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Wasserstein distances form a family of metrics on spaces of probability measures that have recently seen many applications. However, statistical analysis in these spaces is complex due to the nonlinearity of Wasserstein spaces. One potential solution to this problem is Linear Optimal Transport (LOT). This method allows one to find a Euclidean embedding, called LOT embedding, of measures in some Wasserstein spaces, but some information is lost in this embedding. So, to understand whether statistical analysis relying on LOT embeddings can make valid inferences about original data, it is helpful to quantify how well these embeddings describe that data. To answer this question, we present a decomposition of the Fr\'echet variance of a set of measures in the 2-Wasserstein space, which allows one to compute the percentage of variance explained by LOT embeddings of those measures. We then extend this decomposition to the Fused Gromov-Wasserstein setting. We also present several experiments that explore the relationship between the dimension of the LOT embedding, the percentage of variance explained by the embedding, and the classification accuracy of machine learning classifiers built on the embedded data. We use the MNIST handwritten digits dataset, IMDB-50000 dataset, and Diffusion Tensor MRI images for these experiments. Our results illustrate the effectiveness of low dimensional LOT embeddings in terms of the percentage of variance explained and the classification accuracy of models built on the embedded data.

Miniaturization of electronic components has led to overheating, increasing power consumption and causing early circuit failures. Conventional heat dissipation methods are becoming inadequate due to limited surface area and higher short-circuit risks. This study presents a fast, low-cost, and scalable technique using 2D hexagonal boron nitride (hBN) coatings to enhance heat dissipation in commercial electronics. Inexpensive hBN layers, applied by drop casting or spray coating, boost thermal conductivity at IC surfaces from below 0.3 W/m-K to 260 W/m-K, resulting in over double the heat flux and convective heat transfer. This significantly reduces operating temperatures and power consumption, as demonstrated by a 17.4% reduction in a coated audio amplifier circuit board. Density functional theory indicates enhanced interaction between 2D hBN and packaging materials as a key factor. This approach promises substantial energy and cost savings for large-scale electronics without altering existing manufacturing processes., Comment: 27 Pages, 5 Figures

Exploring nuclear physics through the fundamental constituents of the strong force -- quarks and gluons -- is a formidable challenge. While numerical calculations using lattice quantum chromodynamics offer the most promising approach for this pursuit, practical implementation is arduous, especially due to the uncontrollable growth of quark-combinatorics, the so-called Wick-contraction problem of nuclei. We present here two novel methods providing a state-of-the-art solution to this problem. In the first, we exploit randomized algorithms inspired from computational number theory to detect and eliminate redundancies that arise in Wick contraction computations. Our second method explores facilities for automation of tensor computations -- in terms of efficient utilization of specialized hardware, algorithmic optimizations, as well as ease of programming and the potential for automatic code generation -- that are offered by new programming models inspired by applications in machine learning (e.g., TensorFlow). We demonstrate the efficacy of our methods by computing two-point correlation functions for Deuteron, Helium-3, Helium-4 and Lithium-7, achieving at least an order of magnitude improvement over existing algorithms with efficient implementation on GPU-accelerators. Additionally, we discover an intriguing characteristic shared by all the nuclei we study: specific spin-color combinations dominate the correlation functions, hinting at a potential connection to an as-yet-unidentified symmetry in nuclei. Moreover finding them beforehand can reduce the computing time further and substantially. Our results, with the efficiency that we achieved, suggest the possibility of extending the applicability of our methods for calculating properties of light nuclei, potentially up to A ~12 and beyond., Comment: 68 pages with 6 tables and 23 figures including an Appendix. To be published in Physical Review D

The strong coupling regime of photons and quantum materials inside optical cavities has emerged as a promising environment for manipulating states of matter with light. Here, in turn, we show that photons bear witness to cavity quantum-electrodynamical modifications of the material, leading to profoundly non-classical properties of light passing through the cavity. By generalizing quantum-optical input-output relations to correlated quantum materials, we study the second-order photon coherence g2(t) and demonstrate that antibunching of transmitted photons serves as direct evidence of light-induced changes to the cavity-embedded material. We show that materials near a quantum critical point can realize a collective many-body photon blockade, enabling the generation of single photons or Einstein-Podolsky-Rosen pairs via leveraging strong matter fluctuations. Our findings provide new routes for interrogating and harnessing cavity-embedded quantum materials as quantum light sources, as a resource for photon-based computation and quantum sensing., Comment: 9+14 pages, 5 figures

