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This study introduces a novel physics-informed neural networks (PINNs) framework designed to model coupled-field problems specifically tailored for heterogeneous poroelastic media. Firstly, a composite neural network is developed where distinct neural networks are dedicated to predicting displacement and pressure variables for each material, employing identical activation functions but trained separately across all other parameters. Secondly, we handle the challenges of heterogeneous material interfaces by the Interface- PINNs (I-PINNs) framework, where different activation functions across any material interface are prescribed to ensure that the discontinuities in solution fields and gradients are accurately captured. We compare the modified PINNs framework with the conventional approach on two one-dimensional benchmark examples for poroelasticity in heterogeneous media. Furthermore, we assess a single neural network architecture, comparing it against the composite neural network proposed in this work. These examples show that the proposed framework demonstrates superior approximation accuracy in both displacements and pressures, and better convergence behavior., Comment: 34 pages, 12 figures, 3 tables

In the absence of any new physics signals at the Large Hadron Collider (LHC), anomalous results at low energy experiments have become the subject of increased attention and scrutiny. We focus on three such results from the LSND, MiniBooNE (MB), and ATOMKI experiments. A 17 MeV pseudoscalar mediator ($a'$) can account for the excess events seen in $^8$Be and $^4$He pair creation transitions in ATOMKI. We incorporate this mediator in a gauge invariant extension of the Standard Model (SM) with a second Higgs doublet and three singlet (seesaw) neutrinos ($N_i, i=1,2,3$). $N_{1,2}$ participate in an interaction in MB and LSND which, with $a'$ as mediator, leads to the production of $e^+ e^-$ pairs. The $N_i$ also lead to mass-squared differences for SM neutrinos in agreement with global oscillation data. We first show that such a model offers a clean and natural joint solution to the MB and LSND excesses. We then examine the possibility of a common solution to all three anomalies. Using the values of the couplings to the quarks and electrons which are required to explain pair creation nuclear transition data for $^8$Be and $^4$He in ATOMKI, we show that these values lead to excellent fits for MB and LSND data as well, allowing for a common solution. We obtain a representative solution space for this, in the context of an important constraint that comes from the decay $K^+ \rightarrow \pi^+ a' \, (a'\rightarrow e^+e^-$). We also discuss other constraints on the model from both collider and non-collider experiments as well as those from electroweak precision data, stability and unitarity. We compute the contributions to the electron and muon $g-2$ up to two loops for our model and discuss the results in the context of the current theoretical and empirical scenario vis a vis these parameters. Finally, we discuss future tests of the model in upcoming experiments., Comment: 39 pages, 6 figures and 3 tables

We present an efficient physics-informed neural networks (PINNs) framework, termed Adaptive Interface-PINNs (AdaI-PINNs), to improve the modeling of interface problems with discontinuous coefficients and/or interfacial jumps. This framework is an enhanced version of its predecessor, Interface PINNs or I-PINNs (Sarma et al.; https://dx.doi.org/10.2139/ssrn.4766623), which involves domain decomposition and assignment of different predefined activation functions to the neural networks in each subdomain across a sharp interface, while keeping all other parameters of the neural networks identical. In AdaI-PINNs, the activation functions vary solely in their slopes, which are trained along with the other parameters of the neural networks. This makes the AdaI-PINNs framework fully automated without requiring preset activation functions. Comparative studies on one-dimensional, two-dimensional, and three-dimensional benchmark elliptic interface problems reveal that AdaI-PINNs outperform I-PINNs, reducing computational costs by 2-6 times while producing similar or better accuracy., Comment: 17 pages, 8 figures, 6 tables

Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption, particularly for on-device learning where computational resources are limited. Various alternatives to BP, including random feedback alignment, forward-forward, and local classifiers, have been explored to address these challenges. These methods have their advantages, but they can encounter difficulties when dealing with intricate visual tasks or demand considerable computational resources. In this paper, we propose a novel Local Learning rule inspired by neural activity Synchronization phenomena (LLS) observed in the brain. LLS utilizes fixed periodic basis vectors to synchronize neuron activity within each layer, enabling efficient training without the need for additional trainable parameters. We demonstrate the effectiveness of LLS and its variations, LLS-M and LLS-MxM, on multiple image classification datasets, achieving accuracy comparable to BP with reduced computational complexity and minimal additional parameters. Furthermore, the performance of LLS on the Visual Wake Word (VWW) dataset highlights its suitability for on-device learning tasks, making it a promising candidate for edge hardware implementations., Comment: 14 pages, 4 figures

Recent studies have focused on the relationship between charge density wave (CDW) collective electronic ground states and nontrivial topological states. Using angle-resolved photoemission and density functional theory, we establish that YTe$_3$ is a CDW-induced Kramers nodal line (KNL) metal, a newly proposed topological state of matter. YTe$_3$ is a non-magnetic quasi-2D chalcogenide with a CDW wave vector ($q_{\rm cdw}$) of 0.2907c$^*$. Scanning tunneling microscopy and low energy electron diffraction revealed two orthogonal CDW domains, each with a unidirectional CDW and similar YTe$_3$. The effective band structure (EBS) computations, using DFT-calculated folded bands, show excellent agreement with ARPES because a realistic x-ray crystal structure and twin domains are considered in the calculations. The Fermi surface and ARPES intensity plots show weak shadow bands displaced by $q_{\rm cdw}$ from the main bands. These are linked to CDW modulation, as the EBS calculation confirms. Bilayer split main and shadow bands suggest the existence of crossings, according to theory and experiment. DFT bands, including spin-orbit coupling, indicate a nodal line along the $\Sigma$ line from multiple band crossings perpendicular to the KNL. Additionally, doubly degenerate bands are only found along the KNL at all energies, with some bands dispersing through the Fermi level.

