May 2023 arXiv papers — page 142

Yun Hua, Shang Gao, Wenhao Li, Haosheng Chen

In multi-agent reinforcement learning, each agent acts to maximize its individual accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect how others perceive them, resulting in selfish behaviors that undermine global performance. The externality theory, defined as ``the activities of one economic actor affect the activities

Piotr Sowinski, Maria Ganzha, Marcin Paprzycki

RDF streaming has been explored by the Semantic Web community from many angles, resulting in multiple task formulations and streaming methods. However, for many existing formulations of the problem, reliably benchmarking streaming solutions has been challenging due to the lack of well-described and appropriately diverse benchmark datasets. Existing datasets

Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli

A large variety of real-world Reinforcement Learning (RL) tasks is characterized by a complex and heterogeneous structure that makes end-to-end (or flat) approaches hardly applicable or even infeasible. Hierarchical Reinforcement Learning (HRL) provides general solutions to address these problems thanks to a convenient multi-level decomposition of the tasks,

Fa-Ting Hong, Li Shen, Dan Xu

Predominant techniques on talking head generation largely depend on 2D information, including facial appearances and motions from input face images. Nevertheless, dense 3D facial geometry, such as pixel-wise depth, plays a critical role in constructing accurate 3D facial structures and suppressing complex background noises for generation. However, dense 3D a

Mario Lázaro, Luis M. García-Raffi

Hyperuniformity is a property of certain heteroneous media in which density fluctuations in the long wavelength range decay to zero. In reciprocal space this behavior translates into a decay of Fourier intensities in the range near small wavenumbers. In this paper quasiperiodic tilings constructed by word concatenation are under study. The lattice is generat

Jean-Michel Tucny, Mihir Durve, Andrea Montessori, Sauro Succi

The prediction non-equilibrium transport phenomena in disordered media is a difficult problem for conventional numerical methods. An example of a challenging problem is the prediction of gas flow fields through porous media in the rarefied regime, where resolving the six-dimensional Boltzmann equation or its numerical approximations is computationally too de

Yingjie Tian, Yiqi Wang, Xianda Guo, Zheng Zhu

In recent years, soft prompt learning methods have been proposed to fine-tune large-scale vision-language pre-trained models for various downstream tasks. These methods typically combine learnable textual tokens with class tokens as input for models with frozen parameters. However, they often employ a single prompt to describe class contexts, failing to capt

Olivier Hamant

Unstoppable feedback loops and tipping points in socio-ecological systems are the main threats to sustainability. These behaviors have been extensively studied, notably to predict, and arguably deviate, dead-end trajectories. Behind the apparent complexity of such interaction networks, systems analysts have identified a small group of repeated patterns in al

Tan Van Vu, Keiji Saito

Considerable attention has been devoted to microscopic heat engines in both theoretical and experimental aspects. Notably, the fundamental limits pertaining to power and efficiency, as well as the tradeoff relations between these two quantities, have been intensively studied. This study aims to shed further light on the ultimate limits of heat engines by exp

Suraj Rajendran, Weishen Pan, Mert R. Sabuncu, Yong Chen

Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clinical and cost benefits remain limited due to challenges in data privacy, heterogeneous data sources, and the inability to fully leverage multiple data modalities. In this perspecti

Wenxiang Cong, Ge Wang

Fluorescence molecular tomography (FMT) is a promising modality for non-invasive imaging of internal fluorescence agents in biological tissues especially in small animal models, with applications in diagnosis, therapy, and drug design. In this paper, we present a new fluorescent reconstruction algorithm that combines time-resolved fluorescence imaging data w

Batool Salehi, Utku Demir, Debashri Roy, Suyash Pradhan

Creating a digital world that closely mimics the real world with its many complex interactions and outcomes is possible today through advanced emulation software and ubiquitous computing power. Such a software-based emulation of an entity that exists in the real world is called a 'digital twin'. In this paper, we consider a twin of a wireless millimeter-wave

Mobina Naeemi, Mohamad Reza Esmaeili, Iraj Abedi

Today, intensity-modulated radiation therapy (IMRT) is one of the methods used to treat brain tumors. In conventional treatment planning methods, after identifying planning target volume (PTV), and organs at risk (OARs), and determining the limitations for them to receive radiation, the dose distribution is performed based on optimization algorithms, which i

