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Auto-SPICE is the first fully automated framework leveraging large language models (LLMs) to generate Simulation Programs with Integrated Circuit Emphasis (SPICE) netlists. It addresses a long-standing challenge in automating netlist generation for analog circuits within circuit design automation. Automating this workflow could accelerate the creation of finetuned LLMs for analog circuit design and verification. We identify key challenges in this automation and evaluate the multi-modal capabilities of state-of-the-art LLMs, particularly GPT-4, to address these issues. We propose a three-step workflow to overcome current limitations: labeling analog circuits, prompt tuning, and netlist verification. This approach aims to create an end-to-end SPICE netlist generator from circuit schematic images, tackling the long-standing hurdle of accurate netlist generation. Our framework demonstrates significant performance improvements, tested on approximately 2,100 schematics of varying complexity. We open-source this solution for community-driven development.

We investigate the concept of algorithmic replicability introduced by Impagliazzo et al. 2022, Ghazi et al. 2021, Ahn et al. 2024 in an online setting. In our model, the input sequence received by the online learner is generated from time-varying distributions chosen by an adversary (obliviously). Our objective is to design low-regret online algorithms that, with high probability, produce the exact same sequence of actions when run on two independently sampled input sequences generated as described above. We refer to such algorithms as adversarially replicable. Previous works (such as Esfandiari et al. 2022) explored replicability in the online setting under inputs generated independently from a fixed distribution; we term this notion as iid-replicability. Our model generalizes to capture both adversarial and iid input sequences, as well as their mixtures, which can be modeled by setting certain distributions as point-masses. We demonstrate adversarially replicable online learning algorithms for online linear optimization and the experts problem that achieve sub-linear regret. Additionally, we propose a general framework for converting an online learner into an adversarially replicable one within our setting, bounding the new regret in terms of the original algorithm's regret. We also present a nearly optimal (in terms of regret) iid-replicable online algorithm for the experts problem, highlighting the distinction between the iid and adversarial notions of replicability. Finally, we establish lower bounds on the regret (in terms of the replicability parameter and time) that any replicable online algorithm must incur.

Dysgraphia is a learning disorder that affects handwriting abilities, making it challenging for children to write legibly and consistently. Early detection and monitoring are crucial for providing timely support and interventions. This study applies deep learning techniques to address the dual tasks of dysgraphia detection and optical character recognition (OCR) on handwriting samples from children with potential dysgraphic symptoms. Using a dataset of handwritten samples from Malaysian schoolchildren, we developed a custom Convolutional Neural Network (CNN) model, alongside VGG16 and ResNet50, to classify handwriting as dysgraphic or non-dysgraphic. The custom CNN model outperformed the pre-trained models, achieving a test accuracy of 91.8% with high precision, recall, and AUC, demonstrating its robustness in identifying dysgraphic handwriting features. Additionally, an OCR pipeline was created to segment and recognize individual characters in dysgraphic handwriting, achieving a character recognition accuracy of approximately 43.5%. This research highlights the potential of deep learning in supporting dysgraphia assessment, laying a foundation for tools that could assist educators and clinicians in identifying dysgraphia and tracking handwriting progress over time. The findings contribute to advancements in assistive technologies for learning disabilities, offering hope for more accessible and accurate diagnostic tools in educational and clinical settings.

The unique connectivity of kagome lattices gives rise to topological properties, such as flat bands and Dirac cones. When combined with ferromagnetism and a chemical potential near the 2D Dirac points, this structure offers the potential to realize the highly sought-after topological Chern magnetotransport. Recently, there was considerable excitement surrounding this possibility in the ferrimagnetic kagome metal TbMn$_\mathbf{6}$Sn$_\mathbf{6}$. However, density functional theory (DFT) calculations reveal that the 2D Chern gap lies well above the Fermi energy, challenging its relevance in the observed anomalous Hall conductivity. Here, we investigate YMn$_\mathbf{6}$Sn$_\mathbf{5.45}$Ga$_\mathbf{0.55}$, a compound with similar crystallographic, magnetic, and electronic properties to TbMn$_\mathbf{6}$Sn$_\mathbf{6}$. Our findings show that the intrinsic anomalous Hall conductivity in this material, while comparable in magnitude to that in TbMn$_\mathbf{6}$Sn$_\mathbf{6}$, is fully three-dimensional, thus providing experimental evidence that Hall conductivity in this class of materials does not originate from 2D Chern gaps. Additionally, we confirm that the newly proposed empirical scaling relation for extrinsic Hall conductivity is universally governed by spin fluctuations.

The ubiquitous noise in quantum system hinders the advancement of quantum information processing and has driven the emergence of different hardware-efficient quantum error correction protocols. Among them, qubits with structured noise, especially with biased noise, are one of the most promising platform to achieve fault-tolerance due to the high error thresholds of quantum error correction codes tailored for them. Nevertheless, their quantum operations are challenging and the demonstration of their performance beyond the fault-tolerant threshold remain incomplete. Here, we leverage Schr\"odinger cat states in a scalable planar superconducting nonlinear oscillator to thoroughly characterize the high-fidelity single-qubit quantum operations with systematic quantum tomography and benchmarking tools, demonstrating the state-of-the-art performance of operations crossing the fault-tolerant threshold of the XZZX surface code. These results thus embody a transformative milestone in the exploration of quantum systems with structured error channels. Notably, our framework is extensible to other types of structured-noise systems, paving the way for systematic characterization and validation of novel quantum platforms with structured noise., Comment: 19 pages, 12 figures

In many causal learning problems, variables of interest are often not all measured over the same observations, but are instead distributed across multiple datasets with overlapping variables. Tillman et al. (2008) presented the first algorithm for enumerating the minimal equivalence class of ground-truth DAGs consistent with all input graphs by exploiting local independence relations, called ION. In this paper, this problem is formulated as a more computationally efficient answer set programming (ASP) problem, which we call ION-C, and solved with the ASP system clingo. The ION-C algorithm was run on random synthetic graphs with varying sizes, densities, and degrees of overlap between subgraphs, with overlap having the largest impact on runtime, number of solution graphs, and agreement within the output set. To validate ION-C on real-world data, we ran the algorithm on overlapping graphs learned from data from two successive iterations of the European Social Survey (ESS), using a procedure for conducting joint independence tests to prevent inconsistencies in the input., Comment: 18 pages, 4 figures

