February 2024 arXiv papers — page 88

Liang Shi, Edvard Bakhitov, Kenneth Hung, Brian Karrer

Bipartite Experiments are randomized experiments where the treatment is applied to a set of units (randomization units) that is different from the units of analysis, and randomization units and analysis units are connected through a bipartite graph. The scale of experimentation at large online platforms necessitates both accurate inference in the presence of

Jeffrey Streets, Charles Strickland-Constable, Fridrich Valach

We develop a theory of Ricci flow for metrics on Courant algebroids which unifies and extends the analytic theory of various geometric flows, yielding a general tool for constructing solutions to supergravity equations. We prove short time existence and uniqueness of solutions on compact manifolds, in turn showing that the Courant isometry group is preserved

Guangya Wan, Yunsheng Lu, Yuqi Wu, Mengxuan Hu

Causal discovery (CD) and Large Language Models (LLMs) have emerged as transformative fields in artificial intelligence that have evolved largely independently. While CD specializes in uncovering cause-effect relationships from data, and LLMs excel at natural language processing and generation, their integration presents unique opportunities for advancing ca

Andrzej Łuczak

The paper is devoted to the investigation of Segal's entropy in semifinite von Neumann algebras. The following questions are dealt with: semicontinuity, the 'ideal-like' structure of the linear span of the set of operators with finite entropy, and topological properties of the set of operators with finite as well as infinite entropy. In our analysis, full ge

Tristan Bester, Benjamin Rosman

Financial inclusion ensures that individuals have access to financial products and services that meet their needs. As a key contributing factor to economic growth and investment opportunity, financial inclusion increases consumer spending and consequently business development. It has been shown that institutions are more profitable when they provide marginal

Albert Roura

Light-pulse atom interferometers based on single-photon transitions are a promising tool for gravitational-wave detection in the mid-frequency band and the search for ultralight dark-matter fields. Here we present a novel measurement scheme that enables their use as freely falling clocks directly measuring relativistic time-dilation effects. The proposal is

A. A. Vasil'eva

In this paper, we obtain order estimates for the Kolmogorov widths of periodic Sobolev classes with restictions on derivatives of order $r_j$ with respect to $j$th variable in metrics $L_{p_j}$ ($1\le j\le d$).

Diego B. S. Audiffred, André V. G. Cavalieri, Pedro P. C. Brito, Eduardo Martini

Reactive flow control has been shown as a promising tool to improve, among other aspects, the aerodynamic characteristics of an aircraft. This paper focuses on the use of reactive flow control to attenuate Tollmien-Schlichting (TS) waves over a wing profile. TS waves are an instability mechanism that is one of the first stages of boundary layer transition to

J. M. Jackson, J. S. Whitaker, E. T. Chambers, R. Simon

Using the upGREAT instrument on SOFIA, we have imaged [C II] 157.74 and [O I] 63.18 micron line emission from a bright photodissociation region (PDR) associated with an ionized ``bubble'' located in the Nessie Nebula, a filamentary infrared dark cloud. A comparison with ATCA data reveals a classic PDR structure, with a uniform progression from ionized gas, t

Anthony Kiggundu, Bin Han, Dennis Krummacker, Hans D. Schotten

Emerging trends in communication systems, such as network softwarization, functional disaggregation, and multi-access edge computing (MEC), are reshaping both the infrastructural landscape and the application ecosystem. These transformations introduce new challenges for packet transmission, task offloading, and resource allocation under stringent service-lev

Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi R. Fung

The increasing demand for personalized interactions with large language models (LLMs) calls for methodologies capable of accurately and efficiently identifying user opinions and preferences. Retrieval augmentation emerges as an effective strategy, as it can accommodate a vast number of users without the costs from fine-tuning. Existing research, however, has

Gautam Rajendrakumar Gare, Tom Fox, Beam Chansangavej, Amita Krishnan

Accurate and interpretable diagnostic models are crucial in the safety-critical field of medicine. We investigate the interpretability of our proposed biomarker-based lung ultrasound diagnostic pipeline to enhance clinicians' diagnostic capabilities. The objective of this study is to assess whether explanations from a decision tree classifier, utilizing biom

Geoffrey R. Grimmett

A path in a graph $G$ is called non-self-touching if two vertices are neighbours in the path if and only if they are neighbours in the graph. We investigate the existence of doubly infinite non-self-touching paths in infinite plane graphs. The matching graph $G_*$ of an infinite plane graph $G$ is obtained by adding all diagonals to all faces, and it plays a