Incorporating nanomaterials into hydrogels allows for the creation of versatile materials with properties that can be precisely tailored by manipulating their nanoscale structures, leading to a wide range of bulk properties. Investigating the structural and property characteristics of composite hydrogels is crucial in tailoring their performance for specific applications. This study focuses on investigating the correlation between the structural arrangement and properties of a composite hydrogel of thermoresponsive polymer, gelatin, and light-responsive antimicrobial porous gold nanorods, $PAuNR$. The rheo-mechanical properties of the composite hydrogels are correlated with their nanoscale structural characteristics, investigated using small-angle neutron scattering ($SANS$). Analysis of $SANS$ data reveals a decrease in the fractal dimension of $PAuNRs$ incorporated hydrogel matrix, as compared to pure gelatin. Incorporating $PAuNRs$ results in formation of softer composite hydrogel as evident from decrease in viscoelastic moduli, critical yield strain, denaturation temperature and swelling ratio. Our results demonstrates that the structural modulation at the nanoscale can be precisely controlled through adjusting $PAuNRs$ concentration and temperature providing an fabrication mechanism for hydrogels with desired elastic properties. The reduced elasticity of the composite hydrogel and light sensitive/antimicrobial property of the $PAuNRs$ makes this system suitable for specific biomedical applications, such as tissue engineering, device fabrication and stimuli based controlled drug delivery devices respectively., Comment: Under review: The Journal of Chemical Physics (JCP24-AR-04120)

Azurin and its derived peptides, notably p28, exhibit significant anticancer properties, primarily by stabilizing the tumor suppressor protein p53 and preventing its degradation. Previous studies have shown that p28 binds to p53's DNA-binding domain, protecting it from degradation mechanisms. Expanding on these findings, our research explored whether p28 acts on additional cancer pathways beyond p53 stabilization. Specifically, we examined the interactions between p28 and Human Double Minute 2 (HDM2), a protein that downregulates p53's tumor-suppressive activity by binding to its transactivation domain (TAD). HDM2 is crucial in diminishing p53's function, and our study aimed to determine if p28 disrupts this HDM2-p53 interaction. Using HADDOCK docking and molecular dynamics simulations, we identified three stable conformations of the HDM2-p28 complex. These conformations effectively block HDM2's hydrophobic pocket, allowing for sustained inter-chain interactions and showing favorable binding energies. Further analysis pinpointed essential residues in these interactions, and we calculated interaction energies using the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method. Our findings reveal that by blocking HDM2's binding sites, p28 helps maintain p53's transcriptional activity, thus enhancing its tumor-suppressive functions, including apoptosis and cell cycle arrest in cancer cells. This study enhances understanding of azurin-derived peptides' anticancer mechanisms and highlights p28's potential as a peptide-based anticancer agent. These findings also suggest the possibility of designing additional peptide therapies targeting HDM2 and other cancer-related pathways, opening new directions in anticancer therapeutics.

This paper presents a detailed system description of our entry for the CHiPSAL 2025 shared task, focusing on language detection, hate speech identification, and target detection in Devanagari script languages. We experimented with a combination of large language models and their ensembles, including MuRIL, IndicBERT, and Gemma-2, and leveraged unique techniques like focal loss to address challenges in the natural understanding of Devanagari languages, such as multilingual processing and class imbalance. Our approach achieved competitive results across all tasks: F1 of 0.9980, 0.7652, and 0.6804 for Sub-tasks A, B, and C respectively. This work provides insights into the effectiveness of transformer models in tasks with domain-specific and linguistic challenges, as well as areas for potential improvement in future iterations., Comment: 13 pages, Submitted to CHIPSAL workshop @ COLING 2025

Language agents have demonstrated promising capabilities in automating web-based tasks, though their current reactive approaches still underperform largely compared to humans. While incorporating advanced planning algorithms, particularly tree search methods, could enhance these agents' performance, implementing tree search directly on live websites poses significant safety risks and practical constraints due to irreversible actions such as confirming a purchase. In this paper, we introduce a novel paradigm that augments language agents with model-based planning, pioneering the innovative use of large language models (LLMs) as world models in complex web environments. Our method, WebDreamer, builds on the key insight that LLMs inherently encode comprehensive knowledge about website structures and functionalities. Specifically, WebDreamer uses LLMs to simulate outcomes for each candidate action (e.g., "what would happen if I click this button?") using natural language descriptions, and then evaluates these imagined outcomes to determine the optimal action at each step. Empirical results on two representative web agent benchmarks with online interaction -- VisualWebArena and Mind2Web-live -- demonstrate that WebDreamer achieves substantial improvements over reactive baselines. By establishing the viability of LLMs as world models in web environments, this work lays the groundwork for a paradigm shift in automated web interaction. More broadly, our findings open exciting new avenues for future research into 1) optimizing LLMs specifically for world modeling in complex, dynamic environments, and 2) model-based speculative planning for language agents., Comment: 18 pages, 6 figures, 4 tables

