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Robot caregiving should be personalized to meet the diverse needs of care recipients -- assisting with tasks as needed, while taking user agency in action into account. In physical tasks such as handover, bathing, dressing, and rehabilitation, a key aspect of this diversity is the functional range of motion (fROM), which can vary significantly between individuals. In this work, we learn to predict personalized fROM as a way to generalize robot decision-making in a wide range of caregiving tasks. We propose a novel data-driven method for predicting personalized fROM using functional assessment scores from occupational therapy. We develop a neural model that learns to embed functional assessment scores into a latent representation of the user's physical function. The model is trained using motion capture data collected from users with emulated mobility limitations. After training, the model predicts personalized fROM for new users without motion capture. Through simulated experiments and a real-robot user study, we show that the personalized fROM predictions from our model enable the robot to provide personalized and effective assistance while improving the user's agency in action. See our website for more visualizations: https://emprise.cs.cornell.edu/grace/., Comment: 10 pages, 5 figures, Accepted to IEEE/ACM International Conference on Human-Robot Interaction (HRI), 2025

Transformer-based models have achieved remarkable results in natural language processing (NLP) tasks such as text classification and machine translation. However, their computational complexity and resource demands pose challenges for scalability and accessibility. This research proposes a hybrid quantum-classical transformer model that integrates a quantum-enhanced attention mechanism to address these limitations. By leveraging quantum kernel similarity and variational quantum circuits (VQC), the model captures intricate token dependencies while improving computational efficiency. Experimental results on the IMDb dataset demonstrate that the quantum-enhanced model outperforms the classical baseline across all key metrics, achieving a 1.5% improvement in accuracy (65.5% vs. 64%), precision, recall, and F1 score. Statistical significance tests validate these improvements, highlighting the robustness of the quantum approach. These findings illustrate the transformative potential of quantum-enhanced attention mechanisms in optimizing NLP architectures for real-world applications., Comment: 23 pages, 9 figures, 5 tables

Foundation models hold transformative potential for medical imaging, but their clinical utility requires rigorous evaluation to address their strengths and limitations. This study introduces an evaluation framework for assessing the clinical impact and translatability of SAM, MedSAM, and SAM2, using musculoskeletal MRI as a case study. We tested these models across zero-shot and finetuned paradigms to assess their ability to process diverse anatomical structures and effectuate clinically reliable biomarkers, including cartilage thickness, muscle volume, and disc height. We engineered a modular pipeline emphasizing scalability, clinical relevance, and workflow integration, reducing manual effort and aligning validation with end-user expectations. Hierarchical modeling revealed how dataset mixing, anatomical complexity, and MRI acquisition parameters influence performance, providing insights into the role of imaging refinements in improving segmentation accuracy. This work demonstrates how clinically focused evaluations can connect computational advancements with tangible applications, creating a pathway for foundation models to address medical challenges. By emphasizing interdisciplinary collaboration and aligning technical innovation with clinical priorities, our framework provides a roadmap for advancing machine learning technologies into scalable and impactful biomedical solutions.

Deep learning surrogate models have become pivotal in enabling model-driven materials discovery to achieve exceptional properties. However, ensuring the accuracy and reliability of predictions from these models, trained on limited and sparse material datasets remains a significant challenge. This study introduces an integrated deep learning framework for predicting the synthesis, microstructure, and mechanical properties of ceramic aerogels, leveraging physics-based models such as Lattice Boltzmann simulations for microstructure formation and stochastic finite element methods for mechanical property calculations. To address the computational demands of repeated physics-based simulations required for experimental calibration and material design, a linked surrogate model is developed, leveraging Convolutional Neural Networks (CNNs) for stochastic microstructure generation and microstructure-to-mechanical property mapping. To overcome challenges associated with limited training datasets from expensive physical modeling, CNN training is formulated within a Bayesian inference framework, enabling robust uncertainty quantification in predictions. Numerical results highlight the strengths and limitations of the linked surrogate framework, demonstrating its effectiveness in predicting properties of aerogels with pore sizes and morphologies similar to the training data (in-distribution) and its ability to interpolate to new microstructural features between training data (out-of-distribution).

This study describes the impact of non-zero value of strange quark mass $(m_{s})$ and number density of baryons $(n)$ on the structure, stability and maximum mass of strange stars. We derive an exact relativistic solution of the Einstein field equation using the Tolman-IV metric potential and modified MIT bag model EoS, $p_{r}=\frac{1}{3}(\rho-4B')$, where $B'$ is a function of bag constant $B$, $m_{s}$ and baryon number density $(n)$. Following CERN's findings, transition of phase from hadronic matter to Quark-Gluon Plasma (QGP) may occur at high densities in presence of favourable conditions. The standard MIT bag model, with a constant $B$, fails to explain such transition properly. Introducing a finite $m_{s}$ and Wood-Saxon parametrisation for $B$, dependent on baryon number density $(n)$, provides a more realistic EoS to address such phase transition. Both $m_{s}$ and $n$ constrain the EoS, making it softer as $m_{s}$ increases. Solutions to the TOV equations reveal that for massless strange quarks, maximum mass is 2.01 $M_{\odot}$ and corresponding radius is 10.96 Km when $n=0.66~fm^{-3}$. These values decrease to 1.99 $M_{\odot}$ and 1.96 $M_{\odot}$, with corresponding radii of 10.88 Km and 10.69 Km for $m_{s}=50$ and $100~MeV$ respectively having same $n$ value. It is interesting to note that a corelation exists between $n$ and $m_{s}$. The hadronic to quark matter transition occurs at higher values of $n$, when $m_{s}$ increases such as $n\geq0.484,~0.489$ and $0.51~fm^{-3}$ for $m_{s}=50$ and $100~MeV$ respectively. Beyond these values, the energy per baryon $(\mathcal{E_{B}})$ drops below $930.4~MeV$, indicating a complete transition to quark matter. For physical analysis, we have considered $n~(=0.578~fm^{-3})$ which lies in the stable region with $B(n)=70~MeV/fm^{3}$. The model provides a viable description of strange stars, satisfying all necessary physical requirements., Comment: 19 pages, 16 figures

