October 2024 arXiv papers — page 10

Yiqi Yang, Hongye Fu

We present a novel black-box jailbreaking framework that integrates multiple LLM-as-Attacker strategies to deliver highly transferable and effective attacks. The framework is grounded in three key insights from prior jailbreaking research and practice: ensemble approaches outperform single methods in exposing aligned LLM vulnerabilities, malicious instructio

Zepeng Wu, Chen Gao, Feng Zhang, Shunqing Wu

The superionic state is a phase of matter in which liquid-like ionic mobility coexists with a solid crystalline lattice. Recently identified in Earth's inner core (IC), this state has attracted considerable attention for its unique kinetic behavior and geophysical implications. However, the ab initio phase diagram describing the equilibrium between the super

Xiao Guo, Xiaohong Liu, Iacopo Masi, Xiaoming Liu

Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, we present a hierarchical fine-grained formulation for IFDL representation learning. Specifically, we first represent forgery attributes of

Nalin Tiwary, Vardhan Dongre, Sanil Arun Chawla, Ashwin Lamani

Recent advancements in Large Language Model (LLM)-based frameworks have extended their capabilities to complex real-world applications, such as interactive web navigation. These systems, driven by user commands, navigate web browsers to complete tasks through multi-turn dialogues, offering both innovative opportunities and significant challenges. Despite the

Matilda Knierim, Sahil Jain, Murat Han Aydoğan, Kenneth Mitra

Agent learning from human interaction often relies on explicit signals, but implicit social cues, such as prosody in speech, could provide valuable information for more effective learning. This paper advocates for the integration of prosody as a teaching signal to enhance agent learning from human teachers. Through two exploratory studies--one examining voic

Kansuke Nunota, Takahiro Sumi, Naoki Koshimoto, Nicholas J. Rattenbury

We present measurements of the microlensing optical depth and event rate toward the Galactic bulge using the dataset from the 2006--2014 MOA-II survey, which covers 22 bulge fields spanning ~42 deg^2 between -5 deg < l < 10 deg and -7 deg < b < -1 deg. In the central region with |l|<5 deg, we estimate an optical depth of {\tau} = [1.75+-0.04]*10^-6exp[(0.34+

Pengyu Chen, Harry Shu, Wenjing Tang, Christina Yu

Biofouling represents a critical challenge in marine transportation, healthcare, and food manufacturing, among other industries, as it promotes contamination and increases maintenance costs. Zwitterionic polymers, known for their exceptional antifouling properties, offer a promising solution for biofouling deterrence. Despite the rapid development of zwitter

Mario Shannon

Given an Anosov flow on a closed 3-manifold, we are interested in the problem of whether or not making non-trivial Fried surgeries along a finite set of periodic orbits can produce a flow equivalent to itself. We show that for some suspension Anosov flows, there exist infinitely many pairs of periodic orbits satisfying this property.

Jia Zhang, Xin-Jian Wen

In this work we make use of the Nambu-Jona-Lasinio model to investigate thermodynamic properties of magnetized three-flavor quark matter. The non-equilibrium Tsallis distribution is characterized by a dimensionless non-extensive parameter $q$. We find that the system always undergoes a crossover transition for all given $q$ values and the pseudo-critical tem

Tatsuya Miki, Hiroaki Ikeda, Michi-To Suzuki, Shintaro Hoshino

Chiral and axial materials offer platforms for intriguing phenomena, such as cross-correlated responses and chirality-induced spin selectivity. However, quantifying the properties of such materials has generally been considered challenging. Here, we demonstrate that the spatial distribution of the electron chirality, represented by $\Psi^\dagger \gamma^5 \Ps

Jerome Gilles, Travis Meyer, Pamela K. Douglas

Concurrent EEG-fMRI recordings are advantageous over serial recordings, as they offer the ability to explore the relationship between both signals without the compounded effects of nonstationarity in the brain. Nonetheless, analysis of simultaneous recordings is challenging given that a number of noise sources are introduced into the EEG signal even after MR

Hiroki Isobe, Naoto Nagaosa

Nonlinear transport phenomena in condensed matter reflect the geometric nature, quantum coherence, and many-body correlation of electronic states. Electric currents in solids are classified into (i) Ohmic current, (ii) supercurrent, and (iii) geometric or topological current. While the nonlinear current-voltage $(I$-$V)$ characteristics of the former two cat