Jihyung Kil, Farideh Tavazoee, Dongyeop Kang, Joo-Kyung Kim

Visual Question Answering (VQA) often involves diverse reasoning scenarios across Vision and Language (V&L). Most prior VQA studies, however, have merely focused on assessing the model's overall accuracy without evaluating it on different reasoning cases. Furthermore, some recent works observe that conventional Chain-of-Thought (CoT) prompting fails to gener

Shijia Feng, Michael Wray, Brian Sullivan, Youngkyoon Jang

Determining when people are struggling allows for a finer-grained understanding of actions that complements conventional action classification and error detection. Struggle detection, as defined in this paper, is a distinct and important task that can be identified without explicit step or activity knowledge. We introduce the first struggle dataset with thre

Guillaume Bornet, Gabriel Emperauger, Cheng Chen, Francisco Machado

We implement and characterize a protocol that enables arbitrary local controls in a dipolar atom array, where the degree of freedom is encoded in a pair of Rydberg states. Our approach relies on a combination of local addressing beams and global microwave fields. Using this method, we directly prepare two different types of three-atom entangled states, inclu

Jessica Royer, Casey Paquola, Sofie L. Valk, Matthias Kirschner

Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends, or gradients, in brain structure and function, offering a framework to unify principles of brain organization across multipl

Anthony Kiggundu, Bin Han, Dennis Krummacker, Hans D. Schotten

Use-case-specific network slicing in decentralized multi-tenancy cloud environments is a promising approach to bridge the gap between the demand and supply of resources in next-generation communication networks. Our findings associate different slice profiles to queues in a multi-server setting, such that tenants continuously assess their preferences and mak

Ning Ning, Jing Wu

In this paper, we study a broad class of McKean-Vlasov stochastic variational inequalities (MVSVIs), where both the drift coefficient $b$ and the diffusion coefficient $\sigma$ depend on time $t$, the state $X_t$ and its distribution $\mu_t$. We establish the strong well-posedness, when $b$ is superlinear growth and locally Lipschitz continuous, and $\sigma$

Sara Shashaani, Ozge Surer, Matthew Plumlee, Seth Guikema

Decision trees built with data remain in widespread use for nonparametric prediction. Predicting probability distributions is preferred over point predictions when uncertainty plays a prominent role in analysis and decision-making. We study modifying a tree to produce nonparametric predictive distributions. We find the standard method for building trees may

Runcong Zhao, Qinglin Zhu, Hainiu Xu, Jiazheng Li

Existing datasets for narrative understanding often fail to represent the complexity and uncertainty of relationships in real-life social scenarios. To address this gap, we introduce a new benchmark, Conan, designed for extracting and analysing intricate character relation graphs from detective narratives. Specifically, we designed hierarchical relationship

Indu L. Shakya, Falah H. Ali

We propose a novel nonorthogonal multiple access (NOMA) scheme referred as adaptive constellation multiple access (ACMA) which addresses key limitations of existing NOMA schemes for beyond 5G wireless systems. Unlike the latter, that are often constrained in choices of allocation of power, modulations and phases to allow enough separation of clusters from us

Harris Daniels, Jeremy Rouse

Let $E$ be an elliptic curve over a number field $L$ and for a finite set $S$ of primes, let $\rho_{E,S} : {\rm Gal}(\overline{L}/L) \to {\rm GL}_{2}(\mathbb{Z}_{S})$ be the $S$-adic Galois representation. If $L \cap \mathbb{Q}(\zeta_{n}) = \mathbb{Q}$ for all positive integers $n$ whose prime factors are in $S$, then $\det \rho_{E,S} : {\rm Gal}(\overline{L

Lakmal Silva, Michael Unterkalmsteiner, Krzysztof Wnuk

Software documentation often struggles to catch up with the pace of software evolution. The lack of correct, complete, and up-to-date documentation results in an increasing number of documentation defects which could introduce delays in integrating software systems. In our previous study on a bug analysis tool called MultiDimEr, we provided evidence that doc

Venkata Sai Praneeth Karempudi, Sairam Sri Vatsavai, Ishan Thakkar, Oluwaseun Adewunmi Alo

Over the past few years, several microring resonator (MRR)-based analog photonic architectures have been proposed to accelerate general matrix-matrix multiplications (GEMMs), which are found in abundance in deep learning workloads.These architectures have dramatically grown in popularity because they offer exceptional throughput and energy efficiency compare

Samuel Corona-Aquino, Zeferino Ibarra-Borja, Omar Calderón-Losada, Bruno Piccirillo

