May 2024 arXiv papers — page 55

Kaiwen Zheng, Guande He, Jianfei Chen, Fan Bao

Denoising diffusion bridge models (DDBMs) are a powerful variant of diffusion models for interpolating between two arbitrary paired distributions given as endpoints. Despite their promising performance in tasks like image translation, DDBMs require a computationally intensive sampling process that involves the simulation of a (stochastic) differential equati

Dublin M. Nichols, Jameson G. Berg, Takashi Taniguchi, Kenji Watanabe

Surface acoustic waves (SAWs) on piezoelectric insulators can generate dynamic periodic potentials inside one-dimensional and two-dimensional materials. These periodic potentials have been utilized or proposed for various applications, including acoustoelectric charge pumping. In this study, we investigate acoustoelectric charge pumping in graphene with very

Puja Mukherjee, Ujjal Debnath, Himanshu Chaudhary, G. Mustafa

In this paper, we have investigated the phenomenon of accelerated cosmic expansion in the late universe and the mass accretion process of a 4-dimensional Einstein-Aether black hole. Starting with the basics of Einstein-Aether gravity theory, we have first considered the field equations and two eminent models of Chaplygin gas, viz. generalized cosmic Chaplygi

Mikayla Calitis

In this study, the reliability of identified risk factors associated with osteoporosis is investigated using a new clustering-based method on electronic medical records. This study proposes utilizing a new CLustering Iterations Framework (CLIF) that includes an iterative clustering framework that can adapt any of the following three components: clustering, f

Shentong Mo, Yapeng Tian

In recent developments, the Mamba architecture, known for its selective state space approach, has shown potential in the efficient modeling of long sequences. However, its application in image generation remains underexplored. Traditional diffusion transformers (DiT), which utilize self-attention blocks, are effective but their computational complexity scale

Shraddha Barke, Emmanuel Anaya Gonzalez, Saketh Ram Kasibatla, Taylor Berg-Kirkpatrick

Many structured prediction and reasoning tasks can be framed as program synthesis problems, where the goal is to generate a program in a domain-specific language (DSL) that transforms input data into the desired output. Unfortunately, purely neural approaches, such as large language models (LLMs), often fail to produce fully correct programs in unfamiliar DS

Nerito Oliveira Aminde, Tiago Roux Oliveira, Liu Hsu

This paper discusses the design of an extremum seeking controller that relies on a monitoring function for a class of SISO uncertain nonlinear systems characterized by arbitrary and uncertain relative degree. Our demonstration illustrates the feasibility of achieving an arbitrarily small proximity to the desired optimal point through output feedback. The cor

Alexander DeRieux, Walid Saad

Collaboration is a key challenge in distributed multi-agent reinforcement learning (MARL) environments. Learning frameworks for these decentralized systems must weigh the benefits of explicit player coordination against the communication overhead and computational cost of sharing local observations and environmental data. Quantum computing has sparked a pote

Xiangrui Xu, Qiao Zhang, Rui Ning, Chunsheng Xin

The prevalent use of Transformer-like models, exemplified by ChatGPT in modern language processing applications, underscores the critical need for enabling private inference essential for many cloud-based services reliant on such models. However, current privacy-preserving frameworks impose significant communication burden, especially for non-linear computat

A. Barsode, S. J. Kapadia, P. Ajith

We use the non-observation of strong lensing of gravitational waves (GWs) in the first three observation runs of LIGO-Virgo detectors to constrain the fraction of dark matter in the form of compact objects in the mass range $10^{6}-10^{9}~{\mathrm{M}_\odot}$. Using a Bayesian formalism supplemented by astrophysical simulations of strong lensing of GWs, we co

Yang Li, Daniel Agyei Asante, Changsheng Zhao, Ernie Chang

Large language models (LLMs) significantly enhance the performance of various applications, but they are computationally intensive and energy-demanding. This makes it challenging to deploy them on devices with limited resources, such as personal computers and mobile/wearable devices, and results in substantial inference costs in resource-rich environments li

Moorad Alexanian

We introduce a modified Jaynes-Cummings model with single-photon cavity radiation field but with the atomic system instead of exchanging a single photon as in the Jaynes-Cummings model, it exchanges instead a squeezed photon with squeezing parameter r. This allows us to interpolate between the Rabi model, r = infinity, and the Jaynes-Cummings model, r = 0, b

