March 2023 arXiv papers — page 64

Cesare Alippi, Daniele Zambon

The well-known Kalman filters model dynamical systems by relying on state-space representations with the next state updated, and its uncertainty controlled, by fresh information associated with newly observed system outputs. This paper generalizes, for the first time in the literature, Kalman and extended Kalman filters to discrete-time settings where inputs

Guy Nir, Eran O. Ofek, David Polishook, Barak Zackay

Measuring the size distribution of small (km-scale) KBOs can help constrain models of Solar System formation and planetary migration. Such small, distant bodies are hard to detect with current or planned telescopes, but can be identified as sub-second occultations of background stars. We present the analysis of data from the Weizmann Fast Astronomical Survey

Barbara Olmi

Pulsar Wind Nebulae (PWNe) shine at multi-wavelengths and are expected to constitute the largest class of gamma-ray sources in our Galaxy. They are known to be very efficient particle accelerators: the Crab nebula, the PWNe class prototype, is the unique firmly identified leptonic PeVatron of the Galaxy to date, and most of the PeVatrons recently detected by

T. Biekötter, S. Heinemeyer, G. Weiglein

The CMS collaboration has recently reported the results of a low-mass Higgs-boson search in the di-photon final state based on the full Run 2 data set with refined analysis techniques. The new results show an excess of events at a mass of about 95 GeV with a local significance of $2.9\,\sigma$, confirming a previously reported excess at about the same mass a

Haisong Liu, Tao Lu, Yihui Xu, Jia Liu

In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data. Previous methods either employ a complex pipeline that splits the joint task into independent stages, or fuse 2D and 3D information in an ``early-fusion'' or ``late-fusion'' manner. Such one-size-fits-all approaches suffer from a dilemm

Orri Steinn Guðfinnsson, Týr Vilhjálmsson, Martin Eineborg, Torfi Thorhallsson

This paper considers the automatic classification of herding behavior in the cluttered low-visibility environment that typically surrounds towed fishing gear. The paper compares three convolutional and attention-based deep action recognition network architectures trained end-to-end on a small set of video sequences captured by a remotely controlled camera an

Emi Kou, Tetiana Moskalets, Bachir Moussallam

We perform a detailed study of the consequences of isospin symmetry in the Cabibbo favoured $D\to\Kbar\pi\pi$ decays. These processes are important for precision testing of the Standard Model and for hadronic physics. Combining isospin symmetry with a dispersive reconstruction theorem we derive a representation in terms of one-variable functions which allows

Michel Duprez, Vanessa Lleras, Alexei Lozinski, Killian Vuillemot

Thanks to a finite element method, we solve numerically parabolic partial differential equations on complex domains by avoiding the mesh generation, using a regular background mesh, not fitting the domain and its real boundary exactly. Our technique follows the phi-FEM paradigm, which supposes that the domain is given by a level-set function. In this paper,

Xiaoxu Meng, Weikai Chen, Bo Yang

Recent progress in neural implicit functions has set new state-of-the-art in reconstructing high-fidelity 3D shapes from a collection of images. However, these approaches are limited to closed surfaces as they require the surface to be represented by a signed distance field. In this paper, we propose NeAT, a new neural rendering framework that can learn impl

M. Agundez, O. Roncero, N. Marcelino, C. Cabezas

We carried out an observational search for the recently discovered molecule H2NC, and its more stable isomer H2CN, toward eight cold dense clouds (L1544, L134N, TMC-2, Lupus-1A, L1489, TMC-1 NH3, L1498, and L1641N) and two diffuse clouds (B0415+379 and B0355+508) in an attempt to constrain its abundance in different types of interstellar regions and shed lig

Scott Baldridge, Ben McCarty

In this paper, we use a topological quantum field theory (TQFT) to define families of new homology theories of a $2$-dimensional CW complex of a smooth closed surface. The dimensions of these homology groups can be used to count the number of ways that each face of the CW complex can be colored with one of $n$ colors so that no two adjacent faces have the sa

Valerio Lucarini, Mickaël Chekroun

Klaus Hasselmann's revolutionary intuition in climate science was to take advantage of the stochasticity associated with fast weather processes to probe the slow dynamics of the climate system. This has led to fundamentally new ways to study the response of climate models to perturbations, and to perform detection and attribution for climate change signals.

