November 2024 arXiv papers — page 166

Sungho Cho, Ian P. Roberts

In full-duplex millimeter-wave (mmWave) systems, the effects of beam squint and the frequency-selectivity of self-interference exacerbate over wide bandwidths. This complicates the use of beamforming to cancel self-interference when communicating over bandwidths on the order of gigahertz. In this work, we present the first analog beamforming codebooks tailor

Yu-Zhe Liu, Panyue Zhou

For any arbitrary string almost gentle algebra, we consider specific subsets of its quiver's arrow set, denoted by $\mathcal{R}$. For each such $\mathcal{R}$, we introduce the finitely generated module $M_{\mathcal{R}}$ and define its associated $\mathcal{R}$-endomorphism algebra $A_{\mathcal{R}}$. In this paper, we show that the representation type of a str

Jack Coleman, Daozhi Han, Xiaoming Wang

We propose a novel, highly efficient, mean-reverting-SAV-BDF2-based, long-time unconditionally stable numerical scheme for a class of finite-dimensional nonlinear models important in geophysical fluid dynamics. The scheme is highly efficient in that only a fixed symmetric positive definite linear problem (with varying right-hand sides) is solved at each time

Amer Essakine, Yanqi Cheng, Chun-Wun Cheng, Lipei Zhang

Implicit Neural Representations (INRs) have emerged as a paradigm in knowledge representation, offering exceptional flexibility and performance across a diverse range of applications. INRs leverage multilayer perceptrons (MLPs) to model data as continuous implicit functions, providing critical advantages such as resolution independence, memory efficiency, an

Zehao Xiao, Cees G. M. Snoek

Machine learning algorithms have achieved remarkable success across various disciplines, use cases and applications, under the prevailing assumption that training and test samples are drawn from the same distribution. Consequently, these algorithms struggle and become brittle even when samples in the test distribution start to deviate from the ones observed

Amr Abo-eleneen, Menna Helmy, Alaa Awad Abdellatif, Aiman Erbad

In the face of increasing demand for zero-touch networks to automate network management and operations, two pivotal concepts have emerged: "Learn to Slice" (L2S) and "Slice to Learn" (S2L). L2S involves leveraging Artificial intelligence (AI) techniques to optimize network slicing for general services, while S2L centers on tailoring network slices to meet th

Devanshi Arora, Matteo Salomoni, Yacine Haddad, Isabel Frank

In particle physics, homogeneous calorimeters are used to measure the energy of particles as they interact with the detector material. Although not as precise as trackers or muon detectors, these calorimeters provide valuable insights into the properties of particles by analyzing their energy deposition patterns. Recent advances in material science, notably

Nicolau S. Aiex

We construct a curvature varifold that does not admit a decomposition whose components are curvature varifolds.

Shiro Tamiya, Masato Koashi, Hayata Yamasaki

A major challenge in fault-tolerant quantum computation (FTQC) is to reduce both space overhead -- the large number of physical qubits per logical qubit -- and time overhead -- the long physical gate sequences per logical gate. We prove that a protocol using non-vanishing-rate quantum low-density parity-check (LDPC) codes, combined with concatenated Steane c

Mingyo Seo, H. Andy Park, Shenli Yuan, Yuke Zhu

Cross-embodiment imitation learning enables policies trained on specific embodiments to transfer across different robots, unlocking the potential for large-scale imitation learning that is both cost-effective and highly reusable. This paper presents LEGATO, a cross-embodiment imitation learning framework for visuomotor skill transfer across varied kinematic

Nadav Kohen

We give a formula for the density of $0$ in the sequence of generalized Motzkin numbers, $M^{a, b}_n$, modulo a prime, $p$, in terms of the first $p$ generalized central trinomial coefficients $T^{a, b}_n\bmod p$ (with $n<p$). We apply our method to various other sequences to obtain similar formulas. We also prove that $T^{a, b}_{p-1-n}\equiv (b^2-4a^2)^{\fr

Kshiti Sneh Rai, Ilya Kull, Patrick Emonts, Jordi Tura

Estimating spectral gaps of quantum many-body Hamiltonians is a highly challenging computational task, even under assumptions of locality and translation-invariance. Yet, the quest for rigorous gap certificates is motivated by their broad applicability, ranging from many-body physics to quantum computing and classical sampling techniques. Here we present a g