Mariano Zeron, Meng Wu, Ignacio Ruiz

When the Orthogonal Chebyshev Sliding Technique was introduced it was applied to a portfolio of swaps and swaptions within the context of the FRTB-IMA capital calculation. The computational cost associated to the computation of the ES values - an essential component of the capital caluclation under FRTB-IMA - was reduced by more than $90\%$ while passing PLA

Yansong Li, Zhixing Tan, Paula Branco, Yang Liu

Parameter-Efficient Fine-Tuning (PEFT) provides a practical way for users to customize Large Language Models (LLMs) with their private data in LLM service scenarios. However, the inherently sensitive nature of private data demands robust privacy preservation measures during the customization of LLM services to ensure data security, maintain user trust, and c

Sylvain Nougarede, Alice Diot, Elie Maza, Alain Samson

The Check-All-That-Apply (CATA) method was compared to the Adapted-Pivot-Test (APT) method, a recently published method based on pair comparisons between a coded wine and a reference sample, called pivot, and using a set list of attributes as in CATA. Both methods were compared using identical wines, correspondence analyses and Chi-square test of independenc

Joseph Reichert

Several of the CMS experiment's latest results on direct searches for new physics are presented. In particular, an emphasis is made to highlight the new models, unexplored final states, and innovative tools for discovery that these searches focus on.

H. Arthur Weldon

The Hilbert energy-momentum tensor for gauge-fixed non-Abelian gauge theories, defined by the variational derivative of the action with respect to the space-time metric, is a tensor under general coordinate transformations, symmetric in its indices, and BRST invariant. The canonical energy-momentum tensor has none of these properties but the canonical Hamilt

Nicholas Hartman, Kevin He

In applications where the study data are collected within cluster units (e.g., patients within transplant centers), it is often of interest to estimate and perform inference on the treatment effects of the cluster units. However, it is well-established that cluster-level confounding variables can bias these assessments, and many of these confounding factors

Philip Möller, Olga Varghese

A group $G$ is said to be just infinite if $G$ itself is infinite but all proper quotients of $G$ are finite. We show that a Coxeter group $W_\Gamma$ is just infinite if and only if $\Gamma$ is isomorphic to one of the following graphs: $\widetilde{A}_1$, $\widetilde{A}_n (n\geq 2)$, $\widetilde{B}_n (n\geq 3)$, $\widetilde{C}_n (n\geq 2)$, $\widetilde{D}_n(

Georgina Dransfield, Mathilde Timmermans, Amaury H. M. J. Triaud, Martín Dévora-Pajares

A new generation of observatories is enabling detailed study of exoplanetary atmospheres and the diversity of alien climates, allowing us to seek evidence for extraterrestrial biological and geological processes. Now is therefore the time to identify the most unique planets to be characterised with these instruments. In this context, we report on the discove

A. Cieply, P. C. Bruns

The paper presents a first time attempt on a combined fit of the $K^{-}p$ low-energy data and the $\pi\Sigma$ photoproduction mass spectra, performed without fixing the meson-baryon rescattering amplitudes to a specific $\pi\Sigma - \bar{K}N$ coupled channels model obtained from fitting exclusively the $K^{-}p$ data. The formalism adopted to describe the pho

António Girão, Robert Hancock

Recently, Dragani\'c, Munh\'a Correia, Sudakov and Yuster showed that every tournament on $(2+o(1))k^2$ vertices contains a $1$-subdivision of a transitive tournament on $k$ vertices, which is tight up to a constant factor. We prove a counterpart of their result for immersions. Let $f(k)$ be the smallest integer such that any tournament on at least $f(k)$ ve

Maryann M. Gitonga

This paper proposes a 3D attention-based U-Net architecture for multi-region segmentation of brain tumors using a single stacked multi-modal volume created by combining three non-native MRI volumes. The attention mechanism added to the decoder side of the U-Net helps to improve segmentation accuracy by de-emphasizing healthy tissues and accentuating malignan

Gábor Hegedüs, Gyula Károlyi

An almost cover of a finite set in the affine space is a collection of hyperplanes that together cover all points of the set except one. Using the polynomial method, we determine the minimum size of an almost cover of the vertex set of the permutohedron and address a few related questions.

