May 2023 arXiv papers — page 16

Saira Bano, Pietro Cassarà, Nicola Tonellotto, Alberto Gotta

The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike traditional centralized learning methods that are specific to certain geographical areas and inappropriate for others, we propose a generalized model that uses Federated Learning (FL) for channel estimation

Michel Plüss, Jan Deriu, Yanick Schraner, Claudio Paonessa

We present STT4SG-350 (Speech-to-Text for Swiss German), a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. We make the corpus publicly available. It contains 343 hours of s

S. A. Deshmukh, H. -G. Ludwig

Binary molecules such as CO, OH, CH, CN, and C$_2$ are often used as abundance indicators in stars. These species are usually assumed to be formed in chemical equilibrium. The time-dependent effects of hydrodynamics can affect the formation and dissociation of these species and may lead to deviations from chemical equilibrium. We aim to model departures from

Cheuk Yin Lee, Yimin Xiao

Let $u(t, x) = (u_1(t, x), \dots, u_d(t, x))$ be the solution to the systems of nonlinear stochastic heat equations \[ \begin{split} \frac{\partial}{\partial t} u(t, x) &= \frac{\partial^2}{\partial x^2} u(t, x) + \sigma(u(t, x)) \dot{W}(t, x),\\ u(0, x) &= u_0(x), \end{split} \] where $t \ge 0$, $x \in \mathbb{R}$, $\dot{W}(t, x) = (\dot{W}_1(t, x), \dots,

Simon R Davies, Richard Macfarlane, William J Buchanan

Crypto-ransomware remains a significant threat to governments and companies alike, with high-profile cyber security incidents regularly making headlines. Many different detection systems have been proposed as solutions to the ever-changing dynamic landscape of ransomware detection. In the majority of cases, these described systems propose a method based on t

Kouki Wakita, Yoshiki Miyauchi, Youhei Akimoto, Atsuo Maki

A dynamic model for an automatic berthing and unberthing controller has to estimate harbor maneuvers, which include berthing, unberthing, approach maneuvers to berths, and entering and leaving the port. When the dynamic model is estimated by the system identification, a large number of tests or trials are required to measure the various motions of harbor man

Franco Flandoli, Francesco Russo

Diffusion with stochastic transport is investigated here when the random driving process is a very general Gaussian process, including Fractional Brownian motion. The purpose is the comparison with a deterministic PDE, which in certain cases represents the equation for the mean value. From this equation we observe a reduced dissipation property for small tim

F. C. L. Crespi, J. Ljungvall, A. Lopez-Martens, C. Michelagnoli

AGATA is a modern $\gamma$-ray spectrometer for in-beam nuclear structure studies, based on $\gamma$-ray tracking. Since more than a decade, it has been operated performing experimental physics campaigns in different international laboratories (LNL, GSI, GANIL). This paper reviews the obtained results concerning the performances of $\gamma$-ray tracking in A

Georges Gras

We give an elementary criterion for the norm of the fundamental unit $\varepsilon_K$ of $K=\mathbb{Q}(\sqrt M)$, $M$ square-free. More precisely, if $\varepsilon_K = a+b\sqrt M$, $a,b \in \mathbb{Z}$ or $\frac{1}{2}\mathbb{Z}$, its norm ${\bf S}_K$ only depends on $m := {\bf gcd} \big(\frac{a+1}{{\bf gcd}(a+1,b)}, M\big)$ and $m' := {\bf gcd} \big(\frac{a-1}

Zhuocheng Gong, Jiahao Liu, Qifan Wang, Yang Yang

While transformer-based pre-trained language models (PLMs) have dominated a number of NLP applications, these models are heavy to deploy and expensive to use. Therefore, effectively compressing large-scale PLMs becomes an increasingly important problem. Quantization, which represents high-precision tensors with low-bit fix-point format, is a viable solution.