Jeremy Redding, Nick Plewacki, Himakar Ganti, Luis Bravo

The objective of this work is to assess the impact of parameter uncertainty on hypersonic aerothermal surface heating predictions in Reynolds-Averaged Navier-Stokes (RANS) simulations using non-intrusive uncertainty quantification (UQ) techniques. RANS-based models are considered indispensable tools in computational fluid dynamics (CFD) analysis for the iter

Ujjal Debnath, Himanshu Chaudhary, Niyaz Uddin Molla, S. K. J. Pacif

We present a novel dynamical dark energy model within the frameworks of both Einstein gravity and Horava-Lifshitz gravity. Utilizing CDMMA parametrization of the dark energy equation of state $\omega(z)$, we derive solutions to the field equations. By employing recent cosmological datasets, such as cosmic chronometer datasets, Type Ia Supernovae datasets, an

Megan Tjandrasuwita, Jie Xu, Armando Solar-Lezama, Wojciech Matusik

Robots are often built from standardized assemblies, (e.g. arms, legs, or fingers), but each robot must be trained from scratch to control all the actuators of all the parts together. In this paper we demonstrate a new approach that takes a single robot and its controller as input and produces a set of modular controllers for each of these assemblies such th

Xuchen Cao, Thomas Faulkner

We compute the spectral form factor of the modular Hamiltonian $K=-\ln\rho_A$ associated to the reduced density matrix of a Haar random state. A ramp is demonstrated and we find an analytic expression for its slope. Our method involves an application of the replica trick, where we first calculate the correlator $<\text{tr}\rho_A^n\;\text{tr}\rho_A^m>$ at lar

Pedro Enrique Iturria-Rivera, Raimundas Gaigalas, Medhat Elsayed, Majid Bavand

Extended Reality (XR) services will revolutionize applications over 5th and 6th generation wireless networks by providing seamless virtual and augmented reality experiences. These applications impose significant challenges on network infrastructure, which can be addressed by machine learning algorithms due to their adaptability. This paper presents a Multi-

Juan Miguel Lopez Alcaraz, Nils Strodthoff

Despite the excelling performance of machine learning models, understanding their decisions remains a long-standing goal. Although commonly used attribution methods from explainable AI attempt to address this issue, they typically rely on associational rather than causal relationships. In this study, within the context of time series classification, we intro

Erin Griffin, Rahul Poddar, Ramesh Sharma, William Wylie

In this paper we expand on the work of the first author on ambient obstruction solitons, which are self-similar solutions to the ambient obstruction flow. Our main result is to show that any closed ambient obstruction soliton is ambient obstruction flat and has constant scalar curvature. We show, in fact, that the first part of this result is true for a more

Warlley H. Campos, Poliana H. Penteado, Julian Zanon, Paulo E. Faria Junior

Dual topological insulators (DTIs) are simultaneously protected by time-reversal and crystal symmetries, representing advantageous alternatives to conventional topological insulators. By combining ab initio calculations and the $\mathbf{k}\cdot\mathbf{p}$ approach, here, we investigate the electronic band structure of a Na$_2$CdSn triatomic layer and derive

Marco Paul E. Apolinario, Arani Roy, Kaushik Roy

Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption, particularly for on-device learning where computational resources are limited. Various alternatives to BP, including random feedback alignment, forward-forward, and local classifiers, have been explored

Joachim Kopp, Pedro Machado, Margot MacMahon, Ivan Martinez-Soler

Faithful energy reconstruction is foundational for precision neutrino experiments like DUNE, but is hindered by uncertainties in our understanding of neutrino--nucleus interactions. Here, we demonstrate that dense neural networks are very effective in overcoming these uncertainties by estimating inaccessible kinematic variables based on the observable part o

Anders Sundelin, Javier Gonzalez-Huerta, Richard Torkar, Krzysztof Wnuk

Context: In software development organizations employing weak or collective ownership, different teams are allowed and expected to autonomously perform changes in various components. This creates diversity both in the knowledge of, and in the responsibility for, individual components. Objective: Our objective is to understand how and why different teams intr

Eric He, C. M. Wilson, R. Ganesh

Based on its simple valence electron configuration, we may expect lithium to have straightforward physical properties that are easily explained. However, solid lithium, when cooled below 77 K, develops a complex structure that has been debated for decades. A close parallel is found in sodium below 36 K where the crystal structure still remains unresolved. In

Hanjuan Huang, Hao-Jia Song, Hsing-Kuo Pao

We surely enjoy the larger the better models for their superior performance in the last couple of years when both the hardware and software support the birth of such extremely huge models. The applied fields include text mining and others. In particular, the success of LLMs on text understanding and text generation draws attention from researchers who have w