Jun Jiang, Yunhe Sheng, Chenchang Zhu

In this paper, we give the necessary and sufficient conditions of the integrability of relative Rota-Baxter Lie algebras via double Lie groups, matched pairs of Lie groups and factorization of diffeomorphisms respectively. We use the integrability of Rota-Baxter operators to characterize whether the Poisson-Lie group integrating a factorizable Lie bialgebra

Meng Shen, Yuzhi Liu, Qinglin Zhao, Wei Wang

As blockchain applications become increasingly widespread, there is a rising demand for on-chain data queries. However, existing schemes for on-chain data queries face a challenge between verifiability and efficiency. Queries on blockchain databases can compromise the authenticity of the query results, while schemes that utilize on-chain Authenticated Data S

Edgardo Franzin, Antonia M. Frassino, Jorge V. Rocha

Tidal Love numbers of anti-de Sitter black holes are understood as linear response coefficients governing how the holographically dual plasma polarizes when the geometry of the space, in which the plasma lives, is deformed. So far, this picture has been applied only to black branes with plane wave perturbations. We fill the gap in the literature by performin

Hyun-Soo Kim, Isaac H. Kim, Daniel Ranard

Many-body ground state preparation is an important subroutine used in the simulation of physical systems. In this paper, we introduce a flexible and efficient framework for obtaining a state preparation circuit for a large class of many-body ground states. We introduce polynomial-time classical algorithms that take reduced density matrices over $\mathcal{O}(

Lijun Zhu, Qianbiao Liu, Xiangrong Wang

The discovery of the unusual magnetoresistance (UMR) during the rotation of magnetization in the plane perpendicular to the electric current, which has been typically attributed to magnetization-dependent interfacial reflection of spin current, has brought remarkable impacts on the understanding and application of a variety of spintronic phenomena. Here, we

Carlos H. Cardonha, Arvind U. Raghunathan

Passenger transportation is a core aspect of a railway company's business, with ticket sales playing a central role in generating revenue. Profitable operations in this context rely heavily on the effectiveness of reject-or-assign policies for coach reservations. As in traditional revenue management, uncertainty in demand presents a significant challenge, pa

Thomas C. K. Ng, Stefano Rinaldi, Otto A. Hannuksela

The challenge of understanding the Universe's dynamics, particularly the Hubble tension, requires precise measurements of the Hubble constant. Building upon the existing spectral-siren method, which capitalizes on population information from gravitational-wave sources, this paper explores an alternative way to analyze the population data to obtain the cosmol

Shuo Feng, Bo Liu, Yifan, Shan

Wire bending is a technique used in manufacturing to mass-produce items such as clips, mounts, and braces. Recent advances in programmable wire bending have made this process increasingly accessible for custom fabrication. However, CNC wire benders are controlled using Computer Aided Manufacturing (CAM) software, without design tools, making custom designs c

Daniel M C Hallack, Guillem Sole-Mari, Saif Farhat, Diogo Bolster

The effective mixing behavior of solutes in porous media is fundamentally connected to the development of a local mixing interface between the two initial solutions, which is characterized by a complex lamellar structure. The deformation of the interface is controlled by the interplay of advection and diffusion, which generate the mechanisms of lamella stret

Margaux Kerdraon, Albane Théry, Marc Pascual, Stéphanie Descroix

We report the pinching of an elongated liquid droplet that is confined in a rectangular microchannel. The droplet pinching is induced by a local variation of the channel topography and can lead to its break-up. The modification of the channel topography is either induced by a reversible local dilation of the channel bottom wall or by a confinement gradient t

Youpeng Zhao, Jun Wang

Large Language Models (LLMs) represent a revolutionary advancement in the contemporary landscape of artificial general intelligence (AGI). As exemplified by ChatGPT, LLM-based applications necessitate minimal response latency and maximal throughput for inference serving. However, due to the unpredictability of LLM execution, the first-come-first-serve (FCFS)

Chung-Han Hsieh, Xiao-Rou Yu

This paper addresses a novel \emph{cost-sensitive} distributionally robust log-optimal portfolio problem, where the investor faces \emph{ambiguous} return distributions, and a general convex transaction cost model is incorporated. The uncertainty in the return distribution is quantified using the \emph{Wasserstein} metric, which captures distributional ambig

