May 2024 arXiv papers — page 6

Lorenzo Innocenti, Giacomo Blanco, Luca Barco, Claudio Rossi

Urban heat islands, defined as specific zones exhibiting substantially higher temperatures than their immediate environs, pose significant threats to environmental sustainability and public health. This study introduces a novel machine-learning model that amalgamates data from the Sentinel-3 satellite, meteorological predictions, and additional remote sensin

J. Woillez, R. Petrov, R. Abuter, F. Allouche

Context: MATISSE, the mid-infrared spectro-imaging instrument of VLTI, was designed to deliver its advertised performance when paired with an external second generation fringe tracker. Science observation started in 2019, demonstrating imaging capabilities and faint science target observations. Now, The GRAVITY fringe tracker stabilizes the MATISSE fringes w

Demetrianos Gavriel, Haralambos Panagopoulos, Gregoris Spanoudes

We study the renormalization of a complete set of gauge-invariant gluon nonlocal operators in lattice perturbation theory. We determine the mixing pattern under renormalization of these operators using symmetry arguments, which extend beyond perturbation theory. Additionally, we derive the renormalization factors of the operators within the modified Minimal

Emiel van Miltenburg

This short position paper provides a manually curated list of non-English image captioning datasets (as of May 2024). Through this list, we can observe the dearth of datasets in different languages: only 23 different languages are represented. With the addition of the Crossmodal-3600 dataset (Thapliyal et al., 2022, 36 languages) this number increases somewh

Hyeonjun Lee, Sehyun Hwang, Suha Kwak

This paper introduces a novel approach to learning instance segmentation using extreme points, i.e., the topmost, leftmost, bottommost, and rightmost points, of each object. These points are readily available in the modern bounding box annotation process while offering strong clues for precise segmentation, and thus allows to improve performance at the same

Pierre-Henri Chavanis

We discuss the kinetic theory of stellar systems and two-dimensional vortices and stress their analogies. We recall the derivation of the Landau and Lenard-Balescu equations from the Klimontovich formalism. These equations take into account two-body correlations and are valid at the order $1/N$, where $N$ is the number of particles in the system. They have t

Xiaoyun Gan, Shanyu Gan, Taizhi Su, Peng Liu

With heightened awareness of data privacy protection, Federated Learning (FL) has attracted widespread attention as a privacy-preserving distributed machine learning method. However, the distributed nature of federated learning also provides opportunities for backdoor attacks, where attackers can guide the model to produce incorrect predictions without affec

Do-Hyung Kim

In this paper, we will show that certain types of symplectic homology can be used as an invariant of 3-dimensional Besse manifolds, which are strict contact manifolds with periodic Reeb flow. For simplicity, we will assume our Besse structures to be a trivial plane bundle. To identify Besse manifolds with such a condition, we actually compute the first Chern

Wenbo Yu, Hao Fang, Bin Chen, Xiaohang Sui

Gradient Inversion Attacks invert the transmitted gradients in Federated Learning (FL) systems to reconstruct the sensitive data of local clients and have raised considerable privacy concerns. A majority of gradient inversion methods rely heavily on explicit prior knowledge (e.g., a well pre-trained generative model), which is often unavailable in realistic

Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein, Ron Levie

Message Passing Neural Networks (MPNNs) are a staple of graph machine learning. MPNNs iteratively update each node's representation in an input graph by aggregating messages from the node's neighbors, which necessitates a memory complexity of the order of the number of graph edges. This complexity might quickly become prohibitive for large graphs provided th

Yiming Li, Gil Fernandes, David Benton, Antonin Billaud

Mode-division multiplexing has shown its ability to significantly increase the capacity of free-space optical communications. An accurate alignment is crucial to enable such links due to possible performance degradation induced by mode crosstalk and narrow beam divergence. Conventionally, a beacon beam is necessary for system alignment due to multiple local

Fernando Acebes, David Poza, Jose M Gonzalez-Varona, Javier Pajares

Obtaining a viable schedule baseline that meets all project constraints is one of the main issues for project managers. The literature on this topic focuses mainly on methods to obtain schedules that meet resource restrictions and, more recently, financial limitations. The methods provide different viable schedules for the same project, and the solutions wit

Tiago Sousa, Benoît Ries, Nicolas Guelfi

United Nations have declared the current decade (2021-2030) as the "UN Decade on Ecosystem Restoration" to join R\&D forces to fight against the ongoing environmental crisis. Given the ongoing degradation of earth ecosystems and the related crucial services that they offer to the human society, ecosystem restoration has become a major society-critical issue.