Swati S. Priyadarsini, Karthik Gururangan, Jun Shen, Piotr Piecuch

An accurate determination of singlet-triplet gaps in biradicals, including cyclobutadiene in the automerization barrier region where one has to balance the substantial nondynamical many-electron correlation effects characterizing the singlet ground state with the predominantly dynamical correlations of the lowest-energy triplet, remains a challenge for many

Chang Li, Ruoyu Wang, Lijuan Liu, Jun Du

Text-to-music (TTM) generation, which converts textual descriptions into audio, opens up innovative avenues for multimedia creation. Achieving high quality and diversity in this process demands extensive, high-quality data, which are often scarce in available datasets. Most open-source datasets frequently suffer from issues like low-quality waveforms and low

Hugo Sant'Anna

This paper examines the labor market impacts of the 2015 Mariana Dam disaster in Brazil. It contrasts two theoretical models: an urban spatial equilibrium model and a factor of production model, with diverging perspectives on environmental influences on labor outcomes. Utilizing rich national administrative and spatial data, the study reveals that the unusua

Zhe Li, Bicheng Ying, Zidong Liu, Chaosheng Dong

Federated Learning (FL) offers a promising framework for collaborative and privacy-preserving machine learning across distributed data sources. However, the substantial communication costs associated with FL significantly challenge its efficiency. Specifically, in each communication round, the communication costs scale linearly with the model's dimension, wh

Chak Fong Chong, Jielong Guo, Xu Yang, Wei Ke

Multi-label image classification datasets are often partially labeled where many labels are missing, posing a significant challenge to training accurate deep classifiers. However, the powerful Mixup sample-mixing data augmentation cannot be well utilized to address this challenge, as it cannot perform linear interpolation on the unknown labels to construct a

William McClymont, Sandro Tacchella, Francesco D'Eugenio, Callum Witten

The peculiar nebular emission displayed by galaxies in the early Universe presents a unique opportunity to gain insight into the regulation of star formation in extreme environments. We investigate 500 (109) galaxies with deep NIRSpec/PRISM observations from the JADES survey at $z>2$ ($z>5.3$), finding 52 (26) galaxies with Balmer line ratios more than $1\si

Basabendu Barman, Md Riajul Haque, Óscar Zapata

We explore the possibility of explaining the observed dark matter (DM) relic abundance, along with matter-antimatter asymmetry, entirely from the evaporation of primordial black holes (PBH) beyond the semi-classical approximation. We find that, depending on the timing of modification to the semi-classical approximation and the efficiency of the backreaction,

Elizabeth Champion, Zihao Wang, Rayleigh Parker, Machiel Blok

Qudits hold great promise for efficient quantum computation and the simulation of high-dimensional quantum systems. Utilizing a local Hilbert space of dimension d > 2 is known to speed up certain quantum algorithms relative to their qubit counterparts given efficient local qudit control and measurement. However, the direct realization of high-dimensional rot

Anthony Salib, Georg S. Weiss

We study both one and two-phase minimisers of the Dirichlet-area energy $$E(v) = \int_{B_1} \vert\nabla v\vert^2 + Per(\{v>0\},B_1).$$ In the two-phase case, we show that the energies $$E_{\varepsilon}(v) = \int_{B_1}\vert\nabla v\vert^2 + \frac{1}{\varepsilon}W\left(\frac{v}{\varepsilon^{1/2}}\right),$$ $\Gamma$-converge to $E$ as $\varepsilon \to 0$, where

Biswajit Banerjee, Samanta Macera, Alessio Ludovico De Santis, Alessio Mei

Observing and understanding the origin of the very-high-energy (VHE) spectral component in gamma-ray bursts (GRBs) has been challenging because of the lack of sensitivity in MeV-GeV observations, so far. The majestic GRB 221009A, known as the brightest of all times (BOAT), offers a unique opportunity to identify spectral components during the prompt and earl

Campbell McLauchlan, György P. Gehér, Alexandra E. Moylett

Floquet codes are an intriguing generalisation of stabiliser and subsystem codes, which can provide good fault-tolerant characteristics while benefiting from reduced connectivity requirements in hardware. A recent question of interest has been how to run Floquet codes on devices which have defective -- and therefore unusable -- qubits. This is an under-studi

Almut Beige, Ana Predojević, Anja Metelmann, Anna Sanpera

Data show that the presence of women in quantum science is affected by a number of detriments and their percentage decreases even further for higher positions. Beyond data, from our shared personal experiences as female tenured quantum physics professors, we believe that the current model of scientific leadership, funding, and authority fails to represent ma

