December 2023 arXiv papers — page 59

Sofia Sturari, Veronica Varzi, Pietro Aprà, Adam Britel

Nanodiamonds (NDs) gained increasing attention in multiple research areas due to the possibility of tuning their physical and chemical features by functionalizing their surface. This has a crucial impact on their electrical properties, which are essential in applications such as the development of innovative sensors and in the biomedical field. The great int

Damien de Mijolla, Jonathan Holdship, Serena Viti, Johannes Heyl

Molecular emission from the galactic and extragalactic interstellar medium (ISM) is often used to determine the physical conditions of the dense gas. However, even from spatially resolved regions, the observed molecules are not necessarily arising from a single component. Disentangling multiple gas components is often a degenerate problem in radiative transf

William T. Gullion, Stephen J. Walsh

Coordinate exchange (CEXCH) is a popular algorithm for generating exact optimal experimental designs. The authors of CEXCH advocated for a highly greedy implementation - one that exchanges and optimizes single element coordinates of the design matrix. We revisit the effect of greediness on CEXCHs efficacy for generating highly efficient designs. We implement

Hang Xu, Alessandro Perelli

In this work, we present a novel self-supervised method for Low Dose Computed Tomography (LDCT) reconstruction. Reducing the radiation dose to patients during a CT scan is a crucial challenge since the quality of the reconstruction highly degrades because of low photons or limited measurements. Supervised deep learning methods have shown the ability to remov

Ethan Q. Williams, Chandrasekhar Ramanathan

Coherence times of spin qubits in solid-state platforms are often limited by the presence of a spin bath. While some properties of these typically dark bath spins can be indirectly characterized via the central qubit, it is important to characterize their properties by direct measurement. Here we use pulsed electron paramagnetic resonance (pEPR) based Carr-P

Sara Ayoubi, Giulio Grassi, Giovanni Pau, Kyle Jamieson

LoLa is a novel multi-path system for video conferencing applications over cellular networks. It provides significant gains over single link solutions when the link quality over different cellular networks fluctuate dramatically and independently over time, or when aggregating the throughput across different cellular links improves the perceived video qualit

YoonHaeng Hur, Yuehaw Khoo

Computational difficulty of quadratic matching and the Gromov-Wasserstein distance has led to various approximation and relaxation schemes. One of such methods, relying on the notion of distance profiles, has been widely used in practice, but its theoretical understanding is limited. By delving into the statistical complexity of the previously proposed metho

Giulia Tomagra, Alice Re, Veronica Varzi, Pietro Aprà

MicroGraphited-Diamond-Multi Electrode Arrays ({\mu}G-D-MEAs) can be successfully used to reveal, in real time, quantal exocytotic events occurring from many individual neurosecretory cells and/or from many neurons within a network. As {\mu}G-D-MEAs arrays are patterned with up to 16 sensing microelectrodes, each of them recording large amounts of data revea

Zachary R. Madin, Jonathan Lawry, Edmund R. Hunt

An important real-world application of multi-robot systems is multi-robot patrolling (MRP), where robots must carry out the activity of going through an area at regular intervals. Motivations for MRP include the detection of anomalies that may represent security threats. While MRP algorithms show some maturity in development, a key potential advantage has be

Shishir Agrawal, Luis David Garcia Puente, Minho Kim, Flavia Sancier-Barbosa

Asymptotic goodness-of-fit methods in contingency table analysis can struggle with sparse data, especially in multi-way tables where it can be infeasible to meet sample size requirements for a robust application of distributional assumptions. However, algebraic statistics provides exact alternatives to these classical asymptotic methods that remain viable ev

Prem Raj, Aniruddha Singhal, Vipul Sanap, L. Behera

Picking unseen objects from clutter is a difficult problem because of the variability in objects (shape, size, and material) and occlusion due to clutter. As a result, it becomes difficult for grasping methods to segment the objects properly and they fail to singulate the object to be picked. This may result in grasp failure or picking of multiple objects to

Scott Lawrence, Yukari Yamauchi

An extreme learning machine is a neural network in which only the weights in the last layer are changed during training; for such networks training can be performed efficiently and deterministically. We use an extreme learning machine to construct a control variate that tames the sign problem in the classical Ising model at imaginary external magnetic field.

