October 2024 arXiv papers — page 14

Siyu Wang, Kehui Yao

Traditional decision trees are limited by axis-orthogonal splits, which can perform poorly when true decision boundaries are oblique. While oblique decision tree methods address this limitation, they often face high computational costs, difficulties with multi-class classification, and a lack of effective feature selection. In this paper, we introduce LDATre

Midas Segers, Enrico Skoruppa, Helmut Schiessel, Enrico Carlon

A stretched DNA molecule which is also under- or overwound, undergoes a buckling transition forming intertwined looped domains called plectonemes. Here we develop a simple theory that extends the two-phase model of stretched supercoiled DNA, allowing for the coexistence of multiple plectonemic domains by including positional and length distribution entropies

Jordan Meyer, Nick Padgett, Cullen Miller, Laura Exline

We present Public Domain 12M (PD12M), a dataset of 12.4 million high-quality public domain and CC0-licensed images with synthetic captions, designed for training text-to-image models. PD12M is the largest public domain image-text dataset to date, with sufficient size to train foundation models while minimizing copyright concerns. Through the Source.Plus plat

Haimanti Bhattacharya, Subhasish Dugar, Sanchaita Hazra, Bodhisattwa Prasad Majumder

We investigate how low-quality AI advisors, lacking quality disclosures, can help spread text-based lies while seeming to help people detect lies. Participants in our experiment discern truth from lies by evaluating transcripts from a game show that mimicked deceptive social media exchanges on topics with objective truths. We find that when relying on low-qu

Tejaswini Medi, Steffen Jung, Margret Keuper

Deep neural networks are susceptible to adversarial attacks and common corruptions, which undermine their robustness. In order to enhance model resilience against such challenges, Adversarial Training (AT) has emerged as a prominent solution. Nevertheless, adversarial robustness is often attained at the expense of model fairness during AT, i.e., disparity in

Ziang Yan, Angus H. Wright, Nora Elisa Chisari, Christos Georgiou

Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the high number density and extensive depth of the data. However, the complicated selection effects in wide and deep surveys can potentially cause significant bias in the angular two-poi

MAGIC Collaboration, K. Abe, S. Abe, J. Abhir

We study a flaring activity of the HSP Mrk421 that was characterized from radio to very-high-energy (VHE; E $>0.1$TeV) gamma rays with MAGIC, Fermi-LAT, Swift, XMM-Newton and several optical and radio telescopes. These observations included, for the first time for a gamma-ray flare of a blazar, simultaneous X-ray polarization measurements with IXPE. We find

Mark G. Raizen, Aaron D. Barr

A new method for efficient isotope separation is proposed. It is based on efficient photoionization of atoms by a continuous-wave laser using resonant-enhancement in an ultra-large volume optical cavity. This method should enable higher efficiency than the existing state of the art and could be used as an alternative to radiochemistry. It should also allow s

Changhong Mou, Samuel N. Stechmann, Nan Chen

Sea ice plays a crucial role in the climate system, particularly in the Marginal Ice Zone (MIZ), a transitional area consisting of fragmented ice between the open ocean and consolidated pack ice. As the MIZ expands, understanding its dynamics becomes essential for predicting climate change impacts. However, the role of clouds in these processes has been larg

Siddharth Barman, Soroush Ebadian, Mohamad Latifian, Nisarg Shah

We introduce a model of fair division with market values, where indivisible goods must be partitioned among agents with (additive) subjective valuations, and each good additionally has a market value. The market valuation can be viewed as a separate additive valuation that holds identically across all the agents. We seek allocations that are simultaneously f

Keqin Bao, Ming Yan, Yang Zhang, Jizhi Zhang

Frequently updating Large Language Model (LLM)-based recommender systems to adapt to new user interests -- as done for traditional ones -- is impractical due to high training costs, even with acceleration methods. This work explores adapting to dynamic user interests without any model updates by leveraging In-Context Learning (ICL), which allows LLMs to lear

Mihai I. Florea

The gradient mapping norm is a strong and easily verifiable stopping criterion for first-order methods on composite problems. When the objective exhibits the quadratic growth property, the gradient mapping norm minimization problem can be solved by online parameter-free and adaptive first-order schemes with near-optimal worst-case rates. In this work we addr