We recall a resummation procedure in QED to extract the zero momentum mode in soft photon emission and present an ansatz about a possible mechanism for the forward peak characterizing elastic proton proton scattering., Comment: 8 pages, 4 figures

Sign languages are the language of hearing-impaired people who use visuals like the hand, facial, and body movements for communication. There are different signs and gestures representing alphabets, words, and phrases. Nowadays approximately 300 sign languages are being practiced worldwide such as American Sign Language (ASL), Chinese Sign Language (CSL), Indian Sign Language (ISL), and many more. Sign languages are dependent on the vocal language of a place. Unlike vocal or spoken languages, there are no helping words in sign language like is, am, are, was, were, will, be, etc. As only a limited population is well-versed in sign language, this lack of familiarity of sign language hinders hearing-impaired people from communicating freely and easily with everyone. This issue can be addressed by a sign language recognition (SLR) system which has the capability to translate the sign language into vocal language. In this paper, a continuous SLR system is proposed using a deep learning model employing Long Short-Term Memory (LSTM), trained and tested on an ISL primary dataset. This dataset is created using MediaPipe Holistic pipeline for tracking face, hand, and body movements and collecting landmarks. The system recognizes the signs and gestures in real-time with 88.23% accuracy., Comment: 14 pages, 4 figures, Wireless Pers Commun

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

We consider the problem of counting the copies of a length-$k$ pattern $\sigma$ in a sequence $f \colon [n] \to \mathbb{R}$, where a copy is a subset of indices $i_1 < \ldots < i_k \in [n]$ such that $f(i_j) < f(i_\ell)$ if and only if $\sigma(j) < \sigma(\ell)$. This problem is motivated by a range of connections and applications in ranking, nonparametric statistics, combinatorics, and fine-grained complexity, especially when $k$ is a small fixed constant. Recent advances have significantly improved our understanding of counting and detecting patterns. Guillemot and Marx [2014] demonstrated that the detection variant is solvable in $O(n)$ time for any fixed $k$. Their proof has laid the foundations for the discovery of the twin-width, a concept that has notably advanced parameterized complexity in recent years. Counting, in contrast, is harder: it has a conditional lower bound of $n^{\Omega(k / \log k)}$ [Berendsohn, Kozma, and Marx 2019] and is expected to be polynomially harder than detection as early as $k = 4$, given its equivalence to counting $4$-cycles in graphs [Dudek and Gawrychowski, 2020]. In this work, we design a deterministic near-linear time $(1+\varepsilon)$-approximation algorithm for counting $\sigma$-copies in $f$ for all $k \leq 5$. Combined with the conditional lower bound for $k=4$, this establishes the first known separation between approximate and exact algorithms for pattern counting. Interestingly, our algorithm leverages the Birg\'e decomposition -- a sublinear tool for monotone distributions widely used in distribution testing -- which, to our knowledge, has not been applied in a pattern counting context before.

We give a construction of Quantum Low-Density Parity Check (QLDPC) codes with near-optimal rate-distance tradeoff and efficient list decoding up to the Johnson bound in polynomial time. Previous constructions of list decodable good distance quantum codes either required access to a classical side channel or were based on algebraic constructions that preclude the LDPC property. Our construction relies on new algorithmic results for codes obtained via the quantum analog of the distance amplification scheme of Alon, Edmonds, and Luby [FOCS 1995]. These results are based on convex relaxations obtained using the Sum-of-Squares hierarchy, which reduce the problem of list decoding the distance amplified codes to unique decoding the starting base codes. Choosing these base codes to be the recent breakthrough constructions of good QLDPC codes with efficient unique decoders, we get efficiently list decodable QLDPC codes.

Coherent Elastic Neutrino-Nucleus (CE$\nu$NS) and Elastic Neutrino-Electron Scattering (E$\nu$ES) data are exploited to constrain "chiral" $U(1)_{X}$ gauged models with light vector mediator mass. These models fall under a distinct class of new symmetries called Dark Hypercharge Symmetries. A key feature is the fact that the $Z'$ boson can couple to all Standard Model fermions at tree level, with the $U(1)_X$ charges determined by the requirement of anomaly cancellation. Notably, the charges of leptons and quarks can differ significantly depending on the specific anomaly cancellation solution. As a result, different models exhibit distinct phenomenological signatures and can be constrained through various experiments. In this work, we analyze the recent data from the COHERENT experiment, along with results from Dark Matter (DM) direct detection experiments such as XENONnT, LUX-ZEPLIN, and PandaX-4T, and place new constraints on three benchmark models. Additionally, we set constraints from a performed analysis of TEXONO data and discuss the prospects of improvement in view of the next-generation DM direct detection DARWIN experiment., Comment: 44 pages, 13 figures, 3 tables

