October 2024 arXiv papers — page 96

Nirmalya Brahma, Katelin Schutz

Some of the most stringent constraints on physics beyond the Standard Model (BSM) arise from considerations of particle emission from astrophysical plasmas. However, many studies assume that particle production occurs in an isotropic plasma environment. This condition is rarely (if ever) met in astrophysical settings, for instance due to the ubiquitous prese

German Gritsai, Anastasia Voznyuk, Andrey Grabovoy, Yury Chekhovich

The rapid development of autoregressive Large Language Models (LLMs) has significantly improved the quality of generated texts, necessitating reliable machine-generated text detectors. A huge number of detectors and collections with AI fragments have emerged, and several detection methods even showed recognition quality up to 99.9% according to the target me

Qin Liu, Fei Wang, Chaowei Xiao, Muhao Chen

Existing preference alignment is a one-size-fits-all alignment mechanism, where the part of the large language model (LLM) parametric knowledge with non-preferred features is uniformly blocked to all the users. However, this part of knowledge can be useful to advanced users whose expertise qualifies them to handle these information. The one-size-fits-all ali

Yukun Huang, Sanxing Chen, Hongyi Cai, Bhuwan Dhingra

Large Language Models (LLMs) are often augmented with external contexts, such as those used in retrieval-augmented generation (RAG). However, these contexts can be inaccurate or intentionally misleading, leading to conflicts with the model's internal knowledge. We argue that robust LLMs should demonstrate situated faithfulness, dynamically calibrating their

Anjali B. Yelikar, Richard O'Shaughnessy, Daniel Wysocki, Leslie Wade

Gravitational waves from inspiralling neutron stars carry information about matter at extreme gravity and density. The binary neutron star (BNS) event GW170817 provided, for the first time, insight into dense matter through this window. Since then, another BNS (GW190425) and several neutron star-black hole events have been detected, although the tidal measur

Rodrigo González Laiz, Tobias Schmidt, Steffen Schneider

Self-supervised learning (SSL) approaches have brought tremendous success across many tasks and domains. It has been argued that these successes can be attributed to a link between SSL and identifiable representation learning: Temporal structure and auxiliary variables ensure that latent representations are related to the true underlying generative factors o

Shaozhe Hao, Xuantong Liu, Xianbiao Qi, Shihao Zhao

We introduce BiGR, a novel conditional image generation model using compact binary latent codes for generative training, focusing on enhancing both generation and representation capabilities. BiGR is the first conditional generative model that unifies generation and discrimination within the same framework. BiGR features a binary tokenizer, a masked modeling

Danial Langeroodi, Jens Hjorth, Andrea Ferrara, Christa Gall

Interstellar dust links the formation of the first stars to the rocky planet we inhabit by playing a pivotal role in the cooling and fragmentation of molecular clouds, and catalyzing the formation of water and organic molecules. Despite its central role, the origin of dust and its formation timescale remain unknown. Some models favor rapid production in supe

Joshua Engels, Logan Riggs, Max Tegmark

Sparse autoencoders (SAEs) are a promising technique for decomposing language model activations into interpretable linear features. However, current SAEs fall short of completely explaining model performance, resulting in "dark matter": unexplained variance in activations. This work investigates dark matter as an object of study in its own right. Surprisingl

Baiqi Li, Zhiqiu Lin, Wenxuan Peng, Jean de Dieu Nyandwi

Vision-language models (VLMs) have made significant progress in recent visual-question-answering (VQA) benchmarks that evaluate complex visio-linguistic reasoning. However, are these models truly effective? In this work, we show that VLMs still struggle with natural images and questions that humans can easily answer, which we term natural adversarial samples

Xiongtao Zhou, Jie He, Lanyu Chen, Jingyu Li

Multimodal Chain of Thought (MCoT) is a popular prompting strategy for improving the performance of multimodal large language models (MLLMs) across a range of complex reasoning tasks. Despite its popularity, there is a notable absence of automated methods for evaluating the quality of reasoning steps in MCoT. To address this gap, we propose Multimodal Chain-

Peimeng Guan, Mark A. Davenport

Inverse problems aim to reconstruct unseen data from corrupted or perturbed measurements. While most work focuses on improving reconstruction quality, generalization accuracy and robustness are equally important, especially for safety-critical applications. Model-based architectures (MBAs), such as loop unrolling methods, are considered more interpretable an

