November 2022 arXiv papers — page 63

Adam Logan, Jared Weinstein

We investigate a notion of "higher modularity" for elliptic curves over function fields. Given such an elliptic curve $E$ and an integer $r\geq 1$, we say that $E$ is $r$-modular when there is an algebraic correspondence between a stack of $r$-legged shtukas, and the $r$-fold product of $E$ considered as an elliptic surface. The (known) case $r=1$ is analogo

G. W. Marcy

A search for laser light from the directions of Alpha Centauri A and B was performed by examining 15362 optical, high-resolution spectra obtained between 2004 and 2018. None of the spectra exhibit laser emission lines. The threshold was 10% of the continuum intensity of the spectra of both stars at all wavelengths between 3850 and 6900 {\AA}. This search wou

Leping Li, Hongqiang Song, Hardi Peter, Lakshmi Pradeep Chitta

A magnetic flux rope (FR), hosting hot plasma, is thought to be central to the physics of coronal mass ejection. Such FRs are widely observed with passbands of the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), that are sensitive to emission from the hot plasma around 10 MK. In contrast, observations of warmer (around 1 MK)

Shitao Li, Minjia Shi, Huizhou Liu

Linear codes with small hulls over finite fields have been extensively studied due to their practical applications in computational complexity and information protection. In this paper, we develop a general method to determine the exact value of $D_4^H(n,k,1)$ for $n\leq 12$ or $k\in \{1,2,3,n-1,n-2,n-3\}$, where $D_4^H(n,k,1)$ denotes the largest minimum di

Vittorio Basso, Carlo P. Sasso, Martino LoBue, Karl G. Sandeman

We investigate the magnetocaloric effect obtained by the rotation of a magnetic field applied to an exchange-coupled multilayer system composed of two different ferromagnetic (FM) materials. We specifically consider a system in which the two FMs have perpendicular uniaxial anisotropy axes and utilise conditions which yield a reorientation of the total magnet

Shaohua Zhi, Yinghui Wang, Haonan Xiao, Ti Bai

Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for tumor motion management in image-guided radiation therapy (IGRT). However, current 4D-MRI suffers from low spatial resolution and strong motion artifacts owing to the long acquisition time and patients' respiratory variations; these limitations, if not managed properly, can adv

Hsi-Wei Yen, Patrick Koch, Chin-Fei Lee, Naomi Hirano

To study transportation of magnetic flux from large to small scales in protostellar sources, we analyzed the Nobeyama 45-m N2H+ (1-0), JCMT 850 um polarization, and ALMA C18O (2-1) and 1.3 mm and 0.8 mm (polarized) continuum data of the Class 0 protostar HH 211. The magnetic field strength in the dense core on a 0.1 pc scale was estimated with the single-dis

Yanting Zhang, Zhenzhen Lou

The spectral extrema problems on forbidding minors have aroused wide attention. Very recently, Zhai and Lin [J. Combin. Theory Ser. B 157 (2022) 184--215] determined the extremal graph with maximum adjacency spectral radius among all $K_{s,t}$-minor free graphs of sufficiently large order. The matrix $A_{\alpha}(G)$ is a generalization of the adjacency matri

Benjamin A. Miller, Zohair Shafi, Wheeler Ruml, Yevgeniy Vorobeychik

Identifying shortest paths between nodes in a network is a common graph analysis problem that is important for many applications involving routing of resources. An adversary that can manipulate the graph structure could alter traffic patterns to gain some benefit (e.g., make more money by directing traffic to a toll road). This paper presents the Force Path

Rachid Benbrik, Mohammed Boukidi, Stefano Moretti, Souad Semlali

Recently the CMS collaboration reported a $\sim 3 \sigma$ local excess in the di-photon spectrum at 96 GeV. The same mass range concurs with a $\sim2 \sigma$ local excess in the $b\bar{b}$ invariant mass spectrum in four-jet events collected at LEP. In this contribution we show that at 1$\sigma$ level the 2HDM type-III can perfectly fit both excesses simulta

Nasser Heydari, Kazuo Muroi

The bisection of trapezoids by transversal lines has many examples in Babylonian mathematics. In this article, we study a similar problem in Elamite mathematics, inscribed on a clay tablet held in the collection of the Louvre Museum and thought to date from between 1894--1595 BC. We seek to demonstrate that this problem is different from typical Babylonian p

