March 2023 arXiv papers — page 105

Timon Barlag, Miika Hannula, Juha Kontinen, Nina Pardal

Semiring semantics for first-order logic provides a way to trace how facts represented by a model are used to deduce satisfaction of a formula. Team semantics is a framework for studying logics of dependence and independence in diverse contexts such as databases, quantum mechanics, and statistics by extending first-order logic with atoms that describe depend

Thomas Wong, Mauricio Barahona

We present a robust deep incremental learning framework for regression tasks on financial temporal tabular datasets which is built upon the incremental use of commonly available tabular and time series prediction models to adapt to distributional shifts typical of financial datasets. The framework uses a simple basic building block (decision trees) to build

Lucas Maison, Yannick Estève

Thanks to the rise of self-supervised learning, automatic speech recognition (ASR) systems now achieve near-human performance on a wide variety of datasets. However, they still lack generalization capability and are not robust to domain shifts like accent variations. In this work, we use speech audio representing four different French accents to create fine-

Sayan Bandyapadhyay, William Lochet, Saket Saurabh

Clustering with capacity constraints is a fundamental problem that attracted significant attention throughout the years. In this paper, we give the first FPT constant-factor approximation algorithm for the problem of clustering points in a general metric into $k$ clusters to minimize the sum of cluster radii, subject to non-uniform hard capacity constraints.

Javier Coronado-Blázquez

The determination of distance is fundamental in astrophysics. Gamma-ray sources are poorly characterized in this sense, as the limited angular resolution and poor photon-count statistics in gamma-ray astronomy makes it difficult to associate them to a multiwavelength object with known redshift. Taking the 1794 active galactic nuclei (AGNs) with known redshif

Marc Arnaudon, Koléhè Coulibaly-Pasquier, Laurent Miclo

We investigate renormalized curvature flow (RCF) and stochastic renormalized curvature flow (SRCF) for convex sets in the plane.RCF is the gradient descent flow for logarithm of $\sigma/\lambda^2$ where $\sigma$ is the perimeter and $\lambda$ is the volume. SRCF is RCF perturbated by a Brownian noise and has the remarkable property that it can be intertwined

Svante Janson

We survey the definition and some elementary properties of real trees. There are no new results, as far as we know. One purpose is to give a number of different definitions and show the equivalence between them. We discuss also, for example, the four-point inequality, the length measure and the connection to the theory of Gromov hyperbolic spaces. Several ex

Manjunath Omana Kuttan, Jan Steinheimer, Kai Zhou, Marcus Bleicher

The centrality determination and the estimated fluctuations of number of participant nucleons $N_{part}$ in Au-Au collisions at 1.23 $A$GeV beam kinetic energy suffers from severe model dependencies. Comparing the Glauber Monte Carlo (MC) and UrQMD transport models, it is shown that $N_{part}$ is a strongly model dependant quantity. In addition, for any give

Wolf-Jürgen Beyn, Thorsten Hüls

In this work we provide detailed estimates of maximal principal angles between subspaces and we analyze their smoothness for smoothly varying subspaces. This leads to a new definition of angular values for linear dynamical systems in continuous time. We derive some of their properties complementary to the theory of angular values developed in [W.-J. Beyn, G.

Pierre-Jean Meyer

The literature on reachability analysis methods for neural networks currently only focuses on uncertainties on the network's inputs. In this paper, we introduce two new approaches for the reachability analysis of neural networks with additional uncertainties on their internal parameters (weight matrices and bias vectors of each layer), which may open the fie

J. Dimock, Cheng Yuan

We study flow of renormalization group (RG) transformations for the massless Gross-Neveu model in a non-perturbative formulation. The model is defined on a d=2 dimensional Euclidean space with a finite volume. The quadratic approximation to the flow stays bounded after suitable renormalization. We show that for weak coupling this property also is true for th

Monika Drzewiecka, Aleksander Ivanov, Bartosz Mokry

Let $G$ be a closed highly homogeneous subgroup of $S_{\infty}$ not involving circular orderings. We show that the closure of a conjugacy class from $G$ contains a conjugacy class which is comeagre in it. Furthermore, we show that the family of finite partial maps extendable to elements of this conjugacy class has the cofinal amalgamation property. Similar s

Biao Fu, Minpeng Liao, Kai Fan, Zhongqiang Huang

A popular approach to streaming speech translation is to employ a single offline model with a wait-k policy to support different latency requirements, which is simpler than training multiple online models with different latency constraints. However, there is a mismatch problem in using a model trained with complete utterances for streaming inference with par