We demonstrate an experimental protocol for the preparation and control of heralded single photons in inhomogeneously polarized states, such as Vector Vortex and Full Poincar\'e beam states. A laser beam is shaped by a voltage-controlled spin-to-orbital angular momentum converter q-plate device which eliminates the need for an interferometer for the robust p

Philipp Wiesener, Stefan Förster, Milena Merkel, Bertram Schulze Lammers

The complex atomic structures and defects of metal-oxide surfaces are vital for a variety of applications in material science and chemistry. While scanning probe microscopy allows accessing atomic-scale structures in real space, elemental discrimination and defect characterization usually rely on indirect assumptions and extensive theoretical modelling. By i

Todor Antić, Jelena Glišić, Milan Milivojčević

We deal with the problem of decomposing a complete geometric graph into plane star-forests. In particular, we disprove a recent conjecture by Pach, Saghafian and Schnider by constructing for each $n$ a complete geometric graph on $n$ vertices which can be decomposed into $\frac{n}{2}+1$ plane star-forests. Additionally we prove that for even $n$, every decom

Joachim Frenkler

We prove the non-linear stability of a large class of spherically symmetric equilibrium solutions of both the collisonless Boltzmann equation and of the Euler equations in MOND. This is the first such stability result that is proven with mathematical rigour in MOND. While we strive to prove our stability theorems, we develop new, genuinely Mondian ideas how

Utku Saglam, Deniz Yavuz

We present an analytical and numerical study of electromagnetic modes in micro and nano fibers (MNFs) where the electric and magnetic fields of the modes are not necessarily orthogonal to each other. We first investigate these modes for different fiber structures including photonic crystal fibers. We then discuss two specific applications of these modes: (1)

Huynh Khanh Vi Tran, Jürgen Börstler, Nauman Bin Ali, Michael Unterkalmsteiner

Background: Systematic literature studies (SLS) have become a core research methodology in Evidence-based Software Engineering (EBSE). Search completeness, ie, finding all relevant papers on the topic of interest, has been recognized as one of the most commonly discussed validity issues of SLSs. Aim: This study aims at raising awareness on the issues related

Paul Seurin, Koroush Shirvan

Optimizing the fuel cycle cost through the optimization of nuclear reactor core loading patterns involves multiple objectives and constraints, leading to a vast number of candidate solutions that cannot be explicitly solved. To advance the state-of-the-art in core reload patterns, we have developed methods based on Deep Reinforcement Learning (DRL) for both

Nathan Stromberg, Rohan Ayyagari, Monica Welfert, Sanmi Koyejo

Existing methods for last layer retraining that aim to optimize worst-group accuracy (WGA) rely heavily on well-annotated groups in the training data. We show, both in theory and practice, that annotation-based data augmentations using either downsampling or upweighting for WGA are susceptible to domain annotation noise, and in high-noise regimes approach th

S. S. Apostolov, O. V. Usatenko, V. A. Yampol'skii, S. S. Melnyk

We study two-state (dichotomous, telegraph) random ergodic continuous-time processes with dynamics depending on their past. We take into account the history of process in an explicit form by introducing an integral non-local memory term into the conditional probability function. We start from an expression for the conditional transition probability function

Shane P. Kelly, Jamir Marino

In this work we investigate the conditions for quantum back action to result in a phase transition in the information dynamics of a monitored system. We introduce a framework that captures a wide range of experiments encompassing probes comprised of projective measurements and probes which more generally transfer quantum information from the system to a quan

Bárbara Cabrales, James R. A. Davenport, Sofia Z. Sheikh, Steve Croft

The SETI Ellipsoid is a strategy for technosignature candidate selection which assumes that extraterrestrial civilizations who have observed a galactic-scale event -- such as supernova 1987A -- may use it as a Schelling point to broadcast synchronized signals indicating their presence. Continuous wide-field surveys of the sky offer a powerful new opportunity

Soumava Kumar Roy, Ilia Badanin, Sina Honari, Pascal Fua

Occlusions remain one of the key challenges in 3D body pose estimation from single-camera video sequences. Temporal consistency has been extensively used to mitigate their impact but the existing algorithms in the literature do not explicitly model them. Here, we apply this by representing the deforming body as a spatio-temporal graph. We then introduce a re

Benjamin Reichman, Larry Heck

Dense passage retrieval (DPR) is the first step in the retrieval augmented generation (RAG) paradigm for improving the performance of large language models (LLM). DPR fine-tunes pre-trained networks to enhance the alignment of the embeddings between queries and relevant textual data. A deeper understanding of DPR fine-tuning will be required to fundamentally

