July 2023 arXiv papers — page 63

Raphael Boige, Yannis Flet-Berliac, Arthur Flajolet, Guillaume Richard

Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains, including NLP, vision, and biology. Recent approaches involve pre-training transformer models on vast amounts of unlabeled data, serving as a starting point for efficiently solving downstream tasks. In reinforcement learning, researchers have recently adap

Eduardo A. Jagla, Alberto G. Rojo

We present the chiral knife edge rattleback, an alternative version of previously presented systems that exhibit spin inversion. We offer a full treatment of the model using qualitative arguments, analytical solutions as well as numerical results. We treat a reduced, one--mode problem which not only contains the essence of the physics of spin inversion, but

Daniel Schwalbe-Koda, Daniel E. Widdowson, Tuan Anh Pham, Vitaliy A. Kurlin

Zeolites are inorganic materials known for their diversity of applications, synthesis conditions, and resulting polymorphs. Although their synthesis is controlled both by inorganic and organic synthesis conditions, computational studies of zeolite synthesis have focused mostly on organic template design. In this work, we use a strong distance metric between

Aditya Nalgunda Ganesh, Dhruval Pobbathi Badrinath, Harshith Mohan Kumar, Priya SS

Modern approaches for vision-centric environment perception for autonomous navigation make extensive use of self-supervised monocular depth estimation algorithms that output disparity maps. However, when this disparity map is projected onto 3D space, the errors in disparity are magnified, resulting in a depth estimation error that increases quadratically as

Arefeh Abbasi, Tian Chen, Bastien F. G. Aymon, Pedro M. Reis

A design concept is introduced for the building block, a dot, of programmable braille readers utilizing bistable shell buckling, magnetic actuation, and pneumatic loading. The design process is guided by Finite Element simulations, which are initially validated through precision experiments conducted on a scaled-up, single-shell model system. Then, the simul

Qingfa Xiao, Shuangyin Li, Lei Chen

The enhancement of unsupervised learning of sentence representations has been significantly achieved by the utility of contrastive learning. This approach clusters the augmented positive instance with the anchor instance to create a desired embedding space. However, relying solely on the contrastive objective can result in sub-optimal outcomes due to its ina

Maureen Cohen, Paul I. Palmer, Adiv Paradise, Massimo A. Bollasina

Transmission spectroscopy supports the presence of uncharacterised, light-scattering and -absorbing aerosols in the atmospheres of many exoplanets. The complexity of factors influencing the formation, 3-D transport, radiative impact, and removal of aerosols makes it challenging to match theoretical models to the existing data. Our study simplifies these fact

Zhonghao Wang, Zijia Lu, Bo Jin, Haiying Deng

Large language models (LLMs) have shown remarkable capabilities in generating high-quality text and making predictions based on large amounts of data, including the media domain. However, in practical applications, the differences between the media's use cases and the general-purpose applications of LLMs have become increasingly apparent, especially Chinese.

Tao Ni, Francesco Pesavento, Mirco Zaccariotto, Ugo Galvanetto

This paper presents a hybrid modeling approach for simulating hydraulic fracture propagation in saturated porous media: ordinary state-based peridynamics is used to describe the behavior of the solid phase, including the deformation and crack propagation, while FEM is used to describe the fluid flow and to evaluate the pore pressure. Classical Biot poroelast

Seonghyeon Ye, Doyoung Kim, Sungdong Kim, Hyeonbin Hwang

Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction. However, previous studies have mainly focused on coarse-grained evaluation (i.e. overall preference-based evaluation), which limits interpretability since it does

Marcel Beetz, Abhirup Banerjee, Vicente Grau

Global single-valued biomarkers of cardiac function typically used in clinical practice, such as ejection fraction, provide limited insight on the true 3D cardiac deformation process and hence, limit the understanding of both healthy and pathological cardiac mechanics. In this work, we propose the Point Cloud Deformation Network (PCD-Net) as a novel geometri

R. El Jurdi, G. Varoquaux, O. Colliot

Medical segmentation models are evaluated empirically. As such an evaluation is based on a limited set of example images, it is unavoidably noisy. Beyond a mean performance measure, reporting confidence intervals is thus crucial. However, this is rarely done in medical image segmentation. The width of the confidence interval depends on the test set size and

Rubén M. Cabezón, Domingo García-Senz

The smoothed particle hydrodynamic technique is strongly based on the proper choice of interpolation functions. This statement is particularly relevant for the study of subsonic fluxes and turbulence, where inherent small errors in the averaging procedures introduce excessive damping on the smallest scales. To mitigate these errors we can increase both the n