V. Plastovets, A. S. Mel'nikov, A. I. Buzdin

We study the coherent dynamic interaction of a time-dependent spin-splitting field with the homogeneous superconducting order parameter $\Delta(t)$ mediated by spin-orbit coupling using the time-dependent Bogoliubov-de Gennes theory. In the first part of the work we show that linear response of the superconductor is strongly affected by the Zeeman field and

Xudong Xie, Zhen Zhu, Zijie Wu, Zhiliang Xu

This paper aims for a new generation task: non-stationary multi-texture synthesis, which unifies synthesizing multiple non-stationary textures in a single model. Most non-stationary textures have large scale variance and can hardly be synthesized through one model. To combat this, we propose a multi-scale generator to capture structural patterns of various s

Yinan Shen, Jingyang Li, Jian-Feng Cai, Dong Xia

High-dimensional linear regression under heavy-tailed noise or outlier corruption is challenging, both computationally and statistically. Convex approaches have been proven statistically optimal but suffer from high computational costs, especially since the robust loss functions are usually non-smooth. More recently, computationally fast non-convex approache

Vishesh Jain, Marcus Michelen, Huy Tuan Pham, Thuy-Duong Vuong

Let $G$ be a graph on $n$ vertices of maximum degree $\Delta$. We show that, for any $\delta > 0$, the down-up walk on independent sets of size $k \leq (1-\delta)\alpha_c(\Delta)n$ mixes in time $O_{\Delta,\delta}(k\log{n})$, thereby resolving a conjecture of Davies and Perkins in an optimal form. Here, $\alpha_{c}(\Delta)n$ is the NP-hardness threshold for

Shuwen Sun, Lihong Feng, Hoon Seng Chan, Tamara Miličić

A non-intrusive model order reduction (MOR) method that combines features of the dynamic mode decomposition (DMD) and the radial basis function (RBF) network is proposed to predict the dynamics of parametric nonlinear systems. In many applications, we have limited access to the information of the whole system, which motivates non-intrusive model reduction. O

Yuji Tachikawa, Mayuko Yamashita

We construct and study a morphism of spectra implementing the Anderson duality of topological modular forms ($\mathrm{TMF}$). Its differential version will then be introduced, allowing us to pair elements of $\pi_d\mathrm{TMF}$ with spin manifolds whose boundaries are equipped with string structure. A few negative-degree elements of $\pi_d\mathrm{TMF}$ will

Barry F. Madore, Wendy L. Freedman Kayla A. Owens, In Sung Jang

We present an extensive grid of numerical simulations quantifying the uncertainties in measurements of the Tip of the Red Giant Branch (TRGB). These simulations incorporate a luminosity function composed of 2 magnitudes of red giant branch (RGB) stars leading up to the tip, with asymptotic giant branch (AGB) stars contributing exclusively to the luminosity f

Jia Shen, Yifan Wang, Milad Azizkhani, Deqiang Qiu

Concentric Tube Robots (CTR) have the potential to enable effective minimally invasive surgeries. While extensive modeling and control schemes have been proposed in the past decade, limited efforts have been made to improve the trajectory tracking performance from the perspective of manipulability , which can be critical to generate safe motion and feasible

Kai Gu, Peng Fang, Zhiwei Sun, Rui du

We present a rigorous theoretical analysis of the convergence rate of the deep mixed residual method (MIM) when applied to a linear elliptic equation with various types of boundary conditions. The MIM method has been proposed as a more effective numerical approximation method compared to the deep Galerkin method (DGM) and deep Ritz method (DRM) in various ca

L. Kunyansky, E. McDugald, B. Shearer

Currently, theory of ray transforms of vector and tensor fields is well developed, but the Radon transforms of such fields have not been fully analyzed. We thus consider linearly weighted and unweighted longitudinal and transversal Radon transforms of vector fields. As usual, we use the standard Helmholtz decomposition of smooth and fast decreasing vector fi

Weichao Zheng

Structural superlubricity, one of the most important concepts in modern tribology, has attracted lots of interest in both fundamental research and practical applications. However, the underlying model, known as the Prandtl-Tomlinson (PT) model, is oversimplified and not for real processes, despite its prevalence in frictional and structural lubricant studies

