July 2023 arXiv papers — page 94

O. S. Awolude, E. Cator, H. Don

We study random walks on $\mathbb{Z}$ which have a linear (or almost linear) drift towards 0 in a range around 0. This drift leads to a metastable Gaussian distribution centered at zero. We give specific, fast growing, time windows where we can explicitely bound the distance of the distribution of the walk to an appropriate Gaussian. In this way we give a so

Kang Du, Yu Xiang, Ilya Soloveychik

Identifying the direct causes or causal parents of a target variable is crucial for scientific discovery. Focusing on linear models, the invariant prediction framework was built upon the invariance principle, namely, the conditional distribution of the target variable given its causal parents is invariant across multiple environments or experimental conditio

Yuzhou Gu, Zhao Song, Lichen Zhang

Quadratic programming is a ubiquitous prototype in convex programming. Many machine learning problems can be formulated as quadratic programming, including the famous Support Vector Machines (SVMs). Linear and kernel SVMs have been among the most popular models in machine learning over the past three decades, prior to the deep learning era. Generally, a quad

Fan Shi, Bin Li, Xiangyang Xue

The abstract visual reasoning ability in human intelligence benefits discovering underlying rules in the novel environment. Raven's Progressive Matrix (RPM) is a classic test to realize such ability in machine intelligence by selecting from candidates. Recent studies suggest that solving RPM in an answer-generation way boosts a more in-depth understanding of

Po-Yi Huang, Wen-Fong Ke

For a fixed integer $k$, we define a sequence $A_k=(a_k(n))_{n\geq0}$ and a corresponding sparse subsequence $S_k$ using the cardinality of the $n$-th symmetric power of the set $\{1,2,\ldots, k\}$. For $k\in\{2,\dots,8\}$, we find recursive formulas for $S_k$, and show that the values $a_{k}(0)$, $a_{k}(1)$, and $a_{k}(3)$ are sufficient for constructing $A

S. Saroon, B. Dias, T. Tsujimotto, M. C. Parisi

The chemical evolution history of the Small Magellanic Cloud (SMC) has been a matter of debate for decades. The challenges in understanding the SMC chemical evolution are related to a very slow star formation rate (SFR) combined with bursts triggered by the multiple interactions between the SMC and the Large Magellanic Cloud, a significant (~0.5 dex) metalli

Alzayat Saleh, Md Mehedi Hasan, Herman W Raadsma, Mehar S Khatkar

Accurate weight estimation and morphometric analyses are useful in aquaculture for optimizing feeding, predicting harvest yields, identifying desirable traits for selective breeding, grading processes, and monitoring the health status of production animals. However, the collection of phenotypic data through traditional manual approaches at industrial scales

Ahad Mohammadzadeha, Akbar Heidarzadeh, Hailey Becker, Jorge Valilla Robles

A comprehensive literature review on recently rediscovered Co- and/or CoNi-based superalloys, strengthened by the {\gamma}' phase, revealed a relationship between the configurational entropy of the system and the {\gamma}' solvus temperature. This study was conducted on a high Cr CoNi-based superalloy system with high configurational entropy to test our hypo

Yihan Cang, Ming Chen, Kaibin Huang

In this paper, we study joint batching and (task) scheduling to maximise the throughput (i.e., the number of completed tasks) under the practical assumptions of heterogeneous task arrivals and deadlines. The design aims to optimise the number of batches, their starting time instants, and the task-batch association that determines batch sizes. The joint optim

Umesh Shankar

A $k$-Stirling permutation of order $n$ is said to be "flattened" if the leading terms of its increasing runs are in ascending order. We show that flattened $k$-Stirling permutations of order $n+1$ are in bijection correspondence with a colored variant of type $B$ set partitions of $[-n,n]$, introduced by D.G.L. Wang. Using the theory of weighted labelled st

Hinata Aoki, Takao Yamanaka

Neural Radiance Fields (NeRF) has been applied to various tasks related to representations of 3D scenes. Most studies based on NeRF have focused on a small object, while a few studies have tried to reconstruct large-scale scenes although these methods tend to require large computational cost. For the application of NeRF to large-scale scenes, a method based