Daniel Philipov, Vardhan Dongre, Gokhan Tur, Dilek Hakkani-Tür

Embodied agents designed to assist users with tasks must engage in natural language interactions, interpret instructions, execute actions, and communicate effectively to resolve issues. However, collecting large-scale, diverse datasets of situated human-robot dialogues to train and evaluate such agents is expensive, labor-intensive, and time-consuming. To ad

Hyeon-Taek Han, Dae-Hyeok Lee, Heon-Gyu Kwak

Brain-computer interface (BCI) technology enables direct interaction between humans and computers by analyzing brain signals. Electroencephalogram (EEG) is one of the non-invasive tools used in BCI systems, providing high temporal resolution for real-time applications. However, EEG signals are often affected by a low signal-to-noise ratio, physiological arti

Jerome Gilles

Some recent methods, like the Empirical Mode Decomposition (EMD), propose to decompose a signal accordingly to its contained information. Even though its adaptability seems useful for many applications, the main issue with this approach is its lack of theory. This paper presents a new approach to build adaptive wavelets. The main idea is to extract the diffe

Jerome Gilles, Giang Tran, Stanley Osher

A recently developed new approach, called ``Empirical Wavelet Transform'', aims to build 1D adaptive wavelet frames accordingly to the analyzed signal. In this paper, we present several extensions of this approach to 2D signals (images). We revisit some well-known transforms (tensor wavelets, Littlewood-Paley wavelets, ridgelets and curvelets) and show that

B. Lajci, D. H. J. O'Dell, J. Mumford

We consider the topological properties of the so-called central spin model that consists of $N$ identical spins coupled to a single distinguishable central spin which arises in physical systems such as circuit-QED and bosonic Josephson junctions coupled to an impurity atom. The model closely corresponds to the Su-Schrieffer-Heeger (SSH) model except that the

R. Tyler Sutherland

We discuss a general technique for using quantum logic spectroscopy to perform quantum non-demolition (QND) measurements that determine which of two subspaces a logic ion is in. We then show how to use the scheme to perform high fidelity state preparation and measurement (SPAM) and non-destructive leakage detection, as well as how this would reduce the engin

Bingjun Li, Mostafa Karami, Masum Shah Junayed, Sheida Nabavi

Understanding the intricate cellular environment within biological tissues is crucial for uncovering insights into complex biological functions. While single-cell RNA sequencing has significantly enhanced our understanding of cellular states, it lacks the spatial context necessary to fully comprehend the cellular environment. Spatial transcriptomics (ST) add

Łukasz Staniszewski, Łukasz Kuciński, Kamil Deja

Diffusion Models achieve state-of-the-art performance in generating new samples but lack a low-dimensional latent space that encodes the data into editable features. Inversion-based methods address this by reversing the denoising trajectory, transferring images to their approximated starting noise. In this work, we thoroughly analyze this procedure and focus

Zekai Miao, Xingyu Zhang, Qingfei Song, Qingyong Meng

In this work, we propose a decoupled support vector regression (SVR) approach for direct canonical polyadic decomposition (CPD) of a potential energy surface (PES) through a set of discrete training energy data. This approach, denoted by CPD-SVR, is able to directly construct the PES in CPD with a more compressed form than previously developed Gaussian proce

Hajar Sakai, Sarah S. Lam, Mohammadsadegh Mikaeili, Joshua Bosire

Patient experience and care quality are crucial for a hospital's sustainability and reputation. The analysis of patient feedback offers valuable insight into patient satisfaction and outcomes. However, the unstructured nature of these comments poses challenges for traditional machine learning methods following a supervised learning paradigm. This is due to t

Osoro B. Ogutu, Edward J. Oughton

Broadband connectivity is a tool for catalyzing socio-economic development and reducing the societal inequalities. Recent studies have investigated the supporting role of broadband in addressing Sustainable Development Goals (SDGs). Relationally, emerging ultra-dense broadband networks such as 5/6G have been linked to increased power consumption and more car

Si-Hyun Kim, Sung-Jin Kim, Dae-Hyeok Lee

Brain-computer interface (BCI) enables direct communication between the brain and external devices by decoding neural signals, offering potential solutions for individuals with motor impairments. This study explores the neural signatures of motor execution (ME) and motor imagery (MI) tasks using EEG signals, focusing on four conditions categorized as sense-r