Bao Quoc Tang, Bao-Ngoc Tran

Reactions with enzymes are critical in biochemistry, where the enzymes act as catalysis in the process. One of the most used mechanisms for modeling enzyme-catalyzed reactions is the Michaelis-Menten (MM) kinetic. In the ODE level, i.e. concentrations are only on time-dependent, this kinetic can be rigorously derived from mass action law using quasi-steady-s

Hailong Qiu

In this study, we prove rigourous bounds on the error and stability analysis of deep learning methods for the nonstationary Magneto-hydrodynamics equations. We obtain the approximate ability of the neural network by the convergence of a loss function and the convergence of a Deep Neural Network (DNN) to the exact solution. Moreover, we derive explicit error

Aby Philip, Soorya Rethinasamy, Vincent Russo, Mark M. Wilde

Quantifying entanglement is an important task by which the resourcefulness of a quantum state can be measured. Here, we develop a quantum algorithm that tests for and quantifies the separability of a general bipartite state by using the quantum steering effect, the latter initially discovered by Schr\"odinger. Our separability test consists of a distributed

Mário A. T. Figueiredo, Catarina A. Oliveira

Distinguishing cause from effect using observations of a pair of random variables is a core problem in causal discovery. Most approaches proposed for this task, namely additive noise models (ANM), are only adequate for quantitative data. We propose a criterion to address the cause-effect problem with categorical variables (living in sets with no meaningful o

Hengyuan Zhao, Hao Luo, Yuyang Zhao, Pichao Wang

Parameter-Efficient Transfer Learning (PETL) aims at efficiently adapting large models pre-trained on massive data to downstream tasks with limited task-specific data. In view of the practicality of PETL, previous works focus on tuning a small set of parameters for each downstream task in an end-to-end manner while rarely considering the task distribution sh

Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, In So Kweon

This survey reviews the progress of diffusion models in generating images from text, ~\textit{i.e.} text-to-image diffusion models. As a self-contained work, this survey starts with a brief introduction of how diffusion models work for image synthesis, followed by the background for text-conditioned image synthesis. Based on that, we present an organized rev

Károly J. Böröczky, Danylo Radchenko, João P. G. Ramos

We prove explicit stability estimates for the sphere packing problem in dimensions 8 and 24, showing that, in the lattice case, if a lattice is $\sim \varepsilon$ close to satisfying the optimal density, then it is, in a suitable sense, $O(\varepsilon^{1/2})$ close to the $E_8$ and Leech lattices, respectively. In the periodic setting, we prove that, under t

Amélie Piveteau, Alastair A. Abbott, Sadiq Muhammad, Mohamed Bourennane

We show that weakly entangled states can improve communication over a qubit channel using only separate, interference-free, measurements of individual photons. We introduce a communication task corresponding to the cryptographic primitive known as secret sharing and show that all steerable two-qubit isotropic states provide a quantum advantage in the success

Yan-Yan Hou, Jian Li, Xiu-Bo Chen, Chong-Qiang Ye

Label propagation is an essential semi-supervised learning method based on graphs, which has a broad spectrum of applications in pattern recognition and data mining. This paper proposes a quantum semi-supervised classifier based on label propagation. Considering the difficulty of graph construction, we develop a variational quantum label propagation (VQLP) m

Roland Wagner, Jenny Niebsch, Ronny Ramlau

Modern Giant Segmented Mirror Telescopes (GSMTs) like the Extremely Large Telescope, which is currently under construction, depend heavily on Adaptive Optics (AO) systems to correct for atmospheric distortions. However, a residual blur always remains in the astronomical images corrected by Single Conjugate AO (SCAO) systems due to fitting and bandwidth error

Zhaoyang Liu, Tingxuan Han, Donald B. Rubin, Ke Deng

Re-randomization has gained popularity as a tool for experiment-based causal inference due to its superior covariate balance and statistical efficiency compared to classic randomized experiments. However, the basic re-randomization method, known as ReM, and many of its extensions have been deemed sub-optimal as they fail to prioritize covariates that are mor

Christopher I. Calle, Shaunak D. Bopardikar

In this work, we consider a sensor selection drawn at random by a sampling with replacement policy for a linear time-invariant dynamical system subject to process and measurement noise. We employ the Kalman filter to estimate the state of the system. However, the statistical properties of the filter are not deterministic due to the stochastic selection of se

