February 2024 arXiv papers — page 161

Han Yingxiao, Fang Mi, Xia Liuye, Gao Hongya

We present two generalizations of the classical Stampacchia Lemma which contain a non-decreasing non-negative function $g$, and give applications. As a first application, we deal with variational integrals of the form $$ {\cal J} (u;\Omega) = \int_{\Omega}\ f(x,Du{(x)})dx. $$ We consider a minimizer $u: \Omega \subset \mathbb R^n \to \mathbb R $ among all fu

Kathy Q. Ji, Dax T. X. Zhang

The polynomial of the major index ${\rm maj}_W (\sigma)$ over the subset $T$ of the Coxeter group $W$ is called the Mahonian polynomial over $T$, where ${\rm maj}_W (\sigma)$ is a Mahonian statistic of an element $\sigma \in T$, whereas the polynomial of the major index ${\rm maj}_W (\sigma)$ with the sign $(-1)^{\ell_W(\sigma)}$ over the subset $T$ is refer

David J. Hemmer

Mullineux defined an involution on the set of $e$-regular partitions of $n$. When $e=p$ is prime, these partitions label irreducible symmetric group modules in characteristic $p$. Mullineux's conjecture, since proven, was that this ``Mullineux map" described the effect on the labels of taking the tensor product with the one-dimensional signature representati

Anton Lavrouk, Ian Ligon, Tarek Naous, Jonathan Zheng

The Stanceosaurus corpus (Zheng et al., 2022) was designed to provide high-quality, annotated, 5-way stance data extracted from Twitter, suitable for analyzing cross-cultural and cross-lingual misinformation. In the Stanceosaurus 2.0 iteration, we extend this framework to encompass Russian and Spanish. The former is of current significance due to prevalent m

Wei Jiang, Chunmei Su, Ganghui Zhang

We propose a novel class of temporal high-order parametric finite element methods for solving a wide range of geometric flows of curves and surfaces. By incorporating the backward differentiation formulae (BDF) for time discretization into the BGN formulation, originally proposed by Barrett, Garcke, and N\"urnberg (J. Comput. Phys., 222 (2007), pp.~441--467)

Abdelrahman Hosny, Sherief Reda

The remarkable achievements of machine learning techniques in analyzing discrete structures have drawn significant attention towards their integration into combinatorial optimization algorithms. Typically, these methodologies improve existing solvers by injecting learned models within the solving loop to enhance the efficiency of the search process. In this

J. R. Fuentes, Matias Castro-Tapia, Andrew Cumming

The origin of large magnetic fields ($\gtrsim 10^6~\mathrm{G}$) in isolated white dwarfs is not clear. One possible explanation is that crystallization of the star's core drives compositional convection, which when combined with the star's rotation, can drive a dynamo. However, whether convection is efficient enough to explain the large intensity of the obse

Jiashi Yang

A complete and systematic derivation of the purely macroscopic theory of rigid semiconductors is given. It is based on a five-continuum model of charged and interpenetrating continua, and the applications of the relevant laws of physics to the continua. The theory is within the quasistatic approximation of electrostatics and is limited to nonmagnetizable mat

Kim Morrison, Noah Snyder, Dylan P. Thurston

We find a single two-parameter skein relation on trivalent graphs, the quantum exceptional relation, that specializes to a skein relation associated to each exceptional Lie algebra (in the adjoint representation). If a slight strengthening of Deligne's conjecture on the existence of a (classical) exceptional series is true, then this relation holds for a new

Qing Li, Zhihang Hu, Yixuan Wang, Lei Li

Bioinformatics has witnessed a paradigm shift with the increasing integration of artificial intelligence (AI), particularly through the adoption of foundation models (FMs). These AI techniques have rapidly advanced, addressing historical challenges in bioinformatics such as the scarcity of annotated data and the presence of data noise. FMs are particularly a

Shrutika Vishal Thengane, Yu Xiang Tan, Marcel Bartholomeus Prasetyo, Malika Meghjani

The underwater world remains largely unexplored, with Autonomous Underwater Vehicles (AUVs) playing a crucial role in sub-sea explorations. However, continuous monitoring of underwater environments using AUVs can generate a significant amount of data. In addition, sending live data feed from an underwater environment requires dedicated on-board data storage

Sijin Lu, Pengyu Xu, Bing Liu, Hongjian Sun

Posts in software Q\&A sites often consist of three main parts: title, description and code, which are interconnected and jointly describe the question. Existing tag recommendation methods often treat different modalities as a whole or inadequately consider the interaction between different modalities. Additionally, they focus on extracting information direc

Feng Liu, Tengteng Huang, Qianjing Zhang, Haotian Yao

Multi-view 3D object detection systems often struggle with generating precise predictions due to the challenges in estimating depth from images, increasing redundant and incorrect detections. Our paper presents Ray Denoising, an innovative method that enhances detection accuracy by strategically sampling along camera rays to construct hard negative examples.