Chenguang Wang, Zhang-Hua Fu, Pinyan Lu, Tianshu Yu

Efficiently training a multi-task neural solver for various combinatorial optimization problems (COPs) has been less studied so far. Naive application of conventional multi-task learning approaches often falls short in delivering a high-quality, unified neural solver. This deficiency primarily stems from the significant computational demands and a lack of ad

Maryam Daryalal, Ayse N. Arslan, Merve Bodur

In this work, we design primal and dual bounding methods for multistage adaptive robust optimization (MSARO) problems motivated by two decision rules rooted in the stochastic programming literature. From the primal perspective, this is achieved by applying decision rules that restrict the functional forms of only a certain subset of decision variables result

Weichao Zheng

Permeative flows, known for the explanation of the anomalous viscosity (10^5 Poise) in cholesterics at low shear rates, are still under debate due to the difficulty of experiments. Here we use the Surface Force Balance, in which uniform domains with regular circular defects are formed, to probe the forces generated by compression in the direction of the heli

Indranil Biswas, Pralay Chatterjee, Chandan Maity

In the paper "The second cohomology of nilpotent orbits in classical Lie algebras, Kyoto J. Math. 60 (2020), no. 2, 717-799" by I. Biswas, P. Chatterjee, and C. Maity, explicit descriptions of the second and first real de Rham cohomology groups of a general homogeneous space of a Lie group are given, extending an earlier result in "On the exactness of Kostan

Weichao Zheng

The stick-slip phenomenon widely exists in contact mechanics, from the macroscale to the nanoscale. During cholesteric-nematic unwinding by external fields, there is controversy regarding the role of planar surface anchoring, which may induce discontinuous stick-slip behaviors despite the well-known continuous transitions observed in past experiments. Here,

Pablo Mora

I discuss how the factorization of the invariant trace used to define Chern-Simons branes in a space-time with a Chern-Simons action for a space-time group introduces new relationships between the coupling constants of the extended objects of diverse dimensions, and an enhanced gauge invariance for a suitable choice of these coupling constants, owing to an e

Chidi Agbo, Hoda Mehrpouyan

Safety and security are the two most important properties of industrial control systems (ICS), and their integration is necessary to ensure that safety goals do not undermine security goals and vice versa. Sometimes, safety and security co-engineering leads to conflicting requirements or violations capable of impacting the normal behavior of the system. Iden

Frieder Ladisch

We study finite groups $G$ with elements $g$ such that $\lvert \mathbf{C}_G(g)\rvert = \lvert G:G' \rvert$. (Such elements generalize fixed-point-free automorphisms of finite groups.) We show that these groups have a unique conjugacy class of nilpotent supplements for the commutator subgroup and, using the classification of finite simple groups, that these g

Takuzo Okada

A Fano variety of Picard number $1$ is said to be \textit{birationally solid} if it is not birational to a Mori fiber space over a positive dimensional base. In this paper we complete the classification of quasi-smooth birationally solid Fano $3$-fold weighted hypersurfaces.

L. P. Nizhnik

We study the Cauchy problem for the (2+1) integrable nonlinear Schr\"odinger equation by the inverse scattering transform (IST) method. This Cauchy problem with given initial data and boundary data at infinity is reduced by IST to the Cauchy problem for the linear Schr\"odinger equation, in which the potential is expressed in terms of boundary data. The resu

Christoph Adam, Jorge Castelo, Alberto García Martín-Caro, Miguel Huidobro

Boson Stars are, at present, hypothetical compact stellar objects whose existence, however, could resolve several enigmas of current astrophysics. If they exist, either as independent astrophysical entities or as a matter admixture of more standard compact stars, then their imprints can probably be observed in the not-too-distant future from the gravitationa

Ido Lavi, Nicolas Meunier, Olivier Pantz

In this work, we propose and compare three numerical methods to handle the one-phase Hele-Shaw problem with surface tension in dimension two by using three variational approaches in the spirit of the seminal works \cite{Otto, Gia_Otto}.