A Grad-Shafranov equation (GSE) valid for compact quasisymmetric stellarators is derived by an asymptotic expansion around a vacuum field carried to first order. We obtain an equation for the existence of flux surfaces leading up to the GSE. The flux surface label must simultaneously satisfy the existence equation and the GSE, which generally leads to an overdetermined problem. We show how the overdetermined problem can be resolved within our model for a class of hybrid devices similar to that studied by Henneberg and Plunk (S. Henneberg and G. Plunk, PRR 2024). We are also able to solve the existence equation for flux surfaces analytically in the most general case by introducing a special coordinate system. In future work, we plan to carry out an optimization seeking to minimize the error in our GSE while obeying the flux surface existence equation. This should be faster than a conventional stellarator optimization and will allow us to find solutions outside the class of hybrid devices., Comment: 8 pages, 6 figures, 1 table. Submitted to Physics of Plasmas

Bed-to-wheelchair transferring is a ubiquitous activity of daily living (ADL), but especially challenging for caregiving robots with limited payloads. We develop a novel algorithm that leverages the presence of other assistive devices: a Hoyer sling and a wheelchair for coarse manipulation of heavy loads, alongside a robot arm for fine-grained manipulation of deformable objects (Hoyer sling straps). We instrument the Hoyer sling and wheelchair with actuators and sensors so that they can become intelligent agents in the algorithm. We then focus on one subtask of the transferring ADL -- tying Hoyer sling straps to the sling bar -- that exemplifies the challenges of transfer: multi-agent planning, deformable object manipulation, and generalization to varying hook shapes, sling materials, and care recipient bodies. To address these challenges, we propose CART-MPC, a novel algorithm based on turn-taking multi-agent model predictive control that uses a learned neural dynamics model for a keypoint-based representation of the deformable Hoyer sling strap, and a novel cost function that leverages linking numbers from knot theory and neural amortization to accelerate inference. We validate it in both RCareWorld simulation and real-world environments. In simulation, CART-MPC successfully generalizes across diverse hook designs, sling materials, and care recipient body shapes. In the real world, we show zero-shot sim-to-real generalization capabilities to tie deformable Hoyer sling straps on a sling bar towards transferring a manikin from a hospital bed to a wheelchair. See our website for supplementary materials: https://emprise.cs.cornell.edu/cart-mpc/.

Social skills training targets behaviors necessary for success in social interactions. However, traditional classroom training for such skills is often insufficient to teach effective communication -- one-to-one interaction in real-world scenarios is preferred to lecture-style information delivery. This paper introduces a framework that allows instructors to collaborate with large language models to dynamically design realistic scenarios for students to communicate. Our framework uses these scenarios to enable student rehearsal, provide immediate feedback, and visualize performance for both students and instructors. Unlike traditional intelligent tutoring systems, instructors can easily co-create scenarios with a large language model without technical skills. Additionally, the system generates new scenario branches in real time when existing options do not fit the student's response.

We use Priestley duality as a new tool to study maximal $d$-spectra of arithmetic frames, both with and without units. We pay special attention to when the maximal $d$-spectrum is compact or Hausdorff. Various necessary and sufficient conditions are given, including a construction of an arithmetic frame with a unit whose maximal $d$-spectrum is not Hausdorff, thus resolving an open problem in the literature.

Several astrophysical and cosmological observations suggest the existence of dark matter (DM) through its gravitational effects, yet its nature remains elusive. Despite the lack of DM signals from direct detection experiments, efforts continue to focus on the indirect detection of DM from DM-rich astrophysical objects. Dwarf spheroidal galaxies (dSphs) are among the most promising targets for such searches. In this work, we aim to investigate the expected DM capture rate from the stellar component of 10 nearby DM-rich dSphs, assuming that the accumulated DM eventually annihilates into light, long-lived mediators (LLLMs) which decay into gamma rays outside the dSphs. We analyze nearly 16 years of {\it Fermi}-LAT data to search for DM annihilation through LLLMs, and, from the observed stacked flux upper limits, set limits on the DM-nucleon scattering cross section using model-independent methods. Additionally, we incorporate the Sommerfeld Enhancement (SE) effect into the DM annihilation process, obtaining bounds on the DM-nucleon scattering cross-section of $\sim~10^{-36} {\rm cm}^2$ for DM masses around 100 GeV. This allows us to explore an alternative avenue for exploring DM phenomena from dSphs and compare our results with the bounds reported by direct DM detection experiments and other celestial bodies., Comment: 25 pages, 6 figures, Comments are welcome

Aiming for more effective experiment design, such as in video content advertising where different content options compete for user engagement, these scenarios can be modeled as multi-arm bandit problems. In cases where limited interactions are available due to external factors, such as the cost of conducting experiments, recommenders often face constraints due to the small number of user interactions. In addition, there is a trade-off between selecting the best treatment and the ability to personalize and contextualize based on individual factors. A popular solution to this dilemma is the Contextual Bandit framework. It aims to maximize outcomes while incorporating personalization (contextual) factors, customizing treatments such as a user's profile to individual preferences. Despite their advantages, Contextual Bandit algorithms face challenges like measurement bias and the 'curse of dimensionality.' These issues complicate the management of numerous interventions and often lead to data sparsity through participant segmentation. To address these problems, we introduce the Weighted Allocation Probability Adjusted Thompson Sampling (WAPTS) algorithm. WAPTS builds on the contextual Thompson Sampling method by using a dynamic weighting parameter. This improves the allocation process for interventions and enables rapid optimization in data-sparse environments. We demonstrate the performance of our approach on different numbers of arms and effect sizes.

