March 2023 arXiv papers — page 16

Per Calissendorff, Matthew De Furio, Michael Meyer, Loïc Albert

We report the discovery of the first brown dwarf binary system with a Y dwarf primary, WISE J033605.05$-$014350.4, observed with NIRCam on JWST with the F150W and F480M filters. We employed an empirical point spread function binary model to identify the companion, located at a projected separation of 84 milliarcseconds, position angle of 295 degrees, and wit

Gino Isidori, Felix Wilsch, Daniel Wyler

The striking success of the Standard Model in explaining precision data and, at the same time, its lack of explanations for various fundamental phenomena, such as dark matter or the baryon asymmetry of the universe, suggests new physics at an energy scale much larger than the electroweak scale. In the absence of a short-range-long-range conspiracy, the Stand

Brandon C. Rayhaun

A (1+1)D unitary bosonic rational conformal field theory (RCFT) may be organized according to its genus, a tuple $(c,\mathscr{C})$ consisting of its central charge $c$ and a unitary modular tensor category $\mathscr{C}$ which describes the (2+1)D topological quantum field theory (TQFT) for which its maximally extended chiral algebra forms a holomorphic bound

C. M. Byrne, E. R. Stanway

Uncertainties in stellar population models, both in terms of stellar evolution and stellar spectra, translate into uncertainties in our interpretation of stellar populations in galaxies, since stars are the source of most of the light we receive from them. Observations by JWST are revealing high-redshift galaxies in great detail, which must then be compared

Yuxuan Zhang, Naren Manjunath, Ryohei Kobayashi, Maissam Barkeshli

The theory of topological phases of matter predicts invariants protected only by crystalline symmetry, yet it has been unclear how to extract these from microscopic calculations in general. Here we show how to extract a set of many-body invariants $\{\Theta_{\text{o}}^{\pm}\}$, where ${\text{o}}$ is a high symmetry point, from partial rotations in (2+1)D inv

Vivian Rogers, Swati Chaudhary, Richard Nguyen, Jean Anne Incorvia

We propose a novel analog memory device utilizing the gigantic magnetic Weyl semimetal (MWSM) domain wall (DW) magnetoresistance. We predict that the nucleation of domain walls between contacts will strongly modulate the conductance and allow for multiple memory states, which has been long sought-after for use in magnetic random access memories or memristive

Philip Boyle Smith, Ying-Hsuan Lin, Yuji Tachikawa, Yunqin Zheng

We classify two-dimensional purely chiral conformal field theories which are defined on two-dimensional surfaces equipped with spin structure and have central charge less than or equal to 16, and discuss their duality webs. This result can be used to confirm that the list of non-supersymmetric ten-dimensional heterotic string theories found in the late 1980s

Chi Sun, Kristian Mæland, Asle Sudbø

We consider a planar heterostructure consisting of a normal metal in proximity to an antiferromagnetic insulator, with an interlayer exchange coupling between the metal and the insulator. The coupling to the two sublattices of the antiferromagnetic insulator is allowed to be asymmetric. An effective electron-electron interaction in the normal metal, mediated

Charity Woodrum, Raphael E. Hviding, Rachael C. Amaro, Katie Chamberlain

The search for exoplanets has become a focal point of astronomical research, captivating public attention and driving scientific inquiry; however, the rush to confirm exoplanet discoveries has often overlooked potential alternative explanations leading to a scientific consensus that is overly reliant on untested assumptions and limited data. We argue that th

Weihao Yu, Pan Zhou, Shuicheng Yan, Xinchao Wang

Inspired by the long-range modeling ability of ViTs, large-kernel convolutions are widely studied and adopted recently to enlarge the receptive field and improve model performance, like the remarkable work ConvNeXt which employs 7x7 depthwise convolution. Although such depthwise operator only consumes a few FLOPs, it largely harms the model efficiency on pow

Tengda Han, Max Bain, Arsha Nagrani, Gül Varol

The objective of this paper is an automatic Audio Description (AD) model that ingests movies and outputs AD in text form. Generating high-quality movie AD is challenging due to the dependency of the descriptions on context, and the limited amount of training data available. In this work, we leverage the power of pretrained foundation models, such as GPT and

Lawrence Yunliang Chen, Baiyu Shi, Roy Lin, Daniel Seita

Many fabric handling and 2D deformable material tasks in homes and industry require singulating layers of material such as opening a bag or arranging garments for sewing. In contrast to methods requiring specialized sensing or end effectors, we use only visual observations with ordinary parallel jaw grippers. We propose SLIP: Singulating Layers using Interac

