May 2023 arXiv papers — page 63

Alaa Maalouf, Murad Tukan, Noel Loo, Ramin Hasani

Dataset Distillation is the task of synthesizing small datasets from large ones while still retaining comparable predictive accuracy to the original uncompressed dataset. Despite significant empirical progress in recent years, there is little understanding of the theoretical limitations/guarantees of dataset distillation, specifically, what excess risk is ac

Michal Macek, Georgy Zinchenko, Vera Musilova, Pavel Urban

The present study investigates the non-Oberbeck-Boussinesq (NOB) effects which arise due to the temperature dependence of material properties in cryogenic helium experiments of turbulent Rayleigh-B\'enard convection. They are manifest as a difference of the measured mean temperature at the center of the closed cell, $T_c$, from the arithmetic mean temperatur

Jin Lei, Antonio M. Moro

Inclusive breakup is an important reaction mechanism of reactions induced by weakly bound nuclei. The Ichimura, Austern, and Vincent (IAV) model is widely used to analyze inclusive breakup processes and is based on a Distorted Wave Born Approximation (DWBA). However, the validity of the DWBA form for inclusive breakup requires further exploration. In this st

Huangjun Zhu

Projective measurements in quantum theory have a very simple algebraic definition, but their information theoretic significance is quite elusive. Here we introduce a simple order relation based on the concentration of Fisher information, which complements the familiar data-processing order. Under this order relation, the information theoretic significance of

Mark Deutel, Georgios Kontes, Christopher Mutschler, Jürgen Teich

Deploying deep neural networks (DNNs) on microcontrollers (TinyML) is a common trend to process the increasing amount of sensor data generated at the edge, but in practice, resource and latency constraints make it difficult to find optimal DNN candidates. Neural architecture search (NAS) is an excellent approach to automate this search and can easily be comb

Hong Liu, James H. Cullen, Dimitrie Culcer

Spin currents are key to spin torque devices, but determining the proper spin current is non-trivial. Here we derive a general quantum-mechanical formula for the intrinsic proper spin current showing that it is topological and can be finite in the gap. For topological insulators with an out of plane magnetization and the chemical potential in the surface sta

Nan Pu, Zhun Zhong, Xinyuan Ji, Nicu Sebe

Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes, given labeled data of known classes. To meet the recent decentralization trend in the community, we introduce a practical yet challenging task, namely Federated GCD (Fed-GCD), where the training data are distributively stored in local clients and cannot be

Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan O. Arik

Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. This is made possible by their strong few and zero-shot abilities -- they can effectively learn from a handful of handcrafted, completed responses ("in-context examples"), or are prompted to reason spon

Aswanth Kumar, Ratish Puduppully, Raj Dabre, Anoop Kunchukuttan

Large language models have demonstrated the capability to perform on machine translation when the input is prompted with a few examples (in-context learning). Translation quality depends on various features of the selected examples, such as their quality and relevance, but previous work has predominantly focused on individual features in isolation. In this p

Linyi Yang, Yaoxiao Song, Xuan Ren, Chenyang Lyu

Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differs from the training data distribution. This poses important questions about the robustness of NLP models and their high accuracy, which may be artificially inflated due to their un

Guangping Zhang, Dongsheng Li, Hansu Gu, Tun Lu

Understanding the evolution of online news communities is essential for designing more effective news recommender systems. However, due to the lack of appropriate datasets and platforms, the existing literature is limited in understanding the impact of recommender systems on this evolutionary process and the underlying mechanisms, resulting in sub-optimal sy

Harry J. Davies, Ghena Hammour, Marek Zylinski, Amir Nassibi

The Ear-ECG provides a continuous Lead I electrocardiogram (ECG) by measuring the potential difference related to heart activity using electrodes that can be embedded within earphones. The significant increase in wearability and comfort afforded by Ear-ECG is often accompanied by a corresponding degradation in signal quality - a common obstacle that is share

Bin Hong, ZhongZhou Ren, Chen Wu, XueLing Mu

Oscillation modes of compact stars, in general, can serve as a fingerprint in determining the equation of state (EOS) of dense matter. In this study, we examine the impact of symmetry energy slope ($L$) on the oscillation frequencies of neutron stars (NSs) with nucleon-nucleon short range correlation (SRC) and admixed dark matter (DM) for the first time with