Su Hyeong Lee, Qingqi Zhang, Risi Kondor

Inner product-based decoders are among the most influential frameworks used to extract meaningful data from latent embeddings. However, such decoders have shown limitations in representation capacity in numerous works within the literature, which have been particularly notable in graph reconstruction problems. In this paper, we provide the first theoretical

Quang Dao, Aayush Jain

Over the past few decades, we have seen a proliferation of advanced cryptographic primitives with lossy or homomorphic properties built from various assumptions such as Quadratic Residuosity, Decisional Diffie-Hellman, and Learning with Errors. These primitives imply hard problems in the complexity class $SZK$ (statistical zero-knowledge); as a consequence,

Liuxian Zhao, Chunlin Li, Xuxu Zhuang, Hao Guo

In this paper, a modified Maxwell's fish-eye lens is proposed in order to achieve super-resolution imaging. This lens possesses elevated refractive index profile compared with the traditional Maxwell's fish-eye lens. The refractive index profile is achieved with variable thickness configuration defined in a sheet plate structure, to realise desired changes i

SungWoo Youn, Junu Jeong, Yannis K. Semertzidis

The axion offers a well-motivated solution to two fundamental questions in modern physics: the strong CP problem and the dark matter mystery. Cavity haloscopes, exploiting resonant enhancement of photon signals, provide the most sensitive searches for axion dark matter in the microwave region. However, current experimental sensitivities are limited to the O(

Marco Ripà

For every non-negative integer $a$ and positive integer $b$, the congruence speed of the tetration $^{b}a$ is the difference between the number of the rightmost digits of $^{b}a$ that are the same as those of $^{b+1}a$ and the number of the rightmost digits of $^{b-1}a$ that are the same as those of $^{b}a$. In the decimal numeral system, if the given base $

Aoran Xiao, Weihao Xuan, Heli Qi, Yun Xing

The recent Segment Anything Model (SAM) has demonstrated remarkable zero-shot capability and flexible geometric prompting in general image segmentation. However, SAM often struggles when handling various unconventional images, such as aerial, medical, and non-RGB images. This paper presents CAT-SAM, a ConditionAl Tuning network that adapts SAM toward various

Yichen Li, Yun Peng, Yintong Huo, Michael R. Lyu

Large Language Models (LLMs) have achieved remarkable success in code completion, as evidenced by their essential roles in developing code assistant services such as Copilot. Being trained on in-file contexts, current LLMs are quite effective in completing code for single source files. However, it is challenging for them to conduct repository-level code comp

Saswat Das, Marco Romanelli, Ferdinando Fioretto

Ensuring privacy-preserving inference on cryptographically secure data is a well-known computational challenge. To alleviate the bottleneck of costly cryptographic computations in non-linear activations, recent methods have suggested linearizing a targeted portion of these activations in neural networks. This technique results in significantly reduced runtim

Zhixuan Chu, Yan Wang, Feng Zhu, Lu Yu

The advent of large language models (LLMs) such as ChatGPT, PaLM, and GPT-4 has catalyzed remarkable advances in natural language processing, demonstrating human-like language fluency and reasoning capacities. This position paper introduces the concept of Professional Agents (PAgents), an application framework harnessing LLM capabilities to create autonomous

Hao Wang, Jinzhe Jiang, Xin Zhang, Chen Li

As Large Language Models make a breakthrough in natural language processing tasks (NLP), multimodal technique becomes extremely popular. However, it has been shown that multimodal NLP are vulnerable to adversarial attacks, where the outputs of a model can be dramatically changed by a perturbation to the input. While several defense techniques have been propo

Aklant K. Bhowmick, Laura Blecha, Paul Torrey, Luke Zoltan Kelley

The first "seeds" of supermassive black holes (BH) can range from $\sim10^2-10^6~M_{\odot}$. However, the lowest mass seeds ($\lesssim10^3 M_{\odot}$) are inaccessible to most cosmological simulations due to resolution limitations. We present our new BRAHMA suite of cosmological simulations that uses a novel flexible seeding approach to represent low mass se