Pawel Bruckman de Renstrom

The Standard Model predicts several rare Higgs boson decay channels, among which are the decays to a Z boson and a photon, to a low-mass lepton pair and a photon, and to a meson and a photon. The observation of some of these decays could open the possibility of studying the CP and coupling properties of the Higgs boson in a complementary way to other analyse

Charilaos Efthymiou, Thomas P. Hayes, Daniel Stefankovic, Eric Vigoda

We study the mixing time of the single-site update Markov chain, known as the Glauber dynamics, for generating a random independent set of a tree. Our focus is obtaining optimal convergence results for arbitrary trees. We consider the more general problem of sampling from the Gibbs distribution in the hard-core model where independent sets are weighted by a

Shijin Gong, Xinyu Zhang

When artificial neural networks have demonstrated exceptional practical success in a variety of domains, investigations into their theoretical characteristics, such as their approximation power, statistical properties, and generalization performance, have concurrently made significant strides. In this paper, we construct a novel theory for understanding the

Kensuke Mukai, Takao Yamanaka

Zero padding is often used in convolutional neural networks to prevent the feature map size from decreasing with each layer. However, recent studies have shown that zero padding promotes encoding of absolute positional information, which may adversely affect the performance of some tasks. In this work, a novel padding method called Peripheral Prediction Padd

Enrico Giunchiglia, Joohyung Lee, Vladimir Lifschitz, Hudson Turner

This paper continues the line of work on representing properties of actions in nonmonotonic formalisms that stresses the distinction between being "true" and being "caused", as in the system of causal logic introduced by McCain and Turner and in the action language C proposed by Giunchiglia and Lifschitz. The only fluents directly representable in language C

Santiago Serrano, MengKe Feng, Wee Han Lim, Amanda E. Seedhouse

High fidelity qubit readout is critical in order to obtain the thresholds needed to implement quantum error correction protocols and achieve fault-tolerant quantum computing. Large-scale silicon qubit devices will have densely-packed arrays of quantum dots with multiple charge sensors that are, on average, farther away from the quantum dots, entailing a redu

Joohyung Lee, Yunsong Meng

Recently Ferraris, Lee and Lifschitz proposed a new definition of stable models that does not refer to grounding, which applies to the syntax of arbitrary first-order sentences. We show its relation to the idea of loop formulas with variables by Chen, Lin, Wang and Zhang, and generalize their loop formulas to disjunctive programs and to arbitrary first-order

Michael Bartholomew, Joohyung Lee

In classical logic, nonBoolean fluents, such as the location of an object, can be naturally described by functions. However, this is not the case in answer set programs, where the values of functions are pre-defined, and nonmonotonicity of the semantics is related to minimizing the extents of predicates but has nothing to do with functions. We extend the fir

Chen Chen, Ella T. Lifset, Yichen Han, Arkajyoti Roy

While voice user interfaces offer increased accessibility due to hands-free and eyes-free interactions, older adults often have challenges such as constructing structured requests and perceiving how such devices operate. Voice-first user interfaces have the potential to address these challenges by enabling multimodal interactions. Standalone voice + touchscr

Jeongsu Bok

Transverse Single Spin Asymmetries (TSSAs) in transversely polarized proton-proton collisions ($p^{\uparrow}+p$) have been a fruitful source for studying the spin structure of the proton. In the 2015 RHIC data taking periods, collisions of polarized protons with nuclei ($p^{\uparrow}+A$) were made for the first time. The measurements of TSSAs in $p^{\uparrow

Vincent Démery, Andrea Gambassi

The motion of a colloidal probe in a complex fluid, such as a micellar solution, is usually described by the generalized Langevin equation, which is linear. However, recent numerical simulations and experiments have shown that this linear model fails when the probe is confined, and that the intrinsic dynamics of the probe is actually non-linear. Noting that

Guandong Li, Mengxia Ye

Deep neural networks have faced many problems in hyperspectral image classification, including the ineffective utilization of spectral-spatial joint information and the problems of gradient vanishing and overfitting that arise with increasing depth. In order to accelerate the deployment of models on edge devices with strict latency requirements and limited c

Ashley Montanaro, Stasja Stanisic

Variational Monte Carlo (VMC) methods are used to sample classically from distributions corresponding to quantum states which have an efficient classical description. VMC methods are based on performing a number of steps of a Markov chain starting with samples from a simple initial distribution. Here we propose replacing this initial distribution with sample

