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Image classification is a computer vision task where a model analyzes an image to categorize it into a specific label. Vision Transformers (ViT) improve this task by leveraging self-attention to capture complex patterns and long range relationships between image patches. However, a key challenge for ViTs is efficiently incorporating multiscale feature representations, which is inherent in CNNs through their hierarchical structure. In this paper, we introduce the Scale-Aware Graph Attention Vision Transformer (SAG-ViT), a novel framework that addresses this challenge by integrating multi-scale features. Using EfficientNet as a backbone, the model extracts multi-scale feature maps, which are divided into patches to preserve semantic information. These patches are organized into a graph based on spatial and feature similarities, with a Graph Attention Network (GAT) refining the node embeddings. Finally, a Transformer encoder captures long-range dependencies and complex interactions. The SAG-ViT is evaluated on benchmark datasets, demonstrating its effectiveness in enhancing image classification performance., Comment: 10 pages, 4 figures, 3 tables

The rapid proliferation of large language models (LLMs) has increased the volume of machine-generated texts (MGTs) and blurred text authorship in various domains. However, most existing MGT benchmarks include single-author texts (human-written and machine-generated). This conventional design fails to capture more practical multi-author scenarios, where the user refines the LLM response for natural flow, coherence, and factual correctness. Our paper introduces the Benchmark of Expert-edited Machine-generated Outputs (Beemo), which includes 6.5k texts written by humans, generated by ten instruction-finetuned LLMs, and edited by experts for various use cases, ranging from creative writing to summarization. Beemo additionally comprises 13.1k machine-generated and LLM-edited texts, allowing for diverse MGT detection evaluation across various edit types. We document Beemo's creation protocol and present the results of benchmarking 33 configurations of MGT detectors in different experimental setups. We find that expert-based editing evades MGT detection, while LLM-edited texts are unlikely to be recognized as human-written. Beemo and all materials are publicly available.

The interstellar medium of the Milky Way contains turbulent plasma with structures driven by energetic processes that fuel star formation and shape the evolution of our Galaxy. Radio waves from pulsars are scattered off the small (au-scale and below) structures, resulting in frequency-dependent interference patterns that are modulated in time because of the relative motions of the pulsar, Earth, and plasma. Power spectral analyses of these patterns show parabolic arcs with curvatures that encode the locations and kinematics of individual structures. Here we report the discovery of at least 25 distinct plasma structures in the direction of the brilliant millisecond pulsar, PSR J0437$-$4715, in observations obtained with the MeerKAT radio telescope. Four arcs reveal structures within 5000 au of the pulsar, from a series of shocks induced as the pulsar and its wind interact with the ambient insterstellar medium. The measured radial distance and velocity of the main shock allows us to solve the shock geometry and space velocity of the pulsar in three dimensions, while the velocity of another structure unexpectedly indicates a back flow from the direction of the shock or pulsar-wind tail. The remaining 21 arcs represent a surprising abundance of structures sustained by turbulence within the Local Bubble -- a region of the interstellar medium thought to be depleted of gas by a series of supernova explosions about 14 Myr ago., Comment: 46 pages, 10 figures, 1 table, submitted to Nature Astronomy

PSR J0737$-$3039A/B is unique among double neutron star systems. Its near-perfect edge-on orbit causes the fast spinning pulsar A to be eclipsed by the magnetic field of the slow spinning pulsar B. Using high-sensitivity MeerKAT radio observations combined with updated constraints on the system geometry, we studied the impact of these eclipses on the incident polarization properties of pulsar A. Averaging light curves together after correcting for the rotation of pulsar B revealed enormous amounts of circular polarization and rapid changes in the linear polarization position angle, which occur at phases where emission from pulsar A is partially transmitted through the magnetosphere of pulsar B. These behaviours confirm that the eclipse mechanism is the result of synchrotron absorption in a relativistic pair-plasma confined to the closed-field region of pulsar B's truncated dipolar magnetic field. We demonstrate that changes in circular polarization handedness throughout the eclipses are directly tied to the average line of sight magnetic field direction of pulsar B, from which we unambiguously determine the complete magnetic and viewing geometry of the pulsar., Comment: 8 pages, 6 figures. Accepted for publication in MNRAS

Strongly lensed quasars can be used to constrain cosmological parameters through time-delay cosmography. Models of the lens masses are a necessary component of this analysis. To enable time-delay cosmography from a sample of $\mathcal{O}(10^3)$ lenses, which will soon become available from surveys like the Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Euclid Wide Survey, we require fast and standardizable modeling techniques. To address this need, we apply neural posterior estimation (NPE) for modeling galaxy-scale strongly lensed quasars from the Strong Lensing Insights into the Dark Energy Survey (STRIDES) sample. NPE brings two advantages: speed and the ability to implicitly marginalize over nuisance parameters. We extend this method by employing sequential NPE to increase precision of mass model posteriors. We then fold individual lens models into a hierarchical Bayesian inference to recover the population distribution of lens mass parameters, accounting for out-of-distribution shift. After verifying our method using simulated analogs of the STRIDES lens sample, we apply our method to 14 Hubble Space Telescope single-filter observations. We find the population mean of the power-law elliptical mass distribution slope, $\gamma_{\text{lens}}$, to be $\mathcal{M}_{\gamma_{\text{lens}}}=2.13 \pm 0.06$. Our result represents the first population-level constraint for these systems. This population-level inference from fully automated modeling is an important stepping stone towards cosmological inference with large samples of strongly lensed quasars.

