April 2023 arXiv papers — page 107

Jiaqi Hou

Let $\psi$ be a Hecke-Maass form on a compact congruence arithmetic hyperbolic 3-manifold $X$, and let $Y$ be a hyperbolic surface in $X$ that is not necessarily closed. We obtain a power saving result over the local bound for the period of $\psi$ along $Y$, by applying the method of arithmetic amplification developed by Iwaniec and Sarnak.

E. Zhixuan Zeng, Yuhao Chen, Alexander Wong

Object pose estimation is a critical task in robotics for precise object manipulation. However, current techniques heavily rely on a reference 3D object, limiting their generalizability and making it expensive to expand to new object categories. Direct pose predictions also provide limited information for robotic grasping without referencing the 3D model. Ke

Rachel Grotheer, Shuang Li, Anna Ma, Deanna Needell

Due to the explosive growth of large-scale data sets, tensors have been a vital tool to analyze and process high-dimensional data. Different from the matrix case, tensor decomposition has been defined in various formats, which can be further used to define the best low-rank approximation of a tensor to significantly reduce the dimensionality for signal compr

Jialun Zhang, Greg Morrisett, Gang Tan

File formats specify how data is encoded for persistent storage. They cannot be formalized as context-free grammars since their specifications include context-sensitive patterns such as the random access pattern and the type-length-value pattern. We propose a new grammar mechanism called Interval Parsing Grammars IPGs) for file format specifications. An IPG

Amirmohammad Sarfi, Zahra Karimpour, Muawiz Chaudhary, Nasir M. Khalid

Recently, a number of iterative learning methods have been introduced to improve generalization. These typically rely on training for longer periods of time in exchange for improved generalization. LLF (later-layer-forgetting) is a state-of-the-art method in this category. It strengthens learning in early layers by periodically re-initializing the last few l

V. G. Kupriyanov, M. A. Kurkov, P. Vitale

We consider gauge theories on Poisson manifolds emerging as semiclassical approximations of noncommutative spacetime with Lie algebra type noncommutativity. We prove an important identity, which allows to obtain simple and manifestly gauge-covariant expressions for the Euler-Lagrange equations of motion, the Bianchi and the Noether identities. We discuss the

Ilja Klebanov

Jensen's inequality is ubiquitous in measure and probability theory, statistics, machine learning, information theory and many other areas of mathematics and data science. It states that, for any convex function $f\colon K \to \mathbb{R}$ defined on a convex domain $K \subseteq \mathbb{R}^{d}$ and any random variable $X$ taking values in $K$, $\mathbb{E}[f(X

Shimiao Li, Amritanshu Pandey, Larry Pileggi

Cyberthreats are an increasingly common risk to the power grid and can thwart secure grid operations. We propose to extend contingency analysis to include cyberthreat evaluations. However, unlike the traditional N-1 or N-2 contingencies, cyberthreats (e.g., MadIoT) require simulating hard-to-solve N-k (with k >> 2) contingencies in a practical amount of time

Dhruv Mubayi, Jacques Verstraete

We consider the maximum chromatic number of hypergraphs consisting of cliques that have pairwise small intersections. Designs of the appropriate parameters produce optimal constructions, but these are known to exist only when the number of cliques is exponential in the clique size. We construct near designs where the number of cliques is polynomial in the cl

Aleksandr Dekhovich, Marcel H. F. Sluiter, David M. J. Tax, Miguel A. Bessa

Physics-informed neural networks (PINNs) have recently become a powerful tool for solving partial differential equations (PDEs). However, finding a set of neural network parameters that lead to fulfilling a PDE can be challenging and non-unique due to the complexity of the loss landscape that needs to be traversed. Although a variety of multi-task learning a

Florent Burdairon, Jacques Magnaudet

Recent laboratory experiments have revealed that important insights into the physical processes involved in the wind-driven generation of surface waves may be obtained by varying the viscosity of the carrying liquid over several orders of magnitude. The present paper reports on the development of a companion approach aimed at studying similar phenomena throu

Abdul Sittar, Dunja Mladenic, Marko Grobelnik

News media is one of the most effective mechanisms for spreading information internationally, and many events from different areas are internationally relevant. However, news coverage for some news events is limited to a specific geographical region because of information spreading barriers, which can be political, geographical, economic, cultural, or lingui

Alexander Shen

This note provides a simplified exposition of the proof of hierarchical Kraft lemma proven by Barmpalias and Lewis-Pye and its consequences for the oracle use in the Ku\v{c}era--G\'acs theorem (saying that every sequence is Turing reducible to a random one).

