Search

Biologists study Diatoms, a fundamental algae, to assess the health of aquatic systems. Diatom specimens have traditionally been preserved on analog slides, where a single slide can contain thousands of these microscopic organisms. Digitization of these collections presents both metadata challenges and opportunities. This paper reports on metadata research aimed at providing access to a digital portion of the Academy of Natural Sciences' Diatom Herbarium, Drexel University. We report results of a 3-part study covering 1) a review of relevant metadata standards and a microscopy metadata framework shared by Hammer et al., 2) a baseline metadata alignment mapping current diatom metadata properties to standard metadata types, and 3) a metadata risk analysis associated with the course of standard data curation practices. This research is part of an effort involving the transfer of these digital slides to an new system, DataFed, to support global accessible. The final section of this paper includes a conclusion and discusses next steps., Comment: 13 pages, 2 figures, 1 table, to be published in MTSR 2024 conference proceedings

Resting-state EEG data in neuroscience research serve as reliable markers for user identification and reveal individual-specific traits. Despite this, the use of resting-state data in EEG classification models is limited. In this work, we propose a feature concatenation approach to enhance decoding models' generalization by integrating resting-state EEG, aiming to improve motor imagery BCI performance and develop a user-generalized model. Using feature concatenation, we combine the EEGNet model, a standard convolutional neural network for EEG signal classification, with functional connectivity measures derived from resting-state EEG data. The findings suggest that although grounded in neuroscience with data-driven learning, the concatenation approach has limited benefits for generalizing models in within-user and across-user scenarios. While an improvement in mean accuracy for within-user scenarios is observed on two datasets, concatenation doesn't benefit across-user scenarios when compared with random data concatenation. The findings indicate the necessity of further investigation on the model interpretability and the effect of random data concatenation on model robustness.

Brain-computer interfaces (BCIs) enable direct interaction between users and computers by decoding brain signals. This study addresses the challenges of detecting P300 event-related potentials in electroencephalograms (EEGs) and integrating these P300 responses for character spelling, particularly within oddball paradigms characterized by uneven P300 distribution, low target probability, and poor signal-to-noise ratio (SNR). This work proposes a weighted ensemble spatio-sequential convolutional neural network (WE-SPSQ-CNN) to improve classification accuracy and SNR by mitigating signal variability for character identification. We evaluated the proposed WE-SPSQ-CNN on dataset II from the BCI Competition III, achieving P300 classification accuracies of 69.7\% for subject A and 79.9\% for subject B across fifteen epochs. For character recognition, the model achieved average accuracies of 76.5\%, 87.5\%, and 94.5\% with five, ten, and fifteen repetitions, respectively. Our proposed model outperformed state-of-the-art models in the five-repetition and delivered comparable performance in the ten and fifteen repetitions.

On-screen keyboard eye-typing systems are limited due to the lack of predictive text and user-centred approaches, resulting in low text entry rates and frequent recalibration. This work proposes integrating the prediction by partial matching (PPM) technique into a tree-based virtual keyboard. We developed the Flex-Tree on-screen keyboard using a two-stage tree-based character selection system with ten commands, testing it with three degree of PPM (PPM1, PPM2, PPM3). Flex-Tree provides access to 72 English characters, including upper- and lower-case letters, numbers, and special characters, and offers functionalities like the delete command for corrections. The system was evaluated with sixteen healthy volunteers using two specially designed typing tasks, including the hand-picked and random-picked sentences. The spelling task was performed using two input modalities: (i) a mouse and (ii) a portable eye-tracker. Two experiments were conducted, encompassing 24 different conditions. The typing performance of Flex-Tree was compared with that of a tree-based virtual keyboard with an alphabetic arrangement (NoPPM) and the Dasher on-screen keyboard for new users. Flex-Tree with PPM3 outperformed the other keyboards, achieving average text entry speeds of 27.7 letters/min with a mouse and 16.3 letters/min with an eye-tracker. Using the eye-tracker, the information transfer rates at the command and letter levels were 108.4 bits/min and 100.7 bits/min, respectively. Flex-Tree, across all three degree of PPM, received high ratings on the system usability scale and low-weighted ratings on the NASA Task Load Index for both input modalities, highlighting its user-centred design.