Ren Li, Benoît Guillard, Pascal Fua

Many approaches to draping individual garments on human body models are realistic, fast, and yield outputs that are differentiable with respect to the body shape on which they are draped. However, they are either unable to handle multi-layered clothing, which is prevalent in everyday dress, or restricted to bodies in T-pose. In this paper, we introduce a par

Zhijun Jiang, Zhenlong Zhang, Sergei Prokhorenko, Yousra Nahas

An atomistic effective Hamiltonian technique is used to investigate the finite-temperature energy storage properties of a ferroelectric nanocomposite consisting of an array of BaTiO$_{3}$ nanowires embedded in a SrTiO$_{3}$ matrix, for electric field applied along the long axis of the nanowires. We find that the energy density \textit{versus} temperature cur

Poushali Sengupta, Yan Zhang, Sabita Maharjan, Frank Eliassen

Explainability of AI models is an important topic that can have a significant impact in all domains and applications from autonomous driving to healthcare. The existing approaches to explainable AI (XAI) are mainly limited to simple machine learning algorithms, and the research regarding the explainability-accuracy tradeoff is still in its infancy especially

Zeng Da

In a multi-agent environment, In order to overcome and alleviate the non-stationarity of the multi-agent environment, the mainstream method is to adopt the framework of Centralized Training Decentralized Execution (CTDE). This thesis is based on the framework of CTDE, and studies the cooperative decision-making of multi-agent based on the Multi-Agent Proxima

Guangke Chen, Yedi Zhang, Zhe Zhao, Fu Song

Current adversarial attacks against speaker recognition systems (SRSs) require either white-box access or heavy black-box queries to the target SRS, thus still falling behind practical attacks against proprietary commercial APIs and voice-controlled devices. To fill this gap, we propose QFA2SR, an effective and imperceptible query-free black-box attack, by l

Georgios Birmpas, Tomer Ezra, Stefano Leonardi, Matteo Russo

We introduce the study of designing allocation mechanisms for fairly allocating indivisible goods in settings with interdependent valuation functions. In our setting, there is a set of goods that needs to be allocated to a set of agents (without disposal). Each agent is given a private signal, and his valuation function depends on the signals of all agents.

Sangwoo Mo, Minkyu Kim, Kyungmin Lee, Jinwoo Shin

Vision-language models, such as contrastive language-image pre-training (CLIP), have demonstrated impressive results in natural image domains. However, these models often struggle when applied to specialized domains like remote sensing, and adapting to such domains is challenging due to the limited number of image-text pairs available for training. To addres

Marcello Bullo, Seifallah Jardak, Pietro Carnelli, Deniz Gündüz

Deep learning (DL) models have emerged as a promising solution for the Internet of Things (IoT). However, due to their computational complexity, DL models consume significant amounts of energy, which can rapidly drain the battery and compromise the performance of IoT devices. For sustainable operation, we consider an edge device with a rechargeable battery a

Kunhao Liu, Fangneng Zhan, Jiahui Zhang, Muyu Xu

Open-vocabulary segmentation of 3D scenes is a fundamental function of human perception and thus a crucial objective in computer vision research. However, this task is heavily impeded by the lack of large-scale and diverse 3D open-vocabulary segmentation datasets for training robust and generalizable models. Distilling knowledge from pre-trained 2D open-voca

Alessandro Barone, Shoji Hashimoto, Andreas Jüttner, Takashi Kaneko

We address the nonperturbative calculation of the inclusive decay rate of semileptonic $B_{(s)}$-meson decays from lattice QCD. Precise Standard-Model predictions are key ingredients in searches for new physics, and this type of computation may eventually provide new insight into the long-standing tension between the inclusive and exclusive determinations of

Hai Hu, Ziyin Zhang, Weifang Huang, Jackie Yan-Ki Lai

In this work, we revisit linguistic acceptability in the context of large language models. We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale acceptability dataset for a non-Indo-European language. It is verified by native speakers and is the first acceptability dataset that comes with two sets of labels: a linguist lab

Lennard Görges, Christoph Nega, Lorenzo Tancredi, Fabian J. Wagner

In this manuscript, we elaborate on a procedure to derive $\epsilon$-factorised differential equations for multi-scale, multi-loop classes of Feynman integrals that evaluate to special functions beyond multiple polylogarithms. We demonstrate the applicability of our approach to diverse classes of problems, by working out $\epsilon$-factorised differential eq

