Author: "Behera A" / Publication Year Range: Last 10 years - Searchworks@Jio Institute Digital Library Search Results

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57,552 results on '"Behera A"'

1. IJCM_103A: Cost Benefit Analysis Related to Community Acquired Pneumonia inpatients among Geriatric Age Group-A Study Conducted in A Tertiary Care Hospital

Author: Behera Abhimanyu, Sethy Debasish, Pradhan Somen Kumar, and Satapathy Durga Madhab
Subjects: cap, geriatric, economic burden, Public aspects of medicine, RA1-1270
Abstract: Background: Community-acquired pneumonia (CAP) is a common acute infectious disease among elderly people, and is associated with high rates of hospital admission and mortality. Old Age and male sex are known risk factors for CAP among adults and many comorbidity are also risk factors for CAP, such as chronic obstructive pulmonary disease (COPD), diabetes, cancer, dementia, congestive heart failure, and liver function failure. Objective: This study aimed to estimate the economic burden of community- acquired pneumonia (CAP) among elderly patients in Berhampur, Odisha. In addition, the study evaluated the relationship between total treatment cost and CAP risk factors. Methodology: A cross-sectional study was conducted in the IPD of MKCG MKCG Medical College of Berhampur in the period of September-November 2023. Purposive sampling was used and data was collected using semi structured questionnaire and analyzed by MS Excel 2017 Results: Among 348 are inpatient CAP episodes the mean ages were 63±8 years in outpatient group and 70±4 yrs in inpatient groups respectively. The Median treatment cost is Rs.3000/-INR per outpatient episode and Rs 10060/-INR per inpatient episode.Old patient, Male sex, Diabetes, Chronic obstructive Pulmonary Disease, liver dysfunction were associated with increased treatment cost. Conclusion: The economic burden of CAP might be decreased by reducing the number of hospitalizations for mild CAP and the incidence of severe CAP. Therefore, preventative care (e.g. oral hygiene or pneumococcus vaccination) is recommended for patients with related risk factors, such as male sex, older age, diabetes, chronic obstructive pulmonary disease.
Published: 2024
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2. Microwave assisted synthesis of novel spiro diarylidenes and their antimicrobial assay

Author: Shroff Sabita, Mohanta Prajna Parimita, Baitharu Iswar, Bag Bhawani Prasad, and Behera Ajaya Kumar
Subjects: pyrazolone, michaelis addition, aldol condensation, antibacterial activity, irradiation, antifungal activity, Chemistry, QD1-999
Abstract: A rapid and high yield microwave assisted synthesis of a series of novel 7,9-bis-(arylidene)-4-methyl-2,6,10-triphenyl-2,3-diazaspiro[4,5]dec-3- -ene-1,8-dione has been explored. The spiro diarylidene derivatives having nitrogen atom and α,β-unsaturated ketone moiety were synthesized by aldol condensation between 4-methyl-2,6,10-triphenyl-2,3-diazaspiro[4,5]dec-3-ene-1,8- -dione and corresponding aryl aldehydes followed by dehydration. The synthesized series of novel spirodiarylidene derivatives were characterized using IR, 1H- and 13C-NMR and mass spectra. Density functional theory (DFT) study was performed by Gaussian 09 software. The antimicrobial activities of the synthesized derivatives were evaluated against two pathogenic Gram-positive and Gram-negative bacterial strain and three pathogenic fungal species by disk diffusion method. The minimum inhibitory concentration was determined by the microbroth dilution technique. The results of the present study demonstrated that the examined compounds marked 5a and 5c, possessing 4-NO2 and 5-Br-2-OH substituents, are found to be more active against Gram-positive bacterium Staphylococcus aureus, 8 and 16 μg mL-1, respectively, and moderately active against Gram-negative bacteria Pseudomonas aeruginosa and Escherichia coli, compared to other synthetic derivatives. However, none of the synthesized derivatives showed any activity against Streptococcus pyrogenes. Compound 5e, possessing 2,4,6-(OCH3)3C6H3 moiety, exhibited broad spectrum activity against all fungal strains under study, but showed no antibacterial activity.
Published: 2022
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3. A common generalization of convolved (u, v)-Lucas first and second kinds p-polynomials

Author: Behera Adikanda and Ray Prasanta Kumar
Subjects: (u, v)-lucas first kind p-polynomials, (u, v)-lucas second kind p-polynomials, convolved (u,v)-lucas first kind p-polynomials, convolved (u, v)-lucas second kind p-polynomials, 11b37, 11b39, Mathematics, QA1-939
Abstract: In this note the convolved (u, v)-Lucas first kind and the convolved (u, v)-Lucas second kind p-polynomials are introduced and study some of their properties. Several identities related to the common generalization of convolved (u, v)-Lucas first and second kinds p-polynomials are also presented.
Published: 2021
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4. Thermoelectric performance of a minimally nonlinear voltage probe and voltage-temperature probe heat engine with broken time-reversal symmetry

Author: Behera, Jayasmita, Bedkihal, Salil, Agarwalla, Bijay Kumar, and Bandyopadhyay, Malay
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: We investigate the thermoelectric performance of a voltage probe and voltage-temperature probe minimally nonlinear irreversible heat engine with broken time-reversal symmetry by considering extended Onsager relations, where a nonlinear power dissipation term ($-\gamma_h{J_L^N}^2$) is added to the heat current. The analytical expressions for efficiency at a given power and efficiency at maximum power, in terms of asymmetry parameters and generalized figures of merit, are derived and analyzed. Our analysis with broken time-reversal symmetry and nonlinear effect reveals two universal bounds on the efficiency at maximum power (EMP) that overshoot the Curzon-Ahlborn (CA) limit. Although the analytical expressions of the bounds on EMP look similar, their respective Carnot efficiency and the asymmetry parameter set the difference between these heat engines which can be proved numerically. We consider a triple-dot Aharonov-Bohm heat engine with a voltage probe and voltage-temperature probe for our numerical simulation experiment. Unlike the voltage probe, the voltage-temperature probe heat engine requires an anisotropy in the system in addition to the magnetic field to break the time-reversal symmetry. In both probe cases, we observe high efficiency with low output power when the tunneling strength $t$ is less than the coupling strength $\gamma$ and vice-versa for $t>\gamma$. Our numerical experiment demonstrates that the efficiency at maximum output power and efficiency at a given power can be enhanced by increasing the power dissipation strength ($\gamma_h$) although the output power remains unaffected. The added nonlinear term helps us to surpass the CA limit of the upper bound on efficiency at maximum power for both cases with and without time-reversal symmetry.
Published: 2024

5. Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks

Author: Phukan, Orchid Chetia, Koshal, Devyani, Behera, Swarup Ranjan, Buduru, Arun Balaji, and Sharma, Rajesh
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound, 68T45, I.2.7
Abstract: Speech forensic tasks (SFTs), such as automatic speaker recognition (ASR), speech emotion recognition (SER), gender recognition (GR), and age estimation (AE), find use in different security and biometric applications. Previous works have applied various techniques, with recent studies focusing on applying speech foundation models (SFMs) for improved performance. However, most prior efforts have centered on building individual models for each task separately, despite the inherent similarities among these tasks. This isolated approach results in higher computational resource requirements, increased costs, time consumption, and maintenance challenges. In this study, we address these challenges by employing a multi-task learning strategy. Firstly, we explore the various state-of-the-art (SOTA) SFMs by extracting their representations for learning these SFTs and investigating their effectiveness at each task specifically. Secondly, we analyze the performance of the extracted representations on the SFTs in a multi-task learning framework. We observe a decline in performance when SFTs are modeled together compared to individual task-specific models, and as a remedy, we propose multi-view learning (MVL). Views are representations from different SFMs transformed into distinct abstract spaces by characteristics unique to each SFM. By leveraging MVL, we integrate these diverse representations to capture complementary information across tasks, enhancing the shared learning process. We introduce a new framework called TANGO (Task Alignment with iNter-view Gated Optimal transport) to implement this approach. With TANGO, we achieve the topmost performance in comparison to individual SFM representations as well as baseline fusion techniques across benchmark datasets such as CREMA-D, emo-DB, and BAVED.
Published: 2024

6. Beyond Speech and More: Investigating the Emergent Ability of Speech Foundation Models for Classifying Physiological Time-Series Signals

Author: Phukan, Orchid Chetia, Behera, Swarup Ranjan, Girish, Akhtar, Mohd Mujtaba, Buduru, Arun Balaji, and Sharma, Rajesh
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Signal Processing, 68T45, I.2.7
Abstract: Despite being trained exclusively on speech data, speech foundation models (SFMs) like Whisper have shown impressive performance in non-speech tasks such as audio classification. This is partly because speech shares some common traits with audio, enabling SFMs to transfer effectively. In this study, we push the boundaries by evaluating SFMs on a more challenging out-of-domain (OOD) task: classifying physiological time-series signals. We test two key hypotheses: first, that SFMs can generalize to physiological signals by capturing shared temporal patterns; second, that multilingual SFMs will outperform others due to their exposure to greater variability during pre-training, leading to more robust, generalized representations. Our experiments, conducted for stress recognition using ECG (Electrocardiogram), EMG (Electromyography), and EDA (Electrodermal Activity) signals, reveal that models trained on SFM-derived representations outperform those trained on raw physiological signals. Among all models, multilingual SFMs achieve the highest accuracy, supporting our hypothesis and demonstrating their OOD capabilities. This work positions SFMs as promising tools for new uncharted domains beyond speech.
Published: 2024

7. SeQuiFi: Mitigating Catastrophic Forgetting in Speech Emotion Recognition with Sequential Class-Finetuning

Author: Jain, Sarthak, Phukan, Orchid Chetia, Behera, Swarup Ranjan, Buduru, Arun Balaji, and Sharma, Rajesh
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound, 68T45, I.2.7
Abstract: In this work, we introduce SeQuiFi, a novel approach for mitigating catastrophic forgetting (CF) in speech emotion recognition (SER). SeQuiFi adopts a sequential class-finetuning strategy, where the model is fine-tuned incrementally on one emotion class at a time, preserving and enhancing retention for each class. While various state-of-the-art (SOTA) methods, such as regularization-based, memory-based, and weight-averaging techniques, have been proposed to address CF, it still remains a challenge, particularly with diverse and multilingual datasets. Through extensive experiments, we demonstrate that SeQuiFi significantly outperforms both vanilla fine-tuning and SOTA continual learning techniques in terms of accuracy and F1 scores on multiple benchmark SER datasets, including CREMA-D, RAVDESS, Emo-DB, MESD, and SHEMO, covering different languages.
Published: 2024

