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Generating realistic human motion is essential for many computer vision and graphics applications. The wide variety of human body shapes and sizes greatly impacts how people move. However, most existing motion models ignore these differences, relying on a standardized, average body. This leads to uniform motion across different body types, where movements don't match their physical characteristics, limiting diversity. To solve this, we introduce a new approach to develop a generative motion model based on body shape. We show that it's possible to train this model using unpaired data by applying cycle consistency, intuitive physics, and stability constraints, which capture the relationship between identity and movement. The resulting model generates diverse, physically plausible, and dynamically stable human motions that are both quantitatively and qualitatively more realistic than current state-of-the-art methods. More details are available on our project page https://CarstenEpic.github.io/humos/., Comment: Accepted in ECCV'24. Project page: https://CarstenEpic.github.io/humos/

The broad physics reach of the LUX-ZEPLIN (LZ) experiment covers rare phenomena beyond the direct detection of dark matter. We report precise measurements of the extremely rare decay of $^{124}$Xe through the process of two-neutrino double electron capture (2$\nu$2EC), utilizing a $1.39\,\mathrm{kg} \times \mathrm{yr}$ isotopic exposure from the first LZ science run. A half-life of $T_{1/2}^{2\nu2\mathrm{EC}} = (1.09 \pm 0.14_{\text{stat}} \pm 0.05_{\text{sys}}) \times 10^{22}\,\mathrm{yr}$ is observed with a statistical significance of $8.3\,\sigma$, in agreement with literature. First empirical measurements of the KK capture fraction relative to other K-shell modes were conducted, and demonstrate consistency with respect to recent signal models at the $1.4\,\sigma$ level., Comment: 15 pages, 3 figures

Denote by $A(p, n, k)$ the number of commuting $p$-tuples of permutations on $[n]$ that have exactly $k$ distinct orbits. It was conjectured in~\cite{abdesselam2023log} that $A(p, n, k)$ is log-concave with respect to $k$ for every $p\geq 2, n\geq 3$, and the log-concavity was proved in ``$p=\infty$" case. In this paper, we prove that for $k=n-\alpha$, the log-concavity for $A(p, n, k)$ holds for every $p\geq 2$ for sufficiently large $n$., Comment: 8 pages, The version to appear in the Research in Number Theory

Plumes are considered to play an important role in the origin of solar wind. However, an understanding of their thermodynamic evolution is not complete. Here, we perform a detailed study of a plume inside a coronal hole throughout its lifetime, using the observations from the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI). We find that the plume's formation is preceded by frequent occurrences of small-scale jets and jet-lets at its base, leading to the gradual development of plume haze. The plume rapidly developed within the first six hours into its well-known morphology. Light curves from all EUV channels exhibit a similar profile, suggesting its multi-thermal nature and intensity modulation over its lifespan. Moreover, the photospheric magnetic field dynamics at the plume's base are highly correlated with its light curve in 171~{\AA}. We calculate outflow velocities, observed prominently in the 171~{\AA} passband and mildly in the 193~{\AA} and 211~{\AA} passbands, with median speeds lower in higher temperature bands but occasionally comparable to the respective sound speeds. When data is averaged over larger spatial scales, the plume appears iso-thermal along its length, with constant temperature throughout its lifetime. However, an analysis of the differential emission measure at full resolution reveals the presence of higher-temperature plasma, indicating internal temperature structures within the plume. These results provide new insights into the formation, dynamics, and thermal properties of coronal plumes, placing tighter constraints on models to understand their thermodynamic evolution and potential role in the solar wind.

Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and Frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. EEG, a non-invasive tool for recording brain activity, has shown potential in distinguishing AD from FTD and mild cognitive impairment (MCI). Previous studies have utilized various EEG features, such as subband power and connectivity patterns to differentiate these conditions. However, artifacts in EEG signals can obscure crucial information, necessitating advanced signal processing techniques. This study aims to develop a deep learning-based classification system for dementia by analyzing scout time-series signals from deep brain regions, specifically the hippocampus, amygdala, and thalamus. The study utilizes scout time series extracted via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique. The time series is converted to image representations using continuous wavelet transform (CWT) and fed as input to deep learning models. Two high-density EEG datasets are utilized to check for the efficacy of the proposed method: the online BrainLat dataset (comprising AD, FTD, and healthy controls (HC)) and the in-house IITD-AIIA dataset (including subjects with AD, MCI, and HC). Different classification strategies and classifier combinations have been utilized for the accurate mapping of classes on both datasets. The best results were achieved by using a product of probabilities from classifiers for left and right subcortical regions in conjunction with the DenseNet model architecture. It yields accuracies of 94.17$\%$ and 77.72$\%$ on the BrainLat and IITD-AIIA datasets, respectively. This highlights the potential of this approach for early and accurate differentiation of neurodegenerative disorders.

