April 2023 arXiv papers — page 108

Sanath K. Devalapurkar, Max L. Misterka

The goal of this article is to study some basic algebraic and combinatorial properties of "generalized $n$-series" over a commutative ring $R$, which are functions $s: \mathbf{Z}_{\geq 0} \to R$ satisfying a mild condition. A special example of generalized $n$-series is given by the $q$-integers $\frac{q^n-1}{q-1} \in \mathbf{Z}[\![q-1]\!]$. Given a generali

Sovesh Mohapatra, Advait Gosai, Gottfried Schlaug

Brain extraction is a critical preprocessing step in various neuroimaging studies, particularly enabling accurate separation of brain from non-brain tissue and segmentation of relevant within-brain tissue compartments and structures using Magnetic Resonance Imaging (MRI) data. FSL's Brain Extraction Tool (BET), although considered the current gold standard f

Archana Aravindan, Weizhe Liu, Gabriela Canalizo, Sylvain Veilleux

Feedback likely plays a crucial role in resolving discrepancies between observed and theoretical predictions of dwarf galaxy properties. Stellar feedback was once believed to be sufficient to explain these discrepancies, but it has thus far failed to fully reconcile theory and observations. The recent discovery of energetic galaxy-wide outflows in dwarf gala

Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu, Bang An

Our work addresses the critical issue of distinguishing text generated by Large Language Models (LLMs) from human-produced text, a task essential for numerous applications. Despite ongoing debate about the feasibility of such differentiation, we present evidence supporting its consistent achievability, except when human and machine text distributions are ind

Hoang Nhan Luu, Tao Liu, Jing Ren, Tom Broadhurst

Pulsar timing arrays (PTAs) can detect disturbances in the fabric of spacetime on a galactic scale by monitoring the arrival time of pulses from millisecond pulsars (MSPs). Recent advancements have enabled the use of $\gamma$-ray radiation emitted by MSPs, in addition to radio waves, for PTA experiments. Wave dark matter (DM), a prominent class of DM candida

Nathan McDonald

Biological organisms must learn how to control their own bodies to achieve deliberate locomotion, that is, predict their next body position based on their current position and selected action. Such learning is goal-agnostic with respect to maximizing (minimizing) an environmental reward (penalty) signal. A cognitive map learner (CML) is a collection of three

Emilie Josephs, Camilo Fosco, Aude Oliva

The development of technologies for easily and automatically falsifying video has raised practical questions about people's ability to detect false information online. How vulnerable are people to deepfake videos? What technologies can be applied to boost their performance? Human susceptibility to deepfake videos is typically measured in laboratory settings,

Yu-Hsuan Teng, Karin M. Sandstrom, Jiayi Sun, Munan Gong

The CO-to-H$_2$ conversion factor ($\alpha_\rm{CO}$) is central to measuring the amount and properties of molecular gas. It is known to vary with environmental conditions, and previous studies have revealed lower $\alpha_\rm{CO}$ in the centers of some barred galaxies on kpc scales. To unveil the physical drivers of such variations, we obtained ALMA Band 3,

Somayye Rostami, Douglas G. Down, George Karakostas

With the current high levels of energy consumption of data centers, reducing power consumption by even a small percentage is beneficial. We propose a framework for thermal-aware workload distribution in a data center to reduce cooling power consumption. The framework includes linearization of the general optimization problem and proposing a heuristic to appr

Luke C. Powers, Jessica Libby-Roberts, Andrea S. J. Lin, Caleb I. Cañas

Using both ground-based transit photometry and high-precision radial velocity (RV) spectroscopy, we confirm the planetary nature of TOI-3785 b. This transiting Neptune orbits an M2-Dwarf star with a period of ~4.67 days, a planetary radius of 5.14 +/- 0.16 Earth Radii, a mass of 14.95 +4.10, -3.92 Earth Masses, and a density of 0.61 +0.18, -0.17 g/cm^3. TOI-

Massimo Gobbino, Nicola Picenni

We consider generalized solutions of the Perona-Malik equation in dimension one, defined as all possible limits of solutions to the semi-discrete approximation in which derivatives with respect to the space variable are replaced by difference quotients. Our first result is a pathological example in which the initial data converge strictly as bounded variatio

