March 2023 arXiv papers — page 57

Christoph Spenke

Let $p$ be a prime. This papers aims at investigating sheaf cohomology of a broader class of $p$-adic period domains, other then the Drinfeld's upper half space (cf. \cite{O2}). Concretely, we let $\mathbf{G}$ be a split connected reductive group over $\mathbb{Q}_p$ and restrict our attention to the $p$-adic period domain $\mathscr{F}^{\mathrm{wa}}$ which pa

Fabio Saggese, Kimmo Kansanen, Petar Popovski

Efficient integration of reconfigurable intelligent surfaces (RISs) into the current wireless network standard is not a trivial task due to the overhead generated by performing channel estimation (CE) and phase-shift optimization. In this paper, we propose a framework enabling the coexistence between orthogonal-frequency division multiplexing (OFDM) and RIS

Xin Gong, Jintao Peng, Dong Yang, Zhan Shu

This paper studies the leader-following consensuses of uncertain and nonlinear multi-agent systems against composite attacks (CAs), including Denial of Service (DoS) attacks and actuation attacks (AAs). A double-layer control framework is formulated, where a digital twin layer (TL) is added beside the traditional cyber-physical layer (CPL), inspired by the r

D. P. Pattnaik, C. Andrews, M. D. Cropper, A. Balanov

Diffusive memristors continue to receive tremendous interest due to their ability to emulate biological neurons and thus aid the development of bio-inspired computation technology. A major issue with the diffusive memristor is the inability to reliably control the formation of the conduction filaments which affects both the device functionality and reproduci

Jürgen Struckmeier, Claus Riedel

Noether and Lie symmetry analyses based on point transformations that depend on time and spatial coordinates will be reviewed for a general class of time-dependent Hamiltonian systems. The resulting symmetries are expressed in the form of generators whose time-dependent coefficients follow as solutions of sets of ordinary differential (``auxiliary'') equatio

Gabriel Mittag, Babak Naderi, Vishak Gopal, Ross Cutler

Current state-of-the-art video quality models, such as VMAF, give excellent prediction results by comparing the degraded video with its reference video. However, they do not consider temporal distortions (e.g., frame freezes or skips) that occur during videoconferencing calls. In this paper, we present a data-driven approach for modeling such distortions aut

Jiaming Na, Varuna De-Silva

Object detection is one of the most important and fundamental aspects of computer vision tasks, which has been broadly utilized in pose estimation, object tracking and instance segmentation models. To obtain training data for object detection model efficiently, many datasets opt to obtain their unannotated data in video format and the annotator needs to draw

Melody Sepahpour-Fard, Michael Quayle, Maria Schuld, Taha Yasseri

Human beings adapt their language to the audience they interact with. To study the impact of audience and gender in a natural setting, we choose a domain where gender plays a particularly salient role: parenting. We collect posts from the three popular parenting Subreddits (i.e., topical communities on Reddit) r/Daddit, r/Mommit, and r/Parenting. These three

Dawei Shen

In 2003, Klainerman and Nicol\`o \cite{Kl-Ni} proved the stability of Minkowski in the case of the exterior of an outgoing null cone. Relying on the method used in \cite{Kl-Ni}, Caciotta and Nicol\`o \cite{Ca-Ni} proved the stability of Kerr spacetime in external regions, i.e. outside an outgoing null cone far away from the Kerr event horizon. In this paper,

Chen Feng, Ioannis Patras

Deep learning has achieved great success in recent years with the aid of advanced neural network structures and large-scale human-annotated datasets. However, it is often costly and difficult to accurately and efficiently annotate large-scale datasets, especially for some specialized domains where fine-grained labels are required. In this setting, coarse lab

Filip Broćić

Given an open neighborhood $W$ of the zero section in the cotangent bundle of $N$ we define a distance-like function $\rho_W$ on $N$ using certain symplectic embeddings from the standard ball $B^{2n}(r)$ to $W$. We show that when $W$ is the unit disc-cotangent bundle of a Riemannian metric on $N$, $\rho_W$ recovers the metric. As an intermediate step, we giv