Zhecheng Yuan, Sizhe Yang, Pu Hua, Can Chang

Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization. Despite the focus on algorithms aimed at resolving visual generalization problems, we argue that the devil is in the existing benchmarks as they are restricted to isolated tasks

Youqi Song

Jet substructure variables aim to reveal details of the parton fragmentation and hadronization processes that create a jet. By removing collinear radiation while maintaining the soft radiation components, one can construct CollinearDrop jet observables, which have enhanced sensitivity to the soft phase space within jets. We present a CollinearDrop jet measur

Pramit Kumar Pal, Debarshi Dutta, Attreyee Mandal, Dipshika Das

The Covid-19 pandemic has changed the way humans interact with their environment. Common touch surfaces such as elevator switches and ATM switches are hazardous to touch as they are used by countless people every day, increasing the chance of getting infected. So, a need for touch-less interaction with machines arises. In this paper, we propose a method of r

Oleg Kovalenko

We solve two continuous extremal problems on the classes of monotone functions: in the first problem we find extremal values for a line integral of a coordinate-wise monotone function of two variables from a rearrange\-ment-invariant class of functions; in the second one we find extremal values for the expectation of a random process with monotone trajectori

S K H Auluck

The observed scaling of neutron yield in the Dense Plasma Focus (DPF) as the fourth power of the current in the plasma was the principal driver of the growth of DPF research in its early days. Subsequent discovery of failure of this scaling law was also the principal reason for its abandonment by major laboratories. Attempts to understand this failure of sca

Leonardo Martínez-Sandoval, Edgardo Roldán-Pensado

In this note we study a conjecture by Jer\'onimo-Castro, Magazinov and Sober\'on which generalized a question posed by Dol'nikov. Let $F_1,F_2,\dots,F_n$ be families of translates of a convex compact set $K$ in the plane so that each two sets from distinct families intersect. We show that, for some $j$, $\bigcup_{i\neq j}F_i$ can be pierced by at most $4$ po

Ao Jin, Fan Zhang, Panfeng Huang

To avoid complex constraints of the traditional nonlinear method for tethered space robot (TSR) deployment, this paper proposes a data-driven optimal control framework with an improved deep learning based Koopman operator that could be applied to complex environments. In consideration of TSR's nonlinearity, its finite dimensional lifted representation is der

Junpeng Wang, Shixia Liu, Wei Zhang

The past decade has witnessed a plethora of works that leverage the power of visualization (VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML, keeps growing at a fast pace. To better organize the enormous works and shed light on the developing trend of VIS4ML, we provide a systematic review of these works through this s

Ruinian Chang, Jingbang Chen, Ian Munro, Richard Peng

The $\textit{Abelian Sandpile}$ model is a well-known model used in exploring $\textit{self-organized criticality}$. Despite a large amount of work on other aspects of sandpiles, there have been limited results in efficiently computing the terminal state, known as the $\textit{sandpile prediction}$ problem. On graphs with special structures, we present algor

Yufei Wang, Yi Yu, Wenhan Yang, Lanqing Guo

Previous raw image-based low-light image enhancement methods predominantly relied on feed-forward neural networks to learn deterministic mappings from low-light to normally-exposed images. However, they failed to capture critical distribution information, leading to visually undesirable results. This work addresses the issue by seamlessly integrating a diffu

P. Zhang, R. Soria, S. Zhang, L. Ji

We investigate the dynamical properties of low frequency quasi-periodic oscillations (QPOs) observed from the black hole X-ray binary MAXI J1820+070 during the early part of its 2018 outburst, when the system was in a bright hard state. To this aim, we use a series of observations from the Hard X-ray Modulation Telescope Insight-HXMT, and apply a wavelet dec

Lei Pan, Wuyang Luan, Yuan Zheng, Qiang Fu

Most existing 3D instance segmentation methods are derived from 3D semantic segmentation models. However, these indirect approaches suffer from certain limitations. They fail to fully leverage global and local semantic information for accurate prediction, which hampers the overall performance of the 3D instance segmentation framework. To address these issues

Andrew Shi, Per-Olof Persson

Mesh optimization procedures are generally a combination of node smoothing and discrete operations which affect a small number of elements to improve the quality of the overall mesh. These procedures are useful as a post-processing step in mesh generation procedures and in applications such as fluid simulations with severely deforming domains. In order to pe

Yu. L. Sachkov

Left-invariant Lorentzian structures on the 2D solvable non-Abelian Lie group are studied. Sectional curvature, attainable sets, Lorentzian length maximizers, distance, spheres, and infinitesimal isometries are described.