The synthesis of heavy elements in supernovae is affected by low-energy (n,p) and (p,n) reactions on unstable nuclei, yet experimental data on such reaction rates are scarce. The SECAR (SEparator for CApture Reactions) recoil separator at FRIB (Facility for Rare Isotope Beams) was originally designed to measure astrophysical reactions that change the mass of a nucleus significantly. We used a novel approach that integrates machine learning with ion-optical simulations to find an ion-optical solution for the separator that enables the measurement of (p,n) reactions, despite the reaction leaving the mass of the nucleus nearly unchanged. A new measurement of the $^{58}$Fe(p,n)$^{58}$Co reaction in inverse kinematics with a 3.66$\pm$0.12 MeV/nucleon $^{58}$Fe beam (corresponding to 3.69$\pm$0.12 MeV proton energy in normal kinematics) yielded a cross-section of 20.3$\pm$6.3 mb and served as a benchmark for the new technique demonstrating its effectiveness in achieving the required performance criteria. This novel approach marks a significant advancement in experimental nuclear astrophysics, as it paves the way for studying astrophysically important (p,n) reactions on unstable nuclei produced at FRIB.

In recent years, computational Time-of-Flight (ToF) imaging has emerged as an exciting and a novel imaging modality that offers new and powerful interpretations of natural scenes, with applications extending to 3D, light-in-flight, and non-line-of-sight imaging. Mathematically, ToF imaging relies on algorithmic super-resolution, as the back-scattered sparse light echoes lie on a finer time resolution than what digital devices can capture. Traditional methods necessitate knowledge of the emitted light pulses or kernels and employ sparse deconvolution to recover scenes. Unlike previous approaches, this paper introduces a novel, blind ToF imaging technique that does not require kernel calibration and recovers sparse spikes on a continuum, rather than a discrete grid. By studying the shared characteristics of various ToF modalities, we capitalize on the fact that most physical pulses approximately satisfy the Strang-Fix conditions from approximation theory. This leads to a new mathematical formulation for sparse super-resolution. Our recovery approach uses an optimization method that is pivoted on an alternating minimization strategy. We benchmark our blind ToF method against traditional kernel calibration methods, which serve as the baseline. Extensive hardware experiments across different ToF modalities demonstrate the algorithmic advantages, flexibility and empirical robustness of our approach. We show that our work facilitates super-resolution in scenarios where distinguishing between closely spaced objects is challenging, while maintaining performance comparable to known kernel situations. Examples of light-in-flight imaging and light-sweep videos highlight the practical benefits of our blind super-resolution method in enhancing the understanding of natural scenes., Comment: 27 pages

Conventional digitization based on the Shannon-Nyquist method, implemented via analog-to-digital converters (ADCs), faces fundamental limitations. High-dynamic-range (HDR) signals often get clipped or saturated in practice. Given a fixed bit budget, one must choose between minimizing quantization noise or accommodating HDR inputs. The Unlimited Sensing Framework (USF) eliminates saturation by incorporating nonlinear folding in analog hardware, resulting in modulo signals. Quantizing or digitizing modulo signals enables low quantization noise as the modulo representation maps HDR signals into low-dynamic-range (LDR) samples. In the context of USF, the core innovation of this paper is a novel, low-cost, integrator-based efficient modulo ADC hardware implementation that imposes no restrictions on folding rates, enabling significantly HDR capture. The feasibility of this design is demonstrated by hardware experiments showcasing clear advantages across different quantitative performance metrics. These include capturing HDR signals with a 60-fold increase in dynamic range, achieving up to 5 Effective Number of Bits (ENOBs), and improving Signal-to-Noise and Distortion (SINAD) by 30 dB., Comment: IEEE Sensors (Conference), 2024

Autonomous agents require the capability to identify dynamic objects in their environment for safe planning and navigation. Incomplete and erroneous dynamic detections jeopardize the agent's ability to accomplish its task. Dynamic detection is a challenging problem due to the numerous sources of uncertainty inherent in the problem's inputs and the wide variety of applications, which often lead to use-case-tailored solutions. We propose a robust learning-free approach to segment moving objects in point cloud data. The foundation of the approach lies in modelling each voxel using a hidden Markov model (HMM), and probabilistically integrating beliefs into a map using an HMM filter. The proposed approach is tested on benchmark datasets and consistently performs better than or as well as state-of-the-art methods with strong generalized performance across sensor characteristics and environments. The approach is open-sourced at https://github.com/vb44/HMM-MOS., Comment: Accepted to the IEEE IROS 2024 workshop on Long-Term Perception for Autonomy in Dynamic Human-shared Environments: What Do Robots Need?

We present the localization and host galaxy of FRB 20190208A, a repeating source of fast radio bursts (FRBs) discovered using CHIME/FRB. As part of the PRECISE repeater localization program on the EVN, we monitored FRB 20190208A for 65.6 hours at $\sim1.4$ GHz and detected a single burst, which led to its VLBI localization with 260 mas uncertainty (2$\sigma$). Follow-up optical observations with the MMT Observatory ($i\gtrsim 25.7$ mag (AB)) found no visible host at the FRB position. Subsequent deeper observations with the GTC, however, revealed an extremely faint galaxy ($r=27.32 \pm0.16$ mag), very likely ($99.95 \%$) associated with FRB 20190208A. Given the dispersion measure of the FRB ($\sim580$ pc cm$^{-3}$), even the most conservative redshift estimate ($z_{\mathrm{max}}\sim0.83$) implies that this is the lowest-luminosity FRB host to date ($\lesssim10^8L_{\odot}$), even less luminous than the dwarf host of FRB 20121102A. We investigate how localization precision and the depth of optical imaging affect host association, and discuss the implications of such a low-luminosity dwarf galaxy. Unlike the other repeaters with low-luminosity hosts, FRB 20190208A has a modest Faraday rotation measure of a few tens of rad m$^{-2}$, and EVN plus VLA observations reveal no associated compact persistent radio source. We also monitored FRB 20190208A for 40.4 hours over 2 years as part of the \'ECLAT repeating FRB monitoring campaign on the Nan\c{c}ay Radio Telescope, and detected one burst. Our results demonstrate that, in some cases, the robust association of an FRB with a host galaxy will require both high localization precision, as well as deep optical follow-up., Comment: submitted to ApJL

Involving subject matter experts in prompt engineering can guide LLM outputs toward more helpful, accurate, and tailored content that meets the diverse needs of different domains. However, iterating towards effective prompts can be challenging without adequate interface support for systematic experimentation within specific task contexts. In this work, we introduce PromptHive, a collaborative interface for prompt authoring, designed to better connect domain knowledge with prompt engineering through features that encourage rapid iteration on prompt variations. We conducted an evaluation study with ten subject matter experts in math and validated our design through two collaborative prompt-writing sessions and a learning gain study with 358 learners. Our results elucidate the prompt iteration process and validate the tool's usability, enabling non-AI experts to craft prompts that generate content comparable to human-authored materials while reducing perceived cognitive load by half and shortening the authoring process from several months to just a few hours.

