May 2023 arXiv papers — page 130

David Melhart, Julian Togelius, Benedikte Mikkelsen, Christoffer Holmgård

Video games are one of the richest and most popular forms of human-computer interaction and, hence, their role is critical for our understanding of human behaviour and affect at a large scale. As artificial intelligence (AI) tools are gradually adopted by the game industry a series of ethical concerns arise. Such concerns, however, have so far not been exten

Paul-Andi Nagy, Uwe Semmelmann

We study the integrability to second order of infinitesimal Einstein deformations on compact Riemannian and in particular on K\"ahler manifolds. We find a new way of expressing the necessary and sufficient condition for integrability to second order, which also gives a very clear and compact way of writing the Koiso obstruction. As an application we consider

Lingqi Zhang, Mohamed Wahib, Peng Chen, Jintao Meng

Iterative stencils are used widely across the spectrum of High Performance Computing (HPC) applications. Many efforts have been put into optimizing stencil GPU kernels, given the prevalence of GPU-accelerated supercomputers. To improve the data locality, temporal blocking is an optimization that combines a batch of time steps to process them together. Under

Emma O'Neill, Julie Carson-Berndsen

Automatic Speech Recognition (ASR) systems exhibit the best performance on speech that is similar to that on which it was trained. As such, underrepresented varieties including regional dialects, minority-speakers, and low-resource languages, see much higher word error rates (WERs) than those varieties seen as 'prestigious', 'mainstream', or 'standard'. This

Ajay Chandra, Martin Hairer, Martin Peev

We prove local (in space and time) well-posedness for a mildly regularised version of the stochastic quantisation of the Yukawa$_{2}$ Euclidean field theory with a self-interacting boson. Our regularised dynamic is still singular but avoids non-local divergences, allowing us to use a version of the Da Prato--Debussche argument. This model is a test case for

Bidisha Bhatt, Soumik Mukhopadhyay, Krishnacharya Khare

Stability of thin liquid films on a surface can be controlled using an external stimuli, such as electric field, temperature or light by manipulating the total excess free energy of the system. It has been previously shown that thin lubricating films on slippery surfaces can be destabilized via spinodal mechanism using external electric field, which return t

Si-Guo Fang, Dong Huang, Chang-Dong Wang, Jian-Huang Lai

The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets. To avoid the post-processing via k-means during the bipartite graph partitioning, the constrained Laplacian rank (CLR) is often utilized for constraining the number of connected components (i.e., c

Jessica Park, Susan Stepney, Irene D'Amico

We show strong positive spatial correlations in the qubits of a D-Wave 2000Q quantum annealing chip that are connected to qubits outside their own unit cell. By simulating the dynamics of spin networks, we then show that correlation between nodes is affected by a number of factors. The different connectivity of qubits within the network means that informatio

Laura Jahn, Rasmus K. Rendsvig

Social media feeds typically favor posts according to user engagement. The most ubiquitous type of engagement (and the type we study) is *likes*. Users customarily take engagement metrics such as likes as a neutral proxy for quality and authority. This incentivizes like manipulation to influence public opinion through *coordinated inauthentic behavior* (CIB)

Jiwei Zhang, Zhimin Zhang, Chengchao Zhao

Numerous error estimates have been carried out on various numerical schemes for subdiffusion equations. Unfortunately most error bounds suffer from a factor $1/(1-\alpha)$ or $\Gamma(1-\alpha)$, which blows up as the fractional order $\alpha\to 1^-$, a phenomenon not consistent with regularity of the continuous problem and numerical simulations in practice.

Yongdan Yang, Ying Li, Xiaosi Xu, Xiao Yuan

Estimating the eigenvalue or energy gap of a Hamiltonian H is vital for studying quantum many-body systems. Particularly, many of the problems in quantum chemistry, condensed matter physics, and nuclear physics investigate the energy gap between two eigenstates. Hence, how to efficiently solve the energy gap becomes an important motive for researching new qu

Raja Solanki, Bina Patel, Lakhan V. Jaybhaye, P. K. Sahoo

In this article, we investigate the observed cosmic acceleration in the framework of a cosmological $f(R,L_m)$ model dominated by bulk viscous matter in an anisotropic background. We consider the LRS Bianchi type I metric and derive the Friedmann equations that drive the gravitational interactions in $f(R,L_m)$ gravity. Further, we assume the functional form

