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The rapid proliferation of mobile devices and advancements in wireless technologies have given rise to a new era of healthcare delivery through mobile health (mHealth) applications. Design Science Research (DSR) is a widely used research paradigm that aims to create and evaluate innovative artifacts to solve real-world problems. This paper presents a comprehensive framework for employing DSR in mHealth application projects to address healthcare challenges and improve patient outcomes. We discussed various DSR principles and methodologies, highlighting their applicability and importance in developing and evaluating mHealth applications. Furthermore, we present several case studies to exemplify the successful implementation of DSR in mHealth projects and provide practical recommendations for researchers and practitioners., Comment: IEEE Transactions on Engineering Management (2024)

Spin spirals form inside the magnetic layers of antiferromagnetic and noncollinearly-coupled magnetic multilayers in the presence of an external magnetic field. This spin structure can be modeled to extract the direct exchange stiffness of the magnetic layers and the strength of the interlayer exchange coupling across the spacer layer. In this article, we discuss three models to describe the evolution of the spin spiral with the strength of the external magnetic field in these coupled structures: discrete energy, discrete torque, and continuous torque. These models are expanded to accommodate multilayers with any number of ferromagnetic layers, any combination of material parameters, and asymmetry. We compare their performance when fitting to the measured magnetization data of a range of sputtered samples with one or multiple ferromagnetic layers on either side of the spacer. We find that the discrete models produce better fits than the continuous for asymmetric and multi-ferromagnetic structures and exhibit much better computational scaling with high numbers of atomic layers than the continuous model. For symmetric, single-layered structures, the continuous model produces the same fit statistics and outperforms the discrete models. Lastly, we demonstrate the methods by which one can use interfacial layers to measure the exchange stiffness of magnetic layers with low interlayer exchange coupling. An open-access website has been provided to allow the fitting of magnetization as a function of field in arbitrary coupled structures using the discrete energy model., Comment: 16 pages, 10 figures, 4 tables

Conformal Autoencoders are a neural network architecture that imposes orthogonality conditions between the gradients of latent variables towards achieving disentangled representations of data. In this letter we show that orthogonality relations within the latent layer of the network can be leveraged to infer the intrinsic dimensionality of nonlinear manifold data sets (locally characterized by the dimension of their tangent space), while simultaneously computing encoding and decoding (embedding) maps. We outline the relevant theory relying on differential geometry, and describe the corresponding gradient-descent optimization algorithm. The method is applied to standard data sets and we highlight its applicability, advantages, and shortcomings. In addition, we demonstrate that the same computational technology can be used to build coordinate invariance to local group actions when defined only on a (reduced) submanifold of the embedding space.

Long-horizon decision-making tasks present significant challenges for LLM-based agents due to the need for extensive planning over multiple steps. In this paper, we propose a hierarchical framework that decomposes complex tasks into manageable subgoals, utilizing separate LLMs for subgoal prediction and low-level action generation. To address the challenge of creating training signals for unannotated datasets, we develop a reward model that leverages multimodal environment feedback to automatically generate reward signals. We introduce Environment Preference Optimization (EPO), a novel method that generates preference signals from the environment's feedback and uses them to train LLM-based agents. Extensive experiments on ALFRED demonstrate the state-of-the-art performance of our framework, achieving first place on the ALFRED public leaderboard and showcasing its potential to improve long-horizon decision-making in diverse environments., Comment: EMNLP 2024

Both real and virtual photons can be involved in light-matter interactions. A famous example of the observable implications of virtual photons -- vacuum fluctuations of the quantum electromagnetic field -- is the Casimir effect. Since quantum vacuum effects are weak, various mechanisms have been proposed to enhance and engineer them, ranging from static, e.g., strong optical resonances, to dynamic, e.g., systems with moving boundaries or time-varying optical properties, or a combination of them. In this Letter, we discuss the role of material nonlocality (spatial dispersion) in dynamical Casimir effects in time-varying frequency-dispersive nanophotonic systems. We first show that local models may lead to nonphysical predictions, such as diverging emission rates of entangled polariton pairs. We then theoretically demonstrate that nonlocality regularizes this behavior by correcting the asymptotic response of the system for large wavevectors and reveals physical effects missed by local models, including a significant broadening of the emission rate distribution, which are relevant for future experimental observations. Our work sheds light on the importance of nonlocal effects in this new frontier of nanophotonics.

The spread of a graph $G$ is the difference $\lambda_1 - \lambda_n$ between the largest and smallest eigenvalues of its adjacency matrix. Breen, Riasanovsky, Tait and Urschel recently determined the graph on $n$ vertices with maximum spread for sufficiently large $n$. In this paper, we study a related question of maximizing the difference $\lambda_{i+1} - \lambda_{n-j}$ for a given pair $(i, j)$ over all graphs on $n$ vertices. We give upper bounds for all pairs $(i, j)$, exhibit an infinite family of pairs where the bound is tight, and show that for the pair $(1, 0)$ the extremal example is unique. These results contribute to a line of inquiry pioneered by Nikiforov aiming to maximize different linear combinations of eigenvalues over all graphs on $n$ vertices., Comment: 9 pages, 2 figures

For multiple scientific endeavors it is common to measure a phenomenon of interest in more than one ways. We make observations of objects from several different perspectives in space, at different points in time; we may also measure different properties of a mixture using different types of instruments. After collecting this heterogeneous information, it is necessary to be able to synthesize a complete picture of what is `common' across its sources: the subject we ultimately want to study. However, isolated (`clean') observations of a system are not always possible: observations often contain information about other systems in its environment, or about the measuring instruments themselves. In that sense, each observation may contain information that `does not matter' to the original object of study; this `uncommon' information between sensors observing the same object may still be important, and decoupling it from the main signal(s) useful. We introduce a neural network autoencoder framework capable of both tasks: it is structured to identify `common' variables, and, making use of orthogonality constraints to define geometric independence, to also identify disentangled `uncommon' information originating from the heterogeneous sensors. We demonstrate applications in several computational examples.