Jannat Ara Meem, Muhammad Shihab Rashid, Yue Dong, Vagelis Hristidis

Existing work on Temporal Question Answering (TQA) has predominantly focused on questions anchored to specific timestamps or events (e.g. "Who was the US president in 1970?"). Little work has studied questions whose temporal context is relative to the present time (e.g. "Who was the previous US president?"). We refer to this problem as Present-Anchored Tempo

A. Ianniccari, A. J. Iovino, A. Kehagias, D. Perrone

We show that the abundance of primordial black holes, if formed through the collapse of large fluctuations generated during inflation and unless the power spectrum of the curvature perturbation is very peaked, is always dominated by the broadest profile of the compaction function, even though statistically it is not the most frequent. The corresponding thres

Bryson Kagy, Seth Sullivant

Phylogenetic networks are generalizations of trees that allow for the modeling of non-tree like evolutionary processes. Split networks give a useful way to construct networks with intuitive distance structures induced from the associated split graph. We explore the polyhedral geometry of distance matrices built from circular split systems which have the adde

James Moore, Aaron M. Graham, Manos M. Tentzeris, Vincent Fusco

This study introduces an novel approach to the design of low-profile superdirective antenna arrays, employing parasitic elements. The proposed design concept was verified through the analysis of a two-element antenna array consisting of strip dipoles, operating at a frequency of $3.5$ GHz within the sub-6 5G frequency band. The antenna array was optimized fo

Ozan Dernek, Santosh Kumar Radha, Jerome Jackson, Walter R. L. Lambrecht

KCoO2 has been found in 1975 to exist in a unique structure with P4/nmm spacegroup with Co in a square pyramidal coordination with the Co atoms in the plane linked by O in a square arrangement reminiscent of the cuprates but its electronic structure has not been studied until now. Unlike Co atoms in LiCoO2 and NaCoO2 in octahedral coordination, which are non

Hans-Erik Andersen, Göran Ståhl, Bruce D. Cook, Douglas C. Morton

Model-assisted, two-stage forest survey sampling designs provide a means to combine airborne remote sensing data, collected in a sampling mode, with field plot data to increase the precision of national forest inventory estimates, while maintaining important properties of design-based inventories, such as unbiased estimation and quantification of uncertainty

Samuel McCauley, Benjamin Moseley, Aidin Niaparast, Shikha Singh

This paper leverages the framework of algorithms-with-predictions to design data structures for two fundamental dynamic graph problems: incremental topological ordering and cycle detection. In these problems, the input is a directed graph on $n$ nodes, and the $m$ edges arrive one by one. The data structure must maintain a topological ordering of the vertice

Avimita Chatterjee, Debarshi Kundu, Swaroop Ghosh

In the evolving landscape of quantum computing, determining the most efficient parameters for Quantum Error Correction (QEC) is paramount. Various quantum computers possess varied types and amounts of physical noise. Traditionally, simulators operate in a forward paradigm, taking parameters such as distance, rounds, and physical error to output a logical err

Pornthep Pongchalee, Brian W. J. McNeil

A study is made of the saturation mechanism of a single superradiant spike of radiation in a Free Electron Laser. A one-dimensional (1D) computer model is developed using the Puffin, un-averaged FEL simulation code, which allows sub-radiation wavelength evolution of both the spike radiation field and the electron dynamics to be modelled until the highly non-

Junbo Li, Zichen Miao, Qiang Qiu, Ruqi Zhang

Bayesian neural networks (BNNs) offer uncertainty quantification but come with the downside of substantially increased training and inference costs. Sparse BNNs have been investigated for efficient inference, typically by either slowly introducing sparsity throughout the training or by post-training compression of dense BNNs. The dilemma of how to cut down m

Christopher Cedzich, Jake Fillman

We give a criterion based on reflection symmetries in the spirit of Jitomirskaya--Simon to show absence of point spectrum for (split-step) quantum walks and Cantero--Moral--Vel\'azquez (CMV) matrices. To accomplish this, we use some ideas from a recent paper by the authors and their collaborators to implement suitable reflection symmetries for such operators

Prachi Khatri, Cristiano Porciani, Emilio Romano-Díaz, Daniel Seifried

Aims. We present a new sub-grid model, HYACINTH -- HYdrogen And Carbon chemistry in the INTerstellar medium in Hydro simulations -- for computing the non-equilibrium abundances of ${\rm H_2}$ and its carbon-based tracers, namely ${\rm CO}$, ${\rm C}$, and ${\rm C^+}$, in cosmological simulations of galaxy formation. Methods. The model accounts for the unreso