Megumi Harada, Tatsuya Horiguchi

This manuscript is a contributed chapter in the forthcoming CRC Press volume, titled the Handbook of Combinatorial Algebraic Geometry: Subvarieties of the Flag Variety. The book, as a whole, is aimed at a diverse audience of researchers and graduate students seeking an expository introduction to the area. In our chapter, we give an overview of some of the pa

R. Ayala, A. Cabral

In this work we obtain boundedness results for fractional operators associated with Schr\"odinger operators $\ \mathcal{L}=-\Delta+V$ on weighted variable Lebesgue spaces. These operators include fractional integrals and their respective commutators. Particularly, we obtain weighted inequalities of the type $L^{p(\cdot)}$-$L^{q(\cdot)}$ and estimates of the

Xiaoyin Chen, Sam Wiseman

Given BM25's enduring competitiveness as an information retrieval baseline, we investigate to what extent it can be even further improved by augmenting and re-weighting its sparse query-vector representation. We propose an approach to learning an augmentation and a re-weighting end-to-end, and we find that our approach improves performance over BM25 while re

Alireza Amirshahi, Nicolas Kirsch, Jonathan Reymond, Saleh Baghersalimi

The acquisition of survey responses is a crucial component in conducting research aimed at comprehending public opinion. However, survey data collection can be arduous, time-consuming, and expensive, with no assurance of an adequate response rate. In this paper, we propose a pioneering approach for predicting survey responses by examining quotations using ma

R. González-Albaladejo, L. L. Bonilla

The harmonically confined Vicsek model displays qualitative and quantitative features observed in natural insect swarms. It exhibits a scale free transition between single and multicluster chaotic phases. Finite size scaling indicates that this unusual phase transition occurs at zero confinement [Physical Review E 107, 014209 (2023)]. While the evidence of t

Youwang Xiao, Xinhui Li, Jing Wang, Ming Li

The violation of Bell inequality not only provides the most radical departure of quantum theory from classical concepts, but also paves the way of applications in such as device independent randomness certification. Here, we derive the tight upper bound of the maximum quantum value for chained Bell inequality with arbitrary number of measurements on each par

Victoria Lin, Louis-Philippe Morency, Dimitrios Dimitriadis, Srinagesh Sharma

In real-world machine learning systems, labels are often derived from user behaviors that the system wishes to encourage. Over time, new models must be trained as new training examples and features become available. However, feedback loops between users and models can bias future user behavior, inducing a presentation bias in the labels that compromises the

Zhijie Fan, Chao Zhang, Youjin Deng

Simulating long-range interacting systems is a challenging task due to its computational complexity that the computational effort for each local update is of order $\cal{O}$$(N)$, where $N$ is the size of system. Recently, a technique, called hereby the clock factorized quantum Monte Carlo method, was developed on the basis of the so-called factorized Metrop

Viktor Hangya, Alexander Fraser

Due to the broad range of social media platforms, the requirements of abusive language detection systems are varied and ever-changing. Already a large set of annotated corpora with different properties and label sets were created, such as hate or misogyny detection, but the form and targets of abusive speech are constantly evolving. Since, the annotation of

Efe Bozkir, Süleyman Özdel, Mengdi Wang, Brendan David-John

The latest developments in computer hardware, sensor technologies, and artificial intelligence can make virtual reality (VR) and virtual spaces an important part of human everyday life. Eye tracking offers not only a hands-free way of interaction but also the possibility of a deeper understanding of human visual attention and cognitive processes in VR. Despi

Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada

Self-supervised learning general-purpose audio representations have demonstrated high performance in a variety of tasks. Although they can be optimized for application by fine-tuning, even higher performance can be expected if they can be specialized to pre-train for an application. This paper explores the challenges and solutions in specializing general-pur

Zirui Zhao, Wee Sun Lee, David Hsu

Large-scale task planning is a major challenge. Recent work exploits large language models (LLMs) directly as a policy and shows surprisingly interesting results. This paper shows that LLMs provide a commonsense model of the world in addition to a policy that acts on it. The world model and the policy can be combined in a search algorithm, such as Monte Carl

Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart

The success of over-parameterized neural networks trained to near-zero training error has caused great interest in the phenomenon of benign overfitting, where estimators are statistically consistent even though they interpolate noisy training data. While benign overfitting in fixed dimension has been established for some learning methods, current literature

Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai

Stein Variational Gradient Descent (SVGD) is a nonparametric particle-based deterministic sampling algorithm. Despite its wide usage, understanding the theoretical properties of SVGD has remained a challenging problem. For sampling from a Gaussian target, the SVGD dynamics with a bilinear kernel will remain Gaussian as long as the initializer is Gaussian. In

Giuseppe De Laurentis

I present new features of the open-source Python package lips, which leverages the newly developed pyadic and syngular libraries. These developments enable the generation and manipulation of massless phase-space configurations beyond real kinematics, defined in terms of four-momenta or Weyl spinors, not only over complex numbers ($\mathbb{C}$), but now also

Ke Liang, Lingyuan Meng, Sihang Zhou, Siwei Wang

Inductive relation reasoning for knowledge graphs, aiming to infer missing links between brand-new entities, has drawn increasing attention. The models developed based on Graph Inductive Learning, called GraIL-based models, have shown promising potential for this task. However, the uni-directional message-passing mechanism hinders such models from exploiting

Jan Nagel

We show that the variable cohomology of a general complete intersection of quadrics can be identified with the intersection cohomology of a double covering. As a consequence, we show that the middle cohomology of a general complete intersection of four quadrics in an odd-dimensional projective space is isomorphic to the middle cohomology of a resolution of s

Samarth Bhargav, Anne Schuth, Claudia Hauff

We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a searcher is trying to find an existing music entity, but is unable to succeed as they cannot accurately recall important identifying information. ToT information needs are characterized by complexity, verbosity, uncertainty, and possible false memories. We make four contributions. (1)

Kailai Yang, Tianlin Zhang, Sophia Ananiadou

In Emotion Recognition in Conversations (ERC), the emotions of target utterances are closely dependent on their context. Therefore, existing works train the model to generate the response of the target utterance, which aims to recognise emotions leveraging contextual information. However, adjacent response generation ignores long-range dependencies and provi

Rodrigo Wilkens, Leonardo Zilio, Aline Villavicencio

Much recent effort has been devoted to creating large-scale language models. Nowadays, the most prominent approaches are based on deep neural networks, such as BERT. However, they lack transparency and interpretability, and are often seen as black boxes. This affects not only their applicability in downstream tasks but also the comparability of different arc

Shiqi Chen, Siyang Gao, Junxian He

Detecting factual errors in summaries has been an important and challenging subject in summarization research. Inspired by the emergent ability of large language models (LLMs), we explore evaluating factual consistency of summaries by directly prompting LLMs. We present a comprehensive empirical study to assess the ability of LLMs as factual consistency eval

Thales B. S. F. Rodrigues, B. F. Rizzuti

In this work, we demonstrate explicitly the unified nature of electric and magnetic fields, from the principles of special relativity and Lorentz transformations of the electromagnetic field tensor. Using an operational approach we construct the tensor and its corresponding transformation law, based on the principle of relativity. Our work helps to elucidate

Lawrence C Paulson

ALEXANDRIA is an ERC-funded project that started in 2017, with the aim of bringing formal verification to mathematics. The past six years have seen great strides in the formalisation of mathematics and also in some relevant technologies, above all machine learning. Six years of intensive formalisation activity seem to show that even the most advanced results

David Green, Peter Huston, Kyle Kawagoe, David Penneys

Boundaries of Walker-Wang models have been used to construct commuting projector models which realize chiral unitary modular tensor categories (UMTCs) as boundary excitations. Given a UMTC $\mathcal{A}$ representing the Witt class of an anomaly, the article [arXiv:2208.14018] gave a commuting projector model associated to an $\mathcal{A}$-enriched unitary fu

Zhenshan Bing, Yuan Meng, Yuqi Yun, Hang Su

Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters. In this paper, we propose a nonparametric deep clustering framework that employs an infinite mixture of Gaussians as a prior. Our framework utilizes a mem

Lan Jiang, Haoyang Huang, Dongdong Zhang, Rui Jiang

Training models with varying capacities can be advantageous for deploying them in different scenarios. While high-capacity models offer better performance, low-capacity models require fewer computing resources for training and inference. In this work, we propose a novel one-stop training framework to jointly train high-capacity and low-capactiy models. This

Peng Xu, Lin Zhang, Xuanzhou Liu, Jiaqi Sun

Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures. However, these methods inherit issues from the conventional NAS methods, such as high computational cost and optimization difficulty. More importantly, previous NAS methods have ignored the uniquen