Post-training quantization (PTQ) is a technique used to optimize and reduce the memory footprint and computational requirements of machine learning models. It has been used primarily for neural networks. For Brain-Computer Interfaces (BCI) that are fully portable and usable in various situations, it is necessary to provide approaches that are lightweight for storage and computation. In this paper, we propose the evaluation of post-training quantization on state-of-the-art approaches in brain-computer interfaces and assess their impact on accuracy. We evaluate the performance of the single-trial detection of event-related potentials representing one major BCI paradigm. The area under the receiver operating characteristic curve drops from 0.861 to 0.825 with PTQ when applied on both spatial filters and the classifier, while reducing the size of the model by about $\times$ 15. The results support the conclusion that PTQ can substantially reduce the memory footprint of the models while keeping roughly the same level of accuracy.

Deep Learning Recommendation Models (DLRMs) have become increasingly popular and prevalent in today's datacenters, consuming most of the AI inference cycles. The performance of DLRMs is heavily influenced by available bandwidth due to their large vector sizes in embedding tables and concurrent accesses. To achieve substantial improvements over existing solutions, novel approaches towards DLRM optimization are needed, especially, in the context of emerging interconnect technologies like CXL. This study delves into exploring CXL-enabled systems, implementing a process-in-fabric-switch (PIFS) solution to accelerate DLRMs while optimizing their memory and bandwidth scalability. We present an in-depth characterization of industry-scale DLRM workloads running on CXL-ready systems, identifying the predominant bottlenecks in existing CXL systems. We, therefore, propose PIFS-Rec, a PIFS-based scheme that implements near-data processing through downstream ports of the fabric switch. PIFS-Rec achieves a latency that is 3.89x lower than Pond, an industry-standard CXL-based system, and also outperforms BEACON, a state-of-the-art scheme, by 2.03x.

We investigated the Re-based kagome superconductor Re$_2$Zr through various measurements, including resistivity, magnetization, specific heat, and muon spin rotation and relaxation spectroscopy. These results suggest that Re$_2$Zr is a moderately coupled potential two-gap superconductor. Zero-field muon relaxation data indicate the possible presence of a time-reversal symmetry-breaking state in the superconducting ground state. Our investigation identifies Re$_{2}$Zr as a new unconventional superconductor with a potential complex order parameter that warrants considerable experimental and theoretical interest., Comment: 9 pages, 6 figures

Community health workers (CHWs) provide last-mile healthcare services but face challenges due to limited medical knowledge and training. This paper describes the design, deployment, and evaluation of ASHABot, an LLM-powered, experts-in-the-loop, WhatsApp-based chatbot to address the information needs of CHWs in India. Through interviews with CHWs and their supervisors and log analysis, we examine factors affecting their engagement with ASHABot, and ASHABot's role in addressing CHWs' informational needs. We found that ASHABot provided a private channel for CHWs to ask rudimentary and sensitive questions they hesitated to ask supervisors. CHWs trusted the information they received on ASHABot and treated it as an authoritative resource. CHWs' supervisors expanded their knowledge by contributing answers to questions ASHABot failed to answer, but were concerned about demands on their workload and increased accountability. We emphasize positioning LLMs as supplemental fallible resources within the community healthcare ecosystem, instead of as replacements for supervisor support.

The development of quantum processors capable of handling practical fluid flow problems represents a distant yet promising frontier. Recent strides in quantum algorithms, particularly linear solvers, have illuminated the path toward quantum solutions for classical fluid flow solvers. However, assessing the capability of these quantum linear systems algorithms (QLSAs) in solving ideal flow equations on real hardware is crucial for their future development in practical fluid flow applications. In this study, we examine the capability of a canonical QLSA, the Harrow-Hassidim-Lloyd (HHL) algorithm, in accurately solving the system of linear equations governing an idealized fluid flow problem, specifically the Hele-Shaw flow. Our investigation focuses on analyzing the accuracy and computational cost of the HHL solver. To gauge the stability and convergence of the solver, we conduct shots-based simulations on quantum simulators. Furthermore, we share insights gained from executing the HHL solver on superconducting quantum devices. To mitigate errors arising from qubit measurement, gate operations, and qubit decoherence inherent in quantum devices, we employ various error suppression and mitigation techniques. Our preliminary assessments serve as a foundational step towards enabling more complex quantum utility scale evaluation of using QLSA for solving fluid flow problems.

