February 2024 arXiv papers — page 151

Camila Fernandez, Pierre Gaillard, Joseph de Vilmarest, Olivier Wintenberger

We introduce an online mathematical framework for survival analysis, allowing real time adaptation to dynamic environments and censored data. This framework enables the estimation of event time distributions through an optimal second order online convex optimization algorithm-Online Newton Step (ONS). This approach, previously unexplored, presents substantia

Bertrand Berche, Ernesto Medina

A very famous ``test'' of the General Theory of Relativity (GTR) is the advance of Mercury's perihelion (and of other planets too). To be more precise, this is not a prediction of General Relativity, since the anomaly was known in the XIXth century, but no consistent explanation had been found yet at the time GTR was elaborated. Einstein came up with a solut

Michel Caffarel, Pierre del Moral, Luc de Montella

The Diffusion Monte Carlo method with constant number of walkers, also called Stochastic Reconfiguration as well as Sequential Monte Carlo, is a widely used Monte Carlo methodology for computing the ground-state energy and wave function of quantum systems. In this study, we present the first mathematically rigorous analysis of this class of stochastic method

Yan-Chao Li, Yuan-Hang Zhou, Yuan Zhang, Hai-Qing Lin

From the perspective of low-lying excited states, we study the deconfined quantum critical point (DQCP) in a one-dimensional quantum spin chain by means of the entanglement entropy and fidelity. Our results show that there is a close connection between the reconstruction of low-lying excitation spectra and the DQCP. The precise position of the critical point

Christophe Pouzat, Morgan André

After reviewing the behavioral studies of working memory and of the cellular substrate of the latter, we argue that metastable states constitute candidates for the type of transient information storage required by working memory. We then present a simple neural network model made of stochastic units whose synapses exhibit short-term facilitation. The Markov

Margaux Brégère, Julie Keisler

This work formulates model selection as an infinite-armed bandit problem, namely, a problem in which a decision maker iteratively selects one of an infinite number of fixed choices (i.e., arms) when the properties of each choice are only partially known at the time of allocation and may become better understood over time, via the attainment of rewards.Here,

Jiyi Zhang, Han Fang, Ee-Chien Chang

In forensic investigations of machine learning models, techniques that determine a model's data domain play an essential role, with prior work relying on large-scale corpora like ImageNet to approximate the target model's domain. Although such methods are effective in finding broad domains, they often struggle in identifying finer-grained classes within thos

Chen Sun, Wayne M. Saslow

In metals with finite magnetization $\vec{M}$, experiment shows that transverse polarized dc spin currents $\vec{J}_{i}$ both decay and precess on crossing a finite sample thickness. The present work uses Onsager's irreversible thermodynamics, with $\vec{M}$ and $\vec{J}_{i}$ as fundamental variables, to develop a theory with aspects of the Landau-Lifshitz t

Jinpeng Zhang, Changjuan Zhang, Xiaoping Wang

We have developed an efficient and unconditionally energy-stable method for simulating droplet formation dynamics. Our approach involves a novel time-marching scheme based on the scalar auxiliary variable technique, specifically designed for solving the Cahn-Hilliard-Navier-Stokes phase field model with variable density and viscosity. We have successfully ap

Marco Volponi, Saiva Huck, Ruggero Caravita, Jakub Zielinski

A powerful and robust control system is a crucial, often neglected, pillar of any modern, complex physics experiment that requires the management of a multitude of different devices and their precise time synchronisation. The AEgIS collaboration presents CIRCUS, a novel, autonomous control system optimised for time-critical experiments such as those at CERN'

Roman Koshkin, Katsuhito Sudoh, Satoshi Nakamura

Decoder-only large language models (LLMs) have recently demonstrated impressive capabilities in text generation and reasoning. Nonetheless, they have limited applications in simultaneous machine translation (SiMT), currently dominated by encoder-decoder transformers. This study demonstrates that, after fine-tuning on a small dataset comprising causally align

Douadi Drihem

In this paper, we identify the duals of Triebel-Lizorkin spaces of generalized smoothness. In some particular cases these function spaces are just weighted Triebel-Lizorkin spaces. To do these, we will be working at the level of sequence spaces. The $\varphi $-transform characterization of these function spaces in the sense of Frazier and Jawerth, and new we

Sankarshan Damle, Varul Srivastava, Sujit Gujar

The recently proposed Transaction Fee Mechanism (TFM) literature studies the strategic interaction between the miner of a block and the transaction creators (or users) in a blockchain. In a TFM, the miner includes transactions that maximize its utility while users submit fees for a slot in the block. The existing TFM literature focuses on satisfying standard

