July 2023 arXiv papers — page 89

Jean Paulo dos S. Carvalho, Rodolpho Vilhena de Moraes, Antonio Fernando Bertachini de A. Prado

In this work, the orbital evolution of these objects that are located in the geostationary orbit (GEO) is analyzed. Knowing this, the possibility of using a solar sail is considered to help to clean the space environment. The main natural environmental perturbations that act in the orbit of the debris are considered in the dynamics. Such forces acting in the

L. L. Ankile, B. S. Ham, K. Mao, E. Shin

When assisting human users in reinforcement learning (RL), we can represent users as RL agents and study key parameters, called \emph{user traits}, to inform intervention design. We study the relationship between user behaviors (policy classes) and user traits. Given an environment, we introduce an intuitive tool for studying the breakdown of "user types": b

Sam Blake

In this paper we describe a deep learning--based probabilistic algorithm for integer factorisation. We use Lawrence's extension of Fermat's factorisation algorithm to reduce the integer factorisation problem to a binary classification problem. To address the classification problem, based on the ease of generating large pseudo--random primes, a corpus of trai

Tyler Westenbroek, Jacob Levy, David Fridovich-Keil

We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data. In recent years, policy gradient methods have emerged as a promising paradigm for training control policies in simulation. However, these approaches often remain too data inefficient or unreliable to train on real robotic hardware. In this pa

Guang Ping He

Finding gradients is a crucial step in training machine learning models. For quantum neural networks, computing gradients using the parameter-shift rule requires calculating the cost function twice for each adjustable parameter in the network. When the total number of parameters is large, the quantum circuit must be repeatedly adjusted and executed, leading

Yi Zhang, Artur Palha, Marc Gerritsma, Qinghe Yao

In this work, we introduce a mass, energy, enstrophy and vorticity conserving (MEEVC) mixed finite element discretization for two-dimensional incompressible Navier-Stokes equations as an alternative to the original MEEVC scheme proposed in [A. Palha and M. Gerritsma, J. Comput. Phys., 2017]. The present method can incorporate no-slip boundary conditions. Con

Andrew Suk

Let $h(n)$ be the minimum integer such that every complete $n$-vertex simple topological graph contains an edge that crosses at most $h(n)$ other edges. In 2009, Kyn\v{c}l and Valtr showed that $h(n) = O(n^2/\log^{1/4} n)$, and in the other direction, gave constructions showing that $h(n) = \Omega(n^{3/2})$. In this paper, we prove that $h(n) = O(n^{7/4})$.

Emanuel Milman, Joe Neeman

Sullivan's multi-bubble isoperimetric conjectures in $n$-dimensional Euclidean and spherical spaces assert that standard bubbles uniquely minimize total perimeter among all $q-1$ bubbles enclosing prescribed volume, for any $q \leq n+2$. The double-bubble conjecture on $\mathbb{R}^3$ was confirmed by Hutchings-Morgan-Ritor\'e-Ros (and later extended to $\mat

Neerav Karani, Neel Dey, Polina Golland

Neural network prediction probabilities and accuracy are often only weakly-correlated. Inherent label ambiguity in training data for image segmentation aggravates such miscalibration. We show that logit consistency across stochastic transformations acts as a spatially varying regularizer that prevents overconfident predictions at pixels with ambiguous labels

Lech Duraj, Filip Konieczny, Krzysztof Potępa

We develop a framework for algorithms finding the diameter in graphs of bounded distance Vapnik-Chervonenkis dimension, in (parameterized) subquadratic time complexity. The class of bounded distance VC-dimension graphs is wide, including, e.g. all minor-free graphs. We build on the work of Ducoffe et al. [SODA'20, SIGCOMP'22], improving their technique. With

Steven Moore, Huy A. Nguyen, Tianying Chen, John Stamper

Multiple-choice questions with item-writing flaws can negatively impact student learning and skew analytics. These flaws are often present in student-generated questions, making it difficult to assess their quality and suitability for classroom usage. Existing methods for evaluating multiple-choice questions often focus on machine readability metrics, withou

C. O. Ascencio, Wei Jiang, D. J. P. de Sousa, Seungjun Lee

We propose a spin Hall effect (SHE) enhancement mechanism due to Kramers-Weyl point (KWP) alignment in chiral topological semimetals with high Chern numbers (CNs). Through model Hamiltonian calculations, we identify enhancements in the intrinsic spin Hall conductivity (SHC) and the spin Hall angle (SHA). Such enhancements, attributed to a unique high CN KWP

