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Convolutional Neural Networks (CNNs) have made remarkable strides; however, they remain susceptible to vulnerabilities, particularly in the face of minor image perturbations that humans can easily recognize. This weakness, often termed as 'attacks', underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel Non-Uniform Illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely-accepted datasets including CIFAR10, TinyImageNet, and CalTech256, focusing on image classification with 12 different NUI attack models. The resilience of VGG, ResNet, MobilenetV3-small and InceptionV3 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models' classification accuracy when subjected to NUI attacks, indicating their vulnerability under non-uniform illumination. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models' resilience against NUI attacks.

Alzheimer's Disease (AD) is a neurodegenerative disease affecting millions of individuals across the globe. As the prevalence of this disease continues to rise, early diagnosis is crucial to improve clinical outcomes. Neural networks, specifically Convolutional Neural Networks (CNNs), are promising tools for diagnosing individuals with Alzheimer's. However, neural networks such as ANNs and CNNs typically yield lower validation accuracies when fed lower quantities of data. Hence, Generative Adversarial Networks (GANs) can be utilized to synthesize data to augment these existing MRI datasets, potentially yielding higher validation accuracies. In this study, we use this principle while examining a novel application of the SSMI metric in selecting high-quality synthetic data generated by our GAN to compare its accuracies with shuffled data generated by our GAN. We observed that incorporating GANs with an SSMI metric returned the highest accuracies when compared to a traditional dataset.

We present a methodology to generate low-latency, high spatio-temporal resolution thermospheric density estimates using publicly available Low Earth Orbit (LEO) spacecraft ephemerides. This provides a means of generating density estimates that can be used in a data-assimilative context by the satellite operations and thermosphere communities. It also contributes to the data base of high-resolution density estimates during geomagnetic storms -- which remains one of the major gaps for the development and benchmarking of density models. Using accelerometer-derived densities from the Gravity Recovery And Climate Experiment Follow-On (GRACE-FO) spacecraft as truth, our method surpasses Energy Dissipation Rate-Type density retrieval techniques and three widely used operational density models in terms of accuracy: EDR (103.37%), JB2008 (85.43%), DTM2000 (52.73%), and NRLMSISE-00 (12.31%). We demonstrate the robustness of our methodology during a critical time for spacecraft operators -- attempting to operate in the presence of geomagnetic storms, by reconstructing density profiles along the orbits of three LEO satellites during 80 geomagnetic storms. These profiles exhibit high spatial and temporal resolution compared to three operational thermospheric models, highlighting the operational applicability and potential for their use in model validation. Our findings suggest that the increasing availability of precise orbit determination data offers a valuable, yet underutilized, resource that could provide a significant improvement to data assimilative thermospheric models, ultimately enhancing both spacecraft operations and thermospheric modeling efforts., Comment: 29 pages, 6 figures

Defects are inevitable during the manufacturing processes of materials. Presence of these defects and their dynamics significantly influence the responses of materials. A thorough understanding of dislocation dynamics of different types of materials under various conditions is essential for analyzing the performance of the materials. Ductility of a material is directly related with the movement and rearrangement of dislocations under applied load. In this work, we look into the dynamics of dislocations in ductile and brittle materials using simplified two dimensional discrete dislocation dynamics (2D-DDD) simulation. We consider Aluminium (Al) and Tungsten (W) as representative examples of ductile and brittle materials respectively. We study the velocity distribution, strain field, dislocation count, and junction formation during interactions of the dislocations within the domain. Furthermore, we study the probability densities of dislocation motion for both materials. In mesoscale, moving dislocations can be considered as particle diffusionm, which are often stochastic and super-diffusive. Classical diffusion models fail to account for these phenomena and the long-range interactions of dislocations. Therefore, we propose the nonlocal transport model for the probability density and obtained the parameters of nonlocal operators using a machine learning framework.

We show in this letter how a heavy $(\mathcal{O}(TeV))$ invisible $Z'$ gauge boson that will practically be out of reach of the Large Hadron Collider (LHC), can be discovered at the future muon collider. The new force carrier has a relatively stronger coupling with the beyond standard model (BSM) sector, while its interaction with the SM fields is much weaker. This weaker coupling is induced through mixing mechanisms, specifically via gauge kinetic mixing and the $Z-Z'$ mixing. We consider a scenario where the new gauge boson decays mostly to charge-neutral long-lived particles and/or dark matter (DM). We show how producing and detecting this heavier invisible $Z'$, that will be beyond the reach of even the very high luminosity LHC, becomes possible if it is produced in association with an energetic photon at the future muon collider. The on-shell production of the $Z'$ will lead to a peak in the photon energy distribution, following the so-called radiative return phenomena and can lead to the accurate determination of the $Z'$ mass and its interaction with SM particles., Comment: 6 pages, 4 figures, 5 tables

Large wildlife image collections from camera traps are crucial for biodiversity monitoring, offering insights into species richness, occupancy, and activity patterns. However, manual processing of these data is time-consuming, hindering analytical processes. To address this, deep neural networks have been widely adopted to automate image analysis. Despite their growing use, the impact of model training decisions on downstream ecological metrics remains unclear. Here, we analyse camera trap data from an African savannah and an Asian sub-tropical dry forest to compare key ecological metrics derived from expert-generated species identifications with those generated from deep neural networks. We assess the impact of model architecture, training data noise, and dataset size on ecological metrics, including species richness, occupancy, and activity patterns. Our results show that while model architecture has minimal impact, large amounts of noise and reduced dataset size significantly affect these metrics. Nonetheless, estimated ecological metrics are resilient to considerable noise, tolerating up to 10% error in species labels and a 50% reduction in training set size without changing significantly. We also highlight that conventional metrics like classification error may not always be representative of a model's ability to accurately measure ecological metrics. We conclude that ecological metrics derived from deep neural network predictions closely match those calculated from expert labels and remain robust to variations in the factors explored. However, training decisions for deep neural networks can impact downstream ecological analysis. Therefore, practitioners should prioritize creating large, clean training sets and evaluate deep neural network solutions based on their ability to measure the ecological metrics of interest.

In this study, we tackle the challenge of limited labeled data for low-resource languages in ASR, focusing on Hindi. Specifically, we explore pseudo-labeling, by proposing a generic framework combining multiple ideas from existing works. Our framework integrates multiple base models for transcription and evaluators for assessing audio-transcript pairs, resulting in robust pseudo-labeling for low resource languages. We validate our approach with a new benchmark, IndicYT, comprising diverse YouTube audio files from multiple content categories. Our findings show that augmenting pseudo labeled data from YouTube with existing training data leads to significant performance improvements on IndicYT, without affecting performance on out-of-domain benchmarks, demonstrating the efficacy of pseudo-labeled data in enhancing ASR capabilities for low-resource languages. The benchmark, code and models developed as a part of this work will be made publicly available.