There is considerable debate and evidence about how governments should regulate contractors and other firms, but little on how government should regulate schools. In the first phase of this study, we focus on the correlation between indices of state charter school policies and measures of charter quantity (market share) and three measures of quality: statewide student achievement growth from CREDO, closure of low-performing charter schools, and charter entry into low-performing school districts. States with no charter caps, multiple charter authorizers, and stronger contract renewal standards have higher charter market shares. We also see evidence of a quality-quantity trade-off. The regression coefficients on eight of the 11 policy variables are of opposite signs in the quality and quantity analyses. The positive correlation between charter market share and the number of charter authorizers motivates a follow-up analysis in which we test whether this correlation reflects causation. Using difference-in-differences analysis, we find evidence that adding a statewide authorizing body increased the statewide charter market share gradually over time.

Charter schools are privately operated public schools funded by the government with oversight from school districts, state education departments, or other government authorizers. This gives charter schools autonomy from many government rules and regulations, allows them to specialize and innovate in particular types of education, and gives parents more choices. With this autonomy also comes some additional accountability. If a charter school is not meeting the terms of its agreement, the government can close it or turn control over to another charter operator. However, there is little evidence about the appropriate mix of policies, the balance between autonomy and government oversight, and the trade-offs involved. This brief describes how charter regulation varies across states, shows how those regulations are related to charter school outcomes, and provides evidence of their policy effects. The purpose is to help address the larger question: how should the government regulate charter schools?

Antiferromagnetic spintronics offers the potential for higher-frequency operations compared to ferromagnetic spintronics and improved insensitivity to magnetic fields. However, previous electrical techniques to detect antiferromagnetic dynamics have required millimeter-scale samples to achieve measurable signals. Here we demonstrate direct electrical detection of antiferromagnetic resonance in devices 1000 times smaller using spin-filter tunneling in micron-scale PtTe$_2$/bilayer CrSBr/graphite junctions in which the tunnel barrier is the van der Waals antiferromaget CrSBr. This sample geometry allows not only efficient detection, but also electrical control of the antiferromagnetic resonance through spin-orbit torque from the PtTe$_2$ electrode. The ability to efficiently detect and control antiferromagnetic resonance provides the means to make detailed studies of the physics governing these high-frequency dynamics and to pursue applications including radiation sources, modulators, and detectors.

In this article, we propose a novel standalone hybrid Spiking-Convolutional Neural Network (SC-NN) model and test on using image inpainting tasks. Our approach uses the unique capabilities of SNNs, such as event-based computation and temporal processing, along with the strong representation learning abilities of CNNs, to generate high-quality inpainted images. The model is trained on a custom dataset specifically designed for image inpainting, where missing regions are created using masks. The hybrid model consists of SNNConv2d layers and traditional CNN layers. The SNNConv2d layers implement the leaky integrate-and-fire (LIF) neuron model, capturing spiking behavior, while the CNN layers capture spatial features. In this study, a mean squared error (MSE) loss function demonstrates the training process, where a training loss value of 0.015, indicates accurate performance on the training set and the model achieved a validation loss value as low as 0.0017 on the testing set. Furthermore, extensive experimental results demonstrate state-of-the-art performance, showcasing the potential of integrating temporal dynamics and feature extraction in a single network for image inpainting., Comment: 7 Pages, 3 figures, and 2 tables

Three-dimensional doped Dirac insulators foster simply connected (in both topological and trivial regimes) and annular (deep inside the topological regime) Fermi surfaces (FSs) in the normal state, and allow on-site repulsions among fermions with opposite spin ($U_1$) and parity ($U_2$) eigenvalues. From an unbiased leading-order (one-loop) renormalization group analysis, controlled by a suitable $\epsilon$ expansion, we show that this system develops strong propensity toward the nucleation of scalar $s$-wave and odd-parity pseudoscalar $p$-wave pairings, favored by repulsive $U_1$ and $U_2$ interactions, respectively, irrespective of the underlying FS topology. Our results can be pertinent for the observed superconductivity in various doped narrow gap semiconductors, and the theoretical foundation can readily be applied to investigate similar phenomenon in various doped topological materials., Comment: 5 Pages, 1 Figure, 2 Tables (Supplemental Material as ancillary file)

We consider the motion of a large number of heavy particles in a Newtonian fluid occupying a bounded spatial domain. When we say "heavy", we mean a particle with a mass density that approaches infinity at an appropriate rate as its radius vanishes. We show that the collective effect of heavy particles on the fluid motion is similar to the Brinkman perturbation of the Navier-Stokes system identified in the homogenization process.