John O. Dabiri, Michael F. Howland, Matthew K. Fu, Roni H. Goldshmid

Accurate measurements of atmospheric flows at meter-scale resolution are essential for a broad range of sustainability applications, including optimal design of wind and solar farms, safe and efficient urban air mobility, monitoring of environmental phenomena such as wildfires and air pollution dispersal, and data assimilation into weather and climate models

Bradley Jenks, Aly-Joy Ulusoy, Filippo Pecci, Ivan Stoianov

This paper investigates the problem of integrating optimal pressure management and self-cleaning controls in dynamically adaptive water distribution networks. We review existing single-objective valve placement and control problems for minimizing average zone pressure (AZP) and maximizing self-cleaning capacity (SCC). Since AZP and SCC are conflicting object

Subrato Bharati, M. Rubaiyat Hossain Mondal, Prajoy Podder

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in t

Aarne Talman, Hande Celikkanat, Sami Virpioja, Markus Heinonen

This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks. We apply the approach to standard tasks in natural language inference (NLI) and demonstrate the effectiveness of the method in terms of prediction accuracy and correlation with human annotation disagreements. We

J. W. T. Keeble, M. Drissi, A. Rojo-Francàs, B. Juliá-Díaz

We compute the ground-state properties of fully polarized, trapped, one-dimensional fermionic systems interacting through a gaussian potential. We use an antisymmetric artificial neural network, or neural quantum state, as an ansatz for the wavefunction and use machine learning techniques to variationally minimize the energy of systems from 2 to 6 particles.

Yuansi Chen, Khashayar Gatmiry, Minhui Jiang

We analyze the mixing time of Metropolized Hamiltonian Monte Carlo (HMC) with the leapfrog integrator to sample from a distribution on $\mathbb{R}^d$ whose log-density is smooth, has Lipschitz Hessian in Frobenius norm and satisfies isoperimetry. We bound the gradient complexity to reach $\epsilon$ error in total variation distance from a warm start by $\til

S. Mironov, M. Valencia-Villegas

We consider the Quartic Horndeski theory with torsion on a FLRW background in the second order formalism. We show that there is a one parameter family of Quartic Horndeski Cartan Lagrangians and all such theories only modify the dispersion relations of the graviton and the scalar perturbation that are usually found in the standard Horndeski theory on a torsi

Haoxiang Yu, Hsiao-Yuan Chen, Sangsu Lee, Sriram Vishwanath

With the rising emergence of decentralized and opportunistic approaches to machine learning, end devices are increasingly tasked with training deep learning models on-devices using crowd-sourced data that they collect themselves. These approaches are desirable from a resource consumption perspective and also from a privacy preservation perspective. When the

Amit Dutta Banik

If multiple thermal weakly interacting massive particle (WIMP) dark matter candidates exist, then their capture and annihilation dynamics inside a massive stars such as Sun could change from conventional method of study. With a simple correction to time evolution of dark matter (DM) number abundance inside the Sun for multiple dark matter candidates, signifi

Kazi Ranjibul Islam, Ipsita Mandal

Using a quantum Boltzmann equation framework, we analyse the nature of generic low-energy deformations of a critical Fermi surface, which exists at the non-Fermi liquid fixed point of a system consisting of fermions interacting with massless bosons. The non-Fermi liquid behaviour arises due to the itinerant quasiparticles of the Fermi surface interacting str

Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu

Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data. Among both sets of graph SSL techniques, the masked graph autoencoders (e.g., GraphMAE)--one type of generative method--have recently produced promising results. The ide

Qiong Li, Ran Wang, Xiaohui Fan, Xue-Bing Wu

The formation of the first supermassive black holes is expected to have occurred in some most pronounced matter and galaxy overdensities in the early universe. We have conducted a sub-mm wavelength continuum survey of 54 $z\sim6$ quasars using the Submillimeter Common-User Bolometre Array-2 (SCUBA2) on the James Clerk Maxwell Telescope (JCMT) to study the en

Jin Xu, Yangning Li, Xiangjin Xie, Yinghui Li

Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs), which play an important role in knowledge engineering. Recently, EA with dangling entities has been proposed as a more realistic setting, which assumes that not all entities have corresponding equivalent entities. In this paper, we focus on this setting. Some

Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang

Temporal relation prediction in incomplete temporal knowledge graphs (TKGs) is a popular temporal knowledge graph completion (TKGC) problem in both transductive and inductive settings. Traditional embedding-based TKGC models (TKGE) rely on structured connections and can only handle a fixed set of entities, i.e., the transductive setting. In the inductive set