We consider a gossiping network, where a source node sends updates to a network of $n$ gossiping nodes. Meanwhile, the connectivity topology of the gossiping network changes over time, among a finite number of connectivity ''states,'' such as the fully connected graph, the ring graph, the grid graph, etc. The transition of the connectivity graph among the possible options is governed by a finite state continuous time Markov chain (CTMC). When the CTMC is in a particular state, the associated graph topology of the gossiping network is in the way indicated by that state. We evaluate the impact of time-varying graph topologies on the freshness of information for nodes in the network. We use the version age of information metric to quantify the freshness of information at the nodes. Using a method similar to the first passage percolation method, we show that, if one of the states of the CTMC is the fully connected graph and the transition rates of the CTMC are constant, then the version age of a typical node in the network scales logarithmically with the number of nodes, as in the case if the network was always fully connected. That is, there is no loss in the age scaling, even if the network topology deviates from full connectivity, in this setting. We perform numerical simulations and analyze more generally how having different topologies and different CTMC rates (that might depend on the number of nodes) affect the average version age scaling of a node in the gossiping network.

Modern Text-to-Image (T2I) Diffusion models have revolutionized image editing by enabling the generation of high-quality photorealistic images. While the de facto method for performing edits with T2I models is through text instructions, this approach non-trivial due to the complex many-to-many mapping between natural language and images. In this work, we address exemplar-based image editing -- the task of transferring an edit from an exemplar pair to a content image(s). We propose ReEdit, a modular and efficient end-to-end framework that captures edits in both text and image modalities while ensuring the fidelity of the edited image. We validate the effectiveness of ReEdit through extensive comparisons with state-of-the-art baselines and sensitivity analyses of key design choices. Our results demonstrate that ReEdit consistently outperforms contemporary approaches both qualitatively and quantitatively. Additionally, ReEdit boasts high practical applicability, as it does not require any task-specific optimization and is four times faster than the next best baseline., Comment: First three authors contributed equally to this work

Kinetically constrained spin chains serve as a prototype for structured ergodicity breaking in isolated quantum systems. We show that such a system exhibits a hierarchy of degenerate steady states when driven by incoherent pump and loss at the boundary. By tuning the relative pump and loss and how local the constraints are, one can stabilize mixed steady states, noiseless subsystems, and various decoherence-free subspaces, all of which preserve large amounts of information. We also find that a dipole-conserving bulk suppresses current in steady state. These exact results based on the flow in Hilbert space hold regardless of the specific Hamiltonian or drive mechanism. Our findings show that a competition of kinetic constraints and local drives can induce different forms of ergodicity breaking in open systems, which should be accessible in quantum simulators., Comment: 8 pages, 2 figures + Appendix + Notebook to reproduce data

Modeling of large populations of binary stellar systems is an intergral part of a many areas of astrophysics, from radio pulsars and supernovae to X-ray binaries, gamma-ray bursts, and gravitational-wave mergers. Binary population synthesis codes that employ self-consistently the most advanced physics treatment available for stellar interiors and their evolution and are at the same time computationally tractable have started to emerge only recently. One element that is still missing from these codes is the ability to generate the complete time evolution of binaries with arbitrary initial conditions using pre-computed three-dimensional grids of binary sequences. Here we present a highly interpretable method, from binary evolution track interpolation. Our method implements simulation generation from irregularly sampled time series. Our results indicate that this method is appropriate for applications within binary population synthesis and computational astrophysics with time-dependent simulations in general. Furthermore we point out and offer solutions to the difficulty surrounding evaluating performance of signals exhibiting extreme morphologies akin to discontinuities., Comment: 15 pages, 11 figures, Submitted to ApJ

We introduce a new class of $U(1)_X$ symmetries where all Standard Model fermions are "chiral", i.e. the left and right-handed components have different charges under the $U(1)_X$ symmetry. Gauge anomaly cancellation is achieved by introducing three Standard Model gauge singlet dark fermions ($f^i$; $i=1,2,3$) charged under this symmetry. We systematically present chiral solutions for cases in which (a) one, (b) two, or (c) all three generations of Standard Model fermions are charged under the $U(1)_X$ symmetry. The $U(1)_X$ charges of these dark fermions are uniquely determined by anomaly cancellation conditions. These new fermions belong to the dark sector, with the lightest of them being a good dark matter candidate. Additionally, the $Z'$ gauge boson mediates interactions between the dark and visible sectors, and we call this $U(1)_X$ symmetry as the "Dark HyperCharge" symmetry. Using a benchmark model, we explore phenomenological implications in the heavy $Z'$ case ($M_{Z'} > M_Z$), analyzing collider constraints and examining the lightest dark fermion's viability as dark matter. Our analysis shows that it satisfies all current DM constraints over a wide range of dark matter mass., Comment: 35 pages, 12 figures, 5 tables