8. Design, Localization, Perception, and Control for GPS-Denied Autonomous Aerial Grasping and Harvesting

Author: Kumar, Ashish and Behera, Laxmidhar
Subjects: Computer Science - Robotics
Abstract: In this paper, we present a comprehensive UAV system design to perform the highly complex task of off-centered aerial grasping. This task has several interdisciplinary research challenges which need to be addressed at once. The main design challenges are GPS-denied functionality, solely onboard computing, and avoiding off-the-shelf costly positioning systems. While in terms of algorithms, visual perception, localization, control, and grasping are the leading research problems. Hence in this paper, we make interdisciplinary contributions: (i) A detailed description of the fundamental challenges in indoor aerial grasping, (ii) a novel lightweight gripper design, (iii) a complete aerial platform design and in-lab fabrication, and (iv) localization, perception, control, grasping systems, and an end-to-end flight autonomy state-machine. Finally, we demonstrate the resulting aerial grasping system Drone-Bee achieving a high grasping rate for a highly challenging agricultural task of apple-like fruit harvesting, indoors in a vertical farming setting (Fig. 1). To our knowledge, such a system has not been previously discussed in the literature, and with its capabilities, this system pushes aerial manipulation towards 4th generation.
Published: 2024

9. Thrust Microstepping via Acceleration Feedback in Quadrotor Control for Aerial Grasping of Dynamic Payload

Author: Kumar, Ashish and Behera, Laxmidhar
Subjects: Computer Science - Robotics
Abstract: In this work, we propose an end-to-end Thrust Microstepping and Decoupled Control (TMDC) of quadrotors. TMDC focuses on precise off-centered aerial grasping of payloads dynamically, which are attached rigidly to the UAV body via a gripper contrary to the swinging payload. The dynamic payload grasping quickly changes UAV's mass, inertia etc, causing instability while performing a grasping operation in-air. We identify that to handle unknown payload grasping, the role of thrust controller is crucial. Hence, we focus on thrust control without involving system parameters such as mass etc. TMDC is based on our novel Thrust Microstepping via Acceleration Feedback (TMAF) thrust controller and Decoupled Motion Control (DMC). TMAF precisely estimates the desired thrust even at smaller loop rates while DMC decouples the horizontal and vertical motion to counteract disturbances in the case of dynamic payloads. We prove the controller's efficacy via exhaustive experiments in practically interesting and adverse real-world cases, such as fully onboard state estimation without any positioning sensor, narrow and indoor flying workspaces with intense wind turbulence, heavy payloads, non-uniform loop rates, etc. Our TMDC outperforms recent direct acceleration feedback thrust controller (DA) and geometric tracking control (GT) in flying stably for aerial grasping and achieves RMSE below 0.04m in contrast to 0.15m of DA and 0.16m of GT.
Published: 2024

10. A correspondence between Hebbian unlearning and steady states generated by nonequilibrium dynamics

Author: Behera, Agnish Kumar, Du, Matthew, Jagadisan, Uday, Sastry, Srikanth, Rao, Madan, and Vaikuntanathan, Suriyanarayanan
Subjects: Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics
Abstract: The classic paradigms for learning and memory recall focus on strengths of synaptic couplings and how these can be modulated to encode memories. In a previous paper [A. K. Behera, M. Rao, S. Sastry, and S. Vaikuntanathan, Physical Review X 13, 041043 (2023)], we demonstrated how a specific non-equilibrium modification of the dynamics of an associative memory system can lead to increase in storage capacity. In this work, using analytical theory and computational inference schemes, we show that the dynamical steady state accessed is in fact similar to those accessed after the operation of a classic unsupervised scheme for improving memory recall, Hebbian unlearning or ``dreaming". Together, our work suggests how nonequilibrium dynamics can provide an alternative route for controlling the memory encoding and recall properties of a variety of synthetic (neuromorphic) and biological systems., Comment: 15 pages, 13 figures
Published: 2024

11. High-Speed Stereo Visual SLAM for Low-Powered Computing Devices

Author: Kumar, Ashish, Park, Jaesik, and Behera, Laxmidhar
Subjects: Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract: We present an accurate and GPU-accelerated Stereo Visual SLAM design called Jetson-SLAM. It exhibits frame-processing rates above 60FPS on NVIDIA's low-powered 10W Jetson-NX embedded computer and above 200FPS on desktop-grade 200W GPUs, even in stereo configuration and in the multiscale setting. Our contributions are threefold: (i) a Bounded Rectification technique to prevent tagging many non-corner points as a corner in FAST detection, improving SLAM accuracy. (ii) A novel Pyramidal Culling and Aggregation (PyCA) technique that yields robust features while suppressing redundant ones at high speeds by harnessing a GPU device. PyCA uses our new Multi-Location Per Thread culling strategy (MLPT) and Thread-Efficient Warp-Allocation (TEWA) scheme for GPU to enable Jetson-SLAM achieving high accuracy and speed on embedded devices. (iii) Jetson-SLAM library achieves resource efficiency by having a data-sharing mechanism. Our experiments on three challenging datasets: KITTI, EuRoC, and KAIST-VIO, and two highly accurate SLAM backends: Full-BA and ICE-BA show that Jetson-SLAM is the fastest available accurate and GPU-accelerated SLAM system (Fig. 1).
Published: 2024

12. pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topology

Author: Behera, Monik Raj and Chakraborty, Suchetana
Subjects: Statistics - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Computer Science and Game Theory, Computer Science - Machine Learning
Abstract: Conventional federated learning frameworks suffer from several challenges including performance bottlenecks at the central aggregation server, data bias, poor model convergence, and exposure to model poisoning attacks, and limited trust in the centralized infrastructure. In the current paper, a novel game theory-based approach called pFedGame is proposed for decentralized federated learning, best suitable for temporally dynamic networks. The proposed algorithm works without any centralized server for aggregation and incorporates the problem of vanishing gradients and poor convergence over temporally dynamic topology among federated learning participants. The solution comprises two sequential steps in every federated learning round, for every participant. First, it selects suitable peers for collaboration in federated learning. Secondly, it executes a two-player constant sum cooperative game to reach convergence by applying an optimal federated learning aggregation strategy. Experiments performed to assess the performance of pFedGame in comparison to existing methods in decentralized federated learning have shown promising results with accuracy higher than 70% for heterogeneous data.
Published: 2024
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13. A New World in the Depths of Microcrypt: Separating OWSGs and Quantum Money from QEFID

Author: Behera, Amit, Malavolta, Giulio, Morimae, Tomoyuki, Mour, Tamer, and Yamakawa, Takashi
Subjects: Quantum Physics, Computer Science - Cryptography and Security
Abstract: While in classical cryptography, one-way functions (OWFs) are widely regarded as the "minimal assumption," the situation in quantum cryptography is less clear. Recent works have put forward two concurrent candidates for the minimal assumption in quantum cryptography: One-way state generators (OWSGs), postulating the existence of a hard search problem with an efficient verification algorithm, and EFI pairs, postulating the existence of a hard distinguishing problem. Two recent papers [Khurana and Tomer STOC'24; Batra and Jain FOCS'24] showed that OWSGs imply EFI pairs, but the reverse direction remained open. In this work, we give strong evidence that the opposite direction does not hold: We show that there is a quantum unitary oracle relative to which EFI pairs exist, but OWSGs do not. In fact, we show a slightly stronger statement that holds also for EFI pairs that output classical bits (QEFID). As a consequence, we separate, via our oracle, QEFID, and one-way puzzles from OWSGs and several other Microcrypt primitives, including efficiently verifiable one-way puzzles and unclonable state generators. In particular, this solves a problem left open in [Chung, Goldin, and Gray Crypto'24]. Using similar techniques, we also establish a fully black-box separation (which is slightly weaker than an oracle separation) between private-key quantum money schemes and QEFID pairs. One conceptual implication of our work is that the existence of an efficient verification algorithm may lead to qualitatively stronger primitives in quantum cryptography., Comment: Minor corrections and typos were addressed in version 2
Published: 2024

14. A review on Aharonov-Bohm quantum machines: Thermoelectric heat engines and diodes

Author: Bedkihal, Salil, Behera, Jayasmita, and Bandyopadhyay, Malay
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: The study of heat-to-work conversion has gained significant attention in recent years, highlighting the potential of nanoscale systems to achieve energy conversion in steady-state devices without any macroscopic moving parts. This review examines the theoretical frameworks governing the steady-state flows of quantum particles like electrons, photons, and phonons within various mesoscopic or nanoscale devices, such as thermoelectric heat engines in the context of quantum dot Aharonov-Bohm (AB) interferometric configurations. Quantum interference effects hold great promise for enhancing the thermoelectric transport properties of such quantum devices by allowing more precise control over energy levels and transport pathways. Driven quantum dot AB networks can maintain quantum coherence and provide precise experimental control. Unlike bulk systems, nanoscale systems like quantum dots reveal distinct quantum interference phenomena, including sharp features in transmission spectra and Fano resonances. This review highlights the distinction between optimization methods that produce boxcar functions and coherent control methods that result in complex interference patterns. It reveals that the effective design of thermoelectric heat engines requires careful tailoring of quantum interference and the magnetic field-induced effects to enhance performance. We emphasize how magnetic fields can change the bounds of power or efficiency. These machines with broken time-reversal symmetry provide insights into directional dependencies and asymmetries in quantum transport. We offer a thorough overview of past and current research on quantum thermoelectric heat engines using the AB effect and present a detailed review of three-terminal AB heat engines, where broken time-reversal symmetry can induce a coherent diode effect. We cover bounds on power and efficiency in systems with broken time-reversal symmetry.
Published: 2024

15. Instability behavior of odd viscosity-induced viscous fluid over a vibrating bed

Author: Hossain, Md. Mouzakkir, Saha, Mrityunjoy, Behera, Harekrushna, and Ghosh, Sukhendu
Subjects: Physics - Fluid Dynamics
Abstract: The manuscript focuses on the theoretical stability analysis of the viscous liquid over a vibrating inclined rigid bed when the fluid undergoes an impact of odd viscosity. Such an impact emerges in the classical fluid owing to the broken time-reversal symmetry. The rigid bottom vibrates in streamwise and cross-stream directions. The time-dependent Orr-Sommerfeld eigenvalue problem is obtained using the normal mode approach and resolved based on the Chebyshev-collocation method and the Floquet theory. The effect of the odd viscosity coefficient on the different types of instability gravitational, subharmonic, and harmonic are identified. The gravitational instability arises in the longwave region, whereas the resonated wave instability appears in the finite wavenumber region. The gravitational instability is generated in the fluid flow owing to the gravity driving force, whereas the subharmonic instability appears for lower forcing amplitude and the harmonic instability emerges for comparatively higher forcing amplitude. It is found that the subharmonic and harmonic resonances appear when the forcing amplitude surpasses its critical value. A higher odd viscosity leads to stabilizing the gravitational instability, whereas a larger odd viscosity diminishes the subharmonic resonance along with the harmonic resonance instigated at a high forcing amplitude. Further, a new instability, named shear instability in the finite wavenumber range emerges together with the aforementioned three instabilities when the Reynolds number is sufficiently high with a low angle of inclination and becomes weaker when the time-reversal symmetry breaks., Comment: 16 pages, 22 figures
Published: 2024