Tomoya Iwasaki, Kanji Tanaka, Kenta Tsukahara

Visual place classification from a first-person-view monocular RGB image is a fundamental problem in long-term robot navigation. A difficulty arises from the fact that RGB image classifiers are often vulnerable to spatial and appearance changes and degrade due to domain shifts, such as seasonal, weather, and lighting differences. To address this issue, multi

Nandiraju Gireesh, Ayush Agrawal, Ahana Datta, Snehasis Banerjee

The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes. It is a fundamental task for an assistive robot in a home or a factory. Existing methods for MultiON have viewed this as a direct extension of Object Navigation (ON), the task of localising an instance of one object class, and are pre-sequen

C. A. Sonego, P. M. T. Vianez, H. Li, J. C. Lashley

We report the observation of an unexpected quadratic temperature dependence of the heat capacity in the vanadium sulphide metal V5S8 at low temperatures which is independent of applied magnetic field. We find that the behaviour of the heat capacity is consistent with an unconventional phonon spectrum which is linear in wavevector in the c direction but quadr

Matthew T. C. Li, Youssef Marzouk, Olivier Zahm

We investigate the approximation of high-dimensional target measures as low-dimensional updates of a dominating reference measure. This approximation class replaces the associated density with the composition of: (i) a feature map that identifies the leading principal components or features of the target measure, relative to the reference, and (ii) a low-dim

Carsten Carstensen, Neela Nataraj, Gopikrishnan C. Remesan, Devika Shylaja

A unified framework for fourth-order semilinear problems with trilinear nonlinearity and general source allows for quasi-best approximation with lowest-order finite element methods. This paper establishes the stability and a priori error control in the piecewise energy and weaker Sobolev norms under minimal hypotheses. Applications include the stream functio

Jeff Guo, Philippe Schwaller

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose a signific

Gong Chen, Jason Murphy

We prove stability estimates for the problem of recovering the nonlinearity from scattering data. We focus our attention on nonlinear Schr\"odinger equations of the form \[ (i\partial_t+\Delta)u = a(x)|u|^p u \] in three space dimensions, with $p\in[\tfrac43,4]$ and $a\in W^{1,\infty}$.

ALICE Collaboration

The first measurement of the cross section for incoherent photonuclear production of J/$\psi$ vector mesons as a function of the Mandelstam $|t|$ variable is presented. The measurement was carried out with the ALICE detector at midrapidity, $|y|<0.8$, using ultra-peripheral collisions of Pb nuclei at a centre-of-mass energy per nucleon pair of $\sqrt{s_{\mat

CYGNO collaboration

The nature of dark matter is still unknown and an experimental program to look for dark matter particles in our Galaxy should extend its sensitivity to light particles in the GeV mass range and exploit the directional information of the DM particle motion. The CYGNO project is studying a gaseous time projection chamber operated at atmospheric pressure with a

Dániel L Barabási, Ginestra Bianconi, Ed Bullmore, Mark Burgess

The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network sc

Rémi Leluc, François Portier, Johan Segers, Aigerim Zhuman

A novel linear integration rule called $\textit{control neighbors}$ is proposed in which nearest neighbor estimates act as control variates to speed up the convergence rate of the Monte Carlo procedure on metric spaces. The main result is the $\mathcal{O}(n^{-1/2} n^{-s/d})$ convergence rate -- where $n$ stands for the number of evaluations of the integrand

Andreas Padalkin, Manish Kumar, Christian Scheideler

We are considering the geometric amoebot model where a set of $n$ amoebots is placed on the triangular grid. An amoebot is able to send information to its neighbors, and to move via expansions and contractions. Since amoebots and information can only travel node by node, most problems have a natural lower bound of $\Omega(D)$ where $D$ denotes the diameter o

Bingchen Zhao, Xin Wen, Kai Han

In this paper, we address the problem of generalized category discovery (GCD), \ie, given a set of images where part of them are labelled and the rest are not, the task is to automatically cluster the images in the unlabelled data, leveraging the information from the labelled data, while the unlabelled data contain images from the labelled classes and also n

Yu Li, Wenjia Zhang

In this paper, we establish the rigidity of the generalized cylinder $N^n \times \mathbb R^{m-n}$, or a quotient thereof, in the space of Ricci shrinkers equipped with the pointed-Gromov-Hausdorff topology. Here, $N$ is a stable Einstein manifold that has an obstruction of order $3$. The proof is based on a quantitative characterization of the rigidity of co