Reinforcement Learning (RL) is increasingly adopted to train agents that can deal with complex sequential tasks, such as driving an autonomous vehicle or controlling a humanoid robot. Correspondingly, novel approaches are needed to ensure that RL agents have been tested adequately before going to production. Among them, mutation testing is quite promising, especially under the assumption that the injected faults (mutations) mimic the real ones. In this paper, we first describe a taxonomy of real RL faults obtained by repository mining. Then, we present the mutation operators derived from such real faults and implemented in the tool muPRL. Finally, we discuss the experimental results, showing that muPRL is effective at discriminating strong from weak test generators, hence providing useful feedback to developers about the adequacy of the generated test scenarios., Comment: Accepted at ICSE '25

Optical Music Recognition (OMR) automates the transcription of musical notation from images into machine-readable formats like MusicXML, MEI, or MIDI, significantly reducing the costs and time of manual transcription. This study explores knowledge discovery in OMR by applying instance segmentation using Mask R-CNN to enhance the detection and delineation of musical symbols in sheet music. Unlike Optical Character Recognition (OCR), OMR must handle the intricate semantics of Common Western Music Notation (CWMN), where symbol meanings depend on shape, position, and context. Our approach leverages instance segmentation to manage the density and overlap of musical symbols, facilitating more precise information retrieval from music scores. Evaluations on the DoReMi and MUSCIMA++ datasets demonstrate substantial improvements, with our method achieving a mean Average Precision (mAP) of up to 59.70\% in dense symbol environments, achieving comparable results to object detection. Furthermore, using traditional computer vision techniques, we add a parallel step for staff detection to infer the pitch for the recognised symbols. This study emphasises the role of pixel-wise segmentation in advancing accurate music symbol recognition, contributing to knowledge discovery in OMR. Our findings indicate that instance segmentation provides more precise representations of musical symbols, particularly in densely populated scores, advancing OMR technology. We make our implementation, pre-processing scripts, trained models, and evaluation results publicly available to support further research and development., Comment: 8 pages content and one references, accepted version at the International Conference on Knowledge Discovery and Information Retrieval 2024, Porto, Portugal

Object detection and global localization play a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to multi-layered 3D Scene Graphs for semantic scene understanding. This article proposes BOX3D, a novel multi-modal and lightweight scheme for localizing objects of interest by fusing the information from RGB camera and 3D LiDAR. BOX3D is structured around a three-layered architecture, building up from the local perception of the incoming sequential sensor data to the global perception refinement that covers for outliers and the general consistency of each object's observation. More specifically, the first layer handles the low-level fusion of camera and LiDAR data for initial 3D bounding box extraction. The second layer converts each LiDAR's scan 3D bounding boxes to the world coordinate frame and applies a spatial pairing and merging mechanism to maintain the uniqueness of objects observed from different viewpoints. Finally, BOX3D integrates the third layer that supervises the consistency of the results on the global map iteratively, using a point-to-voxel comparison for identifying all points in the global map that belong to the object. Benchmarking results of the proposed novel architecture are showcased in multiple experimental trials on public state-of-the-art large-scale dataset of urban environments., Comment: Presented in MED 2024

The POEMMA-Balloon with Radio (PBR) is a proposed payload to fly on a NASA Super Pressure Balloon (SPB). It will act as a pathfinder of the Probe Of Extreme Multi-Messenger Astrophysics (POEMMA) detector. PBR will consist of an innovative hybrid focal surface featuring a Fluorescence Camera (FC, based on Multi-Anode Photomultiplier Tubes [MAPMTs], 1.05 $\mu$s time resolution) and a Cherenkov Camera (based on SiPMs, 10 ns time resolution), both mounted on the same tiltable frame that can point from nadir up to 13$^\circ$ above the horizon. The FC's main scientific goal is to observe, for the first time, the fluorescence emission of Extensive Air Showers produced by Ultra-High Energy Cosmic Rays from sub-orbital altitudes. This measurement will validate the detection strategy for future space-based missions, such as POEMMA. As a secondary goal, the FC will perform a search for macroscopic dark matter through slowly evolving showers that will leave a signal similar to (but distinct from) a meteor. PBR targets a launch in 2027 as a payload of an ultra-long duration balloon flight with a duration of up to 100 days.

Russian cyberattacks on Ukraine largely failed to produce meaningful outcomes not merely due to robust Ukrainian cyber defenses but were instead primarily a result of Ukraine's effective cyber resilience.

A proper vertex coloring of the graph $G$ such that each vertex dominates at least one color class and the cardinalities of the color classes differ by at most $1$ is called an equitable dominator coloring of $G$. The minimum number of colors used in this coloring is called the equitable dominator chromatic number (EDCN), represented by $\chi_{ed}(G)$. This article explores the concept of equitable dominator coloring for the line graph $L(G)$ of some graph classes.

