May 2023 arXiv papers — page 94

Jinxiang Liu, Yu Wang, Chen Ju, Chaofan Ma

The objective of Audio-Visual Segmentation (AVS) is to localise the sounding objects within visual scenes by accurately predicting pixel-wise segmentation masks. To tackle the task, it involves a comprehensive consideration of both the data and model aspects. In this paper, first, we initiate a novel pipeline for generating artificial data for the AVS task w

Joe J Bradley, Vincent A Martinez, Jochen Arlt, John R Royer

Differential dynamic microscopy (DDM) can be used to extract mean particle size from videos of suspensions. However, many suspensions have multimodal particle size distributions (PSDs), for which this is not a sufficient description. After clarifying how different particle sizes contribute to the signal in DDM, we show that standard DDM analysis can extract

Jiexing Gao, Yurii Zakharian

In the paper, we describe in operator form classes of PDEs that admit PINN's error estimation. Also, for $L^p$ spaces, we obtain a Bramble-Hilbert type lemma that is a tool for PINN's residuals bounding.

Gang Chen, Victoria Huang

Policy gradient algorithms are an important family of deep reinforcement learning techniques. Many past research endeavors focused on using the first-order policy gradient information to train policy networks. Different from these works, we conduct research in this paper driven by the believe that properly utilizing and controlling Hessian information associ

Elisa Bassignana, Filip Ginter, Sampo Pyysalo, Rob van der Goot

Relation Extraction (RE) remains a challenging task, especially when considering realistic out-of-domain evaluations. One of the main reasons for this is the limited training size of current RE datasets: obtaining high-quality (manually annotated) data is extremely expensive and cannot realistically be repeated for each new domain. An intermediate training s

Oliver Görlitz, Daniel Hausmann, Merlin Humml, Dirk Pattinson

There is a wide range of modal logics whose semantics goes beyond relational structures, and instead involves, e.g., probabilities, multi-player games, weights, or neighbourhood structures. Coalgebraic logic serves as a unifying semantic and algorithmic framework for such logics. It provides uniform reasoning algorithms that are easily instantiated to partic

Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B. Tenenbaum

Recent work has considered whether large language models (LLMs) can function as planners: given a task, generate a plan. We investigate whether LLMs can serve as generalized planners: given a domain and training tasks, generate a program that efficiently produces plans for other tasks in the domain. In particular, we consider PDDL domains and use GPT-4 to sy

Zhifu Gao, Zerui Li, Jiaming Wang, Haoneng Luo

This paper introduces FunASR, an open-source speech recognition toolkit designed to bridge the gap between academic research and industrial applications. FunASR offers models trained on large-scale industrial corpora and the ability to deploy them in applications. The toolkit's flagship model, Paraformer, is a non-autoregressive end-to-end speech recognition

Hyungseob Shin, Hyeongyu Kim, Sewon Kim, Yohan Jun

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) can alleviate this problem, which makes it possible to use annotated

Mingyu Guo

We study worst-case VCG redistribution mechanism design for the public project problem. We use a multilayer perceptron (MLP) with ReLU activation to model the payment function and use mixed integer programming (MIP) to solve for the worst-case type profiles that maximally violate the mechanism design constraints. We collect these worst-case type profiles and

Hardik Parwana, Ruiyang Wang, Dimitra Panagou

This paper addresses synthesizing receding-horizon controllers for nonlinear, control-affine dynamical systems under multiple incompatible hard and soft constraints. Handling incompatibility of constraints has mostly been addressed in literature by relaxing the soft constraints via slack variables. However, this may lead to trajectories that are far from the

Adam G. Kline, Stephanie E. Palmer

Renormalization group (RG) methods are emerging as tools in biology and computer science to support the search for simplifying structure in distributions over high-dimensional spaces. We show that mixture models can be thought of as having multiple coexisting, exactly independent RG flows, each with its own notion of scale. We define this property as ``multi

Malte J. M. J. Becher, Julia Jagosz, Rahel-Manuela Neubieser, Jan-Lucas Wree

Thin films of molybendum disulfide grown via thermal atomic layer deposition at low temperatures, suitable for temperature sensible substrates, can be amorphous. To avoid a high temperature post treatment of the whole sample, which can cause thermal degradation of the substrate or other layers, a ultrashort pulse (usp) laser-induced transformation to crystal