Dongliang Wang, Yikun Nie, Gaolei Hu, Hon Ki Tsang

Reservoir computing (RC) is a leading machine learning algorithm for information processing due to its rich expressiveness. A new RC paradigm has recently emerged, showcasing superior performance and delivering more interpretable results with shorter training data sets and training times, representing the next generation of RC computing. This work presents t

Yufei Wang, Zhihao Li, Lanqing Guo, Wenhan Yang

Recently, 3D Gaussian Splatting (3DGS) has become a promising framework for novel view synthesis, offering fast rendering speeds and high fidelity. However, the large number of Gaussians and their associated attributes require effective compression techniques. Existing methods primarily compress neural Gaussians individually and independently, i.e., coding a

Jin-Hee Lee, Jae-Keun Lee, Je-Seok Kim, Soon Kwon

To ensure safe urban driving for autonomous platforms, it is crucial not only to develop high-performance object detection techniques but also to establish a diverse and representative dataset that captures various urban environments and object characteristics. To address these two issues, we have constructed a multi-class 3D LiDAR dataset reflecting diverse

Christina Winkler, Paula Harder, David Rolnick

Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations. This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work,

Miaomiao Cai, Lei Chen, Yifan Wang, Haoyue Bai

Collaborative Filtering (CF) typically suffers from the significant challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to a significant accuracy gap between popular and unpopular items. It not only hinders accurate user preference understanding but also exacerbates the Matthew effect in recommendation

Takaya Tanaka, Yutaka Yamaguti

Successive image generation using cyclic transformations is demonstrated by extending the CycleGAN model to transform images among three different categories. Repeated application of the trained generators produces sequences of images that transition among the different categories. The generated image sequences occupy a more limited region of the image space

Kaustubh Rajwade, Joeri van Leeuwen

Ephemeral Fast Radio Bursts (FRBs) must be powered by some of the most energetic processes in the Universe. That makes them highly interesting in their own right and as precise probes for estimating cosmological parameters. This field thus poses a unique challenge: FRBs must be detected promptly and immediately localised and studied based only on that single

Diabul Haque

The Japanese real estate market, valued over 35 trillion USD, offers significant investment opportunities. Accurate rent and price forecasting could provide a substantial competitive edge. This paper explores using alternative data variables to predict real estate performance in 1100 Japanese municipalities. A comprehensive house price index was created, cov

Ziyu W. Yang, Jie Zhang, Maocai Pi, Xubin Ye

Bulk magnetic and thermodynamic measurements, along with mean-field calculations, were conducted on the ferromagnetic K3Gd5(PO4)6 powders. No magnetic ordering was observed until 2 K, while the application of an external field B > 1 T resulted in the splitting of the Gd3+ ground state multiplet and induced a non-cooperative Schottky effect. The average neare

Erik Pohle, Aysajan Abidin, Bart Preneel

Garbling schemes are vital primitives for privacy-preserving protocols and secure two-party computation. This paper presents a projective garbling scheme that assigns $2^n$ values to wires in a circuit comprising XOR and unary projection gates. A generalization of FreeXOR allows the XOR of wires with $2^n$ values to be very efficient. We then analyze the per

Huan-Yu Liu, Xiaoshui Lin, Zhao-Yun Chen, Cheng Xue

The rapid development of quantum computers has enabled demonstrations of quantum advantages on various tasks. However, real quantum systems are always dissipative due to their inevitable interaction with the environment, and the resulting non-unitary dynamics make quantum simulation challenging with only unitary quantum gates. In this work, we present an inn

Zhaorui Tan, Chengrui Zhang, Xi Yang, Jie Sun

Generalized category discovery presents a challenge in a realistic scenario, which requires the model's generalization ability to recognize unlabeled samples from known and unknown categories. This paper revisits the challenge of generalized category discovery through the lens of information maximization (InfoMax) with a probabilistic parametric classifier.