Hieu Tran, Junda Wang, Yujan Ting, Weijing Huang

Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, yet they often struggle with maintaining factual accuracy, particularly in knowledge-intensive domains like healthcare. This study introduces LEAF: Learning and Evaluation Augmented by Fact-Checking, a novel approach designed to enhance the factual r

Ziming Lin, Fang Han

In a landmark paper, Abadie and Imbens (2008) showed that the naive bootstrap is inconsistent when applied to nearest neighbor matching estimators of the average treatment effect with a fixed number of matches. Since then, this finding has inspired numerous efforts to address the inconsistency issue, typically by employing alternative bootstrap methods. In c

Chen Hao Xia, Leonard Deuschle, Jiang Cao, Alexander Maeder

Band-to-band tunnelling field-effect transistors (TFETs) have long been considered as promising candidates for future low-power logic applications. However, fabricated TFETs rarely reach sub-60 mV/dec sub-threshold swings (SS) at room temperature. Previous theoretical studies identified Auger processes as possible mechanisms for the observed degradation of S

Ricardo Falcon-Perez, Ruohan Gao, Gregor Mueckl, Sebastia V. Amengual Gari

For augmented (AR) and virtual reality (VR) applications, accurate estimates of the acoustic characteristics of a scene are critical for creating a sense of immersion. However, directly estimating Room-impulse Responses (RIRs) from scene geometry is often a challenging, data-expensive task. We propose a method to instead infer spatially-distributed acoustic

Ahalya Ravendran, Mitch Bryson, Donald G. Dansereau

Drones have revolutionized the fields of aerial imaging, mapping, and disaster recovery. However, the deployment of drones in low-light conditions is constrained by the image quality produced by their on-board cameras. In this paper, we present a learning architecture for improving 3D reconstructions in low-light conditions by finding features in a burst. Ou

Chase D. Latour, Mark Klose, Jessie K. Edwards, Zoey Song

Pregnancy loss is recognized as an important competing event in studies of prenatal medication use. However, a healthy live birth also precludes subsequent adverse pregnancy outcomes, yet these events are often censored. Using Monte Carlo simulation, we examine bias that results from failure to account for healthy live birth as a competing event in estimates

Morgan P. Opie

We enumerate complex rank $n$ topological vector bundles on $\mathbb CP^{n+1}$ with prescribed Chern classes. This extends work of Atiyah and Rees in the case $n=2$ and work of Hu in the case that all Chern classes are zero.

Lovis Kwasi Armah, Igor Melnykov

Effectively applying the K-means algorithm to clustering tasks with incomplete features remains an important research area due to its impact on real-world applications. Recent work has shown that unifying K-means clustering and imputation into one single objective function and solving the resultant optimization yield superior results compared to handling imp

D. J. Garcia, J. G. Sereni, A. A. Aligia

We have studied theoretically the specific heat of a large number of non-frustrated magnetic structures described by the Heisenberg model for systems with total angular momentum $J=7/2$, corresponding to the 4f$^7$ configuration of Gd$^{+3}$ and Eu$^{+2}$. For a given critical temperature (determined by the magnitude of the exchange interactions), we find th

Felix Koehler, Simon Niedermayr, Rüdiger Westermann, Nils Thuerey

We introduce the Autoregressive PDE Emulator Benchmark (APEBench), a comprehensive benchmark suite to evaluate autoregressive neural emulators for solving partial differential equations. APEBench is based on JAX and provides a seamlessly integrated differentiable simulation framework employing efficient pseudo-spectral methods, enabling 46 distinct PDEs acro

Jaewon Kim, Ehud Altman, Jong Yeon Lee

Quantum error correction (QEC) codes are fundamentally linked to quantum phases of matter: the degenerate ground state manifold corresponds to the code space, while topological excitations represent error syndromes. Building on this concept, the Sachdev-Ye-Kitaev (SYK) model, characterized by its extensive quasi-ground state degeneracy, serves as a constant

Zbigniew Burdak, Patryk Pagacz

In the paper we fully describe Taylor spectrum of pairs of isometries given by diagrams. In most cases both isometries in such pairs have non-trivial shift part and its Taylor spectrum is a proper subset (of Lebesgue measure in $(0,π^2)$) of the closed bidisc.