M. Cea, M. Grossi, S. Monaco, E. Rico

In this manuscript, we explore the intersection of QML and TN in the context of the one-dimensional ANNNI model with a transverse field. The study aims to concretely connect QML and TN by combining them in various stages of algorithm construction, focusing on phase diagram reconstruction for the ANNNI model, with supervised and unsupervised techniques. The m

Cheng Chu, Zhenxiao Fu, Yilun Xu, Gang Huang

Trapped-Ion (TI) technology offers potential breakthroughs for Noisy Intermediate Scale Quantum (NISQ) computing. TI qubits offer extended coherence times and high gate fidelity, making them appealing for large-scale NISQ computers. Constructing such computers demands a distributed architecture connecting Quantum Charge Coupled Devices (QCCDs) via quantum ma

Chan Park, Mats Stensrud, Eric Tchetgen Tchetgen

Scientists regularly pose questions about treatment effects on outcomes conditional on a post-treatment event. However, causal inference in such settings requires care, even in perfectly executed randomized experiments. Recently, the conditional separable effect (CSE) was proposed as an interventionist estimand that corresponds to scientifically meaningful q

Thomas W. Baumgarte, Stuart L. Shapiro

Long-period radio transients have unusual properties that challenge their interpretation as pulsars or magnetars. We examine whether they might instead be powered by primordial black holes (PBHs) making repeated passages through a host star, thereby providing a signature of elusive dark-matter candidates. We demonstrate that constraints derived from the tran

R. Scott Barrows, Mar Mezcua, Julia M. Comerford, Daniel Stern

Hyper-luminous X-ray sources (HLXs) are extragalactic off-nuclear X-ray sources with luminosities exceeding the theoretical limit for accretion onto stellar-mass compact objects. Many HLXs may represent intermediate-mass black holes (IMBHs) deposited in galaxy halos through mergers, and properties of the stellar cores surrounding HLXs provide powerful constr

Anthony Houppe

We use perturbation theory to construct a family of time-dependent microstrata: a set of non-extremal solutions of IIB supergravity asymptotic to $AdS_3 \times S^3 \times T^4$. Our construction shows that the "special locus" constraints of arXiv:2307.13021 can be broken by allowing the solutions to depend on time. We study the secular terms appearing in the

Oliver Friedrich, ChunJun Cao, Sean M. Carroll, Gong Cheng

The holographic principle suggests that regions of space contain fewer physical degrees of freedom than would be implied by conventional quantum field theory. Meanwhile, in Hilbert spaces of large dimension $2^n$, it is possible to define $N \gg n$ Pauli algebras that are nearly anti-commuting (but not quite) and which can be thought of as "overlapping degre

Ingrid Pelisoli, Laura Chomiuk, Jay Strader, T. R. Marsh

Radio emission has been detected from tens of white dwarfs, in particular in accreting systems. Additionally, radio emission has been predicted as a possible outcome of a planetary system around a white dwarf. We searched for 3 GHz radio continuum emission in 846,000 candidate white dwarfs previously identified in Gaia using the Very Large Array Sky Survey (

Matija Medvidović, Javier Robledo Moreno

Variational quantum calculations have borrowed many tools and algorithms from the machine learning community in the recent years. Leveraging great expressive power and efficient gradient-based optimization, researchers have shown that trial states inspired by deep learning problems can accurately model many-body correlated phenomena in spin, fermionic and qu

Christian Ecker, Konrad Topolski, Matti Järvinen, Alina Stehr

We carry out an in-depth analysis of the prompt-collapse behaviour of binary neutron star (BNS) mergers. To this end, we perform more than $80$ general relativistic BNS merger simulations using a family of realistic Equations of State (EOS) with different stiffness, which feature a first order deconfinement phase transition between hadronic and quark matter.

