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Scent marks play a crucial role in both territorial and sexual communication in many species. We investigated how free-ranging dogs respond to scent marks from individuals of different identities in terms of sex and group, across varying strategic locations within their territory. Both male and female dogs showed heightened interest in scent marks compared to control, exhibiting stronger territorial responses,. with males being more territorial than females. Overmarking behaviour was predominantly observed in males, particularly in response to male scent marks and those from neighbouring groups. Behavioural cluster analysis revealed distinct responses to different scent marks, with neighbouring group male scents eliciting the most distinct reactions. Our findings highlight the multifaceted role of scent marks in free-ranging dog communication, mediating both territorial defence and intrasexual competition. The differential responses based on the identity and gender of the scent-marker emphasize the complexity of olfactory signalling in this species. This study contributes to understanding the social behaviour of dogs in their natural habitat, and opens up possibilities for future explorations in the role of olfactory cues in the social dynamics of the species.

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

Large-scale data is crucial for learning realistic and capable driving policies. However, it can be impractical to rely on scaling datasets with real data alone. The majority of driving data is uninteresting, and deliberately collecting new long-tail scenarios is expensive and unsafe. We propose asymmetric self-play to scale beyond real data with additional challenging, solvable, and realistic synthetic scenarios. Our approach pairs a teacher that learns to generate scenarios it can solve but the student cannot, with a student that learns to solve them. When applied to traffic simulation, we learn realistic policies with significantly fewer collisions in both nominal and long-tail scenarios. Our policies further zero-shot transfer to generate training data for end-to-end autonomy, significantly outperforming state-of-the-art adversarial approaches, or using real data alone. For more information, visit https://waabi.ai/selfplay ., Comment: ECCV 2024

Despite advancements in Neural Implicit models for 3D surface reconstruction, handling dynamic environments with arbitrary rigid, non-rigid, or deformable entities remains challenging. Many template-based methods are entity-specific, focusing on humans, while generic reconstruction methods adaptable to such dynamic scenes often require additional inputs like depth or optical flow or rely on pre-trained image features for reasonable outcomes. These methods typically use latent codes to capture frame-by-frame deformations. In contrast, some template-free methods bypass these requirements and adopt traditional LBS (Linear Blend Skinning) weights for a detailed representation of deformable object motions, although they involve complex optimizations leading to lengthy training times. To this end, as a remedy, this paper introduces TFS-NeRF, a template-free 3D semantic NeRF for dynamic scenes captured from sparse or single-view RGB videos, featuring interactions among various entities and more time-efficient than other LBS-based approaches. Our framework uses an Invertible Neural Network (INN) for LBS prediction, simplifying the training process. By disentangling the motions of multiple entities and optimizing per-entity skinning weights, our method efficiently generates accurate, semantically separable geometries. Extensive experiments demonstrate that our approach produces high-quality reconstructions of both deformable and non-deformable objects in complex interactions, with improved training efficiency compared to existing methods., Comment: Accepted in NeuRIPS 2024

Sequentially interacting with articulated objects is crucial for a mobile manipulator to operate effectively in everyday environments. To enable long-horizon tasks involving articulated objects, this study explores building scene-level articulation models for indoor scenes through autonomous exploration. While previous research has studied mobile manipulation with articulated objects by considering object kinematic constraints, it primarily focuses on individual-object scenarios and lacks extension to a scene-level context for task-level planning. To manipulate multiple object parts sequentially, the robot needs to reason about the resultant motion of each part and anticipate its impact on future actions.We introduce \ourtool{}, a full-stack approach for long-horizon manipulation tasks with articulated objects. The robot maps the scene, detects and physically interacts with articulated objects, collects observations, and infers the articulation properties. For sequential tasks, the robot plans a feasible series of object interactions based on the inferred articulation model. We demonstrate that our approach repeatably constructs accurate scene-level kinematic and geometric models, enabling long-horizon mobile manipulation in a real-world scene. Code and additional results are available at https://chengchunhsu.github.io/KinScene/

A measurement of the double-spin asymmetry from electron-$^{3}$He scattering in the threshold region of two- and three-body breakup of $^{3}$He was performed at Jefferson Lab, for Q$^{2}$ values of 0.1 and 0.2 (GeV/$c$)$^{2}$. The results of this measurement serve as a stringent test of our understanding of few-body systems. When compared with calculations from plane wave impulse approximation and Faddeev theory, we found that the Faddeev calculations, which use modern nuclear potentials and prescriptions for meson-exchange currents, demonstrate an overall good agreement with data.

We perform the first search for $C\!P$ violation in ${D_{(s)}^{+}\to{}K_{S}^{0}K^{-}\pi^{+}\pi^{+}}$ decays. We use a combined data set from the Belle and Belle II experiments, which study $e^+e^-$ collisions at center-of-mass energies at or near the $\Upsilon(4S)$ resonance. We use 980 fb$^{-1}$ of data from Belle and 428 fb$^{-1}$ of data from Belle~II. We measure six $C\!P$-violating asymmetries that are based on triple products and quadruple products of the momenta of final-state particles, and also the particles' helicity angles. We obtain a precision at the level of 0.5% for $D^+\to{}K_{S}^{0}K^{-}\pi^{+}\pi^{+}$ decays, and better than 0.3% for $D^+_{s}\to{}K_{S}^{0}K^{-}\pi^{+}\pi^{+}$ decays. No evidence of $C\!P$ violation is found. Our results for the triple-product asymmetries are the most precise to date for singly-Cabibbo-suppressed $D^+$ decays. Our results for the other asymmetries are the first such measurements performed for charm decays., Comment: 21 pages, 10 figures