Emerging marketplaces for large language models and other large-scale machine learning (ML) models appear to exhibit market concentration, which has raised concerns about whether there are insurmountable barriers to entry in such markets. In this work, we study this issue from both an economic and an algorithmic point of view, focusing on a phenomenon that reduces barriers to entry. Specifically, an incumbent company risks reputational damage unless its model is sufficiently aligned with safety objectives, whereas a new company can more easily avoid reputational damage. To study this issue formally, we define a multi-objective high-dimensional regression framework that captures reputational damage, and we characterize the number of data points that a new company needs to enter the market. Our results demonstrate how multi-objective considerations can fundamentally reduce barriers to entry -- the required number of data points can be significantly smaller than the incumbent company's dataset size. En route to proving these results, we develop scaling laws for high-dimensional linear regression in multi-objective environments, showing that the scaling rate becomes slower when the dataset size is large, which could be of independent interest.

Transition-metal-based carbides (TMCs), renowned for their exceptional hardness, mechanical strength, and thermal properties, have recently emerged as promising candidates for topological superconductivity. In this study, we synthesized ReC in the NaCl structure at ambient pressure by substituting Mo or W at the Re-site. We investigated the superconducting properties of Re$_{1-x}$T$_{x}$C (where T = Mo, W) for $x = 0.5$ using magnetization, resistivity and specific heat measurements. These compounds display type-II, fully gapped, weakly coupled superconductivity with high critical fields, establishing them as new members of superconducting ultra-hard materials at ambient pressure and paving the way for superconducting device applications under extreme conditions., Comment: 6 pages, 4 figures

This paper studies gravitational lensing degeneracies in the wave-optics regime, focusing on lensed gravitational waves (GWs). Considering lensing degeneracies as re-scaling (or transformations) of arrival time delay surface, we can divide them into local and global types. Local degeneracies only affect the time delay surface in localized regions, whereas global degeneracies re-scale the whole time delay surface by a constant while keeping the various observed image properties unchanged. We show that local degeneracies can be broken in the wave-optics regime since lensing effects become sensitive to the overall arrival time delay surface and not only to the time delay values at the image positions. On the other hand, global degeneracies (such as similarity transformation, prismatic transformation, and mass-sheet degeneracy) multiply the amplification factor by a constant factor (let us say, $\lambda$). However, in GW lensing, as the GW signal amplitude depends on the source distance, it turns out that $\lambda$ is completely degenerate with the Hubble constant, similar to what we see in geometric optics. Hence, with the lensing of GWs, global degeneracies are as hard to break in wave optics as they are in geometric optics., Comment: 5 pages, 1 figure. Published in Phys. Rev. D

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate-scale quantum era's inherent external noise issues. This paper discusses the integration of QC as a computational accelerator within classical scientific high-performance computing (HPC) systems. By leveraging a broad spectrum of simulators and hardware technologies, we propose a hardware-agnostic framework for augmenting classical HPC with QC capabilities. Drawing on the HPC expertise of the Oak Ridge National Laboratory (ORNL) and the HPC lifecycle management of the Department of Energy (DOE), our approach focuses on the strategic incorporation of QC capabilities and acceleration into existing scientific HPC workflows. This includes detailed analyses, benchmarks, and code optimization driven by the needs of the DOE and ORNL missions. Our comprehensive framework integrates hardware, software, workflows, and user interfaces to foster a synergistic environment for quantum and classical computing research. This paper outlines plans to unlock new computational possibilities, driving forward scientific inquiry and innovation in a wide array of research domains.

We introduce a novel, \textit{fully} quantum hash (FQH) function within the quantum walk on a cycle framework. We incorporate deterministic quantum computation with a single qubit to replace classical post-processing, thus increasing the inherent security. Further, our proposed hash function exhibits zero collision rate and high reliability. We further show that it provides $ > 50\%$ avalanche on average, and is highly sensitive to the initial conditions. We show comparisons of several performance metrics for the proposed FQH with different settings as well as with existing protocols to prove its efficacy. FQH requires minimal quantum resources to produce a large hash value, providing security against the birthday attack. This innovative approach thus serves as an efficient hash function and lays the foundation for potential advancements in quantum cryptography by integrating the fully quantum hash generation protocol.