Sk Md Adil Imam, Arunava Mukherjee, B. K. Agrawal, Gourab Banerjee

Background: The equations of state (EoSs) which determine the properties of neutron stars (NSs) are often characterized by the iso-scalar and iso-vector nuclear matter parameters (NMPs). Recent attempts to relate the radius and tidal deformability of a NS to the individual NMPs have been inconclusive. These properties display strong correlations with the pre

Francisco Fernandez, Manuel Otero, Ma. Belén Oviedo, Daniel E. Barraco

Silicon anodes hold great promise for next-generation Li-ion batteries. The main obstacle to exploiting their high performance is the challenge of linking experimental observations to atomic structures due to the amorphous nature of Li-Si alloys. We unveil the atomistic-scale structures of amorphous Li-Si using our recently developed density functional tight

Christoph Hertrich, Yixin Tao, László A. Végh

Optimal auction design is a fundamental problem in algorithmic game theory. This problem is notoriously difficult already in very simple settings. Recent work in differentiable economics showed that neural networks can efficiently learn known optimal auction mechanisms and discover interesting new ones. In an attempt to theoretically justify their empirical

Wei Xue, Yongliang Shen, Wenqi Ren, Jietian Guo

Taxonomy completion, enriching existing taxonomies by inserting new concepts as parents or attaching them as children, has gained significant interest. Previous approaches embed concepts as vectors in Euclidean space, which makes it difficult to model asymmetric relations in taxonomy. In addition, they introduce pseudo-leaves to convert attachment cases into

Christopher R. Keefe, Matthew R. Dillon, Chloe Herman, Mary Jewell

Study reproducibility is essential to corroborate, build on, and learn from the results of scientific research but is notoriously challenging in bioinformatics, which often involves large data sets and complex analytic workflows involving many different tools. Additionally many biologists aren't trained in how to effectively record their bioinformatics analy

Chunming He, Kai Li, Yachao Zhang, Guoxia Xu

Weakly-Supervised Concealed Object Segmentation (WSCOS) aims to segment objects well blended with surrounding environments using sparsely-annotated data for model training. It remains a challenging task since (1) it is hard to distinguish concealed objects from the background due to the intrinsic similarity and (2) the sparsely-annotated training data only p

George Trenins, Lars Meuser, Hannah Bertschi, Odysseas Vavourakis

We develop a new simulation technique based on path-integral molecular dynamics for calculating ground-state tunneling splitting patterns from ratios of symmetrized partition functions. In particular, molecular systems are rigorously projected onto their J = 0 rotational state by an "Eckart spring" that connects two adjacent beads in a ring polymer. Using th

Tomasz Dubiel-Teleszynski, Konstantinos Kalogeropoulos, Nikolaos Karouzakis

The importance of unspanned macroeconomic variables for Dynamic Term Structure Models has been intensively discussed in the literature. To our best knowledge the earlier studies considered only linear interactions between the economy and the real-world dynamics of interest rates in DTSMs. We propose a generalized modelling setup for Gaussian DTSMs which allo

Dong Zhang, Shimin Li, Xin Zhang, Jun Zhan

Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT. However, current speech-language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer. In this paper, we propose SpeechGPT, a large language mode

Dominic Breit, Thamsanqa Castern Moyo, Andreas Prohl, Jörn Wichmann

We study the 2D Navier-Stokes equation with transport noise subject to periodic boundary conditions. Our main result is an error estimate for the time-discretisation showing a convergence rate of order (up to) 1/2. It holds with respect to mean square error convergence, whereas previously such a rate for the stochastic Navier-Stokes equations was only known

Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jingyuan Wang

Large language models (LLMs) encode a large amount of world knowledge. However, as such knowledge is frozen at the time of model training, the models become static and limited by the training data at that time. In order to further improve the capacity of LLMs for knowledge-intensive tasks, we consider augmenting LLMs with the large-scale web using search eng

Saptarshi Nath, Christos Peridis, Eseoghene Ben-Iwhiwhu, Xinran Liu

Lifelong learning agents aim to learn multiple tasks sequentially over a lifetime. This involves the ability to exploit previous knowledge when learning new tasks and to avoid forgetting. Modulating masks, a specific type of parameter isolation approach, have recently shown promise in both supervised and reinforcement learning. While lifelong learning algori

Tatsuro Kawamoto

The entropy of random graph ensembles has gained widespread attention in the field of graph theory and network science. We consider microcanonical ensembles of simple graphs with prescribed degree sequences. We demonstrate that the mean-field approximations of the generating function using the Chebyshev-Hermite polynomials provide estimates for the entropy o

