February 2024 arXiv papers — page 135

Jingze Li, Xiaoyue Liu, Mathieu Dahan, Benoit Montreuil

Hyperconnected relay transportation enables using a relay system of short-haul drivers to deliver long-haul shipments collectively, which helps address root causes of trucker shortage issues by transforming working conditions with potentials of daily returning home, accessing consistent schedules, and facilitating load matching. This paper investigates hyper

Saurabh Bhausaheb Zinjad, Amrita Bhattacharjee, Amey Bhilegaonkar, Huan Liu

Crafting the ideal, job-specific resume is a challenging task for many job applicants, especially for early-career applicants. While it is highly recommended that applicants tailor their resume to the specific role they are applying for, manually tailoring resumes to job descriptions and role-specific requirements is often (1) extremely time-consuming, and (

Lu Chen, Wei Huang, Ruqing Zhang, Wei Chen

Instruction tuning on a mixture of tasks has improved zero-shot capabilities in natural language processing (NLP). Nevertheless, existing methods often learn features that exhibit correlations between instruction-formatted samples and target labels, rather than causal relationships. Termed as ``spurious correlation'' in statistics, such a correlation may cha

Christos A. Athanasiadis

The local $h$-polynomial was introduced by Stanley as a fundamental enumerative invariant of a triangulation $\Delta$ of a simplex. This polynomial is known to have nonnegative and symmetric coefficients and is conjectured to be $\gamma$-positive when $\Delta$ is flag. This paper shows that the local $h$-polynomial has the stronger property of being real-roo

Aamil Shaik, Eileen T. Meyer, Karthik Reddy, Sibasish Laha

The origin of X-ray emission from the resolved kiloparsec-scale jets and hotspots of many active galactic nuclei (AGN) remains uncertain, particularly where the X-ray emission is separate from the radio-optical synchrotron component. Possible explanations include synchrotron emission from a second electron population and external Compton or synchrotron self-

Ira Rai, Vandana Vinayak, Richard Gordon

A galaxy cluster, such as RX J2129, sometimes produces two or more gravitationally lensed images of more distant galaxies. We attempt to regard pairs of these images as stereo pairs. While not successful due to the small disparity angles involved, we suggest that with the 1011 light amplification anticipated from the Solar Gravitational Lens (SGL), individua

Cong Xu, Zhangchi Zhu, Jun Wang, Jianyong Wang

Large language models (LLMs) have gained much attention in the recommendation community; some studies have observed that LLMs, fine-tuned by the cross-entropy loss with a full softmax, could achieve state-of-the-art performance already. However, these claims are drawn from unobjective and unfair comparisons. In view of the substantial quantity of items in re

Egor E. Chitorkin, Natalia P. Bondarennko

In this paper, the inverse Sturm-Liouville problem with distribution potential and with polynomials of the spectral parameter in one of the boundary conditions is considered. We for the first time prove local solvability and stability of this inverse problem in the general non-self-adjoint case, taking possible splitting of multiple eigenvalues into account.

Yuansheng Zhao, Kenji Shiraishi, Tetsuo Narita, Atsushi Oshiyama

The stable and metastable configurations of interstitial Mg in GaN and its migration energy barriers are studied from first-principles calculations. In addition to the conventional octahedral (O, global energy minimum) and tetrahedral (T, metastable) interstitial sites, we discover two new metastable interstitial complexes with formation energy lower than or

Yaxuan Song, Jianan Fan, Dongnan Liu, Weidong Cai

Source-free domain adaptation (SFDA) alleviates the domain discrepancy among data obtained from domains without accessing the data for the awareness of data privacy. However, existing conventional SFDA methods face inherent limitations in medical contexts, where medical data are typically collected from multiple institutions using various equipment. To addre

Prasoon Ambalathankandy, Yafei Ou, Masayuki Ikebe

Halo artifacts significantly impact display quality. We propose a method to reduce halos in Local Histogram Equalization (LHE) algorithms by separately addressing dark and light variants. This approach results in visually natural images by exploring the relationship between lateral inhibition and halo artifacts in the human visual system.