Gyungchoon Go, Durga Prasad Goli, Nanse Esaki, Yaroslav Tserkovnyak

The scalar spin chirality, which characterizes the fundamental unit of noncoplanar spin structures, plays an important role in rich chiral physics of magnetic materials. In particular, the intensive research efforts over the past two decades have demonstrated that the scalar spin chirality is the source of various novel Hall transports in solid-state systems

Erfan Hajihashemi, Yanning Shen

Conformal prediction is an uncertainty quantification method that constructs a prediction set for a previously unseen datum, ensuring the true label is included with a predetermined coverage probability. Adaptive conformal prediction has been developed to address data distribution shifts in dynamic environments. However, the efficiency of prediction sets var

Wenwen Chen, Bin Han, Yao Zhu, Anke Schmeink

As a promising technology, physical layer security (PLS) enhances security by leveraging the physical characteristics of communication channels. However, it commonly takes the legitimate user more effort to secure its data, compared to that required by the eavesdropper to intercept the communication. To address this imbalance, we propose a physical layer dec

Tomoya Hayata, Yoshimasa Hidaka, Dan Kondo

We propose a novel method to distinguish states of matter by identifying spontaneous symmetry breaking on extended objects, such as vortices, even in the absence of a bulk phase transition. As a specific example, we investigate the phase transition on superfluid vortices in the Higgs-confinement crossover using a $\mathrm{U}(1)_\mathrm{gauge} \times \mathrm{

Jin Xiao, Bowei Zhang, Qianyu He, Jiaqing Liang

While Large language models (LLMs) have become excellent writing assistants, they still struggle with quotation generation. This is because they either hallucinate when providing factual quotations or fail to provide quotes that exceed human expectations. To bridge the gap, we systematically study how to evaluate and improve LLMs' performance in quotation ge

Yongjiang Wu, Yongtao Li, Lihua Feng, Jiuqiang Liu

The well-known Erd\H{o}s--Ko--Rado theorem states that for $n> 2k$, every intersecting family of $k$-sets of $[n]:=\{1,\ldots ,n\}$ has at most $ {n-1 \choose k-1}$ sets, and the extremal family consists of all $k$-sets containing a fixed element (called a full star). The Hilton--Milner theorem provides a stability result by determining the maximum size of a

Vaidehi S. Paliya

Relativistic jets manifest some of the most intriguing activities in the nuclear regions of active galaxies. Identifying the most powerful relativistic jets permits us to probe the most luminous accretion systems and, in turn, the most massive black holes. This paper reports the identification of one such object, PMN J1310$-$5552 ($z=1.56$), a blazar candida

Yansong Qu, Zixuan Xu, Zilin Huang, Zihao Sheng

Semantic scene completion (SSC) is essential for achieving comprehensive perception in autonomous driving systems. However, existing SSC methods often overlook the high deployment costs in real-world applications. Traditional architectures, such as 3D Convolutional Neural Networks (3D CNNs) and self-attention mechanisms, face challenges in efficiently captur

Jinshuai Bai, Zhongya Lin, Yizheng Wang, Jiancong Wen

Numerical methods for contact mechanics are of great importance in engineering applications, enabling the prediction and analysis of complex surface interactions under various conditions. In this work, we propose an energy-based physics-informed neural network (PINNs) framework for solving frictionless contact problems under large deformation. Inspired by mi

Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang, Fabian Isensee

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To addre

Lingfeng Sun, Yixiao Wang, Pin-Yun Hung, Changhao Wang

Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance and collaborative interaction, such as indoor spaces. Unlike static or predictably moving obstacles, human behavior is inherently complex and unpredictable, stemming from dynamic in

Chunyan Zeng, Yuhao Zhao, Zhifeng Wang

This paper introduces a modeling approach that employs multi-level global processing, encompassing both short-term frame-level and long-term sample-level feature scales. In the initial stage of shallow feature extraction, various scales are employed to extract multi-level features, including Mel-Frequency Cepstral Coefficients (MFCC) and pre-Fbank log energy

Siyuan Su, Jie Zhang, Charles Radin, Harry L. Swinney

A layer of monodisperse circular steel disks in a nearly square horizontal cell forms, for shear amplitudes SA $\le$ 0.08, hexagonal close-packed crystallites that grow and merge until a single crystal fills the container. Increasing the shear amplitude leads to another reproducible regime, 0.21 $\le$ SA $\le$ 0.27, where a few large polycrystallites grow, s