Nucleon structure functions, as measured in lepton-nucleon scattering, have historically provided a critical observable in the study of partonic dynamics within the nucleon. However, at very large parton momenta it is both experimentally and theoretically challenging to extract parton distributions due to the probable onset of non-perturbative contributions and the unavailability of high precision data at critical kinematics. Extraction of the neutron structure and the d-quark distribution have been further challenging due to the necessity of applying nuclear corrections when utilizing scattering data from a deuteron target to extract free neutron structure. However, a program of experiments has been carried out recently at the energy-upgraded Jefferson Lab electron accelerator aimed at significantly reducing the nuclear correction uncertainties on the d-quark distribution function at large partonic momentum. This allows leveraging the vast body of deuterium data covering a large kinematic range to be utilized for d-quark parton distribution function extraction. We present new data from experiment E12-10-002 carried out in Jefferson Lab Hall C on the deuteron to proton cross-section ratio at large BJorken-x. These results significantly improve the precision of existing data, and provide a first look at the expected impact on quark distributions extracted from global parton distribution function fits.

High-entropy alloys (HEAs) are metallic materials with solid solutions stabilized by high mixing entropy. Some exhibit excellent strength, often accompanied by additional properties such as magnetic, invar, corrosion, or cryogenic response. This has spurred efforts to discover new HEAs, but the vast compositional search space has made these efforts challenging. Here we present a framework to predict and optimize the yield strength of face-centered cubic (FCC) HEAs, using CoCrFeMnNi-based alloys as a case study due to abundant available data. Our novel Residual Hybrid Learning Model (RELM) integrates Random Forest and Gradient Boosting, enhanced by material attribute data, to handle sparse, skewed datasets for real-world alloys. A hybrid Generative Adversarial Network-Variational Autoencoder model explores new alloy compositions beyond existing datasets. By incorporating processing parameters, which determine the microstructure and thus strength, RELM achieves an R$^2$ score of 0.915, surpassing traditional models. SHapley Additive Explanations (SHAP) and Partial Dependencies enhance interpretability, revealing composition-processing-property relationships, as validated by experiments, including X-ray diffraction, SEM analysis, and tensile testing. The model discovered two novel Co$_{20}$Cr$_{16}$Fe$_{20}$Mn$_{16}$Ni$_{24}$Al$_4$ and Co$_{24}$Cr$_{12}$Fe$_{12}$Mn$_{16}$Ni$_{28}$Al$_4$Si$_4$ HEAs with a maximum possible yield strength of 842 and 937 MPa, significantly exceeding previously reported values for these alloy systems. This study pioneers interpretable machine learning in alloy design, providing a rigorous, data-driven approach to discovering, processing, and optimizing real-world materials. The findings highlight the critical role of both compositional and post-fabrication processing parameters in advancing the understanding of composition-processing-property relationships.

Navigating and understanding complex environments over extended periods of time is a significant challenge for robots. People interacting with the robot may want to ask questions like where something happened, when it occurred, or how long ago it took place, which would require the robot to reason over a long history of their deployment. To address this problem, we introduce a Retrieval-augmented Memory for Embodied Robots, or ReMEmbR, a system designed for long-horizon video question answering for robot navigation. To evaluate ReMEmbR, we introduce the NaVQA dataset where we annotate spatial, temporal, and descriptive questions to long-horizon robot navigation videos. ReMEmbR employs a structured approach involving a memory building and a querying phase, leveraging temporal information, spatial information, and images to efficiently handle continuously growing robot histories. Our experiments demonstrate that ReMEmbR outperforms LLM and VLM baselines, allowing ReMEmbR to achieve effective long-horizon reasoning with low latency. Additionally, we deploy ReMEmbR on a robot and show that our approach can handle diverse queries. The dataset, code, videos, and other material can be found at the following link: https://nvidia-ai-iot.github.io/remembr

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-box and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into a pre-acquired Piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and a piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiment, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research., Comment: 25 pages, 7 figures in main text, 2 figures in Supp Mat

Coherent states, being the closest analog to classical states of wave systems, are well known to possess special properties that set them apart from most other quantum optical states. For example, they are robust against photon loss and do not easily get entangled upon interaction with a beamsplitter, and hence are called ``pointer states'', which is often attributed to them being eigenstates of the annihilation operator. Here we provide insights into a topological argument for their robustness using two separate but exact mappings of a prototypical quantum optics model - the driven Jaynes-Cummings model. The first mapping is based on bosonization and refermionization of the Jaynes-Cummings model into the fermionic Su-Schrieffer-Heeger model hosting zero-energy topologically protected edge states. The second mapping is based on the algebra of deformed f-oscillators. We choose these mappings to explicitly preserve the translational symmetry of the model along a Fock-state ladder basis, which is important for maintaining the symmetry-protected topology of such 1D lattices. In addition, we show that the edge state form is preserved even when certain chiral symmetry is broken, corresponding to a single-photon drive for the quantum optics model that preserves the coherent state; however, the addition of two-photon drive immediately disturbs the edge state form, as confirmed by numerical simulations of the mapped SSH model; this is expected since two-photon drive strongly perturbs the coherent state into a squeezed state. Our theory sheds light on a fundamental reason for the robustness of coherent states, both in existence and entanglement -- an underlying connection to topology.