Preference tuning of large language models (LLMs) relies on high-quality human preference data, which is often expensive and time-consuming to gather. While existing methods can use trained reward models or proprietary model as judges for preference annotation, they have notable drawbacks: training reward models remain dependent on initial human data, and using proprietary model imposes license restrictions that inhibits commercial usage. In this paper, we introduce customized density ratio (CDR), a training-free and highly effective method that leverages off-the-shelf LLMs for preference data annotation. Our approach uses the log-density ratio between a better-aligned LLM and a less aligned LLM as a reward signal. We explores 221 different LLMs pairs and empirically demonstrate that increasing the performance gap between paired LLMs correlates with better reward generalization. Furthermore, we show that tailoring the density ratio reward function with specific criteria and preference exemplars enhances performance across domains and within target areas. In our experiment using density ratio from a pair of Mistral-7B models, CDR achieves a RewardBench score of 82.6, outperforming the best trained reward functions from same model class and demonstrating competitive performance against SoTA models in Safety (91.0) and Reasoning (88.0) domains. We use CDR to annotate an on-policy preference dataset with which we preference tune Llama-3-8B-Instruct with SimPO. Using reward signals from two relatively weak models, our approach pushes Llama-3-8B to achieve a 37.4% (+15.1%) win rate on ArenaHard and a 40.7% (+17.8%) win rate on Length-Controlled AlpacaEval 2.0, along with a score of 8.0 on MT-Bench.

Whether considering rare astrophysical events on cosmological scales or unresolved stellar populations, accurate models must account for the integrated contribution from the entire history of star formation upon which that population is built. Here, we describe the second version of POSYDON, an open-source binary population synthesis code based on extensive grids of detailed binary evolution models computed using the MESA code, which follows both stars' structures as a binary system evolves through its complete evolution from the zero-age main sequence, through multiple phases of mass transfer and supernovae, to their death as compact objects. To generate synthetic binary populations, POSYDON uses advanced methods to interpolate between our large, densely spaced grids of simulated binaries. In our updated version of POSYDON, we account for the evolution of stellar binaries across a cosmological range of metallicities, extending from $10^{-4}\,Z_{\odot}$ to $2\,Z_{\odot}$, including grids specifically focused on the Small and Large Magellanic Clouds ($0.2\,Z_{\odot}$ and $0.45\,Z_{\odot}$). In addition to describing our model grids and detailing our methodology, we outline several improvements to POSYDON. These include the incorporation of single stars in stellar populations, a treatment for stellar mergers, and a careful modeling of "reverse-mass transferring" binaries, in which an once-accreting star later becomes a donor star. Our simulations are focused on binaries with at least one high-mass component, such as those that host neutron stars and black holes, and we provide post-processing methods to account for the cosmological evolution of metallicity and star formation as well as rate calculations for gravitational wave events, gamma-ray bursts, and other transients., Comment: 48 pages, 26 figures, submitted to AAS Journals

The shuffle of a non-empty countable set $ S $ of linear orders is the (unique up to isomorphism) linear order $ \Xi(S) $ obtained by fixing a coloring function $ \chi: \mathbb{Q} \to S $ having fibers dense in $ \mathbb{Q} $ and replacing each rational $ q $ in $ (\mathbb{Q}, <) $ with an isomorphic copy of $ \chi(q) $. We prove that any two countable shuffles that embed as convex subsets into each other are order isomorphic., Comment: 9 pages, 1 figure

Fast-mode magnetohydrodynamic (MHD) waves in the solar corona are often known to be produced by solar flares and eruptive prominences. We here simulate the effect of the interaction of an external perturbation on a magnetic null in the solar corona which results in the formation of a current sheet (CS). Once the CS undergoes a sufficient extension in its length and squeezing of its width, it may go unstable to the formation of multiple impulsive plasmoids. Eventually, the plasmoids merge with one another to form larger plasmoids and/or are expelled from the sheet. The formation, motion and coalescence of plasmoids with each other and with magnetic Y-points at the outer periphery of the extended CS are found to generate wave-like perturbations. An analysis of the resultant quasi-periodic variations of pressure, density, velocity and magnetic field at certain locations in the model corona indicate that these waves are predominantly fast-mode magnetoacoustic waves. For typical coronal parameters, the resultant propagating waves carry an energy flux of $\mathrm{10^{5}~\mathrm{erg~cm^{-2}~s^{-1}}}$ to a large distance of at least 60 Mm away from the current sheet. In general, we suggest that both waves and reconnection play a role in heating the solar atmosphere and driving the solar wind and may interact with one another in a manner that we refer to as a $"$Symbiosis of WAves and Reconnection (SWAR)$"$., Comment: 24 pages, 13 figures, Accepted for publication in The Astrophysical Journal