Given two four-dimensional symplectic manifolds, together with knots in their boundaries, we define an ``anchored symplectic embedding'' to be a symplectic embedding, together with a two-dimensional symplectic cobordism between the knots (in the four-dimensional cobordism determined by the embedding). We use techniques from embedded contact homology to determine quantitative critera for when anchored symplectic embeddings exist, for many examples of toric domains. In particular we find examples where ordinarily symplectic embeddings exist, but they cannot be upgraded to anchored symplectic embeddings unless one enlarges the target domain., Comment: 30 pages, 3 figures

The newly discovered Wormhole C--metric is a solution of Einstein's field equation coupled with a phantom scalar field which describes the accelerated wormholes. In the zero acceleration limit the solution reduces to an asymptotically flat wormhole. For certain range of parameter space this solution doesn't possess any horizon, thus making it a viable candidate of wormhole. To completely unveil this property we have studied the topological properties of this spacetime and shown that the throat is marginally connected. In the aforementioned range of parameters, the spacetime doesn't posses any photon orbit confirming the absence of shadow. We further analysed the stability of this spacetime under scalar perturbation. Under the usual boundary conditions (outgoing waves at both spatial infinities) there exists a continuous spectra. On the contrary one may achieve the quantization of the modes by exploiting a different but physically intuitive boundary condition. The lowest lying mode behaves as normal mode, and the imaginary part comes into play for the modes corresponding to first overtone number $(n=1)$ marking the onset of quasi-nomral modes for all azimuthal quantum number, $L$. We have also argued that the spacetime has a tendency to hold the excitation in it due to the external perturbation, rather than a fast de-excitation., Comment: 31 pages, 5 figures

Nonclassicality in composite quantum systems depicts several puzzling manifestations, with Einstein-Podolsky-Rosen entanglement, Schr\"odinger steering, and Bell nonlocality being the most celebrated ones. In addition to those, an unentangled quantum state can also exhibit nonclassicality, as evidenced from notions such as quantum discord and work deficit. Here, we propose a general framework to investigate nonclassical correlations in multipartite quantum states. The distinct signatures left on observable quantities, depending on whether the sub-parts of a composite system are probed separately or jointly, provide an operational avenue to construct different quantifiers that faithfully capture signatures of nonclassicality in quantum states. Along the line we unveil an intriguing phenomenon referred to as `nonlocal locking of observable quantities', where the value of an observable quantity gets locked in the correlation of a nonclassical state. Our approach reduces the experimental demand for verification of nonclassicality in composite systems and can find applications for enhanced energy storage in quantum thermodynamical devices., Comment: Comments are welcome

Nonlinear dynamics play an important role in the analysis of signals. A popular, readily interpretable nonlinear measure is Permutation Entropy. It has recently been extended for the analysis of graph signals, thus providing a framework for non-linear analysis of data sampled on irregular domains. Here, we introduce a continuous version of Permutation Entropy, extend it to the graph domain, and develop a ordinal activation function akin to the one of neural networks. This is a step towards Ordinal Deep Learning, a potentially effective and very recently posited concept. We also formally extend ordinal contrasts to the graph domain. Continuous versions of ordinal contrasts of length 3 are also introduced and their advantage is shown in experiments. We also integrate specific contrasts for the analysis of images and show that it generalizes well to the graph domain allowing a representation of images, represented as graph signals, in a plane similar to the entropy-complexity one. Applications to synthetic data, including fractal patterns and popular non-linear maps, and real-life MRI data show the validity of these novel extensions and potential benefits over the state of the art. By extending very recent concepts related to permutation entropy to the graph domain, we expect to accelerate the development of more graph-based entropy methods to enable nonlinear analysis of a broader kind of data and establishing relationships with emerging ideas in data science.

In time-domain radio astronomy with arrays, voltages from individual antennas are added together with proper delay and fringe correction to form the beam in real-time. In order to achieve the correct phased addition of antenna voltages one has to also correct for the ionospheric and instrumental gains. Conventionally this is done using observations of a calibrator source located near to the target field. This scheme is sub-optimal since it does not correct for the variation of the gains with time and position in the sky. Further, since the ionospheric phase variation is typically most rapid at the longest baselines, the most distant antennas are often excluded while forming the beam. We present here a different methodology ("in-field phasing"), in which the gains are obtained in real-time using a model of the intensity distribution in the target field, which overcomes all of these drawbacks. We present observations with the upgraded Giant Metrewave Radio Telescope (uGMRT) which demonstrates that in-field phasing does lead to a significant improvement in sensitivity. We also show, using observations of the millisecond pulsar J1120$-$3618 that this in turn leads to a significant improvement of measurements of the Dispersion Measure and Time of Arrival. Finally, we present test observations of the GMRT discovered eclipsing black widow pulsar J1544+4937 showing that in-field phasing leads to improvement in the measurement of the cut-off frequency of the eclipse., Comment: Accepted for publication in the ApJ

Neural Machine Translation (NMT) remains a formidable challenge, especially when dealing with low-resource languages. Pre-trained sequence-to-sequence (seq2seq) multi-lingual models, such as mBART-50, have demonstrated impressive performance in various low-resource NMT tasks. However, their pre-training has been confined to 50 languages, leaving out support for numerous low-resource languages, particularly those spoken in the Indian subcontinent. Expanding mBART-50's language support requires complex pre-training, risking performance decline due to catastrophic forgetting. Considering these expanding challenges, this paper explores a framework that leverages the benefits of a pre-trained language model along with knowledge distillation in a seq2seq architecture to facilitate translation for low-resource languages, including those not covered by mBART-50. The proposed framework employs a multilingual encoder-based seq2seq model as the foundational architecture and subsequently uses complementary knowledge distillation techniques to mitigate the impact of imbalanced training. Our framework is evaluated on three low-resource Indic languages in four Indic-to-Indic directions, yielding significant BLEU-4 and chrF improvements over baselines. Further, we conduct human evaluation to confirm effectiveness of our approach. Our code is publicly available at https://github.com/raypretam/Two-step-low-res-NMT., Comment: Published at Seventh LoResMT Workshop at ACL 2024