The Small and Large Magellanic Clouds are the only galaxies outside our own in which radio pulsars have been discovered to date. The sensitivity of the MeerKAT radio interferometer offers an opportunity to search for a population of more distant extragalactic pulsars. The TRAPUM (TRansients And PUlsars with MeerKAT) collaboration has performed a radio-domain search for pulsars and transients in the dwarf star-forming galaxies Sextans A and B, situated at the edge of the local group 1.4 Mpc away. We conducted three 2-hour multi-beam observations at L-band (856-1712 MHz) with the full array of MeerKAT. No pulsars were found down to a radio pseudo-luminosity upper limit of 7.9$\pm$0.4 Jy kpc$^{2}$ at 1400 MHz, which is 28 times more sensitive than the previous limit from the Murriyang telescope. This luminosity is 30 per cent greater than that of the brightest known radio pulsar and sets a cut-off on the luminosity distributions of the entire Sextans A and B galaxies for unobscured radio pulsars beamed in our direction. A Fast Radio Burst was detected in one of the Sextans A observations at a Dispersion Measure (DM) of 737 pc cm$^{-3}$. We believe this is a background event not associated with the dwarf galaxy due to its large DM and its S/N being strongest in the wide-field incoherent beam of MeerKAT., Comment: 11 pages, 9 figures, 5 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society

This paper investigates the stereographic projection of points along the Nyquist plots of single input single output (SISO) linear time invariant (LTI) systems subject to probabilistic uncertainty. At each frequency, there corresponds a complex-valued random variable with given probability distribution in the complex plane. The chordal distance between the stereographic projections of this complex value and the corresponding value for a nominal model, as per the well-known Nu-Gap metric of Vinnicombe, is also a random quantity. The main result provides the cumulative density function (CDF) of the chordal distance at a given frequency. Such a stochastic distance framework opens up a fresh and a fertile research direction on probabilistic robust control theory.

Tumors in the brain result from abnormal cell growth within the brain tissue, arising from various types of brain cells. When left undiagnosed, they lead to severe neurological deficits such as cognitive impairment, motor dysfunction, and sensory loss. As the tumor grows, it causes an increase in intracranial pressure, potentially leading to life-threatening complications such as brain herniation. Therefore, early detection and treatment are necessary to manage the complications caused by such tumors to slow down their growth. Numerous works involving deep learning (DL) and artificial intelligence (AI) are being carried out to assist physicians in early diagnosis by utilizing the scans obtained through Magnetic Resonance Imaging (MRI). Our research proposes DL frameworks for localizing, segmenting, and classifying the grade of these gliomas from MRI images to solve this critical issue. In our localization framework, we enhance the LinkNet framework with a VGG19- inspired encoder architecture for improved multimodal tumor feature extraction, along with spatial and graph attention mechanisms to refine feature focus and inter-feature relationships. Following this, we integrated the SeResNet101 CNN model as the encoder backbone into the LinkNet framework for tumor segmentation, which achieved an IoU Score of 96%. To classify the segmented tumors, we combined the SeResNet152 feature extractor with an Adaptive Boosting classifier, which yielded an accuracy of 98.53%. Our proposed models demonstrated promising results, with the potential to advance medical AI by enabling early diagnosis and providing more accurate treatment options for patients., Comment: 36 pages, 27 figures, 5 tables

We study the symmetries of the Liouville superoperator of one dimensional parametric oscillators, especially the so-called squeeze-driven Kerr oscillator, and discover a remarkable quasi-spin symmetry $su(2)$ at integer values of the ratio $\eta =\omega /K$ of the detuning parameter $\omega$ to the Kerr coefficient $K$, which reflects the symmetry previously found for the Hamiltonian operator. We find that the Liouvillian of an $su(2)$ representation $\left\vert j,m_{j}\right\rangle$ has a characteristic double-ellipsoidal structure, and calculate the relaxation time $T_{X}$ for this structure. We then study the phase transitions of the Liouvillian which occur as a function of the parameters $\xi =\varepsilon _{2}/K$ and $\eta=\omega /K$. Finally, we study the temperature dependence of the spectrum of eigenvalues of the Liouvillian. Our findings may have applications in the generation and stabilization of states of interest in quantum computing., Comment: 36 pages, 23 figures, to appear in J. Phys. A: Math. Theor

PSR J1618-3921 is one of five known millisecond pulsars (MSPs) in eccentric orbits (eMPSs) located in the Galactic plane, whose formation is poorly understood. Earlier studies of these objects revealed significant discrepancies between observation and predictions from standard binary evolution scenarios of pulsar-Helium white dwarf binaries. We conducted observations with the L-band receiver of the MeerKAT radio telescope and the UWL receiver of the Parkes Murriyang radio telescope between 2019 and 2021. These data were added to archival observations. We perform an analysis of this joint 23-year-dataset. We use the recent observations to give a brief account of the emission properties of J1618-3921, including a Rotating Vector model fit of the linear polarisation position angle of the pulsar. The long timing baseline allowed for a highly significant measurement of the rate of advance of periastron of $\dot{\omega}$. We can only report a low significance detection of the orthometric Shapiro delay parameters $h_3$ and $\varsigma$, leading to mass estimates of the total and individual binary masses. We detect an unexpected change in the orbital period of, which is an order of magnitude larger and carries an opposite sign to what is expected from Galactic acceleration and the Shklovskii effect. We also detect a significant second derivative of the spin frequency. Furthermore, we report an unexpected, abrupt change of the mean pulse profile in June 2021 with unknown origin. We propose that the anomalous $\dot{P_b}$ and $\ddot{f}$ indicate an additional varying acceleration due to a nearby mass, i.e., the J1618-3921 binary system is likely part of a hierarchical triple. This finding suggests that at least some eMSPs might have formed in triple star systems. Although the uncertainties are large, the binary companion mass is consistent with the $P_b$ - $M_{WD}$ relation., Comment: 17 pages, 11 figures, 3 tables