This paper introduces a new variant of domination-related coloring of graphs, which is a combination of their dominator coloring and equitable coloring called the equitable dominator coloring. An equitable coloring is a proper coloring in which the number of vertices in each color class differs by at most one. In this newly introduced coloring, an additional condition of equitability is added to the existing concept of dominator coloring. The minimum number of colors used in this coloring is called the equitable dominator chromatic number, represented by $\chi_{ed}(G)$. The concept of equitable dominator coloring is explored for basic graph classes. The equitable dominator chromatic number is obtained for the same and some observations on the bounds are made in this paper.

We present a multi-species partial differential equation (PDE) model for tumor growth and a an algorithm for calibrating the model from magnetic resonance imaging (MRI) scans. The model is designed for glioblastoma (GBM) brain tumors. The modeled species correspond to proliferative, infiltrative, and necrotic tumor cells. The model calibration is formulated as an inverse problem and solved a PDE-constrained optimization method. The data that drives the calibration is derived by a single multi-parametric MRI image. This a typical clinical scenario for GBMs. The unknown parameters that need to be calibrated from data include ten scalar parameters and the infinite dimensional initial condition (IC) for proliferative tumor cells. This inverse problem is highly ill-posed as we try to calibrate a nonlinear dynamical system from data taken at a single time. To address this ill-posedness, we split the inversion into two stages. First we regularize the IC reconstruction by solving a single-species compressed sensing problem. Then, using the IC reconstruction, we invert for model parameters using a weighted regularization term. We construct the regularization term by using auxiliary 1D inverse problems. We apply our proposed scheme to clinical data. We compare our algorithm with single-species reconstruction and unregularized reconstructions. Our scheme enables the stable estimation of non-observable species and quantification of infiltrative tumor cells.

This paper explores key aspects of the theory and applications of conformable fractional order systems. It begins by establishing the existence and uniqueness of the evolution operator for a class of non-autonomous homogeneous systems. Using the Schauder fixed point theorem and the theory of linear evolution systems, we delve into the existence of mild solutions for a class of non-autonomous conformable fractional semi-linear systems. Additionally, the paper addresses the exact null controllability of abstract systems. We present an example to demonstrate the efficiency of the results., Comment: 24 Pages, 0 figures

We propose Segment Any Mesh (SAMesh), a novel zero-shot method for mesh part segmentation that overcomes the limitations of shape analysis-based, learning-based, and current zero-shot approaches. SAMesh operates in two phases: multimodal rendering and 2D-to-3D lifting. In the first phase, multiview renders of the mesh are individually processed through Segment Anything 2 (SAM2) to generate 2D masks. These masks are then lifted into a mesh part segmentation by associating masks that refer to the same mesh part across the multiview renders. We find that applying SAM2 to multimodal feature renders of normals and shape diameter scalars achieves better results than using only untextured renders of meshes. By building our method on top of SAM2, we seamlessly inherit any future improvements made to 2D segmentation. We compare our method with a robust, well-evaluated shape analysis method, Shape Diameter Function (ShapeDiam), and show our method is comparable to or exceeds its performance. Since current benchmarks contain limited object diversity, we also curate and release a dataset of generated meshes and use it to demonstrate our method's improved generalization over ShapeDiam via human evaluation. We release the code and dataset at https://github.com/gtangg12/samesh

Since true communities within real-world networks are rarely known, synthetic networks with planted ground truths are valuable for evaluating the performance of community detection methods. Of the synthetic network generation tools available, Stochastic Block Models (SBMs) produce networks with ground truth clusters that well approximate input parameters from real-world networks and clusterings. However, we show that SBMs can produce disconnected ground truth clusters, even when given parameters from clusterings where all clusters are connected. Here we describe the REalistic Cluster Connectivity Simulator (RECCS), a technique that modifies an SBM synthetic network to improve the fit to a given clustered real-world network with respect to edge connectivity within clusters, while maintaining the good fit with respect to other network and cluster statistics. Using real-world networks up to 13.9 million nodes in size, we show that RECCS, applied to stochastic block models, results in synthetic networks that have a better fit to cluster edge connectivity than unmodified SBMs, while providing roughly the same quality fit for other network and clustering parameters as unmodified SBMs., Comment: 12 pages, 5 figures

The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to systematically plan for better outcomes. In this article, we propose and evaluate the use of causal machine learning for developing supply chain risk intervention models, and demonstrate its use with a case study in supply chain risk management in the maritime engineering sector. Our findings highlight that causal machine learning enhances decision-making processes by identifying changes that can be achieved under different supply chain interventions, allowing "what-if" scenario planning. We therefore propose different machine learning developmental pathways for for predicting risk, and planning for interventions to minimise risk and outline key steps for supply chain researchers to explore causal machine learning.

This paper proposes a height profile model for drop-on-demand printing of UV curable ink. Existing models includesuperposition of single drops, numerical models, and graphic-based model. They are either too complicated or over simplified.Graphic model intends to find a sweet spot in between, however, accuracy is marginally improved from superposition modelwhile it demands more computation. The proposed model aimsto achieve the same as graphic model by introducing volumeand area propagation matrices to reflect the localized ink flowfrom higher location to the lower, while avoiding the detailedphysics behind it. This model assumes a constant volume andarea propagation of subsequent drop due to height profile difference. It is validated with experiments on single drop, 2-drop and 3-drop line printing. Stability of this model is analyzed.. Usingroot mean square (RMS) error as benchmark, proposed modelachieves 6.6% along the center row and 7.4% overall, better thanexisting models.