This study effectively reconstructs a cosmological model utilizing covariant $f(Q)$ gravity within Connection-III and FLRW spacetime. The dynamic behavior of the reconstructed model is thoroughly analyzed using the Hubble parameter $H(z)$ and various observational datasets. Our robust findings demonstrate that the model displays quintessence behavior at the present epoch and converges to the $\Lambda$CDM model at late time. It is confirmed through comprehensive evaluations against energy conditions that the Null Energy Condition remains positive throughout cosmic evolution, and the Dominant Energy Condition is consistently satisfied. The Strong Energy Condition is initially fulfilled in the early Universe but violated in the late epoch. Moreover, scalar perturbations extensively assess stability, affirming the strength of the model with respect to the Hubble parameter. This research offers compelling insights into cosmic acceleration, suggesting that $f(Q)$ gravity can effectively displace the $\Lambda$CDM model and provides a convincing alternative explanation for the current accelerating expansion of the Universe without relying on the cosmological constant., Comment: 11 pages, 4 figures

Side channel attacks (SCAs) remain a significant threat to the security of cryptographic systems in modern embedded devices. Even mathematically secure cryptographic algorithms, when implemented in hardware, inadvertently leak information through physical side channel signatures such as power consumption, electromagnetic (EM) radiation, light emissions, and acoustic emanations. Exploiting these side channels significantly reduces the search space of the attacker. In recent years, physical countermeasures have significantly increased the minimum traces to disclosure (MTD) to 1 billion. Among them, signature attenuation is the first method to achieve this mark. Signature attenuation often relies on analog techniques, and digital signature attenuation reduces MTD to 20 million, requiring additional methods for high resilience. We focus on improving the digital signature attenuation by an order of magnitude (MTD 200M). Additionally, we explore possible attacks against signature attenuation countermeasure. We introduce a Voltage drop Linear region Biasing (VLB) attack technique that reduces the MTD to over 2000 times less than the previous threshold. This is the first known attack against a physical side-channel attack (SCA) countermeasure. We have implemented an attack detector with a response time of 0.8 milliseconds to detect such attacks, limiting SCA leakage window to sub-ms, which is insufficient for a successful attack., Comment: Extended from CICC. Now under revision at Journal of Solid-State Circuits

CRISPR (clustered regularly interspaced short palindromic repeat)- based diagnostics are at the forefront of rapid detection platforms of infectious diseases. The integration of reverse transcription-loop-mediated isothermal amplification (RT-LAMP) with CRISPR-Cas protein systems has led to the creation of advanced one-pot assays. The sensitivity of these assays has been bolstered by the utilization of a thermophilic Cas12 protein, BrCas12b, and its engineered variant, which exhibits enhanced thermal stability and allows for broader operation temperatures of the assay. Here, we perform all-atom molecular dynamics (MD) simulations on wild-type and mutant BrCas12b to reveal the mechanism of stabilization conferred by the mutation. High-temperature simulations reveal a small structural change along with greater flexibility in the PAM-interacting domain of the mutant BrCas12b, with marginal structural and flexibility changes in the other mutated domains. Comparative essential dynamics analysis between the wild-type and mutant BrCas12b at both ambient and elevated temperatures provides insights into the stabilizing effects of the mutations. Our findings not only offer a comprehensive insight into the dynamic alterations induced by mutations but reveal important motions in BrCas12b, important for the rational design of diagnostic and therapeutic platforms of Cas12 proteins.

We present the properties of two candidate massive ($M_\star\sim10^{11}M_\odot$) and dusty ($A_{\rm v}>2.5$ mag) galaxies at $z=5-7$ in the first 0.28 deg$^2$ of the COSMOS-Web survey. One object is spectroscopically confirmed at $z_{\rm spec}=5.051$, while the other has a robust $z_{\rm phot}=6.7\pm0.3$. Thanks to their extremely red colors ($F277W-F444W\sim1.7$ mag), these galaxies satisfy the nominal color-selection for the widely-studied ``little red dot" (LRD) population with the exception of their spatially-resolved morphologies. The morphology of our targets allows us to conclude that their red continuum is dominated by highly obscured stellar emission and not by reddened nuclear activity. Using a variety of SED-fitting tools and star formation histories, we estimate the stellar masses to be $\log(M_\star)=11.32^{+0.07}_{-0.15}$ $M_\odot$ and $\log(M_\star)=11.2^{+0.1}_{-0.2}$ $M_\odot$, respectively, with a red continuum emission dominated by a recent episode of star formation. We then compare their number density to the halo mass function to infer stellar baryon fractions of $\epsilon_\star\sim0.25$ and $\epsilon_\star\sim0.5$. Both are significantly higher than what is commonly observed in lower-z galaxies or more dust-obscured galaxies at similar redshifts. With very bright ultra-high-z Lyman-Break Galaxies and some non-AGN dominated LRDs, such ``extended" LRDs represent another population that may require very efficient star formation at early times., Comment: 11 pages, 4 figures, 1 table. Accepted for publication in ApJL

An interacting Holographic dark energy (HDE) with different infra-red (IR) cutoffs (Hubble horizon and future event horizon) is investigated in the background dynamics of flat Friedmann Lemaitre Robertson Walker (FLRW) universe where gravitational particle creation effects via different form of particle creation rates (1) $\Gamma=3\beta H$ and (2) $\Gamma=3\alpha H_{0}+3\beta H$ are considered. The created particles are considered to be pressureless Dark Matter (DM) which interacts with the HDE through a phenomenological choice of interaction term $Q=3\gamma H \rho_{m}$. We obtain an analytic solution of the cosmological dynamics with Hubble horizon as IR cutoff when the creation rate is taken as $\Gamma=3 \beta H$. We find that the interacting HDE from the Hubble horizon as the IR cutoff can give the late-time acceleration and non-interacting cannot give. On the other hand, employing the Hubble horizon and the future event as IR cutoffs for the model of HDE does not provide the analytic solution when the creation rate is taken as $\Gamma=3\alpha H_{0}+3\beta H$. We then analyze the model separately using the dynamical systems theory. From the analysis, the model (with Hubble horizon as IR cutoff) provides two sets of critical points. One can give a late-time accelerated universe evolving in quintessence, the cosmological constant, or the phantom era. But, it does not show any matter-dominated era. On the other hand, by applying the future event as an IR cutoff, the model provides the complete evolution of the universe. It also exhibits the late-time scaling attractor gives the possible solution of the coincidence problem. Global dynamics of the model are investigated by defining the appropriate Lyapunov function. Finally, the adiabatic sound speeds of all the models have been calculated and plotted numerically to find the stability of the models., Comment: 27 pages, 10 captioned figures

This study investigates the causal relationships between Clinical Dementia Ratings (CDR) and its six domain scores across two distinct aging datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Longitudinal Aging Study of India (LASI). Using Directed Acyclic Graphs (DAGs) derived from Bayesian network models, we analyze the dependencies among domain scores and their influence on the global CDR. Our approach leverages the PC algorithm to estimate the DAG structures for both datasets, revealing notable differences in causal relationships and edge strengths between the Western and Indian populations. The analysis highlights a stronger dependency of CDR scores on memory functions in both datasets, but with significant variations in edge strengths and node degrees. By contrasting these findings, we aim to elucidate population-specific differences and similarities in dementia progression, providing insights that could inform targeted interventions and improve understanding of dementia across diverse demographic contexts., Comment: 7 pages, 2 figures, 3 tables