Junjie Hu, Chunxiang Xu, Zhe Jiang, Jiwu Cao

Mining attacks allow adversaries to obtain a disproportionate share of the mining reward by deviating from the honest mining strategy in the Bitcoin system. Among them, the most well-known are selfish mining (SM), block withholding (BWH), fork after withholding (FAW) and bribery mining. In this paper, we propose two novel mining attacks: bribery semi-selfish

Lutz Mertenskötter, Markus Kantner

We present a statistical inference approach to estimate the frequency noise characteristics of ultra-narrow linewidth lasers from delayed self-heterodyne beat note measurements using Bayesian inference. Particular emphasis is on estimation of the intrinsic (Lorentzian) laser linewidth. The approach is based on a statistical model of the measurement process,

Marco Discacciati, Tommaso Vanzan

This paper derives optimal coefficients for optimized Schwarz iterations for the time-dependent Stokes-Darcy problem using an innovative strategy to solve a nonstandard min-max problem. The coefficients take into account both physical and discretization parameters that characterize the coupled problem, and they guarantee the robustness of the associated doma

Gal Yona, Or Honovich, Itay Laish, Roee Aharoni

Ensuring that large language models (LMs) are fair, robust and useful requires an understanding of how different modifications to their inputs impact the model's behaviour. In the context of open-text generation tasks, however, such an evaluation is not trivial. For example, when introducing a model with an input text and a perturbed, "contrastive" version o

David Young, Douglas Leith

We develop a novel latent-bandit algorithm for tackling the cold-start problem for new users joining a recommender system. This new algorithm significantly outperforms the state of the art, simultaneously achieving both higher accuracy and lower regret.

Luca Becchetti, Vincenzo Bonifaci, Emilio Cruciani, Francesco Pasquale

Opinion diffusion is a crucial phenomenon in social networks, often underlying the way in which a collective of agents develops a consensus on relevant decisions. The voter model is a well-known theoretical model to study opinion spreading in social networks and structured populations. Its simplest version assumes that an updating agent will adopt the opinio

Lorenzo Sonnino, Shaswot Shresthamali, Yuan He, Masaaki Kondo

DNNs are widely used but face significant computational costs due to matrix multiplications, especially from data movement between the memory and processing units. One promising approach is therefore Processing-in-Memory as it greatly reduces this overhead. However, most PIM solutions rely either on novel memory technologies that have yet to mature or bit-se

Jinglong Gao, Xiao Ding, Bing Qin, Ting Liu

Causal reasoning ability is crucial for numerous NLP applications. Despite the impressive emerging ability of ChatGPT in various NLP tasks, it is unclear how well ChatGPT performs in causal reasoning. In this paper, we conduct the first comprehensive evaluation of the ChatGPT's causal reasoning capabilities. Experiments show that ChatGPT is not a good causal

Pragya Katyayan, Nisheeth Joshi

Question Answering (QA) has proved to be an arduous challenge in the area of natural language processing (NLP) and artificial intelligence (AI). Many attempts have been made to develop complete solutions for QA as well as improving significant sub-modules of the QA systems to improve the overall performance through the course of time. Questions are the most

Piotr Arcab, Bartosz Mirecki, Marzena Stefaniuk, Monika Pawlowska

Laser-based lensless digital holographic microscopy (LDHM) is often spoiled by considerable coherent noise factor. We propose a novel LDHM method with significantly limited coherent artifacts, e.g., speckle noise and parasitic interference fringes. It is achieved by incorporating a rotating diffuser, which introduces partial spatial coherence and preserves h

Yuan Tian, Zheng Zhang, Zheng Ning, Toby Jia-Jun Li

Relational databases play an important role in business, science, and more. However, many users cannot fully unleash the analytical power of relational databases, because they are not familiar with database languages such as SQL. Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they

Pavel Kolesnikov, Farukh Mashurov, Bauyrzhan Sartayev

For a non-associative algebra $A$ with a derivation $d$, its derived algebra $A^{(d)}$ is the same space equipped with new operations $a\succ b = d(a)b$, $a\prec b = ad(b)$, $a,b\in A$. Given a variety ${\rm Var}$ of algebras, its derived variety is generated by all derived algebras $A^{(d)}$ for all $A$ in ${\rm Var}$ and for all derivations $d$ of $A$. The

Georgios Papaioannou, Lin Zhao, Mikael Nybacka, Jenny Jerrelind

Teleoperation is considered as a viable option to control fully automated vehicles (AVs) of Level 4 and 5 in special conditions. However, by bringing the remote drivers in the loop, their driving experience should be realistic to secure safe and comfortable remote control.Therefore, the remote control tower should be designed such that remote drivers receive

