November 2024 arXiv papers — page 178

Lennart Bittel, Francesco Anna Mele, Antonio Anna Mele, Salvatore Tirone

Gaussian states of bosonic quantum systems enjoy numerous technological applications and are ubiquitous in nature. Their significance lies in their simplicity, which in turn rests on the fact that they are uniquely determined by two experimentally accessible quantities, their first and second moments. But what if these moments are only known approximately, a

Elyjah Kiyooka, Chotivut Tangchingchai, Leo Noirot, Axel Leblanc

Gatemons are superconducting qubits resembling transmons, with a gate-tunable semiconducting weak link as the Josephson element. Here, we report a gatemon device featuring an aluminum microwave circuit on a Ge/SiGe heterostructure embedding a Ge quantum well. Owing to the superconducting proximity effect, the high-mobility two-dimensional hole gas confined i

Eunhyuk Park, Junbeom Kim, Seok-Hwan Park, Osvaldo Simeone

This work investigates a collaborative sensing and data collection system in which multiple unmanned aerial vehicles (UAVs) sense an area of interest and transmit images to a cloud server (CS) for processing. To accelerate the completion of sensing missions, including data transmission, the sensing task is divided into individual private sensing tasks for ea

Melvyn B. Nathanson

This paper describes problems concerning the range of cardinalities of sumsets and restricted sumsets of finite subsets of the integers and finite subsets of ordered abelian groups.

David A. Lowe, Yiru Wang, Juanyi Yang

We begin by reexamining the holographic reconstruction of scalar fields in four-dimensional anti-de Sitter spacetime, adopting a purely Lorentzian signature derivation, reproducing earlier results of HKLL and generalizing to arbitrary boundary metrics. The approach is extended to gravitational perturbations, focussing on perturbations around $AdS_{4}$ and sh

Zachary A. Johnson, Nathaniel R. Shaffer, Michael S. Murillo

Laboratory plasma production almost always preferentially heats either the ions or electrons, leading to a two-temperature state. High-fidelity modeling of these systems can be achieved with density functional theory molecular dynamics in the two-temperature, adiabatic electron limit. Motivated by this, we construct a statistical mechanics framework for the

Alexander Iksanov, Ruslan Kostohryz

Buraczewski et al (2023) proved a functional limit theorem (FLT) and a law of the iterated logarithm (LIL) for a random Dirichlet series $\sum_{k\geq 2}(\log k)^\alpha k^{-1/2-s}\eta_k$ as $s\to 0+$, where $\alpha>-1/2$ and $\eta_1$, $\eta_2,\ldots$ are independent identically distributed random variables with zero mean and finite variance. We prove a FLT an

Yin Wang, Fatima Ebrahimi, Hongke Lu, Jeremy Goodman

The standard magnetorotational instability (SMRI) is widely believed to be responsible for the observed accretion rates in astronomical disks. It is a linear instability triggered in the differentially rotating ionized disk flow by a magnetic field component parallel to the rotation axis. Most studies focus on axisymmetric SMRI in conventional base flows wit

Saubhik Sarkar, Abolfazl Bayat

Wannier-Stark localization has been proven to be a resource for quantum-enhanced sensitivity for precise estimation of a gradient field. An extremely promising feature of such probes is their ability to showcase such enhanced scaling even dynamically with system size, on top of the quadratic scaling in time. In this paper, we address the issue of decoherence

Yang Yue, Yulin Wang, Bingyi Kang, Yizeng Han

MLLMs have demonstrated remarkable comprehension and reasoning capabilities with complex language and visual data. These advances have spurred the vision of establishing a generalist robotic MLLM proficient in understanding complex human instructions and accomplishing various embodied tasks. However, developing MLLMs for real-world robots is challenging due

Jing Song, Zi-Ying Yang, Eulogio Oset

This paper investigates the decay process $\Lambda_b \to \Sigma_c^{++} D^- K^-$ with the objective of finding a predicted molecular state with isospin $I=1$, $J^P=2^+$ of $D^* K^*$ nature, plus finding support for the $P_c(4312)$ state as made out of $\Sigma_c \bar{D}$. The mass distribution of the $D^- K^-$ system shows distinct features as a consequence of

Milan Pasteka

The object of observation in present paper is statistical independence of real sequences and its description as independence with re spect to certain class of densities.