Ling Yang, Zhilin Huang, Yang Song, Shenda Hong

Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and their relations in images. Most existing methods address this challenge by using scene layouts, which are image-like representations of scene graphs designed to capture the coarse str

Liping Yin, Albert Chua

In the past decade, exemplar-based texture synthesis algorithms have seen strong gains in performance by matching statistics of deep convolutional neural networks. However, these algorithms require regularization terms or user-added spatial tags to capture long range constraints in images. Having access to a user-added spatial tag for all situations is not a

Kyohei Shibano, Tohru Nakajima, Gento Mogi

The system proposed in this study uses zero-knowledge proof (ZKP) to verify the traceability of wood recorded in a public blockchain. Wood is a byproduct of several states, ranging from standing trees to logs, lumber, and wood products (hereinafter ``wood objects''). The advantage of using the blockchain for record keeping is that participants can freely rec

Donato Scarcella

In a previous work [Asymptotically quasiperiodic solutions for time-dependent Hamiltonians, arXiv preprint arXiv:2211.06623 (2022)], we consider time-dependent perturbations of a Hamiltonian vector field having an invariant torus supporting quasiperiodic solutions. Assuming the perturbation decays polynomially fast as time tends to infinity, we prove the exi

B. M. Roberts, C. J. Fairhall, J. S. M. Ginges

Motivated by recent measurements for several alkali-metal atoms and alkali-metal-like ions, we perform a detailed study of electric dipole (E1) transition amplitudes in K, Ca+, Rb, Sr+, Cs, Ba+, Fr, and Ra+, which are of interest for studies of atomic parity violation, electric dipole moments, and polarizabilities. Using the all-orders correlation potential

Swetha Nadella, Pramiti Barua, Jeremy C. Hagler, David J. Lamb

In this paper, our focus is on enhancing steering angle prediction for autonomous driving tasks. We initiate our exploration by investigating two veins of widely adopted deep neural architectures, namely ResNets and InceptionNets. Within both families, we systematically evaluate various model sizes to understand their impact on performance. Notably, our key

Yanzhe Ke, Zhigang Song, Qing-Dong Jiang

A material with symmetry breaking inside can transmit the symmetry breaking to its vicinity by vacuum electromagnetic fluctuations. Here, we show that vacuum quantum fluctuations proximate to a parity-symmetry-broken material can induce a chirality-dependent spectral shift of chiral molecules, resulting in a chemical reaction process that favors producing on

Jongoh Jeong, Jong-Hwan Kim

Road scene understanding tasks have recently become crucial for self-driving vehicles. In particular, real-time semantic segmentation is indispensable for intelligent self-driving agents to recognize roadside objects in the driving area. As prior research works have primarily sought to improve the segmentation performance with computationally heavy operation

Zhuo-Rui Pan, Wei Ren, Xi-Ming Sun

This paper addresses the safe stabilization problem of stochastic nonlinear time-delay systems. Based on theKrasovskii approach, we first propose a stochastic control Lyapunov-Krasovskii functional to guarantee the stabilization objective and a stochastic control barrier-Krasovskii functional to ensure the safety objective. Both functionals are developed res

Stefan Osłowski, Mark A. Walker

A pulsar dynamic spectrum is an inline digital hologram of the interstellar medium; it encodes information on the propagation paths by which signals have travelled from source to telescope. To decode the hologram it is necessary to "retrieve" the phases of the wavefield from intensity measurements, which directly gauge only the field modulus, by imposing add

Constantin Kogler

We reduce the local limit theorem for a non-compact semisimple Lie group acting on its symmetric space to establishing that a natural operator associated to the measure is quasicompact. Under strong Diophantine assumptions on the underlying measure, we deduce the necessary spectral results for the operator in question. We thereby give the first examples of f

Jiaxu Tian, Dapeng Zhi, Si Liu, Peixin Wang

The intrinsic complexity of deep neural networks (DNNs) makes it challenging to verify not only the networks themselves but also the hosting DNN-controlled systems. Reachability analysis of these systems faces the same challenge. Existing approaches rely on over-approximating DNNs using simpler polynomial models. However, they suffer from low efficiency and

Kostas I. Psaromiligkos

We prove that character sheaves have nilpotent singular support in any characteristic, partially extending the work of Mirkovic, Vilonen and independently Ginzburg to positive characteristic. We do this by introducing a category of tame perverse sheaves and studying its properties.