Kun Su, Kaizhi Qian, Eli Shlizerman, Antonio Torralba

Modeling sounds emitted from physical object interactions is critical for immersive perceptual experiences in real and virtual worlds. Traditional methods of impact sound synthesis use physics simulation to obtain a set of physics parameters that could represent and synthesize the sound. However, they require fine details of both the object geometries and im

Nathaniel Glover, Tomasz Tkocz, Katarzyna Wyczesany

We prove a dimension-free stability result for polydisc slicing due to Oleszkiewicz and Pelczy\'nski (2000). Intriguingly, compared to the real case, there is an additional asymptotic maximiser. In addition to Fourier-analytic bounds, we crucially rely on a self-improving feature of polydisc slicing, established via probabilistic arguments.

Philippe Brax, Carsten van de Bruck, Eleonora Di Valentino, William Giarè

We revisit the possibility of using cosmological observations to constrain models that involve interactions between neutrinos and dark matter. We show that small-scale measurements of the cosmic microwave background with a few per cent accuracy are critical to uncover unique signatures from models with tiny couplings that would require a much higher sensitiv

Zoey Guo, Yiwen Tang, Ray Zhang, Dong Wang

Understanding 3D scenes from multi-view inputs has been proven to alleviate the view discrepancy issue in 3D visual grounding. However, existing methods normally neglect the view cues embedded in the text modality and fail to weigh the relative importance of different views. In this paper, we propose ViewRefer, a multi-view framework for 3D visual grounding

Adrián Pérez-Salinas, Hao Wang, Xavier Bonet-Monroig

The parameters of the quantum circuit in a variational quantum algorithm induce a landscape that contains the relevant information regarding its optimization hardness. In this work we investigate such landscapes through the lens of information content, a measure of the variability between points in parameter space. Our major contribution connects the informa

Md Mostafijur Rahman, Radu Marculescu

Transformers have shown great success in medical image segmentation. However, transformers may exhibit a limited generalization ability due to the underlying single-scale self-attention (SA) mechanism. In this paper, we address this issue by introducing a Multi-scale hiERarchical vIsion Transformer (MERIT) backbone network, which improves the generalizabilit

Vibashan VS, Ning Yu, Chen Xing, Can Qin

Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human effort, limiting the scalability to annotate novel (new) categories. To alleviate this problem, Open-Vocabulary (OV) methods leverage large-scale image-caption pairs and vision-la

Xiaoxue Chen, Yuhang Zheng, Yupeng Zheng, Qiang Zhou

Nowadays, many visual scene understanding problems are addressed by dense prediction networks. But pixel-wise dense annotations are very expensive (e.g., for scene parsing) or impossible (e.g., for intrinsic image decomposition), motivating us to leverage cheap point-level weak supervision. However, existing pointly-supervised methods still use the same arch

Gergely Harcos, Jesse Thorner

Let $\pi$ and $\pi'$ be cuspidal automorphic representations of $\mathrm{GL}(n)$ and $\mathrm{GL}(n')$ with unitary central characters. We establish a new zero-free region for all $\mathrm{GL}(1)$-twists of the Rankin-Selberg $L$-function $L(s,\pi\times\pi')$, generalizing Siegel's celebrated work on Dirichlet $L$-functions. As an application, we prove the f

Changming Yue, Philipp Werner

The anomalous self-energy plays an important role in the analysis of superconducting states. Its spectral weight provides information on the pairing glue of superconductors, but it can change in sign. In many numerical approaches, for example Monte Carlo methods based on the Nambu formalism, the anomalous self-energy is obtained on the Matsubara axis, and no

Guan Zhe Hong, Yin Cui, Ariel Fuxman, Stanley H. Chan

In this paper, we study how the granularity of pretraining labels affects the generalization of deep neural networks in image classification tasks. We focus on the "fine-to-coarse" transfer learning setting, where the pretraining label space is more fine-grained than that of the target problem. Empirically, we show that pretraining on the leaf labels of Imag

Yuhang Jiang, Ramakanth Kavuluru

Combination drug therapies are treatment regimens that involve two or more drugs, administered more commonly for patients with cancer, HIV, malaria, or tuberculosis. Currently there are over 350K articles in PubMed that use the "combination drug therapy" MeSH heading with at least 10K articles published per year over the past two decades. Extracting combinat

