November 2024 arXiv papers — page 129

Neel Jawale, Navneet Kaur, Amy Santoso, Xiaohai Hu

Safely handling objects and avoiding slippage are fundamental challenges in robotic manipulation, yet traditional techniques often oversimplify the issue by treating slippage as a binary occurrence. Our research presents a framework that both identifies slip incidents and measures their severity. We introduce a set of features based on detailed vector field

Asmit Nayak, Shirley Zhang, Yash Wani, Rishabh Khandelwal

Deceptive patterns in digital interfaces manipulate users into making unintended decisions, exploiting cognitive biases and psychological vulnerabilities. These patterns have become ubiquitous on various digital platforms. While efforts to mitigate deceptive patterns have emerged from legal and technical perspectives, a significant gap remains in creating us

Rahma Heni

The concept of input-to-state stability (ISS) proposed in the late 1980s is one of the central notions in robust nonlinear control. ISS has become indispensable for various branches of nonlinear systems theory, such as robust stabilization of nonlinear systems, design of nonlinear observers, analysis of large-scale networks, etc. The success of the ISS theor

Dinh Hai Le, Felix Kronowetter, Yan Kei Chiang, Marcus Maeder

The ability to concentrate sound energy with a tunable focal point is essential for a wide range of acoustic applications, offering precise control over the location and intensity of sound pressure maxima. However, conventional acoustic metalenses are typically passive, with fixed focal positions, limiting their versatility. A significant obstacle in achievi

SeungHeon Doh, Keunwoo Choi, Daeyong Kwon, Taesu Kim

A conversational music retrieval system can help users discover music that matches their preferences through dialogue. To achieve this, a conversational music retrieval system should seamlessly engage in multi-turn conversation by 1) understanding user queries and 2) responding with natural language and retrieved music. A straightforward solution would be a

Yuki Izumida

We derive the law of entropy non-decrease directly from the Kelvin-Planck principle for simple and compound systems without using the Clausius inequality. A key of the derivation is a new formulation of entropy in terms of work by a Carnot machine operating between a system and a single heat reservoir at fixed temperature, which is equivalent to Clausius ent

David John Needham, John Christopher Meyer

This note establishes sharp time-asymptotic algebraic rate bounds for the classical evolution problem of Fujita, but with sublinear rather than superlinear exponent. A transitional stability exponent is identified, which has a simple reciprocity relation with the classical Fujita critical blow-up exponent.

Masatoshi Suzuki

We show that the sign constancy for the values of certain weighted summatory functions of the von Mangoldt function implies the Riemann hypothesis or the generalized Riemann hypothesis for Dirichlet $L$-functions. While such sign constancy is challenging to establish individually, we prove that the summatory functions under study have constant signs on avera

Henry H. Hsieh, John W. Noonan, Michael S. P. Kelley, Dennis Bodewits

We report the detection of water vapor associated with main-belt comet 358P/PANSTARRS on UT 2024 January 8-9 using the NIRSPEC instrument aboard JWST. We derive a water production rate of Q(H2O)=(5.0+/-0.2)x10^25 molecules/s, marking only the second direct detection of sublimation products of any kind from a main-belt comet, after 238P/Read. Similar to 238P,

Rutuja Gurav, Elena Massara, Xiaomei Song, Kimberly Sinclair

Extended human presence beyond low-Earth orbit (BLEO) during missions to the Moon and Mars will pose significant challenges in the near future. A primary health risk associated with these missions is radiation exposure, primarily from galatic cosmic rays (GCRs) and solar proton events (SPEs). While GCRs present a more consistent, albeit modulated threat, SPE

Mansi Girdhar, Kuchan Park, Wencong Su, Junho Hong

In recent years, critical infrastructure and power grids have increasingly been targets of cyber-attacks, causing widespread and extended blackouts. Digital substations are particularly vulnerable to such cyber incursions, jeopardizing grid stability. This paper addresses these risks by proposing a cybersecurity framework that leverages software-defined netw

Kira M. Düsterwald, Samo Hromadka, Makoto Yamada

The performance of unsupervised methods such as clustering depends on the choice of distance metric between features, or ground metric. Commonly, ground metrics are decided with heuristics or learned via supervised algorithms. However, since many interesting datasets are unlabelled, unsupervised ground metric learning approaches have been introduced. One pro

Amin Farjudian, Achim Jung

We introduce a continuous domain for function spaces over topological spaces which are not core-compact. Notable examples of such topological spaces include the real line with the upper limit topology, which is used in solution of initial value problems with temporal discretization, and various infinite dimensional Banach spaces which are ubiquitous in funct