Mass estimates of black holes (BHs) in the centers of Active Galactic Nuclei (AGN) often rely on the radius-luminosity relation. However, this relation, usually probed by reverberation mapping (RM), is poorly constrained in the high-luminosity and high-redshift ends due to the very long expected lag times. Multiply imaged AGN may thus offer a unique opportunity to explore the radius-luminosity relation at these ends. In addition to comprising several magnified images which enable a more efficient light-curve sampling, the time delay between multiple images of strongly lensed quasars can also aid in making such RM measurements feasible on reasonable timescales: If the time delay is, for example, of the order of the expected time lag, changes in the emission lines in the leading image can be observed around the same time as the changes in the continuum in the trailing image. In this work we probe the typical time-delay distribution in galaxy-cluster lenses and estimate the number of both high-mass ($\sim10^9-10^{10}$ M$_{\odot}$), and high-redshift ($z\gtrsim4-12$) quasars that are expected to be strongly lensed by clusters. We find that less than one, very massive (and luminous, L$_{UV}>10^{46.5}$ erg s$^{-1}$) multiply imaged quasar is expected across the sky down to 30 AB mag. Nonetheless, up to several dozen thousand M$_{BH}\sim10^{6}$-$10^{8}$ M$_{\odot}$ broad-line AGN at $z>4$ should be multiply imaged by galaxy clusters and detectable with JWST, hundreds with $\textit{Euclid}$ and several thousands with the $\textit{Roman}$ Space Telescope, across the whole sky. These could supply an important calibration for the BH mass scaling in the early Universe., Comment: To be submitted. Comments welcome. 14 pages, 3 figures, 1 table

Outflowing gas from supermassive black holes in the centers of active galaxies has been postulated as a major contributor to galactic evolution. To explore the interaction between narrow-line region (NLR) outflows and their host galaxies, we use Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) spectra and Wide Field Camera 3 (WFC3) images of 15 nearby (z < 0.02) active galactic nuclei (AGN) to determine the extents and geometries of their NLRs. We combine new HST WFC3 continuum and [O III] $\lambda$5007A images of 11 AGN with 4 archival AGN to match existing spectra from HST STIS. For the 6 AGN with suitable long-slit coverage of their NLRs, we use isophotal fitting of ground-based images, continuum-subtracted [O III] images, and the STIS spectra, to resolve, measure, and de-project the gas kinematics to the plane of the host galaxy disk and distinguish NLR outflows from galaxy rotation and/or kinematically disturbed gas. We find an average [O III] extent of $\sim$680pc with a correlation between gas extent and [O III] luminosity of R$_\mathrm{[O III]}$ $\propto$ L$_{\text{[O III]}}^{0.39}$. The measured extents depend strongly on the depth of the [O III] images, highlighting the importance of adopting uniform thresholds when analyzing scaling relationships. The outflows reach from 39-88% of the full NLR extents, and we find that all 6 of the AGN with STIS coverage of their entire NLRs show strong kinematic evidence for outflows, despite previous uncertainty for these AGN. This suggests that NLR outflows are ubiquitous in moderate luminosity AGN and that standard criteria for kinematic modeling are essential for identifying outflows., Comment: Accepted for publication in ApJ on July 9, 2024. The paper has 21 pages, 10 figures, and 6 tables. Version two includes minor corrections to match the journal publication. The calibrated data are available through MAST at: https://archive.stsci.edu/hlsp/nlr-agn

Can the rapid advances in code generation, function calling, and data analysis using large language models (LLMs) help automate the search and verification of hypotheses purely from a set of provided datasets? To evaluate this question, we present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. The benchmark is designed to systematically assess current model capabilities in discovery tasks and provide a useful resource for improving them. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering, by manually deriving discovery workflows from published papers to approximate the real-world challenges faced by researchers, where each task is defined by a dataset, its metadata, and a discovery goal in natural language. We additionally provide 903 synthetic tasks to conduct controlled evaluations across task complexity. Furthermore, our structured formalism of data-driven discovery enables a facet-based evaluation that provides useful insights into different failure modes. We evaluate several popular LLM-based reasoning frameworks using both open and closed LLMs as baselines on DiscoveryBench and find that even the best system scores only 25%. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress., Comment: Website: https://github.com/allenai/discoverybench

Neural operators such as the Fourier Neural Operator (FNO) have been shown to provide resolution-independent deep learning models that can learn mappings between function spaces. For example, an initial condition can be mapped to the solution of a partial differential equation (PDE) at a future time-step using a neural operator. Despite the popularity of neural operators, their use to predict solution functions over a domain given only data over the boundary (such as a spatially varying Dirichlet boundary condition) remains unexplored. In this paper, we refer to such problems as boundary-to-domain problems; they have a wide range of applications in areas such as fluid mechanics, solid mechanics, heat transfer etc. We present a novel FNO-based architecture, named Lifting Product FNO (or LP-FNO) which can map arbitrary boundary functions defined on the lower-dimensional boundary to a solution in the entire domain. Specifically, two FNOs defined on the lower-dimensional boundary are lifted into the higher dimensional domain using our proposed lifting product layer. We demonstrate the efficacy and resolution independence of the proposed LP-FNO for the 2D Poisson equation., Comment: Accepted by ICML 2024 AI for Science Workshop