Takuya Okuda, Aswin Parayil Mana, Hiroki Sukeno

Calderbank-Shor-Steane (CSS) codes are a class of quantum error correction codes that contains the toric code and fracton models. A procedure called foliation defines a cluster state for a given CSS code. We use the CSS chain complex and its tensor product with other chain complexes to describe the topological structure in the foliated cluster state, and arg

Alex Gough, Cora Uhlemann

Ultralight candidates for dark matter can present wavelike features on astrophysical scales. Full wave based simulations of such candidates are currently limited to box sizes of 1--10 Mpc/$h$ on a side, limiting our understanding of the impact of wave dynamics on the scale of the cosmic web. We present a statistical analysis of density fields produced by per

Sarah Pearson, Ana Bonaca, Yingtian Chen, Oleg Y. Gnedin

Thin stellar streams originating from globular clusters are among the most sensitive tracers of low-mass dark-matter subhalos. Joint analysis of the entire population of stellar streams will place the most robust constraints on the dark-matter subhalo mass function, and therefore the nature of dark matter. Here we use a hierarchical model of globular cluster

Ningyuan Guo, Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen

The abundance of dark matter haloes is a key cosmological probe in forthcoming galaxy surveys. The theoretical understanding of the halo mass function (HMF) is limited by our incomplete knowledge of the origin of non-universality and its cosmological parameter dependence. We present a deep learning model which compresses the linear matter power spectrum into

Benjamin Concepcion, Yasunori Nomura, Kyle Ritchie, Samuel Weiss

Black hole complementarity posits that the interior of a black hole is not independent from its Hawking radiation. This leads to an apparent violation of causality: the interior can be acausally affected by operators acting solely on the radiation. We argue that this perspective is misleading and that the black hole interior must be viewed as existing in the

Luke Corcoran, Marius de Leeuw, Balázs Pozsgay

We initiate a systematic study of integrable models for spin chains with constrained Hilbert spaces; we focus on spin-1/2 chains with the Rydberg constraint. We extend earlier results for medium-range spin chains to the constrained Hilbert space, and formulate an integrability condition. This enables us to construct new integrable models with fixed interacti

Radha Mastandrea, Benjamin Nachman, Tilman Plehn

Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and should be observed in the near future at the High-Luminosity LHC. We explore how to improve the sensitivity to physics beyond the Standard Model through per-event kinematics for di-Hi

Gabriel Bliard

Superconformal Ward identities are revisited in the context of superconformal line defects. Multipoint correlators of topological operators inserted on superconformal lines are studied. In particular, it is known that protected operators preserving enough of the supersymmetry become topological after performing a topological twist. By definition, such a corr

Sebastian Baum, Cassandra Little, Paola Sala, Joshua Spitz

We present a novel experimental concept to search for proton decay. Using paleo-detectors, ancient minerals acquired from deep underground which can hold traces of charged particles, it may be possible to conduct a search for $p \to \bar{\nu} K^+$ via the track produced at the endpoint of the kaon. Such a search is not possible on Earth due to large atmosphe

Arpit Raj, Patrick A. Lee, Gregory A. Fiete

We consider a planar superconducting-normal-metal (SN) junction with both inelastic and spin-flip scattering processes present. In the diffusive limit, we use a one-dimensional formulation of the Usadel equation to compute the self-consistent energy dependence of the single-particle density of states as a function of distance from the interface on both the s

Xuanjia Zhao, Jian Guan, Congyi Fan, Dongli Xu

Drag-based image editing using generative models provides precise control over image contents, enabling users to manipulate anything in an image with a few clicks. However, prevailing methods typically adopt $n$-step iterations for latent semantic optimization to achieve drag-based image editing, which is time-consuming and limits practical applications. In

Atsushi Nitanda

Mean-field Langevin dynamics (MFLD) minimizes an entropy-regularized nonlinear convex functional defined over the space of probability distributions. MFLD has gained attention due to its connection with noisy gradient descent for mean-field two-layer neural networks. Unlike standard Langevin dynamics, the nonlinearity of the objective functional induces part

Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Aman Chadha

The mining of adverse drug events (ADEs) is pivotal in pharmacovigilance, enhancing patient safety by identifying potential risks associated with medications, facilitating early detection of adverse events, and guiding regulatory decision-making. Traditional ADE detection methods are reliable but slow, not easily adaptable to large-scale operations, and offe