Graphene is a privileged 2D platform for hosting confined light-matter excitations known as surface plasmon-polaritons (SPPs), as it possesses low intrinsic losses with a high degree of optical confinement. However, the inherently isotropic optical properties of graphene limit its ability to guide and focus SPPs, making it less suitable than anisotropic elliptical and hyperbolic materials as a platform for polaritonic lensing and canalization. Here, we present the graphene/CrSBr heterostructure as an engineered 2D interface that hosts highly anisotropic SPP propagation over a wide range of frequencies in the mid-infrared and terahertz. Using a combination of scanning tunneling microscopy (STM), scattering-type scanning near-field optical microscopy (s-SNOM), and first-principles calculations, we demonstrate mutual doping in excess of 10$^{13}$ cm$^{-2}$ holes/electrons between the interfacial layers of graphene/CrSBr heterostructures. SPPs in graphene activated by charge transfer interact with charge-induced anisotropic intra- and interband transitions in the interfacial doped CrSBr, leading to preferential SPP propagation along the quasi-1D chains that compose each CrSBr layer. This multifaceted proximity effect both creates SPPs and endows them with anisotropic transport and propagation lengths that differ by an order-of-magnitude between the two in-plane crystallographic axes of CrSBr.

In recent years, there have been attempts to increase the kernel size of Convolutional Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers' (ViTs) self-attention blocks. That approach, however, quickly hit an upper bound and saturated way before achieving a global receptive field. In this work, we demonstrate that by leveraging the Wavelet Transform (WT), it is, in fact, possible to obtain very large receptive fields without suffering from over-parameterization, e.g., for a $k \times k$ receptive field, the number of trainable parameters in the proposed method grows only logarithmically with $k$. The proposed layer, named WTConv, can be used as a drop-in replacement in existing architectures, results in an effective multi-frequency response, and scales gracefully with the size of the receptive field. We demonstrate the effectiveness of the WTConv layer within ConvNeXt and MobileNetV2 architectures for image classification, as well as backbones for downstream tasks, and show it yields additional properties such as robustness to image corruption and an increased response to shapes over textures. Our code is available at https://github.com/BGU-CS-VIL/WTConv., Comment: Accepted to ECCV 2024

Studies of Galactic HII regions are of crucial importance for studying star formation and the evolution of the interstellar medium. Gaining an insight into their physical characteristics contributes to a more comprehensive understanding of these phenomena. The GLOSTAR project aims to provide a GLObal view on STAR formation in the Milky Way by performing an unbiased and sensitive survey. This is achieved by using the extremely wideband (4{-}8 GHz) C-band receiver of the Karl G. Jansky Very Large Array and the Effelsberg 100 m telescope. Using radio recombination lines observed in the GLOSTAR survey with the VLA in D-configuration with a typical line sensitivity of 1{\sigma} {\sim} 3.0 mJy beam{^-1} at {\sim} 5 km s{^-1} and an angular resolution of 25", we cataloged 244 individual Galactic HII regions and derived their physical properties. We examined the mid-infrared (MIR) morphology of these HII regions and find that a significant portion of them exhibit a bubble-like morphology in the GLIMPSE 8 {\mu}m emission. We also searched for associations with the dust continuum and sources of methanol maser emission, other tracers of young stellar objects, and find that 48\% and 14\% of our HII regions, respectively, are coextensive with those. We measured the electron temperature for a large sample of HII regions within Galactocentric distances spanning from 1.6 to 13.1 kpc and derived the Galactic electron temperature gradient as {\sim} 372 {\pm} 28 K kpc{^-1} with an intercept of 4248 {\pm} 161 K, which is consistent with previous studies., Comment: Accepted for publication in A&A

The strength distributions of the Isoscalar Giant Monopole Resonance (ISGMR) and Isoscalar Giant Quadrupole Resonance (ISGQR) in 142,146-150Nd have been determined via inelastic alpha-particle scattering with the Grand Raiden (GR) Spectrometer at the Research Center for Nuclear Physics (RCNP), Japan. In the deformed nuclei 146-150Nd, the ISGMR strength distributions exhibit a splitting into two components, while the nearly spherical nucleus 142Nd displays a single peak in the ISGMR strength distribution. A noteworthy achievement in this study is the first-time detection of overtones in the Isoscalar Giant Quadrupole Resonance (ISGQR) strength distributions within Nd isotopes at an excitation energy around 25 MeV obtained through Multipole Decomposition Analysis (MDA)., Comment: Accepted for publication in Physics Letters B