JWST has enabled detecting and spatially resolving the heavily dust-attenuated stellar populations of sub-millimeter galaxies, revealing detail that was previously inaccessible. In this work we construct a sample of 289 sub-millimeter galaxies with detailed joint ALMA and JWST constraints in the COSMOS field. Sources are originally selected using the SCUBA-2 instrument and have archival ALMA observations from various programs. Their JWST NIRCam imaging is from COSMOS-Web and PRIMER. We extract multi-wavelength photometry in a manner that leverages the unprecedented near-infrared spatial resolution of JWST, and fit the data with spectral energy distribution models to derive photometric redshifts, stellar masses, star-formation rates and optical attenuation. The sample has an average z=2.6, A_V=2.5, SFR=270 and log(M*)=11.1. There are 81 (30%) galaxies that have no previous optical/near-infrared detections, including 75% of the z>4 sub-sample (n=28). The faintest observed near-infrared sources have the highest redshifts and largest A_V=4. In a preliminary morphology analysis we find that ~10% of our sample exhibit spiral arms and 5% host stellar bars, with one candidate bar found at z>3. Finally, we find that the clustering of JWST galaxies within 10 arcseconds of a sub-mm galaxy is a factor of 2 greater than what is expected based on either random clustering or the distribution of sources around any red galaxy irrespective of a sub-mm detection., Comment: 37 pages (15 for RGBs + references), 14 figures, submitted to ApJ

Our objective is to investigate the distribution of dust and associated large-scale structures of the Galaxy using optical linear polarization measurements of various open clusters located at different distances in the Galactic anticenter direction. We present R-band linear polarization observations of stars towards five open clusters: Kronberger~1, Berkeley~69, Berkeley~71, Berkeley~19, and King~8 in the anticenter direction. The polarization observations were carried out using AIMPOL instrument mounted on the 104 cm Sampurnanand telescope of ARIES, Nainital, making it the first study to target the polarization observations towards distant clusters ($\sim$6~kpc). We combined the observed polarization data with the distance information from the Gaia space telescope to infer the dust distribution along the line of sight. The variation in the degree of polarization and extinction with distance reveals the presence of multiple dust layers along each cluster direction. In addition, common foreground dust layers detected towards different cluster directions highlight the presence of global features such as spiral arms. Our results show that the dust clouds at 2~kpc towards Berkeley~69 and Berkeley~71 coincide with the Perseus arm, while the dust layer at $\sim$4~kpc towards distant clusters, Berkeley~19 and King~8, indicates the presence of the Outer arm. The large-scale dust distribution obtained by combining our polarization results with the previous polarization studies of nearby open clusters suggests that the anticenter direction is characterized by low extinction, homogeneous dust distribution with somewhat uniform orientation of the plane-of-sky component of the magnetic field along the line of sight. Our study demonstrates the utility of polarization as a tool to study the large-scale dust distribution., Comment: 22 Pages, 14 Figures, 7 Tables, Accepted for publication in Astronomy & Astrophysics Journal

Discerning chaos in quantum systems is an important problem as the usual route of Lyapunov exponents in classical systems is not straightforward in quantum systems. A standard route is the comparison of statistics derived from model physical systems to those from random matrix theory (RMT) ensembles, of which the most popular is the nearest-neighbour-spacings distribution (NNSD), which almost always shows good agreement with chaotic quantum systems. However, even in these cases, the long-range statistics (like number variance, spectral rigidity etc.), which are also more difficult to calculate, often show disagreements with RMT. As such, a more stringent test for chaos in quantum systems, via an analysis of intermediate-range statistics is needed, which will additionally assess the extent of agreement with RMT universality. In this paper, we deduce the effective level-repulsion parameters and the corresponding Wigner-surmise-like results of the next-nearest-neighbor spacing distribution (nNNSD) for integrable systems (semi-Poissonian statistics) as well as the three classical quantum-chaotic Wigner-Dyson regimes, by stringent comparisons to numerical RMT models and benchmarking against our exact analytical results for $3\times 3$ Gaussian matrix models, along with a semi-analytical form for the nNNSD in the Orthogonal-to-Unitary symmetry crossover. To illustrate the robustness of these RMT based results, we test these predictions against the nNNSD obtained from quantum chaotic models as well as disordered lattice spin models. This reinforces the Bohigas-Giannoni-Schmit and the Berry-Tabor conjectures, extending the associated universality to longer range statistics. In passing, we also highlight the equivalence of nNNSD in the apparently distinct Orthogonal-to-Unitary and diluted-Symplectic-to-Unitary crossovers., Comment: 19 Pages, 15 Figures

In this paper, we have introduced a numerical method for solving the Friedmann equations of modified $f(\mathcal{G})$ gravity in the presence of pressureless matter, enabling us to predict the redshift behavior of the Hubble expansion rate. To evaluate the credibility of the model, we applied a Bayesian MCMC technique using late-time cosmic observations to impose limitations on the free parameters of the theory. Our results suggest that the $f(\mathcal{G})$ model can reproduce the low-redshift behavior of the standard $\Lambda$CDM model, but there are significant differences at high redshifts, leading to the absence of a standard matter-dominated epoch. We also examined the profiles of cosmographic parameters using the model parameter values from the standard range to verify the intermediate epochs. Our analysis shows that the highly promising $f(\mathcal{G})$ model is a feasible candidate for explaining the current epochs. We presented a dynamical system analysis framework to examine the stability of the model. Our study identified critical points depicting various phases of the Universe and explained the evolutionary epochs. We demonstrated that the model effectively captures the evolution of energy components over cosmic time, supporting its validity as an alternate explanation for the observed acceleration of the Universe., Comment: 15 pages, 8 figures

Engineering magnetism in layered magnets could result in novel phenomena related to two-dimensional (2D) magnetism, which can be useful for fundamental research and practical applications. Extensive doping efforts such as substitution and intercalation have been adopted to tune antiferromagnetic (AFM) properties in M2P2X6 compounds. The substitutional doping in this material family has mainly focused on bimetallic substitution. Recently, the metal substitution can also be extended to more than two metal elements, leading to medium and high-entropy alloys (MEAs and HEAs), which are fairly underexplored in layered magnetic systems including M2P2X6. In this work, we explored the magnetic properties of the previously unreported Cu- and Cr-substituted Ni2P2S6 i.e., CuxNi2(1-x)CrxP2S6. Our study reveals a relatively systematic evolution of AFM phases with substitution than that observed in traditional bimetallic substitution in M2P2X6. Furthermore, the Cu and Cr substitutions in Ni2P2S6 are found to enhance the ferromagnetic (FM) correlation, which is also accompanied by a possible weak FM phase at low temperatures for the intermediate compositions from 0.32 to 0.80. Our work provides a strategy to establish ferromagnetism in AFM M2P2X6 that can also be used for property tuning in other layered magnets.