Ismail Can Yagmur, Hasan F. Ates, Bahadir K. Gunturk

Accurate multispectral image matching presents significant challenges due to non-linear intensity variations across spectral modalities, extreme viewpoint changes, and the scarcity of labeled datasets. Current state-of-the-art methods are typically specialized for a single spectral difference, such as visibleinfrared, and struggle to adapt to other modalitie

Welberth Kennedy, Sebastião dos Anjos Sousa-Júnior, Natanael C. Costa, Raimundo R. dos Santos

The lack of both nesting and a van Hove singularity at half filling, together with the presence of Dirac cones makes the honeycomb lattice a special laboratory to explore strongly correlated phenomena. For instance, at zero temperature the repulsive [attractive] Hubbard model only undergoes a transition to an antiferromagnetic [$s$-wave superconducting degen

Irmak Bukey, Michael Feffer, Chris Donahue

We propose an efficient workflow for high-quality offline alignment of in-the-wild performance audio and corresponding sheet music scans (images). Recent work on audio-to-score alignment extends dynamic time warping (DTW) to be theoretically able to handle jumps in sheet music induced by repeat signs-this method requires no human annotations, but we show tha

Jing-Yang You, Chih-En Hsu, Mauro Del Ben, Zhenglu Li

Recent angle-resolved photoemission spectroscopy (ARPES) experiments on a kagome metal CsV$_3$Sb$_5$ revealed distinct multimodal dispersion kinks and nodeless superconducting gaps across multiple electron bands. The prominent photoemission kinks suggest a definitive coupling between electrons and certain collective modes, yet the precise nature of this inte

Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz

Super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) offers a high-resolution view of microvascular structures. Yet, ULM image quality heavily relies on precise microbubble (MB) detection. Despite the crucial role of localization algorithms, there has been limited focus on the practical pitfalls in MB detection tasks such as sett

Muhammad Rizki Oktavian, Anirudh Tunga, Jonathan Nistor, James Tusar

One of the challenges in operating nuclear power plants is to decide the amount of fuel needed in a cycle. Large-scale nuclear power plants are designed to operate at base load, meaning that they are expected to always operate at full power. Economically, a nuclear power plant should burn enough fuel to maintain criticality until the end of a cycle (EOC). If

Andrew B. Pace

I present the Local Volume Database (LVDB), a catalog of the observed properties of dwarf galaxies and star clusters in the Local Group and Local Volume. The LVDB includes positional, structural, kinematic, chemical, and dynamical parameters for dwarf galaxies and star clusters. I discuss the motivation, structure, construction, and future expansion plans of

Piotr T. Chruściel, Wan Cong, Théophile Quéau, Raphaela Wutte

We analyze existence and properties of solutions of two-dimensional general relativistic initial data sets with a negative cosmological constant, both on spacelike and characteristic surfaces. A new family of such vacuum, spacelike data parameterised by poles at the conformal boundary at infinity is constructed. We review the notions of global Hamiltonian ch

Lorenzo Micalizzi, Eleuterio F. Toro

The numerical flux determines the performance of numerical methods for solving hyperbolic partial differential equations (PDEs). In this work, we compare a selection of 8 numerical fluxes in the framework of nonlinear semidiscrete finite volume (FV) schemes, based on Weighted Essentially Non-Oscillatory (WENO) spatial reconstruction and Deferred Correction (

Davide Lauria, JiHo Park, Yuan Hu, W. Brent Lindquist

We address the problem of asset pricing in a market where there is no risky asset. Previous work developed a theoretical model for a shadow riskless rate (SRR) for such a market in terms of the drift component of the state-price deflator for that asset universe. Assuming asset prices are modeled by correlated geometric Brownian motion, in this work we develo

Burak Ahmet Ozden, Erdogan Aydin, Fatih Cogen

Current wireless communication technologies are insufficient in the face of ever-increasing demands. Therefore, novel and high-performance communication systems are needed. In this paper, a novel high data rate and high-performance index modulation scheme called double media-based modulation (DMBM) is proposed. The DMBM system doubles the number of mirror ac

Kuchan Park, Mansi Girdhar, Junho Hong, Wencong Su

Substation Automation Systems (SAS) that adhere to the International Electrotechnical Commission (IEC) 61850 standard have already been widely implemented across various on-site local substations. However, the digitalization of substations, which involves the use of cyber system, inherently increases their vulnerability to cyberattacks. This paper proposes t