Eva Bayer-Fluckiger

Let X be a complex projective K3 surface, and let T(X) be its transcendental lattice; the characteristic polynomials of the isometries of T(X) induced by automorphisms of X are powers of cyclotomic polynomials. Which powers of cyclotomic polynomials occur ? The aim of this note is to answer this question, as well as related ones, and give an alternative appr

Georgi Ganev, Kai Xu, Emiliano De Cristofaro

Generative models trained with Differential Privacy (DP) can produce synthetic data while reducing privacy risks. However, navigating their privacy-utility tradeoffs makes finding the best models for specific settings/tasks challenging. This paper bridges this gap by profiling how DP generative models for tabular data distribute privacy budgets across rows a

Adrien Laurent, Hans Munthe-Kaas

The exotic aromatic Butcher series were originally introduced for the calculation of order conditions for the high order numerical integration of ergodic stochastic differential equations in $\mathbb{R}^d$ and on manifolds. We prove in this paper that exotic aromatic B-series satisfy a universal geometric property, namely that they are characterised by local

Atsuki Yamaguchi, Hiroaki Ozaki, Terufumi Morishita, Gaku Morio

Masked language modeling (MLM) is a widely used self-supervised pretraining objective, where a model needs to predict an original token that is replaced with a mask given contexts. Although simpler and computationally efficient pretraining objectives, e.g., predicting the first character of a masked token, have recently shown comparable results to MLM, no ob

Luca Herranz-Celotti, Ermal Rrapaj

The softmax attention mechanism has emerged as a noteworthy development in the field of Artificial Intelligence research, building on the successes of Transformer-based architectures. However, their ever increasing sizes necessitate ever increasing computational memory, that limits their usage. We propose KgV, a sigmoid gating mechanism that, in conjunction

Farnik Nikakhtar, Laya Parkavousi, Muhammad Sahimi, M. Reza Rahimi Tabar

Stochastic processes are encountered in many contexts, ranging from generation sizes of bacterial colonies and service times in a queueing system to displacements of Brownian particles and frequency fluctuations in an electrical power grid. If such processes are Markov, then their probability distribution is governed by the Kramers-Moyal (KM) equation, a par

AJ Miller, Shamel Fahmi, Matthew Chignoli, Sangbae Kim

We propose MIMOC: Motion Imitation from Model-Based Optimal Control. MIMOC is a Reinforcement Learning (RL) controller that learns agile locomotion by imitating reference trajectories from model-based optimal control. MIMOC mitigates challenges faced by other motion imitation RL approaches because the references are dynamically consistent, require no motion

Alexander I. Bobenko, Carl O. R. Lutz

We discuss a notion of discrete conformal equivalence for decorated piecewise euclidean surfaces (PE-surface), that is, PE-surfaces with a choice of circle about each vertex. It is closely related to inversive distance and hyperideal circle patterns. Under the assumption that the circles are non-intersecting, we proof the corresponding discrete uniformizatio

Henrique Branquinho, Nuno Lourenço, Ernesto Costa

Spiking Neural Networks (SNNs) have attracted recent interest due to their energy efficiency and biological plausibility. However, the performance of SNNs still lags behind traditional Artificial Neural Networks (ANNs), as there is no consensus on the best learning algorithm for SNNs. Best-performing SNNs are based on ANN to SNN conversion or learning with s

Yichen Zhu, Jian Yuan, Bo Jiang, Tao Lin

Data with missing values is ubiquitous in many applications. Recent years have witnessed increasing attention on prediction with only incomplete data consisting of observed features and a mask that indicates the missing pattern. Existing methods assume that the training and testing distributions are the same, which may be violated in real-world scenarios. In

Chu Wu, Jingyu Zhao, Qiaomu Hu, Rui Zeng

Optical diffractive neural networks have triggered extensive research with their low power consumption and high speed in image processing. In this work, we propose a reconfigurable digital all-optical diffractive neural network (R-ODNN) structure. The optical neurons are built with Sb2Se3 phase-change material, making our network reconfigurable, digital, and

Haocheng Hua, Jie Xu

Future sixth-generation (6G) networks are envisioned to provide both sensing and communications functionalities by using densely deployed base stations (BSs) with massive antennas operating in millimeter wave (mmWave) and terahertz (THz). Due to the large number of antennas and the high frequency band, the sensing and communications will operate within the n