Shuhei Kitamura, Aya S. Ihara

Worldviews may differ significantly according to political orientation. Even a single word can have a completely different meaning depending on political orientation. However, no direct evidence has been obtained on differences in the semantic processing of single words in naturalistic information between individuals with different political orientations. Th

Zheng Chen, Martin Dahl, Erik G. Larsson

In this work, we focus on the communication aspect of decentralized learning, which involves multiple agents training a shared machine learning model using decentralized stochastic gradient descent (D-SGD) over distributed data. In particular, we investigate the impact of broadcast transmission and probabilistic random access policy on the convergence perfor

Rajdeep Dutta, Qincheng Wang, Ankur Singh, Dhruv Kumarjiguda

This paper presents a novel RL algorithm, S-REINFORCE, which is designed to generate interpretable policies for dynamic decision-making tasks. The proposed algorithm leverages two types of function approximators, namely Neural Network (NN) and Symbolic Regressor (SR), to produce numerical and symbolic policies, respectively. The NN component learns to genera

Maha Riad, Vinicius Renan de Carvalho, Fatemeh Golpayegani

Value-alignment in normative multi-agent systems is used to promote a certain value and to ensure the consistent behavior of agents in autonomous intelligent systems with human values. However, the current literature is limited to incorporation of effective norms for single value alignment with no consideration of agents' heterogeneity and the requirement of

Shivani Singh Rathore, Bharti Nathani, Nisheeth Joshi, Pragya Katyayan

In this paper, we have shown a script conversion (transliteration) technique that converts Sindhi text in the Devanagari script to the Perso-Arabic script. We showed this by incorporating a hybrid approach where some part of the text is converted using a rule base and in case an ambiguity arises then a probabilistic model is used to resolve the same. Using t

Andrzej Lingas, Mia Persson

A monotone Boolean circuit is composed of OR gates, AND gates and input gates corresponding to the input variables and the Boolean constants. It is $q$-multilinear if for each its output gate $o$ and for each prime implicant $s$ of the function computed at $o$, the arithmetic version of the circuit resulting from the replacement of OR and AND gates by additi

Edvin Olofsson, Jan Marcus Dahlström

Non-perturbative resonant multiphoton ionization $(1+1)$ is studied using the resolvent operator technique. Scaling parameters for effective two-level Hamiltonians are computed for hydrogen and helium atoms to provide a quantitative description of Rabi oscillations at XUV wavelengths, which were recently observed using a seeded Free-Electron Laser [S. Nandi

Olga Bokareva, Oliver Kühn

Transition metal complexes for photochemical applications often feature a high density of electron-vibrational states characterized by nonadiabatic and spin-orbit couplings. Overall, the dynamics after photoexcitation is shaped by rapid transitions between states of different character and multiplicity. Even though transient absorption experiments enable cha

Matthias Raddant, Martin Bertau, Gerald Steiner

This paper introduces a novel method for estimating material flows, with a focus on tracing phosphate flows from mining countries to those using phosphate in agricultural production. Our approach integrates data on phosphate rock extraction, fertilizer use, and international trade of phosphate-related products. A key advantage of this method is that it does

Xinze Lyu, Sundar Aditya, Junghoon Kim, Bruno Clerckx

In multi-user multi-antenna communications, it is well-known in theory that Rate-Splitting Multiple Access (RSMA) can achieve a higher spectral efficiency than both Space Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA). However, an experimental evaluation of RSMA's performance, relative to SDMA and NOMA, is missing in the literature

Radhika Sharma, Pragya Katyayan, Nisheeth Joshi

In this paper, we have shown a method of improving the quality of neural machine translation by translating/transliterating name entities as a preprocessing step. Through experiments we have shown the performance gain of our system. For evaluation we considered three types of name entities viz person names, location names and organization names. The system w

Margherita Disertori, Franz Merkl, Silke W. W. Rolles

We show that the vertex-reinforced jump process on the $d$-dimensional lattice with long-range jumps is transient in any dimension $d$ as long as the initial weights do not decay too fast. The main ingredients in the proof are: an analysis of the corresponding random environment on finite boxes, a comparison with a hierarchical model, and the reduction of th