A. L. Patel, J. S. Urquhart, A. Y. Yang, T. J. T Moore

This study uses archival high frequency continuum data to expand the search for Hypercompact HII regions and determine the conditions at which they appear, as this stage high mass star formation is short-lived and rare. We use 23 GHz continuum data taken towards methanol masers, which are an excellent signpost for very young embedded high-mass protostars. We

Mingen Pan

This paper establishes the equivalence between Local Differential Privacy (LDP) and a global limit on learning any knowledge specific to a queried object. However, an output from an LDP query is not necessarily required to provide exact amount of knowledge equal to the upper bound of the learning limit. The LDP guarantee can overestimate the amount of knowle

Ben Morris, Hans Oberschelp, Hamilton Samraj Santhakumar

In the bounded retrieval model, the adversary can leak a certain amount of information from the message sender's computer (e.g., 10 percent of the hard drive). Bellare, Kane and Rogaway give an efficient symmetric encryption scheme in the bounded retrieval model. Their scheme uses a giant key (a key so large only a fraction of it can be leaked.) One property

Erandi Chavez, Imke de Pater, Erin Redwing, Edward M. Molter

Using archival near-infrared observations from the Keck and Lick Observatories and the Hubble Space Telescope, we document the evolution of Neptune's cloud activity from 1994 to 2022. We calculate the fraction of Neptune's disk that contained clouds, as well as the average brightness of both cloud features and cloud-free background over the planet's disk. We

A. Flores, R. C. de Lamare, K. V. Mishra

In this paper, we address two crucial challenges in the design of cell-free (CF) systems: degradation in the performance of CF systems by imperfect channel state information at the transmitter (CSIT) and high computational/signaling loads arising from the increasing number of distributed antennas and parameters to be exchanged. To mitigate the effects of imp

Konstantinos Palapanidis, Despoina Pazouli

We use the geometric optics approximation to derive the stability criteria for the Rayleigh shearing instability and the magnetorotational instability. We examine the cases where each criterion is relevant by looking into the magnitude of the magnetic field using a small dimensionless parameter. Examining all the orders of this parameter in the characteristi

Gengrui Zhang, Fei Pan, Sofia Tijanic, Hans-Arno Jacobsen

This paper proposes PrestigeBFT, a novel leader-based BFT consensus algorithm that addresses the weaknesses of passive view-change protocols. Passive protocols blindly rotate leadership among servers on a predefined schedule, potentially selecting unavailable or slow servers as leaders. PrestigeBFT proposes an active view-change protocol using reputation mec

Thomas Thurnher, George Haller, Shobhit Jain

We use the recent theory of Spectral Submanifolds (SSM) for model reduction of nonlinear mechanical systems subject to parametric excitations. Specifically, we develop expressions for higher-order nonautonomous terms in the parameterization of SSMs and their reduced dynamics. We provide these results both for general first-order as well as second-order mecha

Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych

Data quality is crucial for training accurate, unbiased, and trustworthy machine learning models as well as for their correct evaluation. Recent works, however, have shown that even popular datasets used to train and evaluate state-of-the-art models contain a non-negligible amount of erroneous annotations, biases, or artifacts. While practices and guidelines

Jingqing Zhang, Kai Sun, Akshay Jagadeesh, Mahta Ghahfarokhi

Recent studies have demonstrated promising performance of ChatGPT and GPT-4 on several medical domain tasks. However, none have assessed its performance using a large-scale real-world electronic health record database, nor have evaluated its utility in providing clinical diagnostic assistance for patients across a full range of disease presentation. We perfo

Akihiro Higashitani, Satoshi Murai, Masahiko Yoshinaga

Given a rational polytope $P \subset \mathbb R^d$, the numerical function counting lattice points in the integral dilations of $P$ is known to become a quasi-polynomial, called the Ehrhart quasi-polynomial $\mathrm{ehr}_P$ of $P$. In this paper we study the following problem: Given a rational $d$-polytope $P \subset \mathbb R^d$, is there a nice way to know

Fernando Montenegro-Dos Santos, Francisco Pérez-Galarce, Carlos Monardes-Concha, Sergio Cruz-Zárate

Vaccination campaigns have saved thousands of lives, reaching the farthest places in the world. These campaigns have required substantial investments and accurate coordination between several actors within the vaccine supply chain. Despite these successful strategies, the outbreak of COVID-19 has altered the objectives and rules of undertaking vaccine campai