Distributed Denial of Service (DDoS) attacks are a major concern in network security, as they overwhelm systems with excessive traffic, compromise sensitive data, and disrupt network services. Accurately detecting these attacks is crucial to protecting network infrastructure. Traditional approaches, such as single Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) algorithms like Decision Trees (DTs) and Support Vector Machines (SVMs), struggle to extract the diverse features needed for precise classification, resulting in suboptimal performance. This research addresses this gap by introducing a novel approach for DDoS attack detection. The proposed method combines three distinct CNN architectures: SA-Enabled CNN with XGBoost, SA-Enabled CNN with LSTM, and SA-Enabled CNN with Random Forest. Each model extracts features at multiple scales, while self-attention mechanisms enhance feature integration and relevance. The weighted ensemble approach ensures that both prominent and subtle features contribute to the final classification, improving adaptability to evolving attack patterns and novel threats. The proposed method achieves a precision of 98.71%, an F1-score of 98.66%, a recall of 98.63%, and an accuracy of 98.69%, outperforming traditional methods and setting a new benchmark in DDoS attack detection. This innovative approach addresses critical limitations in current models and advances the state of the art in network security., Comment: 19 pages, 3 tables, 9 figures

Brain tumors are abnormalities that can severely impact a patient's health, leading to life-threatening conditions such as cancer. These can result in various debilitating effects, including neurological issues, cognitive impairment, motor and sensory deficits, as well as emotional and behavioral changes. These symptoms significantly affect a patient's quality of life, making early diagnosis and timely treatment essential to prevent further deterioration. However, accurately segmenting the tumor region from medical images, particularly MRI scans, is a challenging and time-consuming task that requires the expertise of radiologists. Manual segmentation can also be prone to human errors. To address these challenges, this research leverages the synergy of SeNet and ResNet architectures within an encoder-decoder framework, designed specifically for glioma detection and segmentation. The proposed model incorporates the power of SeResNet-152 as the backbone, integrated into a robust encoder-decoder structure to enhance feature extraction and improve segmentation accuracy. This novel approach significantly reduces the dependency on manual tasks and improves the precision of tumor identification. Evaluation of the model demonstrates strong performance, achieving 87% in Dice Coefficient, 89.12% in accuracy, 88% in IoU score, and 82% in mean IoU score, showcasing its effectiveness in tackling the complex problem of brain tumor segmentation., Comment: 9 pages, 6 figures, 1 table

The Large Magellanic Cloud (LMC) presents a unique environment for pulsar population studies due to its distinct star formation characteristics and proximity to the Milky Way. As part of the TRAPUM (TRAnsients and PUlsars with MeerKAT) Large Survey Project, we are using the core array of the MeerKAT radio telescope (MeerKAT) to conduct a targeted search of the LMC for radio pulsars at L-band frequencies, 856-1712$\,$MHz. The excellent sensitivity of MeerKAT, coupled with a 2-hour integration time, makes the survey 3 times more sensitive than previous LMC radio pulsar surveys. We report the results from the initial four survey pointings which has resulted in the discovery of seven new radio pulsars, increasing the LMC radio pulsar population by 30 per cent. The pulse periods of these new pulsars range from 278 to 1690$\,$ms, and the highest dispersion measure is 254.20$\,$pc$\,$cm$^{-3}$. We searched for, but did not find any significant pulsed radio emission in a beam centred on the SN$\,$1987A remnant, establishing an upper limit of 6.3$\,{\mu}$Jy on its minimum flux density at 1400$\,$MHz., Comment: 12 pages, 4 figures, 4 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society

We report new radio timing solutions from a three-year observing campaign conducted with the MeerKAT and Murriyang telescopes for nine Small Magellanic Cloud pulsars, increasing the number of characterised rotation-powered extragalactic pulsars by 40 per cent. We can infer from our determined parameters that the pulsars are seemingly all isolated, that six are ordinary pulsars, and that three of the recent MeerKAT discoveries have a young characteristic age of under 100 kyr and have undergone a spin-up glitch. Two of the sources, PSRs J0040$-$7337 and J0048$-$7317, are energetic young pulsars with spin-down luminosities of the order of 10$^{36}$ erg s$^{-1}$. They both experienced a large glitch, with a change in frequency of about 30 $\mu$Hz, and a frequency derivative change of order $-10^{-14}$ Hz s$^{-1}$. These glitches, the inferred glitch rate, and the properties of these pulsars (including potentially high inter-glitch braking indices) suggest these neutron stars might be Vela-like repeating glitchers and should be closely monitored in the future. The position and energetics of PSR J0048$-$7317 confirm it is powering a new Pulsar Wind Nebula (PWN) detected as a radio continuum source; and similarly the association of PSR J0040$-$7337 with the PWN of Supernova Remnant (SNR) DEM S5 (for which we present a new Chandra image) is strengthened. Finally, PSR J0040$-$7335 is also contained within the same SNR but is a chance superposition. It has also been seen to glitch with a change of frequency of $10^{-2}$ $\mu$Hz. This work more than doubles the characterised population of SMC radio pulsars., Comment: 20 pages, 13 figures, accepted for publication in Monthly Notices of the Royal Astronomical Society

Understanding emotions is a fundamental aspect of human communication. Integrating audio and video signals offers a more comprehensive understanding of emotional states compared to traditional methods that rely on a single data source, such as speech or facial expressions. Despite its potential, multimodal emotion recognition faces significant challenges, particularly in synchronization, feature extraction, and fusion of diverse data sources. To address these issues, this paper introduces a novel transformer-based model named Audio-Video Transformer Fusion with Cross Attention (AVT-CA). The AVT-CA model employs a transformer fusion approach to effectively capture and synchronize interlinked features from both audio and video inputs, thereby resolving synchronization problems. Additionally, the Cross Attention mechanism within AVT-CA selectively extracts and emphasizes critical features while discarding irrelevant ones from both modalities, addressing feature extraction and fusion challenges. Extensive experimental analysis conducted on the CMU-MOSEI, RAVDESS and CREMA-D datasets demonstrates the efficacy of the proposed model. The results underscore the importance of AVT-CA in developing precise and reliable multimodal emotion recognition systems for practical applications., Comment: 38 Pages, 9 Tables, 12 Figures