Xinyun Zhang, Haochen Tan, Han Wu, Bei Yu

Humans learn language via multi-modal knowledge. However, due to the text-only pre-training scheme, most existing pre-trained language models (PLMs) are hindered from the multi-modal information. To inject visual knowledge into PLMs, existing methods incorporate either the text or image encoder of vision-language models (VLMs) to encode the visual informatio

Ines Martin, Yoann Pertot, Olivier Albert, Thomas Pinoteau

We present a 40-MHz ultrafast optical parametric amplifier (OPA), tunable from 0.8 to 1 $\mu$m, with a relative intensity noise (RIN) matching the shot-noise floor (-160 dB/Hz) above 2 MHz. The OPA is pumped by a 20-W Kerr-lens mode-locked Ytterbium laser and seeded by a a supercontinuum generated in an all-normal-dispersion (ANDi) fiber. With an average out

Wenzheng Lu, Leonarde de S. Menezes, Andreas Tittl, Haoran Ren

Active metasurfaces provide unique advantages for on-demand light manipulation at a subwavelength scale for emerging applications of 3D displays, augmented/virtual reality (AR/VR) glasses, holographic projectors and light detection and ranging (LiDAR). These applications put stringent requirements on switching speed, cycling duration, controllability over in

Wei-Lin Chen, An-Zi Yen, Cheng-Kuang Wu, Hen-Hsen Huang

Language models (LMs) that jointly generate end-task answers as well as free-text rationales are known as self-rationalization models. Recent works demonstrate great performance gain for self-rationalization by few-shot prompting LMs with rationale-augmented exemplars. However, the ability to benefit from explanations only emerges with large-scale LMs, which

Taras Banakh

Following Will Brian, we define a metric space $X$ to be $Banakh$ if all nonempty spheres of positive radius $r$ in $X$ have cardinality $2$ and diameter $2r$. Standard examples of Banakh spaces are subgroups of the real line. In this paper we study the geometry of Banakh spaces, characterize Banakh spaces which are isometric to subgroups of the real line, a

Y. Ge, K. Wang, A. Duarte-Cabral, A. R. Pettitt

Context. Filamentary structures in the interstellar medium are closely related to star formation. Dense gas mass fraction (DGMF) or clump formation efficiency in large-scale filaments possibly determine their hosting star formation activities. Aims. We aim to automatically identify large-scale filaments, characterize them, investigate their association with

Stephan Leitner

Organizations face numerous challenges posed by unexpected events such as energy price hikes, pandemic disruptions, terrorist attacks, and natural disasters, and the factors that contribute to organizational success in dealing with such disruptions often remain unclear. This paper analyzes the roles of top-down and bottom-up organizational structures in prom

Sameep Dahal, Francesco d'Amore, Henrik Lievonen, Timothé Picavet

One of the cornerstones of the distributed complexity theory is the derandomization result by Chang, Kopelowitz, and Pettie [FOCS 2016]: any randomized LOCAL algorithm that solves a locally checkable labeling problem (LCL) can be derandomized with at most exponential overhead. The original proof assumes that the number of random bits is bounded by some funct

Laura Jahn, Rasmus K. Rendsvig, Jacob Stærk-Østergaard

Coordinated inauthentic behavior is used as a tool on social media to shape public opinion by elevating or suppressing topics using systematic engagements -- e.g. through *likes* or similar reactions. In an honest world, reactions may be informative to users when selecting on what to spend their attention: through the wisdom of crowds, summed reactions may h

Janice L. Scealy, Kassel L. Hingee, John T. Kent, Andrew T. A. Wood

The restricted polynomially-tilted pairwise interaction (RPPI) distribution gives a flexible model for compositional data. It is particularly well-suited to situations where some of the marginal distributions of the components of a composition are concentrated near zero, possibly with right skewness. This article develops a method of tractable robust estimat

Philipp Spitzer, Niklas Kühl, Daniel Heinz, Gerhard Satzger

As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and to pass it on to novices. While this knowledge transfer has traditionally taken place through personal interaction, it lacks scalability and requires si

Gabriele Meoni, Roberto Del Prete, Federico Serva, Alix De Beussche

Nowadays, there is growing interest in applying Artificial Intelligence (AI) on board Earth Observation (EO) satellites for time-critical applications, such as natural disaster response. However, the unavailability of raw satellite data currently hinders research on lightweight pre-processing techniques and limits the exploration of end-to-end pipelines, whi