Elisabetta Carlini, Valentina Coscetti, Marco Pozza

We examine the numerical approximation of time-dependent Hamilton-Jacobi equations on networks, providing a convergence error estimate for the semi-Lagrangian scheme introduced in (Carlini and Siconolfi, 2023), where convergence was proven without an error estimate. We derive a convergence error estimate of order one-half. This is achieved showing the equiva

Abhishek Sharma, Ramin Nateghi, Marina Ayad, Lee A. D. Cooper

The placenta forms a critical barrier to infection through pregnancy, labor and, delivery. Inflammatory processes in the placenta have short-term, and long-term consequences for offspring health. Digital pathology and machine learning can play an important role in understanding placental inflammation, and there have been very few investigations into methods

Ruotong Wang, Xinyi Zhou, Lin Qiu, Joseph Chee Chang

AI agents are increasingly tasked with making proactive suggestions in online spaces where groups collaborate, yet risk being unhelpful or even annoying if they fail to match group preferences or behave in socially inappropriate ways. Fortunately, group spaces have a rich history of prior interactions and affordances for social feedback that can support grou

Kim Conger, Gregory Rudnick, Rose A. Finn, Gianluca Castignani

Recent theoretical work and targeted observational studies suggest that filaments are sites of galaxy preprocessing. The aim of the WISESize project is to directly probe galaxies over the full range of environments to quantify and characterize extrinsic galaxy quenching in the local Universe. In this paper, we use GALFIT to measure the infrared 12$\mu$m ($R_

Mustafa Umut Kazancıoğlu, Mohammad Sadek

The list of all groups that can appear as torsion subgroups of elliptic curves over number fields of degree $d$, $d=4,5,6$, is not completely determined. However, the list of groups $\Phi^{\infty}(d)$, $d=4,5,6$, that can be realized as torsion subgroups for infinitely many non-isomorphic elliptic curves over these fields are known. We address the question o

Christian El Emam, Nathaniel Sagman

We prove that, on the $\mathrm{SL}(3,\mathbb R)$ Hitchin component, the Goldman symplectic form and the Labourie-Loftin complex structure are compatible and together determine a (mapping class group invariant) pseudo-K\"ahler structure.

Qingwen Pu, Yuan Zhu, Junqing Wang, Hong Yang

This study employed over 100 hours of high-altitude drone video data from eight intersections in Hohhot to generate a unique and extensive dataset encompassing high-density urban road intersections in China. This research has enhanced the YOLOUAV model to enable precise target recognition on unmanned aerial vehicle (UAV) datasets. An automated calibration al

Claire E. Stevenson, Alexandra Pafford, Han L. J. van der Maas, Melanie Mitchell

In people, the ability to solve analogies such as "body : feet :: table : ?" emerges in childhood, and appears to transfer easily to other domains, such as the visual domain "( : ) :: < : ?". Recent research shows that large language models (LLMs) can solve various forms of analogies. However, can LLMs generalize analogy solving to new domains like people ca

Chenliang Zhou, Alejandro Sztrajman, Gilles Rainer, Fangcheng Zhong

We introduce the physically based neural bidirectional reflectance distribution function (PBNBRDF), a novel, continuous representation for material appearance based on neural fields. Our model accurately reconstructs real-world materials while uniquely enforcing physical properties for realistic BRDFs, specifically Helmholtz reciprocity via reparametrization

Vladimir Lipp, Victor Tkachenko, Ichiro Inoue, Philip Heimann

Intense femtosecond x-ray pulses produced by an x-ray free-electron laser can trigger irreversible structural transitions in crystalline solids. For instance, irradiation of diamond can lead to graphitization and, at higher deposited doses, to amorphization. Our Monte Carlo simulations of irradiated diamond under realistic experimental conditions demonstrate

Shahab Kavousinejad

Nanorobots are a promising development in targeted drug delivery and the treatment of neurological disorders, with potential for crossing the blood-brain barrier (BBB). These small devices leverage advancements in nanotechnology and bioengineering for precise navigation and targeted payload delivery, particularly for conditions like brain tumors, Alzheimer's

Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier, Yann LeCun

Decoder-only Transformers often struggle with complex reasoning tasks, particularly arithmetic reasoning requiring multiple sequential operations. In this work, we identify representation collapse in the model's intermediate layers as a key factor limiting their reasoning capabilities. To address this, we propose Sequential Variance-Covariance Regularization

Ethan Baron, Victor Hau, Zeke Weng

Using data from professional bouldering competitions from 2008 to 2022, we train a logistic regression to predict climber results and measure climber skill. However, this approach is limited, as a single numeric coefficient per climber cannot adequately capture the intricacies of climbers' varying strengths and weaknesses in different boulder problems. For e