Jiaqi Yin, Yingjie Li, Daniel Robinson, Cunxi Yu

Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e.g., computation, I/O, and memory-bound) edge computing systems. While efficient execution of their computational graph requires an effective scheduling algorithm,

Patrick J. Stofanak, Cheng-Nian Xiao, Inanc Senocak

We delineate the structure of steady laminar flows within a stably stratified, valley-shaped triangular cavity heated from below through linear stability analysis and Navier-Stokes simulations. We derive an exact solution to the quiescent conduction state, and characterize the flow via the stratification perturbation parameter, $\Pi_s$, which is a measure of

William Baldwin, Xia Liang, Johan Klarbring, Milos Dubajic

Metal halide perovskites are multifunctional semiconductors with tunable structures and properties. They are highly dynamic crystals with complex octahedral tilting patterns and strongly anharmonic atomic behaviour. In the higher temperature, higher symmetry phases of these materials, several complex structural features have been observed. The local structur

Kairi Furui, Masahito Ohue

In recent years, free energy perturbation (FEP) calculations have garnered increasing attention as tools to support drug discovery. The lead optimization mapper (Lomap) was proposed as an algorithm to calculate the relative free energy between ligands efficiently. However, Lomap requires checking whether each edge in the FEP graph is removable, which necessi

Manuel Febrero-Bande, Pedro Galeano, Eduardo García-Portugués, Wenceslao González-Manteiga

A goodness-of-fit test for the Functional Linear Model with Scalar Response (FLMSR) with responses Missing at Random (MAR) is proposed in this paper. The test statistic relies on a marked empirical process indexed by the projected functional covariate and its distribution under the null hypothesis is calibrated using a wild bootstrap procedure. The computati

Adam Brown, Sarah D'Angelo, Ben Holtz, Ciera Jaspen

Beyond self-report data, we lack reliable and non-intrusive methods for identifying flow. However, taking a step back and acknowledging that flow occurs during periods of focus gives us the opportunity to make progress towards measuring flow by isolating focused work. Here, we take a mixed-methods approach to design a logs-based metric that leverages machine

Digvijay Boob, Qi Deng, Mohammad Khalafi

The monotone Variational Inequality (VI) is a general model with important applications in various engineering and scientific domains. In numerous instances, the VI problems are accompanied by function constraints that can be data-driven, making the usual projection operator challenging to compute. This paper presents novel first-order methods for the functi

Woocheol Choi, Myeong-Su Lee, Seok-Bae Yun

In this paper, we consider the online proximal mirror descent for solving the time-varying composite optimization problems. For various applications, the algorithm naturally involves the errors in the gradient and proximal operator. We obtain sharp estimates on the dynamic regret of the algorithm when the regular part of the cost is convex and smooth. If the

Lv Tang, Haoke Xiao, Bo Li

SAM is a segmentation model recently released by Meta AI Research and has been gaining attention quickly due to its impressive performance in generic object segmentation. However, its ability to generalize to specific scenes such as camouflaged scenes is still unknown. Camouflaged object detection (COD) involves identifying objects that are seamlessly integr

Sergio Carbajo

Through the Fiat Lux seminar series at the University of California, Los Angeles, Volume 1 presents the first pedagogical and academic plans to destabilize hegemonic logics and hierarchies in STEM studies and knowledge production.

Lukas Meyer, Andreas Gilson, Oliver Scholz, Marc Stamminger

In plant phenotyping, accurate trait extraction from 3D point clouds of trees is still an open problem. For automatic modeling and trait extraction of tree organs such as blossoms and fruits, the semantically segmented point cloud of a tree and the tree skeleton are necessary. Therefore, we present CherryPicker, an automatic pipeline that reconstructs photo-

Christine Lescop

This elementary article introduces easy-to-manage invariants of genus one knots in homology 3-spheres. To prove their invariance, we investigate properties of an invariant of 3-dimensional genus two homology handlebodies called the Alexander form. The Alexander form of a 3-manifold E with boundary contains all Reidemeister torsions of link exteriors obtained

Rui Graça, Brian McReynolds, Tobi Delbruck

The operation of the DVS event camera is controlled by the user through adjusting different bias parameters. These biases affect the response of the camera by controlling - among other parameters - the bandwidth, sensitivity, and maximum firing rate of the pixels. Besides determining the response of the camera to input signals, biases significantly impact it