Xuying Ning, Wujiang Xu, Tianxin Wei, Xiaolei Liu

Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across multiple domains to mitigate data sparsity and cold-start challenges in Single-Domain Sequential Recommendation. Existing methods primarily rely on shared users (overlapping users) to learn transferable interest representations. However, these approaches have limited information pr

Fernando Acebes, David Poza, Jose Manuel Gonzalez-Varona, Adolfo Lopez-Paredes

Earned duration management (EDM) is a methodology for project schedule management (PSM) that can be considered an alternative to earned value management (EVM). EDM provides an estimation of deviations in schedule and a final project duration estimation. There is a key difference between EDM and EVM: In EDM, the value of activities is expressed as work period

Michael W. Hansen, Dénes Sexty

We study complex Langevin simulations of a toy model as well as QCD, supplemented with a dynamical stabilization (DS) term, which was proposed to regularize the complexified process at lower temperatures. We compare the results to reweghting from zero chemical potential to measure the bias that the inclusion of the stabilization term causes, depending on its

David Curto, Fernando Acebes, Jose M Gonzalez-Varona, David Poza

In construction projects, contingency reserves have traditionally been estimated based on a percentage of the total project cost, which is arbitrary and, thus, unreliable in practical cases. Monte Carlo simulation provides a more reliable estimation. However, works on this topic have focused exclusively on the effects of aleatoric uncertainty, but ignored th

Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi, Yusuke Miyao

Thinking about the future is one of the important activities that people do in daily life. Futurists also pay a lot of effort into figuring out possible scenarios for the future. We argue that the exploration of this direction is still in an early stage in the NLP research. To this end, we propose three argument generation tasks in the financial application

Germán Molpeceres, Kenji Furuya, Yuri Aikawa

We investigate the role of carbon monoxide ice in the chemical evolution of prestellar cores using astrochemical rate equation models. We constrain the ratios of the binding energies on CO ice and H$_{2}$O ice for a series of adsorbates deemed important in diffusive chemistry on H$_{2}$O ices. We later include these ratios in our chemical reaction network mo

Jose M. Gonzalez-Varona, Natalia Martin-Cruz, Fernando Acebes, Javier Pajares

In this paper, we apply a case study approach to advance current understanding of what effects public co-funding of R&D projects have on project team members' perceived complexity. We chose an R&D project carried out by an industrial SME in northern Spain. The chosen research strategy was a qualitative approach, and sixteen employees participated in the proj

Jona Cappelle, Liesbet Van der Perre, Emma Fitzgerald, Simon Ravyts

The Internet of Things (IoT) can support the evolution towards a digital and green future. However, the introduction of the technology clearly has in itself a direct adverse ecological impact. This paper assesses this impact at both the IoT-node and at the network side. For the nodes, we show that the electronics production of devices comes with a carbon foo

Sören Schleibaum, Lu Feng, Sarit Kraus, Jörg P. Müller

In the evolving landscape of human-centered AI, fostering a synergistic relationship between humans and AI agents in decision-making processes stands as a paramount challenge. This work considers a problem setup where an intelligent agent comprising a neural network-based prediction component and a deep reinforcement learning component provides advice to a h

Erwin Luesink, Juan Giraldo, Bernard Geurts, Johann Hurink

DC networks play an important role within the ongoing energy transition. In this context, simulations of designed and existing networks and their corresponding assets are a core tool to get insights and form a support to decision-making. Hereby, these simulations of DC networks are executed in the time domain. Due to the involved high frequencies and the use

Laura Cabello, Uchenna Akujuobi

Aspect-Based Sentiment Analysis (ABSA) involves extracting opinions from textual data about specific entities and their corresponding aspects through various complementary subtasks. Several prior research has focused on developing ad hoc designs of varying complexities for these subtasks. In this paper, we present a generative framework extensible to any ABS

Xiangxi Li, Yuhan Li, Minyu Feng, Jürgen Kurths

There are complex interactions between antibody levels and epidemic propagation, the antibody level of an individual influences the probability of infection, and the spread of the virus influences the antibody level of each individual. There exist some viruses that, in their natural state, cause antibody levels in an infected individual to gradually decay. W

Pengwei Zhan, Zhen Xu, Qian Tan, Jie Song

Large language models (LLMs) demonstrate exceptional instruct-following ability to complete various downstream tasks. Although this impressive ability makes LLMs flexible task solvers, their performance in solving tasks also heavily relies on instructions. In this paper, we reveal that LLMs are over-sensitive to lexical variations in task instructions, even