Xiaoyan Xing, Konrad Groh, Sezer Karaoglu, Theo Gevers

The purpose of intrinsic decomposition is to separate an image into its albedo (reflective properties) and shading components (illumination properties). This is challenging because it's an ill-posed problem. Conventional approaches primarily concentrate on 2D imagery and fail to fully exploit the capabilities of 3D data representation. 3D point clouds offer

Aniruddh Raghu, Payal Chandak, Ridwan Alam, John Guttag

Self-supervised learning (SSL) for clinical time series data has received significant attention in recent literature, since these data are highly rich and provide important information about a patient's physiological state. However, most existing SSL methods for clinical time series are limited in that they are designed for unimodal time series, such as a se

Kanchana Ranasinghe, Michael Ryoo

Recent contrastive language image pre-training has led to learning highly transferable and robust image representations. However, adapting these models to video domains with minimal supervision remains an open problem. We explore a simple step in that direction, using language tied self-supervised learning to adapt an image CLIP model to the video domain. A

Olga I. Vinogradova, Elena F. Silkina

When electrolyte solutions are confined in micro- and nanochannels their conductivity is significantly different from those in a bulk phase. Here we revisit the theory of this phenomenon by focusing attention on the reduction in the ion mobility with the concentration of salt and a consequent impact to the conductivity of a monovalent solution, from bulk to

Xiaolong Li, Hao-Yue Liu, Xin-An Ren

We prove matrix Li-Yau-Hamilton estimates for positive solutions to the heat equation and the backward conjugate heat equation, both coupled with the K\"ahler-Ricci flow. As an application, we obtain a monotonicity formula.

Emily Moser, Nicholas Battaglia, Stefania Amodeo

The Sunyaev-Zeldovich thermal (tSZ) and kinetic (kSZ) effects can be used to constrain the thermodynamic properties of pressure and density, respectively, of galaxies and their surrounding regions. As SZ observations continue to improve, it is important to understand any modeling systematics when inferring properties from the data. Thus, a pipeline to forwar

Carlo De Michele, Gennaro Coppola

This paper proposes a hierarchy of numerical fluxes for the compressible flow equations which are kinetic-energy and pressure equilibrium preserving and asymptotically entropy conservative, i.e., they are able to arbitrarily reduce the numerical error on entropy production due to the spatial discretization. The fluxes are based on the use of the harmonic mea

Javier Gonzalez-Conde, Thomas W. Watts, Pablo Rodriguez-Grasa, Mikel Sanz

Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency of this process leads to a major bottleneck for the application of these algorithms. Here, we present and compare two efficient methods for the amplitude encoding of real polyn

Oscar Javier Gomez Sanchez, Guan-Hao Peng, Wei-Hua Li, Ching-Hung Shih

A light beam can be spatially structured in the complex amplitude to possess orbital angular momentum (OAM), which introduces a new degree of freedom alongside the intrinsic spin angular momentum (SAM) associated with circular polarization. Moreover, super-imposing two twisted lights with distinct SAM and OAM produces a vector vortex beam (VVB) in non-separa

Muhammad Osama Khan, Yi Fang

Despite the rapid progress in self-supervised learning (SSL), end-to-end fine-tuning still remains the dominant fine-tuning strategy for medical imaging analysis. However, it remains unclear whether this approach is truly optimal for effectively utilizing the pre-trained knowledge, especially considering the diverse categories of SSL that capture different t

Eduardo C. Garrido-Merchán, Sara Lumbreras-Sancho

This paper adds to the efforts of evolutionary ethics to naturalize morality by providing specific insights derived from a computational ethics view. We propose a stylized model of human decision-making, which is based on Reinforcement Learning, one of the most successful paradigms in Artificial Intelligence. After the main concepts related to Reinforcement

Gennadiy Feldman

Let $X$ be a locally compact Abelian group with the connected component of zero of dimension 1. Let $\xi_1$ and $\xi_2$ be independent random variables with values in $X$ with nonvanishing characteristic functions. We prove that if a topological automorphism $\alpha$ of the group $X$ satisfies the condition ${{\rm Ker}(I+\alpha)=\{0\}}$ and the conditional d