Easy-plane quantum magnets are strikingly similar to superconductors, allowing for spin supercurrent and an effective superconducting phase stemming from their $U(1)$ rotation symmetry around the $z$-axis. We uncover a generalized fractional Josephson effect with a periodicity that increases linearly with system size in one-dimensional spin-$1/2$ chains at selected anisotropies and phase-fixing boundary fields. The effect combines arbitrary integer periodicities in a single system, exceeding the $4\pi $ and $8\pi $ periodicity of superconducting Josephson effects of Majorana zero modes and other exotic quasiparticles. We reveal a universal energy-phase relation and connect the effect to the recently discovered phantom helices., Comment: 11 pages, 5 figures, 3 ancillary videos

Modern automatic speech recognition (ASR) systems are typically trained on more than tens of thousands hours of speech data, which is one of the main factors for their great success. However, the distribution of such data is typically biased towards common accents or typical speech patterns. As a result, those systems often poorly perform on atypical accented speech. In this paper, we present accent clustering and mining schemes for fair speech recognition systems which can perform equally well on under-represented accented speech. For accent recognition, we applied three schemes to overcome limited size of supervised accent data: supervised or unsupervised pre-training, distributionally robust optimization (DRO) and unsupervised clustering. Three schemes can significantly improve the accent recognition model especially for unbalanced and small accented speech. Fine-tuning ASR on the mined Indian accent speech using the proposed supervised or unsupervised clustering schemes showed 10.0% and 5.3% relative improvements compared to fine-tuning on the randomly sampled speech, respectively.

Video understanding typically requires fine-tuning the large backbone when adapting to new domains. In this paper, we leverage the egocentric video foundation models (Ego-VFMs) based on video-language pre-training and propose a parameter-efficient adaptation for egocentric video tasks, namely Ego-VPA. It employs a local sparse approximation for each video frame/text feature using the basis prompts, and the selected basis prompts are used to synthesize video/text prompts. Since the basis prompts are shared across frames and modalities, it models context fusion and cross-modal transfer in an efficient fashion. Experiments show that Ego-VPA excels in lightweight adaptation (with only 0.84% learnable parameters), largely improving over baselines and reaching the performance of full fine-tuning.

The integration of large language models (LLMs) with vision-language (VL) tasks has been a transformative development in the realm of artificial intelligence, highlighting the potential of LLMs as a versatile general-purpose chatbot. However, the current trend in this evolution focuses on the integration of vision and language to create models that can operate in more diverse and real-world contexts. We present a novel approach, termed Bottleneck Adapter, specifically crafted for enhancing the multimodal functionalities of these complex models, enabling joint optimization of the entire multimodal LLM framework through a process known as Multimodal Model Tuning (MMT). Our approach utilizes lightweight adapters to connect the image encoder and LLM without the need for large, complex neural networks. Unlike the conventional modular training schemes, our approach adopts an end-to-end optimization regime, which, when combined with the adapters, facilitates the joint optimization using a significantly smaller parameter set. Our method exhibits robust performance with 90.12\% accuracy, outperforming both human-level performance (88.4\%) and LaVIN-7B (89.41\%).

In this paper, we address the problem of joint allocation of transmit and jamming power at the source and destination, respectively, to enhance the long-term cumulative secrecy performance of an energy-harvesting wireless communication system until it stops functioning in the presence of an eavesdropper. The source and destination have energy-harvesting devices with limited battery capacities. The destination also has a full-duplex transceiver to transmit jamming signals for secrecy. We frame the problem as an infinite-horizon Markov decision process (MDP) problem and propose a reinforcement learning-based optimal joint power allocation (OJPA) algorithm that employs a policy iteration (PI) algorithm. Since the optimal algorithm is computationally expensive, we develop a low-complexity sub-optimal joint power allocation (SJPA) algorithm, namely, reduced state joint power allocation (RSJPA). Two other SJPA algorithms, the greedy algorithm (GA) and the naive algorithm (NA), are implemented as benchmarks. In addition, the OJPA algorithm outperforms the individual power allocation (IPA) algorithms termed individual transmit power allocation (ITPA) and individual jamming power allocation (IJPA), where the transmit and jamming powers, respectively, are optimized individually. The results show that the OJPA algorithm is also more energy efficient. Simulation results show that the OJPA algorithm significantly improves the secrecy performance compared to all SJPA algorithms. The proposed RSJPA algorithm achieves nearly optimal performance with significantly less computational complexity marking it the balanced choice between the complexity and the performance. We find that the computational time for the RSJPA algorithm is around 75 percent less than the OJPA algorithm.

The virtual Z gate has been established as an important tool for performing quantum gates on various platforms, including but not limited to superconducting systems. Many such platforms offer a limited set of calibrated gates and compile other gates, such as the Y gate, using combinations of X and virtual Z gates. Here, we show that the method of compilation has important consequences in an open quantum system setting. Specifically, we experimentally demonstrate that it is crucial to choose a compilation that is symmetric with respect to virtual Z rotations. This is particularly pronounced in dynamical decoupling (DD) sequences, where improper gate decomposition can result in unintended effects such as the implementation of the wrong sequence. Our findings indicate that in many cases the performance of DD is adversely affected by the incorrect use of virtual Z gates, compounding other coherent pulse errors. In addition, we identify another source of coherent errors: interference between consecutive pulses that follow each other too closely. This work provides insights into improving general quantum gate performance and optimizing DD sequences in particular.