Dean Wyatte, Fatemeh Tahmasbi, Ming Li, Thomas Markovich

Modern large language models (LLMs) represent a paradigm shift in what can plausibly be expected of machine learning models. The fact that LLMs can effectively generate sensible answers to a diverse range of queries suggests that they would be useful in customer support applications. While powerful, LLMs have been observed to be prone to hallucination which

Li Gong, Alex R. Wright, Kullervo Hynynen, David E. Goertz

To address existing challenges with intravascular mechanical thrombectomy devices, a novel ultrasound-enhanced aspiration approach is being developed to mechanically degrade clots using cavitation. This method employs standing waves within a mm-scale hollow cylindrical transducer to generate high pressures sufficient to perform histotripsy on clots situated

Ke Fan, Junshu Tang, Weijian Cao, Ran Yi

Text-to-motion synthesis is a crucial task in computer vision. Existing methods are limited in their universality, as they are tailored for single-person or two-person scenarios and can not be applied to generate motions for more individuals. To achieve the number-free motion synthesis, this paper reconsiders motion generation and proposes to unify the singl

Victor Hugo Pereira Rodrigues, Tiago Roux Oliveira, Miroslav Krstić, Tamer Başar

This paper introduces a new method to achieve stable convergence to Nash equilibrium in duopoly noncooperative games. Inspired by the recent fixed-time Nash Equilibrium seeking (NES) as well as prescribed-time extremum seeking (ES) and source seeking schemes, our approach employs a distributed sliding mode control (SMC) scheme, integrating extremum seeking w

Pallabi Ghosh, Ashis Kr. Chakraborty, Soumen Dey

Estimation of software reliability often poses a considerable challenge, particularly for critical softwares. Several methods of estimation of reliability of software are already available in the literature. But, so far almost nobody used the concept of size of a bug for estimating software reliability. In this article we make used of the bug size or the eve

Qi Jia, Xiaodian Chen, Shu Wang, Licai Deng

We obtain the largest catalog of multi-mode $\delta$ Sct stars in the northern sky to date using the Zwicky Transient Facility (ZTF) Data Release 20 (DR20). The catalog includes 2254 objects, of which 2181 are new to our study. Among these multi-mode $\delta$ Sct stars, 2142 objects are double-mode $\delta$ Sct, while 109 objects are triple-mode $\delta$ Sct

Virginia K. Felkner, Jennifer A. Thompson, Jonathan May

Social biases in LLMs are usually measured via bias benchmark datasets. Current benchmarks have limitations in scope, grounding, quality, and human effort required. Previous work has shown success with a community-sourced, rather than crowd-sourced, approach to benchmark development. However, this work still required considerable effort from annotators with

Yuchi Wang, Junliang Guo, Jianhong Bai, Runyi Yu

Recent talking avatar generation models have made strides in achieving realistic and accurate lip synchronization with the audio, but often fall short in controlling and conveying detailed expressions and emotions of the avatar, making the generated video less vivid and controllable. In this paper, we propose a novel text-guided approach for generating emoti

Feng Liang, Akio Kodaira, Chenfeng Xu, Masayoshi Tomizuka

This paper introduces StreamV2V, a diffusion model that achieves real-time streaming video-to-video (V2V) translation with user prompts. Unlike prior V2V methods using batches to process limited frames, we opt to process frames in a streaming fashion, to support unlimited frames. At the heart of StreamV2V lies a backward-looking principle that relates the pr

Tamar Ervin, Stuart D. Bale, Samuel T. Badman, Trevor A. Bowen

During Parker Solar Probe (Parker) Encounter 15 (E15), we observe an 18-hour period of near subsonic ($\mathrm{M_S \sim}$ 1) and sub-Alfv\'enic (SA), $\mathrm{M_A}$ <<< 1, slow speed solar wind from 22 to 15.6 R$_\odot$. As the most extreme SA interval measured to date and skirting the solar wind sonic point, it is the deepest Parker has probed into the form

Shashata Sawmya, Linghao Kong, Ilia Markov, Dan Alistarh

Disentangling polysemantic neurons is at the core of many current approaches to interpretability of large language models. Here we attempt to study how disentanglement can be used to understand performance, particularly under weight sparsity, a leading post-training optimization technique. We suggest a novel measure for estimating neuronal entanglement: the

Xudong Han, Nobuyuki Oishi, Yueying Tian, Elif Ucurum

Many Multi-Object Tracking (MOT) approaches exploit motion information to associate all the detected objects across frames. However, many methods that rely on filtering-based algorithms, such as the Kalman Filter, often work well in linear motion scenarios but struggle to accurately predict the locations of objects undergoing complex and non-linear movements