Bushra Alshehhi, Areg Karapetyan, Khaled Elbassioni, Sid Chi-Kin Chau

With the electrification of transportation, the rising uptake of electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and stability jeopardized. To accommodate these new loads cost-effectively, modern power grids require coordinated or ``smart'' charging strategies capable of optimizing EV charging sche

Elisa Bassignana, Filip Ginter, Sampo Pyysalo, Rob van der Goot

Most research in Relation Extraction (RE) involves the English language, mainly due to the lack of multi-lingual resources. We propose Multi-CrossRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. Multi-CrossRE is a machine translated version of CrossRE (Bassignana and Plank, 2022), wi

Patrick Diehl, Steven R. Brandt, Max Morris, Nikunj Gupta

Many scientific high performance codes that simulate e.g. black holes, coastal waves, climate and weather, etc. rely on block-structured meshes and use finite differencing methods to iteratively solve the appropriate systems of differential equations. In this paper we investigate implementations of an extremely simple simulation of this type using various pr

Dobrik Georgiev, Danilo Numeroso, Davide Bacciu, Pietro Liò

Solving NP-hard/complete combinatorial problems with neural networks is a challenging research area that aims to surpass classical approximate algorithms. The long-term objective is to outperform hand-designed heuristics for NP-hard/complete problems by learning to generate superior solutions solely from training data. Current neural-based methods for solvin

Song He, Jie Yang, Yu-Xuan Zhang, Zi-Xuan Zhao

In this work, we investigate the time evolution of the pseudo-(R\'enyi) entropy after local primary operator quenches in 2D CFTs with $T\bar T/J\bar T$-deformation. Using perturbation theory, we analyze the corrections to the second pseudo-R\'enyi entropy at the late time, which exhibit a universal form, while its early-time behavior is model-dependent. More

Tianhe Wu, Shuwei Shi, Haoming Cai, Mingdeng Cao

Blind Omnidirectional Image Quality Assessment (BOIQA) aims to objectively assess the human perceptual quality of omnidirectional images (ODIs) without relying on pristine-quality image information. It is becoming more significant with the increasing advancement of virtual reality (VR) technology. However, the quality assessment of ODIs is severely hampered

A. Arelakis, J. M. Arnau, J. L. Berral, A. Call

Vitamin-V is a 2023-2025 Horizon Europe project that aims to develop a complete RISC-V open-source software stack for cloud services with comparable performance to the cloud-dominant x86 counterpart and a powerful virtual execution environment for software development, validation, verification, and test that considers the relevant RISC-V ISA extensions for c

Petch Khunpetch, Arghya Majee, Hu Ruixuan, Rudolf Podgornik

We analyze the changes in the vicinal acidity (pH) at a spherical amphiphilic membrane. The membrane is assumed to contain solvent accessible, embedded, dissociable, charge regulated moieties. Basing our approach on the linear Debye-H\"uckel as well as the non-linear Poisson-Boltzmann theory, together with the general Frumkin-Fowler-Guggenheim adsorption iso

Nikolay Kacharov, Maria-Rosa L. Cioni

The Large Magellanic Cloud (LMC) has a complex dynamics driven by both internal and external processes. The external forces are due to tidal interactions with the Small Magellanic Cloud and the Milky Way, while internally its dynamics mainly depends on the stellar, gas, and dark matter mass distributions. Despite the overall complexity of the system, very of

Shouhei Ma

In this paper we study mixed Hodge structures on the cohomology of locally symmetric varieties and give an application to modular forms. After proving vanishing of some Hodge numbers, we focus on the weight filtration on the last Hodge subspace of the middle degree cohomology. We prove that the weight filtration coincides with the corank filtration on the sp

Zhijie Xie, Shenghui Song

The development of Policy Iteration (PI) has inspired many recent algorithms for Reinforcement Learning (RL), including several policy gradient methods that gained both theoretical soundness and empirical success on a variety of tasks. The theory of PI is rich in the context of centralized learning, but its study under the federated setting is still in the i

Arshin Rezazadeh, Hanan Lutfiyya

Cloud, fog, and edge computing integration with future mobile Internet-of-Things (IoT) devices and related applications in 5G/6G networks will become more practical in the coming years. Containers became the de facto virtualization technique that replaced Virtual Memory (VM). Mobile IoT applications, e.g., intelligent transportation and augmented reality, in