Hailiang Li, Yan Huo, Yan Wang, Xu Yang

As the modern CPU, GPU, and NPU chip design complexity and transistor counts keep increasing, and with the relentless shrinking of semiconductor technology nodes to nearly 1 nanometer, the placement and routing have gradually become the two most pivotal processes in modern very-large-scale-integrated (VLSI) circuit back-end design. How to evaluate routabilit

Vinayak Gupta, Rahul Goel, Sirikonda Dhawal, P. J. Narayanan

Traditional Radiance Field (RF) representations capture details of a specific scene and must be trained afresh on each scene. Semantic feature fields have been added to RFs to facilitate several segmentation tasks. Generalised RF representations learn the principles of view interpolation. A generalised RF can render new views of an unknown and untrained scen

Mengqi Chen, Bin Guo, Hao Wang, Haoyu Li

Persuasion, as one of the crucial abilities in human communication, has garnered extensive attention from researchers within the field of intelligent dialogue systems. We humans tend to persuade others to change their viewpoints, attitudes or behaviors through conversations in various scenarios (e.g., persuasion for social good, arguing in online platforms).

Sheng Jin, Xueying Jiang, Jiaxing Huang, Lewei Lu

Inspired by the outstanding zero-shot capability of vision language models (VLMs) in image classification tasks, open-vocabulary object detection has attracted increasing interest by distilling the broad VLM knowledge into detector training. However, most existing open-vocabulary detectors learn by aligning region embeddings with categorical labels (e.g., bi

Jae Choon Cha, Taehee Kim

We show that for a winding number zero satellite operator $P$ on the knot concordance group, if the axis of $P$ has nontrivial self-pairing under the Blanchfield form of the pattern, then the image of the iteration $P^n$ generates an infinite rank subgroup for each $n$. Furthermore, the graded quotients of the filtration of the knot concordance group associa

Hao Xu

Starting from Polchinski's thought experiment on how to distinguish between pure and thermal states, we construct a specific system to study the interaction between qubit and cavity quantum field theory (QFT) in order to provide a more operational point of view. Without imposing any restrictions on the initial states of qubit and cavity QFT, we compute the e

Julio C. Rangel, Tarcisio Mendes de Farias, Ana Claudia Sima, Norio Kobayashi

The recent success of Large Language Models (LLM) in a wide range of Natural Language Processing applications opens the path towards novel Question Answering Systems over Knowledge Graphs leveraging LLMs. However, one of the main obstacles preventing their implementation is the scarcity of training data for the task of translating questions into correspondin

H. Y. Yuan, Yaroslav Blanter

Surface plasmons in two-dimensional (2D) electron systems have attracted great attention for their promising light-matter applications. However, the excitation of a surface plasmon, in particular, transverse-electric (TE) surface plasmon, remains an outstanding challenge due to the difficulty to conserve energy and momentum simultaneously in the normal 2D ma

Hansam Cho, Jonghyun Lee, Seoung Bum Kim, Tae-Hyun Oh

Text-guided diffusion models have become a popular tool in image synthesis, known for producing high-quality and diverse images. However, their application to editing real images often encounters hurdles primarily due to the text condition deteriorating the reconstruction quality and subsequently affecting editing fidelity. Null-text Inversion (NTI) has made

Tao Sun, Chad Zanocco, June Flora, Ram Rajagopal

A resilient energy infrastructure is crucial for addressing increasing extreme weather and climate risks. The undergrounding of the power system is one approach to building such resiliency. In this study, we introduce Grid Underground Distribution Statistics (GUDS) for the US, the first nationwide comprehensive assessment of underground electricity distribut

Yu Wang, Yifan Gao, Xiusi Chen, Haoming Jiang

Existing Large Language Models (LLMs) usually remain static after deployment, which might make it hard to inject new knowledge into the model. We aim to build models containing a considerable portion of self-updatable parameters, enabling the model to integrate new knowledge effectively and efficiently. To this end, we introduce MEMORYLLM, a model that compr

Luyao Ren, Xing Zhang, Ziyue Hua, Yanyan Jiang

Reducing test inputs that trigger bugs is crucial for efficient debugging. Delta debugging is the most popular approach for this purpose. When test inputs need to conform to certain specifications, existing delta debugging practice encounters a validity problem: it blindly applies reduction rules, producing a large number of invalid test inputs that do not s

