arXiv papers from December 2024

Tzu-Yung Huang, David A. Hopper, Kaisarbek Omirzakhov, Mohamad Hossein Idjadi

As quantum networks expand and are deployed outside research laboratories, a need arises to design and integrate compact control electronics for each memory node. It is essential to understand the performance requirements for such systems, especially concerning tolerable levels of noise, since these specifications dramatically affect a system's design comple

Kevin S. Becker, Kristina D. Launey, Andreas Ekström, Tomáš Dytrych

We discover a surprising relation between the collective motion of nucleons within atomic nuclei, traditionally understood to be driven by long-range correlations, and short-range nucleon-nucleon interactions. Specifically, we find that quadrupole collectivity in low-lying states of $^6$Li and $^{12}$C, calculated with state-of-the-art ab initio techniques,

S. Saraf, S. Buchman, C. Y. Lui, S. Wang

Precision AC resonant capacitance bridges, with a planar printed circuit board transformer using an ungapped MnZn ferrite core, have shown excellent noise performance in high-precision measurements. As many applications use an ensemble of bridges, consistency of performance is critical to the functionality of the systems. If part of the same manufacturing ba

Josiah Park

In the following, we study the minimization of polynomial potentials $ f(t) $ on the unit circle, where the potentials take the form \[ f(t) = \sum_{i=1}^n b_i x^{2i}, \quad b_i \in \mathbb{R}. \] This form arises in the context of truncations of expansions of $ p $-frame potentials. One approach to minimize these potentials involves rewriting the integral a

Mauro A. P. Goncalves, Monica Graf, Marek Pasciak, Jiri Hlinka

This paper analyzes how the ferroelectric properties of cubic-like BiFeO$_3$ nanoparticles are affected by different terminations and charge distributions at the surface using ab-initio-based atomistic computational experiments. Our findings unveil multiple multidomain configurations and illustrate how the different order parameters evolve towards the surfac

David E. Crawford, Yi Zeng, Judith Vidal, Jianjun Dong

Ultrafast and nanoscale heat conduction demands a unified theoretical framework that rigorously bridges macroscopic transport equations with microscopic material properties derived from statistical physics.Existing empirical generalizations of Fourier's law often lack a solid microscopic foundation, failing to connect observed non-Fourier behavior with under

Wei-Yang Liu, Ismail Zahed, Yong Zhao

We outline a general framework for evaluating the non-perturbative soft functions in the QCD instanton vacuum. In particular, from the soft function we derive the Collins-Soper (CS) kernel, which drives the rapidity evolution of the transverse-momentum-dependent parton distributions. The resulting CS kernel, when supplemented with the perturbative contributi

Zac Bailey, Riddhi Bandyopadhyay, Shadia Habbal, Miloslav Druckmüller

The solar atmosphere displays a sharp temperature gradient, starting from spicules in the chromosphere at $2 \times 10^4$ K, outward into the corona exceeding $10^6$ K. Plasma turbulence produced by the transverse motion of magnetic fields anchored in the photosphere is likely the energy source producing this gradient. However, very little is known about the

John Sturt, Evgeny Kozik

Diagrammatic expansions are a paradigmatic and powerful tool of quantum many-body theory. Their evaluation to high order, e.g., by the Diagrammatic Monte Carlo technique, can provide unbiased results in strongly correlated and challenging regimes. However, calculating a factorial number of terms to acceptable precision remains very costly even for state-of-t

Amir M. Ebrahimi, Bram Adams, Gustavo A. Oliva, Ahmed E. Hassan

Software applications that run on a blockchain platform are known as DApps. DApps are built using smart contracts, which are immutable after deployment. Just like any real-world software system, DApps need to receive new features and bug fixes over time in order to remain useful and secure. However, Ethereum lacks native solutions for post-deployment smart c

Akash Kumar Panda, Bart Kosko

An adaptive multiexpert mixture of feedback causal models can approximate missing or phantom nodes in large-scale causal models. The result gives a scalable form of \emph{big knowledge}. The mixed model approximates a sampled dynamical system by approximating its main limit-cycle equilibria. Each expert first draws a fuzzy cognitive map (FCM) with at least o

Stefania Ketzetzi, Lorenzo Caprini, Vivien Willems, Laura Alvarez

Cells and microorganisms employ dynamic shape changes to enable steering and avoidance for efficient spatial exploration and collective organization. In contrast, active colloids, their synthetic counterparts, currently lack similar abilities and strategies. Through physical interactions alone, here we create active colloidal molecules that spontaneously rec