Chih-Yuan Chiu, Chinmay Maheshwari, Pan-Yang Su, Shankar Sastry

Arc-based traffic assignment models (TAMs) are a popular framework for modeling traffic network congestion generated by self-interested travelers who sequentially select arcs based on their perceived latency on the network. However, existing arc-based TAMs either assign travelers to cyclic paths, or do not extend to networks with bi-directional arcs (or edge

Shuhuai Ren, Aston Zhang, Yi Zhu, Shuai Zhang

This work proposes POMP, a prompt pre-training method for vision-language models. Being memory and computation efficient, POMP enables the learned prompt to condense semantic information for a rich set of visual concepts with over twenty-thousand classes. Once pre-trained, the prompt with a strong transferable ability can be directly plugged into a variety o

Razhan Hameed, Sina Ahmadi, Fatemeh Daneshfar

Sentiment analysis is the process of identifying and extracting subjective information from text. Despite the advances to employ cross-lingual approaches in an automatic way, the implementation and evaluation of sentiment analysis systems require language-specific data to consider various sociocultural and linguistic peculiarities. In this paper, the collect

Kamal Kishor Rajak, Pavan Pahilani, Harsh Patel, Bhavtosh Kikani

Silver nanoparticles (AgNP's) possess inherent biological potentials that have obliged an alternative, eco-friendly, sustainable approach to "Green Synthesis." In the present study, we synthesized Green Silver Nanoparticles (GAgNP's) using Curcuma longa L. (C. longa) flower extract as a reducing and capping agent. The synthesized GAgNP's were characterized u

Emil A. Atz, Brian M. Walsh, Connor J. O'Brien

The CuPID CubeSat Observatory was a 6U cubesat launched into low-Earth orbit with a ride-share opportunity in Fall 2021. The mission was supported by NASA's Heliophysics division and motivated scientifically with the objective to image X-rays produced in the magnetosphere. After launch, the team was unable to communicate with the spacecraft. This document pr

Nicolas Mascot

We extend our method to compute division polynomials of Jacobians of curves over Q to curves over Q(t), in view of computing mod ell Galois representations occurring in the \'etale cohomology of surfaces over Q. Although the division polynomials which we obtain are unfortunately too complicated to achieve this last goal, we still obtain explicit families of

Shaojie Tang, Jing Yuan, Twumasi Mensah-Boateng

Submodular function optimization has numerous applications in machine learning and data analysis, including data summarization which aims to identify a concise and diverse set of data points from a large dataset. It is important to implement fairness-aware algorithms when dealing with data items that may contain sensitive attributes like race or gender, to p

Yi-Jun Chang

In the LOCAL model, low-diameter decomposition is a useful tool in designing algorithms, as it allows us to shift from the general graph setting to the low-diameter graph setting, where brute-force information gathering can be done efficiently. Recently, Chang and Su [PODC 2022] showed that any high-conductance network excluding a fixed minor contains a high

Javier Aramayona, Kai-Uwe Bux, Heejoung Kim, Christopher J. Leininger

For every $n\ge 2$, the {\em surface Houghton group} $\mathcal B_n$ is defined as the asymptotically rigid mapping class group of a surface with exactly $n$ ends, all of them non-planar. The groups $\mathcal B_n$ are analogous to, and in fact contain, the braided Houghton groups. These groups also arise naturally in topology: every monodromy homeomorphisms o

Biswadeep Chakraborty, Saibal Mukhopadhyay

Energy and data-efficient online time series prediction for predicting evolving dynamical systems are critical in several fields, especially edge AI applications that need to update continuously based on streaming data. However, current DNN-based supervised online learning models require a large amount of training data and cannot quickly adapt when the under

D. K. Watson

Typically visualized from an independent particle viewpoint, the Pauli principle's role in collective motion is analyzed leading to a reimagination of the microscopic dynamics underlying superfluidity/superconductivity and a reinterpretation of several interrelated phenomena: Cooper pairs, the Fermi sea, and Pauli blocking. The current approach, symmetry-inv

Irina Aref'eva, Igor Volovich

Black holes violate the third law of thermodynamics in its standard formulation. Schwarzschild black hole entropy is inverse proportional to the square of the temperature $S=1/(16 \pi T^2) $ and tends to infinity rather than zero when the temperature goes to zero. We search for quantum statistical models with such exotic thermodynamic behaviour. It is shown