Özgür Akarsu, Eoin Ó Colgáin, Anjan A. Sen, M. M. Sheikh-Jabbari

In Adil et al. 2023, we reported an increasing trend in $S_8$ with effective redshift $z_{\textrm{eff}}$ based on $f \sigma_8(z)$ constraints over the redshift range $0 \lesssim z \lesssim 2$, and predicted that this trend would be observable in independent datasets. Recently, the studies by Artis et al. and the ACT+DESI collaboration appeared, presenting da

Hadi Khalilia, Jahna Otterbacher, Gabor Bella, Shandy Darma

Lexical-semantic resources (LSRs), such as online lexicons and wordnets, are fundamental to natural language processing applications as well as to fields such as linguistic anthropology and language preservation. In many languages, however, such resources suffer from quality issues: incorrect entries, incompleteness, but also the rarely addressed issue of bi

Tassilo Wald, Constantin Ulrich, Stanislav Lukyanenko, Andrei Goncharov

Self-Supervised Learning (SSL) presents an exciting opportunity to unlock the potential of vast, untapped clinical datasets, for various downstream applications that suffer from the scarcity of labeled data. While SSL has revolutionized fields like natural language processing and computer vision, its adoption in 3D medical image computing has been limited by

Michael Crawshaw, Mingrui Liu

In federated learning, it is common to assume that clients are always available to participate in training, which may not be feasible with user devices in practice. Recent works analyze federated learning under more realistic participation patterns, such as cyclic client availability or arbitrary participation. However, all such works either require strong a

Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney

Cardiac image segmentation is essential for automated cardiac function assessment and monitoring of changes in cardiac structures over time. Inspired by coarse-to-fine approaches in image analysis, we propose a novel multitask compositional segmentation approach that can simultaneously localize the heart in a cardiac image and perform part-based segmentation

Guan Zhe Hong, Yin Cui, Ariel Fuxman, Stanley Chan

Recent studies show that pretraining a deep neural network with fine-grained labeled data, followed by fine-tuning on coarse-labeled data for downstream tasks, often yields better generalization than pretraining with coarse-labeled data. While there is ample empirical evidence supporting this, the theoretical justification remains an open problem. This paper

Vivien Sainte Fare Garnot, Lynsay Spafford, Jelle Lever, Christian Sigg

Phenology, the timing of cyclical plant life events such as leaf emergence and coloration, is crucial in the bio-climatic system. Climate change drives shifts in these phenological events, impacting ecosystems and the climate itself. Accurate phenology models are essential to predict the occurrence of these phases under changing climatic conditions. Existing

Di Ni, Hungtang Ko, Radhika Nagpal

The schooling behavior of fish is hypothesized to confer many survival benefits, including foraging success, safety from predators, and energy savings through hydrodynamic interactions when swimming in formation. Underwater robot collectives may be able to achieve similar benefits in future applications, e.g. using formation control to achieve efficient spat

Kazutomo Yoshii, Mohamed El-Hadedy

The advent of chiplet technology introduces cutting-edge opportunities for constructing highly heterogeneous platforms with specialized accelerators. However, the HPC community currently lacks expertise in hardware development, a gap that must be bridged to leverage these advancements. Additionally, technologies like chiplet is cutting-edge with limited educ

Jerry Yao-Chieh Hu, Dennis Wu, Han Liu

We study the optimal memorization capacity of modern Hopfield models and Kernelized Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories. We present a tight analysis by establishing a connection between the memory configuration of KHMs and spherical codes from information theory. Specifically, we treat the stored memory set as

Anudeep Surendran, David Pinto-Ramos, Rafael Menezes, Ricardo Martinez-Garcia

Spatial patterning is common in ecological systems and has been extensively studied via different modeling approaches. Individual-based models (IBMs) accurately describe nonlinear interactions at the organism level and the stochastic spatial dynamics that drives pattern formation, but their computational cost scales quickly with system complexity, limiting t

Emanuel Taschner, Georgios Deskos, Michael B. Kuhn, Jan-Willem van Wingerden

The actuator line method (ALM) is an approach commonly used to represent lifting and dragging devices like wings and blades in large-eddy simulations (LES). The crux of the ALM is the projection of the actuator point forces onto the LES grid by means of a Gaussian regularisation kernel. The minimum width of the kernel is constrained by the grid size; however

Chulin Xie, Yangsibo Huang, Chiyuan Zhang, Da Yu

Large language models (LLMs) achieve good performance on challenging reasoning benchmarks, yet could also make basic reasoning mistakes. This contrasting behavior is puzzling when it comes to understanding the mechanisms behind LLMs' reasoning capabilities. One hypothesis is that the increasingly high and nearly saturated performance on common reasoning benc