PSR J1227$-$6208 is a 34.53-ms recycled pulsar with a massive companion. This system has long been suspected to belong to the emerging class of massive recycled pulsar-ONeMg white dwarf systems such as PSR J2222$-$0137, PSR J1528$-$3146 and J1439$-$5501. Here we present an updated emission and timing analysis with more than 11 years of combined Parkes and MeerKAT data, including 19 hours of high-frequency data from the newly installed MeerKAT S-band receivers. We measure a scattering timescale of 1.22 ms at 1 GHz with a flat scattering index 3.33<$\beta$<3.62, and a mean flux density of 0.53-0.62 mJy at 1 GHz with a steep spectral index 2.06<$\alpha$<2.35. Around 15% of the emission is linearly and circularly polarised, but the polarisation angle does not follow the rotating vector model. Thanks to the sensitivity of MeerKAT, we successfully measure a rate of periastron advance of 0.0171(11) deg/yr, and a Shapiro delay with an orthometric amplitude of 3.6$\pm$0.5 $\mu$s and an orthometric shape of 0.85$\pm$0.05. The main source of uncertainty in our timing analysis is chromatic correlated dispersion measure noise, which we model as a power law in the Fourier space thanks to the large frequency coverage provided by the Parkes UWL receiver. Assuming general relativity and accounting for the measurements across all the implemented timing noise models, the total mass, companion mass, pulsar mass and inclination angle are constrained at 2.3

Farmers face various challenges when it comes to identifying diseases in rice leaves during their early stages of growth, which is a major reason for poor produce. Therefore, early and accurate disease identification is important in agriculture to avoid crop loss and improve cultivation. In this research, we propose a novel hybrid deep learning (DL) classifier designed by extending the Squeeze-and-Excitation network architecture with a channel attention mechanism and the Swish ReLU activation function. The channel attention mechanism in our proposed model identifies the most important feature channels required for classification during feature extraction and selection. The dying ReLU problem is mitigated by utilizing the Swish ReLU activation function, and the Squeeze-andExcitation blocks improve information propagation and cross-channel interaction. Upon evaluation, our model achieved a high F1-score of 99.76% and an accuracy of 99.74%, surpassing the performance of existing models. These outcomes demonstrate the potential of state-of-the-art DL techniques in agriculture, contributing to the advancement of more efficient and reliable disease detection systems., Comment: 17 pages, 4 tables, 10 figures

Alzheimer's disease (AD) is the most common form of neurodegeneration, which impacts millions of people each year. Diagnosing and classifying AD accurately with neuroimaging data is an ongoing challenge in the field of medicine. Traditional Convolutional Neural Networks (CNNs) are good at capturing low-level information from images, but their capability to extract high-level minuscule particles is suboptimal, which is a significant challenge in detecting AD from MRI scans. To overcome this, we propose a novel Granular Feature Integration method to combine information extraction at different scales combined with an efficient information flow. We also propose a Bi-Focal Perspective mechanism to highlight focus on subtle neurofibrillary tangles and amyloid plaques in MRI scans. Our model yielded an F1-Score of 99.31%, a precision of 99.24%, and a recall of 99.51%, which shows a major improvement in comparison to existing state-of-the-art (SOTA) CNNs., Comment: 17 pages, 10 figures, 6 tables

We extend the notion of Cantor-Kantorovich distance between Markov chains introduced by (Banse et al., 2023) in the context of Markov Decision Processes (MDPs). The proposed metric is well-defined and can be efficiently approximated given a finite horizon. Then, we provide numerical evidences that the latter metric can lead to interesting applications in the field of reinforcement learning. In particular, we show that it could be used for forecasting the performance of transfer learning algorithms., Comment: Presented at the 26th International Symposium on Mathematical Theory of Networks and Systems (Cambridge, UK)

Breast cancer is a leading cause of mortality worldwide, and demands the critical need for early and accurate diagnostic tools. Ultrasound imaging is a widely used modality for breast cancer screening, yet the precise segmentation and classification of tumors in these images are challenging due to variations in tumor morphology and image quality. To address these challenges, we propose novel deep learning (DL) frameworks leveraging a precision mapping mechanism (PMM) along with a component-specific feature enhancement module (CSFEM) to improve breast cancer lesion segmentation and identification. Our PPM ensures that the segmentation accurately reflects the true shape and extent of the tumor by meticulously delineating their boundaries. The CSFEM focuses on extracting and amplifying features unique to different tumor types, enabling the model to effectively distinguish between benign, malignant, and normal tissues. Integrating PMM and CSFEM into our segmentation model yielded an accuracy of 98.1%, an IoU of 96.9%, and a Dice Coefficient of 97.2%. Similarly, our classification model achieved an accuracy of 99.2%, with F1-score, precision, and recall values of 99.1%, 99.3%, and 99.1%, respectively. Our results indicate significant improvement in evaluation metrics in comparison to state-of-the-art (SOTA) models, demonstrating the effectiveness of precision mapping and component-specific feature enhancement in advancing breast cancer lesion analysis., Comment: 29 pages, 15 figures, 6 tables

Bug localization refers to the identification of source code files which is in a programming language and also responsible for the unexpected behavior of software using the bug report, which is a natural language. As bug localization is labor-intensive, bug localization models are employed to assist software developers. Due to the domain difference between source code files and bug reports, modern bug-localization systems, based on deep learning models, rely heavily on embedding techniques that project bug reports and source code files into a shared vector space. The creation of an embedding involves several design choices, but the impact of these choices on the quality of embedding and the performance of bug localization models remains unexplained in current research. To address this gap, our study evaluated 14 distinct embedding models to gain insights into the effects of various design choices. Subsequently, we developed bug localization models utilizing these embedding models to assess the influence of these choices on the performance of the localization models. Our findings indicate that the pre-training strategies significantly affect the quality of the embedding. Moreover, we discovered that the familiarity of the embedding models with the data has a notable impact on the bug localization model's performance. Notably, when the training and testing data are collected from different projects, the performance of the bug localization models exhibits substantial fluctuations.