Adam L. Shaw, Ran Finkelstein, Richard Bing-Shiun Tsai, Pascal Scholl

Current optical atomic clocks do not utilize their resources optimally. In particular, an exponential gain in sensitivity could be achieved if multiple atomic ensembles were to be controlled or read-out individually, even without entanglement. However, controlling optical transitions locally remains an outstanding challenge for neutral atom based clocks and

Shaoxu Li, Ye Pan

We present Instant Neural Radiance Fields Stylization, a novel approach for multi-view image stylization for the 3D scene. Our approach models a neural radiance field based on neural graphics primitives, which use a hash table-based position encoder for position embedding. We split the position encoder into two parts, the content and style sub-branches, and

Arman Rashidi, Robert Kealhofer, Alexander C. Lygo, Victor Huang

Hybrid structures between conventional, s-wave superconductors and two-dimensional topological insulators (2D TIs) are a promising route to topological superconductivity. Here, we investigate planar Josephson junctions fabricated from hybrid structures that use thin films of cadmium arsenide (Cd3As2) as the 2D TI material. Measurements of superconducting int

Maxim A. Olshanskii

The paper studies the equilibrium configurations of inextensible elastic membranes exhibiting lateral fluidity. Using a continuum description of the membrane's motions based on the surface Navier--Stokes equations with bending forces, the paper derives differential equations governing the mechanical equilibrium. The equilibrium conditions are found to be ind

In-Gu Shin, Jennifer C. Yee, Weicheng Zang, Hongjing Yang

As a part of the ``Systematic KMTNet Planetary Anomaly Search" series, we report five new planets (namely, OGLE-2016-BLG-1635Lb, MOA-2016-BLG-532Lb, KMT-2016-BLG-0625Lb, OGLE-2016-BLG-1850Lb, and KMT-2016-BLG-1751Lb) and one planet candidate (KMT-2016-BLG-1855), which were found by searching $2016$ KMTNet prime fields. These $buried$ planets show a wide rang

Matteo Negri, Clarissa Lauditi, Gabriele Perugini, Carlo Lucibello

The Hopfield model is a paradigmatic model of neural networks that has been analyzed for many decades in the statistical physics, neuroscience, and machine learning communities. Inspired by the manifold hypothesis in machine learning, we propose and investigate a generalization of the standard setting that we name Random-Features Hopfield Model. Here $P$ bin

Howard Zhong, Mark Hamilton

Non-Fungible Tokens (NFTs) are non-interchangeable assets, usually digital art, which are stored on the blockchain. Preliminary studies find that female and darker-skinned NFTs are valued less than their male and lighter-skinned counterparts. However, these studies analyze only the CryptoPunks collection. We test the statistical significance of race and gend

Franciele do Carmo Silva, Ana Paula de Souza, Eleonesio Strey, Sueli I. R. Costa

Multilevel lattice codes, such as those associated to Constructions $C$, $\overline{D}$, D and D', have relevant applications in communications. In this paper, we investigate some properties of lattices obtained via Constructions D and D' from $q$-ary linear codes. Connections with Construction A, generator matrices, expressions and bounds for the lattice vo

Luca Di Giammarino, Emanuele Giacomini, Leonardo Brizi, Omar Salem

The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of Simultaneous Localization and Mapping (SLAM) systems. To achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now retain higher resolutions that enable the creation of point cloud images resembling those taken by conventional cameras. Nevertheless

A. Löhr, M. Ivanov, M. Khokhlova

We develop a mathematically simple yet accurate model for the single-pulse non-resonant impulsive alignment of thermal ensembles of linear molecules. We find that our molecular alignment model not only provides a simplification for analytical and numerical calculations, but also establishes intuitive connections between system parameters, such as gas tempera

Pietro Brighi, Marko Ljubotina, Dmitry A. Abanin, Maksym Serbyn

The many-body localization (MBL) proximity effect is an intriguing phenomenon where a thermal bath localizes due to the interaction with a disordered system. The interplay of thermal and non-ergodic behavior in these systems gives rise to a rich phase diagram, whose exploration is an active field of research. In this work, we study a bosonic Hubbard model fe

L. R. Dreglea Sidorov, N. Sidorov, D. Sidorov

The theory of linear Fredholm integral-functional equations of the second kind with linear functionals and with a parameter is considered. The necessary and sufficient conditions are obtained for the coefficients of the equation and those parameter values, in the nighbohood of which the equation has solutions. The leading terms of the asymptotics of the solu

Ruyi Lian, Haibin Ling

Estimating the 6-DoF pose of a rigid object from a single RGB image is a crucial yet challenging task. Recent studies have shown the great potential of dense correspondence-based solutions, yet improvements are still needed to reach practical deployment. In this paper, we propose a novel pose estimation algorithm named CheckerPose, which improves on three ma