Jiaqi Sun, Lin Zhang, Guangyi Chen, Kun Zhang

Graph neural networks aim to learn representations for graph-structured data and show impressive performance, particularly in node classification. Recently, many methods have studied the representations of GNNs from the perspective of optimization goals and spectral graph theory. However, the feature space that dominates representation learning has not been

Mitsuki Yoshida, Kanji Tanaka, Ryogo Yamamoto, Daiki Iwata

Semantic localization, i.e., robot self-localization with semantic image modality, is critical in recently emerging embodied AI applications (e.g., point-goal navigation, object-goal navigation, vision language navigation) and topological mapping applications (e.g., graph neural SLAM, ego-centric topological map). However, most existing works on semantic loc

Himmet Kaplan, Ralf-Peter Mundani, Heiko Rölke, Albert Weichselbraun

Predicting market movements based on the sentiment of news media has a long tradition in data analysis. With advances in natural language processing, transformer architectures have emerged that enable contextually aware sentiment classification. Nevertheless, current methods built for the general financial market such as FinBERT cannot distinguish asset-spec

Andrew Bolt, Conrad Sanderson, Joel Janek Dabrowski, Carolyn Huston

Wildfire propagation is a highly stochastic process where small changes in environmental conditions (such as wind speed and direction) can lead to large changes in observed behaviour. A traditional approach to quantify uncertainty in fire-front progression is to generate probability maps via ensembles of simulations. However, use of ensembles is typically co

Han Zhou, Wenyi Tian

In this work, two Crank-Nicolson schemes without corrections are developed for sub-diffusion equations. First, we propose a Crank-Nicolson scheme without correction for problems with regularity assumptions only on the source term. Second, since the existing Crank-Nicolson schemes have a severe reduction of convergence order for solving sub-diffusion equation

Eduardo M. K. Souza

We intend to contribute to the Collatz dynamics problem by seeking to analyze the Collatz conjecture from the tree of numbers sequences. First, we show numerically that the distribution of odd numbers has an initial transient, and proceeds to a power law growth to its maximum. Second, using the formulation that uses only odd numbers, we present analytically

Akira Kitaoka, Riki Eto

We show the convergence of Wasserstein inverse reinforcement learning for multi-objective optimizations with the projective subgradient method by formulating an inverse problem of the multi-objective optimization problem. In addition, we prove convergence of inverse reinforcement learning (maximum entropy inverse reinforcement learning, guided cost learning)

Qingyu Chen, Yan Hu, Xueqing Peng, Qianqian Xie

The rapid growth of biomedical literature poses challenges for manual knowledge curation and synthesis. Biomedical Natural Language Processing (BioNLP) automates the process. While Large Language Models (LLMs) have shown promise in general domains, their effectiveness in BioNLP tasks remains unclear due to limited benchmarks and practical guidelines. We perf

Joseph Ben Geloun, Andreas G. A. Pithis, Johannes Thürigen

Field theories with combinatorial non-local interactions such as tensor invariants are interesting candidates for describing a phase transition from discrete quantum-gravitational to continuum geometry. In the so-called cyclic-melonic potential approximation of a tensorial field theory on the $r$-dimensional torus it was recently shown using functional renor

T. Guérin, M. Dolgushev, O. Bénichou, R. Voituriez

We consider the kinetics of the imperfect narrow escape problem, i.e. the time it takes for a particle diffusing in a confined medium of generic shape to reach and to be adsorbed by a small, imperfectly reactive patch embedded in the boundary of the domain, in two or three dimensions. Imperfect reactivity is modeled by an intrinsic surface reactivity $\kappa

Christopher L. Fryer, Eric Burns, Aimee Hungerford, Samar Safi-Harb

Core-collapse supernova explosions play a wide role in astrophysics by producing compact remnants (neutron stars, black holes) and the synthesis and injection of many heavy elements into their host Galaxy. Because they are produced in some of the most extreme conditions in the universe, they can also probe physics in extreme conditions (matter at nuclear den

Chenghao Li, Chaoning Zhang

ChatGPT and its improved variant GPT4 have revolutionized the NLP field with a single model solving almost all text related tasks. However, such a model for computer vision does not exist, especially for 3D vision. This article first provides a brief view on the progress of deep learning in text, image and 3D fields from the model perspective. Moreover, this

Wei Sun

In this paper, we shall study existence of weak solutions to complex Hessian equations. With appropriate assumptions, it is possible to obtain weak solutions in pluripotential sense.