Taebin Kim, Lili Wang, Randy Lai, Sangho Yoon

Post-stratification is often used to estimate treatment effects with higher efficiency. However, the majority of existing post-stratification frameworks depend on prior knowledge of the distributions of covariates and assume that the units are classified into post-strata without error. We propose a novel method to determine a proper stratification rule by ma

Florent Foucaud, Esther Galby, Liana Khazaliya, Shaohua Li

Treewidth (tw) is an important parameter that, when bounded, yields tractability for many problems. For example, graph problems expressible in Monadic Second Order (MSO) logic and QUANTIFIED SAT or, more generally, QUANTIFIED CSP, are FPT parameterized by the tw of the input's (primal) graph plus the length of the MSO-formula [Courcelle, Information & Comput

Jake Taylor, Vivien Parmentier

The search for clouds on the dayside of hot Jupiters has been disadvantaged due to hot Jupiters having a limited number of high quality space-based observations. To date, retrieval studies have found no evidence for grey clouds on the dayside, however none of these studies explored the impact of scattering clouds. In this study we reanalyse the dayside emiss

Daniel S. Freed

The anomaly of a quantum field theory is an expression of its projective nature. This starting point quickly leads to its manifestation as a special kind of field theory: a once-categorified invertible theory. We arrive at this statement through a general discussion of projectivity and a discussion of projectivity in quantum mechanics. We conclude with a gen

Jules Fraunié, Rayan Jamil, Richard Kantelberg, Sébastien Roux

For the past few years, 2D ferroelectric materials have attracted strong interest for their potential in future nanoelectronics devices. The recent discovery of 2D ferroelectricity in twisted layers of insulating hexagonal boron nitride, one of the most used 2D materials, has opened the route to its integration into complex van der Waals heterostructures com

Maria Nektaria Minaidi, Charilaos Papaioannou, Alexandros Potamianos

In this paper, we study the problem of producing a comprehensive video summary following an unsupervised approach that relies on adversarial learning. We build on a popular method where a Generative Adversarial Network (GAN) is trained to create representative summaries, indistinguishable from the originals. The introduction of the attention mechanism into t

Mathieu Gonod, Miguel Angel Suarez, Carlos Chacon Avila, Vage Karakhanyan

Purpose: We introduce a novel concept of a compact multiprobe scintillator detector and demonstrate its applicability in HDR-brachytherapy. Our fabricated seven-probe system is sufficiently narrow to be inserted in a brachytherapy needle or in a catheter. Methods: Our multiprobe detection system results from the parallel implementation of a miniaturized scin

Bernd Jung, Gerhard Jung

To investigate the impact of non-linear interactions on dynamic coarse graining, we study a simplified model system, featuring a tracer particle in a complex environment. Using a projection operator formalism and computer simulations, we systematically derive generalized Langevin equations describing the dynamics of this particle. We compare different kinds

Skylar Wolfgang Wurster, Hanqi Guo, Tom Peterka, Han-Wei Shen

We present a neural network approach to compute stream functions, which are scalar functions with gradients orthogonal to a given vector field. As a result, isosurfaces of the stream function extract stream surfaces, which can be visualized to analyze flow features. Our approach takes a vector field as input and trains an implicit neural representation to le

Alexander Petrovsky, Yomna Youssef, Kirill Myasoedov, Artem Timoshenko

This paper focuses on Passable Obstacles Aware (POA) planner - a novel navigation method for two-wheeled robots in a highly cluttered environment. The navigation algorithm detects and classifies objects to distinguish two types of obstacles - passable and unpassable. Our algorithm allows two-wheeled robots to find a path through passable obstacles. Such a so

Skylar Wolfgang Wurster, Tianyu Xiong, Han-Wei Shen, Hanqi Guo

Scene representation networks (SRNs) have been recently proposed for compression and visualization of scientific data. However, state-of-the-art SRNs do not adapt the allocation of available network parameters to the complex features found in scientific data, leading to a loss in reconstruction quality. We address this shortcoming with an adaptively placed m

Debesh Jha, Vanshali Sharma, Neethi Dasu, Nikhil Kumar Tomar

Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance. These challenges include data availability, biased outcomes, data quality, lack of transparency, and underperformance on unseen datasets from different distributions. The scarcity of large-scale, precisely labeled, and diverse