Anna Gusakova, Zakhar Kabluchko

Consider $d+2$ i.i.d. random points $X_1,\ldots, X_{d+2}$ in $\mathbb R^d$. In this note, we compute the probability that their convex hull is a simplex focusing on three specific distributional settings: (i) the distribution of $X_1$ is multivariate standard normal; (ii) the density of $X_1$ is proportional to $(1-\|x\|^2)^{\beta}$ on the unit ball (the bet

X. H. Mo

For charmonium's decaying to the final states involving merely light quarks, in light of SU(3) flavor symmetry, a systematic parametrization scheme is established, which involving binary decays, ternary decays and radiative decays.

El Houcine El Fatimi

This study, our main topic is to devlop a new deep-learning approachs for plant leaf disease identification and detection using leaf image datasets. We also discussed the challenges facing current methods of leaf disease detection and how deep learning may be used to overcome these challenges and enhance the accuracy of disease detection. Therefore, we have

Francesca Sammarruca, Tomiwa Ajagbonna

The profile of a neutron star probes a very large range of densities, from the density of iron up to several times the density of saturated nuclear matter, and thus no theory of hadrons can be considered reliable if extended to those regions. We emphasize the importance of taking contemporary ab initio theories of nuclear and neutron matter as the baseline f

Tyrrell B. McAllister, Jason S. Williford

We prove that a rational pseudointegral triangle with exactly one lattice point in its interior has at most $9$ lattice points on its boundary, where a polygon $P$ is called pseudointegral if the Ehrhart function of $P$ is a polynomial. We further show that such a triangle never has exactly $7$ lattice points on its boundary. Our results determine the set of

Magdalena Kołodziej, Stephan Brons, Mikołaj Dubiel, George N. Farah

Objective. The objective of the presented study was to evaluate the feasibility of a coded-mask (CM) gamma camera for real-time range verification in proton therapy, addressing the need for a precise and efficient method of treatment monitoring. Approach. A CM gamma camera prototype was tested in clinical conditions. The setup incorporated a scintillator-bas

Trey Anderson, W. Melnitchouk, N. Sato

We perform a global QCD analysis of unpolarized parton distribution functions (PDFs) in the proton, including new $W +$\,charm production data from $pp$ collisions at the LHC and semi-inclusive pion and kaon production data in lepton-nucleon deep-inelastic scattering, both of which have been suggested for constraining the strange quark PDF. Compared with a b

Phuc Nguyen, Miao Li, Alexandra Morgan, Rima Arnaout

Generative models hold great potential, but only if one can trust the evaluation of the data they generate. We show that many commonly used quality scores for comparing two-dimensional distributions of synthetic vs. ground-truth data give better results than they should, a phenomenon we call the "grade inflation problem." We show that the correlation score,

Ali Behrouz, Peilin Zhong, Vahab Mirrokni

Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention. While recurrent models aim to compress the data into a fixed-size memory (called hidden state), attention allows attending to the entire context window, capturing the direct dependencies of all tokens. This more accurate modeling

Z. L. Idema, M. D. Campanell

We show that ion trapping in virtual cathodes can raise the transmitted current of emitted electrons much closer to the full emission than is predicted by theories without trapped ions. The transmitted current is controlled by the well voltage whose value must adjust to balance the creation of low-energy ions within the well, and their leakage over the well.

Reihaneh Hosseini, Krishna Kumar

The effective stress parameter, $\chi$, is essential for calculating the effective stress in unsaturated soils. Experimental measurements have captured different relationships between $\chi$ and the degree of saturation, $S_r$; however, they have not been able to justify the particular shapes of the $\chi$-$S_r$ curves. Theoretical solutions express $\chi$ a

Carolina van Baalen, Stefania Ketzetzi, Anushka Tintor, Lucio Isa

Active colloidal particles typically exhibit a pronounced affinity for accumulating and being captured at boundaries. Here, we engineer long-range repulsive interactions between colloids that self-propel under an electric field and patterned obstacles. As a result of these interactions, particles turn away from obstacles and avoid accumulation. We show that

Kazuki Irie

Do autoregressive Transformer language models require explicit positional encodings (PEs)? The answer is 'no' provided they have more than one layer -- they can distinguish sequences with permuted tokens without the need for explicit PEs. This follows from the fact that a cascade of (permutation invariant) set processors can collectively exhibit sequence-sen