Gecheng Chen, Zeyu Yang, Chengwen Luo, Jianqiang Li

Modern industrial fault diagnosis tasks often face the combined challenge of distribution discrepancy and bi-imbalance. Existing domain adaptation approaches pay little attention to the prevailing bi-imbalance, leading to poor domain adaptation performance or even negative transfer. In this work, we propose a self-degraded contrastive domain adaptation (Sd-C

Sujoy Chakraborty, Arjun Paul

Let $X$ be a smooth complex projective variety equipped with an action of a linear algebraic group $G$ over $\mathbb{C}$. Let $D$ be a reduced effective divisor on $X$ that is invariant under the $G$--action on $X$. Let $s_D$ be the canonical section of $\mathcal{O}_X(D)$ vanishing along $D$. Given a positive integer $r$, consider the stack $\mathfrak{X} :=

Di Wang

Isospin symmetry, as the most precise flavor symmetry, can be used to extract information about hadronic dynamics. The effective Hamiltonian for bottom quark weak decay is zero under the isospin lowering operators $I_-^n$, which allows us to generate isospin sum rules through several master formulas. In this work, we derive the master formulas of isospin sum

Xun An

The widespread presence of Use-After-Free (UAF) vulnerabilities poses a serious threat to software security, with dangling pointers being considered the primary cause of these vulnerabilities. However, existing methods for defending against UAF vulnerabilities by eliminating dangling pointers need to interrupt the program's execution when encountering pointe

Gong-Chu Li, Lei Chen, Si-Qi Zhang, Xu-Song Hong

Entanglement plays a fundamental role in quantum physics and information processing. Here, we develop an unbiased estimator for mixed-state entanglement in the few-shot scenario and directly estimate it using random unitary evolution in a photonic system. As a supplement to traditional projective measurements, we incorporate Bell measurements on qubit-pairs,

Alex Amenta, Pascal Auscher, Moritz Egert

The preliminary material of the monograph (arXiv:1607.03852) written by the first two authors contains two major imprecisions that necessitates a number of (in the end harmless) changes throughout the entire text. One is about identification of abstract and concrete Hardy spaces for perturbed Dirac operators, the other one about interpolation of quasi-Banach

Wei-Can Yang, Makoto Tsubota, Muneto Nitta, Hua-Bi Zeng

The three-body problem, from the chaotic motions of celestial bodies to complex microscopic particle interactions, has always been one of the most foundational yet intricate challenges in physics since its establishment. A key breakthrough in this domain is the Efimov effect, which represents a significant stride in what is now known as Efimov physics. Our s

Jianhao Ding, Zhiyu Pan, Yujia Liu, Zhaofei Yu

Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low energy budget on neuromorphic hardware. However, they still face challenges in lacking sufficient robustness to guard safety-critical applications such as autonomous driving. Many studies have been conducted to defend SNNs from the threat of adversarial attacks. This pape

Ruyi Zha, Tao Jun Lin, Yuanhao Cai, Jiwen Cao

3D Gaussian splatting (3DGS) has shown promising results in image rendering and surface reconstruction. However, its potential in volumetric reconstruction tasks, such as X-ray computed tomography, remains under-explored. This paper introduces R$^2$-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction. By carefully deriving X-r

Sili Huang, Jifeng Hu, Hechang Chen, Lichao Sun

In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-and-error manner when treating RL tasks as an across-episodic sequential prediction problem. Despite the self-im

Dario Javier Zamora, Roberto Artuso

Motion in bounded domains is a fundamental concept in various fields, including billiard dynamics and random walks on finite lattices, with important applications in physics, ecology and biology. An important universal property related to the average return time to the boundary, the Mean Path Length Theorem (MPLT), has been proposed theoretically and confirm

Hengrui Zhang, Liancheng Fang, Qitian Wu, Philip S. Yu

Generative models play an important role in missing data imputation in that they aim to learn the joint distribution of full data. However, applying advanced deep generative models (such as Diffusion models) to missing data imputation is challenging due to 1) the inherent incompleteness of the training data and 2) the difficulty in performing conditional inf

Mark Hammond, Neil T. Lewis, Sasha Boone, Xueqing Chen

Eclipse mapping uses the shape of the eclipse of an exoplanet to measure its two-dimensional structure. Light curves are mostly composed of longitudinal information, with the latitudinal information only contained in the brief ingress and egress of the eclipse. This imbalance can lead to a spuriously confident map, where the longitudinal structure is constra