Frederic Adjewa, Moez Esseghir, Leila Merghem-Boulahia

In this paper, we present an adaptive framework designed for the continuous detection, identification and classification of emerging attacks in network traffic. The framework employs a transformer encoder architecture, which captures hidden patterns in a bidirectional manner to differentiate between malicious and legitimate traffic. Initially, the framework

Samuel G. B. Johnson, Amir-Hossein Karimi, Yoshua Bengio, Nick Chater

Although AI has become increasingly smart, its wisdom has not kept pace. In this article, we examine what is known about human wisdom and sketch a vision of its AI counterpart. We analyze human wisdom as a set of strategies for solving intractable problems-those outside the scope of analytic techniques-including both object-level strategies like heuristics [

Rafic Nader, Florent Autrusseau, Vincent L'Allinec, Romain Bourcier

We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree, including the cerebral arteries, bifurcations and intracranial aneurysms. This model intends to provide a substantial dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms.

MohammadReza Aghdaee, Oluwafemi S. Ojambati

Metallic nano-objects play crucial roles in diverse fields, including biomedical imaging, nanomedicine, spectroscopy, and photocatalysis. Nano-objects with sizes that are less than 15 nm exhibit extremely low light scattering cross-sections, posing a significant challenge for optical detection. A possible approach to enhance the optical detection is to explo

Sorouralsadat Fatemi, Yuheng Hu, Maryam Mousavi

Large Language Models (LLMs) have demonstrated impressive capabilities across diverse Natural Language Processing (NLP) tasks, including language understanding, reasoning, and generation. However, general-domain LLMs often struggle with financial tasks due to the technical and specialized nature of financial texts. This study investigates the efficacy of ins

Jake McGrath, Brian Kent, Colin Johnson, José Alvarado

Myosin motors are fundamental biological actuators, powering diverse mechanical tasks in eukaryotic cells via ATP hydrolysis. Recent work revealed that myosin's velocity-dependent detachment rate can bridge actomyosin dynamics to macroscale Hill muscle predictions. However, the influence of this microscale unbinding, which we characterize by a dimensionless

Rocco Duvenhage, Kyle Oerder, Keagan van den Heuvel

Quantum detailed balance is formulated in terms of elementary transitions, in close analogy to detailed balance in a classical Markov chain on a finite set of points. An elementary transition is taken to be a pure state of two copies of the quantum system, as a quantum analogue of an ordered pair of classical points representing a classical transition from t

Pranav Guruprasad, Harshvardhan Sikka, Jaewoo Song, Yangyue Wang

Vision-language-action (VLA) models represent a promising direction for developing general-purpose robotic systems, demonstrating the ability to combine visual understanding, language comprehension, and action generation. However, systematic evaluation of these models across diverse robotic tasks remains limited. In this work, we present a comprehensive eval

Jongjun M. Lee, Hyun-Woo Lee, Myung-Joong Hwang

We study the instability of antiferromagnets with easy-axis anisotropy under a magnetic field, uncovering single or even multiple phase transitions at the boundary between non-collinear and collinear spin orderings. Near the phase boundary, the entanglement between the sublattice magnons diverges due to the interplay among antiferromagnetic exchange interact

Zehan Qi, Xiao Liu, Iat Long Iong, Hanyu Lai

Large language models (LLMs) have shown remarkable potential as autonomous agents, particularly in web-based tasks. However, existing LLM web agents heavily rely on expensive proprietary LLM APIs, while open LLMs lack the necessary decision-making capabilities. This paper introduces WebRL, a self-evolving online curriculum reinforcement learning framework de

Wei Cheng, Juncheng Mu, Xianfang Zeng, Xin Chen

Texturing is a crucial step in the 3D asset production workflow, which enhances the visual appeal and diversity of 3D assets. Despite recent advancements in Text-to-Texture (T2T) generation, existing methods often yield subpar results, primarily due to local discontinuities, inconsistencies across multiple views, and their heavy dependence on UV unwrapping o

Yuqi Luo, Chenyang Song, Xu Han, Yingfa Chen

Activation sparsity denotes the existence of substantial weakly-contributed elements within activation outputs that can be eliminated, benefiting many important applications concerned with large language models (LLMs). Although promoting greater activation sparsity within LLMs deserves deep studies, existing works lack comprehensive and quantitative research