Xuenan Xu, Zhiling Zhang, Zelin Zhou, Pingyue Zhang

Compared with ample visual-text pre-training research, few works explore audio-text pre-training, mostly due to the lack of sufficient parallel audio-text data. Most existing methods incorporate the visual modality as a pivot for audio-text pre-training, which inevitably induces data noise. In this paper, we propose to utilize audio captioning to generate te

Ming Han, Tao Wang, Jianglin Wu, Huan Zhou

Weak degeneracy is a variation of degeneracy which shares many nice properties of degeneracy. In particular, if a graph $G$ is weakly $d$-degenerate, then for any $(d + 1)$-list assignment $L$ of $G$, one can construct an $L$-coloring of $G$ by a modified greedy coloring algorithm. It is known that planar graphs of girth 5 are 3-choosable and locally planar

Pascal Peter

Probabilistic diffusion models enjoy increasing popularity in the deep learning community. They generate convincing samples from a learned distribution of input images with a wide field of practical applications. Originally, these approaches were motivated from drift-diffusion processes, but these origins find less attention in recent, practice-oriented publ

Nico Reski, Aris Alissandrakis, Andreas Kerren

The use of head-mounted display technologies for virtual reality experiences is inherently single-user-centred, allowing for the visual immersion of its user in the computer-generated environment. This isolates them from their physical surroundings, effectively preventing external visual information cues, such as the pointing and referral to an artifact by a

Erik Ostrowski, Muhammad Shafique

One key bottleneck of employing state-of-the-art semantic segmentation networks in the real world is the availability of training labels. Conventional semantic segmentation networks require massive pixel-wise annotated labels to reach state-of-the-art prediction quality. Hence, several works focus on semantic segmentation networks trained with only image-lev

Alessandra Azzollini, Steven Neil Shore, Paul Kuin

A novel method is presented which can pin down the time the accretion disk re-established itself in the RS Oph system after it experienced a nova disruption. The method is based on the re-ionisation of the ejecta by photoionisation from the radiation released in the boundary layer from accretion.

Roman Korkin, Ivan Oseledets, Aleksandr Katrutsa

This study considers the object localization problem and proposes a novel multiparticle Kalman filter to solve it in complex and symmetric environments. Two well-known classes of filtering algorithms to solve the localization problem are Kalman filter-based methods and particle filter-based methods. We consider these classes, demonstrate their complementary

Muhammad Danial Khilji

The feature matching is a basic step in matching different datasets. This article proposes shows a new hybrid model of a pretrained Natural Language Processing (NLP) based model called BERT used in parallel with a statistical model based on Jaccard similarity to measure the similarity between list of features from two different datasets. This reduces the tim

Erik Ostrowski, Bharath Srinivas Prabakaran, Muhammad Shafique

Reliable classification and detection of certain medical conditions, in images, with state-of-the-art semantic segmentation networks, require vast amounts of pixel-wise annotation. However, the public availability of such datasets is minimal. Therefore, semantic segmentation with image-level labels presents a promising alternative to this problem. Neverthele

Noam Wies, Yoav Levine, Amnon Shashua

In-context learning is a surprising and important phenomenon that emerged when modern language models were scaled to billions of learned parameters. Without modifying a large language model's weights, it can be tuned to perform various downstream natural language tasks simply by including concatenated training examples of these tasks in its input. Though dis

Do T. Nga, Anh D. Phan, Thudsaphungthong Julie, Nam B. Le

We theoretically investigate photothermal heating of ultra-flexible metamaterials, which are obtained by randomly mixing TiN nanoparticles in polydimethylsiloxane (PDMS). Due to the plasmonic properties of TiN nanoparticles, incident light is perfectly absorbed in a broadband range (300-3000 nm) to generate heat within these metamaterials. Under irradiation

Maxime Fortier Bourque

The well-rounded retract for $\mathrm{SL}_n(\mathbb{Z})$ is defined as the set of flat tori of unit volume and dimension $n$ whose systoles generate a finite-index subgroup in homology. This set forms an equivariant spine of minimal dimension for the space of flat tori. For both the Outer space $X_n$ of metric graphs of rank $n$ and the Teichm\"uller space $