Mohammad Harakeh, Michael Ban, Géry de Saxcé

In a previous paper, we proposed a symplectic version of Brezis-Ekeland-Nayroles principle based on the concepts of Hamiltonian inclusions and symplectic polar functions. We applied it to the standard plasticity. The object of this work is to extend the previous formalism to non associated plasticity. For this aim, we generalize the concept of bipotential to

P. Virtanen, R. P. S. Penttilä, P. Törmä, A. Díez-Carlón

We analyze the properties of flat-band superconductor junctions that behave differently from ordinary junctions containing only metals with Fermi surfaces. In particular, we show how in the tunneling limit the critical Josephson current between flat-band superconductors is inversely proportional to the pair potential, how the quantum geometric contribution t

Yibo Wu, Musa Furkan Keskin, Ulf Gustavsson, Gonzalo Seco-Granados

Distributed massive multiple-input multiple-output networks utilize a large number of distributed access points (APs) to serve multiple user equipments (UEs), offering significant potential for both communication and localization. However, these networks require frequent phase and time calibration between distributed antennas due to oscillator phase drifts,

M. I. Ayzatsky

In this work we present the results of calculation of the electric field distribution in the inhomogeneous accelerating section on the base of generalized coupled modes theory. It was shown that the proposed coupled differential equations correctly describe electromagnetic field distribution in the regular part of the section under consideration, which is an

Chris Achard

Language models can achieve high accuracy on natural language tasks such as NLI, but performance suffers on manually created adversarial examples. We investigate the performance of a language model trained on the Stanford Natural Language Inference (SNLI) corpus on a manually created adversarial test set. We then improve the model's performance by fine tunin

Tamas Gombor, Charlotte Kristjansen, Vasileios Moustakis, Xin Qian

Invoking a quantum dressing procedure as well as the representation theory of twisted Yangians we derive a number of summation formulas for the overlap between integrable matrix product states and Bethe eigenstates which involve only eigenvalues of fused transfer matrices and which are valid in the presence of inhomogeneities as well as twists. Although the

Wentao Fan, Hakan E. Türeci

Model order reduction encompasses mathematical techniques aimed at reducing the complexity of mathematical models in simulations while retaining the essential characteristics and behaviors of the original model. This is particularly useful in the context of large-scale dynamical systems, which can be computationally expensive to analyze and simulate. Here, w

Samuel Meyer, Ferdinand Wagner

As a consequence of Efimov's proof of rigidity of the $\infty$-category of localising motives, Efimov and Scholze have constructed refinements of localising invariants such as $\operatorname{THH}$ and $\operatorname{TC}^-$. These refinements often contain vastly more information than the original invariant. In this article we explain a general recipe how to

Junjie Wu, Tsz Ting Chung, Kai Chen, Dit-Yan Yeung

Despite the outstanding performance in vision-language reasoning, Large Vision-Language Models (LVLMs) might generate hallucinated contents that do not exist in the given image. Most existing LVLM hallucination benchmarks are constrained to evaluate the object-related hallucinations. However, the potential hallucination on the relations between two objects,

E. F. Zhou, C. R. Ding, J. M. Yao, B. Bally

We extend a nuclear ab initio framework based on chiral two- and three-nucleon interactions to investigate shape coexistence and the degradation of the $N=20$ magic number in both even-even and odd-even magnesium isotopes. The quantum-number projected generator coordinate method, combined with the in-medium similarity renormalization group (IMSRG), is employ

R. K. Singh, Stanislav Burov

In transport processes across materials like glasses, living cells, and porous media, the probability density function of displacements exhibits exponential decay rather than Gaussian behavior. We show that this universal behavior of rare events, termed Laplace tails, emerges even when correlations and memory affect the dynamics. Using a renormalization-base

Navyansh Mahla, Kshitij Sharad Jadhav, Ganesh Ramakrishnan

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, particularly in task generalization for both text and vision data. While fine-tuning these models can significantly enhance their performance on specific downstream tasks, it often requires high-quality data that cannot be shared due to privacy concerns. Federated

