March 2023 arXiv papers — page 6

Ashwini B P, R Sumathi, Sudhira H S

In this information era commuters prefer to know a reliable travel time to plan ahead of their journey using both public and private modes. In this direction reliability analysis using the location data of the buses is conducted in two folds in the current work; (i) Reliability analysis of a public transit service at route level, and (ii) Travel time reliabi

Li Jiayu, Pan Shujing

In this article, we will study the isoperimetric problem by introducing a mean curvature type flow in the Riemannian manifold endowed with a non-trivial conformal vector field. This flow preserves the volume of the bounded domain enclosed by a star-shaped hypersurface and decreases the area of hypersurface under certain conditions. We will prove the long tim

Jan Philipp Bange, David Schmitt, Wiebke Bennecke, Giuseppe Meneghini

Excitons are two-particle correlated bound states that are formed due to Coulomb interaction between single-particle holes and electrons. In the solid-state, cooperative interactions with surrounding quasiparticles can strongly tailor the exciton properties and potentially even create new correlated states of matter. It is thus highly desirable to access suc

Yanjie Dong, Luya Wang, Yuanfang Chi, Jia Wang

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-sh

Maryam Hadipour, Soroush Haseli

In the context of quantum information, a quantum battery refers to a system composed of quantum particles that can store and release energy in a way that is governed by the principles of quantum mechanics. The study of open quantum batteries is motivated by the fact that real-world quantum systems are almost never perfectly isolated from their environment. O

Yijie Zheng, George S. D. Gordon

Ultra-thin multimode optical fiber imaging promises next-generation medical endoscopes reaching high image resolution for deep tissues. However, current technology suffers from severe optical distortion, as the fiber's calibration is sensitive to bending and temperature and thus requires in vivo re-measurement with access to a single end only. We present a n

Haiming Yao, Wei Luo, Wenyong Yu

In this paper, we introduce the novel state-of-the-art Dual-attention Transformer and Discriminative Flow (DADF) framework for visual anomaly detection. Based on only normal knowledge, visual anomaly detection has wide applications in industrial scenarios and has attracted significant attention. However, most existing methods fail to meet the requirements. I

Colin Drewes, Olivia Weng, Andres Meza, Alric Althoff

Cloud FPGAs strike an alluring balance between computational efficiency, energy efficiency, and cost. It is the flexibility of the FPGA architecture that enables these benefits, but that very same flexibility that exposes new security vulnerabilities. We show that a remote attacker can recover "FPGA pentimenti" - long-removed secret data belonging to a prior

L. Ren, C. Robert, M. M. Glazov, M. A. Semina

We have investigated the exciton fine structure in atomically thin WSe2 -based van der Waals heterostructures where the density of optical modes at the location of the semiconductor monolayer can be tuned. The energy splitting $\Delta$ between the bright and dark exciton has been measured by photoluminescence spectroscopy. We demonstrate that $\Delta$ can be

Rafael S. Oyamada, Gabriel M. Tavares, Sylvio Barbon Junior, Paolo Ceravolo

Process simulation is gaining attention for its ability to assess potential performance improvements and risks associated with business process changes. The existing literature presents various techniques, generally grounded in process models discovered from event log data or built upon deep learning algorithms. These techniques have specific strengths and l

Rafael Stahl, Daniel Mueller-Gritschneder, Ulf Schlichtmann

Memory optimization for deep neural network (DNN) inference gains high relevance with the emergence of TinyML, which refers to the deployment of DNN inference tasks on tiny, low-power microcontrollers. Applications such as audio keyword detection or radar-based gesture recognition are heavily constrained by the limited memory on such tiny devices because DNN

Kaihua Qin, Stefanos Chaliasos, Liyi Zhou, Benjamin Livshits

The use of blockchains for automated and adversarial trading has become commonplace. However, due to the transparent nature of blockchains, an adversary is able to observe any pending, not-yet-mined transactions, along with their execution logic. This transparency further enables a new type of adversary, which copies and front-runs profitable pending transac

Tiago Ribeiro, Stephanie Brandl, Anders Søgaard, Nora Hollenstein

We present WebQAmGaze, a multilingual low-cost eye-tracking-while-reading dataset, designed as the first webcam-based eye-tracking corpus of reading to support the development of explainable computational language processing models. WebQAmGaze includes webcam eye-tracking data from 600 participants of a wide age range naturally reading English, German, Spani