Minghao Li, Shihao Xia, Youlin Wang, Minglong Lv

A thermodynamic formalism describing the efficiency of information learning is proposed, which is applicable for stochastic thermodynamic systems with multiple internal degree of freedom. The learning rate, entropy production rate (EPR), and entropy flow from the system to the environment under coarse-grained dynamics are derived. The Cauchy-Schwarz inequali

Peishi Li, Zichao Xiao, Ming Li, Rang Liu

Integrated sensing and communication (ISAC) is a promising technology in future wireless systems owing to its efficient hardware and spectrum utilization. In this paper, we consider a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC system and propose a novel waveform design to provide better radar ranging performance by

Minki Kang, Jin Myung Kwak, Jinheon Baek, Sung Ju Hwang

Language models have achieved impressive performances on dialogue generation tasks. However, when generating responses for a conversation that requires factual knowledge, they are far from perfect, due to an absence of mechanisms to retrieve, encode, and reflect the knowledge in the generated responses. Some knowledge-grounded dialogue generation methods tac

Saira Bano, Achilles Machumilane, Pietro Cassarà, Alberto Gotta

With the advent of 5G and the anticipated arrival of 6G, there has been a growing research interest in combining mobile networks with Non-Terrestrial Network platforms such as low earth orbit satellites and Geosynchronous Equatorial Orbit satellites to provide broader coverage for a wide range of applications. However, integrating these platforms is challeng

Iwona Anna Sputowska

$\Sigma$ is a new correlation measure, quite recently introduced to heavy-ion physics. This measure, defined in the independent source model as a strongly intensive quantity, is expected to be free of the effects of system volume and volume fluctuations. This paper discusses the forward-backward correlation quantified with the $\Sigma$ observable calculated

Yifei Yue

With the rapid development of social media such as Twitter and Weibo, detecting keywords from a huge volume of text data streams in real-time has become a critical problem. The keyword detection problem aims at searching important information from massive text data to reflect the most important events or topics. However, social media data usually has unique

Julius Serbenta

In my thesis, I present one particular example of the formalism capable of describing the propagation of a family of light rays in a curved spacetime. It is based on the resolvent operator of the geodesic deviation equation for null geodesics which is known as the bilocal geodesic operator (BGO) formalism. The BGO formalism generalizes the standard treatment

Xingyu Fu, Sheng Zhang, Gukyeong Kwon, Pramuditha Perera

The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge. Recently, pre-trained Language Models (PLM) such as GPT-3 have been applied to the task and shown to be powerful world knowledge sources. However, these methods suffer from low knowledge coverage caused by PL

Annie Adhikary

Autism spectrum disorder is one of the leading neurodevelopmental disorders in our world, present in over 1% of the population and rapidly increasing in prevalence, yet the condition lacks a robust, objective, and efficient diagnostic. Clinical diagnostic criteria rely on subjective behavioral assessments, which are prone to misdiagnosis as they face limitat

Joseph Enguehard

Explaining predictions based on multivariate time series data carries the additional difficulty of handling not only multiple features, but also time dependencies. It matters not only what happened, but also when, and the same feature could have a very different impact on a prediction depending on this time information. Previous work has used perturbation-ba

Mario Fuest, Johannes Lankeit

Our main result shows that the mass $2\pi$ is critical for the minimal Keller-Segel system \begin{align}\label{prob:abstract}\tag{$\star$} \begin{cases} u_t = \Delta u - \nabla \cdot (u \nabla v), \\ v_t = \Delta v - v + u, \end{cases} \end{align} considered in a quarter disc $\Omega = \{\,(x_1, x_2) \in \mathbb R : x_1 > 0, x_2 > 0, x_1^2 + x_2^2 < R^2\,\}$

Jiaxin Gao, Wenbo Hu, Yuntian Chen

Long-term time series forecasting (LTSF) is a crucial aspect of modern society, playing a pivotal role in facilitating long-term planning and developing early warning systems. While many Transformer-based models have recently been introduced for LTSF, a doubt have been raised regarding the effectiveness of attention modules in capturing cross-time dependenci

Gilles Dowek

The Stratified Foundations are a restriction of naive set theory where the comprehension scheme is restricted to stratifiable propositions. It is known that this theory is consistent and that proofs strongly normalize in this theory. Deduction modulo is a formulation of first-order logic with a general notion of cut. It is known that proofs normalize in a th

Daniel Goodair, Dan Crisan

We introduce an analogue to Kato's Criterion regarding the inviscid convergence of stochastic Navier-Stokes flows to the strong solution of the deterministic Euler equation. Our assumptions cover additive, multiplicative and transport type noise models. This is achieved firstly for the typical noise scaling of $\nu^\frac{1}{2}$, before considering a new para

Georg C. Ganzenmüller, Sebastian Hütter, Martin Reder, Andreas Prahs

This work discusses the origin of temperature rise during the collision welding process. The different physical irreversible and reversible mechanisms which act as heat sources are described: isentropic compression work, shock dissipation, plasticity, and phase transitions. The temperature increase due to these effects is quantified in a continuum mechanics