V. I. Korzyuk, J. V. Rudzko

For the telegraph equation with a nonlinear potential given in the first quadrant, we consider the first and the second mixed problem, for which we study issues related to the absence and non-uniqueness of classical solutions.

Takemi Nakamura

The monotonicity of the scalar curvature of the state space equipped with the Bogoliubov-Kubo-Mori metric under more mixing a state is an important conjecture called the Petz conjecture. From the standpoint of quantum statistical mechanics, the quantum exponential family, a special submanifold of the state space, is central rather than the full state space.

Colin Guillarmou, Thibault Lefeuvre, Gabriel P. Paternain

Let $\Sigma$ be a smooth closed oriented surface of genus $\geq 2$. We prove that two metrics on $\Sigma$ with the same marked length spectrum and Anosov geodesic flow are isometric via an isometry isotopic to the identity. The proof combines microlocal tools with the geometry of complex curves.

Jiahang Li, Steffen Waldherr, Wolfram Weckwerth

One central goal of systems biology is to infer biochemical regulations from large-scale OMICS data. Many aspects of cellular physiology and organism phenotypes could be understood as a result of the metabolic interaction network dynamics. Previously, we have derived a mathematical method addressing this problem using metabolomics data for the inverse calcul

Eric Moffat, Adam Freese, Ian Cloët, Thomas Donohoe

The feasibility of extracting generalized parton distributions (GPDs) from deeply-virtual Compton scattering (DVCS) data has recently been questioned because of the existence of an infinite set of so-called ''shadow GPDs'' (SGPDs). These SGPDs depend on the process and manifest as multiple solutions (at a fixed scale $Q^2$) to the inverse problem that needs

Jennifer Haase, Paul H. P. Hanel

A widespread view is that Artificial Intelligence cannot be creative. We tested this assumption by comparing human-generated ideas with those generated by six Generative Artificial Intelligence (GAI) chatbots: $alpa.\!ai$, $Copy.\!ai$, ChatGPT (versions 3 and 4), $Studio.\!ai$, and YouChat. Humans and a specifically trained AI independently assessed the qual

Giovanni Morrone, Samuele Cornell, Luca Serafini, Enrico Zovato

Recent works show that speech separation guided diarization (SSGD) is an increasingly promising direction, mainly thanks to the recent progress in speech separation. It performs diarization by first separating the speakers and then applying voice activity detection (VAD) on each separated stream. In this work we conduct an in-depth study of SSGD in the conve

Jefferson Hernandez, Ruben Villegas, Vicente Ordonez

We propose ViC-MAE, a model that combines both Masked AutoEncoders (MAE) and contrastive learning. ViC-MAE is trained using a global featured obtained by pooling the local representations learned under an MAE reconstruction loss and leveraging this representation under a contrastive objective across images and video frames. We show that visual representation

Aleksandr Beilinson

The article shows that the photons and electrons in the states with "deinterlaced" spins, which are isomorphic to their usual wave state "Feynman paths", but also having a power to penetrate infinitely distant in arbitrarily small time.

Luciana L. Couso, Alfonso Soler-Bistue, Ariel A. Aptekmann, Ignacio E. Sanchez

Microbes are often discussed in terms of dichotomies such as copiotrophic/oligotrophic and fast/slow-growing microbes, defined using the characterisation of microbial growth in isolated cultures. The dichotomies are usually qualitative and/or study-specific, sometimes precluding clear-cut results interpretation. We are able to interpret microbial dichotomies

Deniz Kus, Valentin Rappel

The classical Pieri formula gives a multiplicity free expansion of an irreducible module with a fundamental one for the complex general linear group. In this article we replace the tensor product by the fusion product and prove an analogue Pieri formula for higher level Demazure modules for the affine Lie algebra $\widehat{\mathfrak{sl}}_{n+1}$. To be more p

Mihajlo Cekić, Thibault Lefeuvre

On a Riemannian manifold $(M, g)$ with Anosov geodesic flow, the problem of recovering a connection from the knowledge of traces of its holonomies along primitive closed geodesics is known as the holonomy inverse problem. In this paper, we prove H\"older type stability estimates for this inverse problem: 1) locally, near generic connections; 2) globally, for

Saizhe Ding, Jinze Chen, Yang Wang, Yu Kang

Event cameras, such as dynamic vision sensors (DVS), are biologically inspired vision sensors that have advanced over conventional cameras in high dynamic range, low latency and low power consumption, showing great application potential in many fields. Event cameras are more sensitive to junction leakage current and photocurrent as they output differential s