Huajie Li

In an infinitesimal variant of Guo-Jacquet trace formulae, the regular semi-simple terms are expressed as noninvariant weighted orbital integrals on two global infinitesimal symmetric spaces. We prove some relations between the Fourier transforms of invariant weighted orbital integrals on the corresponding local infinitesimal symmetric spaces. These relation

Leonardo Blas, Luca Luceri, Emilio Ferrara

With its lenient moderation policies and long-standing associations with potentially unlawful activities, Telegram has become an incubator for problematic content, frequently featuring conspiratorial, hyper-partisan, and fringe narratives. In the political sphere, these concerns are amplified by reports of Telegram channels being used to organize violent act

Sebastian Damrich, Philipp Berens, Dmitry Kobak

Persistent homology is a popular computational tool for analyzing the topology of point clouds, such as the presence of loops or voids. However, many real-world datasets with low intrinsic dimensionality reside in an ambient space of much higher dimensionality. We show that in this case traditional persistent homology becomes very sensitive to noise and fail

Jiwei Wang, Simone Baldi, Henk J. van Waarde

The objective of model reference control is to design a controller that regulates the system&#39;s behavior so as to match a specified reference model. This paper investigates necessary and sufficient conditions for model reference control from a data-driven perspective, when only a set of data generated by the system is utilized to directly accomplish the m

Théo Marty

We prove that in dimension 3, Anosov flows which are $\mathbb{R}$-covered and skewed are orbit equivalent to Reeb-Anosov flows. We characterize the existence of an invariant contact form or of a Birkhoff section with a given boundary, in terms of linking numbers between two invariant signed measures. Furthermore, we prove the existence of open book decomposi

H. Huet, P. R. Ramesh, S. C. Wein, N. Coste

Measurement-based quantum computing offers a promising route towards scalable, universal photonic quantum computation. This approach relies on the deterministic and efficient generation of photonic graph states in which many photons are mutually entangled with various topologies. Recently, deterministic sources of graph states have been demonstrated with qua

Madina Yergibay, Tleukhan Mussin, Saltanat Seitzhan, Daryn Kenzhebek

While most tactile sensors rely on measuring pressure, insights from continuum mechanics suggest that measuring shear strain provides critical information for tactile sensing. In this work, we introduce an optical tactile sensing principle based on shear strain detection. A silicone rubber layer, dyed with color inks, is used to quantify the shear magnitude

Dae-Hyeok Lee, Sung-Jin Kim, Si-Hyun Kim

The detection of pilots' mental states is critical, as abnormal mental states have the potential to cause catastrophic accidents. This study demonstrates the feasibility of using deep learning techniques to classify different fatigue levels, specifically a normal state, low fatigue, and high fatigue. To the best of our knowledge, this is the first study to c

Gajan Mohan Raj, Michael G. Morley, Mohammad Eslami

Diabetic retinopathy is the leading cause of vision loss in working-age adults worldwide, yet under-resourced regions lack ophthalmologists. Current state-of-the-art deep learning systems struggle at these institutions due to limited generalizability. This paper explores a novel federated learning system for diabetic retinopathy diagnosis with the EfficientN

Wenhao Xu, Andrey A. Bagrov, Farhan T. Chowdhury, Luke D. Smith

Magnon-condensation, which emerges in pumped bosonic systems at room temperature, continues to garner great interest for its long-lived coherence. While traditionally formulated in terms of Bose-Einstein condensation, which typically occurs at ultra-low temperatures, it could potentially also be explained by Fr\"ohlich-condensation, a hypothesis of Bose-Eins

Yutong Gao, Vince D. Calhoun, Robyn L. Miller

Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor regional neural activity, providing a rich and complex spatiotemporal data structure. Deep le

Matej Klemen, Martin Božič, Špela Arhar Holdt, Marko Robnik-Šikonja

Spell-checkers are valuable tools that enhance communication by identifying misspelled words in written texts. Recent improvements in deep learning, and in particular in large language models, have opened new opportunities to improve traditional spell-checkers with new functionalities that not only assess spelling correctness but also the suitability of a wo

Marc Vila, Veronika Sunko, Joel E. Moore

Altermagnets, magnetic materials with zero magnetization and spin-split band structure, have gained tremendous attention recently for their rich physics and potential applications. Here, we report on a microscopic tight-binding model that unveils a unique coupling between orbitals and spins in $d$-wave altermagnets which gives rise to momentum-dependent and