Inkyu Shin, Dahun Kim, Qihang Yu, Jun Xie

Video Panoptic Segmentation (VPS) aims to achieve comprehensive pixel-level scene understanding by segmenting all pixels and associating objects in a video. Current solutions can be categorized into online and near-online approaches. Evolving over the time, each category has its own specialized designs, making it nontrivial to adapt models between different

James Cruickshank, Bill Jackson, Shinichi Tanigawa

We show that, if $\Gamma$ is a point group of $\mathbb{R}^{k+1}$ of order two for some $k\geq 2$ and $\mathcal S$ is a $k$-pseudomanifold which has a free automorphism of order two, then either $\mathcal S$ has a $\Gamma$-symmetric infinitesimally rigid realisation in $\mathbb{R}^{k+1}$ or $k=2$ and $\Gamma$ is a half-turn rotation group.This verifies a conj

Sandra E. Safo, Han Lu

We develop scalable randomized kernel methods for jointly associating data from multiple sources and simultaneously predicting an outcome or classifying a unit into one of two or more classes. The proposed methods model nonlinear relationships in multiview data together with predicting a clinical outcome and are capable of identifying variables or groups of

Kui Cao, Su-Peng Kou

We present a systematic study of statistical mechanics for non-Hermitian quantum systems. Our work reveals that the stability of a non-Hermitian system necessitates the existence of a single path-dependent conserved quantity, which, in conjunction with the system's Hamiltonian, dictates the equilibrium state. By elucidating the relationship between the Hamil

Pierre Aboulker, Guillaume Aubian, Pierre Charbit

The dichromatic number $\vec\chi(D)$ of a digraph $D$ is the minimum size of a partition of its vertices into acyclic induced subgraphs. We denote by $\lambda(D)$ the maximum local edge connectivity of a digraph $D$. Neumann-Lara proved that for every digraph $D$, $\vec\chi(D) \leq \lambda(D) + 1$. In this paper, we characterize the digraphs $D$ for which $\

Kevin J. Kelly, Pedro A. N. Machado, Nityasa Mishra, Louis E. Strigari

We study the impact of time-dependent solar cycles in the atmospheric neutrino rate at DUNE and Hyper-Kamiokande (HK), focusing in particular on the flux below 1 GeV. Including the effect of neutrino oscillations for the upward-going component that travels through the Earth, we find that across the solar cycle the amplitude of time variation is about $\pm5\%

Wei-Jhe Huang, Jheng-Hsien Yeh, Min-Hung Chen, Gueter Josmy Faure

The goal of spatial-temporal action detection is to determine the time and place where each person's action occurs in a video and classify the corresponding action category. Most of the existing methods adopt fully-supervised learning, which requires a large amount of training data, making it very difficult to achieve zero-shot learning. In this paper, we pr

Norberto Adrian Goussies, Kenji Hata, Shruthi Prabhakara, Abhishek Amit

We present a novel self-contained camera-projector tabletop system with a lamp form-factor that brings digital intelligence to our tables. We propose a real-time, on-device, learning-based touch detection algorithm that makes any tabletop interactive. The top-down configuration and learning-based algorithm makes our method robust to the presence of clutter,

S. K. Rajput

The effect of the rigid top surface on the onset of phototactic bioconvection is investigated using linear stability theory for a suspension of forward-scattering phototactic algae in this article. The suspension is uniformly illuminated by both diffuse and oblique collimated flux. The nature of disturbance of bio-convective instability transits from a stati

Piotr Pstrągowski

Inspired by the work of Hahn-Raksit-Wilson, we introduce a variant of the even filtration which is naturally defined on $\mathbf{E}_{1}$-rings and their modules. We show that our variant satisfies flat descent and so agrees with the Hahn-Raksit-Wilson filtration on ring spectra of arithmetic interest, showing that various "motivic" filtrations are in fact in

Wei Cui, Hongfei Shu, Wei Song, Juntao Wang

We investigate the proposed holographic duality between the TsT transformation of IIB string theory on AdS$_3\times {\cal N}$ with NS-NS flux and a single-trace $T\bar{T}$ deformation of the symmetric orbifold CFT. We present a non-perturbative calculation of two-point correlation functions using string theory and demonstrate their consistency with those of