Weilin Zhao, Yuxiang Huang, Xu Han, Zhiyuan Liu

Recently, there has been a demand to deploy Large Language Models (LLMs) on personal devices such as laptops and smartphones. These LLMs have different model variants when handling different tasks. However, personal devices have limited resources and require reduced storage overhead. To address this, there are two key methods available: the first is model co

Michael P. Casey

For a set $X$ of $N$ points in $\mathbb{R}^D$, the Johnson-Lindenstrauss lemma provides random linear maps that approximately preserve all pairwise distances in $X$ -- up to multiplicative error $(1\pm \epsilon)$ with high probability -- using a target dimension of $O(\epsilon^{-2}\log(N))$. Certain known point sets actually require a target dimension this l

Sai Pradeep Chakka, Sunil Kumar Vengalil, Neelam Sinha

Timeseries classification as stochastic (noise-like) or non-stochastic (structured), helps understand the underlying dynamics, in several domains. Here we propose a two-legged matrix decomposition-based algorithm utilizing two complementary techniques for classification. In Singular Value Decomposition (SVD) based analysis leg, we perform topological analysi

Xuyi Jia, Chunlin Gong, Wen Ji, Chunna Li

It's difficult to accurately predict the flow with shock waves over an aircraft due to the flow's strongly nonlinear characteristics. In this study, we propose an accuracy-enhanced flow prediction method that fuses deep learning and reduced-order model to achieve fast flow field prediction for various aerodynamic shapes. First, we establish the convolutional

Lin Jiao, Wei Wu, Si-Yuan Bai, Jun-Hong An

Quantum metrology pursues the physical realization of higher-precision measurements to physical quantities than the classically achievable limit by exploiting quantum features, such as entanglement and squeezing, as resources. It has potential applications in developing next-generation frequency standards, magnetometers, radar, and navigation. However, the u

Zhun Yang, Adam Ishay, Joohyung Lee

We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neu

Adam Ishay, Zhun Yang, Joohyung Lee

Large language models (LLMs), such as GPT-3 and GPT-4, have demonstrated exceptional performance in various natural language processing tasks and have shown the ability to solve certain reasoning problems. However, their reasoning capabilities are limited and relatively shallow, despite the application of various prompting techniques. In contrast, formal log

Raiga Kashiwagi, Kazunari Iwasaki, Kohji Tomisaka

Filamentary molecular clouds are regarded as the place where newborn stars are formed. In particular, a hub region, a place where it appears as if several filaments are colliding, often indicates active star formation. To understand the star formation in filament structures, we investigate the collisions between two filaments using two-dimensional magnetohyd

Aditya Gupta, Samarth Chitgopekar, Alexander Kim, Joseph Jiang

We present our work on electric bicycles (e-bikes) and their implications for policymakers in the United States. E-bikes have gained significant popularity as a fast and eco-friendly transportation option. As we strive for a sustainable energy plan, understanding the growth and impact of e-bikes is crucial for policymakers. Our mathematical modeling offers i

Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang

Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning. These issues could be partially addressed by introducing external knowledge graphs (KG) in LLM reasoning. In this paper, we propose a new LLM-KG integratin

Zhun Yang, Adam Ishay, Joohyung Lee

While large language models (LLMs), such as GPT-3, appear to be robust and general, their reasoning ability is not at a level to compete with the best models trained for specific natural language reasoning problems. In this study, we observe that a large language model can serve as a highly effective few-shot semantic parser. It can convert natural language

Ru Huang, Kai Chang, Huan He, Ruipeng Li

We propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in algebraic multigrid (AMG) methods, addressing the well-known issue of increasing operator complexity. Guided by the AMG theory on spectrally equivalent coarse-grid operators, we have developed novel ML algorithms that utilize neural networks (NNs) co

Chung I Lu

We evaluate benchmark deep reinforcement learning algorithms on the task of portfolio optimisation using simulated data. The simulator to generate the data is based on correlated geometric Brownian motion with the Bertsimas-Lo market impact model. Using the Kelly criterion (log utility) as the objective, we can analytically derive the optimal policy without