Ilkin Aliyev, Tosiron Adegbija

The highly sparse activations in Spiking Neural Networks (SNNs) can provide tremendous energy efficiency benefits when carefully exploited in hardware. The behavior of sparsity in SNNs is uniquely shaped by the dataset and training hyperparameters. This work reveals novel insights into the impacts of training on hardware performance. Specifically, we explore

Ilkin Aliyev, Tosiron Adegbija

Spiking Neural Networks (SNNs) have become popular for their more bio-realistic behavior than Artificial Neural Networks (ANNs). However, effectively leveraging the intrinsic, unstructured sparsity of SNNs in hardware is challenging, especially due to the variability in sparsity across network layers. This variability depends on several factors, including th

Zuowei Liu, Zi-Wei Tang

Gravitational wave (GW) signals arising from binary neutron star mergers offer new, sensitive probes to ultralight mediators. Here we analyze the GW signals in the GW170817 event detected by the LIGO/Virgo collaboration to impose constraints on the ultralight isospin-violating mediator that has different couplings to protons and neutrons. Neutron stars, whic

Jinyang Li, Jason Bonacum, Selim M. Shahriar

Large momentum transfer (LMT) is an important technique for magnifying the phase shift accumulated in an atom interferometer. Existing approaches to implement Raman-transition-based LMT all involve physically swapping the propagation directions of the two counterpropagating Raman beams repeatedly, which could significantly complicate the experimental system.

Ryo Ishizuka, Kei Nakazato

In this paper, we prove "prismatic Kunz's theorem" which states that a complete Noetherian local ring $R$ of residue characteristic $p$ is a regular local ring if and only if the Frobenius lift on a prismatic complex of (a derived enhancement of) $R$ over a specific prism $(A, I)$ is faithfully flat. This generalizes classical Kunz's theorem from the perspec

Jesús Chacón, Hector Vargas, Gonzalo Farias, Jose Sánchez

Designing and developing web-enabled remote laboratories for pedagogical purposes is not an easy task. Often, developers (generally, educators who know the subjects they teach but lack of the technical and programming skills required to build Internet-based educational applications) end up discarding the idea of exploring these new teaching and learning expe

Michael J. Gill, Adam Mammoliti, Ian M. Wanless

A Latin square of order $n$ is an $n\times n$ matrix in which each row and column contains each of $n$ symbols exactly once. For $\epsilon>0$, we show that with high probability a uniformly random Latin square of order $n$ has no proper subsquare of order larger than $n^{1/2}\log^{1/2+\epsilon}n$. Using this fact we present a canonical labelling algorithm fo

Juhyun Oh, Eunsu Kim, Inha Cha, Alice Oh

This paper explores the assumption that Large Language Models (LLMs) skilled in generation tasks are equally adept as evaluators. We assess the performance of three LLMs and one open-source LM in Question-Answering (QA) and evaluation tasks using the TriviaQA (Joshi et al., 2017) dataset. Results indicate a significant disparity, with LLMs exhibiting lower p

Dictino Chaos, Jesús Chacón, Jose Antonio Lopez-Orozco, Sebastian Dormido

This paper describes the design and implementation of a virtual and remote laboratory based on Easy Java Simulations (EJS) and LabVIEW. The main application of this laboratory is to improve the study of sensors in Mobile Robotics, dealing with the problems that arise on the real world experiments. This laboratory allows the user to work from their homes, tel

Santi Roca-Fàbrega, Ji-hoon Kim, Joel R. Primack, Minyong Jung

In this fourth paper from the AGORA Collaboration, we study the evolution down to redshift $z=2$ and below of a set of cosmological zoom-in simulations of a Milky Way mass galaxy by eight of the leading hydrodynamic simulation codes. We also compare this CosmoRun suite of simulations with dark matter-only simulations by the same eight codes. We analyze gener

Meredith L. Anderson, Ran Jing, Juan C. Pacheco Garcia, Ilyoung Yang

Soft robots have immense potential given their inherent safety and adaptability, but challenges in soft actuator forces and design constraints have limited scaling up soft robots to larger sizes. Electrothermal shape memory alloy (SMA) artificial muscles have the potential to create these large forces and high displacements, but consistently using these musc

Jiankun Hou, Jiefu Zhu, Ruixin Ma, Boyi Xue

Non-Hermitian degeneracies reveal intriguing and non-trivial behaviors in open physical systems. Examples like Parity-Time (PT) symmetry breaking, topological encircling chirality, and enhanced sensing near an exceptional point (EP) are often associated with the abrupt nature of the phase transition around these degeneracies. Here we experimentally observe a

Mandeep Deka, Ashwani Assam, Ganesh Natarajan

We present a generic framework for gradient reconstruction schemes on unstructured meshes using the notion of a dyadic sum-vector product. The proposed formulation reconstructs centroidal gradients of a scalar from its directional derivatives along specific directions in a suitably defined neighbourhood. We show that existing gradient reconstruction schemes