This paper proposes an improved model of height profile for drop-on-demand printing of UV curable ink. Unlike previous model, the proposed model propagates volume and covered area based on height difference between adjacent drops. Height profile is then calculated from the propagated volume and area. Measurements of 2-drop and 3-drop patterns are used to experimentally compute model parameters. The parameters are used to predict and validate height profiles of 4 and more drops in a straight line. Using the same root mean square (RMS) error as benchmark, this model achieves 5.9% RMS height profile error on 4-drop lines. This represents more than 60% reduction from graph-based model and an improvement from our previous effort.

This study uses methods from futures studies to develop a set of ten self-consistent scenarios for Earth's 1000-year future, which can serve as examples for defining technosignature search strategies. We apply a novel worldbuilding pipeline that evaluates the dimensions of human needs in each scenario as a basis for defining the observable properties of the technosphere. Our scenarios include three with zero-growth stability, two that have collapsed into a stable state, one that oscillates between growth and collapse, and four that continue to grow. Only one scenario includes rapid growth that could lead to interstellar expansion. We examine absorption spectral features for a few scenarios to illustrate that nitrogen dioxide can serve as a technosignature to distinguish between present-day Earth, pre-agricultural Earth, and an industrial 1000-year future Earth. Three of our scenarios are spectrally indistinguishable from pre-agricultural Earth, even though these scenarios include expansive technospheres. Up to nine of these scenarios could represent steady-state examples that could persist for much longer timescales, and it remains possible that short-duration technospheres could be the most abundant. Our scenario set provides the basis for further systematic thinking about technosignature detection as well as for imagining a broad range of possibilities for Earth's future., Comment: Submitted to Technological Forecasting and Social Change

Recently, there has been interest in determining the viscoelastic properties of polymeric liquids and other complex fluids by means of Diffusing Wave Spectroscopy (DWS). In this technique, light-scattering spectroscopy is applied to highly turbid fluids containing optical probe particles. The DWS spectrum is used to infer the time-dependent mean-square displacement and time-dependent diffusion coefficient $D$ of the probes. From $D$, values for the storage modulus $G'(\omega)$ and the loss modulus $G''(\omega)$ are obtained. This paper is primarily concerned with the inference of the mean-square displacement from a DWS spectrum. However, in much of the literature, central to the inference that is said to yield $D$ is an invocation $g^{(1)}(t) = \exp(- 2 q^{2} \overline{X(t)^{2}})$ of the Gaussian Approximation for the field correlation function $g^{(1)}(t)$ of the scattered light in terms of the mean-square displacement $\overline{X(t)^{2}}$ of a probe particle during time $t$. Experiment and simulation both show that the Gaussian approximation is invalid for probes in polymeric liquids and other complex fluids. In this paper, we obtain corrections to the Gaussian approximation that will assist in interpreting DWS spectra of probes in polymeric liquids. The corrections reveal that these DWS spectra receive contributions from higher moments $\overline{X(t)^{2n}}$, $n >1$, of the probe displacement distribution function., Comment: arXiv admin note: substantial text overlap with arXiv:cond-mat/0505075

Given a partition $\lambda$, we write $e_j(\lambda)$ for the $j^{\textrm{th}}$ elementary symmetric polynomial $e_j$ evaluated at the parts of $\lambda$ and $e_jp_A(n)$ for the sum of $e_j(\lambda)$ as $\lambda$ ranges over the set of partitions of $n$ with parts in $A$. For $e_jp_A(n)$, we prove analogs of the classical formula for the partition function, $p(n)=1/n \sum_{k=0}^{n-1}\sigma_1(n-k)p(k)$, where $\sigma_1$ is the sum of divisors function. We prove several congruences for $e_2p_4(n)$, the sum of $e_2$ over the set of partitions of $n$ into four parts. Define the function $\textrm{pre}_j(\lambda)$ to be the multiset of monomials in $e_j(\lambda)$, which is itself a partition. If $\mathcal A$ is a set of partitions, we define $\textrm{pre}_j(\mathcal A)$ to be the set of partitions $\textrm{pre}_j(\lambda)$ as $\lambda$ ranges over $\mathcal A$. If $\mathcal P(n)$ is the set of all partitions of $n$, we conjecture that the number of odd partitions in $\textrm{pre}_2(\mathcal P(n))$ is at least the number of distinct partitions. We prove some results about $\textrm{pre}_2(\mathcal B(n))$, where $\mathcal B(n)$ is the set of binary partitions of $n$. We conclude with conjectures on the log-concavity of functions related to $e_jp(n)$, the sum of $e_j(\lambda)$ for all $\lambda\in \mathcal P(n)$., Comment: 17 pages

Quantum money is the first invention in quantum information science, promising advantages over classical money by simultaneously achieving unforgeability, user privacy, and instant validation. However, standard quantum money relies on quantum memories and long-distance quantum communication, which are technologically extremely challenging. Quantum "S-money" tokens eliminate these technological requirements while preserving unforgeability, user privacy, and instant validation. Here, we report the first full experimental demonstration of quantum S-tokens, proven secure despite errors, losses and experimental imperfections. The heralded single-photon source with a high system efficiency of 88.24% protects against arbitrary multi-photon attacks arising from losses in the quantum token generation. Following short-range quantum communication, the token is stored, transacted, and verified using classical bits. We demonstrate a transaction time advantage over intra-city 2.77 km and inter-city 60.54 km optical fibre networks, compared with optimal classical cross-checking schemes. Our implementation demonstrates the practicality of quantum S-tokens for applications requiring high security, privacy and minimal transaction times, like financial trading and network control. It is also the first demonstration of a quantitative quantum time advantage in relativistic cryptography, showing the enhanced cryptographic power of simultaneously considering quantum and relativistic physics., Comment: 74 pages, 6 figures