Junwei Su, Difan Zou, Chuan Wu

Memory-based Dynamic Graph Neural Networks (MDGNNs) are a family of dynamic graph neural networks that leverage a memory module to extract, distill, and memorize long-term temporal dependencies, leading to superior performance compared to memory-less counterparts. However, training MDGNNs faces the challenge of handling entangled temporal and structural depe

Sungyoon Kim, Mert Pilanci

In this paper, we study the optimality gap between two-layer ReLU networks regularized with weight decay and their convex relaxations. We show that when the training data is random, the relative optimality gap between the original problem and its relaxation can be bounded by a factor of O(log n^0.5), where n is the number of training samples. A simple applic

Tao Li, Qing-Wen Wang, Xin-Fang Zhang

The quaternion biconjugate gradient (QBiCG) method, as a novel variant of quaternion Lanczos-type methods for solving the non-Hermitian quaternion linear systems, does not yield a minimization property. This means that the method possesses a rather irregular convergence behavior, which leads to numerical instability. In this paper, we propose a new structure

Daniil Asafov, Ilia Pavlov

Two-dimensional semi-Dirac models describe a family of novel materials that have anisotropic dispersion with relativistic-type spectrum along one of the spatial directions and non-relativistic along another. In the present paper we perform a detailed analysis of Landau levels collapse for such models in perpendicular magnetic field and in-plane electric fiel

Yu-Fei Liu, Wen-Jing Xing, Xiang-Yu Wu, Shanshan Cao

We study the charmed meson and baryon production and elliptic flow in ultra-relativistic nucleus-nucleus collisions at the LHC energies. The space-time evolution of quark-gluon plasma (QGP) produced in these energetic collisions is obtained via the (3+1)-dimensional CLVisc hydrodynamics model, the heavy quark dynamics inside the QGP is simulated using an imp

Shivvrat Arya, Tahrima Rahman, Vibhav Gogate

Probabilistic circuits (PCs) such as sum-product networks efficiently represent large multi-variate probability distributions. They are preferred in practice over other probabilistic representations such as Bayesian and Markov networks because PCs can solve marginal inference (MAR) tasks in time that scales linearly in the size of the network. Unfortunately,

Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen

We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods. Core to the framework is a self-discovery process where LLMs select multiple atomic reasoning modules such as critical thinking and step-by-step thinking, an

Alexandru I. Suciu

Each complex hyperplane arrangement gives rise to a Milnor fibration of its complement. Although the Betti numbers of the Milnor fiber $F$ can be expressed in terms of the jump loci for rank 1 local systems on the complement, explicit formulas are still lacking in full generality, even for $b_1(F)$. We study here the "generic" case (in which $b_1(F)$ is as s

Sreejan Kumar, Raja Marjieh, Byron Zhang, Declan Campbell

Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a paradigm similar to the game of telephone, where one person observes a stimulus and reproduces it for the next to form a chain of reproductions. Past serial reproduction experiments typically employ a single

Caitlin Freeman, Arun Niddish Mahendran, Vishesh Vikas

Locomotion gaits are fundamental for control of soft terrestrial robots. However, synthesis of these gaits is challenging due to modeling of robot-environment interaction and lack of a mathematical framework. This work presents an environment-centric, data-driven and fault-tolerant probabilistic Model-Free Control (pMFC) framework that allows for soft multi-

Yujin Kim, Chin-Chia Hsu

Large Language Models (LLMs) hold the potential to perform a variety of text processing tasks and provide textual explanations for proposed actions or decisions. In the era of hybrid work, LLMs can provide intelligent decision support for workers who are designing their hybrid work plans. In particular, they can offer suggestions and explanations to workers

Wes Hellwig, Xian Shi, William A. Sirignano

A three-dimensional flamelet model considering vortex stretching with unitary Lewis number is used to simulate diluted hydrogen-oxygen diffusion flames. Non-reacting nitrogen is used as the diluent gas in the fuel stream. Unitary Lewis number provides a common thermal and mass diffusivity from which to create scalar dissipation rate. Both stable and unstable

He Zhao, Vassili Kitsios, Terence J. O'Kane, Edwin V. Bonilla

We study the problem of automatically discovering Granger causal relations from observational multivariate time-series data.Vector autoregressive (VAR) models have been time-tested for this problem, including Bayesian variants and more recent developments using deep neural networks. Most existing VAR methods for Granger causality use sparsity-inducing penalt

Rajsekhar Mohapatra, Eliot Quataert

Massive elliptical galaxies harbor large amounts of hot gas ($T\gtrsim10^6~\mathrm{K}$) in their interstellar medium (ISM) but are typically quiescent in star formation. Active-galactic nuclei (AGNs) jets and Type Ia supernovae (SNIa) inject energy into the ISM which offsets its radiative losses and keeps it hot. SNIa deposit their energy locally within the