Xinkai Liu, Mahdi Boloursaz Mashhadi, Li Qiao, Yi Ma

Generative diffusion models (GDMs) have recently shown great success in synthesizing multimedia signals with high perceptual quality, enabling highly efficient semantic communications in future wireless networks. In this paper, we develop an intent-aware generative semantic multicasting framework utilizing pre-trained diffusion models. In the proposed framew

Jiakuang He, Dongqing Wu

Time-variant standard Sylvester-conjugate matrix equations are presented as early time-variant versions of the complex conjugate matrix equations. Current solving methods include Con-CZND1 and Con-CZND2 models, both of which use ode45 for continuous model. Given practical computational considerations, discrete these models is also important. Based on Euler-f

Amritansh Kwatra, Tobias Weinberg, Ilan Mandel, Ritik Batra

As tools for designing and manufacturing hardware become more accessible, smaller producers can develop and distribute novel hardware. However, processes for supporting end-user hardware troubleshooting or routine maintenance aren't well defined. As a result, providing technical support for hardware remains ad-hoc and challenging to scale. Inspired by patter

Robert Long, Jeff Sebo, Patrick Butlin, Kathleen Finlinson

In this report, we argue that there is a realistic possibility that some AI systems will be conscious and/or robustly agentic in the near future. That means that the prospect of AI welfare and moral patienthood, i.e. of AI systems with their own interests and moral significance, is no longer an issue only for sci-fi or the distant future. It is an issue for

Selva Chandrasekaran Selvaraj

This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of crystal structures and employ GNNs to predict multiple properties simultaneously. All crystal structures are from the Materi

Jan Ambjorn, Jakub Gizbert-Studnicki, Andrzej Goerlich, Daniel Nemeth

Causal Dynamical Triangulations (CDT) is a lattice theory of quantum gravity. It is shown how to identify the IR and the UV limits of this lattice theory with similar limits studied using the continuum, functional renormalization group (FRG) approach. The main technical tool in this study will be the so-called two-point function. It will allow us to identify

Raghunathan Ramakrishnan

At large quantum numbers, the probability densities for particle-in-a-box or simple harmonic oscillator converge to the classical result upon coarse-graining the quantum mechanical probability densities by introducing a finite resolution in the measurement of the particle's position. This resolution in the position can be related to the resolution of the sec

Vijay Joshi, Iver Band

Recent advancements in large language models, including GPT-4 and its variants, and Generative AI-assisted coding tools like GitHub Copilot, ChatGPT, and Tabnine, have significantly transformed software development. This paper analyzes how these innovations impact productivity and software test development metrics. These tools enable developers to generate c

Samarth Sriram, Sashank Kaushik Sridhar, Avik Dutt

Topological effects manifest in a variety of lattice geometries. While square lattices, due to their simplicity, have been used for models supporting nontrivial topology, several exotic topological phenomena such as Dirac points, Weyl points and Haldane phases are most commonly supported by non-square lattices. Examples of prototypical non-square lattices in

Shangkun Sun, Ruyang Liu, Haoran Tang, Yixiao Ge

In the past year, video-based large language models (Video LLMs) have achieved impressive progress, particularly in their ability to process long videos through extremely extended context lengths. However, this comes at the cost of significantly increased computational overhead due to the massive number of visual tokens, making efficiency a major bottleneck.

Christian Carrick, Michael A. Hill

We use the slice filtration to study the $MU$-homology of the fixed points of connective models of Lubin--Tate theory studied by Hill--Hopkins--Ravenel and Beaudry--Hill--Shi--Zeng. We show that, unlike their periodic counterparts $EO_n$, the $MU$ homology of $BP^{((G))}\langle m\rangle^G$ usually fails to be even and torsion free. This can only happen when

Shafaq Gulzar Elahi, Martine Schut, Andrew Dana, Alexey Grinin

The Quantum Gravity Mediated Entanglement (QGEM) protocol offers a novel method to probe the quantumness of gravitational interactions at non-relativistic scales. This protocol leverages the Stern-Gerlach effect to create $\mathcal{O}(\sim \mu m)$ spatial superpositions of two nanodiamonds (mass $\sim 10^{-15}$ kg) with NV spins, which are then allowed to in

Maximilian Kruse, Sebastian Krumscheid

In this paper, we present a theoretical and computational workflow for the non-parametric Bayesian inference of drift and diffusion functions of autonomous diffusion processes. We base the inference on the partial differential equations arising from the infinitesimal generator of the underlying process. Following a problem formulation in the infinite-dimensi