Erik Ostrowski, Bharath Srinivas Prabakaran, Muhammad Shafique

Achieving high-quality semantic segmentation predictions using only image-level labels enables a new level of real-world applicability. Although state-of-the-art networks deliver reliable predictions, the amount of handcrafted pixel-wise annotations to enable these results are not feasible in many real-world applications. Hence, several works have already ta

Erwin de Gelder, Kingsley Adjenughwure, Jeroen Manders, Ron Snijders

Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk, restricting their applicability to scenarios where these assumptions are valid. In response to this limitation,

Shaoqing Li

It is difficult to identify a G-C fullerene directly from its dimensions as its lattice is not proportional to that of its archetype in general, although they have the same three-dimensional shape. In this paper, the area scale factor of a G-C fullerene is proved to be an integer, which can be calculated from its dimensions. All the G-C transformations are k

Jan Leusmann, Chao Wang, Michael Gienger, Albrecht Schmidt

Recently the field of Human-Robot Interaction gained popularity, due to the wide range of possibilities of how robots can support humans during daily tasks. One form of supportive robots are socially assistive robots which are specifically built for communicating with humans, e.g., as service robots or personal companions. As they understand humans through a

Marouan Mizmizi, Dario Tagliaferri, Damiano Badini, Umberto Spagnolini

Target-to-user (T2U) association is a prerequisite to fully exploit the potential of the sensing function in communication-centric integrated sensing and communication (ISAC) systems, e.g., for beam and blockage management. This letter proposes to purposely mount a RIS on the roof of the vehicular user equipment (VUE), which can serve as an intentional back-

Chuanan Wei

Recently, Sun [preprint, arXiv: 2210.07238v7] proposed two conjectural series for the mathematical constant $\zeta(4)$ and two conjectural series for the mathematical constant $\zeta(5)$. In terms of the operator method and two hypergeometric transformations, we prove these four conjectures. Furthermore, we also find some new series for the two constants in

Tim Puphal, Benedict Flade, Daan de Geus, Julian Eggert

We consider the problem of intelligently navigating through complex traffic. Urban situations are defined by the underlying map structure and special regulatory objects of e.g. a stop line or crosswalk. Thereon dynamic vehicles (cars, bicycles, etc.) move forward, while trying to keep accident risks low. Especially at intersections, the combination and inter

Abinash Agasti, Puduru Viswanadha Reddy, Bharath Bhikkaji

In this article an $n$-pursuer versus $m$-evader reach-avoid differential game in 3D space is studied. A team of evaders aim to reach a stationary target while avoiding capture by a team of pursuers. The multiplayer scenario is formulated in a differential game framework. This article provides an optimal solution for the particular case of $n=m=1$ and extend

Viet Hoang Pham, Hyo-Sung Ahn

This paper designs a distributed least square solution method for a linear algebraic equation over a multiagent network. The coefficient matrix is divided into multiple blocks, and each agent only knows a subset of these blocks. The designed method is discrete-time and based on a proximal ADMM algorithm. By applying the designed method, each agent can find i

Lilybelle Cowland Kellock, Vladimir Dokchitser

The parity conjecture has a long and distinguished history. It gives a way of predicting the existence of points of infinite order on elliptic curves without having to construct them, and is responsible for a wide range of unexplained arithmetic phenomena. It is one of the main consequences of the Birch and Swinnerton-Dyer conjecture and lets one calculate t

Benjamin Steinberg

We give a simple proof that the orbit space of the $p$-subgroup complex of a finite group is contractible using Brown-Forman discrete Morse theory. This result was originally conjectured by Webb and proved by Symonds.

Disha, Sucheta Dutt

The aim of this paper is to determine the algebraic structure of multidimensional cyclic codes over a finite chain ring $\mathfrak{R}$. An algorithm to find the generator polynomials of $n$ dimensional ($n$D) cyclic codes of length $m_{1}m_{2}\dots m_{n}$ over $\mathfrak{R}$ has been developed using the generator polynomials of cyclic codes over $\mathfrak{R

Connor Roberts, Zigan Zhen

We explore the properties of run-and-tumble particles moving in a piecewise-linear "ratchet" potential by deriving analytic results for the system's steady-state probability density, current, entropy production rate, extractable power, and thermodynamic efficiency. The ratchet's broken spatial symmetry rectifies the particles' self-propelled motion, resultin

Eleni Stai, Lesia Mitridati, Ioannis Stavrakakis, Evangelia Kokolaki

This work introduces a decentralized mechanism for the fair and efficient allocation of limited renewable energy sources among consumers in an energy community. In the proposed non-cooperative game, the self-interested community members independently decide whether to compete or not for access to RESs during peak hours and shift their loads analogously. In t