Mahrokh Abdollahi Lorestani, Thilina Ranbaduge, Thierry Rakotoarivelo

With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. The widespread exposure of such location data poses significant privacy risks to users, as it can lead to re-identification, the inference of sensitive information, and even physical threats. In this sur

Jeffrey A. Cloninger, Raine Harris, Kristine L. Haley, Randy M. Sterbentz

The use of metal van der Waals contacts and the implicit reduction in Fermi-level pinning in contacted semiconductors has led to remarkable device optimizations. For example, using graphene as an electrical contact allows for tunable Schottky barriers in transistors and barristors. In this study, we present a double Schottky barrier model and apply it to bar

Tomoyuki Kagaya, Thong Jing Yuan, Yuxuan Lou, Jayashree Karlekar

Owing to recent advancements, Large Language Models (LLMs) can now be deployed as agents for increasingly complex decision-making applications in areas including robotics, gaming, and API integration. However, reflecting past experiences in current decision-making processes, an innate human behavior, continues to pose significant challenges. Addressing this,

Jae-Hwan Choi

This paper investigates the existence, uniqueness, and regularity of solutions to evolution equations with time-measurable pseudo-differential operators in weighted mixed-norm Sobolev-Lipschitz spaces. We also explore trace embedding and continuity of solutions.

Jinyang Li, Timothy Kovachy, Jason Bonacum, Selim M. Shahriar

We analyze theoretically the sensitivity of accelerometry and rotation sensing with a point source interferometer employing large momentum transfer (LMT) and present a design of an inertial measurement unit (IMU) that can measure rotation around and acceleration along each of the three axes. In this design, the launching technique is used to realize the LMT

Trilok Padhi, Ugur Kursuncu, Yaman Kumar, Valerie L. Shalin

The digital landscape continually evolves with multimodality, enriching the online experience for users. Creators and marketers aim to weave subtle contextual cues from various modalities into congruent content to engage users with a harmonious message. This interplay of multimodal cues is often a crucial factor in attracting users' attention. However, this

Yun Soo Myung

We investigate the shadow cast of black holes immersed in a dark matter halo. We use the M87* shadow data obtained by the EHT collaboration to constrain two parameters ($M,a$) of dark matter halo surrounding a black hole with mass $M_{\rm bh}$. For $a\ge10.8M$, we find the favored region (shadow bound), while the disfavored region is found for $a<10.8M$ when

Daniel D. Spiegel, Markus J. Pflaum

We prove that the space $\mathscr{P}(\mathfrak{A})$ of pure states of a nonelementary, simple, separable, real rank zero $C^*$-algebra $\mathfrak{A}$ has trivial homotopy groups of all orders when $\mathscr{P}(\mathfrak{A})$ is equipped with the weak* topology. The convex-valued and finite-dimensional selection theorems of Michael are used to deform a family

Michael C. Kopreski

Let $\Sigma$ be a compact, orientable surface of genus $g$, and let $\Gamma$ be a relation on $\pi_0(\partial \Sigma)$ such that the prescribed arc graph $\mathcal{A}(\Sigma,\Gamma)$ is Gromov-hyperbolic and non-trivial. We show that $\operatorname{asdim} \mathcal{A}(\Sigma,\Gamma) \geq -\chi(\Sigma) - 1$, from which we prove that the asymptotic dimension of

Chunsheng Zuo, Michael Guerzhoy

As we show in this paper, the prediction for output token $n+1$ of Transformer architectures without one of the mechanisms of positional encodings and causal attention is invariant to permutations of input tokens $1, 2, ..., n-1$. Usually, both mechanisms are employed and the symmetry with respect to the input tokens is broken. Recently, it has been shown th

Manuel Beato Vásquez, Melvin Arias Polanco

Historically and to date, the continuity equation has served as a consistency criterion for the development of physical theories. Employing Clifford's geometric algebras, a system of continuity equations for a generalised multivector of the space-time algebra (STA) is constructed. Associated with this continuity system, a system of wave equations is construc

Hafiz Oyediran, William Turner, Kyungki Kim, Matthew Barrows

An obstacle toward construction robotization is the lack of methods to plan robot operations within the entire construction planning process. Despite the strength in modeling construction site conditions, 4D BIM technologies cannot perform construction robot task planning considering the contexts of given work environments. To address this limitation, this s