Haoyuan Li, Yanpeng Zhou, Yihan Zeng, Hang Xu

3D Shape represented as point cloud has achieve advancements in multimodal pre-training to align image and language descriptions, which is curial to object identification, classification, and retrieval. However, the discrete representations of point cloud lost the object's surface shape information and creates a gap between rendering results and 2D correspon

Yu Luo, Satoshi Tsujimoto, Hao Yang

Exceptional extensions of a class of Laurent biorthogonal polynomials (the so-called Hendriksen-van Rossum polynomials) have been presented by the authors recently. This is achieved through Darboux transformations of generalized eigenvalue problems. In this paper, we discuss the recurrence relations satisfied by these exceptional Laurent biorthogonal polynom

Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu

Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data, as predicted by scal

Pol Mestres, Carlos Nieto-Granda, Jorge Cortés

This paper proposes a distributed controller synthesis framework for safe navigation of multi-agent systems. We leverage control barrier functions to formulate collision avoidance with obstacles and teammates as constraints on the control input for a state-dependent network optimization problem that encodes team formation and the navigation task. Our algorit

Yifan Xiong, Yuting Jiang, Ziyue Yang, Lei Qu

Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the widespread use of hardware redundancies. However, these redundancies can inadvertently lead to hidden degradation, so called "gray failure", for AI workloads, significantly affecting end-to-end performance and concealing performance issues, which complicates root cau

Sayak Bose, Jason M. TenBarge, Troy Carter, Michael Hahn

We report the first experimental detection of a reflected Alfv\'en wave from an Alfv\'en-speed gradient under conditions similar to those in coronal holes. The experiments were conducted in the Large Plasma Device at the University of California, Los Angeles. We present the experimentally measured dependence of the coefficient of reflection versus the wave i

S. W. Duchesne, A. Botteon, B. S. Koribalski, F. Loi

Clusters of galaxies have been found to host Mpc-scale diffuse, non-thermal radio emission in the form of central radio halos and peripheral relics. Turbulence and shock-related processes in the intra-cluster medium are generally considered responsible for the emission, though details of these processes are still not clear. The low surface brightness makes d

Steven Golob, Sikha Pentyala, Anuar Maratkhan, Martine De Cock

Synthetic data generation (SDG) has become increasingly popular as a privacy-enhancing technology. It aims to maintain important statistical properties of its underlying training data, while excluding any personally identifiable information. There have been a whole host of SDG algorithms developed in recent years to improve and balance both of these aims. Ma

Henry Pinkard, Cherry Liu, Fanice Nyatigo, Daniel A. Fletcher

Computational microscopy, in which hardware and algorithms of an imaging system are jointly designed, shows promise for making imaging systems that cost less, perform more robustly, and collect new types of information. Often, the performance of computational imaging systems, especially those that incorporate machine learning, is sample-dependent. Thus, stan

Amin Karimi Monsefi, Payam Karisani, Mengxi Zhou, Stacey Choi

Standard modern machine-learning-based imaging methods have faced challenges in medical applications due to the high cost of dataset construction and, thereby, the limited labeled training data available. Additionally, upon deployment, these methods are usually used to process a large volume of data on a daily basis, imposing a high maintenance cost on medic

Zhang Hongna, Cheng Haitian, Wang Suming, Zhang Wenhua

The research on elasto-inertial turbulence (EIT), a new type of turbulent flow, has reached the stage of identifying the minimal flow unit (MFU). On this issue, direct numerical simulations (DNSs) of FENE-P fluid flow in two-dimensional channels with variable sizes are conducted in this study. We demonstrate the existence of MFU for EIT to be self-sustained.

Xinhai Hou, Cheng Jiang, Akhil Kondepudi, Yiwei Lyu

Whole slide imaging is fundamental to biomedical microscopy and computational pathology. Previously, learning representations for gigapixel-sized whole slide images (WSIs) has relied on multiple instance learning with weak labels, which do not annotate the diverse morphologic features and spatial heterogeneity of WSIs. A high-quality self-supervised learning

Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma

We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks. Premier-TACO leverages a subset of multitask offline datasets for pretraining a general feature representation, which captures critical environmental dynamics and is fine-tuned using minima

Dhruba Jyoti Gogoi, Supakchai Ponglertsakul

This work deals with the scalar quasinormal modes using higher order WKB method and black hole shadow in non-minimal Einstein Yang-Mills theory. To validate the results of quasinormal modes, time domain profiles are also investigated. We found that with an increase in the magnetic charge of the black hole, the ring-down gravitational wave increases non-linea