The constraint satisfaction problem asks to decide if a set of constraints over a relational structure $\mathcal{A}$ is satisfiable (CSP$(\mathcal{A})$). We consider CSP$(\mathcal{A} \cup \mathcal{B})$ where $\mathcal{A}$ is a structure and $\mathcal{B}$ is an alien structure, and analyse its (parameterized) complexity when at most $k$ alien constraints are allowed. We establish connections and obtain transferable complexity results to several well-studied problems that previously escaped classification attempts. Our novel approach, utilizing logical and algebraic methods, yields an FPT versus pNP dichotomy for arbitrary finite structures and sharper dichotomies for Boolean structures and first-order reducts of $(\mathbb{N},=)$ (equality CSPs), together with many partial results for general $\omega$-categorical structures.

Consider a scenario where a harmfulness detection metric is employed by a system to filter unsafe responses generated by a Large Language Model. When analyzing individual harmful and unethical prompt-response pairs, the metric correctly classifies each pair as highly unsafe, assigning the highest score. However, when these same prompts and responses are concatenated, the metric's decision flips, assigning the lowest possible score, thereby misclassifying the content as safe and allowing it to bypass the filter. In this study, we discovered that several harmfulness LLM-based metrics, including GPT-based, exhibit this decision-flipping phenomenon. Additionally, we found that even an advanced metric like GPT-4o is highly sensitive to input order. Specifically, it tends to classify responses as safe if the safe content appears first, regardless of any harmful content that follows, and vice versa. This work introduces automatic concatenation-based tests to assess the fundamental properties a valid metric should satisfy. We applied these tests in a model safety scenario to assess the reliability of harmfulness detection metrics, uncovering a number of inconsistencies.

As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging. Although daunting, this problem is not new: Neuroscientists and cognitive scientists have accumulated decades of experience analyzing a particularly complex system - the brain. In this work, we argue that interpreting both biological and artificial neural systems requires analyzing those systems at multiple levels of analysis, with different analytic tools for each level. We first lay out a joint grand challenge among scientists who study the brain and who study artificial neural networks: understanding how distributed neural mechanisms give rise to complex cognition and behavior. We then present a series of analytical tools that can be used to analyze biological and artificial neural systems, organizing those tools according to Marr's three levels of analysis: computation/behavior, algorithm/representation, and implementation. Overall, the multilevel interpretability framework provides a principled way to tackle neural system complexity; links structure, computation, and behavior; clarifies assumptions and research priorities at each level; and paves the way toward a unified effort for understanding intelligent systems, may they be biological or artificial.

We introduce the notion of "Banach metrics" on finitely generated infinite groups. This extends the notion of a Cayley graph (as a metric space). Our motivation comes from trying to detect the existence of virtual homomorphisms into Z, the additive group of integers. We show that detection of such homomorphisms through metric functional boundaries of Cayley graphs isn't always possible. However, we prove that it is always possible to do so through a metric functional boundary of some Banach metric on the group.

Hindi, one of the most spoken language of India, exhibits a diverse array of accents due to its usage among individuals from diverse linguistic origins. To enable a robust evaluation of Hindi ASR systems on multiple accents, we create a benchmark, LAHAJA, which contains read and extempore speech on a diverse set of topics and use cases, with a total of 12.5 hours of Hindi audio, sourced from 132 speakers spanning 83 districts of India. We evaluate existing open-source and commercial models on LAHAJA and find their performance to be poor. We then train models using different datasets and find that our model trained on multilingual data with good speaker diversity outperforms existing models by a significant margin. We also present a fine-grained analysis which shows that the performance declines for speakers from North-East and South India, especially with content heavy in named entities and specialized terminology.

A result of Gevrey regularity is ascertained for a semigroup which models a fluid-structure interaction problem. In this model, the fluid evolves in a piecewise smooth or convex geometry $\mathcal{O}$. On a portion of the boundary, a fourth order plate equation is coupled with the fluid through pressure and matching velocities. The key to obtaining the conclusion of Gevrey regularity is an appropriate estimation of the resolvent of the associated $C_0$-semigroup operator. Moreover, a numerical scheme and example is provided which empirically demonstrates smoothing of the fluid-structure semigroup., Comment: 14 pages, 5 figures

In this work, we apply a phase-field modeling framework to elucidate the interplay between nucleation and kinetics in the dynamic evolution of twinning interfaces. The key feature of this phase-field approach is the ability to transparently and explicitly specify nucleation and kinetic behavior in the model, in contrast to other regularized interface models. We use this to study 2 distinct problems where it is essential to explicitly specify the kinetic and nucleation behavior governing twin evolution. First, we study twinning interfaces in 2-d. When these interfaces are driven to move, we find that significant levels of twin nucleation occur ahead of the moving interface. Essentially, the finite interface velocity and the relaxation time of the stresses ahead of the interface allows for nucleation to occur before the interface is able to propagate to that point. Second, we study the growth of needle twins in antiplane elasticity. We show that both nucleation and anisotropic kinetics are essential to obtain predictions of needle twins. While standard regularized interface approaches do not permit the transparent specification of anisotropic kinetics, this is readily possible with the phase-field approach that we have used here., Comment: To appear in Journal of Applied Mechanics