Gregorio Baldi, Nicholas Miller, Matthew Stover, Emmanuel Ullmo

We investigate and compare applications of the Zilber-Pink conjecture and dynamical methods to rigidity problems for arithmetic real and complex hyperbolic lattices. Along the way we obtain new general results about reconstructing a variation of Hodge structure from its typical Hodge locus that may be of independent interest. Applications to Siu's immersion

Jinqiu Jin, Sihao Ding, Wenjie Wang, Fuli Feng

Common click-through rate (CTR) prediction recommender models tend to exhibit feature-level bias, which leads to unfair recommendations among item groups and inaccurate recommendations for users. While existing methods address this issue by adjusting the learning of CTR models, such as through additional optimization objectives, they fail to consider how the

Paula Fraga-Lamas, Tiago M. Fernández-Caramés, Manuel Suárez-Albela, Luis Castedo

The Internet of Things (IoT) is undeniably transforming the way that organizations communicate and organize everyday businesses and industrial procedures. Its adoption has proven well suited for sectors that manage a large number of assets and coordinate complex and distributed processes. This survey analyzes the great potential for applying IoT technologies

Sunanda Prabhu Gaunkar, Ellen Askey, Meira Chasman, Koksuke Takaira

The complexity of science and its frequent lack of accessibility often creates disinterest among the general public. Furthermore, there exists a gap between the public perception of science and the reality of scientific research, which severely limits the scope of public engagement with science. A new docuseries, Curiosity-The Making of a Scientist, created

Brenda Y. Miao, Christopher YK Williams, Ebenezer Chinedu-Eneh, Travis Zack

Prescription contraceptives play a critical role in supporting women's reproductive health. With nearly 50 million women in the United States using contraceptives, understanding the factors that drive contraceptives selection and switching is of significant interest. However, many factors related to medication switching are often only captured in unstructure

PandaX Collaboration, Abdusalam Abdukerim, Zihao Bo, Wei Chen

We propose a major upgrade to the existing PandaX-4T experiment in the China Jinping Underground Laboratory. The new experiment, PandaX-xT, will be a multi-ten-tonne liquid xenon, ultra-low background, and general-purpose observatory. The full-scaled PandaX-xT contains a 43-tonne liquid xenon active target. Such an experiment will significantly advance our f

M. Nowak, S. Rowther, S. Lacour, F. Meru

HD 142527 A is a young and massive Herbig Ae/Be star surrounded by a highly structured disc. The disc shows numerous morphological structures, such as spiral arms, a horseshoe region of dust emission, a set of shadows cast by an inner disc on the outer disc, and a large cavity extending from $\simeq{}$30 au to $\simeq{}$130 au. HD 142527 A also has a lower m

M. Nowak, S. Lacour, R. Abuter, J. Woillez

Aims. As part of the ongoing GRAVITY+ upgrade of the Very Large Telescope Interferometer infrastructure, we aim to improve the performance of the GRAVITY Fringe-Tracker, and to enable its use by other instruments. Methods. We modify the group delay controller to consistently maintain tracking in the white light fringe, characterised by a minimum group delay.

John Ellis, Brian D. Fields, Rebecca Surman

Two heavy elements essential to human biology are thought to have been produced by the astrophysical $r$-process, which occurs in neutron-rich environments: iodine is a constituent of thyroid hormones that affect many physiological processes including growth and development, body temperature and heart rate, and bromine is essential for tissue development and

Ali Khajegili Mirabadi, Graham Archibald, Amirali Darbandsari, Alberto Contreras-Sanz

Cancer subtyping is one of the most challenging tasks in digital pathology, where Multiple Instance Learning (MIL) by processing gigapixel whole slide images (WSIs) has been in the spotlight of recent research. However, MIL approaches do not take advantage of inter- and intra-magnification information contained in WSIs. In this work, we present GRASP, a nove

Diego Kozlowski, Thema Monroe-White, Vincent Larivière, Cassidy R. Sugimoto

The US higher education system concentrates the production of science and scientists within a few institutions. This has implications for minoritized scholars and the topics with which they are disproportionately associated. This paper examines topical alignment between institutions and authors of varying intersectional identities, and the relationship with

I. Yahniuk, M. Hild, L. E. Golub, J. Amann

We report the observation of the terahertz-induced ratchet effect in graphene-based two-dimensional (2D) metamaterials. The metamaterial consists of a graphite gate patterned with an array of triangular antidots placed under a graphene monolayer. We show that the ratchet current appears due to the noncentrosymmetry of the periodic structure unit cell. The ra