Nowadays, transit timing variations (TTVs) are proving to be a very valuable tool in exoplanetary science to detect exoplanets by observing variations in transit times. To study the transit timing variation of the hot Jupiter, TrES-2b, we have combined 64 high-quality transit light curves from all seven sectors of NASA's Transiting Exoplanet Survey Satellite (TESS) along with 60 best-quality light curves from the ground-based facility Exoplanet Transit Database (ETD) and 106 mid-transit times from the previous works. From the precise transit timing analysis, we have observed a significant improvement in the orbital ephemerides, but we did not detect any short period TTVs that might result from an additional body. The inability to detect short-term TTVs further motivates us to investigate long-term TTVs, which might be caused by orbital decay, apsidal precession, Applegate mechanism, and $R{\phi}$mer effect and the orbital decay appeared to be a better explanation for the observed TTV with $\Delta BIC$ = 4.32. The orbital period of the hot Jupiter TrES-2b appears to be shrinking at a rate of $-5.58 \pm 1.81$ ms/yr. Assuming this decay is primarily caused by tidal dissipation within the host star, we have subsequently calculated the stellar tidal quality factor value to be 9900, which is 2 to 3 orders of magnitude smaller than the theoretically predicted values for other hot-Jupiter systems and its low value indicates more efficient tidal dissipation within the host star. Additional precise photometric and radial velocity observations are required to pinpoint the cause of the change in the orbital period., Comment: 38 pages, accepted by AJ on 2nd August

We investigate the potential of epitaxial (001)p-NiO/(0001)n-ZnO heterostructures grown on (0001)sapphire substrates by pulsed laser deposition technique for ultraviolet photodetector application. Our study reveals that in the self-powered mode, these devices can serve as effective photodetectors for the UV-A band (320-400 nm) with response time as short as 400 microseconds. Peak responsivity as high as 5mA/W at zero bias condition have been achieved. These devices also show a very high level of stability under repeated on/off illumination cycles over a long period of time. Furthermore, we find that the response time of these detectors can be controlled from several microseconds to thousands of seconds by applying bias both in the forward and the reverse directions. This persistent photoconductivity effect has been explained in terms of the field induced change in the capture barrier height associated with certain traps located at the junction., Comment: 10 Pages

Let the complex reflection group $G(m,p,n)$ act on the unit polydisc $\mathbb D^n$ in $\mathbb C^n.$ A $\boldsymbol\Theta_n$-contraction is a commuting tuple of operators on a Hilbert space having $$\overline{\boldsymbol\Theta}_n:=\{\boldsymbol\theta(z)=(\theta_1(z),\ldots,\theta_n(z)):z\in\overline{\mathbb D}^n\}$$ as a spectral set, where $\{\theta_i\}_{i=1}^n$ is a homogeneous system of parameters associated to $G(m,p,n).$ A plethora of examples of $\boldsymbol\Theta_n$-contractions is exhibited. Under a mild hypothesis, it is shown that these $\boldsymbol\Theta_n$-contractions are mutually unitarily inequivalent. These inequivalence results are obtained concretely for the weighted Bergman modules under the action of the permutation groups and the dihedral groups. The division problem is shown to have negative answers for the Hardy module and the Bergman module on the bidisc. A Beurling-Lax-Halmos type representation for the invariant subspaces of $\boldsymbol\Theta_n$-isometries is obtained., Comment: 23 pages. arXiv admin note: text overlap with arXiv:1301.2837

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

Continuous wavefront sensing benefits space observatories in on-orbit optical performance maintenance. To measure the phase of a wavefront, phase retrieval is an attractive technique as it uses multiple point spread function (PSF) images that are acquired by the telescope itself without extra metrology systems nor complicated calibration. The focus diverse phase retrieval utilizes PSFs from predetermined defocused positions to enhance the dynamic range of the algorithm. We describe an updated visible light active optics testbed with the addition of a linear motorized focus stage. The performance of the phase retrieval algorithm in broadband is tested under various cases. While broadband pass filters have advantages in higher signal-to-noise ratio (SNR), the performance of phase retrieval can be restricted due to blurred image caused by diffraction and increased computing cost. We used multiple bandpass filters (10 nm, 88 nm, and 150 nm) and investigated effects of bandwidth on the accuracy and required image acquisition conditions such as SNR, reaching accuracies below 20 nm RMS wavefront error at the widest bandwidth. We also investigated the dynamic range of the phase retrieval algorithm depending on the bandwidth and required amount of defocus to expand dynamic range. Finally, we simulated the continuous wavefront sensing and correction loop with a range of statistically generated representative telescope disturbance time series to test for edge cases.

We present a comprehensive study on enhancing the thermoelectric (TE) performance of bilayer graphene (BLG) through irradiation with arbitrarily polarized light, focusing on $AA$- and $AB$-stacked configurations with zigzag edges. Utilizing a combination of tight-binding theory and density functional theory (DFT), we systematically analyze the impact of light irradiation on electronic and phononic transport properties. Light irradiation alters the electronic hopping parameters, creating an asymmetric transmission function, which significantly increases the Seebeck coefficient, thereby boosting the overall {\it figure of merit} (FOM). For the phononic contribution, DFT calculations reveal that $AB$-stacked BLG exhibits lower lattice thermal conductivity compared to $AA$-stacked, attributed to enhanced anharmonic scattering and phonon group velocity. The combined analysis shows that FOM exceeds unity in both stacking types, with notable improvements near the irradiation-induced gap. Additionally, we explore the dependence of FOM on the system dimensions and temperature, demonstrating that light-irradiated BLG holds great promise for efficient thermoelectric energy conversion and waste heat recovery. Our results show favorable responses over a wide range of irradiation parameters. These findings provide crucial insights into optimizing BLG for advanced TE applications through light-induced modifications., Comment: 12 pages, 12 figures, and supporting material. Comments are welcome