Fernando Acebes, David Curto, Juan de Anton, Felix Villafanez

Risk management is a fundamental discipline in project management, which includes, among others, quantitative risk analysis. Throughout several years of teaching, we have observed difficulties in students performing Monte Carlo Simulation within the quantitative analysis of risks. This article aims to present MCSimulRisk as a teaching tool that allows studen

Kidist Amde Mekonnen

Deep learning algorithms have a large number of trainable parameters often with sizes of hundreds of thousands or more. Training this algorithm requires a large amount of training data and generating a sufficiently large dataset for these algorithms is costly\cite{noguchi2019image}. GANs are generative neural networks that use two deep learning networks that

Qi Wang, Xueyi Wang, Jiefeng Liu

This paper establishes a categorical framework for phase spaces of Lie algebras, pre-Lie bialgebras, Manin triples, classical s-matrices, and relative Rota-Baxter operators by introducing the concept of coherent homomorphisms. Starting with endo pre-Lie algebras (pre-Lie algebras equipped with endomorphisms), we extend classical constructions to this enhance

Yukai Zhang, Ao Xu, Zihao Li, Tieru Wu

In the realm of Artificial Intelligence (AI), the importance of Explainable Artificial Intelligence (XAI) is increasingly recognized, particularly as AI models become more integral to our lives. One notable single-instance XAI approach is counterfactual explanation, which aids users in comprehending a model's decisions and offers guidance on altering these d

Aaron Haag, Vlad Argatu, Oliver Lohse

Large Language Models (LLMs) have achieved impressive performance in text understanding and have become an essential tool for building smart assistants. Originally focusing on text, they have been enhanced with multimodal capabilities in recent works that successfully built visual instruction following assistants. As far as the graph modality goes, however,

Andronikos Paliathanasis

We explore the introduction of the cosmological constant via equivalent transformations in cosmology. We consider the Wheeler-DeWitt equation for the CDM universe and we construct the Hamilton-Jacobi action for the $\Lambda$CDM model. We discuss how this approach allows us to relate different physical systems, providing insights into the role of the cosmolog

Tomislav Antic, Andrew Keane, Tomislav Capuder

Hosting capacity (HC) and dynamic operating envelopes (DOEs), defined as dynamic, time-varying HC, are calculated using three-phase optimal power flow (OPF) formulations. Due to the computational complexity of such optimisation problems, HC and DOE are often calculated by introducing certain assumptions and approximations, including the linearised OPF formul

Xiaojin Zhang, Yahao Pang, Yan Kang, Wei Chen

Individuals and businesses have been significantly benefited by Large Language Models (LLMs) including PaLM, Gemini and ChatGPT in various ways. For example, LLMs enhance productivity, reduce costs, and enable us to focus on more valuable tasks. Furthermore, LLMs possess the capacity to sift through extensive datasets, uncover underlying patterns, and furnis

Mingda Li, Xinyu Li, Yifan Chen, Wenfeng Xuan

Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Language Models (LMs). Our experiments reveal that this example-level performance inconsistency exists not only between retrieval-augmented and retrieval-free LM but also among differ

Fernando Acebes, José Manuel González-Varona, Adolfo López-Paredes, Javier Pajares

The project managers who deal with risk management are often faced with the difficult task of determining the relative importance of the various sources of risk that affect the project. This prioritisation is crucial to direct management efforts to ensure higher project profitability. Risk matrices are widely recognised tools by academics and practitioners i

Mengxiao Zhang, Ramiro Deo-Campo Vuong, Haipeng Luo

We consider the problem of online multi-agent Nash social welfare (NSW) maximization. While previous works of Hossain et al. [2021], Jones et al. [2023] study similar problems in stochastic multi-agent multi-armed bandits and show that $\sqrt{T}$-regret is possible after $T$ rounds, their fairness measure is the product of all agents' rewards, instead of the

Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro

Interactive-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in which a learner aims at maximizing unobservable rewards through interacting with an environment and observing reward-dependent feedback on the taken actions. To deal with personalized rewards that are ubiquitous in applications such as recommendation systems, Maghakian et al. [

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using data samples with an integrated luminosity of $4.67~\mathrm{fb}^{-1}$ collected by the BESIII detector operating at the BEPCII collider, we search for the process $e^+e^- \rightarrow \eta' \psi(2S)$ at center-of-mass energies from $4.66$ to $4.95~\mathrm{GeV}$. No significant signal is observed, and upper limits for the Born cross sections $\sigma^B(e^

