May 2023 arXiv papers — page 113

Yewei Yuan, Chao Wang, Bei Wang, Zhao-Yun Chen

Quantum comparators and modular arithmetic are fundamental in many quantum algorithms. Current research mainly focuses on operations between two quantum states. However, various applications, such as integer factorization, optimization, option pricing, and risk analysis, commonly require one of the inputs to be classical. It requires many ancillary qubits, e

Patrick Zhong, Federico Rossi, Dylan A. Shell

A challenging category of robotics problems arises when sensing incurs substantial costs. This paper examines settings in which a robot wishes to limit its observations of state, for instance, motivated by specific considerations of energy management, stealth, or implicit coordination. We formulate the problem of planning under uncertainty when the robot's o

Md Rabiul Hasan, Zhichao Liu, Asif Rahman

The awareness of energy consumption is gaining much more attention in manufacturing due to its economic and sustainability benefits. An energy consumption model is needed for quantifying the consumption and predicting the impact of various process parameters in manufacturing. This paper aims to develop an energy consumption model for Direct Energy Deposition

Ibsal Assi, J. P. F. LeBlanc

We present a symbolic algorithm for treating perturbative expansions of Hamiltonians with general two-body interactions. The method, formally equivalent to determinant Monte Carlo methods, merges well-known analytics with the recently developed symbolic integration tool, algorithmic Matsubara integration (AMI) that allows for the evaluation of the imaginary

Aníbal Utreras-Alarcón, Eric G. Cavalcanti, Howard M. Wiseman

The Wigner's friend thought experiment has gained a resurgence of interest in recent years thanks to no-go theorems that extend it to Bell-like scenarios. One of these, by us and co-workers, showcased the contradiction that arises between quantum theory and a set of assumptions, weaker than those in Bell's theorem, which we named "local friendliness". Using

Sebastian Maldonado, Carla Vairetti, Ignacio Figueroa

As with any task, the process of building machine learning models can benefit from prior experience. Meta-learning for classifier selection leverages knowledge about the characteristics of different datasets and/or the past performance of machine learning techniques to inform better decisions in the current modeling process. Traditional meta-learning approac

O. D. Dalkarov, V. A. Karmanov, E. A. Kupriyanova

As known, some approximate approaches to the hadron scattering from nuclei work rather well far beyond the limits of their applicability. This was explained by cancellation of the contributions (non-adiabatic and off-shell effects) omitted in these approaches. Moreover, in some cases (in particular, for the reaction $\bar{p}d \to e^+e^-n$) this cancellation

Xinyuan Tian, Yiting Wang, Selena Wang, Yi Zhao

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in non-invasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence

Taiqiang Wu, Cheng Hou, Shanshan Lao, Jiayi Li

Knowledge Distillation (KD) is a predominant approach for BERT compression. Previous KD-based methods focus on designing extra alignment losses for the student model to mimic the behavior of the teacher model. These methods transfer the knowledge in an indirect way. In this paper, we propose a novel Weight-Inherited Distillation (WID), which directly transfe

Baishuai Zuo, Chuancun Yin, Jing Yao

In this paper, we propose the multivariate range Value-at-Risk (MRVaR) and the multivariate range covariance (MRCov) as two risk measures and explore their desirable properties in risk management. In particular, we explain that such range-based risk measures are appropriate for risk management of regulation and investment purposes. The multivariate range cor

Angelos Mantzaflaris, Bernard Mourrain, Nelly Villamizar, Beihui Yuan

Geometrically continuous splines are piecewise polynomial functions defined on a collection of patches which are stitched together through transition maps. They are called $G^{r}$-splines if, after composition with the transition maps, they are continuously differentiable functions to order $r$ on each pair of patches with stitched boundaries. This type of s

Zhen Long, Ce Zhu, Jie Chen, Zihan Li

Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in the self-representation tensor. Current tensor decompositions for MSC suffer from highly unbalanced unfolding matrices or rotation sensitivity, failing to fully explore inter/intra-view information. Using the advanced tensor network, namely, multi-scale entanglement renor

L. Hernández-Sánchez, I. Ramos-Prieto, F. Soto-Eguibar, H. M. Moya-Cessa

It is well known that AC Stark shifts have an impact on the dynamics of atoms interacting with a near-resonant quantized single-mode cavity field, which is relevant for single-atom micromasers. In this study, we demonstrate that when the field is in a squeezed coherent state, the micromaser lines are highly sensitive to the squeezing parameter. Furthermore,