Thang Pham, Steven Senger, Michael Tait, Nguyen Thu-Huyen

Let $G$ be a graph and $U\subset V(G)$ be a set of vertices. For each $v\in U$, let $h_v\colon U\to \{0, 1\}$ be the function defined by \[h_v(u)=\begin{cases} &1 ~\mbox{if}~u\sim v, u\in U\\&0 ~\mbox{if}~u\not\sim v, u\in U\end{cases},\] and set $\mathcal{H}(U):=\{h_v\colon v\in U\}$. The first purpose of this paper is to study the following question: What

Herve Bergeron, Jaime de Cabo Martin, Jean-Pierre Gazeau, Przemyslaw Malkiewicz

We study a semiclassical model of the mixmaster universe. We first derive the quantum model and then introduce its semiclassical approximation. We employ a general integral quantization method that respects the symmetries of the model given by the affine and the Weyl-Heisenberg groups, and can produce a wide class of quantum models. The semiclassical approxi

H. Srinivasan, V. K. Sharma, V. G. Sakai, S. Mitra

A crossover from a non-Gaussian to Gaussian sub-diffusion has been observed ubiquitously in various polymeric/molecular glass-formers. We have developed a framework which generalizes the fractional Brownian motion (fBm) model to incorporate non-Gaussian features by introducing a jump kernel. We illustrate that the non-Gaussian fBm (nGfBm) model accurately ch

Tomasz Kochanek

We prove an Ulam type stability result for a non-associative version of the multiplicativity equation, that is, $T(xy)=\Psi(T(x),T(y))$, where $T$ is a unital bounded operator acting from an amenable Banach algebra to a dual Banach algebra.

Ruihuang Li, Chenhang He, Shuai Li, Yabin Zhang

The representative instance segmentation methods mostly segment different object instances with a mask of the fixed resolution, e.g., 28*28 grid. However, a low-resolution mask loses rich details, while a high-resolution mask incurs quadratic computation overhead. It is a challenging task to predict the optimal binary mask for each instance. In this paper, w

Edwin H. Lock, Sergey V. Gerus

Analytically, without magnetostatic approximation, the problem of electromagnetic wave propagation along arbitrary direction in a tangentially magnetized bihyrotropic layer has been solved. It is found that one can bring the Maxwell equations for this problem to the fourth order differential equation and the obtained biquadratic characteristic equation deter

Josh Shelton, Samuel Crew, Philippe H. Trinh

The higher-order Stokes phenomenon can emerge in the asymptotic analysis of many problems governed by singular perturbations. Indeed, over the last two decades, the phenomena has appeared in many physical applications, from acoustic and optical wave phenomena and gravity-capillary ripples, to models of crystal growth and equatorial Kelvin waves. It emerges i

Kateryna Lutsai, Christoph H. Lampert

This research is aimed to solve the tweet/user geolocation prediction task and provide a flexible methodology for the geotagging of textual big data. The suggested approach implements neural networks for natural language processing (NLP) to estimate the location as coordinate pairs (longitude, latitude) and two-dimensional Gaussian Mixture Models (GMMs). The

Yunfeng Fan, Wenchao Xu, Haozhao Wang, Jiaqi Zhu

Online Class-Incremental (OCI) learning has sparked new approaches to expand the previously trained model knowledge from sequentially arriving data streams with new classes. Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones. Existing literatur

Rebeca C. Motta, Káthia M. de Oliveira, Guilherme H. Travassos

Context: The Internet of Things (IoT) has brought expectations for software inclusion in everyday objects. However, it has challenges and requires multidisciplinary technical knowledge involving different areas that should be combined to enable IoT software systems engineering. Goal: To present an evidence-based roadmap for IoT development to support develop

Matthew C. Childs, Benjamin D. Horne

A concern among content moderation researchers is that hard moderation measures, such as banning content producers, will push users to more extreme information environments. Research in this area is still new, but predominately focuses on one-way migration (from mainstream to alt-tech) due to this concern. However, content producers on alt-tech social media

Vladislav Zubko, Natan Eismont, Konstantin Fedyaev, Andrey Belyaev

A problem of constructing the trajectory of a spacecraft flight to Venus within the framework of a mission including landing of a lander in a given region of the planet's surface is being considered. A new celestial mechanics related method based on the use of gravity assist maneuver near Venus is proposed to transfer the spacecraft to a heliocentric orbit r