Kidist Amde Mekonnen, Nicola Dall'Asen, Paolo Rota

Diffusion Probabilistic Models (DPMs) have emerged as a powerful class of deep generative models, achieving remarkable performance in image synthesis tasks. However, these models face challenges in terms of widespread adoption due to their reliance on sequential denoising steps during sample generation. This dependence leads to substantial computational requ

Haiyu Zhang, Xinyuan Chen, Yaohui Wang, Xihui Liu

Current 4D generation methods have achieved noteworthy efficacy with the aid of advanced diffusion generative models. However, these methods lack multi-view spatial-temporal modeling and encounter challenges in integrating diverse prior knowledge from multiple diffusion models, resulting in inconsistent temporal appearance and flickers. In this paper, we pro

Ben Moonen

We give a precise classification, in terms of Shimura data, of all 1-dimensional Shimura subvarieties of a moduli space of polarized abelian varieties.

Davide Coppola, Hwee Kuan Lee

This study explores the impact of adversarial perturbations on Convolutional Neural Networks (CNNs) with the aim of enhancing the understanding of their underlying mechanisms. Despite numerous defense methods proposed in the literature, there is still an incomplete understanding of this phenomenon. Instead of treating the entire model as vulnerable, we propo

Hanseul Cho, Jaeyoung Cha, Pranjal Awasthi, Srinadh Bhojanapalli

Even for simple arithmetic tasks like integer addition, it is challenging for Transformers to generalize to longer sequences than those encountered during training. To tackle this problem, we propose position coupling, a simple yet effective method that directly embeds the structure of the tasks into the positional encoding of a (decoder-only) Transformer. T

Jaime A. Teixeira da Silva, Serhii Nazarovets

Here, we note how academics, journals and publishers should no longer refer to the social media platform Twitter as such, rather as X. Relying on Google Scholar, we found 16 examples of papers published in the last months of 2023 - essentially during the transition period between Twitter and X - that used Twitter and X, but in different ways. Unlike that tra

Shuzhou Yang, Yu Wang, Haijie Li, Jiarui Meng

Single image-to-3D generation is pivotal for crafting controllable 3D assets. Given its under-constrained nature, we attempt to leverage 3D geometric priors from a novel view diffusion model and 2D appearance priors from an image generation model to guide the optimization process. We note that there is a disparity between the generation priors of these two d

Zhiwei Wang, Yongkang Wang, Wen Zhang

Accurately predicting antibody-antigen binding residues, i.e., paratopes and epitopes, is crucial in antibody design. However, existing methods solely focus on uni-modal data (either sequence or structure), disregarding the complementary information present in multi-modal data, and most methods predict paratopes and epitopes separately, overlooking their spe

Cuiyun Yuan, Sisi Xu, Yang Li, Enzhuo Quan

Purpose: We aims to examine the impact of different head tilt angles on the dose distribution in the whole-brain target area and organs at risk. It also aims to determine the head tilt angle to achieve optimal radiation therapy outcomes. Methods: CT images were collected from 8 brain metastases patients at 5 different groups of head tilt angle. The treatment

Weichao Zhao, Hezhen Hu, Wengang Zhou, Yunyao Mao

Sign language recognition (SLR) has long been plagued by insufficient model representation capabilities. Although current pre-training approaches have alleviated this dilemma to some extent and yielded promising performance by employing various pretext tasks on sign pose data, these methods still suffer from two primary limitations: 1) Explicit motion inform

Rikuto Ota, Keito Kobayashi, Keisuke Hayakawa, Shun Kanai

Stochastic magnetic tunnel junctions (s-MTJ) is a promising component of probabilistic bit (p-bit), which plays a pivotal role in probabilistic computers. For a standard cell structure of the p-bit, s-MTJ is desired to be insensitive to voltage across the junction over several hundred millivolts. In conventional s-MTJs with a reference layer having a fixed m

Ao Xu, Tieru Wu

Counterfactual explanation generation is a powerful method for Explainable Artificial Intelligence. It can help users understand why machine learning models make specific decisions, and how to change those decisions. Evaluating the robustness of counterfactual explanation algorithms is therefore crucial. Previous literature has widely studied the robustness