In this work, we explore the entanglement structure of two $N = Z$ nuclei, $^{20}$Ne and $^{22}$Na using single-orbital entanglement entropy within the No-Core Shell Model (NCSM) framework for two realistic interactions, INOY and N$^3$LO. We begin with the determination of the optimal frequencies based on the variation of ground-state (g.s.) binding energy with NCSM parameters, $N_{max}$ and $\hbar \Omega$, followed by an analysis of the total single-orbital entanglement entropy, $S_{tot}$, for the g.s. of $^{20}$Ne and $^{22}$Na. Our results show that $S_{tot}$ increases with $N_{max}$ and decreases with $\hbar \Omega$ after reaching a maximum. We use $S_{tot}$ to guide the selection of an additional set of optimal frequencies that can enhance electromagnetic transition strengths. We also calculate the low-energy spectra and $S_{tot}$ for four low-lying states of $^{20}$Ne and six low-lying states of $^{22}$Na. Finally, we calculate a few $E2$ and one $M1$ transition strengths, finding that N$^3$LO provides better results for $B(E2; 5^+_1 \to 3^+_1$) and INOY performs well for the $B(M1; 0_1^+ \to 1_1^+)$ transition in the $^{22}$Na nucleus while considering the first set of optimal frequencies. We also observe that the second set of optimal frequencies enhances electromagnetic transition strengths, particularly for the states with large and comparable $S_{tot}$. Also, for both nuclei, the $S_{tot}$ for INOY and N$^3$LO are close while considering the second set of optimal frequencies, suggesting that the calculated $S_{tot}$ are more dependent on $\hbar \Omega$ than the interactions employed for the same model space defined by the $N_{max}$ parameter., Comment: 15 pages, 5 figures

Coronal Mass Ejections (CMEs) are subject to changes in their direction of propagation, tilt, and other properties as they interact with the variable solar wind. We investigated the heliospheric propagation of 15 Earth-impacting CMEs observed during April 2010 to August 2018 in the field of view (FOV) of the Heliospheric Imager (HI) onboard the STEREO. About half of the 15 events followed self-similar expansion up to 40 $R_\odot$. The remaining events showed deflection either in latitude, longitude, or a tilt change. Only two events showed significant rotation in the HI1 FOV. We also use toroidal and cylindrical flux rope fitting on the in situ observations of interplanetary magnetic field (IMF) and solar wind parameters to estimate the tilt at L1 for these two events. Although the sample size is small, this study suggests that CME rotation is not very common in the heliosphere. We attributed the observed deflections and rotations of CMEs to a combination of factors, including their interaction with the ambient solar wind and the influence of the ambient magnetic field. These findings contribute to our understanding of the complex dynamics involved in CME propagation and highlight the need for comprehensive modeling and observational studies to improve space weather prediction. In particular, HI observations help us to connect observations near the Sun and near Earth, improving our understanding of how CMEs move through the heliosphere., Comment: Accepted for publication in the Astrophysical Journal

Soft polymers are ubiquitous materials found in nature and as engineering materials with properties varying from rate-independent to significantly rate-dependent depending on the crosslinking mechanisms. Current fracture toughness measures such as energy release rate are non-unique for rate-dependent soft materials for varying loading profiles and specimen geometries. Works on modeling fracture in rate-dependent soft polymers are limited to crack tip stress field analyses or crack tip driving force approaches for specific pre-cracked geometries. We have developed a model to predict damage initiation and growth in soft polymers based on a generalized multi-mechanism gradient damage framework. We propose and show that a critical value of stress work $\mathit{W}_{cr}$ can uniquely quantify the total energy dissipation per unit referential volume associated with the complete failure of a material point under a loading mode. $\mathit{W}_{cr}$ can be evaluated using homogeneous deformation experiments and without a constitutive model. $\mathit{W}_{cr}$ is demonstrated to be approximately constant with strain rate for two rate-dependent soft polymers and different loading modes for an elastomer. We propose the energetic contribution to $\mathit{W}_{cr}$ as a suitable damage initiation criterion. The proposed initiation criterion in the damage model enabled successful predictions of fracture in an important rate-stiffening soft polymer Polyborosiloxane in a variety of experiments involving different specimen geometries and loading conditions. The model also provides a consistent energy density estimate for fracture-associated microstructural processes in Polyborosiloxane. The broader applicability of the fracture model is shown by its ability to predict fracture in an elastomer (EPDM) and another viscous soft polymer (EPS25 vitrimer)., Comment: A version of this work has been submitted for peer review