S. Cornejo, F. S. Masset, F. J. Sánchez-Salcedo

A planetary embryo embedded in a gaseous disc can grow by pebble accretion while subjected to a gravitational force from the disc that changes its orbital elements. Usually, that force is considered to arise from the Lindblad and corotation resonances with the embryo. However, more important contributions exist for low-mass planets. Radiative thermal diffusi

Vaishnavi Patil, Matthew Evanusa, Joseph JaJa

Generative modeling and self-supervised learning have in recent years made great strides towards learning from data in a completely unsupervised way. There is still however an open area of investigation into guiding a neural network to encode the data into representations that are interpretable or explainable. The problem of unsupervised disentanglement is o

Brendan Conway-Smith, Robert L. West

Attempts to import dual-system descriptions of System-1 and System-2 into AI have been hindered by a lack of clarity over their distinction. We address this and other issues by situating System-1 and System-2 within the Common Model of Cognition. Results show that what are thought to be distinctive characteristics of System-1 and 2 instead form a spectrum of

Liuyi Lan, Xuanjin Cheng, Li Xing, Xuekui Zhang

Motivation: Precision medicine is a major trend in the future of medicine. It aims to provide tailored medical treatment and prevention strategies based on an individual's unique characteristics and needs. Biomarker is the primary source of patients' unique features used in precision medicine. We often need to investigate many cutoff values of a continuous b

Meng Qin

Graph representation learning (a.k.a. network embedding) is a significant topic of network analysis, due to its effectiveness to support various graph inference tasks. In this paper, we study the representation learning with multiple information sources in attributed graphs. Recent studies usually focus on several specific sources (e.g., high-order proximity

Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf

While Convolutional Neural Networks (CNNs) have long been investigated and applied, as well as theorized, we aim to provide a slightly different perspective into their nature -- through the perspective of their Hessian maps. The reason is that the loss Hessian captures the pairwise interaction of parameters and therefore forms a natural ground to probe how t

Wei Zhou, Chung Ting Ma, S. Joseph Poon

Mn4N thin film is one of the potential magnetic mediums for spintronic devices due to its ferrimagnetism with low magnetization, large perpendicular magnetic anisotropy (PMA), thermal stability, and large domain wall velocity. A recent experiment confirmed the existence of tunable magnetic skyrmions in MgO/Mn4N/CuxPt1-x(x=0,0.5,0.9,0.95), and density functio

Amit R. Dhawan, Tristan Farrow, Ashley Marshall, Alex Ghorbal

Ultranarrow bandwidth single-photon sources operating at room-temperature are of vital importance for viable optical quantum technologies at scale, including quantum key distribution, cloud based quantum information processing networks, and quantum metrology. Here we show a room-temperature ultranarrow bandwidth single-photon source generating polarised phot

Nikolai Larkin, Marcos Padilha

We consider an initial-boundary value problem for the 4D Navier-Stokes equations posed on bounded smooth domains. We prove the existence and uniqiueness of regular solutions as well as their exponential decay and additional regularity properties have been established assuming restrictions on initial data.

Zeyuan Hu, Ziang Yuan

The detection of malicious websites has become a critical issue in cybersecurity. Therefore, this paper offers a comprehensive review of data-driven methods for detecting malicious websites. Traditional approaches and their limitations are discussed, followed by an overview of data-driven approaches. The paper establishes the data-feature-model-extension pip

Fanchen Bu, Shinhwan Kang, Kijung Shin

What are the relations between the edge weights and the topology in real-world graphs? Given only the topology of a graph, how can we assign realistic weights to its edges based on the relations? Several trials have been done for edge-weight prediction where some unknown edge weights are predicted with most edge weights known. There are also existing works o

Jiho Shin, Moshi Wei, Junjie Wang, Lin Shi

Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges because of the fundamentally different nature and construction from general programming tasks, especially for developers who do not have ML backgrounds. Automatic code generation that produces a code snippet from a natural lang

Ivan Cheltsov, Igor Krylov, Jesus Martinez-Garcia, Evgeny Shinder

We classify non-factorial nodal Fano threefolds with $1$ node and class group of rank $2$.