Hideki Maeda, Cristian Martinez

Without specifying a matter field nor imposing energy conditions, we study Killing horizons in $n(\ge 3)$-dimensional static solutions in general relativity with an $(n-2)$-dimensional Einstein base manifold. Assuming linear relations $p_{\rm r}\simeq\chi_{\rm r} \rho$ and $p_2\simeq\chi_{\rm t} \rho$ near a Killing horizon between the energy density $\rho$,

Fabio Bacchini, Alexander A. Philippov

Electromagnetic fundamental and harmonic emission is ubiquitously observed throughout the heliosphere, and in particular it is commonly associated with the occurrence of Type II and III solar radio bursts. Classical analytic calculations for the plasma-emission process, though useful, are limited to idealized situations; a conclusive numerical verification o

R. Pakmor, I. R. Seitenzahl, A. J. Ruiter, S. A. Sim

Theoretical and observational approaches to settling the important questions surrounding the progenitor systems and the explosion mechanism of normal Type Ia supernovae have thus far failed. With its unique capability to obtain continuous spectra through the near- and mid-infrared, JWST now offers completely new insights into Type Ia supernovae. In particula

Carlo Musolino, Raphaël Duqué, Luciano Rezzolla

Using a set of general-relativistic magnetohydrodynamics simulations that include proper neutrino transfer, we assess for the first time the role played by the fallback accretion onto the remnant from a binary neutron-star merger over a timescale of hundreds of seconds. In particular, we find that, independently of the equation of state, the properties of th

Yvonne A. Fichtner, Jonathan Mackey, Luca Grassitelli, Emilio Romano-Díaz

Energy and momentum feedback from stars is a key element of models for galaxy formation and interstellar medium dynamics, but resolving the relevant length scales to directly include this feedback remain out of reach of current-generation simulations. We aim to constrain the energy feedback by winds, photoionisation and supernovae (SNe) from massive stars. W

Nicole Sabina Ticea, Julian May-Mann, Jiewen Xiao, Erez Berg

We study the effects of quasiperiodicity on the stability of conventional and unconventional superconductors. Quasiperiodicity is modelled using the three-dimensional Aubry-Andre model, a system in which electrons are coupled to a translation-symmetry-breaking potential that is incommensurate with the underlying lattice. Upon increasing the strength of the q

Yair Carmon, Oliver Hinder

We prove impossibility results for adaptivity in non-smooth stochastic convex optimization. Given a set of problem parameters we wish to adapt to, we define a "price of adaptivity" (PoA) that, roughly speaking, measures the multiplicative increase in suboptimality due to uncertainty in these parameters. When the initial distance to the optimum is unknown but

Luca Vannucci, José Ferreira Neto, Claudia Piccinini, Athanasios Paralikis

Within optical quantum information processing, single-photon sources based on a two-level system in a semiconductor material allow for on-demand generation of single photons. To initiate the spontaneous emission process, it is necessary to efficiently populate the excited state. However, reconciling the requirement for on-demand excitation with both high eff

Junfei Xiao, Zheng Xu, Alan Yuille, Shen Yan

This paper demonstrates that a progressively aligned language model can effectively bridge frozen vision encoders and large language models (LLMs). While the fundamental architecture and pre-training methods of vision encoders and LLMs have been extensively studied, the architecture and training strategy of vision-language adapters vary significantly across

D. Adamová, G. Agakishiev, A. Andronic, D. Antończyk

The Fourier harmonics, $v_2$ and $v_3$ of negative pions are measured at center-of-mass energy per nucleon pair of $\sqrt{s_{\mathrm{NN}}}$= 17.3 GeV around midrapidity by the CERES/NA45 experiment at the CERN SPS in 0--30\% central PbAu collisions with a mean centrality of 5.5\%. The analysis is performed in two centrality bins as a function of the transver

Chia-Ling Tsai, Hui-Yun Su, Shen-Feng Sung, Wei-Yang Lin

Stroke is a common disabling neurological condition that affects about one-quarter of the adult population over age 25; more than half of patients still have poor outcomes, such as permanent functional dependence or even death, after the onset of acute stroke. The aim of this study is to investigate the efficacy of diffusion-weighted MRI modalities combining

Mukund Srinath, Pranav Venkit, Maria Badillo, Florian Schaub

Privacy policies are crucial for informing users about data practices, yet their length and complexity often deter users from reading them. In this paper, we propose an automated approach to identify and visualize data practices within privacy policies at different levels of detail. Leveraging crowd-sourced annotations from the ToS;DR platform, we experiment

Moritz Stephan, Alexander Khazatsky, Eric Mitchell, Annie S Chen

The diversity of contexts in which large language models (LLMs) are deployed requires the ability to modify or customize default model behaviors to incorporate nuanced requirements and preferences. A convenient interface to specify such model adjustments is high-level verbal feedback, such as "Don't use emojis when drafting emails to my boss." However, while