Shutong Jin, Ruiyu Wang, Florian T. Pokorny

Even though large-scale text-to-image generative models show promising performance in synthesizing high-quality images, applying these models directly to image editing remains a significant challenge. This challenge is further amplified in video editing due to the additional dimension of time. This is especially the case for editing real-world videos as it n

Payam Jome Yazdian, Rachel Lagasse, Hamid Mohammadi, Eric Liu

We introduce MotionScript, a novel framework for generating highly detailed, natural language descriptions of 3D human motions. Unlike existing motion datasets that rely on broad action labels or generic captions, MotionScript provides fine-grained, structured descriptions that capture the full complexity of human movement including expressive actions (e.g.,

Gbetondji J-S Dovonon, Jakob Zeitler

Multi-fidelity Bayesian Optimisation (MFBO) has been shown to generally converge faster than single-fidelity Bayesian Optimisation (SFBO) (Poloczek et al. (2017)). Inspired by recent benchmark papers, we are investigating the long-run behaviour of MFBO, based on observations in the literature that it might under-perform in certain scenarios (Mikkola et al. (

Fan Cheng, Vladimir Shuvayev, Mark Douvidzon, Lev Deych

We experimentally demonstrate and numerically analyze large arrays of whispering gallery resonators. Using fluorescent mapping, we measure the spatial distribution of the cavity-ensemble's resonances, revealing that light reaches distant resonators in various ways, including while passing through dark gaps, resonator groups, or resonator lines. Energy spatia

Jiexiang Huang

Let $(X,L)$ be a polarized K3 surface of genus $g$ and $C_{en} \subset X$ be the curve of singular points of nodal elliptic curves in $|L|$. When $(X,L)$ is generic of genus two, Huybrechts observed that the curve $C_{en}$ is a constant cycle curve and conjectured that this remains true for higher genus cases. In this note, we show that the conjecture holds

Himanshu Singh

One of the central challenge for extracting governing principles of dynamical system via Dynamic Mode Decomposition (DMD) is about the limit data availability or formally called as Limited Data Acquisition in the present paper. In the interest of discovering the governing principles for a dynamical system with limited data acquisition, we provide a variant o

Geoffrey van Dover, Josh Javor, Jourdan Ewoldt, Ha Eun Lee

Understanding the structural and functional development of human-induced pluripotent stem-cell-derived cardiomyocytes is essential to engineering cardiac tissue that enables pharmaceutical testing, modeling diseases, and designing therapies. Here we use a method not commonly applied to biological materials, small angle X-ray scattering, to characterize the s

Arthur Leroy, Ai Ling Teh, Frank Dondelinger, Mauricio A. Alvarez

Interrogating the evolution of biological changes at early stages of life requires longitudinal profiling of molecules, such as DNA methylation, which can be challenging with children. We introduce a probabilistic and longitudinal machine learning framework based on multi-mean Gaussian processes (GPs), accounting for individual and gene correlations across t

Oscar Fuentealba, Marc Henneaux

The asymptotic structure of gravity in $D=6$ spacetime dimensions is described at spatial infinity in the asymptotically flat context through Hamiltonian (ADM) methods. Special focus is given on the BMS supertranslation subgroup. It is known from previous studies that the BMS group contains more supertranslations as one goes from $D=4$ to $D=5$. Indeed, whil

Alina Ostafe, Igor E. Shparlinski

We consider the set $\mathcal M_n(\mathbb Z; H)$ of $n\times n$-matrices with integer elements of size at most $H$ and obtain upper bounds on the number of matrices from $\mathcal M_n(\mathbb Z; H)$, for which the characteristic polynomial has a fixed discriminant $d$. When $d=0$, this corresponds to counting matrices with a repeated eigenvalue, and thus is

Sharath Nittur Sridhar, Maciej Szankin, Fang Chen, Sairam Sundaresan

Recent one-shot Neural Architecture Search algorithms rely on training a hardware-agnostic super-network tailored to a specific task and then extracting efficient sub-networks for different hardware platforms. Popular approaches separate the training of super-networks from the search for sub-networks, often employing predictors to alleviate the computational

Clement Ruah, Osvaldo Simeone, Jakob Hoydis, Bashir Al-Hashimi

Embodying the principle of simulation intelligence, digital twin (DT) systems construct and maintain a high-fidelity virtual model of a physical system. This paper focuses on ray tracing (RT), which is widely seen as an enabling technology for DTs of the radio access network (RAN) segment of next-generation disaggregated wireless systems. RT makes it possibl

Guneet Singh Kohli, Shantipriya Parida, Sambit Sekhar, Samirit Saha

Building LLMs for languages other than English is in great demand due to the unavailability and performance of multilingual LLMs, such as understanding the local context. The problem is critical for low-resource languages due to the need for instruction sets. In a multilingual country like India, there is a need for LLMs supporting Indic languages to provide