The conformal scalar propagator as an explicit function of the Schwarzschild black-hole space-time has been established in the preceding three projects. The present project examines the precise relation which that propagator has to the amplitude the Schwarzschild black hole emit a scalar particle in a particular mode of positive energy. It is thereby established that the effect of infinite time-dilation on the event horizon results in both, the thermal radiation in the background of a static exterior space-time geometry and the detraction from thermality if energy-conservation is properly imposed as a constraint on scalar propagation. The derivation of the latter signifies an equivalent approach to the result of Parikh and Wilczek from the semi-classical perspective of the distant observer. From that perspective for that matter, Hawking radiation emerges in both contexts as the exclusive consequence of infinite time-dilation on the event horizon. These results are consistent with Hawking radiation as the exclusive consequence of the causal structure of the Schwarzschild black-hole space-time both, in a static exterior geometry and in such dynamical exterior geometry as energy-conservation signifies. The extension of the thermal case to other spin-fields and black-hole geometries is discussed., Comment: 22 pages, 3 figures. To appear in International Journal of Modern Physics D

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices

The CHIPS R&D project focuses on development of low-cost water Cherenkov neutrino detectors through novel design strategies and resourceful engineering. This work presents an end-to-end DAQ solution intended for a recent 5 kt CHIPS prototype, which is largely based on affordable mass-produced components. Much like the detector itself, the presented instrumentation is composed of modular arrays that can be scaled up and easily serviced. A single such array can carry up to 30 photomultiplier tubes (PMTs) accompanied by electronics that generate high voltage in-situ and deliver time resolution of up to 0.69 ns. In addition, the technology is compatible with the White Rabbit timing system, which can synchronize its elements to within 100 ps. While deployment issues did not permit the presented DAQ system to operate beyond initial evaluation, the presented hardware and software successfully passed numerous commissioning tests that demonstrated their viability for use in a large-scale neutrino detector, instrumented with thousands of PMTs., Comment: 30 pages, 28 figures, submitted to MDPI Applied Sciences, Special Issue: Advanced Neutrino Detector Development and Application

Community detection approaches resolve complex networks into smaller groups (communities) that are expected to be relatively edge-dense and well-connected. The stochastic block model (SBM) is one of several approaches used to uncover community structure in graphs. In this study, we demonstrate that SBM software applied to various real-world and synthetic networks produces poorly-connected to disconnected clusters. We present simple modifications to improve the connectivity of SBM clusters, and show that the modifications improve accuracy using simulated networks.

We continue the exploration of a question of dice relabeling posed by Gallian and Rusin: Given $n$ dice, each labeled 1 through $m$, how many ways are there to relabel the dice without changing the frequencies of the possible sums? We answer this question in the case where $n = 2$ and $m$ is a product of three prime numbers. We also explore more general questions. We find a method for decomposing two $m$-sided dice into two dice of different sizes and give some preliminary results on relabeling two dice of different sizes. Finally, we refine a result of the aforementioned authors in the case where m is a prime power.

The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index \gamma. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, \gamma_run(f) = \gamma + \beta \ln(f/f_ref). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter \beta consistent with no running, \beta \in [-0.80,2.96], and an inconclusive Bayes factor, B(RPL vs. CPL) = 0.69 +- 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero \beta. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO-Virgo-KAGRA., Comment: 17 pages, 4 figures, 2 tables

The stellar obliquity distribution of warm-Jupiter systems is crucial for constraining the dynamical history of Jovian exoplanets, as the warm Jupiters' tidal detachment likely preserves their primordial obliquity. However, the sample size of warm-Jupiter systems with measured stellar obliquities has historically been limited compared to that of hot Jupiters, particularly in hot-star systems. In this work, we present newly obtained sky-projected stellar obliquity measurements for warm-Jupiter systems, TOI-559, TOI-2025, TOI-2031, TOI-2485, TOI-2524, and TOI-3972, derived from the Rossiter-McLaughlin effect, and show that all six systems display alignment with a median measurement uncertainty of 13 degrees. Combining these new measurements with the set of previously reported stellar obliquity measurements, our analysis reveals that single-star warm-Jupiter systems tend to be aligned, even around hot stellar hosts. This alignment exhibits a 3.4-$\sigma$ deviation from the $T_{\rm eff}-\lambda$ dependency observed in hot-Jupiter systems, where planets around cool stars tend to be aligned, while those orbiting hot stars show considerable misalignment. The current distribution of spin-orbit measurements for Jovian exoplanets indicates that misalignments are neither universal nor primordial phenomena affecting all types of planets. The absence of misalignments in single-star warm-Jupiter systems further implies that many hot Jupiters, by contrast, have experienced a dynamically violent history., Comment: 18 pages, 2 figures, accepted for publication in ApJL