Ivan Palaia, Adelchi J. Asta, Megh Dutta, Patrick B. Warren

A parallel plate capacitor containing an electrolytic solution is the simplest model of a supercapacitor, or electric double layer capacitor. Using both analytical and numerical techniques, we solve the Poisson-Nernst-Planck equations for such a system, describing the mean-field charging dynamics of the capacitor, when a constant potential difference is abru

Daniel Funke, Peter Sanders

Yao graphs are geometric spanners that connect each point of a given point set to its nearest neighbor in each of $k$ cones drawn around it. Yao graphs were introduced to construct minimum spanning trees in $d$ dimensional spaces. Moreover, they are used for instance in topology control in wireless networks. An optimal \Onlogn time algorithm to construct Yao

Arijit Halder, Supratik Banerjee, Anando G. Chatterjee, Manohar K. Sharma

A detailed study of small-scale Hall magnetohydrodynamic dynamo has been performed both analytically and numerically. Assuming the magnetic field and the current to be separate fields, the contribution of the Hall term has been decomposed into two parts and their individual contributions have been studied separately. Calculating the scale-separated transfer

Ali Emami Kopaei, Krzysztof Sacha, Lingzhen Guo

It was recently discovered that a crystalline many-body state can exist in the phase space of a closed dynamical system. A phase space crystal can be an anomalous Chern insulator that supports chiral topological transport without breaking physical time-reversal symmetry [L. Guo et al., Phys. Rev. B 105, 094301 (2022)]. In this work, we further study the effe

Marian Aprodu, Gavril Farkas, Claudiu Raicu, Alexander I. Suciu

The resonance varieties are cohomological invariants that are studied in a variety of topological, combinatorial, and geometric contexts. We discuss their scheme structure in a general algebraic setting and introduce various properties that ensure the reducedness of the associated projective resonance scheme. We prove an asymptotic formula for the Hilbert se

Yiqi Tang, Ryan Martin

High-dimensional linear models have been widely studied, but the developments in high-dimensional generalized linear models, or GLMs, have been slower. In this paper, we propose an empirical or data-driven prior leading to an empirical Bayes posterior distribution which can be used for estimation of and inference on the coefficient vector in a high-dimension

Bharath Srinivas Prabakaran, Erik Ostrowski, Muhammad Shafique

Weakly Supervised Semantic Segmentation (WSSS) relying only on image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for generating a large number of pixel-wise masks in a given dataset. However, most state-of-the-art image-level WSSS techniques lack an understanding of the geometric features embedded in

Bharath Srinivas Prabakaran, Paul Hamelmann, Erik Ostrowski, Muhammad Shafique

Ultrasound imaging is one of the most prominent technologies to evaluate the growth, progression, and overall health of a fetus during its gestation. However, the interpretation of the data obtained from such studies is best left to expert physicians and technicians who are trained and well-versed in analyzing such images. To improve the clinical workflow an

Ziyi Liu, Yongchun Fang

In general-sum games, the interaction of self-interested learning agents commonly leads to socially worse outcomes, such as defect-defect in the iterated stag hunt (ISH). Previous works address this challenge by sharing rewards or shaping their opponents' learning process, which require too strong assumptions. In this paper, we demonstrate that agents traine

Minseok Seo, Hakjin Lee, Doyi Kim, Junghoon Seo

Future frame prediction has been approached through two primary methods: autoregressive and non-autoregressive. Autoregressive methods rely on the Markov assumption and can achieve high accuracy in the early stages of prediction when errors are not yet accumulated. However, their performance tends to decline as the number of time steps increases. In contrast

Adam Nahum, Kay Joerg Wiese

We analyze the renormalization-group (RG) flows of two effective Lagrangians, one for measurement induced transitions of monitored quantum systems and one for entanglement transitions in random tensor networks. These Lagrangians, previously proposed on grounds of replica symmetry, are derived in a controlled regime for an illustrative family of tensor networ

Rajanikant Ghate, Nayan Kalnad, Rahee Walambe, Ketan Kotecha

Automated depression screening and diagnosis is a highly relevant problem today. There are a number of limitations of the traditional depression detection methods, namely, high dependence on clinicians and biased self-reporting. In recent years, research has suggested strong potential in machine learning (ML) based methods that make use of the user's passive