Kazuki Daikuhara, Tadayuki Kodama, Jose M. Pérez-Martínez, Rhythm Shimakawa

Low-mass galaxies at high redshifts are the building blocks of more massive galaxies at later times and are thus key populations for understanding galaxy formation and evolution. We have made deep narrow-band observations for two protoclusters and the general field in COSMOS at $z$ $\sim$ 2. In a clumpy young protocluster, USS1558$-$003, at $z$ = 2.53, we fi

Marc Hovemann, Markus Weimar

In this paper we investigate Besov-Morrey spaces $\mathcal{N}^{s}_{u,p,q}(\Omega)$ and Besov-type spaces $B^{s,\tau}_{p,q}(\Omega)$ of positive smoothness defined on Lipschitz domains $\Omega \subset \mathbb{R}^d$ as well as on $\mathbb{R}^d$. We combine the Hedberg-Netrusov approach to function spaces with distinguished kernel representations due to Triebel

Alvaro M. Aparicio-Morales, Enrique Moguel, Luis Mariano Bibbo, Alejandro Fernandez

Quantum computing represents a revolutionary computational paradigm with the potential to address challenges beyond classical computers' capabilities. The development of robust quantum software is indispensable to unlock the full potential of quantum computing. Like classical software, quantum software is expected to be complex and extensive, needing the est

Yu-Cheng Hui

In this study, we investigate photon-photon scattering in ultra-peripheral heavy ion collisions (UPCs). We start by deriving an effective Lagrangian from first principles and then apply factorization techniques from Soft-Collinear effective theory (SCET). This approach allows us to decompose the photon-photon scattering cross-section into two primary factors

Jinting Li, Xi Chen, Danfang Zhang, Wenzhang Wang

High-precision gyroscopes in space are essential for fundamental physics research and navigation. Due to its potential high precision, the cold atom gyroscope is expected to be the next generation of gyroscopes in space. Here, we report the first realization of a cold atom gyroscope, which was demonstrated by the atom interferometer installed in the China Sp

Lik-Chun Chan, Tsz-Fung Kok, Emily S. C. Ching

Spontaneous brain activity in the absence of external stimuli is not random but contains complex dynamical structures such as neuronal avalanches with power-law duration and size distributions. These experimental observations have been interpreted as supporting evidence for the hypothesis that the brain is operating near a critical point of phase transition

Nikolay Plotnikov, Alexander Antonov

In this paper, we introduce four comprehensive datasets for the Chuvash language, aiming to support and enhance linguistic research and technological development for this underrepresented language. These datasets include a monolingual dataset, a parallel dataset with Russian, a parallel dataset with English, and an audio dataset. Each dataset is meticulously

Lirong Gao, Ru Peng, Yiming Zhang, Junbo Zhao

Prompt recovery in large language models (LLMs) is crucial for understanding how LLMs work and addressing concerns regarding privacy, copyright, etc. The trend towards inference-only APIs complicates this task by restricting access to essential outputs for recovery. To tackle this challenge, we extract prompt-related information from limited outputs and iden

Javier Naranjo-Alcazar, Jordi Grau-Haro, Pedro Zuccarello, David Almenar

Insect pest control poses a global challenge, affecting public health, food safety, and the environment. Diseases transmitted by mosquitoes are expanding beyond tropical regions due to climate change. Agricultural pests further exacerbate economic losses by damaging crops. The Sterile Insect Technique (SIT) emerges as an eco-friendly alternative to chemical

Hengchao Shi, Xinyi Liu, Ming Zheng, Wen Yu

Case-control sampling is a commonly used retrospective sampling design to alleviate imbalanced structure of binary data. When fitting the logistic regression model with case-control data, although the slope parameter of the model can be consistently estimated, the intercept parameter is not identifiable, and the marginal case proportion is not estimatable, e

Xuyang Wu, Zhiyuan Peng, Krishna Sravanthi Rajanala Sai, Hsin-Tai Wu

Effective passage retrieval and reranking methods have been widely utilized to identify suitable candidates in open-domain question answering tasks, recent studies have resorted to LLMs for reranking the retrieved passages by the log-likelihood of the question conditioned on each passage. Although these methods have demonstrated promising results, the perfor

Jiahao Yu, Haozheng Luo, Jerry Yao-Chieh Hu, Wenbo Guo

Recent advances in Large Language Models (LLMs) have led to impressive alignment where models learn to distinguish harmful from harmless queries through supervised finetuning (SFT) and reinforcement learning from human feedback (RLHF). In this paper, we reveal a subtle yet impactful weakness in these aligned models. We find that simply appending multiple end