Aleksei Gorshkov

Most of the known Fourier transforms associated with the equations of mathematical physics have a trivial kernel, and an inversion formula as well as the Parseval equality are fulfilled. In other words, the system of the eigenfunctions involved in the definition of the integral transform is complete. Here we will study Fourier transform associated with the d

Vu Trong Luong, Nguyen Duc Huy, Nguyen Van Minh, Nguyen Ngoc Vien

In this paper we study the asymptotic behavior of solutions of fractional differential equations of the form $ D^{\alpha}_Cu(t)=Au(t)+f(t), u(0)=x, 0<\alpha\le1, ( *) $ where $D^{\alpha}_Cu(t)$ is the derivative of the function $u$ in the Caputo's sense, $A$ is a linear operator in a Banach space $\X$ that may be unbounded and $f$ satisfies the property that

João M. Souto-Maior

This article investigates mechanism-based explanations for a well-known empirical pattern in sociology of education, namely, that Black-White unequal access to school resources -- defined as advanced coursework -- is the highest in racially diverse and majority-White schools. Through an empirically calibrated and validated agent-based model, this study explo

Wenqi Xian, Aljaž Božič, Noah Snavely, Christoph Lassner

Recent methods for 3D reconstruction and rendering increasingly benefit from end-to-end optimization of the entire image formation process. However, this approach is currently limited: effects of the optical hardware stack and in particular lenses are hard to model in a unified way. This limits the quality that can be achieved for camera calibration and the

William Barker

In this paper we consider traveling waves for a diffusive Nicholson Blowflies Equation with different discrete time delays in the diffusion term and birth function. We construct quasi upper and lower solutions via the monotone iteration method. This also allows for the construction of C2 upper and lower solutions, and then traveling wave solutions. We then p

Jack W. Davidson, Jason D. Hiser, Anh Nguyen-Tuong

The open-source Helix++ project improves the security posture of computing platforms by applying cutting-edge cybersecurity techniques to diversify and harden software automatically. A distinguishing feature of Helix++ is that it does not require source code or build artifacts; it operates directly on software in binary form--even stripped executables and li

Livio De Fabrizio

Kinetic Alfv\'en Waves (KAWs) are generated in magnetized space and laboratory plasmas due to a continuous shear Alfv\'en wave (SAW) spectrum and, unlike SAWs, are characterized by microscale perpendicular structures of the order of the thermal ion Larmor radius. This has important consequences on heating, acceleration and transport processes connected with

Claudia Chaio, Alfredo González Chaio, Isabel Pratti, María José Souto Salorio

Let $\mathcal{A}$ be an additive $k-$category and $\mathbf{C}_{\equiv m}(\mathcal{A})$ be the category of $m-$periodic objects. For any integer $m>1$, we study conditions under which the compression functor ${\mathcal F}_m :\mathbf{C}^{b}(\mathcal{A}) \rightarrow \mathbf{C}_{\equiv m}(\mathcal{A})$ preserves or reflects irreducible morphisms. Moreover, we fi

Arkan Hassan, Julio Gea-Banacloche

We study the interaction of a V-type atom in a cavity with incident single- and two-photon wavepackets and derive an exact formula, valid in all parameter regimes, relating the spectrum of the outgoing wavepackets to the incident one. We present detailed results for several special input pulses, and consider the potential performance of the system as a CPHAS

Shahed Rezaei, Ahmad Moeineddin, Ali Harandi

We applied physics-informed neural networks to solve the constitutive relations for nonlinear, path-dependent material behavior. As a result, the trained network not only satisfies all thermodynamic constraints but also instantly provides information about the current material state (i.e., free energy, stress, and the evolution of internal variables) under a

Chen Liu, Matthias Jobst, Liyuan Guo, Xinyue Shi

In the past few years, more and more AI applications have been applied to edge devices. However, models trained by data scientists with machine learning frameworks, such as PyTorch or TensorFlow, can not be seamlessly executed on edge. In this paper, we develop an end-to-end code generator parsing a pre-trained model to C source libraries for the backend usi