Peihao Wang, Ruisi Cai, Yuehao Wang, Jiajun Zhu

Structured State Space Models (SSMs) have emerged as alternatives to transformers. While SSMs are often regarded as effective in capturing long-sequence dependencies, we rigorously demonstrate that they are inherently limited by strong recency bias. Our empirical studies also reveal that this bias impairs the models' ability to recall distant information and

Nicholas B. Andrews, Kristi A. Morgansen

Relative pose (position and orientation) estimation is an essential component of many robotics applications. Fiducial markers, such as the AprilTag visual fiducial system, yield a relative pose measurement from a single marker detection and provide a powerful tool for pose estimation. In this paper, we perform a Lie algebraic nonlinear observability analysis

Team OLMo, Pete Walsh, Luca Soldaini, Dirk Groeneveld

We present OLMo 2, the next generation of our fully open language models. OLMo 2 includes a family of dense autoregressive language models at 7B, 13B and 32B scales with fully released artifacts -- model weights, full training data, training code and recipes, training logs and thousands of intermediate checkpoints. In this work, we describe our modified mode

Davide Italiano, Chris Cummins

Compilers are complex, and significant effort has been expended on testing them. Techniques such as random program generation and differential testing have proved highly effective and have uncovered thousands of bugs in production compilers. The majority of effort has been expended on validating that a compiler produces correct code for a given input, while

Maryam Hakimzadeh, Carlos Mora-Corral, Noel Walkington, Giuseppe Buscarnera

Strongly anisotropic geomaterials undergo fracture under compressive loading. This paper applies a phase-field fracture model to study this fracture process. While phase-field fracture models have several advantages, they provide unphysical predictions when the stress state is complex and includes compression that can cause crack faces to contact. Building o

Xindi Wu, Mengzhou Xia, Rulin Shao, Zhiwei Deng

Training vision-language models via instruction tuning relies on large data mixtures spanning diverse tasks and domains, yet these mixtures frequently include redundant information that increases computational costs without proportional gains. Existing methods typically rely on task-agnostic heuristics to estimate data importance, limiting their effectivenes

Pete Rigas

We compute the action-angle coordinates for an Ising type model whose L-operator has been previously studied in the literature by Bazhanov and Sergeev. In comparison to computations with such operators that have been examined previously by the author for the 4-vertex, 6-vertex, and 20-vertex, models, computations for asymptotically approximating a collection

René Brandenberg, Florian Grundbacher

We consider two well-known problems: upper bounding the volume of lower dimensional ellipsoids contained in convex bodies given their John ellipsoid, and lower bounding the volume of ellipsoids containing projections of convex bodies given their Loewner ellipsoid. For the first problem, we use the John asymmetry to unify a tight upper bound for the general c

Chenji Fu

Let $F$ be a non-archimedean local field. Let $\overline{F}$ be an algebraic closure of $F$. Let $G$ be a connected reductive group over $F$. Let $\varphi$ be an elliptic $L$-parameter. For every irreducible representation $\pi$ of $G(F)$ with Fargues--Scholze $L$-parameter $\varphi$, we prove that there exists a finite set of irreducible representations $\{

Suttisak Wizadwongsa, Jinfan Zhou, Edward Li, Jeong Joon Park

Recent AI-based 3D content creation has largely evolved along two paths: feed-forward image-to-3D reconstruction approaches and 3D generative models trained with 2D or 3D supervision. In this work, we show that existing feed-forward reconstruction methods can serve as effective latent encoders for training 3D generative models, thereby bridging these two par

David Solomon

We lay the foundations for a broad algebraic theory encompassing SICs in the hope of elucidating their heuristic connections with Stark units. What emerges is a greatly generalised set-up with added structure and potential for applications in other areas. Let $A$ and $B$ be finite modules for a commutative ring $R$, $C$ a finite abelian group and $\lambda: A

Andrzej Derdzinski, Yunhee Euh, Sinhwi Kim, JeongHyeong Park

Riemannian four-manifolds in which the triple contraction of the curvature tensor against itself yields a functional multiple of the metric are called weakly Einstein. We focus on weakly Einstein K\"ahler surfaces. We provide several conditions characterizing those K\"ahler surfaces which are weakly Einstein, classify weakly Einstein K\"ahler surfaces having

David R. Smith, Yeonghoon Noh, Insang Yoo, Divya Pande

We propose an approach to extracting equivalent circuit models for waveguide-fed, resonant metamaterial elements, such as the complementary, electric inductive-capacitive element (cELC). From the scattering parameters of a single waveguide-fed cELC, effective electric and magnetic polarizabilities can be determined that can be expressed in terms of equivalen