Aswin Suresh, Chi-Hsuan Wu, Matthias Grossglauser

We propose an interpretable model to score the bias present in web documents, based only on their textual content. Our model incorporates assumptions reminiscent of the Bradley-Terry axioms and is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. While prior approaches based on absolute bias classif

William Consagra, Lipeng Ning, Yogesh Rathi

Inferring brain connectivity and structure \textit{in-vivo} requires accurate estimation of the orientation distribution function (ODF), which encodes key local tissue properties. However, estimating the ODF from diffusion MRI (dMRI) signals is a challenging inverse problem due to obstacles such as significant noise, high-dimensional parameter spaces, and sp

Lenny Fukshansky, Camilla Hollanti

In this editorial survey we introduce the special issue of the journal Communications in Mathematics on the topic in the title of the article. Our main goal is to briefly outline some of the main aspects of this important area at the intersection of theory and applications, providing the context for the articles showcased in this special issue.

Richard Nickl, Edriss S. Titi

We consider a non-linear Bayesian data assimilation model for the periodic two-dimensional Navier-Stokes equations with initial condition modelled by a Gaussian process prior. We show that if the system is updated with sufficiently many discrete noisy measurements of the velocity field, then the posterior distribution eventually concentrates near the ground

Aritro Pathak

We give new arguments for sums and products of sufficient numbers of arbitrary central Cantor sets to produce large open intervals. We further discuss the same question for $C^1$ images of such central Cantor sets. This gives another perspective on the results obtained by Astels through a different formulation on the thickness of these Cantor sets. There has

Marco Buratti, Anamari Nakic

A $2$-$(v,k,\lambda)$ design is additive (or strongly additive) if it is possible to embed it in a suitable abelian group $G$ in such a way that its block set is contained in (or coincides with) the set of all the zero-sum $k$-subsets of $G$. Explicit results on the additivity or strong additivity of symmetric designs and subspace 2-designs are presented. In

Farhan Hanif, Debarshi Das, Jonathan Halliwell, Dipankar Home

A defining signature of classical systems is "in principle measurability" without disturbance: a feature manifestly violated by quantum systems. We describe a multi-interferometer experimental setup that can, in principle, reveal the nonclassicality of a spatial superposition-sourced gravitational field if an irreducible disturbance is caused by a measuremen

Bocheng Chen, Nikolay Ivanov, Guangjing Wang, Qiben Yan

Federated Learning (FL) is a distributed machine learning (ML) paradigm, aiming to train a global model by exploiting the decentralized data across millions of edge devices. Compared with centralized learning, FL preserves the clients' privacy by refraining from explicitly downloading their data. However, given the geo-distributed edge devices (e.g., mobile,

Akhila Krishna K, Ravi Kant Gupta, Nikhil Cherian Kurian, Pranav Jeevan

The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection. Convolutional neural networks often neglect the spatial relationships within histopathological images, which can limit their accuracy. Graph neural networks (GNNs) offer a promising solution by coding the spatial relationships with

Elena Tiukhova, Emiliano Penaloza, María Óskarsdóttir, Bart Baesens

Leveraging network information for predictive modeling has become widespread in many domains. Within the realm of referral and targeted marketing, influencer detection stands out as an area that could greatly benefit from the incorporation of dynamic network representation due to the continuous evolution of customer-brand relationships. In this paper, we pre

Adam Simon Levine, Tye Lidman, Lisa Piccirillo

In this article, we give new means of constructing and distinguishing closed exotic four-manifolds. Using Heegaard Floer homology, we define new closed four-manifold invariants that are distinct from the Seiberg--Witten and Bauer--Furuta invariants and can remain distinct in covers. Our constructions include exotic definite manifolds with fundamental group $

Amine Marrakchi, Mikael de la Salle

The aim of this note is to present a ``type III'' generalization of a distribution lemma of Connes. We then derive, following Vidick, consequences on infinite-dimensional quantum strategies for non-local games.