Shengbo Liu, Wen Wu, Liqun Fu, Kaige Qu

Full-duplex (FD) technique can remarkably boost the network capacity in the millimeter wave (mmWave) bands by enabling simultaneous transmission and reception. However, due to directional transmission and large bandwidth, the throughput and fairness performance of a mmWave FD network are affected by deafness and directional hidden-node (HN) problems and seve

Xinyu Mu, Shulin Lyu

We study the Hankel determinant generated by a Gaussian weight with Fisher-Hartwig singularities of root type at $t_j$, $j=1,\cdots ,N$. It characterizes a type of average characteristic polynomial of matrices from Gaussian unitary ensembles. We derive the ladder operators satisfied by the associated monic orthogonal polynomials and three compatibility condi

Yixun Liang, Hao He, Ying-cong Chen

Generalizable neural surface reconstruction techniques have attracted great attention in recent years. However, they encounter limitations of low confidence depth distribution and inaccurate surface reasoning due to the oversimplified volume rendering process employed. In this paper, we present Reconstruction TRansformer (ReTR), a novel framework that levera

Takahiro Uto, Bertrand Evrard, Kenji Watanabe, Takashi Taniguchi

Laser induced shift of atomic states due to the AC-Stark effect has played a central role in cold-atom physics and facilitated their emergence as analog quantum simulators. Here, we explore this phenomena in an atomically thin layer of semiconductor MoSe$_2$, which we embedded in a heterostructure enabling charge tunability. Shining an intense pump laser wit

Théo Gnassounou, Rémi Flamary, Alexandre Gramfort

In many machine learning applications on signals and biomedical data, especially electroencephalogram (EEG), one major challenge is the variability of the data across subjects, sessions, and hardware devices. In this work, we propose a new method called Convolutional Monge Mapping Normalization (CMMN), which consists in filtering the signals in order to adap

Lanfeng Zhong, Xin Liao, Shaoting Zhang, Guotai Wang

Segmentation of pathological images is a crucial step for accurate cancer diagnosis. However, acquiring dense annotations of such images for training is labor-intensive and time-consuming. To address this issue, Semi-Supervised Learning (SSL) has the potential for reducing the annotation cost, but it is challenged by a large number of unlabeled training imag

Chen Min, Liang Xiao, Dawei Zhao, Yiming Nie

Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR-based solutions. The existing multi-camera algorithms primarily rely on monocular 2D pre-training. However, the monocular 2D pre-training overlooks the spatial and temporal correlations among the multi-camera s

Olivier Aubert, Benjamin Hervy, Guillaume Raschia, Françoise Rubellin

In the 18th century in Paris, only two public theatres could officially perform comedies: the Com{\'e}die-Fran{\c c}aise, and the Com{\'e}die-Italienne. The latter was much less well known. By studying a century of accounting registers, we aim to learn more about its successful plays, its actors, musicians, set designers, and all the small trades necessary f

Antoine Borel, Théo Habrant-Claude, Federico Rapisarda, Jakob Reichel

We report on the coupling of a reconfigurable high Q fiber micro-cavity to an organic color center grafted to a carbon nanotube for telecom wavelength emission of single photons in the Purcell regime. Using three complementary approaches we assess various figures of merit of this tunable single photon source and of the cavity quantum electrodynamical effects

Nicholas Furtak-Wells, Benjamin Dawson, Thomas Mann, Gin Jose

In three dimensions, dipole-dipole interactions which alter atomic level shifts and spontaneous decay rates only persist over distances comparable to the wavelength of the emitted light. In this paper we show that it is possible to significantly extend the range of these interactions with the help of a partially transparent asymmetric mirror interface. Suppo

Olivier Aubert, Thomas Scherer, Jasper Stratil

Instrumental genesis through interdisciplinary collaboration-reflections on the emergence of a visualisation framework for video annotation data XML This paper presents, discusses and reflects on the development of a visualization framework for the analysis of the temporal dynamics of audiovisual expressivity. The main focus lies on the instrumental genesis

Guangzhi Sun, Chao Zhang, Phil Woodland

The incorporation of biasing words obtained through contextual knowledge is of paramount importance in automatic speech recognition (ASR) applications. This paper proposes an innovative method for achieving end-to-end contextual ASR using graph neural network (GNN) encodings based on the tree-constrained pointer generator method. GNN node encodings facilitat