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO3)n/(SrTiO3)n superlattices was directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that were previously inaccessible. Their reconfiguration and competition were quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics were quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity were drastically altered by light, holding promise for light-programmable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for remote control of adaptive nanoscale domain networks.

The Cs$_2$LiYCl$_6$:Ce (CLYC) elpasolite scintillator is known for its response to fast and thermal neutrons along with good $\gamma$-ray energy resolution. While the $^{35}$Cl($n,p$) reaction has been identified as a potential means for CLYC-based fast neutron spectroscopy in the absence of time-of-flight (TOF), previous efforts to functionalize CLYC as a fast neutron spectrometer have been thwarted by the inability to isolate proton interactions from $^{6}$Li($n,\alpha$) and $^{35}$Cl($n,\alpha$) signals. This work introduces a new approach to particle discrimination in CLYC for fission spectrum neutrons using a multi-gate charge integration algorithm that provides excellent separation between protons and heavier charged particles. Neutron TOF data were collected using a $^{252}$Cf source, an array of EJ-309 organic liquid scintillators, and a $^6$Li-enriched CLYC scintillator outfitted with fast electronics. Modal waveforms were constructed corresponding to the different reaction channels, revealing significant differences in the pulse characteristics of protons and heavier charged particles at ultrafast, fast, and intermediate time scales. These findings informed the design of a pulse shape discrimination algorithm, which was validated using the TOF data. This study also proposes an iterative subtraction method to mitigate contributions from confounding reaction channels in proton and heavier charged particle pulse height spectra, opening the door for CLYC-based fast neutron and $\gamma$-ray spectroscopy while preserving sensitivity to thermal neutron capture signals., Comment: 9 pages, 8 figures

The rise of unifying frameworks that enable seamless interoperability of Large Language Models (LLMs) has made LLM-LLM collaboration for open-ended tasks a possibility. Despite this, there have not been efforts to explore such collaborative writing. We take the next step beyond human-LLM collaboration to explore this multi-LLM scenario by generating the first exclusively LLM-generated collaborative stories dataset called CollabStory. We focus on single-author ($N=1$) to multi-author (up to $N=5$) scenarios, where multiple LLMs co-author stories. We generate over 32k stories using open-source instruction-tuned LLMs. Further, we take inspiration from the PAN tasks that have set the standard for human-human multi-author writing tasks and analysis. We extend their authorship-related tasks for multi-LLM settings and present baselines for LLM-LLM collaboration. We find that current baselines are not able to handle this emerging scenario. Thus, CollabStory is a resource that could help propel an understanding as well as the development of techniques to discern the use of multiple LLMs. This is crucial to study in the context of writing tasks since LLM-LLM collaboration could potentially overwhelm ongoing challenges related to plagiarism detection, credit assignment, maintaining academic integrity in educational settings, and addressing copyright infringement concerns. We make our dataset and code available at \texttt{\url{https://github.com/saranya-venkatraman/multi_llm_story_writing}}.

The paper investigates the techniques of quantum computation in metrological predictions, with a particular emphasis on enhancing prediction potential through variational parameter estimation. The applicability of quantum simulations and quantum metrology techniques for modelling complex physical systems and achieving high-resolution measurements are proposed. The impacts of various parameter distributions and learning rates on predictive accuracy are investigated. Modelling the time evolution of physical systems Hamiltonian simulation and the product formula procedure are adopted. The time block method is analyzed in order to reduce simulation errors, while the Schatten-infinite norm is used to evaluate the simulation precision. Methodology requires estimation of optimized parameters by minimizing loss functions and resource needs. For this purpose, the mathematical formulations of Cramer Rao Bound and Fischer Information are indispensable requirements. The impact of learning rates on regulating the loss function for various parameter values. Using parameterized quantum circuits, the article outlines a four-step procedure for extracting information. This method involves the preparation of input states, the evolution of parameterized quantum states, the measurement of outputs, and the estimation of parameters based on multiple measurements. The study analyses variational unitary circuits with optimized parameter estimation for more precise predictions. The findings shed light on the effects of normal parameter distributions and learning rates on attaining the most optimal state and comparison with classical Long Short Term Memory (LSTM) predictions, providing valuable insights for the development of more appropriate approaches in quantum computing., Comment: conference. arXiv admin note: substantial text overlap with arXiv:2406.05767

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data, $\mathbf{x}\sim p^{\rm post}(\mathbf{x})\propto p(\mathbf{x})r(\mathbf{x})$, in a model that consists of a diffusion generative model prior $p(\mathbf{x})$ and a black-box constraint or likelihood function $r(\mathbf{x})$. We state and prove the asymptotic correctness of a data-free learning objective, relative trajectory balance, for training a diffusion model that samples from this posterior, a problem that existing methods solve only approximately or in restricted cases. Relative trajectory balance arises from the generative flow network perspective on diffusion models, which allows the use of deep reinforcement learning techniques to improve mode coverage. Experiments illustrate the broad potential of unbiased inference of arbitrary posteriors under diffusion priors: in vision (classifier guidance), language (infilling under a discrete diffusion LLM), and multimodal data (text-to-image generation). Beyond generative modeling, we apply relative trajectory balance to the problem of continuous control with a score-based behavior prior, achieving state-of-the-art results on benchmarks in offline reinforcement learning., Comment: Code: https://github.com/GFNOrg/diffusion-finetuning