Jacob Bernstein, Arunima Bhattacharya

We study notions of asymptotic regularity for a class of minimal submanifolds of complex hyperbolic space that includes minimal Lagrangian submanifolds. As an application, we show a relationship between an appropriate formulation of Colding-Minicozzi entropy and a quantity we call the CR-volume that is computed from the asymptotic geometry of such submanifol

V. E. Adler

We study reductions of the Volterra lattice corresponding to stationary equations for the additional, noncommutative subalgebra of symmetries. It is shown that, in the case of general position, such a reduction is equivalent to the stationary equation for a sum of the scaling symmetry and the negative flows, and is written as $(m+1)$-component difference equ

Aritro Pathak

Starting with a trivial periodic flow on $\mathbb{S}M$, the unit tangent bundle of a genus two surface, we perform a Dehn-type surgery on the manifold around a tubular neighborhood of a curve on $\mathbb{S}M$ that projects to a self-intersecting closed geodesic on $M$, to get a surgered flow which restricted to the surgery region is ergodic with respect to t

JETSCAPE Collaboration, Y. Tachibana, A. Angerami, R. Arora

We utilize event-by-event Monte Carlo simulations within the JETSCAPE framework to examine scale-dependent jet-medium interactions in heavy-ion collisions. The reduction in jet-medium interaction during the early high-virtuality stage, where the medium is resolved at a short distance scale, is emphasized as a key element in explaining multiple jet observable

Anum Munir

Single nucleotide polymorphisms (SNPs) are variations at specific locations in DNA. Sequence responsible for marking genes associated with diseases or tracking inherited diseases within The family. These variations in the Rb1 gene can cause Retinoblastoma and cancer in the retina Of one eye or both, Osteosarcoma, Melanoma, Leukemias, Lungs, and Breast cancer

Aritro Pathak

We reduce the earlier known optimal shear parameter for which ergodicity is established in the linked twist map with two linear shears in opposite sense, in the most general setting. Further, here we obtain ergodicity with possibly only one-fold twists in either lobe, while earlier results only applied for twist parameters at least 2. Almost hyperbolicity is

Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu

Diffusion models have recently emerged as powerful generative priors for solving inverse problems. However, training diffusion models in the pixel space are both data-intensive and computationally demanding, which restricts their applicability as priors for high-dimensional real-world data such as medical images. Latent diffusion models, which operate in a m

Tian Yu Liu, Aditya Golatkar, Stefano Soatto

We introduce Tangent Attention Fine-Tuning (TAFT), a method for fine-tuning linearized transformers obtained by computing a First-order Taylor Expansion around a pre-trained initialization. We show that the Jacobian-Vector Product resulting from linearization can be computed efficiently in a single forward pass, reducing training and inference cost to the sa

Honglin Zhu

For simple graphs $X$ and $Y$ on $n$ vertices, the friends-and-strangers graph $\mathsf{FS}(X,Y)$ is the graph whose vertex set consists of all bijections $\sigma: V(X) \to V(Y)$, where two bijections $\sigma$ and $\sigma'$ are adjacent if and only if they agree on all but two adjacent vertices $a, b \in V(X)$ such that $\sigma(a), \sigma(b) \in V(Y)$ are ad

Massimo Candela, Valerio Luconi, Alessio Vecchio

The topology of the Internet and its geographic properties received significant attention during the last years, not only because they have a deep impact on the performance experienced by users, but also because of legal, political, and economic reasons. In this paper, the global Internet is studied in terms of path locality, where a path is defined as local

Hiroki Takahasi

We investigate mixing properties of piecewise affine non-Markovian maps acting on $[0,1]^2$ or $[0,1]^3$ and preserving the Lebesgue measure, which are natural generalizations of the {\it heterochaos baker maps} introduced in [Y. Saiki, H. Takahasi, J. A. Yorke. Nonlinearity 34 (2021) 5744-5761]. These maps are skew products over uniformly expanding or hyper

N. Tariq, F. A. Khan, S. A. Moqurrab, G. Srivastava

The proliferation of the Internet and mobile devices has resulted in malicious bots access to genuine resources and data. Bots may instigate phishing, unauthorized access, denial-of-service, and spoofing attacks to mention a few. Authentication and testing mechanisms to verify the end-users and prohibit malicious programs from infiltrating the services and d

Charles Stahl

We construct a new subsystem code in three dimensions that exhibits single-shot error correction in a user-friendly and transparent way. As this code is a subsystem version of coupled toric codes, we call it the intertwined toric code (ITC). Although previous codes share the property of single-shot error correction, the ITC is distinguished by its physically

Hamza Riaz, Alan F. Smeaton

Domain generalisation involves pooling knowledge from source domain(s) into a single model that can generalise to unseen target domain(s). Recent research in domain generalisation has faced challenges when using deep learning models as they interact with data distributions which differ from those they are trained on. Here we perform domain generalisation on