Xin Gu, Ming Li, Libo Zhang, Fan Chen

High-quality training triplets (instruction, original image, edited image) are essential for instruction-based image editing. Predominant training datasets (e.g., InsPix2Pix) are created using text-to-image generative models (e.g., Stable Diffusion, DALL-E) which are not trained for image editing. Accordingly, these datasets suffer from inaccurate instructio

Gang Zhang, Weiling Yang, Xian'an Jin

Let $G$ be a graph and $k \geq 3$ an integer. A subset $D \subseteq V(G)$ is a $k$-clique (resp., cycle) isolating set of $G$ if $G-N[D]$ contains no $k$-clique (resp., cycle). In this paper, we prove that every connected graph with maximum degree at most $k$, except $k$-clique, can be partitioned into $k+1$ disjoint $k$-clique isolating sets, and that every

Xuelin Liu, Yanfei Zhu, Shucheng Zhu, Pengyuan Liu

Proper moral beliefs are fundamental for language models, yet assessing these beliefs poses a significant challenge. This study introduces a novel three-module framework to evaluate the moral beliefs of four prominent large language models. Initially, we constructed a dataset containing 472 moral choice scenarios in Chinese, derived from moral words. The dec

Sen Shao, Wei-Chi Chiu, Tao Hou, Naizhou Wang

Chiral charge density waves (CDWs) have attracted intense interest due to their exotic quantum properties, yet the microscopic origin of structural chirality emerging from correlated charge order remains elusive. Here, we reveal that the interlayer phases of CDW wave vectors, an overlooked degree of freedom, play a crucial role in driving chiral structural d

Hanyang Yuan, Jiarong Xu, Renhong Huang, Mingli Song

Graph neural networks (GNNs) have attracted considerable attention due to their diverse applications. However, the scarcity and quality limitations of graph data present challenges to their training process in practical settings. To facilitate the development of effective GNNs, companies and researchers often seek external collaboration. Yet, directly sharin

Vincent Blanloeil, Osamu Saeki

In our previous paper, we obtained several results concerning cobordisms of algebraic knots associated with Brieskorn polynomials: for example, under certain conditions, we showed that the exponents are cobordism invariants. In this paper, we further obtain new results concerning the Fox--Milnor type relations, decomposition of the algebraic cobordism class

Grigorios Giotopoulos, Hisham Sati, Urs Schreiber

The conjectured symmetries of M-theory famously involve (1.) brane-extended super-symmetry (the M-algebra) and (2.) exceptional duality-symmetry (the $\mathfrak{e}_{11}$-algebra); but little attention has been given to their inevitable combination. In this little note, we highlight (by combining results available in the literature) that the $local$ exception

Kenya Murata, Atsushi Kakogawa

This paper reports a practical articulated wheeled in-pipe inspection robot "AIRo-7.1" which is waterproof and dustproof, and can adapt to 3 to 4 in inner diameters. The joint torque can be adjusted by a PWM open-loop control. The middle joint angle can be controlled by a position feedback control system while the other two joints are bent by torsional sprin

Rujun Gao, Xiaosu Guo, Xiaodi Li, Arun Balajiee Lekshmi Narayanan

This study explores the feasibility of using large language models (LLMs), specifically GPT-4o (ChatGPT), for automated grading of conceptual questions in an undergraduate Mechanical Engineering course. We compared the grading performance of GPT-4o with that of human teaching assistants (TAs) on ten quiz problems from the MEEN 361 course at Texas A&M Univers

Yao Ji, Bo-Xuan Shi, Jian Wang, Ye-Fan Wang

We accomplish for the first time the two-loop computation of the leading-twist contribution to the pion electromagnetic form factor by employing the effective field theory formalism rigorously. The next-to-next-to-leading-order short-distance matching coefficient is determined by evaluating the appropriate $5$-point QCD amplitude with the modern multi-loop t

Simon Diemert, Caleb Shortt, Jens H. Weber

CONTEXT: Assurance Cases (ACs) are prepared to argue that the system's desired quality attributes (e.g., safety or security) are satisfied. While there is strong adoption of ACs, practitioners are often left asking an important question: are we confident that the claims made by the case are true? While many confidence assessment methods (CAMs) exist, little

Yuqing Xiao, John Grundy, Anuradha Madugalla

Growth of the older adult population has led to an increasing interest in technology-supported aged care. However, the area has some challenges such as a lack of caregivers and limitations in understanding the emotional, social, physical, and mental well-being needs of seniors. Furthermore, there is a gap in the understanding between developers and ageing pe