Tiago M. Fernández-Caramés, Paula Fraga-Lamas, Manuel Suárez-Albela, Luis Castedo

The Internet of Things (IoT) is a distributed system of physical objects that requires the seamless integration of hardware (e.g., sensors, actuators, electronics) and network communications in order to collect and exchange data. IoT smart objects need to be somehow identified to determine the origin of the data and to automatically detect the elements aroun

Ted Fujimoto, Joshua Suetterlein, Samrat Chatterjee, Auroop Ganguly

Research in machine learning is making progress in fixing its own reproducibility crisis. Reinforcement learning (RL), in particular, faces its own set of unique challenges. Comparison of point estimates, and plots that show successful convergence to the optimal policy during training, may obfuscate overfitting or dependence on the experimental setup. Althou

Jiaqi Liang, Defeng Liu, Sanjay Dominik Jena, Andrea Lodi

Bike-sharing systems (BSS) provide a sustainable urban mobility solution, but ensuring their reliability requires effective rebalancing strategies to address stochastic demand and prevent station imbalances. This paper proposes reinforcement learning (RL) algorithms for dynamic rebalancing problem with multiple vehicles, introducing and comparing two RL appr

Yan Shen, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao

Domain adversarial adaptation in a continual setting poses a significant challenge due to the limitations on accessing previous source domain data. Despite extensive research in continual learning, the task of adversarial adaptation cannot be effectively accomplished using only a small number of stored source domain data, which is a standard setting in memor

Yuxi Chen, Chuanfei Dong, Li-Jen Chen, Menelaos Sarantos

In the vicinity of Earth's orbit, the typical solar wind Alfv\'{e}n Mach number exceeds 5, and the super-Alfv\'{e}nic solar wind drives a conventional magnetosphere configuration. However, at the ejecta phase of an interplanetary coronal mass ejection (ICME) event, the Alfv\'{e}n Mach number may experience a significant reduction due to the intensified inter

Linus Aronsson, Morteza Haghir Chehreghani

Correlation clustering is a flexible framework for partitioning data based solely on pairwise similarity or dissimilarity information, without requiring the number of clusters as input. However, in many practical scenarios, these pairwise similarities are not available a priori and must be obtained through costly measurements or human feedback. This motivate

Fabio Nobile, Thomas Trigo Trindade

We perform an error analysis of a fully discretised Streamline Upwind Petrov Galerkin Dynamical Low Rank (SUPG-DLR) method for random time-dependent advection-dominated problems. The time integration scheme has a splitting-like nature, allowing for potentially efficient computations of the factors characterising the discretised random field. The method allow

Xi Jia, Wenqi Lu, Xinxing Cheng, Jinming Duan

In unsupervised medical image registration, the predominant approaches involve the utilization of a encoder-decoder network architecture, allowing for precise prediction of dense, full-resolution displacement fields from given paired images. Despite its widespread use in the literature, we argue for the necessity of making both the encoder and decoder learna

Youngdae Lee, Dae-Sik Moon, Sang Chul Kim, Hong Soo Park

We present the discovery of a peculiar dwarf nova KSP-OT-201712a using high-cadence, multi-color observations made with the Korea Microlensing Telescope Network. KSP-OT-201712a exhibits a rare presence of outbursts during standstills as well as strong H${\alpha}$ emission for a dwarf nova below the period minimum with an orbital period of 58.75 $\pm$ 0.02 mi

Yang Liu, Huang Fang, Yunfeng Cai, Mingming Sun

Knowledge graph embedding (KGE) models achieved state-of-the-art results on many knowledge graph tasks including link prediction and information retrieval. Despite the superior performance of KGE models in practice, we discover a deficiency in the expressiveness of some popular existing KGE models called \emph{Z-paradox}. Motivated by the existence of Z-para

Tianshi Li, Lorrie Faith Cranor, Yuvraj Agarwal, Jason I. Hong

Apple and Google introduced their versions of privacy nutrition labels to the mobile app stores to better inform users of the apps' data practices. However, these labels are self-reported by developers and have been found to contain many inaccuracies due to misunderstandings of the label taxonomy. In this work, we present Matcha, an IDE plugin that uses auto

V. Adibekyan, S. G. Sousa, S. C. C. Barros, E. Delgado Mena

The determination of planetary densities from the masses derived with the radial velocity (RV) and transit-timing variation (TTV) methods reveals discrepancies. Specifically, planets detected through RV exhibit higher densities than those detected through TTV, even though their radii are similar. We explored the possibility that the discrepant mass/densities