Michele N. Notarnicola, Stefano Olivares

Noiseless linear amplifiers (NLAs) provide a powerful tool to achieve long-distance continuous-variable quantum key distribution (CV-QKD) in the presence of realistic setups with non unit reconciliation efficiency. We address a NLA-assisted CV-QKD protocol implemented via realistic physical NLAs, namely, quantum scissors (QS) and single-photon catalysis (SPC

Syed Samiul Alam, Samiul Based Shuvo, Shams Nafisa Ali, Fardeen Ahmed

Ocular Toxoplasmosis (OT), is a common eye infection caused by T. gondii that can cause vision problems. Diagnosis is typically done through a clinical examination and imaging, but these methods can be complicated and costly, requiring trained personnel. To address this issue, we have created a benchmark study that evaluates the effectiveness of existing pre

Xingyuan Li, Jinyuan Liu, Yixin Lei, Long Ma

3D object detection plays a crucial role in numerous intelligent vision systems. Detection in the open world inevitably encounters various adverse scenes, such as dense fog, heavy rain, and low light conditions. Although existing efforts primarily focus on diversifying network architecture or training schemes, resulting in significant progress in 3D object d

Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu

Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model, which often lack flexibility, precision, and g

Gogulapati Sreedurga

In an indivisible participatory budgeting (PB) framework, we have a limited budget that is to be distributed among a set of projects, by aggregating the preferences of voters for the projects. All the prior work on indivisible PB assumes that each project has only one possible cost. In this work, we let each project have a set of permissible costs, each refl

Iyanuoluwa Shode, David Ifeoluwa Adelani, Jing Peng, Anna Feldman

Africa has over 2000 indigenous languages but they are under-represented in NLP research due to lack of datasets. In recent years, there have been progress in developing labeled corpora for African languages. However, they are often available in a single domain and may not generalize to other domains. In this paper, we focus on the task of sentiment classifi

Aryo Pradipta Gema, Michał Kobiela, Achille Fraisse, Ajitha Rajan

The SARS-CoV-2 pandemic has emphasised the importance of developing a universal vaccine that can protect against current and future variants of the virus. The present study proposes a novel conditional protein Language Model architecture, called Vaxformer, which is designed to produce natural-looking antigenicity-controlled SARS-CoV-2 spike proteins. We eval

Gili Bielous, Reshef Meir

This paper offers a framework for the study of strategic behavior in proxy voting, where non-active voters delegate their votes to active voters. We further study how proxy voting affects the strategic behavior of non-active voters and proxies (active voters) under complete and partial information. We focus on the median voting rule for single-peaked prefere

Dimitrios Mitsotakis

We consider a new splitting based on the Sherman-Morrison-Woodbury formula, which is particularly effective with iterative methods for the numerical solution of large linear systems. These systems involve matrices that are perturbations of circulant or block circulant matrices, which commonly arise in the discretization of differential equations using finite

Alexander Klump, Mladen Savov

For a stochastic process $(X_t)_{t\geq 0}$ we establish conditions under which the inverse first-passage time problem has a solution for any random variable $\xi >0$. For Markov processes we give additional conditions under which the solutions are unique and solutions corresponding to ordered initial states fulfill a comparison principle. As examples we show

Yichen Guo, Eric de Sturler, Tim Warburton

We consider stopping criteria that balance algebraic and discretization errors for the conjugate gradient algorithm applied to high-order finite element discretizations of Poisson problems. Firstly, we introduce a new stopping criterion that suggests stopping when the norm of the linear system residual is less than a small fraction of an error indicator deri

Mohammad Loni, Aditya Mohan, Mehdi Asadi, Marius Lindauer

Sparse Neural Networks (SNNs) can potentially demonstrate similar performance to their dense counterparts while saving significant energy and memory at inference. However, the accuracy drop incurred by SNNs, especially at high pruning ratios, can be an issue in critical deployment conditions. While recent works mitigate this issue through sophisticated pruni

Yulin Sun, Deepak Vij, Fenge Li, Wenjian Guo

Serverless computing is a popular cloud computing paradigm, which requires low response latency to handle on-demand user requests. There are two prominent techniques employed for reducing the response latency: keep fully initialized containers alive (Warm Container) or reduce the new container startup (cold start) latency. This paper presents the 3rd contain