Meng Ye, Dong Yang, Mikael Kanski, Leon Axel

We propose a novel neural deformable model (NDM) targeting at the reconstruction and modeling of 3D bi-ventricular shape of the heart from 2D sparse cardiac magnetic resonance (CMR) imaging data. We model the bi-ventricular shape using blended deformable superquadrics, which are parameterized by a set of geometric parameter functions and are capable of defor

Dong Guo, Xionghong He, Pengcheng Li, Zhi Qin

The HIRFL-CSR external-target experiment (CEE) under construction is expected to provide novel opportunities to the studies of the thermodynamic properties, namely the equation of state of nuclear matter (nEOS) with heavy ion collisions at a few hundreds MeV/u beam energies. Based on Geant 4 packages, the fast simulations of the detector responses to the col

Abhinandan Kumar Pun, Mohammed Javed, David S. Doermann

The problem of change detection in images finds application in different domains like diagnosis of diseases in the medical field, detecting growth patterns of cities through remote sensing, and finding changes in legal documents and contracts. However, this paper presents a survey on core techniques and rules to detect changes in different versions of a docu

Hung D. Nguyen, Lekun Wang

We study the long time statistics of a two-dimensional Hamiltonian system in the presence of Gaussian white noise. While the original dynamics is known to exhibit finite time explosion, we demonstrate that under the impact of the stochastic forcing as well as a deterministic perturbation, the solutions are exponentially attractive toward the unique invariant

Zhaoxing Gao, Ruey S. Tsay

This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an effective way to bridge the gap between predictors and the target variable of interest by scaling and combining the predictors and their lagged values, resulting i

Yuanshuo Cheng, Mingwen Shao, Yecong Wan, Chao Wang

Existing All-In-One image restoration (IR) methods usually lack flexible modeling on various types of degradation, thus impeding the restoration performance. To achieve All-In-One IR with higher task dexterity, this work proposes an efficient Dynamic Reference Modeling paradigm (DRM-IR), which consists of task-adaptive degradation modeling and model-based im

Elias Ventre, Aden Forrow, Nitya Gadhiwala, Parijat Chakraborty

A core challenge for modern biology is how to infer the trajectories of individual cells from population-level time courses of high-dimensional gene expression data. Birth and death of cells present a particular difficulty: existing trajectory inference methods cannot distinguish variability in net proliferation from cell differentiation dynamics, and hence

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin

In this study, we present a novel dataset for training machine learning models translating between OpenMP Fortran and C++ code. To ensure reliability and applicability, the dataset is created from a range of representative open-source OpenMP benchmarks. It is also refined using a meticulous code similarity test. The effectiveness of our dataset is assessed u

J. A. Mendoza-Fierro, L. M. Arévalo Aguilar

In this work, we prove the generation of genuine tripartite continuous-variable entanglement in the unitary dynamics of the simultaneous measurement process of position and momentum observables raised by Arthurs and Kelly, considering a measurement configuration where the system under examination is a rotated, displaced, and squeezed vacuum state. Under thes

Kifu Kurokawa, Shunsuke Isono, Yoshimitsu Kohama, So Kunisada

The currently established electronic phase diagram of cuprates is based on a study of single- and double-layered compounds. These CuO$_2$ planes, however, are directly contacted with dopant layers, thus inevitably disordered with an inhomogeneous electronic state. Here, we solve this issue by investigating a 6-layered Ba$_2$Ca$_5$Cu$_6$O$_{12}$(F,O)$_2$ with

Sarah Barrington, Romit Barua, Gautham Koorma, Hany Farid

Synthetic-voice cloning technologies have seen significant advances in recent years, giving rise to a range of potential harms. From small- and large-scale financial fraud to disinformation campaigns, the need for reliable methods to differentiate real and synthesized voices is imperative. We describe three techniques for differentiating a real from a cloned

Wenxin Xu, Hexin Jiang, Xuefeng Liang, Ying Zhou

Subjective time-series regression (STR) tasks have gained increasing attention recently. However, most existing methods overlook the label distribution bias in STR data, which results in biased models. Emerging studies on imbalanced regression tasks, such as age estimation and depth estimation, hypothesize that the prior label distribution of the dataset is