Radheesh Sharma Meda, Amir Barati Farimani

Identifying drug-target interactions is essential for developing effective therapeutics. Binding affinity quantifies these interactions, and traditional approaches rely on computationally intensive 3D structural data. In contrast, language models can efficiently process sequential data, offering an alternative approach to molecular representation. In the cur

Parmita Mondal, Swetadri Vasan Setlur Nagesh, Sam Sommers-Thaler, Allison Shields

Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to br

Jun-Hyuk Kim, Seungeon Kim, Won-Hee Lee, Dokwan Oh

Designing a fast and effective entropy model is challenging but essential for practical application of neural codecs. Beyond spatial autoregressive entropy models, more efficient backward adaptation-based entropy models have been recently developed. They not only reduce decoding time by using smaller number of modeling steps but also maintain or even improve

Berkay Kaplan, Buhe Li

First responders risk their lives to reduce property damage and prevent injuries during disasters. Among first responders, firefighters work with fires in residential properties, forests, or other locations where fire occurs. We built the PyroGuardian system that uses wearable modules to transmit unit information over Long Range (LoRa) to an Android tablet.

Alexander Kleshchev

We study blocks of the double covers of symmetric and alternating groups. The main result is a `local' description, up to Morita equivalence, of arbitrary defect RoCK blocks of these groups in terms of generalized Schur superalgebras corresponding to an explicit Brauer tree superalgebra. In view of the recent results on Brou\'e's Conjecture for these groups,

Hidetsugu Sakaguchi, Boris A. Malomed

We report results of systematic analysis of confined steadily rotating patterns in the two-component BEC including the spin-orbit coupling (SOC) of the Rashba type, which acts in the interplay with the attractive or repulsive intra-component and inter-component nonlinear interactions and confining potential. The analysis is based on the system of the Gross-P

Savitha Sam Abraham, Sourav Garg, Feras Dayoub

Recent research in Vision Language Navigation (VLN) has overlooked the development of agents' inquisitive abilities, which allow them to ask clarifying questions when instructions are incomplete. This paper addresses how agents can recognize "when" they lack sufficient information, without focusing on "what" is missing, particularly in VLN tasks with vague i

Parand A. Alamdari, Soroush Ebadian, Ariel D. Procaccia

We consider the challenge of AI value alignment with multiple individuals that have different reward functions and optimal policies in an underlying Markov decision process. We formalize this problem as one of policy aggregation, where the goal is to identify a desirable collective policy. We argue that an approach informed by social choice theory is especia

Yuto Watanabe, Shinichi Nishihaya, Markus Kriener, Ayano Nakamura

Rare earth intermetallic compounds crystallized in AlB$_2$-type and its low-symmetry derivative CeCu$_2$-type structures potentially host diverse frustrated magnetic structures and rich magnetotransport phenomena. We report the film growth of CeCu$_2$-type EuZn$_2$ by molecular beam epitaxy and the observation of topological Hall responses highly contrastive

Yi-An Wang, Linhu Li

We discover that the interplay between Hilbert space fragmentation and multiple non-Hermitian pumping channels leads to distinct non-Hermitian skin effect (NHSE) in real and Fock spaces. Using an extended Hatano-Nelson model with next-nearest neighbor hopping and a strong interaction as an example, we find that two fermions loaded in the lattice exhibit diff

John Ben S. Temones

In this paper, we give a generalization on the error correcting capability of twisted centralizer codes obtained from a fixed rank 1 matrix. In particular, we fix the combinatorial matrix which is obtained by getting the linear combination of the matrix whose all entries are 1 and the identity matrix of order n. Results reveal that such codes have a dimensio

Louis Golowich, Venkatesan Guruswami

We present new constructions of quantum codes of linear or close-to-linear distance and dimension with low-weight stabilizers. Only a few constructions of such codes were previously known, and were primarily based on a specific operation from homological algebra, namely the balanced product. In contrast, our constructions are based on a more basic and widely

Tharindu Adikari, Haider Al-Lawati, Jason Lam, Zhenhua Hu

We consider the problem of stragglers in distributed computing systems. Stragglers, which are compute nodes that unpredictably slow down, often increase the completion times of tasks. One common approach to mitigating stragglers is work replication, where only the first completion among replicated tasks is accepted, discarding the others. However, discarding