Leo Pauly, Wassim Rharbaoui, Carl Shneider, Arunkumar Rathinam

Estimating the pose of an uncooperative spacecraft is an important computer vision problem for enabling the deployment of automatic vision-based systems in orbit, with applications ranging from on-orbit servicing to space debris removal. Following the general trend in computer vision, more and more works have been focusing on leveraging Deep Learning (DL) me

Christos Plachouras, Marius Miron

Music rearrangement involves reshuffling, deleting, and repeating sections of a music piece with the goal of producing a standalone version that has a different duration. It is a creative and time-consuming task commonly performed by an expert music engineer. In this paper, we propose a method for automatically rearranging music recordings that takes into ac

Justus Bogner, Sebastian Kotstein, Timo Pfaff

Context: Web APIs are one of the most used ways to expose application functionality on the Web, and their understandability is important for efficiently using the provided resources. While many API design rules exist, empirical evidence for the effectiveness of most rules is lacking. Objective: We therefore wanted to study 1) the impact of RESTful API design

Etor Arza, Josu Ceberio, Ekhiñe Irurozki, Aritz Pérez

An experimental comparison of two or more optimization algorithms requires the same computational resources to be assigned to each algorithm. When a maximum runtime is set as the stopping criterion, all algorithms need to be executed in the same machine if they are to use the same resources. Unfortunately, the implementation code of the algorithms is not alw

Mahmoud Zaher, Emil Björnson, Marina Petrova

The existence of unknown interference is a prevalent problem in wireless communication networks. Especially in multi-user multiple-input multiple-output (MIMO) networks, where a large number of user equipments are served on the same time-frequency resources, the outage performance may be dominated by the unknown interference arising from scheduling variation

Eric G. Cavalcanti, Andrea Di Biagio, Carlo Rovelli

Lawrence et al. have presented an argument purporting to show that "relative facts do not exist" and, consequently, "Relational Quantum Mechanics is incompatible with quantum mechanics". The argument is based on a GHZ-like contradiction between constraints satisfied by measurement outcomes in an extended Wigner's friend scenario. Here we present a strengthen

Weikun Zhang, Jianke Zhu

With the growing popularity of neural rendering, there has been an increasing number of neural implicit multi-view reconstruction methods. While many models have been enhanced in terms of positional encoding, sampling, rendering, and other aspects to improve the reconstruction quality, current methods do not fully leverage the information among neighboring p

Meng Zheng

This paper introduces and explores a new programming paradigm, Model-based Programming, designed to address the challenges inherent in applying deep learning models to real-world applications. Despite recent significant successes of deep learning models across a range of tasks, their deployment in real business scenarios remains fraught with difficulties, su

Jie Xu, Lu Lu, Sen Yang, Bilin Liang

METHODS: First, a set of evaluation criteria is designed based on a comprehensive literature review. Second, existing candidate criteria are optimized for using a Delphi method by five experts in medicine and engineering. Third, three clinical experts design a set of medical datasets to interact with LLMs. Finally, benchmarking experiments are conducted on t

Richard Westerhof, Richard Atherton, Vasilios Andrikopoulos

Cloud computing has become the de facto paradigm for delivering software to system users, with organizations and enterprises of all sizes making use of cloud services in some way. On the surface, adopting the cloud appears to be a very efficient approach for offloading concerns such as infrastructure management, logistics, and most importantly for this work,

Francesco Coppini, Paolo Maria Santini

The Ablowitz-Ladik (AL) equations are distinguished integrable discretizations of the focusing and defocusing nonlinear Schr\"odinger (NLS) equations. In a previous paper (arXiv:2305.04857) we have studied the effect of the modulation instability of the homogeneous background solution of the AL equations in the periodic setting, showing in particular that bo

Robin Chemnitz, Maximilian Engel, Péter Koltai

We consider linear cocycles taking values in $\textup{SL}_d(\mathbb{R})$ driven by homeomorphic transformations of a smooth manifold, in discrete and continuous time. We show that any discrete-time cocycle can be extended to a continuous-time cocycle, while preserving its characteristic properties. We provide a necessary and sufficient condition under which

R. Farmer, M. Renzo, Y. Götberg, E. Bellinger

Thorne-$\.Z$ytkow objects (T$\.Z$O) are potential end products of the merger of a neutron star with a non-degenerate star. In this work, we have computed the first grid of evolutionary models of T$\.Z$Os with the MESA stellar evolution code. With these models, we predict several observational properties of T$\.Z$Os, including their surface temperatures and l