This work presents a detailed study of the electronic structure, phonon dispersion, Z2 invariant calculation, and Fermi surface of the newly discovered kagome superconductor CsV3Sb5, using density functional theory (DFT). The phonon dispersion in the pristine state reveals two negative modes at the M and L points of the Brillouin zone, indicating lattice instability. CsV3Sb5 transitions into a structurally stable 2x2x1 charge density wave (CDW) phase, confirmed by positive phonon modes. The electronic band structure shows several Dirac points near the Fermi level, with a narrow gap opening due to spin-orbit coupling (SOC), though the effect of SOC on other bands is minimal. In the pristine phase, this material exhibits a quasi-2D cylindrical Fermi surface, which undergoes reconstruction in the CDW phase. We calculated quantum oscillation frequencies using Onsager's relation, finding good agreement with experimental results in the CDW phase. To explore the topological properties of CsV3Sb5, we computed the Z2 invariant in both pristine and CDW phases, resulting in a value of (u0; u1u2u3) = (1; 000), suggesting the strong topological nature of this material. Our detailed analysis of phonon dispersion, electronic bands, Fermi surface mapping, and Z2 invariant provides insights into the topological properties, CDW order, and unconventional superconductivity in AV3Sb5 (A = K, Rb, and Cs)., Comment: To be published in APL Quantum

In this paper, we calculate the relativistic corrections to the Zeeman effect for hydrogen-like atoms based on the Generalized Uncertainty Principle (GUP). We propose a relativistic GUP algebra using the Stetsko and Tkachuk approximation and incorporate these corrections into the Zeeman effect. In the relativistic limit, our results recover previously derived GUP corrections as well as the standard Lande energy shift expression when GUP effects are absent. This work presents a generalized expression that accounts for both relativistic and GUP corrections to the Zeeman effect.

The kagome lattice, known for its strong frustration in two dimensions, hosts a variety of exotic magnetic and electronic states. A variation of this geometry, where the triangular motifs are twisted to further reduce symmetry, has recently revealed even more complex physics. HoAgGe exemplifies such a structure, with magnetic and electronic properties believed to be driven by strong in-plane anisotropy of the Ho spins, effectively acting as a two-dimensional spin ice. In this study, using a combination of magnetization, Hall conductivity measurements, and density functional theory calculations, we demonstrate how various spin-ice states, stabilized by external magnetic fields, influence the Fermi surface topology. More interestingly, we observe sharp transitions in Hall conductivity without concurrent changes in magnetization when an external magnetic field is applied along a particular crystallographic direction, underscoring the role of strong magnetic frustration and providing a new platform for exploring the interplay between magnetic frustration, electronic topology, and crystalline symmetry. These results also highlight the limitations of a simple spin-ice model, suggesting that a more sophisticated framework is necessary to capture the subtle experimental nuances observed., Comment: 17 pages, 11 figures

Garbage production and littering are persistent global issues that pose significant environmental challenges. Despite large-scale efforts to manage waste through collection and sorting, existing approaches remain inefficient, leading to inadequate recycling and disposal. Therefore, developing advanced AI-based systems is less labor intensive approach for addressing the growing waste problem more effectively. These models can be applied to sorting systems or possibly waste collection robots that may produced in the future. AI models have grown significantly at identifying objects through object detection. This paper reviews the implementation of AI models for classifying trash through object detection, specifically focusing on using YOLO V5 for training and testing. The study demonstrates how YOLO V5 can effectively identify various types of waste, including plastic, paper, glass, metal, cardboard, and biodegradables., Comment: 8 pages, 8 figures

Today, we are open-sourcing our core speech recognition and diarization models for non-commercial use. We are releasing both a full production pipeline for developers as well as pared-down research models for experimentation. Rev hopes that these releases will spur research and innovation in the fast-moving domain of voice technology. The speech recognition models released today outperform all existing open source speech recognition models across a variety of long-form speech recognition domains.

Digital acquisition of high bandwidth signals is particularly challenging when Nyquist rate sampling is impractical. This has led to extensive research in sub-Nyquist sampling methods, primarily for spectral and sinusoidal frequency estimation. However, these methods struggle with high-dynamic-range (HDR) signals that can saturate analog-to-digital converters (ADCs). Addressing this, we introduce a novel sub-Nyquist spectral estimation method, driven by the Unlimited Sensing Framework (USF), utilizing a multi-channel system. The sub-Nyquist USF method aliases samples in both amplitude and frequency domains, rendering the inverse problem particularly challenging. Towards this goal, our exact recovery theorem establishes that $K$ sinusoids of arbitrary amplitudes and frequencies can be recovered from $6K + 4$ modulo samples, remarkably, independent of the sampling rate or folding threshold. In the true spirit of sub-Nyquist sampling, via modulo ADC hardware experiments, we demonstrate successful spectrum estimation of HDR signals in the kHz range using Hz range sampling rates (0.078\% Nyquist rate). Our experiments also reveal up to a 33-fold improvement in frequency estimation accuracy using one less bit compared to conventional ADCs. These findings open new avenues in spectral estimation applications, e.g., radars, direction-of-arrival (DoA) estimation, and cognitive radio, showcasing the potential of USF., Comment: 18 pages, 8 figures, accepted to IEEE Trans. on Signal Processing