We study time-dependent density segregation of granular mixtures flowing over an inclined plane. Discrete Element Method (DEM) simulations in a periodic box are performed for granular mixtures of same size and different density particles flowing under the influence of gravity. In addition, a continuum model is developed to solve the momentum balance equations along with species transport equation by accounting for the inter-coupling of segregation and rheology. The particle force-based density segregation theory has been used along with the $\mu-I$ rheology to predict evolution of flow properties with time for binary and multicomponent mixtures. The effect of particle arrangements on the transient evolution of flow properties for three different initial configurations is investigated using both continuum and DEM simulations. Continuum predictions for various flow properties of interest such as species concentration, velocity, pressure, and shear stress at different time instants are compared with DEM simulations. The results from the discrete and continuum models are found to be in good agreement with each other for well-mixed and heavy-near-base initial configurations. However, the continuum model is unable to predict the flow evolution for the light-near-base initial configuration. DEM simulations reveal the presence of an instability driven, quick segregation for this configuration which is not predicted by the one dimensional model and requires generalization to three dimensions.

In healthcare and medical diagnostics, Visual Question Answering (VQA) mayemergeasapivotal tool in scenarios where analysis of intricate medical images becomes critical for accurate diagnoses. Current text-based VQA systems limit their utility in scenarios where hands-free interaction and accessibility are crucial while performing tasks. A speech-based VQA system may provide a better means of interaction where information can be accessed while performing tasks simultaneously. To this end, this work implements a speech-based VQA system by introducing a Textless Multilingual Pathological VQA (TMPathVQA) dataset, an expansion of the PathVQA dataset, containing spoken questions in English, German & French. This dataset comprises 98,397 multilingual spoken questions and answers based on 5,004 pathological images along with 70 hours of audio. Finally, this work benchmarks and compares TMPathVQA systems implemented using various combinations of acoustic and visual features., Comment: conference (Accepted at Interspeech 2024)

Metaverse virtual reality (VR) applications enable users to socialise, work, entertain, and study online with immersive experiences beyond the classic PC-based interactions. While the 360-degree immersion enables users to be fully engaged in a virtual scenario, suboptimal Quality-of-Experience (QoE) like poorly displayed 3D graphics, disruptive loading time, or motion lagging caused by degraded network Quality-of-Service (QoS) can be perceived by users much worse (such as dizziness) than a monitor visualisation. This paper empirically measures user QoE of metaverse VR caused by network QoS. Specifically, by focusing on both public social hubs and private user-created events in three popular metaverse VR applications (Rec Room, VRChat and MultiverseVR), we first identify three metrics, including environment freeze level, peripheral content loading time, and control response time, that describe metaverse user experience. By tuning three network QoS parameters (bandwidth, latency, and packet loss), we benchmark each QoE metric's level from excellent to unplayable. Key insights are revealed, such as freeze of metaverse virtual environment is resilient to latency but sensitive to packet loss, and private user-created events demand better network conditions than public social hubs, providing a reference for ISPs to optimise their network QoS for superlative metaverse user experience., Comment: Accepted in Proc. IEEE MetaCom, Hong Kong, China, Aug 2024

We introduce deterministic benchmarking (DB), a protocol designed to identify the interplay of coherent and incoherent errors overlooked by randomized benchmarking (RB) and related benchmarking methods. DB provides a set of four parameters that characterize both incoherent and coherent errors in the single-qubit gate set. Furthermore, DB reveals asymmetries in gate performance induced by strong relaxation errors ($T_1$). We experimentally demonstrate DB using a superconducting transmon qubit and support these results with a simple analytical model and master equation simulations. Our findings uncover critical errors missed by conventional RB and point to strategies to mitigate these errors., Comment: 13 pages, 5 figures, comments are welcome

Agile software development is gaining popularity among software developers due to its benefits. As the interest in agile software development grows, there is an increasing focus on investigating sustainability within this field. This study aimed to explore sustainability within agile software development in the Finnish software industry and, through gathered experiences, contribute to the software engineering roadmap 2030. Using an interview approach, we conducted an empirical study within the Finnish software industry to achieve this goal. The findings indicate a growing interest among experts in integrating sustainability into agile software development. The results show that the Scrum methodology is the most popular approach in the Finnish software industry, and addressing different sustainability dimensions can have a ripple effect on each other. The study proposes three key elements to be considered in the software engineering roadmap 2030: integrating sustainability into software engineering education, creating sustainability tools and frameworks, and assessing the energy efficiency of libraries used in software development.