Abdesselam Ferdi

Computer-aided diagnosis (CAD) systems have greatly improved the interpretation of medical images by radiologists and surgeons. However, current CAD systems for fracture detection in X-ray images primarily rely on large, resource-intensive detectors, which limits their practicality in clinical settings. To address this limitation, we propose a novel lightwei

Timothy Jakobi, Matt Garratt, Mandayam Srinivasan, Sridhar Ravi

The ability to fly through openings in vegetation allows insects like bees to access otherwise unreachable food sources. The specific visual strategies employed by flying insects during aperture negotiation tasks remain unknown. In this study, we investigated the visual and geometric parameters of apertures that influence traversing honeybees. We recorded ho

Kim Sung-Bin, Kim Jun-Seong, Junseok Ko, Yewon Kim

We propose SoundBrush, a model that uses sound as a brush to edit and manipulate visual scenes. We extend the generative capabilities of the Latent Diffusion Model (LDM) to incorporate audio information for editing visual scenes. Inspired by existing image-editing works, we frame this task as a supervised learning problem and leverage various off-the-shelf m

Daniel B. Hier, Michael D. Carrithers, Thanh Son Do, Tayo Obafemi-Ajayi

Clinician notes are a rich source of patient information but often contain inconsistencies due to varied writing styles, colloquialisms, abbreviations, medical jargon, grammatical errors, and non-standard formatting. These inconsistencies hinder the extraction of meaningful data from electronic health records (EHRs), posing challenges for quality improvement

Daniel W. Piasecki

We demonstrate the existence of a complex Hilbert Space with Hermitian operators for calculations in \textit{classical} electromagnetism that parallels the Hilbert Space of quantum mechanics. The axioms of this classical theory are the so-called Dirac-von Neumann axioms, however, with classical potentials in place of the wavefunction and the indeterministic

Mehran Ebrahimi, Adrian Butscher, Hyunmin Cheong, Francesco Iorio

The design space of dynamic multibody systems (MBSs), particularly those with flexible components, is considerably large. Consequently, having a means to efficiently explore this space and find the optimum solution within a feasible timeframe is crucial. It is well known that for problems with several design variables, sensitivity analysis using the adjoint

Mark Zakharov, Farzaneh Rabiei Kashanaki, Jose Renau

Large Language Models (LLMs) based agents are transforming the programming language landscape by facilitating learning for beginners, enabling code generation, and optimizing documentation workflows. Hardware Description Languages (HDLs), with their smaller user community, stand to benefit significantly from the application of LLMs as tools for learning new

Xiang-Gen Xia

In this paper, we rethink delay Doppler channels (also called doubly selective channels). We prove that no modulation schemes (including the current active VOFDM/OTFS) can compensate a non-trivial Doppler spread well. We then discuss some of the existing methods to deal with time-varying channels, in particular time-frequency (TF) coding in an OFDM system. T

A. Nasr, A. Elsonbaty, M. A. Seoud, M. Anwar

This manuscript introduces Diophantine labeling, a new way of labeling of the vertices for finite simple undirected graphs with some divisibility condition on the edges. Maximal graphs admitting Diophantine labeling are investigated and their number of edges are computed. Some number-theoretic techniques are used to characterize vertices of maximum degree an

Maize Chico, Thomas W. Mattman, Alex Richards

The reciprocal of the Ihara zeta function of a graph is a polynomial invariant introduced by Ihara in 1966. Scott and Storm gave a method to determine the coefficients of the polynomial. Here we simplify their calculation and determine the zeta function for all graphs of rank two. We verify that it is a complete invariant for such graphs: If $G_1$ and $G_2$

Burak Kocuk, Diego Moran Ramirez

We study the integrality gap of convex mixed-integer programs, that is, the difference between the optimal value of such a problem and the optimal value of its continuous relaxation. We study classes of convex sets whose associated optimization problem have finite integrality gap: Dirichlet convex sets, sets with full-dimensional recession cones and sets tha

Tianfu Wang, Mingyang Xie, Haoming Cai, Sachin Shah

Transparent surfaces, such as glass, create complex reflections that obscure images and challenge downstream computer vision applications. We introduce Flash-Split, a robust framework for separating transmitted and reflected light using a single (potentially misaligned) pair of flash/no-flash images. Our core idea is to perform latent-space reflection separa