Maitreya Prafulla Chitale, Uday Bindal, Rajakrishnan Rajkumar, Rahul Mishra

Summarizing movie screenplays presents a unique set of challenges compared to standard document summarization. Screenplays are not only lengthy, but also feature a complex interplay of characters, dialogues, and scenes, with numerous direct and subtle relationships and contextual nuances that are difficult for machine learning models to accurately capture an

Anay Pattanaik, Lav R. Varshney

This paper considers an online reinforcement learning algorithm that leverages pre-collected data (passive memory) from the environment for online interaction. We show that using passive memory improves performance and further provide theoretical guarantees for regret that turns out to be near-minimax optimal. Results show that the quality of passive memory

Shingo Akama, Shin'ichi Hirano, Shuichiro Yokoyama

It is known that stochastic gravitational wave backgrounds (SGWBs) have anisotropies generated by squeezed-type tensor non-Gaussianities originating from scalar-tensor-tensor (STT) and tensor-tensor-tensor cubic interactions. While the squeezed tensor non-Gaussianities in the standard slow-roll inflation with the Bunch-Davies vacuum state are suppressed due

Geoffrey Mo, Carl-Johan Haster, Erik Katsavounidis

A major challenge in gravitational-wave multi-messenger astrophysics is the imprecise localization of gravitational-wave compact binary mergers. We investigate the use of a method to include galaxy catalog information in performing parameter estimation of these events. We test its effectiveness with the gravitational-wave events GW170817, GW190425, and GW190

Jiaxin Lu, Yongqing Liang, Huijun Han, Jiacheng Hua

Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. The reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlyi

Louis Golowich, Ting-Chun Lin

For every integer $r\geq 2$ and every $\epsilon>0$, we construct an explicit infinite family of quantum LDPC codes supporting a transversal $C^{r-1}Z$ gate with length $N$, dimension $K\geq N^{1-\epsilon}$, distance $D\geq N^{1/r}/\operatorname{poly}(\log N)$, and stabilizer weight $w\leq\operatorname{poly}(\log N)$. The previous state of the art constructio

Qingtao Chen, Shengmao Zhu

This is the third article in a series devoted to the study of the asymptotic expansions of various quantum invariants related to the twist knots. In this paper, by using the saddle point method developed by Ohtsuki and Yokota, we obtain an asymptotic expansion formula for the Reshetikhin-Turaev invariants of closed hyperbolic 3-manifolds obtained by doing in

Shengjie Sun, Runze Liu, Jiafei Lyu, Jing-Wen Yang

Large Language Models (LLMs) have shown significant potential in designing reward functions for Reinforcement Learning (RL) tasks. However, obtaining high-quality reward code often involves human intervention, numerous LLM queries, or repetitive RL training. To address these issues, we propose CARD, a LLM-driven Reward Design framework that iteratively gener

Daiqi Gao, Hsin-Yu Lai, Predrag Klasnja, Susan A. Murphy

We consider reinforcement learning (RL) for a class of problems with bagged decision times. A bag contains a finite sequence of consecutive decision times. The transition dynamics are non-Markovian and non-stationary within a bag. All actions within a bag jointly impact a single reward, observed at the end of the bag. For example, in mobile health, multiple

Marco Abbadini, Francesca Guffanti

We describe the layer of quantifier alternation depth at most one of the quantifier completion of a Boolean doctrine over a small category. This amounts to a doctrinal version of Herbrand's theorem for formulas with quantifier alternation depth at most one modulo a universal theory. The resulting construction satisfies a universal property that makes it the

Nicolas Bousquet, Sébastien Zeitoun

In local certification, vertices of a $n$-vertex graph perform a local verification to check if a given property is satisfied by the graph. This verification is performed thanks to certificates, which are pieces of information that are given to the vertices. In this work, we focus on the local certification of $P_5$-freeness, and we prove a $O(n^{3/2})$ uppe

Li-Cheng Tsai

The Stochastic Heat Flow (SHF) emerges as the scaling limit of directed polymers in random environments and the noise-mollified Stochastic Heat Equation (SHE), specifically at the critical dimension of two and near the critical temperature. The prior work Caravenna Sun Zygouras (2023) established the first construction of finite-dimensional distributions by

Charles Paquette, Job Daisie Rock, Emine Yıldırım

We study the representation category of thread quivers and their quotients. A thread quiver is a quiver in which some arrows have been replaced by totally ordered sets. Pointwise finite-dimensional (pwf) representations of such a thread quiver admit a Krull-Remak-Schmidt-Azumaya decomposition. We show that an indecomposable representation is induced from an