Vicente Saavedra-Araya

In this paper, we introduce a new technique to study the distribution in residue classes of sets of integers with digit and sum-of-digits restrictions. From our main theorem, we derive a necessary and sufficient condition for integers with missing digits to be uniformly distributed in arithmetic progressions, extending previous results going back to the work

Katerina Korre, Arianna Muti, Federico Ruggeri, Alberto Barrón-Cedeño

Hate speech relies heavily on cultural influences, leading to varying individual interpretations. For that reason, we propose a Semantic Componential Analysis (SCA) framework for a cross-cultural and cross-domain analysis of hate speech definitions. We create the first dataset of hate speech definitions encompassing 493 definitions from more than 100 culture

Weixi Yi, Yipei Wang, Natasha Thorley, Alexander Ng

Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement. However, measuring diffusion patterns in DWI sequences can be time-consuming, prone to artifacts and sensitive to imaging parameters. While machine learning

Borut Jurčič Zlobec

We present a numerical solution of the nonlinear differential equation for a pendulum with an elastic string on the rotating Earth, for different values of string stiffness at different geographic latitudes.

Geoffroy Oudoumanessah, Thomas Coudert, Luc Meyer, Aurelien Delphin

With the rapid advancements in medical data acquisition and production, increasingly richer representations exist to characterize medical information. However, such large-scale data do not usually meet computing resource constraints or algorithmic complexity, and can only be processed after compression or reduction, at the potential loss of information. In t

Vibhhu Sharma, Bryan Wilder

Machine learning is increasingly used to select which individuals receive limited-resource interventions in domains such as human services, education, development, and more. However, it is often not apparent what the right quantity is for models to predict. Policymakers rarely have access to data from a randomized controlled trial (RCT) that would enable acc

Futoon M. Abushaqra, Hao Xue, Yongli Ren, Flora D. Salim

Addressing the challenges of irregularity and concept drift in streaming time series is crucial for real-world predictive modelling. Previous studies in time series continual learning often propose models that require buffering long sequences, potentially restricting the responsiveness of the inference system. Moreover, these models are typically designed fo

Alireza Sharifian, Andreas Athenodorou, Pedro Bicudo

We compute the spectra of open flux tubes formed between a static quark-antiquark pair for various gauge groups in the large-$N_c$ limit, focusing on different symmetries manifested by the quantum numbers of angular momentum, parity, and charge conjugation. Specifically, we present spectra from $N_c=3$ up to $N_c=6$ and for eight different irreducible repres

Vivek Vasan, Anuj Agrawal, Alexander Nico-Katz, Jerry Horgan

We consider quantum networks, where entangled photon pairs are distributed using fibre optic links from a centralized source to entangling nodes. The entanglement is then stored (via an entanglement swap) in entangling nodes' quantum memories until used in, e.g., distributed quantum computing, quantum key distribution, quantum sensing, and other applications

Daito Igarashi, Masashi Konyo, Satoshi Tadokoro

In recent years, there has been a growing demand for realistic haptic experiences in VR. This study employs the Intensity Segment Modulation (ISM) method to convert high-frequency vibrations into low-frequency amplitude-modulated waves on VR devices. We verified the generation of high-immersion vibration stimuli synchronized with audio. Additionally, we comp

Shuchen Guo, Ehsan Latif, Yifan Zhou, Xuan Huang

This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts, particularly for student constructed responses in science assessments. The research addresses a key gap in the field by exploring how multi-agent systems, called AutoFeedback, can improve the quality of GenAI-generated feedback, ove

Jessica Irons, Patrick Cooper, Melanie McGrath, Shahroz Tariq

Artificial intelligence (AI) tools are now prevalent in many knowledge work industries. As AI becomes more capable and interactive, there is a growing need for guidance on how to employ AI most effectively. The A2C framework (Tariq, Chhetri, Nepal & Paris, 2024) distinguishes three decision-making modes for engaging AI: automation (AI completes a task, inclu

Neelabhro Roy, Mani H. Dhullipalla, Gourav Prateek Sharma, Dimos V. Dimarogonas

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexib

Julian Minder, Kevin Du, Niklas Stoehr, Giovanni Monea

When making predictions, a language model must trade off how much it relies on its context vs. its prior knowledge. Choosing how sensitive the model is to its context is a fundamental functionality, as it enables the model to excel at tasks like retrieval-augmented generation and question-answering. In this paper, we search for a knob which controls this sen