The polarization position angles (PPA) of time samples with high linear polarization often show two parallel tracks across the pulsar profile that follow the rotating vector model (RVM). This feature support coherent curvature radiation (CCR) as the underlying mechanism of radio emission from pulsars, where the parallel tracks of the PPA represent the orthogonal extraordinary X and ordinary O eigen modes of strongly magnetized pair plasma. However, the frequency evolution of these high linearly polarized signals remains unexplored. In this work we explore the flux density spectral nature of high linearly polarized signals by studying the emission from PSR J0332+5434 over a frequency range between 300 MHz and 750 MHz, using the Giant Metrewave Radio Telescope. The pulsar average profile comprises of a central core and a pair of conal components. We find the high linearly polarized time samples to be broadband in nature and in many cases they resemble a narrow spiky feature in the conal regions. These spiky features are localised within a narrow pulse longitude, over the entire frequency range, and their spectral shapes sometimes resemble an inverted parabolic shape. In all such cases the PPA are exclusively along one of the orthogonal RVM tracks, likely corresponding to the X-mode. The inverted spectral shape can in principle be explained if the high linearly polarized emission in these time samples are formed due to incoherent addition of CCR from a large number of charged solitons (charge bunches) exciting the X-mode., Comment: Accepted for publication in ApJ

Development of methods to control the directional and spectral characteristics of thermal radiation from metallic surfaces is a critical factor enabling many important thermal management applications. In this paper, we study the thermal emission properties of functionalized aluminum surfaces produced through femtosecond laser surface processing (FLSP). These types of surfaces have recently been found to exhibit near-unity broadband omnidirectional emissivity. However, their ultrabroadband absorption response includes visible and near-infrared (IR) radiation, in addition to the mid-IR range, which limits their use as daytime passive radiative cooling devices. Here, we present ways to solve this problem by demonstrating a new design that uses a dielectric Bragg visible light reflector to accurately control the thermal emission spectra of the FLSP surface with the goal of achieving high performance daytime radiative cooling operation. In addition, we propose other designs based on dielectric multilayer structures to further tailor and control the spectra and thermal emission angles of the FLSP surfaces leading to narrowband and broadband directional thermal radiation. The presented photonic engineering approach combined with FLSP structures will be beneficial to various emerging applications, such as radiative cooling, thermal sensing, and thermophotovoltaics.

We examine the evaporation-induced coalescence of two droplets undergoing freezing by conducting numerical simulations employing the lubrication approximation. When two sessile drops undergo freezing in close vicinity over a substrate, they interact with each other through the gaseous phase and the simultaneous presence of evaporation/condensation. In an unsaturated environment, the evaporation flux over the two volatile sessile drops is asymmetric, with lower evaporation in the region between the two drops. This asymmetry in the evaporation flux generates an asymmetric curvature in each drop, which results in a capillary flow that drives the drops closer to each other, eventually leading to their coalescence. This capillary flow, driven by evaporation, competes with the upward movement of the freezing front, depending on the relative humidity in the surrounding environment. We found that higher relative humidity reduces the evaporative flux, delaying capillary flow and impeding coalescence by restricting contact line motion. For a constant relative humidity, the substrate temperature governs the coalescence phenomenon, and resulting condensation can accelerate this process. Interestingly, lower substrate temperatures are observed to facilitate faster propagation of the freezing front, which, in turn, restricts coalescence., Comment: 34 pages, 16 figures. arXiv admin note: text overlap with arXiv:2312.03271

The Landau-Nordita conference that was held in Moscow in 1981 was the first place where I met Lars. This "marvellous meeting", as Lars quoted in one of his reminiscence article, was organised by Alan Luther. It was an event that generated a long-lasting friendship of scientists from East and West, provided a background for the future collaborations and, in particular, my own collaboration with Lars on a supersymmetric high-spin extension of the Poincar\'e group., Comment: 13 pages and 3 photos

It can be difficult to interpret a coefficient of an uncertain model. A slope coefficient of a regression model may change as covariates are added or removed from the model. In the context of high-dimensional data, there are too many model extensions to check. However, as we show here, it is possible to efficiently search, with a branch and bound algorithm, for maximum and minimum values of that adjusted slope coefficient over a discrete space of regularized regression models. Here we introduce our algorithm, along with supporting mathematical results, an example application, and a link to our computer code, to help researchers summarize high-dimensional data and assess the stability of regression coefficients in uncertain models.

Clay minerals are abundant natural materials used widely in coatings, construction materials, ceramics, as well as being a component of drilling fluids. Here, we present the effect of steady and oscillatory preshear on organophilic modified clay gels in synthetic oil. Both platelet and needle-like particles are used as viscosifiers in drilling fluid formulations. For both particles the plateau modulus exhibits a similar concentration dependence, G_P ~ c^3.9, whereas the yield strain is {\gamma}_y ~ c^(-1) for the platelets and {\gamma}_y ~ c^-1.7 for the needles. Mixtures of the two follow an intermediate behavior: at low concentrations their elasticity and yield strain follows that of needle particles while at higher concentrations it exhibits a weaker power law dependence. Furthermore, upon varying the preshear history, the gel viscoelastic properties can be significantly tuned. At lower (higher) clay concentrations, preshear at specific oscillatory strain amplitudes or steady shear rates, may induce a hardening (softening) of the dispersions and, at all concentrations, a lowering of the shear strain. Hence, in needle dispersions preshear resulted in changes in the volume fraction dependence of the elastic modulus from G_P ~ c^3.9 to G_P ~ c^2.5 and of the yield strain from {\gamma}_y ~ c^-1.7 to {\gamma}_y ~ c^-1. However, small angle X-ray scattering showed not much structural changes, within the q-range covered. Our findings indicate ways to design colloidal organoclay dispersions with a mechanical response that can be tuned at will.