Guillermo Diaz, Iker Sobron, Iñaki Eizmendi, Iratxe landa

The phase of the channel state information (CSI) is underutilized as a source of information in wireless sensing due to its sensitivity to synchronization errors of the signal reception. A linear transformation of the phase is commonly applied to correct linear offsets and, in a few cases, some filtering in time or frequency is carried out to smooth the data

Shanjian Tang, Guang Yang

In this paper, we study the multi-dimensional backward stochastic differential equations (BSDEs) whose generator depends also on the mean of both variables. When the generator is diagonally quadratic, we prove that the BSDE admits a unique local solution with a fixed point argument. When the generator has a logarithmic growth of the off-diagonal elements (i.

Nuno Crokidakis, Marcio Argollo de Menezes, Daniel O. Cajueiro

We present an overview of the complex systems field using ChatGPT as a representation of the community's understanding. ChatGPT has learned language patterns and styles from a large dataset of internet texts, allowing it to provide answers that reflect common opinions, ideas, and language patterns found in the community. Our exploration covers both teaching

James Belk, Liam Stott

We prove that the "pseudo-$F_4$" group is isomorphic to $F_4$, answering a question of Brin. Both of these groups can be described as fast groups of homeomorphisms of the interval generated by bumps, as introduced by Bleak, Brin, Kassabov, Moore, and Zaremsky. The proof uses a representation of fast groups as Guba-Sapir diagram groups in order to leverage kn

Shaotong Zhu, Michael Wan, Elaheh Hatamimajoumerd, Kashish Jain

We present an end-to-end computer vision pipeline to detect non-nutritive sucking (NNS) -- an infant sucking pattern with no nutrition delivered -- as a potential biomarker for developmental delays, using off-the-shelf baby monitor video footage. One barrier to clinical (or algorithmic) assessment of NNS stems from its sparsity, requiring experts to wade thr

Wei Wei, Jiahuan Zhou, Hongze Li, Ying Wu

It is critical that the models pay attention not only to accuracy but also to the certainty of prediction. Uncertain predictions of deep models caused by noisy data raise significant concerns in trustworthy AI areas. To explore and handle uncertainty due to intrinsic data noise, we propose a novel method called ALUM to simultaneously handle the model uncerta

Yifeng Gong, Ge Sun, Aditya Nair, Aditya Bidwai

Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or otherwise inaccessible environments. Although most legged robotics research to date typically focuses on traversing these challenging environments, many legged platform demonstrations have also included "moving an object" as a way of doing tangible work. Legged robots

Sumit Kumar, Kummari Mallesham, Prahlad Sharma, Saurabh Kumar Singh

Let $f$ be an Hecke eigenform for the group $\Gamma_{0}(q)$ and $\chi_{d}$ be a primitive quadratic character of conductor $|d|$. In this article, we prove an asymptotic for the second moment of the derivative of $L(s, f \otimes \chi_{8d})$ at the central point $1/2$, which was previously known under GRH by Petrow \cite{petrow}.

Josefine Gaarn, Ben Burningham, Jacqueline K. Faherty, Channon Visscher

At the lowest masses, the distinction between brown dwarfs and giant exoplanets is often blurred and literature classifications rarely reflect the deuterium burning boundary. Atmospheric characterisation may reveal the extent to which planetary formation pathways contribute to the population of very-low mass brown dwarfs, by revealing if their abundance dist

Eustasio del Barrio, Alberto González Sanz

For a probability P in $R^d$ its center outward distribution function $F_{\pm}$, introduced in Chernozhukov et al. (2017) and Hallin et al. (2021), is a new and successful concept of multivariate distribution function based on mass transportation theory. This work proves, for a probability P with density locally bounded away from zero and infinity in its sup

Wei Wei, Jiahuan Zhou, Ying Wu

It is broadly known that deep neural networks are susceptible to being fooled by adversarial examples with perturbations imperceptible by humans. Various defenses have been proposed to improve adversarial robustness, among which adversarial training methods are most effective. However, most of these methods treat the training samples independently and demand

Hongpeng Cao, Yanbing Mao, Lui Sha, Marco Caccamo

Deep reinforcement learning (DRL) has achieved tremendous success in many complex decision-making tasks of autonomous systems with high-dimensional state and/or action spaces. However, the safety and stability still remain major concerns that hinder the applications of DRL to safety-critical autonomous systems. To address the concerns, we proposed the Phy-DR

Ruben Interian, Francisco A. Rodrigues

In this work, we investigate the evolution of polarization, influence, and domination in online interaction networks. Twitter data collected before and during the 2022 Brazilian elections is used as a case study. From a theoretical perspective, we develop a methodology called d-modularity that allows discovering the contribution of specific groups to network

Leonardo Maltoni

We study the extension groups between (modular) Wakimoto sheaves in type $\tilde{A_1}$. Firstly we determine them completely over characteristic zero fields. Secondly we describe a dg model which allows us to compute these groups in the antispherical category for arbitrary coefficients.