Edward S. Harake, Joseph R. Linzey, Cheng Jiang, Rushikesh S. Joshi

Objective. Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automated measurement tools have the promise of rapid and consistent measurements, but existing tools are still limite

Jascha Sohl-Dickstein

Some fractals -- for instance those associated with the Mandelbrot and quadratic Julia sets -- are computed by iterating a function, and identifying the boundary between hyperparameters for which the resulting series diverges or remains bounded. Neural network training similarly involves iterating an update function (e.g. repeated steps of gradient descent),

Zihong Chen

Consider a closed monotone symplectic manifold $(M,\omega)$. \cite{Gan2} constructed a cyclic open-closed map, which goes from the cyclic homology of the Fukaya category of $M$ to the $S^1$-equivariant quantum cohomology of $M$. In this paper, we show that with mod $p$ coefficients, Ganatra's cyclic open-closed map is compatible with a certain $\mathbb{Z}/p$

Toufik Zaimi

A well-known result, due to Meyer, states that the set P of Pisot numbers, generating a real algebraic number field K, is uniformly discrete and relatively dense in the set of positive real number. In the present paper, we show that P is contained is the set, say D, of the differences of elements of P, and the complement of P in D is not finite. Also, we pro

Hidenori Iwakiri, Tomoya Kamijima, Shinji Ito, Akiko Takeda

Time-varying optimization problems are prevalent in various engineering fields, and the ability to solve them accurately in real-time is becoming increasingly important. The prediction-correction algorithms used in smooth time-varying optimization can achieve better accuracy than that of the time-varying gradient descent (TVGD) algorithm. However, none of th

Shuhei Sugiura, Ryo Ariizumi, Masaya Tanemura, Toru Asai

In this paper, we propose a method for estimating the algebraic Riccati equation (ARE) with respect to an unknown discrete-time system from the system state and input observation. The inverse optimal control (IOC) problem asks, ``What objective function is optimized by a given control system?'' The inverse linear quadratic regulator (ILQR) problem is an IOC

Isao Kishimoto, Mako Kouga, Shigenori Seki, Tomohiko Takahashi

We construct closed string vertex operators with various ghost numbers in addition to the conventional ones, using the Faddeev-Popov procedure for the gauge fixing of the conformal Killing group, from matter primary fields. We find that these operators give solutions to the descent equations in the framework of the BRST formalism. Similarly, we also construc

Yixiao Zhang, Yukara Ikemiya, Gus Xia, Naoki Murata

Recent advances in text-to-music generation models have opened new avenues in musical creativity. However, music generation usually involves iterative refinements, and how to edit the generated music remains a significant challenge. This paper introduces a novel approach to the editing of music generated by such models, enabling the modification of specific

Asaf Ferber, Jie Han, Dingjia Mao, Roman Vershynin

We show that every $(n,d,\lambda)$-graph contains a Hamilton cycle for sufficiently large $n$, assuming that $d\geq \log^{6}n$ and $\lambda\leq cd$, where $c=\frac{1}{70000}$. This significantly improves a recent result of Glock, Correia and Sudakov, who obtained a similar result for $d$ that grows polynomially with $n$. The proof is based on a new result re

Zihan Dong, Xinyu Fan, Zhiyuan Peng

Financial market predictions utilize historical data to anticipate future stock prices and market trends. Traditionally, these predictions have focused on the statistical analysis of quantitative factors, such as stock prices, trading volumes, inflation rates, and changes in industrial production. Recent advancements in large language models motivate the int

Saurabh Kumar, Shashi Ranjan Kumar, Abhinav Sinha

This paper investigates a pursuit-evasion problem involving three agents: a pursuer, an evader, and a defender. Cooperative guidance laws are developed for the evader-defender team that guarantee interception of the pursuer by the defender before it reaches the vicinity of the evader. Unlike heuristic methods, optimal control, differential game formulation,

Koki Shinada, Robert Peters

We present the optical activity induced by the orbital magnetic moment in metals and superconductors using Green's function formalization. We show that an apparent singularity of the optical activity vanishes in the normal state; however, it remains finite in the superconducting state and is related to the superconducting Edelstein effect, ensuring the missi

Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot

In this work, we demonstrate continuous-time radar-inertial and lidar-inertial odometry using a Gaussian process motion prior. Using a sparse prior, we demonstrate improved computational complexity during preintegration and interpolation. We use a white-noise-on-acceleration motion prior and treat the gyroscope as a direct measurement of the state while prei