Chenghao Li, Chaoning Zhang, Joseph Cho, Atish Waghwase

Generative AI has made significant progress in recent years, with text-guided content generation being the most practical as it facilitates interaction between human instructions and AI-generated content (AIGC). Thanks to advancements in text-to-image and 3D modeling technologies, like neural radiance field (NeRF), text-to-3D has emerged as a nascent yet hig

S. E. Motta, J. D. Turner, B. Stappers, R. P. Fender

In MeerKAT observations pointed at a Galactic X-ray binary located on the Galactic plane we serendipitously discovered a radio nebula with cometary-like morphology. The feature, which we named `the Mini Mouse' based on its similarity with the previously discovered `Mouse' nebula, points back towards the previously unidentified candidate supernova remnant G45

Andre Oliveira, Vania Neves, Alexandre Plastino, Ana Carla Bibiano

In collaborative software development, multiple contributors frequently change the source code in parallel to implement new features, fix bugs, refactor existing code, and make other changes. These simultaneous changes need to be merged into the same version of the source code. However, the merge operation can fail, and developer intervention is required to

Simona D'Evangelista, Margherita Lelli-Chiesa

We survey basic results concerning Prym varieties, the Prym-Brill-Noether theory initiated by Welters, and Brill-Noether theory of general \'etale double covers of curves of genus g>=2. We then specialize to curves on Nikulin surfaces and show that \'etale double covers of curves on Nikulin surfaces of standard type do not satisfy Welters' Theorem. On the ot

Pardis Semnani, Elina Robeva

We consider the problem of learning a directed graph $G^\star$ from observational data. We assume that the distribution which gives rise to the samples is Markov and faithful to the graph $G^\star$ and that there are no unobserved variables. We do not rely on any further assumptions regarding the graph or the distribution of the variables. Particularly, we a

Salomon Kabongo, Jennifer D'Souza, Sören Auer

The purpose of this work is to describe the Orkg-Leaderboard software designed to extract leaderboards defined as Task-Dataset-Metric tuples automatically from large collections of empirical research papers in Artificial Intelligence (AI). The software can support both the main workflows of scholarly publishing, viz. as LaTeX files or as PDF files. Furthermo

Xinlong Dong, Xuemin Shen, Xiaowen Sun, Yuhao Bai

Multiferroic tunnel junctions (MFTJs) based on two-dimensional (2D) van der Waals heterostructures with sharp and clean interfaces at the atomic scale are crucial for applications in nanoscale multi-resistive logic memory devices. The recently discovered sliding ferroelectricity in 2D van der Waals materials has opened new avenues for ferroelectric-based dev

Giulio Corsi

Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addres

Jiahao Liu, Jiang Wu, Jinyu Chen, Miao Hu

Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregation method to generate personalized models, in which weights are determined by the loss value or model paramete

Marc Leinweber, Hannes Hartenstein

Asynchronous Byzantine Atomic Broadcast (ABAB) promises simplicity in implementation as well as increased performance and robustness in comparison to partially synchronous approaches. We adapt the recently proposed DAG-Rider approach to achieve ABAB with $n\geq 2f+1$ processes, of which $f$ are faulty, with only a constant increase in message size. We levera

Tobias Ehring, Bernard Haasdonk

Numerical methods for the optimal feedback control of high-dimensional dynamical systems typically suffer from the curse of dimensionality. In the current presentation, we devise a mesh-free data-based approximation method for the value function of optimal control problems, which partially mitigates the dimensionality problem. The method is based on a greedy

André O. Françani, Marcos R. O. A. Maximo

Estimating the camera's pose given images from a single camera is a traditional task in mobile robots and autonomous vehicles. This problem is called monocular visual odometry and often relies on geometric approaches that require considerable engineering effort for a specific scenario. Deep learning methods have been shown to be generalizable after proper tr

Mohsen Ghaffari, Julian Portmann

Chatterjee, Gmyr, and Pandurangan [PODC 2020] recently introduced the notion of awake complexity for distributed algorithms, which measures the number of rounds in which a node is awake. In the other rounds, the node is sleeping and performs no computation or communication. Measuring the number of awake rounds can be of significance in many settings of distr