The orange carotenoid protein (OCP) is the water-soluble mediator of non-photochemical quenching in cyanobacteria, a crucial photoprotective mechanism in response to excess illumination. OCP converts from a globular, inactive state (OCPo) to an extended, active conformation (OCPr) under high-light conditions, resulting in a concomitant redshift in the absorption of the bound carotenoid. Here, OCP was trapped in either the active or inactive state by fixing each protein conformation in trehalose-sucrose glass. Glass-encapsulated OCPo did not convert under intense illumination and OCPr did not convert in darkness, allowing the optical properties of each conformation to be determined at room temperature. We measured pump wavelength-dependent transient absorption of OCPo in glass films and found that initial OCP photoproducts are still formed, despite the glass preventing completion of the photocycle. By comparison to the pump wavelength dependence of the OCPo to OCPr photoconversion yield in buffer, we show that the long-lived carotenoid singlet-like feature (S*) is associated with ground-state heterogeneity within OCPo, rather than triggering OCP photoconversion.

The sensitivity of the MeerKAT radio interferometer is an opportunity to probe deeper into the population of rare and faint extragalactic pulsars. The TRAPUM (TRAnsients and PUlsars with MeerKAT) collaboration has conducted a radio-domain search for accelerated pulsars and transients in the Small Magellanic Cloud (SMC). This partially targeted survey, performed at L-band (856-1712 MHz) with the core array of the MeerKAT telescope in 2-h integrations, is twice as sensitive as the latest SMC radio pulsar survey. We report the discovery of seven new SMC pulsars, doubling this galaxy's radio pulsar population and increasing the total extragalactic population by nearly a quarter. We also carried out a search for accelerated millisecond pulsars in the SMC Globular Cluster NGC 121 using the full array of MeerKAT. This improved the previous upper limit on pulsed radio emission from this cluster by a factor of six. Our discoveries reveal the first radio pulsar-PWN systems in the SMC, with only one such system previously known outside our galaxy (the "Crab pulsar twin" in the Large Magellanic Cloud, PSR J0540$-$6919). We associate the 59 ms pulsar discovery PSR J0040$-$7337, now the fastest spinning radio pulsar in the SMC, with the bow-shock Pulsar Wind Nebula (PWN) of Supernova Remnant DEM S5. We also present a new young pulsar with a 79 ms period, PSR J0048$-$7317, in a PWN recently discovered in a MeerKAT radio continuum image. Using the multi-beam capability of MeerKAT, we localised our pulsar discoveries, and two previous Murriyang discoveries, to a positional uncertainty of a few arcseconds., Comment: 32 pages, 14 figures, 10 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society

We present the description and initial results of the TRAPUM (TRAnsients And PUlsars with MeerKAT) search for pulsars associated with supernova remnants (SNRs), pulsar wind nebulae and unidentified TeV emission. The list of sources to be targeted includes a large number of well-known candidate pulsar locations but also new candidate SNRs identified using a range of criteria. Using the 64-dish MeerKAT radio telescope, we use an interferometric beamforming technique to tile the potential pulsar locations with coherent beams which we search for radio pulsations, above a signal-to-noise of 9, down to an average flux density upper limit of 30 $\mu$Jy. This limit is target-dependent due to the contribution of the sky and nebula to the system temperature. Coherent beams are arranged to overlap at their 50 per cent power radius, so the sensitivity to pulsars is not degraded by more than this amount, though realistically averages around 65 per cent if every location in the beam is considered. We report the discovery of two new pulsars; PSR J1831$-$0941 is an adolescent pulsar likely to be the plerionic engine of the candidate PWN G20.0+0.0, and PSR J1818$-$1502 appears to be an old and faint pulsar that we serendipitously discovered near the centre of a SNR already hosting a compact central object. The survey holds importance for better understanding of neutron star birth rates and the energetics of young pulsars., Comment: 17 pages, 6 figures, 3 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society

The objective of the paper is to determine the complete solutions for the Diophantine equation $x^2 + 3^{\alpha}113^{\beta} = y^{\mathfrak{n}}$ in positive integers $x$ and $y$ (where $x, y \geq 1$), non-negative exponents $\alpha$ and $\beta$, and an integer $\mathfrak{n}\geq 3$, subject to the condition $\text{gcd}(x, y) = 1$.

We unravel the distinct roles each cation plays in phase evolution, stability, and properties within Mg1/5Co1/5Ni1/5Cu1/5Zn1/5O high-entropy oxide (HEO) by integrating experimental findings, thermodynamic analyses, and first-principles predictions. Our approach is through sequentially removing one cation at a time from the five-component high-entropy oxide to create five four-component derivatives. Bulk synthesis experiments indicate that Mg, Ni, and Co act as rock salt phase stabilizers whereas only Mg and Ni enthalpically enhance single-phase rock salt stability in thin film growth; synthesis conditions dictate whether Co is a rock salt phase stabilizer or destabilizer. By examining the competing phases and oxidation state preferences using pseudo-binary phase diagrams and first-principles calculations, we resolve the stability differences between bulk and thin film for all compositions. We systematically explore HEO macroscopic property sensitivity to cation selection employing both predicted and measured optical spectra. This study establishes a framework for understanding high-entropy oxide synthesizability and properties on a per-cation basis that is broadly applicable to tailoring functional property design in other high-entropy materials., Comment: Saeed S. I. Almishal and Jacob T. Sivak contributed equally to this work