Matthew Dawson, Vishwa Dewage, Mishko Mitkovski, Gestur Olafsson

We study quantum harmonic analysis (QHA) on the Bergman space $\mathcal{A}^2(\mathbb{B}^n)$ over the unit ball in $\mathbb{C}^n$. We formulate a Wiener's Tauberian theorem, and characterizations of the radial Toeplitz algebra over $\mathcal{A}^2(\mathbb{B}^n)$. We discuss the $\alpha$-Berezin transform and investigate the question of approximations by Toepli

Hongbo Li, Haikuan Zhu, Sikai Zhong, Ningna Wang

This paper introduces a new learning-based method, NASM, for anisotropic surface meshing. Our key idea is to propose a graph neural network to embed an input mesh into a high-dimensional (high-d) Euclidean embedding space to preserve curvature-based anisotropic metric by using a dot product loss between high-d edge vectors. This can dramatically reduce the c

Mahsa Bazzaz, Seth Cooper

Generative Adversarial Networks (GANs) are unsupervised models designed to learn and replicate a target distribution. The vanilla versions of these models can be extended to more controllable models. Conditional Generative Adversarial Networks (CGANs) extend vanilla GANs by conditioning both the generator and discriminator on some additional information (lab

Tassilo Wald, Constantin Ulrich, Gregor Köhler, David Zimmerer

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to their internal high dimensionality and complexity. To address this, one approach is to measure the similarity

Emanuel Carneiro, Giuseppe Negro, Diogo Oliveira e Silva

We prove a new family of sharp $L^2(\mathbb S^{d-1})\to L^4(\mathbb R^d)$ Fourier extension inequalities from the unit sphere $\mathbb S^{d-1}\subset \mathbb R^d$, valid in arbitrary dimensions $d\geq 3$.

Pengkun Liu, Shuna Ni, Stanislav I. Stoliarov, Pingbo Tang

Fire patterns, consisting of fire effects that offer insights into fire behavior and origin, are traditionally classified based on investigators' visual observations, leading to subjective interpretations. This study proposes a framework for quantitative fire pattern classification to support fire investigators, aiming for consistency and accuracy. The frame

Lu-Xin Ren, Jun-Xian Wang, Jia-Lai Kang

Utilizing a range of techniques including multi-band light curves, softness ratio analysis, structure functions, rms spectra, cross-correlation functions, and ratios of spectra from different intervals, we present a comprehensive study of the complex X-ray spectral variability in Seyfert 1 galaxy Ark 120, through re-analyzing its six XMM-Newton observations

Patrick R. Stollenwerk, Stephen H. Southworth, Francesco Granato, Amy Renne

We describe a new instrument, the Argonne Auger Radioisotope Microscope (ARM), capable of characterizing the Auger electron emission of radionuclides, including candidates relevant in nuclear medicine. Our approach relies on event-by-event ion-electron coincidence, time-of-flight, and spatial readout measurement to determine correlated electron multiplicity

Tobias Boege, Liam Solus

We derive an implicit description of the image of a semialgebraic set under a birational map, provided that the denominators of the map are positive on the set. For statistical models which are globally rationally identifiable, this yields model-defining constraints which facilitate model membership testing, representation learning, and model equivalence tes

Mahsa Bazzaz, Seth Cooper

Procedurally generated levels created by machine learning models can be unsolvable without further editing. Various methods have been developed to automatically repair these levels by enforcing hard constraints during the post-processing step. However, as levels increase in size, these constraint-based repairs become increasingly slow. This paper proposes us

Safiere Kuijpers, Laura Scarabosio

We consider the Bayesian approach to the inverse problem of recovering the shape of an object from measurements of its scattered acoustic field. Working in the time-harmonic setting, we focus on a Helmholtz transmission problem and then extend our results to an exterior Dirichlet (sound-soft) problem. It is well known that higher frequencies yield higher res

Dong Shu, Mengnan Du

In-context learning can help Large Language Models (LLMs) to adapt new tasks without additional training. However, this performance heavily depends on the quality of the demonstrations, driving research into effective demonstration selection algorithms to optimize this process. These algorithms assist users in selecting the best $k$ input-label pairs (demons

H. Adhikary, P. Adrich, K. K. Allison, N. Amin

This paper presents the multiplicity of neutral and charged hadrons produced in 90 GeV$/c$ proton-carbon interactions from a dataset taken by the NA61/SHINE experiment in 2017. Particle identification via dE/dx was performed for the charged hadrons $\pi^\pm$, $K^\pm$, and $p / \bar{p}$; the neutral hadrons $K^0_S$, $\Lambda$, and $\bar{\Lambda}$ were identif