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

For millions of adults with mobility limitations, eating meals is a daily challenge. A variety of robotic systems have been developed to address this societal need. Unfortunately, end-user adoption of robot-assisted feeding is limited, in part because existing devices are unable to seamlessly grasp, manipulate, and feed diverse foods. Recent works seek to address this issue by creating new algorithms for food acquisition and bite transfer. In parallel to these algorithmic developments, however, we hypothesize that mechanical intelligence will make it fundamentally easier for robot arms to feed humans. We therefore propose Kiri-Spoon, a soft utensil specifically designed for robot-assisted feeding. Kiri-Spoon consists of a spoon-shaped kirigami structure: when actuated, the kirigami sheet deforms into a bowl of increasing curvature. Robot arms equipped with Kiri-Spoon can leverage the kirigami structure to wrap-around morsels during acquisition, contain those items as the robot moves, and then compliantly release the food into the user's mouth. Overall, Kiri-Spoon combines the familiar and comfortable shape of a standard spoon with the increased capabilities of soft robotic grippers. In what follows, we first apply a stakeholder-driven design process to ensure that Kiri-Spoon meets the needs of caregivers and users with physical disabilities. We next characterize the dynamics of Kiri-Spoon, and derive a mechanics model to relate actuation force to the spoon's shape. The paper concludes with three separate experiments that evaluate (a) the mechanical advantage provided by Kiri-Spoon, (b) the ways users with disabilities perceive our system, and (c) how the mechanical intelligence of Kiri-Spoon complements state-of-the-art algorithms. Our results suggest that Kiri-Spoon advances robot-assisted feeding across diverse foods, multiple robotic platforms, and different manipulation algorithms., Comment: 20 pages, 15 figures

A consistent finite-temperature microscopic theory for the response of strongly coupled superfluid fermionic systems is formulated. We start from the general many-body Hamiltonian with the vacuum (bare) two-fermion interaction and derive the equation of motion (EOM) for the thermally averaged two-time two-fermion correlation function, which determines the spectrum of the system under study. The superfluidity is introduced via the Bogoliubov transformation of the fermionic field operators, and the entire formalism is carried out in the basis of Bogoliubov's quasiparticles, keeping the complete $4\times 4$ block matrix structure of the two-fermion EOM. Static and dynamical interaction kernels of the resulting EOM are discussed. The least correlated static kernel reproduces the known finite-temperature quasiparticle random phase approximation, while the dynamical kernel was previously largely unknown in the finite-temperature superfluid theory. The major focus is then placed on the latter kernel, which is completed and advanced to a factorized form, enabling a minimal truncation of the many-body problem with keeping important effects of emergent collectivity and mapping to the quasiparticle-vibration coupling (qPVC). As in the zero-temperature and non-superfluid cases, the qPVC can be associated with a new order parameter, qPVC vertex. In the thermal superfluid theory, the latter vertex as well as the components of the dynamical kernel acquire an extended form including thermally unblocked transition amplitudes., Comment: Article, 12 pages

Humblebragging is a phenomenon where individuals present self-promotional statements under the guise of modesty or complaints. For example, a statement like, "Ugh, I can't believe I got promoted to lead the entire team. So stressful!", subtly highlights an achievement while pretending to be complaining. Detecting humblebragging is important for machines to better understand the nuances of human language, especially in tasks like sentiment analysis and intent recognition. However, this topic has not yet been studied in computational linguistics. For the first time, we introduce the task of automatically detecting humblebragging in text. We formalize the task by proposing a 4-tuple definition of humblebragging and evaluate machine learning, deep learning, and large language models (LLMs) on this task, comparing their performance with humans. We also create and release a dataset called HB24, containing 3,340 humblebrags generated using GPT-4o. Our experiments show that detecting humblebragging is non-trivial, even for humans. Our best model achieves an F1-score of 0.88. This work lays the foundation for further exploration of this nuanced linguistic phenomenon and its integration into broader natural language understanding systems., Comment: Under review at ARR

Over the past decade, several image-processing methods and algorithms have been proposed for identifying plant diseases based on visual data. DNN (Deep Neural Networks) have recently become popular for this task. Both traditional image processing and DNN-based methods encounter significant performance issues in real-time detection owing to computational limitations and a broad spectrum of plant disease features. This article proposes a novel technique for identifying and localising plant disease based on the Quad-Tree decomposition of an image and feature learning simultaneously. The proposed algorithm significantly improves accuracy and faster convergence in high-resolution images with relatively low computational load. Hence it is ideal for deploying the algorithm in a standalone processor in a remotely operated image acquisition and disease detection system, ideally mounted on drones and robots working on large agricultural fields. The technique proposed in this article is hybrid as it exploits the advantages of traditional image processing methods and DNN-based models at different scales, resulting in faster inference. The F1 score is approximately 0.80 for four disease classes corresponding to potato and tomato crops.

Fantasy sports, particularly fantasy cricket, have garnered immense popularity in India in recent years, offering enthusiasts the opportunity to engage in strategic team-building and compete based on the real-world performance of professional athletes. In this paper, we address the challenge of optimizing fantasy cricket team selection using reinforcement learning (RL) techniques. By framing the team creation process as a sequential decision-making problem, we aim to develop a model that can adaptively select players to maximize the team's potential performance. Our approach leverages historical player data to train RL algorithms, which then predict future performance and optimize team composition. This not only represents a huge business opportunity by enabling more accurate predictions of high-performing teams but also enhances the overall user experience. Through empirical evaluation and comparison with traditional fantasy team drafting methods, we demonstrate the effectiveness of RL in constructing competitive fantasy teams. Our results show that RL-based strategies provide valuable insights into player selection in fantasy sports., Comment: 8 Pages including references, Accepted to CODS-COMAD 2024 conference

We establish Gaussian approximation bounds for covariate and rank-matching-based Average Treatment Effect (ATE) estimators. By analyzing these estimators through the lens of stabilization theory, we employ the Malliavin-Stein method to derive our results. Our bounds precisely quantify the impact of key problem parameters, including the number of matches and treatment balance, on the accuracy of the Gaussian approximation. Additionally, we develop multiplier bootstrap procedures to estimate the limiting distribution in a fully data-driven manner, and we leverage the derived Gaussian approximation results to further obtain bootstrap approximation bounds. Our work not only introduces a novel theoretical framework for commonly used ATE estimators, but also provides data-driven methods for constructing non-asymptotically valid confidence intervals.