G. Bejarano, D. Rodríguez, J. M. Lemos, M. Vargas

This brief deals with the satisfaction of the daily cooling demand by a hybrid system that consists of a vapour-compression refrigeration cycle and a thermal energy storage (TES) unit, based on phase change materials. The addition of the TES tank to the original refrigeration plant allows to schedule the cooling production regardless of the instantaneous dem

Artem Vysogorets, Anna Dawid, Julia Kempe

The second-order properties of the training loss have a massive impact on the optimization dynamics of deep learning models. Fort & Scherlis (2019) discovered that a large excess of positive curvature and local convexity of the loss Hessian is associated with highly trainable initial points located in a region coined the "Goldilocks zone". Only a handful of

Shanshan Han, Qifan Zhang, Weizhao Jin, Zhaozhuo Xu

This paper explores multi-agent systems and identify challenges that remain inadequately addressed. By leveraging the diverse capabilities and roles of individual agents, multi-agent systems can tackle complex tasks through agent collaboration. We discuss optimizing task allocation, fostering robust reasoning through iterative debates, managing complex and l

Kien Do, Dung Nguyen, Hung Le, Thao Le

In this paper, we study the "dataset bias" problem from a statistical standpoint, and identify the main cause of the problem as the strong correlation between a class attribute u and a non-class attribute b in the input x, represented by p(u|b) differing significantly from p(u). Since p(u|b) appears as part of the sampling distributions in the standard maxim

Payam Delgosha, Hamed Hassani, Ramtin Pedarsani

We have widely observed that neural networks are vulnerable to small additive perturbations to the input causing misclassification. In this paper, we focus on the $\ell_0$-bounded adversarial attacks, and aim to theoretically characterize the performance of adversarial training for an important class of truncated classifiers. Such classifiers are shown to ha

Sugandha Sharma, Guy Davidson, Khimya Khetarpal, Anssi Kanervisto

Achieving human-AI alignment in complex multi-agent games is crucial for creating trustworthy AI agents that enhance gameplay. We propose a method to evaluate this alignment using an interpretable task-sets framework, focusing on high-level behavioral tasks instead of low-level policies. Our approach has three components. First, we analyze extensive human ga

Constantin Bacuta, Cristina Bacuta

We consider a model convection-diffusion problem and present useful connections between the finite differences and finite element discretization methods. We introduce a general upwinding Petrov-Galerkin discretization based on bubble modification of the test space and connect the method with the general upwinding approach used in finite difference discretiza

Martin Lüdtke

The Chabauty--Kim method and its refined variant by Betts and Dogra aim to cut out the $S$-integral points $X(\mathbb{Z}_S)$ on a curve inside the $p$-adic points $X(\mathbb{Z}_p)$ by producing enough Coleman functions vanishing on them. We derive new functions in the case of the thrice-punctured line when $S$ contains two primes. We describe an algorithm fo

Kaan Kale, Homa Esfahanizadeh, Noel Elias, Oguzhan Baser

With the exponential growth in data volume and the emergence of data-intensive applications, particularly in the field of machine learning, concerns related to resource utilization, privacy, and fairness have become paramount. This paper focuses on the textual domain of data and addresses challenges regarding encoding sentences to their optimized representat

Jiahao Huang, Min Zhuang, Chaohong Lee

Entanglement-enhanced quantum metrology explores the utilization of quantum entanglement to enhance measurement precision. When particles in a probe are prepared into a quantum entangled state, they collectively accumulate information about the physical quantity to be measured, leading to an improvement in measurement precision beyond the standard quantum li

Aobo Chen

We present a proof of scale-invariant boundary Harnack principle for uniform domains when the underlying space satisfies a scale-invariant elliptic Harnack inequality. Our approach does not assume the underlying space to be geodesic. Additionally, the existence of Green functions is also not assumed beforehand and is ensured by a recent result from M. T. Bar

Zihan Ding, Amy Zhang, Yuandong Tian, Qinqing Zheng

We introduce Diffusion World Model (DWM), a conditional diffusion model capable of predicting multistep future states and rewards concurrently. As opposed to traditional one-step dynamics models, DWM offers long-horizon predictions in a single forward pass, eliminating the need for recursive queries. We integrate DWM into model-based value estimation, where

Zewei Shi, Ruoxi Sun, Jieshan Chen, Jiamou Sun

Deceptive patterns are design practices embedded in digital platforms to manipulate users, representing a widespread and long-standing issue in the web and mobile software development industry. Legislative actions highlight the urgency of globally regulating deceptive patterns. However, despite advancements in detection tools, a significant gap exists in ass