Stam Nicolis

We review the idea, put forward in 1982, by Parisi and Sourlas, that the bath of fluctuations, with which a physical system is in equilibrium, can be resolved by the superpartners of the degrees of freedom, defined by the classical action. This implies, in particular, that fermions can be described in terms of their superpartners, using the Nicolai map. We f

Koji Uryu, Shijun Yoshida, Eric Gourgoulhon, Charalampos Markakis

We present numerical solutions for stationary and axisymmetric equilibriums of compact stars associated with extremely strong magnetic fields. The interior of the compact stars is assumed to satisfy ideal magnetohydrodynamic (MHD) conditions, while in the region of negligible mass density the force-free conditions or electromagnetic vacuum are assumed. Solvi

Jozef Bobok, Jernej Činč, Piotr Oprocha, Serge Troubetzkoy

We survey the current state-of-the-art about the dynamical behavior of continuous Lebesgue measure-preserving maps on one-dimensional manifolds.

Hajo Holzmann, Bernhard Klar

We suggest novel correlation coefficients which equal the maximum correlation for a class of bivariate Lancaster distributions while being only slightly smaller than maximum correlation for a variety of further bivariate distributions. In contrast to maximum correlation, however, our correlation coefficients allow for rank and moment-based estimators which a

Chairote Piyakulworawat, Asiri Thennakoon, Junjie Yang, Hideki Yoshizawa

Thermodynamics studies of a prototypical quasi-two-dimensional frustrated magnet Ba$_2$Sn$_2$ZnCr$_{7p}$Ga$_{10-7p}$O$_{22}$ where the magnetic Cr$^{3+}$ ions are arranged in a triangular network of bipyramids show that the magnetic zero-point entropy for $p=0.98$ is 55(1)\% of the entropy expected when the Cr$^{3+}$ moments are fully disordered. Furthermore

Jian Ma, Mingjun Zhao, Chen Chen, Ruichen Wang

Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user instructions.Although the synthesis performance is fascinating, one significant limitation of current image generation models is their insufficient ability to generate text coherent

G. W. Bowden, M. Brown

Space launches produce ionospheric disturbances which can be observed through measurements such as Global Navigation Satellite System signal delays. Here we report observations and numerical simulations of the ionospheric depletion due to a Small-Lift Launch Vehicle. The case examined was the launch of a Rocket Lab Electron at 22:30 UTC on March 22, 2021. De

Jacob Kryczka

This work is the first in a series of papers that, among other things, extends the formalism of diolic differential calculus, wherein a new context for obtaining differential calculus in vector bundles was established. Here we provide a modest but interesting generalization of this formalism to include a class of vector bundles with additional inner structur

L. Mátyás, I. F. Barna

In the description of transport phenomena an important aspect represents the diffusion. In certain cases the diffusion may appear together with convection. In this paper we study the diffusion equation with the self similar Ansatz. With an appropriate change of variables we found original solutions of diffusion equation for infinite horizon. Here we present

Linfeng Wen, Chengying Gao, Changqing Zou

Content affinity loss including feature and pixel affinity is a main problem which leads to artifacts in photorealistic and video style transfer. This paper proposes a new framework named CAP-VSTNet, which consists of a new reversible residual network and an unbiased linear transform module, for versatile style transfer. This reversible residual network can

Yasha Neiman

Using Krasnov's formulation of General Relativity (GR), we develop a lightcone ansatz for self-dual gravity (along with linearized anti-self-dual perturbations) in the Poincare patch of de Sitter space. This amounts to a generalization of Plebanski's "second heavenly equation" to non-zero cosmological constant. The only interaction vertices are cubic ones, f

Hang Liu, Liuming Liu, Peng Sun, Wei Sun

In heavy quark limit, the lowest-lying charmed baryons with two light quarks can form an SU(3) triplet and sextet. The $\Xi_c$ in the SU(3) triplet and $\Xi_c'$ in the sextet have the same $J^{PC}$ quantum number and can mix due to the finite charm quark mass and the fact the strange quark is heavier than the up/down quark. We explore the $\Xi_c$-$\Xi_c'$ mi

Claude Formanek, Callum Rhys Tilbury, Jonathan Shock, Kale-ab Tessera

'Reincarnation' in reinforcement learning has been proposed as a formalisation of reusing prior computation from past experiments when training an agent in an environment. In this paper, we present a brief foray into the paradigm of reincarnation in the multi-agent (MA) context. We consider the case where only some agents are reincarnated, whereas the others