Suttisak Wizadwongsa, Worameth Chinchuthakun, Pramook Khungurn, Amit Raj

Despite the remarkable success of diffusion models in image generation, slow sampling remains a persistent issue. To accelerate the sampling process, prior studies have reformulated diffusion sampling as an ODE/SDE and introduced higher-order numerical methods. However, these methods often produce divergence artifacts, especially with a low number of samplin

V. Gorshkov, I. Gusev, P. Dokukin, V. Kaftan

Major results of researches conducted by Russian geodesists in 2019-2022 on the topics of the International Association of Geodesy (IAG) of the International Union of Geodesy and Geophysics (IUGG) are presented in this issue. This report is prepared by the Section of Geodesy of the National Geophysical Committee of Russia. In the report prepared for the XXVI

Woon-Seng Gan, Dongyuan Shi, Xiaoyi Shen

This paper presents some recent algorithms developed by the authors for real-time adaptive active noise (AANC) control systems. These algorithms address some of the common challenges faced by AANC systems, such as speaker saturation, system divergence, and disturbance rejection. Speaker saturation can introduce nonlinearity into the adaptive system and degra

Jun Wei, Yiwen Hu, Shuguang Cui, S. Kevin Zhou

Limited by expensive pixel-level labels, polyp segmentation models are plagued by data shortage and suffer from impaired generalization. In contrast, polyp bounding box annotations are much cheaper and more accessible. Thus, to reduce labeling cost, we propose to learn a weakly supervised polyp segmentation model (i.e., WeakPolyp) completely based on boundin

Stella Krell, Julien Moatti

We made a comparison between a Discrete Duality Finite Volume (DDFV) scheme and a Hybrid Finite Volume (HFV) scheme for a drift-diffusion model with mixed boundary conditions on general meshes. Both schemes are based on a nonlinear discretisation of the convection-diffusion fluxes, which ensures the positivity of the discrete densities. We investigate the be

Raffaella Mulas

We consider two different notions of graph colouring, namely, the $t$-periodic colouring for vertices that has been introduced in 1974 by Bondy and Simonovits, and the periodic colouring for oriented edges that has been recently introduced in the context of spectral theory of non-backtracking operators. For each of these two colourings, we introduce the corr

Christopher Cedzich, Jake Fillman, Long Li, Darren Ong

We investigate the symmetries of so-called generalized extended CMV matrices. It is well-documented that problems involving reflection symmetries of standard extended CMV matrices can be subtle. We show how to deal with this in an elegant fashion by passing to the class of generalized extended CMV matrices via explicit diagonal unitaries in the spirit of Can

Xiaobing Li, Jiale Hou, Haozhen Situ, Cai Zhang

In 2021, Wu et al. presented a multi-party quantum summation scheme exploiting the entanglement properties of d-dimensional Bell states (Wu et al. in Quantum Inf Process 20:200, 2021). In particular, the authors proposed a three-party quantum summation protocol and then extended their work to a multi-party case. It is claimed that their protocol is secure ag

Borja Rodríguez-Gálvez, Arno Blaas, Pau Rodríguez, Adam Goliński

The mechanisms behind the success of multi-view self-supervised learning (MVSSL) are not yet fully understood. Contrastive MVSSL methods have been studied through the lens of InfoNCE, a lower bound of the Mutual Information (MI). However, the relation between other MVSSL methods and MI remains unclear. We consider a different lower bound on the MI consisting

Sándor Kajántó

We present a simple method for proving Rellich inequalities on Riemannian manifolds with constant, non-positive sectional curvature. The method is built upon simple convexity arguments, integration by parts, and the so-called Riccati pairs, which are based on the solvability of a Riccati-type ordinary differential inequality. These results can be viewed as t

Lukas Nakamura

We introduce a pseudo-metric on the contactomorphism group of any contact manifold $(M,\xi)$ with a cooriented contact structure $\xi$. It is the contact analogue of a corresponding semi-norm in Hofer's geometry, and on certain classes of contact manifolds, its lift to the universal cover can be viewed as a continuous version of the integer valued bi-invaria

Samuel J. Dunham, Eirik Endeve, Anthony Mezzacappa, John M. Blondin

We present numerical results from a parameter study of the standing accretion shock instability (SASI), investigating the impact of general relativity (GR) on the dynamics. Using GR hydrodynamics with GR gravity, and nonrelativistic (NR) hydrodynamics with Newtonian gravity, in an idealized model setting, we vary the initial radius of the shock and, by varyi

Renato Kunz, Fatemeh Banaie, Abhinav Sharma, Carina I. Hausladen

Digital democracy and new forms for direct digital participation in policy making gain unprecedented momentum. This is particularly the case for preferential voting methods and decision-support systems designed to promote fairer, more inclusive and legitimate collective decision-making processes in citizens assemblies, participatory budgeting and elections.