Christoph Langenbruch

Lepton universality, meaning the equal coupling of the electroweak gauge bosons to the different lepton flavours, is a central property of the Standard Model (SM). Tests of lepton universality in ratios of rates of $b\to s\ell^+\ell^-$ decays involving electron and muon final states profit from precise SM predictions, free from hadronic uncertainties. They t

Weronika Hryniewska, Piotr Czarnecki, Jakub Wiśniewski, Przemysław Bombiński

Prevention is better than cure. This old truth applies not only to the prevention of diseases but also to the prevention of issues with AI models used in medicine. The source of malfunctioning of predictive models often lies not in the training process but reaches the data acquisition phase or design of the experiment phase. In this paper, we analyze in deta

Eric Rosen, Devesh K. Jha

We address the problem of teleoperating an industrial robot manipulator via a commercially available Virtual Reality (VR) interface. Previous works on VR teleoperation for robot manipulators focus primarily on collaborative or research robot platforms (whose dynamics and constraints differ from industrial robot arms), or only address tasks where the robot's

Joanna Krynski, Nigar Sultana, Youn Seok Lee, Vadim Makarov

Single-photon avalanche detectors (SPADs) are well-suited for satellite-based quantum communication because of their advantageous operating characteristics as well as their relatively straightforward and robust integration into satellite payloads. However, space-borne SPADs will encounter damage from space radiation, which usually manifests itself in the for

Ilya Gorshkov, Andrey Mamontov, Alexey Staroletov

We describe all finite connected 3-transposition groups whose Matsuo algebras have nontrivial factors that are Jordan algebras. As a corollary, we show that if F is a field of characteristic 0, then there exist infinitely many primitive axial algebras of Jordan type 1/2 over F that are not factors of Matsuo algebras. As an illustrative example, we prove this

Yanfu Wu, Zengqian Ding, Kanglin Xiong, Jiagui Feng

Intrigued by the discovery of the long lifetime in the {\alpha}-Ta/Al2O3-based Transmon qubit, researchers recently found {\alpha}-Ta film is a promising platform for fabricating multi-qubits with long coherence time. To meet the requirements for integrating superconducting quantum circuits, the ideal method is to grow {\alpha}-Ta film on a silicon substrate

Yuzhen Zheng, Shuming Li, Zengqian Ding, Kanglin Xiong

Tantalum and aluminum on sapphire are widely used platforms for qubits of long coherent time. As quantum chips scale up, the number of Josephson junctions on Sapphire increases. Thus, both the uniformity and stability of the junctions are crucial to quantum devices, such as scalable superconducting quantum computer circuit, and quantum-limited amplifiers. By

Yameng Zhang, Long Bai, Li Liu, Hongliang Ren

Due to its non-invasive and painless characteristics, wireless capsule endoscopy has become the new gold standard for assessing gastrointestinal disorders. Omissions, however, could occur throughout the examination since controlling capsule endoscope can be challenging. In this work, we control the magnetic capsule endoscope for the coverage scanning task in

Yanjun Li, Ravesh Sukhnandan, Jeffrey P. Gill, Hillel J. Chiel

The Synthetic Nervous System (SNS) is a biologically inspired neural network (NN). Due to its capability of capturing complex mechanisms underlying neural computation, an SNS model is a candidate for building compact and interpretable NN controllers for robots. Previous work on SNSs has focused on applying the model to the control of legged robots and the de

Tingting Qin, Gaopeng Duan, Aming Li

Detecting the driver nodes of complex networks has garnered significant attention recently to control complex systems to desired behaviors, where nodes represent system components and edges encode their interactions. Driver nodes, which are directly controlled by external inputs, play a crucial role in controlling all network nodes. While many approaches hav

Amartya Mukherjee, Jun Liu

This paper proposes an actor-critic algorithm for controlling the temperature of a battery pack using a cooling fluid. This is modeled by a coupled 1D partial differential equation (PDE) with a controlled advection term that determines the speed of the cooling fluid. The Hamilton-Jacobi-Bellman (HJB) equation is a PDE that evaluates the optimality of the val

Martijn Bartelds, Nay San, Bradley McDonnell, Dan Jurafsky

The performance of automatic speech recognition (ASR) systems has advanced substantially in recent years, particularly for languages for which a large amount of transcribed speech is available. Unfortunately, for low-resource languages, such as minority languages, regional languages or dialects, ASR performance generally remains much lower. In this study, we