Ajay Sharma, Raj Prince, Debanjan Bose

Quasi-periodic oscillations (QPOs) have been detected in many Fermi-detected bright blazars. In this letter, we report multiple QPOs detected in a non-blazar AGN PKS 0521-36 searched over the entire 15 years of Fermi-LAT data. QPOs are detected at 268 days, at 295 days, and at 806 days timescales with more than 3$\sigma$ significance. The QPO detected at 806

Juan F. Castel, Susana Cebrián, Theopisti Dafni, David Díez-Ibáñez

The TREX-DM detector, a low background chamber with microbulk Micromegas readout, was commissioned in the underground laboratory of Canfranc (LSC) in 2018. Since then, data taking campaigns have been carried out with Argon and Neon mixtures, at different pressures from 1 to 4 bar. By achieving a low energy threshold of 1 keV$_{ee}$ and a background level of

Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman

Deep Learning (DL) workloads have rapidly increased in popularity in enterprise clusters and several new cluster schedulers have been proposed in recent years to support these workloads. With rapidly evolving DL workloads, it is challenging to quickly prototype and compare scheduling policies across workloads. Further, as prior systems target different aspec

Anna Batra, Iris Zhou, Suh Young Choi, Chongjiu Gao

Prior work has argued for a more justice-centered approach to postsecondary computing education by emphasizing ethics, identity, and political vision. In this experience report, we examine how postsecondary students of diverse gender and racial identities experience a justice-centered Data Structures and Algorithms designed for undergraduate non-computer sci

Clément Grisi, Geert Litjens, Jeroen van der Laak

Vision Transformers (ViTs) have ushered in a new era in computer vision, showcasing unparalleled performance in many challenging tasks. However, their practical deployment in computational pathology has largely been constrained by the sheer size of whole slide images (WSIs), which result in lengthy input sequences. Transformers faced a similar limitation whe

Dominic Flocco, Jonad Pulaj, Carl Yerger

Graph pebbling is a combinatorial game played on an undirected graph with an initial configuration of pebbles. A pebbling move consists of removing two pebbles from one vertex and placing one pebble on an adjacent vertex. The pebbling number of a graph is the smallest number of pebbles necessary such that, given any initial configuration of pebbles, at least

Nicolas Perez, Daryl Preece

Optical bottle beams, characterized by their unique three-dimensional dark core, have garnered substantial interest due to their potential applications across multiple domains of science and technology. This paper delves into the current methods used to create these beams and provides a method to obscure their nodal planes through coaxial non-interfering ort

Louis Rosenberg, Gregg Willcox, Hans Schumann, Ganesh Mani

Conversational Swarm Intelligence (CSI) is a communication technology that enables large, networked groups (25 to 2500 people) to hold real-time conversational deliberations online. Modeled on the dynamics of biological swarms, CSI enables the reasoning benefits of small-groups with the collective intelligence benefits of large-groups. In this pilot study, g

Alessandro Mastrototaro, Jimmy Olsson

Being the most classical generative model for serial data, state-space models (SSM) are fundamental in AI and statistical machine learning. In SSM, any form of parameter learning or latent state inference typically involves the computation of complex latent-state posteriors. In this work, we build upon the variational sequential Monte Carlo (VSMC) method, wh

George M. Bergman

For $P$ a poset, the dimension of $P$ is defined to be the least cardinal $\kappa$ such that $P$ is embeddable in a direct product of $\kappa$ totally ordered sets. We study the behavior of this function on finite-dimensional (not necessarily finite) posets. In general, the dimension dim($P$ x $Q$) of a product of two posets can be smaller than dim($P$) + di

Rene Allerstorfer, Andreas Bluhm, Harry Buhrman, Matthias Christandl

Signal loss poses a significant threat to the security of quantum cryptography when the chosen protocol lacks loss-tolerance. In quantum position verification (QPV) protocols, even relatively small loss rates can compromise security. The goal is thus to find protocols that remain secure under practically achievable loss rates. In this work, we modify the usu

Raúl A. Briceño, Robert G. Edwards, Miller Eaton, Carlos González-Arciniegas

In recent years, applications of quantum simulation have been developed to study properties of strongly interacting theories. This has been driven by two factors: on the one hand, needs from theorists to have access to physical observables that are prohibitively difficult to study using classical computing; on the other hand, quantum hardware becoming increa