Fateh Kaakai, Shridhar "Shreeder" Adibhatla, Ganesh Pai, Emmanuelle Escorihuela

We give a first rigorous characterization of Operational Design Domains (ODDs) for Machine Learning (ML)-based aeronautical products. Unlike in other application sectors (such as self-driving road vehicles) where ODD development is scenario-based, our approach is data-centric: we propose the dimensions along which the parameters that define an ODD can be exp

Usama Sardar, Sarwan Ali, Muhammad Sohaib Ayub, Muhammad Shoaib

Nanobodies (Nb) are monomeric heavy-chain fragments derived from heavy-chain only antibodies naturally found in Camelids and Sharks. Their considerably small size (~3-4 nm; 13 kDa) and favorable biophysical properties make them attractive targets for recombinant production. Furthermore, their unique ability to bind selectively to specific antigens, such as t

Cheng Chen, Yifan Zhao, Jia Li

Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets. Existing literature mainly focuses on label completion and co-occurrence learning while facing difficulties with the most common single-positive label mann

Jason M. Klusowski, Jonathan W. Siegel

We study the fundamental limits of matching pursuit, or the pure greedy algorithm, for approximating a target function $ f $ by a linear combination $f_n$ of $n$ elements from a dictionary. When the target function is contained in the variation space corresponding to the dictionary, many impressive works over the past few decades have obtained upper and lowe

Ze Lu, Yalei Lv, Wenqi Wang, Pengfei Xiong

Deep generative approaches have obtained great success in image inpainting recently. However, most generative inpainting networks suffer from either over-smooth results or aliasing artifacts. The former lacks high-frequency details, while the latter lacks semantic structure. To address this issue, we propose an effective Frequency-Spatial Complementary Netwo

Jingyi Xu, Hieu Le, Dimitris Samaras

Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing, based on only a few annotated exemplars. In this paper, we point out that the task of counting objects of interest when there are multiple object classes in the image (namely, multi-class object count

Yuki Yonemoto, Yuto Nakashima, Shunsuke Inenaga

We consider labeled directed graphs where each vertex is labeled with a non-empty string. Such labeled graphs are also known as non-linear texts in the literature. In this paper, we introduce a new problem of comparing two given labeled graphs, called the SEQ-IC-LCS problem on labeled graphs. The goal of SEQ-IC-LCS is to compute the the length of the longest

Yinglun Xu, Bhuvesh Kumar, Jacob Abernethy

Efficient learning in multi-armed bandit mechanisms such as pay-per-click (PPC) auctions typically involves three challenges: 1) inducing truthful bidding behavior (incentives), 2) using personalization in the users (context), and 3) circumventing manipulations in click patterns (corruptions). Each of these challenges has been studied orthogonally in the lit

Nikhil Vemgal, Elaine Lau, Doina Precup

Reinforcement Learning (RL) algorithms aim to learn an optimal policy by iteratively sampling actions to learn how to maximize the total expected return, $R(x)$. GFlowNets are a special class of algorithms designed to generate diverse candidates, $x$, from a discrete set, by learning a policy that approximates the proportional sampling of $R(x)$. GFlowNets e

M. H. Kim

In the high-energy $p+p$ collisions, the transverse single spin asymmetry for very forward neutron production has been interpreted by an interference between $\pi$ (spin flip) and $a_1$ (spin non-flip) exchange with a non-zero phase shift. The $\pi$ and $a_1$ exchange model predicted the neutron asymmetry would increase in magnitude with transverse momentum

Itai Arieli, Yakov Babichenko, Fedor Sandomirskiy

Agents receive private signals about an unknown state. The resulting joint belief distributions are complex and lack a simple characterization. Our key insight is that, when conditioned on the state, the structure of belief distributions simplifies: feasibility constrains only the marginal distributions of individual agents across states, with no joint const

CMS Collaboration

A search for high-mass dimuon resonance production in association with one or more b quark jets is presented. The study uses proton-proton collision data collected with the CMS detector at the LHC corresponding to an integrated luminosity of 138 fb$^{-1}$ at a center-of-mass energy of 13 TeV. Model-independent limits are derived on the number of signal event