Daniele Bartoli, Mohit Pal, Pantelimon Stanica, Tommaso Toccotelli

Constructing permutation polynomials over finite fields, particularly those with simple algebraic structure in multiple variables, is a fundamental problem with applications in cryptography and coding theory. Recently, Li and Kaleyski (IEEE Trans. Inf. Theory, 2024) generalized two sporadic quadratic APN permutations into infinite families of trivariate func

Jean-Christophe Pain

In this article we obtain, using an expression of the digamma function $\psi(x)$ due to Mikolas, integral representations of the zeta function of odd arguments $\zeta(2p+1)$ for any positive value of $p$. The integrand consists of the product of a polynomial by one or two elementary trigonometric functions. Examples for the first values of the argument are g

Karthik Subramaniam Eswaran, Ali Emami Kopaei, Krzysztof Sacha

Solutions of the wave equations for time-independent disordered media can exhibit Anderson localization where instead of wave propagation we observe their localization around different points in space. Photonic time crystals are spatially homogeneous media in which the refractive index changes periodically in time, leading to the formation of bands in the wa

Donald M Davis, W Stephen Wilson

We compute the $E_2$ page of the Adams spectral sequence converging to the connective KO-theory of the second mod 2 Eilenberg-MacLane space, $ko_*(K(Z/2,2))$. This required a careful analysis of the structure of $H^*(K(Z/2,2);Z_2)$ as a module over the subalgebra of the Steenrod algebra generated by $Sq^1$ and $Sq^2$. Complete analysis of the spectral sequen

Roman Bushuiev, Anton Bushuiev, Niek F. de Jonge, Adamo Young

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even wh

Siwen Quan, Junhao Yu, Ziming Nie, Muze Wang

Point cloud data now are popular data representations in a number of three-dimensional (3D) vision research realms. However, due to the limited performance of sensors and sensing noise, the raw data usually suffer from sparsity, noise, and incompleteness. This poses great challenges to down-stream point cloud processing tasks. In recent years, deep-learning-

Leonard Storcks, Tobias Buck

We present jf1uids, a one-dimensional fluid solver that can, by virtue of a geometric formulation of the Euler equations, model radially symmetric fluid problems in a conservative manner, i.e., without losing mass or energy. For spherical problems, such as ideal supernova explosions or stellar wind-blown bubble expansions, simulating only along a radial dime

Ruyang Li, Tengfei Zhang, Heng Zhang, Tiejun Liu

This report presents our team's technical solution for participating in Track 3 of the 2024 ECCV ROAD++ Challenge. The task of Track 3 is atomic activity recognition, which aims to identify 64 types of atomic activities in road scenes based on video content. Our approach primarily addresses the challenges of small objects, discriminating between single objec

Kassiani Papasotiriou, Srijan Sood, Shayleen Reynolds, Tucker Balch

Investment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), offers opportunities to enhance the equity rating process. This paper explores the application of LLMs to generate multi-horizon stock ratings by ingesting diverse datasets. Traditi

Mingkun Zhang, Keping Bi, Wei Chen, Quanrun Chen

Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated by subtle manipulations, since we make judgments only based on essential factors. Inspired by this observation, we attempt to model label generation with essential label-causative

Yiruo Cheng, Kelong Mao, Ziliang Zhao, Guanting Dong

Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing large language models (LLMs) through external knowledge retrieval. Despite its widespread attention, existing academic research predominantly focuses on single-turn RAG, leaving a significant gap in addressing the complexities of multi-turn conversations found in real-world app

Tianxiang Wu, Minxin Nie, Ziqiang Cao

The Multimodal Large Language Models (MLLMs) have activated the capabilitiesof Large Language Models (LLMs) in solving visual-language tasks by integratingvisual information. The prevailing approach in existing MLLMs involvesemploying an image encoder to extract visual features, converting thesefeatures into visual tokens via an adapter, and then integrating

Matteo Migliorini

We prove that Thompson's group $T$ and, more generally, all the Higman-Thompson groups $T_n$ have quadratic Dehn function.

Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk

We study the density estimation problem defined as follows: given $k$ distributions $p_1, \ldots, p_k$ over a discrete domain $[n]$, as well as a collection of samples chosen from a ``query'' distribution $q$ over $[n]$, output $p_i$ that is ``close'' to $q$. Recently~\cite{aamand2023data} gave the first and only known result that achieves sublinear bounds i

Haiyuan Li, Hari Madhukumar, Peizheng Li, Yuelin Liu

Deep Reinforcement Learning (DRL) has emerged as a powerful solution for meeting the growing demands for connectivity, reliability, low latency and operational efficiency in advanced networks. However, most research has focused on theoretical analysis and simulations, with limited investigation into real-world deployment. To bridge the gap and support practi

Gaurav N. Gadbail, Sanjay Mandal, P. K. Sahoo, Kazuharu Bamba

The accelerated expansion of the universe has been widely confirmed, posing challenges to the standard $\Lambda$CDM model, particularly the cosmological coincidence problem. This has motivated the exploration of modified gravity theories, including non-metricity gravity, which explains cosmic acceleration without dark energy. In this work, we incorporate a q

Matthias Flotho, Philipp Flotho, Andreas Keller

Visualization of multidimensional, categorical data is a common challenge across scientific areas and, in particular, the life sciences. The goal is to create a comprehensive overview of the underlying data which allows to assess multiple variables intuitively. One application where such visualizations are particularly useful is pathway analysis, where we ch

Maciej K. Wozniak, Hariprasath Govindarajan, Marvin Klingner, Camille Maurice

Recent self-supervised clustering-based pre-training techniques like DINO and Cribo have shown impressive results for downstream detection and segmentation tasks. However, real-world applications such as autonomous driving face challenges with imbalanced object class and size distributions and complex scene geometries. In this paper, we propose S3PT a novel

Xiangcen Wu, Yipei Wang, Qianye Yang, Natasha Thorley

Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to develop deep learning models that improve the overall cancer diagnostic accuracy, by classifying radiologist-i

Rasha Karakchi, Ryan Karbowniczak

While IoT devices provide significant benefits, their rapid growth results in larger data volumes, increased complexity, and higher security risks. To manage these issues, techniques like encryption, compression, and mapping are used to process data efficiently and securely. General-purpose and AI platforms handle these tasks well, but mapping in natural lan

Nicolas Chauvaux, Adrian Kneip, Christoph Posch, Kofi Makinwa

Compute-in-memory (CIM) accelerators for spiking neural networks (SNNs) are promising solutions to enable $\mu$s-level inference latency and ultra-low energy in edge vision applications. Yet, their current lack of flexibility at both the circuit and system levels prevents their deployment in a wide range of real-life scenarios. In this work, we propose a nov

Yuta Yamauchi, Genya Kobayashi, Shonosuke Sugasawa

Count data frequently arises in biomedical applications, such as the length of hospital stay. However, their discrete nature poses significant challenges for appropriately modeling conditional quantiles, which are crucial for understanding heterogeneous effects and variability in outcomes. To solve the practical difficulty, we propose a novel general Bayesia

Guangzeng Yi

For all $s\in[0,1]$ and $t\in(0,s]\cup [2-s,2)$, we find the supremum of numbers $\omega\in(0,2)$ such that $\text{I}_\omega(\mu\ast\sigma) \lesssim 1$, where $\mu$ is any Borel measure on $B(1)$ with $\text{I}_t(\mu)\leq 1$ and $\sigma$ is any $(s,1)$-Frostman measure on a $C^2$-graph with non-zero curvature. As an application, we use this to show the sharp

Junqi Zhao, Zhijin Fang, Shu Li, Shaohui Yang

Large language models (LLMs) are essential in natural language processing but often struggle with inference speed and computational efficiency, limiting real-time deployment. The key-value (KV) cache mechanism reduces computational overhead in transformer models, but challenges in maintaining contextual understanding remain. In this paper, we propose BUZZ, a

Ferdinand Wagner

In this article, we'll introduce a $q$-variant of Witt vectors and de Rham-Witt complexes. This variant is closely related to the Habiro ring of a number field constructed by Garoufalidis, Scholze, Wheeler, and Zagier, to $q$-Hodge cohomology, and to $\operatorname{THH}(-/\mathrm{ku})$. While most of these connections will only be explored in forthcoming wor

Tengfei Zhang, Heng Zhang, Ruyang Li, Qi Deng

This report presents our team's solutions for the Track 1 of the 2024 ECCV ROAD++ Challenge. The task of Track 1 is spatiotemporal agent detection, which aims to construct an "agent tube" for road agents in consecutive video frames. Our solutions focus on the challenges in this task, including extreme-size objects, low-light scenarios, class imbalance, and f