Despite recent advancements in federated learning (FL) for medical image diagnosis, addressing data heterogeneity among clients remains a significant challenge for practical implementation. A primary hurdle in FL arises from the non-IID nature of data samples across clients, which typically results in a decline in the performance of the aggregated global model. In this study, we introduce FedMRL, a novel federated multi-agent deep reinforcement learning framework designed to address data heterogeneity. FedMRL incorporates a novel loss function to facilitate fairness among clients, preventing bias in the final global model. Additionally, it employs a multi-agent reinforcement learning (MARL) approach to calculate the proximal term $(\mu)$ for the personalized local objective function, ensuring convergence to the global optimum. Furthermore, FedMRL integrates an adaptive weight adjustment method using a Self-organizing map (SOM) on the server side to counteract distribution shifts among clients' local data distributions. We assess our approach using two publicly available real-world medical datasets, and the results demonstrate that FedMRL significantly outperforms state-of-the-art techniques, showing its efficacy in addressing data heterogeneity in federated learning. The code can be found here~{\url{https://github.com/Pranabiitp/FedMRL}}., Comment: Accepted to MICCAI 2024

Multi-level qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decoherence than qubits due to increased loss channels, noise sensitivity, and crosstalk. To address these challenges, we develop protocols for dynamical decoupling (DD) of qudit systems based on the Heisenberg-Weyl group. We implement and experimentally verify these DD protocols on a superconducting transmon processor that supports qudit operation based on qutrits $(d=3)$ and ququarts $(d=4)$. Specifically, we demonstrate single-qudit DD sequences to decouple qutrits and ququarts from system-bath-induced decoherence. We also introduce two-qudit DD sequences designed to suppress the detrimental cross-Kerr couplings between coupled qudits. This allows us to demonstrate a significant improvement in the fidelity of time-evolved qutrit Bell states. Our results highlight the utility of leveraging DD to enable scalable qudit-based quantum computing., Comment: 12 pages, 5 figures, comments are welcome

Single-step Coulomb excitation of $^{46,48,49,50}$Ti is presented. A complete set of $E2$ matrix elements for the quintuplet of states in $^{49}$Ti, centered on the $2^+$ core excitation, was measured for the first time. A total of nine $E2$ matrix elements are reported, four of which were previously unknown. $^{49}_{22}$Ti$_{27}$ shows a $20\%$ quenching in electric quadrupole transition strength as compared to its semi-magic $^{50}_{22}$Ti$_{28}$ neighbour. This $20\%$ quenching, while empirically unprecedented, can be explained with a remarkably simple two-state mixing model, which is also consistent with other ground-state properties such as the magnetic dipole moment and electric quadrupole moment. A connection to nucleon transfer data and the quenching of single-particle strength is also demonstrated. The simplicity of the $^{49}$Ti-$^{50}$Ti pair (i.e., approximate single-$j$ $0f_{7/2}$ valence space and isolation of yrast states from non-yrast states) provides a unique opportunity to disentangle otherwise competing effects in the ground-state properties of atomic nuclei, the emergence of collectivity, and the role of proton-neutron interactions., Comment: 8 pages, 5 figures, accepted in Physics Letters B

Rossby wave instability (RWI) is considered the underlying mechanism to crescent-shaped azimuthal asymmetries, discovered in (sub-)millimeter dust continuum of many protoplanetary disks. Previous works on linear theory were conducted in the hydrodynamic limit. Nevertheless, protoplanetary disks are likely magnetized and weakly ionized. We examine the influence of magnetic fields and non-ideal magnetohydrodynamic (MHD) effects - namely, Ohmic resistivity, Hall drift, and ambipolar diffusion - on the RWI unstable modes. We perform radially global linear analyses, employing constant azimuthal ($B_\phi$) or vertical ($B_z$) background magnetic fields. It is found that, in the ideal MHD regime, magnetism can either enhance or diminish RWI growth. Strong non-ideal MHD effects cause RWI growth rates to recover hydrodynamic results. The sign of Hall Els\"{a}sser number subtly complicates the results, and vertical wavenumbers generically diminish growth rates., Comment: 13 pages, 4 figures, submitted to MNRAS

We explore both experimental and theoretical aspects of the superconducting properties in the distinctive layered caged compound, Ba$_{3}$Ir$_{4}$Ge$_{16}$. Our approach integrates muon spin rotation and relaxation ($\mu$SR) measurements with magnetization and heat capacity experiments, accompanied by first-principle calculations. The compound's bulk superconductivity is unequivocally established through DC magnetization measurements, revealing a critical temperature ($T_\mathrm{C}$) of 5.7 K. A noteworthy characteristic observed in the low-temperature superfluid density is its saturating behavior, aligning with the features typical of conventional Bardeen-Cooper-Schrieffer (BCS) superconductors. The assessment of moderate electron-phonon coupling superconductivity is conducted through transverse field $\mu$SR measurements, yielding a superconducting gap to $T_\mathrm{C}$ ratio ($2\Delta(0)/k_\mathrm{B}T_\mathrm{C}$) of 4.04, a value corroborated by heat capacity measurements. Crucially, zero field $\mu$SR measurements dismiss the possibility of any spontaneous magnetic field emergence below $T_\mathrm{C}$, highlighting the preservation of time-reversal symmetry. Our experimental results are reinforced by first-principles density functional calculations, underscoring the intricate interplay between crystal structure and superconducting order parameter symmetry in polyhedral caged compounds. This comprehensive investigation enhances our understanding of the nuanced relationship between crystal structure and superconductivity in such unique compounds.