Organizers of QueerInAI, Nathan Dennler, Anaelia Ovalle, Ashwin Singh

Bias evaluation benchmarks and dataset and model documentation have emerged as central processes for assessing the biases and harms of artificial intelligence (AI) systems. However, these auditing processes have been criticized for their failure to integrate the knowledge of marginalized communities and consider the power dynamics between auditors and the co

Amani Mohammed Alqarni, Daniel Timko, Muhammad Lutfor Rahman

Facial Recognition Technology (FRT) is a pioneering field of mass surveillance that sparks privacy concerns and is considered a growing threat in the modern world. FRT has been widely adopted in the Kingdom of Saudi Arabia to improve public services and surveillance. Accordingly, the following study aims to understand the privacy and security concerns, trust

Guanlin Liu, Lifeng Lai

Due to the broad range of applications of multi-agent reinforcement learning (MARL), understanding the effects of adversarial attacks against MARL model is essential for the safe applications of this model. Motivated by this, we investigate the impact of adversarial attacks on MARL. In the considered setup, there is an exogenous attacker who is able to modif

Y. -H. Bao, J. -N. Xu, Y. Ye, J. J. Zhang

We prove a one-to-one correspondence between the operadic ideals of the operad $\As$ and $T$-ideals. As a consequence, we show that $\As$ is noetherian and that every proper operadic ideal of $\ias$ is generated by a single element.

Ruth E. Kastner

I dissent from the standard assertion of a "Two Times Problem," in which physical time is taken as being at odds with the human sense of a "flow of time." I provide a brief overview of the case to be made for the contrary view: namely, that physical theory is indeed consistent with a genuine temporal dynamism that takes into account the quantum level in conn

Andrew Sheinis, Samuel C. Barden, Jennifer Sobeck, The MSE Team

The Maunakea Spectroscopic Explorer (MSE) is a massively multiplexed spectroscopic survey facility that will replace the Canada-France-Hawaii Telescope over the next two decades. This 12.5-meter telescope, with its 1.5 square degree field-of-view, will observe 18,000-20,000 astronomical targets in every pointing from 0.36-1.80 microns at low/moderate resolut

Guanlin Liu, Zhihan Zhou, Han Liu, Lifeng Lai

Robust reinforcement learning (RL) aims to find a policy that optimizes the worst-case performance in the face of uncertainties. In this paper, we focus on action robust RL with the probabilistic policy execution uncertainty, in which, instead of always carrying out the action specified by the policy, the agent will take the action specified by the policy wi

Graciela B. Gelmini, Jonah Hyman

In post-inflation axion-like particle (ALP) models, a stable domain wall network forms if the model's potential has multiple minima. This system must annihilate before dominating the Universe's energy density, producing ALPs and gravitational waves (a process we dub "catastrogenesis," or "creation via annihilation"). We examine the possibility that the gravi

Yan Liang, Peter Melchior, ChangHoon Hahn, Jeff Shen

We present an unsupervised search for outliers in the Bright Galaxy Survey (BGS) dataset from the DESI Early Data Release. This analysis utilizes an autoencoder to compress galaxy spectra into a compact, redshift-invariant latent space, and a normalizing flow to identify low-probability objects. The most prominent outliers show distinctive spectral features

Jiahui Huang, Leonid Sigal, Kwang Moo Yi, Oliver Wang

We present Interactive Neural Video Editing (INVE), a real-time video editing solution, which can assist the video editing process by consistently propagating sparse frame edits to the entire video clip. Our method is inspired by the recent work on Layered Neural Atlas (LNA). LNA, however, suffers from two major drawbacks: (1) the method is too slow for inte

Siliang Ma, Yong Xu

Bounding box regression (BBR) has been widely used in object detection and instance segmentation, which is an important step in object localization. However, most of the existing loss functions for bounding box regression cannot be optimized when the predicted box has the same aspect ratio as the groundtruth box, but the width and height values are exactly d

Cole Hugelmeyer

We present a category theoretical generalization of the Goussarov theorem for finite type invariants, relating generating sets for generalized finite type theories with diagrams systems for the corresponding topological objects. We will demonstrate this correspondence through a few examples including the standard finite type theory and its relationship with

Ofek Gila, Michael T. Goodrich, Robert E. Tarjan

We define simple variants of zip trees, called zip-zip trees, which provide several advantages over zip trees, including overcoming a bias that favors smaller keys over larger ones. We analyze zip-zip trees theoretically and empirically, showing, e.g., that the expected depth of a node in an $n$-node zip-zip tree is at most $1.3863\log n-1+o(1)$, which match

Ryan M. Aronson, John A. Evans

We introduce stabilized spline collocation schemes for the numerical solution of nonlinear, hyperbolic conservation laws. A nonlinear, residual-based viscosity stabilization is combined with a projection stabilization-inspired linear operator to stabilize the scheme in the presence of shocks and prevent the propagation of spurious, small-scale oscillations.