Jiahua Tian, Yi-Nan Wang

We study the 7D Symmetry Topological Field Theory (SymTFT) associated to a 6D SCFT from the IIB/F-theory geometric engineering approach. The 6D (2,0) or (1,0) SCFT is constructed from IIB on a non-compact complex surface possibly with 7-branes. We derive the general form of 7D SymTFT actions from the compactification of IIB action on the boundary link of the

Daniele Andreucci, Anatoli F. Tedeev

We consider the Cauchy problem in the Euclidean space for a doubly degenerate parabolic equation with a space-dependent exponential weight, roughly speaking of the type of the exponential of a power of the distance from the origin. We assume here the solutions of the Cauchy problem to be globally integrable in space (in the appropriate weighted sense) and no

Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao

We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability. In

Hongyu Chen, Chengcheng Chen, Fei Wang, Yuhu Shi

Recent advancements in synthetic aperture radar (SAR) ship detection using deep learning have significantly improved accuracy and speed, yet effectively detecting small objects in complex backgrounds with fewer parameters remains a challenge. This letter introduces RSNet, a lightweight framework constructed to enhance ship detection in SAR imagery. To ensure

Aymene Mohammed Bouayed, Samuel Deslauriers-Gauthier, Adrian Iaccovelli, David Naccache

Interpreting the decisions of Convolutional Neural Networks (CNNs) is essential for understanding their behavior, yet explainability remains a significant challenge, particularly for self-supervised models. Most existing methods for generating saliency maps rely on ground truth labels, restricting their use to supervised tasks. EigenCAM is the only notable l

Judah Van Zandt, Erik Petigura

Understanding the relationship between close-in small planets (CS) and distant giants (DG) is central to understanding the formation of planetary systems like our own. Most studies of this connection have found evidence for a positive correlation, though significant statistical and systematic uncertainties remain due to differences in sample size, target sel

Kartik Bhide, Valerie Lang

Precise measurements of the electromagnetic dipole moments of charged leptons are powerful probes of physics beyond the Standard Model of particle physics. It is essential to study avenues for optimizing the measurement strategies for the anomalous magnetic dipole moment (AMDM) and the electric dipole moment (EDM) of the $\tau$-lepton which have recently bec

Kanzhi Cheng, Yantao Li, Fangzhi Xu, Jianbing Zhang

Chain-of-thought (CoT) has proven to improve the reasoning capability of large language models (LLMs). However, due to the complexity of multimodal scenarios and the difficulty in collecting high-quality CoT data, CoT reasoning in multimodal LLMs has been largely overlooked. To this end, we propose a simple yet effective self-training framework, R3V, which i

Ahmed Kharrufa, Sami Alghamdi, Abeer Aziz, Christopher Bull

This work takes a pedagogical lens to explore the implications of generative AI (GenAI) models and tools, such as ChatGPT and GitHub Copilot, in a semester-long 2nd-year undergraduate Software Engineering Team Project. Qualitative findings from survey (39 students) and interviews (eight students) provide insights into the students' views on the impact of Gen

Ozgur Akarsu, Bilal Bulduk, Antonio De Felice, Nihan Katırcı

While $f(T)$ gravity has shown considerable potential in addressing cosmological tensions, we explore previously overlooked solution spaces that hold further promise. We examine the case where the customary assumption of a strictly positive effective DE density may not apply, offering new possibilities. Focusing on $f(T) = T e^{T_*/T}$, we investigate cosmol

Robert J. Kunsch, Marcin Wnuk

We study approximation of the embedding $\ell_p^m \hookrightarrow \ell_q^m$, $1 \leq p < q \leq \infty$, based on randomized algorithms that use up to $n$ arbitrary linear functionals as information on a problem instance where $n \ll m$. By analysing adaptive methods we show upper bounds for which the information-based complexity $n$ exhibits only a $(\log\l

Debjyoti Saha Roy, Byron C. Wallace, Javed A. Aslam

State-of-the-art Extreme Multi-Label Text Classification models rely on multi-label attention to focus on key tokens in input text, but learning good attention weights is challenging. We introduce PLANT - Pretrained and Leveraged Attention - a plug-and-play strategy for initializing attention. PLANT works by planting label-specific attention using a pretrain