Low frequency critical fluctuations in magnetic materials encode important information about the physics of magnetic ordering, especially in the associated critical exponents. While a number of techniques have been established to study magnetic critical fluctuations in bulk materials, few approaches maintain the required microscopic resolution, temporal range, and signal sensitivity to quantitatively analyze critical fluctuations in magnetic phases of 2D materials. Using nitrogen-vacancy (NV) centers in diamond as quantum probes, we implement $T_2$ (spin decoherence) noise magnetometry to quantitatively study critical dynamics in a tri-layer sample of the Van der Waals magnetic material CrSBr. We characterize critical fluctuations across the magnetic phase transition in CrSBr by analyzing the NV spin echo coherence decay on time scales that approach the characteristic fluctuation correlation time $\tau_c$ at criticality, allowing us to study the temperature dependence of critical slowing down. By modelling the spin echo decoherence using theoretical models for critical dynamics, we are able to extract the critical exponent $\nu$ for the correlation length. We find a value for $\nu$ which deviates from the Ising prediction and suggests the role of long-range dipolar interactions in modifying the critical behavior of magnetic fluctuation modes in CrSBr at the 2D limit. We further compare the divergence of correlation length in CrSBr to the predicted exponential divergence for 2D XY criticality, and find evidence suggesting the possibility of such behavior in a temperature window near $T_C$ where static magnetic domains are absent. Our work provides a first demonstration of the capability of decoherence based NV noise magnetometry to quantitatively analyze critical scaling laws in 2D materials., Comment: 9 pages, 4 figures

Upcoming galaxy surveys aim to map the Universe with unprecedented precision, depth and sky coverage. The galaxy bispectrum is a prime source of information as it allows us to probe primordial non-Gaussianity (PNG), a key factor in differentiating various models of inflation. On the scales where local PNG is strongest, Doppler and other relativistic effects become important and need to be included. We investigate the detectability of relativistic and local PNG contributions in the galaxy bispectrum. We compute the signal-to-noise ratio for the detection of the bispectrum including such effects. Furthermore, we perform information matrix forecasts on the local PNG parameter $f_{\rm NL}$ and on the parametrised amplitudes of the relativistic corrections. Finally, we quantify the bias on the measurement of $f_{\rm NL}$ that arises from neglecting relativistic effects. Our results show that detections of both first- and second-order relativistic effects are promising with forthcoming spectroscopic survey specifications -- and are largely unaffected by the uncertainty in $f_{\rm NL}$. Conversely, we show for the first time that neglecting relativistic corrections in the galaxy bispectrum can lead to $>\!1.5\sigma(f_{\rm NL})$ shift on the detected value of $f_{\rm NL}$, highlighting the importance of including relativistic effects in our modelling., Comment: 28 pages, 8 figures, comments are welcome

Traditional supervised 3D medical image segmentation models need voxel-level annotations, which require huge human effort, time, and cost. Semi-supervised learning (SSL) addresses this limitation of supervised learning by facilitating learning with a limited annotated and larger amount of unannotated training samples. However, state-of-the-art SSL models still struggle to fully exploit the potential of learning from unannotated samples. To facilitate effective learning from unannotated data, we introduce LLM-SegNet, which exploits a large language model (LLM) to integrate task-specific knowledge into our co-training framework. This knowledge aids the model in comprehensively understanding the features of the region of interest (ROI), ultimately leading to more efficient segmentation. Additionally, to further reduce erroneous segmentation, we propose a Unified Segmentation loss function. This loss function reduces erroneous segmentation by not only prioritizing regions where the model is confident in predicting between foreground or background pixels but also effectively addressing areas where the model lacks high confidence in predictions. Experiments on publicly available Left Atrium, Pancreas-CT, and Brats-19 datasets demonstrate the superior performance of LLM-SegNet compared to the state-of-the-art. Furthermore, we conducted several ablation studies to demonstrate the effectiveness of various modules and loss functions leveraged by LLM-SegNet., Comment: Under Review

The classic house allocation problem is primarily concerned with finding a matching between a set of agents and a set of houses that guarantees some notion of economic efficiency (e.g. utilitarian welfare). While recent works have shifted focus on achieving fairness (e.g. minimizing the number of envious agents), they often come with notable costs on efficiency notions such as utilitarian or egalitarian welfare. We investigate the trade-offs between these welfare measures and several natural fairness measures that rely on the number of envious agents, the total (aggregate) envy of all agents, and maximum total envy of an agent. In particular, by focusing on envy-free allocations, we first show that, should one exist, finding an envy-free allocation with maximum utilitarian or egalitarian welfare is computationally tractable. We highlight a rather stark contrast between utilitarian and egalitarian welfare by showing that finding utilitarian welfare maximizing allocations that minimize the aforementioned fairness measures can be done in polynomial time while their egalitarian counterparts remain intractable (for the most part) even under binary valuations. We complement our theoretical findings by giving insights into the relationship between the different fairness measures and conducting empirical analysis.

H II regions are the signposts of massive ($M\geq\,8\,M_\odot$) star-forming sites in our Galaxy. It has been observed that the ionizing photon rate inferred from the radio continuum emission of H II regions is significantly lower ($\sim$ 90%) than that inferred from far-infrared fluxes measured by IRAS. This discrepancy in the ionizing photon rates may arise due to there being significant amounts of dust within the H II regions or the presence of extended emission that is undetected by high-resolution radio interferometric observations. Here, we study a sample of eight compact and ultracompact H II regions with extended emission to explore its role in resolving the discrepancy. We have used observations at the uGMRT (1.25-1.45 GHz) and data from the GLOSTAR survey (4-8 GHz) to estimate the ionizing photon rate from the radio continuum emission. We have also estimated the ionizing photon rate from the infrared luminosity by fitting a spectral energy distribution function to the infrared data from the GLIMPSE, MIPSGAL, and Hi-GAL surveys. The excellent sensitivity of the radio observations to extended emission allows us to investigate the actual fraction of ionizing photons that are absorbed by dust in compact and ultracompact H II regions. Barring one source, we find a direct association between the radio continuum emission from the compact and diffuse components of the H II region. Our study shows that the ionizing photon rates estimated using the radio and infrared data are within reasonable agreement (5-28%) if we include the extended emission. We also find multiple candidate ionizing stars in all our sources, and the ionizing photon rates from the radio observations and candidate stars are in reasonable agreement., Comment: Accepted for publication in A&A. 22 pages, 9 figures, 6 tables