Shan Jiang, Benoît Fauqué, Kamran Behnia

The temperature dependence of the phase space for electron-electron (e-e) collisions leads to a T-square contribution to electrical resistivity of metals. Umklapp scattering are identified as the origin of momentum loss due to e-e scattering in dense metals. However, in dilute metals like lightly doped strontium titanate, the origin of T-square electrical re

Santiago Flum, J. Andres Montoya

We prove that NL is strictly contained in P. We get this separation as a corollary of the following result: the set of context-free languages is not contained in NL. The reader should recall that CFL is contained in DTIME(n^3)

Jonayet Miah, Muntasir Mamun, Md Minhazur Rahman, Md Ishtyaq Mahmud

Mobile phones and other electronic gadgets or devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data. Mobile health data use mobile devices to gather clinical health data and track patient vitals in real-time. Our study is aimed to give decisions for small or big sports teams on whether

Weijie Du, James P. Vary

We propose a framework for computing the structure and dynamics for second-quantized many-nucleon Hamiltonians on quantum computers. We develop an oracle-based Hamiltonian input model that computes the many-nucleon states and nonzero Hamiltonian matrix elements of the many-nucleon system. With our Fock-state based input model, we show how to implement the sp

Jason Vander Woude, Peter Dixon, A. Pavan, Jamie Radcliffe

A partition $\mathcal{P}$ of $\mathbb{R}^d$ is called a $(k,\varepsilon)$-secluded partition if, for every $\vec{p} \in \mathbb{R}^d$, the ball $\overline{B}_{\infty}(\varepsilon, \vec{p})$ intersects at most $k$ members of $\mathcal{P}$. A goal in designing such secluded partitions is to minimize $k$ while making $\varepsilon$ as large as possible. This par

Weiwei Sun, Pengjie Ren, Zhaochun Ren

Knowledge selection is the key in knowledge-grounded dialogues (KGD), which aims to select an appropriate knowledge snippet to be used in the utterance based on dialogue history. Previous studies mainly employ the classification approach to classify each candidate snippet as "relevant" or "irrelevant" independently. However, such approaches neglect the inter

Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi, Adil Salim

Variational inference (VI) seeks to approximate a target distribution $\pi$ by an element of a tractable family of distributions. Of key interest in statistics and machine learning is Gaussian VI, which approximates $\pi$ by minimizing the Kullback-Leibler (KL) divergence to $\pi$ over the space of Gaussians. In this work, we develop the (Stochastic) Forward

Sadia Nourin, Van Tran, Xi Jiang, Kevin Bock

Since 2006, Turkmenistan has been listed as one of the few Internet enemies by Reporters without Borders due to its extensively censored Internet and strictly regulated information control policies. Existing reports of filtering in Turkmenistan rely on a small number of vantage points or test a small number of websites. Yet, the country's poor Internet adopt

Rakesh Arul, Kishan Menghrajani, Marie S. Rider, Rohit Chikkaraddy

Strong coupling of molecular vibrations with light creates polariton states, enabling control over many optical and chemical properties. However, the near-field signatures of strong coupling are difficult to map as most cavities are closed systems. Surface-enhanced Raman microscopy of open metallic gratings under vibrational strong coupling enables the obser

Marcio G B de Avellar, Alexandre A S Junior, André H G Lopes, André L S Carneiro

We explore confidential computing in the context of CBDCs using Microsoft's CCF framework as an example. By developing an experiment and comparing different approaches and performance and security metrics, we seek to evaluate the effectiveness of confidential computing to improve the privacy, security, and performance of CBDCs. Preliminary results suggest th

Marcelo Guzman, Xiaofei Guo, Corentin Coulais, David Carpentier

Topological materials can host edge and corner states that are protected from disorder and material imperfections. In particular, the topological edge states of mechanical structures present unmatched opportunities for achieving robust responses in wave guiding, sensing, computation, and filtering. However, determining whether a mechanical structure is topol

Brice Ravon, Paul Méhaignerie, Yohann Machu, Andrés Durán Hernández

Circular Rydberg atoms (CRAs), i.e., Rydberg atoms with maximal orbital momentum, are highly promising for quantum computation, simulation and sensing. They combine long natural lifetimes with strong inter-atomic interactions and coupling to electromagnetic fields. Trapping individual CRAs is essential to harness these unique features. We report the first de

Caoimhe M. Rooney, Natasha E. Batalha, Mark S. Marley

Approximate methods to estimate solutions to the radiative transfer equation are essential for the understanding of atmospheres of exoplanets and brown dwarfs. The simplest and most popular choice is the "two-stream method" which is often used to produce simple yet effective models for radiative transfer in scattering and absorbing media. Toon et al. (1989)