Johnny Tian-Zheng Wei, Ryan Yixiang Wang, Robin Jia

Detecting whether copyright holders' works were used in LLM pretraining is poised to be an important problem. This work proposes using data watermarks to enable principled detection with only black-box model access, provided that the rightholder contributed multiple training documents and watermarked them before public release. By applying a randomly sampled

Dylan Zhang, Justin Wang, Francois Charton

Instruction tuning -- fine-tuning a large language model (LLM) on pairs of instructions and desired outcomes -- is an approach that enables pre-trained language models to perform real-world tasks and follow human instructions. Its practical success depends on the model learning a broader set of instructions than those it was trained on. Yet the factors that

Ziru Chen, Michael White, Raymond Mooney, Ali Payani

In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method. We investigate the practical utility of two advanced planning methods, iterative correction and tree search. We present a comprehensive analysis of how discrimination

Leonardo Azalim de Oliveira, Edelberto Franco Silva

The fifth generation of the telecommunication networks (5G) established the service-oriented paradigm on the mobile networks. In this new context, the 5G Core component has become extremely flexible so, in addition to serving mobile networks, it can also be used to connect devices from the so-called non-3GPP networks, which contains technologies such as WiFi

julien Delaunay

This thesis explores the generation of local explanations for already deployed machine learning models, aiming to identify optimal conditions for producing meaningful explanations considering both data and user requirements. The primary goal is to develop methods for generating explanations for any model while ensuring that these explanations remain faithful

Ziyang Wang, Chao Ma

Medical image segmentation is increasingly reliant on deep learning techniques, yet the promising performance often come with high annotation costs. This paper introduces Weak-Mamba-UNet, an innovative weakly-supervised learning (WSL) framework that leverages the capabilities of Convolutional Neural Network (CNN), Vision Transformer (ViT), and the cutting-ed

Zhaolin Gao, Kianté Brantley, Thorsten Joachims

Recent developments in LLMs offer new opportunities for assisting authors in improving their work. In this paper, we envision a use case where authors can receive LLM-generated reviews that uncover weak points in the current draft. While initial methods for automated review generation already exist, these methods tend to produce reviews that lack detail, and

Tsung-Wei Ke, Nikolaos Gkanatsios, Katerina Fragkiadaki

Diffusion policies are conditional diffusion models that learn robot action distributions conditioned on the robot and environment state. They have recently shown to outperform both deterministic and alternative action distribution learning formulations. 3D robot policies use 3D scene feature representations aggregated from a single or multiple camera views

Shengzhi Li, Rongyu Lin, Shichao Pei

Multi-modal large language models (MLLMs) are expected to support multi-turn queries of interchanging image and text modalities in production. However, the current MLLMs trained with visual-question-answering (VQA) datasets could suffer from degradation, as VQA datasets lack the diversity and complexity of the original text instruction datasets with which th

Veyis Gunes, Jean-Yves Botquelen, Odile Bohnke

A method of electronic conductivity measurement is presented. It combines two well known methods of electrochemistry: cyclic voltammetry and chronoamperometry. This DC technique uses the Hebb/Wagner approach to block ionic conduction when steady state conditions are reached and allows electronic conduction of solid electrolytes to be determined. In order to

Zongyu Wu, Hongcheng Gao, Yueze Wang, Xiang Zhang

Text-to-Image (T2I) models have shown great performance in generating images based on textual prompts. However, these models are vulnerable to unsafe input to generate unsafe content like sexual, harassment and illegal-activity images. Existing studies based on image checker, model fine-tuning and embedding blocking are impractical in real-world applications

Boryana Hadzhiyska, Lehman Garrison, Daniel J. Eisenstein, Simone Ferraro

A measurement of a primordial non-Gaussianity (PNG) signal through late- or early-Universe probes has the potential to transform our understanding of the physics of the primordial Universe. $N$-body simulations, such as the public \textsc{AbacusPNG} set presented in this study, consisting of 9 boxes, each of size $L_{\rm box} = 2~{\rm Gpc}/h$ and particle ma

Clodoaldo Ragazzo, Lucas Ruiz dos Santos

In this study, we formulate a set of differential equations for a binary system to describe the secular-tidal evolution of orbital elements, rotational dynamics, and deformation (flattening), under the assumption that one body remains spherical while the other is slightly aspherical throughout the analysis. By applying singular perturbation theory, we analyz

Emil Raaholt Ingelsten, Anton Frisk Kockum, Ariadna Soro

Giant atoms are quantum emitters that can couple to light at multiple discrete points. Such atoms have been shown to interact without decohering via a one-dimensional waveguide. Here, we study how giant atoms behave when coupled to a two-dimensional square lattice of coupled cavities, an environment characterized by a finite energy band and band gaps. In par