Bo Zhou, Jiapeng Xie, Yan Pan, Jiajie Wu

Identifying moving objects is an essential capability for autonomous systems, as it provides critical information for pose estimation, navigation, collision avoidance, and static map construction. In this paper, we present MotionBEV, a fast and accurate framework for LiDAR moving object segmentation, which segments moving objects with appearance and motion f

Renato P. dos Santos

This study explores the potential of Generative AI chatbots (GenAIbots) such as ChatGPT and Bing Chat, in Chemistry education, within a constructionist theoretical framework. A single-case study methodology was used to analyse extensive interaction logs between students and both AI systems in simulated Chemistry learning experiences. The results highlight th

Bela Gipp, André Greiner-Petter, Moritz Schubotz, Norman Meuschke

This project investigated new approaches and technologies to enhance the accessibility of mathematical content and its semantic information for a broad range of information retrieval applications. To achieve this goal, the project addressed three main research challenges: (1) syntactic analysis of mathematical expressions, (2) semantic enrichment of mathemat

Yuling Yao, Luiz Max Carvalho, Diego Mesquita, Yann McLatchie

Combining predictions from different models is a central problem in Bayesian inference and machine learning more broadly. Currently, these predictive distributions are almost exclusively combined using linear mixtures such as Bayesian model averaging, Bayesian stacking, and mixture of experts. Such linear mixtures impose idiosyncrasies that might be undesira

Julio C. Sanchez, Hanspeter Schaub

This manuscript develops a simultaneous navigation and gravity estimation strategy around a small body. The scheme combines dynamical model compensation with a mascon gravity fit. Dynamical compensation adds the unmodeled acceleration to the filter state. Consequently, the navigation filter is able to generate an on-orbit position-unmodeled acceleration data

Jasper Müller, Sai Kireet Patri, Tobias Fehenberger, Helmut Griesser

The rapidly growing traffic demands in fiber-optical networks require flexibility and accuracy in configuring lightpaths, for which fast and accurate quality of transmission (QoT) estimation is of pivotal importance. This paper introduces a machine learning (ML)-based QoT estimation approach that meets these requirements. The proposed gradient-boosting ML mo

Claus Kiefer

I investigate the question whether G\"odel's undecidability theorems play a crucial role in the search for a unified theory of physics. I conclude that unless the structure of space-time is fundamentally discrete we can never decide whether a given theory is the final one or not. This is relevant for both canonical quantum gravity and string theory.

Senthil Kumar Jagatheesaperumal, Zhaohui Yang, Qianqian Yang, Chongwen Huang

To facilitate the deployment of digital twins in Metaverse, the paradigm with semantic awareness has been proposed as a means for enabling accurate and task-oriented information extraction with inherent intelligence. However, this framework requires all devices in the Metaverse environment to be directly linked with the semantic model to enable faithful inte

Jasper Müller, Ognjen Jovanovic, Tobias Fehenberger, Gabriele Di Rosa

Continued cost- and power-efficient capacity scaling in optical networks is imperative to keep pace with ever-increasing traffic demands. In this paper, we investigate multi-wavelength transponders as a potential way forward. Suitable system architectures and realistic specifications of multi-wavelength transponders are identified and analyzed in terms of tr

Piotr T. Chruściel

We finish the proof of the no-hair theorem for stationary, analytic, connected, suitably regular, four dimensional vacuum black holes. We show how to define the surface gravity and the angular velocity of horizons without assuming analyticity. We point out that, under the usual regularity conditions, vacuum near-horizon geometries are Kerrian without assumin

Kai Cheng, Xinzhe Li, Lijuan Che

The development of unsupervised Video Anomaly Detection (VAD) relies on technologies in the field of signal processing. Since the anomaly is quite ambiguous and unbounded, different detection demands may often be raised even in one scenario. Thus, we propose to design the configurable VAD with flexible solutions targeting to solve the issue that previous met

Miguel A. Cascales Sandoval, A Hierro-Rodríguez, S. Ruiz-Gómez, L. Skoric

The Interlayer Dzyaloshinskii-Moriya interaction (IL-DMI) chirally couples spins in different ferromagnetic layers of multilayer heterostructures. So far, samples with IL-DMI have been investigated utilizing magnetometry and magnetotransport techniques, where the interaction manifests as a tunable chiral exchange bias field. Here, we investigate the nanoscal