Handling haphazard streaming data, such as data from edge devices, presents a challenging problem. Over time, the incoming data becomes inconsistent, with missing, faulty, or new inputs reappearing. Therefore, it requires models that are reliable. Recent methods to solve this problem depend on a hedging-based solution and require specialized elements like auxiliary dropouts, forked architectures, and intricate network design. We observed that hedging can be reduced to a special case of weighted residual connection; this motivated us to approximate it with plain self-attention. In this work, we propose HapNet, a simple baseline that is scalable, does not require online backpropagation, and is adaptable to varying input types. All present methods are restricted to scaling with a fixed window; however, we introduce a more complex problem of scaling with a variable window where the data becomes positionally uncorrelated, and cannot be addressed by present methods. We demonstrate that a variant of the proposed approach can work even for this complex scenario. We extensively evaluated the proposed approach on five benchmarks and found competitive performance.

We give a conjectural formula for the characteristic number of rational cuspidal curves in the projective plane by extending the idea of Kontsevich's recursion formula (namely, pulling back the equality of two divisors in the four pointed moduli space). The key geometric input that is needed here is that in the closure of rational cuspidal curves, there are two component rational curves which are tangent to each other at the nodal point. While this fact is geometrically quite believable, we haven't as yet proved it; hence our formula is for the moment conjectural. The answers that we obtain agree with what has been computed earlier Ran, Pandharipande, Zinger and Ernstrom and Kennedy. We extend this technique (modulo another conjecture) to obtain the characteristic number of rational quartics with an E6 singularity., Comment: 19 pages, 8 figures. Comments are welcome

This paper introduces a novel reference-free (RF) audio quality metric called the RF-Generative Machine Listener (RF-GML), designed to evaluate coded mono, stereo, and binaural audio at a 48 kHz sample rate. RF-GML leverages transfer learning from a state-of-the-art full-reference (FR) Generative Machine Listener (GML) with minimal architectural modifications. The term "generative" refers to the model's ability to generate an arbitrary number of simulated listening scores. Unlike existing RF models, RF-GML accurately predicts subjective quality scores across diverse content types and codecs. Extensive evaluations demonstrate its superiority in rating unencoded audio and distinguishing different levels of coding artifacts. RF-GML's performance and versatility make it a valuable tool for coded audio quality assessment and monitoring in various applications, all without the need for a reference signal., Comment: Pre-review version submitted to ICASSP 2025

Among the popular TE materials, selenides and tellurides are the benchmarks of high-efficiency systems. However, for the high-temperature application (>700 K), it is required to rely on the silicides and the oxides due to their exceptional thermal stability. ZnO is among the first few oxides in the field of thermoelectricity. Al-doped ZnO is a proven material for its high-temperature thermoelectric applications. However, the high grain boundary resistance limits further improvement of the efficiency of this oxide. Band-engineering, band-modification is a successful approach in lowering the grain boundary resistance. The addition of graphite and graphite-based materials at the grain boundaries is shown to serve this purpose. In this work, graphite powder is added in varying proportions to Al-doped ZnO triangular microcrystals. Thus, prepared materials are characterized to confirm the formation and investigate the nature of interface, morphology, etc. TE parameters such as electrical conductivity, Seebeck coefficient, and thermal conductivity of those materials also have been measured. The theoretical calculation of TE efficiency zT often differs from the actual experimental results due to the wide range of preparation methods, leading to changes in porosity, the nature and density defects, and several other factors. In this paper, an effort has been made to estimate the maximum achievable power factor (PFmax) from the measured TE parameters of this set of samples by the Jonker and Ioffe analysis. Based on the predicted PFmax, an appropriate material composition has been identified to achieve that same. Subsequently, including the measured parameters the TE efficiency (zT) is calculated. Further, a sudden dip observed in the thermal conductivity at the high-temperature range (625 K - 1000 K) of the prepared undoped ZnO graphite composite is investigated in this paper.

Construction of quantum analogs starting from classical stochastic processes have been previously introduced. In this paper, we generate a large class of self contained quantum extensions by symmetry operations. We show that the relaxation processes for different quantum extensions are different and that is supported by the measure of coherence, the the probability of reaching the equilibrium, decay of the domain walls and purity. However, the coherence measure based on the L1-norm does not capture the speed of the relaxation process. We also show that the finite size scaling of coherence exists for both short and long times., Comment: 7 pages, 8 figures : This is the first draft and will be edited further before peer review

In many temporal datasets, the parameters of the underlying distribution may change abruptly at unknown times. Detecting these changepoints is crucial for numerous applications. While this problem has been extensively studied for linear data, there has been remarkably less research on bivariate angular data. For the first time, we address the changepoint problem for the mean direction of toroidal and spherical data, which are types of bivariate angular data. By leveraging the intrinsic geometry of a curved torus, we introduce the concept of the ``square'' of an angle. This leads us to define the ``curved dispersion matrix'' for bivariate angular random variables, analogous to the dispersion matrix for bivariate linear random variables. Using this analogous measure of the ``Mahalanobis distance,'' we develop two new non-parametric tests to identify changes in the mean direction parameters for toroidal and spherical distributions. We derive the limiting distributions of the test statistics and evaluate their power surface and contours through extensive simulations. We also apply the proposed methods to detect changes in mean direction for hourly wind-wave direction measurements and the path of the cyclonic storm ``Biporjoy,'' which occurred between 6th and 19th June 2023 over the Arabian Sea, western coast of India., Comment: arXiv admin note: text overlap with arXiv:2403.00508