Yincen Qu, Chao Ma, Xiangying Dai, Hui Zhou

In the industry, numerous tasks are deployed online. Traditional approaches often tackle each task separately by its own network, which leads to excessive costs for developing and scaling models, especially in the context of large language models. Although multi-task methods can save costs through parameter sharing, they often struggle to outperform single-t

Jacob Calvert, Shunhao Oh, Dana Randall

Spin systems are fundamental models of statistical physics that provide insight into collective behavior across scientific domains. Their interest to computer science stems in part from the deep connection between the phase transitions they exhibit and the computational complexity of sampling from the probability distributions they describe. Our focus is on

M. Zeeshan Gul, M. Sharif, Shajee Shahid, Faisal Javed

This paper explores static wormhole solutions in f(Q,T) theory, where Q is the non-metricity and T is the trace of energy-momentum tensor. We derive the field equations that describe gravitational phenomena in the existence of non-metricity and matter source terms. We examine different models of this theory to determine the explicit expressions of matter con

Diantong Li, Fengxue Zhang, Chong Liu, Yuxin Chen

Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization. In practice, regulatory or safety concerns often impose additional thresholds on certain attributes of the experimental outcomes. Previous work has primarily focused on constrained single-objective optimizat

Qin-Qin Wang, Shaojun Dong, Xiao-Wei Li, Xiao-Ye Xu

Noise-enhanced applications in open quantum walk (QW) have recently seen a surge due to their ability to improve performance. However, verifying the success of open QW is challenging, as mixed-state tomography is a resource-intensive process, and implementing all required measurements is almost impossible due to various physical constraints. To address this

Soumyaranjan Khuntia, Wageesh Mishra, Yuming Wang, Sudheer K Mishra

Most earlier studies have been limited to estimating the kinematic evolution of coronal mass ejections (CMEs), and only limited efforts have been made to investigate their thermodynamic evolution. We focus on the interplay of the thermal properties of CMEs with their observed global kinematics. We implement the Flux rope Internal State (FRIS) model to estima

B. Kaviya, R. Suresh, V. K. Chandrasekar

This research investigates the dynamics of a forced Lienard oscillator featuring asymmetric potential wells. We provide compelling evidence of extreme events (EE) in the system by manipulating the height of the potential well. In the case of a symmetric well, the system exhibits chaotic behavior, with the trajectory irregularly traversing between the two wel

Zihan Qin, Jialei Xu, Wenbo Zhao, Junjun Jiang

Depth estimation under adverse conditions remains a significant challenge. Recently, multi-spectral depth estimation, which integrates both visible light and thermal images, has shown promise in addressing this issue. However, existing algorithms struggle with precise pixel-level feature matching, limiting their ability to fully exploit geometric constraints

Rui Peng, Wangze Xu, Luyang Tang, Liwei Liao

Despite the substantial progress of novel view synthesis, existing methods, either based on the Neural Radiance Fields (NeRF) or more recently 3D Gaussian Splatting (3DGS), suffer significant degradation when the input becomes sparse. Numerous efforts have been introduced to alleviate this problem, but they still struggle to synthesize satisfactory results e

Ying Zhang, Qiang Li, Hongli Liu, Liu Yang

Radio Frequency Fingerprint Identification (RFFI) technology uniquely identifies emitters by analyzing unique distortions in the transmitted signal caused by non-ideal hardware. Recently, RFFI based on deep learning methods has gained popularity and is seen as a promising way to address the device authentication problem for Internet of Things (IoT) systems.

Mingcheng He, Huaqing Wu, Conghao Zhou, Shisheng Hu

Resource slicing in low Earth orbit satellite networks (LSN) is essential to support diversified services. In this paper, we investigate a resource slicing problem in LSN to reserve resources in satellites to achieve efficient resource provisioning. To address the challenges of non-stationary service demands, inaccurate prediction, and satellite mobility, we

Yandong Gu, Dang-Zheng Liu

Inspired by the study of edge statistics of random band matrices, we investigate random walks on large $d$-dimensional periodic lattices, whose transition matrices are determined by discretized density functions. Under certain moment assumption on the density, we prove local limit theorems for random walks in three different regimes according to the bandwidt

Bing Liu, Chengcheng Zhao, Li Chai, Peng Cheng

This paper studies privacy-preserving resilient vector consensus in multi-agent systems against faulty agents, where normal agents can achieve consensus within the convex hull of their initial states while protecting state vectors from being disclosed. Specifically, we consider a modification of an existing algorithm known as Approximate Distributed Robust C