Caoimhe M. Rooney, Natasha E. Batalha, Mark S. Marley

A significant challenge in radiative transfer theory for atmospheres of exoplanets and brown dwarfs is the derivation of computationally efficient methods that have adequate fidelity to more precise, numerically demanding solutions. In this work, we extend the capability of the first open-source radiative transfer model for computing the reflected light of e

Connor Donovan, Danielle Paulson, Pablo Soberón

Krasnosselsky's art gallery theorem gives a combinatorial characterization of star-shaped sets in Euclidean spaces, similar to Helly's characterization of finite families of convex sets with non-empty intersection. We study colorful and quantitative variations of Krasnosselsky's result. In particular, we are interested in conditions on a set $K$ that guarant

Johannes Hirth, Viktoria Horn, Gerd Stumme, Tom Hanika

Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ord

Daniel Silvestre

Set-valued state estimation when in the presence of uncertainties in the model have been addressed in the literature essentially following three main approaches: i) interval arithmetic of the uncertain dynamics with the estimates; ii) factorizing the uncertainty into matrices with unity rank; and, iii) performing the convex hull for the vertices of the uncer

Matieyendou Lamboni

Mathematical models are sometime given as functions of independent input variables and equations or inequations connecting the input variables. A probabilistic characterization of such models results in treating them as functions with non-independent variables. Using the distribution function or copula of such variables that comply with such equations or ine

Xiang Hu, Enrico Rossi, Yafis Barlas

Inversion asymmetry in bilayer graphene can be tuned by the displacement field. As a result, the band dispersion in biased bilayer graphene acquires flat band regions near the Dirac points along with a non-trivial band geometry. We analyze the effect of inversion symmetry on the critical temperature and superfluid stiffness of the superconducting state of AB

Hanjing Wang, Dhiraj Joshi, Shiqiang Wang, Qiang Ji

Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs. To build a trusted AI system, it is therefore critical to accurately quantify the prediction uncertainties. While current efforts focus on improving uncertainty quantification accuracy and efficiency, there is a need to identify unce

Jieming Bian, Lei Wang, Kun Yang, Cong Shen

Over the past few years, Federated Learning (FL) has become a popular distributed machine learning paradigm. FL involves a group of clients with decentralized data who collaborate to learn a common model under the coordination of a centralized server, with the goal of protecting clients' privacy by ensuring that local datasets never leave the clients and tha

Paul Lathrop, Beth Boardman, Sonia Martínez

In this paper, we present a novel formulation of traditional sampling-based motion planners as database-oracle structures that can be solved via quantum search algorithms. We consider two complementary scenarios: for simpler sparse environments, we formulate the Quantum Full Path Search Algorithm (q-FPS), which creates a superposition of full random path sol

Dmitry E. Pelinovsky, Michael Plum

We consider a family of regularized defocusing nonlinear Schrodinger (NLS) equations proposed in the context of the cubic NLS equation with a bounded dispersion relation. The time evolution is well-posed if the black soliton is perturbed by a small perturbation in the Sobolev space $H^s(\R)$ with s > 1/2. We prove that the black soliton is spectrally stable

Kavya Puthuveetil, Sasha Wald, Atharva Pusalkar, Pratyusha Karnati

Robotic caregivers could potentially improve the quality of life of many who require physical assistance. However, in order to assist individuals who are lying in bed, robots must be capable of dealing with a significant obstacle: the blanket or sheet that will almost always cover the person's body. We propose a method for targeted bedding manipulation over

Kalervo Jarvelin, Yu-Wei Chang, Pertti Vakkari

The paper analyses the citation impact of Library and Information Science, LIS for short, research articles published in 31 leading international LIS journals in 2015. The main research question is: to what degree do authors' disciplinary composition in association with other content characteristics of LIS articles affect their citation impact? The impact is

Ze Wang, Jiang Wang, Zicheng Liu, Qiang Qiu

In this paper, we show that a binary latent space can be explored for compact yet expressive image representations. We model the bi-directional mappings between an image and the corresponding latent binary representation by training an auto-encoder with a Bernoulli encoding distribution. On the one hand, the binary latent space provides a compact discrete im