16. The hypothetical track-length fitting algorithm for energy measurement in liquid argon TPCs

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Alex, N. S., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Choi, G., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., Gago, A. M., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. 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Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.
Published: 2024

17. Evaluating the galaxy formation histories predicted by a neural network in pure dark matter simulations

Author: Chittenden, Harry George, Behera, Jayashree, and Tojeiro, Rita
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: We investigate a series of galaxy properties computed using the merger trees and environmental histories from dark matter only cosmological simulations, using the predictive semi-recurrent neural network outlined in Chittenden and Tojeiro (2023), and using stochastic improvements presented in our companion paper: Behera, Tojeiro and Chittenden (2024). We apply these methods to the dark matter only runs of the IllustrisTNG simulations to understand the effects of baryon removal, and to the gigaparsec-volume pure dark matter simulation Uchuu, to understand the effects of the lower resolution or alternative metrics for halo properties. We find that the machine learning model recovers accurate summary statistics derived from the predicted star formation and stellar metallicity histories, and correspondent spectroscopy and photometry. However, the inaccuracies of the model's application to dark simulations are substantial for low mass and slowly growing haloes. For these objects, the halo mass accretion rate is exaggerated due to the lack of stellar feedback, yet the formation of the halo can be severely limited by the absence of low mass progenitors in a low resolution simulation. Furthermore, differences in the structure and environment of higher mass haloes results in an overabundance of red, quenched galaxies. These results signify progress towards a machine learning model which builds high fidelity mocks based on a physical interpretation of the galaxy-halo connection, yet they illustrate the need to account for differences in halo properties and the resolution of the simulation., Comment: 21 pages, 17 figures, submitted to MNRAS alongside companion paper: arXiv:2409.16548
Published: 2024

18. Representation Loss Minimization with Randomized Selection Strategy for Efficient Environmental Fake Audio Detection

Author: Phukan, Orchid Chetia, Girish, Akhtar, Mohd Mujtaba, Behera, Swarup Ranjan, Choudhury, Nitin, Buduru, Arun Balaji, Sharma, Rajesh, and Prasanna, S. R Mahadeva
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound, 68T45, I.2.7
Abstract: The adaptation of foundation models has significantly advanced environmental audio deepfake detection (EADD), a rapidly growing area of research. These models are typically fine-tuned or utilized in their frozen states for downstream tasks. However, the dimensionality of their representations can substantially lead to a high parameter count of downstream models, leading to higher computational demands. So, a general way is to compress these representations by leveraging state-of-the-art (SOTA) unsupervised dimensionality reduction techniques (PCA, SVD, KPCA, GRP) for efficient EADD. However, with the application of such techniques, we observe a drop in performance. So in this paper, we show that representation vectors contain redundant information, and randomly selecting 40-50% of representation values and building downstream models on it preserves or sometimes even improves performance. We show that such random selection preserves more performance than the SOTA dimensionality reduction techniques while reducing model parameters and inference time by almost over half., Comment: Submitted to ICASSP 2025
Published: 2024

19. Optimised neural network predictions of galaxy formation histories using semi-stochastic corrections

Author: Behera, Jayashree, Tojeiro, Rita, and Chittenden, Harry George
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present a novel methodology to improve neural network (NN) predictions of galaxy formation histories by incorporating semi-stochastic corrections to account for short-timescale variability. Our paper addresses limitations in existing models that capture broad trends in galaxy evolution, but fail to reproduce the bursty nature of star formation and chemical enrichment, resulting in inaccurate predictions of key observables such as stellar masses, optical spectra, and colour distributions. We introduce a simple technique to add stochastic components by utilizing the power spectra of galaxy formation histories. We justify our stochastic approach by studying the correlation between the phases of the halo mass assembly and star-formation histories in the IllustrisTNG simulation, and we find that they are correlated only on timescales longer than 6 Gyr, with a strong dependence on galaxy type. Building on NNs developed in Chittenden & Tojeiro (2023), trained on hydrodynamical simulations from the IllustrisTNG project, which predict time-resolved star formation and stellar metallicity histories for central and satellite galaxies based solely on the properties and evolution of their dark matter halos and environments, this approach successfully recovers realistic variability in galaxy properties at short timescales. It significantly improves the accuracy of predicted stellar masses, metallicities, spectra, and colour distributions and provides a powerful framework for generating large, realistic mock galaxy catalogs, while also enhancing our understanding of the complex interplay between galaxy evolution and dark matter halo assembly., Comment: 18 pages, 20 figures, submitted to MNRAS alongside companion paper - arXiv:2409.16079
Published: 2024

20. Avengers Assemble: Amalgamation of Non-Semantic Features for Depression Detection

Author: Phukan, Orchid Chetia, Behera, Swarup Ranjan, Singh, Shubham, Singh, Muskaan, Rajan, Vandana, Buduru, Arun Balaji, Sharma, Rajesh, and Prasanna, S. R. Mahadeva
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound, 68T45, I.2.7
Abstract: In this study, we address the challenge of depression detection from speech, focusing on the potential of non-semantic features (NSFs) to capture subtle markers of depression. While prior research has leveraged various features for this task, NSFs-extracted from pre-trained models (PTMs) designed for non-semantic tasks such as paralinguistic speech processing (TRILLsson), speaker recognition (x-vector), and emotion recognition (emoHuBERT)-have shown significant promise. However, the potential of combining these diverse features has not been fully explored. In this work, we demonstrate that the amalgamation of NSFs results in complementary behavior, leading to enhanced depression detection performance. Furthermore, to our end, we introduce a simple novel framework, FuSeR, designed to effectively combine these features. Our results show that FuSeR outperforms models utilizing individual NSFs as well as baseline fusion techniques and obtains state-of-the-art (SOTA) performance in E-DAIC benchmark with RMSE of 5.51 and MAE of 4.48, establishing it as a robust approach for depression detection., Comment: Submitted to ICASSP 2025
Published: 2024

21. Strong Alone, Stronger Together: Synergizing Modality-Binding Foundation Models with Optimal Transport for Non-Verbal Emotion Recognition

Author: Phukan, Orchid Chetia, Akhtar, Mohd Mujtaba, Girish, Behera, Swarup Ranjan, Kalita, Sishir, Buduru, Arun Balaji, Sharma, Rajesh, and Prasanna, S. R Mahadeva
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound, 68T45, I.2.7
Abstract: In this study, we investigate multimodal foundation models (MFMs) for emotion recognition from non-verbal sounds. We hypothesize that MFMs, with their joint pre-training across multiple modalities, will be more effective in non-verbal sounds emotion recognition (NVER) by better interpreting and differentiating subtle emotional cues that may be ambiguous in audio-only foundation models (AFMs). To validate our hypothesis, we extract representations from state-of-the-art (SOTA) MFMs and AFMs and evaluated them on benchmark NVER datasets. We also investigate the potential of combining selected foundation model representations to enhance NVER further inspired by research in speech recognition and audio deepfake detection. To achieve this, we propose a framework called MATA (Intra-Modality Alignment through Transport Attention). Through MATA coupled with the combination of MFMs: LanguageBind and ImageBind, we report the topmost performance with accuracies of 76.47%, 77.40%, 75.12% and F1-scores of 70.35%, 76.19%, 74.63% for ASVP-ESD, JNV, and VIVAE datasets against individual FMs and baseline fusion techniques and report SOTA on the benchmark datasets., Comment: Submitted to ICASSP 2025
Published: 2024

22. Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models

Author: Phukan, Orchid Chetia, Jain, Sarthak, Behera, Swarup Ranjan, Buduru, Arun Balaji, Sharma, Rajesh, and Prasanna, S. R Mahadeva
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Machine Learning, Computer Science - Sound, 68T45, I.2.7
Abstract: In this study, for the first time, we extensively investigate whether music foundation models (MFMs) or speech foundation models (SFMs) work better for singing voice deepfake detection (SVDD), which has recently attracted attention in the research community. For this, we perform a comprehensive comparative study of state-of-the-art (SOTA) MFMs (MERT variants and music2vec) and SFMs (pre-trained for general speech representation learning as well as speaker recognition). We show that speaker recognition SFM representations perform the best amongst all the foundation models (FMs), and this performance can be attributed to its higher efficacy in capturing the pitch, tone, intensity, etc, characteristics present in singing voices. To our end, we also explore the fusion of FMs for exploiting their complementary behavior for improved SVDD, and we propose a novel framework, FIONA for the same. With FIONA, through the synchronization of x-vector (speaker recognition SFM) and MERT-v1-330M (MFM), we report the best performance with the lowest Equal Error Rate (EER) of 13.74 %, beating all the individual FMs as well as baseline FM fusions and achieving SOTA results., Comment: Submitted to ICASSP 2025
Published: 2024

23. An efficient wavelet-based physics-informed neural networks for singularly perturbed problems

Author: Pandey, Himanshu, Singh, Anshima, and Behera, Ratikanta
Subjects: Computer Science - Machine Learning
Abstract: Physics-informed neural networks (PINNs) are a class of deep learning models that utilize physics as differential equations to address complex problems, including ones that may involve limited data availability. However, tackling solutions of differential equations with oscillations or singular perturbations and shock-like structures becomes challenging for PINNs. Considering these challenges, we designed an efficient wavelet-based PINNs (W-PINNs) model to solve singularly perturbed differential equations. Here, we represent the solution in wavelet space using a family of smooth-compactly supported wavelets. This framework represents the solution of a differential equation with significantly fewer degrees of freedom while still retaining in capturing, identifying, and analyzing the local structure of complex physical phenomena. The architecture allows the training process to search for a solution within wavelet space, making the process faster and more accurate. The proposed model does not rely on automatic differentiations for derivatives involved in differential equations and does not require any prior information regarding the behavior of the solution, such as the location of abrupt features. Thus, through a strategic fusion of wavelets with PINNs, W-PINNs excel at capturing localized nonlinear information, making them well-suited for problems showing abrupt behavior in certain regions, such as singularly perturbed problems. The efficiency and accuracy of the proposed neural network model are demonstrated in various test problems, i.e., highly singularly perturbed nonlinear differential equations, the FitzHugh-Nagumo (FHN), and Predator-prey interaction models. The proposed design model exhibits impressive comparisons with traditional PINNs and the recently developed wavelet-based PINNs, which use wavelets as an activation function for solving nonlinear differential equations., Comment: 17 pages, 12 figures
Published: 2024