Ilkka Helenius, Hannu Paukkunen

We apply the SACOT-$m_{\rm T}$ general-mass variable flavour number scheme (GM-VFNS) to the inclusive B-meson production in hadronic collisions at next-to-leading order in perturbative Quantum Chromodynamics. In the GM-VFNS approach one matches the fixed-order heavy-quark production cross sections, accurate at low transverse momentum ($p_{\rm T}$), with the

Gabriel Calvo, Carmen Armero, Luigi Spezia

Sports data analytics is a relevant topic in applied statistics that has been growing in importance in recent years. In basketball, a player or team has a hot hand when their performance during a match is better than expected or they are on a streak of making consecutive shots. This phenomenon has generated a great deal of controversy with detractors claimin

Damian Andrew Tamburri, Francesca Arcelli Fontana, Riccardo Roveda, Valentina Lenarduzzi

Technical debt occurs in many different forms across software artifacts. One such form is connected to software architectures where debt emerges in the form of structural anti-patterns across architecture elements, namely, architecture smells. As defined in the literature, ``Architecture smells are recurrent architectural decisions that negatively impact int

Sebastian M. F. Raupach, Mariia Sidorova, Alexej D. Semenov

Superconducting nanostrip single-photon detectors (SNSPD) are wide-spread tools in photonic quantum technologies. Here, we study afterpulsing in commercial SNSPD made of amorphous superconducting material. We find that the probability of an afterpulse is not a constant but depends on the mean number of photons per light pulse including mean numbers much less

Giulio Morpurgo

Using the fact that the number of combinations $p_{1}$, $p_{2}$, where $p_{1}$ and $p_{2}$ are odd primes, with $p_{1} \leq p_{2}$ and $p_{1} + p_{2} \leq 2N$ is equal to the total number of Goldbach pairs for all the even integers from 6 to $2N$, we derive in a simple way an accurate estimate of the number of Goldbach pairs for $N$

Maximilian Bernhard, Niklas Strauß, Matthias Schubert

Change detection in remote sensing imagery is essential for a variety of applications such as urban planning, disaster management, and climate research. However, existing methods for identifying semantically changed areas overlook the availability of semantic information in the form of existing maps describing features of the earth's surface. In this paper,

Sigurdur Hafstein, Aneel Tanwani

With the objective of developing computational methods for stability analysis of switched systems, we consider the problem of finding the minimal lower bounds on average dwell-time that guarantee global asymptotic stability of the origin. Analytical results in the literature quantifying such lower bounds assume existence of multiple Lyapunov functions that s

Wenxuan Pan, Feifei Zhao, Yi Zeng, Bing Han

The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking Neural Network (SNN) based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence because of its brain-inspired structure and the po

Kan Zheng, Haojun Yang, Ziqiang Ying, Pengshuo Wang

Beamforming techniques have been widely used in the millimeter wave (mmWave) bands to mitigate the path loss of mmWave radio links as the narrow straight beams by directionally concentrating the signal energy. However, traditional mmWave beam management algorithms usually require excessive channel state information overhead, leading to extremely high computa

Bernard W. Silverman, Lax Chan, Kyle Vincent

Multiple systems estimation using a Poisson loglinear model is a standard approach to quantifying hidden populations where data sources are based on lists of known cases. Information criteria are often used for selecting between the large number of possible models. Confidence intervals are often reported conditional on the model selected, providing an over-o

Luca Maestrini, Aishwarya Bhaskaran, Matt P. Wand

A recent article on generalised linear mixed model asymptotics, Jiang et al. (2022), derived the rates of convergence for the asymptotic variances of maximum likelihood estimators. If $m$ denotes the number of groups and $n$ is the average within-group sample size then the asymptotic variances have orders $m^{-1}$ and $(mn)^{-1}$, depending on the parameter.

Rafael Ferreira da Silva, Rosa M. Badia, Venkat Bala, Debbie Bard

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing

Daiki Maeda

Let G be a 3-dimensional connected Lie group with a left-invariant nondegenerate CR structure. We show that all the chains on G are closed if and only if G is CR equivalent to one of the left-invariant spherical CR structures on SU(2), SO(3) and a quotient group of the Heisenberg group.