This paper introduces a Scalable Hierarchical Aware Convolutional Neural Network (SHA-CNN) model architecture for Edge AI applications. The proposed hierarchical CNN model is meticulously crafted to strike a balance between computational efficiency and accuracy, addressing the challenges posed by resource-constrained edge devices. SHA-CNN demonstrates its efficacy by achieving accuracy comparable to state-of-the-art hierarchical models while outperforming baseline models in accuracy metrics. The key innovation lies in the model's hierarchical awareness, enabling it to discern and prioritize relevant features at multiple levels of abstraction. The proposed architecture classifies data in a hierarchical manner, facilitating a nuanced understanding of complex features within the datasets. Moreover, SHA-CNN exhibits a remarkable capacity for scalability, allowing for the seamless incorporation of new classes. This flexibility is particularly advantageous in dynamic environments where the model needs to adapt to evolving datasets and accommodate additional classes without the need for extensive retraining. Testing has been conducted on the PYNQ Z2 FPGA board to validate the proposed model. The results achieved an accuracy of 99.34%, 83.35%, and 63.66% for MNIST, CIFAR-10, and CIFAR-100 datasets, respectively. For CIFAR-100, our proposed architecture performs hierarchical classification with 10% reduced computation while compromising only 0.7% accuracy with the state-of-the-art. The adaptability of SHA-CNN to FPGA architecture underscores its potential for deployment in edge devices, where computational resources are limited. The SHA-CNN framework thus emerges as a promising advancement in the intersection of hierarchical CNNs, scalability, and FPGA-based Edge AI.

We investigate the `intrinsic' H$\alpha$ EW distributions of $z \sim 0.4 - 2.2$ narrowband-selected H$\alpha$ samples from HiZELS and DAWN using a forward modeling approach. We find an EW - stellar mass anti-correlation with steepening slopes $-0.18\pm0.03$ to $-0.24^{+0.06}_{-0.08}$ at $z \sim 0.4$ and $z\sim 2.2$, respectively. Typical EW increases as $(1+z)^{1.78^{+0.22}_{-0.23}}$ for a $10^{10}$ M$_\odot$ emitter from $15^{+2.4}_{-2.3}$\r{A} ($z \sim 0.4$) to $67.7^{+10.4}_{-10.0}$\r{A} ($z \sim 2.2$) and is steeper with decreasing stellar mass highlighting the high EW nature of low-mass high-$z$ systems. We model this redshift evolving anti-correlation, $W_0(M,z)$, and find it produces H$\alpha$ luminosity and SFR functions strongly consistent with observations validating the model and allowing us to use $W_0(M,z)$ to investigate the relative contribution of H$\alpha$ emitters towards cosmic SF. We find EW$_0 > 200$ \r{A} emitters contribute significantly to cosmic SF activity at $z \sim 1.5 - 2$ making up $\sim 40$% of total SF consistent with sSFR $> 10^{-8.5}$ yr$^{-1}$ ($\sim 45 - 55$%). Overall, this highlights the importance of high EW systems at high-$z$. Our $W_0(M,z)$ model also reproduces the cosmic sSFR evolution found in simulations and observations and show that tension between the two can simply arise from selection effects in observations. Lastly, we forecast Roman and Euclid grism surveys using $W_0(M,z)$ including observational efficiency and limiting resolution effects where we predict $\sim 24000$ and $\sim 30000$ $0.5 < z < 1.9$ H$\alpha$ emitters per deg$^{-2}$, respectively, down to $>5\times10^{-17}$ erg s$^{-1}$ cm$^{-2}$ including $10^{7.2 - 8}$ M$_\odot$ galaxies at $z > 1$ with EW$_0 >1000$\r{A}. Both Roman and Euclid will enable us to observe with unprecedented detail some of the most bursty/high EW, low-mass star-forming galaxies near cosmic noon., Comment: 25 pages, 12 Figures, and 12 Tables. Submitted to MNRAS. Comments are welcomed. (Abridged Abstract)

Memory management is necessary with the increasing number of multi-connected AI devices and data bandwidth issues. For this purpose, high-speed multi-port memory is used. The traditional multi-port memory solutions are hard-bounded to a fixed number of ports for read or write operations. In this work, we proposed a pseudo-quad-port memory architecture. Here, ports can be configured (1-port, 2-port, 3-port, 4-port) for all possible combinations of read/write operations for the 6T static random access memory (SRAM) memory array, which improves the speed and reduces the bandwidth for data transfer. The proposed architecture improves the bandwidth of data transfer by 4x. The proposed solution provides 1.3x and 2x area efficiency as compared to dual-port 8T and quad-port 12T SRAM. All the design and performance analyses are done using 65nm CMOS technology.

This paper presents the Hybrid Overestimating Approximate Adder designed to enhance the performance in processing engines, specifically focused on edge AI applications. A novel Plus One Adder design is proposed as an incremental adder in the RCA chain, incorporating a Full Adder with an excess 1 alongside inputs A, B, and Cin. The design approximates outputs to 2 bit values to reduce hardware complexity and improve resource efficiency. The Plus One Adder is integrated into a dynamically reconfigurable HOAA, allowing runtime interchangeability between accurate and approximate overestimation modes. The proposed design is demonstrated for multiple applications, such as Twos complement subtraction and Rounding to even, and the Configurable Activation function, which are critical components of the Processing engine. Our approach shows 21 percent improvement in area efficiency and 33 percent reduction in power consumption, compared to state of the art designs with minimal accuracy loss. Thus, the proposed HOAA could be a promising solution for resource-constrained environments, offering ideal trade-offs between hardware efficiency vs computational accuracy.

Major Depressive Disorder (MDD) is a pervasive mental health condition that affects 300 million people worldwide. This work presents a novel, BiLSTM-based tri-modal model-level fusion architecture for the binary classification of depression from clinical interview recordings. The proposed architecture incorporates Mel Frequency Cepstral Coefficients, Facial Action Units, and uses a two-shot learning based GPT-4 model to process text data. This is the first work to incorporate large language models into a multi-modal architecture for this task. It achieves impressive results on the DAIC-WOZ AVEC 2016 Challenge cross-validation split and Leave-One-Subject-Out cross-validation split, surpassing all baseline models and multiple state-of-the-art models. In Leave-One-Subject-Out testing, it achieves an accuracy of 91.01%, an F1-Score of 85.95%, a precision of 80%, and a recall of 92.86%., Comment: Keywords: Multi-Modal Neural Networks, Deep Learning, Large Language Models, Depression Diagnosis, Biomedical Informatics, DAIC-WOZ