David van Wijk

Control barrier functions (CBFs) and safety-critical control have seen a rapid increase in popularity in recent years, predominantly applied to systems in aerospace, robotics and neural network controllers. Control barrier functions can provide a computationally efficient method to monitor arbitrary primary controllers and enforce state constraints to ensure

Min Xiong, Kaiyang Huang, Yang Liu, Rui Yao

Electromagnetic transient (EMT) simulation is a crucial tool for power system dynamic analysis because of its detailed component modeling and high simulation accuracy. However, it suffers from computational burdens for large power grids since a tiny time step is typically required for accuracy. This paper proposes an efficient and accurate semi-analytical ap

P. Rodriguez-Fernandez, N. T. Howard, A. Saltzman, S. Kantamneni

This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is d

S. I. Atwood, V. V. Mkhitaryan, S. Dhileepkumar, C. Nuibe

We report the observation of a three-photon resonant transition of charge-carrier spins in an organic light-emitting diode using electrically detected magnetic resonance (EDMR) spectroscopy at room temperature. Under strong magnetic-resonant drive (drive field $B_1$ ~ static magnetic field $B_0$), a $B_0$-field swept EDMR line emerges when $B_0$ is approxima

Anupriya Kumari, Devansh Bhardwaj, Sukrit Jindal

Machine learning models have demonstrated remarkable success across diverse domains but remain vulnerable to adversarial attacks. Empirical defense mechanisms often fail, as new attacks constantly emerge, rendering existing defenses obsolete, shifting the focus to certification-based defenses. Randomized smoothing has emerged as a promising technique among n

Gurjyot Sethi, Bowen Xia, Dongwook Kim, Hang Liu

Chiral exact flat bands (FBs) at charge neutrality have attracted much recent interest, presenting an intriguing condensed-matter system to realize exact many-body phenomena, as specifically shown in "magic angle" twisted bilayer graphene for superconductivity and triangulene-based superatomic graphene for excitonic condensation. Yet, no generic physical mod

Li Ding, Lee Spector

One potential drawback of using aggregated performance measurement in machine learning is that models may learn to accept higher errors on some training cases as compromises for lower errors on others, with the lower errors actually being instances of overfitting. This can lead to both stagnation at local optima and poor generalization. Lexicase selection is

S. Alekhin, M. V. Garzelli, S. Kulagin, S. -O. Moch

We investigate the impact of the recently released FNAL-E906 (SeaQuest) data on the ratio of proton-deuteron and proton-proton Drell-Yan production cross-sections on the sea quark PDFs. We find that they have constraining power on the light-quark sea isospin asymmetry $(\bar{d}-\bar{u})(x)$ and on the $(\bar{d}/\bar{u})(x)$ ratio at large longitudinal moment

Moses Openja, Foutse Khomh, Armstrong Foundjem, Zhen Ming

Recently, machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their inductive nature, ensuring the quality of these systems remains a significant challenge for the research community. Unlike traditional software built deductively by writing explicit rules, ML/DL systems infer rules from training data.

Adam Kraus, Brian Simanek

We consider families of polynomial lemniscates in the complex plane and determine if they bound a Jordan domain. This allows us to find examples of regions for which we can calculate the projection of $\bar{z}$ to the Bergman space of the bounded region. Such knowledge has applications to the calculation of torsional rigidity.

Zexiang Hu, Ajay Jha, Katarzyna Siewierska, Ross Smith

The magnetization of amorphous DyCo3 and TbCo3 is studied by magnetometry, anomalous Hall effect and magneto-optic Kerr effect to understand the temperature-dependent magnetic structure. A square magnetic hysteresis loop with perpendicular magnetic anisotropy and coercivity that reaches 3.5 T in the vicinity of the compensation temperature is seen in thin fi

Robert Littlejohn, Jonathan Rawlinson, Joseph Subotnik

This article describes a method for calculating higher order or nonadiabatic corrections in Born-Oppenheimer theory and its interaction with the translational degrees of freedom. The method uses the Wigner-Weyl correspondence to map nuclear operators into functions on the classical phase space and the Moyal star product to represent operator multiplication o

Eman Abdullah AlOmar, Mohamed Wiem Mkaouer, Ali Ouni

Code refactoring is widely recognized as an essential software engineering practice to improve the understandability and maintainability of the source code. The Extract Method refactoring is considered as "Swiss army knife" of refactorings, as developers often apply it to improve their code quality. In recent years, several studies attempted to recommend Ext