Michael Clancy, David B. Kaplan

Recently Weyl edge states on manifolds in dimension $d+1$ with a connected $d$-dimensional boundary were proposed as candidates for lattice regularization of chiral gauge theories, for even $d$. The examples considered to date include solid cylinders in any odd dimension, and the 3-ball with boundary $S^2$. Here we consider the general case of a $(d+1)$-dime

Guillaume Aubrun, Alexander Müller-Hermes

Given an operator $\phi:X\rightarrow Y$ between Banach spaces, we consider its tensor powers $\phi^{\otimes k}$ as operators from the $k$-fold injective tensor product of $X$ to the $k$-fold projective tensor product of $Y$. We show that after taking the $k$th root, the operator norm of $\phi^{\otimes k}$ converges to the $2$-dominated norm $\gamma^*_2(\phi)

Amir Burshtein, David Shuliutsky, Roman Kuzmin, Vladimir E. Manucharyan

Instantons, semi-classical trajectories of quantum tunneling in imaginary time, have long been used to study thermodynamic and transport properties in a myriad of condensed matter and high energy systems. A recent experiment in superconducting circuits [Phys. Rev. Lett. 126, 197701, (2021)] provided first evidence for direct dynamical signatures of instanton

Mathias S. Feinler, Bernadette N. Hahn

When solving inverse problems, one has to deal with numerous potential sources of model inexactnesses, like object motion, calibration errors, or simplified data models. Regularized Sequential Subspace Optimization (ReSeSOp) allows to compensate for such inaccuracies within the reconstruction step by employing consecutive projections onto suitably defined su

Subrata Ghosh, Massimiliano Righi, Andrea Macrelli, Francesco Goto

Nanoporous carbon materials containing small domains of sp2-carbon with highly disordered structures are promising for supercapacitor applications. Herein, we synthesize amorphous carbon nanofoam with 98% volumetric void fraction and low mass density of around 30 mg/cm3 by pulsed laser deposition at room temperature. With the unavoidable oxygen functional gr

Jiangkun Yu, Houari Bettahar, Hakan Kandemir, Quan Zhou

Soft robotic manipulators are generally slow despite their great adaptability, resilience, and compliance. This limitation also extends to current soft robotic micromanipulators. Here, we introduce FilMBot, a 3-DOF film-based, electromagnetically actuated, soft kinematic robotic micromanipulator achieving speeds up to 2117 {\deg}/s and 2456 {\deg}/s in {\alp

Subrata Ghosh, Massimiliano Righi, Simone Melesi, Yu Qiu

Carbon atomic wires, a linear atomic chain of sp-carbon, is theoretically predicted to have around five times higher surface area than graphene, notable charge mobilities, as well as excellent optical and thermal properties. Despite these impressive properties, the properties of sp-carbon as an electrochemical energy-storage electrode have not been reported

Javier Gonzalez-Conde, Dylan Lewis, Sachin S. Bharadwaj, Mikel Sanz

Computational fluid dynamics (CFD) is a specialised branch of fluid mechanics that utilises numerical methods and algorithms to solve and analyze fluid-flow problems. One promising avenue to enhance CFD is the use of quantum computing, which has the potential to resolve nonlinear differential equations more efficiently than classical computers. Here, we try

Fabien Nießen, Paul Paschmanns

Personnel scheduling problems have received considerable academic attention due to their relevance in various real-world applications. These problems involve preparing feasible schedules for an organization's employees and often account for factors such as qualifications of workers and holiday requests, resulting in complex constraints. While certain version

Run Luo, Zikai Song, Longze Chen, Yunshui Li

Multi-Object Tracking (MOT) aims to associate multiple objects across video frames and is a challenging vision task due to inherent complexities in the tracking environment. Most existing approaches train and track within a single domain, resulting in a lack of cross-domain generalizability to data from other domains. While several works have introduced natu

Sorin-Mihai Grad, Felipe Lara, Raúl T. Marcavillaca

Introduced by Polyak in 1966, the class of strongly quasiconvex functions includes some interesting nonconvex members, like the square root of the Euclidean norm or ratios with a nonnegative strongly convex numerator and a concave and positive denominator. This survey collects the vast majority of the results involving strongly quasiconvex functions availabl

Pau Rodriguez, Arno Blaas, Michal Klein, Luca Zappella

The increasing capabilities of large generative models and their ever more widespread deployment have raised concerns about their reliability, safety, and potential misuse. To address these issues, recent works have proposed to control model generation by steering model activations in order to effectively induce or prevent the emergence of concepts or behavi