The dissipative spectral form factor (DSFF) has recently emerged as a diagnostic tool for assessing non-integrability or chaos in non-Hermitian systems. It extends the concept of the spectral form factor (SFF), a widely used measure for similar investigations in Hermitian systems. In this study, we concentrate on the elliptic Ginibre unitary ensemble (eGinUE), which interpolates between the non-Hermitian limit of Ginibre unitary ensemble (GinUE) and the Hermitian limit of Gaussian unitary ensemble (GUE) by varying a symmetry parameter. We derive exact finite-dimension expression and large-dimension approximation for the DSFF of eGinUE. A key finding of this work is a compelling ``scaling relationship" between the DSFF of eGinUE and that of the GUE or GinUE. This scaling relationship also reveals a previously unobserved connection between the DSFF of GinUE and the SFF of GUE. The DSFF for eGinUE displays a \emph{dip-ramp-plateau} structure in the GinUE and GUE limits, as well as in the crossover region, with differences in time scales that are well-explained by the aforementioned scaling relationship. Additionally, depending on the symmetry regime within the crossover, we provide various estimates for the Thouless time ($T_\mathrm{Th}$) and Heisenberg time ($T_\mathrm{H}$), which correspond to the dip-ramp and ramp-plateau transitions, respectively. Our analytical results are compared with Monte Carlo simulations of the eGinUE random matrix model, showing excellent agreement., Comment: 9 pages, 3 figures

Recently, layered materials transition metal thiophosphate MPX3 (M = transition metals, X = S or Se) have gained significant attention because of their rich magnetic, optical, and electronic properties. Specifically, the diverse magnetic structures and the robustness of magnetism in the two-dimensional limit have made them prominent candidates to study two-dimensional magnetism. Numerous efforts such as substitutions and interlayer intercalations have been made to tune the properties of these materials, which has greatly deepened the understanding of the underlying mechanisms that govern the properties. In this work, we focus on modifying the magnetism of Ising-type antiferromagnet FePS3 using electrochemical lithium intercalation. Our work unveils the effectiveness of electrochemical intercalation as a controllable tool to modulating magnetism, including tuning magnetic ordering temperature and inducing low temperature spin-glass state, offering an approach for implementing this material into applications.

Photometric redshifts are widely used in studies of dusty star-forming galaxies (DSFGs), but catastrophic photo-$z$ failure can undermine all redshift-dependent results. Here we report the spectroscopic redshift confirmation of COSBO-7, a strongly lensed DSFG in the COSMOS-PRIMER field. Recently, using 10 bands of JWST NIRCam and MIRI imaging data on COSBO-7, Ling et al. (2024) reported a photometric redshift solution of $z\gtrsim7.0$, favored by four independent spectral energy distribution (SED) fitting codes, and therefore providing an appealing candidate of the most distant massive DSFG. This photo-$z$ solution was also supported by a single line detection in ALMA Band 3 consistent with CO(7-6) at $z=7.46$. However, our new ALMA observations robustly detect two lines in Band 6 identified as CO(7-6) and [CI](2-1) at $z_{\rm spec}=2.625$, and thus the Band 3 line as CO(3-2). The three robust line detections decidedly place COSBO-7 at $z=2.625$, refuting the photo-$z$ solution. We derive physical parameters by fitting NIR-to-mm photometry and lens modeling, revealing that COSBO-7 is a main-sequence galaxy. We examine possible reasons for this photo-$z$ failure and attribute it to (1) the likely underestimation of photometric uncertainty at 0.9$\mu$m, and (2) the lack of photometry at wavelengths beyond 20$\mu$m. Notably, we recover a bona-fide $z_{\rm phot}\sim 2.3$ by including the existing MIPS $24\mu$m photometry, demonstrating the critical importance of mid-infrared data for photo-$z$ robustness. This work highlights a common challenge in modeling SEDs of DSFGs, cautioning against the reliability of photometric redshifts, as well as pseudo-spectroscopic redshifts based on single line detection., Comment: Submitted to A&A Letter

Magnetotransport, the response of electrical conduction to external magnetic field, acts as an important tool to reveal fundamental concepts behind exotic phenomena and plays a key role in enabling spintronic applications. Magnetotransport is generally sensitive to magnetic field orientations. In contrast, efficient and isotropic modulation of electronic transport, which is useful in technology applications such as omnidirectional sensing, is rarely seen, especially for pristine crystals. Here we propose a strategy to realize extremely strong modulation of electron conduction by magnetic field which is independent of field direction. GdPS, a layered antiferromagnetic semiconductor with resistivity anisotropies, supports a field-driven insulator-to-metal transition with a paradoxically isotropic gigantic negative magnetoresistance insensitive to magnetic field orientations. This isotropic magnetoresistance originates from the combined effects of a near-zero spin-orbit coupling of Gd3+-based half-filling f-electron system and the strong on-site f-d exchange coupling in Gd atoms. Our results not only provide a novel material system with extraordinary magnetotransport that offers a missing block for antiferromagnet-based ultrafast and efficient spintronic devices, but also demonstrate the key ingredients for designing magnetic materials with desired transport properties for advanced functionalities., Comment: 44 pages, 18 figures

The present study aims to optimize the structural design of a Z-type flow lithium-ion battery pack with a forced air-cooling system (FACS) known as BTMS (Battery Thermal Management System). The main goal is to minimize Tmax (maximum temperature) and \delta Tmax (maximum temperature difference) while ensuring an even airflow distribution within the battery module. The present study thoroughly investigates critical factors such as the inlet air velocity, tapered inlet manifold, and the number of secondary outlets to evaluate their impact on thermal performance and airflow uniformity within the battery module. Increasing the inlet air velocity from 3 to 4.5 m/s significantly improves the thermal cooling performance of the BTMS, resulting in a decrease of 4.57 {\deg}C (10.05%) in Tmax and 0.29 {\deg}C (9.79%) in \delta Tmax compared to the original 3 m/s velocity. Further, the study assesses the significance of a tapered inlet manifold as a critical factor, revealing its substantial impact on cooling performance and temperature reductions in battery cells 3-9. It also facilitates a more uniform airflow distribution, decreasing the velocity difference between channel 9 and channel 1 from 3.32 m/s to 2.50 m/s. Incorporating 7 secondary outlets significantly improves the heat dissipation ability of the BTMS, resulting in a decrease of 0.894 {\deg}C (2.18%) in Tmax and 2.23 {\deg}C (72.84%) in \delta Tmax compared to the configuration with 0 secondary outlets. By optimizing these parameters, the aim is to enhance BTMS's capabilities, improving LIB packs' performance and reliability.

Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subject to extensive missingness due to a complex combination of factors. In this work, we study the problem of imputation of missing step count data, one of the most ubiquitous forms of wearable sensor data. We construct a novel and large scale data set consisting of a training set with over 3 million hourly step count observations and a test set with over 2.5 million hourly step count observations. We propose a domain knowledge-informed sparse self-attention model for this task that captures the temporal multi-scale nature of step-count data. We assess the performance of the model relative to baselines and conduct ablation studies to verify our specific model designs., Comment: Accepted by Conference on Health, Inference, and Learning (CHIL) 2024

We searched the Chandra and XMM archives for observations of 900 green pea galaxies to find AGN signatures. Green peas are low-mass galaxies with prominent emission lines, similar in size and star formation rate to high-redshift dwarf galaxies. Of the 29 observations found, 9 show X-ray detections with $S/N>3$. The 2-10 keV X-ray luminosity for these 9 sources exceeds $10^{40}~\mathrm{erg~s}^{-1}$, with 2 sources exceeding $10^{41}~\mathrm{erg~s}^{-1}$, suggesting the presence of intermediate-mass black holes (IMBH) or low-luminosity AGN (LLAGN) with BH masses between $100-10^6M_\mathrm{\odot}$. All X-ray detected sources (plus 6 additional sources) show He~II$\lambda4686$ emission and a broad component of the H$\alpha$ emission line, indicating winds. The line widths of the broad H$\alpha$ and He II$\lambda4686$ emitting gas clouds are weakly correlated ($R^{2}=0.15$), suggesting He II$\lambda4686$ emission is inconsistent with winds from super-Eddington accretors. However, the ratio of X-ray luminosity to star formation rate shows an anti-correlation with metallicity in 5 out of 9 X-ray detected sources, implying ultraluminous X-ray sources are key contributors to the observed X-ray luminosity. This could be due to super-Eddington accretors or IMBH. The X-ray emission is much higher than that produced by Wolf-Rayet stars and supernovae-driven winds. Thus, the X-ray luminosity in these 9 sources can only be explained by black holes with masses over $100~M_\mathrm{\odot}$. Our findings suggest the presence of LLAGN in these galaxies, with broad H$\alpha$ line widths implying BH masses of $10^4-10^6M_\mathrm{\odot}$. Given Green Peas' role as significant Lyman Continuum leakers, LLAGN in these galaxies could have contributed significantly to cosmic reionization., Comment: Submitted to ApJ. 17 pages, 11 figures and 3 tables. Comments welcome

The power distribution system is evolving in the form of an intelligent grid. The proliferation of distributed energy resources (DERs) makes the previously passive system active and more complicated. With the adoption of de-carbonization principles, large-scale coal and nuclear power plants are being gradually replaced by renewables and carbon-free DERs. With this rapid transformation, the power system operates with less inertia and minimal margins. In recent years, power systems have been facing apocalyptic weather events more frequently, and large-scale blackouts have become regular. After the complete or partial blackouts, the power system goes through different stages before it reaches the normal operating condition. The load restoration is the stage where the power system is fully established after the blackouts; however, due to the limiting ramping rates of centralized generation, the energization of large amounts of loads is delayed by some time. To mitigate the negative impact of ramping rates of centralized generation, DERs in distribution systems are proposed to serve the loads in both transmission and distribution systems in coordination with limited centralized generation in the transmission system. The problem is formulated as a centralized or integrated transmission and distribution (T$\&$D) coordination model. The modified IEEE 14 bus test case and IEEE 13 node test feeders are used to validate the proposed strategy; the results indicate the validity of the proposed model.

We study a scotogenic model augmented with an additional U(1) gauge and a discrete Z2 symmetry. The lightest Z2-odd particle in our model becomes the dark matter (DM) candidate while tiny neutrino masses are realized at one loop. We explore the parameter space of the model for which the DM relic density is satisfied, and the correct low-energy neutrino observables are reproduced. The extended gauge symmetry includes beyond Standard Model (SM) particle spectrum consisting of vector-like fermions and scalars. We also highlight possible collider signatures of these particles at the LHC., Comment: 43 pages, 13 figures, 13 tables

As Large Language Models (LLMs) perform (and sometimes excel at) more and more complex cognitive tasks, a natural question is whether AI really understands. The study of understanding in LLMs is in its infancy, and the community has yet to incorporate well-trodden research in philosophy, psychology, and education. We initiate this, specifically focusing on understanding algorithms, and propose a hierarchy of levels of understanding. We use the hierarchy to design and conduct a study with human subjects (undergraduate and graduate students) as well as large language models (generations of GPT), revealing interesting similarities and differences. We expect that our rigorous criteria will be useful to keep track of AI's progress in such cognitive domains., Comment: 13 pages, 8 figures

Motor imagery-based BCI systems have been promising and gaining popularity in rehabilitation and Activities of daily life(ADL). Despite this, the technology is still emerging and has not yet been outside the laboratory constraints. Channel reduction is one contributing avenue to make these systems part of ADL. Although Motor Imagery classification heavily depends on spatial factors, single channel-based classification remains an avenue to be explored thoroughly. Since Fisher's ratio and Pearson Correlation are powerful measures actively used in the domain, we propose an integrated framework (FRPC integrated framework) that integrates Fisher's Ratio to select the best channel and Pearson correlation to select optimal filter banks and extract spectral and temporal features respectively. The framework is tested for a 2-class motor imagery classification on 2 open-source datasets and 1 collected dataset and compared with state-of-art work. Apart from implementing the framework, this study also explores the most optimal channel among all the subjects and later explores classes where the single-channel framework is efficient.

Heavy quarks, and the hadrons containing them, are excellent probes of the QCD medium formed in high-energy heavy-ion collisions, as they provide direct information on the transport properties of the medium and how quarks color-neutralize into hadrons. Large theoretical and phenomenological efforts have been dedicated thus far to assess the diffusion of charm and bottom quarks in the quark-gluon plasma and their subsequent hadronization into heavy-flavor (HF) hadrons. However, the fireball formed in heavy-ion collisions also features an extended hadronic phase, and therefore any quantitative analysis of experimental observables needs to account for rescattering of charm and bottom hadrons. This is further reinforced by the presence of a QCD cross-over transition and the notion that the interaction strength is maximal in the vicinity of the pseudo-critical temperature. We review existing approaches for evaluating the interactions of open HF hadrons in a hadronic heat bath and the pertinent results for scattering amplitudes, spectral functions and transport coefficients. While most of the work to date has focused on $D$ mesons, we also discuss excited states as well as HF baryons and the bottom sector. Both the HF hadro-chemistry and bottom observables will play a key role in future experimental measurements. We also conduct a survey of transport calculations in heavy-ion collisions that have included effects of hadronic HF diffusion and assess their sensitivity to various observables., Comment: 60 pages, 45 figures. Review article sent to Physics Reports journal

Widespread default involves substantial deadweight costs which could be countered by injecting capital into failing firms. Injections have positive spillovers that can trigger a repayment cascade. But which firms should a regulator bailout so as to minimize the total injection of capital while ensuring solvency of all firms? While the problem is, in general, NP-hard, for a wide range of networks that arise from a stochastic block model, we show that the optimal bailout can be implemented by a simple policy that targets firms based on their characteristics and position in the network. Specific examples of the setting include core-periphery networks.