Julian Müllner, Marcel Moosbrugger, Laura Kovács

We show that computing the strongest polynomial invariant for single-path loops with polynomial assignments is at least as hard as the Skolem problem, a famous problem whose decidability has been open for almost a century. While the strongest polynomial invariants are computable for affine loops, for polynomial loops the problem remained wide open. As an int

Rodolfo Batista Negri, Antonio Fernando Bertachini de Almeida Prado

This article presents a path-following control law for autonomous orbital maintenance of small body missions. The control law is robust, stable, and capable of controlling only the orbital geometry, allowing the spacecraft to operate with idle-thruster periods. It is entirely analytical and suitable for real-time operations. The control law is inspired by th

Zakaria Marah

We consider the problem of pricing American Exchange options driven by a L\'evy process. We study the properties of American Exchange options, we represented it as the sum of the price of the corresponding European exchange option price and an early exercise premium. Secondly, we show some properties of the free boundary and give an approximative formula of

Ozgur Akarsu, Eleonora Di Valentino, Suresh Kumar, Rafael C. Nunes

We present a comprehensive analysis of the $\Lambda_{\rm s}$CDM model, which explores the recent conjecture suggesting a rapid transition of the Universe from anti-de Sitter vacua to de Sitter vacua (viz., the cosmological constant switches sign from negative to positive) at redshift ${z_\dagger\sim 2}$, inspired by the graduated dark energy (gDE) model. Our

Shivaramakrishna Singirala, Dinesh Kumar Singha, Rukmani Mohanta

We investigate neutrino magnetic moment, triplet scalar dark matter in a Type-II radiative seesaw scenario. With three vector-like fermion doublets and two scalar triplets, we provide a loop level setup for the electromagnetic vertex of neutrinos. All the scalar multiplet components constitute the total dark matter abundance of the Universe and also their sc

Merle M. Reimann, Florian A. Kunneman, Catharine Oertel, Koen V. Hindriks

As social robots see increasing deployment within the general public, improving the interaction with those robots is essential. Spoken language offers an intuitive interface for the human-robot interaction (HRI), with dialogue management (DM) being a key component in those interactive systems. Yet, to overcome current challenges and manage smooth, informativ

Leandro O. Souza, Earl T. Barr, Justyna Petke, Eduardo S. Almeida

For companies producing related products, a Software Product Line (SPL) is a software reuse method that improves time-to-market and software quality, achieving substantial cost reductions.These benefits do not come for free. It often takes years to re-architect and re-engineer a codebase to support SPL and, once adopted, it must be maintained. Current SPL pr

Johan Ziruo Ye, Thomas Ørkild, Peter Lempel Søndergaard, Søren Hauberg

Digital dentistry has made significant advancements, yet numerous challenges remain. This paper introduces the FDI 16 dataset, an extensive collection of tooth meshes and point clouds. Additionally, we present a novel approach: Variational FoldingNet (VF-Net), a fully probabilistic variational autoencoder for point clouds. Notably, prior latent variable mode

Dave Griffiths, Elizabeth Wilson, Iván Paz, Alex McLean

In this paper, we explore how textile pattern-making can be a useful activity for live coders used to manipulating software. We ran an algorithmic patterns workshop in July 2022 -- with a node at "on the fly" festival in Barcelona, a node in Sheffield and the workshop leader in Penryn -- where we created an activity recreating ancient patterns by weaving on

Wentao Zhu, Xiaoxuan Ma, Dongwoo Ro, Hai Ci

Human motion generation aims to generate natural human pose sequences and shows immense potential for real-world applications. Substantial progress has been made recently in motion data collection technologies and generation methods, laying the foundation for increasing interest in human motion generation. Most research within this field focuses on generatin

I H Hutchinson

Recent critical remarks, published in "Annalen der Physik", about the present author's analysis of electron and ion holes and their stability are addressed and shown to be misunderstandings and misrepresentations.