Heming Yao, Jérôme Lüscher, Benjamin Gutierrez Becker, Josep Arús-Pous

Colonoscopy plays a crucial role in the diagnosis and prognosis of various gastrointestinal diseases. Due to the challenges of collecting large-scale high-quality ground truth annotations for colonoscopy images, and more generally medical images, we explore using self-supervised features from vision transformers in three challenging tasks for colonoscopy ima

Roberto Francisco de Lima Junior, Luiz Fernando Paes de Barros Presta, Lucca Santos Borborema, Vanderson Nogueira da Silva

This paper presents a detailed case study examining the application of Large Language Models (LLMs) in the construction of test cases within the context of software engineering. LLMs, characterized by their advanced natural language processing capabilities, are increasingly garnering attention as tools to automate and enhance various aspects of the software

Zirong Chen, Xutong Sun, Yuanhe Li, Meiyi Ma

Emergency and non-emergency response systems are essential services provided by local governments and critical to protecting lives, the environment, and property. The effective handling of (non-)emergency calls is critical for public safety and well-being. By reducing the burden through non-emergency callers, residents in critical need of assistance through

Elliot Creager

Machine Learning (ML) is an expressive framework for turning data into computer programs. Across many problem domains -- both in industry and policy settings -- the types of computer programs needed for accurate prediction or optimal control are difficult to write by hand. On the other hand, collecting instances of desired system behavior may be relatively m

S. Carlip

General relativity becomes vastly simpler in three spacetime dimensions: all vacuum solutions have constant curvature, and the moduli space of solutions can be almost completely characterized. As a result, this lower dimensional setting becomes an ideal test bed for a wide range of approaches to quantum gravity, from reduced phase phase space quantization to

Sangwook Ryu, Bjoern Schenke, Chun Shen, Wenbin Zhao

Within a (3+1)D viscous hydrodynamic model we compute anisotropic flow in small system collisions as performed at the Relativistic Heavy Ion Collider and measured by the STAR and PHENIX Collaborations. We emphasize the importance of the rapidity dependence of the geometry for interpreting the differences encountered in measurements by the two collaborations.

Bathiya Samarakoon, Terrance M. Figy

In this paper, we present predictions for scattering cross-section the of Higgs boson pair production via photon fusion at future muon colliders, focusing specifically the production processes $\mu^+\mu^- \rightarrow \gamma\gamma \rightarrow h^0h^0, A^0A^0$. We investigated the impact of three choices the photon structure functions on cross-section predictio

Kyoung-Bum Huh, Hyun-Sik Jeong, Juan F. Pedraza

Recently, the concept of spread complexity, Krylov complexity for states, has been introduced as a measure of the complexity and chaoticity of quantum systems. In this paper, we study the spread complexity of the thermofield double state within \emph{integrable} systems that exhibit saddle-dominated scrambling. Specifically, we focus on the Lipkin-Meshkov-Gl

Aidan Herderschee, Juan Maldacena

We compute the three graviton amplitude in the Banks-Fischler-Shenker-Susskind matrix model for M-theory. Even though the three point amplitude is determined by super Poincare invariance in eleven dimensional M-theory, it requires a non-trivial computation in the matrix model. We consider a configuration where all three gravitons carry non-zero longitudinal

Liuyang Ding, Lena Sabidussi, Brian C. Holloway, Marcus Hultmark

A turbulent pipe flow experiment was conducted where the surface of the pipe was oscillated azimuthally over a wide range of frequencies, amplitudes and Reynolds number. The drag was reduced by as much as 30\%. Past work has suggested that the drag reduction scales with the velocity amplitude of the motion, its period, or the Reynolds number. Here, we find t

Priti Oli, Rabin Banjade, Jeevan Chapagain, Vasile Rus

Assessing student's answers and in particular natural language answers is a crucial challenge in the field of education. Advances in machine learning, including transformer-based models such as Large Language Models(LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across dive

Evan C. Johnson, Antonia Musso, José F. Negron, Mark A. Lewis

Bark beetles are significant forest pests, with some species capable of causing widespread tree mortality. Among these, the mountain pine beetle (MPB) stands out for its exceptionally destructive outbreak in the 2000s. We use MPB as a case study to explore the concept of =excitable dynamics, where ephemeral perturbations produce large excursions from equilib