In the past decade, social media platforms have been used for information dissemination and consumption. While a major portion of the content is posted to promote citizen journalism and public awareness, some content is posted to mislead users. Among different content types such as text, images, and videos, memes (text overlaid on images) are particularly prevalent and can serve as powerful vehicles for propaganda, hate, and humor. In the current literature, there have been efforts to individually detect such content in memes. However, the study of their intersection is very limited. In this study, we explore the intersection between propaganda and hate in memes using a multi-agent LLM-based approach. We extend the propagandistic meme dataset with coarse and fine-grained hate labels. Our finding suggests that there is an association between propaganda and hate in memes. We provide detailed experimental results that can serve as a baseline for future studies. We will make the experimental resources publicly available to the community., Comment: propaganda, hate-speech, disinformation, misinformation, fake news, LLMs, GPT-4, multimodality, multimodal LLMs

We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algorithms based on a suite of metrics. The package, named Blending Toolkit (BTK), serves as a modular, flexible, easy-to-install, and simple-to-use interface for exploring and analyzing systematic effects related to blended galaxies in cosmological surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST). BTK has three main components: (1) a set of modules that perform fast image simulations of blended galaxies, using the open source image simulation package GalSim; (2) a module that standardizes the inputs and outputs of existing deblending algorithms; (3) a library of deblending metrics commonly defined in the galaxy deblending literature. In combination, these modules allow researchers to explore the impacts of galaxy blending in cosmological surveys. Additionally, BTK provides researchers who are developing a new deblending algorithm a framework to evaluate algorithm performance and make principled comparisons with existing deblenders. BTK includes a suite of tutorials and comprehensive documentation. The source code is publicly available on GitHub at https://github.com/LSSTDESC/BlendingToolKit., Comment: 15 pages, 9 figures, 2 tables

Let $C$ be an irreducible smooth complex projective curve of genus $g$, with $g_C \geqslant 2$. Let $E$ be a vector bundle on $C$ of rank $r$, with $r\geqslant 2$. Let $\mc Q:=\mc Q(E,\,d)$ be the Quot Scheme parameterizing torsion quotients of $E$ of degree $d$. We explicitly describe all deformations of $\mc Q$., Comment: Final version; Revista Matem\'atica Complutense (to appear)

This paper introduces a dependent toroidal distribution, to analyze astigmatism data following cataract surgery. Rather than utilizing the flat torus, we opt to represent the bivariate angular data on the surface of a curved torus, which naturally offers smooth edge identifiability and accommodates a variety of curvatures: positive, negative, and zero. Beginning with the area-uniform toroidal distribution on this curved surface, we develop a five-parameter-dependent toroidal distribution that harnesses its intrinsic geometry via the area element to model the distribution of two dependent circular random variables. We show that both marginal distributions are Cardioid, with one of the conditional variables also following a Cardioid distribution. This key feature enables us to propose a circular-circular regression model based on conditional expectations derived from circular moments. To address the high rejection rate (approximately 50%) in existing acceptance-rejection sampling methods for Cardioid distributions, we introduce an exact sampling method based on a probabilistic transformation. Additionally, we generate random samples from the proposed dependent toroidal distribution through suitable conditioning. This bivariate distribution and the regression model are applied to analyze astigmatism data arising in the follow-up of one and three months due to cataract surgery., Comment: NA

Advanced Driver Assistance Systems (ADAS) alert drivers during safety-critical scenarios but often provide superfluous alerts due to a lack of consideration for drivers' knowledge or scene awareness. Modeling these aspects together in a data-driven way is challenging due to the scarcity of critical scenario data with in-cabin driver state and world state recorded together. We explore the benefits of driver modeling in the context of Forward Collision Warning (FCW) systems. Working with real-world video dataset of on-road FCW deployments, we collect observers' subjective validity rating of the deployed alerts. We also annotate participants' gaze-to-objects and extract 3D trajectories of the ego vehicle and other vehicles semi-automatically. We generate a risk estimate of the scene and the drivers' perception in a two step process: First, we model the movement of vehicles in a given scenario as a joint trajectory forecasting problem. Then, we reason about the drivers' risk perception of the scene by counterfactually modifying the input to the forecasting model to represent the drivers' actual observations of vehicles in the scene. The difference in these behaviours gives us an estimate of driver behaviour that accounts for their actual (inattentive) observations and their downstream effect on overall scene risk. We compare both a learned scene representation as well as a more traditional ``worse-case'' deceleration model to achieve the future trajectory forecast. Our experiments show that using this risk formulation to generate FCW alerts may lead to improved false positive rate of FCWs and improved FCW timing.

We report new pion electroproduction measurements in the $\Delta(1232)$ resonance, utilizing the SHMS - HMS magnetic spectrometers of Hall C at Jefferson Lab. The data focus on a region that exhibits a strong and rapidly changing interplay of the mesonic cloud and quark-gluon dynamics in the nucleon. The results are in reasonable agreement with models that employ pion cloud effects and chiral effective field theory calculations, but at the same time they suggest that an improvement is required to the theoretical calculations and provide valuable input that will allow their refinements. The data illustrate the potential of the magnetic spectrometers setup in Hall C towards the study the $\Delta(1232)$ resonance. These first reported results will be followed by a series of measurements in Hall C, that will expand the studies of the $\Delta(1232)$ resonance offering a high precision insight within a wide kinematic range from low to high momentum transfers.