Jesper Hauch, Christoffer Riis, Francisco C. Pereira

The ability to learn polynomials and generalize out-of-distribution is essential for simulation metamodels in many disciplines of engineering, where the time step updates are described by polynomials. While feed forward neural networks can fit any function, they cannot generalize out-of-distribution for higher-order polynomials. Therefore, this paper collect

Calvin Chau, Jan Křetínský, Stefanie Mohr

Abstraction is a key verification technique to improve scalability. However, its use for neural networks is so far extremely limited. Previous approaches for abstracting classification networks replace several neurons with one of them that is similar enough. We can classify the similarity as defined either syntactically (using quantities on the connections b

Fang Kong, Shuai Li

The problem of matching markets has been studied for a long time in the literature due to its wide range of applications. Finding a stable matching is a common equilibrium objective in this problem. Since market participants are usually uncertain of their preferences, a rich line of recent works study the online setting where one-side participants (players)

Shalin Parekh

A result of Arcones implies that if a measure-preserving linear operator $S$ on an abstract Wiener space $(X,H,\mu)$ is strongly mixing, then the set of limit points of the random sequence $((2\log n)^{-1/2}S^n(x))_{n\in\mathbb N}$ equals the unit ball of $H$ for a.e. $x \in X$, which may be seen as a generalization of the classical Strassen's law of the ite

Anna Melnykova, Patricia Reynaud-Bouret, Adeline Samson

We develop several statistical tests of the determinant of the diffusion coefficient of a stochastic differential equation, based on discrete observations on a time interval $[0,T]$ sampled with a time step $\Delta$. Our main contribution is to control the test Type I and Type II errors in a non asymptotic setting, i.e. when the number of observations and th

Agnid Banerjee, Abhishek Ghosh

For $s \in [1/2, 1)$, let $u$ solve $(\partial_t - \Delta)^s u = Vu$ in $\mathbb R^{n} \times [-T, 0]$ for some $T>0$ where $||V||_{ C^2(\mathbb R^n \times [-T, 0])} < \infty$. We show that if for some $0< c< T$ and $\epsilon>0$ $$\frac{1}{c} \int_{[-c,0]} u^2(x, t) dt \leq Ce^{-|x|^{2+\epsilon}}\ \forall x \in \mathbb R^n,$$ then $u \equiv 0$ in $\mathbb R^

Jennifer Harnett, Simon Weir, Davide Michieletto

In this paper we investigate the effects of varying cation valency and concentration on the rheology of entangled lambda DNA solutions. We show that monovalent cations moderately increase the viscoelasticty of the solutions mainly by stabilising linear condensation of lambda DNA ``monomers'' via hybridisation of their sticky ends. On the contrary, divalent c

Aniello Murano, Daniel Neider, Martin Zimmermann

In multi-agent system design, a crucial aspect is to ensure robustness, meaning that for a coalition of agents A, small violations of adversarial assumptions only lead to small violations of A's goals. In this paper we introduce a logical framework for robust strategic reasoning about multi-agent systems. Specifically, inspired by recent works on robust temp

Annesha Karmakar, G. Anil Kumar, Bhavika, V. Anand

The use of programmable hardware devices is imperative for digital based pulse shape discrimination (PSD) to differentiate between various types of radiation. This work reports the development of a PSD algorithm based on tail area and total area, eliminating the need for programmable hardware. The pulses were collected using BC501 detector and Pu-Be source f

Alina Landowska, Marek Robak, Maciej Skorski

Anticipation is a fundamental human cognitive ability that involves thinking about and living towards the future. While language markers reflect anticipatory thinking, research on anticipation from the perspective of natural language processing is limited. This study aims to investigate the futures projected by futurists on Twitter and explore the impact of

Chendi Qu, Jianping He, Xiaoming Duan, Shukun Wu

Trajectory and control secrecy is an important issue in robotics security. This paper proposes a novel algorithm for the control input inference of a mobile agent without knowing its control objective. Specifically, the algorithm first estimates the target state by applying external perturbations. Then we identify the objective function based on the inverse

YuanDong Wang, Zhen-Gang Zhu, Gang Su

Magnons are electrically neutral bosonic quasiparticles emerging as collective spin excitations of magnetically ordered materials, and play a central role in the next-generation spintronics owing to its obviating Joule heating. A difficult obstacle for quantum magnonics is that the magnons do not couple to the external electric field directly so that a direc

Rodolfo Batista Negri, Antonio Fernando Bertachini de Almeida Prado

This paper introduces the Circular Restricted n-Body Problem (CRNBP), an extension of the bicircular restricted four-body problem (BCR4BP) designed to describe the dynamics of an n-body system. In the CRNBP, each massive body in the system is constrained to follow a Keplerian motion, similar to the BCR4BP's artificial constraint. The CRNBP is an efficient al

Artūras Dubickas, Min Sha, Igor E. Shparlinski

In this paper, we use some of our previous results to improve an upper bound of Bayer-Fluckiger, Borello and Jossen on the Euclidean minima of algebraic number fields. Our bound depends on the degree $n$ of the field, its signature, discriminant and the Hermite constant in dimension $n$.