Binan Gu, Lou Kondic, Linda J. Cummings

Pore-size gradients are often used in the design of membrane filters to increase filter lifetime and ensure fuller use of the initial membrane pore volume. In this work, we impose pore-size gradients in the setting of a membrane filter with an internal network of interconnected tube-like pores. We model the flow and foulant transport through the filter using

Brian Harvie

We prove that the Inverse Mean Curvature Flow of a non-star-shaped, mean-convex embedded sphere in $\mathbb{R}^{n+1}$ with symmetry about an axis and sufficiently long, thick necks exists for all time and homothetically converges to a round sphere as $t \rightarrow \infty$. Our approach is based on a localized version of the parabolic maximum principle. We a

Ricardo García-Pelayo

We present here two new techniques to solve the one-dimensional random flight. The first one is an expansion in the number of collisions. The second one is the obtention of a Fourier series. This second technique can be applied to an arbitrary number of dimensions. Some mathematical identities are obtained.

Daniel C. Mayer

Cyclic number fields of odd prime degree are constructed as ray class fields over the rational number field. They are collected in multiplets sharing a common conductor and discriminant. The algorithms are implemented in Magma and applied to all cyclic quintic and cyclic cubic fields with conductors below 100000. Our primary attention is devoted to the theor

Lucas C. F. Ferreira, Jhean E. Pérez-López, Julio C. Valencia-Guevara

We are concerned with bilinear estimates and uniqueness of mild solutions for the Navier-Stokes equations in critical spaces. For that, we construct general settings in which estimates for the bilinear term of the mild formulation hold true without using auxiliary norms such as Kato time-weighted ones. We first obtain necessary conditions in abstract critica

Ricardo García-Pelayo

The probability density function of the random flight with isotropic initial conditions is obtained by an expansion in the number of collisions and the in the spatial harmonics of the solution, as in a Fourier series. The method holds for any dimension and is worked out in detail for the three dimensional case. In this case the probability density functions

Juliana J. Park, Yu-Kun Lu, Alan O. Jamison, Wolfgang Ketterle

Trapping ultracold molecules in conservative traps is essential for applications -- such as quantum state-controlled chemistry, quantum simulations, and quantum information processing. These applications require high densities or phase-space densities. We report magnetic trapping of NaLi molecules in the triplet ground state at high density ($\approx 10^{11}

Alberto Caron, Gianluca Baio, Ioanna Manolopoulou

In this paper, we address the challenge of performing counterfactual inference with observational data via Bayesian nonparametric regression adjustment, with a focus on high-dimensional settings featuring multiple actions and multiple correlated outcomes. We present a general class of counterfactual multi-task deep kernels models that estimate causal effects

Masahito Hasegawa

We argue that operads provide a general framework for dealing with polynomials and combinatory completeness of combinatory algebras, including the classical $\mathbf{SK}$-algebras, linear $\mathbf{BCI}$-algebras, planar $\mathbf{BI}(\_)^\bullet$-algebras as well as the braided $\mathbf{BC^\pm I}$-algebras. We show that every extensional combinatory algebra g

Pablo A. Ferrari, Chiara Franceschini, Dante G. E. Grevino, Herbert Spohn

We study the hydrodynamics of the hard rod model proposed by Boldrighini, Dobrushin and Soukhov by describing the displacement of each quasiparticle with respect to the corresponding ideal gas particle as a height difference in a related field. Starting with a family of nonhomogeneous Poisson processes contained in the position-velocity-length space $\mathbb

Chia-Wen Kuo, Chih-Yao Ma, Judy Hoffman, Zsolt Kira

In Vision-and-Language Navigation (VLN), researchers typically take an image encoder pre-trained on ImageNet without fine-tuning on the environments that the agent will be trained or tested on. However, the distribution shift between the training images from ImageNet and the views in the navigation environments may render the ImageNet pre-trained image encod

Somayajulu L. N. Dhulipala, Promit Chakroborty, Michael D. Shields, Wen Jiang

The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel proposed to be used for multiple modern nuclear technologies. Therefore, characterizing its safety is vital for the reliable operation of nuclear technologies. However, the TRISO fuel failure probabilities are small and the computational model is time consuming to evaluate them

Zhaiming Shen, Ming-Jun Lai, Sheng Li

Local clustering aims at extracting a local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be thought as a sparse solution to a linear system. In this