Weimin Sheng, Yinhang Wang, Jie Wu

In this paper, we first give some new characterizations of geodesic spheres in the hyperbolic space by the condition that hypersurface has constant weighted shifted mean curvatures, or constant weighted shifted mean curvature ratio, which generalize the result of Hu-Wei-Zhou \cite{HWZ23}. Secondly, we investigate several rigidity problems for hypersurfaces i

Soo Yong Lee, Sunwoo Kim, Fanchen Bu, Jaemin Yoo

How would randomly shuffling feature vectors among nodes from the same class affect graph neural networks (GNNs)? The feature shuffle, intuitively, perturbs the dependence between graph topology and features (A-X dependence) for GNNs to learn from. Surprisingly, we observe a consistent and significant improvement in GNN performance following the feature shuf

Pragnya Ramjee, Bhuvan Sachdeva, Satvik Golechha, Shreyas Kulkarni

The healthcare landscape is evolving, with patients seeking reliable information about their health conditions and available treatment options. Despite the abundance of information sources, the digital age overwhelms individuals with excess, often inaccurate information. Patients primarily trust medical professionals, highlighting the need for expert-endorse

Rajesh Ranjan Patra, Sarit Maitra

In this article, a non-smooth predator-prey dynamical system is considered. Here, we discuss about sustainable harvesting in a Filippov predator-prey system, which can produce yield and at the same time prevent over-exploitation of bioresources. The local and global stability analysis of the two subsystems, with and without harvesting, are studied. Furthermo

Xi Chen, Yang Cai, Yuan Wu, Bo Xiong

Recently, MBConv blocks, initially designed for efficiency in resource-limited settings and later adapted for cutting-edge image classification performances, have demonstrated significant potential in image classification tasks. Despite their success, their application in semantic segmentation has remained relatively unexplored. This paper introduces a novel

Chaojun Xiao, Pengle Zhang, Xu Han, Guangxuan Xiao

Large language models (LLMs) have emerged as a cornerstone in real-world applications with lengthy streaming inputs (e.g., LLM-driven agents). However, existing LLMs, pre-trained on sequences with a restricted maximum length, cannot process longer sequences due to the out-of-domain and distraction issues. Common solutions often involve continual pre-training

Yijun Tian, Yikun Han, Xiusi Chen, Wei Wang

Transferring the reasoning capability from stronger large language models (LLMs) to smaller ones has been quite appealing, as smaller LLMs are more flexible to deploy with less expense. Among the existing solutions, knowledge distillation stands out due to its outstanding efficiency and generalization. However, existing methods suffer from several drawbacks,

Gilles Baechler, Srinivas Sunkara, Maria Wang, Fedir Zubach

Screen user interfaces (UIs) and infographics, sharing similar visual language and design principles, play important roles in human communication and human-machine interaction. We introduce ScreenAI, a vision-language model that specializes in UI and infographics understanding. Our model improves upon the PaLI architecture with the flexible patching strategy

Chirag Agarwal, Sree Harsha Tanneru, Himabindu Lakkaraju

Large Language Models (LLMs) are deployed as powerful tools for several natural language processing (NLP) applications. Recent works show that modern LLMs can generate self-explanations (SEs), which elicit their intermediate reasoning steps for explaining their behavior. Self-explanations have seen widespread adoption owing to their conversational and plausi

Viktor Stein, Sebastian Neumayer, Nicolaj Rux, Gabriele Steidl

Commonly used $f$-divergences of measures, e.g., the Kullback-Leibler divergence, are subject to limitations regarding the support of the involved measures. A remedy is regularizing the $f$-divergence by a squared maximum mean discrepancy (MMD) associated with a characteristic kernel $K$. We use the kernel mean embedding to show that this regularization can

Jiajun Ma, Qinghua Chen, Xiaosong Chen, Jingfang Fan

Ignoring the differences between countries, human reproductive and dispersal behaviors can be described by some standardized models, so whether there is a universal law of population growth hidden in the abundant and unstructured data from various countries remains unclear. The age-specific population data constitute a three-dimensional tensor containing mor

Masato Sato, Nozomu Tominaga, Sergei I. Blinnikov, Marat Sh. Potashov

Core-collapse supernovae (CCSNe) are the terminal explosions of massive stars. While most massive stars explode as iron-core-collapse supernovae (FeCCSNe), slightly less massive stars explode as electron-capture supernovae (ECSNe), shaping the low-mass end of CCSNe. ECSNe was proposed $\sim 40$ years ago and first-principles simulations also predict their su