Wiphu Rujopakarn, Christina C. Williams, Emanuele Daddi, Malte Schramm

We present an identification of dust-attenuated star-forming galactic-disk substructures in a typical star-forming galaxy (SFG), UDF2, at $z = 2.696$. To date, substructures containing significant buildup of stellar mass and actively forming stars have yet to be found in typical (i.e., main-sequence) SFGs at $z > 2$. This is due to the strong dust attenuatio

Cong Zou, Wei Chen

It was the L_2 L_infinity performance index that for the first time is initiated into the discussion on state estimation of delayed MJNNs with with partially known transition probabilities, which provides a more general promotion for the estimation error.The WTOD protocol is adopted to dispatch the sensor nodes so as to effectively alleviate the updating fre

Francesco Di Carlo, Nazanin Nezami, Hadis Anahideh, Abolfazl Asudeh

Despite the potential benefits of machine learning (ML) in high-risk decision-making domains, the deployment of ML is not accessible to practitioners, and there is a risk of discrimination. To establish trust and acceptance of ML in such domains, democratizing ML tools and fairness consideration are crucial. In this paper, we introduce FairPilot, an interact

John Dickerson, Bistra Dilkina, Yu Ding, Swati Gupta

This workshop Report Out focuses on the foundational elements of trustworthy AI and OR technology, and how to ensure all AI and OR systems implement these elements in their system designs. Four sessions on various topics within Trustworthy AI were held, these being Fairness, Explainable AI/Causality, Robustness/Privacy, and Human Alignment and Human-Computer

Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu

Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT). In this paper, we systematically investigate the advantages and challenges of LLMs for MMT by answering two questions: 1) How well do LLMs perform in translating massive languages? 2) Which factors affect LLMs' performance in translation? W

CUPID collaboration, K. Alfonso, A. Armatol, C. Augier

CUPID is a next-generation bolometric experiment aiming at searching for neutrinoless double-beta decay with ~250 kg of isotopic mass of $^{100}$Mo. It will operate at $\sim$10 mK in a cryostat currently hosting a similar-scale bolometric array for the CUORE experiment at the Gran Sasso National Laboratory (Italy). CUPID will be based on large-volume scintil

Mengjin Dong, Long Xie, Sandhitsu R. Das, Jiancong Wang

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate c

Jizhizi Li, Jing Zhang, Dacheng Tao

Image matting refers to extracting precise alpha matte from natural images, and it plays a critical role in various downstream applications, such as image editing. Despite being an ill-posed problem, traditional methods have been trying to solve it for decades. The emergence of deep learning has revolutionized the field of image matting and given birth to mu

Julian Frattini, Lloyd Montgomery, Davide Fucci, Jannik Fischbach

Requirements quality literature abounds with publications presenting artifacts, such as data sets and tools. However, recent systematic studies show that more than 80% of these artifacts have become unavailable or were never made public, limiting reproducibility and reusability. In this work, we report on an attempt to recover those artifacts. To that end, w

Liping Xin, Xuhui Han, Huali Li, Bing Zhang

Long gamma-ray bursts (GRBs), which signify the end-life collapsing of very massive stars, are produced by extremely relativistic jets colliding into circumstellar medium. Huge energy is released both in the first few seconds, namely the internal dissipation phase that powers prompt emissions, and in the subsequent self-similar jet-deceleration phase that pr

Arundhati Banerjee, Soham Phade, Stefano Ermon, Stephan Zheng

We study how a principal can efficiently and effectively intervene on the rewards of a previously unseen learning agent in order to induce desirable outcomes. This is relevant to many real-world settings like auctions or taxation, where the principal may not know the learning behavior nor the rewards of real people. Moreover, the principal should be few-shot

Ohad Trabelsi

The All-Pairs Max-Flow problem has gained significant popularity in the last two decades, and many results are known regarding its fine-grained complexity. Despite this, wide gaps remain in our understanding of the time complexity for several basic variants of the problem. In this paper, we aim to bridge these gaps by providing algorithms, conditional lower

Zhirui Hu, Youzuo Lin, Qiang Guan, Weiwen Jiang

Recently, we have been witnessing the scale-up of superconducting quantum computers; however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to leveraging the power of quantum computing. Although there exist error mitigation or error-aware designs for quantum applications, the inherent fluctuation of noise (a.k.a., instab