Erik Aurell, Ryoichi Kawai

G\"oran Lindblad in 1983 published a monograph on non-equilibrium thermodynamics. We here summarize the contents of this book, and provide a perspective on its relation to later developments in statistical physics and quantum physics. We high-light two aspects. The first is the idea that while all unitaries can be allowed in principle, different theories res

Mattia Sinigaglia, Luca Bertaccini, Luca Valente, Angelo Garofalo

Emerging applications in the IoT domain require ultra-low-power and high-performance end-nodes to deal with complex near-sensor-data analytics. Domains such as audio, radar, and Structural Health Monitoring require many computations to be performed in the frequency domain rather than in the time domain. We present ECHOES, a System-On-a-Chip (SoC) composed of

Samuel J. Harris, N. R. McDonald

Penguins huddling in a cold wind are represented by a two-dimensional, continuum model. The huddle boundary evolves due to heat loss to the huddle exterior and through the reorganisation of penguins as they seek to regulate their heat production within the huddle. These two heat transfer mechanisms, along with area, or penguin number, conservation, gives a f

Benjamin C. B. Symons, David Galvin, Emre Sahin, Vassil Alexandrov

Quantum computing is gaining popularity across a wide range of scientific disciplines due to its potential to solve long-standing computational problems that are considered intractable with classical computers. One promising area where quantum computing has potential is in the speed-up of NP-hard optimisation problems that are common in industrial areas such

Nicholas Aidoo

For any given sum of squares domain in $\mathbb{C}^n,$ we reduce the complexity in Catlin's multitype techniques by giving a complete normalization of the geometry. Using this normalization result, we present a more elementary proof of the equality of the Catlin multitype and the commutator multitype for such domains when both invariants are finite. Finally,

Chihiro Iwamoto, Shinsuke Ota, Reiko Kojima, Hiroshi Tokieda

A prototype Dual Gain Multilayer Thick Gas Electron Multilyer (DG-M-THGEM) with an active area of 10 cm $\times$ 10 cm was manufactured aiming at the production of a large-volume active-target time projection chamber which can work under the condition of high-intensity heavy-ion beam injections. The DG-M-THGEM has a alternating structure of electrodes and in

Kurusch Ebrahimi-Fard, Frederic Patras

Recent works have explored relations between classical and quantum statistical physics on the one hand and Voiculescu's theory of free probability on the other. Motivated by these results, the present work focuses on the notion of effective action, which is closely related to the large deviation rate function in classical probability and one-particle irreduc

Yudai Suzuki, Keita Yokoyama

We investigate some Weihrauch problems between $\mathsf{ATR}_2$ and $\mathsf{C}_{\omega^\omega}$ . We show that the fixed point theorem for monotone operators on the Cantor space (a weaker version of the Knaster-Tarski theorem) is not Weihrauch reducible to $\mathsf{ATR}_2$. Furthermore, we introduce the $\omega$-model reflection $\mathsf{ATR}_2^{\mathrm{rfn

Andrew Draganov, Jakob Rødsgaard Jørgensen, Katrine Scheel Nellemann, Davide Mottin

tSNE and UMAP are popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. Despite their popularity, however, little work has been done to study their full span of differences. We theoretically and experimentally evaluate the space of parameters in both tSNE and UMAP and observe that a single one -- the nor

Francesco Masillo

Due to the exponential growth of genomic data, constructing dedicated data structures has become the principal bottleneck in common bioinformatics applications. In particular, the Burrows-Wheeler Transform (BWT) is the basis of some of the most popular self-indexes for genomic data, due to its known favourable behaviour on repetitive data. Some tools that ex

Peiyu Jing, Ravi Seshadri, Takanori Sakai, Ali Shamshiripour

The distributional impacts of congestion pricing have been widely studied in the literature and the evidence on this is mixed. Some studies find that pricing is regressive whereas others suggest that it can be progressive or neutral depending on the specific spatial characteristics of the urban region, existing activity and travel patterns, and the design of

Yohe Kono, Minoru Koizumi

In continuous process plants controlled by model predictive control, model-plant mismatch (MPM), due to the aging of processes, causes degradation of control performance. We propose a concept called Model Life Extension (MLE) and its implementation to mitigate this degradation in a non-invasive manner. The purpose of MLE is to continually update (re-identify

Fateme Abbasi, Sandip Banerjee, Jarosław Byrka, Parinya Chalermsook

We consider the well-studied Robust $(k, z)$-Clustering problem, which generalizes the classic $k$-Median, $k$-Means, and $k$-Center problems. Given a constant $z\ge 1$, the input to Robust $(k, z)$-Clustering is a set $P$ of $n$ weighted points in a metric space $(M,\delta)$ and a positive integer $k$. Further, each point belongs to one (or more) of the $m$