Xinzhu Liang, Joseph M. Lukens, Sanjaya Lohani, Brian T. Kirby

This work systematically compares parallel implementations of consistent (asymptotically unbiased) Bayesian deep learning algorithms: sequential Monte Carlo sampler (SMC$_\parallel$) or Markov chain Monte Carlo (MCMC$_\parallel$). We provide a proof of convergence for SMC$_\parallel$ showing that it theoretically achieves the same level of convergence as a s

Joanna Li, Jonathan J. Wang, Dvira Segal

We investigate phonon thermal transport of fullerene-based single-molecule junctions by employing classical molecular dynamics simulations. The thermal conductances of fullerene monomers, dimers, and trimers are computed through three distinct molecular dynamics methods, by following the equilibration dynamics in one method, and using two other nonequilibriu

Cheng Li, Mengzhou Chen, Jindong Wang, Sunayana Sitaram

Large language models (LLMs) are reported to be partial to certain cultures owing to the training data dominance from the English corpora. Since multilingual cultural data are often expensive to collect, existing efforts handle this by prompt engineering or culture-specific pre-training. However, they might overlook the knowledge deficiency of low-resource c

Quinn Fisher, Haoming Meng, Vardan Papyan

Mixup is a data augmentation strategy that employs convex combinations of training instances and their respective labels to augment the robustness and calibration of deep neural networks. Despite its widespread adoption, the nuanced mechanisms that underpin its success are not entirely understood. The observed phenomenon of Neural Collapse, where the last-la

Ben Wang, Jiqun Liu, Jamshed Karimnazarov, Nicolas Thompson

Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks. However, users still face challenges initializing and refining prompts, and their cognitive barriers and biased perceptions further impede task completion. These issues reflect broader challenges identified within the f

Eleanor Dunlop, Judy Cunningham, Paul Adorno, Shari Fatupaito

Australian agriculture supplies many horticultural commodities to domestic and international markets; however, food composition data for many commodities are outdated or unavailable. We produced an up-to-date, nationally representative dataset of up to 148 nutrients and related components in 92 Australian-grown fruit (fresh n=39, dried n=6), vegetables (n=43

Rahnuma Rahman, Samiran Ganguly, Supriyo Bandyopadhyay

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. "Binary" stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and -1, with the probability of being in either level determined by an external bias. "Analog" stochastic neurons (ASNs), in contrast, can

Lehel Csillag, Anish Agashe, Damianos Iosifidis

Schr\"odinger connections are a special class of affine connections, which despite being metric incompatible, preserve length of vectors under autoparallel transport. In the present paper, we introduce a novel coordinate-free formulation of Schr\"odinger connections. After recasting their basic properties in the language of differential geometry, we show tha

Seiji Kameno, Yuichi Harikane, Satoko Sawada-Satoh, Tsuyoshi Sawada

We report sub-pc-scale observations of the 321-GHz H$_2$O emission line in the radio galaxy NGC 1052. The H$_2$O line emitter size is constrained in $< 0.6$ milliarcsec distributed on the continuum core component. The brightness temperature exceeding $10^6$ K and the intensity variation indicate certain evidence for maser emission. The maser spectrum consist

Ziqiao Shang, Bin Liu, Fengmao Lv, Fei Teng

For the Facial Action Unit (AU) detection task, accurately capturing the subtle facial differences between distinct AUs is essential for reliable detection. Additionally, AU detection faces challenges from class imbalance and the presence of noisy or false labels, which undermine detection accuracy. In this paper, we introduce a novel contrastive learning fr

Chenguang Zhang, Zhihang Yuan, Xingchen Li, Guangyu Sun

Deep neural networks are widely deployed in many fields. Due to the in-situ computation (known as processing in memory) capacity of the Resistive Random Access Memory (ReRAM) crossbar, ReRAM-based accelerator shows potential in accelerating DNN with low power and high performance. However, despite power advantage, such kind of accelerators suffer from the hi

Benjamin J. Zhang, Siting Liu, Wuchen Li, Markos A. Katsoulakis

We focus on the fundamental mathematical structure of score-based generative models (SGMs). We first formulate SGMs in terms of the Wasserstein proximal operator (WPO) and demonstrate that, via mean-field games (MFGs), the WPO formulation reveals mathematical structure that describes the inductive bias of diffusion and score-based models. In particular, MFGs