Jikang Wang

We study complete, connected and simply connected $n$-dim Riemannian manifold $M$ satisfying Ricci curvature lower bound. Further more, suppose that $M$ admits discrete isometric group actions $G$ so that the diameter of the quotient space $\mathrm{diam}(M/G)$ is bounded. In particular, for any $n$-manifold $N$ satisfying $\mathrm{diam}(N) \le D$ and $\mathr

Serena Della Corte, Laurens Van Mieghem, Antonis Papapantoleon, Jonas Papazoglou-Hennig

We consider the supervised learning problem of learning the price of an option or the implied volatility given appropriate input data (model parameters) and corresponding output data (option prices or implied volatilities). The majority of articles in this literature considers a (plain) feed forward neural network architecture in order to connect the neurons

Vladimir Batagelj

The standard and fractional projections are extended from binary two-mode networks to weighted two-mode networks. Some interesting properties of the extended projections are proved.

Sophia M. Walls, Ian J. Ford

Bell inequalities rely on an assumption that the probabilities of adopting configurations of hidden variables describing a system prior to measurement are independent of the choice of measured physical property, also known as measurement independence. Weakening this assumption could alter the inequalities to accommodate experimental data whilst maintaining l

Friedrich Schuessler, Francesca Mastrogiuseppe, Srdjan Ostojic, Omri Barak

The relation between neural activity and behaviorally relevant variables is at the heart of neuroscience research. When strong, this relation is termed a neural representation. There is increasing evidence, however, for partial dissociations between activity in an area and relevant external variables. While many explanations have been proposed, a theoretical

Donghua Wang, Wen Yao, Tingsong Jiang, Chao Li

Physical adversarial attacks against deep neural networks (DNNs) have recently gained increasing attention. The current mainstream physical attacks use printed adversarial patches or camouflage to alter the appearance of the target object. However, these approaches generate conspicuous adversarial patterns that show poor stealthiness. Another physical deploy

Peyman Gholami, Hulya Seferoglu

Two widely considered decentralized learning algorithms are Gossip and random walk-based learning. Gossip algorithms (both synchronous and asynchronous versions) suffer from high communication cost, while random-walk based learning experiences increased convergence time. In this paper, we design a fast and communication-efficient asynchronous decentralized l

Feng-Yi Liao, Lijun Ding, Yang Zheng

The spectral bundle method developed by Helmberg and Rendl is well-established for solving large-scale semidefinite programs (SDPs) in the dual form, especially when the SDPs admit $\textit{low-rank primal solutions}$. Under mild regularity conditions, a recent result by Ding and Grimmer has established fast linear convergence rates when the bundle method ca

An-Hung Hsiao, Li-Hsiang Shen, Chen-Yi Chang, Chun-Jie Chiu

Wireless indoor localization has attracted significant amount of attention in recent years. Using received signal strength (RSS) obtained from WiFi access points (APs) for establishing fingerprinting database is a widely utilized method in indoor localization. However, the time-variant problem for indoor positioning systems is not well-investigated in existi

Hongkuan Zhou, Da Zheng, Xiang Song, George Karypis

Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph representation learning and have demonstrated superior performance in many real-world applications. However, their node memory favors smaller batch sizes to capture more dependencies in graph events and needs to be maintained synchronously across all trainers. As a result, existi

Yijiang Li, Santanu S. Dey, Nikolaos V. Sahinidis

Gas networks are used to transport natural gas, which is an important resource for both residential and industrial customers throughout the world. The gas network design problem is generally modelled as a nonconvex mixed-integer nonlinear integer programming problem (MINLP). The challenges of solving the resulting MINLP arise due to the nonlinearity and nonc