Zack Weinstein

Strong-to-weak spontaneous symmetry breaking (SWSSB) has recently emerged as a universal feature of quantum mixed-state phases of matter. While various information-theoretic diagnostics have been proposed to define and characterize SWSSB phases, relating these diagnostics to observables which can be efficiently and scalably probed on modern quantum devices r

Tanmay Parekh, Pradyot Prakash, Alexander Radovic, Akshay Shekher

Research has shown the effectiveness of reasoning (e.g., Chain-of-Thought), planning (e.g., SelfAsk), and retrieval augmented generation strategies to improve the performance of Large Language Models (LLMs) on various tasks, such as question answering. However, using a single fixed strategy to answer different kinds of questions is suboptimal in performance

Peter Belcak, Roger Wattenhofer

It is staggering that words of the English language, which are on average represented by 5--6 bytes of ASCII, require as much as 24 kilobytes when served to large language models. We show that there is room for more information in every token embedding. We demonstrate that 1--3-layer transformers are capable of encoding and subsequently decoding standard Eng

Laura Aspirot, Ernesto Mordecki, Andres Sosa

We obtain a verification theorem for solving a Dynkin game driven by a L\'evy process. The result requires finding two averaging functions that, composed respectively with the supremum and the infimum of the process, summed, and taked the expectation, provide the value function of the game. The optimal stopping rules are the respective hitting times of the s

Gustavo G. Cambrainha, Daniel M. Castro, Nivaldo A. P. de Vasconcelos, Pedro Carelli

The critical brain hypothesis posits that neural systems operate near a phase transition, optimizing the processing of information. While scale invariance and non-Gaussian dynamics--hallmarks of criticality--have been observed in brain activity, a direct link between criticality and behavioral performance remains unexplored. Here, we use a phenomenological r

Muhammad Reza Qorib, Alham Fikri Aji, Hwee Tou Ng

Error type information has been widely used to improve the performance of grammatical error correction (GEC) models, whether for generating corrections, re-ranking them, or combining GEC models. Combining GEC models that have complementary strengths in correcting different error types is very effective in producing better corrections. However, system combina

Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu

We introduce the "Belief State Transformer", a next-token predictor that takes both a prefix and suffix as inputs, with a novel objective of predicting both the next token for the prefix and the previous token for the suffix. The Belief State Transformer effectively learns to solve challenging problems that conventional forward-only transformers struggle wit

Jacob Denson

For any bounded, regulated function $m: [0,\infty) \to \mathbb{C}$, consider the family of operators $\{ T_R \}$ on the sphere $S^d$ such that $T_R f = m(k/R) f$ for any spherical harmonic $f$ of degree $k$. We completely characterize the compactly supported functions $m$ for which the operators $\{ T_R \}$ are uniformly bounded on $L^p(S^d)$, in the range $

Ritik Roongta, Mitchell Zhou, Ben Stock, Rachel Greenstadt

Recent years have seen a sharp rise in adblocker use, driven by increased web tracking and personalized ads. However, a significant issue for adblocker users is the web breakages they encounter, which worsens their browsing experience and often leads them to turn off their adblockers. Despite efforts by filter list maintainers to create rules that minimize t

Ludovico Napoli, Márton Karsai, Esteban Moro

The accurate modeling of individual movement in cities has significant implications for policy decisions across various sectors. Existing research emphasizes the universality of human mobility, positing that simple models can capture population-level movements. However, population-level accuracy does not guarantee consistent performance across all individual

Emanuele Marconato, Sébastien Lachapelle, Sebastian Weichwald, Luigi Gresele

We analyze identifiability as a possible explanation for the ubiquity of linear properties across language models, such as the vector difference between the representations of "easy" and "easiest" being parallel to that between "lucky" and "luckiest". For this, we ask whether finding a linear property in one model implies that any model that induces the same

Yanir A. Rubinstein

Convex geometry and complex geometry have long had fascinating interactions. This survey offers a tour of a few.