Emilio A. Lauret, Benjamin Linowitz

The spectral geometry of negatively curved manifolds has received more attention than its positive curvature counterpart. In this paper we will survey a variety of spectral geometry results that are known to hold in the context of hyperbolic manifolds and discuss the extent to which analogous results hold in the setting of spherical manifolds. We conclude wi

Yuzeng Li, Qicheng Zhang, Chunyin Qiu

Recently, the higher-order topological phases from the chiral AIII symmetry classes are characterized by a Z topological invariant known as the multipole chiral numbers, which indicate the number of degenerate zero-energy corner states at each corner. Here, we report the first experimental realization of higher-order topological insulators protected by multi

Juyoung Yun, Sol Choi, Francois Rameau, Byungkon Kang

With the increasing complexity of machine learning models, managing computational resources like memory and processing power has become a critical concern. Mixed precision techniques, which leverage different numerical precisions during model training and inference to optimize resource usage, have been widely adopted. However, access to hardware that support

Ilia A. Iakovlev, Oleg M. Sotnikov, Ivan V. Dyakonov, Evgeniy O. Kiktenko

Analyzing the properties of complex quantum systems is crucial for further development of quantum devices, yet this task is typically challenging and demanding with respect to required amount of measurements. A special attention to this problem appears within the context of characterizing outcomes of noisy intermediate-scale quantum devices, which produce qu

Marcel Heisler, Christian Becker-Asano

This paper describes, how current Machine Learning (ML) techniques combined with simple rule-based animation routines make an android robot head an embodied conversational agent with ChatGPT as its core component. The android robot head is described, technical details are given of how lip-sync animation is being achieved, and general software design decision

Zhaoran Liu, Takahiro Morishita, Tadayuki Kodama

We study the morphological properties of mid-infrared selected galaxies at $1.0<z<1.7$ in the SMACS J0723.3-7327 cluster field, to investigate the mechanisms of galaxy mass assembly and structural formation at cosmic noon. We develop a new algorithm to decompose the dust and stellar components of individual galaxies by utilizing high-resolution images in the

S. P. Hatkar, D. P. Tadas, A. S. Agrawal, S. D. Katore

In this paper, a new class of string and holographic dark energy (HDE) cosmological model in the context of the $f(R)$ theory of gravity using the Kasner metric is considered. The exact solution of the field equations is obtained using the relation between the average scale factor and the scalar function $f(R)$. It is observed that the universe is accelerati

Zachary T. P. Fried, Kin Long Kelvin Lee, Alex N. Byrne, Brett A. McGuire

Machine learning techniques have been previously used to model and predict column densities in the TMC-1 dark molecular cloud. In interstellar sources further along the path of star formation, such as those where a protostar itself has been formed, the chemistry is known to be drastically different from that of largely quiescent dark clouds. To that end, we

Shibshankar Dey, Ali Kaan Kurbanzade, Esma S. Gel, Joseph Mihaljevic

During various stages of the COVID-19 pandemic, countries implemented diverse vaccine management approaches, influenced by variations in infrastructure and socio-economic conditions. This article provides a comprehensive overview of optimization models developed by the research community throughout the COVID-19 era, aimed at enhancing vaccine distribution an

Sarah Wingert, Christian Becker-Asano

When researching on the acceptance of robots in Human-Robot-Interaction the Uncanny Valley needs to be considered. Reusable and standardized measures for it are essential. In this paper one such questionnaire got translated into German. The translated indices got evaluated (n=140) for reliability with Cronbach's alpha. Additionally the items were tested with

Chang Zeng, Xin Wang, Xiaoxiao Miao, Erica Cooper

The ability of countermeasure models to generalize from seen speech synthesis methods to unseen ones has been investigated in the ASVspoof challenge. However, a new mismatch scenario in which fake audio may be generated from real audio with unseen genres has not been studied thoroughly. To this end, we first use five different vocoders to create a new datase

Helmut Friedrich

This article introduces the notions of asymptotic dust and asymptotic radiation equations of state. With these non-linear generalizations of the well known dust or (incoherent) radiation equations of state the perfect-fluid equations loose any conformal covariance or privilege. We analyse the conformal field equations induced with these equations of state. I

Michele Farisco, Gianluca Baldassarre, Emilio Cartoni, Antonia Leach

Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of shortcomings with regards to different application domains and goals. These limitations are arguably both conceptual (e.g., related to underlying theoretical models, such as symbolic vs. connectionist), and operational (e.g., related to robustness and ability to