Aashay Arora, Jonathan Guiang, Diego Davila, Frank Würthwein

Due to the increased demand of network traffic expected during the HL-LHC era, the T2 sites in the USA will be required to have 400Gbps of available bandwidth to their storage solution. With the above in mind we are pursuing a scale test of XRootD software when used to perform Third Party Copy transfers using the HTTP protocol. Our main objective is to under

Isidora Chara Tourni, Derry Wijaya

Unsupervised Neural Machine Translation (UNMT) focuses on improving NMT results under the assumption there is no human translated parallel data, yet little work has been done so far in highlighting its advantages compared to supervised methods and analyzing its output in aspects other than translation accuracy. We focus on three very diverse languages, Frenc

Neel R Vora, Amir Hajighasemi, Cody T. Reynolds, Amirmohammad Radmehr

Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases. However, the real-time transmission of the significant corpus physiological signals over extended periods consumes substantial power and time, limiting the viability of

Kaushik Venkatesh Krishnamurthy

This research focuses on enabling Northeastern University's Husky, a multi-modal quadrupedal robot, to navigate narrow paths akin to various animals in nature. The Husky is equipped with thrusters to stabilize its body during dynamic maneuvers, addressing challenges inherent in aerial-legged systems. The approach involves modeling the robot as HROM (Husky Re

Andrei Khrennikov

We develop the contextual measurement model (CMM) which is used for clarification of the quantum foundations. This model matches with Bohr's views on the role of experimental contexts. CMM is based on contextual probability theory which is connected with generalized probability theory. CMM covers measurements in classical, quantum, and semi-classical physics

Jing Cui, Yufei Han, Yuzhe Ma, Jianbin Jiao

Backdoor attacks in reinforcement learning (RL) have previously employed intense attack strategies to ensure attack success. However, these methods suffer from high attack costs and increased detectability. In this work, we propose a novel approach, BadRL, which focuses on conducting highly sparse backdoor poisoning efforts during training and testing while

Julia Chuzhoy, Sanjeev Khanna

The maximum bipartite matching problem is among the most fundamental and well-studied problems in combinatorial optimization. A beautiful and celebrated combinatorial algorithm of Hopcroft and Karp (1973) shows that maximum bipartite matching can be solved in $O(m \sqrt{n})$ time on a graph with $n$ vertices and $m$ edges. For the case of very dense graphs,

Shohei Wakayama, Nisar Ahmed

We introduce a new variant of contextual multi-armed bandits (CMABs) called observation-augmented CMABs (OA-CMABs) wherein a robot uses extra outcome observations from an external information source, e.g. humans. In OA-CMABs, external observations are a function of context features and thus provide evidence on top of observed option outcomes to infer hidden

Harbir Antil, Rohit Khandelwal, Umarkhon Rakhimov

This article provides quasi-optimal a priori error estimates for an optimal control problem constrained by an elliptic obstacle problem where the finite element discretization is carried out using the symmetric interior penalty discontinuous Galerkin method. The main proofs are based on the improved $L^2$-error estimates for the obstacle problem, the discret

Ke Wu, Xia Zhu, Stephan W. Anderson, Xin Zhang

Anatomy-specific RF receive coil arrays routinely adopted in magnetic resonance imaging (MRI) for signal acquisition, are commonly burdened by their bulky, fixed, and rigid configurations, which may impose patient discomfort, bothersome positioning, and suboptimal sensitivity in certain situations. Herein, leveraging coaxial cables' inherent flexibility and

Ivan Vitev, Weiyao Ke

Heavy meson production in reactions with nuclei is an active new frontier to understand QCD dynamics and the process of hadronization in nuclear matter. Measurements in various colliding systems at RHIC and LHC, including Pb-Pb, Xe-Xe, O-O, p-Pb, and p-O, enable precision tests of the medium-size, temperature, and mass dependencies of the in-medium parton pr

Rachael Hill, Madelyn Polzin, Zachary Spielman, Casey Kovesdi

The purpose of this style guide is to assist developers in designing effective and consistent-looking user interfaces for accelerator control rooms. A similar purpose is to help developers avoid the creation of user interfaces that needlessly stray from the accepted standard set forth in this document. This way, all interfaces combined will look congruous. T

Zezhong Zhang, Feng Bao, Guannan Zhang

The impressive expressive power of deep neural networks (DNNs) underlies their widespread applicability. However, while the theoretical capacity of deep architectures is high, the practical expressive power achieved through successful training often falls short. Building on the insights gained from Neural ODEs, which explore the depth of DNNs as a continuous

Wenwen Li

GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data, and massive computing power to solve problems with high automation and intelligence. This paper reviews the progress of AI in social science research, highlighting important advancements in using GeoAI to fill critical data