Modeling legal reasoning and argumentation justifying decisions in cases has always been central to AI & Law, yet contemporary developments in legal NLP have increasingly focused on statistically classifying legal conclusions from text. While conceptually simpler, these approaches often fall short in providing usable justifications connecting to appropriate legal concepts. This paper reviews both traditional symbolic works in AI & Law and recent advances in legal NLP, and distills possibilities of integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. We identify open challenges and discuss the potential of modern NLP models and methods that integrate

We perform broadband ($0.7-100$ keV) spectral analysis of five hard state observations of the low-mass back hole X-ray binary GX~339--4 taken by AstroSat during the rising phase of three outbursts from $2019$ to $2022$. We find that the outburst in 2021 was the only successful/full outburst, while the source was unable to make transition to the soft state during the other two outbursts in 2019 and 2022. Our spectral analysis employs two different model combinations, requiring two separate Comptonizing regions and their associated reflection components, and soft X-ray excess emission. The harder Comptonizing component dominates the overall bolometric luminosity, while the softer one remains relatively weak. Our spectral fits indicate that the disk evolves with the source luminosity, where the inner disk radius decreases with increasing luminosity. However, the disk remains substantially truncated throughout all the observations at the source luminosity of $\sim2-8\%\times$ of the Eddington luminosity. We note that our assumption of the soft X-ray excess emission as disk blackbody may not be realistic, and this kind of soft excess may arise due the non-homogeneity in the disk/corona geometry. Our temporal analysis deriving the power density spectra suggests that the break frequency increases with the source luminosity. Furthermore, our analysis demonstrates a consistency between the inner disk radii estimated from break frequency of the power density spectra and those obtained from the reflection modelling, supporting the truncated disk geometry in the hard state., Comment: 15 Pages, 7 Figures, 3 Tables, Accepted for publication in ApJ

JWST has revealed a population of compact and extremely red galaxies at $z>4$, which likely host active galactic nuclei (AGN). We present a sample of 434 ``little red dots'' (LRDs), selected from the 0.54 deg$^2$ COSMOS-Web survey. We fit galaxy and AGN SED models to derive redshifts and physical properties; the sample spans $z\sim5$-$9$ after removing brown dwarf contaminants. We consider two extreme physical scenarios: either LRDs are all AGN, and their continuum emission is dominated by the accretion disk, or they are all compact star-forming galaxies, and their continuum is dominated by stars. If LRDs are AGN-dominated, our sample exhibits bolometric luminosities $\sim10^{45-47}$ erg\,s$^{-1}$, spanning the gap between JWST AGN in the literature and bright, rare quasars. We derive a bolometric luminosity function (LF) $\sim100$ times the (UV-selected) quasar LF, implying a non-evolving black hole accretion density of $\sim10^{-4}$ M$_\odot$ yr$^{-1}$ Mpc$^{-3}$ from $z\sim2$-$9$. By contrast, if LRDs are dominated by star formation, we derive stellar masses $\sim10^{8.5-10}\,M_\odot$. MIRI/F770W is key to deriving accurate stellar masses; without it, we derive a mass function inconsistent with $\Lambda$CDM. The median stellar mass profile is broadly consistent with the maximal stellar mass surface densities seen in the nearby universe, though the most massive $\sim50$\% of objects exceed this limit, requiring substantial AGN contribution to the continuum. Nevertheless, stacking all available X-ray, mid-IR, far-IR/sub-mm, and radio data yields non-detections. Whether dominated by dusty AGN, compact star-formation, or both, the high masses/luminosities and remarkable abundance of LRDs implies a dominant mode of early galaxy/SMBH growth., Comment: 27 pages, 13 figures. Submitted to ApJ, comments welcome! Data access at https://github.com/hollisakins/akins24_cw

A near memory hardware accelerator, based on a novel direct path computational model, for real-time emulation of radio frequency systems is demonstrated. Our evaluation of hardware performance uses both application-specific integrated circuits (ASIC) and field programmable gate arrays (FPGA) methodologies: 1). The ASIC testchip implementation, using TSMC 28nm CMOS, leverages distributed autonomous control to extract concurrency in compute as well as low latency. It achieves a $518$ MHz per channel bandwidth in a prototype $4$-node system. The maximum emulation range supported in this paradigm is $9.5$ km with $0.24$ $\mu$s of per-sample emulation latency. 2). The FPGA-based implementation, evaluated on a Xilinx ZCU104 board, demonstrates a $9$-node test case (two Transmitters, one Receiver, and $6$ passive reflectors) with an emulation range of $1.13$ km to $27.3$ km at $215$ MHz bandwidth.

In this paper we consider the problem of developing a computational model for emulating an RF channel. The motivation for this is that an accurate and scalable emulator has the potential to minimize the need for field testing, which is expensive, slow, and difficult to replicate. Traditionally, emulators are built using a tapped delay line model where long filters modeling the physical interactions of objects are implemented directly. For an emulation scenario consisting of $M$ objects all interacting with one another, the tapped delay line model's computational requirements scale as $O(M^3)$ per sample: there are $O(M^2)$ channels, each with $O(M)$ complexity. In this paper, we develop a new ``direct path" model that, while remaining physically faithful, allows us to carefully factor the emulator operations, resulting in an $O(M^2)$ per sample scaling of the computational requirements. The impact of this is drastic, a $200$ object scenario sees about a $100\times$ reduction in the number of per sample computations. Furthermore, the direct path model gives us a natural way to distribute the computations for an emulation: each object is mapped to a computational node, and these nodes are networked in a fully connected communication graph. Alongside a discussion of the model and the physical phenomena it emulates, we show how to efficiently parameterize antenna responses and scattering profiles within this direct path framework. To verify the model and demonstrate its viability in hardware, we provide several numerical experiments produced using a cycle level C++ simulator of a hardware implementation of the model.

A wide variety of feed gases are used to generate low-temperature plasmas for the microelectronics and the sustainability applications. These plasmas often have a complex combination of reactive and non-reactive species which may have spatial and temporal variations in the density, the temperature and the energy. Accurate knowledge of these parameters and their variations is critically important for understanding and advancing these applications through validated and predictive modeling and design of relevant devices. Laser-induced fluorescence (LIF) provides both spatial and temporally resolved information about the plasma-produced radicals, ions, and metastables. However, the use of this powerful diagnostic tool requires the knowledge of optical transitions including excitation and fluorescence wavelengths which may not be available or scattered through a huge literature domain. In this manuscript, we collected, analyzed and compiled the available transitions for laser-induced fluorescence for more than 160 chemical species relevant to the microelectronics industry and the sustainability applications. A list of species with overlapping LIF excitation and fluorescence wavelengths have been identified. This summary is intended to serve as a data reference for LIF transitions and should be updated in the future.