Marie Doumic, Sophie Hecht, Benoit Perthame, Diane Peurichard

Systems describing the long-range interaction between individuals have attracted a lot of attention in the last years, in particular in relation with living systems. These systems are quadratic, written under the form of transport equations with a nonlocal self-generated drift. We establish the localisation limit, that is the convergence of nonlocal to local

Xiaoxiao Miao, Xin Wang, Erica Cooper, Junichi Yamagishi

Speaker anonymization aims to conceal a speaker's identity while preserving content information in speech. Current mainstream neural-network speaker anonymization systems disentangle speech into prosody-related, content, and speaker representations. The speaker representation is then anonymized by a selection-based speaker anonymizer that uses a mean vector

Ziting Chen, Kin To Wong, Bojeong Seo, Mingchen Huang

Accurately measuring magnetic fields is essential for magnetic-field sensitive experiments in fields like atomic, molecular, and optical physics, condensed matter experiments, and other areas. However, since many experiments are conducted in an isolated vacuum environment that is inaccessible to experimentalists, it can be challenging to accurately determine

Belle Collaboration, C. -Y. Chang, M. -Z. Wang, I. Adachi

We search for the $B^0\to p\bar{\Sigma}^0\pi^-$ decay with $\bar{\Sigma}^0 \to \bar{\Lambda}\gamma$, where the $\gamma$ is not measured, using a data sample corresponding to an integrated luminosity of 711 $\rm{fb^{-1}}$ which contains 772 $\times$ $10^{6}$ $B\bar{B}$ pairs, collected around the $\Upsilon$(4S) resonance with the Belle detector at the KEKB as

Melissa Mozifian, Tristan Sylvain, Dave Evans, Lili Meng

Attention-based sequential recommendation methods have shown promise in accurately capturing users' evolving interests from their past interactions. Recent research has also explored the integration of reinforcement learning (RL) into these models, in addition to generating superior user representations. By framing sequential recommendation as an RL problem

Ranajit Mondal, Hisay Lama, Kirti Chandra Sahu

Drying complex fluids is a common phenomenon where a liquid phase transforms into a dense or porous solid. This transformation involves several physical processes, such as the diffusion of liquid molecules into the surrounding atmosphere and the movement of dispersed phases through evaporation-driven flow. As a result, the solute forming a dried deposit exhi

Tommy Liu, Amanda Barnard

In this paper, we introduce the idea of decomposing the residuals of regression with respect to the data instances instead of features. This allows us to determine the effects of each individual instance on the model and each other, and in doing so makes for a model-agnostic method of identifying instances of interest. In doing so, we can also determine the

Wen Teng, Jiulin Jin, Yu Zhang

In this paper, we introduce the notion of embedding tensor on 3-Hom-Lie algebras and naturally induce 3-Hom-Leibniz algebras. Moreover, the cohomology theory of embedding tensors on 3-Hom-Lie algebras is defined. As an application, we show that if two linear deformations of an embedding tensor on a 3-Hom-Lie algebra are equivalent, then their infinitesimals

Xuanloc Leu, Xuan-Hoai Thi Nguyen, Jinhyoung Lee

We present a novel geometric approach for determining the unique structure of a Hamiltonian and establishing an instability criterion for quantum quadratic systems. Our geometric criterion provides insights into the underlying geometric perspective of instability: A quantum quadratic system is dynamically unstable if and only if its Hamiltonian is hyperbolic

Fabio Paparella, Theo Hofman, Mauro Salazar

This paper studies mobility systems that incorporate a substantial solar energy component, generated not only on the ground, but also through solar roofs installed on vehicles, directly covering a portion of their energy consumption. In particular, we focus on Solar-electric Autonomous Mobility-on-Demand systems, whereby solar-electric autonomous vehicles pr

Ambre Davat, Véronique Aubergé, Gang Feng

One of the challenges of telepresence robotics is to provide ubiquitous social-interpersonalimmersion. In order to achieve this, there is a need to understand and model the factors that wouldallow the users to control the transmission of their vocal productions, and to give them perception,proprioception and inter-proprioception of this control. This model f

Ambre Davat, Gang Feng, Véronique Aubergé

In this study, we present a new experiment in order to study the Lombard effect in telepresence robotics. In this experiment, one person talks with a robot controled remotely by someone in a different room. The remote pilot (R) is immersed in both environments, while the local interlocutor (L) interacts directly with the robot. In this context, the position