Lauren Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff

We propose a framework for two-player infinite-dimensional games with cooperative or competitive structure. These games take the form of coupled partial differential equations in which players optimize over a space of measures, driven by either a gradient descent or gradient descent-ascent in Wasserstein-2 space. We characterize the properties of the Nash eq

Joong-Ku Lee, Donghyeon Kim, Seong-Su Park, Jiye Lee

This paper presents a bimanual haptic display based on collaborative robot arms. We address the limitations of existing robot arm-based haptic displays by optimizing the setup configuration and implementing inertia/friction compensation techniques. The optimized setup configuration maximizes workspace coverage, dexterity, and haptic feedback capability while

Maijia Su, Mayssa Dabaghi, Marco Broccardo

Stochastic ground motion models (GMMs) are gaining popularity and momentum among engineers to perform time-history analysis of structures and infrastructures. This paper aims to review and validate hierarchical stochastic GMMs, with a focus on identifying their ''optimal'' configuration. We introduce the word ''hierarchical'' as its formulation contains two

Nicholas Jeon, Xiaoning Qian, Lamin SaidyKhan, Paul de Figueiredo

Long non-coding RNAs (lncRNAs) serve as crucial regulators in numerous biological processes. Although they share sequence similarities with messenger RNAs (mRNAs), lncRNAs perform entirely different roles, providing new avenues for biological research. The emergence of next-generation sequencing technologies has greatly advanced the detection and identificat

Mark Tygert

Prior works have demonstrated many advantages of cumulative statistics over the classical methods of reliability diagrams, ECEs (empirical, estimated, or expected calibration errors), and ICIs (integrated calibration indices). The advantages pertain to assessing calibration of predicted probabilities, comparison of responses from a subpopulation to the respo

Aakash Sorathiya, Gouri Ginde

With the increasing proliferation of mobile applications in our everyday experiences, the concerns surrounding ethics have surged significantly. Users generally communicate their feedback, report issues, and suggest new functionalities in application (app) reviews, frequently emphasizing safety, privacy, and accountability concerns. Incorporating these revie

Boxun Xu, Junyoung Hwang, Pruek Vanna-iampikul, Sung Kyu Lim

Spiking neural networks (SNNs) are powerful models of spatiotemporal computation and are well suited for deployment on resource-constrained edge devices and neuromorphic hardware due to their low power consumption. Leveraging attention mechanisms similar to those found in their artificial neural network counterparts, recently emerged spiking transformers hav

Alexandria Leto, Cecilia Aguerrebere, Ishwar Bhati, Ted Willke

Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs). Two separate systems form the RAG pipeline, the retriever and the reader, and the impact of each on downstream task performance is not well-understood. Here, we work towards the goal of understanding ho

Mohamed Abul Hassan, Pu Sun, Xiangnan Zhou, Lisanne Kraft

This study introduces a novel data-centric approach to improve real-time surgical guidance using fiber-based fluorescence lifetime imaging (FLIm). A key aspect of the methodology is the accurate detection of the aiming beam, which is essential for localizing points used to map FLIm measurements onto the tissue region within the surgical field. The primary ch

David J. Wilson, Cynthia S. Froning, Girish M. Duvvuri, Allison Youngblood

We present 5-1x10^7 Angstrom spectral energy distributions (SEDs) for twelve M dwarf stars covering spectral types M0-M8. Our SEDs are provided for community use as a sequel to the Measurements of the Ultraviolet Spectral Characteristics of Low-mass Exoplanetary Systems (MUSCLES) survey. The twelve stars include eight known exoplanet hosts and four stars cho

S. A. Pustilnik, Y. A. Perepelitsyna, A. S. Vinokurov, E. S. Egorova

DDO68 is a star-forming (SF) dwarf galaxy residing in a nearby void. Its gas metallicity is among the lowest known in the local Universe, with parameter 12+log(O/H) in the range of 6.96-7.3 dex. Six of its SF regions are located in or near the so-called 'Northern Ring', in which the Hubble Space Telescope (HST) images reveal many luminous young stars. We pre

Yang Gu, Hengyu You, Jian Cao, Muran Yu

Building effective machine learning (ML) workflows to address complex tasks is a primary focus of the Automatic ML (AutoML) community and a critical step toward achieving artificial general intelligence (AGI). Recently, the integration of Large Language Models (LLMs) into ML workflows has shown great potential for automating and enhancing various stages of t