Amin Bakhshandeh, Yan Levin

We investigate charge regulation of nanoparticles in concentrated suspensions, focusing on the effect of different statistical ensembles. We find that the choice of ensemble does not affect the mean charge of nanoparticles, but significantly alters the magnitude of its fluctuation. Specifically, we compared the behaviors of colloidal charge fluctuations in t

Andrés F. Vargas, Andrew Melatos

Stochastic temporal wandering of the spin frequency $\nu$ of a rotation-powered pulsar (i.e.~the achromatic component of timing noise unrelated to interstellar propagation) affects the accuracy with which the secular braking torque can be measured. Observational studies confirm that pulsars with anomalous braking indices $\vert n \vert = \vert \nu \ddot{\nu}

Haozheng Yu, Lu He, Bing Jian, Weiwei Feng

Indoor 360 panoramas have two essential properties. (1) The panoramas are continuous and seamless in the horizontal direction. (2) Gravity plays an important role in indoor environment design. By leveraging these properties, we present PanelNet, a framework that understands indoor environments using a novel panel representation of 360 images. We represent an

P. G. de Oliveira, A. S. T. Pires

Topology applied to condensed matter is an important area of research and technology, and topological magnetic excitations have recently become an active field of study. This paper presents a general discussion of magnon Hall transport in two-dimensional antiferromagnets. Although the Chern number is zero for a collinear antiferromagnet, we offer a general d

Moshe Adrian, Guy Henniart, Eyal Kaplan, Masao Oi

We consider the split special orthogonal group $\mathrm{SO}_{N}$ defined over a $p$-adic field. We determine the structure of any $L$-packet of $\mathrm{SO}_{N}$ containing a simple supercuspidal representation (in the sense of Gross--Reeder). We also determine its endoscopic lift to a general linear group. Combined with the explicit local Langlands correspo

Rodger I. Thompson

The primary purpose of this work is the provision of accurate, analytic, evolutionary templates for cosmological parameters and fundamental constants in a dynamical cosmology. A flat quintessence cosmology with a dark energy potential that has the mathematical form of the Higgs potential is the specific cosmology and potential addressed in this work. These t

R. Rouzegar, M. A. Wahada, A. L. Chekhov, W. Hoppe

We study femtosecond spin currents through MgO tunneling barriers in CoFeB(2 nm)|MgO($d$)|Pt(2 nm) stacks by terahertz emission spectroscopy. To obtain transport information independent of extrinsic experimental factors, we determine the complex-valued spin conductance $\tilde{G}_d (\omega)$ of the MgO layer (thickness d= 0-6 {\AA} over a wide frequency rang

Candice Schumann, Gbolahan O. Olanubi, Auriel Wright, Ellis Monk

Understanding different human attributes and how they affect model behavior may become a standard need for all model creation and usage, from traditional computer vision tasks to the newest multimodal generative AI systems. In computer vision specifically, we have relied on datasets augmented with perceived attribute signals (e.g., gender presentation, skin

Otto C. W. Kong, Hock King Ting

The famous equation $E=mc^2$ is a version of particle mass being essentially the magnitude of the (energy-)momentum four-vector in the setting of `relativistic' dynamics, which can be seen as dictated by the Poincaré symmetry adopted as the relativity symmetry. However, as Einstein himself suggested, the naive notion of momentum as mass times velocity ma

Rong Wang, Chun-Mei Zhang

Reference-frame-independent quantum key distribution was proposed to generate a string of secret keys without a shared reference frame. Based on the Bloch sphere, however, the security analysis in previous methods is only independent on azimuthal angle, while a reference frame is determined by both polar angle and azimuthal angle. Here, we propose a 3 \times

Einari Vaaras, Manu Airaksinen, Sampsa Vanhatalo, Okko Räsänen

The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings. This information could be used for developmental research and to support clinical decision-making, such as detection of developmental problems and guiding of their therapeutic

Dongwei Zhao, Vladimir Dvorkin, Stefanos Delikaraoglou, Alberto J. Lamadrid L.