Nino Bašić, Martin Knor, Riste Škrekovski

Let $W(G)$ be the Wiener index of a graph $G$. We say that a vertex $v \in V(G)$ is a \v{S}olt\'es vertex in $G$ if $W(G - v) = W(G)$, i.e. the Wiener index does not change if the vertex $v$ is removed. In 1991, \v{S}olt\'es posed the problem of identifying all connected graphs $G$ with the property that all vertices of $G$ are \v{S}olt\'es vertices. The onl

Yu Ge, Hedieh Khosravi, Fan Jiang, Hui Chen

Positioning with 5G signals generally requires connection to several base stations (BSs), which makes positioning more demanding in terms of infrastructure than communications. To address this issue, there have been several theoretical studies on single BS positioning, leveraging high-resolution angle and delay estimation and multipath exploitation possibili

Qiang Li, Shujian Yu, Kristoffer H Madsen, Vince D Calhoun

Pairwise metrics are often employed to estimate statistical dependencies between brain regions, however they do not capture higher-order information interactions. It is critical to explore higher-order interactions that go beyond paired brain areas in order to better understand information processing in the human brain. To address this problem, we applied mu

R. C. L. Bruni, Luiz F. Ferreira, Diego M. Rodrigues

We study the magnetic field effects on the quantum critical point (QCP) in the holographic Weyl semimetal model. We show that it increases quadratically with the magnetic field for weak field and linear with the magnetic field for strong field. Our findings are compatible with previous results in the literature from other approaches.

Muhammad Amith, Licong Cui, Kirk Roberts, Cui Tao

Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health agencies have expressed an interest in model cards report for research studies using machine-learning based AI. Previously, we have developed an ontology model for model card reports

Oren Ben-Bassat, Jeroen Hekking

This work presents a generalization of derived blow-ups and of the derived deformation to the normal bundle from derived algebraic geometry to any geometric context. The latter is our proposed globalization of a derived algebraic context, itself a generalization of the theory of simplicial commutative rings. One key difference between a geometric context and

Lukas Höllein, Ang Cao, Andrew Owens, Justin Johnson

We present Text2Room, a method for generating room-scale textured 3D meshes from a given text prompt as input. To this end, we leverage pre-trained 2D text-to-image models to synthesize a sequence of images from different poses. In order to lift these outputs into a consistent 3D scene representation, we combine monocular depth estimation with a text-conditi

Rupert Hölzl, Philip Janicki

A left-computable number $x$ is called regainingly approximable if there is a computable increasing sequence $(x_n)_n$ of rational numbers converging to $x$ such that $x - x_n < 2^{-n}$ for infinitely many $n \in \mathbb{N}$; and it is called nearly computable if there is such an $(x_n)_n$ such that for every computable increasing function $s \colon \mathbb{

Kengo Okura

We consider a certain class of simplicial complexes which includes the independence complexes of forests. We show that if a simplicial complex $K$ belongs to this class, then the polyhedral join $\mathcal{Z}^*_{K}(\underline{X}, \emptyset)$ is homotopy equivalent to a wedge sum of CW complexes of the form $\Sigma^r X_{i_1} * X_{i_2} * \cdots * X_{i_k}$, wher

Abhinav Singh, Ivo F. Sbalzarini, Anas Obeidat

We present a consistent mesh-free numerical scheme for solving the incompressible Navier-Stokes equations. Our method is based on entropically damped artificial compressibility for imposing the incompressibility constraint explicitly, and the Discretization-Corrected Particle Strength Exchange (DC-PSE) method to consistently discretize the differential opera

Kosuke Kimura, Hideaki Ishii

For networked control systems, cyber-security issues have gained much attention in recent years. In this paper, we consider the so-called zero dynamics attacks, which form an important class of false data injection attacks, with a special focus on the effects of quantization in a sampled-data control setting. When the attack signals must be quantized, some e

Simon Brandhorst, Davide Cesare Veniani

We provide an algorithm to compute generators of the orthogonal group of the discriminant group associated to an integral quadratic lattice over the integers. We give a closed formula for its order.