Tim Jahn, Bangti Jin

In recent years, new regularization methods based on (deep) neural networks have shown very promising empirical performance for the numerical solution of ill-posed problems, e.g., in medical imaging and imaging science. Due to the nonlinearity of neural networks, these methods often lack satisfactory theoretical justification. In this work, we rigorously dis

Zhuang Li, Levon Haroutunian, Raj Tumuluri, Philip Cohen

Post-editing has proven effective in improving the quality of text generated by large language models (LLMs) such as GPT-3.5 or GPT-4, particularly when direct updating of their parameters to enhance text quality is infeasible or expensive. However, relying solely on smaller language models for post-editing can limit the LLMs' ability to generalize across do

Levi Tegg, Ingrid E. McCarroll, Se-Ho Kim, Renelle Dubosq

The application of atom probe tomography (APT) to frozen liquids is limited by difficulties in specimen preparation. Here, we report on the use of nanoporous Cu needles as a physical framework to hold water ice for investigation using APT. Nanoporous Cu needles are prepared by the electropolishing and dealloying of Cu-Mn matchstick precursors. Cryogenic scan

Yingru Li, Liangqi Liu, Wenqiang Pu, Hao Liang

This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through bandit feedback alongside strategic decision-making. We introduce Thompson Sampling (TS)-based algorithms that exploit the information of opponents' actions and reward structures, lead

Hazem Ibrahim, Fengyuan Liu, Yasir Zaki, Talal Rahwan

Citations are widely considered in scientists' evaluation. As such, scientists may be incentivized to inflate their citation counts. While previous literature has examined self-citations and citation cartels, it remains unclear whether scientists can purchase citations. Here, we compile a dataset of ~1.6 million profiles on Google Scholar to examine instance

Duc V. Nguyen, Dung V. Duong

This paper proposes a scalable lattice-Boltzmann computational framework (SBoTFlow) for simulations of flexible moving objects in an incompressible fluid flow. Behavior of fluid flow formed from moving boundaries of flexible-object motions is obtained through the multidirect forcing immersed boundary scheme associated with the lattice Boltzmann equation with

Alberto Canonaco, Christian Haesemeyer, Amnon Neeman, Paolo Stellari

In this article we prove that all the inclusions between the 'classical' and naturally defined full triangulated subcategories of a weakly approximable triangulated category are intrinsic (in one case under a technical condition). This extends all the existing results about subcategories of weakly approximable triangulated categories. Together with a forthco

Sugata Mandal

Let $ K$ be a field admitting a Galois extension $L$ of degree $n$, denoting the Galois group as $G = \gal(L/K)$. Our focus lies on the space $\sym_K(L)$ of symmetric $K$-bilinear forms on $L$. We establish a decomposition of $\sym_K(L)$ into direct sum of $K$-subspaces $A^{\sigma_i}$, where $\sigma_i \in G$. Notably, these subspaces $ A^{\sigma_i}$ exhibit

Debashish Mondal, Tanay Nag

Considering a non-Hermitian version of $p$-wave Kitaev chain in the presence of additional second nearest neighbour tunneling, we study dynamical quantum phase transition (DQPT) which accounts for the vanishing Loschmidt amplitude. The locus of the Fisher's zero traces a continuous path on the complex time plane for the Hermitian case while it becomes discon

Yinan Shen, Dong Xia, Wen-Xin Zhou

This paper addresses the challenge of integrating sequentially arriving data within the quantile regression framework, where the number of features is allowed to grow with the number of observations, the horizon is unknown, and memory is limited. We employ stochastic sub-gradient descent to minimize the empirical check loss and study its statistical properti

Haihui Yang, Xiaojun Quan

Chinese grammatical error correction (CGEC) faces serious overcorrection challenges when employing autoregressive generative models such as sequence-to-sequence (Seq2Seq) models and decoder-only large language models (LLMs). While previous methods aim to address overcorrection in Seq2Seq models, they are difficult to adapt to decoder-only LLMs. In this paper

Shao-Long Chen, Yu-Qi Xiao

Neutrinoless double beta ($0\nu\beta\beta$) decay is the most promising way to determine whether neutrinos are Majorana particles. There are many experiments based on different isotopes searching for $0\nu\beta\beta$ decay. Combining the searches of $0\nu\beta\beta$ decay in multiple isotopes provides a possible method to distinguish operators and different