Extreme natural phenomena are occurring more frequently everyday in the world, challenging, among others, the infrastructure of communication networks. For instance, the devastating earthquakes in Turkiye in early 2023 showcased that, although communications became an imminent priority, existing mobile communication systems fell short with the operational requirements of harsh disaster environments. In this article, we present a novel framework for robust, resilient, adaptive, and open source sixth generation (6G) radio access networks (Open6GRAN) that can provide uninterrupted communication services in the face of natural disasters and other disruptions. Advanced 6G technologies, such as reconfigurable intelligent surfaces (RISs), cell-free multiple-input-multiple-output, and joint communications and sensing with increasingly heterogeneous deployment, consisting of terrestrial and non-terrestrial nodes, are robustly integrated. We advocate that a key enabler to develop service and management orchestration with fast recovery capabilities will rely on an artificial-intelligence-based radio access network (RAN) controller. To support the emergency use case spanning a larger area, the integration of aerial and space segments with the terrestrial network promises a rapid and reliable response in the case of any disaster. A proof-of-concept that rapidly reconfigures an RIS for performance enhancement under an emergency scenario is presented and discussed., Comment: Submitted to IEEE VTM

We propose a path for making quantitative analyses of mean-square displacement curves of polymer chains in the melt or in solution. The approach invokes a general functional form that accurately describes $g(t) \equiv \langle (\Delta x(t))^{2} \rangle$ for all times at which $g(t)$ was measured, and that gives values for the logarithmic derivative of $g(t)$ for the same times. By these means we can readily distinguish between regimes in which $g(t)$ follows a power law in time, does not follow a power law in time, or has an inflection point. In a power-law regime, the method accurately determines the exponent, without imposing any assumption as to the exponent's value., Comment: 9 pages, 3 figures

The relationship between Ly$\alpha$ forest opacity and local galaxy density (the opacity-density relation) is a key observational test of late reionization models. Using narrow-band surveys of z=5.7 Ly$\alpha$ emitters centered on quasar sight lines, Christenson et al. (2023) showed that two of the most transmissive forest segments at this redshift intersect galaxy underdensities. This is in tension with models of a strongly fluctuating ionizing background, including some late reionization models, which predict that the vast majority of these segments should intersect overdensities where the ionizing intensity is strongest. We use radiative transfer simulations to explore in detail the opacity-density relation in late reionization models. Fields like the one toward quasar PSO J359-06 -- the more underdense of the two transmissive sight lines in Christenson et al. (2023) -- typically contain recently reionized gas in cosmic voids where the hot temperatures and low densities enhance Ly$\alpha$ transmission. The opacity-density relation's transmissive end is sensitive to the amount of neutral gas in voids, and its morphology, set by the reionization source clustering. These effects are, however, degenerate. We demonstrate that models with very different source clustering can yield similar opacity-density relations when their reionization histories are calibrated to match Ly$\alpha$ forest mean flux measurements at z<6. In models with fixed source clustering, a lower neutral fraction increases the likelihood of intersecting hot, recently reionized gas in voids, increasing the likelihood of observing PSO J359-06. For instance, the probability of observing this field is 15% in a model with neutral fraction $x_{\rm HI}=5\%$ at z=5.7, three times more likely than in a model with $x_{\rm HI}=15\%$. The opacity-density relation may thus provide a complementary probe of reionization's end., Comment: 31 pages, 17 Figures

Training a neural network is a monolithic endeavor, akin to carving knowledge into stone: once the process is completed, editing the knowledge in a network is nearly impossible, since all information is distributed across the network's weights. We here explore a simple, compelling alternative by marrying the representational power of deep neural networks with the flexibility of a database. Decomposing the task of image classification into image similarity (from a pre-trained embedding) and search (via fast nearest neighbor retrieval from a knowledge database), we build a simple and flexible visual memory that has the following key capabilities: (1.) The ability to flexibly add data across scales: from individual samples all the way to entire classes and billion-scale data; (2.) The ability to remove data through unlearning and memory pruning; (3.) An interpretable decision-mechanism on which we can intervene to control its behavior. Taken together, these capabilities comprehensively demonstrate the benefits of an explicit visual memory. We hope that it might contribute to a conversation on how knowledge should be represented in deep vision models -- beyond carving it in "stone" weights., Comment: Adding link to code at https://github.com/google-deepmind/visual-memory

The rapid advancements in Large Language Models (LLMs) have led to significant improvements in various natural language processing tasks. However, the evaluation of LLMs' legal knowledge, particularly in non-English languages such as Arabic, remains under-explored. To address this gap, we introduce ArabLegalEval, a multitask benchmark dataset for assessing the Arabic legal knowledge of LLMs. Inspired by the MMLU and LegalBench datasets, ArabLegalEval consists of multiple tasks sourced from Saudi legal documents and synthesized questions. In this work, we aim to analyze the capabilities required to solve legal problems in Arabic and benchmark the performance of state-of-the-art LLMs. We explore the impact of in-context learning and investigate various evaluation methods. Additionally, we explore workflows for generating questions with automatic validation to enhance the dataset's quality. We benchmark multilingual and Arabic-centric LLMs, such as GPT-4 and Jais, respectively. We also share our methodology for creating the dataset and validation, which can be generalized to other domains. We hope to accelerate AI research in the Arabic Legal domain by releasing the ArabLegalEval dataset and code: https://github.com/Thiqah/ArabLegalEval