Keith G. Mills, Muhammad Fetrat Qharabagh, Weichen Qiu, Fred X. Han

Layer fusion techniques are critical to improving the inference efficiency of deep neural networks (DNN) for deployment. Fusion aims to lower inference costs by reducing data transactions between an accelerator's on-chip buffer and DRAM. This is accomplished by grouped execution of multiple operations like convolution and activations together into single exe

Ebrahim Navid Sadjadi, Jesus Garcia, Jose M. Molina, Akbar Hashemi Borzabadi

This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the model to adaptively gain the optimum value

Natasha Kirova, Serguei Brazovskii

Phase transformations induced by short optical pulses are mainstream in studies on the dynamics of cooperative electronic states. We present a semi-phenomenological modeling of spacio-temporal effects expected when optical excitons are intricate with the order parameter as in, e.g., organic compounds with neutral-ionic ferroelectric phase transitions. A conc

Lorenzo Frattarolo

Financial crises are usually associated with increased cross-sectional dependence between asset returns, causing asymmetry between the lower and upper tail of return distribution. The detection of asymmetric dependence is now understood to be essential for market supervision, risk management, and portfolio allocation. I propose a non-parametric test procedur

Soren Blomquist, Anil Kumar, Whitney K. Newey

This paper introduces an estimator for the average of heterogeneous elasticities of taxable income (ETI), addressing key econometric challenges posed by nonlinear budget sets. Building on an isoelastic utility framework, we derive a linear-in-logs taxable income specification that incorporates the entire budget set while allowing for individual-specific ETI

Mohammad Omar Sahtout, Haiyan Wang, Santosh Ghimire

This article considers the impact of different thresholding methods to the Nearest Shrunken Centroid algorithm, which is popularly referred as the Prediction Analysis of Microarrays (PAM) for high-dimensional classification. PAM uses soft thresholding to achieve high computational efficiency and high classification accuracy but in the price of retaining too

Timothy Ferguson

We prove that if a weight is a Bekoll\'{e}-Bonami weight for some $q$ and it satisfies another simple condition that depends on $0 < p < \infty$, then the operator taking a function to its harmonic conjugate is bounded on the harmonic Bergman space $a^p$. One part of our results uses a certain special type of good lambda inequality.

Erica Heller, Kuangyi Xu, Zachery Harris, M. Hassan Arbab

Significance: Changes in the structure of tissue occur in many disease processes, such as the boundaries of cancerous tumors and burn injuries. Spectroscopic and polarimetric alterations of terahertz light caused by Mie scattering patterns has the potential to be a diagnostic marker. Aim: We present an analysis of Monte Carlo simulation of Mie scattering of

Claudia Cornella, David Curtin, Gordan Krnjaic, Micah Mellors

The Froggatt-Nielsen (FN) mechanism offers an elegant explanation for the observed masses and mixings of Standard Model fermions. In this work, we systematically study FN models in the lepton sector, identifying a broad range of charge assignments ("textures") that naturally yield viable masses and mixings for various neutrino mass generation mechanisms. Usi

Taejoon Kim, Haiyan Wang

High-dimensional sparse matrix data frequently arise in various applications. A notable example is the weighted word-word co-occurrence count data, which summarizes the weighted frequency of word pairs appearing within the same context window. This type of data typically contains highly skewed non-negative values with an abundance of zeros. Another example i

Arghya Maity, Ahana Ghoshal

The thermodynamic uncertainty relation (TUR) is a fundamental principle in non-equilibrium thermodynamics that relates entropy production to fluctuations in a system, establishing a trade-off between the precision of an observable and the thermodynamic cost. Investigating TUR violations challenges classical thermodynamic limits, offering the potential for im

Alec J. Linot, Haotian Hang, Eva Kanso, Kunihiko Taira

Data-driven modeling of collective dynamics is a challenging problem because emergent phenomena in multi-agent systems are often shaped by long-range interactions among individuals. For example, in bird flocks and fish schools, long-range vision and flow coupling drive individual behaviors across the collective. Such collective motion can be modeled using gr

Hang Yang, Hao Chen, Hui Guo, Yineng Chen

Accurate and efficient question-answering systems are essential for delivering high-quality patient care in the medical field. While Large Language Models (LLMs) have made remarkable strides across various domains, they continue to face significant challenges in medical question answering, particularly in understanding domain-specific terminologies and perfo

Kyle Gao, Liangzhi Li, Hongjie He, Dening Lu

Recently released open-source pre-trained foundational image segmentation and object detection models (SAM2+GroundingDINO) allow for geometrically consistent segmentation of objects of interest in multi-view 2D images. Users can use text-based or click-based prompts to segment objects of interest without requiring labeled training datasets. Gaussian Splattin