Samuel T. Spencer, Vikas Joshi, Alison M. W. Mitchell

In what will likely be a litany of generative-model-themed arXiv submissions celebrating April the 1st, we evaluate the capacity of state-of-the-art transformer models to create a paper detailing the detection of a Pulsar Wind Nebula with a non-existent Imaging Atmospheric Cherenkov Telescope (IACT) Array. We do this to evaluate the ability of such models to

Qian Wang, Kamran Paynabar

Point cloud data are widely used in manufacturing applications for process inspection, modeling, monitoring and optimization. The state-of-art tensor regression techniques have effectively been used for analysis of structured point cloud data, where the measurements on a uniform grid can be formed into a tensor. However, these techniques are not capable of h

Hong Shaopeng, Liu Xiangdong

In this paper, we consider Fredlin-Wentzell type large deviation principle (LDP) of multidimensional reflected stochastic partial differential equations in a convex domain, allowing for oblique direction of reflection. To prove the LDP, a sufficient condition for the weak convergence method and penalized method plays an important role.

Alexander Filusch, Holger Fehske

Flat bands can be divided into singular and non-singular ones according to the behavior of their Bloch wave function around band-crossing points in momentum space. We analyze the flat band in the Dice model, which can be tuned by a uniaxial strain in the zigzag direction and a Haldane-type next-nearest neighbor interaction, and derive the topological phase d

Takayuki Miura, Toshiki Shibahara, Masanobu Kii, Atsunori Ichikawa

Privacy protection with synthetic data generation often uses differentially private statistics and model parameters to quantitatively express theoretical security. However, these methods do not take into account privacy protection due to the randomness of data generation. In this paper, we theoretically evaluate R\'{e}nyi differential privacy of the randomne

G. P. Curbera, S. Okada, W. J. Ricker

The finite Hilbert transform $T$ is a classical (singular) kernel operator which is continuous in every rearrangement invariant space $X$ over $(-1,1)$ having non-trivial Boyd indices. For $X=L^p$, $1<p<\infty$, this operator has been intensively investigated since the 1940's (also under the guise of the ``airfoil equation''). Recently, the extension and inv

Maria Fuwa

We propose a system for passive magnetic levitation and three-dimensional harmonic trapping of soft ferromagnets. Our protocol utilizes the magnetic field gradient for vertical trapping, and the finite size effect of the Meissner effect for horizontal trapping. We provide numerical and analytical estimations of possible mechanical dissipations to show that o

Andreas Gustavsson

We obtain a Lagrangian for a lightlike dimensional reduction of the nonabelian M5 brane theory in six dimension. We assume that the six-manifold has at least one conformal Killing spinor and two commuting lightlike Killing vector fields, and we perform the dimensional reduction along one of these lightlike directions.

Ayokunle Olalekan Ige, Mohd Halim Mohd Noor

In recent times, various modules such as squeeze-and-excitation, and others have been proposed to improve the quality of features learned from wearable sensor signals. However, these modules often cause the number of parameters to be large, which is not suitable for building lightweight human activity recognition models which can be easily deployed on end de

Mario Beraha, Stefano Favaro

Completely random measures (CRMs) provide a broad class of priors, arguably, the most popular, for Bayesian nonparametric (BNP) analysis of trait allocations. As a peculiar property, CRM priors lead to predictive distributions that share the following common structure: for fixed prior's parameters, a new data point exhibits a Poisson (random) number of ``new

Julia Plavnik, Sean Sanford, Dalton Sconce

Tambara and Yamagami investigated a simple set of fusion rules with only one non-invertible object, and proved under which circumstances those rules could be given a coherent associator. We consider a generalization of such fusion rules to the setting where simple objects are no longer required to be split simple. Over the real numbers, this means that objec

Jinwoo Kim, Janghyuk Choi, Ho-Jin Choi, Seon Joo Kim

Object-centric learning (OCL) aspires general and compositional understanding of scenes by representing a scene as a collection of object-centric representations. OCL has also been extended to multi-view image and video datasets to apply various data-driven inductive biases by utilizing geometric or temporal information in the multi-image data. Single-view i

Georgios Papadopoulos, Fran Silavong, Sean Moran

Finding relevant and high-quality datasets to train machine learning models is a major bottleneck for practitioners. Furthermore, to address ambitious real-world use-cases there is usually the requirement that the data come labelled with high-quality annotations that can facilitate the training of a supervised model. Manually labelling data with high-quality