Lilian Aline Weber, Peter Thestrup Waade, Nicolas Legrand, Anna Hedvig Møller

Hierarchical Bayesian models of perception and learning feature prominently in contemporary cognitive neuroscience where, for example, they inform computational concepts of mental disorders. This includes predictive coding and hierarchical Gaussian filtering (HGF), which differ in the nature of hierarchical representations. In this work, we present a new cla

Leonardo Maini

Let $(A_x)_{x\in\mathbb{R}^d}$ be a locally integrable, centered, weakly stationary random field, i.e. $\mathbb{E}[A_x]=0$, ${\rm Cov}(A_x,A_y)=K(x-y)$, $\forall x,y\in\mathbb{R}^d$, with measurable covariance function $K:\mathbb{R}^d\rightarrow\mathbb{R}$. Assuming only that $w_t:=\int_{\{|z|\le t\}}K(z)dz$ is regularly varying (which encompasses the classi

Martin Böhm, Jarosław Byrka, Mateusz Lewandowski, Jan Marcinkowski

Submodularity in combinatorial optimization has been a topic of many studies and various algorithmic techniques exploiting submodularity of a studied problem have been proposed. It is therefore natural to ask, in cases where the cost function of the studied problem is not submodular, whether it is possible to approximate this cost function with a proxy submo

Matias D. Cattaneo, Xinwei Ma, Yusufcan Masatlioglu

Barseghyan and Molinari (2023) give sufficient conditions for semi-nonparametric point identification of parameters of interest in a mixture model of decision-making under risk, allowing for unobserved heterogeneity in utility functions and limited consideration. A key assumption in the model is that the heterogeneity of risk preferences is unobservable but

Yong Cai, Mian Zhu, Yun-Song Piao

Primordial black holes (PBHs) and the violation of the null energy condition (NEC) have significant implications for our understanding of the very early universe. We present a novel approach to generate PBHs via the NEC violation in a single-field inflationary scenario. In our scenario, the universe transitions from a first slow-roll inflation stage with a H

Shuang Liu, Xianzhong Zheng, Dongdong Shi, Zheng Cai

Characterizing the structural properties of galaxies in high-redshift protoclusters is key to our understanding of the environmental effects on galaxy evolution in the early stages of galaxy and structure formation. In this study, we assess the structural properties of 85 and 87 Halpha emission-line candidates (HAEs) in the densest regions of two massive pro

Kyriakos Stylianopoulos, Mattia Merluzzi, Paolo Di Lorenzo, George C. Alexandropoulos

In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new data are continuously generated/collected by a set of devices and are handled through a dynamic queueing system. Buildin

Akiri Surely

The adoption of modern technologies for use in healthcare has become an inevitable change. The emergence of artificial intelligence drives this digital disruption. Artificial intelligence has augmented machine capabilities to act like and interact with human beings. As the healthcare industry adopts technology in most areas, an area in healthcare that is tou

Liang Chen, Shuming Ma, Dongdong Zhang, Furu Wei

While multilingual neural machine translation has achieved great success, it suffers from the off-target issue, where the translation is in the wrong language. This problem is more pronounced on zero-shot translation tasks. In this work, we find that failing in encoding discriminative target language signal will lead to off-target and a closer lexical distan

Di Yang, Yihao Huang, Qing Guo, Felix Juefei-Xu

The adversarial patch attack aims to fool image classifiers within a bounded, contiguous region of arbitrary changes, posing a real threat to computer vision systems (e.g., autonomous driving, content moderation, biometric authentication, medical imaging) in the physical world. To address this problem in a trustworthy way, proposals have been made for certif

Nadav Borenstein, Natalia da Silva Perez, Isabelle Augenstein

NLP methods can aid historians in analyzing textual materials in greater volumes than manually feasible. Developing such methods poses substantial challenges though. First, acquiring large, annotated historical datasets is difficult, as only domain experts can reliably label them. Second, most available off-the-shelf NLP models are trained on modern language

Jiandong Liu, Lan Zhang, Chaojie Lv, Ting Yu

In modern distributed computing applications, such as federated learning and AIoT systems, protecting privacy is crucial to prevent adversarial parties from colluding to steal others' private information. However, guaranteeing the utility of computation outcomes while protecting all parties' data privacy can be challenging, particularly when the parties' pri