Arthur G. Wasserman

For any compact Lie group $G$ and any $n$ we construct a smooth $G$-manifold $U_n(G)$ such that any smooth $n$-dimensional $G$-manifold can be embedded in $U_n(G)$ with a trivial normal bundle. Furthermore, we show that such embeddings are unique up to equivariant isotopy It is shown that the (inverse limit) of the cohomology of such spaces gives rise to nat

Taejoon Kim, Haiyan Wang

In this article, we present a model for analyzing the cooccurrence count data derived from practical fields such as user-item or item-item data from online shopping platform, cooccurring word-word pairs in sequences of texts. Such data contain important information for developing recommender systems or studying relevance of items or words from non-numerical

Cecilia Stari, Marcos Abreu, Martín Monteiro, Arturo C. Marti

The square inverse law with distance plays an important role in many fields of physics covering electromagnetism, optics or acoustics. However, as every law in physics has its range of validity. We propose an open-inquiry laboratory where we challenge these concepts by proposing experiments where the intensity of light decays linearly or even remains constan

Daniel J. Heimsoth, Rebecca Kowalski, Danielle H. Speller, Calvin W. Johnson

Using tellurium dioxide as a target, we calculate uncertainties on 90% upper confidence limits of Galilean effective field theory (Galilean EFT) couplings to a weakly-interacting massive particle (WIMP) dark matter candidate due to uncertainties in nuclear shell models. We find that these uncertainties in naturally-occurring tellurium isotopes are comparable

Navid Afrasiabian, Colin Denniston

The Lattice Boltzmann Method (LBM) has emerged as a powerful tool in computational fluid dynamics and material science. However, standard LBM formulation imposes some limitations on the applications of the method, particularly compressible fluids. In this paper, we introduce a new velocity discretization method to overcome some of these challenges. In this n

Sarah M. Hooper, Hui Xue

Biomedical imaging modalities often produce high-resolution, multi-dimensional images that pose computational challenges for deep neural networks. These computational challenges are compounded when training transformers due to the self-attention operator, which scales quadratically with context length. Recent developments in long-context models have potentia

N. Bradley Fox, Benjamin Bruyns

The standard voting methods in the United States, plurality and ranked choice (or instant runoff) voting, are susceptible to significant voting failures. These flaws include Condorcet and majority failures as well as monotonicity and no-show paradoxes. We investigate alternative ranked choice voting systems using variations of the points-based Borda count wh

Tomek Rutowski, Amir Harati, Elizabeth Shriberg, Yang Lu

Mental health risk prediction is a growing field in the speech community, but many studies are based on small corpora. This study illustrates how variations in test and train set sizes impact performance in a controlled study. Using a corpus of over 65K labeled data points, results from a fully crossed design of different train/test size combinations are pro

David O'Gara, Cliff C. Kerr, Daniel J. Klein, Mickaël Binois

Advances in computing power and data availability have led to growing sophistication in mechanistic mathematical models of social dynamics. Increasingly these models are used to inform real-world policy decision-making, often with significant time sensitivity. One such modeling approach is agent-based modeling, which offers particular strengths for capturing

Geoffery Agorku, Sarah Hernandez, Maria Falquez, Subhadipto Poddar

This study presents a machine learning approach to predict the number of barges transported by vessels on inland waterways using tracking data from the Automatic Identification System (AIS). While AIS tracks the location of tug and tow vessels, it does not monitor the presence or number of barges transported by those vessels. Understanding the number and typ

H. K. Carley, M. K. -H. Kiessling

This reply to Dr. Smolyakov's comment [2] on our PRL paper [1] was solicited by PRL on Nov. 17, 2021 and submitted to PRL on Nov. 30, 2021. Regretfully, the editors of PRL decided not to publish Dr. Smolyakov's comment; hence, not our reply to it. However, since Dr. Smolyakov made his comment publicly available at the arXiv.org in June 2022 (of which we lear

Luis A. Lastras, Barry M. Trager, Jonathan Lenchner, Wojciech Szpankowski

Information theory has provided foundations for the theories of several application areas critical for modern society, including communications, computer storage, and AI. A key aspect of Shannon's 1948 theory is a sharp lower bound on the number of bits needed to encode and communicate a string of symbols. When he introduced the theory, Shannon famously excl