Lei Wang, Jun Liu, Liang Zheng, Tom Gedeon

Video sequences exhibit significant nuisance variations (undesired effects) of speed of actions, temporal locations, and subjects' poses, leading to temporal-viewpoint misalignment when comparing two sets of frames or evaluating the similarity of two sequences. Thus, we propose Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE) for sequence pairs. In par

Sarah Schömbs, Jorge Goncalves, Wafa Johal

In the light of recent advances in embodied data visualizations, we aim to shed light on agency in the context of data visualization. To do so, we introduce Data Agency and Data-Agent Interplay as potential terms and research focus. Furthermore, we exemplify the former in the context of human-robot interaction, and identify future challenges and research que

Javier Ferrer, Francisco Chicano, José Antonio Ortega Toro

In Software Product Lines (SPLs) it may be difficult or even impossible to test all the products of the family because of the large number of valid feature combinations that may exist. Thus, we want to find a minimal subset of the product family that allows us to test all these possible combinations (pairwise). Furthermore, when testing a single product is a

Mairembam Kelvin Singh, A. Surjalal Sharma, N. Nimai Singh, Moirangthem Shubhakanta Singh

Complexity is often exhibited in dynamical systems, where certain parameters evolve with time in a strange and chaotic nature. These systems lack predictability and are common in the physical world. Dissipative systems are one of such systems where the volume of the phase space contracts with time. On the other hand, we employ dimensionality reduction techni

Sourav Mishra, Shirin Dora, Suresh Sundaram

Algorithms designed for addressing typical supervised classification problems can only learn from a fixed set of samples and labels, making them unsuitable for the real world, where data arrives as a stream of samples often associated with multiple labels over time. This motivates the study of task-agnostic continual multi-label learning problems. While algo

Osamu Fujino

We discuss Iitaka's theory of quasi-Albanese maps in details. We also give a detailed proof of Kawamata's theorem on the quasi-Albanese maps for varieties of the logarithmic Kodaira dimension zero. Note that Iitaka's theory is an application of Deligne's mixed Hodge theory for smooth algebraic varieties.

Mike Nkongolo

The rise of ransomware attacks has necessitated the development of effective strategies for identifying and mitigating these threats. This research investigates the utilization of a feature selection algorithm for distinguishing ransomware-related and benign transactions in both Bitcoin (BTC) and United States Dollar (USD). Leveraging the UGRansome dataset,

Subhadeep Paul, Shanjukta Nath

The Spatial AutoRegressive model (SAR) is commonly used in studies involving spatial and network data to estimate the spatial or network peer influence and the effects of covariates on the response, taking into account the dependence among units. While the model can be efficiently estimated with a Quasi maximum likelihood approach (QMLE), the detrimental eff

Gil Goffer, Denis Osin, Ekaterina Rybak

We study Frattini subgroups of various generalizations of hyperbolic groups. For any countable group $G$ admitting a general type action on a hyperbolic space $S$, we show that the induced action of the Frattini subgroup $\Phi(G)$ on $S$ has bounded orbits. This implies that $\Phi(G)$ is "small" compared to $G$; in particular, $|G:\Phi(G)|=\infty$. In contra

Yu-Wei Yu, Hai-Ming Zhang, Ying-Ying Gan, Xin-Ke Hu

We report on the detection of extended $\gamma$-ray emission from lobes in the radio galaxy NGC 6251 using observation data of Fermi Large Area Telescope (Fermi-LAT). The maximum likelihood analysis results show that a radio morphology template provides a better fit than a point-like source description for the observational data at a confidence level of 8.1$

Amin Ullah, Guilin Qi, Saddam Hussain, Irfan Ullah

Smart cities stand as pivotal components in the ongoing pursuit of elevating urban living standards, facilitating the rapid expansion of urban areas while efficiently managing resources through sustainable and scalable innovations. In this regard, as emerging technologies like Artificial Intelligence (AI), the Internet of Things (IoT), big data analytics, an

Sacha Huriot-Tattegrain, Glynn Winskel

Probabilistic concurrent/distributed strategies have so far not been investigated thoroughly in the context of imperfect information, where the Player has only partial knowledge of the moves made by the Opponent. In a situation where the Player and Opponent can make concurrent moves according to the game, and the Player cannot see the move of the Opponent, t

Reem Alfaidi, Christopher Messenger

The pre-merger detection of gravitational waves from the early inspiral phase of compact binary coalescence events would allow the observation of the earlier stages of the merger in the electromagnetic band. This would significantly impact multi-messenger astronomy, giving astronomers potential access to rich new information. Here, we introduce a proof-of-co