Lavanya Elluri, Varun Mandalapu, Piyush Vyas, Nirmalya Roy

Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using sophisticated techniques to breach security systems and steal sensitive data. This paper aims to comprehensively survey the latest advancements in cybercrime prediction, highlighting the relevant research. For this purpose, we reviewed more than 150 research artic

George Stepaniants, Alasdair D. Hastewell, Dominic J. Skinner, Jan F. Totz

Despite rapid progress in live-imaging techniques, many complex biophysical and biochemical systems remain only partially observable, thus posing the challenge to identify valid theoretical models and estimate their parameters from an incomplete set of experimentally accessible time series. Here, we combine sensitivity methods and ranked-choice model selecti

Konstantin Emil Thiel, Daniel Kocher, Nikolaus Augsten, Thomas Hütter

Density-based clustering aims to find groups of similar objects (i.e., clusters) in a given dataset. Applications include, e.g., process mining and anomaly detection. It comes with two user parameters ({\epsilon}, MinPts) that determine the clustering result, but are typically unknown in advance. Thus, users need to interactively test various settings until

Cheng-Yen Yang, Alan Yu Shyang Tan, Melanie J. Underwood, Charlotte Bodie

The aim of in-trawl catch monitoring for use in fishing operations is to detect, track and classify fish targets in real-time from video footage. Information gathered could be used to release unwanted bycatch in real-time. However, traditional multi-object tracking (MOT) methods have limitations, as they are developed for tracking vehicles or pedestrians wit

José Menéndez, Chi Xu, John Kouvetakis

A consistent methodology is presented to extract carrier concentrations in n-type Ge from measurements of the infrared dielectric function and the Hall effect. In the case of the optical measurements, usually carried out using spectroscopic ellipsometry, the carrier concentration is affected by the doping dependence of the conductivity effective mass, which

Muntasir Mamun, Md Ishtyaq Mahmud, Mahabuba Meherin, Ahmed Abdelgawad

The most deadly and life-threatening disease in the world is lung cancer. Though early diagnosis and accurate treatment are necessary for lowering the lung cancer mortality rate. A computerized tomography (CT) scan-based image is one of the most effective imaging techniques for lung cancer detection using deep learning models. In this article, we proposed a

J. C. de Albuquerque, L. R. S. de Assis, M. L. M. Carvalho, A. Salort

In this article we study the asymptotic behavior of anisotropic nonlocal nonstandard growth seminorms and modulars as the fractional parameter goes to 1. This gives a so-called Bourgain-Brezis-Mironescu type formula for a very general family of functionals. In the particu\-lar case of fractional Sobolev spaces with variable exponent, we point out that our pr

Ziyang Li, Jiani Huang, Mayur Naik

We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation that is based on the relational data mode

Yida Mu, Ye Jiang, Freddy Heppell, Iknoor Singh

The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challenging for citizens to differentiate between accurate and inaccurate information about COVID-19. This motivated us to carry out a comparative study of the characteristics of COVID-1

Oleksandra Gasanova, Lisa Nicklasson

We introduce a family of toric algebras defined by maximal chains of a finite distributive lattice. Applying results on stable set polytopes we conclude that every such algebra is normal and Cohen-Macaulay, and give an interpretation of its Krull dimension in terms of the combinatorics of the underlying lattice. When the lattice is planar, we show that the c

Shengzhi Luan, Enze Chen, Joel John, Stavros Gaitanaros

Cellular solids and micro-lattices are a class of lightweight architected materials that have been established for their unique mechanical, thermal, and acoustic properties. It has been shown that by tuning material architecture, a combination of topology and solid(s) distribution, one can design new material systems, also known as metamaterials, with superi

Jean-Baptiste Touchais, Pascal Simon, Andrej Mesaros

In one-dimensional disordered systems with a chiral symmetry it is well-known that electrons at energy $E = 0$ avoid localization and simultaneously exhibit a diverging density of states (DOS). For $N$ coupled chains with zero-correlation-length disorder, the diverging DOS remains for odd $N$, but a vanishing DOS is found for even $N$. We use a thin spinless

Ian Colwell, Virisha Timmaraju, Alexander Wise

We present a deep-learning based approach for measuring small planetary radial velocities in the presence of stellar variability. We use neural networks to reduce stellar RV jitter in three years of HARPS-N sun-as-a-star spectra. We develop and compare dimensionality-reduction and data splitting methods, as well as various neural network architectures includ