Haoliang Wang, Chen Zhao, Feng Chen

Open-set domain generalization addresses a real-world challenge: training a model to generalize across unseen domains (domain generalization) while also detecting samples from unknown classes not encountered during training (open-set recognition). However, most existing approaches tackle these issues separately, limiting their practical applicability. To ove

Mahdi Morafah, Hojin Chang, Chen Chen, Bill Lin

Federated Learning (FL) enables collaborative model training across decentralized edge devices while preserving data privacy. However, existing FL methods often assume clean annotated datasets, impractical for resource-constrained edge devices. In reality, noisy labels are prevalent, posing significant challenges to FL performance. Prior approaches attempt l

Martin Kolodziejczyk, Michela Ottobre, Gideon Simpson

This paper is concerned with the problem of counting solutions of stationary nonlinear Partial Differential Equations (PDEs) when the PDE is known to admit more than one solution. We suggest tackling the problem via a sampling-based approach. We test our proposed methodology on the McKean-Vlasov PDE, more precisely on the problem of determining the number of

Sanjay Jain, Tzeh Yuan Neoh, Frank Stephan

We show that the CNF satisfiability problem (SAT) can be solved in time $O^*(1.1199^{(d-2)n})$, where $d$ is either the maximum number of occurrences of any variable or the average number of occurrences of all variables if no variable occurs only once. This improves upon the known upper bound of $O^*(1.1279^{(d-2)n})$ by Wahlstr$\ddot{\text{o}}$m (SAT 2005)

Simone Betteti, Giacomo Baggio, Francesco Bullo, Sandro Zampieri

Firing rate models are dynamical systems widely used in applied and theoretical neuroscience to describe local cortical dynamics in neuronal populations. By providing a macroscopic perspective of neuronal activity, these models are essential for investigating oscillatory phenomena, chaotic behavior, and associative memory processes. Despite their widespread

Midia Yousefi, Yao Qian, Junkun Chen, Gang Wang

End-to-end speech translation (ST), which translates source language speech directly into target language text, has garnered significant attention in recent years. Many ST applications require strict length control to ensure that the translation duration matches the length of the source audio, including both speech and pause segments. Previous methods often

Maximilian O'Keeffe

Suppose $A$ is a subset of $\{1, \dotsc, N\}$ which does not contain any configurations of the form $x,x+\lfloor n^c \rfloor$ where $n \neq 0$ and $1<c<\frac{6}{5}$. We show that the density of $A$ relative to the first $N$ integers is $O_c(N^{1-\frac{6}{5c}})$. More generally, given a smooth and regular real valued function $h$ with "growth rate" $c \in (1,

Maximilian O'Keeffe

Let $(X,\mu)$ be an arbitrary measure space equipped with a family of pairwise commuting measure preserving transformations $T_1, \dotsc, T_m$. We prove that the ergodic averages \[ A_{N;X}^{P_1, \dotsc, P_m}f = \frac{1}{N} \sum_{n=1}^N T_1^{\lfloor P_1(n) \rfloor} \dotsm T_m^{\lfloor P_m(n) \rfloor} f \] converge pointwise $\mu$-almost everywhere as $N \to

Maximilian O'Keeffe

We prove long variational estimates for the bilinear ergodic averages \[ A_{N;X}(f,g)(x) = \frac{1}{N} \sum_{n=1}^N f(T^{\lfloor \sqrt{n} \rfloor}x) g(T^nx) \] on an arbitrary measure preserving system $(X,\mu,T)$ for the full expected range, i.e. whenever $f \in L^{p_1}(X)$ and $g \in L^{p_2}(X)$ with $1<p_1,p_2<\infty$. In particular, if $\frac{1}{p}=\frac

Taesoo Song, Jiaxing Zhao, Ilia Grishmanovskii

Heavy quark mass in QGP is related to the heavy quark potential at a large distance. In this study we test three different heavy quark potentials, namely, the free energy, the internal energy of the heavy quark pair in QGP, and the unscreened potential, which was recently proposed by the HotQCD Collaboration, through the thermal production of charm quarks in

Russell Keith, Hung La

This paper presents a scheduling algorithm that divides a manufacturing/warehouse floor into zones that an Autonomous Mobile Robot (AMR) will occupy and complete part pick-up and drop-off tasks. Each zone is balanced so that each AMR will share each task equally. These zones change over time to accommodate fluctuations in production and to avoid overloading