Haoyu Wang, Shuo Wang, Yukun Yan, Xujia Wang

Open-source large language models (LLMs) have gained significant strength across diverse fields. Nevertheless, the majority of studies primarily concentrate on English, with only limited exploration into the realm of multilingual abilities. In this work, we therefore construct an open-source multilingual supervised fine-tuning dataset. Different from previou

Pengyu Dai, Yafei Ou, Yuqiao Yang, Yang Liu

Accurate tooth identification and segmentation in Cone Beam Computed Tomography (CBCT) dental images can significantly enhance the efficiency and precision of manual diagnoses performed by dentists. However, existing segmentation methods are mainly developed based on large data volumes training, on which their annotations are extremely time-consuming. Meanwh

Miguel Ángel Domínguez-Ríos, Francisco Chicano, Enrique Alba, Isabel María del Águila

The Next Release Problem consists in selecting a subset of requirements to develop in the next release of a software product. The selection should be done in a way that maximizes the satisfaction of the stakeholders while the development cost is minimized and the constraints of the requirements are fulfilled. Recent works have solved the problem using exact

Yinling Zhang, Nan Chen, Jerome Vialard, Xianghui Fang

Understanding ENSO dynamics has tremendously improved over the past decades. However, one aspect still poorly understood or represented in conceptual models is the ENSO diversity in spatial pattern, peak intensity, and temporal evolution. In this paper, a physics-informed auto-learning framework is developed to derive ENSO stochastic conceptual models with v

Yinghao Song, Zhiyuan Cao, Wanhong Xiang, Sifan Long

Low-light image enhancement (LLIE) restores the color and brightness of underexposed images. Supervised methods suffer from high costs in collecting low/normal-light image pairs. Unsupervised methods invest substantial effort in crafting complex loss functions. We address these two challenges through the proposed TroubleMaker Learning (TML) strategy, which e

Prasoon Ambalathankandy, Yafei Ou, Sae Kaneko, Masayuki Ikebe

Grayscale images are essential in image processing and computer vision tasks. They effectively emphasize luminance and contrast, highlighting important visual features, while also being easily compatible with other algorithms. Moreover, their simplified representation makes them efficient for storage and transmission purposes. While preserving contrast is im

Jungho Kim, Sang-ri Yi, Ziqi Wang

We introduce a method to construct a stochastic surrogate model from the results of dimensionality reduction in forward uncertainty quantification. The hypothesis is that the high-dimensional input augmented by the output of a computational model admits a low-dimensional representation. This assumption can be met by numerous uncertainty quantification applic

Yifei Chen, Lambert Schomaker, Francisco Cruz

In reinforcement learning, reward shaping is an efficient way to guide the learning process of an agent, as the reward can indicate the optimal policy of the task. The potential-based reward shaping framework was proposed to guarantee policy invariance after reward shaping, where a potential function is used to calculate the shaping reward. In former work, w

Haoyi Niu, Jianming Hu, Guyue Zhou, Xianyuan Zhan

The burgeoning fields of robot learning and embodied AI have triggered an increasing demand for large quantities of data. However, collecting sufficient unbiased data from the target domain remains a challenge due to costly data collection processes and stringent safety requirements. Consequently, researchers often resort to data from easily accessible sourc

Ahmad-Reza Ehyaei, Ali Shirali, Samira Samadi

Counterfactual explanations provide individuals with cost-optimal recommendations to achieve their desired outcomes. However, when a significant number of individuals seek similar state modifications, this individual-centric approach can inadvertently create competition and introduce unforeseen costs. Additionally, disregarding the underlying data distributi

Jiaqi Chen, Yuxian Jiang, Jiachen Lu, Li Zhang

Leveraging large language models (LLMs), autonomous agents have significantly improved, gaining the ability to handle a variety of tasks. In open-ended settings, optimizing collaboration for efficiency and effectiveness demands flexible adjustments. Despite this, current research mainly emphasizes fixed, task-oriented workflows and overlooks agent-centric or

Jialue Li, Xiao Zhang

Inspired by interaction of gravitational waves and dark matters, we study the Bondi-Sachs formalism for Einstein massless scalar field with zero cosmological constant. We provide asymptotic expansions for the Bondi-Sachs metrics as well as the scalar fields and prove the peeling property. We also prove the positivity of the Bondi energy-momentum under condit

Kejia Zhu

We study the connection between the fundamental groups of complex hyperbolic manifolds and those of spaces arising from the (relative) strict hyperbolization process due to Charney--Davis and Davis--Januszkiewicz--Weinberger. Viewing a non-uniform lattice $\Gamma$ in $\text{PU}(n,1)$ as a relatively hyperbolic group with respect to its cusp subgroups in the