Guillermo Aparicio-Estrems, Abel Gargallo-Peiró, Xevi Roca

We detail how to use Newton's method for distortion-based curved $r$-adaption to a discrete high-order metric field while matching a target geometry. Specifically, we combine two terms: a distortion measuring the deviation from the target metric; and a penalty term measuring the deviation from the target boundary. For this combination, we consider four ingre

Ioannis Markakis

We introduce a notion of signature whose sorts form a direct category, and study computads for such signatures. Algebras for such a signature are presheaves with an interpretation of every function symbol of the signature, and we describe how computads give rise to signatures. Generalising work of Batanin, we show that computads with certain generator-preser

Felix Brandt, Matthias Greger, René Romen

Random serial dictatorship (RSD) is a randomized assignment rule that - given a set of $n$ agents with strict preferences over $n$ houses - satisfies equal treatment of equals, ex post efficiency, and strategyproofness. For $n \le 3$, Bogomolnaia and Moulin (2001) have shown that RSD is characterized by these axioms. Extending this characterization to arbitr

Denis Kochan, Andreas Costa, Iaroslav Zhumagulov, Igor Žutić

Nonreciprocal phenomena in the normal state are well established and key to many commercial applications. In contrast, superconducting analogs, such as the superconducting diode effect (SDE), are only starting to be experimentally explored and pose significant challenges to their theoretical understanding. In this work we put forth a phenomenological picture

Graeme Clayton, Cody S. Hansen

Let $N$ be an odd perfect number. Let $\omega(N)$ be the number of distinct prime factors of $N$ and let $\Omega(N)$ be the total number (counting multiplicity) of prime factors of $N$. We prove that $\frac{99}{37}\omega(N) - \frac{187}{37} \leq \Omega(N)$ and that if $3\nmid N$, then $\frac{51}{19}\omega(N)-\frac{46}{19} \leq \Omega(N)$.

Qiang Li, Mingjuan Yang, Hongwei Song, Yongle Li

Globally accurate full-dimensional ground state potential energy surface (PES) for the Cl($^2$P) + XCl $\to$ HCl + Cl($^2$P) reaction, a prototypical heavy-light-heavy abstract reaction, is developed using permutation invariant polynomial neural network (PIP-NN) method and embedded atom neural network (EANN) method, with the corresponding total root mean squ

Juan Pedro Hecht, Hugo Daniel Scolnik

We present an improved post-quantum version of Sakalauskas matrix power function key agreement protocol, using rectangular matrices instead of the original square ones. Sakalauskas matrix power function is an efficient and secure way to generate a shared secret key, and using rectangular matrices provides additional flexibility and security. This method redu

Nati Ofir, Yotam Ben Shoshan, Ran Badanes, Boris Sherman

This work is addressing the problem of defect anomaly detection based on a clean reference image. Specifically, we focus on SEM semiconductor defects in addition to several natural image anomalies. There are well-known methods to create a simulation of an artificial reference image by its defect specimen. In this work, we introduce several applications for t

Ron Ofir, Pietro Lorenzetti, Michael Margaliot

We derive a sufficient condition guaranteeing that a singularly perturbed linear time-varying system is strongly monotone with respect to a matrix cone $C$ of rank $k$. This implies that the singularly perturbed system inherits the asymptotic properties of systems that are strongly monotone with respect to $C$, which include convergence to the set of equilib

Colton Crum, Patrick Tinsley, Aidan Boyd, Jacob Piland

The performance of convolutional neural networks has continued to improve over the last decade. At the same time, as model complexity grows, it becomes increasingly more difficult to explain model decisions. Such explanations may be of critical importance for reliable operation of human-machine pairing setups, or for model selection when the "best" model amo

Jorge F. Urbán, Petros Stefanou, Clara Dehman, José A. Pons

Using Physics-Informed Neural Networks (PINNs) to solve a specific boundary value problem is becoming more popular as an alternative to traditional methods. However, depending on the specific problem, they could be computationally expensive and potentially less accurate. The functionality of PINNs for real-world physical problems can significantly improve if

Zohreh Hajiakhondi-Meybodi, Arash Mohammadi, Jamshid Abouei, Konstantinos N. Plataniotis

Mobile Edge Caching (MEC) integrated with Deep Neural Networks (DNNs) is an innovative technology with significant potential for the future generation of wireless networks, resulting in a considerable reduction in users' latency. The MEC network's effectiveness, however, heavily relies on its capacity to predict and dynamically update the storage of caching