Quynh T. Nguyen, Christopher A. Pattison

In a model of fault-tolerant quantum computation with quick and noiseless polyloglog-time auxiliary classical computation, we construct a fault tolerance protocol with constant-space and $\widetilde{O}(\log N)$-time overhead, where $\widetilde{O}(\cdot)$ hides sub-polylog factors. Our construction utilizes constant-rate quantum locally testable codes (qLTC),

Jie Wu, Mengfei Sun, Jin Li

TDI is essential in space-based GW detectors, effectively reducing laser noise and improving detection precision. Space-based GW detectors provide a unique opportunity to probe GW polarizations, including possible additional modes that may signal deviations from general relativity and alternative gravity theories. In this study, we examine the impacts of sec

Yu-Ang Cheng, Ivan Felipe Rodriguez, Sixuan Chen, Kohitij Kar

Current neural network models of primate vision focus on replicating overall levels of behavioral accuracy, often neglecting perceptual decisions' rich, dynamic nature. Here, we introduce a novel computational framework to model the dynamics of human behavioral choices by learning to align the temporal dynamics of a recurrent neural network (RNN) to human re

Shaobo Gan, Ruibin Xi, Jiagang Yang, Rusong Zheng

Let $\mathscr{X}^r(M)$ be the set of $C^r$ vector fields on a boundaryless compact Riemannian manifold $M$. Given a vector field $X_0\in\mathscr{X}^r(M)$ and a compact invariant set $\Gamma$ of $X_0$, we consider the closed subset $\mathscr{X}^r(M,\Gamma)$ of $\mathscr{X}^r(M)$, consisting of all $C^r$ vector fields which coincide with $X_0$ on $\Gamma$. Stu

Junming Lin, Zheng Fang, Chi Chen, Zihao Wan

The rapid development of Multimodal Large Language Models (MLLMs) has expanded their capabilities from image comprehension to video understanding. However, most of these MLLMs focus primarily on offline video comprehension, necessitating extensive processing of all video frames before any queries can be made. This presents a significant gap compared to the h

Zhi-Wei Wei, Shao-Ming Fei

Quantum imaginarity and quantum nonlocality capture the essence of quantumness of a physical system from different aspects. We establish a connection between the imaginarity and quantum nonlocality in any two-qubit states. Based on the imaginarity $l_1$-norm and relative entropy, we first derive complementary relations among the quantum imaginarities with re

Elijah Pelofske, Georg Hahn, Hristo Djidjev

Posiform planting is a method for constructing QUBO problems with a single unique planted solution that can be tailored to arbitrary connectivity graphs. In this study we investigate making posiform planted QUBOs computationally harder by fusing many smaller random discrete coefficient spin-glass Ising models, whose global minimum energy is computed classica

Myunghyun Song

This paper develops an econometric framework and tools for the identification and inference of a structural parameter in general bunching designs. We present point and partial identification results, which generalize previous approaches in the literature. The key assumption for point identification is the analyticity of the counterfactual density, which defi

Wenjun Wang, Jiacheng Lu, Kejia Chen, Zheng Liu

Graph similarity computation (GSC) aims to quantify the similarity score between two graphs. Although recent GSC methods based on graph neural networks (GNNs) take advantage of intra-graph structures in message passing, few of them fully utilize the structures presented by edges to boost the representation of their connected nodes. Moreover, previous cross-g

Arnab Ganguly

The paper studies asymptotic properties of estimators of multidimensional stochastic differential equations driven by Brownian motions from high-frequency discrete data. Consistency and central limit properties of a class of estimators of the diffusion parameter and an approximate maximum likelihood estimator of the drift parameter based on a discretized lik

Qiuyu Liang, Weihua Wang, Feilong Bao, Guanglai Gao

Hyperbolic rotation is commonly used to effectively model knowledge graphs and their inherent hierarchies. However, existing hyperbolic rotation models rely on logarithmic and exponential mappings for feature transformation. These models only project data features into hyperbolic space for rotation, limiting their ability to fully exploit the hyperbolic spac

Luiz Pessoa

Despite their widespread utility across domains, basic network models face fundamental limitations when applied to complex biological systems, particularly in neuroscience. This paper critically examines these limitations and explores potential extensions and alternative frameworks better suited to capture the adaptive nature of biological systems. Key chall