Zhepeng Cen, Yao Liu, Siliang Zeng, Pratik Chaudhari

Language models are often trained to maximize the likelihood of the next token given past tokens in the training dataset. However, during inference time, they are utilized differently, generating text sequentially and auto-regressively by using previously generated tokens as input to predict the next one. Marginal differences in predictions at each step can

Fangjun Hu, Xun Gao

Tackling output sampling noise due to finite shots of quantum measurement is an unavoidable challenge when extracting information in machine learning with physical systems. A technique called Eigentask Learning was developed recently as a framework for learning with infinite input training data in the presence of output sampling noise. In the work of Eigenta

Colin Crowley, Tao Gong, Connor Simpson

Let $W$ be a finite group generated by reflections of a lattice $M$. If a lattice polytope $P \subset M \otimes_{\mathbb Z}\mathbb R$ is preserved by $W$, then we show that the quotient of the projective toric variety $X_P$ by $W$ is isomorphic to the toric variety $X_{P \cap D}$, where $D$ is a fundamental domain for the action of $W$. This answers a questi

Joey Li, Amos Chan, Thorsten B. Wahl

We numerically investigate the effect of coupling a two-dimensional many-body localized system to a finite heat bath, using shallow quantum circuits as a variational ansatz. Specifically, we simulate optical lattice experiments with two components of ultracold bosons, where only one species is subject to a random disorder potential and the other acts as a he

Sanxing Chen, Yukun Huang, Bhuwan Dhingra

We show that existing evaluations for assessing the factuality of news from conventional sources, such as claims on fact-checking websites, result in high accuracies over time for LLM-based detectors-even after their knowledge cutoffs. This suggests that recent popular false information from such sources can be easily identified due to its likely presence in

Pedro Terán, José M. Zapata

We prove by counterexample that a large deviation principle established by Chen and Feng [{\em Comm. Statist. Theory Methods} {\bf 45} (2016), 400--412] in the framework of sublinear expectations is incorrect. That implies that the rate function cannot, in general, be obtained by computing the Fenchel transform of the cumulant generating function, as is the

Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic, Dan Alistarh

The high computational costs of large language models (LLMs) have led to a flurry of research on LLM compression, via methods such as quantization, sparsification, or structured pruning. A new frontier in this area is given by dynamic, non-uniform compression methods, which adjust the compression levels (e.g., sparsity) per-block or even per-layer in order t

Mauricio Che, Fernando Galaz-García, Martin Kerin, Jaime Santos-Rodríguez

In this paper we study the isometric rigidity of certain classes of metric spaces with respect to the $p$-Wasserstein space. We prove that spaces that split a separable Hilbert space are not isometrically rigid with respect to $\mathbb{P}_2$. We then prove that infinite rays are isometrically rigid with respect to $\mathbb{P}_p$ for any $p\geq 1$, whereas ta

Henry W. Lin, Zechuan Zheng

The D0-brane/Banks-Fischler-Shenker-Susskind matrix theory is a strongly coupled quantum system with an interesting gravity dual. We develop a scheme to derive bootstrap bounds on simple correlators in the matrix theory at infinite $N$ at zero energy by imposing the supercharge equations of motion. By exploiting SO(9) symmetry, we are able to consider single

Guohui Cai, Ying Cai, Zeyu Zhang, Yuanzhouhan Cao

Lung cancer remains one of the leading causes of morbidity and mortality worldwide, making early diagnosis critical for improving therapeutic outcomes and patient prognosis. Computer-aided diagnosis systems, which analyze computed tomography images, have proven effective in detecting and classifying pulmonary nodules, significantly enhancing the detection ra

Rishemjit Kaur, Shuchen Zhang, Bhavika Berwal, Sonalika Ray

Herbs and spices each contain about 3000 phytochemicals on average and there is much traditional knowledge on their health benefits. However, there is a lack of systematic study to understand the relationship among herbs and spices, their phytochemical constituents, their potential health benefits, and their usage in regional cuisines. Here we use a network-

G. D. Rublev, S. A. Murzov

This paper introduces a novel methodology for modeling stationary shock waves in porous materials, which employs the recently developed moving window technique. The core of this method is the iterative adjustment of the reference frame to the boundary conditions that regulate the entry and exit of Lagrangian particles from a fixed computational domain, which

Geoffrey Mo, Kishalay De, Eli Wiston, Nayana A. J.