We study the fluctuations, particularly the inequality of fluctuations, in cryptocurrency prices over the last ten years. We calculate the inequality in the price fluctuations through different measures, such as the Gini and Kolkata indices, and also the $Q$ factor (given by the ratio between the highest value and the average value) of these fluctuations. We compare the results with the equivalent quantities in some of the more prominent national currencies and see that while the fluctuations (or inequalities in such fluctuations) for cryptocurrencies were initially significantly higher than national currencies, over time the fluctuation levels of cryptocurrencies tend towards the levels characteristic of national currencies. We also compare similar quantities for a few prominent stock prices., Comment: Invited contribution for Physica A Spl. Issue on "Crypto's Global Impact"

We study fermionic quantum spin liquids (QSLs) on the three-dimensonal trillium lattice of corner-sharing triangles. We are motivated by recent experimental and theoretical investigations that have explored various classical and quantum spin liquid states on similar networks of triangular motifs with strong geometric frustration. Using the framework of Projective Symmetry Groups (PSG), we obtain a classification of all symmetric $\mathsf{Z}_2$ and $\mathsf{U}(1)$ QSLs on the trillium lattice. We find 2 $\mathsf{Z}_2$ spin-liquids, and a single $\mathsf{U}(1)$ spin-liquid which is proximate to one of the $\mathsf{Z}_2$ states. The small number of solutions reflects the constraints imposed by the two non-symmorphic symmetries in the space group of trillium. Using self-consistency conditions of the mean-field equations, we obtain the spinon band-structure and spin structure factors corresponding to these states. All three of our spin liquids are gapless at their saddle points: the $\mathsf{Z}_2$ QSLs are both nodal, while the $\mathsf{U}(1)$ case hosting a spinon Fermi surface. One of our $\mathsf{Z}_2$ spin liquids hosts a stable gapless nodal star, that is protected by projective symmetries against additions of further neighbour terms in the mean field ansatz. We comment on directions for further work.

Although foundational vision-language models (VLMs) have proven to be very successful for various semantic discrimination tasks, they still struggle to perform faithfully for fine-grained categorization. Moreover, foundational models trained on one domain do not generalize well on a different domain without fine-tuning. We attribute these to the limitations of the VLM's semantic representations and attempt to improve their fine-grained visual awareness using generative modeling. Specifically, we propose two novel methods: Generative Class Prompt Learning (GCPL) and Contrastive Multi-class Prompt Learning (CoMPLe). Utilizing text-to-image diffusion models, GCPL significantly improves the visio-linguistic synergy in class embeddings by conditioning on few-shot exemplars with learnable class prompts. CoMPLe builds on this foundation by introducing a contrastive learning component that encourages inter-class separation during the generative optimization process. Our empirical results demonstrate that such a generative class prompt learning approach substantially outperform existing methods, offering a better alternative to few shot image recognition challenges. The source code will be made available at: https://github.com/soumitri2001/GCPL., Comment: Accepted in BMVC 2024

In this work we explore the application of AI to robotic welding. Robotic welding is a widely used technology in many industries, but robots currently do not have the capability to detect welding defects which get introduced due to various reasons in the welding process. We describe how deep-learning methods can be applied to detect weld defects in real-time by recording the welding process with microphones and a camera. Our findings are based on a large database with more than 4000 welding samples we collected which covers different weld types, materials and various defect categories. All deep learning models are trained in an unsupervised fashion because the space of possible defects is large and the defects in our data may contain biases. We demonstrate that a reliable real-time detection of most categories of weld defects is feasible both from audio and video, with improvements achieved by combining both modalities. Specifically, the multi-modal approach achieves an average Area-under-ROC-Curve (AUC) of 0.92 over all eleven defect types in our data. We conclude the paper with an analysis of the results by defect type and a discussion of future work., Comment: 21 pages

The critical phase boundary of a system, in general, can depend on one or more parameters. We show that by calculating the Gini index ($g$) of any suitably defined response function of a system, the critical phase boundary can always be reduced to that of a single parameter, starting from $g=0$ and terminating at $g=g_f$, where $g_f$ is a universal number for a given universality class. We demonstrate the construction with analytical and numerical calculations of mean field transverse field Ising model and site diluted Ising model on the Bethe lattice, respectively. Both models have two parameter phase boundaries -- transverse field and Temperature for the first case and site dilution and temperature in the second case. Both can be reduced to single parameter transition points in terms of the Gini index. The method is generally applicable for any multi-parameter critical transition., Comment: 5 pages, 4 figures