Nikiforos Mimikos-Stamatopoulos, Benjamin J. Zhang, Markos A. Katsoulakis

Through an uncertainty quantification (UQ) perspective, we show that score-based generative models (SGMs) are provably robust to the multiple sources of error in practical implementation. Our primary tool is the Wasserstein uncertainty propagation (WUP) theorem, a model-form UQ bound that describes how the $L^2$ error from learning the score function propaga

Edith Cohen, Haim Kaplan, Yishay Mansour, Shay Moran

We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise definition is much more nuanced than it appears, to the extent that a single all-encompassing definition may not exist. Thus, we employ a different strategy and aim to "sandwich" the con

Maleknaz Nayebi, Homayoon Farrahi, Guenther Ruhe

Market and user characteristics of mobile apps make their release management different from proprietary software products and web services. Despite the wealth of information regarding users' feedback on an app, an in-depth analysis of app releases is difficult due to the inconsistency and uncertainty of the information. To better understand and potentially i

Michael te Vrugt, Raphael Wittkowski

In the past years, the amount of research on active matter has grown extremely rapidly, a fact that is reflected in particular by the existence of more than 600 review articles on this topic. Moreover, the field has become very diverse, ranging from theoretical studies of the statistical mechanics of active particles to applied work on medical applications o

Abhinav Patil, Jaap Jumelet, Yu Ying Chiu, Andy Lapastora

This paper introduces Filtered Corpus Training, a method that trains language models (LMs) on corpora with certain linguistic constructions filtered out from the training data, and uses it to measure the ability of LMs to perform linguistic generalization on the basis of indirect evidence. We apply the method to both LSTM and Transformer LMs (of roughly comp

Yongtao Huang, I-Ling Yen, Farokh Bastani

The Internet of Things (IoT) necessitates robust access control mechanisms to secure a vast array of interconnected devices. Most of the existing IoT systems in practice use centralized solutions. We identify the problems in such solutions and adopt the blockchain based decentralized access control approach. Though there are works in the literature that use

Uzu Lim

In this expository article, we outline a basic theory of group (co)homology and prove a cohomological formulation of the Local Reciprocity Law: $${\rm Gal}(L/K)^{\rm ab} \cong H_T^{-2}({\rm Gal}(L/K),\mathbb{Z}) \cong H_T^{0}({\rm Gal}(L/K),L^\times) \cong \frac{K^\times}{{\rm Nm}_{L/K}(L^\times)}$$ We first recall basic facts about local fields and homologi

Rafael Sumitani, João Victor Amorim, Augusto Mafra, Mirlaine Crepalde

Electronic Design Automation (EDA) tools are software applications used by engineers in the design, development, simulation, and verification of electronic systems and integrated circuits. These tools typically process specifications written in a Hardware Description Language (HDL), such as Verilog, SystemVerilog or VHDL. Thus, effective testing of these too

Hongyu Jin, Panos Papadimitratos

Medium Access Control (MAC) address randomization is a key component for privacy protection in Wi-Fi networks. Current proposals periodically change the mobile device MAC addresses when it disconnects from the Access Point (AP). This way frames cannot be linked across changes, but the mobile device presence is exposed as long as it remains connected: all its

Tong Wan, Juliette Pimbert, Reshawna L. Chapple, Ying Cao

Group work is commonly adopted in university science laboratories. However, student small-group discourse in university science labs is rarely investigated. We aim to bridge the gap in the literature by characterizing student discourse group roles in inquiry-based science labs. The instructional context for the study was a summer program hosted at a private

Michał Marczenko, Michał Szymański, Pok Man Lo, Bithika Karmakar

We discuss the modeling of the hadronic phase of QCD at finite magnetic field in the framework of hadron resonance gas (HRG). We focus on the statistical description of particle yields that include contribution from resonance decays. We demonstrate that the swift increase in the number of protons with magnetic field predicted in the HRG is due to the ill-def

Sahan Liyanaarachchi, Kanchana Thilakarathna, Sennur Ulukus

In many federated learning (FL) models, a common strategy employed to ensure the progress in the training process, is to wait for at least $M$ clients out of the total $N$ clients to send back their local gradients based on a reporting deadline $T$, once the parameter server (PS) has broadcasted the global model. If enough clients do not report back within t

Nolan Dey, Shane Bergsma, Joel Hestness

Several challenges make it difficult for sparse neural networks to compete with dense models. First, setting a large fraction of weights to zero impairs forward and gradient signal propagation. Second, sparse studies often need to test multiple sparsity levels, while also introducing new hyperparameters (HPs), leading to prohibitive tuning costs. Indeed, the