D. Vohl, H . K. Vedantham, J. W. T. Hessels, C. G. Bassa

The repeating FRB 20121102A was localized to a star-forming region in a dwarf galaxy and found to be co-located with a persistent radio source (PRS). FRB 20190520B is only the second known source sharing phenomenology akin to FRB 20121102A's, with similar burst activity, host galaxy properties, as well as being associated with a PRS. PRS emission is potentia

Cora A. Dimmig, Kevin C. Wolfe, Joseph Moore

Planning for multi-robot teams in complex environments is a challenging problem, especially when these teams must coordinate to accomplish a common objective. In general, optimal solutions to these planning problems are computationally intractable, since the decision space grows exponentially with the number of robots. In this paper, we present a novel appro

Jorge Ignacio González Cázares, Feng Lin, Aleksandar Mijatović

We construct a fast exact algorithm for the simulation of the first-passage time, jointly with the undershoot and overshoot, of a tempered stable subordinator over an arbitrary non-increasing absolutely continuous function. We prove that the running time of our algorithm has finite exponential moments and provide bounds on its expected running time with expl

Dongqing Wang, Tong Zhang, Sabine Süsstrunk

We propose NEMTO, the first end-to-end neural rendering pipeline to model 3D transparent objects with complex geometry and unknown indices of refraction. Commonly used appearance modeling such as the Disney BSDF model cannot accurately address this challenging problem due to the complex light paths bending through refractions and the strong dependency of sur

Toby S. Cubitt

For any local Hamiltonian H, I construct a local CPT map and stopping condition which converges to the ground state subspace of H. Like any ground state preparation algorithm, this algorithm necessarily has exponential run-time in general (otherwise BQP=QMA), even for gapped, frustration-free Hamiltonians (otherwise BQP is in NP). However, this dissipative q

Roberta Tripodi, Chiara Feruglio, Francisca Kemper, Francesca Civano

We present ALMA Band 9 continuum observation of the ultraluminous quasi-stellar object (QSO) SDSS J0100+2802, providing a $\sim 10\sigma$ detection at $\sim 670$ GHz. SDSS J0100+2802 is the brightest QSO with the most massive super massive black hole (SMBH) known at $z>6$, and we study its dust spectral energy distribution in order to determine the dust prop

Jiří Rosický, Giacomo Tendas

We introduce enriched notions of purity depending on the left class $\mathcal E$ of a factorization system on the base $\mathcal V$ of enrichment. Ordinary purity is given by the class of surjective mappings in the category of sets. Under specific assumptions, covering enrichment over quantale-valued metric spaces, $\omega$-complete posets, and quasivarietie

David Roodman

Mainly through regression discontinuity designs, Khanna (2023a) studies the impacts of a primary schooling expansion in India in the 1990s. Absent from the data set are four districts close to the modeled treatment discontinuity. Incorporating them cuts the impact of intention to treat on schooling attainment by two-thirds and the impact on wages by a third.

Kunal Jain, Prabuchandran K. J., Tejas Bodas

Bayesian Optimization (BO) is used to find the global optima of black box functions. In this work, we propose a practical BO method of function compositions where the form of the composition is known but the constituent functions are expensive to evaluate. By assuming an independent Gaussian process (GP) model for each of the constituent black-box function,

Nuno Crokidakis

In this work we study a simple mathematical model to analyze the emergence and control of radicalization phenomena, motivated by the recent far-right extremist events in Brazil, occurred in January 8, 2023. For this purpose, we considered a compartmental SIS-like model that takes into account only the right electors, for simplicity. The model considers radic

Zsolt Páles, Mahmood Kamil Shihab

The main purpose of this paper is to introduce various convexity concepts in terms of a positive Chebyshev system $\omega$ and give a systematic investigation of the relations among them. We generalize a celebrated theorem of Bernstein--Doetsch to the setting of $\omega$-Jensen convexity. We also give sufficient conditions for the existence of discontinuous

Xiang Chen, Hao Li, Mingqiang Li, Jinshan Pan

Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens fro

J. Mah, B. Bitsch

Super-Mercuries, rocky exoplanets with bulk iron mass fraction of more than 60 per cent, appear to be preferentially hosted by stars with higher iron mass fraction than the Earth. It is unclear whether these iron-rich planets can form in the disc, or if giant impacts are necessary. Here we investigate the formation of super-Mercuries in their natal protoplan