Federico Bongiorno

The aim of this article is to give a rigorous geometric interpretation of the completion of a ring with respect to an ideal. To this end, we define the infinitesimal neighbourhood of an immersion of formal schemes as the largest possible thickening. Further, we show that immersions of formal schemes locally of formal finite presentation admit the existence o

Qiang Wang, Di Kong, Fengyin Lin, Yonggang Qi

Creative sketch is a universal way of visual expression, but translating images from an abstract sketch is very challenging. Traditionally, creating a deep learning model for sketch-to-image synthesis needs to overcome the distorted input sketch without visual details, and requires to collect large-scale sketch-image datasets. We first study this task by usi

Wenjie Du, Yiyuan Yang, Linglong Qian, Jun Wang

PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series with missing values. Particularly, it provides easy access to diverse algorithms categorized into five tasks: imputation, forecasting, anomaly detection, classification, and clustering. The included models represent a diverse set of me

Yuexiong Ding, Muyang Liu, Xiaowei Luo

The occlusion issues of computer vision (CV) applications in construction have attracted significant attention, especially those caused by the wide-coverage, crisscrossed, and immovable scaffold. Intuitively, removing the scaffold and restoring the occluded visual information can provide CV agents with clearer site views and thus help them better understand

Bohan Zhang, Anastasios Panagiotelis, Yanfei Kang

This paper presents a formal framework and proposes algorithms to extend forecast reconciliation to discrete-valued data to extend forecast reconciliation to discrete-valued data, including low counts. A novel method is introduced based on recasting the optimisation of scoring rules as an assignment problem, which is solved using quadratic programming. The p

Yudi Li, Min Tang, Yun Yang, Ruofeng Tong

We present a novel learning method to predict the cloth deformation for skeleton-based characters with a two-stream network. The characters processed in our approach are not limited to humans, and can be other skeletal-based representations of non-human targets such as fish or pets. We use a novel network architecture which consists of skeleton-based and mes

Chaozhong Xue, Yongqi Dong, Jiaqi Liu, Yijun Liao

Medical waste recycling and treatment has gradually drawn concerns from the whole society, as the amount of medical waste generated is increasing dramatically, especially during the pandemic of COVID-19. To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i.e., medical waste classification & monit

Michał Zając, Tinne Tuytelaars, Gido M. van de Ven

Class-incremental learning (CIL) is a particularly challenging variant of continual learning, where the goal is to learn to discriminate between all classes presented in an incremental fashion. Existing approaches often suffer from excessive forgetting and imbalance of the scores assigned to classes that have not been seen together during training. In this s

Jessica Afalla, Joselito Muldera, Semmi Takamizawa, Takumi Fukuda

Terahertz (THz) time-domain emission spectroscopy was performed on layered 2H-MoSe2 and 2H-WSe2. The THz emission shows an initial cycle attributed to surge currents and is followed by oscillations attributed to coherent interlayer phonon modes. To obtain the frequencies of the interlayer vibrations, analysis of the THz emission waveforms were performed, sep

Valentina Giunchiglia, Sharon Curtis, Stephen Smith, Naomi Allen

Automated online and App-based cognitive assessment tasks are becoming increasingly popular in large-scale cohorts and biobanks due to advantages in affordability, scalability and repeatability. However, the summary scores that such tasks generate typically conflate the cognitive processes that are the intended focus of assessment with basic visuomotor speed

Yong Liu, Chenyu Li, Jianmin Wang, Mingsheng Long

Real-world time series are characterized by intrinsic non-stationarity that poses a principal challenge for deep forecasting models. While previous models suffer from complicated series variations induced by changing temporal distribution, we tackle non-stationary time series with modern Koopman theory that fundamentally considers the underlying time-variant

G. Jeyakodi, Arkadeep Pal, Debapratim Gupta, K. Sarukeswari

According to the World Health Organization (WHO), cancer is the second leading cause of death globally. Scientific research on different types of cancers grows at an ever-increasing rate, publishing large volumes of research articles every year. The insight information and the knowledge of the drug, diagnostics, risk, symptoms, treatments, etc., related to g

Yuma Koizumi, Heiga Zen, Shigeki Karita, Yifan Ding

This paper introduces a new speech dataset called ``LibriTTS-R'' designed for text-to-speech (TTS) use. It is derived by applying speech restoration to the LibriTTS corpus, which consists of 585 hours of speech data at 24 kHz sampling rate from 2,456 speakers and the corresponding texts. The constituent samples of LibriTTS-R are identical to those of LibriTT