Despite advancements in Autoencoders (AEs) for tasks like dimensionality reduction, representation learning and data generation, they remain vulnerable to adversarial attacks. Variational Autoencoders (VAEs), with their probabilistic approach to disentangling latent spaces, show stronger resistance to such perturbations compared to deterministic AEs; however, their resilience against adversarial inputs is still a concern. This study evaluates the robustness of VAEs against non-targeted adversarial attacks by optimizing minimal sample-specific perturbations to cause maximal damage across diverse demographic subgroups (combinations of age and gender). We investigate two questions: whether there are robustness disparities among subgroups, and what factors contribute to these disparities, such as data scarcity and representation entanglement. Our findings reveal that robustness disparities exist but are not always correlated with the size of the subgroup. By using downstream gender and age classifiers and examining latent embeddings, we highlight the vulnerability of subgroups like older women, who are prone to misclassification due to adversarial perturbations pushing their representations toward those of other subgroups.

In a previous paper with the same title, we gave an upper bound for the exponent of uniform rational approximation to a quadruple of $\mathbb{Q}$-linearly independent real numbers in geometric progression. Here, we explain why this upper bound is not optimal., Comment: 24 pages

Existing algorithms for human body part segmentation have shown promising results on challenging datasets, primarily relying on end-to-end supervision. However, these algorithms exhibit severe performance drops in the face of domain shifts, leading to inaccurate segmentation masks. To tackle this issue, we introduce POSTURE: \underline{Po}se Guided Un\underline{s}upervised Domain Adap\underline{t}ation for H\underline{u}man Body Pa\underline{r}t S\underline{e}gmentation - an innovative pseudo-labelling approach designed to improve segmentation performance on the unlabeled target data. Distinct from conventional domain adaptive methods for general semantic segmentation, POSTURE stands out by considering the underlying structure of the human body and uses anatomical guidance from pose keypoints to drive the adaptation process. This strong inductive prior translates to impressive performance improvements, averaging 8\% over existing state-of-the-art domain adaptive semantic segmentation methods across three benchmark datasets. Furthermore, the inherent flexibility of our proposed approach facilitates seamless extension to source-free settings (SF-POSTURE), effectively mitigating potential privacy and computational concerns, with negligible drop in performance.

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement., Comment: 90 pages, 22 figures, roadmap, neuromorphic

In this research, we examine the minsum flow problem in dynamic path networks where flows are represented as discrete and weighted sets. The minsum flow problem has been widely studied for its relevance in finding evacuation routes during emergencies such as earthquakes. However, previous approaches often assume that individuals are separable and identical, which does not adequately account for the fact that some groups of people, such as families, need to move together and that some groups may be more important than others. To address these limitations, we modify the minsum flow problem to support flows represented as discrete and weighted sets. We also propose a 2-approximation pseudo-polynomial time algorithm to solve this modified problem for path networks with uniform capacity.

We propose a novel model for decomposing grayscale images into three distinct components: the structural part, representing sharp boundaries and regions with strong light-to-dark transitions; the smooth part, capturing soft shadows and shades; and the oscillatory part, characterizing textures and noise. To capture the homogeneous structures, we introduce a combination of $L^0$-gradient and curvature regularization on level lines. This new regularization term enforces strong sparsity on the image gradient while reducing the undesirable staircase effects as well as preserving the geometry of contours. For the smoothly varying component, we utilize the $L^2$-norm of the Laplacian that favors isotropic smoothness. To capture the oscillation, we use the inverse Sobolev seminorm. To solve the associated minimization problem, we design an efficient operator-splitting algorithm. Our algorithm effectively addresses the challenging non-convex non-smooth problem by separating it into sub-problems. Each sub-problem can be solved either directly using closed-form solutions or efficiently using the Fast Fourier Transform (FFT). We provide systematic experiments, including ablation and comparison studies, to analyze our model's behaviors and demonstrate its effectiveness as well as efficiency.

In this paper, we explore the properties of loss curvature with respect to input data in deep neural networks. Curvature of loss with respect to input (termed input loss curvature) is the trace of the Hessian of the loss with respect to the input. We investigate how input loss curvature varies between train and test sets, and its implications for train-test distinguishability. We develop a theoretical framework that derives an upper bound on the train-test distinguishability based on privacy and the size of the training set. This novel insight fuels the development of a new black box membership inference attack utilizing input loss curvature. We validate our theoretical findings through experiments in computer vision classification tasks, demonstrating that input loss curvature surpasses existing methods in membership inference effectiveness. Our analysis highlights how the performance of membership inference attack (MIA) methods varies with the size of the training set, showing that curvature-based MIA outperforms other methods on sufficiently large datasets. This condition is often met by real datasets, as demonstrated by our results on CIFAR10, CIFAR100, and ImageNet. These findings not only advance our understanding of deep neural network behavior but also improve the ability to test privacy-preserving techniques in machine learning.