Xinyi Zhou, Reza Zafarani, Emilio Ferrara

The COVID-19 pandemic has gained worldwide attention and allowed fake news, such as ``COVID-19 is the flu,'' to spread quickly and widely on social media. Combating this coronavirus infodemic demands effective methods to detect fake news. To this end, we propose a method to infer news credibility from hashtags involved in news dissemination on social media,

Alexey Bondal, Alexei Rosly

We construct a twist-closed enhancement of the category ${\mathcal D}^b_{\rm coh}(X)$, the bounded derived category of complexes of ${\mathcal O}_X$-modules with coherent cohomology, by means of the DG-category of $\bar\partial$-superconnections. Then we apply the techniques of $\bar\partial$-superconnections to define Chern classes and Bott-Chern classes of

Grigori Rozenblum, Nikolai Vasilevski

We consider commutative C* -algebras of Toeplitz operators in the weighted Bergman space on the unit ball in $\mathbb{C}^{\mathbf{n}}$. For the algebras of elliptic type we find a new representation, namely as the algebra of operators which are functions of certain collections of commuting unbounded self-adjoint operators in the Bergman space.

Noah Riggenbach

We study the algebraic $K$-theory of rings of the form $R[x]/x^e$. We do this via trace methods and filtrations on topological Hochschild homology and related theories by quasisyntomic sheaves. We produce computations for $R$ a perfectoid ring in terms of the big Witt vectors of $R$, for $R$ a smooth curve over a perfectoid ring in terms of the prismatic coh

Abdelrahman Zayed, Prasanna Parthasarathi, Goncalo Mordido, Hamid Palangi

Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity concerns. Prediction models may discover, use, or amplify spurious correlations based on gender or other protected personal characteristics, thus discriminating against marginalized groups. Mitigating gender bias has become

Yutaka Nagashima, Zijin Xu, Ningli Wang, Daniel Sebastian Goc

Proof by induction plays a central role in formal verification. However, its automation remains as a formidable challenge in Computer Science. To solve inductive problems, human engineers often have to provide auxiliary lemmas manually. We automate this laborious process with template-based conjecturing, a novel approach to generate auxiliary lemmas and use

Barak Sabbagh, Sinwook Park, Gilad Yossifon

Microfluidic channels with embedded ion permselective medium under the application of electric current are commonly used for electrokinetic processes as on-chip ion concentration polarization (ICP) and bioparticle preconcentration to enhance biosensing. Herein, we demonstrate the ability to dynamically control the electrically driven ion transport by integra

Konrad Aguilar, Jiahui Yu

Given a unital AF-algebra $A$ equipped with a faithful tracial state, we equip each (norm-closed two-sided) ideal of $A$ with a metrized quantum vector bundle structure, when canonically viewed as a module over $A$, in the sense of Latr\'emoli\`ere using previous work of the first author and Latr\'emoli\`ere. Moreover, we show that convergence of ideals in t

Peter G. Casazza, Laura De Carli, Tin T. Tran

This paper investigates scalable frame in ${\mathbb R}^n$. We define the reduced diagram matrix of a frame and use it to classify scalability of the frame under some conditions. We give a new approach to the scaling problem by breaking the problem into two smaller ones, each of which is easily solved, giving a simple way to check scaling. Finally, we study t

Peng Shi

The study rederives the fundamental equations of fluid flow and examines the inherent relationship between momentum conservation and mechanical energy conservation. It is shown that the material derivative of velocity is to depict the acceleration of fluid particles in Eulerian perspective, and momentum conservation and mechanical energy conservation are the

Steffen Ridderbusch, Sina Ober-Blöbaum, Paul Goulart

Computing the distribution of trajectories from a Gaussian Process model of a dynamical system is an important challenge in utilizing such models. Motivated by the computational cost of sampling-based approaches, we consider approximations of the model's output and trajectory distribution. We show that previous work on uncertainty propagation, focussed on di

Sergey A. Melikhov

We prove an "abelian, locally compact" Whitehead theorem in fine shape: A fine shape morphism between locally connected finite-dimensional locally compact separable metrizable spaces with trivial $\pi_0$ and $\pi_1$ is a fine shape equivalence if and only if it induces isomorphisms on the $\pi_i$ (=the Steenrod-Sitnikov homotopy groups). We show by an exampl

Russell B. Thompson

Polymer self-consistent field theory techniques are used to derive quantum density functional theory without the use of the theorems of density functional theory. Instead, a free energy is obtained from a partition function that is constructed directly from a Hamiltonian, so that the results are, in principle, valid at finite temperatures. The main governing