Type Ia supernovae arise from the thermonuclear explosions of white dwarfs in multiple star systems. A rare sub-class of SNe Ia exhibit signatures of interaction with circumstellar material (CSM), allowing for direct constraints on companion material. While most known events show evidence for dense nearby CSM identified via peak-light spectroscopy (as SNe Ia

Shyam Narayanan, Václav Rozhoň, Jakub Tětek, Mikkel Thorup

Suppose we have a memory storing $0$s and $1$s and we want to estimate the frequency of $1$s by sampling. We want to do this I/O-efficiently, exploiting that each read gives a block of $B$ bits at unit cost; not just one bit. If the input consists of uniform blocks: either all 1s or all 0s, then sampling a whole block at a time does not reduce the number of

Rang Liu, Ming Li, Qian Liu

In this paper, we explore cooperative sensing and communication within cell-free integrated sensing and communication (ISAC) systems. Specifically, multiple transmit access points (APs) collaboratively serve multiple communication users while simultaneously illuminating a potential target, with a separate sensing AP dedicated to collecting echo signals for t

Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng

Positional bias in large language models (LLMs) hinders their ability to effectively process long inputs. A prominent example is the "lost in the middle" phenomenon, where LLMs struggle to utilize relevant information situated in the middle of the input. While prior research primarily focuses on single pieces of relevant information, real-world applications

Junyu Cao, Ruijiang Gao, Esmaeil Keyvanshokooh

Human doctors frequently recommend actionable recourses that allow patients to modify their conditions to access more effective treatments. Inspired by such healthcare scenarios, we propose the Recourse Linear UCB ($\textsf{RLinUCB}$) algorithm, which optimizes both action selection and feature modifications by balancing exploration and exploitation. We furt

David R. Johnson, Joyce Chew, Siddharth Viswanath, Edward De Brouwer

In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). The filter-combine framework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and naturally suggests many interesting families of MNNs which can be interpreted as the manifold analog of various popular GNNs. W

Bernhard Haeupler, Richard Hladík, Vaclav Rozhon, Robert E. Tarjan

Although Dijkstra's algorithm has near-optimal time complexity for the problem of finding a shortest path from a given vertex $s$ to a given vertex $t$, in practice other algorithms are often superior on huge graphs. A prominent example is bidirectional search, which concurrently executes Dijkstra's algorithm forward from $s$ and backward from $t$, and stops

Asif Manzoor Hasan, Alex Jose Elias, Florian Menter, Rene Pecnik

We introduce a novel approach to derive compressibility corrections for Reynolds-averaged Navier-Stokes (RANS) models. Using this approach, we derive variable-property corrections for wall-bounded flows that take into account the distinct scaling characteristics of the inner and outer layers, extending the earlier work of Otero Rodriguez et al. [Int. J. Heat

Joel T. Clemmer, Jeremy B. Lechman

The role of plastic deformation in the high-pressure compaction of granular material is investigated using bonded particle model simulations. Grains are discretized into a set of computational particles connected by pairwise bonds. Bonds are harmonic up to a plastic onset strain $\epsilon_p$ above which they yield, capping out at a maximum force and producin

Raghuveer Thirukovalluru, Bhuwan Dhingra

Training-free embedding methods directly leverage pretrained large language models (LLMs) to embed text, bypassing the costly and complex procedure of contrastive learning. Previous training-free embedding methods have mainly focused on optimizing embedding prompts and have overlooked the benefits of utilizing the generative abilities of LLMs. We propose a n

Sandeep Nagar, Girish Varma

The inverse of an invertible convolution is an important operation that comes up in Normalizing Flows, Image Deblurring, etc. The naive algorithm for backpropagation of this operation using Gaussian elimination has running time $O(n^3)$ where $n$ is the number of pixels in the image. We give a fast parallel backpropagation algorithm with running time $O(\sqr

Yuxiang Lu, Shengcao Cao, Yu-Xiong Wang

Vision Foundation Models (VFMs) have demonstrated outstanding performance on numerous downstream tasks. However, due to their inherent representation biases originating from different training paradigms, VFMs exhibit advantages and disadvantages across distinct vision tasks. Although amalgamating the strengths of multiple VFMs for downstream tasks is an intu

Michael JQ Zhang, Zhilin Wang, Jena D. Hwang, Yi Dong

We examine diverging preferences in human-labeled preference datasets. We develop a taxonomy of disagreement sources spanning ten categories across four high-level classes and find that the majority of disagreements are due to factors such as task underspecification or response style. Our findings challenge a standard assumption in reward modeling methods th

Aarsh Shekhar, Mincheol Kim

Each year, the lack of efficient data standardization and interoperability in cancer care contributes to the severe lack of timely and effective diagnosis, while constantly adding to the burden of cost, with cancer costs nationally reaching over $208 billion in 2023 alone. Traditional methods regarding clinical trial enrollment and clinical care in oncology