Timely disease diagnosis is challenging due to increasing disease burdens and limited clinician availability. AI shows promise in diagnosis accuracy but faces real-world application issues due to insufficient validation in clinical workflows and diverse populations. This study addresses gaps in medical AI downstream accountability through a case study on age-related macular degeneration (AMD) diagnosis and severity classification. We designed and implemented an AI-assisted diagnostic workflow for AMD, comparing diagnostic performance with and without AI assistance among 24 clinicians from 12 institutions with real patient data sampled from the Age-Related Eye Disease Study (AREDS). Additionally, we demonstrated continual enhancement of an existing AI model by incorporating approximately 40,000 additional medical images (named AREDS2 dataset). The improved model was then systematically evaluated using both AREDS and AREDS2 test sets, as well as an external test set from Singapore. AI assistance markedly enhanced diagnostic accuracy and classification for 23 out of 24 clinicians, with the average F1-score increasing by 20% from 37.71 (Manual) to 45.52 (Manual + AI) (P-value < 0.0001), achieving an improvement of over 50% in some cases. In terms of efficiency, AI assistance reduced diagnostic times for 17 out of the 19 clinicians tracked, with time savings of up to 40%. Furthermore, a model equipped with continual learning showed robust performance across three independent datasets, recording a 29% increase in accuracy, and elevating the F1-score from 42 to 54 in the Singapore population.

Common measures of accuracy used to assess the performance of automatic speech recognition (ASR) systems, as well as human transcribers, conflate multiple sources of error. Stylistic differences, such as verbatim vs non-verbatim, can play a significant role in ASR performance evaluation when differences exist between training and test datasets. The problem is compounded for speech from underrepresented varieties, where the speech to orthography mapping is not as standardized. We categorize the kinds of stylistic differences between 6 transcription versions, 4 human- and 2 ASR-produced, of 10 hours of African American English (AAE) speech. Focusing on verbatim features and AAE morphosyntactic features, we investigate the interactions of these categories with how well transcripts can be compared via word error rate (WER). The results, and overall analysis, help clarify how ASR outputs are a function of the decisions made by the training data's human transcribers., Comment: Published in Interspeech 2024 Proceedings, 5 pages excluding references, 5 figures

Fusion frames are widely studied for their applications in recovering signals from large data. These are proved to be very useful in many areas, such as, distributed processing, wireless sensor networks, packet encoding. Inspired by the work of Bemrose et al.\cite{Be16}, this paper delves into the properties and characterizations of weaving fusion frames.

Feedback control of open quantum systems is of fundamental importance for practical applications in various contexts, ranging from quantum computation to quantum error correction and quantum metrology. Its use in the context of thermodynamics further enables the study of the interplay between information and energy. However, deriving optimal feedback control strategies is highly challenging, as it involves the optimal control of open quantum systems, the stochastic nature of quantum measurement, and the inclusion of policies that maximize a long-term time- and trajectory-averaged goal. In this work, we employ a reinforcement learning approach to automate and capture the role of a quantum Maxwell's demon: the agent takes the literal role of discovering optimal feedback control strategies in qubit-based systems that maximize a trade-off between measurement-powered cooling and measurement efficiency. Considering weak or projective quantum measurements, we explore different regimes based on the ordering between the thermalization, the measurement, and the unitary feedback timescales, finding different and highly non-intuitive, yet interpretable, strategies. In the thermalization-dominated regime, we find strategies with elaborate finite-time thermalization protocols conditioned on measurement outcomes. In the measurement-dominated regime, we find that optimal strategies involve adaptively measuring different qubit observables reflecting the acquired information, and repeating multiple weak measurements until the quantum state is "sufficiently pure", leading to random walks in state space. Finally, we study the case when all timescales are comparable, finding new feedback control strategies that considerably outperform more intuitive ones. We discuss a two-qubit example where we explore the role of entanglement and conclude discussing the scaling of our results to quantum many-body systems., Comment: 16+10 pages, 21 figures

We present X-ray to radio frequency observations of the bright long gamma-ray burst GRB 210702A. Our ALMA 97.5 GHz observations show a significant rebrightening by a factor of ~2 beginning at 8.2 days post-burst and rising to peak brightness at 18.1 days before declining again. This is the first such rebrightening seen in a millimeter afterglow light curve. A standard forward shock model in a stellar wind circumburst medium can explain most of our X-ray, optical and millimeter observations prior to the rebrightening, but significantly over-predicts the self-absorbed radio emission, and cannot explain the millimeter rebrightening. We investigate possible explanations for the millimeter rebrightening and find that energy injection or a reverse shock from a late-time shell collision are plausible causes. Similar to other bursts, our radio data may require alternative scenarios such as a thermal electron population or a structured jet to explain the data. Our observations demonstrate that millimeter light curves can exhibit some of the rich features more commonly seen in optical and X-ray afterglow light curves, motivating further millimeter wavelength studies of GRB afterglows., Comment: 22 pages, 10 figures, accepted for publication in ApJ

Current observational evidence reveals that fast radio bursts (FRBs) exhibit bandwidths ranging from a few dozen MHz to several GHz. Traditional FRB searches primarily employ matched filter methods on time series collapsed across the entire observational bandwidth. However, with modern ultra-wideband receivers featuring GHz-scale observational bandwidths, this approach may overlook a significant number of events. We investigate the efficacy of sub-banded searches for FRBs, a technique seeking bursts within limited portions of the bandwidth. These searches aim to enhance the significance of FRB detections by mitigating the impact of noise outside the targeted frequency range, thereby improving signal-to-noise ratios. We conducted a series of Monte Carlo simulations, for the $400$-MHz bandwidth Parkes 21-cm multi-beam (PMB) receiver system and the Parkes Ultra-Wideband Low (UWL) receiver, simulating bursts down to frequency widths of about $100$\,MHz. Additionally, we performed a complete reprocessing of the high-latitude segment of the High Time Resolution Universe South survey (HTRU-S) of the Parkes-Murriyang telescope using sub-banded search techniques. Simulations reveal that a sub-banded search can enhance the burst search efficiency by $67_{-42}^{+133}$ % for the PMB system and $1433_{-126}^{+143}$ % for the UWL receiver. Furthermore, the reprocessing of HTRU led to the confident detection of eighteen new bursts, nearly tripling the count of FRBs found in this survey. These results underscore the importance of employing sub-banded search methodologies to effectively address the often modest spectral occupancy of these signals., Comment: Accepted for publication (A&A)