Structured optical fields have led to several ground-breaking techniques in classical imaging and microscopy. At the same time, in the quantum domain, position-momentum entangled photon fields have been shown to have several unique features that can lead to beyond-classical imaging and microscopy capabilities. Therefore, it is natural to expect that position-momentum entangled two-photon fields that are structured can push the boundaries of quantum imaging and microscopy even further beyond. Nonetheless, the existing experimental schemes are able to produce either structured two-photon fields without position-momentum entanglement, or position-momentum entangled two-photon fields without structures. In this article, by manipulating the phase-matching condition of the spontaneous parametric down-conversion process, we report experimental generation of two-photon fields with various structures in their spatial correlations. We experimentally measure the minimum bound on the entanglement of formation and thereby verify the position-momentum entanglement of the structured two-photon field. We expect this work to have important implications for quantum technologies related to imaging and sensing., Comment: 9 pages, 6 figures

Quantum Support Vector Machine is a kernel-based approach to classification problems. We study the applicability of quantum kernels to financial data, specifically our self-curated Dhaka Stock Exchange (DSEx) Broad Index dataset. To the best of our knowledge, this is the very first systematic research work on this dataset on the application of quantum kernel. We report empirical quantum advantage in our work, using several quantum kernels and proposing the best one for this dataset while verifying the Phase Space Terrain Ruggedness Index metric. We estimate the resources needed to carry out these investigations on a larger scale for future practitioners., Comment: 5 pages, 6 figures

We investigate the efficacy of a systematic planetary nebula (PN) search in the Census of the Local Universe (CLU) narrowband (H$\alpha$) survey that covers a considerably larger sky region of above declination $-20^\circ$ than most previous surveys. Using PNe observed by the Isaac Newton Telescope Photometric H$\alpha$ Survey (IPHAS) as validation, we are able to visually recover 432 out of 441 cataloged PNe (98\%) within the CLU dataset, with 5 sources having unusable CLU images and 4 missed due to limitations of imaging quality. Moreover, the reference PNe are conventionally divided into three PN classes in decreasing order of identification confidence given their spectra and morphologies. We record consistently high recovery rate across all classes: 95\% of True, 71\% of Likely, and 81\% of Possible sources are readily recovered. To further demonstrate the ability of CLU to find new PNe, we undertake a preliminary search of compact PNe within a sub-region of the validation catalog, mainly utilizing the significance of narrow-band colors ($\Sigma$) as a metric for identification. In a $200\,\rm deg^2$ region, we search the CLU source catalog and find 31 PN candidates after automated and visual scrutiny, of which 12 are new sources not appearing in previous studies. As a demonstration of our ongoing follow-up campaign, we present medium-resolution optical spectra of six candidates and notice that four of them show emission signatures characteristic of confirmed PNe. As we refine our selection methods, CLU promises to provide a systematic catalog of PNe spanning $2/3$ of the sky., Comment: 40 pages, 12 figures, 3 tables; submitted to the Publications of the Astronomical Society of the Pacific

Dzyaloshinskii-Moriya interaction (DMI) plays a crucial role to stabilize the exotic topologically stable skyrmion spin-textures in the noncentrosymmetric crystals. The recent discovery of biskyrmions and skyrmions in the globally centrosymmetric crystals has raised debate about the role of the DMI in causing the spin textures, since DMI vanishes in such crystal structures. Theoretical studies, on the other hand, suggest non-vanishing DMI even if there is local inversion symmetry breaking in an otherwise globally centrosymmetric crystal structure. Motivated by such theoretical predictions, we present here the results of a systematic crystal structure study of two skyrmion-hosting Ni2In-type centrosymmetric hexagonal compounds, MnNiGa and MnPtGa, using the atomic pair distribution function (PDF) technique. Our result provides information about structural correlations in the short-range (SR), medium-range (MR) and long-range (LR) regimes simultaneously. The analysis of the experimental PDFs, obtained from high flux, high energy and high-Q synchrotron x-ray powder diffraction patterns, reveal that the local SR structure of both MnNiGa and MnPtGa compounds corresponds to the noncentrosymmetric trigonal space group P3m1, while the structure in the MR+LR regimes remains hexagonal in the centrosymmetric P63/mmc space group. These findings are also supported by theoretical DFT calculations. Our results in conjunction with the previous theoretical predictions, provide a rationale for the genesis of skyrmions in centrosymmetric materials in terms of non-vanishing DMI due to local inversion symmetry breaking. We believe that our findings would encourage a systematic search of skyrmionic textures and other topological phenomena in a vast family of centrosymmetric materials.

Recent gain in popularity of AI conversational agents has led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences. Additionally, psychological risks have often been presented as a sub-category within broader AI-related risks in past taxonomy works, leading to under-representation of the impact of psychological risks of AI use. To address these challenges, our work presents a novel risk taxonomy focusing on psychological risks of using AI gathered through lived experience of individuals. We employed a mixed-method approach, involving a comprehensive survey with 283 individuals with lived mental health experience and workshops involving lived experience experts to develop a psychological risk taxonomy. Our taxonomy features 19 AI behaviors, 21 negative psychological impacts, and 15 contexts related to individuals. Additionally, we propose a novel multi-path vignette based framework for understanding the complex interplay between AI behaviors, psychological impacts, and individual user contexts. Finally, based on the feedback obtained from the workshop sessions, we present design recommendations for developing safer and more robust AI agents. Our work offers an in-depth understanding of the psychological risks associated with AI conversational agents and provides actionable recommendations for policymakers, researchers, and developers., Comment: 25 pages, 2 figures, 4 tables; Corrected typos

Diabetic Retinopathy is one of the most familiar diseases and is a diabetes complication that affects eyes. Initially, diabetic retinopathy may cause no symptoms or only mild vision problems. Eventually, it can cause blindness. So early detection of symptoms could help to avoid blindness. In this paper, we present some experiments on some features of diabetic retinopathy, like properties of exudates, properties of blood vessels and properties of microaneurysm. Using the features, we can classify healthy, mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative stages of DR. Support Vector Machine, Random Forest and Naive Bayes classifiers are used to classify the stages. Finally, Random Forest is found to be the best for higher accuracy, sensitivity and specificity of 76.5%, 77.2% and 93.3% respectively., Comment: 5 pages, 9 figures, 2 tables. International Conference on Advanced Engineering, Technology and Applications (ICAETA-2021), Istanbul, Turkey