Travis Whyte, David J. Wilson, Christopher E. Thomas

The first determination of doubly-charmed isospin-0 coupled-channel $DD^\ast-D^\ast D^\ast$ scattering amplitudes from lattice QCD is presented. The finite-volume spectrum is computed for three lattice volumes with a light-quark mass corresponding to $m_\pi\approx 391$ MeV and is used to extract the scattering amplitudes in $J^P = 1^+$ via the L\"{u}scher qu

Grace Tompkins, Joel A Dubin, Michael Wallace

Many observational studies feature irregular longitudinal data, where the observation times are not common across individuals in the study. Further, the observation times may be related to the longitudinal outcome. In this setting, failing to account for the informative observation process may result in biased causal estimates. This can be coupled with other

Andres Algaba, Carmen Mazijn, Vincent Holst, Floriano Tori

Citation practices are crucial in shaping the structure of scientific knowledge, yet they are often influenced by contemporary norms and biases. The emergence of Large Language Models (LLMs) introduces a new dynamic to these practices. Interestingly, the characteristics and potential biases of references recommended by LLMs that entirely rely on their parame

Chunjiang Ge, Sijie Cheng, Ziming Wang, Jiale Yuan

High-resolution Large Multimodal Models (LMMs) encounter the challenges of excessive visual tokens and quadratic visual complexity. Current high-resolution LMMs address the quadratic complexity while still generating excessive visual tokens. However, the redundancy in visual tokens is the key problem as it leads to more substantial compute. To mitigate this

Maleknaz Nayebi, Homayoon Farrahi, Ada Lee, Henry Cho

The increasing attraction of mobile apps has inspired researchers to analyze apps from different perspectives. As with any software product, apps have different attributes such as size, content maturity, rating, category, or number of downloads. Current research studies mostly consider sampling across all apps. This often results in comparisons of apps being

Petia Arabadjieva, Alexandru Gheorghiu, Victor Gitton, Tony Metger

A proof of quantumness is an efficiently verifiable interactive test that an efficient quantum computer can pass, but all efficient classical computers cannot (under some cryptographic assumption). Such protocols play a crucial role in the certification of quantum devices. Existing single-round protocols (like asking the quantum computer to factor a large nu

John Wilson Peoples, John Harlim

We introduce a numerical method for approximating arbitrary differential operators on vector fields in the weak form given point cloud data sampled randomly from a $d$ dimensional manifold embedded in $\mathbb{R}^n$. This method generalizes the local linear mesh method to the local curved mesh method, thus, allowing for the estimation of differential operato

Louis Foucard, Samar Khanna, Yi Shi, Chi-Kuei Liu

In this paper, we propose SpotNet: a fast, single stage, image-centric but LiDAR anchored approach for long range 3D object detection. We demonstrate that our approach to LiDAR/image sensor fusion, combined with the joint learning of 2D and 3D detection tasks, can lead to accurate 3D object detection with very sparse LiDAR support. Unlike more recent bird's-

Boyang Zheng, Jinjin Gu, Shijun Li, Chao Dong

The success of large language models (LLMs) has fostered a new research trend of multi-modality large language models (MLLMs), which changes the paradigm of various fields in computer vision. Though MLLMs have shown promising results in numerous high-level vision and vision-language tasks such as VQA and text-to-image, no works have demonstrated how low-leve

Bruce Reed, Maya Stein

The Erd\H{o}s-S\'os Conjecture states that every graph with average degree exceeding $k-1$ contains every tree with $k$ edges as a subgraph. We prove that there are $\delta>0$ and $k_0\in\mathbb N$ such that the conjecture holds for every tree $T$ with $k \ge k_0$ edges and every graph $G$ with $|V(G)| \le (1+\delta)|V(T)|$.