Delgermaa Gankhuyag, Cristina Olaverri-Monreal

In recent years, autonomous vehicles have become increasingly popular, leading to extensive research on their safe and efficient operation. Understanding road yielding behavior is crucial for incorporating the appropriate driving behavior into algorithms. This paper focuses on investigating drivers' yielding behavior at unsignalized intersections. We quantif

Antoine Rondelet, Quintus Kilbourn

A growing body of literature is aimed at designing private mempools in blockchains. The ultimate goal of this research is addressing several phenomena broadly classed under MEV with sandwich attacks as the canonical example. The literature has primarily viewed MEV as a problem arising from oversights in distributed systems and cryptographic protocol design a

Georgel Savin, Ammar Asseri, Josiah Dykstra, Jonathan Goohs

Industry standard frameworks are now widespread for labeling the high-level stages and granular actions of attacker and defender behavior in cyberspace. While these labels are used for atomic actions, and to some extent for sequences of actions, there remains a need for labeled data from realistic full-scale attacks. This data is valuable for better understa

Kai-Uwe Bux, Joachim Hilgert, Tobias Weich

We compare the spectral properties of two kinds of linear operators characterizing the (classical) geodesic flow and its quantization on connected locally finite graphs without dead ends. The first kind are transfer operators acting on vector spaces associated with the set of non backtracking paths in the graphs. The second kind of operators are averaging op

Xinke Li, Junchi Lu, Henghui Ding, Changsheng Sun

With the growth of 3D sensing technology, deep learning system for 3D point clouds has become increasingly important, especially in applications like autonomous vehicles where safety is a primary concern. However, there are also growing concerns about the reliability of these systems when they encounter noisy point clouds, whether occurring naturally or intr

Benjamin Sanderse, Francesc Xavier Trias

A new energy-consistent discretization of the viscous dissipation function in incompressible flows is proposed. It is implied by choosing a discretization of the diffusive terms and a discretization of the local kinetic energy equation and by requiring that continuous identities like the product rule are mimicked discretely. The proposed viscous dissipation

Ken Mori, Kai Storms, Steven Peters

Having efficient testing strategies is a core challenge that needs to be overcome for the release of automated driving. This necessitates clear requirements as well as suitable methods for testing. In this work, the requirements for perception modules are considered with respect to relevance. The concept of relevance currently remains insufficiently defined

Torben G. Andersen, Viktor Todorov, Bo Zhou

This paper focuses on the task of detecting local episodes involving violation of the standard It\^o semimartingale assumption for financial asset prices in real time that might induce arbitrage opportunities. Our proposed detectors, defined as stopping rules, are applied sequentially to continually incoming high-frequency data. We show that they are asympto

Marcelo A. Santos, Antonio Ferramosca, Guilherme V. Raffo

This work proposes a finite-horizon optimal control strategy to solve the tracking problem while providing avoidance features to the closed-loop system. Inspired by the set-point tracking model predictive control (MPC) framework, the central idea of including artificial variables into the optimal control problem is considered. This approach allows us to add

Dimitri Meunier, Zhu Li, Arthur Gretton, Samory Kpotufe

Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target task. The main aim of theoretical guarantees on the subject is to establish the extent to which convergence rates -- in learning a common representation -- \emph{may scale with the n

Wenwei Gu, Jinyang Liu, Zhuangbin Chen, Jianping Zhang

Cloud systems are susceptible to performance issues, which may cause service-level agreement violations and financial losses. In current practice, crucial metrics are monitored periodically to provide insight into the operational status of components. Identifying performance issues is often formulated as an anomaly detection problem, which is tackled by anal

P. C. N. Verheijen, M. Haghi, M. Lazar, D. Goswami

In this paper, we propose a parallel shooting algorithm for solving nonlinear model predictive control problems using sequential quadratic programming. This algorithm is built on a two-phase approach where we first test and assess sequential convergence over many initial trajectories in parallel. However, if none converge, the algorithm starts varying the Ne