Yining Xu, Sheng Zhou

Reconfigurable intelligent surface (RIS) is a promising solution to deal with the blockage-sensitivity of millimeter wave band and reduce the high energy consumption caused by network densification. However, deploying large scale RISs may not bring expected performance gain due to significant channel estimation overhead and non-negligible reflected interfere

Huai-Yu Wang

The author's opinion is that the negative energy solutions of the Dirac equation mean that a particle can be of negative kinetic energy (NKE) besides positive kinetic energy (PKE). We think that NKE particles are dark ones and NKE matter is dark matter. In our previous works, the dark matter theory of the NKE version and the dark energy theory that matched d

Maohao Shen, J. Jon Ryu, Soumya Ghosh, Yuheng Bu

This paper questions the effectiveness of a modern predictive uncertainty quantification approach, called \emph{evidential deep learning} (EDL), in which a single neural network model is trained to learn a meta distribution over the predictive distribution by minimizing a specific objective function. Despite their perceived strong empirical performance on do

Anuj Gautam, Tarun Kumar Yadav, Kent Seamons, Scott Ruoti

Password-based authentication faces various security and usability issues. Password managers help alleviate some of these issues by enabling users to manage their passwords effectively. However, malicious client-side scripts and browser extensions can steal passwords after they have been autofilled by the manager into the web page. In this paper, we explore

David G. Costanzo, Mark L. Lewis, Stefano Schmidt, Eyob Tsegaye

Let $G$ be a finite group and construct a graph $\Delta(G)$ by taking $G\setminus\{1\}$ as the vertex set of $\Delta(G)$ and by drawing an edge between two vertices $x$ and $y$ if $\langle x,y\rangle$ is cyclic. Let $K(G)$ be the set consisting of the universal vertices of $\Delta(G)$ along the identity element. For a solvable group $G$, we present a necessa

Farhad Farokhi

Barycentric and pairwise quantum Renyi leakages are proposed as two measures of information leakage for privacy and security analysis in quantum computing and communication systems. These quantities both require minimal assumptions on the eavesdropper, i.e., they do not make any assumptions on the eavesdropper's attack strategy or the statistical prior on th

John Hewitt, Sarah Chen, Lanruo Lora Xie, Edward Adams

We introduce model editing with canonical examples, a setting in which (1) a single learning example is provided per desired behavior, (2) evaluation is performed exclusively out-of-distribution, and (3) deviation from an initial model is strictly limited. A canonical example is a simple instance of good behavior, e.g., The capital of Mauritius is Port Louis

Yuan Xu, Chongwen Huang, Wei Li, Yongxu Zhu

The millimeter wave (mmWave) has received considerable interest due to its expansive bandwidth and high frequency. However, a noteworthy challenge arises from its vulnerability to blockages, leading to reduced coverage and achievable rates. To address these limitations, a potential solution is to deploy distributed reconfigurable intelligent surfaces (RISs),

Takuya Yoda

String scattering amplitudes are typically expressed in formal integrals which diverge in physical kinematic regions. Recently the problem of divergence was cured by redefining integration contours. In this paper, we apply the new integration contour to evaluate time-delay of the Veneziano amplitude. Thimble analysis of the new integration contour tells us t

Hao Song, Wei Lin, Wei Song, Man Wang

To enhance precision and comprehensiveness in identifying targets in electric power construction monitoring video, a novel target recognition algorithm utilizing infrared imaging is explored. This algorithm employs a color processing technique based on a local linear mapping method to effectively recolor monitoring images. The process involves three key step

Yuta Saito, Jihan Yao, Thorsten Joachims

We study off-policy learning (OPL) of contextual bandit policies in large discrete action spaces where existing methods -- most of which rely crucially on reward-regression models or importance-weighted policy gradients -- fail due to excessive bias or variance. To overcome these issues in OPL, we propose a novel two-stage algorithm, called Policy Optimizati

Kecheng Chen, Elena Gal, Hong Yan, Haoliang Li

In this work, we propose to tackle the problem of domain generalization in the context of \textit{insufficient samples}. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a domain-invariant representation based on the probabilistic framework by mapping each data point into probabilistic embeddings. Specificall

Zhenglin Zhou, Fan Ma, Hehe Fan, Zongxin Yang

Creating digital avatars from textual prompts has long been a desirable yet challenging task. Despite the promising results achieved with 2D diffusion priors, current methods struggle to create high-quality and consistent animated avatars efficiently. Previous animatable head models like FLAME have difficulty in accurately representing detailed texture and g