24. High-Order Evolving Graphs for Enhanced Representation of Traffic Dynamics

Author: Humnabadkar, Aditya, Sikdar, Arindam, Cave, Benjamin, Zhang, Huaizhong, Bakaki, Paul, and Behera, Ardhendu
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We present an innovative framework for traffic dynamics analysis using High-Order Evolving Graphs, designed to improve spatio-temporal representations in autonomous driving contexts. Our approach constructs temporal bidirectional bipartite graphs that effectively model the complex interactions within traffic scenes in real-time. By integrating Graph Neural Networks (GNNs) with high-order multi-aggregation strategies, we significantly enhance the modeling of traffic scene dynamics, providing a more accurate and detailed analysis of these interactions. Additionally, we incorporate inductive learning techniques inspired by the GraphSAGE framework, enabling our model to adapt to new and unseen traffic scenarios without the need for retraining, thus ensuring robust generalization. Through extensive experiments on the ROAD and ROAD Waymo datasets, we establish a comprehensive baseline for further developments, demonstrating the potential of our method in accurately capturing traffic behavior. Our results emphasize the value of high-order statistical moments and feature-gated attention mechanisms in improving traffic behavior analysis, laying the groundwork for advancing autonomous driving technologies. Our source code is available at: https://github.com/Addy-1998/High_Order_Graphs, Comment: Accepted manuscript - 2nd Workshop on Vision-Centric Autonomous Driving (VCAD) as part of European Conference on Computer Vision (ECCV) 2024
Published: 2024

25. Collective motion using quantum-like entanglement of neighbours in perceptual space

Author: Beuria, Jyotiranjan, Chaurasiya, Mayank, and Behera, Laxmidhar
Subjects: Physics - Physics and Society, Condensed Matter - Statistical Mechanics, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Quantum Physics
Abstract: We explore the collective motion of self-propelled agents through the lens of quantum-like entanglement in visual perception. This approach makes the non-linear dynamics in ordinary space a derivative of dynamics in the Hilbert space of perception. By appropriate choice of operators in this quantum-inspired model, we show how the familiar Vicsek model, a well-known paradigm for flocking behaviour, naturally emerges. Furthermore, we extend the analysis to explore how different types of entangled quantum states-such as GHZ states, W states, and cluster states-affect collective motion. Each type of entanglement introduces unique coordination patterns, interaction strength, and information sharing among agents, leading to different collective behaviours., Comment: 8 pages, 7 figures, 1 table
Published: 2024

26. Computation of $M$-QDR decomposition of tensors and applications

Author: Panigrahy, Krushnachandra, Karmakar, Biswarup, Sahoo, Jajati Keshari, Behera, Ratikanta, and Mohapatra, Ram N.
Subjects: Mathematics - Numerical Analysis
Abstract: The theory and computation of tensors with different tensor products play increasingly important roles in scientific computing and machine learning. Different products aim to preserve different algebraic properties from the matrix algebra, and the choice of tensor product determines the algorithms that can be directly applied. This study introduced a novel full-rank decomposition and $M$-$\mc{QDR}$ decomposition for third-order tensors based on $M$-product. Then, we designed algorithms for computing these two decompositions along with the Moore-Penrose inverse, and outer inverse of the tensors. In support of these theoretical results, a few numerical examples were discussed. In addition, we derive exact expressions for the outer inverses of tensors using symbolic tensor (tensors with polynomial entries) computation. We designed efficient algorithms to compute the Moore-Penrose inverse of symbolic tensors. The prowess of the proposed $M$-$\mc{QDR}$ decomposition for third-order tensors is applied to compress lossy color images., Comment: 23
Published: 2024

27. Photo-induced molecular growth of benzonitrile in the gas phase

Author: Behera, Nihar Ranjan, Kanakati, Arun Kumar, Thakkar, Pratikkumar, Payra, Siddhartha Sankar, Dutta, Saurav, Barik, Saroj, Lenka, Yash, and Aravind, G
Subjects: Physics - Chemical Physics
Abstract: Due to the absorption of high energetic ultraviolet (UV) photons by the surface layers of the cold molecular clouds, only low energetic photons are able to penetrate into the inner regions of these clouds. This leads to lower photo-ionization yield of molecules of higher ionization potential in these environments. However, here we have experimentally shown the ionization of Benzonitrile molecule using 266nm (4.66eV) photons. The low intensity and unfocused laser irradiation of benzonitrile molecules results extensive fragmentation. Moreover, the ion-neutral reactions among the cationic fragments and neutral fragments shows promising molecular mass growth.
Published: 2024

28. $M$-QR decomposition and hyperpower iterative methods for computing outer inverses of tensors

Author: Behera, Ratikanta, Panigrahy, Krushnachandra, Sahoo, Jajati Keshari, and Wei, Yimin
Subjects: Mathematics - Numerical Analysis
Abstract: The outer inverse of tensors plays increasingly significant roles in computational mathematics, numerical analysis, and other generalized inverses of tensors. In this paper, we compute outer inverses with prescribed ranges and kernels of a given tensor through tensor QR decomposition and hyperpower iterative method under the M-product structure, which is a family of tensor-tensor products, generalization of the t-product and c-product, allows us to suit the physical interpretations across those different modes. We discuss a theoretical analysis of the nineteen-order convergence of the proposed tensor-based iterative method. Further, we design effective tensor-based algorithms for computing outer inverses using M-QR decomposition and hyperpower iterative method. The theoretical results are validated with numerical examples demonstrating the appropriateness of the proposed methods., Comment: 23 pages
Published: 2024

29. Renormalized stochastic entropy solution for degenerate parabolic-hyperbolic equations with Levy noise

Author: Behera, Soumya Ranjan and Majee, Ananta K
Subjects: Mathematics - Analysis of PDEs, Mathematics - Probability
Abstract: In this article, we establish the well-posedness theory for renormalized entropy solutions of a degenerate parabolic-hyperbolic PDE perturbed by a multiplicative Levy noise with general L1-data on the unbounded domain. By using a suitable approximation procedure based on the vanishing viscosity technique and bounded data, we prove the existence of a renormalized entropy solution to the underlying problem. The uniqueness of the solution is settled by adapting Kru\v{z}kov's doubling the variables technique in the presence of noise.
Published: 2024

30. Homogeneous Dirichlet problem for degenerate parabolic-hyperbolic PDE driven by Levy noise

Author: Behera, Soumya Ranjan and Majee, Ananta K
Subjects: Mathematics - Analysis of PDEs
Abstract: In this article, we study the homogeneous Dirichlet problem for a degenerate parabolic-hyperbolic PDE perturbed by Levy noise. In particular, we develop the well-posedness theory of entropy solution based on the Kru\v{z}kov's semi-entropy formulation. In comparison to the pioneered work by Bauzet et al. (J. Funct. Anal. 266, (2014), 2503-2545), concerning the existence and uniqueness of entropy solution for the Dirichlet problem for conservation laws driven by Brownian noise, our present analysis involves a simpler approach to obtain the global Kato's inequality.
Published: 2024

31. DUNE Phase II: Scientific Opportunities, Detector Concepts, Technological Solutions

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D. M., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Barham~Alzás, P., Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chakraborty, S., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cortez, A. F. V., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Fernández-Posada, D., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., M~Gago, A., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Groetschla, F. T., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, A., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Hadavand, H., Haegel, L., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hart, A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Hellmuth, P., Henry, S., Hernández-García, J., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., King, B., Kirby, B., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Kozhukalov, V., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kreslo, I., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kuźniak, M., Kvasnicka, J., Labree, T., Lackey, T., Lalău, I., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laudrain, A., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Silverio, D. Leon, Lepin, L. M., -Y~Li, J., Li, S. W., Li, Y., Liao, H., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lokajicek, M., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., Lord, T., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Meazza, L., Meddage, V. C. N., Mefodiev, A., Mehta, B., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Mercuri, A. C. E. A., Meregaglia, A., Messier, M. D., Metallo, S., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Micallef, J., Miccoli, A., Michna, G., Milincic, R., Miller, F., Miller, G., Miller, W., Mineev, O., Minotti, A., Miralles, L., Miranda, O. G., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Mogan, A., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. 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Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.
Published: 2024

32. Non-linear Analysis Based ECG Classification of Cardiovascular Disorders

Author: Behera, Suraj Kumar, Bhattacharya, Debanjali, Aithal, Ninad, and Sinha, Neelam
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Multi-channel ECG-based cardiac disorders detection has an impact on cardiac care and treatment. Limitations of existing methods included variation in ECG waveforms due to the location of electrodes, high non-linearity in the signal, and amplitude measurement in millivolts. The present study reports a non-linear analysis-based methodology that utilizes Recurrence plot visualization. The patterned occurrence of well-defined structures, such as the QRS complex, can be exploited effectively using Recurrence plots. This Recurrence-based method is applied to the publicly available Physikalisch-Technische Bundesanstalt (PTB) dataset from PhysioNet database, where we studied four classes of different cardiac disorders (Myocardial infarction, Bundle branch blocks, Cardiomyopathy, and Dysrhythmia) and healthy controls, achieving an impressive classification accuracy of 100%. Additionally, t-SNE plot visualizations of the latent space embeddings derived from Recurrence plots and Recurrence Quantification Analysis features reveal a clear demarcation between the considered cardiac disorders and healthy individuals, demonstrating the potential of this approach., Comment: 23 pages, 9 Figures, 3 Tables
Published: 2024

33. First Measurement of the Total Inelastic Cross-Section of Positively-Charged Kaons on Argon at Energies Between 5.0 and 7.5 GeV

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Barham~Alzás, P., Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chakraborty, S., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. 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M., Gwon, S., Habig, A., Hadavand, H., Haegel, L., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Hellmuth, P., Henry, S., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. 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J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and 7 GeV/$c$ beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be 380$\pm$26 mbarns for the 6 GeV/$c$ setting and 379$\pm$35 mbarns for the 7 GeV/$c$ setting.
Published: 2024

34. Angular dependent measurement of electron-ion recombination in liquid argon for ionization calorimetry in the ICARUS liquid argon time projection chamber