Understanding urban mobility patterns and analyzing how people move around cities helps improve the overall quality of life and supports the development of more livable, efficient, and sustainable urban areas. A challenging aspect of this work is the collection of mobility data by means of user tracking or travel surveys, given the associated privacy concerns, noncompliance, and high cost. This work proposes an innovative AI-based approach for synthesizing travel surveys by prompting large language models (LLMs), aiming to leverage their vast amount of relevant background knowledge and text generation capabilities. Our study evaluates the effectiveness of this approach across various U.S. metropolitan areas by comparing the results against existing survey data at different granularity levels. These levels include (i) pattern level, which compares aggregated metrics like the average number of locations traveled and travel time, (ii) trip level, which focuses on comparing trips as whole units using transition probabilities, and (iii) activity chain level, which examines the sequence of locations visited by individuals. Our work covers several proprietary and open-source LLMs, revealing that open-source base models like Llama-2, when fine-tuned on even a limited amount of actual data, can generate synthetic data that closely mimics the actual travel survey data, and as such provides an argument for using such data in mobility studies., Comment: 13 pages, 10 Figures

An explicit quantization of Chern-Simons theory leads to an identity between sums of the Kac-Weyl characters. One can use this identity to prove inequalities that constrain the fusion coefficients $N_{\mu\nu}^l$ in the case of RCFTs that descend from current algebras. It also leads to a statement regarding the conjugacy symmetry of the sums of squares of fusion coefficients for current algebras admitting complex representations., Comment: 8 pages

The concept of frames, initially introduced by Duffin and Schaeffer, gained substantial recognition decades later when Daubechies, Grossman, and Meyer highlighted its significance. Since then, frame theory has become a fundamental and widely applicable tool across diverse branches of Mathematics, Physics, and Engineering Sciences. Driven by the extensive applications of model space frames in dynamical sampling, the study delves into an exploration of frames and their properties within the model space and RKHS. Moreover, the study systematically investigates the intrinsic features of model space frames and its reproducing counterparts, thoroughly examining and analyzing various characterizations of the same. Furthermore, several examples are provided to validate the results, demonstrating the practical applicability and correctness of the theoretical findings.

Altermagnets (AMs) are a new class of magnetic materials that combine the beneficial spintronics properties of ferromagnets and antiferromagnets, garnering significant attention recently. Here, we have identified altermagnetism in a layered intercalated transition metal diselenide, CoNb$_4$Se$_8$, which crystallizes with an ordered sublattice of intercalated Co atoms between NbSe$_2$ layers. Single crystals are synthesized, and the structural characterizations are performed using single crystal diffraction and scanning tunneling microscopy. Magnetic measurements reveal easy-axis antiferromagnetism below 168 K. Density functional theory (DFT) calculations indicate that A-type antiferromagnetic ordering with easy-axis spin direction is the ground state, which is verified through single crystal neutron diffraction experiments. Electronic band structure calculations in this magnetic state display spin-split bands, confirming altermagnetism in this compound. The layered structure of CoNb$_4$Se$_8$ presents a promising platform for testing various predicted properties associated with altermagnetism.

With wide-field phased array feed technology, the Australian Square Kilometre Array Pathfinder (ASKAP) is ideally suited to search for seemingly rare radio transient sources. The Commensal Real-time ASKAP Fast Transient (CRAFT) Survey Science Project has developed instrumentation to continuously search for fast radio transients (duration $\lesssim$ 1 second) with ASKAP, with a particular focus on finding and localising Fast Radio Bursts (FRBs). Of particular interest are Fast Radio Bursts (FRBs). Since 2018, the CRAFT survey has been searching for FRBs and other fast transients by incoherently adding the intensities received by individual ASKAP antennas, and then correcting for the impact of frequency dispersion on these short-duration signals in the resultant incoherent sum (ICS) in real-time. This low-latency detection enables the triggering of voltage buffers, which facilitates the localisation of the transient source and the study spectro-polarimetric properties at high time resolution. Here we report the sample of 43 FRBs discovered in this CRAFT/ICS survey to date. This includes 22 FRBs that had not previously been reported: 16 FRBs localised by ASKAP to $\lesssim$ 1 arcsec and 6 FRBs localised to approximately 10 arcmin. Of the new arcsecond-localised FRBs, we have identified and characterised host galaxies (and measured redshifts) for 11. The median of all 30 measured host redshifts from the survey to date is z = 0.23. We summarise results from the searches, in particular those contributing to our understanding of the burst progenitors and emission mechanisms, and on the use of bursts as probes of intervening media. We conclude by foreshadowing future FRB surveys with ASKAP using a coherent detection system that is currently being commissioned., Comment: 37 pages, 23 Figures, 7 Tables. Submitted for publication in PASA

We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the traditional mathematical models, particularly the Markov model. The findings indicate that while QGANs demonstrate potential in approximating complex distributions, the Markov model consistently outperforms in accurately replicating the real data distribution. This comparison underlines the challenges and avenues for refinement in time series data generation using quantum computing techniques. It emphasizes the need for further optimization of quantum models to better align with real-world data characteristics., Comment: 8 pages

We investigate the potential of bio-inspired evolutionary algorithms for designing quantum circuits with specific goals, focusing on two particular tasks. The first one is motivated by the ideas of Artificial Life that are used to reproduce stochastic cellular automata with given rules. We test the robustness of quantum implementations of the cellular automata for different numbers of quantum gates The second task deals with the sampling of quantum circuits that generate highly entangled quantum states, which constitute an important resource for quantum computing. In particular, an evolutionary algorithm is employed to optimize circuits with respect to a fitness function defined with the Mayer-Wallach entanglement measure. We demonstrate that, by balancing the mutation rate between exploration and exploitation, we can find entangling quantum circuits for up to five qubits. We also discuss the trade-off between the number of gates in quantum circuits and the computational costs of finding the gate arrangements leading to a strongly entangled state. Our findings provide additional insight into the trade-off between the complexity of a circuit and its performance, which is an important factor in the design of quantum circuits.