Giovanni Fantuzzi, Federico Fuentes

We describe a `discretize-then-relax' strategy to globally minimize integral functionals over functions $u$ in a Sobolev space subject to Dirichlet boundary conditions. The strategy applies whenever the integral functional depends polynomially on $u$ and its derivatives, even if it is nonconvex. The `discretize' step uses a bounded finite element scheme to a

V. S. Filinov, R. A. Syrovatka, P. R. Levashov

Path integral Monte Carlo simulations in the Wigner approach to quantum mechanics has been applied to calculate momentum and spin--resolved radial distribution functions of the strongly correlated soft--sphere quantum fermions. The obtained spin--resolved radial distribution functions demonstrate arising triplet clusters of fermions, that is the consequence

Atefe Akbari-Bardaskan

Electromagnetic Interference that is created by any methods can have an important effect on electronic and telecommunication devices. Thus, the effect of electromagnetic waves on communication systems is particularly important. Nowadays, the slot antennas are an inseparable part of many communication devices. In this paper, two methods have been introduced t

Simran Tinani

This paper focuses on devising methods for producing collisions in algebraic hash functions that may be seen as generalized forms of the well-known Z\'emor and Tillich-Z\'emor hash functions. In contrast to some of the previous approaches, we attempt to construct collisions in a structured and deterministic manner by constructing messages with triangular or

Yixuan Zhou, Peiyu Yang, Yi Qu, Xing Xu

Semi-supervised anomaly detection (SSAD) methods have demonstrated their effectiveness in enhancing unsupervised anomaly detection (UAD) by leveraging few-shot but instructive abnormal instances. However, the dominance of homogeneous normal data over anomalies biases the SSAD models against effectively perceiving anomalies. To address this issue and achieve

Xiaogang Peng, Hao Wen, Yikai Luo, Xiao Zhou

In recent years, the task of weakly supervised audio-visual violence detection has gained considerable attention. The goal of this task is to identify violent segments within multimodal data based on video-level labels. Despite advances in this field, traditional Euclidean neural networks, which have been used in prior research, encounter difficulties in cap

Gyu Whan Chang, Alfred Geroldinger

For a given family $(G_i)_{i \in \N}$ of finitely generated abelian groups, we construct a Dedekind domain $D$ having the following properties. \begin{enumerate} \item $\Pic(D) \cong \bigoplus_{i \in \N}G_i$. \item For each $i \in \N$, there exists a submonoid $S_i \subseteq D^{\bullet}$ with $\Pic (D_{S_i}) \cong G_i$. \item Each class of $\Pic (D)$ and of

Merlin Humml, Lutz Schröder, Dirk Pattinson

Alternating-time temporal logic (ATL) and its extensions, including the alternating-time $\mu$-calculus (AMC), serve the specification of the strategic abilities of coalitions of agents in concurrent game structures. The key ingredient of the logic are path quantifiers specifying that some coalition of agents has a joint strategy to enforce a given goal. Thi

Heinrich Dinkel, Weiji Zhuang, Zhiyong Yan, Yongqing Wang

The currently most prominent algorithm to train keyword spotting (KWS) models with deep neural networks (DNNs) requires strong supervision i.e., precise knowledge of the spoken keyword location in time. Thus, most KWS approaches treat the presence of redundant data, such as noise, within their training set as an obstacle. A common training paradigm to deal w

Peng Ding

I developed the lecture notes based on my ``Causal Inference'' course at the University of California Berkeley over the past seven years. Since half of the students were undergraduates, my lecture notes only required basic knowledge of probability theory, statistical inference, and linear and logistic regressions.

S. E. Pyatovsky

In the paper, a comparative primary cosmic rays (PCR) comparative analysis by $E_0$ and the spectra of variable stars by periods is carried out in order to establish the causes of irregularities in the spectrum of PCR by $E_0$. The study was performed using the public database of the KASCADE-Grande experiment and GCVS and ZTF variable star catalogues. It has

Fangchao Long, Mahdi Ghorbani-Asl, Kseniia Mosina, Yi Li

Layered magnetic materials are becoming a major platform for future spin-based applications. Particularly the air-stable van der Waals compound CrSBr is attracting considerable interest due to its prominent magneto-transport and magneto-optical properties. In this work, we observe a transition from antiferromagnetic to ferromagnetic behavior in CrSBr crystal