Accommodating the uncertain and variable renewable energy sources (VRES) in electricity markets requires sophisticated and scalable tools to achieve market efficiency. To account for the uncertain imbalance costs in the real-time market while remaining compatible with the existing sequential market-clearing structure, our work adopts an uncertainty-informed

Otto C. W. Kong

The Galilei group has been taken as the fundamental symmetry for 'nonrelativistic' physics, quantum or classical. Our fully group theoretical formulation approach to the quantum theory asks for some adjustments. We present a sketch of the full picture here, emphasizing aspects that are different from the more familiar picture. The analysis involves a

Teanna Barrett, Quan Ze Chen, Amy X. Zhang

To investigate the well-observed racial disparities in computer vision systems that analyze images of humans, researchers have turned to skin tone as more objective annotation than race metadata for fairness performance evaluations. However, the current state of skin tone annotation procedures is highly varied. For instance, researchers use a range of untest

Yongwan Gim, Kyushik Min

Recent algorithms of time-series anomaly detection have been evaluated by applying a Point Adjustment (PA) protocol. However, the PA protocol has a problem of overestimating the performance of the detection algorithms because it only depends on the number of detected abnormal segments and their size. We propose a novel evaluation protocol called the Point-Ad

Sarmad Mehrdad, S. Farokh Atashzar

Recently skew-t mixture models have been introduced as a flexible probabilistic modeling technique taking into account both skewness in data clusters and the statistical degree of freedom (S-DoF) to improve modeling generalizability, and robustness to heavy tails and skewness. In this paper, we show that the state-of-the-art skew-t mixture models fundamental

Xi Yang, Ge Gao, Min Chi

Apprenticeship learning (AL) is a process of inducing effective decision-making policies via observing and imitating experts' demonstrations. Most existing AL approaches, however, are not designed to cope with the evolving reward functions commonly found in human-centric tasks such as healthcare, where offline learning is required. In this paper, we propose

Evgenia Gasnikova, Alexander Gasnikov, Demyan Yarmoshik, Meruza Kubentaeva

In this paper we consider multi-stages traffic assignment with several demand layers, user types and network types. We consider two stages: demand matrix calculation (Entropy Wilson's model) and traffic assignment models (Beckmann or Nesterov--de Palma). For the traffic assignment stage we use dual reformulation and combine these stages as a saddle-point pro

Ciyuan Zhang, Sebin Gracy, Tamer Basar, Philip E. Pare

This paper proposes a novel discrete-time multi-virus susceptible-infected-recovered (SIR) model that captures the spread of competing epidemics over a population network. First, we provide sufficient conditions for the infection level of all the viruses over the networked model to converge to zero in exponential time. Second, we propose an observation model

Xiaoying Zhang, Baolin Peng, Kun Li, Jingyan Zhou

Building end-to-end task bots and maintaining their integration with new functionalities using minimal human efforts is a long-standing challenge in dialog research. Recently large language models (LLMs) have demonstrated exceptional proficiency in conversational engagement and adherence to instructions across various downstream tasks. In this work, we intro

Jan Bernauer, Ross Corliss, Susan Gardner, Michael Hasinoff

This paper gives an overview of the scientific opportunities at the ARIEL electron accelerator identified in open discussion at the workshop, including applications in hadron structure, astrophysical processes, tests of quantum electrodynamics, dark matter and other BSM physics, and material science.

Jerry Anunrojwong, Santiago R. Balseiro, Omar Besbes

A seller wants to sell an item to $n$ buyers. Buyer valuations are drawn i.i.d. from a distribution unknown to the seller; the seller only knows that the support is included in $[a, b]$. To be robust, the seller chooses a DSIC mechanism that optimizes the worst-case performance relative to the ideal expected revenue the seller could have collected with knowl

Markelle Kelly, Aakriti Kumar, Padhraic Smyth, Mark Steyvers

Improving our understanding of how humans perceive AI teammates is an important foundation for our general understanding of human-AI teams. Extending relevant work from cognitive science, we propose a framework based on item response theory for modeling these perceptions. We apply this framework to real-world experiments, in which each participant works alon

Samir Huseynov

This paper investigates the causal impact of negatively and positively toned ChatGPT Artificial Intelligence (AI) discussions on US students' anticipated labor market outcomes. Our findings reveal students reduce their confidence regarding their future earnings prospects after exposure to AI debates, and this effect is more pronounced after reading discussio