Understanding the first billion years of the universe requires studying two critical epochs: the Epoch of Reionization (EoR) and Cosmic Dawn (CD). However, due to limited data, the properties of the Intergalactic Medium (IGM) during these periods remain poorly understood, leading to a vast parameter space for the global 21cm signal. Training an Artificial Neural Network (ANN) with a narrowly defined parameter space can result in biased inferences. To mitigate this, the training dataset must be uniformly drawn from the entire parameter space to cover all possible signal realizations. However, drawing all possible realizations is computationally challenging, necessitating the sampling of a representative subset of this space. This study aims to identify optimal sampling techniques for the extensive dimensionality and volume of the 21cm signal parameter space. The optimally sampled training set will be used to train the ANN to infer from the global signal experiment. We investigate three sampling techniques: random, Latin Hypercube (stratified), and Hammersley Sequence (quasi-Monte Carlo) sampling, and compare their outcomes. Our findings reveal that sufficient samples must be drawn for robust and accurate ANN model training, regardless of the sampling technique employed. The required sample size depends primarily on two factors: the complexity of the data and the number of free parameters. More free parameters necessitate drawing more realizations. Among the sampling techniques utilized, we find that ANN models trained with Hammersley Sequence sampling demonstrate greater robustness compared to those trained with Latin Hypercube and Random sampling., Comment: 30 pages, 17 figures, comments are welcome, prepared for submission to JCAP

Simulating plasma physics on quantum computers is difficult because most problems of interest are nonlinear, but quantum computers are not naturally suitable for nonlinear operations. In weakly nonlinear regimes, plasma problems can be modeled as wave-wave interactions. In this paper, we develop a quantization approach to convert nonlinear wave-wave interaction problems to Hamiltonian simulation problems. We demonstrate our approach using two qubits on a superconducting device. Unlike a photonic device, a superconducting device does not naturally have the desired interactions in its native Hamiltonian. Nevertheless, Hamiltonian simulations can still be performed by decomposing required unitary operations into native gates. To improve experimental results, we employ a range of error mitigation techniques. Apart from readout error mitigation, we use randomized compilation to transform undiagnosed coherent errors into well-behaved stochastic Pauli channels. Moreover, to compensate for stochastic noise, we rescale exponentially decaying probability amplitudes using rates measured from cycle benchmarking. We carefully consider how different choices of product-formula algorithms affect the overall error and show how a trade-off can be made to best utilize limited quantum resources. This study provides an example of how plasma problems may be solved on near-term quantum computing platforms., Comment: 26 pages, 5 figures

The Facial Action Coding System (FACS) for studying facial expressions is manual and requires significant effort and expertise. This paper explores the use of automated techniques to generate Action Units (AUs) for studying facial expressions. We propose an unsupervised approach based on Principal Component Analysis (PCA) and facial keypoint tracking to generate data-driven AUs called PCA AUs using the publicly available DISFA dataset. The PCA AUs comply with the direction of facial muscle movements and are capable of explaining over 92.83 percent of the variance in other public test datasets (BP4D-Spontaneous and CK+), indicating their capability to generalize facial expressions. The PCA AUs are also comparable to a keypoint-based equivalence of FACS AUs in terms of variance explained on the test datasets. In conclusion, our research demonstrates the potential of automated techniques to be an alternative to manual FACS labeling which could lead to efficient real-time analysis of facial expressions in psychology and related fields. To promote further research, we have made code repository publicly available., Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in [LNCS,volume 14301], and is available online at https://doi.org/10.1007/978-3-031-45170-6_85

Pretraining egocentric vision-language models has become essential to improving downstream egocentric video-text tasks. These egocentric foundation models commonly use the transformer architecture. The memory footprint of these models during pretraining can be substantial. Therefore, we pretrain SViTT-Ego, the first sparse egocentric video-text transformer model integrating edge and node sparsification. We pretrain on the EgoClip dataset and incorporate the egocentric-friendly objective EgoNCE, instead of the frequently used InfoNCE. Most notably, SViTT-Ego obtains a +2.8% gain on EgoMCQ (intra-video) accuracy compared to LAVILA large, with no additional data augmentation techniques other than standard image augmentations, yet pretrainable on memory-limited devices.

Cancer clinics capture disease data at various scales, from genetic to organ level. Current bioinformatic methods struggle to handle the heterogeneous nature of this data, especially with missing modalities. We propose PARADIGM, a Graph Neural Network (GNN) framework that learns from multimodal, heterogeneous datasets to improve clinical outcome prediction. PARADIGM generates embeddings from multi-resolution data using foundation models, aggregates them into patient-level representations, fuses them into a unified graph, and enhances performance for tasks like survival analysis. We train GNNs on pan-Squamous Cell Carcinomas and validate our approach on Moffitt Cancer Center lung SCC data. Multimodal GNN outperforms other models in patient survival prediction. Converging individual data modalities across varying scales provides a more insightful disease view. Our solution aims to understand the patient's circumstances comprehensively, offering insights on heterogeneous data integration and the benefits of converging maximum data views.