Sergey A. Melikhov

In this paper we obtain results indicating that fine shape is tractable and "not too strong" even in the non-locally compact case, and can be used to better understand infinite-dimensional metrizable spaces and their homology theories. We show that every Polish space $X$ is fine shape equivalent to the limit of an inverse sequence of simplicial maps between

Yuqin Jiang, Faxi Yuan, Hamed Farahmand, Kushal Acharya

The ability to measure and track the speed and trajectory of a community's post-disaster recovery is essential to inform resource allocation and prioritization. The current survey-based approaches to examining community recovery, however, have significant lags and put the burden of data collection on affected people. Also, the existing literature lacks quant

Gilles Mordant, Stephen Zhang

We consider the conjecture proposed in Matsumoto, Zhang and Schiebinger (2022) suggesting that optimal transport with quadratic regularisation can be used to construct a graph whose discrete Laplace operator converges to the Laplace--Beltrami operator. We derive first order optimal potentials for the problem under consideration and find that the resulting so

Elon Lindenstrauss, Amir Mohammadi, Zhiren Wang

We prove effective equidistribution theorems, with polynomial error rate, for orbits of the unipotent subgroups of $\operatorname{SL}_2(\mathbb R)$ in arithmetic quotients of $\operatorname{SL}_2(\mathbb C)$ and $\operatorname{SL}_2(\mathbb R)\times\operatorname{SL}_2(\mathbb R)$. The proof is based on the use of a Margulis function, tools from incidence geo

Sayandip Ghosh, Guido Menichetti, Mikhail I. Katsnelson, Marco Polini

Two-dimensional honeycomb ferromagnets offer the unprecedented opportunity to study interactions between collective modes that in standard bulk ferromagnets do not cross paths. Indeed, they harbor an optical spin-wave branch, i.e. a spin wave which disperses weakly near the Brillouin zone center. When doped with free carriers, they also host the typical gapl

Jin Lu, Xingpeng Li

There has been growing interest in integrating hydrogen storage into power grids with high renewable penetration levels. The economic benefits and power grid reliability are both essential for hydrogen storage integration. In this paper, an annual scheduling model (ASM) for energy hubs (EH) coupled power grids is proposed to investigate the annual benefits o

Dmitriy Akimov, Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov

Offline reinforcement learning aims to train a policy on a pre-recorded and fixed dataset without any additional environment interactions. There are two major challenges in this setting: (1) extrapolation error caused by approximating the value of state-action pairs not well-covered by the training data and (2) distributional shift between behavior and infer

Mohamed O Darwish, Mohammad Sadek

A polynomial with rational coefficients is said to be pure with respect to a rational prime $p$ if its Newton polygon has one slope. In this article, we prove that the number of irreducible factors of the $n$-th iterate of a pure polynomial over the rational field $\mathbb Q$ is bounded independent of $n$. In other words, we show that pure polynomials are {\

Leyun Feng, Youhua Jiang, Christian Machado, Wonjae Choi

Droplets coalescing on flat surfaces tend to end up with the smaller droplet migrating into the larger one. We report a counter-intuitive droplet coalescence pattern on a superhydrophilic cylindrical wire, where the larger droplet is pulled toward the smaller one. Consequently, the center of the combined mass significantly moves toward, often beyond, the ori

Jie Huang, Kevin Chen-Chuan Chang

Entities and relationships between entities are vital in the real world. Essentially, we understand the world by understanding entities and relations. For instance, to understand a field, e.g., computer science, we need to understand the relevant concepts, e.g., machine learning, and the relationships between concepts, e.g., machine learning and artificial i

Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Dmitry Akimov

Training large neural networks is known to be time-consuming, with the learning duration taking days or even weeks. To address this problem, large-batch optimization was introduced. This approach demonstrated that scaling mini-batch sizes with appropriate learning rate adjustments can speed up the training process by orders of magnitude. While long training

Fabian Reimers, Müfit Sezer

We consider a finite permutation group acting naturally on a vector space $V$ over a field $\Bbbk$. A well known theorem of G\"obel asserts that the corresponding ring of invariants $\Bbbk[V]^G$ is generated by invariants of degree at most $\binom{\dim V}{2}$. In this note we show that if the characteristic of $\Bbbk$ is zero then the top degree of vector co