Ting-Chun Lin

A major goal in quantum computing is to build a fault-tolerant quantum computer. One approach involves quantum low-density parity-check (qLDPC) codes that support transversal non-Clifford gates. In this work, we provide a large family of such codes. The key insight is to interpret the logical operators of qLDPC codes as geometric surfaces and use the interse

Luca Butera, Giovanni De Felice, Andrea Cini, Cesare Alippi

In forecasting multiple time series, accounting for the individual features of each sequence can be challenging. To address this, modern deep learning methods for time series analysis combine a shared (global) model with local layers, specific to each time series, often implemented as learnable embeddings. Ideally, these local embeddings should encode meanin

Chuang Yang, Renhe Jiang, Xiaohang Xu, Chuan Xiao

Free-space trajectory similarity calculation, e.g., DTW, Hausdorff, and Frechet, often incur quadratic time complexity, thus learning-based methods have been proposed to accelerate the computation. The core idea is to train an encoder to transform trajectories into representation vectors and then compute vector similarity to approximate the ground truth. How

Salvatore Baldino

In this thesis, we develop WKB techniques for the finite difference Schrodinger equation, following the construction of the WKB approach for the standard differential Schrodinger equation. In particular, we will develop an all-order WKB algorithm to get arbitrary hbar-corrections and construct a general quantum momentum, underlining the various properties of

Jan-Samuel Wagner, Dave DeCaprio, Abishek Chiffon Muthu Raja, Jonathan M. Holman

We introduce Controller-Embedded Language Model Interactions (CELI), a framework that integrates control logic directly within language model (LM) prompts, facilitating complex, multi-stage task execution. CELI addresses limitations of existing prompt engineering and workflow optimization techniques by embedding control logic directly within the operational

Daniel Baumartz, Mevlüt Bagci, Alexander Henlein, Maxim Konca

If sentiment analysis tools were valid classifiers, one would expect them to provide comparable results for sentiment classification on different kinds of corpora and for different languages. In line with results of previous studies we show that sentiment analysis tools disagree on the same dataset. Going beyond previous studies we show that the sentiment to

Chun Yin Kong, Picasso Vasquez, Makan Farhoodimoghadam, Chris Brandt

In the rapidly evolving landscape of veterinary healthcare, integrating machine learning (ML) clinical decision-making tools with electronic health records (EHRs) promises to improve diagnostic accuracy and patient care. However, the seamless integration of ML classifiers into existing EHRs in veterinary medicine is frequently hindered by the rigidity of EHR

Huy Tuan Pham, Dmitrii Zakharov

A set of integers $A$ is non-averaging if there is no element $a$ in $A$ which can be written as an average of a subset of $A$ not containing $a$. We show that the largest non-averaging subset of $\{1, \ldots, n\}$ has size $n^{1/4+o(1)}$, thus solving the Erd\H{o}s-Straus problem. We also determine the largest size of a non-averaging set in a $d$-dimensiona

Ce Sui, Deaglan J. Bartlett, Shivam Pandey, Harry Desmond

Current and future large scale structure surveys aim to constrain the neutrino mass and the equation of state of dark energy. We aim to construct accurate and interpretable symbolic approximations to the linear and nonlinear matter power spectra as a function of cosmological parameters in extended $\Lambda$CDM models which contain massive neutrinos and non-c

Mikael Berggren, Maria Teresa Núñez Pardo de Vera, Jenny List

The direct pair-production of the superpartner of the $\tau$-lepton, the $\widetilde{\tau}$, is one of the most interesting channels to search for SUSY in: the $\widetilde{\tau}$ is likely to be the lightest of the scalar leptons, and is one of the most experimentally chalanging ones. The current model-independent $\widetilde{\tau}$ limits come from LEP, whi

Ameya Daigavane, Bodhi P. Vani, Darcy Davidson, Saeed Saremi

Conformational ensembles of protein structures are immensely important both for understanding protein function and drug discovery in novel modalities such as cryptic pockets. Current techniques for sampling ensembles such as molecular dynamics (MD) are computationally inefficient, while many recent machine learning methods do not transfer to systems outside

Morteza Ghaderi Aram, Hao Guo, Mingsheng Yin, Tommy Svensson

Digital twinning is becoming increasingly vital in the design and real-time control of future wireless networks by providing precise cost-effective simulations, predictive insights, and real-time data integration. This paper explores the application of digital twinning in optimizing wireless communication systems within urban environments, where building arr