Despite their high predictive accuracies, current machine learning systems often exhibit systematic biases stemming from annotation artifacts or insufficient support for certain classes in the dataset. Recent work proposes automatic methods for identifying and explaining systematic biases using keywords. We introduce DISCERN, a framework for interpreting systematic biases in text classifiers using language explanations. DISCERN iteratively generates precise natural language descriptions of systematic errors by employing an interactive loop between two large language models. Finally, we use the descriptions to improve classifiers by augmenting classifier training sets with synthetically generated instances or annotated examples via active learning. On three text-classification datasets, we demonstrate that language explanations from our framework induce consistent performance improvements that go beyond what is achievable with exemplars of systematic bias. Finally, in human evaluations, we show that users can interpret systematic biases more effectively (by over 25% relative) and efficiently when described through language explanations as opposed to cluster exemplars., Comment: 20 pages, 9 figures, 15 tables; Accepted to EMNLP 2024

Our goal is to model and experimentally assess trust evolution to predict future beliefs and behaviors of human-robot teams in dynamic environments. Research suggests that maintaining trust among team members in a human-robot team is vital for successful team performance. Research suggests that trust is a multi-dimensional and latent entity that relates to past experiences and future actions in a complex manner. Employing a human-robot collaborative task, we design an optimal assistance-seeking strategy for the robot using a POMDP framework. In the task, the human supervises an autonomous mobile manipulator collecting objects in an environment. The supervisor's task is to ensure that the robot safely executes its task. The robot can either choose to attempt to collect the object or seek human assistance. The human supervisor actively monitors the robot's activities, offering assistance upon request, and intervening if they perceive the robot may fail. In this setting, human trust is the hidden state, and the primary objective is to optimize team performance. We execute two sets of human-robot interaction experiments. The data from the first experiment are used to estimate POMDP parameters, which are used to compute an optimal assistance-seeking policy evaluated in the second experiment. The estimated POMDP parameters reveal that, for most participants, human intervention is more probable when trust is low, particularly in high-complexity tasks. Our estimates suggest that the robot's action of asking for assistance in high-complexity tasks can positively impact human trust. Our experimental results show that the proposed trust-aware policy is better than an optimal trust-agnostic policy. By comparing model estimates of human trust, obtained using only behavioral data, with the collected self-reported trust values, we show that model estimates are isomorphic to self-reported responses.

The existence of a mass gap of $3-5\,M_{\odot}$ between the heaviest neutron stars (NSs) and the lightest black holes (BHs), inferred from the BH mass distribution in low mass X-ray binaries (LMXBs), has been suggested for decades. The recently reported gravitational-wave source GW230529 has been confidently identified as a NSBH merger, with the BH mass falling within this lower mass gap. This detection provides strong evidence against the existence of the latter and introduces new implications for the coalescing NSBH population, including a revised BH mass distribution and an updated local merger rate. In this study, we employ POSYDON, a binary population synthesis code that integrates detailed single- and binary-star models, to investigate coalescing NSBH binaries formed through isolated binary evolution. In particular, we focus on the BH mass distribution of the intrinsic NSBH merger population. We find that, with a high common-envelope efficiency of $\alpha_{\rm{CE}} =2 $, the BH masses in NSBH mergers concentrate in the lower mass gap, aligning more closely with observations. However, after accounting for the constraints of the selection bias against mass-gap BHs in LMXBs, which suggests that the maximum NS birth mass is below $\simeq 2\,M_{\odot}$, we find that introducing a high $\alpha_{\rm{CE}}$ is not required to match observations. Additionally, we explore the impact of core-collapse supernova kicks. Finally, we present the property distributions of observable NSBH mergers from our simulation and find that they match well with the observations. We find that the fraction of electromagnetic counterparts in observable populations is $\approx 4-30\%$, depending on different NS equations of state. Future detections of coalescing NSBH binaries would provide invaluable insights into SN mechanisms, common envelope evolution, and NS physics., Comment: 9 pages, 4 figures. Comments are welcome!