Ashish Singh, Ashutosh Singh, Prateek Agarwal, Zixuan Huang

Captions are crucial for understanding scientific visualizations and documents. Existing captioning methods for scientific figures rely on figure-caption pairs extracted from documents for training, many of which fall short with respect to metrics like helpfulness, explainability, and visual-descriptiveness [15] leading to generated captions being misaligned

Leander Girrbach, Anders Christensen, Ole Winther, Zeynep Akata

Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning. In this work, however, we find both theoretically and empirically that the variance of network weights and spatial concentration of large weights are the main factors that impact neural persistence

Yumeng Li, Margret Keuper, Dan Zhang, Anna Khoreva

Emerging large-scale text-to-image generative models, e.g., Stable Diffusion (SD), have exhibited overwhelming results with high fidelity. Despite the magnificent progress, current state-of-the-art models still struggle to generate images fully adhering to the input prompt. Prior work, Attend & Excite, has introduced the concept of Generative Semantic Nursin

James Branch, Nikolaos Diamantis, Wissam Raji, Larry Rolen

Given the L-series of a half-integral weight cusp form, we construct a cohomology class with coefficients in a finite dimensional vector space in a way that parallels the Eichler cohomology in the integral weight case. We also define a lift of half-integral weight cusp forms to integral weight modular forms that is compatible with the $L$-series of the respe

Kartheek Kumar Reddy Nareddy, Abijith Jagannath Kamath, Chandra Sekhar Seelamantula

The choice of the sensing matrix is crucial in compressed sensing. Random Gaussian sensing matrices satisfy the restricted isometry property, which is crucial for solving the sparse recovery problem using convex optimization techniques. However, tight-frame sensing matrices result in minimum mean-squared-error recovery given oracle knowledge of the support o

Huhe Han

For any Wulff shape $\mathcal{W}$, its dual Wulff shape and spherical Wulff shape $\widetilde{\mathcal{W}}$ can be defined naturally. A self-dual Wulff shape is a Wulff shape equaling its dual Wulff shape exactly. In this paper, we show that if a spherical convex polytope $P$ is of constant width $\delta$, then $\delta=\pi/2$. As an application of this fact,

ALICE Collaboration

Measurements of inclusive charged-particle jet production in pp and p-Pb collisions at center-of-mass energy per nucleon-nucleon collision $\sqrt{s_{\rm NN}} = 5.02$ TeV and the corresponding nuclear modification factor $R_{\rm pPb}^{\rm ch\,jet}$ are presented, using data collected with the ALICE detector at the LHC. Jets are reconstructed in the central ra

Pauline Chassonnery, Jenny Paupert, Anne Lorsignol, Childérick Sévérac

The Extra-Cellular-Matrix (ECM) is a complex interconnected 3D network that provides structural support for the cells and tissues and defines organ architecture key for their healthy functioning. However, the intimate mechanisms by which ECM acquire their 3D architecture are still largely unknown. In this paper, we address this question by means of a 3D indi

Adam Lahouari, Jean-Philip Piquemal, Johannes Richardi

The self-assembled monolayers of alkane thiolates on Ag (111) surfaces and nanoparticles are studied using molecular dynamics. Reactive force fields allow simulations of very large systems such as nanoparticles of 10 nm. Stable (sqrt(7) X sqrt(7))R19.1{\deg} assemblies are obtained as experimentally observed for these systems. Only nanoparticles smaller than

Kevin Wildrick

Among all Poincar\'e inequality spaces, we define the class of Cheeger fractals, which includes the sub-Riemannian Heisenberg group. We show that there is no bi-Lipschitz embedding $\iota$ of any Cheeger fractal $X$ into any Banach space $V$ with the following property: there exists a bounded Euclidean domain $\Omega$ such that for any Lipschitz mapping $f \

Xiaolin Zhang, Mengjie Wang, Jiliang Jing

In this paper we complete a systematic study on quasinormal modes (QNMs) and late time tails for scalar, Dirac and Maxwell fields on a spherically symmetric Schwarzschild-like black hole with a global monopole in the Einstein-bumblebee theory. To look for QNMs, we solve the equations of motion numerically by employing both the matrix and the WKB methods, and

Shenglong Hu, Defeng Sun, Kim-Chuan Toh

In this paper, we present a method to certify the approximation quality of a low rank tensor to a given third order symmetric tensor. Under mild assumptions, best low rank approximation is attained if a control parameter is zero or quantified quasi-optimal low rank approximation is obtained if the control parameter is positive.This is based on a primal-dual