Saikat Mondal, Mohammad Masudur Rahman, Chanchal K. Roy

On the Stack Overflow (SO) Q&A site, users often request solutions to their code-related problems (e.g., errors, unexpected behavior). Unfortunately, they often miss required code snippets during their question submission, which could prevent their questions from getting prompt and appropriate answers. In this study, we conduct an empirical study investigati

Cheyanne Shariat, Yasuhiro Hasegawa, Bradley M. S. Hansen, Tze Yeung Mathew Yu

Most, if not all, sun-like stars host one or more planets, making multi-planetary systems commonplace in our galaxy. We utilize hundreds of multi-planet simulations to explore the origin of such systems, focusing on their orbital architecture. The first set of simulations assumes in-situ assembly of planetary embryos, while the second explores planetary migr

Baiyili Liu, Songsong Ji, Gang Pang, Shaoqiang Tang

In this paper, we design a series of matching boundary conditions for a two-dimensional compound honeycomb lattice, which has an explicit and simple form, high computing efficiency and good effectiveness of suppressing boundary reflections. First, we formulate the dynamic equations and calculate the dispersion relation for the harmonic honeycomb lattice, the

Gangming Zhao, Chaoqi Chen, Wenhao He, Chengwei Pan

Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. However, in many real-world cases, target data usually emerge sequentially and have continuously evolving distributions. Restoring and adapting to such target data results in escalating computational and resource consumption over time. Hence, it i

Xingzhi Zhan

Thomassen's chord conjecture from 1976 states that every longest cycle in a $3$-connected graph has a chord. This is one of the most important unsolved problems in graph theory. We pose a new conjecture which implies Thomassen's conjecture. It involves bound vertices in a longest path between two vertices in a $k$-connected graph. We also give supporting evi

Ryo Ohkawa, Jun'ichi Shiraishi

We study $K$-theoretic integrals over famed quiver moduli via wall-crossing phenomena. We study the chainsaw quiver varieties, and consider generating functions defined by two types of $K$-theoretic classes. In particular, we focus on integrals over the handsaw quiver varieties of type $A_{1}$, and get functional equations for each of them. We also give expl

Kali Krishna Kota, Praful D. Mankar

This paper aims to explore reconfigurable intelligent surface (RIS) integration in a millimeter wave (mmWave) communication system with low-complexity transceiver architecture under imperfect CSI assumption. Towards this, we propose a RIS-aided system with a fully analog (FA) architecture at the base station. However, to overcome the disadvantage of single-u

Woohyeok Jo, Jongil Park, Kyungbae Park

Let $S$ be a rational homology complex projective plane with quotient singularities. The algebraic Montgomery-Yang problem conjectures that the number of singular points of $S$ is at most three if its smooth locus is simply-connected. In this paper, we leverage results from the study of smooth 4-manifolds, including the Donaldson diagonalization theorem and

Saikat Mondal, Suborno Deb Bappon, Chanchal K. Roy

Prompt design plays a crucial role in shaping the efficacy of ChatGPT, influencing the model's ability to extract contextually accurate responses. Thus, optimal prompt construction is essential for maximizing the utility and performance of ChatGPT. However, sub-optimal prompt design may necessitate iterative refinement, as imprecise or ambiguous instructions

Chen Wang, Sarah Erfani, Tansu Alpcan, Christopher Leckie

Anomaly detection in decision-making sequences is a challenging problem due to the complexity of normality representation learning and the sequential nature of the task. Most existing methods based on Reinforcement Learning (RL) are difficult to implement in the real world due to unrealistic assumptions, such as having access to environment dynamics, reward

Lu Wen, Qihun Zhang, Zhenghao Feng, Yuanyuan Xu

Radiotherapy is a primary treatment for cancers with the aim of applying sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs at risk (OARs). Convolutional neural networks (CNNs) have automated the radiotherapy plan-making by predicting the dose maps. However, current CNN-based methods ignore the remarkabl

Yoshito Ishiki

For a metrizable space, we consider the space of all metrics generating the same topology of the metrizable space, and this space of metrics is equipped with the supremum metric. In this paper, for every metrizable space, we establish that the space of metrics on the metrizable space is Baire. We also show that the set of all complete metrics is comeager in