Author: ICARUS collaboration, Abratenko, P., Abrego-Martinez, N., Aduszkiewic, A., Akbar, F., Soplin, L. Aliaga, Pons, M. Artero, Asaadi, J., Badgett, W. F., Baibussinov, B., Behera, B., Bellini, V., Benocci, R., Berger, J., Berkman, S., Bertolucci, S., Betancourt, M., Bonesini, M., Boone, T., Bottino, B., Braggiotti, A., Brailsford, D., Brice, S. J., Brio, V., Brizzolari, C., Budd, H. S., Campani, A., Campos, A., Carber, D., Carneiro, M., Terrazas, I. Caro, Carranza, H., Fernandez, F. Castillo, Castro, A., Centro, S., Cerati, G., Chatterjee, A., Cherdack, D., Cherubini, S., Chitirasreemadam, N., Cicerchia, M., Coan, T. E., Cocco, A., Convery, M. R., Cooper-Troendle, L., Copello, S., Dange, A. A., de Roeck, A., Di Domizio, S., Di Noto, L., Di Stefano, C., Di Ferdinando, D., Diwan, M., Dolan, S., Domine, L., Donati, S., Drielsma, F., Dyer, J., Dytman, S., Falcone, A., Farnese, C., Fava, A., Ferrari, A., Gallice, N., Garcia, F. G., Gatto, C., Gibin, D., Gioiosa, A., Gu, W., Guglielmi, A., Gurung, G., Hausner, H., Heggestuen, A., Howard, B., Howell, R., Ingratta, I., James, C., Jang, W., Jwa, Y. -J., Kashur, L., Ketchum, W., Kim, J. S., Koh, D. -H., Larkin, J., Li, Y., Mariani, C., Marshall, C. M., Martynenko, S., Mauri, N., McFarland, K. S., Méndez, D. P., Menegolli, A., Meng, G., Miranda, O. G., Mogan, A., Moggi, N., Montagna, E., Montanari, C., Montanari, A., Mooney, M., Moreno-Granados, G., Mueller, J., Murphy, M., Naples, D., Nguyen, V. C. L., Palestini, S., Pallavicini, M., Paolone, V., Papaleo, R., Pasqualini, L., Patrizii, L., Paudel, L., Petrillo, G., Petta, C., Pia, V., Pietropaolo, F., Poppi, F., Pozzato, M., Putnam, G., Qian, X., Rappoldi, A., Raselli, G. L., Repetto, S., Resnati, F., Ricci, A. M., Riccobene, G., Richards, E., Rosenberg, M., Rossella, M., Roy, P., Rubbia, C., Saad, M., Saha, S., Sala, P., Samanta, S., Sapienza, P., Scaramelli, A., Scarpelli, A., Schmitz, D., Schukraft, A., Senadheera, D., Seo, S-H., Sergiampietri, F., Sirri, G., Smedley, J. S., Smith, J., Stanco, L., Stewart, J., Tanaka, H. A., Tapia, F., Tenti, M., Terao, K., Terranova, F., Togo, V., Torretta, D., Torti, M., Tortorici, F., Triozzi, R., Tsai, Y. -T., Tufanli, S., Usher, T., Varanini, F., Ventura, S., Vicenzi, M., Vignoli, C., Viren, B., Williams, Z., Wilson, R. J., Wilson, P., Wolfs, J., Wongjirad, T., Wood, A., Worcester, E., Wospakrik, M., Yu, H., Yu, J., Zani, A., Zennamo, J., Zettlemoyer, J., Zhang, C., and Zucchelli, S.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: This paper reports on a measurement of electron-ion recombination in liquid argon in the ICARUS liquid argon time projection chamber (LArTPC). A clear dependence of recombination on the angle of the ionizing particle track relative to the drift electric field is observed. An ellipsoid modified box (EMB) model of recombination describes the data across all measured angles. These measurements are used for the calorimetric energy scale calibration of the ICARUS TPC, which is also presented. The impact of the EMB model is studied on calorimetric particle identification, as well as muon and proton energy measurements. Accounting for the angular dependence in EMB recombination improves the accuracy and precision of these measurements.
Published: 2024

35. Calibration and simulation of ionization signal and electronics noise in the ICARUS liquid argon time projection chamber

Author: ICARUS collaboration, Abratenko, P., Abrego-Martinez, N., Aduszkiewic, A., Akbar, F., Soplin, L. Aliaga, Pons, M. Artero, Asaadi, J., Badgett, W. F., Baibussinov, B., Behera, B., Bellini, V., Benocci, R., Berger, J., Berkman, S., Bertolucci, S., Betancourt, M., Bonesini, M., Boone, T., Bottino, B., Braggiotti, A., Brailsford, D., Brice, S. J., Brio, V., Brizzolari, C., Budd, H. S., Campani, A., Campos, A., Carber, D., Carneiro, M., Terrazas, I. Caro, Carranza, H., Fernandez, F. Castillo, Castro, A., Centro, S., Cerati, G., Chatterjee, A., Cherdack, D., Cherubini, S., Chitirasreemadam, N., Cicerchia, M., Coan, T. E., Cocco, A., Convery, M. R., Cooper-Troendle, L., Copello, S., Dange, A. A., de Roeck, A., Di Domizio, S., Di Noto, L., Di Stefano, C., Di Ferdinando, D., Diwan, M., Dolan, S., Domine, L., Donati, S., Drielsma, F., Dyer, J., Dytman, S., Falcone, A., Farnese, C., Fava, A., Ferrari, A., Gallice, N., Garcia, F. G., Gatto, C., Gibin, D., Gioiosa, A., Gu, W., Guglielmi, A., Gurung, G., Hausner, H., Heggestuen, A., Howard, B., Howell, R., Ingratta, I., James, C., Jang, W., Jwa, Y. -J., Kashur, L., Ketchum, W., Kim, J. S., Koh, D. -H., Larkin, J., Li, Y., Mariani, C., Marshall, C. M., Martynenko, S., Mauri, N., McFarland, K. S., Méndez, D. P., Menegolli, A., Meng, G., Miranda, O. G., Mogan, A., Moggi, N., Montagna, E., Montanari, C., Montanari, A., Mooney, M., Moreno-Granados, G., Mueller, J., Murphy, M., Naples, D., Nguyen, V. C. L., Palestini, S., Pallavicini, M., Paolone, V., Papaleo, R., Pasqualini, L., Patrizii, L., Paudel, L., Petrillo, G., Petta, C., Pia, V., Pietropaolo, F., Poppi, F., Pozzato, M., Putnam, G., Qian, X., Rappoldi, A., Raselli, G. L., Repetto, S., Resnati, F., Ricci, A. M., Riccobene, G., Richards, E., Rosenberg, M., Rossella, M., Roy, P., Rubbia, C., Saad, M., Saha, S., Sala, P., Samanta, S., Sapienza, P., Scaramelli, A., Scarpelli, A., Schmitz, D., Schukraft, A., Senadheera, D., Seo, S-H., Sergiampietri, F., Sirri, G., Smedley, J. S., Smith, J., Stanco, L., Stewart, J., Tanaka, H. A., Tapia, F., Tenti, M., Terao, K., Terranova, F., Togo, V., Torretta, D., Torti, M., Tortorici, F., Triozzi, R., Tsai, Y. -T., Tufanli, S., Usher, T., Varanini, F., Ventura, S., Vicenzi, M., Vignoli, C., Viren, B., Williams, Z., Wilson, R. J., Wilson, P., Wolfs, J., Wongjirad, T., Wood, A., Worcester, E., Wospakrik, M., Yu, H., Yu, J., Zani, A., Zennamo, J., Zettlemoyer, J., Zhang, C., and Zucchelli, S.
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: The ICARUS liquid argon time projection chamber (LArTPC) neutrino detector has been taking physics data since 2022 as part of the Short-Baseline Neutrino (SBN) Program. This paper details the equalization of the response to charge in the ICARUS time projection chamber (TPC), as well as data-driven tuning of the simulation of ionization charge signals and electronics noise. The equalization procedure removes non-uniformities in the ICARUS TPC response to charge in space and time. This work leverages the copious number of cosmic ray muons available to ICARUS at the surface. The ionization signal shape simulation applies a novel procedure that tunes the simulation to match what is measured in data. The end result of the equalization procedure and simulation tuning allows for a comparison of charge measurements in ICARUS between Monte Carlo simulation and data, showing good performance with minimal residual bias between the two.
Published: 2024

36. Supernova Pointing Capabilities of DUNE

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bracinik, J., Braga, D., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calin, M., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. 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P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. 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L., Pinaroli, G., Pinchault, J., Pitts, K., Plows, K., Plunkett, R., Pollack, C., Pollman, T., Polo-Toledo, D., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Potekhin, M., Potenza, R., Pozimski, J., Pozzato, M., Prakash, T., Pratt, C., Prest, M., Psihas, F., Pugnere, D., Qian, X., Queen, J., Raaf, J. L., Radeka, V., Rademacker, J., Radics, B., Rafique, A., Raguzin, E., Rai, M., Rajagopalan, S., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Ray, R., Razafinime, H., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reggiani-Guzzo, M., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renner, E., Renshaw, A., Rescia, S., Resnati, F., Restrepo, Diego, Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Roeth, A. J., Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Ross-Lonergan, M., Roy, N., Roy, P., Rubbia, C., Ruggeri, A., Ferreira, G. Ruiz, Russell, B., Ruterbories, D., Rybnikov, A., Saa-Hernandez, A., Saakyan, R., Sacerdoti, S., Sahoo, S. K., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., Bravo, A. Sánchez, Sanchez-Lucas, P., Sandberg, V., Sanders, D. A., Sanfilippo, S., Sankey, D., Santoro, D., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Schuld, B., Segade, A., Segreto, E., Selyunin, A., Senise, C. R., Sensenig, J., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Shaw, K., Shaw, T., Shchablo, K., Shen, J., Shepherd-Themistocleous, C., Sheshukov, A., Shi, W., Shin, S., Shivakoti, S., Shoemaker, I., Shooltz, D., Shrock, R., Siddi, B., Siden, M., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smedley, J., Smith, E., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Snowden-Ifft, D., Nunes, M. Soares, Sobel, H., Soderberg, M., Sokolov, S., Salinas, C. J. Solano, Söldner-Rembold, S., Soleti, S. R., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Sotnikov, A., Soto-Oton, J., Sousa, A., Soustruznik, K., Spinella, F., Spitz, J., Spooner, N. J. C., Spurgeon, K., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thiebault, A., Thomas, S., Thompson, A., Thorn, C., Timm, S. C., Tiras, E., Tishchenko, V., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truchon, J., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tunnell, C., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valder, S., Valdiviesso, G. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Van de Water, R. G., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Oliva, D. Vargas, Vasina, S., Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Vizcaya-Hernandez, A., Vrba, T., Vuong, Q., Waldron, A. V., Wallbank, M., Walsh, J., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Wenzel, H., Westerdale, S., Wetstein, M., Whalen, K., Whilhelmi, J., White, A., Whitehead, L. H., Whittington, D., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: High Energy Physics - Experiment, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics, Nuclear Experiment, Physics - Instrumentation and Detectors
Abstract: The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on $^{40}$Ar and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called ``brems flipping'', as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE's burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage., Comment: 25 pages, 16 figures
Published: 2024

37. Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical

Author: Behera, Adarsh Prasad, Daubaris, Paulius, Bravo, Iñaki, Gallego, José, Morabito, Roberto, Widmer, Joerg, and Champati, Jaya Prakash Varma
Subjects: Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: On-device inference holds great potential for increased energy efficiency, responsiveness, and privacy in edge ML systems. However, due to less capable ML models that can be embedded in resource-limited devices, use cases are limited to simple inference tasks such as visual keyword spotting, gesture recognition, and predictive analytics. In this context, the Hierarchical Inference (HI) system has emerged as a promising solution that augments the capabilities of the local ML by offloading selected samples to an edge server or cloud for remote ML inference. Existing works demonstrate through simulation that HI improves accuracy. However, they do not account for the latency and energy consumption on the device, nor do they consider three key heterogeneous dimensions that characterize ML systems: hardware, network connectivity, and models. In contrast, this paper systematically compares the performance of HI with on-device inference based on measurements of accuracy, latency, and energy for running embedded ML models on five devices with different capabilities and three image classification datasets. For a given accuracy requirement, the HI systems we designed achieved up to 73% lower latency and up to 77% lower device energy consumption than an on-device inference system. The key to building an efficient HI system is the availability of small-size, reasonably accurate on-device models whose outputs can be effectively differentiated for samples that require remote inference. Despite the performance gains, HI requires on-device inference for all samples, which adds a fixed overhead to its latency and energy consumption. Therefore, we design a hybrid system, Early Exit with HI (EE-HI), and demonstrate that compared to HI, EE-HI reduces the latency by up to 59.7% and lowers the device's energy consumption by up to 60.4%.
Published: 2024

38. Ferromagnetic CrBr$_3$-Induced Graphene Spintronics

Author: Behera, Sushant Kumar and Ramamurthy, Praveen C
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Applied Physics
Abstract: Our proposed spin valve prototype showcases a sophisticated design featuring a two-dimensional graphene bilayer positioned between layers of ${CrBr}_3$ ferromagnetic insulators. In this model, proximity coupling plays a pivotal role, influencing the magnetization orientations of the graphene layers and significantly impacting the \textit{in-plane} conductivity of the ${CrBr}_3$ layers. In this present work, we position the graphene bilayer between two layers of the ferromagnetic insulator ${CrBr}_3$ to establish this configuration. Using density functional theory, we conduct detailed computations to analyze the electronic structure of this sandwiched system. Our findings reveal a notable finite gap at specific \textit{k}-points, particularly evident in the antiparallel configuration of the magnetizations. This finding represents a significant advancement in spintronics, underscoring the potential of our spin valve prototype to drive innovation in electronic device technologies., Comment: 5 pages, 4 figures
Published: 2024

39. Study of $\chi_{bJ}(2P)\to\omega\Upsilon(1S)$ at Belle

Author: Belle Collaboration, Stottler, Z. S., Pedlar, T. K., Fulsom, B. G., Adachi, I., Adamczyk, K., Aihara, H., Said, S. Al, Asner, D. M., Atmacan, H., Aushev, T., Ayad, R., Babu, V., Banerjee, Sw., Bauer, M., Behera, P., Belous, K., Bennett, J., Bernlochner, F., Bessner, M., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bonvicini, G., Borah, J., Bozek, A., Branchini, P., Browder, T. E., Budano, A., Campajola, M., Cao, L., Červenkov, D., Chang, M. -C., Cheon, B. G., Chilikin, K., Cho, H. E., Cho, K., Choi, S. -K., Choi, Y., Choudhury, S., Cinabro, D., Das, S., De Nardo, G., De Pietro, G., Dhamija, R., Di Capua, F., Doležal, Z., Dong, T. V., Dubey, S., Ecker, P., Epifanov, D., Ferber, T., Ferlewicz, D., Gaur, V., Garmash, A., Giri, A., Goldenzweig, P., Graziani, E., Gu, T., Guan, Y., Gudkova, K., Hadjivasiliou, C., Hara, T., Hayasaka, K., Hazra, S., Hedges, M. T., Herrmann, D., Hou, W. -S., Hsu, C. -L., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Iwasaki, Y., Jacobs, W. W., Jia, S., Jin, Y., Kaliyar, A. B., Kawasaki, T., Kiesling, C., Kim, C. H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kodyš, P., Korobov, A., Korpar, S., Kovalenko, E., Križan, P., Krokovny, P., Kuhr, T., Kumar, M., Kumar, R., Kumara, K., Kuzmin, A., Kwon, Y. -J., Lai, Y. -T., Lam, T., Laurenza, M., Lee, S. C., Levit, D., Lewis, P., Li, L. K., Libby, J., Lieret, K., Liventsev, D., Luo, T., Ma, Y., Masuda, M., Maurya, S. K., Meier, F., Merola, M., Miyabayashi, K., Mohanty, G. B., Nakamura, I., Nakao, M., Natochii, A., Nayak, L., Nisar, N. K., Nishida, S., Ogawa, K., Ogawa, S., Ono, H., Oskin, P., Pakhlov, P., Pakhlova, G., Pang, T., Pardi, S., Park, J., Park, S. -H., Patra, S., Paul, S., Pestotnik, R., Piilonen, L. E., Podobnik, T., Prencipe, E., Prim, M. T., Rout, N., Russo, G., Sandilya, S., Sangal, A., Santelj, L., Savinov, V., Schnell, G., Schwanda, C., Seino, Y., Senyo, K., Shan, W., Shapkin, M., Sharma, C., Shiu, J. -G., Sokolov, A., Solovieva, E., Starič, M., Sumihama, M., Sutcliffe, W., Takizawa, M., Tanida, K., Tenchini, F., Tiwary, R., Uchida, M., Unno, Y., Uno, S., Vahsen, S. E., Varner, G., Wang, D., Wang, E., Wang, M. -Z., Watanuki, S., Werbycka, O., Won, E., Yabsley, B. D., Yan, W., Yin, J. H., Yuan, C. Z., Yuan, L., Yusa, Y., Zhang, Z. P., Zhilich, V., and Zhukova, V.
Subjects: High Energy Physics - Experiment
Abstract: We report a study of the hadronic transitions $\chi_{bJ}(2P)\to\omega\Upsilon(1S)$, with $\omega\to\pi^{+}\pi^{-}\pi^{0}$, using $28.2\times10^6~\Upsilon(3S)$ mesons recorded by the Belle detector. We present the first evidence for the near--threshold transition $\chi_{b0}(2P)\to\omega\Upsilon(1S)$, the analog of the charm sector decay $\chi_{c1}(3872)\to\omega J/\psi$, with a branching fraction of $B\big(\chi_{b0}(2P)\to\omega\Upsilon(1S)\big) = \big(0.55\pm0.19\pm0.07\big)\%$. We also obtain branching fractions of $B\big(\chi_{b1}(2P)\to\omega\Upsilon(1S)\big) = \big(2.39{}^{+0.20}_{-0.19}\pm0.24\big)\%$ and $B\big(\chi_{b2}(2P)\to\omega\Upsilon(1S)\big) = \big(0.47{}^{+0.13}_{-0.12}\pm0.06\big)\%$, confirming the measurement of the $\omega$ transitions of the $J=1,2~P$--wave states. The ratio for the $J=2$ to $J=1$ transitions is also measured and found to differ by 3.3 standard deviations from the expected value in the QCD multipole expansion., Comment: 6 pages, 2 figures
Published: 2024

40. Ultrafast high-temperature sintering of dense and textured alumina

Author: Behera, Rohit Pratyush, Reavley, Matthew Jun-Hui, Du, Zehui, Lip, Gan Chee, and Ferrand, Hortense Le
Subjects: Condensed Matter - Materials Science, Condensed Matter - Other Condensed Matter
Abstract: Crystallographic texture engineering in ceramics is essential to achieve direction-specific properties. Current texture engineering methods are time-consuming, energy extensive, or can lead to unnecessary diffusion of added dopants. Herein, we explore ultrafast high-temperature sintering (UHS) to prepare dense and textured alumina using templated grain growth (TGG). From a slurry containing alumina microplatelets coated with Fe3O4 nanoparticles dispersed in a matrix of alumina nanoparticles, green bodies with oriented microplatelets were prepared using magnetic assisted slip casting (MASC). The effects of the sintering temperature, time and heating rate on the density and microstructure of the obtained ceramics were then studied. We found that TGG occurs for a temperature range between 1640 and 1780 {\deg}C and 10 s sintering time. Sintering at 1700 {\deg}C for 10 s led to dense and textured alumina with anisotropic grains thanks to the Fe3O4 coating, which did not have the time to diffuse. The highest texture and relative density were obtained with a heating rate of ~5,500 {\deg}C/min, leading to texture-dependent anisotropic mechanical properties. This study opens new avenues for fabricating textured ceramics in ultra-short times., Comment: 37 pages, 19 figures, includes main manuscript and abstract
Published: 2024
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41. Effect of interfacial Fe3O4 nanoparticles on the microstructure and mechanical properties of textured alumina densified by ultrafast high-temperature sintering

Author: Behera, Rohit Pratyush, Ng, Andrew Yun Ru, Du, Zehui, Gan, Chee Lip, and Ferrand, Hortense Le
Subjects: Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Alumina microplatelets coated with a small amount of Fe3O4 can be oriented via a rotating magnetic field to create texture. After ultrafast high-temperature sintering (UHS), Fe atoms are found at the grain boundaries and within the grains, influencing the mechanical properties. Here, we compare the microstructure and mechanical properties of textured alumina prepared with and without Fe3O4 and sintered using UHS or conventional sintering (CS). Microstructural analysis using electron backscattering diffraction (EBSD) indicates that Fe3O4 induces crystallographic defects in the ceramic after UHS. Nanoindentation measurements enlighten that the presence of Fe3O4 leads to plastic flow that increases the energy dissipation, reaching ~122 % at a maximum load of 1900 mN compared to pristine samples. Overall, due to the concentrated effects of Fe3O4 after UHS, the flexural strength and fracture toughness values are higher than the other two samples, reaching values of ~287 MPa and 7 MPa.m0.5, respectively. These results could be leveraged to produce stronger and tougher ceramics., Comment: 10 pages, 11 figures, contains main manuscript and supplementary file
Published: 2024
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42. Dynamics of colloidal rods rotating in viscoelastic media

Author: Narinder, N, Behera, Jyotiprakash, and Ghosh, Ambarish
Subjects: Condensed Matter - Soft Condensed Matter
Abstract: We experimentally investigate the in-plane rotational motion of ferromagnetic colloidal rods immersed in viscoelastic media and subjected to a rotating magnetic field. Unexpectedly, we observe significant angular velocity even at field frequencies an order of magnitude exceeding the step-out frequency, a regime where rods typically cease rotating in Newtonian fluids. This anomalous behavior arises from the interplay between the rapid rod actuation driven by the external field and the slower microstructural relaxation of the viscoelastic fluid. A minimal model incorporating memory effects quantitatively captures our experimental findings. Our study demonstrate a rather general case of microrheological probe dynamics in viscoelastic media where the behavior beyond step-out frequency depends strongly on the rheological parameters medium. Additionally, we derive an analytical expression for the rod orientation in the high-frequency limit, providing a potential method for extracting rheological parameters.
Published: 2024

43. Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

Author: Aamir, M., Acar, B., Adamov, G., Adams, T., Adloff, C., Afanasiev, S., Agrawal, C., Ahmad, A., Ahmed, H. A., Akbar, S., Akchurin, N., Akgul, B., Akgun, B., Akpinar, R. O., Aktas, E., AlKadhim, A., Alexakhin, V., Alimena, J., Alison, J., Alpana, A., Alshehri, W., Dominguez, P. Alvarez, Alyari, M., Amendola, C., Amir, R. B., Andersen, S. B., Andreev, Y., Antoszczuk, P. D., Aras, U., Ardila, L., Aspell, P., Avila, M., Awad, I., Aydilek, O., Azimi, Z., Pretel, A. Aznar, Bach, O. A., Bainbridge, R., Bakshi, A., Bam, B., Banerjee, S., Barney, D., Bayraktar, O., Beaudette, F., Beaujean, F., Becheva, E., Behera, P. K., Belloni, A., Bergauer, T., Besancon, M., Bylund, O. Bessidskaia, Bhatt, L., Bhowmil, D., Blekman, F., Blinov, P., Bloch, P., Bodek, A., Boger, a., Bonnemaison, A., Bouyjou, F., Brennan, L., Brondolin, E., Brusamolino, A., Bubanja, I., Perraguin, A. Buchot, Bunin, P., Misura, A. Burazin, Butler-nalin, A., Cakir, A., Callier, S., Campbell, S., Canderan, K., Cankocak, K., Cappati, A., Caregari, S., Carron, S., Carty, C., Cauchois, A., Ceard, L., Cerci, S., Chang, P. J., Chatterjee, R. M., Chatterjee, S., Chattopadhyay, P., Chatzistavrou, T., Chaudhary, M. S., Chauhan, A., Chen, J. A., Chen, J., Chen, Y., Cheng, K., Cheung, H., Chhikara, J., Chiron, A., Chiusi, M., Chokheli, D., Chudasama, R., Clement, E., Mendez, S. Coco, Coko, D., Coskun, K., Couderc, F., Crossman, B., Cui, Z., Cuisset, T., Cummings, G., Curtis, E. M., D'Alfonso, M., D-hler-ball, J., Dadazhanova, O., Damgov, J., Das, I., DasGupta, S., Dauncey, P., Mendes, A. David Tinoco, Davies, G., Davignon, O., DeLa, P. deBarbaroC., DeSilva, M., DeWit, A., Debbins, P., Defranchis, M. M., Delagnes, E., Devouge, P., Dewangan, C., DiGuglielmo, G., Diehl, L., Dilsiz, K., Dincer, G. G., Dittmann, J., Dragicevic, M., Du, D., Dubinchik, B., Dugad, S., Dulucq, F., Dumanoglu, I., Duran, B., Dutta, S., Dutta, V., Dychkant, A., Dünser, M., Edberg, T., Ehle, I. T., Berni, A. El, Elias, F., Eno, S. C., Erdogan, E. N., Erkmen, B., Ershov, Y., Ertorer, E. Y., Extier, S., Eychenne, L., Fedar, Y. E., Fedi, G., De Almeida, J. P. Figueiredo De De Sá Sousa, Alves, B. A. Fontana Santos Santos, Frahm, E., Francis, K., Freeman, J., French, T., Gaede, F., Gandhi, P. K., Ganjour, S., Garcia-Bellido, A., Gastaldi, F., Gazi, L., Gecse, Z., Gerwig, H., Gevin, O., Ghosh, S., Gill, K., Gleyzer, S., Godinovic, N., Goek, M., Goettlicher, P., Goff, R., Golunov, A., Gonultas, B., Martínez, J. D. González, Gorbounov, N., Gouskos, L., Gray, A., Gray, L., Grieco, C., Groenroos, S., Groner, D., Gruber, A., Grummer, A., Grönroos, S., Guilloux, F., Guler, Y., Gungordu, A. D., Guo, J., Guo, K., Guler, E. Gurpinar, Gutti, H. K., Guvenli, A. A., Gülmez, E., Hacisahinoglu, B., Halkin, Y., Machado, G. Hamilton Ilha, Hare, H. S., Hatakeyama, K., Heering, A. H., Hegde, V., Heintz, U., Hinton, N., Hinzmann, A., Hirschauer, J., Hitlin, D., Hos, İ., Hou, B., Hou, X., Howard, A., Howe, C., Hsieh, H., Hsu, T., Hua, H., Hummer, F., Imran, M., Incandela, J., Iren, E., Isildak, B., Jackson, P. S., Jackson, W. J., Jain, S., Jana, P., Jaroslavceva, J., Jena, S., Jige, A., Jordano, P. P., Joshi, U., Kaadze, K., Kafizov, A., Kalipoliti, L., Tharayil, A. Kallil, Kaluzinska, O., Kamble, S., Kaminskiy, A., Kanemura, M., Kanso, H., Kao, Y., Kapic, A., Kapsiak, C., Karjavine, V., Karmakar, S., Karneyeu, A., Kaya, M., Topaksu, A. Kayis, Kaynak, B., Kazhykarim, Y., Khan, F. A., Khudiakov, A., Kieseler, J., Kim, R. S., Klijnsma, T., Kloiber, E. G., Klute, M., Kocak, Z., Kodali, K. R., Koetz, K., Kolberg, T., Kolcu, O. B., Komaragiri, J. R., Komm, M., Kopsalis, I., Krause, H. A., Krawczyk, M. A., Vinayakam, T. R. Krishnaswamy, Kristiansen, K., Kristic, A., Krohn, M., Kronheim, B., Krüger, K., Kudtarkar, C., Kulis, S., Kumar, M., Kumar, N., Kumar, S., Verma, R. Kumar, Kunori, S., Kunts, A., Kuo, C., Kurenkov, A., Kuryatkov, V., Kyre, S., Ladenson, J., Lamichhane, K., Landsberg, G., Langford, J., Laudrain, A., Laughlin, R., Lawhorn, J., Dortz, O. Le, Lee, S. W., Lektauers, A., Lelas, D., Leon, M., Levchuk, L., Li, A. J., Li, J., Li, Y., Liang, Z., Liao, H., Lin, K., Lin, W., Lin, Z., Lincoln, D., Linssen, L., Litomin, A., Liu, G., Liu, Y., Lobanov, A., Lohezic, V., Loiseau, T., Lu, C., Lu, R., Lu, S. Y., Lukens, P., Mackenzie, M., Magnan, A., Magniette, F., Mahjoub, A., Mahon, D., Majumder, G., Makarenko, V., Malakhov, A., Malgeri, L., Mallios, S., Mandloi, C., Mankel, A., Mannelli, M., Mans, J., Mantilla, C., Martinez, G., Massa, C., Masterson, P., Matthewman, M., Matveev, V., Mayekar, S., Mazlov, I., Mehta, A., Mestvirishvili, A., Miao, Y., Milella, G., Mirza, I. R., Mitra, P., Moccia, S., Mohanty, G. B., Monti, F., Moortgat, F., Murthy, S., Music, J., Musienko, Y., Nabili, S., Nayak, S., Nelson, J. W., Nema, A., Neutelings, I., Niedziela, J., Nikitenko, A., Noonan, D., Noy, M., Nurdan, K., Obraztsov, S., Ochando, C., Ogul, H., Olsson, J., Onel, Y., Ozkorucuklu, S., Paganis, E., Palit, P., Pan, R., Pandey, S., Pantaleo, F., Papageorgakis, C., Paramesvaran, S., Paranjpe, M. M., Parolia, S., Parsons, A. G., Parygin, P., Paulini, M., Paus, C., Peñaló, K., Pedro, K., Pekic, V., Peltola, T., Peng, B., Perego, A., Perini, D., Petrilli, A., Pham, H., Pierre-Emile, T., Podem, S. K., Popov, V., Portales, L., Potok, O., Pradeep, P. B., Pramanik, R., Prosper, H., Prvan, M., Qasim, S. R., Qu, H., Quast, T., Trivino, A. Quiroga, Rabour, L., Raicevic, N., Rajpoot, H., Rao, M. A., Rapacz, K., Redjeb, W., Reinecke, M., Revering, M., Roberts, A., Rohlf, J., Rosado, P., Rose, A., Rothman, S., Rout, P. K., Rovere, M., Rumerio, P., Rusack, R., Rygaard, L., Ryjov, V., Sadivnycha, S., Sahin, M. Ö., Sakarya, U., Salerno, R., Saradhy, R., Saraf, M., Sarbandi, K., Sarkisla, M. A., Satyshev, I., Saud, N., Sauvan, J., Schindler, G., Schmidt, A., Schmidt, I., Schmitt, M. H., Sculac, A., Sculac, T., Sedelnikov, A., Seez, C., Sefkow, F., Selivanova, D., Selvaggi, M., Sergeychik, V., Sert, H., Shahid, M., Sharma, P., Sharma, R., Sharma, S., Shelake, M., Shenai, A., Shih, C. W., Shinde, R., Shmygol, D., Shukla, R., Sicking, E., Silva, P., Simsek, C., Simsek, E., Sirasva, B. K., Sirois, Y., Song, S., Song, Y., Soudais, G., Sriram, S., StJacques, R. R., StahlLeiton, A. G., Steen, A., Stein, J., Strait, J., Strobbe, N., Su, X., Sukhov, E., Suleiman, A., Cerci, D. Sunar, Suryadevara, P., Swain, K., Syal, C., Tali, B., Tanay, K., Tang, W., Tanvir, A., Tao, J., Tarabini, A., Tatli, T., Taylor, R., Taysi, Z. C., Teafoe, G., Tee, C. Z., Terrill, W., Thienpont, D., Thomas, R., Titov, M., Todd, C., Todd, E., Toms, M., Tosun, A., Troska, J., Tsai, L., Tsamalaidze, Z., Tsionou, D., Tsipolitis, G., Tsirigoti, M., Tu, R., Polat, S. N. Tural, Undleeb, S., Usai, E., Uslan, E., Ustinov, V., Vernazza, E., Viahin, O., Viazlo, O., Vichoudis, P., Vijay, A., Virdee, T., Voirin, E., Vojinovic, M., Voytishin, N., Vámi, T. Á., Wade, A., Walter, D., Wang, C., Wang, F., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Wanlin, E., Wayne, M., Wetzel, J., Whitbeck, A., Wickwire, R., Wilmot, D., Wilson, J., Wu, H., Xiao, M., Yang, J., Yazici, B., Ye, Y., Yetkin, T., Yi, R., Yohay, R., Yu, T., Yuan, C., Yuan, X., Yuksel, O., YushmanoV, I., Yusuff, I., Zabi, A., Zareckis, D., Zarubin, A., Zehetner, P., Zghiche, A., Zhang, C., Zhang, D., Zhang, H., Zhang, J., Zhang, Z., Zhao, X., Zhong, J., Zhou, Y., and Zorbilmez, Ç.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability
Abstract: A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST
Published: 2024