Jenny Vi Abrajano, Khavee Agustus Botangen, Jovith Nabua, Jenalyn Apanay

Contaminated and polluted water poses significant threats to human health, necessitating vigilant monitoring of water sources for potential contamination. This paper introduces a low-cost Internet of Things (IoT)-based water quality monitoring system designed to address water quality challenges in rural communities, as demonstrated through a case study condu

Simone Baldassarri, Peter Braunsteins, Frank den Hollander, Michel Mandjes

We consider two-opinion voter models on dense dynamic random graphs. Our goal is to understand and describe the occurrence of consensus versus polarisation over long periods of time. The former means that all vertices have the same opinion, the latter means that the vertices split into two communities with different opinions and few disagreeing edges. We con

Piotr Sapiezynski, Levi Kaplan, Alan Mislove, Aleksandra Korolova

Detailed targeting of advertisements has long been one of the core offerings of online platforms. Unfortunately, malicious advertisers have frequently abused such targeting features, with results that range from violating civil rights laws to driving division, polarization, and even social unrest. Platforms have often attempted to mitigate this behavior by r

Mariusz Wisniewski, Paraskevas Chatzithanos, Weisi Guo, Antonios Tsourdos

Deep Reinforcement learning (DRL) is used to enable autonomous navigation in unknown environments. Most research assume perfect sensor data, but real-world environments may contain natural and artificial sensor noise and denial. Here, we present a benchmark of both well-used and emerging DRL algorithms in a navigation task with configurable sensor denial eff

Arman Adibi, Sanjeev Kulkarni, H. Vincent Poor, Taposh Banerjee

This paper addresses the problem of detecting changes when only unnormalized pre- and post-change distributions are accessible. This situation happens in many scenarios in physics such as in ferromagnetism, crystallography, magneto-hydrodynamics, and thermodynamics, where the energy models are difficult to normalize. Our approach is based on the estimation o

A. K. Shargh, C. D. Stiles, J. A. El-Awady

Single-phase body-centered cubic (BCC) refractory multi-principal element alloys (RMPEAs) offer potential for developing alloys with exceptional strength. However, the compositional design space is immense. Exhaustively mapping this space with conventional CALculation of PHAse Diagrams (CALPHAD) is impractical because database coverage and run times scale po

Peter Græns Larsen, Anne E. B. Nielsen, André Eckardt, Francesco Petiziol

Quantum many-body scars are energy eigenstates which fail to reproduce thermal expectation values of local observables, in systems where the rest of the many-body spectrum fulfils eigenstate thermalization. Experimental observation of quantum many-body scars has so far been limited to models with multiple scar states evenly spaced in energy. It is thus an in

Christina Bukas, Harshavardhan Subramanian, Fenja See, Carina Steinchen

High-throughput image analysis in the biomedical domain has gained significant attention in recent years, driving advancements in drug discovery, disease prediction, and personalized medicine. Organoids, specifically, are an active area of research, providing excellent models for human organs and their functions. Automating the quantification of organoids in

Zhenzhen He, Guang-Xing Li

Extracting information from complex data is a challenge shared by multiple frontiers of modern astrophysical research. Among those, analyzing spectra cubes, where the emission is mapped in the position-position-velocity space is a difficult task given the vast amount of information contained within. The cubes often contain a superposition of emissions and ab

Guang-Xing Li, Mengke Zhao, Xing Lu

Understanding the fragmentation of the gas cloud and the formation of massive stars remains one of the most challenging questions of modern astrophysical research. Either the gas fragments in a Jeans-like fashion, after which the fragments grow through accretion, or the fragmentation length is larger than the Jeans length from the start. Despite significant

Simon Lupart, Mohammad Aliannejadi, Evangelos Kanoulas

Conversational Search (CS) involves retrieving relevant documents from a corpus while considering the conversational context, integrating retrieval with context modeling. Recent advancements in Large Language Models (LLMs) have significantly enhanced CS by enabling query rewriting based on conversational context. However, employing LLMs during inference pose

Timothy Heightman, Grzegorz Rajchel-Mieldzioć

The ability to characterise and discern quantum channels is a crucial aspect of noisy quantum technologies. In this work, we explore the problem of distinguishing quantum channels when limited to sub-exponential resources, framed as von Neumann (projective) measurements. We completely characterise equivalence classes of quantum channels with different Kraus

Rui Pu, Chaozhuo Li, Rui Ha, Zejian Chen

Jailbreak attack can be used to access the vulnerabilities of Large Language Models (LLMs) by inducing LLMs to generate the harmful content. And the most common method of the attack is to construct semantically ambiguous prompts to confuse and mislead the LLMs. To access the security and reveal the intrinsic relation between the input prompt and the output f