Perturbative Quantum Chromodynamics corrections and the colour superconductivity indicate that strongly interacting matter can manifest unique physical behaviours under extreme conditions. Motivated by this notion, we investigate the interior structure and properties of quark stars composed of interacting quark matter, which provides a comprehensive avenue to explore the strong interaction effects, within the framework of $f(Q)$ gravity. A unified equation of state is formulated to describe various phases of quark matter, including up-down quark matter $(2SC)$, strange quark matter $(2SC+s)$, and the Colour-Flavor Locked $(CFL)$ phase. By employing a systematic reparametrisation and rescaling, the number of degrees of freedom in the equation of state is significantly reduced. Utilising the Buchdahl-I metric ansatz and a linear $f(Q)$ functional form, $f(Q)=\alpha_{0}+\alpha_{1}Q$, we derive the exact solutions of the Einstein field equations in presence of the unified interacting quark matter equation of state. For the $2SC$ phase, we examine the properties of quark stars composed of up-down quark matter. For the $(2SC+s)$and $CFL$ phases, we incorporate the effects of a finite strange quark mass $(m_{s}\neq0)$. The Tolman-Oppenheimer-Volkoff equations are numerically solved to determine the maximum mass-radius relations for each phase. Our results indicate that the model satisfies key physical criteria, including causality, energy conditions, and stability requirements, ensuring the viability of the configurations. Furthermore, the predicted radii for certain compact star candidates align well with observational data. The study highlights that quark stars composed of interacting quark matter within the $f(Q)$ gravity framework provide a robust and physically consistent stellar model across all considered phases., Comment: 24 pages, 33 figures

Cellular networks rely on handovers (HOs) as a fundamental element to enable seamless connectivity for mobile users. A comprehensive analysis of HOs can be achieved through data from Mobile Network Operators (MNOs); however, the vast majority of studies employ data from measurement campaigns within confined areas and with limited end-user devices, thereby providing only a partial view of HOs. This paper presents the first countrywide analysis of HO performance, from the perspective of a top-tier MNO in a European country. We collect traffic from approximately 40M users for 4 weeks and study the impact of the radio access technologies (RATs), device types, and manufacturers on HOs across the country. We characterize the geo-temporal dynamics of horizontal (intra-RAT) and vertical (inter-RATs) HOs, at the district level and at millisecond granularity, and leverage open datasets from the country's official census office to associate our findings with the population. We further delve into the frequency, duration, and causes of HO failures, and model them using statistical tools. Our study offers unique insights into mobility management, highlighting the heterogeneity of the network and devices, and their effect on HOs.

Collisionless self-gravitating systems, e.g., cold dark matter halos, harbor universal density profiles despite the intricate non-linear physics of hierarchical structure formation, the origin of which has been a persistent mystery. To solve this problem, we develop a self-consistent quasilinear theory (QLT) in action-angle space for the collisionless relaxation of driven, inhomogeneous, self-gravitating systems by perturbing the governing Vlasov-Poisson equations. We obtain a quasilinear diffusion equation (QLDE) for the secular evolution of the mean distribution function $f_0$ of a halo due to linear fluctuations (induced by random perturbations in the force field) that are collectively dressed by self-gravity, a phenomenon described by the response matrix. Unlike previous studies, we treat collective dressing up to all orders. Well-known halo density profiles $\rho(r)$ commonly observed in $N$-body simulations, including the $r^{-1}$ NFW cusp, an Einasto central core, and the $r^{-1.5}$ prompt cusp, emerge as quasi-steady state attractor solutions of the QLDE. The $r^{-1}$ cusp is a constant flux steady-state solution for a constantly accreting massive halo perturbed by small-scale white noise fluctuations induced by substructure. It is an outcome of the universal nature of collisionless relaxation: lower energy particles attract more particles, gain higher effective mass and get less accelerated by the fluctuating force field. The zero-flux steady state solution for an isolated halo is an $f_0$ that is flat in energy, and the corresponding $\rho(r)$ can either be cored or an $r^{-1.5}$ cusp depending on the inner boundary condition. The latter forms around a central dense object, e.g., a compact subhalo or a black hole. We demonstrate for the first time that these halo profiles emerge as quasi-steady state attractors of collisionless relaxation described by a self-consistent QLT., Comment: Submitted to PRX; 9 pages, 1 figure

Transition metal dichalcogenides (TMDs) are a novel class of quantum materials with significant potential in spintronics, optoelectronics, valleytronics, and opto-valleytronics. TMDs exhibit strong spin-orbit coupling, enabling efficient spin-charge interconversion, which makes them ideal candidates for spin-orbit torque-driven spintronic devices. In this study, we investigated the spin-to-charge conversion through ferromagnetic resonance in MoS2/Cu/CoFeB heterostructures with varying Cu spacer thicknesses. The conversion efficiency, quantified by the spin Hall angle, was enhanced by an order of magnitude due to Cu intercalation. Magneto-optic Kerr effect microscopy confirmed that Cu did not significantly modify the magnetic domains, indicating its effectiveness in decoupling MoS2 from CoFeB. This decoupling preserves the spin-orbit coupling (SOC) of MoS2 by mitigating the exchange interaction with CoFeB, as proximity to localized magnetization can alter the electronic structure and SOC. First-principles calculations revealed that Cu intercalation notably enhances the spin Berry curvature and spin Hall conductivity, contributing to the increased spin Hall angle. This study demonstrates that interface engineering of ferromagnet/TMD-based heterostructures can achieve higher spin-to-charge conversion efficiencies, paving the way for advancements in spintronic applications.

Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP). However, traditional methods, whether matching-based or embedding-based, often fall short of judging subtle attributes and delivering satisfactory results. Recent advancements in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm, where LLMs are leveraged to perform scoring, ranking, or selection across various tasks and applications. This paper provides a comprehensive survey of LLM-based judgment and assessment, offering an in-depth overview to advance this emerging field. We begin by giving detailed definitions from both input and output perspectives. Then we introduce a comprehensive taxonomy to explore LLM-as-a-judge from three dimensions: what to judge, how to judge and where to judge. Finally, we compile benchmarks for evaluating LLM-as-a-judge and highlight key challenges and promising directions, aiming to provide valuable insights and inspire future research in this promising research area. Paper list and more resources about LLM-as-a-judge can be found at \url{https://github.com/llm-as-a-judge/Awesome-LLM-as-a-judge} and \url{https://llm-as-a-judge.github.io}., Comment: v5: add new citations; 36 pages, 5 figures

Theory of Mind (ToM) refers to the cognitive ability to infer and attribute mental states to oneself and others. As large language models (LLMs) are increasingly evaluated for social and cognitive capabilities, it remains unclear to what extent these models demonstrate ToM across diverse languages and cultural contexts. In this paper, we introduce a comprehensive study of multilingual ToM capabilities aimed at addressing this gap. Our approach includes two key components: (1) We translate existing ToM datasets into multiple languages, effectively creating a multilingual ToM dataset and (2) We enrich these translations with culturally specific elements to reflect the social and cognitive scenarios relevant to diverse populations. We conduct extensive evaluations of six state-of-the-art LLMs to measure their ToM performance across both the translated and culturally adapted datasets. The results highlight the influence of linguistic and cultural diversity on the models' ability to exhibit ToM, and questions their social reasoning capabilities. This work lays the groundwork for future research into enhancing LLMs' cross-cultural social cognition and contributes to the development of more culturally aware and socially intelligent AI systems. All our data and code are publicly available.

On May 24th, 2023, the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), joined by the Advanced Virgo and KAGRA detectors, began the fourth observing run for a two-year-long dedicated search for gravitational waves. The LIGO Hanford and Livingston detectors have achieved an unprecedented sensitivity to gravitational waves, with an angle-averaged median range to binary neutron star mergers of 152 Mpc and 160 Mpc, and duty cycles of 65.0% and 71.2%, respectively, with a coincident duty cycle of 52.6%. The maximum range achieved by the LIGO Hanford detector is 165 Mpc and the LIGO Livingston detector 177 Mpc, both achieved during the second part of the fourth observing run. For the fourth run, the quantum-limited sensitivity of the detectors was increased significantly due to the higher intracavity power from laser system upgrades and replacement of core optics, and from the addition of a 300 m filter cavity to provide the squeezed light with a frequency-dependent squeezing angle, part of the A+ upgrade program. Altogether, the A+ upgrades led to reduced detector-wide losses for the squeezed vacuum states of light which, alongside the filter cavity, enabled broadband quantum noise reduction of up to 5.2 dB at the Hanford observatory and 6.1 dB at the Livingston observatory. Improvements to sensors and actuators as well as significant controls commissioning increased low frequency sensitivity. This paper details these instrumental upgrades, analyzes the noise sources that limit detector sensitivity, and describes the commissioning challenges of the fourth observing run., Comment: 26 pages, 18 figures

Ambiguity in natural language poses significant challenges to Large Language Models (LLMs) used for open-domain question answering. LLMs often struggle with the inherent uncertainties of human communication, leading to misinterpretations, miscommunications, hallucinations, and biased responses. This significantly weakens their ability to be used for tasks like fact-checking, question answering, feature extraction, and sentiment analysis. Using open-domain question answering as a test case, we compare off-the-shelf and few-shot LLM performance, focusing on measuring the impact of explicit disambiguation strategies. We demonstrate how simple, training-free, token-level disambiguation methods may be effectively used to improve LLM performance for ambiguous question answering tasks. We empirically show our findings and discuss best practices and broader impacts regarding ambiguity in LLMs., Comment: Accepted at the REU Symposium at IEEE BigData 2024

We examine the onset of Anderson localization in three-dimensional systems with structural disorder in the form of lattice irregularities and in the absence of any on-site disordered potential. Analyzing two models with distinct types of lattice regularities, we show that the Anderson localization transition occurs when the strength of the structural disorder is smoothly increased. Performing finite-size scaling analysis of the results, we show that the transition belongs to the same universality class as regular Anderson localization induced by onsite disorder. Our work identifies a new class of structurally disordered lattice models in which destructive interference of matter waves may inhibit transport and lead to a transition between metallic and localized phases., Comment: 8 pages, 6 figures

Quantum mechanics has revolutionized our understanding of information transmission, leading to the development of quantum communication protocols that promise unprecedented security in data transfer. Quantum teleportation, in particular, has emerged as a cornerstone protocol for quantum communication, operating within the constraints of noisy intermediate-scale quantum (NISQ) devices that characterize current quantum hardware. While significant progress has been made in demonstrating quantum teleportation, maintaining reliable high-fidelity communication in practical, noisy environments remains an unsolved challenge, particularly in addressing real-time interference detection and mitigation. Here we show that automated interference detection coupled with a strategic reset protocol significantly enhances the reliability of quantum teleportation under realistic noise conditions. Our system incorporates a novel feedback mechanism that continuously monitors quantum state fidelity and triggers resets when interference is detected, improving both the success rate and robustness of the teleportation process. In 20 experimental trials, our approach achieved an interference detection rate of 65\% and required an average of 3.4 resets per successful teleportation, resulting in a maintained fidelity of 0.92, well above classical limits. This reset mechanism reduced the occurrence of failed transmissions by 40\% compared to non-reset trials, demonstrating its essential role in sustaining high fidelity. These findings establish a practical framework for robust quantum communication in noisy environments, advancing the field toward reliable quantum networks suitable for real-world applications., Comment: 13 pages, 3 figures, 1 table