The development of existing facial coding systems, such as the Facial Action Coding System (FACS), relied on manual examination of facial expression videos for defining Action Units (AUs). To overcome the labor-intensive nature of this process, we propose the unsupervised learning of an automated facial coding system by leveraging computer-vision-based facial keypoint tracking. In this novel facial coding system called the Data-driven Facial Expression Coding System (DFECS), the AUs are estimated by applying dimensionality reduction to facial keypoint movements from a neutral frame through a proposed Full Face Model (FFM). FFM employs a two-level decomposition using advanced dimensionality reduction techniques such as dictionary learning (DL) and non-negative matrix factorization (NMF). These techniques enhance the interpretability of AUs by introducing constraints such as sparsity and positivity to the encoding matrix. Results show that DFECS AUs estimated from the DISFA dataset can account for an average variance of up to 91.29 percent in test datasets (CK+ and BP4D-Spontaneous) and also surpass the variance explained by keypoint-based equivalents of FACS AUs in these datasets. Additionally, 87.5 percent of DFECS AUs are interpretable, i.e., align with the direction of facial muscle movements. In summary, advancements in automated facial coding systems can accelerate facial expression analysis across diverse fields such as security, healthcare, and entertainment. These advancements offer numerous benefits, including enhanced detection of abnormal behavior, improved pain analysis in healthcare settings, and enriched emotion-driven interactions. To facilitate further research, the code repository of DFECS has been made publicly accessible.

We perform the numerical simulations of axisymmetric, relativistic, optically thin jets under the influence of the radiation field of an accretion disk. We show that starting from a very low injection velocity at the base, jets can be accelerated to relativistic terminal speeds when traveling through the radiation field. The jet gains momentum through the interaction with the radiation field. We use a relativistic equation of state for multi-species plasma, which self-consistently calculates the adiabatic index for the jet material. All the jet solutions obtained are transonic in nature. In addition to the acceleration of the jet to relativistic speeds, our results show that the radiation field also acts as a collimating agent. The jets remain well collimated under the effect of radiation pressure. We also show that if the jet starts with a rotational velocity, the radiation field will reduce the angular momentum of the jet beam., Comment: 19 pages, 11 figures, accepted for publication in ApJ

Weakly interacting massive particles (WIMPs) may interact with a virtual pion that is exchanged between nucleons. This interaction channel is important to consider in models where the spin-independent isoscalar channel is suppressed. Using data from the first science run of the LUX-ZEPLIN dark matter experiment, containing 60 live days of data in a 5.5~tonne fiducial mass of liquid xenon, we report the results on a search for WIMP-pion interactions. We observe no significant excess and set an upper limit of $1.5\times10^{-46}$~cm$^2$ at a 90\% confidence level for a WIMP mass of 33~GeV/c$^2$ for this interaction.

We introduce LAVITI, a novel approach to learning language, video, and temporal representations in long-form videos via contrastive learning. Different from pre-training on video-text pairs like EgoVLP, LAVITI aims to align language, video, and temporal features by extracting meaningful moments in untrimmed videos. Our model employs a set of learnable moment queries to decode clip-level visual, language, and temporal features. In addition to vision and language alignment, we introduce relative temporal embeddings (TE) to represent timestamps in videos, which enables contrastive learning of time. Significantly different from traditional approaches, the prediction of a particular timestamp is transformed by computing the similarity score between the predicted TE and all TEs. Furthermore, existing approaches for video understanding are mainly designed for short videos due to high computational complexity and memory footprint. Our method can be trained on the Ego4D dataset with only 8 NVIDIA RTX-3090 GPUs in a day. We validated our method on CharadesEgo action recognition, achieving state-of-the-art results., Comment: CVPR EgoVis Workshop 2024 extended abstract

We report new limits on the kinetic mixing strength of the dark photon spanning the mass range 0.21 -- 1.24 $\mu$eV corresponding to a frequency span of 50 -- 300 MHz. The Dark E-Field Radio experiment is a wide-band search for dark photon dark matter. In this paper we detail changes in calibration and upgrades since our proof-of-concept pilot run. Our detector employs a wide bandwidth E-field antenna moved to multiple positions in a shielded room, a low noise amplifier, wideband ADC, followed by a $2^{24}$-point FFT. An optimal filter searches for signals with Q $\approx10^6$. In nine days of integration, this system is capable of detecting dark photon signals corresponding to $\epsilon$ several orders of magnitude lower than previous limits. We find a 95% exclusion limit on $\epsilon$ over this mass range between $6\times 10^{-15}$ and $6\times 10^{-13}$, tracking the complex resonant mode structure in the shielded room., Comment: 15 pages, 12 figures, submitted to PRD

We present a framework for analyzing plasma flow in a rotating mirror. By making a series of physical assumptions, we reduce the magnetohydrodynamic (MHD) equations in a three-dimensional cylindrical system to a one-dimensional system in a shallow, cuboidal channel within a transverse magnetic field, similar to the Hartmann flow in the ducts. We then solve the system both numerically and analytically for a range of values of the Hartmann number and calculate the dependence of the plasma flow speed on the thickness of the insulating end cap. We observe that the mean flow overshoots and decelerates before achieving a steady-state value, a phenomenon that the analytical model cannot capture. This overshoot is directly proportional to the thickness of the insulating end cap and the external electric field, with a weak dependence on the external magnetic field. Our simplified model can act as a benchmark for future simulations of the supersonic mirror device Compact Magnetic Fusion Experiment (CMFX), which will employ more sophisticated physics and realistic magnetic field geometries., Comment: 15 pages, 6 figures