Yongyi Tang, Kunlun Wang, Dusit Niyato, Wen Chen

In Industry 4.0 systems, a considerable number of resource-constrained Industrial Internet of Things (IIoT) devices engage in frequent data interactions due to the necessity for model training, which gives rise to concerns pertaining to security and privacy. In order to address these challenges, this paper considers a digital twin (DT) and blockchain-assiste

Mufei Li, Viraj Shitole, Eli Chien, Changhai Man

Directed acyclic graphs (DAGs) serve as crucial data representations in domains such as hardware synthesis and compiler/program optimization for computing systems. DAG generative models facilitate the creation of synthetic DAGs, which can be used for benchmarking computing systems while preserving intellectual property. However, generating realistic DAGs is

Jonathan Cordeiro, Shayan Noei, Ying Zou

Large Language Models (LLMs) have shown potential to enhance software development through automated code generation and refactoring, reducing development time and improving code quality. This study empirically evaluates StarCoder2, an LLM optimized for code generation, in refactoring code across 30 open-source Java projects. We compare StarCoder2's performan

Yuyang Zhao, Chung-Ching Lin, Kevin Lin, Zhiwen Yan

Recent developments in 2D visual generation have been remarkably successful. However, 3D and 4D generation remain challenging in real-world applications due to the lack of large-scale 4D data and effective model design. In this paper, we propose to jointly investigate general 3D and 4D generation by leveraging camera and object movements commonly observed in

Wen Fan, Marilyn Rego, Xin Hu, Sanya Dod

Static verification is a powerful method for enhancing software quality, but it demands significant human labor and resources. This is particularly true of static verifiers that reason about heap manipulating programs using an ownership logic. LLMs have shown promise in a number of software engineering activities, including code generation, test generation,

Narotam Singh, Vaibhav Chaudhary, Nimisha Singh, Neha Soni

This paper explores the transformative impact of Generative AI (GenAI) on the business landscape, examining its role in reshaping traditional business models, intensifying market competition, and fostering innovation. By applying the principles of Neo-Schumpeterian economics, the research analyses how GenAI is driving a new wave of "creative destruction," le

Mete Ismayilzada, Claire Stevenson, Lonneke van der Plas

Story-writing is a fundamental aspect of human imagination, relying heavily on creativity to produce narratives that are novel, effective, and surprising. While large language models (LLMs) have demonstrated the ability to generate high-quality stories, their creative story-writing capabilities remain under-explored. In this work, we conduct a systematic ana

Anupa Kumari, MohammadReza Aghdaee, Mathis Van de Voorde, Oluwafemi S. Ojambati

Quantification of nonlinear optical properties is required for nano-optical devices, but they are challenging to measure on a nanomaterial. Here, we harness enhanced optical fields inside a plasmonic nanocavity to mediate efficient nonlinear interactions with the nanomaterials. We performed reflection Z-scan technique at intensity levels of kWcm^2, reaching

Gildardo Martinez, Justin Siu, Steven Dang, Dylan Gage

Differential dynamic microscopy (DDM) typically relies on movies containing hundreds or thousands of frames to accurately quantify motion in soft matter systems. Using movies much shorter in duration produces noisier and less accurate results. This limits the applicability of DDM to situations where the dynamics are stationary over extended times. Here, we i

Nathan Haboury, Mo Kordzanganeh, Alexey Melnikov, Pavel Sekatski

The information plane (Tishby et al. arXiv:physics/0004057, Shwartz-Ziv et al. arXiv:1703.00810) has been proposed as an analytical tool for studying the learning dynamics of neural networks. It provides quantitative insight on how the model approaches the learned state by approximating a minimal sufficient statistics. In this paper we extend this tool to th

Gurvan Mével

G\"ottsche-Schroeter invariants are a genus 0 extension of Block-G\"ottsche invariants. They interpolate between Welschinger invariants involving pairs of complex conjugated points and genus 0 descendant Gromov-Witten invariants. They can be computed by a floor diagram algorithm. In this paper, we show that this floor diagrams recipe actually leads to some i

David Theidel, Viviane Cotte, Philip Heinzel, Houssna Griguer

High harmonic generation is a resource of extremely broad frequency combs of ultrashort light pulses. The non-classical nature of this new quantum source has been recently evidenced in semiconductors by showing that high harmonic generation generates multimode squeezed states of light. Applications in quantum information science require the knowledge of the

Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi

Code large language models (LLMs) have made significant progress in code debugging by directly generating the correct code based on the buggy code snippet. Programming benchmarks, typically consisting of buggy code snippet and their associated test cases, are used to assess the debugging capabilities of LLMs. However, many existing benchmarks primarily focus

Amin Anjomshoaa, Hannah Schuster, Axel Polleres

The Spatial Knowledge Graphs (SKG) are experiencing growing adoption as a means to model real-world entities, proving especially invaluable in domains like crisis management and urban planning. Considering that RDF specifications offer limited support for effectively managing spatial information, it's common practice to include text-based serializations of g

Ian Gemp

This work proposes a novel set of techniques for approximating a Nash equilibrium in a finite, normal-form game. It achieves this by constructing a new reformulation as solving a parameterized system of multivariate polynomials with tunable complexity. In doing so, it forges an itinerant loop from game theory to machine learning and back. We show a Nash equi

Kishi Kobe Yee Francisco, Andrane Estelle Carnicer Apuhin, Myles Joshua Toledo Tan, Mickael Cavanaugh Byers

Personalized medicine (PM) promises to transform healthcare by providing treatments tailored to individual genetic, environmental, and lifestyle factors. However, its high costs and infrastructure demands raise concerns about exacerbating health disparities, especially between high-income countries (HICs) and low- and middle-income countries (LMICs). While H

Marcus Williams, Micah Carroll, Adhyyan Narang, Constantin Weisser

As LLMs become more widely deployed, there is increasing interest in directly optimizing for feedback from end users (e.g. thumbs up) in addition to feedback from paid annotators. However, training to maximize human feedback creates a perverse incentive structure for the AI to resort to manipulative or deceptive tactics to obtain positive feedback from users

Kung-Hsiang Huang, Akshara Prabhakar, Sidharth Dhawan, Yixin Mao

Customer Relationship Management (CRM) systems are vital for modern enterprises, providing a foundation for managing customer interactions and data. Integrating AI agents into CRM systems can automate routine processes and enhance personalized service. However, deploying and evaluating these agents is challenging due to the lack of realistic benchmarks that

N. Andruskiewitsch, I. Heckenberger, L. Vendramin

We classify finite-dimensional Nichols algebras of Yetter-Drinfeld modules with indecomposable support over finite solvable groups in characteristic 0, using a variety of methods including reduction to positive characteristic. As a consequence, all Nichols algebras over groups of odd order are of diagonal type, which allows us to describe all pointed Hopf al

Peter Berczik, Taras Panamarev, Maryna Ishchenko, Bence Kocsis

Context. Many Milky Way globular clusters (GCs) host multiple stellar populations, challenging the traditional view of GCs as single-population systems. It has been suggested that second-generation stars could form in a disk from gas lost by first-generation stars or from external accreted gas. Aims. We investigate how the introduction of a second stellar ge

Ayaki Sunaga

An enhanced mechanism is proposed for the large-amplitude-motion-induced parity-violating frequency by integrating the exact quantum dynamics method with the relativistic electronic structure theory. The torsional wavefunctions and PV frequency shifts are obtained by the exact quantum dynamics method. The potential energy curve and PV energy along the torsio

Shouvik Datta Choudhury

This paper explores the evolution and monotonicity of geometric constants within the framework of extended Ricci flows, incorporating variable coupling parameters. Building on Hamiltons foundational Ricci flow and subsequent extensions by List (2008), we introduce modifications to the extended Ricci flow by varying parameters that affect the interaction betw

Arijit Chatterjee, H. S. Karthik, T. S. Mahesh, A. R. Usha Devi

Quantum theory contravenes classical macrorealism by allowing a system to be in a superposition of two or more physically distinct states, producing physical consequences radically different from that of classical physics. We show that a system, upon subjecting to transform under superposition of unitary operators, exhibits enhanced non-macrorealistic featur

Iain Beaton

A dominating set $S$ in a graph is a subset of vertices such that every vertex is either in $S$ or adjacent to a vertex in $S$. A minimal dominating set $M$ is a dominating set such that $M-v$ is not a dominating set for all $v \in M$. In this paper we introduce a reconfiguration graph $\mathcal{R}(G)$ for minimal dominating sets under a generalization of th

Rongzhen Zhao, Vivienne Wang, Juho Kannala, Joni Pajarinen

Object-Centric Learning (OCL) aims to discover objects in images or videos by reconstructing the input. Representative methods achieve this by reconstructing the input as its Variational Autoencoder (VAE) discrete representations, which suppress (super-)pixel noise and enhance object separability. However, these methods treat features as indivisible units, o