Compressed video action recognition classifies video samples by leveraging the different modalities in compressed videos, namely motion vectors, residuals, and intra-frames. For this purpose, three neural networks are deployed, each dedicated to processing one modality. Our observations indicate that the network processing intra-frames tend to converge to a flatter minimum than the network processing residuals, which in turn converges to a flatter minimum than the motion vector network. This hierarchy in convergence motivates our strategy for knowledge transfer among modalities to achieve flatter minima, which are generally associated with better generalization. With this insight, we propose Progressive Knowledge Distillation (PKD), a technique that incrementally transfers knowledge across the modalities. This method involves attaching early exits (Internal Classifiers - ICs) to the three networks. PKD distills knowledge starting from the motion vector network, followed by the residual, and finally, the intra-frame network, sequentially improving IC accuracy. Further, we propose the Weighted Inference with Scaled Ensemble (WISE), which combines outputs from the ICs using learned weights, boosting accuracy during inference. Our experiments demonstrate the effectiveness of training the ICs with PKD compared to standard cross-entropy-based training, showing IC accuracy improvements of up to 5.87% and 11.42% on the UCF-101 and HMDB-51 datasets, respectively. Additionally, WISE improves accuracy by up to 4.28% and 9.30% on UCF-101 and HMDB-51, respectively.

We explore the exponential parameterization of the Majorana neutrino mass matrix texture. In this regard, the elements of the mass matrix are expressed in terms of their absolute values and arguments. We propose a neutrino mass matrix texture that highlights four correlations among its elements. The mixing scheme obtained from the proposed texture is consistent with experimental observations. In addition, we derive the proposed texture from the $SU(2)_L \times U(1)_Y \times A_4 \times Z_{10}$ group in the light of the seesaw mechanism.

It has been suggested that a $dS_{d+1}$ spacetime of radius $R_{ds}$ has a holographic dual, living at future space-like infinity ${\cal I}^+$, with the bulk wave function being dual to the partition function of the boundary theory, [arXiv:astro-ph/0210603v5]. We consider some aspects of this correspondence. For under damped scalars with mass $M^2R_{ds}^2>{d^2\over4}$, belonging to the principal series, we show that for the Bunch Davies vacuum a suitable source in the boundary theory can be identified in terms of the coherent state representation of the wave function. We argue that terms in the resulting correlation functions, which are independent of the late time cut-off, satisfy the Ward identities of a conformal field theory. We also discuss other ways to identify sources, both in the under damped and the over damped case, where $M^2R_{ds}^2<{d^2\over4}$, and argue that these too can lead to correlators satisfying the Ward identities of a CFT. Some comments on the violation of reflection positivity, and the cut-off dependent terms, along with some explicit checks and sample calculations, are also included.

In this paper, we propose a general distributionally robust framework for performative optimization, where the selected decision can influence the probabilistic distribution of uncertain parameters. Our framework facilitates safe decision-making in scenarios with incomplete information about the underlying decision-dependent distributions, relying instead on accessible reference distributions. To tackle the challenge of decision-dependent uncertainty, we introduce an algorithm named repeated robust risk minimization. This algorithm decouples the decision variables associated with the ambiguity set from the expected loss, optimizing the latter at each iteration while keeping the former fixed to the previous decision. By leveraging the strong connection between distributionally robust optimization and regularization, we establish a linear convergence rate to a performatively stable point and provide a suboptimality performance guarantee for the proposed algorithm. Finally, we examine the performance of our proposed model through an experimental study in strategic classification.

Risk-driven behavior provides a feedback mechanism through which individuals both shape and are collectively affected by an epidemic. We introduce a general and flexible compartmental model to study the effect of heterogeneity in the population with regards to risk tolerance. The interplay between behavior and epidemiology leads to a rich set of possible epidemic dynamics. Depending on the behavioral composition of the population, we find that increasing heterogeneity in risk tolerance can either increase or decrease the epidemic size. We find that multiple waves of infection can arise due to the interplay between transmission and behavior, even without the replenishment of susceptibles. We find that increasing protective mechanisms such as the effectiveness of interventions, the number of risk-averse people in the population, and the duration of intervention usage reduces the epidemic overshoot. When the protection is pushed past a critical threshold, the epidemic dynamics enter an underdamped regime where the epidemic size exactly equals the herd immunity threshold. Lastly, we can find regimes where epidemic size does not monotonically decrease with a population that becomes increasingly risk-averse., Comment: 12 pages, 7 figures (main text)

Deep Neural Networks (DNNs) are ubiquitous in real-world applications, yet they remain vulnerable to errors and adversarial attacks. This work tackles the challenge of applying formal verification to ensure the safety of computer vision models, extending verification beyond image classification to object detection. We propose a general formulation for certifying the robustness of object detection models using formal verification and outline implementation strategies compatible with state-of-the-art verification tools. Our approach enables the application of these tools, originally designed for verifying classification models, to object detection. We define various attacks for object detection, illustrating the diverse ways adversarial inputs can compromise neural network outputs. Our experiments, conducted on several common datasets and networks, reveal potential errors in object detection models, highlighting system vulnerabilities and emphasizing the need for expanding formal verification to these new domains. This work paves the way for further research in integrating formal verification across a broader range of computer vision applications.