Dahuin Jung, Hyungyu Lee, Sungroh Yoon

Imitation learning, in which learning is performed by demonstration, has been studied and advanced for sequential decision-making tasks in which a reward function is not predefined. However, imitation learning methods still require numerous expert demonstration samples to successfully imitate an expert's behavior. To improve sample efficiency, we utilize sel

Jeong-Hoon Ju, Taehyeong Kim, Yeongrak Kim

In this paper, we present a new formula of the determinant tensor $det_n$ for $n \times n$ matrices. In \cite{kim2023newdet4}, Kim, Ju, and Kim found a new formula of $4 \times 4$ determinant tensor $det_4$ which is available when the base field is not of characteristic $2$. Considering some symmetries in that formula, we found a new formula so that \begin{e

Thorsten Hertl

We apply the strategy to study of diffeomorphisms via block diffeomorphisms to the world of positive scalar curvature (psc) metrics. For each closed psc manifold $M$, we construct the cubical set $\widetilde{\mathcal{R}^{+}_{\bullet}}(M)$ of all psc block metrics, which only encodes concordance information of psc metrics within its homotopy type. We show tha

Tanwi Debnath, Shubhadip Nayak, Poulami Bag, Debajyoti Debnath

We numerically study structure and dynamics of single files composed of active particles, as well as, active-passive binary mixtures. Our simulation results show that when the persistent length of self-propelled particles is much larger than the average inter-particle separation and the self-propulsion velocity is larger than the thermal velocity, particles

Taehyeong Kim, Jeong-Hoon Ju, Yeongrak Kim

In this paper, we present a new formula for the determinant of a $4 \times 4$ matrix. We approach via the sparse optimization problem and derive the formula through the Least Absolute Shrinkage and Selection Operator (LASSO). Our formula has the potential to advance understanding of the algebraic structure of determinants, such as an upper bound of the tenso

Ju-Yeon Gyhm, Uwe R. Fischer

We establish a general implementation-independent approach to assess the potential advantage of using highly entangled quantum states between the initial and final states of the charging protocol to enhance the maximum charging power of quantum batteries. It is shown that the impact of entanglement on power can be separated from both the global quantum speed

Jun Wan, Jun Liu, Jie Zhou, Zhihui Lai

Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results. However, when the face image is suffering from large poses, heavy occlusions and complicated illuminations, they cannot learn discriminative feature representations and effective facial shape const

Leonid V. Bogachev, Ruheyan Nuermaimaiti, Jochen Voss

The generalized inverse Gaussian-Poisson (GIGP) distribution proposed by Sichel in the 1970s has proved to be a flexible fitting tool for diverse frequency data, collectively described using the item production model. In this paper, we identify the limit shape (specified as an incomplete gamma function) of the properly scaled diagrammatic representations of

Ahmad Diab, Bolor-Erdene Jagdagdorj, Lynnette Hui Xian Ng, Yu-Ru Lin

White supremacist extremist groups are a significant domestic terror threat in many Western nations. These groups harness the Internet to spread their ideology via online platforms: blogs, chat rooms, forums, and social media, which can inspire violence offline. In this work, we study the persistence and reach of white supremacist propaganda in both online a

Nassim Nicholas Taleb, Pierre Zalloua, Khaled Elbassioni, Andreas Henschel

We discuss the inadequacy of covariances/correlations and other measures in L2 as relative distance metrics under some conditions. We propose a computationally simple heuristic to transform a map based on standard principal component analysis (PCA) (when the variables are asymptotically Gaussian) into an entropy-based map where distances are based on mutual

Hannah Markwig, Sam Payne, Kris Shaw

We compare two partitions of real bitangents to smooth plane quartics into sets of 4: one coming from the closures of connected components of the avoidance locus and another coming from tropical geometry. When both are defined, we use the Tarski principle for real closed fields in combination with the topology of real plane quartics and the tropical geometry

David Morilla-Cabello, Lorenzo Mur-Labadia, Ruben Martinez-Cantin, Eduardo Montijano

The integration of semantic information in a map allows robots to understand better their environment and make high-level decisions. In the last few years, neural networks have shown enormous progress in their perception capabilities. However, when fusing multiple observations from a neural network in a semantic map, its inherent overconfidence with unknown