Elias Stengel-Eskin, Benjamin Van Durme

We illustrate how a calibrated model can help balance common trade-offs in task-oriented parsing. In a simulated annotator-in-the-loop experiment, we show that well-calibrated confidence scores allow us to balance cost with annotator load, improving accuracy with a small number of interactions. We then examine how confidence scores can help optimize the trad

Bin Feng, Tenglong Ao, Zequn Liu, Wei Ju

How to automatically synthesize natural-looking dance movements based on a piece of music is an incrementally popular yet challenging task. Most existing data-driven approaches require hard-to-get paired training data and fail to generate long sequences of motion due to error accumulation of autoregressive structure. We present a novel 3D dance synthesis sys

Alexander Ugarov

The paper describes a potential platform to facilitate academic peer review with emphasis on early-stage research. This platform aims to make peer review more accurate and timely by rewarding reviewers on the basis of peer prediction algorithms. The algorithm uses a variation of Peer Truth Serum for Crowdsourcing (Radanovic et al., 2016) with human raters co

Xingwei He, Zhenghao Lin, Yeyun Gong, A-Long Jin

Many natural language processing (NLP) tasks rely on labeled data to train machine learning models with high performance. However, data annotation is time-consuming and expensive, especially when the task involves a large amount of data or requires specialized domains. Recently, GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot abilit

Tomohiro Yamada

We show that if $N\pm 1=M\varphi(N)$ with $N\neq 15, 255$ composite, then $M<15.76515\log\log\log N$ and $M<16.03235\log\log\omega(N)$, together with similar results for the unitary totient function, Dedekind function, and the sum of unitary divisors.

Yuyang Shi, Valentin De Bortoli, Andrew Campbell, Arnaud Doucet

Solving transport problems, i.e. finding a map transporting one given distribution to another, has numerous applications in machine learning. Novel mass transport methods motivated by generative modeling have recently been proposed, e.g. Denoising Diffusion Models (DDMs) and Flow Matching Models (FMMs) implement such a transport through a Stochastic Differen

Md Azimul Haque, Tong Zhu, Luis Huerta Hernandez, Roba Tounesi

Achieving control over the transport properties of charge-carriers is a crucial aspect of realizing high-performance electronic materials. In metal-halide perovskites, which offer convenient manufacturing traits and tunability for certain optoelectronic applications, this is challenging: The perovskite structure itself, poses fundamental limits to maximum do

Michael Schelchshorn, Fabian Stilp, Marco Weiss, Franz J. Giessibl

The qPlus sensor allows simultaneous measurements of scanning tunneling microscopy (STM) and atomic force microscopy (AFM). Its design for use in frequency modulation AFM (FM-AFM) involves separate electrodes, applied on an oscillating quartz cantilever, for the detection of an electronic tunneling current and the deflection signal. Cable resistance and capa

Tony Junjing Fan, Dae-Sik Moon, Hong Soo Park, Dennis Zaritsky

We present the discovery of 48 new and the analysis of 55, including 7 previously discovered, dwarf galaxy candidates around the giant spiral galaxy NGC~2997 using deep $BVI$ images from the KMTNet Supernova Program. Their $V$-band central surface brightness and total absolute magnitudes are in the range of 20.3--26.7 mag arcsec$^{-2}$ and --(8.02--17.69) ma

Elena Di Bernardino, Thomas Laloë, Cambyse Pakzad

The present article is devoted to the semi-parametric estimation of multivariate expectiles for extreme levels. The considered multivariate risk measures also include the possible conditioning with respect to a functional covariate, belonging to an infinite-dimensional space. By using the first order optimality condition, we interpret these expectiles as sol