Li Zeng, Xiaoliang Wan, Tao Zhou

In this paper, we develop an invertible mapping, called B-KRnet, on a bounded domain and apply it to density estimation/approximation for data or the solutions of PDEs such as the Fokker-Planck equation and the Keller-Segel equation. Similar to KRnet, B-KRnet consists of a series of coupling layers with progressively fewer active transformation dimensions, i

Mauricio Mendez-Ruiz, Jorge Gonzalez-Zapata, Ivan Reyes-Amezcua, Daniel Flores-Araiza

Few-shot learning is a challenging area of research that aims to learn new concepts with only a few labeled samples of data. Recent works based on metric-learning approaches leverage the meta-learning approach, which is encompassed by episodic tasks that make use a support (training) and query set (test) with the objective of learning a similarity comparison

Naoki Seto

We discuss the prospect of identifying a white dwarf binary merger by monitoring disappearance of its nearly monochromatic gravitational wave. For a ten-year operation of the laser interferometer space antenna (LISA), the chance probability of observing such an event is roughly estimated to be 20%. By simply using short-term coherent signal integrations, we

King Fai Yeh, Paris Flood, William Redman, Pietro Liò

Recently, Koopman operator theory has become a powerful tool for developing linear representations of non-linear dynamical systems. However, existing data-driven applications of Koopman operator theory, including both traditional and deep learning approaches, perform poorly on non-linear network dynamics problems as they do not address the underlying geometr

Geoffrey Goodell

This document constitutes a response to a Consultation Paper published by the Bank of England and HM Treasury, "The digital pound: a new form of money for households and businesses?", the latest document in a series that includes "Central Bank Digital Currency: opportunities, challenges and design" in 2020 and "New forms of digital money" in 2021. The Consul

Mingxue Xu, Tongtong Xu, Po-Yu Chen

Machine Learning as a Service (MLaaS) is a popular cloud-based solution for customers who aim to use an ML model but lack training data, computation resources, or expertise in ML. In this case, the training datasets are typically a private possession of the ML or data companies and are inaccessible to the customers, but the customers still need an approach t

Sean Paulsen, Michael Casey

In this work we introduce a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretrainin

Jungang Chen, Eduardo Gildin, John E. Killough

This paper presents a novel surrogate model for modeling subsurface fluid flow with well controls using a physics-informed convolutional recurrent neural network (PICRNN). The model uses a convolutional long-short term memory (ConvLSTM) to capture the spatiotemporal dependencies of the state evolution dynamics in the porous flow. The ConvLSTM is linked to th

Johann Rafelski, Jeremiah Birrell, Andrew Steinmetz, Cheng Tao Yang

We offer a survey of the matter-antimatter evolution within the primordial Universe. While the origin of the tiny matter-antimatter asymmetry has remained one of the big questions in modern cosmology, antimatter itself has played a large role for much of the Universe's early history. In our study of the evolution of the Universe we adopt the position of the

Kaan Önder

We study the dynamics of two-dimensional chiral $SU(N)$ gauge theories with fermions in the symmetric, anti-symmetric and fundamental representations. A consistent infra-red limit of these theories consists of certain coset CFTs. There is also a free fermion phase which shares the same central charge and 't Hooft anomalies but does not coincide with the cose

Marco S. Bianchi

We present the three-point function of two spin-two and one scalar twist-two operators in N=4 SYM up to three perturbative orders at weak coupling, obtained via a direct Feynman diagrammatic calculation.

Haitian Xie

This study presents a novel approach to the density estimation of private values from second-price auctions, diverging from the conventional use of smoothing-based estimators. We introduce a Grenander-type estimator, constructed based on a shape restriction in the form of a convexity constraint. This constraint corresponds to the renowned Myerson regularity

Eugene B. Kolomeisky

Excitations in the form of quantized vortex rings are known to exist in superfluid $^{4}He$ at energies and momenta exceeding those of the Landau phonon-roton spectrum. They form a vortex branch of elementary excitations spectrum which is disconnected from the Landau spectrum. Interference of vortex ring excitations determines wake patterns due to uniformly

Michel L. Lapidus, Machiel van Frankenhuijsen, Edward K. Voskanian

We give a generalization of Lagarias' formula for diffraction by ideal crystals, and we apply it to the lattice case, in preparation for addressing the problem of quasicrystals and complex dimensions posed by Lapidus and van Frankenhuijsen concerning the quasiperiodic properties of the set of complex dimensions of any nonlattice self-similar fractal string.