Liu Xiangdong, Hong Shaopeng

In this paper, we study the asymptotic behavior of randomly perturbed path-dependent stochastic differential equations with small parameter $\vartheta_{\varepsilon}$, when $\varepsilon \rightarrow 0$, $\vartheta_\varepsilon$ goes to $0$. When $\varepsilon \rightarrow 0$, we establish large deviation principle. The proof of the results relies on the weak conv

Yiwu Zhong, Licheng Yu, Yang Bai, Shangwen Li

The abundance of instructional videos and their narrations over the Internet offers an exciting avenue for understanding procedural activities. In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a large-scale dataset of web instructional videos and their narrations, without using human

Xing Huang, Chen-Te Ma

The gauge formulation of Einstein gravity in AdS$_3$ background leads to a boundary theory that breaks modular symmetry and loses the covariant form. We examine the Weyl anomaly for the cylinder and torus manifolds. The divergent term is the same as the Liouville theory when transforming from the cylinder to the sphere. The general Weyl transformation on the

W J P van Tonder, J Links

Several studies have exploited the integrable structure of central spin models to deepen understanding of these fundamental systems. In recent years, an underlying supersymmetry for systems with XX interactions has been uncovered. Here we report that a class of central spin models with XY interactions is also supersymmetric and integrable. The associated Bet

Naveed Akhtar, Mohammad A. A. K. Jalwana

Saliency methods provide post-hoc model interpretation by attributing input features to the model outputs. Current methods mainly achieve this using a single input sample, thereby failing to answer input-independent inquiries about the model. We also show that input-specific saliency mapping is intrinsically susceptible to misleading feature attribution. Cur

Janne Alatalo, Tuomo Sipola, Mika Rantonen

Satellite-based Synthetic Aperture Radar (SAR) images can be used as a source of remote sensed imagery regardless of cloud cover and day-night cycle. However, the speckle noise and varying image acquisition conditions pose a challenge for change detection classifiers. This paper proposes a new method of improving SAR image processing to produce higher qualit

Olivier Briant, Hadrien Cambazard, Diego Cattaruzza, Nicolas Catusse

Warehouses are nowadays the scene of complex logistic problems integrating different decision layers. This paper addresses the Joint Order Batching, Picker Routing and Sequencing Problem with Deadlines (JOBPRSP-D) in rectangular warehouses. To tackle the problem an exponential linear programming formulation is proposed. It is solved with a column generation

Bin Yang, Shanyun Liu, Tao Xu, Chuyu Li

Space-air-ground integrated networks (SAGINs), which have emerged as an expansion of terrestrial networks, provide flexible access, ubiquitous coverage, high-capacity backhaul, and emergency/disaster recovery for mobile users (MUs). While the massive benefits brought by SAGIN may improve the quality of service, unauthorized access to SAGIN entities is potent

Angelo Maurizio Brambilla, Davide del Giudice, Daniele Linaro, Federico Bizzarri

This work presents a technique to estimate on-line the global momentum of an electric power system. It exploits the footprint of the principal frequency system dynamics. Probing tones are injected by a grid-forming converter-interfaced generator and the speed of its virtual rotor is exploited to extrapolate the footprint. The proposed technique is entirely d

Sébastien Da Veiga, Fabrice Gamboa, Thierry Klein, Agnès Lagnoux

The main objective of this paper is to estimate optimally Sobol' indices at any order when a unique input/output i.i.d.\ sample is available. Our approach stands on three main ingredients: semi-parametric estimation theory, high-order kernel estimation (inspired by the paper of Doksum in 1995), and mirror-type transformations as introduced in Bertin 2020 and

Hugo Marques, Andrés González de Castilla, Simon Müller, Irina Smirnova

Recently an improved long-range model for electrolyte solutions was developed that is applicable from infinite dilution to pure salt. This paper tests this claim for the first time applying it to the calculation of liquid-liquid equilibria for mixtures of different ionic liquids (ILs) and water. The conventional Pitzer-Debye-H\"uckel (PDH) equation is compar

Bruno Schapira

We consider the branching capacity of the range of a simple random walk on $\mathbb Z^d$, with $d \ge 5$, and show that it falls in the same universality class as the volume and the capacity of the range of simple random walks and branching random walks. To be more precise we prove a law of large numbers in dimension $d \ge 6$, with a logarithmic correction