Junren Chen, Siyao Yang, Huaqiang Wu, Giacomo Indiveri

Multi-core neuromorphic systems typically use on-chip routers to transmit spikes among cores. These routers require significant memory resources and consume a large part of the overall system's energy budget. A promising alternative approach to using standard CMOS and SRAM-based routers is to exploit the features of memristive crossbar arrays and use them as

Chiara Caiazza, Claudio Cicconetti, Valerio Luconi, Alessio Vecchio

Edge computing is projected to become the dominant form of cloud computing in the future because of the significant advantages it brings to both users (less latency, higher throughput) and telecom operators (less Internet traffic, more local management). However, to fully unlock its potential at scale, system designers and automated optimization systems alik

Gabriel O. Alves

In this thesis we deal with two different topics. In the first half we investigate how the Bayesian formalism can be introduced into the problem of quantum thermometry -- a field which exploits the high level of control in coherent devices to offer enhanced precision for temperature estimation. In particular, we investigate concrete estimation strategies, wi

Tian Yu Liu, Stefano Soatto

Tangent Model Composition (TMC) is a method to combine component models independently fine-tuned around a pre-trained point. Component models are tangent vectors to the pre-trained model that can be added, scaled, or subtracted to support incremental learning, ensembling, or unlearning. Component models are composed at inference time via scalar combination,

Harmony Morris

In this paper, we define a new parameter of a graph as a spin-off of the pebbling number (which is the smallest $t$ such that every supply of $t$ pebbles can satisfy every demand of one pebble). This new parameter is the singular pebbling number, the smallest $t$ such that a player can be given any configuration of at least $t$ pebbles and any target vertex

Marcela Carena, Ian Low, Carlos E. M. Wagner, Ming-Lei Xiao

We study information-theoretic properties of scalar models containing two Higgs doublets $\Phi_a$, where $a=1,2$ is the flavor quantum number. Considering the 2-to-2 scattering $\Phi_a \Phi_b \to \Phi_c \Phi_d$ as a two-qubit system in the flavor subspace and the S-matrix as a quantum logic gate, we analyze the entanglement power of the S-matrix at the tree-

Furkan Ok, Amir Bahrami, Christophe Caloz

We solve the problem of electron scattering at a potential temporal step discontinuity. We show that the Schrodinger equation cannot account for scattering in this problem, necessitating resort to the Dirac equation, and that breaking gauge symmetry requires a vector potential, a scalar potential inducing only Aharonov-Bohm type energy transitions. We derive

Yoshihiko Nishikawa, Ludovic Berthier

We numerically elucidate the microscopic mechanisms controlling the relaxation dynamics of a three-dimensional lattice glass model that has static properties compatible with the approach to a random first-order transition. At low temperatures, the relaxation is triggered by a small population of particles with low-energy barriers forming mobile clusters. The

Rizwaan Mohammed

The Standard Model predicts that the electroweak couplings to the three charged leptons are identical. However, in the last decade, experimental measurements have suggested that semileptonic processes involving taus could have a slightly enhanced decay rate compared to their muonic counterparts. If confirmed, this would be an unambiguous sign of New Physics,

D. F. López, S. Abarghouei Nejad, José G. Pereira

In the de Sitter-invariant approach to gravitation, all solutions to the gravitational field equations are spacetimes that reduce locally to de Sitter. Consequently, besides including an event horizon, the de Sitter-invariant black hole also has a cosmic horizon. Accordingly, it can lodge ordinary and dark energies. Owing to this additional structure concern

Zhen Zhang, Zongren Zou, Ellen Kuhl, George Em Karniadakis

Misfolded tau proteins play a critical role in the progression and pathology of Alzheimer's disease. Recent studies suggest that the spatio-temporal pattern of misfolded tau follows a reaction-diffusion type equation. However, the precise mathematical model and parameters that characterize the progression of misfolded protein across the brain remain incomple

Dean Hazineh, Soon Wei Daniel Lim, Qi Guo, Federico Capasso

Optical metasurfaces composed of precisely engineered nanostructures have gained significant attention for their ability to manipulate light and implement distinct functionalities based on the properties of the incident field. Computational imaging systems have started harnessing this capability to produce sets of coded measurements that benefit certain task

A. R. Olamaei, A. Bazrafshan, M. Ghanaatian

This paper investigates charged black holes within the framework of quintic quasi-topological gravity, focusing on their thermodynamics, conserved quantities, and stability. We construct numerical solutions and explore their thermodynamic properties, supplemented by the study of analytically solvable special cases. By verifying the first law of thermodynamic