Yuichi Harikane, Yechi Zhang, Kimihiko Nakajima, Masami Ouchi

We present a first statistical sample of faint type-1 AGNs at $z>4$ identified by JWST/NIRSpec deep spectroscopy. Among the 185 galaxies at $z_\mathrm{spec}=3.8-8.9$ confirmed with NIRSpec, our systematic search for broad-line emission reveals 10 type-1 AGNs at $z=4.015-6.936$ whose broad component is only seen in the permitted H$\alpha$ line and not in the

Lisa Schwetlick, Sebastian Reich, Ralf Engbert

Humans constantly move their eyes, even during visual fixations, where miniature (or fixational) eye movements occur involuntarily. Fixational eye movements comprise slow components (physiological drift and tremor) and fast components (microsaccades). The complex dynamics of physiological drift can be modeled qualitatively as a statistically self-avoiding ra

Yuhui Guo, Jian Song, Xiaoming Song

In this article, we consider the following stochastic fractional diffusion equation \begin{equation*} \left(\partial^{\beta}+\dfrac{\nu}{2}\left(-\Delta\right)^{\alpha / 2}\right) u(t, x)= \lambda\: I_{0_+}^{\gamma}\left[u(t, x) \dot{W}(t, x)\right] ,\quad t>0,\: x \in \mathbb{R}, \end{equation*} where $\alpha>0$, $\beta\in(0,2]$, $\gamma \ge 0$, $\lambda\ne

Yu-Jhe Li, Tao Xu, Ji Hou, Bichen Wu

We tackle the task of text-to-3D creation with pre-trained latent-based NeRFs (NeRFs that generate 3D objects given input latent code). Recent works such as DreamFusion and Magic3D have shown great success in generating 3D content using NeRFs and text prompts, but the current approach of optimizing a NeRF for every text prompt is 1) extremely time-consuming

Michael Lashkevich, Oleg Lisovyy, Tatiana Ushakova

Form factors in the sinh-Gordon model are studied semiclassically for small values of the parameter $b\sim\hbar^{1/2}$ in the background of a radial classical solution, which describes a heavy exponential operator placed at the origin. For this purpose we use a generalization of the radial quantization scheme, well known for a massless boson field. We introd

Sukrut Rao, Moritz Böhle, Amin Parchami-Araghi, Bernt Schiele

Despite being highly performant, deep neural networks might base their decisions on features that spuriously correlate with the provided labels, thus hurting generalization. To mitigate this, 'model guidance' has recently gained popularity, i.e. the idea of regularizing the models' explanations to ensure that they are "right for the right reasons". While var

Goffredo Giordano, Saravana Prashanth Murali Babu, Barbara Mazzolai

Soft robotics technology can aid in achieving United Nations Sustainable Development Goals (SDGs) and the Paris Climate Agreement through development of autonomous, environmentally responsible machines powered by renewable energy. By utilizing soft robotics, we can mitigate the detrimental effects of climate change on human society and the natural world thro

Aida Abiad, Leonardo de Lima, Dheer Noal Desai, Krystal Guo

The energy of a graph $G$ is the sum of the absolute values of the eigenvalues of the adjacency matrix of $G$. Let $s^+(G), s^-(G)$ denote the sum of the squares of the positive and negative eigenvalues of $G$, respectively. It was conjectured by [Elphick, Farber, Goldberg, Wocjan, Discrete Math. (2016)] that if $G$ is a connected graph of order $n$, then $s

José A. Carrillo, Charles Elbar, Jakub Skrzeczkowski

We provide a rigorous mathematical framework to establish the limit of a nonlocal model of cell-cell adhesion system to a local model. When the parameter of the nonlocality goes to 0, the system tends to a Cahn-Hilliard system with degenerate mobility and cross interaction forces. Our analysis relies on a priori estimates and compactness properties.

Massimo Giovannini

Cosmic gravitons are expected in the MHz-GHz regions that are currently unreachable by the operating wide-band interferometers and where various classes of electromechanical detectors have been proposed through the years. The minimal chirp amplitude detectable by these instruments is often set on the basis of the sensitivities reachable by the detectors curr

Benjamin Terlat

We study the time constant $C(\nu)$ of last passage percolation on the complete directed acyclic graph on the set of non-negative integers, where edges have i.i.d. weights with distribution $\nu$ with support included in $\{-\infty\}\cup\mathbb{R}$. We show that $\nu \mapsto C(\nu)$ is strictly increasing in $\nu$. We also prove that $C(\nu)$ is continuous i