Zhidong Ling, Zihao Li, Pablo Romero, Lifeng Han

This report is the system description of the MaLei team (Manchester and Leiden) for the shared task Plain Language Adaptation of Biomedical Abstracts (PLABA) 2024 (we had an earlier name BeeManc following last year), affiliated with TREC2024 (33rd Text REtrieval Conference https://ir.nist.gov/evalbase/conf/trec-2024). This report contains two sections corres

Clifford Cheung, Ira Z. Rothstein

Is field space infinite? If not, it either loops back on itself or ends altogether. Periodic boundary conditions are of course familiar, but field space endpoints--which appear in real-world systems--are far less explored. In this paper we argue that boundaries in field space are generic, radiatively stable structures that allow for new physics at very low s

Henning Vahlbruch, Moritz Mehmet, Karsten Danzmann, Roman Schnabel

Squeezed states of light belong to the most prominent nonclassical resources. They have compelling applications in metrology, which has been demonstrated by their routine exploitation for improving the sensitivity of a gravitational-wave detector since 2010. Here, we report on the direct measurement of 15 dB squeezed vacuum states of light and their applicat

Yu Han, Aaron Ceross, Sarim Ather, Jeroen H. M. Bergmann

Artificial intelligence (AI) in medical device software (MDSW) represents a transformative clinical technology, attracting increasing attention within both the medical community and the regulators. In this study, we leverage a data-driven approach to automatically extract and analyze AI-enabled medical devices (AIMD) from the National Medical Products Admini

Farbod Jahandar, René Doyon, Étienne Artigau, Neil J. Cook

We extend the methodology introduced by Jahandar et al. (2024) to determine the effective temperature and chemical abundances of 31 slowly-rotating solar neighborhood M dwarfs (M1-M5) using high-resolution spectra from CFHT/SPIRou. This group includes 10 M dwarfs in binary systems with FGK primaries of known metallicity from optical measurements. By testing

Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz

Super-resolution ultrasound (SR-US) is a powerful imaging technique for capturing microvasculature and blood flow at high spatial resolution. However, accurate microbubble (MB) localization remains a key challenge, as errors in localization can propagate through subsequent stages of the super-resolution process, affecting overall performance. In this paper,

Bo-Hsun Chen, Dan Negrut

In this contribution, we introduce the concept of Instance Performance Difference (IPD), a metric designed to measure the gap in performance that a robotics perception task experiences when working with real vs. synthetic pictures. By pairing synthetic and real instances in the pictures and evaluating their performance similarity using perception algorithms,

Prashanth Amireddy, Amik Raj Behera, Manaswi Paraashar, Srikanth Srinivasan

In this work, we show that the class of multivariate degree-$d$ polynomials mapping $\{0,1\}^{n}$ to any Abelian group $G$ is locally correctable with $\widetilde{O}_{d}((\log n)^{d})$ queries for up to a fraction of errors approaching half the minimum distance of the underlying code. In particular, this result holds even for polynomials over the reals or th

Karin Wittmann Wilsmann, Erick R. Castro, Itzhak Roditi, Angela Foerster

This study explores the semiclassical limit of an integrable-chaotic bosonic many-body quantum system, providing nuanced insights into its behavior. We examine classical-quantum correspondences across different interaction regimes of bosons in a triple-well potential, ranging from the integrable to the self-trapping regime, and including the chaotic one. The

Hyewon Jeong, Siddharth Nayak, Taylor Killian, Sanjat Kanjilal

Sepsis is a life-threatening condition defined by end-organ dysfunction due to a dysregulated host response to infection. Although the Surviving Sepsis Campaign has launched and has been releasing sepsis treatment guidelines to unify and normalize the care for sepsis patients, it has been reported in numerous studies that disparities in care exist across the

Serge Vlăduţ

This note corrects the paper \cite{ex}, where lattice sequences having exponentially large kissing numbers were constructed. However it was noted in \cite{dif} that the arguments in that paper are not sufficient. Here we correct the construction which leads to similar results, albeit with much smaller constants.