Langzhang Liang, Sunwoo Kim, Kijung Shin, Zenglin Xu

Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graph-structured data. To empower GNNs in heterophilic graphs, where adjacent nodes exhibit dissimilar labels or features, Signed Message Passing (SMP) has been widely adopted. However, there is a lack of theoretical and empir

Yongfeng Zhao, Ruben Zakine, Adrian Daerr, Yariv Kafri

The Young-Dupr\'e equation is a cornerstone of the equilibrium theory of capillary and wetting phenomena. In the biological world, interfacial phenomena are ubiquitous, from the spreading of bacterial colonies to tissue growth and flocking of birds, but the description of such active systems escapes the realm of equilibrium physics. Here we show how a micros

Hansang Lee, Haeil Lee, Helen Hong

In this paper, we propose a novel data augmentation technique called GenMix, which combines generative and mixture approaches to leverage the strengths of both methods. While generative models excel at creating new data patterns, they face challenges such as mode collapse in GANs and difficulties in training diffusion models, especially with limited medical

Byeonghu Na, Suhyeon Jo, Yeongmin Kim, Il-Chul Moon

Relation extraction (RE) is a fundamental task in natural language processing, aiming to identify relations between target entities in text. While many RE methods are designed for a single sentence or document, cross-document RE has emerged to address relations across multiple long documents. Given the nature of long documents in cross-document RE, extractin

Richard Luo, Austin Peng, Adithya Vasudev, Rishabh Jain

Video is an increasingly prominent and information-dense medium, yet it poses substantial challenges for language models. A typical video consists of a sequence of shorter segments, or shots, that collectively form a coherent narrative. Each shot is analogous to a word in a sentence where multiple data streams of information (such as visual and auditory data

Tony J. Puthenpurakal, Samarendra Sahoo

Let $(A,\mathfrak{m})$ be an analytically un-ramified Noetherian local ring of dimension $d \geq 1$, $I$ a regular $\mathfrak{m}$-primary ideal of $A$ and let $\overline{I}$ be integral closure ideal of $I$. If $A$ is of characteristic $p > 0$ then let $I^*$ denote the tight closure of $I$. Let $G_I(A)=\bigoplus_{n\geq 0}I^n/I^{n+1}$ be the associated graded

Qidong Liu, Xian Wu, Yejing Wang, Zijian Zhang

Sequential recommender systems (SRS) aim to predict users' subsequent choices based on their historical interactions and have found applications in diverse fields such as e-commerce and social media. However, in real-world systems, most users interact with only a handful of items, while the majority of items are seldom consumed. These two issues, known as th

Ayesha Asloob Qureshi, Somayeh Bandari

We prove the componentwise linearity of ideals that satisfy a certain exchange property similar to polymatroidal ideals. We also discuss the componentwise linearity and exchange properties of ideals of $k$-covers of totally balanced weighted hypergraphs.

Gecheng Chen, Rui Tuo

This work focuses on the design of experiments of multi-fidelity computer experiments. We consider the autoregressive Gaussian process model proposed by Kennedy and O'Hagan (2000) and the optimal nested design that maximizes the prediction accuracy subject to a budget constraint. An approximate solution is identified through the idea of multi-level approxima

Nerea Aranjuelo, Siyu Huang, Ignacio Arganda-Carreras, Luis Unzueta

Gaze-annotated facial data is crucial for training deep neural networks (DNNs) for gaze estimation. However, obtaining these data is labor-intensive and requires specialized equipment due to the challenge of accurately annotating the gaze direction of a subject. In this work, we present a generative framework to create annotated gaze data by leveraging the b

Shiliang Zuo

Machine learning has become increasingly popular in informing data-driven policy-making. Policies influence behavior in individuals or populations, and ideally, through observational signals, policy-makers learn which policies are effective. However, in many settings, individual actions cannot be perfectly observed. This issue, known in economics as moral ha

Shaofei Li, Ziqi Zhang, Haomin Jia, Ding Li

Query-based black-box attacks have emerged as a significant threat to machine learning systems, where adversaries can manipulate the input queries to generate adversarial examples that can cause misclassification of the model. To counter these attacks, researchers have proposed Stateful Defense Models (SDMs) for detecting adversarial query sequences and reje