Yida Mu, Mali Jin, Kalina Bontcheva, Xingyi Song

Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a comprehensive understanding of the public's stance towards vaccination on a large scale. However, attitudes towards COVID-19 vaccination, such as pro-vaccine or vaccine hesitancy,

L. N. Kalousis

The purpose of the present article is to demonstrate the calibration of photomultipliers with a gaussian single photoelectron response using a numerical method based on the Discrete Fourier Transform (DFT). Conventional techniques, commonly employed in the literature, use approximate models or brute force numerical calculations of the convolution integrals t

Mikhail R. Gabdullin, Vitalii V. Iudelevich, Sergei V. Konyagin

We prove that $$ \sum_{p \leq x} \frac{1}{\tau(p-1)} \asymp \frac{x}{(\log x)^{3/2}}, \quad \quad \sum_{n \leq x} \frac{1}{\tau(n^2+1)} \asymp \frac{x}{(\log x)^{1/2}}, $$ where $\tau(n)=\sum_{d|n}1$ is the number of divisors of $n$, and the summation in the first sum is over primes.

Dominique Fosse

Given $A:=\left(\begin{smallmatrix}1&1\\0&1\end{smallmatrix}\right)$, $B:=\left(\begin{smallmatrix}1&0\\1&1\end{smallmatrix}\right)$ and $C:=\left(\begin{smallmatrix}1&0\\0&-1\end{smallmatrix}\right)$ three elements of $GL_{2}\left(\mathbb{Z}\right)$, we propose an explicit formula that provides the decomposition of any $M\in GL_{2}\left(\mathbb{Z}\right)$ i

Eric V. Linder

Dark energy is a premier mystery of physics, both theoretical and experimental. As we look to develop plans for high energy physics over the next decade, within a two decade view, we consider benchmarks for revealing the nature of dark energy. We conclude, based on fundamental physical principles detailed below, that understanding will come from experiments

Warren Li

We study the properties of spacelike singularities in spherically symmetric spacetimes obeying the Einstein equations, in the presence of matter. We consider in particular matter described by a scalar field, both in the presence of an electromagnetic field and without. We prove that if a spacelike singularity obeying several reasonable assumptions is formed,

Davide Gerosa, Giulia Fumagalli, Matthew Mould, Giovanni Cavallotto

We present analytical and numerical progress on black-hole binary spin precession at second post-Newtonian order using multi-timescale methods. In addition to the commonly used effective spin which acts as a constant of motion, we exploit the weighted spin difference and show that such reparametrization cures the coordinate singularity that affected the prev

Saikat Roy, Tassilo Wald, Gregor Koehler, Maximilian R. Rokuss

Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting. With the recent introduction of the Segment Anything Model (SAM), this prompt-driven paradigm has entered image segmentation with a hitherto unexplored abundance of capabilities. The purpose of this paper is to conduct an initial ev

Xin-yi Lin, Jian-dong Zhang, Liang Dai, Shun-Jia Huang

When gravitational waves (GWs) pass by a massive object on its way to Earth, a strong gravitational lensing effect will happen. Thus, the GW signal will be amplified, deflected, and delayed in time. Through analyzing the lensed GW waveform, physical properties of the lens can be inferred. On the other hand, neglecting lensing effects in the analysis of GW da

Minho Kim, Jörg Peters

Box splines provide smooth spline spaces as shifts of a single generating function on a lattice and so generalize tensor-product splines. Their elegant theory is laid out in classical papers and a summarizing book. This compendium aims to succinctly but exhaustively survey symmetric low-degree box splines with special focus on two and three variables. Tables

S. Suciu, G. A. Bulzan, T. A. Isdraila, A. M. Palici

Quantum communications bring a paradigm change in internet security by using quantum resources to establish secure keys between parties. Present-day quantum communications networks are mainly point-to-point and use trusted nodes and key management systems to relay the keys. Future quantum networks, including the quantum internet, will have complex topologies

Drew Penney, Bin Li, Lizhong Chen, Jaroslaw J. Sydir

Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership. Effective resource sharing, however, remains an open challenge due to the adverse effects that resource contention can have on high-priority, user-facing workloads with

E. O. Ofek, S. Ben-Ami, D. Polishook, E. Segre

The Large Array Survey Telescope (LAST) is a wide-field visible-light telescope array designed to explore the variable and transient sky with a high cadence. LAST will be composed of 48, 28-cm f/2.2 telescopes (32 already installed) equipped with full-frame backside-illuminated cooled CMOS detectors. Each telescope provides a field of view (FoV) of 7.4 deg^2