Hassan Ashtiani, Mahbod Majid, Shyam Narayanan

We study the problem of learning mixtures of Gaussians with approximate differential privacy. We prove that roughly $kd^2 + k^{1.5} d^{1.75} + k^2 d$ samples suffice to learn a mixture of $k$ arbitrary $d$-dimensional Gaussians up to low total variation distance, with differential privacy. Our work improves over the previous best result [AAL24b] (which requi

Suyash Srivastava, Mihir Mittal

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

Maria Camisassa, J. R. Fuentes, Matthias R. Schreiber, Alberto Rebassa-Mansergas

Recent observations of volume-limited samples of magnetic white dwarfs (WD) have revealed a higher incidence of magnetism in older WDs. Specifically, these studies indicate that magnetism is more prevalent in WDs with fully or partially crystallized cores compared to those with entirely liquid cores. This has led to the recognition of a crystallization-drive

Di Ni, Ved Gund, Landon Ivy, Amit Lal

Integrated micro power generators are crucial components for micro robotic platforms to demonstrate untethered operation and to achieve autonomy. Current micro robotic electrostatic actuators typically require hundreds to thousands of voltages to output sufficient work. Pyroelectricity is one such source of high voltages that can be scaled to small form fact

Rafael Aoude, Andrea Cristofoli, Asaad Elkhidir, Matteo Sergola

Emitted radiation and absorption effects in black hole dynamics lead to inelastic scattering amplitudes. In this paper, we study how these effects introduce an inelasticity function to the $2\rightarrow2$ eikonalised $S$-matrix and how they can be described using unequal mass and spin on-shell amplitudes. To achieve this, we formulate the inelastic coupled-c

Xianghui Yang, Huiwen Shi, Bowen Zhang, Fan Yang

While 3D generative models have greatly improved artists' workflows, the existing diffusion models for 3D generation suffer from slow generation and poor generalization. To address this issue, we propose a two-stage approach named Hunyuan3D 1.0 including a lite version and a standard version, that both support text- and image-conditioned generation. In the f

Wenjie Mei, Dongzhe Zheng, Shihua Li

Neural ODEs (NODEs) are continuous-time neural networks (NNs) that can process data without the limitation of time intervals. They have advantages in learning and understanding the evolution of complex real dynamics. Many previous works have focused on NODEs in concise forms, while numerous physical systems taking straightforward forms, in fact, belong to th

Ryan Shìjié Dù, K. Shafer Smith

The turbulent evolution of the shallow water system exhibits asymmetry in vorticity. This emergent phenomenon can be classified as "balanced", that is, it is not due to the inertial-gravity wave modes. The Quasi-Geostrophic (QG) system, the canonical model for balanced motion, has a symmetric evolution of vorticity, thus misses this phenomenon. Here we prese

Andrey Yu. Konyaev, Vladimir S. Matveev

We repeat, using methods developed for BKM systems, the famous results of S. Novikov (1974), J. Moser (1981, 1982) , and A. Veselov (1980) that relate Schr\"odinger-Hill operators with finite-band spectra, solutions of the Neumann system, and certain solutions of the KdV equations. Our general motivation is to determine whether it is possible to apply invers

Jaroslav Pavličko

In the first part of the work, the equivalence of quantum deterministic and probabilistic processors was investigated. A programmable quantum processor is a device able to transform input data states in a desired way. Deterministic equivalence as well as three types of probabilistic equivalences - strong, weak, and structural - were defined. Necessary and su

Iain Beaton, Sam Schoonhoven

A polynomial is said to be unimodal if its coefficients are non-decreasing and then non-increasing. The domination polynomial of a graph $G$ is the generating function of the number of dominating sets of each cardinality in $G$. In \cite{IntroDomPoly2014} Alikhani and Peng conjectured that all domination polynomials are unimodal. In this paper we show that n

David Trillo, Miguel Navascués

We show that the dynamics of the Di\'osi-Penrose (DP) model of classical gravity can entangle the mechanical degrees of freedom of two separate particles. For standard experiments of gravitationally induced entanglement (GIE), we find that entanglement can be generated if and only if the particles are separated by a distance smaller than some limiting value

Andrea Protani, Lorenzo Giusti, Albert Sund Aillet, Chiara Iacovelli

Machine learning (ML) has the potential to become an essential tool in supporting clinical decision-making processes, offering enhanced diagnostic capabilities and personalized treatment plans. However, outsourcing medical records to train ML models using patient data raises legal, privacy, and security concerns. Federated learning has emerged as a promising