Kurt Butler, Daniel Waxman, Petar M. Djurić

Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have been proposed to study time series that are generated by dynamical systems, where traditional approaches like Granger causality are unreliable. However, CCM often yields inaccurat

Sattar Vakili, Julia Olkhovskaya

Reinforcement learning utilizing kernel ridge regression to predict the expected value function represents a powerful method with great representational capacity. This setting is a highly versatile framework amenable to analytical results. We consider kernel-based function approximation for RL in the infinite horizon average reward setting, also referred to

Grant Wilkins, Sheng Di, Jon C. Calhoun, Robert Underwood

Modern scientific simulations generate massive volumes of data, creating significant challenges for I/O and storage systems. Error-bounded lossy compression (EBLC) offers a solution by reducing data set sizes while preserving data quality within user-specified limits. This study provides the first comprehensive energy characterization of state-of-the-art EBL

Guangliang Liu, Zhiyu Xue, Xitong Zhang, Rongrong Wang

Self-correction is one of the most amazing emerging capabilities of Large Language Models (LLMs), enabling LLMs to self-modify an inappropriate output given a natural language feedback which describes the problems of that output. Moral self-correction is a post-hoc approach correcting unethical generations without requiring a gradient update, making it both

Baekrok Shin, Junsoo Oh, Hanseul Cho, Chulhee Yun

Warm-starting neural network training by initializing networks with previously learned weights is appealing, as practical neural networks are often deployed under a continuous influx of new data. However, it often leads to loss of plasticity, where the network loses its ability to learn new information, resulting in worse generalization than training from sc

Nathan Drenkow, William Paul, Chris Ribaudo, Mathias Unberath

Robustness audits of deep neural networks (DNN) provide a means to uncover model sensitivities to the challenging real-world imaging conditions that significantly degrade DNN performance in-the-wild. Such conditions are often the result of multiple interacting factors inherent to the environment, sensor, or processing pipeline and may lead to complex image d

Matthew Amy, Joseph Lunderville

The phase folding optimization is a circuit optimization used in many quantum compilers as a fast and effective way of reducing the number of high-cost gates in a quantum circuit. However, existing formulations of the optimization rely on an exact, linear algebraic representation of the circuit, restricting the optimization to being performed on straightline

Zdzislaw Brzeźniak, Adam Larios, Isabel Safarik

The Voigt regularization is a technique used to model turbulent flows, offering advantages such as sharing steady states with the Navier-Stokes equations and requiring no modification of boundary conditions; however, the parabolic dissipative character of the equation is lost. In this work we propose and study a generalization of the Voigt regularization tec

Ferenc A. Bartha, Ábel Garab, Tibor Krisztin

We consider state-dependent delay differential equations of the form $$\dot{x}(t) = f(x(t), x(t - r(x_t))),$$ where $f$ is continuously differentiable and fulfills a negative feedback condition in the delayed term. Under suitable conditions on $r$ and $f$, we construct a Morse decomposition of the global attractor, giving some insight into the global dynamic

Federico Zadra, Marcello Seri

This paper explores the relationship between Cartan symmetries, dynamical similarities, and dynamical symmetries in contact Hamiltonian mechanics. By introducing an alternative decomposition of vector fields, we characterize these symmetries and present a novel description in terms of tensor densities. Furthermore, we demonstrate that this framework allows,

A. S. Umar, K. Godbey, C. Simenel

The investigation delves into understanding how the Pauli exclusion principle influences the bare potential between atomic nuclei through the application of advanced theoretical methodologies. Specifically, the application of the novel Frozen-Hartree-Fock (DCFHF) technique is employed. The resulting potentials demonstrate a noticeable repulsion at short dist

Luisa Mao, Garrett Warnell, Peter Stone, Joydeep Biswas

In autonomous robot navigation, terrain cost assignment is typically performed using a semantics-based paradigm in which terrain is first labeled using a pre-trained semantic classifier and costs are then assigned according to a user-defined mapping between label and cost. While this approach is rapidly adaptable to changing user preferences, only preference

Sebastian Burgos

We study the set of irregular points for topologically mixing subshifts of finite type. It is well known that despite the irregular set having zero measure for every invariant measure, it has full topological entropy and full Hausdorff dimension. We establish that for these systems the irregular set is not only abundant in terms of its dimensional properties

Christian Mancas

This paper rigorously and concisely defines, in the context of our (Elementary) Mathematical Data Model ((E)MDM), the mathematical concepts of dyadic relation, reflexivity, irreflexivity, symmetry, asymmetry, transitivity, intransitivity, Euclideanity, inEuclideanity, equivalence, acyclicity, connectivity, the properties that relate them, and the correspondi