Kaede Shiohara, Xingchao Yang, Takafumi Taketomi

The great advancements of generative adversarial networks and face recognition models in computer vision have made it possible to swap identities on images from single sources. Although a lot of studies seems to have proposed almost satisfactory solutions, we notice previous methods still suffer from an identity-attribute entanglement that causes undesired a

Zhenghui Zhao, Lixiang Ru, Chen Wu

Weakly-supervised change detection (WSCD) aims to detect pixel-level changes with only image-level annotations. Owing to its label efficiency, WSCD is drawing increasing attention recently. However, current WSCD methods often encounter the challenge of change missing and fabricating, i.e., the inconsistency between image-level annotations and pixel-level pre

Luis Ferroni, Akihiro Higashitani

This article provides a comprehensive exposition about inequalities that the coefficients of Ehrhart polynomials and $h^*$-polynomials satisfy under various assumptions. We pay particular attention to the properties of Ehrhart positivity as well as unimodality, log-concavity and real-rootedness for $h^*$-polynomials. We survey inequalities that arise when th

Hongyu Qu, Jianyong Qiao, Guangyuan Zhang

In this paper we explore a class of quadratic polynomials having Siegel disks with unbounded type rotation numbers. We prove that any boundary point of Siegel disks of these polynomials is a Lebesgue density point of their filled-in Julia sets, which generalizes the corresponding result of McMullen for bounded type rotation numbers. As an application, this r

Daniil Riabov, Ruslan Gladkov, Olesia Pashina, Andrey Bogdanov

Raman lasers is an actively developing field of nonlinear optics aiming to create efficient frequency converters and various optical sensors. Due to the growing importance of ultracompact chip-scale technologies, there is a constant demand for optical devices miniaturization, however, the development of a nanoscale Raman laser remains a challenging endeavor.

Jean-Baptiste Casteras, Léonard Monsaingeon

In this short note we derive a closed form for the trivariate distribution (position, local time at the origin, and positive occupation time) of the one-dimensional sticky Brownian motion, thereby filling some gaps and fixing some mistakes in the literature.

Svetlana Malysheva

We consider the spectrum of the Sample Covariance matrix $\mathbf{A}_N:= \frac{\mathbf{X}_N \mathbf{X}_N^*}{N}, $ where $\mathbf{X}_N$ is the $P\times N$ matrix with i.i.d. half-heavy tailed entries and $\frac{P}{N}\to y>0$ (the entries of the matrix have variance, but do not have the fourth moment). We derive the Central Limit Theorem for the Stieltjes tran

Jan Matyáš Křišťan, Jakub Svoboda

In this paper, we present novel algorithms that efficiently compute a shortest reconfiguration sequence between two given dominating sets in trees and interval graphs under the Token Sliding model. In this problem, a graph is provided along with its two dominating sets, which can be imagined as tokens placed on vertices. The objective is to find a shortest s

Zhifeng Qian, Mingyu You, Hongjun Zhou, Xuanhui Xu

Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally extended tasks remains a challenge for GCRL. Current works tackled this problem by leveraging planning algorithms to plan intermediate subgoa

Wei Cong, Yang Cong, Gan Sun, Yuyang Liu

Continual learning algorithms which keep the parameters of new tasks close to that of previous tasks, are popular in preventing catastrophic forgetting in sequential task learning settings. However, 1) the performance for the new continual learner will be degraded without distinguishing the contributions of previously learned tasks; 2) the computational cost

Adrian Dacko

We study the central limit distribution $\mu$ for V-monotone independence. Using its Cauchy--Stieltjes transform, we prove that $\mu$ is absolutely continuous with respect to the Lebesgue measure on $\mathbb{R}$ and we give its density $\rho$ in an implicit form. We present a computer generated graph of $\rho$.

Reyhaneh Rahimi, Praveen Ravirathinam, Ardeshir Ebtehaj, Ali Behrangi

This paper presents a deep learning architecture for nowcasting of precipitation almost globally every 30 min with a 4-hour lead time. The architecture fuses a U-Net and a convolutional long short-term memory (LSTM) neural network and is trained using data from the Integrated MultisatellitE Retrievals for GPM (IMERG) and a few key precipitation drivers from

Ondrej Bohdal, Da Li, Timothy Hospedales

Performance of a pre-trained semantic segmentation model is likely to substantially decrease on data from a new domain. We show a pre-trained model can be adapted to unlabelled target domain data by calculating soft-label prototypes under the domain shift and making predictions according to the prototype closest to the vector with predicted class probabiliti