Arun Jambulapati, James R. Lee, Yang P. Liu, Aaron Sidford

For any norms $N_1,\ldots,N_m$ on $\mathbb{R}^n$ and $N(x) := N_1(x)+\cdots+N_m(x)$, we show there is a sparsified norm $\tilde{N}(x) = w_1 N_1(x) + \cdots + w_m N_m(x)$ such that $|N(x) - \tilde{N}(x)| \leq \epsilon N(x)$ for all $x \in \mathbb{R}^n$, where $w_1,\ldots,w_m$ are non-negative weights, of which only $O(\epsilon^{-2} n \log(n/\epsilon) (\log n)

Marek Kadlčík, Adam Hájek, Jürgen Kieslich, Radosław Winiecki

The field of audio captioning has seen significant advancements in recent years, driven by the availability of large-scale audio datasets and advancements in deep learning techniques. In this technical report, we present our approach to audio captioning, focusing on the use of a pretrained speech-to-text Whisper model and pretraining on synthetic captions. W

Zhaohui Yang, Chaohan Cui

With the recent developments in engineering quantum systems, the realization of scalable local-area quantum networks has become viable. However, the design and implementation of a quantum network is a holistic task that is way beyond the scope of an abstract design problem. As such, a testbed on which multiple disciplines can verify the design and implementa

GlueX Collaboration, S. Adhikari, F. Afzal, C. S. Akondi

The GlueX experiment at Jefferson Lab studies photoproduction of mesons using linearly polarized $8.5\,\text{GeV}$ photons impinging on a hydrogen target which is contained within a detector with near-complete coverage for charged and neutral particles. We present measurements of spin-density matrix elements for the photoproduction of the vector meson $\rho$

James Chok, Geoffrey M. Vasil

We propose a new iteration scheme, the Cauchy-Simplex, to optimize convex problems over the probability simplex $\{w\in\mathbb{R}^n\ |\ \sum_i w_i=1\ \textrm{and}\ w_i\geq0\}$. Specifically, we map the simplex to the positive quadrant of a unit sphere, envisage gradient descent in latent variables, and map the result back in a way that only depends on the si

Gang Liu, Haitao Wang

Given a set $P$ of $n$ weighted points and a set $S$ of $m$ disks in the plane, the hitting set problem is to compute a subset $P'$ of points of $P$ such that each disk contains at least one point of $P'$ and the total weight of all points of $P'$ is minimized. The problem is known to be NP-hard. In this paper, we consider a line-constrained version of the p

Yicong He, George K. Atia

Tensor ring (TR) decomposition has recently received increased attention due to its superior expressive performance for high-order tensors. However, the applicability of traditional TR decomposition algorithms to real-world applications is hindered by prevalent large data sizes, missing entries, and corruption with outliers. In this work, we propose a scalab

Chiara Bernardini, Annalisa Cesaroni

We prove existence of a positive radial solution to the Choquard equation $$-\Delta u +V u=(I_\alpha\ast |u|^p)|u|^{p-2}u\qquad\text{in}\,\,\,\Omega$$ with Neumann or Dirichlet boundary conditions, when $\Omega$ is an annulus, or an exterior domain of the form $\mathbb{R}^N\setminus \bar{B}_a(0)$. We provide also a nonexistence result, that is if $p\ge\frac{

Xiaonan Liu, Shiqiang Wang, Yansha Deng, Arumugam Nallanathan

Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. Hierarchical FL (HFL), as a device-edge-cloud aggregation hierarchy, can enjoy both the cloud server's access to more datasets and the edge servers' efficient communica

Antoine Théberge, Christian Desrosiers, Maxime Descoteaux, Pierre-Marc Jodoin

Recently, deep reinforcement learning (RL) has been proposed to learn the tractography procedure and train agents to reconstruct the structure of the white matter without manually curated reference streamlines. While the performances reported were competitive, the proposed framework is complex, and little is still known about the role and impact of its multi

Mateusz Kubiak, Bogdan Szal

In the present paper we will introduce a new class of double sequences called DGM (p, alpha, beta, gamma, r), which is the generalization of a class considered by Szal and Duzinkiewicz. Moreover, we obtained in this note a sufficient condition for the uniform convergence of double sine series with coefficients belonging to this class.