Fernando Albiac, José L. Ansorena

Tsirelson's space $\mathcal{T}$ made its appearance in Banach space theory in 1974 soon to become one of the most significant counterexamples in the theory. Its structure broke the ideal pattern that analysts had conceived for a generic Banach space, thus giving rise to the era of pathological examples. Since then, many authors have contributed to the study

Ilyes Toumi, Andreas Orthey, Alexander von Rohr, Ngo Anh Vien

Automated bin-picking is a prerequisite for fully automated manufacturing and warehouses. To successfully pick an item from an unstructured bin the robot needs to first detect possible grasps for the objects, decide on the object to remove and consequently plan and execute a feasible trajectory to retrieve the chosen object. Over the last years significant p

Minjae Jeon, Lang Tong, Qing Zhao

We consider the co-optimization of flexible household consumption, electric vehicle charging, and behind-the-meter distributed energy resources under the net energy metering tariff. Using a stochastic dynamic programming formulation, we show that the solution to the dynamic programming co-optimization is a procrastination threshold policy that delays and min

Li S. Yifei, Lyle Ungar, João Sedoc

Pre-trained large language models (LLMs) reflect the inherent social biases of their training corpus. Many methods have been proposed to mitigate this issue, but they often fail to debias or they sacrifice model accuracy. We use conceptors--a soft projection method--to identify and remove the bias subspace in LLMs such as BERT and GPT. We propose two methods

Hao Chen, Yue Fan, Yidong Wang, Jindong Wang

Semi-supervised learning (SSL) has shown great promise in leveraging unlabeled data to improve model performance. While standard SSL assumes uniform data distribution, we consider a more realistic and challenging setting called imbalanced SSL, where imbalanced class distributions occur in both labeled and unlabeled data. Although there are existing endeavors

Shuo Chen, Tan Yu, Ping Li

Recently, vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community. MLP-like models achieve competitive performance on a single 2D image classification with less inductive bias without hand-crafted convolution layers. In this work, we explore the effectiveness of MLP-based architectu

Alan Wang, Feng Yi Chang, Siavash Yousefi, Beatrice Li

Living labs have been established across different countries to evaluate how the interaction between humans and buildings can be optimized to improve comfort, health, and energy savings. However, existing living labs can be too project-specific, not scalable, and inflexible for comparison against other labs. Furthermore, the lack of transparency in its softw

Meenakshi Narain, Laura Reina, Alessandro Tricoli, Michael Begel

This report, as part of the 2021 Snowmass Process, summarizes the current status of collider physics at the Energy Frontier, the broad and exciting future prospects identified for the Energy Frontier, the challenges and needs of future experiments, and indicates high priority research areas.

Luciano M. Abreu, Emerson B. S. Corrêa, Elenilson S. Nery

We investigate the combined finite-size and thermo-magnetic effects on the properties of the quark matter, in the context of the two-flavored Nambu--Jona-Lasinio model. In particular, by using the mean-field approximation and the Schwinger proper time method in a toroidal topology with periodic or antiperiodic conditions, we evaluate the chiral phase transit

Zhengqi Li, Qianqian Wang, Forrester Cole, Richard Tucker

We address the problem of synthesizing novel views from a monocular video depicting a complex dynamic scene. State-of-the-art methods based on temporally varying Neural Radiance Fields (aka dynamic NeRFs) have shown impressive results on this task. However, for long videos with complex object motions and uncontrolled camera trajectories, these methods can pr

Shafi Goldwasser, David F. Gruber, Adam Tauman Kalai, Orr Paradise

Neural networks are capable of translating between languages -- in some cases even between two languages where there is little or no access to parallel translations, in what is known as Unsupervised Machine Translation (UMT). Given this progress, it is intriguing to ask whether machine learning tools can ultimately enable understanding animal communication,

E. van der Wateren, C. G. Bassa, S. Cooper, J. -M. Grießmeier

The LOFAR Tied-Array All-Sky Survey (LOTAAS) is the most sensitive untargeted radio pulsar survey performed at low radio frequencies (119--151\,MHz) to date and has discovered 76 new radio pulsars, among which the 23.5-s pulsar J0250+5854, up until recently the slowest-spinning radio pulsar known. Here, we report on the timing solutions of 35 pulsars discove

Arsalan Adil, Andreas Albrecht, Rose Baunach, R. Holman

The simplest single-field inflation models capture all the relevant contributions to the patterns in the Cosmic Microwave Background (CMB) observed today. A key assumption in these models is that the quantum inflationary fluctuations that source such patterns are generated by a particular quantum state -- the Bunch-Davies (BD) state. While this is a well-mot