Malitha Gunawardhana, Chathuki Navanjana, Dinithi Fernando, Nipuna Upeksha

Noise reduction is a crucial aspect of hearing aids, which researchers have been striving to address over the years. However, most existing noise reduction algorithms have primarily been evaluated using English. Considering the linguistic differences between English and Sinhala languages, including variation in syllable structures and vowel duration, it is v

Yuming Qin, Xiaolei Dong, Alain Miranville, Ke Wang

In this paper, we study the strong global attractors for a three dimensional nonclassical diffusion equation with memory. First, we prove the existence and uniqueness of strong solutions for the equations by the Galerkin method. Then we prove the existence of global attractors for the equations in $H^2(\Omega)\cap H^1_0(\Omega)\times L^2_\mu(\mathbb{R}^+;H^2

Yixuan Wang, Weichao Zhou, Jiameng Fan, Zhilu Wang

Neural networks (NNs) playing the role of controllers have demonstrated impressive empirical performances on challenging control problems. However, the potential adoption of NN controllers in real-life applications also gives rise to a growing concern over the safety of these neural-network controlled systems (NNCSs), especially when used in safety-critical

Zakhar Kabluchko, Daniel Rosen, Christoph Thäle

Consider a stationary Poisson process of horospheres in a $d$-dimensional hyperbolic space. In the focus of this note is the total surface area these random horospheres induce in a sequence of balls of growing radius $R$. The main result is a quantitative, non-standard central limit theorem for these random variables as the radius $R$ of the balls and the sp

Xiaoyu Zhang, Jianping Li, Po-Wei Chi, Senthil Chandrasegaran

With the most advanced natural language processing and artificial intelligence approaches, effective summarization of long and multi-topic documents -- such as academic papers -- for readers from different domains still remains a challenge. To address this, we introduce ConceptEVA, a mixed-initiative approach to generate, evaluate, and customize summaries fo

Jonas Bergström, Everett W. Howe, Elisa Lorenzo García, Christophe Ritzenthaler

This paper goes beyond Katz-Sarnak theory on the distribution of curves over finite fields according to their number of rational points, theoretically, experimentally and conjecturally. In particular, we give a formula for the limits of the moments measuring the asymmetry of this distribution for (non-hyperelliptic) curves of genus $g \geq 3$. The experiment

Aiqing Zhu, Tom Bertalan, Beibei Zhu, Yifa Tang

We focus on learning unknown dynamics from data using ODE-nets templated on implicit numerical initial value problem solvers. First, we perform Inverse Modified error analysis of the ODE-nets using unrolled implicit schemes for ease of interpretation. It is shown that training an ODE-net using an unrolled implicit scheme returns a close approximation of an I

Akifumi Okuno, Kazuharu Harada

This study proposes an interpretable neural network-based non-proportional odds model (N$^3$POM) for ordinal regression. N$^3$POM is different from conventional approaches to ordinal regression with non-proportional models in several ways: (1) N$^3$POM is defined for both continuous and discrete responses, whereas standard methods typically treat the ordered

Chaitanya Bhatt, Vijay Nenmeli, Sachindeo Vaidya

We study the classical non-linear dynamics of the $SU(2)$ Yang-Mills matrix model introduced in [1] as a low-energy approximation to two-color QCD. Restricting to the spin-0 sector of the model, we unearth an unexpected tetrahedral symmetry, which endows the dynamics with an extraordinarily rich structure. Amongst other things, we find that the spin-0 sector

Johannes J. Buck

In this paper, we compute for odd fundamental discriminants $D>1$ the Fourier expansion of non-holomorphic elliptic Eisenstein series for $\Gamma_0(D)$ with quadratic nebentypus character $\chi_D$ satisfying a certain plus space condition. For each genus of $\mathbb{Q}(\sqrt{D})$, we obtain an associated plus space condition and corresponding Eisenstein seri

Xiaoyu Zhang, Xiwei Xuan, Alden Dima, Thurston Sexton

With the rapid accumulation of text data produced by data-driven techniques, the task of extracting "data annotations"--concise, high-quality data summaries from unstructured raw text--has become increasingly important. The recent advances in weak supervision and crowd-sourcing techniques provide promising solutions to efficiently create annotations (labels)