David Cohen, Julián A. Norato

This work proposes a gradient-based method to design bone implants using triply-periodic minimal surfaces (TPMS) of spatially varying thickness to maximize bone in-growth. Bone growth into the implant is estimated using a finite element based mechanobiological model considering the magnitude and frequency of in vivo loads, as well as the density distribution

Farinaldo S. Queiroz, Jilberto Zamora-Saa, Ricardo C. Silva, Y. M. Oviedo-Torres

In this work, we use publicly available data from ATLAS collaboration collected at LHC run 2 at a center-of-mass energy of $\sqrt{s}=13$TeV with an integrated luminosity of $139 fb^{-1}$ to derive lower mass limits on the $Z^\prime$ gauge boson associated with the B-L gauge symmetry. Using dilepton data we find that $M_{Z^\prime} > 4$TeV ($6$TeV) for $g_{BL}

Andrew W. Mayo, Charles D. Fortenbach, Dana R. Louie, Courtney D. Dressing

We characterize the atmosphere of the hot super-Neptune WASP-166b ($P = 5.44$ d, $R_p = 6.9 \pm 0.3$ R$_\oplus$, $M_p = 32.1 \pm 1.6$ M$_\oplus$, $T_\mathrm{eq} = 1270 \pm 30$ K) orbiting an F9V star using JWST transmission spectroscopy with NIRISS and NIRSpec ($0.85-5.17$ $\mu$m). With this broad wavelength range, NIRISS provides strong constraints on H$_2$

Tomasz Rutowski, Amir Harati, Yang Lu, Elizabeth Shriberg

Machine learning models for speech-based depression classification offer promise for health care applications. Despite growing work on depression classification, little is understood about how the length of speech-input impacts model performance. We analyze results for speaker-independent depression classification using a corpus of over 1400 hours of speech

Juven Wang

In the standard lore, the baryon asymmetry of the present universe is attributed to the leptogenesis from the sterile right-handed neutrino with heavy Majorana mass decaying into the Standard Model's leptons at the very early universe -- called the Majorana fermion's leptogenesis; while the electroweak sphaleron causes baryogenesis at a later time. In this w

Amirhossein Javaheri, Jiaxi Ying, Daniel P. Palomar, Farokh Marvasti

Graph models provide efficient tools to capture the underlying structure of data defined over networks. Many real-world network topologies are subject to change over time. Learning to model the dynamic interactions between entities in such networks is known as time-varying graph learning. Current methodology for learning such models often lacks robustness to

Alexander Alecio

The Gauss Galerkin Method/Quadrature method of moments (GG-QMoM) closure scheme, introduced by Dawson, closes a truncated set of moment equations of an SDE by a Galerkin approximation of its law in the space of probability measures. Here, results are presented on stationary solutions of the closed equations, irrespective of the number of moments retained (he

Arindam Mallick, Maciej Lewenstein, Jakub Zakrzewski, Marcin Płodzień

We consider the dynamics in the one-dimensional quantum Ising model in which each spin coherently interacts with its phononic mode. The model is motivated by quantum simulators based on Rydberg atoms in tweezers or trapped ions. The configuration of two domain walls simulates the particle-antiparticle connecting string. We concentrate on the effect the local

Yuchuan Tian, Jing Han, Chengcheng Wang, Yuchen Liang

Diffusion models have shown exceptional performance in visual generation tasks. Recently, these models have shifted from traditional U-Shaped CNN-Attention hybrid structures to fully transformer-based isotropic architectures. While these transformers exhibit strong scalability and performance, their reliance on complicated self-attention operation results in

Jiawei Yang, Jiahui Huang, Yuxiao Chen, Yan Wang

We present STORM, a spatio-temporal reconstruction model designed for reconstructing dynamic outdoor scenes from sparse observations. Existing dynamic reconstruction methods often rely on per-scene optimization, dense observations across space and time, and strong motion supervision, resulting in lengthy optimization times, limited generalization to novel vi

Jiageng Mao, Boyi Li, Boris Ivanovic, Yuxiao Chen

Synthesizing photo-realistic visual observations from an ego vehicle's driving trajectory is a critical step towards scalable training of self-driving models. Reconstruction-based methods create 3D scenes from driving logs and synthesize geometry-consistent driving videos through neural rendering, but their dependence on costly object annotations limits thei

Md Salman Rabbi Limon, Curtis Duffee, Zeeshan Ahmad

The development of solid-state batteries (SSBs) is hindered by degradation at solid-solid interfaces due to void formation and contact loss, resulting in increased impedance. Here, we systematically investigate the roles of real and unrecoverable interfacial contact areas at the electrode/Li$_6$PS$_5$Cl interface in driving the impedance rise. By controlling

J. E. Hirsch

In a recent Comment (arXiv:2411.10522, Nat Rev Phys 7, 2 (2025)), fifteen prominent leaders in the field of condensed matter physics declare that hydride superconductivity is real and urge funding agencies to continue to support the field. I question the validity and constructiveness of their argument.