Elie Abdo, Zhongtian Hu

In this paper, we study the coupled Keller-Segel-Navier-Stokes system, which models chemotaxis occuring in ambient viscous fluid. We consider this nonlinear, nonlocal system on a periodic strip, equipped with homogeneous Neumann boundary conditions for the Keller-Segel part and no-slip boundary condition for the fluid part. We prove the simultaneous space-ti

Saebom Leem, Hyunseok Seo

Vision Transformer(ViT) is one of the most widely used models in the computer vision field with its great performance on various tasks. In order to fully utilize the ViT-based architecture in various applications, proper visualization methods with a decent localization performance are necessary, but these methods employed in CNN-based models are still not av

Suchitra Gupta, Joon Hwan Choi, Hojin Jeong, Seung-Cheol Lee

This study investigates the catalytic degradation of ground-level ozone on low-index stoichiometric and reduced CeO$_2$ surfaces using first-principles calculations. The presence of oxygen vacancies on the surface enhances the interaction between ozone and catalyst by serving as active sites for adsorption and decomposition. Our results suggest that the {111

Govind M. Chari, Behçet Açıkmeşe

We implement a fully factorization-free algorithm for nonconvex, free-final-time trajectory optimization. This algorithm is based on sequential convex programming and utilizes an inverse-free, exact discretization procedure to ensure dynamic feasibility of the converged trajectory and PIPG, a fast, first-order conic optimization algorithm as the subproblem s

Hernando Maldonado Colmán, Michael E. Mueller

Large Eddy Simulation (LES) was used to investigate the evolution of the soot size distribution in a series of turbulent nonpremixed bluff body flames, with different bluff body diameters. The new Bivariate Multi-Moment Sectional Method (BMMSM) is employed to characterize the size distribution. BMMSM combines elements of sectional methods and methods of mome

Chengxing Xie, Canyu Chen, Feiran Jia, Ziyu Ye

Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in social science and role-playing applications. However, one fundamental question remains: can LLM agents really simulate human behavior? In this paper, we focus on one critical and elemental behavior in human interactions, trust, and investigate whether LLM

Guoqiang Liang, Jiahao Hu, Qingyue Wang, Shizhou Zhang

Human de-occlusion, which aims to infer the appearance of invisible human parts from an occluded image, has great value in many human-related tasks, such as person re-id, and intention inference. To address this task, this paper proposes a dynamic mask-aware transformer (DMAT), which dynamically augments information from human regions and weakens that from o

Nung Siong Lai, Yi Shen Tew, Xialin Zhong, Jun Yin

In the pursuit of novel catalyst development to address pressing environmental concerns and energy demand, conventional design and optimization methods often fall short due to the complexity and vastness of the catalyst parameter space. The advent of Machine Learning (ML) has ushered in a new era in the field of catalyst optimization, offering potential solu

Hernando Maldonado Colmán, Michael E. Mueller

A joint volume-surface formalism of the Multi-Moment Sectional Method (MMSM) is developed to describe the evolution of soot size distribution in turbulent reacting flows. The bivariate MMSM (or BMMSM) considers three statistical moments per section, including the total soot number density, total soot volume, and total soot surface area per section. A linear

Chuhao Liu, Ke Wang, Jieqi Shi, Zhijian Qiao

Semantic mapping based on the supervised object detectors is sensitive to image distribution. In real-world environments, the object detection and segmentation performance can lead to a major drop, preventing the use of semantic mapping in a wider domain. On the other hand, the development of vision-language foundation models demonstrates a strong zero-shot

Huiqing Zhang, Yifei Xue, Ming Liao, Yizhen Lao

In this study, we introduce BirdNeRF, an adaptation of Neural Radiance Fields (NeRF) designed specifically for reconstructing large-scale scenes using aerial imagery. Unlike previous research focused on small-scale and object-centric NeRF reconstruction, our approach addresses multiple challenges, including (1) Addressing the issue of slow training and rende

Omead Pooladzandi, Xi-Lin Li

We present a novel approach to accelerate stochastic gradient descent (SGD) by utilizing curvature information obtained from Hessian-vector products or finite differences of parameters and gradients, similar to the BFGS algorithm. Our approach involves two preconditioners: a matrix-free preconditioner and a low-rank approximation preconditioner. We update bo

Takaaki Ishii, Yu Nakayama

In the Einstein-Maxwell-charged scalar system with a negative cosmological constant in arbitrary dimensions higher than three, there exists a horizonless charged soliton solution, which we construct explicitly for an arbitrary mass of the scalar in perturbative series in small charge. We find that the stability of the soliton is determined by the validity of