Kane C. Bennett, Alyson M. Stahl, Thomas R. Canfield, Garrett G. Euler

An integrated Equation of State (EOS) and strength/pore-crush/damage model framework is provided for modeling near to source (near-field) ground-shock response, where large deformations and pressures necessitate coupling EOS with pressure-dependent plastic yield and damage. Nonlinear pressure-dependence of strength up to high-pressures is combined with a Mod

Shai M. Chester, Ross Dempsey, Silviu S. Pufu

We combine supersymmetric localization with the numerical conformal bootstrap to bound the scaling dimension and OPE coefficient of the lowest-dimension operator in $\mathcal{N} = 4$ $\text{SU}(N)$ super-Yang-Mills theory for a wide range of $N$ and Yang-Mills couplings $g_\text{YM}$. We find that our bounds are approximately saturated by weak coupling resul

Saad Ullah, Mingji Han, Saurabh Pujar, Hammond Pearce

Large Language Models (LLMs) have been suggested for use in automated vulnerability repair, but benchmarks showing they can consistently identify security-related bugs are lacking. We thus develop SecLLMHolmes, a fully automated evaluation framework that performs the most detailed investigation to date on whether LLMs can reliably identify and reason about s

Wieland Morgenstern, Florian Barthel, Anna Hilsmann, Peter Eisert

3D Gaussian Splatting has recently emerged as a highly promising technique for modeling of static 3D scenes. In contrast to Neural Radiance Fields, it utilizes efficient rasterization allowing for very fast rendering at high-quality. However, the storage size is significantly higher, which hinders practical deployment, e.g. on resource constrained devices. I

Vedang Asgaonkar, Aditya Jain, Abir De

Given a set of observations, feature acquisition is about finding the subset of unobserved features which would enhance accuracy. Such problems have been explored in a sequential setting in prior work. Here, the model receives feedback from every new feature acquired and chooses to explore more features or to predict. However, sequential acquisition is not f

Mengya Zhang, Xiaokuan Zhang, Josh Barbee, Yinqian Zhang

Cross-chain bridges are used to facilitate token and data exchanges across blockchains. Although bridges are becoming increasingly popular, they are still in their infancy and have been attacked multiple times recently, causing significant financial loss. Although there are numerous reports online explaining each of the incidents on cross-chain bridges, they

Sebastian Kräss, Rico Zacher

We prove Li-Yau and Harnack inequalities for systems of linear reaction-diffusion equations. By introducing an additional discrete spatial variable, the system is rewritten as a scalar diffusion equation with an operator sum. For such operators in a mixed continuous and discrete setting, we introduce the hybrid curvature-dimension condition $CD_{hyb} (\kappa

Phuong X. Nguyen, Raghav Chaturvedi, Liguo Ma, Patrick Knuppel

Trions are a three-particle bound state of electrons and holes. Experimental realization of a trion liquid in the degenerate quantum limit would open a wide range of phenomena in quantum many-body physics. However, trions have been observed only as optically excited states in doped semiconductors to date. Here we report the emergence of a degenerate trion li

Vibhuti Bhushan Jha, Kannabiran Seshasayanan, Vassilios Dallas

Generalised two-dimensional (2D) fluid dynamics is characterised by a relationship between a scalar field $q$, called generalised vorticity, and the stream function $\psi$, namely $q = (-\nabla^2)^\frac{\alpha}{2} \psi$. We study the transition of cascades in generalised 2D turbulence by systematically varying the parameter $\alpha$ and investigating its inf

G. Sumbatian, E. Ievlev, A. Yung

We study effective dynamics of the non-supersymmetric two-dimensional $\mathbb{CP}(N-1)$ model in the large $N$ limit. This model is deformed by a mass term $m$ preserving $\mathbb{Z}_N$ symmetry of the Lagrangian. At small $m$ the theory is strongly coupled and resembles the undeformed $\mathbb{CP}(N-1)$ model, while at large $m$ it is in a weakly coupled H

Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti

In multi-agent settings with mixed incentives, methods developed for zero-sum games have been shown to lead to detrimental outcomes. To address this issue, opponent shaping (OS) methods explicitly learn to influence the learning dynamics of co-players and empirically lead to improved individual and collective outcomes. However, OS methods have only been eval