Mateusz Kubiak, Bogdan Szal

In this paper we consider trigonometric series with p-bounded variation coefficients. We presented a sufficient condition for uniform convergance of such series in case p > 1. This condition is significantly weaker than these obtained in the results on this subject known in the literature.

Elisa Kreiss, Krishna Srinivasan, Tiziano Piccardi, Jesus Adolfo Hermosillo

We make a first attempt to characterize image accessibility on Wikipedia across languages, present new experimental results that can inform efforts to assess description quality, and offer some strategies to improve Wikipedia's image accessibility.

Omesh Dhar Dwivedi, Yuri Barsukov, Sierra Jubin, Joseph Vella

Anisotropic etching is a widely used process in semiconductor manufacturing, in particular for micro- and nano-scale texturing of silicon surfaces for black silicon production. The typical process of plasma-assisted etching uses energetic ions to remove material in the vertical direction, creating anisotropic etch profiles. Plasma-less anisotropic etching, c

Nicholas Chimitt, Stanley H. Chan

For the simulation of anisoplanatic optical turbulence, split-step propagation is the gold standard. Within the context of the degradations being limited to phase distortions, one instead may focus on generating the phase realizations directly, a method which has been utilized in previous so-called multi-aperture simulations. Presently, this modality assumes

Jennifer Chien, Margaret Roberts, Berk Ustun

Dynamic learning systems subject to selective labeling exhibit censoring, i.e. persistent negative predictions assigned to one or more subgroups of points. In applications like consumer finance, this results in groups of applicants that are persistently denied and thus never enter into the training data. In this work, we formalize censoring, demonstrate how

Pradeep Fernando, Daniel Zahka, Ada Gavrilovska, Amitabha Roy

Persistent memory (PMEM) devices present an opportunity to retain the flexibility of main memory data structures and algorithms, but augment them with reliability and persistence. The challenge in doing this is to combine replication (for reliability) and failure atomicity (for persistence) with concurrency (for fully utilizing persistent memory bandwidth).

Jasmine M. Cox, Jessica J. Frick, Chen Liu, Zhou Li

Graphene aerogel (GA) is a promising material for thermal management applications across many fields due to its lightweight and thermally insulative properties. However, standard values for important thermal properties, such as thermal conductivity, remain elusive due to the lack of reliable characterization techniques for highly porous materials. Comparativ

Caspar Schwarz-Schilling, Fahad Saleh, Thomas Thiery, Jennifer Pan

We propose a model suggesting that honest-but-rational consensus participants may play timing games, and strategically delay their block proposal to optimize MEV capture, while still ensuring the proposal's timely inclusion in the canonical chain. In this context, ensuring economic fairness among consensus participants is critical to preserving decentralizat

Harrison C. Gottlich, Panagiotis Korfiatis, Adriana V. Gregory, Timothy L. Kline

Methods for automatically flag poor performing-predictions are essential for safely implementing machine learning workflows into clinical practice and for identifying difficult cases during model training. We present a readily adoptable method using sub-models trained on different dataset folds, where their disagreement serves as a surrogate for model confid

AJ Bu, Doron Zeilberger

We show the power of Bruno Buchberger's seminal Groebner Basis algorithm, interfaced, seamlessly, with what we call symbolic dynamical programming, to automatically generate algebraic equations satisfied by the generating functions enumerating so-called Generalized Dyck Walks, i.e. 2D walks that start and end on the x-axis, and never dip below it, for an arb

Dmitrii Karp, Yi Zhang

In this paper, we study power series with coefficients equal to a product of a generic sequence and an explicitly given function of a positive parameter expressible in terms of the Pochhammer symbols. Four types of such series are treated. We show that logarithmic concavity (convexity) of the generic sequence leads to logarithmic concavity (convexity) of the

Alexander Moreno, Jonathan Mei, Luke Walters

Toeplitz Neural Networks (TNNs) (Qin et. al. 2023) are a recent sequence model with impressive results. They require O(n log n) computational complexity and O(n) relative positional encoder (RPE) multi-layer perceptron (MLP) and decay bias calls. We aim to reduce both. We first note that the RPE is a non-SPD (symmetric positive definite) kernel and the Toepl