Natallia Kokash, Leonid Makhnist

In this paper, we address an issue of finding explainable clusters of class-uniform data in labelled datasets. The issue falls into the domain of interpretable supervised clustering. Unlike traditional clustering, supervised clustering aims at forming clusters of labelled data with high probability densities. We are particularly interested in finding cluster

Jiaming Shi

Recently it has been shown that the cosmological dynamics of covariant $f(Q)$ gravity depend on different affine connections. In this paper, two specific $f(Q)$ models are investigated with SNe+CC+BAO+QSO observational data, and the spatial curvature of the universe is studied in covariant $f(Q)$ gravity. It is found that the parameters $\mathcal{X}_0$ and $

Piotr Nowakowski

Let C(a) be the central Cantor set generated by a sequence a with terms in (0,1). It is known that the difference set C(a)-C(a) of C(a) can has one of three possible forms: a finite union of closed intervals, a Cantor set, or a Cantorval. In the previous paper there was proved a sufficient condition for the sequence a which implies that C(a) - C(a) is a Cant

Adam Perrett, Danny Wood, Gavin Brown

This work presents a novel algorithm for transforming a neural network into a spline representation. Unlike previous work that required convex and piecewise-affine network operators to create a max-affine spline alternate form, this work relaxes this constraint. The only constraint is that the function be bounded and possess a well-define second derivative,

Gürkan Kartal, George Simion, Bart Sorée

Coupling of transmon qubits to resonators that serve as storage for information provides alternative routes for quantum computing. Such a scheme paves the way for achieving high qubit connectivity, which is a great challenge in cQED systems. Implementations either involve an ancillary transmon's direct excitation, or virtual photon interactions. Virtual coup

Jihyun Lee, Junbong Jang, Donghwan Kim, Minhyuk Sung

Recent 4D shape representations model continuous temporal evolution of implicit shapes by (1) learning query flows without leveraging shape and articulation priors or (2) decoding shape occupancies separately for each time value. Thus, they do not effectively capture implicit correspondences between articulated shapes or regularize jittery temporal deformati

Prabhat Lankireddy, Sibibalan Jeevanandam, Aditya Chaudhary, P. C. Deshmukh

Algorithms that calculate the current-voltage (I-V) characteristics of a solar cell play an important role in processes that aim to improve the efficiency of a solar cell. I-V characteristics can be obtained from different models used to represent the solar cell, and the single diode model is a simple yet accurate model for common field implementations. Howe

Jialun Pei, Tao Jiang, He Tang, Nian Liu

We propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously calibrates depth and RGB features in the kernel and mask branches to generate instance-aware kernels and mask features. CalibNet consists of three simple modules, a dynamic interactive

Siqiao Xue, Xiaoming Shi, Zhixuan Chu, Yan Wang

Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point processes (TPPs) have emerged as the most natural and competitive models, making a significant impact in both academic and application communities. Despite the emergence of many p

Shahin Heydari, Petr Knobloch, Thoma Wick

In the present work, we investigate a model of the invasion of healthy tissue by cancer cells which is described by a system of nonlinear PDEs consisting of a cross-diffusion-reaction equation and two additional nonlinear ordinary differential equations. We show that when the convective part of the system, the chemotactic term, is dominant, then straightforw

Jiacheng Zhang, Xiangru Lin, Wei Zhang, Kuo Wang

We analyze the DETR-based framework on semi-supervised object detection (SSOD) and observe that (1) the one-to-one assignment strategy generates incorrect matching when the pseudo ground-truth bounding box is inaccurate, leading to training inefficiency; (2) DETR-based detectors lack deterministic correspondence between the input query and its prediction out

Christoph Chiaffrino, Talha Ersoy, Olaf Hohm

We give an interpretation of holography in the form of the AdS/CFT correspondence in terms of homotopy algebras. A field theory such as a bulk gravity theory can be viewed as a homotopy Lie or $L_{\infty}$ algebra. We extend this dictionary to theories defined on manifolds with a boundary, including the conformal boundary of AdS, taking into account the cycl

Yifan Yang, Shuhai Zhang, Zixiong Huang, Yubing Zhang

Neural Radiance Fields (NeRF) is a revolutionary approach for rendering scenes by sampling a single ray per pixel and it has demonstrated impressive capabilities in novel-view synthesis from static scene images. However, in practice, we usually need to recover NeRF from unconstrained image collections, which poses two challenges: 1) the images often have dyn