We present the highest resolution images to date of caustics formed by wave dark matter ($\psi$DM) fluctuations near the critical curves of cluster gravitational lenses. We describe the basic magnification features of $\psi$DM in the source plane at high macromodel magnification and discuss specific differences between the $\psi$DM and standard cold dark matter (CDM) models. The unique generation of demagnified counterimages formed outside the Einstein radius for $\psi$DM is highlighted. Substructure in CDM cannot generate such demagnified images of positive parity, thus providing a definitive way to distinguish $\psi$DM from CDM. Highly magnified background sources with sizes $r\approx 1pc$, or approximately a factor of ten smaller than the expected de Broglie wavelength of $\psi$DM, offer the best possibility of discriminating between $\psi$DM and CDM. These include objects such as very compact stellar clusters at high redshift that JWST is finding in abundance., Comment: 8 pages

We have characterized the ionized, neutral, and warm molecular gas kinematics in the Seyfert 1 galaxy NGC 3227 using observations from the Hubble Space Telescope Space Telescope Imaging Spectrograph, Apache Point Observatory's Kitt Peak Ohio State Multi-Object Spectrograph, Gemini-North's Near-Infrared Integral Field Spectrometer, and the Atacama Large Millimeter Array. We fit multiple Gaussians to several spatially-resolved emission lines observed with long-slit and integral-field spectroscopy and isolate the kinematics based on apparent rotational and outflowing motions. We use the kinematics to determine an orientation for the bicone along which the outflows travel, and find that the biconical structure has an inclination of $40 ^{+5}_{-4}${\deg} from our line of sight, and a half-opening angle with an inner and outer boundary of $47 ^{+6}_{-2}${\deg} and $68 ^{+1}_{-1}${\deg}, respectively. We observe ionized outflows traveling 500 km s$^{-1}$ at distances up to 7$''$ (800 pc) from the SMBH, and disturbed ionized gas up to a distance of 15$''$ (1.7 kpc). Our analysis reveals that the ionized outflows are launched from within 20 pc of the SMBH, at the same location as a bridge of cold gas across the nucleus detected in ALMA CO(2-1) observations. We measure a turnover radius where the gas starts decelerating at a distance of $26 \pm 6$ pc from the AGN. Compared to a turnover radius in the range of $31- 63$ pc from a radiative driving model, we confirm that radiative driving is the dominant acceleration mechanism for the narrow line region (NLR) outflows in NGC 3227., Comment: 26 pages, 17 figures

Microlensed stars recently discovered by JWST & HST follow closely the winding critical curve of A370 along all sections of the ``Dragon Arc" traversed by the critical curve. These transients are fainter than $m_{AB}>26.5$, corresponding to the Asymptotic Giant Branch (AGB) and microlensed by diffuse cluster stars observed with $\simeq 18M_\odot/pc^2$, or about $\simeq 1$\% of the projected dark matter density. Most microlensed stars appear along the inner edge of the critical curve, following an asymmetric band of width $\simeq 4$kpc that is skewed by $-0.7\pm0.2$kpc. Some skewness is expected as the most magnified images should form along the inner edge of the critical curve with negative parity, but the predicted shift is small $\simeq -0.04$kpc and the band of predicted detections is narrow, $\simeq 1.4$kpc. Adding CDM-like dark halos of $10^{6-8}M_\odot$ broadens the band as desired but favours detections along the outer edge of the critical curve, in the wrong direction, where sub-halos generate local Einstein rings. Instead, the interference inherent to ``Wave Dark Matter" as a Bose-Einstein condensate ($\psi$DM) forms a symmetric band of critical curves that favours negative parity detections. A de Broglie wavelength of $\simeq 10$pc matches well the observed $4$kpc band of microlenses and predicts negative skewness $\simeq -0.6$kpc, similar to the data. The implied corresponding boson mass is $\simeq 10^{-22}$eV, in good agreement with estimates from dwarf galaxy cores when scaled by momentum. Further JWST imaging may reveal the pattern of critical curves by simply ``joining the dots" between microlensed stars, allowing wave corrugations of $\psi$DM to be distinguished from CDM sub-halos, Comment: 12 pages, 5 figures