Charles Arnal, Louis Garrigue

We investigate almost-degenerate perturbation theory of eigenvalue problems, using spectral projectors, also named density matrices. When several eigenvalues are close to each other, the coefficients of the perturbative series become singular because inverses of differences between eigenvalues arise as some factors. We remove those artificial singularities i

Zhiqi Huang, Hansi Zeng, Hamed Zamani, James Allan

In this work, we explore a Multilingual Information Retrieval (MLIR) task, where the collection includes documents in multiple languages. We demonstrate that applying state-of-the-art approaches developed for cross-lingual information retrieval to MLIR tasks leads to sub-optimal performance. This is due to the heterogeneous and imbalanced nature of multiling

Yingqing Chen, Christos G. Cassandras

We study the Traffic Light Control (TLC) problem for a traffic network with multiple intersections in an artery, including the effect of transit delays for vehicles moving from one intersection to the next. The goal is to minimize the overall mean waiting time and improve the ``green wave'' properties in such systems. Using a stochastic hybrid system model w

Rodrigo Arruda, Bernardo Carvalho, Alberto Sarmiento

This paper discusses the dynamics of continuum-wise hyperbolic surface homeomorphisms. We prove that $cw_F$-hyperbolic surface homeomorphisms containing only a finite set of spines are $cw_2$-hyperbolic. In the case of $cw_3$-hyperbolic homeomorphisms we prove the finiteness of spines and, hence, that $cw_3$-hyperbolicity implies $cw_2$-hyperbolicity. In the

Yifan Yu, Rosario Pintos Lobo, Michael Cody Riedel, Katherine Bottenhorn

Coordinate-based meta-analysis combines evidence from a collection of Neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-re

Arjun Subramonian, Xingdi Yuan, Hal Daumé, Su Lin Blodgett

Progress in NLP is increasingly measured through benchmarks; hence, contextualizing progress requires understanding when and why practitioners may disagree about the validity of benchmarks. We develop a taxonomy of disagreement, drawing on tools from measurement modeling, and distinguish between two types of disagreement: 1) how tasks are conceptualized and

Zahra Sattari, Bahram Mobasher, Nima Chartab, Daniel D. Kelson

High-resolution imaging of galaxies in rest-frame UV has revealed the existence of giant star-forming clumps prevalent in high redshift galaxies. Studying these sub-structures provides important information about their formation and evolution and informs theoretical galaxy evolution models. We present a new method to identify clumps in galaxies' high-resolut

Thomas Beckers, Tom Z. Jiahao, George J. Pappas

Switching physical systems are ubiquitous in modern control applications, for instance, locomotion behavior of robots and animals, power converters with switches and diodes. The dynamics and switching conditions are often hard to obtain or even inaccessible in case of a-priori unknown environments and nonlinear components. Black-box neural networks can learn

Jielin Yang, Ivan C. Christov, Suchuan Dong

We develop a method for modeling and simulating a class of two-phase flows consisting of two immiscible incompressible dielectric fluids and their interactions with imposed external electric fields in two and three dimensions. We first present a thermodynamically-consistent and reduction-consistent phase field model for two-phase dielectric fluids. The model

Tyler McMaken

Vacuum models of charged or spinning black holes possess two horizons, the inner of which has the oft-overlooked property that gravitational tidal forces initially spaghettifying a freely falling observer will eventually change signs and flatten the observer like a pancake. Inner horizons also induce a classical blueshift instability known as mass inflation,

Cyril Picard, Jürg Schiffmann, Faez Ahmed

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significant challenge for researchers aiming to apply these breakthroughs in engineering design. Synthetic dataset

Julian Burghoff, Leonhard Ackermann, Younes Salahdine, Veronika Bram

In order to improve the detection and classification of malignant melanoma, this paper describes an image-based method that can achieve AUROC values of up to 0.78 without additional clinical information. Furthermore, the importance of the domain gap between two different image sources is considered, as it is important to create usability independent of hardw

Thomas Beckers, Jacob Seidman, Paris Perdikaris, George J. Pappas

Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which determine the behavior of any real-world system. This omission is unfavorable in two ways: The models are not as data-efficient as they could be by incorporating physical prior kno