Jie Xu

In this article, we show that (i) any smooth function on compact Riemann surface with non-empty smooth boundary $ (M, \partial M, g) $ can be realized as a Gaussian curvature function; (ii) any smooth function on $ \partial M $ can be realized as a geodesic curvature function for some metric $ \tilde{g} \in [g] $. The essential steps are the existence result

Atakan Yüksel, Erva Ulusoy, Atabey Ünlü, Tunca Doğan

Automated computational analysis of the vast chemical space is critical for numerous fields of research such as drug discovery and material science. Representation learning techniques have recently been employed with the primary objective of generating compact and informative numerical expressions of complex data. One approach to efficiently learn molecular

Qian Cheng, Doyen Sahoo, Amrita Saha, Wenzhuo Yang

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be

Junjie Zhang, Jiafei Lyu, Xiaoteng Ma, Jiangpeng Yan

Equipped with the trained environmental dynamics, model-based offline reinforcement learning (RL) algorithms can often successfully learn good policies from fixed-sized datasets, even some datasets with poor quality. Unfortunately, however, it can not be guaranteed that the generated samples from the trained dynamics model are reliable (e.g., some synthetic

Emmett L. Wyman, Yakun Xi

We introduce a variation on Kac's question, "Can one hear the shape of a drum?" Instead of trying to identify a compact manifold and its metric via its Laplace--Beltrami spectrum, we ask if it is possible to uniquely identify a point $x$ on the manifold, up to symmetry, from its pointwise counting function \[ N_x(\lambda) = \sum_{\lambda_j \leq \lambda} |e_j

Ali TehraniJamsaz, Hanze Chen, Ali Jannesari

Matching binary to source code and vice versa has various applications in different fields, such as computer security, software engineering, and reverse engineering. Even though there exist methods that try to match source code with binary code to accelerate the reverse engineering process, most of them are designed to focus on one programming language. Howe

László Györfi, Attila Lovas, Miklós Rásonyi

We revisit processes generated by iterated random functions driven by a stationary and ergodic sequence. Such a process is called strongly stable if a random initialization exists, for which the process is stationary and ergodic, and for any other initialization, the difference between the two processes converges to zero almost surely. Under some mild condit

Saebyeok Jeong, Norton Lee, Nikita Nekrasov

We examine two types of half-BPS surface defects $-$ regular monodromy surface defect and canonical surface defect $-$ in four-dimensional gauge theory with $\mathcal{N}=2$ supersymmetry and $\Omega_{\varepsilon_1,\varepsilon_2}$-background. Mathematically, we investigate integrals over the moduli spaces of parabolic framed sheaves over $\mathbb{P}^2$. Using

David Schaich

Maximally supersymmetric Yang--Mills theory (N=4 SYM) is conformal for any value of the coupling. Lattice regularization breaks conformality through the introduction of a non-zero lattice spacing and a finite lattice volume. This proceedings presents ongoing numerical computations of conformal scaling dimensions in lattice N=4 SYM, based on a lattice formula

Vladimir Kazakov, Fedor Levkovich-Maslyuk, Victor Mishnyakov

We present significant evidence that the powerful property of Yangian invariance extends to a new large class of conformally invariant Feynman integrals. Our results apply to planar Feynman diagrams in any spacetime dimension dual to an arbitrary network of intersecting straight lines on a plane (Baxter lattice), with propagator powers determined by the geom

Rayson Laroca, Valter Estevam, Alceu S. Britto, Rodrigo Minetto

This work draws attention to the large fraction of near-duplicates in the training and test sets of datasets widely adopted in License Plate Recognition (LPR) research. These duplicates refer to images that, although different, show the same license plate. Our experiments, conducted on the two most popular datasets in the field, show a substantial decrease i

Ritoban Kundu, Xu Shi, Jean Morrison, Jessica Barrett

Using administrative patient-care data such as Electronic Health Records (EHR) and medical/ pharmaceutical claims for population-based scientific research has become increasingly common. With vast sample sizes leading to very small standard errors, researchers need to pay more attention to potential biases in the estimates of association parameters of intere