In this paper we explore the effects of a Generalized Uncertainty Principle (GUP) on Schwarzschild black hole. In particular, we incorporate the effects of GUP into the Parikh-Wilczek tunneling process for Hawking radiation. To this effect, we observe that results obtained due to GUP correction resemble that of the Reissner-Nordstr\"{om} black hole, showing similarities to the nature of an electric charge. We also find that, within this framework, the emission is not purely thermal, thus addressing the information loss problem through the correlation function., Comment: 7 pages, 1 figure

$TbMn_{6}Sn_{6}$ is a ferrimagnetic material which exhibits a highly unusual phase transition near room temperature where spins remain collinear while the total magnetic moment rotates from out-of-plane to in-plane. The mechanisms underlying this phenomenon have been studied in the quasi-static limit and the reorientation has been attributed to the competing anisotropies of Tb and Mn, whose magnetic moments have very different temperature dependencies. In this work, we present the first measurement of the spin-reorientation transition in $TbMn_{6}Sn_{6}$. By probing very small signals with the transverse magneto-optical Kerr effect (TMOKE) at the Mn M-edge, we show that the re-orientation timescale spans from 12 ps to 24 ps, depending on the laser excitation fluence. We then verify these data with a simple model of spin precession with a temperature-dependent magnetocrystalline anisotropy field to show that the spin reorientation timescale is consistent with the reorientation being driven by very large anisotropies energies on approximately $\approx$ meV scales. Promisingly, the model predicts a possibility of 180o reorientation of the out-of-plane moment over a range of excitation fluences. This could facilitate optically controlled magnetization switching between very stable ground states, which could have useful applications in spintronics or data storage.

Machine learning (ML) models have emerged as powerful tools for accelerating materials discovery and design by enabling accurate predictions of properties from compositional and structural data. These capabilities are vital for developing advanced technologies across fields such as energy, electronics, and biomedicine, potentially reducing the time and resources needed for new material exploration and promoting rapid innovation cycles. Recent efforts have focused on employing advanced ML algorithms, including deep learning - based graph neural network, for property prediction. Additionally, ensemble models have proven to enhance the generalizability and robustness of ML and DL. However, the use of such ensemble strategies in deep graph networks for material property prediction remains underexplored. Our research provides an in-depth evaluation of ensemble strategies in deep learning - based graph neural network, specifically targeting material property prediction tasks. By testing the Crystal Graph Convolutional Neural Network (CGCNN) and its multitask version, MT-CGCNN, we demonstrated that ensemble techniques, especially prediction averaging, substantially improve precision beyond traditional metrics for key properties like formation energy per atom ($\Delta E^{f}$), band gap ($E_{g}$) and density ($\rho$) in 33,990 stable inorganic materials. These findings support the broader application of ensemble methods to enhance predictive accuracy in the field., Comment: 9 pages, 6 figures, 2 tables

Deep learning has significantly advanced the field of gastrointestinal vision, enhancing disease diagnosis capabilities. One major challenge in automating diagnosis within gastrointestinal settings is the detection of abnormal cases in endoscopic images. Due to the sparsity of data, this process of distinguishing normal from abnormal cases has faced significant challenges, particularly with rare and unseen conditions. To address this issue, we frame abnormality detection as an out-of-distribution (OOD) detection problem. In this setup, a model trained on In-Distribution (ID) data, which represents a healthy GI tract, can accurately identify healthy cases, while abnormalities are detected as OOD, regardless of their class. We introduce a test-time augmentation segment into the OOD detection pipeline, which enhances the distinction between ID and OOD examples, thereby improving the effectiveness of existing OOD methods with the same model. This augmentation shifts the pixel space, which translates into a more distinct semantic representation for OOD examples compared to ID examples. We evaluated our method against existing state-of-the-art OOD scores, showing improvements with test-time augmentation over the baseline approach.

A globally distributed IC supply chain brings risks due to untrusted third parties. The risks span inadvertent use of hardware Trojan (HT), inserted Intellectual Property (3P-IP) or Electronic Design Automation (EDA) flows. HT can introduce stealthy HT behavior, prevent an IC work as intended, or leak sensitive data via side channels. To counter HTs, rapidly examining HT scenarios is a key requirement. While Trust-Hub benchmarks are a good starting point to assess defenses, they encompass a small subset of manually created HTs within the expanse of HT designs. Further, the HTs may disappear during synthesis. We propose a large language model (LLM) framework SENTAUR to generate a suite of legitimate HTs for a Register Transfer Level (RTL) design by learning its specifications, descriptions, and natural language descriptions of HT effects. Existing tools and benchmarks are limited; they need a learning period to construct an ML model to mimic the threat model and are difficult to reproduce. SENTAUR can swiftly produce HT instances by leveraging LLMs without any learning period and sanitizing the HTs facilitating their rapid assessment. Evaluation of SENTAUR involved generating effective, synthesizable, and practical HTs from TrustHub and elsewhere, investigating impacts of payloads/triggers at the RTL. While our evaluation focused on HT insertion, SENTAUR can generalize to automatically transform an RTL code to have defined functional modifications.

The AMoRE collaboration searches for neutrinoless double beta decay of $^{100}$Mo using molybdate scintillating crystals via low temperature thermal calorimetric detection. The early phases of the experiment, AMoRE-pilot and AMoRE-I, have demonstrated competitive discovery potential. Presently, the AMoRE-II experiment, featuring a large detector array with about 90 kg of $^{100}$Mo isotope, is under construction.This paper discusses the baseline design and characterization of the lithium molybdate cryogenic calorimeters to be used in the AMoRE-II detector modules. The results from prototype setups that incorporate new housing structures and two different crystal masses (316 g and 517 - 521 g), operated at 10 mK temperature, show energy resolutions (FWHM) of 7.55 - 8.82 keV at the 2.615 MeV $^{208}$Tl $\gamma$ line, and effective light detection of 0.79 - 0.96 keV/MeV. The simultaneous heat and light detection enables clear separation of alpha particles with a discrimination power of 12.37 - 19.50 at the energy region around $^6$Li(n, $\alpha$)$^3$H with Q-value = 4.785 MeV. Promising detector performances were demonstrated at temperatures as high as 30 mK, which relaxes the temperature constraints for operating the large AMoRE-II array.

Effective educational measurement relies heavily on the curation of well-designed item pools (i.e., possessing the right psychometric properties). However, item calibration is time-consuming and costly, requiring a sufficient number of respondents for the response process. We explore using six different LLMs (GPT-3.5, GPT-4, Llama 2, Llama 3, Gemini-Pro, and Cohere Command R Plus) and various combinations of them using sampling methods to produce responses with psychometric properties similar to human answers. Results show that some LLMs have comparable or higher proficiency in College Algebra than college students. No single LLM mimics human respondents due to narrow proficiency distributions, but an ensemble of LLMs can better resemble college students' ability distribution. The item parameters calibrated by LLM-Respondents have high correlations (e.g. > 0.8 for GPT-3.5) compared to their human calibrated counterparts, and closely resemble the parameters of the human subset (e.g. 0.02 Spearman correlation difference). Several augmentation strategies are evaluated for their relative performance, with resampling methods proving most effective, enhancing the Spearman correlation from 0.89 (human only) to 0.93 (augmented human).