Hyperbolic umbilic (HU) is a point singularity of the gravitational lens equation, giving rise to a ring-shaped image formation made of four highly magnified images, off-centred from the lens centre. Recent observations have revealed new strongly lensed image formations near HU singularities, and many more are expected in ongoing and future observations. Like fold/cusp, image formations near HU also satisfy magnification relation ($R_{\rm hu}$), i.e., the signed magnification sum of the four images equals zero. Here, we study how $R_{\rm hu}$ deviates from zero as a function of area ($A_{\rm hu}$) covered by the image formation near HU and the distance ($d$) of the central maxima image (which is part of the HU image formation) from the lens centre for ideal single- and double-component cluster-scale lenses. For lens ellipticity values $\geq0.3$, the central maxima image will form sufficiently far from the lens centre ($d\gtrsim5''$), similar to the observed HU image formations with $R_{\rm hu}\simeq0$. We also find that, in some cases, double-component and actual cluster-scale lenses can lead to large cross-sections for HU image formations for sources at $z\gtrsim5$, effectively increasing the chances to observe HU image formation at high redshifts. Finally, we study the time delay distribution in the observed HU image formations, finding that not only are these images highly magnified, but the relative time delay between various pairs of HU characteristic image formation has a typical value of $\sim100$ days, an order of magnitude smaller than generic five-image formations in cluster lenses, making such image formations optimal targets for time delay cosmography studies., Comment: 14 pages, 11 figures, 1 table. Accepted in OJAp

We investigate the dielectric and impedance spectroscopic investigation of Pr-doped NASICON type Na$_{3+x}$Zr$_{2-x}$Pr$_{x}$Si$_2$PO$_{\rm 12}$ ($x=$ 0.05--0.2) samples as a function of temperature and frequency. The Rietveld refinement of x-ray diffraction patterns confirms the monoclinic phase having C2/c space groups for all the samples. The scanning electron microscopy shows the granular-like structure and energy dispersive x-ray analysis confirms the desired compositions. The temperature (90--400~K) and frequency (20 Hz-2 MHz) dependence of electric permittivity are explained using Maxwell-Wagner-Sillars (MWS) polarization and space charge polarization mechanisms. The dielectric relaxation shows nearly equal activation energy for all the samples with a non-Debye type of relaxation in the measured temperature range. The complex impedance analysis shows the presence of broad grain and grain boundary relaxation peaks. The stretched exponent analysis of electric modulus using the Kohlrausch-Williams-Watts (KWW) function further confirms the non-Debye type of relaxation. Moreover, scaling analysis of the electric modulus shows a similar type of relaxation for all the samples. The {\it a.c.} conductivity data are fitted using modified power law, where the temperature dependence of exponent ($s$) confirms the correlated barrier hopping (CBH) type conduction for all the samples. Our results indicate that the Pr doped NASICON samples are potential candidates for charge storage devices due to their large electric permittivity., Comment: submitted

In 1990, Jakeman (see \cite{jakeman1990statistics}) defined the binomial process as a special case of the classical birth-death process, where the probability of birth is proportional to the difference between a fixed number and the number of individuals present. Later, a fractional generalization of the binomial process was studied by Cahoy and Polito (2012) (see \cite{cahoy2012fractional}) and called it as fractional binomial process (FBP). In this paper, we study second-order properties of the FBP and the long-range behavior of the FBP and its noise process. We also estimate the parameters of the FBP using the method of moments procedure. Finally, we present the simulated sample paths and its algorithm for the FBP., Comment: 18 pages, 4 figures

Brain-computer interface (BCI) systems facilitate unique communication between humans and computers, benefiting severely disabled individuals. Despite decades of research, BCIs are not fully integrated into clinical and commercial settings. It's crucial to assess and explain BCI performance, offering clear explanations for potential users to avoid frustration when it doesn't work as expected. This work investigates the efficacy of different deep learning and Riemannian geometry-based classification models in the context of motor imagery (MI) based BCI using electroencephalography (EEG). We then propose an optimal transport theory-based approach using earth mover's distance (EMD) to quantify the comparison of the feature relevance map with the domain knowledge of neuroscience. For this, we utilized explainable AI (XAI) techniques for generating feature relevance in the spatial domain to identify important channels for model outcomes. Three state-of-the-art models are implemented - 1) Riemannian geometry-based classifier, 2) EEGNet, and 3) EEG Conformer, and the observed trend in the model's accuracy across different architectures on the dataset correlates with the proposed feature relevance metrics. The models with diverse architectures perform significantly better when trained on channels relevant to motor imagery than data-driven channel selection. This work focuses attention on the necessity for interpretability and incorporating metrics beyond accuracy, underscores the value of combining domain knowledge and quantifying model interpretations with data-driven approaches in creating reliable and robust Brain-Computer Interfaces (BCIs)., Comment: 8 pages, 3 figures, 3 tables, draft of the accepted work at IJCNN, WCCI 2024