Hanifa Tidjani, Alberto Tosato, Alexander Ivlev, Corentin Déprez

Gate-defined quantum dots in silicon-germanium heterostructures have become a compelling platform for quantum computation and simulation. Thus far, developments have been limited to quantum dots defined in a single plane. Here, we propose to advance beyond planar systems by exploiting heterostructures with multiple quantum wells. We demonstrate the operation

Ryoki Endo, Xuefeng Liu

This paper solves the open problem of the simplicity of the second Dirichlet eigenvalue for nearly equilateral triangles, offering a complete solution to Conjecture 6.47 posed by R. Laugesen and B. Siudeja in A. Henrot's book ``Shape Optimization and Spectral Theory." Our proof is achieved by introducing a new difference quotient formula for the behavior of

Yuyang Miao, Harry J. Davies, Danilo P. Mandic

Photoplethysmography (PPG) refers to the measurement of variations in blood volume using light and is a feature of most wearable devices. The PPG signals provide insight into the body's circulatory system and can be employed to extract various bio-features, such as heart rate and vascular ageing. Although several algorithms have been proposed for this purpos

Aayushya Agarwal, Carmel Fiscko, Soummya Kar, Larry Pileggi

We introduce a new workflow for unconstrained optimization whereby objective functions are mapped onto a physical domain to more easily design algorithms that are robust to hyperparameters and achieve fast convergence rates. Specifically, we represent optimization problems as an equivalent circuit that are then solved solely as nonlinear circuits using robus

Manel Perucho, Jose López-Miralles, Nectaria A. B. Gizani, José-María Martí

Extragalactic jets are generated as bipolar outflows at the nuclei of active galaxies. Depending on their morphology, they are classified as Fanaroff-Riley type I (centre-brightened) and Fanaroff-Riley type II (edge-brightened) radio jets. However, this division is not sharp and observations of these sources at large scales often show intermediate jet morpho

Eric Brachmann, Tommaso Cavallari, Victor Adrian Prisacariu

Learning-based visual relocalizers exhibit leading pose accuracy, but require hours or days of training. Since training needs to happen on each new scene again, long training times make learning-based relocalization impractical for most applications, despite its promise of high accuracy. In this paper we show how such a system can actually achieve the same a

Vance Faber, Noah Streib

This paper concerns all-to-all network routing on regular digraphs. In previous work we focused on efficient routing in highly symmetric digraphs with low diameter for fixed degree. Here, we show that every connected regular digraph has an all-to-all routing scheme and associated schedule with no waiting. In fact, this routing scheme becomes more efficient a

Lei Li, Jingjing Xu, Qingxiu Dong, Ce Zheng

Language models~(LMs) gradually become general-purpose interfaces in the interactive and embodied world, where the understanding of physical concepts is an essential prerequisite. However, it is not yet clear whether LMs can understand physical concepts in the human world. To investigate this, we design a benchmark VEC that covers the tasks of (i) Visual con

Kirsten Hogenson, Dan Johnston, Suzanne O'Hara

A graph $G$ is equitably $k$-choosable if, for every $k$-uniform list assignment $L$, $G$ is $L$-colorable and each color appears on at most $\left\lceil |V(G)|/k\right\rceil$ vertices. Equitable list-coloring was introduced by Kostochka, Pelsmajer, and West in 2003. They conjectured that a connected graph $G$ with $\Delta(G)\geq 3$ is equitably $\Delta(G)$-

Mingjie Hu, Jie Yan, Liting Chen, Qingwei Lin

This paper investigates the column generation (CG) for solving cutting stock problems (CSP). Traditional CG method, which repeatedly solves a restricted master problem (RMP), often suffers from two critical issues in practice -- the loss of solution quality introduced by linear relaxation of both feasible domain and objective and the high time cost of last i

Felix Küng

We construct a natural generalization of the Grothendieck group $\mathrm{K}_0$ to the case of possibly unpointed categories admitting pushouts by using the concept of heaps recently introduced by Brezinzki. In case of a monoidal category, the defined $\mathrm{K}_0$ is shown to be a truss. It is shown that the construction generalizes the classical $\mathrm{K