Curtis D. Abell, Derek B. Leinweber, Anthony W. Thomas, Jia-Jun Wu

Low-lying baryon resonances have been explored using Hamiltonian Effective Field Theory (HEFT), in a formalism where resonances with a three-quark component are described by both two-particle meson-baryon states and a bare basis state. Here, we investigate the use of multiple bare states in the Hamiltonian, to extend the formalism to higher energy ranges, an

Etash Kumar Guha, Prasanjit Dubey, Xiaoming Huo

In this paper, we derive a novel bound on the generalization error of Magnitude-Based pruning of overparameterized neural networks. Our work builds on the bounds in Arora et al. [2018] where the error depends on one, the approximation induced by pruning, and two, the number of parameters in the pruned model, and improves upon standard norm-based generalizati

Pietro Caputo, Alistair Sinclair

We introduce and analyze a natural class of nonlinear dynamics for spin systems such as the Ising model. This class of dynamics is based on the framework of mass action kinetics, which models the evolution of systems of entities under pairwise interactions, and captures a number of important nonlinear models from various fields, including chemical reaction n

Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh

Despite the demonstrated empirical efficacy of prompt tuning to adapt a pretrained language model for a new task, the theoretical underpinnings of the difference between "tuning parameters before the input" against "the tuning of model weights" are limited. We thus take one of the first steps to understand the role of soft-prompt tuning for transformer-based

Haitao Lin, Yufei Huang, Odin Zhang, Lirong Wu

In recent years, AI-assisted drug design methods have been proposed to generate molecules given the pockets' structures of target proteins. Most of them are atom-level-based methods, which consider atoms as basic components and generate atom positions and types. In this way, however, it is hard to generate realistic fragments with complicated structures. To

Santiago Castro, Oana Ignat, Rada Mihalcea

Joint vision-language models have shown great performance over a diverse set of tasks. However, little is known about their limitations, as the high dimensional space learned by these models makes it difficult to identify semantic errors. Recent work has addressed this problem by designing highly controlled probing task benchmarks. Our paper introduces a mor

Lu Yi, Hanzhi Wang, Zhewei Wei

We study the fundamental problem of sampling independent events, called subset sampling. Specifically, consider a set of $n$ events $S=\{x_1, \ldots, x_n\}$, where each event $x_i$ has an associated probability $p(x_i)$. The subset sampling problem aims to sample a subset $T \subseteq S$, such that every $x_i$ is independently included in $S$ with probabilit

Ronshee Chawla, Daniel Vial, Sanjay Shakkottai, R. Srikant

The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of $N$ agents such that each agent is learning one of $M$ stochastic multi-armed bandits to minimize their group cumulative regret. We develop decentralized algorithms which facilitate

Lorenzo Boccali, Danilo Costarelli, Gianluca Vinti

In this paper, we provide a unifying theory concerning the convergence properties of the so-called max-product Kantorovich sampling operators based upon generalized kernels in the setting of Orlicz spaces. The approximation of functions defined on both bounded intervals and on the whole real axis has been considered. Here, under suitable assumptions on the k

Takahiro Shindo, Taiju Watanabe, Kein Yamada, Hiroshi Watanabe

In recent years, video analysis using Artificial Intelligence (AI) has been widely used, due to the remarkable development of image recognition technology using deep learning. In 2019, the Moving Picture Experts Group (MPEG) has started standardization of Video Coding for Machines (VCM) as a video coding technology for image recognition. In the framework of

A. J. Parameswaran, Mohit Upmanyu

This paper aims to prove that given a isolated complete intersection singularity, the Milnor number will be bounded by a bound depending only on Tjurina number and dimension of the singularity. The proof uses A$\mathfrak{m}$AC (introduced in arXiv:2204.05594) and as with such methods, the bound is purely existential.