AMoRE searches for the signature of neutrinoless double beta decay of $^{100}$Mo with a 100 kg sample of enriched $^{100}$Mo. Scintillating molybdate crystals coupled with a metallic magnetic calorimeter operate at milli-Kelvin temperatures to measure the energy of electrons emitted in the decay. As a demonstration of the full-scale AMoRE, we conducted AMoRE-I, a pre-experiment with 18 molybdate crystals, at the Yangyang Underground Laboratory for over two years. The exposure was 8.02 kg$\cdot$year (or 3.89 kg$_{\mathrm{^{100}Mo}}\cdot$year) and the total background rate near the Q-value was 0.025 $\pm$ 0.002 counts/keV/kg/year. We observed no indication of $0\nu\beta\beta$ decay and report a new lower limit of the half-life of $^{100}$Mo $0\nu\beta\beta$ decay as $ T^{0\nu}_{1/2}>3.0\times10^{24}~\mathrm{years}$ at 90\% confidence level. The effective Majorana mass limit range is $m_{\beta\beta}<$(210--610) meV using nuclear matrix elements estimated in the framework of different models, including the recent shell model calculations., Comment: 8 pages, 5 figures

This paper is concerned with the existence of global-in-time weak solutions to the multicomponent reactive flows inside a moving domain whose shape in time is prescribed. The flow is governed by the 3D compressible Navier-Stokes-Fourier system coupled with the equations of species mass fractions. The fluid velocity is supposed to fulfill the complete slip boundary condition, whereas the heat flux and species diffusion fluxes satisfy the conservative boundary conditions. The existence of weak solutions is obtained by means of suitable approximation techniques. To this end, we need to rigorously analyze the penalization of the boundary behavior, viscosity and the pressure in the weak formulation., Comment: arXiv admin note: text overlap with arXiv:2307.09348

Power side-channel (PSC) analysis is pivotal for securing cryptographic hardware. Prior art focused on securing gate-level netlists obtained as-is from chip design automation, neglecting all the complexities and potential side-effects for security arising from the design automation process. That is, automation traditionally prioritizes power, performance, and area (PPA), sidelining security. We propose a "security-first" approach, refining the logic synthesis stage to enhance the overall resilience of PSC countermeasures. We introduce ASCENT, a learning-and-search-based framework that (i) drastically reduces the time for post-design PSC evaluation and (ii) explores the security-vs-PPA design space. Thus, ASCENT enables an efficient exploration of a large number of candidate netlists, leading to an improvement in PSC resilience compared to regular PPA-optimized netlists. ASCENT is up to 120x faster than traditional PSC analysis and yields a 3.11x improvement for PSC resilience of state-of-the-art PSC countermeasures, Comment: Accepted at 2024 ACM/IEEE International Conference on Computer-Aided Design

This work investigates the potential of tailoring Large Language Models (LLMs), specifically GPT3.5 and GPT4, for the domain of chip testing. A key aspect of chip design is functional testing, which relies on testbenches to evaluate the functionality and coverage of Register-Transfer Level (RTL) designs. We aim to enhance testbench generation by incorporating feedback from commercial-grade Electronic Design Automation (EDA) tools into LLMs. Through iterative feedback from these tools, we refine the testbenches to achieve improved test coverage. Our case studies present promising results, demonstrating that this approach can effectively enhance test coverage. By integrating EDA tool feedback, the generated testbenches become more accurate in identifying potential issues in the RTL design. Furthermore, we extended our study to use this enhanced test coverage framework for detecting bugs in the RTL implementations

We present the results of a nation-wide baseline survey, conducted by us, for the status of Astronomy education among secondary school students in India. The survey was administered in 10 different languages to over 2000 students from diverse backgrounds, and it explored multiple facets of their perspectives on astronomy. The topics included students' views on the incorporation of astronomy in curricula, their grasp of fundamental astronomical concepts, access to educational resources, cultural connections to astronomy, and their levels of interest and aspirations in the subject. We find notable deficiencies in students' knowledge of basic astronomical principles, with only a minority demonstrating proficiency in key areas such as celestial sizes, distances, and lunar phases. Furthermore, access to resources such as telescopes and planetariums remain limited across the country. Despite these challenges, a significant majority of students expressed a keen interest in astronomy. We further analyze the data along socioeconomic and gender lines. Particularly striking were the socioeconomic disparities, with students from resource-poor backgrounds often having lower levels of access and proficiency. Some differences were observed between genders, although not very pronounced. The insights gleaned from this study hold valuable implications for the development of a more robust astronomy curriculum and the design of effective teacher training programs in the future., Comment: 15 pages, 19 figures

We prove asymptotic formulae for small weighted solutions of quadratic congruences of the form $\lambda_1x_1^2+\cdots +\lambda_nx_n^2\equiv \lambda_{n+1}\bmod{p^m}$, where $p$ is a fixed odd prime, $\lambda_1,...,\lambda_{n+1}$ are integer coefficients such that $(\lambda_1\cdots \lambda _{n},p)=1$ and $m\rightarrow \infty$. If $n\ge 6$, $p\ge 5$ and the coefficients are fixed and satisfy $\lambda_1,...,\lambda_n>0$ and $(\lambda_{n+1},p)=1$ (inhomogeneous case), we obtain an asymptotic formula which is valid for integral solutions $(x_1,...,x_n)$ in cubes of side length at least $p^{(1/2+\varepsilon)m}$, centered at the origin. If $n\ge 4$ and $\lambda_{n+1}=0$ (homogeneous case), we prove a result of the same strength for coefficients $\lambda_i$ which are allowed to vary with $m$. These results extend previous results of the first- and the third-named authors and N. Bag., Comment: 20 pages

Large Language Models (LLMs), originally shown to ace various text comprehension tasks have also remarkably been shown to tackle table comprehension tasks without specific training. While previous research has explored LLM capabilities with tabular dataset tasks, our study assesses the influence of $\textit{in-context learning}$,$ \textit{model scale}$, $\textit{instruction tuning}$, and $\textit{domain biases}$ on Tabular Question Answering (TQA). We evaluate the robustness of LLMs on Wikipedia-based $\textbf{WTQ}$ and financial report-based $\textbf{TAT-QA}$ TQA datasets, focusing on their ability to robustly interpret tabular data under various augmentations and perturbations. Our findings indicate that instructions significantly enhance performance, with recent models like Llama3 exhibiting greater robustness over earlier versions. However, data contamination and practical reliability issues persist, especially with WTQ. We highlight the need for improved methodologies, including structure-aware self-attention mechanisms and better handling of domain-specific tabular data, to develop more reliable LLMs for table comprehension.