Motasem Alfarra, Hani Itani, Alejandro Pardo, Shyma Alhuwaider

This paper proposes a novel online evaluation protocol for Test Time Adaptation (TTA) methods, which penalizes slower methods by providing them with fewer samples for adaptation. TTA methods leverage unlabeled data at test time to adapt to distribution shifts. Although many effective methods have been proposed, their impressive performance usually comes at t

Thomas Leonard, Samuel Liu, Harrison Jin, Jean Anne C. Incorvia

The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) make SNNs promising for edge applications that require high energy efficiency. To realize SNNs in hardware, spintronic neuron implementations can bring advantages of scalability and energy efficiency. Domain wall (DW) based magnetic tunnel junction (MTJ) devices are well suited

Anish Ghoshal, Yann Gouttenoire, Lucien Heurtier, Peera Simakachorn

Light primordial black holes (PBHs) with masses smaller than $10^9$ g ($10^{-24} M_\odot$) evaporate before the onset of Big-Bang nucleosynthesis, rendering their detection rather challenging. If efficiently produced, they may have dominated the universe energy density. We study how such an early matter-dominated era can be probed successfully using gravitat

Charles Stahl, Rahul Nandkishore, Oliver Hart

We present a set of generalized quantum loop models which provably exhibit topologically stable ergodicity breaking. These results hold for both periodic and open boundary conditions, and derive from a one-form symmetry (notably not being restricted to sectors of extremal one-form charge). We identify simple models in which this one-form symmetry can be emer

Sera Cremonini, Mirjam Cvetic, Christopher N. Pope, Aritra Saha

We investigate various properties of extremal dyonic static black holes in Einstein-Maxwell-Dilaton theory. Using the fact that the long-range force between two identical extremal black holes always vanishes, we obtain a simple first-order ordinary differential equation for the black hole mass in terms of its electric and magnetic charges. Although this equa

Sylvain Lacroix

In this paper, we study conformal points among the class of $\mathcal{E}$-models. The latter are $\sigma$-models formulated in terms of a current Poisson algebra, whose Lie-theoretic definition allows for a purely algebraic description of their dynamics and their 1-loop RG-flow. We use these results to formulate a simple algebraic condition on the defining d

Thibaut Coudarchet, Fernando Marchesano, David Prieto, Mikel A. Urkiola

The analysis of type IIB flux vacua on warped Calabi--Yau orientifolds becomes considerably involved for a large number of complex structure fields. We however show that, for a quadratic flux superpotential, one can devise simplifying schemes which effectively reduce the large number of equations down to a few. This can be achieved by imposing the vanishing

Eshita Banerjee, Sowgat Muzahid, Joop Schaye, Sean D. Johnson

We present a detailed study of the column density and covering fraction profiles of C IV absorption around 86 redshift $z \approx 3.3$ Ly$\alpha$ emitters (LAEs) detected in 8 Multi-Unit Spectroscopic Explorer (MUSE) fields of $1'\times 1'$ centered on 8 bright background quasars as part of the MUSEQuBES survey. Using Voigt profile fitting of all the C IV ab

Jinchao Zhao, Gabriele La Nave, Philip Phillips

We establish the Hatsugai-Kohmoto model as a stable quartic fixed point (distinct from Wilson-Fisher) by computing the $\beta-$function in the presence of perturbing local interactions. In vicinity of the half-filled doped Mott state, the $\beta-$function vanishes for all local interactions regardless of their sign. The only flow away from the HK model is th

Jiacheng Deng, Chuxin Wang, Jiahao Lu, Jianfeng He

Unsupervised point cloud shape correspondence aims to obtain dense point-to-point correspondences between point clouds without manually annotated pairs. However, humans and some animals have bilateral symmetry and various orientations, which lead to severe mispredictions of symmetrical parts. Besides, point cloud noise disrupts consistent representations for

B. Rodríguez Del Pino, S. Arribas, A. L. Chies-Santos, I. Lamperti

AGN have been generally considered to be less frequent in denser environments due to the lower number of galaxy-galaxy interactions and/or the removal of their gas-rich reservoirs by the dense intergalactic medium. However, recent observational and theoretical works suggest that the effect of ram-pressure stripping might reduce the angular momentum of their