Superconducting Digital (SCD) technology offers significant potential for enhancing the performance of next generation large scale compute workloads. By leveraging advanced lithography and a 300 mm platform, SCD devices can reduce energy consumption and boost computational power. This paper presents a cross-layer modeling approach to evaluate the system-level performance benefits of SCD architectures for Large Language Model (LLM) training and inference. Our findings, based on experimental data and Pulse Conserving Logic (PCL) design principles, demonstrate substantial performance gain in both training and inference. We are, thus, able to convincingly show that the SCD technology can address memory and interconnect limitations of present day solutions for next-generation compute systems., Comment: 8 figures

In this study, an odour-mediated system is developed and studied. In an odor-mediated systems, the sense of smell or odour of species plays a critical role in the interactions between predators and prey. It is widely recognised in scientific literature that these systems are very common and essential across natural ecosystems. These systems are crucial for various behaviors, including foraging, mating, and avoiding predators. In this paper, it is assumed that the presence of prey odour aids the predator in its hunting efforts. It is assumed that both prey and intermediate predators seek refuge against their respective predators upon detecting the odour of their predators. In other words, the odour of predators assists prey species in evaluating the danger and seeking refuge for hiding. This model incorporates the prey species' harvesting as well. We also explore the impact of fading memory on the system by incorporating fractional derivatives into the model. The conditions for both the existence and local stability of the non-negative equilibria are derived. The current model system undergoes both Hopf and transcritical bifurcation when the parameter values are appropriately chosen. The dynamic behaviour of the system is showcased and thoroughly analysed using a range of diagrams, highlighting the the impact of prey refuge and predator odour parameters. This paper extensively examines the long-term impacts of harvesting within the system. The presence of prey odour plays a significant role in promoting a long-term cohabitation dynamic within this specific system. It has been observed that when individuals within the system have a strong memory, it positively affects the stability of the system. Numerical simulations are conducted in order to demonstrate and validate the usefulness of the model being considered, therefore supporting the analytical conclusions.

The incorporation of quantum ansatz with machine learning classification models demonstrates the ability to extract patterns from data for classification tasks. However, taking advantage of the enhanced computational power of quantum machine learning necessitates dealing with various constraints. In this paper, we focus on constraints like finding suitable datasets where quantum advantage is achievable and evaluating the relevance of features chosen by classical and quantum methods. Additionally, we compare quantum and classical approaches using benchmarks and estimate the computational complexity of quantum circuits to assess real-world usability. For our experimental validation, we selected the gene expression dataset, given the critical role of genetic variations in regulating physiological behavior and disease susceptibility. Through this study, we aim to contribute to the advancement of quantum machine learning methodologies, offering valuable insights into their potential for addressing complex classification challenges in various domains., Comment: 5 pages

Recently a one-dimensional Schwarzian type theory was proposed as an effective dual theory of pure gravity in (2+1) dimensional asymptotically flat spacetimes \cite{Bhattacharjee:2023sfd}. This codimension-two `celestial' dual captures the Bekenstein-Hawking entropy of bulk flat cosmologies in semiclassical limit. In this paper, we extend this analysis beyond semiclassical approximation and evaluate the one-loop exact partition function of this celestial dual theory. Our analysis results in novel nontrivial logarithmic corrections to the area term of entropy, appearing from the one-loop path integral., Comment: 19 pages, minor changes and references added

The interaction between macroscopic fluid flow and nanoscale forces has resulted in the formation of long-range assemblies through evaporation-induced self-assembly. Anisotropic gold nanorods (AuNR) can form disordered, smectic, or vertically ordered long-range structures, but controlling their assembly remains a challenge and requires a deeper understanding of fundamental interaction mechanisms. In this work, we established a correlation between the $in~situ$ drying profiles, measured using an optical tensiometer, and deposit pattern, imaged $ex~situ$ using electron microscopy. Increasing particle concentration induced a transition from coffee-ring to uniform deposition at the microscale, while nanoscale structures shifted from isotropic/smectic to vertically aligned crystalline AuNRs. The interplay of capillary and Marangoni flow influences assembly at both macro and nanoscales, with the deposition process and nanoparticle ordering being highly sensitive to interparticle and nanoparticle-substrate interactions. By systematically studying key parameters, we aim to develop a comprehensive framework for the rational design and fabrication of nanomaterials with precisely controlled structure and properties., Comment: 35 pages including SI, 13 figures including SI, corresponding author: Sunita Srivastava (sunita.srivastava@iitb.ac.in)

In this paper we investigate the code properties of holographic fractal geometries initiated in \cite{Pastawski:2016qrs}. We study reconstruction wedges in $AdS_3/CFT_2$ for black hole backgrounds, which are in qualitative agreement with the vacuum-AdS approximation using generalized entanglement entropy in \cite{Bao:2022tgv}. In higher dimensions, we study reconstruction wedges for the infinite, straight strip in $AdS_{d+1}/CFT_{d}$ and clarify the roles of `straight' and `infinite' in their code properties. Lastly, we comment on uberholography from the perspective of complexity transfer and one-shot holography., Comment: 11 pages, 11 figures

We study operators that are products of two orthogonal projections. Our results complement some of the classical results of Crimmins and von Neumann. Particular emphasis has been given to projections associated with inner functions defined on the polydisc., Comment: 21 pages

In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2024, organized alongside the CVPR 2024 in Seattle, Washington, United States. WiCV aims to amplify the voices of underrepresented women in the computer vision community, fostering increased visibility in both academia and industry. We believe that such events play a vital role in addressing gender imbalances within the field. The annual WiCV@CVPR workshop offers a)~opportunity for collaboration between researchers from minority groups, b) mentorship for female junior researchers, c) financial support to presenters to alleviate financial burdens and d)~a diverse array of role models who can inspire younger researchers at the outset of their careers. In this paper, we present a comprehensive report on the workshop program, historical trends from the past WiCV@CVPR events, and a summary of statistics related to presenters, attendees, and sponsorship for the WiCV 2024 workshop., Comment: arXiv admin note: substantial text overlap with arXiv:2309.12768