Debjoy Thakur

The application of deep neural networks in geospatial data has become a trending research problem in the present day. A significant amount of statistical research has already been introduced, such as generalized least square optimization by incorporating spatial variance-covariance matrix, considering basis functions in the input nodes of the neural networks

Chengyang Peng, Victor Paredes, Guillermo A. Castillo, Ayonga Hereid

Safe navigation in real-time is an essential task for humanoid robots in real-world deployment. Since humanoid robots are inherently underactuated thanks to unilateral ground contacts, a path is considered safe if it is obstacle-free and respects the robot's physical limitations and underlying dynamics. Existing approaches often decouple path planning from g

Dahyun Mok, Junghyun Bum, Le Duc Tai, Hyunseung Choo

Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. This paper focuses on referable DR classification to enhance the applicability of the proposed method in clinical practice. We develop an advanced cross-learning DR classification method leveraging transfer learning and cross-attention mechanisms. The prop

Elizabeth Harris, Ali Eshragh, Bishnu Lamichhane, Jordan Shaw-Carmody

The Minimum Volume Covering Ellipsoid (MVCE) problem, characterised by $n$ observations in $d$ dimensions where $n \gg d$, can be computationally very expensive in the big data regime. We apply methods from randomised numerical linear algebra to develop a data-driven leverage score sampling algorithm for solving MVCE, and establish theoretical error bounds a

Danielle Li, Lindsey Raymond, Peter Bergman

This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance exploitation (selecting from groups with proven track records) with exploration (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on supervised learning approaches, are designed solely for exploita

Shivanshu Shekhar, Shreyas Singh, Tong Zhang

Direct Preference Optimization (DPO) has been successfully used to align large language models (LLMs) according to human preferences, and more recently it has also been applied to improving the quality of text-to-image diffusion models. However, DPO-based methods such as SPO, Diffusion-DPO, and D3PO are highly susceptible to overfitting and reward hacking, e

Yohann Tendero, Jerome Gilles

We propose a new way to correct for the non-uniformity (NU) and the noise in uncooled infrared-type images. This method works on static images, needs no registration, no camera motion and no model for the non uniformity. The proposed method uses an hybrid scheme including an automatic locally-adaptive contrast adjustment and a state-of-the-art image denoisin

Will Ferenc, Hannah Kastein, Lauren Lieu, Ryan Wilson

This paper considers cooperative control of robots involving two different testbed systems in remote locations with communication on the internet. This provides us the capability to exchange robots status like positions, velocities and directions needed for the swarming algorithm. The results show that all robots properly follow some leader defined one of th

Saehoon Eo, Namhyun Eun, Moon-Jin Kang

The stability of an irreversible singularity, such as a Riemann shock to the full Euler system, in the absence of any technical conditions on perturbations, remains a major open problem even within mono-dimensional framework. A natural approach to justify such stability is to consider vanishing dissipation (or viscosity) limits of physical viscous flows. We

Linlin Mao, Shefeng Yan, Zeping Sui, Hongbin Li

We consider a distributed detection problem within a wireless sensor network (WSN), where a substantial number of sensors cooperate to detect the existence of sparse stochastic signals. To achieve a trade-off between detection performance and system constraints, multi-bit quantizers are employed at local sensors. Then, two quantization strategies, namely raw

Keito Akiyama

In recent years, learning for neural networks can be viewed as optimization in the space of probability measures. To obtain the exponential convergence to the optimizer, the regularizing term based on Shannon entropy plays an important role. Even though an entropy function heavily affects convergence results, there is almost no result on its generalization,

Jiahui Wang, Yinan Deng, Yi Yang, Yufeng Yue

Recently the dense Simultaneous Localization and Mapping (SLAM) based on neural implicit representation has shown impressive progress in hole filling and high-fidelity mapping. Nevertheless, existing methods either heavily rely on known scene bounds or suffer inconsistent reconstruction due to drift in potential loop-closure regions, or both, which can be at

Jorge González Cázares, David Kramer-Bang, Aleksandar Mijatović

We develop two novel couplings between general pure-jump L\'evy processes in $\R^d$ and apply them to obtain upper bounds on the rate of convergence in an appropriate Wasserstein distance on the path space for a wide class of L\'evy processes attracted to a multidimensional stable process in the small-time regime. We also establish general lower bounds based