Mritula Chandrasekaran, Jarek Francik, Dimitrios Makris

This paper focuses on addressing the problem of data scarcity for gait analysis. Standard augmentation methods may produce gait sequences that are not consistent with the biomechanical constraints of human walking. To address this issue, we propose a novel framework for gait data augmentation by using OpenSIM, a physics-based simulator, to synthesize biomech

Zhenyi Wang, Enneng Yang, Li Shen, Heng Huang

Forgetting refers to the loss or deterioration of previously acquired knowledge. While existing surveys on forgetting have primarily focused on continual learning, forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Forgetting manifests in research fields such as generative models due to generator shifts, and

Daodao Yang

It is proved that $$\int_{T}^{2T} \left|\frac{\zeta\left(\frac{1}{2}+{\rm i} t\right)}{\zeta\left(1+2{\rm i} t\right)}\right|^2 {\rm d} t = \frac{1}{\zeta(2)} T \log T + \left( \frac{\log \frac{2}{\pi} + 2\gamma -1 }{\zeta(2)} -4 \,\frac{\zeta^{\prime}(2)}{\zeta^2(2)} \right) T + O\left(T\, \left(\log T\right)^{-2023} \right) , \quad \forall T \geqslant 100.

Liyuan Ma, Hongxia Wang, Ningyi Leng, Ziyang Yuan

Fourier phase retrieval (FPR) is a challenging task widely used in various applications. It involves recovering an unknown signal from its Fourier phaseless measurements. FPR with few measurements is important for reducing time and hardware costs, but it suffers from serious ill-posedness. Recently, untrained neural networks have offered new approaches by in

Mahsa Farjadnia, Angela Fontan, Alessio Russo, Karl Henrik Johansson

Window-opening and window-closing behaviors play an important role in indoor environmental conditions and therefore have an impact on building energy efficiency. On the other hand, the same environmental conditions drive occupants to interact with windows. Understanding this mutual relationship of interaction between occupants and the residential building is

Adam Keilthy

Using the block filtration as a realisation of the coradical filtration, we study the discrepancy between the depth filtration and the coradical filtration for motivic multiple zeta values. We construct an explicit dictionary between a certain subspace of block graded multiple zeta values and totally odd multiple zeta values and show that all expected relati

Albert Chau, Adam Martens

Lai (2021) used singular Ricci flows, introduced by Kleiner and Lott (2017), to construct a nonnegative Ricci curvature Ricci flow $g(t)$ emerging from an arbitrary 3D complete noncompact Riemannian manifold $(M^3, g_0)$ which has nonnegative Ricci curvature. We show $g(t)$ is complete for positive times provided $g_0$ satisfies a volume ratio lower bound th

Jaime de Miguel-Rodriguez, Fernando Sancho-Caparrini

Neural-symbolic approaches to machine learning incorporate the advantages from both connectionist and symbolic methods. Typically, these models employ a first module based on a neural architecture to extract features from complex data. Then, these features are processed as symbols by a symbolic engine that provides reasoning, concept structures, composabilit

Murad Tukan, Alaa Maalouf, Margarita Osadchy

Deep learning has grown tremendously over recent years, yielding state-of-the-art results in various fields. However, training such models requires huge amounts of data, increasing the computational time and cost. To address this, dataset distillation was proposed to compress a large training dataset into a smaller synthetic one that retains its performance

Mengyuan Zhang, Wotao Yin, Mengchang Wang, Yangbin Shen

Numerical software is usually shipped with built-in hyperparameters. By carefully tuning those hyperparameters, significant performance enhancements can be achieved for specific applications. We developed MindOpt Tuner, a new automatic tuning tool that supports a wide range of numerical software, including optimization and other solvers. MindOpt Tuner uses e

Saeideh Nabipour, Javad Javidan

The size reduction of transistors in the latest flash memory generation has resulted in programming and data erasure issues within these designs. Consequently, ensuring reliable data storage has become a significant challenge for these memory structures. To tackle this challenge, error-correcting codes like BCH (Bose-Chaudhuri-Hocquenghem) codes are employed

Wei-Lin Tu, Laurens Vanderstraeten, Norbert Schuch, Hyun-Yong Lee

Diagrammatic summation is a common bottleneck in modern applications of projected entangled-pair states, especially in computing low-energy excitations of a two-dimensional quantum many-body system. To solve this problem, here we extend the generating function approach for tensor network diagrammatic summation, a scheme previously proposed in the context of