Odelia V. Hartl, Louis E. Strigari

We use new measurements of the M31 proper motion to examine the Milky Way (MW) - M31 orbit and angular momentum. For Local Group (LG) mass consistent with measured values, and assuming the system evolves in isolation, we show a wide range of orbits is possible. We compare to a sample of LG-like systems in the Illustris simulation and find that $\sim 13\%$ of

Houssam Sabri, Benjamin E. Carlson, Sergey S. Pershoguba, Jiadong Zang

We investigate the emergence of topological Hall-like (THE-like) signals in disordered multidomain ferromagnets. Non-monotonic behavior in Hall resistivity, commonly attributed to topological spin textures such as skyrmions, is produced in a random resistor network model without any chirality. It arises from simple mechanisms of the anomalous Hall effect (AH

Christiane Didden

The Oaxaca-Blinder decomposition is a widely used method to explain social disparities. However, assigning causal meaning to its estimated components requires strong assumptions that often lack explicit justification. This article emphasizes the importance of clearly defined estimands and their identification when targeting mediating mechanisms of social dis

Fei Shuang, Kai Liu, Yucheng Ji, Wei Gao

Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurately modeling these extensive defects is crucial for understanding their deformation mechanisms. Existing machine learning interatomic potentials (MLIPs) often fall short in adequately describing these defects, as their significant characteristic si

Lucas Gerth

Let $X$ be a smooth proper rigid analytic space over a complete algebraically closed field extension $K$ of $\mathbb{Q}_p$. We establish a Hodge--Tate decomposition for $X$ with $G$-coefficients, where $G$ is any commutative locally $p$-divisible rigid group. This generalizes the Hodge--Tate decomposition of Faltings and Scholze, which is the case $G=\mathbb

Sheehan Olver

Incorporating symmetries into the numerical solution of differential equations has been a mainstay of research over the last 40 years, however, one aspect is less known and under-utilised: discretisations of partial differential equations that commute with symmetry actions (like rotations, reflections or permutations) can be decoupled into independent system

Wallace Abreu, Luiz Wagner Pereira Biscainho

Audio super-resolution aims to enhance low-resolution signals by creating high-frequency content. In this work, we modify the architecture of AERO (a state-of-the-art system for this task) for music super-resolution. SPecifically, we replace its original Attention and LSTM layers with Mamba, a State Space Model (SSM), across all network layers. Mamba is capa

Safal Raman Aryal

We explore various semantic understandings of dual intuitionistic logic by exploring the relationship between co-Heyting algebras and topological spaces. First, we discuss the relevant ideas in the setting of Heyting algebras and intuitionistic logic, showing organically the progression from the primordial example of lattices of open sets of topological spac

Jaime Ruiz-Serra, Patrick Sweeney, Michael S. Harré

Understanding how individual agents make strategic decisions within collectives is important for advancing fields as diverse as economics, neuroscience, and multi-agent systems. Two complementary approaches can be integrated to this end. The Active Inference framework (AIF) describes how agents employ a generative model to adapt their beliefs about and behav

Jaeseok Choi, Anand Deo, Constantino Lagoa, Anirudh Subramanyam

The scenario approach is widely used in robust control system design and chance-constrained optimization, maintaining convexity without requiring assumptions about the probability distribution of uncertain parameters. However, the approach can demand large sample sizes, making it intractable for safety-critical applications that require very low levels of co

Salma Begum Tamanna, Gias Uddin, Song Wang, Lan Xia

Hallucinations, the tendency to produce irrelevant/incorrect responses, are prevalent concerns in generative AI-based tools like ChatGPT. Although hallucinations in ChatGPT are studied for textual responses, it is unknown how ChatGPT hallucinates for technical texts that contain both textual and technical terms. We surveyed 47 software engineers and produced

Tuck C Choy

This paper is a commentary on Rudolph Peierls's "In defence of measurement" Physics World 19-20, January 1991 to accompany the reprint of this paper for the special volume dedicated to the celebration of the centenary of the discovery of quantum mechanics by Werner Heisenberg, to be published by World Scientific in July 2025.

Ivan-Vanja Boroja, Damjana Kokol Bukovšek, Nik Stopar

We show that any infinite ring has an infinite nonunital compressed commuting graph. We classify all infinite unital rings with finite unital compressed commuting graph, using semidirect product of rings as our main tool. As a consequence we also classify infinite unital rings with only finitely many unital subrings.

Sven Hirsch, Hyun Chul Jang, Yiyue Zhang

In Special Relativity, massless objects are characterized as either vacuum states or as radiation propagating at the speed of light. This distinction extends to General Relativity for asymptotically flat initial data sets (IDS) \((M^n, g, k)\), where vacuum is represented by slices of Minkowski space, and radiation is modeled by slices of \(pp\)-wave spaceti

Peter J. Forrester

Ensembles of complex symmetric, and complex self dual random matrices are known to exhibit local statistical properties distinct from those of the non-Hermitian Ginibre ensembles. On the other hand, in distinction to the latter, the joint eigenvalue probability density function of these two ensembles are not known. Nonetheless, as carried out in the recent w