Suleiman Saka, Sanchari Das

The widespread adoption of telehealth systems has led to a significant increase in the use of healthcare apps among older adults, but this rapid growth has also heightened concerns about the privacy of their health information. While HIPAA in the US and GDPR in the EU establish essential privacy protections for health information, limited research exists on

Mohamed Elsayed, Gautham Vasan, A. Rupam Mahmood

Natural intelligence processes experience as a continuous stream, sensing, acting, and learning moment-by-moment in real time. Streaming learning, the modus operandi of classic reinforcement learning (RL) algorithms like Q-learning and TD, mimics natural learning by using the most recent sample without storing it. This approach is also ideal for resource-con

Elham Gorgannejad, Wouter Deconinck, David S. Armstrong

The precise measurement of parity-violating asymmetries in parity-violating electron scattering experiments is a powerful tool for probing new physics beyond the Standard Model. Achieving the expected precision requires both experimental and post-processing signal corrections. This includes using auxiliary detectors to distinguish the main signal from backgr

Nasser Abdo Saeed Bulkhali, Zhi-Wei Sun

An integer-valued polynomial $P(x,y,z)$ is said to be universal (over $\mathbb Z$) if each nonnegative integer can be written as $P(x,y,z)$ with $x,y,z\in\mathbb Z$. In this paper, we mainly introduce a new technique to determine the universality of some sums in the form $x(a_1x+a_2)/2+y(b_1y+b_2)/2+z(c_1z+c_2)/2$ (with $a_1-a_2,b_1-b_2,c_1-c_2$ all even) co

Shih-Hsin Wang, Justin Baker, Cory Hauck, Bao Wang

The pioneering work of Oono and Suzuki [ICLR, 2020] and Cai and Wang [arXiv:2006.13318] initializes the analysis of the smoothness of graph convolutional network (GCN) features. Their results reveal an intricate empirical correlation between node classification accuracy and the ratio of smooth to non-smooth feature components. However, the optimal ratio that

Guang-Xing Li, Ji-Xuan Zhou, Bing-Qiu Chen

The Radcliffe wave \cite{2020Natur.578..237A} is a 2.7 kpc long, 100 pc wide-like structure in the Galactic disk with a wave-like velocity structure \cite{2022MNRAS.517L.102L,2024arXiv240212596K}. A referent Nature paper \cite{2024arXiv240212596K} treated the Wave as a solid body in the disk plane, modeled its oscillation along the vertical direction, and de

Yang Ba, Michelle V. Mancenido, Rong Pan

Data augmentation that introduces diversity into the input data has long been used in training deep learning models. It has demonstrated benefits in improving robustness and generalization, practically aligning well with other regularization strategies such as dropout and weight decay. However, the underlying mechanism of how diverse training data contribute

Ziyang Liu, Nikos Zygouras

The Critical $2d$ Stochastic Heat Flow (SHF) is a measure valued stochastic process on $\mathbb{R}^2$ that defines a non-trivial solution to the two-dimensional stochastic heat equation with multiplicative space-time noise. Its one-time marginals are a.s. singular with respect to the Lebesgue measure, meaning that the mass they assign to shrinking balls deca

Yinghui Wang, Yilong Ren, Hongmao Qin, Zhiyong Cui

Cyber attacks have become a vital threat to connected autonomous vehicles in intelligent transportation systems. Cyber threat intelligence, as the collection of cyber threat information, provides an ideal approach for responding to emerging vehicle cyber threats and enabling proactive security defense. Obtaining valuable information from enormous cybersecuri

Vincent Eberle, Matteo Guardiani, Margret Westerkamp, Philipp Frank

The eROSITA Early Data Release (EDR) and eROSITA All-Sky Survey (eRASS1) data have already revealed a remarkable number of undiscovered X-ray sources. Using Bayesian inference and generative modeling techniques for X-ray imaging, we aim to increase the sensitivity and scientific value of these observations by denoising, deconvolving, and decomposing the X-ra

Stefano Bolognesi, Kenichi Konishi, Andrea Luzio, Matteo Orso

We study the strong-interaction dynamics of a class of $4D$ chiral $SU(N)$ gauge theories with a fermion in a symmetric second-rank tensor representation and a number of fermions in an anti-antisymmetric tensor representation, extending the previous work on chiral gauge theories such as the Bars-Yankielowicz and the generalized Georgi-Glashow models. The mai