AMoRE-II aims to search for neutrinoless double beta decay with an array of 423 Li$_2$$^{100}$MoO$_4$ crystals operating in the cryogenic system as the main phase of the Advanced Molybdenum-based Rare process Experiment (AMoRE). AMoRE has been planned to operate in three phases: AMoRE-pilot, AMoRE-I, and AMoRE-II. AMoRE-II is currently being installed at the Yemi Underground Laboratory, located approximately 1000 meters deep in Jeongseon, Korea. The goal of AMoRE-II is to reach up to $T^{0\nu\beta\beta}_{1/2}$ $\sim$ 6 $\times$ 10$^{26}$ years, corresponding to an effective Majorana mass of 15 - 29 meV, covering all the inverted mass hierarchy regions. To achieve this, the background level of the experimental configurations and possible background sources of gamma and beta events should be well understood. We have intensively performed Monte Carlo simulations using the GEANT4 toolkit in all the experimental configurations with potential sources. We report the estimated background level that meets the 10$^{-4}$counts/(keV$\cdot$kg$\cdot$yr) requirement for AMoRE-II in the region of interest (ROI) and show the projected half-life sensitivity based on the simulation study.

Primordial black holes bearing magnetic charges may bypass the constraints imposed by Hawking radiation, thereby enabling reasonable present-day populations, even for masses below $10^{15}\,\text{g}$ -- a range previously considered improbable. They could, therefore, conceivably contribute to a component of dark matter. We investigate novel Faraday rotation signatures exhibited by primordial magnetic black holes while also establishing new Parker-type bounds on their populations. For the latter, we bound the dark matter fraction from intergalactic magnetic fields in cosmic voids $\left(f_{\text{DM}} \lesssim 10^{-8}\right)$ and cosmic web filaments $\left(f_{\text{ DM}} \lesssim 10^{-4}\right)$, notably eclipsing previous bounds. Exploring Faraday rotation effects, we discern a pronounced rotation of the polarization angle and the rotation measure values for extremal primordial magnetic black holes with masses $M^{\text{ ex.}}_{\text{ BH}}\gtrsim 10^{-6}~ \text{M}_\odot$. This makes them potentially detectable in current observations. A comparative investigation finds that the effects are notably greater than for a neutron star, like a Magnetar, with a similar magnetic field at the surface. Moreover, the polarization angle maps for primordial magnetic black holes exhibit unique features, notably absent in other astrophysical magnetic configurations. In this context, we also introduce a simple integral measure, offering a quantitative measure for their discrimination in many scenarios. These traits potentially suggest a robust avenue for their observational detection and differentiation., Comment: 52 pages, 13 figures

The Bhutanese government is increasing its utilization of technological approaches such as including Remote Sensing-based knowledge in their decision-making process. This study focuses on crop type and crop extent in Paro, one of the top rice-yielding districts in Bhutan, and employs publicly available NICFI high-resolution satellite imagery from Planet. Two Deep Learning (DL) approaches, point-based (DNN) and patch-based (U-Net), models were used in conjunction with cloud-computing platforms. Three different models per DL approaches (DNN and U-Net) were trained: 1) RGBN channels from Planet; 2) RGBN and elevation data (RGBNE); 3) RGBN and Sentinel-1 (S1) data (RGBNS), and RGBN with E and S1 data (RGBNES). From this comprehensive analysis, the U-Net displayed higher performance metrics across both model training and model validation efforts. Among the U-Net model sets, the RGBN, RGBNE, RGBNS, and RGBNES models had an F1-score of 0.8546, 0.8563, 0.8467, and 0.8500 respectively. An independent model evaluation was performed and found a high level of performance variation across all the metrics. For this independent model evaluation, the U-Net RGBN, RGBNE, RGBNES, and RGBN models displayed the F1-scores of 0.5935, 0.6154, 0.5882, and 0.6582, suggesting U-Net RGBNES as the best model. The study shows that the DL approaches can predict rice. Also, DL methods can be used with the survey-based approaches currently utilized by the Bhutan Department of Agriculture. Further, this study demonstrated the usage of regional land cover products such as SERVIR's RLCMS as a weak label approach to capture different strata addressing the class imbalance problem and improving the sampling design for DL application. Finally, through preliminary model testing and comparisons outlined it was shown that using additional features such as NDVI, EVI, and NDWI did not drastically improve model performance.

In this paper, we study the asymptotic behavior of solutions to the compressible Navier-Stokes system considered on a sequence of spatial domains, whose boundaries exhibit fast oscillations with amplitude and characteristic wave length proportional to a small parameter. Imposing the full-slip boundary conditions we show that in the asymptotic limit the fluid sticks completely to the boundary, provided the oscillations are non-degenerate, meaning not oriented in a single direction.

This paper presents a simulation approach to enhance the power quality of a PV-micro hydro grid supplying both linear consumer load and non-linear industrial load by integrating Shunt Active Power Filter (SAPF), utilizing instantaneous PQ theory and hysteresis current control band logic. The non-linear load draws reactive power and harmonic current from the source thereby affecting the power quality. The integration of the SAPF at the point of common coupling (PCC) offers reactive power and harmonic current compensation, ensuring that the current supply to the grid remains nearly sinusoidal and proportional to the active power. By injecting equal and opposite harmonic components, the SAPF effectively reduces Total Harmonic Distortion (THD) from 7% to 2.96%, thereby enhancing the overall power quality of the PV-micro hydro grid system., Comment: 5 pages, 21 figures, submitted on IEEE powercon 2024 conference