Debjit Pal, Mainak Poddar

A principal torus bundle over a complex manifold with even dimensional fiber and characteristic class of type $(1,1)$ admits a family of regular generalized complex structures (GCS) with the fibers as leaves of the associated symplectic foliation. We show that such a generalized complex structure is equivalent to the product of the complex structure on the b

Uday Kumar M, Sanjay P Bhat, Veeraruna Kavitha, Nandyala Hemachandra

This paper considers the problem of finding a solution to the finite horizon constrained Markov decision processes (CMDP) where the objective as well as constraints are sum of additive and multiplicative utilities. Towards solving this, we construct another CMDP, with only additive utilities under a restricted set of policies, whose optimal value is equal to

Danqin Wu

In multi-turn dialogue generation, responses are not only related to the topic and background of the context but also related to words and phrases in the sentences of the context. However, currently widely used hierarchical dialog models solely rely on context representations from the utterance-level encoder, ignoring the sentence representations output by t

Shuo Ma, Jinzhong Liu, Yu Zhang, Guoliang Lü

We present the first detailed photometric analysis of ATO J108.6991+27.8306 (hereinafter as J108). The short-period close binary J108 was observed by the Nanshan 1 m Wide Field Telescope of the Xinjiang Astronomical Observatory. The obtained BVRI-band light curves were used to determine the photometric solution by using the 2003 version of the Wilson-Devinne

Yu Zhou, Liyuan Guo, Lianghai Jin

Facial expression recognition (FER) is a challenging topic in artificial intelligence. Recently, many researchers have attempted to introduce Vision Transformer (ViT) to the FER task. However, ViT cannot fully utilize emotional features extracted from raw images and requires a lot of computing resources. To overcome these problems, we propose a quaternion or

Tengjun Liu, Yansong Chua, Yiwei Zhang, Yuxiao Ning

Despite its better bio-plausibility, goal-driven spiking neural network (SNN) has not achieved applicable performance for classifying biological spike trains, and showed little bio-functional similarities compared to traditional artificial neural networks. In this study, we proposed the motorSRNN, a recurrent SNN topologically inspired by the neural motor ci

Marcos Paulo Belançon, Marcelo Sandrini, Vitor Santaella Zanuto, Robson Ferrari Muniz

Glass provides mechanical, chemical, and UV protection to solar panels, enabling these devices to withstand weathering for several decades. The increasing demand for solar electricity and the need to reduce anthropogenic carbon emissions require researchers to develop new materials and processes to make solar even more sustainable. Here, we review the curren

Zewen Wu, Jian Tang, Xingyu Chen, Chengzhong Ma

In scenarios involving the grasping of multiple targets, the learning of stacking relationships between objects is fundamental for robots to execute safely and efficiently. However, current methods lack subdivision for the hierarchy of stacking relationship types. In scenes where objects are mostly stacked in an orderly manner, they are incapable of performi

Bastián Reinoso, Ralf S. Klessen, Dominik Schleicher, Simon C. O. Glover

The formation of supermassive stars is believed to be an essential intermediate step for the formation of the massive black hole seeds that become the supermassive black holes powering the quasars observed in the early Universe. Numerical simulations have shown that supermassive stars can form in atomic-cooling halos when protostars reach accretion rates hig

Kechi Zhang, Zhuo Li, Zhi Jin, Ge Li

Source code representation with deep learning techniques is an important research field. There have been many studies that learn sequential or structural information for code representation. But sequence-based models and non-sequence-models both have their limitations. Researchers attempt to incorporate structural information to sequence-based models, but th

Laura Ciobanu, Alex Levine

The survey provides an overview of the work done in the last 10 years to characterise solutions to equations in groups in terms of formal languages. We begin with the work of Ciobanu, Diekert and Elder, who showed that solutions to systems of equations in free groups in terms of reduced words are expressible as EDT0L languages. We provide a sketch of their a

Partha Sarathi Mohapatra, Puduru Viswanadha Reddy

In this letter, we study a class of linear-quadratic mean-field-type difference games with coupled affine inequality constraints. We show that the mean-field-type equilibrium can be characterized by the existence of a multiplier process which satisfies some implicit complementarity conditions. Further, we show that the equilibrium strategies can be computed

Dmitry Beliaev, Michael McAuley, Stephen Muirhead

We derive a covariance formula for the number of excursion or level set components of a smooth stationary Gaussian field on $\mathbb{R}^d$ contained in compact domains. We also present two applications of this formula: (1) for fields whose correlations are integrable we prove that the variance of the component count in large domains is of volume order and gi