How intelligence arises from the brain is a central problem in science. A crucial aspect of intelligence is dealing with uncertainty -- developing good predictions about one's environment, and converting these predictions into decisions. The brain itself seems to be noisy at many levels, from chemical processes which drive development and neuronal activity to trial variability of responses to stimuli. One hypothesis is that the noise inherent to the brain's mechanisms is used to sample from a model of the world and generate predictions. To test this hypothesis, we study the emergence of statistical learning in NEMO, a biologically plausible computational model of the brain based on stylized neurons and synapses, plasticity, and inhibition, and giving rise to assemblies -- a group of neurons whose coordinated firing is tantamount to recalling a location, concept, memory, or other primitive item of cognition. We show in theory and simulation that connections between assemblies record statistics, and ambient noise can be harnessed to make probabilistic choices between assemblies. This allows NEMO to create internal models such as Markov chains entirely from the presentation of sequences of stimuli. Our results provide a foundation for biologically plausible probabilistic computation, and add theoretical support to the hypothesis that noise is a useful component of the brain's mechanism for cognition., Comment: 22 pages, 8 figures

In this article we report the structural stability of Er$_2$Ti$_2$O$_7$ cubic pyrochlore with pressure using x-ray diffraction, Raman spectroscopy, photoluminescence, x-ray absorption and ab-initio calculations. Our studies establish a phase transformation in Er$_2$Ti$_2$O$_7$ from ambient cubic phase to high-pressure orthorhombic (cotunnite) phase, initiated at ~40 GPa. The transformation is sluggish and it does not complete even at the highest measured pressure in our study i.e. ~60.0 GPa. This is further supported by the first principle calculations which reveal that cotunnite phase is energetically more stable than the ambient phase above ~53 GPa. After complete release of pressure, the high-pressure cotunnite phase is retained while the fraction of untransformed pyrochlore phase becomes amorphous. Furthermore, the EXAFS data of the recovered sample at L3 edge of Er3+ ion show an increase in the coordination number of cations from eight at ambient to nine in the high-pressure phase. The mechanism of structural transformation is explained in terms of accumulation of cation antisite defects and subsequent disordering of cations and anions in their respective sublattice. The amorphization of the pyrochlore phase upon release is interpreted as the inability of accommodating the point defects at ambient conditions, which are formed in the pyrochlore lattice under compression.

AI-generated faces have enriched human life, such as entertainment, education, and art. However, they also pose misuse risks. Therefore, detecting AI-generated faces becomes crucial, yet current detectors show biased performance across different demographic groups. Mitigating biases can be done by designing algorithmic fairness methods, which usually require demographically annotated face datasets for model training. However, no existing dataset comprehensively encompasses both demographic attributes and diverse generative methods, which hinders the development of fair detectors for AI-generated faces. In this work, we introduce the AI-Face dataset, the first million-scale demographically annotated AI-generated face image dataset, including real faces, faces from deepfake videos, and faces generated by Generative Adversarial Networks and Diffusion Models. Based on this dataset, we conduct the first comprehensive fairness benchmark to assess various AI face detectors and provide valuable insights and findings to promote the future fair design of AI face detectors. Our AI-Face dataset and benchmark code are publicly available at https://github.com/Purdue-M2/AI-Face-FairnessBench.

We study the prospect of heavy singlet neutrinos as a dark matter (DM) candidate within a neutrinophilic U(1) model, where the Standard Model (SM) is extended with a U(1) gauge symmetry, and neutrino mass and oscillation parameters are explained through an inverse see-saw mechanism. The lightest of the heavy neutrinos plays the role of the DM while the newly introduced scalars and the extra gauge boson Z' act as mediators between the dark sector and the SM sector. We show the range of model parameters where this DM candidate can be accommodated in the Weakly Interacting Massive Particle (WIMP) or Feebly Interacting Massive Particle (FIMP) scenario. The observed DM relic density is achieved via the new gauge boson and singlet scalar portals in the WIMP scenario whereas within the FIMP scenario, these two particles assume a distinct yet pivotal role in generating the observed relic density of dark matter., Comment: 32 pages, 14 figures, 1 table

This study investigates the challenges posed by the dynamic nature of legal multi-label text classification tasks, where legal concepts evolve over time. Existing models often overlook the temporal dimension in their training process, leading to suboptimal performance of those models over time, as they treat training data as a single homogeneous block. To address this, we introduce ChronosLex, an incremental training paradigm that trains models on chronological splits, preserving the temporal order of the data. However, this incremental approach raises concerns about overfitting to recent data, prompting an assessment of mitigation strategies using continual learning and temporal invariant methods. Our experimental results over six legal multi-label text classification datasets reveal that continual learning methods prove effective in preventing overfitting thereby enhancing temporal generalizability, while temporal invariant methods struggle to capture these dynamics of temporal shifts., Comment: Accepted to ACL 2024

Deep Learning has shown great success in reshaping medical imaging, yet it faces numerous challenges hindering widespread application. Issues like catastrophic forgetting and distribution shifts in the continuously evolving data stream increase the gap between research and applications. Continual Learning offers promise in addressing these hurdles by enabling the sequential acquisition of new knowledge without forgetting previous learnings in neural networks. In this survey, we comprehensively review the recent literature on continual learning in the medical domain, highlight recent trends, and point out the practical issues. Specifically, we survey the continual learning studies on classification, segmentation, detection, and other tasks in the medical domain. Furthermore, we develop a taxonomy for the reviewed studies, identify the challenges, and provide insights to overcome them. We also critically discuss the current state of continual learning in medical imaging, including identifying open problems and outlining promising future directions. We hope this survey will provide researchers with a useful overview of the developments in the field and will further increase interest in the community. To keep up with the fast-paced advancements in this field, we plan to routinely update the repository with the latest relevant papers at https://github.com/BioMedIA-MBZUAI/awesome-cl-in-medical ., Comment: 15 pages, 8 figures

We present one high-resolution and a time series of 561 low-resolution follow-up spectroscopic observations of SZ Lyn. It is a high-amplitude Delta Scuti-type pulsating star in a binary system. The photometric observations reveal the existence of radial and non-radial oscillation modes in SZ Lyn. In spectroscopy, the variation of equivalent width of the line profiles reflects the temperature variations. The equivalent widths of the Balmer lines, H-alpha, H-hbeta, and H-gamma were measured over the pulsation cycle of SZ Lyn using time sequence spectra. Hence, the temperature profile of SZ Lyn was derived using the curve of growth analysis. Furthermore, the stellar parameters were determined through the best fit analysis of observed and synthetic high-resolution spectral lines. The best fit determines a model of Teff=6750 K, log(g)=3.5 dex, and vrot=10 km/s for solar abundance., Comment: 8 pages, 4 figures, accepted in the Bulletin of Li\`ege Royal Society of Sciences (Proceedings paper for the 3rd BINA Workshop held at ARIES, India)

Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest, or dynamic functional connectivity matrices with a sliding window approach. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. While deep learning has gained substantial popularity for modeling complex relational data, its application to uncovering the spatiotemporal dynamics of the brain is still limited. We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series and employs a specialized graph neural network for the final classification. Our model, DSAM, leverages temporal causal convolutional networks to capture the temporal dynamics in both low- and high-level feature representations, a temporal attention unit to identify important time points, a self-attention unit to construct the goal-specific connectivity matrix, and a novel variant of graph neural network to capture the spatial dynamics for downstream classification. To validate our approach, we conducted experiments on the Human Connectome Project dataset with 1075 samples to build and interpret the model for the classification of sex group, and the Adolescent Brain Cognitive Development Dataset with 8520 samples for independent testing. Compared our proposed framework with other state-of-art models, results suggested this novel approach goes beyond the assumption of a fixed connectivity matrix and provides evidence of goal-specific brain connectivity patterns, which opens up the potential to gain deeper insights into how the human brain adapts its functional connectivity specific to the task at hand., Comment: 18 Pages, 4 figures