James Oldfield, Christos Tzelepis, Yannis Panagakis, Mihalis A. Nicolaou

Latent image representations arising from vision-language models have proved immensely useful for a variety of downstream tasks. However, their utility is limited by their entanglement with respect to different visual attributes. For instance, recent work has shown that CLIP image representations are often biased toward specific visual properties (such as ob

Takuma Iwata, T. Kousa, Y. Nishioka, K. Ohwada

We have developed a state-of-the-art apparatus for laser-based spin- and angle-resolved photoemission spectroscopy with micrometer spatial resolution (micro-SARPES). This equipment is achieved through the combination of a high-resolution photoelectron spectrometer, a 6-eV laser with high photon flux that is focused down to a few micrometers, a high-precision

Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma

Demand forecasting in the online fashion industry is particularly amendable to global, data-driven forecasting models because of the industry's set of particular challenges. These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption. While standard deep learning forecasting approaches ca

Alberto Rech, Matteo Pagin, Leonardo Badia, Stefano Tomasin

Intelligent reflecting surfaces (IRSs) are being widely investigated as a potential low-cost and energy-efficient alternative to active relays for improving coverage in next-generation cellular networks. However, technical constraints in the configuration of IRSs should be taken into account in the design of scheduling solutions and the assessment of their p

Simonas Draukšas, Vytautas Dūdėnas, Luís Lavoura

The parametrization of the oblique corrections through $S$, $T$, and $U$ -- later extended by $V$, $W$, and $X$ -- is a convenient way of comparing the predictions for various electroweak observables at the one-loop level between the Standard Model and its extensions. That parametrization assumes that the extensions under consideration have ${SU(2)\times U(1

Tian-Hao Zhang, Hai-Bo Qin, Zhi-Hao Lai, Song-Lu Chen

Attention-based encoder-decoder (AED) models have shown impressive performance in ASR. However, most existing AED methods neglect to simultaneously leverage both acoustic and semantic features in decoder, which is crucial for generating more accurate and informative semantic states. In this paper, we propose an Acoustic and Semantic Cooperative Decoder (ASCD

Warren Singh, Timothy Sun

A graph is said to be nearly complete bipartite if it can be obtained by deleting a set of independent edges from a complete bipartite graph. The nonorientable genus of such graphs is known except in a few cases where the sizes of the partite classes differ by at most one, and a maximum matching is deleted. We resolve these missing cases using three classic

Kaiqiang Yu, Cheng Long

Mining cohesive subgraphs from a graph is a fundamental problem in graph data analysis. One notable cohesive structure is $\gamma$-quasi-clique (QC), where each vertex connects at least a fraction $\gamma$ of the other vertices inside. Enumerating maximal $\gamma$-quasi-cliques (MQCs) of a graph has been widely studied. One common practice of finding all MQC

Kaihua Qin, Zhe Ye, Zhun Wang, Weilin Li

Smart contract vulnerabilities have led to significant financial losses, with their increasing complexity rendering outright prevention of hacks increasingly challenging. This trend highlights the crucial need for advanced forensic analysis and real-time intrusion detection, where dynamic analysis plays a key role in dissecting smart contract executions. The

Seungone Kim, Se June Joo, Doyoung Kim, Joel Jang

Language models (LMs) with less than 100B parameters are known to perform poorly on chain-of-thought (CoT) reasoning in contrast to large LMs when solving unseen tasks. In this work, we aim to equip smaller LMs with the step-by-step reasoning capability by instruction tuning with CoT rationales. In order to achieve this goal, we first introduce a new instruc

Gaoxia Zhu, Xiuyi Fan, Chenyu Hou, Tianlong Zhong

ChatGPT, launched in November 2022, has gained widespread attention from students and educators globally, with an online report by Hu (2023) stating it as the fastest-growing consumer application in history. While discussions on the use of ChatGPT in higher education are abundant, empirical studies on its impact on collaborative interdisciplinary learning ar

Yago Fontenla-Seco, Alberto Bugarín-Diz, Manuel Lama

In this paper we present the Process-To-Text (P2T) framework for the automatic generation of textual descriptive explanations of processes. P2T integrates three AI paradigms: process mining for extracting temporal and structural information from a process, fuzzy linguistic protoforms for modelling uncertain terms, and natural language generation for building

M. Bulakhov, A. S. Peletminskii, Yu. V. Slyusarenko

In the context of quantum gases, we obtain a many-body Hamiltonian for spin-3/2 atoms with general multipole (spin, quadrupole, and octupole) exchange interaction by employing the apparatus of irreducible spherical tensor operators. This Hamiltonian implies the finite-range interaction, whereas, for zero-range (contact) potentials parameterized by the $s$-wa