Ni Liu, Meng Luo, J. -Q. Liang

We in this paper study the hermiticity of Hamiltonian and energy spectrum for the SU(1; 1) systems. The Hermitian Hamiltonian can possess imaginary eigenvalues in contrast with the common belief that hermiticity is a suffcient condition for real spectrum. The imaginary eigenvalues are derived in algebraic method with imaginary-frequency boson operators for t

Zhen Wang, Jie Ren, Yu Miao

In this paper, we study the existence and pathwise uniqueness of strong solutions for jump-type McKean-Vlasov SDEs with irregular coefficients but uniform linear growth assumption. Moreover, the propagation of chaos and the convergence rate for Euler's scheme of jump-type McKean-Vlasov SDEs are also obtained by taking advantage of Yamada-Watanabe's approxima

Sanoli Gun, Sunil L Naik

A well known result of Breulmann states that Hecke eigenvalues of Saito-Kurokawa lifts are positive. In this article, we show that the Hecke eigenvalues of an Ikeda lift at primes are positive. Further, we derive lower and upper bounds of these Hecke eigenvalues for all primes $p$. One of the main ingredients involves expressing the Hecke eigenvalues of an I

Kathryn Mann, Jason Fox Manning, Theodore Weisman

We prove a topological stability result for the actions of hyperbolic groups on their Bowditch boundaries. More precisely, we show that a sufficiently small perturbation of the standard boundary action, if assumed on each parabolic subgroup to be a perturbation by semi-conjugacy, is in fact always globally semi-conjugate to the standard action. This proves a

Simon Chamorro, Victor Klemm, Miguel de la Iglesia Valls, Christopher Pal

In recent years, legged and wheeled-legged robots have gained prominence for tasks in environments predominantly created for humans across various domains. One significant challenge faced by many of these robots is their limited capability to navigate stairs, which hampers their functionality in multi-story environments. This study proposes a method aimed at

Naonori Sugiyama

Since galaxy distribution reconstruction effectively reduces non-Gaussian terms in the power spectrum covariance matrix, it has attracted interest not only for Baryon Acoustic Oscillation (BAO) signals but also for various cosmological signal analyses. To this end, this paper presents a novel theoretical model that addresses infrared (IR) effects in the post

Runliang Niu, Jindong Li, Shiqi Wang, Yali Fu

Existing Large Language Models (LLM) can invoke a variety of tools and APIs to complete complex tasks. The computer, as the most powerful and universal tool, could potentially be controlled directly by a trained LLM agent. Powered by the computer, we can hopefully build a more generalized agent to assist humans in various daily digital works. In this paper,

Yin-Zhen Xu

We systematically investigate the electromagnetic form factors of heavy-light pseudo-scalar and vector mesons within the Dyson-Schwinger/Bethe-Salpeter equations framework for the first time. It is found that the charge radius of vector meson is larger than that of its pseudo-scalar counterpart. In heavy-light systems, the flavor symmetry breaking will lead

Karol Kawka, Pawel Kempisty, Konrad Sakowski, Stanislaw Krukowski

On semiconductor growth surfaces, surface reconstructions appear. Estimation of the reconstructed structures is essential for understanding and controlling growth phenomena. In this study, the stability of a mixture of two different surface reconstructions is investigated. Since the number of candidate structures is enormous, the structures sampled by Bayesi

Germán Benitez, Pedro Rizzo

In this paper we propose unifying the categories of cochain complexes $\text{Ch}(\mathcal{C})$ and modules $\widehat{A}\text{-mod}$ over a repetitive algebra $\widehat{A}$. Motivated by their striking similarities and importance, we introduce a novel category encompassing both. Our analysis explores key properties of this unified category, highlighting its p

Sanoli Gun, Sunil L Naik

Let $\tau$ denote the Ramanujan tau function. One is interested in possible prime values of $\tau$ function. Since $\tau$ is multiplicative and $\tau(n)$ is odd if and only if $n$ is an odd square, we only need to consider $\tau(p^{2n})$ for primes $p$ and natural numbers $n \geq 1$. This is a rather delicate question. In this direction, we show that for any

Samir Adly, Jun Huang, Ba Khiet Le

In this paper, we introduce a new sliding mode observer for Lur'e set-valued dynamical systems, particularly addressing challenges posed by uncertainties not within the standard range of observation. Traditionally, most of Luenberger-like observers and sliding mode observer have been designed only for uncertainties in the range of observation. Central to our