Yuqian Yuan, Hang Zhang, Wentong Li, Zesen Cheng

Video Large Language Models (Video LLMs) have recently exhibited remarkable capabilities in general video understanding. However, they mainly focus on holistic comprehension and struggle with capturing fine-grained spatial and temporal details. Besides, the lack of high-quality object-level video instruction data and a comprehensive benchmark further hinders

David Gros

Within computing research, there are two spellings for an increasingly important term - dialogue and dialog. We analyze thousands of research papers to understand this "dialog(ue) debacle". Among publications in top venues that use "dialog(ue)" in the title or abstract, 72% use "dialogue", 24% use "dialog", and 5% use both in the same title and abstract. Thi

Kateryna Melnyk, Lee Friedman, Dmytro Katrychuk, Oleg Komogortsev

Eye movement prediction is a promising area of research with the potential to improve performance and the user experience of systems based on eye-tracking technology. In this study, we analyze individual differences in gaze prediction performance. We use three fundamentally different models within the analysis: the lightweight Long Short-Term Memory network

Blake Dunshee, M. N. Ellingham

We consider seven fundamental properties of cellular embeddings of graphs in compact surfaces, and show that each property can be associated with a point of the Fano plane $F$, in such a way that allowable combinations of properties correspond to projective subspaces of $F$. This Fano framework allows us to deduce a number of implications involving the seven

Abdullah Alchihabi, Yuhong Guo

Graph Neural Networks (GNNs) have demonstrated remarkable efficacy in tackling a wide array of graph-related tasks across diverse domains. However, a significant challenge lies in their propensity to generate biased predictions, particularly with respect to sensitive node attributes such as age and gender. These biases, inherent in many machine learning mode

Alexander Du, Xiujin Liu

This paper proposes PoseLecTr, a graph-based encoder-decoder framework that integrates a novel Legendre convolution with attention mechanisms for six-degree-of-freedom (6-DOF) object pose estimation from monocular RGB images. Conventional learning-based approaches predominantly rely on grid-structured convolutions, which can limit their ability to model high

Christopher M. Hans, Ningyi Liu

The Bayesian elastic net regression model is characterized by the regression coefficient prior distribution, the negative log density of which corresponds to the elastic net penalty function. While Markov chain Monte Carlo (MCMC) methods exist for sampling from the posterior of the regression coefficients given the penalty parameters, full Bayesian inference

M. Ali Bayram, Ali Arda Fincan, Ahmet Semih Gümüş, Banu Diri

Language models have made remarkable advancements in understanding and generating human language, achieving notable success across a wide array of applications. However, evaluating these models remains a significant challenge, particularly for resource-limited languages such as Turkish. To address this gap, we introduce the Turkish MMLU (TR-MMLU) benchmark,

Yicheng Zhu

Tracking surgical modifications based on endoscopic videos is technically feasible and of great clinical advantages; however, it still remains challenging. This report presents a modular pipeline to divide and conquer the clinical challenges in the process. The pipeline integrates frame selection, depth estimation, and 3D reconstruction components, allowing

Yu Jia, Yang Liu, Junliang Lu, Guang Tang

In this work, we investigate a novel production mechanism of vector mesons, exemplified by the production of a neutral vector meson associated with a lepton pair in $e^+e^-$ annihilation, i.e., $e^+e^-\to V l^+l^-$ ($V=J/\psi, \rho^0, \omega, \phi$, and $l=\mu, \tau$). These vector meson production channels can be precisely accounted within QED. The producti

H. Cein Mandujano, Peter Y. Zavalij, Alicia Manjón-Sanz, Huibo Cao

In Co$_x$NbSe$_2$, crystal symmetry, and cobalt site occupation drive the formation of two distinct magnetic phases. At $x = 1/4$, the centrosymmetric structure ($P$6$_3$/$mmc$) promotes Co-Co interactions leading to the formation of an $A$-type antiferromagnetic structure phase with a transition temperature of $T_N^A$ = 169 K. At $x = 1/3$, the non-centrosy