Decoding EEG during motor imagery is pivotal for the Brain-Computer Interface (BCI) system, influencing its overall performance significantly. As end-to-end data-driven learning methods advance, the challenge lies in balancing model complexity with the need for human interpretability and trust. Despite strides in EEG-based BCIs, challenges like artefacts and low signal-to-noise ratio emphasise the ongoing importance of model transparency. This work proposes using post-hoc explanations to interpret model outcomes and validate them against domain knowledge. Leveraging the GradCAM post-hoc explanation technique on the motor imagery dataset, this work demonstrates that relying solely on accuracy metrics may be inadequate to ensure BCI performance and acceptability. A model trained using all EEG channels of the dataset achieves 72.60% accuracy, while a model trained with motor-imagery/movement-relevant channel data has a statistically insignificant decrease of 1.75%. However, the relevant features for both are very different based on neurophysiological facts. This work demonstrates that integrating domain-specific knowledge with XAI techniques emerges as a promising paradigm for validating the neurophysiological basis of model outcomes in BCIs. Our results reveal the significance of neurophysiological validation in evaluating BCI performance, highlighting the potential risks of exclusively relying on performance metrics when selecting models for dependable and transparent BCIs., Comment: 5 pages, 4 figures, 1 table, accepted draft for IEEE EMBC 2024

High-entropy alloy (HEA) superconductors have attracted significant attention due to their exceptional low-temperature mechanical and superconducting properties. We report the synthesis and thorough characterization of an equiatomic HEA superconductor with the composition Sc$_{0.20}$V$_{0.20}$Ti$_{0.20}$Hf$_{0.20}$Nb$_{0.20}$, crystallizing in a body-centered cubic crystal structure (Im3$\bar{m}$). Our investigation, using magnetization, transport, and heat capacity measurements, reveals the presence of weakly coupled, fully gapped superconductivity with a transition temperature of 4.17(3) K and the upper critical field exceeding the Pauli paramagnetic limit. The metallic nature, combined with a high upper critical field, positions it as a promising candidate for applications in superconducting devices., Comment: 7 pages, 5 figures

We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence (SST). SST is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying cases. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the URANS equations. Rather than attempting to model each term separately, it is attractive to explore the capability of machine learning to model groups of terms, i.e., to directly model the force balances. We consider decaying SST which are homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. We consider two time-series ML models: Long Short-Term Memory (LSTM) and Neural Ordinary Differential Equation (NODE). Both models perform accurately and are numerically stable in a posteriori tests. Furthermore, we explore the data requirements of the ML models by extracting physically relevant timescales of the complex system. We find that the ratio of the timescales of the minimum information required by the ML models to accurately capture the dynamics of the SST corresponds to the Reynolds number of the flow. The current framework provides the backbone to explore the capability of such models to capture the dynamics of higher-dimensional complex SST flows.

As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors. However, the design of AI systems rarely accounts for how AI and users shape one another. In this position paper, we argue for the development of formal interaction models which mathematically specify how AI and users shape one another. Formal interaction models can be leveraged to (1) specify interactions for implementation, (2) monitor interactions through empirical analysis, (3) anticipate societal impacts via counterfactual analysis, and (4) control societal impacts via interventions. The design space of formal interaction models is vast, and model design requires careful consideration of factors such as style, granularity, mathematical complexity, and measurability. Using content recommender systems as a case study, we critically examine the nascent literature of formal interaction models with respect to these use-cases and design axes. More broadly, we call for the community to leverage formal interaction models when designing, evaluating, or auditing any AI system which interacts with users.

We report the discovery of transients among star clusters in a distant galaxy that is gravitationally lensed by a foreground galaxy cluster, and explore whether these transients correspond to: (i) intrinsic variations associated with stellar outbursts; or (ii) extrinsic variations imposed through microlensing by intraclusters stars along, perhaps, with primordial black holes. From images at two epochs separated by nearly a year, we discovered ten such transients -- displaying brightness variations of $\sim$10\%--20\% -- among 55 persistent knots identified as young star clusters in the Dragon arc. Two of these transients are associated with a triply-lensed star cluster, permitting a test of intrinsic variability by checking whether their light variations are repeated among the different lensed counterparts with a suitable time delay given their different light arrival times at the observer. Despite considerable care in constructing a lens model for Abell 370 that is optimized at the Dragon arc, we found that the predicted lensing magnifications are not sufficiently accurate to provide a definitive test of intrinsic variability based only on two images -- although such a test will become feasible as more observations are made. On the other hand, we perform simulations demonstrating that the observed level of brightness variations, as well as the observed transient event rate, can be explained entirely by stellar microlensing: as stars in the background star cluster move across the sky relative to intracluster stars, changes in their individual brightnesses can result in an overall change in the brightness of their parent star cluster., Comment: 31 pages, 14 figures. Submitted to the Astrophysical Journal, comments welcomed