An analytical theory is developed for predicting the nonlinear susceptibility of ionic polarization to continuous electromagnetic waves in both bulk and strained thin film ferroelectrics. Using a perturbation method for solving the nonlinear equation of motion for ionic polarization within the framework of Landau-Ginzburg-Devonshire theory, the full second-order nonlinear susceptibility tensor is derived as a function of frequency, temperature, and strain. The theory predicts the coexistence of a significantly enhanced second-order dielectric susceptibility and a relatively low dielectric loss in BaTiO3 films with a strain-stabilized monoclinic ferroelectric phase and in a strained SrTiO3 film near its temperature-driven second-order ferroelectric-to-paraelectric phase transition. This work establishes a theoretical framework for predicting and exploiting nonlinear interactions between THz waves and ferroelectric materials, and more generally, suggests exciting opportunities to strain-engineer nonlinear dynamical properties of ferroelectrics beyond the static and quasi-static limits.

Semantic segmentation is an effective way to perform scene understanding. Recently, segmentation in 3D Bird's Eye View (BEV) space has become popular as its directly used by drive policy. However, there is limited work on BEV segmentation for surround-view fisheye cameras, commonly used in commercial vehicles. As this task has no real-world public dataset and existing synthetic datasets do not handle amodal regions due to occlusion, we create a synthetic dataset using the Cognata simulator comprising diverse road types, weather, and lighting conditions. We generalize the BEV segmentation to work with any camera model; this is useful for mixing diverse cameras. We implement a baseline by applying cylindrical rectification on the fisheye images and using a standard LSS-based BEV segmentation model. We demonstrate that we can achieve better performance without undistortion, which has the adverse effects of increased runtime due to pre-processing, reduced field-of-view, and resampling artifacts. Further, we introduce a distortion-aware learnable BEV pooling strategy that is more effective for the fisheye cameras. We extend the model with an occlusion reasoning module, which is critical for estimating in BEV space. Qualitative performance of DaF-BEVSeg is showcased in the video at https://streamable.com/ge4v51.

Trigonal Cr5Te8, a self-intercalated van der Waals ferromagnet with an out of plane magnetic anisotropy, has long been known to crystallise in a centrosymmetric structure. Through detailed structural analysis together with second harmonic generation experiments, we show that the compound actually adopts a non-centrosymmetric structure. A large anomalous Hall conductivity of 102 {\Omega}^(-1) cm^(-1) at low temperature stems from intrinsic origin, which is larger than any previously reported values in bulk Cr-Te system. In addition, we observe a hump-like feature in the field-dependent Hall resistivity data, resembling a typical topological Hall signal. We demonstrate that the feature is highly tunable and is not related to topological Hall effect even though we observe N\'eel-type skyrmions by Lorentz transmission electron microscopy which is consistent with the non-centrosymmetric structure of the compound., Comment: 26 pages, 8 Figures

We report the discovery of ten new pulsars in the globular cluster Terzan 5 as part of the Transients and Pulsars with MeerKAT (TRAPUM) Large Survey Project. We observed Terzan 5 at L-band (856--1712 MHz) with the MeerKAT radio telescope for four hours on two epochs, and performed acceleration searches of 45 out of 288 tied-array beams covering the core of the cluster. We obtained phase-connected timing solutions for nine discoveries, covering nearly two decades of archival observations from the Green Bank Telescope for all but one. Highlights include PSR J1748$-$2446ao which is an eccentric ($e = 0.32$) wide-orbit (orbital period $P_{\rm b} = 57.55$ d) system. We were able to measure the rate of advance of periastron ($\dot{\omega}$) for this system allowing us to determine a total mass of $3.17 \pm \, 0.02\, \rm M_{\odot}$. With a minimum companion mass ($M_{\rm c}$) of $\sim 0.8\, \rm M_{\odot}$, PSR J1748$-$2446ao is a candidate double neutron star (DNS) system. If confirmed to be a DNS, it would be the fastest spinning pulsar ($P = 2.27$ ms) and the longest orbital period measured for any known DNS system. PSR J1748$-$2446ap has the second highest eccentricity for any recycled pulsar ($e \sim 0.905$) and for this system we can measure the total mass ($1.997 \pm 0.006\, \rm M_{\odot}$) and also estimate the individual pulsar and companion masses. PSR J1748$-$2446ar is an eclipsing redback (minimum $M_{\rm c} \sim 0.34\, \rm M_{\odot}$) system whose properties confirm it to be the counterpart to a previously published source identified in radio and X-ray imaging. With these discoveries, the total number of confirmed pulsars in Terzan 5 is 49, the highest for any globular cluster so far. These discoveries further enhance the rich set of pulsars known in Terzan 5 and provide scope for a deeper understanding of binary stellar evolution, cluster dynamics and ensemble population studies., Comment: 23 pages, 11 figures, 5 tables, published in A&A