Patrick A. Lee

We construct a model which exhibits resistivity going as a power law in temperature $T$, as $T^\alpha$ down to the lowest temperature. There is no residual resistivity because we assume the absence of disorder and momentum relaxation is due to umklapp scattering. Our model consists of a quantum spin liquid state with spinon Fermi surface and a hole Fermi sur

Jonathan Richens, Tom Everitt

It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. However, it is not known if agents must learn causal models in order to generalise to new domains, or if other inductive biases are sufficient. We answer this question, showing that any agent capable of satisfying a regret bound under a large set

Sarath Sivaprasad, Pramod Kaushik, Sahar Abdelnabi, Mario Fritz

Large Language Models (LLMs) are increasingly utilized in autonomous decision-making, where they sample options from vast action spaces. However, the heuristics that guide this sampling process remain under explored. We study this sampling behavior and show that this underlying heuristics resembles that of human decision-making: comprising a descriptive comp

Benjamin L. Edelman, Ezra Edelman, Surbhi Goel, Eran Malach

Large language models have the ability to generate text that mimics patterns in their inputs. We introduce a simple Markov Chain sequence modeling task in order to study how this in-context learning (ICL) capability emerges. In our setting, each example is sampled from a Markov chain drawn from a prior distribution over Markov chains. Transformers trained on

Cong Guo, Fengchen Xue, Jingwen Leng, Yuxian Qiu

Network pruning can reduce the computation cost of deep neural network (DNN) models. However, sparse models often produce randomly-distributed weights to maintain accuracy, leading to irregular computations. Consequently, unstructured sparse models cannot achieve meaningful speedup on commodity hardware built for dense matrix computations. Accelerators are u

Clodoaldo Ragazzo, Lucas Ruiz dos Santos

This study analyzes secular dynamics using averaged equations that detail tidal effects on the motion of two extended bodies in Keplerian orbits. It introduces formulas for energy dissipation within each body of a binary system. The equations, particularly in contexts like the Sun-Mercury system, can be delineated into a fast-slow system. A significant contr

Alok Shukla, Prakash Vedula

The concept of sequency holds a fundamental significance in signal analysis using Walsh basis functions. In this study, we closely examine the concept of sequency and explore the properties of sequency-complete and sequency-ordered matrices. We obtain results on cardinalities of sets containing sequency-complete and sequency-ordered matrices of arbitrary siz

Qichen Xu, Zhuanglin Shen, Alexander Edström, I. P. Miranda

Despite extensive research on magnetic skyrmions and antiskyrmions, a significant challenge remains in crafting nontrivial high-order skyrmionic textures with varying, or even tailor-made, topologies. We address this challenge, by focusing on a construction pathway of skyrmionic metamaterials within a monolayer thin film and suggest several skyrmionic metama

Muhammad Umar Farooq Qaisar, Weijie Yuan, Paolo Bellavista, Guangjie Han

The internet of things (IoT) based wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore

Joshua Chiel, Christopher Jarzynski, Jay Sau

Thouless pumping is a transport phenomenon whereby an insulating, time-periodic Hamiltonian induces a robustly quantized transfer of charge, per driving cycle, in the quasi-adiabatic limit. In this work we apply the shortcuts to adiabaticity (STA) method of counterdiabatic (CD) driving to the Rice-Mele model, an archetypal model for Thouless pumping. We show

M. Garcia-Bosque, C. Sánchez-Azqueta, G. Royo, S. Celma

In this paper, we propose and analyze two different stream ciphers based on a Skew Tent Map and a Modified Logistic Map respectively. In order to improve the randomness of these systems, a single method for increasing the period length of the generated sequences has been applied. The results prove that the randomness of these systems can be severally increas

Tanner Fiez, Houssam Nassif, Yu-Cheng Chen, Sergio Gamez

Adaptive experimental design (AED) methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods. However, the behavior and guarantees of such methods are not well-understood beyond idealized stationary settings. This paper shares lessons learned regarding th

Antoine Song

Let $M$ be a closed, oriented, negatively curved, $n$-dimensional manifold with fundamental group $\Gamma$. Let $S^\infty$ be the unit sphere in $\ell^2(\Gamma)$, on which $\Gamma$ acts by the regular representation. The spherical volume of $M$ is a topological invariant introduced by Besson-Courtois-Gallot. We show that it is equal to the area of an $n$-dim