Strong gravitational magnification by massive galaxy clusters enable us to detect faint background sources, resolve their detailed internal structures, and in the most extreme cases identify and study individual stars in distant galaxies. Highly magnified individual stars allow for a wide range of applications, including studies of stellar populations in distant galaxies and constraining small-scale dark matter structures. However, these applications have been hampered by the small number of events observed, as typically one or a few stars are identified from each distant galaxy. Here, we report the discovery of 46 significant microlensed stars in a single strongly-lensed high-redshift galaxy behind the Abell 370 cluster at redshift of 0.725 when the Universe was half of its current age (dubbed the ``Dragon arc''), based on two observations separated by one year with the James Webb Space Telescope ({\it JWST}). These events are mostly found near the expected lensing critical curves, suggesting that these are magnified individual stars that appear as transients from intracluster stellar microlenses. Through multi-wavelength photometry and colors, we constrain stellar types and find that many of them are consistent with red giants/supergiants magnified by factors of thousands. This finding reveals an unprecedented high occurrence of microlensing events in the Dragon arc, and proves that {\it JWST}'s time-domain observations open up the possibility of conducting statistical studies of high-redshift stars and subgalactic scale perturbations in the lensing dark matter field., Comment: 15 pages, 4 figures, 1 table submitted to Nature Astronomy

Observations of caustic-crossing galaxies at redshift $0.72$ and the number density of events is greater around substructureand the number density of events is greater around substructures, and (ii) negative imaging regime where $\beta<2$. We study the particular case of seven microlensing events found by HST in the Dragon arc (at z=0.725). We find that a population of supergiant stars with a steep LF with $\beta=2.55$ fits the distribution of these events. We identify a small region of high density of microlensing events, and interpret it as evidence of a possible invisible substructure, for which we derive a mass of $\sim 1.3 \times 10^8\,\Msun$ (within its Einstein radius)., Comment: 23 pages, 16 figures

MACS J0600.1-2008 (MACS0600) is an X-ray luminous, massive galaxy cluster at $z_{\mathrm{d}}=0.43$, studied previously by the REionization LensIng Cluster Survey (RELICS) and ALMA Lensing Cluster Survey (ALCS) projects which revealed a complex, bimodal mass distribution and an intriguing high-redshift object behind it. Here, we report on the results of a combined analysis of the extended strong lensing (SL), X-ray, Sunyaev-Zeldovich (SZ), and galaxy luminosity-density properties of this system. Using new JWST and ground-based Gemini-N and Keck data, we obtain 13 new spectroscopic redshifts of multiply imaged galaxies and identify 12 new photometric multiple-image systems and candidates, including two multiply imaged $z\sim7$ objects. Taking advantage of the larger areal coverage, our analysis reveals an additional bimodal, massive SL structure which we measure spectroscopically to lie adjacent to the cluster and whose existence was implied by previous SL-modeling analyses. While based in part on photometric systems identified in ground-based imaging requiring further verification, our extended SL model suggests that the cluster may have the second-largest critical area and effective Einstein radius observed to date, $A_{\mathrm{crit}}\simeq2.16 \mathrm{arcmin}^2$ and $\theta_{\mathrm{E}}=49.7''\pm5.0''$ for a source at $z_{\mathrm{s}}=2$, enclosing a total mass of $M(<\theta_{\mathrm{E}})=(4.7\pm0.7)\times10^{14} \mathrm{M}_{\odot}$. These results are also supported by the galaxy luminosity distribution, the SZ and X-ray data. Yet another, probably related massive cluster structure, discovered in X-rays $5'$ (1.7 Mpc) further north, suggests that MACS0600 is part of an even larger filamentary structure. This discovery adds to several recent detections of massive structures around SL galaxy clusters and establishes MACS0600 as a prime target for future high-redshift surveys with JWST., Comment: Accepted for publication in MNRAS. Version updated to the accepted paper