Ihsan Arharas, Siham Bouhadou, Astrid Hilbert, Youssef Ouknine

We consider the optimal stopping time problem under model uncertainty $R(v)= {\text{ess}\sup\limits}_{ \mathbb{P} \in \mathcal{P}} {\text{ess}\sup\limits}_{\tau \in \mathcal{S}_v} E^\mathbb{P}[Y(\tau) \vert \mathcal{F}_v]$, for every stopping time $v$, set in the framework of families of random variables indexed by stopping times. This setting is more genera

Colin Parellier, Axel Barrau, Silvere Bonnabel

In this paper we provide novel closed-form expressions enabling differentiation of any scalar function of the Kalman filter's outputs with respect to all its tuning parameters and to the measurements. The approach differs from the previous well-known sensitivity equations in that it is based on a backward (matrix) gradient calculation, that leads to drastic

Niccolò Bigagli, Claire Warner, Weijun Yuan, Siwei Zhang

Stable ultracold ensembles of dipolar molecules hold great promise for many-body quantum physics, but high inelastic loss rates have been a long-standing challenge. Recently, it was shown that gases of fermionic molecules can be effectively stabilized through external fields. However, many quantum applications will benefit from molecular ensembles with boson

J. Schou, J. Hirzberger, D. Orozco Suárez, K. Albert

In order to make accurate inferences about the solar interior using helioseismology, it is essential to understand all the relevant physical effects on the observations. One effect to understand is the (complex-valued) ratio of the horizontal to vertical displacement of the p- and f-modes at the height at which they are observed. Unfortunately, it is impossi

Jonathan W. Stallrich, Kade Young, Maria L. Weese, Byran J. Smucker

Supersaturated designs investigate more factors than there are runs, and are often constructed under a criterion measuring a design's proximity to an unattainable orthogonal design. The most popular analysis identifies active factors by inspecting the solution path of a penalized estimator, such as the lasso. Recent criteria encouraging positive correlations

Isaac Neal, Calum Rickard, Steve Shkoller, Vlad Vicol

From an open set of initial data, we construct a family of classical solutions to the 1D nonisentropic compressible Euler equations which form $C^{0,\nu}$ cusps as a first singularity, for any $\nu \in [1/2,1)$. For this range of $\nu$, this is the first result demonstrating the stable formation of such $C^{0,\nu}$ cusp-type singularities, also known as pre-

Ziwen Wang, Yancheng Yuan, Jiaming Ma, Tieyong Zeng

In this paper, we propose a randomly projected convex clustering model for clustering a collection of $n$ high dimensional data points in $\mathbb{R}^d$ with $K$ hidden clusters. Compared to the convex clustering model for clustering original data with dimension $d$, we prove that, under some mild conditions, the perfect recovery of the cluster membership as

Nisha Chahal, Suneel Dutt, Arvind Kumar

In the current work, we present the influence of finite volume and magnetic field on the thermodynamic properties of isospin asymmetric quark matter using the Polyakov loop extended chiral SU(3) quark mean field (PCQMF) model at finite chemical potential and temperature. Within the PCQMF model, we use the scalar and vector field values in mean-field approxim

Carlo Adornetto, Gianluigi Greco

In the field of functional genomics, the analysis of gene expression profiles through Machine and Deep Learning is increasingly providing meaningful insight into a number of diseases. The paper proposes a novel algorithm to perform Feature Selection on genomic-scale data, which exploits the reconstruction capabilities of autoencoders and an ad-hoc defined Ex

Weicheng Kuo, AJ Piergiovanni, Dahun Kim, Xiyang Luo

The development of language models have moved from encoder-decoder to decoder-only designs. In addition, we observe that the two most popular multimodal tasks, the generative and contrastive tasks, are nontrivial to accommodate in one architecture, and further need adaptations for downstream tasks. We propose a novel paradigm of training with a decoder-only

Fabrizio Murgana, Adrian Koenigstein, Dirk H. Rischke

We compute the critical exponents of the O(N) model within the Functional Renormalization Group (FRG) approach. We use recent advances which are based on the observation that the FRG flow equation can be put into the form of an advection-diffusion equation. This allows to employ well-tested hydrodynamical algorithms for its solution. In this study we work in

Abdelaziz Salama, Syed Ali Zaidi, Des McLernon, Mohammed M. H. Qazzaz

Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between

Yue Ma, Michael Hanks, M. S. Kim

Surface codes are the most promising candidates for fault-tolerant quantum computation. Single qudit errors are typically modelled as Pauli operators, to which general errors are converted via randomizing methods. In this Letter, we quantify remaining correlations after syndrome measurement for a qudit 2D surface code subject to non-Pauli errors. Using belie