Today, scientific research is increasingly data-centric and compute-intensive, relying on data and models across distributed sources. However, it still faces challenges in the traditional cooperation mode, due to the high storage and computing cost, geo-location barriers, and local confidentiality regulations. The Jupyter environment has recently emerged and evolved as a vital virtual research environment for scientific computing, which researchers can use to scale computational analyses up to larger datasets and high-performance computing resources. Nevertheless, existing approaches lack robust support of a decentralized cooperation mode to unlock the full potential of decentralized collaborative scientific research, e.g., seamlessly secure data sharing. In this work, we change the basic structure and legacy norms of current research environments via the seamless integration of Jupyter with Ethereum blockchain capabilities. As such, it creates a Decentralized Virtual Research Environment (D-VRE) from private computational notebooks to decentralized collaborative research ecosystem. We propose a novel architecture for the D-VRE and prototype some essential D-VRE elements for enabling secure data sharing with decentralized identity, user-centric agreement-making, membership, and research asset management. To validate our method, we conducted an experimental study to test all functionalities of D-VRE smart contracts and their gas consumption. In addition, we deployed the D-VRE prototype on a test net of the Ethereum blockchain for demonstration. The feedback from the studies showcases the current prototype's usability, ease of use, and potential and suggests further improvements., Comment: We revised the manuscript draft and submitted the revised manuscript to the journal Blockchain: Research and Applications

The Data Acquisition System (DAQ) for the LUX-ZEPLIN (LZ) dark matter detector is described. The signals from 745 PMTs, distributed across three subsystems, are sampled with 100-MHz 32-channel digitizers (DDC-32s). A basic waveform analysis is carried out on the on-board Field Programmable Gate Arrays (FPGAs) to extract information about the observed scintillation and electroluminescence signals. This information is used to determine if the digitized waveforms should be preserved for offline analysis. The system is designed around the Kintex-7 FPGA. In addition to digitizing the PMT signals and providing basic event selection in real time, the flexibility provided by the use of FPGAs allows us to monitor the performance of the detector and the DAQ in parallel to normal data acquisition. The hardware and software/firmware of this FPGA-based Architecture for Data acquisition and Realtime monitoring (FADR) are discussed and performance measurements are described., Comment: 18 pages, 24 figures

To investigate the short-term variability and determine the size of the optical continuum emitting size of intermediate-mass black holes (IMBHs), we carried out high-cadence, multi-band photometric monitoring of a Seyfert 1 galaxy J0249-0815 across two nights, together with a one-night single-band preliminary test. The presence of the broad Ha component in our target was confirmed by recent Palomar/P200 spectroscopic observations, 23 years after Sloan Digital Sky Survey, ruling out the supernovae origin of the broad Ha line. The photometric experiment was primarily conducted utilizing four-channel imagers MuSCAT 3 & 4 mounted on 2-meter telescopes within the Las Cumbres Observatory Global Telescope Network. Despite the expectation of variability, we observed no significant variation (<1.4%) on timescales of 6-10 hours. This non-detection is likely due to substantial host galaxy light diluting the subtle AGN variability. Dual-band preliminary tests and tailored simulations may enhance the possibility of detecting variability and lag in future IMBH reverberation campaigns., Comment: 14 pages, 6 figures, submitted to ApJ, comments welcome

The inertial number-based rheology, popularly known as the JFP model, is well known for describing the rheology of granular materials in the dense flow regime. While most of the recent studies focus on the steady-state rheology of granular materials, the time-dependent rheology of such materials has received less attention. Owing to this fact, we perform three-dimensional DEM simulations of frictional inelastic spheres flowing down an inclined bumpy surface varying over a wide range of inclination angles and restitution coefficients. We show that steady, fully developed flows are possible at inclinations much higher than those predicted from the JFP model rheology. We show that, in addition to a modified effective friction law, the rheological description also needs to account for the stress anisotropy by means of a first and second normal stress difference law.

This paper presents BARKPLUG V.2, a Large Language Model (LLM)-based chatbot system built using Retrieval Augmented Generation (RAG) pipelines to enhance the user experience and access to information within academic settings.The objective of BARKPLUG V.2 is to provide information to users about various campus resources, including academic departments, programs, campus facilities, and student resources at a university setting in an interactive fashion. Our system leverages university data as an external data corpus and ingests it into our RAG pipelines for domain-specific question-answering tasks. We evaluate the effectiveness of our system in generating accurate and pertinent responses for Mississippi State University, as a case study, using quantitative measures, employing frameworks such as Retrieval Augmented Generation Assessment(RAGAS). Furthermore, we evaluate the usability of this system via subjective satisfaction surveys using the System Usability Scale (SUS). Our system demonstrates impressive quantitative performance, with a mean RAGAS score of 0.96, and experience, as validated by usability assessments.