Dan Yang, Binbin Hu, Xiaoyan Yang, Yue Shen

With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users that are interested in specific services. Most pioneering works for user targeting tasks commonly perform similarity-based expansion with a few active users as seeds, suffering from

Leon Bungert, Nicolás García Trillos, Matt Jacobs, Daniel McKenzie

Although deep neural networks have achieved super-human performance on many classification tasks, they often exhibit a worrying lack of robustness towards adversarially generated examples. Thus, considerable effort has been invested into reformulating standard Risk Minimization (RM) into an adversarially robust framework. Recently, attention has shifted towa

Sepehr Ganji, Shirin Behnaminia, Ali Ahangarpour, Erfan Mazaheri

Upcoming cellular networks aim to improve the efficiency and flexibility of mobile networks by incorporating various technologies, such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Network Slicing (NS). There exist open-source projects that implement components of different cellular generations. In this paper, we elaborate

Peiman Mohseni, Nick Duffield, Bani Mallick, Arman Hasanzadeh

Neural processes are a family of probabilistic models that inherit the flexibility of neural networks to parameterize stochastic processes. Despite providing well-calibrated predictions, especially in regression problems, and quick adaptation to new tasks, the Gaussian assumption that is commonly used to represent the predictive likelihood fails to capture m

Shunfa Liu, Chris Gustin, Hanqing Liu, Xueshi Li

The coherent interaction between a two-level system and electromagnetic fields serves as a foundation for fundamental quantum physics and modern photonic quantum technology. A profound example is resonance fluorescence, where the non-classical photon emission appears in the form of a Mollow-triplet when a two-level system is continuously driven by a resonant

Wenhao Luo, Yee Hui Lee, Mohamed Lokman Mohd Yusof, Abdulkadir C. Yucel

This study presents a technique for processing Stepfrequency continuous wave (SFCW) ground penetrating radar (GPR) data to detect tree roots. SFCW GPR is portable and enables precise control of energy levels, balancing depth and resolution trade-offs. However, the high-frequency components of the transmission band suffers from poor penetrating capability and

Efthyvoulos Drousiotis, Alexander M. Phillips, Paul G. Spirakis, Simon Maskell

Bayesian Decision Trees (DTs) are generally considered a more advanced and accurate model than a regular Decision Tree (DT) because they can handle complex and uncertain data. Existing work on Bayesian DTs uses Markov Chain Monte Carlo (MCMC) with an accept-reject mechanism and sample using naive proposals to proceed to the next iteration, which can be slow

Yating Wang, Liu Liu

Robust control design for quantum systems is a challenging and key task for practical technology. In this work, we apply neural networks to learn the control problem for the semiclassical Schr\"odinger equation, where the control variable is the potential given by an external field that may contain uncertainties. Inspired by a relevant work [29], we incorpor

Karl Schlechta

This is a sequel to the author's "Truth and Knowledge", College Publications, 2022, and contains some problems and results in connection with a possible representation for Yablo like structures.

Yu Fu, Yanyan Huang, Shunjie Dong, Yalin Wang

Deep neural networks (DNN) have been designed to predict the chronological age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as a valuable biomarker for the early detection of development-related or aging-related disorders. Recent DNN models for brain age estimations usually rely too much on

Caucher Birkar

In this paper, we investigate singularities on fibrations and related topics. We prove conjectures of McKernan and Shokurov on singularities on Fano type fibrations and a conjecture of the author on singularities on log Calabi-Yau fibrations. From these we derive a variant of a conjecture of McKernan and Prokhorov on rationally connected varieties with nef a

Keerth Rathakumar, David Liebowitz, Christian Walder, Kristen Moore

Colour controlled image generation and manipulation are of interest to artists and graphic designers. Vector Quantised Variational AutoEncoders (VQ-VAEs) with autoregressive (AR) prior are able to produce high quality images, but lack an explicit representation mechanism to control colour attributes. We introduce DualVAE, a hybrid representation model that p

Didier Henrion, Maria Infusino, Salma Kuhlmann, Victor Vinnikov

We formulate a class of nonlinear {evolution} partial differential equations (PDEs) as linear optimization problems on moments of positive measures supported on infinite-dimensional vector spaces. Using sums of squares (SOS) representations of polynomials in these spaces, we can prove convergence of a hierarchy of finite-dimensional semidefinite relaxations

Alexander Gladkov

We consider an initial value problem for a nonlinear parabolic equation with memory under nonlinear nonlocal boundary condition. In this paper we study classical solutions. We establish the existence of a local maximal solution. It is shown that under some conditions a supersolution is not less than a subsolution. We find conditions for the positiveness of s

Junzhe Zhu, Peiye Zhuang, Sanmi Koyejo

The advancements in automatic text-to-3D generation have been remarkable. Most existing methods use pre-trained text-to-image diffusion models to optimize 3D representations like Neural Radiance Fields (NeRFs) via latent-space denoising score matching. Yet, these methods often result in artifacts and inconsistencies across different views due to their subopt