Using MeerKAT, we have discovered three new millisecond pulsars (MSPs) in the bulge globular cluster M62: M62H, M62I, and M62J. All three are in binary systems, which means all ten known pulsars in the cluster are in binaries. M62H has a planetary-mass companion with a median mass $M_{\rm c,med} \sim 3$ M$_{\rm J}$ and a mean density of $\rho \sim 11$ g cm$^{-3}$. M62I has an orbital period of 0.51 days and a $M_{\rm c,med} \sim 0.15$ M$_{\odot}$. Neither of these low-mass systems exhibit eclipses. M62J has only been detected in the two UHF band (816 MHz) observations with a flux density $S_{816} = 0.08$ mJy. The non-detection in the L-band (1284 MHz) indicates it has a relatively steep spectrum ($\beta < -3.1$). We also present 23-yr-long timing solutions obtained using data from the Parkes "Murriyang", Effelsberg and MeerKAT telescopes for the six previously known pulsars. For all these pulsars, we measured the second spin-period derivatives and the rate of change of orbital period caused by the gravitational field of the cluster, and their proper motions. From these measurements, we conclude that the pulsars' maximum accelerations are consistent with the maximum cluster acceleration assuming a core-collapsed mass distribution. Studies of the eclipses of the redback M62B and the black widow M62E at four and two different frequency bands, respectively, reveal a frequency dependence with longer and asymmetric eclipses at lower frequencies. The presence of only binary MSPs in this cluster challenges models which suggest that the MSP population of core-collapsed clusters should be dominated by isolated MSPs., Comment: 22 pages, 8 figures, 6 tables. Accepted for publication in MNRAS

We study certain "geometric-invariant resonant cavitie"' introduced by Liberal et. al in a 2016 Nature Comm. paper, modeled using the transverse magnetic reduction of Maxwell's equations. The cross-section consists of a dielectric inclusion surrounded by an "epsilon-near-zero" (ENZ) shell. When the shell has the right area, its interaction with the inclusion produces a resonance. Mathematically, the resonance is a nontrivial solution of a 2D divergence-form Helmoltz equation $\nabla \cdot \left(\varepsilon^{-1}(x,\omega) \nabla u \right) + \omega^2 \mu u = 0$, where $\varepsilon(x,\omega)$ is the (complex-valued) dielectric permittivity, $\omega$ is the frequency, $\mu$ is the magnetic permeability, and a homogeneous Neumann condition is imposed at the outer boundary of the shell. This is a nonlinear eigenvalue problem, since $\varepsilon$ depends on $\omega$. Use of an ENZ material in the shell means that $\varepsilon(x,\omega)$ is nearly zero there, so the PDE is rather singular. Working with a Lorentz model for the dispersion of the ENZ material, we put the discussion of Liberal et.~al.~on a sound foundation by proving the existence of the anticipated resonance when the loss is sufficiently small. Our analysis is perturbative in character despite the apparently singular form of the PDE. While the existence of the resonance depends only on the area of the ENZ shell, the rate at which it decays depends on the shape of the shell. We consider an associated optimal design problem: what shape shell gives the slowest-decaying resonance? We prove that if the dielectric inclusion is a ball then the optimal shell is a concentric annulus. For an inclusion of any shape, we study a convex relaxation of the design problem using tools from convex duality, and discuss the conjecture that our relaxed problem amounts to considering homogenization-like limits of nearly optimal designs., Comment: 44 pages, 2 Figures. Revised version incorporating referee suggestions with a few more references

Redbacks are millisecond pulsar binaries with low mass, irradiated companions. These systems have a rich phenomenology that can be used to probe binary evolution models, pulsar wind physics, and the neutron star mass distribution. A number of high-confidence redback candidates have been identified through searches for variable optical and X-ray sources within the localisation regions of unidentified but pulsar-like Fermi-LAT gamma-ray sources. However, these candidates remain unconfirmed until pulsations are detected. As part of the TRAPUM project, we searched for radio pulsations from six of these redback candidates with MeerKAT. We discovered three new radio millisecond pulsars, PSRs J0838$-$2527, J0955$-$3947 and J2333$-$5526, confirming their redback nature. PSR J0838$-$2827 remained undetected for two years after our discovery despite repeated observations, likely due to evaporated material absorbing the radio emission for long periods of time. While, to our knowledge, this system has not undergone a transition to an accreting state, the disappearance, likely caused by extreme eclipses, illustrates the transient nature of spider pulsars and the heavy selection bias in uncovering their radio population. Radio timing enabled the detection of gamma-ray pulsations from all three pulsars, from which we obtained 15-year timing solutions. All of these sources exhibit complex orbital period variations consistent with gravitational quadrupole moment variations in the companion stars. These timing solutions also constrain the binary mass ratios, allowing us to narrow down the pulsar masses. We find that PSR J2333$-$5526 may have a neutron star mass in excess of 2 M$_{\odot}$., Comment: 19 pages, 7 figures, accepted for publication in MNRAS

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the ensemble of FNN and LSTM models, capitalizing on their collective learning. This ensemble approach outperforms individual models, ensuring stable and accurate power output predictions. Additionally, machine learning techniques are applied to detect wind turbine performance deterioration, enabling proactive maintenance strategies and health assessment. Crucially, our analysis reveals the uniqueness of each wind turbine, necessitating tailored models for optimal predictions. These insight underscores the importance of providing automatized customization for different turbines to keep human modeling effort low. Importantly, the methodologies developed in this analysis are not limited to wind turbines; they can be extended to predict and optimize performance in various machinery, highlighting the versatility and applicability of our research across diverse industrial contexts., Comment: 19 Pages, 17 Figures, 3 Tables, Submitted at Applied Sciences (MDPI)

Teaching is one of many professions for which personalized feedback and reflection can help improve dialogue and discussion between the professional and those they serve. However, professional development (PD) is often impersonal as human observation is labor-intensive. Data-driven PD tools in teaching are of growing interest, but open questions about how professionals engage with their data in practice remain. In this paper, we present ClassInSight, a tool that visualizes three levels of teachers' discussion data and structures reflection. Through 22 reflection sessions and interviews with 5 high school science teachers, we found themes related to dissonance, contextualization, and sustainability in how teachers engaged with their data in the tool and in how their professional vision, the use of professional expertise to interpret events, shifted over time. We discuss guidelines for these conversational support tools to support personalized PD in professions beyond teaching where conversation and interaction are important.