Dominik P. Pacholski, Lorenzo Ducci, Martin Topinka, Sandro Mereghetti

The INTEGRAL satellite has collected a large amount of data on magnetars in our Galaxy, spanning more than 20 years starting from 2003. The large data set obtained with the IBIS/ISGRI instrument at energies above 20 keV allows us to study both the properties and long-term evolution of their persistent hard X-ray emission and the population characteristics of

Julian Collado, Kevin Stangl

Recent approaches in machine learning often solve a task using a composition of multiple models or agentic architectures. When targeting a composed system with adversarial attacks, it might not be computationally or informationally feasible to train an end-to-end proxy model or a proxy model for every component of the system. We introduce a method to craft a

Ruda Zhang, Negin Alemazkoor

In system analysis and design optimization, multiple computational models are typically available to represent a given physical system. These models can be broadly classified as high-fidelity models, which provide highly accurate predictions but require significant computational resources, and low-fidelity models, which are computationally efficient but less

Maxwell West, Antonio Anna Mele, Martin Larocca, M. Cerezo

Efficiently learning expectation values of a quantum state using classical shadow tomography has become a fundamental task in quantum information theory. In a classical shadows protocol, one measures a state in a chosen basis W after it has evolved under a unitary transformation randomly sampled from a chosen distribution U. In this work we study the case wh

Evgenia Giatzoglou, Panagiotis Vorias, Ryan Kemm, Irene Karayianni

Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (age

Riccardo Borghi

In this paper, the problem of assessing the twistability of a given bona fide cross-spectral density is tackled for the class of Schell-model sources, whose shift-invariant degree of coherence is represented by a real and symmetric function, {denoted as} $\mu(-\bfr)=\mu(\bfr)$. By employing an abstract operatorial language, the problem of determining the hig

Sireesh Gururaja, Yueheng Zhang, Guannan Tang, Tianhao Zhang

Recent years in NLP have seen the continued development of domain-specific information extraction tools for scientific documents, alongside the release of increasingly multimodal pretrained transformer models. While the opportunity for scientists outside of NLP to evaluate and apply such systems to their own domains has never been clearer, these models are d

Nasit S Sony, Xianzhong Ding, Mukesh Singhal

Multi-valued Byzantine agreement (MVBA) protocols are essential for atomic broadcast and fault-tolerant state machine replication in asynchronous networks. Despite advances, challenges persist in optimizing these protocols for communication and computation efficiency. This paper presents a committee-based MVBA protocol (cMVBA), a novel approach that achieves

Ana P. Millán, Hanlin Sun, Joaquín J. Torres

Triadic interactions are general mechanisms by which a node or neuron can regulate directly the link or synapse between other two neurons. The regulation takes place in a familiar way by either depressing or facilitating synaptic transmission. Such interactions are ubiquitous in neural systems, accounting for axo-axonic synapses and tripartite synapses media

CMS Collaboration

Measurements are presented of inclusive and differential cross sections for Z boson associated production of top quark pairs ($\mathrm{t\bar{t}}$Z) and single top quarks (tZq or tWZ). The data were recorded in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. Events with three or more l

Diego A. Robayo Bargans

This article discusses a combinatorial extension of tropical intersection theory to spaces given by glueing quotients of partially open convex polyhedral cones by finitely many automorphisms. This extension is done in terms of linear poic-complexes and poic-fibrations, mainly motivated by the case of the moduli spaces of tropical curves of arbitrary genus an

Rokas Gipiškis, Ayrton San Joaquin, Ze Shen Chin, Adrian Regenfuß

There is an urgent need to identify both short and long-term risks from newly emerging types of Artificial Intelligence (AI), as well as available risk management measures. In response, and to support global efforts in regulating AI and writing safety standards, we compile an extensive catalog of risk sources and risk management measures for general-purpose

Zhenguo Wei, Hao Zhang

This article is devoted to the study of the Schatten class membership of commutators involving singular integral operators. We utilize martingale paraproducts and Hyt\"{o}nen's dyadic martingale technique to obtain sufficient conditions on the weak-type and strong-type Schatten class membership of commutators in terms of Sobolev spaces and Besov spaces respe