Orkhan Khankishiyev, Saeed Salehi, Runar Nygaard, Danny Rehg

Drilling deep geothermal wells has proven to be a challenging endeavor, primarily due to issues such as loss circulation events, material limitations under high temperatures, and the production of corrosive fluids. Furthermore, the substantial upfront costs, coupled with geological and technical obstacles associated with drilling super-hot EGS wells in igneo

Antonio Jimeno Yepes, Yao You, Jan Milczek, Sebastian Laverde

Chunking information is a key step in Retrieval Augmented Generation (RAG). Current research primarily centers on paragraph-level chunking. This approach treats all texts as equal and neglects the information contained in the structure of documents. We propose an expanded approach to chunk documents by moving beyond mere paragraph-level chunking to chunk pri

Carlos Quinto Huamán, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

Nowadays, mobile devices have become the natural substitute for the digital camera, as they capture everyday situations easily and quickly, encouraging users to express themselves through images and videos. These videos can be shared across different platforms exposing them to any kind of intentional manipulation by criminals who are aware of the weaknesses

Owen Bradley, Christopher Plumberg

Exact solutions to the equations of hydrodynamics provide valuable benchmark tests for numerical hydrodynamic codes and also provide useful insights into the nature of hydrodynamic flow. In this paper, we introduce two novel, closely related exact solutions with non-trivial rapidity dependence which are generalizations of the well-known Gubser flow solution

Marcel F. Corchado Albelo, Maria D. Kazachenko, Benjamin J. Lynch

Magnetic reconnection is understood to be the main physical process that facilitates the transformation of magnetic energy into heat, motion, and particle acceleration during solar eruptions. Yet, observational constraints on reconnection region properties and dynamics are limited due to lack of high-cadence and high-spatial-resolution observations. By study

Takamasa Ando

In this work, we explore topological phases of matter obtained by effectively gauging or fermionizing a system, where the Gauss law constraint is only enforced energetically. In contrast to conventional gauging or fermionization, the symmetry that is effectively gauged at low energies still generates a global symmetry that acts on the whole Hilbert space fai

Etienne Krönert, Dalila Hattab, Alain Celisse

The goal of anomaly detection is to identify observations that are generated by a distribution that differs from the reference distribution that qualifies normal behavior. When examining a time series, the reference distribution may evolve over time. The anomaly detector must therefore be able to adapt to such changes. In the online context, it is particular

Rana Shahout, Michael Mitzenmacher

In light of recent work on scheduling with predicted job sizes, we consider the effect of the cost of predictions in queueing systems, removing the assumption in prior research that predictions are external to the system's resources and/or cost-free. In particular, we introduce a novel approach to utilizing predictions, SkipPredict, designed to address their

Gustaf Ahdritz, Tian Qin, Nikhil Vyas, Boaz Barak

We study the feasibility of identifying epistemic uncertainty (reflecting a lack of knowledge), as opposed to aleatoric uncertainty (reflecting entropy in the underlying distribution), in the outputs of large language models (LLMs) over free-form text. In the absence of ground-truth probabilities, we explore a setting where, in order to (approximately) disen

Jorge Maestre Vidal, Marco Antonio Sotelo Monge, Luis Javier García Villalba

This paper introduces a malware detection system for smartphones based on studying the dynamic behavior of suspicious applications. The main goal is to prevent the installation of the malicious software on the victim systems. The approach focuses on identifying malware addressed against the Android platform. For that purpose, only the system calls performed

Jialu Li, Aishwarya Padmakumar, Gaurav Sukhatme, Mohit Bansal

Outdoor Vision-and-Language Navigation (VLN) requires an agent to navigate through realistic 3D outdoor environments based on natural language instructions. The performance of existing VLN methods is limited by insufficient diversity in navigation environments and limited training data. To address these issues, we propose VLN-Video, which utilizes the divers

Pavel Bochev, Justin Owen, Paul Kuberry, Jeffrey Connors

Loosely coupled partitioned methods for multiphysics problems treat each subproblem as a separate entity and advance them independently in time. In so doing these methods enable code reuse, increase concurrency and provide a convenient framework for plug-and-play multiphysics simulations. However, mathematically loosely coupled schemes are equivalent to a si

Jacob K Christopher, Stephen Baek, Ferdinando Fioretto

This paper introduces an approach to endow generative diffusion processes the ability to satisfy and certify compliance with constraints and physical principles. The proposed method recast the traditional sampling process of generative diffusion models as a constrained optimization problem, steering the generated data distribution to remain within a specifie