Daiki Mitsuta, Yasutaka Shimizu

The Survival Energy Model (SEM), as originally introduced by Shimizu et al. (2020), is designed to characterize human bioenergetics by employing diffusion processes or inverse Gaussian processes. While parametric models have been employed to articulate the SEM, they exhibit inherent sensitivity in their parameters and hyperparameters, which in turn introduce

Tongda Xu, Ziran Zhu, Jian Li, Dailan He

Diffusion Inverse Solvers (DIS) are designed to sample from the conditional distribution $p_{\theta}(X_0|y)$, with a predefined diffusion model $p_{\theta}(X_0)$, an operator $f(\cdot)$, and a measurement $y=f(x'_0)$ derived from an unknown image $x'_0$. Existing DIS estimate the conditional score function by evaluating $f(\cdot)$ with an approximated poster

Navid Aftabi, Nima Moradi, Fatemeh Mahroo

Deep neural networks (DNNs) are widely studied in various applications. A DNN consists of layers of neurons that compute affine combinations, apply nonlinear operations, and produce corresponding activations. The rectified linear unit (ReLU) is a typical nonlinear operator, outputting the max of its input and zero. In scenarios like max pooling, where multip

Jonathan Lebensold, Doina Precup, Borja Balle

Report Noisy Max and Above Threshold are two classical differentially private (DP) selection mechanisms. Their output is obtained by adding noise to a sequence of low-sensitivity queries and reporting the identity of the query whose (noisy) answer satisfies a certain condition. Pure DP guarantees for these mechanisms are easy to obtain when Laplace noise is

Yuri F Bilu, Sanoli Gun, Sunil L Naik

In this article, we investigate a non-Archimedean analogue of a question of Atkin and Serre. More precisely, we derive lower bounds for the largest prime factor of non-zero Fourier coefficients of non-CM normalized cuspidal Hecke eigenforms of even weight $k \geq 2$, level $N$ with integer Fourier coefficients. In particular, we show that for such a form $f$

Sriram V. C. Nallani, Gautham Ramachandran

Current approaches to prosthetic control are limited by their reliance on traditional methods, which lack real-time adaptability and intuitive responsiveness. These limitations are particularly pronounced in assistive technologies designed for individuals with diverse cognitive states and motor intentions. In this paper, we introduce a framework that leverag

Sunil L Naik

In this article, we derive lower bounds for the number of distinct prime divisors of families of non-zero Fourier coefficients of non-CM primitive cusp forms and more generally of non-CM primitive Hilbert cusp forms. In particular, for the Ramanujan $\Delta$-function, we show that for any $\epsilon > 0$, there exist infinitely many natural numbers $n$ such t

Xiaokang Wei, Zhuoman Liu, Ping Li, Yan Luximon

We propose SIR, an efficient method to decompose differentiable shadows for inverse rendering on indoor scenes using multi-view data, addressing the challenges in accurately decomposing the materials and lighting conditions. Unlike previous methods that struggle with shadow fidelity in complex lighting environments, our approach explicitly learns shadows for

Jiawei Jiang, Yifan Yang, Jingyuan Wang, Junjie Wu

The electronic map plays a crucial role in geographic information systems, serving various urban managerial scenarios and daily life services. Developing effective Map Entity Representation Learning (MERL) methods is crucial to extracting embedding information from electronic maps and converting map entities into representation vectors for downstream applica

Bryce Jeffrey, Seungmo Kim

5G standalone (SA) rollout is right around the corner. 28 GHz band is considered as one of the main spectrum bands for the 5G SA in many countries. However, the band has already been occupied by uplink of the fixed satellite service (FSS). Due to high equivalent isotropic radiated power (EIRP) adopted by the FSS, the interference that FSS may cause into 5G i

Rostyslav Sipakov, Olena Voloshkina, Anastasiia Kovalova

This research explores the application of quadratic polynomials in Python for advanced data analysis. The study demonstrates how quadratic models can effectively capture nonlinear relationships in complex datasets by leveraging Python libraries such as NumPy, Matplotlib, scikit-learn, and Pandas. The methodology involves fitting quadratic polynomials to the

Andrey Moskalenko, Vlad Shakhuro, Anna Vorontsova, Anton Konushin

Interactive segmentation methods rely on user inputs to iteratively update the selection mask. A click specifying the object of interest is arguably the most simple and intuitive interaction type, and thereby the most common choice for interactive segmentation. However, user clicking patterns in the interactive segmentation context remain unexplored. Accordi