Guido Bosco

Let $C$ be a complete algebraically closed extension of $\mathbb{Q}_p$, and let $\mathfrak{X}$ be a smooth formal scheme over $\mathcal{O}_C$. By the work of Bhatt--Morrow--Scholze, it is known that when $\mathfrak{X}$ is proper, the length of the torsion in the integral $p$-adic \'etale cohomology of the generic fiber $\mathfrak{X}_C$ is bounded above by th

Eric Balkanski, Cherlin Zhu

An important challenge in robust machine learning is when training data is provided by strategic sources who may intentionally report erroneous data for their own benefit. A line of work at the intersection of machine learning and mechanism design aims to deter strategic agents from reporting erroneous training data by designing learning algorithms that are

Riccardo Alberini, Michele Terzano, Gerhard A. Holzapfel, Andrea Spagnoli

Advanced simulations of the mechanical behavior of soft tissues frequently rely on structure-based constitutive models, including smeared descriptions of collagen fibers. Among them, the so-called Discrete Fiber Dispersion (DFD) model is based on a discrete integration of the fiber-strain energy over all the fiber directions. In this paper, we recall the the

Yu Hsuan Liang, Xing Zhang, Garnet Kin-Lic Chan, Timothy C. Berkelbach

We present an efficient implementation of the random phase approximation (RPA) for molecular systems within the domain-based local pair natural orbital (DLPNO) framework. With optimized parameters, DLPNO-RPA achieves approximately 99.9% accuracy in the total correlation energy compared to a canonical implementation, enabling highly accurate reaction energies

Danil Kazimirov, Dmitry Nikolaev, Ekaterina Rybakova, Arseniy Terekhin

Nowadays, the Hough (discrete Radon) transform (HT/DRT) has proved to be an extremely powerful and widespread tool harnessed in a number of application areas, ranging from general image processing to X-ray computed tomography. Efficient utilization of the HT to solve applied problems demands its acceleration and increased accuracy. Along with this, most fast

Sanghyuk Moon, Eve C. Ostriker

A fraction of the dense cores within a turbulent molecular cloud will eventually collapse to form stars. Identifying the physical criteria for instability and analyzing critical core properties is therefore necessary to star formation theory. Here we quantify the characteristics of an ensemble of "critical cores" on the verge of collapse. This critical epoch

Sanghyuk Moon, Eve C. Ostriker

A fundamental issue in star formation is understanding the precise mechanisms leading to the formation of prestellar cores, and their subsequent gravitationally unstable evolution. To address this question, we carefully construct a suite of turbulent, self-gravitating numerical simulations, and analyze the development and collapse of individual prestellar co

Khadija Rais, Mohamed Amroune, Abdelmadjid Benmachiche, Mohamed Yassine Haouam

Variational autoencoder (VAE) is one of the most common techniques in the field of medical image generation, where this architecture has shown advanced researchers in recent years and has developed into various architectures. VAE has advantages including improving datasets by adding samples in smaller datasets and in datasets with imbalanced classes, and thi

Timur Z. Seidov, Maxim A. Gorlach

Tellegen response is a special type of nonreciprocal magneto-electric coupling which long remained elusive in photonics and extremely weak in condensed matter. It is widely accepted that the Tellegen coefficient is restricted by $\chi^2<\varepsilon\mu$, where $\varepsilon$ and $\mu$ are permittivity and permeability of the material. Here, we demonstrate that

Z. Jonny Kong, Nathan Hu, Y. Charlie Hu, Jiayi Meng

With rapid evolution of mobile core network (MCN) architectures, large-scale control-plane traffic (CPT) traces are critical to studying MCN design and performance optimization by the R&D community. The prior-art control-plane traffic generator SMM heavily relies on domain knowledge which requires re-design as the domain evolves. In this work, we study the f

Andrew O. Parry, C. Rascón

Recent studies of wetting in a two-component square-gradient model of interfaces in a fluid mixture, showing three-phase bulk coexistence, have revealed some highly surprising features. Numerical results show that the density profile paths, which form a tricuspid shape in the density plane, have curious geometric properties, while conjectures for the analyti

German Gritsai, Ildar Khabutdinov, Andrey Grabovoy

This paper describes a system designed to distinguish between AI-generated and human-written scientific excerpts in the DAGPap24 competition hosted within the Fourth Workshop on Scientific Document Processing. In this competition the task is to find artificially generated token-level text fragments in documents of a scientific domain. Our work focuses on the