Ian Wright

$\partial\mathbb{B}$ nets are differentiable neural networks that learn discrete boolean-valued functions by gradient descent. $\partial\mathbb{B}$ nets have two semantically equivalent aspects: a differentiable soft-net, with real weights, and a non-differentiable hard-net, with boolean weights. We train the soft-net by backpropagation and then `harden' the

Martin Wieskotten, Marielle Crozet, Bertrand Iooss, Céline Lacaux

In decommissioning projects of nuclear facilities, the radiological characterisation step aims to estimate the quantity and spatial distribution of different radionuclides. To carry out the estimation, measurements are performed on site to obtain preliminary information. The usual industrial practice consists in applying spatial interpolation tools (as the o

Valentin Ruffine, Andreas Hartmann, Michael Schlierf, Elisabeth Fischer-Friedrich

The actin cortex is an active biopolymer network underneath the plasma membrane at the periphery of mammalian cells. It is a major regulator of cell shape through the generation of active cortical tension. In addition, the cortex constitutes a mechanical shield that protects the cell during mechanical agitation. Cortical mechanics is tightly controlled by th

Sam Allen

When predicting future events, it is common to issue forecasts that are probabilistic, in the form of probability distributions over the range of possible outcomes. Such forecasts can be evaluated using proper scoring rules. Proper scoring rules condense forecast performance into a single numerical value, allowing competing forecasters to be ranked and compa

Gilles Dowek

We extend the notion of Heyting algebra to a notion of truth values algebra and prove that a theory is consistent if and only if it has a B-valued model for some non trivial truth values algebra B. A theory that has a B-valued model for all truth values algebras B is said to be super-consistent. We prove that super-consistency is a model-theoretic sufficient

Pengzhi Gao, Liwen Zhang, Zhongjun He, Hua Wu

The multilingual neural machine translation (NMT) model has a promising capability of zero-shot translation, where it could directly translate between language pairs unseen during training. For good transfer performance from supervised directions to zero-shot directions, the multilingual NMT model is expected to learn universal representations across differe

Tomer Raviv, Sangwoo Park, Osvaldo Simeone, Yonina C. Eldar

Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in challenging communication scenarios. However, wireless receiver design poses unique challenges that fundamentally differ from those encountered in traditional deep learning domains.

Jialiang Sun, Wen Yao, Tingsong Jiang, Xiaoqian Chen

Neural architecture search (NAS) has emerged as one successful technique to find robust deep neural network (DNN) architectures. However, most existing robustness evaluations in NAS only consider $l_{\infty}$ norm-based adversarial noises. In order to improve the robustness of DNN models against multiple types of noises, it is necessary to consider a compreh

Junjie Liu, Junlong Liu, Rongxin Jiang, Yaowu Chen

Multi-view Clustering (MVC) has achieved significant progress, with many efforts dedicated to learn knowledge from multiple views. However, most existing methods are either not applicable or require additional steps for incomplete MVC. Such a limitation results in poor-quality clustering performance and poor missing view adaptation. Besides, noise or outlier

Piotr T. Chrusciel, Roger Tagne Wafo, Finnian Gray

We show that the maximal globally hyperbolic solution of the initial-value problem for the higher-dimensional vacuum Einstein equations on two transversally intersecting characteristic hypersurfaces contains a future neighborhood of the hypersurfaces.

Firuz Kamalov, David Santandreu Calong, Ikhlaas Gurrib

The recent high performance of ChatGPT on several standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education. As deep learning is poised to shift the teaching paradigm, it is essential to have a clear understanding of its effects on the current education system to ensure sus

Roberto Montemanni, Luca Maria Gambardella

This paper discusses a heuristic approach for Team Orienteering Problems with Time Windows. The method we propose takes advantage of a solution model based on a hierarchic generalization of the original problem, which is combined with an Ant Colony System algorithm. Computational results on benchmark instances previously adopted in the literature suggest tha

Ruixiang Jiang, Lingbo Liu, Changwen Chen

Recent advances in visual-language models have shown remarkable zero-shot text-image matching ability that is transferable to downstream tasks such as object detection and segmentation. Adapting these models for object counting, however, remains a formidable challenge. In this study, we first investigate transferring vision-language models (VLMs) for class-a