Wenbiao Yin, Zhicheng Liu, Chengqi Zhao, Tao Wang

In speech translation, leveraging multimodal data to improve model performance and address limitations of individual modalities has shown significant effectiveness. In this paper, we harness the complementary strengths of speech and text, which are disparate modalities. We observe three levels of modality gap between them, denoted by Modal input representati

Alireza Darvishy, Rolf Sethe, Ines Engler, Oriane Pierres

This survey analyzed the quality of the PDF documents on online repositories in Switzerland, examining their accessibility for people with visual impairments. Two minimal accessibility features were analyzed: the PDFs had to have tags and a hierarchical heading structure. The survey also included interviews with the managers or heads of multiple Swiss univer

Leah A. Jacobs, Alec McClean, Zach Branson, Edward H. Kennedy

This study examines the relationship between homelessness and recidivism among people on probation with and without behavioral health problems. The study also illustrates a new way to summarize the effect of an exposure on an outcome, the Incremental Propensity Score (IPS) effect, which avoids pitfalls of other approaches commonly used in criminology. We ass

Yuantong Zhang, Baoxin Teng, Daiqin Yang, Zhenzhong Chen

Low-light image enhancement (LLIE) aims to improve the illuminance of images due to insufficient light exposure. Recently, various lightweight learning-based LLIE methods have been proposed to handle the challenges of unfavorable prevailing low contrast, low brightness, etc. In this paper, we have streamlined the architecture of the network to the utmost deg

Yuming Huang, Yi Gu, Chengzhong Xu, Hui Kong

In most urban and suburban areas, pole-like structures such as tree trunks or utility poles are ubiquitous. These structural landmarks are very useful for the localization of autonomous vehicles given their geometrical locations in maps and measurements from sensors. In this work, we aim at creating an accurate map for autonomous vehicles or robots with pole

Julio Backhoff-Veraguas, Mathias Beiglboeck

Motivated by a problem posed by Aldous, our goal is to find the maximal-entropy win-martingale: In a sports game between two teams, the chance the home team wins is initially $x_0 \in (0,1)$ and finally 0 or 1. As an idealization we take a continuous time interval $[0,1]$ and consider the process $M=(M_t)_{t\in [0,1]}$ giving the probability at time $t$ that

Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani, Nathan van de Wouw

This paper proposes a nonlinear estimator for the robust reconstruction of process and sensor faults for a class of uncertain nonlinear systems. The proposed fault estimation method augments the system dynamics with an ultra-local (in time) internal state-space representation (a finite chain of integrators) of the fault vector. Next, a nonlinear state observ

Eklavya Sarkar, Mathew Magimai. -Doss

Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is not limited to modeling human speech alone. Building on this understanding, this paper explores the cross-

H. K. Avetissian, A. G. Ghazaryan, Kh. V. Sedrakian, G. F. Mkrtchian

This paper focuses on investigating high-order harmonic generation (HHG) in graphene quantum dots (GQDs) under intense near-infrared laser fields. To model the GQD and its interaction with the laser field, we utilize a mean-field approach. Our analysis of the HHG power spectrum reveals fine structures and a noticeable enhancement in cutoff harmonics due to t

M. N. Chernodub, V. A. Goy, A. V. Molochkov

The scale (conformal) anomaly can generate an electric current near the boundary of a system in the presence of a static magnetic field. The magnitude of this magnetization current, produced at zero temperature and in the absence of matter, is proportional to a beta function associated with the renormalization of the electric charge. Using first-principle la

Sangmin Bae, June-Woo Kim, Won-Yang Cho, Hyerim Baek

Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases. Since the COVID-19 pandemic, there has been a growing interest in contact-free medical care based on electronic stethoscopes. To this end, cutting-edge deep learning models have been developed to diagnose lung diseases; however, it is still challenging due to the s

Zhonghua Li, Lu Yan

In this paper, we study the explicit expressions of multiple t-star values with an arbitrary number of blocks of twos of general level. We give an expression of a generating function of such values, which generalizes the results for multiple zeta-star values and multiple t-star values. This derived generating function can provide expressions of multiple t-st