Developing accurate machine learning models for oncology requires large-scale, high-quality multimodal datasets. However, creating such datasets remains challenging due to the complexity and heterogeneity of medical data. To address this challenge, we introduce HoneyBee, a scalable modular framework for building multimodal oncology datasets that leverages foundation models to generate representative embeddings. HoneyBee integrates various data modalities, including clinical diagnostic and pathology imaging data, medical notes, reports, records, and molecular data. It employs data preprocessing techniques and foundation models to generate embeddings that capture the essential features and relationships within the raw medical data. The generated embeddings are stored in a structured format using Hugging Face datasets and PyTorch dataloaders for accessibility. Vector databases enable efficient querying and retrieval for machine learning applications. We demonstrate the effectiveness of HoneyBee through experiments assessing the quality and representativeness of these embeddings. The framework is designed to be extensible to other medical domains and aims to accelerate oncology research by providing high-quality, machine learning-ready datasets. HoneyBee is an ongoing open-source effort, and the code, datasets, and models are available at the project repository.

Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling the complex biological mechanisms underlying cancer, thereby enabling effective diagnosis, treatment, and prevention strategies. However, predicting patient outcomes through integration of all available multi-omics data is an under-study research direction. Here, we present SeNMo (Self-normalizing Network for Multi-omics), a deep neural network trained on multi-omics data across 33 cancer types. SeNMo is efficient in handling multi-omics data characterized by high-width (many features) and low-length (fewer samples) attributes. We trained SeNMo for the task of overall survival using pan-cancer data involving 33 cancer sites from Genomics Data Commons (GDC). The training data includes gene expression, DNA methylation, miRNA expression, DNA mutations, protein expression modalities, and clinical data. We evaluated the model's performance in predicting overall survival using concordance index (C-Index). SeNMo performed consistently well in training regime, with the validation C-Index of 0.76 on GDC's public data. In the testing regime, SeNMo performed with a C-Index of 0.758 on a held-out test set. The model showed an average accuracy of 99.8% on the task of classifying the primary cancer type on the pan-cancer test cohort. SeNMo proved to be a mini-foundation model for multi-omics oncology data because it demonstrated robust performance, and adaptability not only across molecular data types but also on the classification task of predicting the primary cancer type of patients. SeNMo can be further scaled to any cancer site and molecular data type. We believe SeNMo and similar models are poised to transform the oncology landscape, offering hope for more effective, efficient, and patient-centric cancer care.

We solve for waves in a polytropic, stratified medium with a spatially varying background magnetic field that points along a horizontal $x$-direction, and with gravity that is directed along the vertical $z$-direction. Force balance determines the magnitude of the background magnetic field, $B_0^2 \sim z^{n+1}$, where $n$ is the polytropic index. Using numerical and asymptotic methods, we deduce an accurate and explicit dispersion relation for fast pressure-driven waves: $\Omega^2 \sim K\left(2m+n\right) \left[1 + (1/M_\mathrm{A})^2 (4-2\gamma+\cos^2\theta-3\cos^4\theta)/4 \right]$. Here, $\Omega$ is the frequency, $K$ the wavenumber, $\theta$ the angle the wave-vector makes with the background magnetic field, $M_\mathrm{A}$ the Alfv\'enic Mach number, and $m$ an integer representing the eigen state. Applications of our result are in magnetoseismology and nonlinear asteroseismology., Comment: 11 pages, 5 figures

LUX-ZEPLIN (LZ) is a tonne-scale experiment searching for direct dark matter interactions and other rare events. It is located at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, USA. The core of the LZ detector is a dual-phase xenon time projection chamber (TPC), designed with the primary goal of detecting Weakly Interacting Massive Particles (WIMPs) via their induced low energy nuclear recoils. Surrounding the TPC, two veto detectors immersed in an ultra-pure water tank enable reducing background events to enhance the discovery potential. Intricate calibration systems are purposely designed to precisely understand the responses of these three detector volumes to various types of particle interactions and to demonstrate LZ's ability to discriminate between signals and backgrounds. In this paper, we present a comprehensive discussion of the key features, requirements, and performance of the LZ calibration systems, which play a crucial role in enabling LZ's WIMP-search and its broad science program. The thorough description of these calibration systems, with an emphasis on their novel aspects, is valuable for future calibration efforts in direct dark matter and other rare-event search experiments.

The first science run of the LUX-ZEPLIN (LZ) experiment, a dual-phase xenon time project chamber operating in the Sanford Underground Research Facility in South Dakota, USA, has reported leading limits on spin-independent WIMP-nucleon interactions and interactions described from a non-relativistic effective field theory (NREFT). Using the same 5.5~t fiducial mass and 60 live days of exposure we report on the results of a relativistic extension to the NREFT. We present constraints on couplings from covariant interactions arising from the coupling of vector, axial currents, and electric dipole moments of the nucleon to the magnetic and electric dipole moments of the WIMP which cannot be described by recasting previous results described by an NREFT. Using a profile-likelihood ratio analysis, in an energy region between 0~keV$_\text{nr}$ to 270~keV$_\text{nr}$, we report 90% confidence level exclusion limits on the coupling strength of five interactions in both the isoscalar and isovector bases., Comment: 7 pages, 4 figures