Alex Abreu, Nicola Pagani

We give an explicit graph formula, in terms of decorated boundary strata classes, for the wall-crossing of universal Brill-Noether classes. More precisely, fix n>0 and d<g , and two stability conditions \phi^-, \phi^+ for degree d compactified universal (over the moduli space of stable n-pointed curves of genus g) Jacobians that lie on opposite sides of a st

James T. Meech

Computing accelerators must significantly improve at least one metric such as: cost, speed, or efficiency for customers to find them useful. They must do this for at least one application that large numbers of users care about to become a commercial success. Furthermore, accelerators must improve the metric that customers care most about for a given applicat

Salomon Kabongo, Jennifer D'Souza, Sören Auer

We present a large-scale empirical investigation of the zero-shot learning phenomena in a specific recognizing textual entailment (RTE) task category, i.e. the automated mining of leaderboards for Empirical AI Research. The prior reported state-of-the-art models for leaderboards extraction formulated as an RTE task, in a non-zero-shot setting, are promising

Sam Hughes, Monika Kudlinska

We prove that amongst the class of free-by-cyclic groups, Gromov hyperbolicity is an invariant of the profinite completion. We show that whenever $G$ is a free-by-cyclic group with first Betti number equal to one, and $H$ is a free-by-cyclic group which is profinitely isomorphic to $G$, the ranks of the fibres and the characteristic polynomials associated to

Alan Li, Angela P. Schoellig

6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. It is particularly challenging in bin-picking applications, where many objects are low-feature and reflective, and self-occlusion between objects of the same type is common. We propose a novel multi-view approach leveraging known camera trans

Peter Davies

In the study of radio networks, the tasks of broadcasting (propagating a message throughout the network) and leader election (having the network agree on a node to designate `leader') are two of the most fundamental global problems, and have a long history of work devoted to them. This work has two divergent strands: some works focus on exploiting the geomet

Matteo D'Achille, Nicolas Curien, Nathanaël Enriquez, Russell Lyons

We study the limit in low intensity of Poisson--Voronoi tessellations in hyperbolic spaces $ \mathbb{H}_{d}$ for $d \geq 2$. In contrast to the Euclidean setting, a limiting nontrivial ideal tessellation $ \mathcal{V}_{d}$ appears as the intensity tends to $0$. The tessellation $ \mathcal{V}_{d}$ is a natural, isometry-invariant decomposition of $ \mathbb{H}

Jozef Bucko, Deniz Soyuer, Lorenz Zwick

Past years have seen various publications attempting to explain the apparent clustering features of trans-Neptunian objects, the most popular explanation being an unconfirmed "Planet 9". The recently proposed Uranus Orbiter and Probe mission by NASA's Planetary Science and Astrobiology Decadal Survey could offer the opportunity to precisely determine Planet

Siddharth Kackar

We consider the information transmission problem in neurons and its possible implications for learning in neural networks. Our approach is based on recent developments in statistical physics and complexity science. Combining sensory information from various modalities for perceptual decision-making offers several advantages and is essential for the survival

Daniel Nkemelu, Harshil Shah, Irfan Essa, Michael L. Best

Given Myanmars historical and socio-political context, hate speech spread on social media has escalated into offline unrest and violence. This paper presents findings from our remote study on the automatic detection of hate speech online in Myanmar. We argue that effectively addressing this problem will require community-based approaches that combine the kno

Nele Callebaut

In these notes we give a pedagogical account of the replica trick derivation of CFT entanglement and its holographic counterpart, i.e. the Lewkowycz Maldacena derivation of the Ryu-Takayanagi formula. The application to an 'island set-up' for the calculation of black hole radiation entropy is briefly discussed. Further topics focused on are the relation to t

Apashanka Das, Biswajit Pandey, Suman Sarkar

We study the colour and star formation rates of paired galaxies in filaments and sheets using the EAGLE simulations. We find that the major pairs with pair separation $<50$ kpc are bluer and more star forming in filamentary environments compared to those hosted in sheet-like environments. This trend reverses beyond a pair separation of $\sim 50$ kpc. The int

Jann H. Ungerer, Alessia Pally, Artem Kononov, Sebastian Lehmann

Tremendous progress in few-qubit quantum processing has been achieved lately using superconducting resonators coupled to gate voltage defined quantum dots. While the strong coupling regime has been demonstrated recently for odd charge parity flopping mode spin qubits, first attempts towards coupling a resonator to even charge parity singlet-triplet spin qubi