Bastien Fernandez, Eric Vernier

Motivated by proving the loss of ergodicity in expanding systems of piecewise affine coupled maps with arbitrary number of units, all-to-all coupling and inversion symmetry, we provide ad-hoc substitutes - namely inversion-symmetric maps of the simplex with arbitrary number of vertices - that exhibit several asymmetric absolutely continuous invariant measure

Peirong Liu, Rui Wang, Pengchuan Zhang, Omid Poursaeed

Objection detection (OD) has been one of the most fundamental tasks in computer vision. Recent developments in deep learning have pushed the performance of image OD to new heights by learning-based, data-driven approaches. On the other hand, video OD remains less explored, mostly due to much more expensive data annotation needs. At the same time, multi-objec

Daniele Ronzani, Shamil Mamedov, Jan Swevers

Many industries extensively use flexible materials. Effective approaches for handling flexible objects with a robot manipulator must address residual vibrations. Existing solutions rely on complex models, use additional instrumentation for sensing the vibrations, or do not exploit the repetitive nature of most industrial tasks. This paper develops an iterati

Kathinka Frieswijk, Lorenzo Zino, A. Stephen Morse, Ming Cao

Motivated by the climate crisis that is currently ravaging the planet, we propose and analyze a novel framework for the evolution of anthropogenic climate impact in which the evolution of human environmental behavior and environmental impact is coupled. Our framework includes a human decision-making process that captures social influence, government policy i

Yehuda Dar, Lorenzo Luzi, Richard G. Baraniuk

We study the generalization behavior of transfer learning of deep neural networks (DNNs). We adopt the overparameterization perspective -- featuring interpolation of the training data (i.e., approximately zero train error) and the double descent phenomenon -- to explain the delicate effect of the transfer learning setting on generalization performance. We st

Simone Giombi, Elizabeth Helfenberger, Himanshu Khanchandani

We study line defects in the fermionic CFTs in the Gross-Neveu-Yukawa universality class in dimensions $2<d<4$. These CFTs may be described as the IR fixed points of the Gross-Neveu-Yukawa (GNY) model in $d=4-\epsilon$, or as the UV fixed points of the Gross-Neveu (GN) model, which can be studied using the large $N$ expansion in $2<d<4$. These models admit n

Zu-Jian Ying

By extracting different levels of topological information a new light is shed on the energy spectrum of the anisotropic quantum Rabi model (QRM) which is the fundamental model of light-matter interactions with indispensable counter-rotating terms in ultra-strong couplings. Besides conventional topological transitions (TTs) at gap closing, abundant unconventi

Marcus Benghi Pinto

Neutron star constraints and {\it ab initio} pQCD evaluations require the EoS representing cold quark matter to be stiff at intermediate baryonic densities and soft at high-$n_B$. Here, I suggest that the three flavor NJL model with a density dependent repulsive coupling, $G_V(\mu)$, can generate an EoS which interpolates between these two regimes. Such an i

Hongrui Jin

Object and person tracking networks powered by Bluetooth and mobile devices have become increasingly popular for purposes of public safety and individual concerns. This essay examines popular commercial tracking networks and their campaigns from Apple, Samsung and Tile with reference to surveillance capitalism and digital privacy, discovering the hidden asse

Arash Amini, Qiyu Sun, Nader Motee

We consider a class of stochastic dynamical networks whose governing dynamics can be modeled using a coupling function. It is shown that the dynamics of such networks can generate geometrically ergodic trajectories under some reasonable assumptions. We show that a general class of coupling functions can be learned using only one sample trajectory from the ne

Subash Sachidananda, Prithvi Gundlapalli, Victor Leong, Soe Moe Thar

Most active quench circuits used for single-photon avalanche photodetectors (APDs) are designed either with discrete components which lack the flexibility of dynamically changing the control parameters, or with custom ASICs which require a long development time and high cost. As an alternative, we present a reconfigurable and robust hybrid design implemented

Wojciech Broniowski, Vanamali Shastry, Enrique Ruiz Arriola

Off-shell effects in generalized parton distributions (GPDs) of the pion, appearing, e.g., in the Sullivan process, are considered. Due to the lack of crossing symmetry, the moments of GPDs involve also odd powers of the skewness (longitudinal momentum transfer) parameter, which results in emergence of new off-shell form factors. With current-algebra techniq