Thomas Blom, Ieke Moerdijk

We show that the particular profinite completion used by Boavida-Horel-Robertson in their study of the Grothendieck-Teichm\"uller group fits in the framework of profinite completion as a left Quillen functor. More precisely, we construct a model category of profinite up-to-homotopy operads based on dendroidal objects in Quick's model category of profinite sp

Timothy E. Amish, Jeffrey T. Auletta, Chad C. Kessens, Joshua R. Smith

In many robotic systems, the holding state consumes power, limits operating time, and increases operating costs. Electrostatic clutches have the potential to improve robotic performance by generating holding torques with low power consumption. A key limitation of electrostatic clutches has been their low specific shear stresses which restrict generated holdi

Haijian Sun, Xiang Ma, Rose Qingyang Hu, Randy Christensen

Electric vehicle (EV) has emerged as a transformative force for the sustainable and environmentally friendly future. To alleviate range anxiety caused by battery and charging facility, dynamic wireless power transfer (DWPT) is increasingly recognized as a key enabler for widespread EV adoption, yet it faces significant technical challenges, primarily in prec

Alexandra Souly, Timon Willi, Akbir Khan, Robert Kirk

Reinforcement learning solutions have great success in the 2-player general sum setting. In this setting, the paradigm of Opponent Shaping (OS), in which agents account for the learning of their co-players, has led to agents which are able to avoid collectively bad outcomes, whilst also maximizing their reward. These methods have currently been limited to 2-

Carolina Araujo, Ana-Maria Castravet

We classify $2$-Fano horospherical varieties with Picard number $1$. We also review all the known examples of $2$-Fano manifolds and investigate the relation between the $2$-Fano condition and different notions of stability. This paper was conceived as a contribution to the Edge Volume: 2018-2022.

María Isabel Cortez, Jaime Gómez

Let $G$ be a non-amenable countable group. We show that every almost automorphic $G$-action on a compact Hausdorff space, with a maximal equicontinuous factor whose phase space is a Cantor set, admits invariant probability measures (this partially answers a question posed by Veech). In particular, every Toeplitz $G$-subshift has a non-empty space of invarian

Sean Reiter, Ion Victor Gosea, Serkan Gugercin

The quadrature-based balanced truncation (QuadBT) framework of arXiv:2104.01006 is a non-intrusive reformulation of balanced truncation (BT), a classical projection-based model-order reduction technique for linear systems. QuadBT is non-intrusive in the sense that it builds approximate balanced truncation reduced-order models entirely from system response da

Christina Hastings Blow, Lijun Qian, Camille Gibson, Pamela Obiomon

Fairness AI aims to detect and alleviate bias across the entire AI development life cycle, encompassing data curation, modeling, evaluation, and deployment-a pivotal aspect of ethical AI implementation. Addressing data bias, particularly concerning sensitive attributes like gender and race, reweighting samples proves efficient for fairness AI. This paper con

Kimi Wenzel, Geoff Kaufman, Laura Dabbish

The ethics of artificial intelligence (AI) systems has risen as an imminent concern across scholarly communities. This concern has propagated a great interest in algorithmic fairness. Large research agendas are now devoted to increasing algorithmic fairness, assessing algorithmic fairness, and understanding human perceptions of fairness. We argue that there

Meshal Alharbi, Mardavij Roozbehani, Munther Dahleh

The problem of sample complexity of online reinforcement learning is often studied in the literature without taking into account any partial knowledge about the system dynamics that could potentially accelerate the learning process. In this paper, we study the sample complexity of online Q-learning methods when some prior knowledge about the dynamics is avai

V. N. Yershov, A. A. Raikov, E. A. Popova

We compare two versions of the GW150914 gravitational wave signal analysis by the LIGO/Virgo collaboration. The first version was published in 2016 by this collaboration along with their announcement of the first experimental detection of gravitational waves. It was based on the gravitational wave waveforms with the fully non-linear general-relativistic trea

Andrei Chertkov, Ivan Oseledets

Deep neural networks (DNNs) are widely used today, but they are vulnerable to adversarial attacks. To develop effective methods of defense, it is important to understand the potential weak spots of DNNs. Often attacks are organized taking into account the architecture of models (white-box approach) and based on gradient methods, but for real-world DNNs this

P M S Sai Krishna

Miyanishi proved that the ring of invariants of any $\mathbb{G}_a$ action on $\mathbb{A}^3$ is $\mathbb{A}^2$, when the field $k$ has zero characteristic. However, it is not known if this result holds when $k$ has positive characteristic. We provide a sufficient condition under which this result holds in positive characteristic. We also prove the following r