S. Nageeb Ali, Andreas Kleiner, Kun Zhang

This paper studies the role of hard information in contractual and market settings in which the receiver can flexibly adjust allocations and transfers in response to the sender's disclosure. These settings include monopoly pricing, bilateral trade with interdependent values, insurance contracting, and policy negotiations. Across these settings, the sender is

Chaeyeon Kim, Joohyun Son, Daesik Hong, Ian P. Roberts

This paper introduces a novel beam tracking scheme for full-duplex ground user terminals aiming to transmit uplink and receive downlink from two low Earth orbit (LEO) satellites at the same time and same frequency. Our proposed technique leverages observed phenomena from a recent measurement campaign to strategically select transmit and receive beams which c

Soohyoung Lee, Dawoon Jeong, Jeong-Dong Lee

Occupational mobility is an emergent strategy to cope with technological unemployment by facilitating efficient labor redeployment. However, previous studies analyzing networks show that the boundaries to smooth mobility are constrained by a fragmented structure in the occupation network. In this study, positing that this structure will significantly change

Jonas Guan, Shon Eduard Verch, Claas Voelcker, Ethan C. Jackson

A computational problem in biological reward-based learning is how credit assignment is performed in the nucleus accumbens (NAc). Much research suggests that NAc dopamine encodes temporal-difference (TD) errors for learning value predictions. However, dopamine is synchronously distributed in regionally homogeneous concentrations, which does not support expli

Yuqian Fu, Yuanheng Zhu, Haoran Li, Zijie Zhao

Efficient exploration is crucial in cooperative multi-agent reinforcement learning (MARL), especially in sparse-reward settings. However, due to the reliance on the unimodal policy, existing methods are prone to falling into the local optima, hindering the effective exploration of better policies. Furthermore, in sparse-reward settings, each agent tends to r

Mariko Kimura, Hitoshi Negoro, Shinya Yamada, Wataru Iwakiri

We performed shot analyses of X-ray and optical sub-second flares observed during the low/hard state of the 2018 outburst in MAXI J1820$+$070. Optical shots were less spread than X-ray shots. The amplitude of X-ray shots was the highest at the onset of the outburst, and they faded at the transition to the intermediate state. The timescale of shots was $\sim$

Alonso Castillo-Ramirez, Maria G. Magaña-Chavez, Luguis de los Santos Baños

A one-dimensional cellular automaton $\tau : A^\mathbb{Z} \to A^\mathbb{Z}$ is a transformation of the full shift defined via a finite neighborhood $S \subset \mathbb{Z}$ and a local function $\mu : A^S \to A$. We study the family of cellular automata whose finite neighborhood $S$ is an interval containing $0$, and there exists a pattern $p \in A^S$ satisfyi

M. Katsuma

The triple-$\alpha$ reaction from the ternary continuum states at off-resonant energies, $\alpha+\alpha+\alpha\rightarrow^{12}$C, remains an open question. This direct process is scrutinized using a non-adiabatic Faddeev hyperspherical harmonics $R$-matrix expansion method, and the derived reaction rates are discussed. After reviewing the model, the resultan

Hsuan-Cheng Wu, Xiantao Li

Hamiltonian systems of ordinary and partial differential equations are fundamental mathematical models spanning virtually all physical scales. A critical property for the robustness and stability of computational methods in such systems is the underlying symplectic structure, which preserves geometric properties like phase-space volume over time and energy c

Tyler E. Korenyi-Both, Nathan J. Falkiewicz, Matthew C. Jones

High-speed flight vehicles, which travel much faster than the speed of sound, are crucial for national defense and space exploration. However, accurately predicting their behavior under numerous, varied flight conditions is a challenge and often prohibitively expensive. The proposed approach involves creating smarter, more efficient machine learning models (

N. Palanque-Delabrouille

Cosmology is entering a very exciting time in its history, when a wealth of cutting-edge experiments are all starting to collect data, or about to. These experiments aim at addressing some of the most intriguing questions in fundamental physics, such as what is the nature of dark matter, is dark energy a cosmological constant or a varying field, what are the

Benedict Aaron Tjandra, Federico Barbero, Michael Bronstein

Despite the successful application of Temporal Graph Networks (TGNs) for tasks such as dynamic node classification and link prediction, they still perform poorly on the task of dynamic node affinity prediction -- where the goal is to predict 'how much' two nodes will interact in the future. In fact, simple heuristic approaches such as persistent forecasts an