This study presents an orbital phase-dependent analysis of three black widow spider millisecond pulsars (BW MSPs), aiming to investigate the magnetic field within the eclipse environment. The ultra-wide-bandwidth low-frequency receiver (UWL) of the Parkes 'Murriyang' radio telescope is utilised for full polarisation observations covering frequencies from 704-4032 MHz. Depolarisation of pulsed emission is observed during the eclipse phase of three BW MSPs namely, J0024-7204J, J1431-4715 and PSR J1959+2048, consistent with previous studies of other BW MSPs. We estimated orbital phase dependent RM values for these MSPs. The wide bandwidth observations also provided the constraints on eclipse cutoff frequency for these BW MSPs. For PSR J0024-7204J, we report temporal variation of the eclipse cutoff frequency coupled with changes in the electron column density within the eclipse medium across six observed eclipses. Moreover, the eclipse cutoff frequency for PSR J1431-4715 is determined to be 1251 $\pm$ 80 MHz, leading to the conclusion that synchrotron absorption is the primary mechanism responsible for the eclipsing. Additionally, for PSR J1959+2048, the estimated cutoff frequency exceeded 1400 MHz, consistent with previous studies. With this investigation, we have doubled the sample size of BW MSPs with orbital phase-resolved studies allowing a better probe to the eclipse environment., Comment: Accepted for publication in ApJ

In this work, we investigate the problem of adapting to the presence or absence of causal structure in multi-armed bandit problems. In addition to the usual reward signal, we assume the learner has access to additional variables, observed in each round after acting. When these variables $d$-separate the action from the reward, existing work in causal bandits demonstrates that one can achieve strictly better (minimax) rates of regret (Lu et al., 2020). Our goal is to adapt to this favorable "conditionally benign" structure, if it is present in the environment, while simultaneously recovering worst-case minimax regret, if it is not. Notably, the learner has no prior knowledge of whether the favorable structure holds. In this paper, we establish the Pareto optimal frontier of adaptive rates. We prove upper and matching lower bounds on the possible trade-offs in the performance of learning in conditionally benign and arbitrary environments, resolving an open question raised by Bilodeau et al. (2022). Furthermore, we are the first to obtain instance-dependent bounds for causal bandits, by reducing the problem to the linear bandit setting. Finally, we examine the common assumption that the marginal distributions of the post-action contexts are known and show that a nontrivial estimate is necessary for better-than-worst-case minimax rates., Comment: Accepted to ICML 2024

We investigate the doping-dependence of the Seebeck coefficient, as calculated from the Kelvin formula, for the Fermi Hubbard model using determinantal quantum Monte Carlo simulations. Our key findings are: (1) Besides the expected hole to electron-like behavior change around half filling, we show that the additional sign change at an electronic density $n_s$ (and correspondingly a hole density $p_s$) is controlled by the opening of a charge gap in the thermodynamic density of states or compressibility and not by the pseudogap scale in the single particle density of states. (2) We find that $n_s(T,U)$ depends strongly on the interaction $U$ and shows an unusual non-monotonic dependence on temperature with a maximum at a temperature $T\approx t$, on the order of the hopping scale. (3) We identify local moment formation close to half filling as the main driver for the anomalous behavior of the thermoelectric transport coefficient., Comment: 7 pages, 7 figures

In autonomous aerial navigation, real-time and energy-efficient obstacle avoidance remains a significant challenge, especially in dynamic and complex indoor environments. This work presents a novel integration of neuromorphic event cameras with physics-driven planning algorithms implemented on a Parrot Bebop2 quadrotor. Neuromorphic event cameras, characterized by their high dynamic range and low latency, offer significant advantages over traditional frame-based systems, particularly in poor lighting conditions or during high-speed maneuvers. We use a DVS camera with a shallow Spiking Neural Network (SNN) for event-based object detection of a moving ring in real-time in an indoor lab. Further, we enhance drone control with physics-guided empirical knowledge inside a neural network training mechanism, to predict energy-efficient flight paths to fly through the moving ring. This integration results in a real-time, low-latency navigation system capable of dynamically responding to environmental changes while minimizing energy consumption. We detail our hardware setup, control loop, and modifications necessary for real-world applications, including the challenges of sensor integration without burdening the flight capabilities. Experimental results demonstrate the effectiveness of our approach in achieving robust, collision-free, and energy-efficient flight paths, showcasing the potential of neuromorphic vision and physics-driven planning in enhancing autonomous navigation systems.

We show nonreciprocal light propagation for single-photon inputs due to quantum noise in coupled optical systems with gain and loss. We consider two parity-time ($\mathcal{PT}$) symmetric linear optical systems consisting of either two directly coupled resonators or two finite-length waveguides evanescently coupled in parallel. One resonator or waveguide is filled with an active gain medium and the other with a passive loss medium. The light propagation is reciprocal in such $\mathcal{PT}$ symmetric linear systems without quantum noise. We show here that light transmission becomes nonreciprocal when we include quantum noises in our modeling, which is essential for a proper physical description. The quantum nonreciprocity is especially pronounced in the $\mathcal{PT}$ broken phase. Transmitted light intensity in the waveguide of incidence is asymmetric for two waveguides even without noise. Quantum noise significantly enhances such asymmetry in the broken phase., Comment: 12 pages, 4 figures