Theo F. Motta, Julian Bernhardt, Michael Buballa, Christian S. Fischer

In this letter, we discuss a novel method to search for inhomogeneous chiral symmetry breaking in theories with fermions. The prime application we have in mind is QCD, but the method is also applicable for other theories, including solid-state applications. It is based on an extension of the chiral susceptibility to inhomogeneous phases and it works as a sta

Vishakha Suresh Kalal, Andrew Parry, Sean MacAvaney

Neural approaches to ranking based on pre-trained language models are highly effective in ad-hoc search. However, the computational expense of these models can limit their application. As such, a process known as knowledge distillation is frequently applied to allow a smaller, efficient model to learn from an effective but expensive model. A key example of t

Venkat S. Malladi, Maria Yazykova, Olesya Melnichenko, Yulia Dubinina

Reproducibility in research remains hindered by complex systems involving data, models, tools, and algorithms. Studies highlight a reproducibility crisis due to a lack of standardized reporting, code and data sharing, and rigorous evaluation. This paper introduces the concept of Continuous Analysis to address the reproducibility challenges in scientific rese

Nikhil Gowda, Srinath Sibi, Sonia Baltodano, Nikolas Martelaro

To increase driver awareness in a fully autonomous vehicle, we developed several haptic interaction prototypes that signal what the car is planning to do next. The goal was to use haptic cues so that the driver could be situation aware but not distracted from the non-driving tasks they may be engaged in. This paper discusses the three prototypes tested and t

Yanjing Wang, Lizhou Wu, Wentao Hong, Yang Ou

Compute eXpress Link (CXL) has emerged as a key enabler of memory disaggregation for future heterogeneous computing systems to expand memory on-demand and improve resource utilization. However, CXL is still in its infancy stage and lacks commodity products on the market, thus necessitating a reliable system-level simulation tool for research and development.

Mason Sharp

We show some preservation results of amenably extending strongly Ulam stable groups under mild decay assumptions, including quantitative preservation of asymptotic bounds under the assumption that the modulus of stability is H\"older continuous of exponent $s>\frac 1 2$ at 0, utilizing some simplistic integral estimates. Additionally, we show some partial re

John Brandon Graham-Knight, Jamil Fayyad, Nourhan Bayasi, Patricia Lasserre

Class imbalance and label noise are pervasive in large-scale datasets, yet much of machine learning research assumes well-labeled, balanced data, which rarely reflects real world conditions. Existing approaches typically address either label noise or class imbalance in isolation, leading to suboptimal results when both issues coexist. In this work, we propos

Badr AlKhamissi, Greta Tuckute, Antoine Bosselut, Martin Schrimpf

Large language models (LLMs) exhibit remarkable capabilities on not just language tasks, but also various tasks that are not linguistic in nature, such as logical reasoning and social inference. In the human brain, neuroscience has identified a core language system that selectively and causally supports language processing. We here ask whether similar specia

Jincheng Huang, Yujie Mo, Xiaoshuang Shi, Lei Feng

The message-passing mechanism of graph convolutional networks (i.e., GCNs) enables label information to reach more unlabeled neighbors, thereby increasing the utilization of labels. However, the additional label information does not always contribute positively to the GCN. To address this issue, we propose a new two-step framework called ELU-GCN. In the firs

Wei-Cheng Wu, Yutian Yan, Hallgrimur David Egilsson, David Park

WebAssembly is a low-level bytecode language designed for client-side execution in web browsers. The need for decompilation techniques that recover high-level source code from WASM binaries has grown as WASM continues to gain widespread adoption and its security concerns. However little research has been done to assess the quality of decompiled code from WAS

Shouvik Datta Choudhury

In this paper, we investigate the evolution of certain functionals involving higher powers of a scalar quantity $F$ under Bernard List's extended Ricci flow on a compact Riemannian manifold. By deriving explicit expressions for the time derivative of integrals of the form $\int_M F^n \cdot \frac{\partial F}{\partial t} \, d\mu$ for various powers $n$, we exp

Lukas Miklautz, Timo Klein, Kevin Sidak, Collin Leiber

This work investigates an important phenomenon in centroid-based deep clustering (DC) algorithms: Performance quickly saturates after a period of rapid early gains. Practitioners commonly address early saturation with periodic reclustering, which we demonstrate to be insufficient to address performance plateaus. We call this phenomenon the "reclustering barr