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains. We have also created an accompanying GitHub repository, where we compile the papers included in this survey: https://github.com/AIoT-MLSys-Lab/AIoT-Survey. This repository will be actively maintained and updated with new research as it becomes available. As both IoT and AI become increasingly critical to our society, we believe AIoT is emerging as an essential research field at the intersection of IoT and modern AI. We hope this survey will serve as a valuable resource for those engaged in AIoT research and act as a catalyst for future explorations to bridge gaps and drive advancements in this exciting field., Comment: Accepted in ACM Transactions on Sensor Networks (TOSN)

Generative large language models (LLMs) exhibit impressive capabilities, which can be further augmented by integrating a pre-trained vision model into the original LLM to create a multimodal LLM (MLLM). However, this integration often significantly decreases performance on natural language understanding and generation tasks, compared to the original LLM. This study investigates this issue using the LLaVA MLLM, treating the integration as a continual learning problem. We evaluate five continual learning methods to mitigate forgetting and identify a technique that enhances visual understanding while minimizing linguistic performance loss. Our approach reduces linguistic performance degradation by up to 15\% over the LLaVA recipe, while maintaining high multimodal accuracy. We also demonstrate the robustness of our method through continual learning on a sequence of vision-language tasks, effectively preserving linguistic skills while acquiring new multimodal capabilities., Comment: NeurIPS 2024 Workshop on Scalable Continual Learning for Lifelong Foundation Models

Context. The black holes discovered using Gaia, especially Gaia BH1 and BH2, have low mass companions of solar-like metallicity in wide orbits. For standard isolated binary evolution formation channels including interactions such an extreme mass ratio is unexpected; especially in orbits of hundreds to thousands of days. Aims. Here, we investigate a non-interacting formation path for isolated binaries to explain the formation of Gaia BH1 and BH2. Methods. We use single star models computed with MESA to constrain the main characteristics of possible progenitors of long-period black hole binaries like Gaia BH1 and BH2. Then, we incorporate these model grids into the binary population synthesis code POSYDON, to explore whether the formation of the observed binaries at solar metallicity is indeed possible. Results. We find that winds of massive stars ($\gtrsim 80\,M_\odot$), especially during the Wolf-Rayet phase, tend to cause a plateau in the initial stellar mass to final black hole mass relation (at about $13\,M_\odot$ in our default wind prescription). However, stellar winds at earlier evolutionary phases are also important at high metallicity, as they prevent the most massive stars from expanding ($<100\,R_\odot$) and filling their Roche lobe. Consequently, the strength of the applied winds affects the range of the final black hole masses in non-interacting binaries, making it possible to form systems similar to Gaia BH1 and BH2. Conclusions. We deduce that wide binaries with a black hole and a low mass companion can form at high metallicity without binary interactions. There could be hundreds of such systems in the Milky Way. The mass of the black hole in binaries evolved through the non-interacting channel can potentially provide insights into the wind strength during the progenitors evolution., Comment: 8+4 pages, 6+6 figures, resubmitted to A&A

Given a network with an ongoing epidemic, the network immunization problem seeks to identify a fixed number of nodes to immunize in order to maximize the number of infections prevented. One of the fundamental computational challenges in network immunization is that the objective function is generally neither submodular nor supermodular. As a result, no efficient algorithm is known to consistently find a solution with a constant approximation guarantee. Traditionally, this problem is addressed using proxy objectives, which offer better approximation properties. However, converting to these indirect optimizations often introduces losses in effectiveness. In this paper, we overcome these fundamental barriers by utilizing the underlying stochastic structures of the diffusion process. Similar to the traditional influence objective, the immunization objective is an expectation that can be expressed as the sum of objectives over deterministic instances. However, unlike the former, some of these terms are not submodular. The key step is proving that this sum has a bounded deviation from submodularity, thereby enabling the greedy algorithm to achieve constant factor approximation. We show that this approximation still stands considering a variety of immunization settings and spread models., Comment: There is a flaw in the proof of Lemma 3.2 that affects the key results of the paper

We perform a structural analysis of large scale jammed packings of monodisperse, frictionless and frictional spheres to elucidate structural signatures of the static structure factor in the low-to-intermediate wavenumber (q) region. We employ discrete element method simulations containing up to eighty million particles, in which the particle friction coefficient(s), including sliding, rolling, and twisting interactions, are varied. At intermediate q values, corresponding to length scales that lie between that of the nearest neighbor primary peak and the system size, we find the emergence of a prepeak - a signature of intermediate range order - that grows with increasing friction. We correlate the emergence of this peak to real space fluctuations in the local particle coordination number, which exhibits a grainy-like fluctuating field throughout the packing process that is retained in the final, mechanically stable state. While the formation of the prepeak shows varying degrees of robustness to packing protocol changes, our results suggest that preparation history may be used to construct packings with variable large length scale structural properties.