Leonardo F. Cavenaghi, Lino Grama, Ricardo M. Martins

In this note, we show that the classical Wallach manifold $\mathrm{SU}(3)/\mathrm{T}^2$-admits metrics of positive intermediate Ricci curvature $(\mathrm{Ric}_d >0)$ for $d = 1, 2, 3, 4, 5$ that lose these properties under the homogeneous Ricci flow for $d=1, 2, 3, 5$. We make the same analyses to the family of Riemannian flag manifolds $\mathrm{SU}(m+2p)/\m

Xiaopeng Zhao, Zhenlin An, Qingrui Pan, Lei Yang

Although Maxwell discovered the physical laws of electromagnetic waves 160 years ago, how to precisely model the propagation of an RF signal in an electrically large and complex environment remains a long-standing problem. The difficulty is in the complex interactions between the RF signal and the obstacles (e.g., reflection, diffraction, etc.). Inspired by

Daichi Takeuchi, Takahiro Tsushima

We study Van der Geer--Van der Vlugt curves in a ramification-theoretic view point. We give explicit formulae on L-polynomials of these curves. As a result, we show that these curves are supersingular and give sufficient conditions for these curves to be maximal or minimal.

Marta Catalano, Hugo Lavenant

Random measures provide flexible parameters for Bayesian nonparametric models. Given two different priors for a random measure, we develop a natural framework to investigate the rate at which the corresponding posteriors merge, as the sample size increases. We define a new distance between the laws of random measures that is built as a Wasserstein distance o

Wei Zhou, Weiwei Jin, Qian Wang, Yifan Wang

Recently, Transformer-based methods for point cloud learning have achieved good results on various point cloud learning benchmarks. However, since the attention mechanism needs to generate three feature vectors of query, key, and value to calculate attention features, most of the existing Transformer-based point cloud learning methods usually consume a large

Huabin Liu, Weiyao Lin, Tieyuan Chen, Yuxi Li

Current few-shot action recognition involves two primary sources of information for classification:(1) intra-video information, determined by frame content within a single video clip, and (2) inter-video information, measured by relationships (e.g., feature similarity) among videos. However, existing methods inadequately exploit these two information sources

Jonas Busk, Mikkel N. Schmidt, Ole Winther, Tejs Vegge

Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting and applying interatomic forces. In these settings, it is crucial to detect when predictions are unreliable to avoid wrong or misleading results. Here, we present a complete framewor

Pablo Viñas Martínez, Esperanza López, Alejandro Bermudez

The quantum simulation of magnetism in trapped-ion systems makes use of the crystal vibrations to mediate pairwise interactions between spins, which are encoded in the internal electronic states of the ions, and measured in experiments that probe the real-time dynamics. These interactions can be accounted for by a long-wavelength relativistic theory, where t

Dylan Braithwaite, Jules Hedges, Toby St Clere Smithe

Bayes' rule tells us how to invert a causal process in order to update our beliefs in light of new evidence. If the process is believed to have a complex compositional structure, we may observe that the inversion of the whole can be computed piecewise in terms of the component processes. We study the structure of this compositional rule, noting that it relat

Ali Baheri, Mykel J. Kochenderfer

Safety validation is a crucial component in the development and deployment of autonomous systems, such as self-driving vehicles and robotic systems. Ensuring safe operation necessitates extensive testing and verification of control policies, typically conducted in simulation environments. High-fidelity simulators accurately model real-world dynamics but enta

Md Rakibul Hasan, Shreya Ghosh, Pradyumna Agrawal, Zhixi Cai

This paper proposes an atomic behaviour intervention strategy using the Pavlok wearable device. Pavlok utilises beeps, vibration and shocks as a mode of aversion technique to help individuals with behaviour modification. While the device can be useful in certain periodic daily life situations, like alarms and exercise notifications, it relies on manual opera

Munib Mesinovic, Peter Watkinson, Tingting Zhu

Heart attack remain one of the greatest contributors to mortality in the United States and globally. Patients admitted to the intensive care unit (ICU) with diagnosed heart attack (myocardial infarction or MI) are at higher risk of death. In this study, we use two retrospective cohorts extracted from the eICU and MIMIC-IV databases, to develop a novel pseudo