Serhii Bardyla, Jonathan Cancino-Manríquez, Vera Fischer, Corey Bacal Switzer

A family $\mathcal{A} \subseteq [\omega]^\omega$ such that for all finite $\{X_i\}_{i\in n}\subseteq \mathcal A$ and $A \in \mathcal{A} \setminus \{X_i\}_{i\in n}$, the set $A \setminus \bigcup_{i \in n} X_i$ is infinite, is said to be ideal independent. We prove that an ideal independent family $\mathcal{A}$ is maximal if and only if $\mathcal A$ is $\mathc

Michael L. Sitko, Ray W. Russell, Zachary C. Long, Korash Assani

We have examined inter-night variability of K2-discovered Dippers that are not close to being viewed edge-on, as determined from previously-reported ALMA images, using the SpeX spectrograph and the NASA Infrared Telescope facility (IRTF). The three objects observed were EPIC 203850058, EPIC 205151387, and EPIC 204638512 (2MASS J16042165-2130284). Using the r

Jin-Wen Kang, Lei Wang, Wei Dai, Sa Wang

This work presents the first theoretical investigation of the medium modification of jet broadening as an event-shape observable in multijet final states due to jet quenching in high-energy nuclear collisions. The partonic spectrum of $pp$ collisions with next-to-leading order (NLO) accuracy at $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV is provided by the POWHEG$+$

Md Tahmidul Alam, Chirag Gupta

A broad TCAD simulation analysis of a monolithic common drain bidirectional GaN HEMT was performed. We used gate-to-gate distances of 4 microns and 6 microns for the devices optimized with two field plates. The breakdown voltages were 675V and 915V respectively. Inclusion of field plates near both the gates produced electric field peaks at the opposite ends

Dongxu Liu, Haiquan Zhao, Yang Zhou

Limited by fixed step-size and sparsity penalty factor, the conventional sparsity-aware normalized subband adaptive filtering (NSAF) type algorithms suffer from trade-off requirements of high filtering accurateness and quicker convergence behavior for sparse system identification. To deal with this problem, this paper proposes variable step-size L0-norm cons

Hongxiang Gao, Xingyao Wang, Zhenghua Chen, Min Wu

Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification, and the introduction of intelligent wearable ECG devices has enabled daily monitoring. However, due to the need for professional expertise in the ECGs interpretation, general public access has once again been restricted, prompting th

Marius Dadarlat

In this article we discuss cohomological obstructions to two kinds of group stability. In the first part, we show that residually finite groups $\Gamma$ which arise as fundamental groups of compact Riemannian manifolds with strictly negative sectional curvature are not uniform-to-local stable with respect to the operator norm if their even Betti numbers $b_{

Ian Barnett

Traditional methods for inference in change point detection often rely on a large number of observed data points and can be inaccurate in non-asymptotic settings. With the rise of mobile health and digital phenotyping studies, where patients are monitored through the use of smartphones or other digital devices, change point detection is needed in non-asympto

Luca Di Luzio, Alfredo Walter Mario Guerrera, Xavier Ponce Díaz, Stefano Rigolin

Non-universal axion models, with the Peccei-Quinn (PQ) symmetry acting on Standard Model (SM) fermions in a generation-dependent way, are typically accompanied by two different sources of flavour violation, dubbed here as infrared (IR) and ultraviolet (UV). The former is due to the flavour violating axion couplings to SM fermions, while the latter arises fro

Noah Bertram, Alex Levinson, Justin Hsu

The fair division literature in economics considers how to divide resources between multiple agents such that the allocation is envy-free: each agent receives their favorite piece. Researchers have developed a variety of fair division protocols for the most standard setting, where the agents want to split a single item, however, the protocols are highly intr

Xiaojin Zhang, Anbu Huang, Lixin Fan, Kai Chen

Federated learning (FL) is a new distributed learning paradigm, with privacy, utility, and efficiency as its primary pillars. Existing research indicates that it is unlikely to simultaneously attain infinitesimal privacy leakage, utility loss, and efficiency. Therefore, how to find an optimal trade-off solution is the key consideration when designing the FL

Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promisi

Kar Balan, Shruti Agarwal, Simon Jenni, Andy Parsons

We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance -- determining the generative mod

Sathish Rajendran, Haoyu Liu, Stephen E. Trask, Baris Key

Development of new polymeric binders can help enable the use of silicon-rich anodes in Li-ion batteries, by providing stronger adhesion to the active material particles. The compositional features that improve interfacial interactions and mechanical properties can often impart electronic conductivity and redox activity to these polymers, which are generally