Divya Kappara, Arup Bose, Madhuchhanda Bhattacharjee

Spatial association measures for univariate static spatial data are widely used. When the data is in the form of a collection of spatial vectors with the same temporal domain of interest, we construct a measure of similarity between the regions' series, using Bergsma's correlation coefficient $\rho$. Due to the special properties of $\rho$, unlike other spat

Peder J. Olesen, Azur Hodžić, Clara M. Velte

We present a method for combining proper orthogonal decomposition (POD) bases optimized with respect to different norms into a single complete basis. We produce a basis combining decompositions optimized with respect to turbulent kinetic energy (TKE) and dissipation rate. The method consists of projecting a data set into the subspace spanned by the lowest se

Ting Tao, Ruyu Liu, Shaohua Pan

This paper concerns a class of DC composite optimization problems which, as an extension of convex composite optimization problems and DC programs with nonsmooth components, often arises in robust factorization models of low-rank matrix recovery. For this class of nonconvex and nonsmooth problems, we propose an inexact linearized proximal algorithm (iLPA) by

Salar Arbabi, Davide Tavernini, Saber Fallah, Richard Bowden

Autonomous agents that drive on roads shared with human drivers must reason about the nuanced interactions among traffic participants. This poses a highly challenging decision making problem since human behavior is influenced by a multitude of factors (e.g., human intentions and emotions) that are hard to model. This paper presents a decision making approach

Anson Ka Long Yip, Patrick Chi-Kit Cheong, Tjonnie Guang Feng Li

Strongly magnetized neutron stars are popular candidates for producing detectable electromagnetic and gravitational-wave signals. Gravitational collapses of neutron stars triggered by a phase transition from hadrons to deconfined quarks in the cores could also release a considerable amount of energy in the form of gravitational waves and neutrinos. Hence, th

Andreas Weingartner

We show that the sequence $(\alpha n)_{n\in \mathcal{B}}$ is uniformly distributed modulo 1, for every irrational $\alpha$, provided $\mathcal{B}$ belongs to a certain family of integer sequences, which includes the prime, almost prime, squarefree, practical, densely divisible and lexicographical numbers. We also give an estimate for the discrepancy if $\alp

Haimei Zhao, Qiming Zhang, Shanshan Zhao, Zhe Chen

Multi-view camera-based 3D object detection has become popular due to its low cost, but accurately inferring 3D geometry solely from camera data remains challenging and may lead to inferior performance. Although distilling precise 3D geometry knowledge from LiDAR data could help tackle this challenge, the benefits of LiDAR information could be greatly hinder

Hoyoung Kim, Minhyeon Oh, Sehyun Hwang, Suha Kwak

Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive. To reduce the annotation cost, we propose a superpixel-based active learning (AL) framework, which collects a dominant label per superpixel instead. To be specific, it consists of adaptive superpixel and sieving mechanisms, fully dedicated to AL. At eac

Deividas Eringis, John Leth, Zheng-Hua Tan, Rafael Wisniewski

In this paper we derive a Probably Approxilmately Correct(PAC)-Bayesian error bound for linear time-invariant (LTI) stochastic dynamical systems with inputs. Such bounds are widespread in machine learning, and they are useful for characterizing the predictive power of models learned from finitely many data points. In particular, with the bound derived in thi

Laura L. Parrilla, Celia Escamilla-Rivera

In this work, we derive the analytical form for a $f(R)$ model that describes a perfect scalar field $\phi$ by assuming the existence of a chameleon mechanism. Based on four statements, at the background and perturbative level, it is possible to relate the extra terms from this theory as a geometrical perfect fluid term, whose has been expressed as possible

Károly J. Böröczky, András Csépai, Ádám Sagmeister

We discuss basic properties of several different width functions in the $n$-dimensional hyperbolic space such as continuity, and we also define a new hyperbolic width as the extension of Leichtweiss' width function. Then we prove a characterization theorem of bodies of constant width regarding the aforementioned notions of hyperbolic width.

Paul Geuchen, Felix Voigtlaender

Complex-valued neural networks (CVNNs) have recently shown promising empirical success, for instance for increasing the stability of recurrent neural networks and for improving the performance in tasks with complex-valued inputs, such as in MRI fingerprinting. While the overwhelming success of Deep Learning in the real-valued case is supported by a growing m

Rodica Andreea Dinu, Martin Vodička

Group-based models appear in algebraic statistics as mathematical models coming from evolutionary biology, respectively the study of mutations of organisms. Both theoretically and in terms of applications, we are interested in determining the algebraic degrees of the phylogenetic varieties coming from these models. These algebraic degrees are called phylogen