Natalia Porqueres, Alan Heavens, Daniel Mortlock, Guilhem Lavaux

We construct a field-based Bayesian Hierarchical Model for cosmic shear that includes, for the first time, the important astrophysical systematics of intrinsic alignments and baryon feedback, in addition to a gravity model. We add to the BORG-WL framework the tidal alignment and tidal torquing model (TATT) for intrinsic alignments and compare them with the n

Aleksandar Bukva, Jurriaan de Gier, Kevin T. Grosvenor, Ro Jefferson

Deep feedforward networks initialized along the edge of chaos exhibit exponentially superior training ability as quantified by maximum trainable depth. In this work, we explore the effect of saturation of the tanh activation function along the edge of chaos. In particular, we determine the line of uniformity in phase space along which the post-activation dis

Trung Ha, Cooper Dix, Brandon M. Matthews, Ohad Shemmer

Weak emission-line quasars (WLQs) are a subset of Type 1 quasars that exhibit extremely weak Ly$\alpha +$N V $\lambda$1240 and/or C IV $\lambda$1549 emission lines. We investigate the relationship between emission-line properties and accretion rate for a sample of 230 `ordinary' Type 1 quasars and 18 WLQs at $z < 0.5$ and $1.5 < z < 3.5$ that have rest-frame

Dibya Ghosh, Chethan Bhateja, Sergey Levine

Passive observational data, such as human videos, is abundant and rich in information, yet remains largely untapped by current RL methods. Perhaps surprisingly, we show that passive data, despite not having reward or action labels, can still be used to learn features that accelerate downstream RL. Our approach learns from passive data by modeling intentions:

Yanpeng Sun, Qiang Chen, Xiaofan Li, Jian Wang

The In-Context Learning (ICL) is to understand a new task via a few demonstrations (aka. prompt) and predict new inputs without tuning the models. While it has been widely studied in NLP, it is still a relatively new area of research in computer vision. To reveal the factors influencing the performance of visual in-context learning, this paper shows that pro

Akash Sinha, Aritra Ghosh, Bijan Bagchi

In this paper, we describe the dynamical symmetries of classical supersymmetric oscillators in one and two spatial (bosonic) dimensions. Our main ingredient is a generalized Poisson bracket which is defined as a suitable classical counterpart to commutators and anticommutators. In one dimension, i.e., in the presence of one bosonic and one fermionic coordina

Jiaao Chen, Aston Zhang, Mu Li, Alex Smola

Diffusion models that are based on iterative denoising have been recently proposed and leveraged in various generation tasks like image generation. Whereas, as a way inherently built for continuous data, existing diffusion models still have some limitations in modeling discrete data, e.g., languages. For example, the generally used Gaussian noise can not han

Jonathan Wittmer, Jacob Badger, Hari Sundar, Tan Bui-Thanh

PDE-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes that are required to accurately compute the PDE solution introduce an enormous number of parameters and require large scale computing resources such as more processors and more memory to solve such systems in a

Aimon Rahman, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu, Vishal M Patel

Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we intr

Subhajit Ghosh, Dinusha Herath Mudiyanselage, Sergey Rumyantsev, Yuji Zhao

We report on the low-frequency electronic noise in (Al$_x$Ga$_{1-x}$)$_2$O$_3$ Schottky barrier diodes. The noise spectral density reveals 1/f dependence, characteristic of the flicker noise, with superimposed Lorentzian bulges at the intermediate current levels (f is the frequency). The normalized noise spectral density in such diodes was determined to be o

Shilong Liu, Tianhe Ren, Jiayu Chen, Zhaoyang Zeng

This paper is concerned with the matching stability problem across different decoder layers in DEtection TRansformers (DETR). We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR. To address this problem, we show that the most important design is to use

Chenwei Lv, Ming Zhu, Sambit Banerjee, Chen-Lung Hung

We investigate external and internal dynamics of a two-level atom strongly coupled to a weakly pumped nanophotonic cavity. We calculate the dipole force, friction force, and stochastic force due to the cavity pump field, and show that a three-dimensional cooling region exists near the surface of a cavity. Using a two-color evanescent field trap as an example

Aaron Lou, Stefano Ermon

Score-based diffusion models learn to reverse a stochastic differential equation that maps data to noise. However, for complex tasks, numerical error can compound and result in highly unnatural samples. Previous work mitigates this drift with thresholding, which projects to the natural data domain (such as pixel space for images) after each diffusion step, b