The fractal and phase transitional properties of each type of pions (i.e. $\pi^{\pm,0}$) through one-dimensional $\eta-$space, at an energy of $\sqrt{s}=13~$TeV, have been studied with the help of the Scaled Factorial Moment (SFM) framework. To generate simulated data sets for $pp$ collisions under the minimum bias (MB) condition at $\sqrt{s}=13~$TeV, we have employed the Monte Carlo-based event simulator PYTHIA. Various parameters such as the Levy index $(\mu)$, degree of multifractality $(r)$, anomalous fractal dimension $(d_q)$, multifractal specific heat $(c)$ and critical exponent $(\nu)$ have been calculated. To study the Bose Einstein(BE) effect due to identical particles (here pions) we have also derived these parameters for mixed pion pairs (i.e. $\{\pi^{+},\pi^{-}\}$, $\{\pi^{+},\pi^{0}\}$ and $\{\pi^{-},\pi^{0}\}$) and we find that the effects of identical particles weakened for the mixture with respect to the individual distributions. The quest for the quark-hadron phase transition has also been conducted within the framework of the Ginzburg-Landau (GL) theory of second-order phase transition. Analysis revealed that for PYTHIA-generated MB events, there is a clear indication of the quark-hadron phase transition according to the GL theory. Furthermore, the values of the multifractal specific heat ($c$) for each $\pi^{+}, \pi^{-}, \pi^{0}$ and the mixture pair data sets of pions generated by PYTHIA model at MB condition, indicate a transition from multifractality to monofractality in $pp$ collisions at $\sqrt{s}=13~$TeV., Comment: 19 pages, 10 Figures ( Total 18 Figures with sub-figures)

We have measured the flavor dependence of multiplicities for pi^+ and pi^- production in semi-inclusive deep-inelastic scattering (SIDIS) on proton and deuteron targets to explore a possible charge symmetry violation in fragmentation functions. The experiment used an electron beam with energies of 10.2 and 10.6 GeV at Jefferson Lab and the Hall-C spectrometers. The electron kinematics spanned the range 0.3

Due to the unprecedented depth of the upcoming ground-based Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory, approximately two-thirds of the galaxies are likely to be affected by blending - the overlap of physically separated galaxies in images. Thus, extracting reliable shapes and photometry from individual objects will be limited by our ability to correct blending and control any residual systematic effect. Deblending algorithms tackle this issue by reconstructing the isolated components from a blended scene, but the most commonly used algorithms often fail to model complex realistic galaxy morphologies. As part of an effort to address this major challenge, we present MADNESS, which takes a data-driven approach and combines pixel-level multi-band information to learn complex priors for obtaining the maximum a posteriori solution of deblending. MADNESS is based on deep neural network architectures such as variational auto-encoders and normalizing flows. The variational auto-encoder reduces the high-dimensional pixel space into a lower-dimensional space, while the normalizing flow models a data-driven prior in this latent space. Using a simulated test dataset with galaxy models for a 10-year LSST survey and a galaxy density ranging from 48 to 80 galaxies per arcmin2 we characterize the aperture-photometry g-r color, structural similarity index, and pixel cosine similarity of the galaxies reconstructed by MADNESS. We compare our results against state-of-the-art deblenders including scarlet. With the r-band of LSST as an example, we show that MADNESS performs better than in all the metrics. For instance, the average absolute value of relative flux residual in the r-band for MADNESS is approximately 29% lower than that of scarlet. The code is publicly available on GitHub., Comment: 20 pages, 19 figures, submitted to Astronomy & Astrophysics

In this work, we investigate the impact of the possibility of a small, subsolar mass compact star, such as the recently reported central compact object of HESS J1731-347, on the equation of state (EOS) of neutron stars. We have used a hybrid approach to the nuclear EOS developed recently where the matter around nuclear saturation density is described by a parametric expansion in terms of nuclear empirical parameters and represented in an agnostic way at higher density using piecewise polytropes. We have incorporated the inputs provided by the latest neutron skin measurement experiments from PREX-II and CREX, simultaneous mass-radius measurements of pulsars PSR J0030+0451 and PSR J0740+6620, and the gravitational wave events GW170817 and GW190425. The main results of the study show the effect of HESS J1731-347 on the nuclear parameters and neutron star observables. Our analysis yields the slope of symmetry energy $L=45.71^{+38.18}_{-22.11}$ MeV, the radius of a $1.4 M_\odot$ star, $R_{1.4}=12.18^{+0.71}_{-0.88}$ km, and the maximum mass of a static star, $M_{\rm max}= 2.14^{+0.26}_{-0.17} M_\odot$ within $90\%$ confidence interval, respectively.

With ongoing advancements in nuclear theory and experimentation, together with a growing body of neutron star (NS) observations, a wealth of information on the equation of state (EOS) for matter at extreme densities has become accessible. Here, we utilize a hybrid EOS formulation that combines an empirical parameterization centered around the nuclear saturation density with a generic three-segment piecewise polytrope model at higher densities. We incorporate data derived from chiral effective field theory ($\chi$EFT), perturbative quantum chromodynamics (pQCD), and from experiments such as PREX-II and CREX. Furthermore, we examine the influence of a total of 129 NS mass measurements up to April 2023, as well as simultaneous mass and radius measurements derived from the X-ray emission from surface hot spots on NSs. Additionally, we consider constraints on tidal properties inferred from the gravitational waves emitted by coalescing NS binaries. To integrate this extensive and varied array of constraints, we utilize a hierarchical Bayesian statistical framework to simultaneously deduce the EOS and the distribution of NS masses. We find that incorporating data from $\chi$EFT significantly tightens the constraints on the EOS of NSs near or below the nuclear saturation density. However, constraints derived from pQCD computations and nuclear experiments such as PREX-II and CREX have minimal impact., Comment: 19 pages, 8 figures