Snehal Singh Tomar, Maitreya Suin, A. N. Rajagopalan

With an unprecedented increase in the number of agents and systems that aim to navigate the real world using visual cues and the rising impetus for 3D Vision Models, the importance of depth estimation is hard to understate. While supervised methods remain the gold standard in the domain, the copious amount of paired stereo data required to train such models

Moïse Blanchard, Alexandre Jacquillat, Patrick Jaillet

This technical report provides additional results for the main paper ``Probabilistic bounds on the $k-$Traveling Salesman Problem ($k-$TSP) and the Traveling Repairman Problem (TRP)''. For the $k-$TSP, we extend the probabilistic bounds derived in the main paper to the case of distributions with general densities. For the TRP, we propose a utility-based noti

Tie-Jiun Hou, Huey-Wen Lin, Mengshi Yan, C. -P. Yuan

We study the impact of lattice data on the determination of the strangeness asymmetry distribution $s_-(x) \equiv s(x) - {\bar s}(x)$ in the general CTEQ-TEA global analysis of parton distribution functions (PDFs) of the proton. Firstly, we find that allowing a nonvanishing $s_-(x)$, at the initial $Q_0=1.3$~GeV scale, in a global PDF analysis leads to a CT1

Kiplang'at Weldon, John Ngechu, Ngatho Everlyne, Nancy Njambi

Over the past 20 years, Kenya's demand for petroleum products has proliferated. This is mainly because this particular commodity is used in many sectors of the country's economy. Exchange rates are impacted by constantly shifting prices, which also impact Kenya's industrial output of commodities. The cost of other items produced and even the expansion of the

Moïse Blanchard, Alexandre Jacquillat, Patrick Jaillet

The $k-$traveling salesman problem ($k$-TSP) seeks a tour of minimal length that visits a subset of $k\leq n$ points. The traveling repairman problem (TRP) seeks a complete tour with minimal latency. This paper provides constant-factor probabilistic approximations of both problems. We first show that the optimal length of the $k$-TSP path grows at a rate of

Qiaomu Miao, Minh Hoai, Dimitris Samaras

Gaze following aims to predict where a person is looking in a scene, by predicting the target location, or indicating that the target is located outside the image. Recent works detect the gaze target by training a heatmap regression task with a pixel-wise mean-square error (MSE) loss, while formulating the in/out prediction task as a binary classification ta

Charles D. Young, Michael D. Graham

A common problem in time series analysis is to predict dynamics with only scalar or partial observations of the underlying dynamical system. For data on a smooth compact manifold, Takens theorem proves a time delayed embedding of the partial state is diffeomorphic to the attractor, although for chaotic and highly nonlinear systems learning these delay coordi

Anup Singh, Kris Demuynck, Vipul Arora

Audio fingerprinting systems must efficiently and robustly identify query snippets in an extensive database. To this end, state-of-the-art systems use deep learning to generate compact audio fingerprints. These systems deploy indexing methods, which quantize fingerprints to hash codes in an unsupervised manner to expedite the search. However, these methods g

Huiming Sun, Jin Ma, Qing Guo, Qin Zou

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inp

Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro

The increasing availability of geometric data has motivated the need for information processing over non-Euclidean domains modeled as manifolds. The building block for information processing architectures with desirable theoretical properties such as invariance and stability is convolutional filtering. Manifold convolutional filters are defined from the mani

Phillip Schneider, Markus Voggenreiter, Abdullah Gulraiz, Florian Matthes

Over the last years, software development in domains with high security demands transitioned from traditional methodologies to uniting modern approaches from software development and operations (DevOps). Key principles of DevOps gained more importance and are now applied to security aspects of software development, resulting in the automation of security-enh

Simin Liu, Changliu Liu, John Dolan

We propose new methods to synthesize control barrier function (CBF)-based safe controllers that avoid input saturation, which can cause safety violations. In particular, our method is created for high-dimensional, general nonlinear systems, for which such tools are scarce. We leverage techniques from machine learning, like neural networks and deep learning,

Diogo Boito, Maarten Golterman, Kim Maltman, Santiago Peris

We describe how recent determinations of exclusive-mode contributions to $a_\mu^{\rm LO,HVP}$, the leading-order hadronic vacuum polarization contribution to the anomalous magnetic moment of the muon, can be used to provide, up to small electromagnetic (EM) corrections accessible from the lattice, a data-based dispersive determination of $a_\mu^{\rm lqc;\, I