Victor G. Lopez, Matthias A. Müller

In this paper, an off-policy reinforcement learning algorithm is designed to solve the continuous-time LQR problem using only input-state data measured from the system. Different from other algorithms in the literature, we propose the use of a specific persistently exciting input as the exploration signal during the data collection step. We then show that, u

F. M. J. Cozijn, M. L. Diouf, W. Ubachs

The saturated absorption spectrum of the hyperfine-less S(0) quadrupole line in the (2-0) band of H$_2$ is measured at $\lambda=1189$ nm, using the NICE-OHMS technique under cryogenic conditions (72~K). It is for the first time that a Lamb dip of a molecular quadrupole transition is recorded. At low (150-200 W) saturation powers a single narrow Lamb dip is o

Alireza Rahimpour, Navid Fallahinia, Devesh Upadhyay, Justin Miller

The performance of the current collision avoidance systems in Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically affected by low light and adverse weather conditions. Collisions with large animals such as deer in low light cause significant cost and damage every year. In this paper, we propose the first AI-based method

Daniel Disegni

We introduce `canonical' classes in the Selmer groups of certain Galois representations with a conjugate-symplectic symmetry. They are images of special cycles in unitary Shimura varieties, and defined uniquely up to a scalar. The construction is a slight refinement of one of Y. Liu, based on the conjectural modularity of Kudla's theta series of special cycl

Yuto Fukushima, Kaishu Kawaguchi, Kenta Kuroda, Masayuki Ochi

We use a pump-probe, spin-, and angle-resolved photoemission spectroscopy (ARPES) with a 10.7 eV laser accessible up to the Brillouin zone edge, and reveal for the first time the entire band structure, including the unoccupied side, for the elemental bismuth (Bi) with the spin-polarized surface states. Our data identify Bi as in a strong topological insulato

Kangyeol Kim, Sunghyun Park, Junsoo Lee, Jaegul Choo

Recent remarkable improvements in large-scale text-to-image generative models have shown promising results in generating high-fidelity images. To further enhance editability and enable fine-grained generation, we introduce a multi-input-conditioned image composition model that incorporates a sketch as a novel modal, alongside a reference image. Thanks to the

Sunghyun Park, Sunghyo Chung, Jungsoo Lee, Jaegul Choo

Despite the recent remarkable improvements in scene text recognition (STR), the majority of the studies focused mainly on the English language, which only includes few number of characters. However, STR models show a large performance degradation on languages with a numerous number of characters (e.g., Chinese and Korean), especially on characters that rarel

Hengjia Li, Tu Zheng, Zhihao Chi, Zheng Yang

Transformer-based networks have achieved impressive performance in 3D point cloud understanding. However, most of them concentrate on aggregating local features, but neglect to directly model global dependencies, which results in a limited effective receptive field. Besides, how to effectively incorporate local and global components also remains challenging.

Lue Wu, Maodong Gao, Jin-Yu Liu, Hao-Jing Chen

Thermal silica is a common dielectric used in all silicon-photonic circuits. And bound hydroxyl ions (Si-OH) can provide a significant component of optical loss in this material on account of the wet nature of the thermal oxidation process. A convenient way to quantify this loss relative to other mechanisms is through OH-absorption at 1380 nm. Here, using ul

Yusen Wu, Bujiao Wu, Yanqi Song, Xiao Yuan

Quantifying the complexity of quantum states is a longstanding key problem in various subfields of science, ranging from quantum computing to the black-hole theory. The lower bound on quantum pure state complexity has been shown to grow linearly with system size [Haferkamp et al., 2022]. However, extending this result to noisy circuit environments, which bet

Muzaffer Adak, Tekin Dereli, Tomi S. Koivisto, Caglar Pala

In the conventional formulation of general relativity, gravity is represented by the metric curvature of Riemannian geometry. There are also alternative formulations in flat affine geometries, wherein the gravitational dynamics is instead described by torsion and nonmetricity. These so called general teleparallel geometries may also have applications in mate

Seonghoon Yu, Paul Hongsuck Seo, Jeany Son

Referring image segmentation (RIS) aims to find a segmentation mask given a referring expression grounded to a region of the input image. Collecting labelled datasets for this task, however, is notoriously costly and labor-intensive. To overcome this issue, we propose a simple yet effective zero-shot referring image segmentation method by leveraging the pre-