The magnetised near detector (ND280) of the T2K long-baseline neutrino oscillation experiment has been recently upgraded aiming to satisfy the requirement of reducing the systematic uncertainty from measuring the neutrinonucleus interaction cross section, which is the largest systematic uncertainty in the search for leptonic charge-parity symmetry violation. A key component of the upgrade is SuperFGD, a 3D segmented plastic scintillator detector made of approximately 2,000,000 optically-isolated 1 cm3 cubes. It will provide a 3D image of GeV neutrino interactions by combining tracking and stopping power measurements of final state particles with sub-nanosecond time resolution. The performance of SuperFGD is characterized by the precision of its response to charged particles as well as the systematic effects that might affect the physics measurements. Hence, a detailed Geant4 based optical simulation of the SuperFGD building block, i.e. a plastic scintillating cube read out by three wavelength shifting fibers, has been developed and validated with the different datasets collected in various beam tests. In this manuscript the description of the optical model as well as the comparison with data are reported., Comment: 31 pages, 15 figures

We introduce QuARC, Quantum Adaptive Routing using Clusters, a novel clustering-based entanglement routing protocol that leverages redundant, multi-path routing through multi-particle projective quantum measurements to enable high-throughput, low-overhead, starvation-free entanglement distribution. At its core, QuARC periodically reconfigures the underlying quantum network into clusters of different sizes, where each cluster acts as a small network that distributes entanglement across itself, and the end-to-end entanglement is established by further distributing between clusters. QuARC does not require a-priori knowledge of any physical parameters, and is able to adapt the network configuration using static topology information, and using local (within-cluster) measurements only. We present a comprehensive simulation-based evaluation that shows QuARC is robust against changes to physical network parameters, and maintains high throughput without starvation even as network sizes scale and physical parameters degrade., Comment: 12 pages, 7 figures

We study algorithms for approximating the spectral density of a symmetric matrix $A$ that is accessed through matrix-vector product queries. By combining a previously studied Chebyshev polynomial moment matching method with a deflation step that approximately projects off the largest magnitude eigendirections of $A$ before estimating the spectral density, we give an $\epsilon\cdot\sigma_\ell(A)$ error approximation to the spectral density in the Wasserstein-$1$ metric using $O(\ell\log n+ 1/\epsilon)$ matrix-vector products, where $\sigma_\ell(A)$ is the $\ell^{th}$ largest singular value of $A$. In the common case when $A$ exhibits fast singular value decay, our bound can be much stronger than prior work, which gives an error bound of $\epsilon \cdot ||A||_2$ using $O(1/\epsilon)$ matrix-vector products. We also show that it is nearly tight: any algorithm giving error $\epsilon \cdot \sigma_\ell(A)$ must use $\Omega(\ell+1/\epsilon)$ matrix-vector products. We further show that the popular Stochastic Lanczos Quadrature (SLQ) method matches the above bound, even though SLQ itself is parameter-free and performs no explicit deflation. This bound explains the strong practical performance of SLQ, and motivates a simple variant of SLQ that achieves an even tighter error bound. Our error bound for SLQ leverages an analysis that views it as an implicit polynomial moment matching method, along with recent results on low-rank approximation with single-vector Krylov methods. We use these results to show that the method can perform implicit deflation as part of moment matching., Comment: 78 pages, 1 figure

Recent advancements in Large Language Models (LLMs) have renewed interest in automatic programming language translation. Encoder-decoder transformer models, in particular, have shown promise in translating between different programming languages. However, translating between a language and its high-performance computing (HPC) extensions remains underexplored due to challenges such as complex parallel semantics. In this paper, we introduce CodeRosetta, an encoder-decoder transformer model designed specifically for translating between programming languages and their HPC extensions. CodeRosetta is evaluated on C++ to CUDA and Fortran to C++ translation tasks. It uses a customized learning framework with tailored pretraining and training objectives to effectively capture both code semantics and parallel structural nuances, enabling bidirectional translation. Our results show that CodeRosetta outperforms state-of-the-art baselines in C++ to CUDA translation by 2.9 BLEU and 1.72 CodeBLEU points while improving compilation accuracy by 6.05%. Compared to general closed-source LLMs, our method improves C++ to CUDA translation by 22.08 BLEU and 14.39 CodeBLEU, with 2.75% higher compilation accuracy. Finally, CodeRosetta exhibits proficiency in Fortran to parallel C++ translation, marking it, to our knowledge, as the first encoder-decoder model for this complex task, improving CodeBLEU by at least 4.63 points compared to closed-source and open-code LLMs.

Communication traits in text-based human-AI conversations play pivotal roles in shaping user experiences and perceptions of systems. With the advancement of large language models (LLMs), it is now feasible to analyze these traits at a more granular level. In this study, we explore the preferences of information workers regarding chatbot communication traits across seven applications. Participants were invited to participate in an interactive survey, which featured adjustable sliders, allowing them to adjust and express their preferences for five key communication traits: formality, personification, empathy, sociability, and humor. Our findings reveal distinct communication preferences across different applications; for instance, there was a preference for relatively high empathy in wellbeing contexts and relatively low personification in coding. Similarities in preferences were also noted between applications such as chatbots for customer service and scheduling. These insights offer crucial design guidelines for future chatbots, emphasizing the need for nuanced trait adjustments for each application.

In the evolving landscape of deep learning, there is a pressing need for more comprehensive datasets capable of training models across multiple modalities. Concurrently, in digital humanities, there is a growing demand to leverage technology for diverse media adaptation and creation, yet limited by sparse datasets due to copyright and stylistic constraints. Addressing this gap, our paper presents a novel dataset comprising Franco-Belgian comics from the 1950s annotated for tasks including depth estimation, semantic segmentation, saliency detection, and character identification. It consists of two distinct and consistent styles and incorporates object concepts and labels taken from natural images. By including such diverse information across styles, this dataset not only holds promise for computational creativity but also offers avenues for the digitization of art and storytelling innovation. This dataset is a crucial component of the AI4VA Workshop Challenges~\url{https://sites.google.com/view/ai4vaeccv2024}, where we specifically explore depth and saliency. Dataset details at \url{https://github.com/IVRL/AI4VA}., Comment: ECCV 2024 Workshop Proceedings