Sebastian Zeng, Florian Graf, Martin Uray, Stefan Huber

We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood gove

Jerome Sieber, Carmen Amo Alonso, Alexandre Didier, Melanie N. Zeilinger

Softmax attention is the principle backbone of foundation models for various artificial intelligence applications, yet its quadratic complexity in sequence length can limit its inference throughput in long-context settings. To address this challenge, alternative architectures such as linear attention, State Space Models (SSMs), and Recurrent Neural Networks

Omar Oukdach, Said Boulite, Abdellatif Elgrou, Lahcen Maniar

This paper deals with a hierarchical multi-objective control problem for forward stochastic parabolic equations with dynamic boundary conditions. The controls are divided into two classes: leaders and followers. The goal of the leaders is of null controllability type while the followers are in charge of letting the state close to prescribed targets in fixed

Bohdan Petryshyn, Mantas Lukoševičius

Recent advancements in Large Language Models (LLMs) and their utilization in code generation tasks have significantly reshaped the field of software development. Despite the remarkable efficacy of code completion solutions in mainstream programming languages, their performance lags when applied to less ubiquitous formats such as OpenAPI definitions. This stu

Jiajin Zhang, Ge Wang, Mannudeep K. Kalra, Pingkun Yan

In medical image analysis, the expertise scarcity and the high cost of data annotation limits the development of large artificial intelligence models. This paper investigates the potential of transfer learning with pre-trained vision-language models (VLMs) in this domain. Currently, VLMs still struggle to transfer to the underrepresented diseases with minima

Roelof G. Hup, Julian P. Merkofer, Alex A. Bhogal, Ruud J. G. van Sloun

Change point detection (CPD) and anomaly detection (AD) are essential techniques in various fields to identify abrupt changes or abnormal data instances. However, existing methods are often constrained to univariate data, face scalability challenges with large datasets due to computational demands, and experience reduced performance with high-dimensional or

Z. C. Santana, M. O. Costa, R. F. L. Holanda, R. Silva

The non-gravitational interaction between the dark components of the Universe could lead to the variation of dark matter energy density standard evolution law. When we assume this scenario, the dark matter energy density follows $\rho_{{dm}}\sim(1+z)^{3 + \epsilon(z)}$ (where $\epsilon(z)=0$ the standard law is recovered). In this paper, we perform a Bayesia

Aldo Garcia Guinto, Matthew Lorentz, Brent Nelson

Given a von Neumann algebra $M$ equipped with a faithful normal strictly semifinite weight $\varphi$, we develop a notion of Murray-von Neumann dimension over $(M,\varphi)$ that is defined for modules over the basic construction associated to the inclusion $M^\varphi \subset M$. For $\varphi=\tau$ a faithful normal tracial state, this recovers the usual Murr

NASA Space Robots Team, Josh Brunner, Kenneth C. Cheung, Erik D. Demaine

We introduce and analyze a model for self-reconfigurable robots made up of unit-cube modules. Compared to past models, our model aims to newly capture two important practical aspects of real-world robots. First, modules often do not occupy an exact unit cube, but rather have features like bumps extending outside the allotted space so that modules can interlo

Alessandro Abate, Mirco Giacobbe, Yannik Schnitzer

We introduce a data-driven approach to computing finite bisimulations for state transition systems with very large, possibly infinite state space. Our novel technique computes stutter-insensitive bisimulations of deterministic systems, which we characterize as the problem of learning a state classifier together with a ranking function for each class. Our pro

Noga Amit, Shafi Goldwasser, Orr Paradise, Guy Rothblum

How can we trust the correctness of a learned model on a particular input of interest? Model accuracy is typically measured on average over a distribution of inputs, giving no guarantee for any fixed input. This paper proposes a theoretically-founded solution to this problem: to train Self-Proving models that prove the correctness of their output to a verifi

Adam Baybutt

We develop novel estimation procedures with supporting econometric theory for a dynamic latent-factor model with high-dimensional asset characteristics, that is, the number of characteristics is on the order of the sample size. Utilizing the Double Selection Lasso estimator, our procedure employs regularization to eliminate characteristics with low signal-to

Venkata Rama Manoj Pola, Ratna Kumar Annabattula

A simple position-dependent body force-based confinement for simulating triaxial tests using the Discrete Element Method is presented. The said method is used to perform triaxial simulations on mono-disperse and segregated assemblies of glass spheres. The macroscopic load response obtained in simulations is validated with experimental load response. A mesh c

Elias Nehme, Rotem Mulayoff, Tomer Michaeli

When solving ill-posed inverse problems, one often desires to explore the space of potential solutions rather than be presented with a single plausible reconstruction. Valuable insights into these feasible solutions and their associated probabilities are embedded in the posterior distribution. However, when confronted with data of high dimensionality (such a

Sebastian Golat, Alex J. Vernon, Francisco J. Rodríguez-Fortuño

Electromagnetic quantities such as energy density, momentum, spin, and helicity bring meaning and intuition to electromagnetism and possess intricate interrelations, particularly prominent in complex non-paraxial near-fields. These quantities are conventionally expressed using electric and magnetic field vectors, yet the electric-magnetic basis is one among