Áron Rozgonyi, Gábor Széchenyi

Enhancing the lifetime of qubits with quantum code-based memories on different quantum hardware is a significant step towards fault-tolerant quantum computing. We theoretically show that the break-even point, i.e., preserving arbitrary quantum information longer than the lifetime of a single idle qubit, can be beaten even with the quantum phase-flip repetiti

Trent Henderson, Annie G. Bryant, Ben D. Fulcher

The variety of complex algorithmic approaches for tackling time-series classification problems has grown considerably over the past decades, including the development of sophisticated but challenging-to-interpret deep-learning-based methods. But without comparison to simpler methods it can be difficult to determine when such complexity is required to obtain

Wei Wei, Peng Wang, Sizhe Wang

This article investigates the challenge of achieving functional tool-use grasping with high-DoF anthropomorphic hands, with the aim of enabling anthropomorphic hands to perform tasks that require human-like manipulation and tool-use. However, accomplishing human-like grasping in real robots present many challenges, including obtaining diverse functional gras

Dongyeop Jang, Tae-Rim Yun, Choong-Yeol Lee, Young-Kyu Kwon

Traditional Korean medicine (TKM) emphasizes individualized diagnosis and treatment. This uniqueness makes AI modeling difficult due to limited data and implicit processes. Large language models (LLMs) have demonstrated impressive medical inference, even without advanced training in medical texts. This study assessed the capabilities of GPT-4 in TKM, using t

Hongen Liu

Although end-to-end video text spotting methods based on Transformer can model long-range dependencies and simplify the train process, it will lead to large computation cost with the increase of the frame size in the input video. Therefore, considering the resolution of ICDAR 2023 DSText is 1080 * 1920 and slicing the video frame into several areas will dest

Alexander Mai, Dor Verbin, Falko Kuester, Sara Fridovich-Keil

We present Neural Microfacet Fields, a method for recovering materials, geometry, and environment illumination from images of a scene. Our method uses a microfacet reflectance model within a volumetric setting by treating each sample along the ray as a (potentially non-opaque) surface. Using surface-based Monte Carlo rendering in a volumetric setting enables

Qian Wang, Huanyi Shui, Thi Tu Trinh Tran, Milad Zafar Nezhad

In the automotive industry, the full cycle of managing in-use vehicle quality issues can take weeks to investigate. The process involves isolating root causes, defining and implementing appropriate treatments, and refining treatments if needed. The main pain-point is the lack of a systematic method to identify causal relationships, evaluate treatment effecti

Sebastian Neumayer, Lénaïc Chizat, Michael Unser

In supervised learning, the regularization path is sometimes used as a convenient theoretical proxy for the optimization path of gradient descent initialized from zero. In this paper, we study a modification of the regularization path for infinite-width 2-layer ReLU neural networks with nonzero initial distribution of the weights at different scales. By expl

Łukasz Kułacz

Dynamic spectrum access systems typically require information about the spectrum occupancy and thus the presence of other users in order to make a spectrum al-location decision for a new device. Simple methods of spectrum occupancy detection are often far from reliable, hence spectrum occupancy detection algorithms supported by machine learning or artificial

Benjamin Egan, Yuri Nikolayevsky

A simple graph is called triangular if every edge of it belongs to a triangle. We conjecture that any graphical degree sequence all terms of which are greater than or equal to 4 has a triangular realisation, and establish this conjecture for a class of biregular graphical degree sequences.

Qihang Fan, Huaibo Huang, Jiyang Guan, Ran He

Vision Transformers (ViTs) have been shown to be effective in various vision tasks. However, resizing them to a mobile-friendly size leads to significant performance degradation. Therefore, developing lightweight vision transformers has become a crucial area of research. This paper introduces CloFormer, a lightweight vision transformer that leverages context

Takumi Kanai, Naoya Sogi, Atsuto Maki, Kazuhiro Fukui

This paper proposes a new method for anomaly detection in time-series data by incorporating the concept of difference subspace into the singular spectrum analysis (SSA). The key idea is to monitor slight temporal variations of the difference subspace between two signal subspaces corresponding to the past and present time-series data, as anomaly score. It is

Chunhua Li, Yoshinori Nishii, Yuji Sagawa, Hideaki Sunagawa

We give a survey on recent developments on nonlinear Schr\"odinger equations with dissipative structure based on the authors' recent works.