The proliferation of scene text in both structured and unstructured environments presents significant challenges in optical character recognition (OCR), necessitating more efficient and robust text spotting solutions. This paper presents FastTextSpotter, a framework that integrates a Swin Transformer visual backbone with a Transformer Encoder-Decoder architecture, enhanced by a novel, faster self-attention unit, SAC2, to improve processing speeds while maintaining accuracy. FastTextSpotter has been validated across multiple datasets, including ICDAR2015 for regular texts and CTW1500 and TotalText for arbitrary-shaped texts, benchmarking against current state-of-the-art models. Our results indicate that FastTextSpotter not only achieves superior accuracy in detecting and recognizing multilingual scene text (English and Vietnamese) but also improves model efficiency, thereby setting new benchmarks in the field. This study underscores the potential of advanced transformer architectures in improving the adaptability and speed of text spotting applications in diverse real-world settings. The dataset, code, and pre-trained models have been released in our Github., Comment: Accepted in ICPR 2024

This paper presents a memristor-based compute-in-memory hardware accelerator for on-chip training and inference, focusing on its accuracy and efficiency against device variations, conductance errors, and input noise. Utilizing realistic SPICE models of commercially available silver-based metal self-directed channel (M-SDC) memristors, the study incorporates inherent device non-idealities into the circuit simulations. The hardware, consisting of 30 memristors and 4 neurons, utilizes three different M-SDC structures with tungsten, chromium, and carbon media to perform binary image classification tasks. An on-chip training algorithm precisely tunes memristor conductance to achieve target weights. Results show that incorporating moderate noise (<15%) during training enhances robustness to device variations and noisy input data, achieving up to 97% accuracy despite conductance variations and input noises. The network tolerates a 10% conductance error without significant accuracy loss. Notably, omitting the initial memristor reset pulse during training considerably reduces training time and energy consumption. The hardware designed with chromium-based memristors exhibits superior performance, achieving a training time of 2.4 seconds and an energy consumption of 18.9 mJ. This research provides insights for developing robust and energy-efficient memristor-based neural networks for on-chip learning in edge applications.

A point cloud is a crucial geometric data structure utilized in numerous applications. The adoption of deep neural networks referred to as Point Cloud Neural Networks (PC- NNs), for processing 3D point clouds, has significantly advanced fields that rely on 3D geometric data to enhance the efficiency of tasks. Expanding the size of both neural network models and 3D point clouds introduces significant challenges in minimizing computational and memory requirements. This is essential for meeting the demanding requirements of real-world applications, which prioritize minimal energy consumption and low latency. Therefore, investigating redundancy in PCNNs is crucial yet challenging due to their sensitivity to parameters. Additionally, traditional pruning methods face difficulties as these networks rely heavily on weights and points. Nonetheless, our research reveals a promising phenomenon that could refine standard PCNN pruning techniques. Our findings suggest that preserving only the top p% of the highest magnitude weights is crucial for accuracy preservation. For example, pruning 99% of the weights from the PointNet model still results in accuracy close to the base level. Specifically, in the ModelNet40 dataset, where the base accuracy with the PointNet model was 87. 5%, preserving only 1% of the weights still achieves an accuracy of 86.8%. Codes are available in: https://github.com/apurba-nsu-rnd-lab/PCNN_Pruning, Comment: Accepted in BMVC 2024

The persistent issue of human bias in recruitment processes poses a formidable challenge to achieving equitable hiring practices, particularly when influenced by demographic characteristics such as gender and race of both interviewers and candidates. Asynchronous Video Interviews (AVIs), powered by Artificial Intelligence (AI), have emerged as innovative tools aimed at streamlining the application screening process while potentially mitigating the impact of such biases. These AI-driven platforms present an opportunity to customize the demographic features of virtual interviewers to align with diverse applicant preferences, promising a more objective and fair evaluation. Despite their growing adoption, the implications of virtual interviewer identities on candidate experiences within AVIs remain underexplored. We aim to address this research and empirical gap in this paper. To this end, we carried out a comprehensive between-subjects study involving 218 participants across six distinct experimental conditions, manipulating the gender and skin color of an AI virtual interviewer agent. Our empirical analysis revealed that while the demographic attributes of the agents did not significantly influence the overall experience of interviewees, variations in the interviewees' demographics significantly altered their perception of the AVI process. Further, we uncovered that the mediating roles of Social Presence and Perception of the virtual interviewer critically affect interviewees' perceptions of fairness (+), privacy (-), and impression management (+).

Colorectal cancer (CRC) is the third most common cause of cancer diagnosed in the United States and the second leading cause of cancer-related death among both genders. Notably, CRC is the leading cause of cancer in younger men less than 50 years old. Colonoscopy is considered the gold standard for the early diagnosis of CRC. Skills vary significantly among endoscopists, and a high miss rate is reported. Automated polyp segmentation can reduce the missed rates, and timely treatment is possible in the early stage. To address this challenge, we introduce \textit{\textbf{\ac{FANetv2}}}, an advanced encoder-decoder network designed to accurately segment polyps from colonoscopy images. Leveraging an initial input mask generated by Otsu thresholding, FANetv2 iteratively refines its binary segmentation masks through a novel feedback attention mechanism informed by the mask predictions of previous epochs. Additionally, it employs a text-guided approach that integrates essential information about the number (one or many) and size (small, medium, large) of polyps to further enhance its feature representation capabilities. This dual-task approach facilitates accurate polyp segmentation and aids in the auxiliary classification of polyp attributes, significantly boosting the model's performance. Our comprehensive evaluations on the publicly available BKAI-IGH and CVC-ClinicDB datasets demonstrate the superior performance of FANetv2, evidenced by high dice similarity coefficients (DSC) of 0.9186 and 0.9481, along with low Hausdorff distances of 2.83 and 3.19, respectively. The source code for FANetv2 is available at https://github.com/xxxxx/FANetv2.