Jacob Golomb, Isaac Legred, Katerina Chatziioannou, Philippe Landry

Neutron star properties depend on both nuclear physics and astrophysical processes, and thus observations of neutron stars offer constraints on both large-scale astrophysics and the behavior of cold, dense matter. In this study, we use astronomical data to jointly infer the universal equation of state of dense matter along with two distinct astrophysical pop

Elias Stengel-Eskin, Peter Hase, Mohit Bansal

Large language models (LLMs) are susceptible to persuasion, which can pose risks when models are faced with an adversarial interlocutor. We take a first step towards defending models against persuasion while also arguing that defense against adversarial (i.e. negative) persuasion is only half of the equation: models should also be able to accept beneficial (

Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus, Daniel Puiu Poenar

Image dehazing is crucial for clarifying images obscured by haze or fog, but current learning-based approaches is dependent on large volumes of training data and hence consumed significant computational power. Additionally, their performance is often inadequate under non-uniform or heavy haze. To address these challenges, we developed the Detail Recovery And

Elias Lumer, Vamse Kumar Subbiah, James A. Burke, Pradeep Honaganahalli Basavaraju

Recent advancements in tool-equipped Agents (LLMs) have enabled complex tasks like secure database interactions and multi-agent code development. However, scaling tool capacity beyond agent reasoning or model limits remains a challenge. In this paper, we address these challenges by introducing Toolshed Knowledge Bases, a tool knowledge base (vector database)

Edith Elkind, Alexander Lam, Mohamad Latifian, Tzeh Yuan Neoh

We study a fair division model where indivisible items arrive sequentially, and must be allocated immediately and irrevocably. Previous work on online fair division has shown impossibility results in achieving approximate envy-freeness under these constraints. In contrast, we consider an informed setting where the algorithm has complete knowledge of future i

Colin Dirren, Mattia Bianchi, Panagiotis D. Grontas, John Lygeros

We study the convex-concave bilinear saddle-point problem $\min_x \max_y f(x) + y^\top Ax - g(y)$, where both, only one, or none of the functions $f$ and $g$ are strongly convex, and suitable rank conditions on the matrix $A$ hold. The solution of this problem is at the core of many machine learning tasks. By employing tools from monotone operator theory, we

Francesca Bartolucci, Marcello Carioni, José A. Iglesias, Yury Korolev

We revisit the mean field parametrization of shallow neural networks, using signed measures on unbounded parameter spaces and duality pairings that take into account the regularity and growth of activation functions. This setting directly leads to the use of unbalanced Kantorovich-Rubinstein norms defined by duality with Lipschitz functions, and of spaces of

Elizabeth Wilson, Mika Satomi, Alex McLean, Deva Schubert

In this paper, we explore how performers' embodied interactions with a Neural Audio Synthesis model allow the exploration of the latent space of such a model, mediated through movements sensed by e-textiles. We provide background and context for the performance, highlighting the potential of embodied practices to contribute to developing explainable AI syste

Javier Bernal, Jim Lawrence

Algorithms based on gradient descent for computing the elastic shape registration of two simple surfaces in 3-dimensional space and therefore the elastic shape distance between them have been proposed by Kurtek, Jermyn, et al., and more recently by Riseth. Their algorithms are designed to minimize a distance function between the surfaces by rotating and repa

Ryan Soh-Eun Shim, Barbara Plank

There is increasing interest in looking at dialects in NLP. However, most work to date still treats dialects as discrete categories. For instance, evaluative work in variation-oriented NLP for English often works with Indian English or African-American Venacular English as homogeneous categories (Faisal et al., 2024; Ziems et al., 2023), yet even within one

Jessica Dai, Nika Haghtalab, Eric Zhao

A canonical desideratum for prediction problems is that performance guarantees should hold not just on average over the population, but also for meaningful subpopulations within the overall population. But what constitutes a meaningful subpopulation? In this work, we take the perspective that relevant subpopulations should be defined with respect to the clus

Namid R. Stillman, Rory Baggott

Deep generative models are becoming increasingly used as tools for financial analysis. However, it is unclear how these models will influence financial markets, especially when they infer financial value in a semi-autonomous way. In this work, we explore the interplay between deep generative models and market dynamics. We develop a form of virtual traders th

Baran Atalar, Carlee Joe-Wong

We consider the contextual combinatorial bandit setting where in each round, the learning agent, e.g., a recommender system, selects a subset of "arms," e.g., products, and observes rewards for both the individual base arms, which are a function of known features (called "context"), and the super arm (the subset of arms), which is a function of the base arm