Abhinendra Singh, Kuniyasu Saitoh

Dense suspensions often exhibit a dramatic response to large external deformation. The recent body of work has related this behavior to transition from an unconstrained lubricated to a constrained frictional state. Here, we use numerical simulations to study the flow behavior and shear-induced diffusion of frictional non-Brownian spheres in two dimensions un

Frederik Banning, Jessica Reale, Michael Roos

This paper proposes an addition to the firm-based perspective on intra-industry profitability differentials by modelling a business organisation as a complex adaptive system. The presented agent-based model introduces an endogenous similarity-based social network and employees' reactions to dynamic management strategies informed by key company benchmarks. Th

Rachel Greenfeld, Mihail N. Kolountzakis

Let $\Omega\subset \mathbb{R}^d$ be a set of finite measure. The periodic tiling conjecture suggests that if $\Omega$ tiles $\mathbb{R}^d$ by translations then it admits at least one periodic tiling. Fuglede's conjecture suggests that $\Omega$ admits an orthogonal basis of exponential functions if and only if it tiles $\mathbb{R}^d$ by translations. Both con

Guilherme Zeus Dantas e Moura, Tibor Jordán, Corwin Silverman

A $d$-dimensional bar-and-joint framework $(G,p)$ with underlying graph $G$ is called universally rigid if all realizations of $G$ with the same edge lengths, in all dimensions, are congruent to $(G,p)$. A graph $G$ is said to be generically universally rigid in $\mathbb{R}^d$ if every $d$-dimensional generic framework $(G,p)$ is universally rigid. In this p

Ashwani Anand, Satya Prakash Nayak, Anne-Kathrin Schmuck

We present a novel method to compute \emph{permissive winning strategies} in two-player games over finite graphs with $ \omega $-regular winning conditions. Given a game graph $G$ and a parity winning condition $\Phi$, we compute a \emph{winning strategy template} $\Psi$ that collects an infinite number of winning strategies for objective $\Phi$ in a concise

Tomoki Goda, Krzysztof Kutak, Sebastian Sapeta

We investigate effects of exact gluon kinematics on the parameters of the Golec-Biernat-W\"usthoff, and Bartels-Golec-Biernat-Kowalski saturation models. The resulting fits show some differences, particularly, in the normalization of the dipole cross section $\sigma_0$. The refitted models are used for the dijet production process in DIS to investigate effec

Elliot Anshelevich, Aris Filos-Ratsikas, Christopher Jerrett, Alexandros A. Voudouris

We consider a social choice setting in which agents and alternatives are represented by points in a metric space, and the cost of an agent for an alternative is the distance between the corresponding points in the space. The goal is to choose a single alternative to (approximately) minimize the social cost (cost of all agents) or the maximum cost of any agen

Aljoscha Düsterhöft, Felix Burkhardt, Björn W. Schuller

We conducted a data collection on the basis of the Google AudioSet database by selecting a subset of the samples annotated with \textit{laughter}. The selection criterion was to be present a communicative act with clear connotation of being either positive (laughing with) or negative (being laughed at). On the basis of this annotated data, we performed two e

Qi Wu, Mingyan Han, Ting Jiang, Chengzhi Jiang

Deep denoising models require extensive real-world training data, which is challenging to acquire. Current noise synthesis techniques struggle to accurately model complex noise distributions. We propose a novel Realistic Noise Synthesis Diffusor (RNSD) method using diffusion models to address these challenges. By encoding camera settings into a time-aware ca

Ming Li, Youjin Deng

The site percolation on the triangular lattice stands out as one of the few exactly solved statistical systems. By initially configuring critical percolation clusters of this model and randomly reassigning the color of each percolation cluster, we obtain coarse-grained configurations by merging adjacent clusters that share the same color. It is shown that th

Bart Holterman, Kees van Deemter

Theory of Mind (ToM) is the ability to understand human thinking and decision-making, an ability that plays a crucial role in social interaction between people, including linguistic communication. This paper investigates to what extent recent Large Language Models in the ChatGPT tradition possess ToM. We posed six well-known problems that address biases in h

Kaiyan Chang, Ying Wang, Haimeng Ren, Mengdi Wang

As large language models (LLMs) like ChatGPT exhibited unprecedented machine intelligence, it also shows great performance in assisting hardware engineers to realize higher-efficiency logic design via natural language interaction. To estimate the potential of the hardware design process assisted by LLMs, this work attempts to demonstrate an automated design