Shihao Wang, Yingfei Liu, Tiancai Wang, Ying Li

In this paper, we propose a long-sequence modeling framework, named StreamPETR, for multi-view 3D object detection. Built upon the sparse query design in the PETR series, we systematically develop an object-centric temporal mechanism. The model is performed in an online manner and the long-term historical information is propagated through object queries fram

Marco Pozzetta

We review recent results on the study of the isoperimetric problem on Riemannian manifolds with Ricci lower bounds. We focus on the validity of sharp second order differential inequalities satisfied by the isoperimetric profile of possibly noncompact Riemannian manifolds with Ricci lower bounds. We give a self-contained overview of the methods employed for t

Eliran Subag

For random systems of $K$ polynomials in $N + 1$ real variables which include the models of Kostlan (1987) and Shub and Smale (1993), we prove that the number of zeros on the unit sphere for $K = N$ or the Hausdorff measure of the zero set for $K < N$ concentrates around its mean as $N\to\infty$. To prove concentration we show that the variance of the latter

Hancheng Ye, Bo Zhang, Tao Chen, Jiayuan Fan

Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance. Previous works focus on either single task model pruning or simply adapting it to multitask scenario, and still face the following problems when handling multitask pruning: 1) Due to the task mismatch, a well-p

Christoforos Neofytidis, Hongbin Sun, Ye Tian, Shicheng Wang

The set of degrees of maps $D(M,N)$, where $M,N$ are closed oriented $n$-manifolds, always contains $0$ and the set of degrees of self-maps $D(M)$ always contains $0$ and $1$. Also, if $a,b\in D(M)$, then $ab\in D(M)$; a set $A\subseteq\mathbb Z$ so that $ab\in A$ for each $a,b\in A$ is called multiplicative. On the one hand, not every infinite set of intege

Dingkang Yang, Zhaoyu Chen, Yuzheng Wang, Shunli Wang

Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked

Minal Suresh Patil, Kary Främling

This work introduces the notion of intermediate concepts based on levels structure to aid explainability for black-box models. The levels structure is a hierarchical structure in which each level corresponds to features of a dataset (i.e., a player-set partition). The level of coarseness increases from the trivial set, which only comprises singletons, to the

Timo Schorlepp, Shanyin Tong, Tobias Grafke, Georg Stadler

We introduce and compare computational techniques for sharp extreme event probability estimates in stochastic differential equations with small additive Gaussian noise. In particular, we focus on strategies that are scalable, i.e. their efficiency does not degrade upon temporal and possibly spatial refinement. For that purpose, we extend algorithms based on

Sebastian Baader, Lukas Lewark, Filip Misev, Paula Truöl

We classify 3-braid knots whose topological 4-genus coincides with their Seifert genus, using McCoy's twisting method and the Xu normal form. In addition, we give upper bounds for the topological 4-genus of positive and strongly quasipositive 3-braid knots.

Kaixun Jiang, Zhaoyu Chen, Hao Huang, Jiafeng Wang

Although Deep Neural Networks (DNNs) have demonstrated excellent performance, they are vulnerable to adversarial patches that introduce perceptible and localized perturbations to the input. Generating adversarial patches on images has received much attention, while adversarial patches on videos have not been well investigated. Further, decision-based attacks

Geonmo Gu, Sanghyuk Chun, Wonjae Kim, HeeJae Jun

This paper proposes a novel diffusion-based model, CompoDiff, for solving zero-shot Composed Image Retrieval (ZS-CIR) with latent diffusion. This paper also introduces a new synthetic dataset, named SynthTriplets18M, with 18.8 million reference images, conditions, and corresponding target image triplets to train CIR models. CompoDiff and SynthTriplets18M tac

James R Neilson, Matthew J McDermott, Kristin A Persson

Synthesis remains a challenge for advancing materials science. A key focus of this challenge is how to enable selective synthesis, particularly as it pertains to metastable materials. This perspective addresses the question: how can ``spectator'' elements, such as those found in double ion exchange (metathesis) reactions, enable selective materials synthesis

Laxmi Prasad Naik, Rakesh Mohan Das, Prasanta K. Panigrahi

Steering is one of the three in-equivalent forms of nonlocal correlations intermediate between Bell nonlocality and entanglement. Schrodinger-Robertson uncertainty relation (SRUR), has been widely used to detect entanglement and steering. However, the steering criterion in earlier works, based on SRUR, did not involve complete inferred-variance uncertainty r

Julia Brandes, Changhao Chen, Igor E. Shparlinski

We obtain new estimates - both upper and lower bounds - on the mean values of the Weyl sums over a small box inside of the unit torus. In particular, we refine recent conjectures of C. Demeter and B. Langowski (2022), and improve some of their results.

Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu

Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been less explored for time series data. A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such

Zhifeng Teng, Jiaming Zhang, Kailun Yang, Kunyu Peng

Seeing only a tiny part of the whole is not knowing the full circumstance. Bird's-eye-view (BEV) perception, a process of obtaining allocentric maps from egocentric views, is restricted when using a narrow Field of View (FoV) alone. In this work, mapping from 360{\deg} panoramas to BEV semantics, the 360BEV task, is established for the first time to achieve

Simon Dahan, Logan Z. J. Williams, Daniel Rueckert, Emma C. Robinson

Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis. While Transformers have excelled as domain-agnostic architectures for sequence-to-sequence learning, the quadratic cost of the self-attention operation

Andrew Lamperski

Spectrum estimation is a fundamental methodology in the analysis of time-series data, with applications including medicine, speech analysis, and control design. The asymptotic theory of spectrum estimation is well-understood, but the theory is limited when the number of samples is fixed and finite. This paper gives non-asymptotic error bounds for a broad cla

Jonatas A. Silva, Fábio C. Carvalho, Antonio R. G. Garcia

The proposal "no boundary" of physicists Hartle and Hawking seeks to build a satisfactory model of the early Universe, in a way that avoids the singularity "Big Bang" of the beginning of the Universe. As a consequence of this proposal, the concept of metric signature change arises, which is approached in different ways in the literature. Here, we reinterpret

Yuexiao Ma, Huixia Li, Xiawu Zheng, Xuefeng Xiao

Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We argue that an overlooked problem of oscillation is in the PTQ methods. In this paper, we take the initiative to explore and present a theoretical proof to explain why such a problem

Adrian E. Feiguin, Christian Helman, Armando A. Aligia

We consider a multiband Hubbard model $H_m$ for Cu and O orbitals in Ba$_{2-x}$Sr$_x$CuO$_{3+\delta}$ similar to the tree-band model for two-dimensional (2D) cuprates. The hopping parameters are obtained from maximally localized Wannier functions derived from \textit{ab initio} calculations. Using the cell perturbation method, we derive both a generalized $t

Chantal Sundqvist, Felix Karbstein

Quasi-elastic scattering processes have long been thought of providing the most promising signal for a first experimental detection of quantum vacuum nonlinearity. A prominent example of such a process is vacuum birefringence. However, these signals are typically strongly background dominated. This problem can be circumvented by inelastic scattering processe

Eldar Fischer, Johann A. Makowsky

In this paper we study the number of finite topologies on an $n$-element set subject to various restrictions.

Kaushiki Mukherjee, Biswajit Paul, Soma Mandal

By combining two objects with no quantum effect one can get an object with quantum effect. Such a phenomenon, often referred to as activation has been analyzed for the notion of steering nonlocality. Activation of steering nonlocality is observed for different classes of mixed entangled states in linear network scenarios. Characterization of arbitrary two qu

Erin Carson, Ieva Daužickaitė

We consider the split-preconditioned FGMRES method in a mixed precision framework, in which four potentially different precisions can be used for computations with the coefficient matrix, application of the left preconditioner, application of the right preconditioner, and the working precision. Our analysis is applicable to general preconditioners. We obtain

Rodrigo Ponce

In this paper we identify, for small $t$ and a fixed $T>0,$ the order $\alpha>0$ in the abstract fractional differential equation $$\partial^\alpha u(t)=Au(t),$$ where the time-fractional derivative $\partial^\alpha$ is understood in the sense of Caputo and Riemann-Liouville, $A$ is a closed (possibly unbounded) linear operator in a Banach space $X,$ and $0<

Hankang Gu, Shangbo Wang, Xiaoguang Ma, Dongyao Jia

Multi-agent Deep Reinforcement Learning (MADRL) based traffic signal control becomes a popular research topic in recent years. To alleviate the scalability issue of completely centralized RL techniques and the non-stationarity issue of completely decentralized RL techniques on large-scale traffic networks, some literature utilizes a regional control approach