44. Pick-or-Mix: Dynamic Channel Sampling for ConvNets

Author: Kumar, Ashish, Kim, Daneul, Park, Jaesik, and Behera, Laxmidhar
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Channel pruning approaches for convolutional neural networks (ConvNets) deactivate the channels, statically or dynamically, and require special implementation. In addition, channel squeezing in representative ConvNets is carried out via 1x1 convolutions which dominates a large portion of computations and network parameters. Given these challenges, we propose an effective multi-purpose module for dynamic channel sampling, namely Pick-or-Mix (PiX), which does not require special implementation. PiX divides a set of channels into subsets and then picks from them, where the picking decision is dynamically made per each pixel based on the input activations. We plug PiX into prominent ConvNet architectures and verify its multi-purpose utilities. After replacing 1x1 channel squeezing layers in ResNet with PiX, the network becomes 25% faster without losing accuracy. We show that PiX allows ConvNets to learn better data representation than widely adopted approaches to enhance networks' representation power (e.g., SE, CBAM, AFF, SKNet, and DWP). We also show that PiX achieves state-of-the-art performance on network downscaling and dynamic channel pruning applications., Comment: Published in Computer Vision and Pattern Recognition (CVPR 2024)
Published: 2024

45. Towards Multilingual Audio-Visual Question Answering

Author: Phukan, Orchid Chetia, Mallick, Priyabrata, Behera, Swarup Ranjan, Narayani, Aalekhya Satya, Buduru, Arun Balaji, and Sharma, Rajesh
Subjects: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, 68T45
Abstract: In this paper, we work towards extending Audio-Visual Question Answering (AVQA) to multilingual settings. Existing AVQA research has predominantly revolved around English and replicating it for addressing AVQA in other languages requires a substantial allocation of resources. As a scalable solution, we leverage machine translation and present two multilingual AVQA datasets for eight languages created from existing benchmark AVQA datasets. This prevents extra human annotation efforts of collecting questions and answers manually. To this end, we propose, MERA framework, by leveraging state-of-the-art (SOTA) video, audio, and textual foundation models for AVQA in multiple languages. We introduce a suite of models namely MERA-L, MERA-C, MERA-T with varied model architectures to benchmark the proposed datasets. We believe our work will open new research directions and act as a reference benchmark for future works in multilingual AVQA., Comment: Accepted to Interspeech 2024
Published: 2024

46. Interfacial Dynamics and Catalytic Behavior of Single Ni Atom Site

Author: Priyanga, G. S. and Behera, S. K.
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Single-atom catalysts (SACs) have garnered significant interest due to their ability to reduce metal particles to the atomic scale, enabling finely tunable local environments and enhanced catalytic properties in terms of reactivity and selectivity. Despite this potential, their application has largely been confined to small-molecule transformations as metal-catalyzed reaction. In this study, we present a diverse single-atom nickel (Ni) catalyst established via a nanoporous carbon (NPC) supported practice. This catalyst represents a breakthrough by achieving the bond formation between carbon and nitrogen and interfacial dynamics in the SAC. The present first principle-based density functional simulations establish the reaction dynamics and catalytic behaviour of such SAC. This dynamic nature comprises an exclusive nitrogen intercalated site showing excellent base effects. This base quickly tunes the interfacial atmosphere, enabling dynamic movement of adatoms into the NPC species, significantly changing the reaction path in Ni SACs due to superior steric effects. The research demonstrates that SACs can extend the capabilities of catalytic systems to include a wider range of complex reactions, offering substantial promise for the development of new, efficient synthetic methods for creating value-added molecular products., Comment: 12 pages, 4 figures
Published: 2024

47. FastAST: Accelerating Audio Spectrogram Transformer via Token Merging and Cross-Model Knowledge Distillation

Author: Behera, Swarup Ranjan, Dhiman, Abhishek, Gowda, Karthik, and Narayani, Aalekhya Satya
Subjects: Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Multimedia, Electrical Engineering and Systems Science - Audio and Speech Processing, 68T10
Abstract: Audio classification models, particularly the Audio Spectrogram Transformer (AST), play a crucial role in efficient audio analysis. However, optimizing their efficiency without compromising accuracy remains a challenge. In this paper, we introduce FastAST, a framework that integrates Token Merging (ToMe) into the AST framework. FastAST enhances inference speed without requiring extensive retraining by merging similar tokens in audio spectrograms. Furthermore, during training, FastAST brings about significant speed improvements. The experiments indicate that FastAST can increase audio classification throughput with minimal impact on accuracy. To mitigate the accuracy impact, we integrate Cross-Model Knowledge Distillation (CMKD) into the FastAST framework. Integrating ToMe and CMKD into AST results in improved accuracy compared to AST while maintaining faster inference speeds. FastAST represents a step towards real-time, resource-efficient audio analysis., Comment: Accepted to Interspeech 2024
Published: 2024

48. Scintillation Light in SBND: Simulation, Reconstruction, and Expected Performance of the Photon Detection System

Author: SBND Collaboration, Abratenko, P., Acciarri, R., Adams, C., Aliaga-Soplin, L., Alterkait, O., Alvarez-Garrote, R., Andreopoulos, C., Antonakis, A., Arellano, L., Asaadi, J., Badgett, W., Balasubramanian, S., Basque, V., Beever, A., Behera, B., Belchior, E., Betancourt, M., Bhat, A., Bishai, M., Blake, A., Bogart, B., Bogenschuetz, J., Brailsford, D., Brandt, A., Brickner, S., Bueno, A., Camilleri, L., Caratelli, D., Carber, D., Carlson, B., Carneiro, M., Castillo, R., Cavanna, F., Chen, H., Chung, S., Cicala, M. F., Coackley, R., Crespo-Anadón, J. I., Cuesta, C., Dalager, O., Darby, R., Del Tutto, M., Di Benedetto, V., Djurcic, Z., Duffy, K., Dytman, S., Ereditato, A., Evans, J. J., Ezeribe, A., Fan, C., Filkins, A., Fleming, B., Foreman, W., Franco, D., Furic, I., Furmanski, A., Gao, S., Garcia-Gamez, D., Gardiner, S., Ge, G., Gil-Botella, I., Gollapinni, S., Green, P., Griffith, W. C., Guenette, R., Guzowski, P., Hagaman, L., Hamer, A., Hamilton, P., Hernandez-Morquecho, M., Hilgenberg, C., Howard, B., Imani, Z., James, C., Jones, R. S., Jung, M., Junk, T., Kalra, D., Karagiorgi, G., Kelly, K., Ketchum, W., King, M., Klein, J., Kotsiopoulou, L., Kroupová, T., Kudryavtsev, V. A., Larkin, J., Lay, H., LaZur, R., Li, J. -Y., Lin, K., Littlejohn, B., Louis, W. C., Luo, X., Machado, A., Machado, P., Mariani, C., Marinho, F., Mastbaum, A., Mavrokoridis, K., McConkey, N., McCusker, B., Meddage, V., Mendez, D., Mooney, M., Moor, A. F., Moura, C. A., Mulleriababu, S., Navrer-Agasson, A., Nebot-Guinot, M., Nguyen, V. C. L., Nicolas-Arnaldos, F., Nowak, J., Oh, S., Oza, N., Palamara, O., Pallat, N., Pandey, V., Papadopoulou, A., Parkinson, H. B., Paton, J., Paulucci, L., Pavlovic, Z., Payne, D., Pelegrina-Gutiérrez, L., Pimentel, V. L., Plows, J., Psihas, F., Putnam, G., Qian, X., Rajagopalan, R., Ratoff, P., Ray, H., Reggiani-Guzzo, M., Roda, M., Ross-Lonergan, M., Safa, I., Sanchez-Castillo, A., Sanchez-Lucas, P., Schmitz, D. W., Schneider, A., Schukraft, A., Scott, H., Segreto, E., Sensenig, J., Shaevitz, M., Slater, B., Soares-Nunes, M., Soderberg, M., Söldner-Rembold, S., Spitz, J., Spooner, N. J. C., Stancari, M., Stenico, G. V., Strauss, T., Szelc, A. M., Totani, D., Toups, M., Touramanis, C., Tung, L., Valdiviesso, G. A., Van de Water, R. G., Vázquez-Ramos, A., Wan, L., Weber, M., Wei, H., Wester, T., White, A., Wilkinson, A., Wilson, P., Wongjirad, T., Worcester, E., Worcester, M., Yadav, S., Yandel, E., Yang, T., Yates, L., Yu, B., Yu, J., Zamorano, B., Zennamo, J., and Zhang, C.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: SBND is the near detector of the Short-Baseline Neutrino program at Fermilab. Its location near to the Booster Neutrino Beam source and relatively large mass will allow the study of neutrino interactions on argon with unprecedented statistics. This paper describes the expected performance of the SBND photon detection system, using a simulated sample of beam neutrinos and cosmogenic particles. Its design is a dual readout concept combining a system of 120 photomultiplier tubes, used for triggering, with a system of 192 X-ARAPUCA devices, located behind the anode wire planes. Furthermore, covering the cathode plane with highly-reflective panels coated with a wavelength-shifting compound recovers part of the light emitted towards the cathode, where no optical detectors exist. We show how this new design provides a high light yield and a more uniform detection efficiency, an excellent timing resolution and an independent 3D-position reconstruction using only the scintillation light. Finally, the whole reconstruction chain is applied to recover the temporal structure of the beam spill, which is resolved with a resolution on the order of nanoseconds., Comment: 21 pages, 17 figures
Published: 2024
Full Text: View/download PDF

49. On the Bures metric, C*-norm, and the quantum metric

Author: Aguilar, Konrad, Behera, Karina, Omland, Tron, and Wu, Nicole
Subjects: Mathematics - Operator Algebras, Mathematics - Functional Analysis, 46L89, 46L30, 58B34
Abstract: We prove that the topology on the density space with respect to a unital C*-algebra and a faithful induced by the C*-norm is finer than the Bures metric topology. We also provide an example when this containment is strict. Next, we provide a metric on the density space induced by a quantum metric in the sense of Rieffel and prove that the induced topology is the same as the topology induced by the Bures metric and C*-norm when the C*-algebra is assumed to be finite dimensional. Finally, we provide an example of when the Bures metric and induced quantum metric are not metric equivalent. Thus, we provide a bridge between these aspects of quantum information theory and noncommutative metric geometry., Comment: 13 pages, 1 figure
Published: 2024

50. Physical and Mechanical Characterization of Ti50Ni(50−X)FeX Shape Memory Alloy Fabricated by Spark Plasma Sintering Process

Author: Parida, Jagadish, Mishra, Subash Chandra, Satapathy, Deepak Kumar, Behera, Kishore Kumar, and Behera, Ajit
Published: 2024
Full Text: View/download PDF

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