Weichuan Deng, Pawel Polak, Abolfazl Safikhani, Ronakdilip Shah

We introduce a unified framework for rapid, large-scale portfolio optimization that incorporates both shrinkage and regularization techniques. This framework addresses multiple objectives, including minimum variance, mean-variance, and the maximum Sharpe ratio, and also adapts to various portfolio weight constraints. For each optimization scenario, we detail

Timo Grothe, Johannes Baumgart, Cornelis J. Nederveen

A formula for the local acoustical admittance in a conical waveguide with viscous and thermal losses given by Nederveen [(1969) Acoustical Aspects of Woodwind Instruments (Frits Knuf, Amsterdam)] is rewritten as an impedance transmission matrix. Based on a self-consistent approximation for the cone, it differs from other one-dimensional transmission matrices

Krzysztof Ptaszynski, Massimiliano Esposito

As previously demonstrated, the entropy production -- a key quantity characterizing the irreversibility of thermodynamic processes -- is related to generation of correlations between degrees of freedom of the system and its thermal environment. This raises the question of whether such correlations are of a classical or quantum nature, namely, whether they ar

Jürgen Struckmeier, Claus Riedel

An exact invariant is derived for $n$-degree-of-freedom Hamiltonian systems with general time-dependent potentials. The invariant is worked out in two equivalent ways. In the first approach, we define a special {\it Ansatz\/} for the invariant and determine its time-dependent coefficients. In the second approach, we perform a two-step canonical transformatio

J. A. Hanna

I point out and diagnose an error in a figure in a textbook on classical physics. The error helps to illustrate a pitfall encountered when dealing with the shapes of objects, and perhaps also reflects general cultural attitudes in physics. Another, less interesting, error is noted in passing.

Christel Baier, Clemens Dubslaff, Patrick Wienhöft, Stefan J. Kiebel

A central task in control theory, artificial intelligence, and formal methods is to synthesize reward-maximizing strategies for agents that operate in partially unknown environments. In environments modeled by gray-box Markov decision processes (MDPs), the impact of the agents' actions are known in terms of successor states but not the stochastics involved.

Samy Lakhal, Alexandre Darmon, Iacopo Mastromatteo, Matteo Marsili

We use an agnostic information-theoretic approach to investigate the statistical properties of natural images. We introduce the Multiscale Relevance (MSR) measure to assess the robustness of images to compression at all scales. Starting in a controlled environment, we characterize the MSR of synthetic random textures as function of image roughness H and othe

Pavel Semenyuk

For a given irrational number $\alpha$ one can define an irrationality measure function $\psi_{\alpha}^{[2]}(t) = \min\limits_{\substack{(q, p)\colon q, p \in\mathbb{Z}, 1\leqslant q\leqslant t, \\ (p, q) \neq (p_n, q_n) ~\forall n\in\mathbb{Z_{+}}}} |q\alpha -p|$, related to the second-best approximations to $\alpha$. In 2017 Moshchevitin studied the corres

Tara Macalister Brough, Alan J. Cain, Jan Philipp Wächter

This paper shows how to construct explicitly an automaton that generates an arbitrary numerical semigroup.

Bálint Hartmann, Shengfeng Deng, Géza Ódor, Jeffrey Kelling

The size distribution of planned and forced outages and following restoration times in power systems have been studied for almost two decades and has drawn great interest as they display heavy tails. Understanding of this phenomenon has been done by various threshold models, which are self-tuned at their critical points, but as many papers pointed out, expla

Sara Botelho-Andrade, Peter G. Casazza, Desai Cheng, Tin Tran

We study $m \times n$ matrices whose columns are of the form \[\{(a_{1j},\ldots, a_{nj}): \quad a_{1j} = \lambda_j,\ a_{ij} = \pm\lambda_j\ , \ \lambda_j >0 ,\ j=1,2,\ldots,n\}.\] We explicitly construct for all $a = (a_1,\ldots, a_{\frac{m(m- 1)}{2}}) \in \mathbb{R}^{\frac{m(m-1)}{2}}$ a matrix of the above form whose rows have pairwise dot product equal to

Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, w

Tommaso Calò, Luigi De Russis

Deep Learning (DL) developers come from different backgrounds, e.g., medicine, genomics, finance, and computer science. To create a DL model, they must learn and use high-level programming languages (e.g., Python), thus needing to handle related setups and solve programming errors. This paper presents DeepBlocks, a visual programming tool that allows DL deve

Tommaso Calò, Luigi De Russis

Design mockups are essential instruments for visualizing and testing design ideas. However, the process of generating mockups can be time-consuming and challenging for designers. In this article, we present and evaluate two different modalities for generating mockup ideas to support designers in their work: (1) a sketch-based approach to generate mockups bas

Michał Bobula, Tomasz Pawłowski

The dust ball collapse is studied in the context of the "rainbow metric" approach (where the matter content is supplemented with a scalar field perturbation) to the Oppenheimer-Snyder collapse scenario within the framework of loop quantum cosmology. The global spacetime structure is determined for this scenario and subsequently used to evaluate the entanglem

Jiaxuan Wang, Alexei V. Sokolov, Girish S. Agarwal

Quantum frequency up-conversion is a cutting-edge technique that leverages the interaction between photons and quantum systems to shift the frequency of single photons from a lower frequency to a higher frequency. If the photon before up-conversion was one of the entangled pair, then it is important to understand how much entanglement is preserved after up-c

Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings

Latent confounding has been a long-standing obstacle for causal reasoning from observational data. One popular approach is to model the data using acyclic directed mixed graphs (ADMGs), which describe ancestral relations between variables using directed and bidirected edges. However, existing methods using ADMGs are based on either linear functional assumpti

Andreas Leitherer, Byung Chul Yeo, Christian H. Liebscher, Luca M. Ghiringhelli

Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials. Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision. Here, we present AI-STEM, an automatic, artificial-intelligence based method, for acc

Jürgen Struckmeier

Stochastic phenomena occurring within charged particle beams can be handled using the Vlasov-Fokker-Planck generalization of the Vlasov equation. In particular, this non-deterministic approach can deal with effects due to Coulomb scattering between the beam particles. Moreover, stochastic phenomena also occur in computer simulations of charged particle beams

Gregory Taroyan

In the present paper, we establish an equivalence between several models of derived geometry. That is, we show that the categories of higher derived stacks they produce are Quillen equivalent. As a result, we tie together a model of derived manifolds constructed by Spivak--Borisov--Noel, a model of Carchedi--Roytenberg, and a model of Behrend--Liao--Xu. By r

Zhiyuan Hu, Yunsheng Li, Jiancheng Lyu, Dashan Gao

The problem of class incremental learning (CIL) is considered. State-of-the-art approaches use a dynamic architecture based on network expansion (NE), in which a task expert is added per task. While effective from a computational standpoint, these methods lead to models that grow quickly with the number of tasks. A new NE method, dense network expansion (DNE

Salim I. Amoukou, Nicolas J. B Brunel

Despite attractive theoretical guarantees and practical successes, Predictive Interval (PI) given by Conformal Prediction (CP) may not reflect the uncertainty of a given model. This limitation arises from CP methods using a constant correction for all test points, disregarding their individual uncertainties, to ensure coverage properties. To address this iss

Daniel J. Campbell, Daniel T. Sneed, Earl F. O'Bannon, Per Söderlind

At room temperature, bismuth undergoes several structural transitions with increasing pressure before taking on a body-centered cubic (bcc) phase at approximately 8 GPa. The bcc structure is stable to the highest measured pressure and its simplicity, along with its high compressibility and atomic number, make it an enticing choice as a pressure calibrant. We

Yukang Cui, Lingbo Cao, Michael V. Basin, Jun Shen

This paper studies the distributed resilient output containment control of heterogeneous multiagent systems against composite attacks, including denial-of-services (DoS) attacks, false-data injection (FDI) attacks, camouflage attacks, and actuation attacks. Inspired by digital twins, a twin layer (TL) with higher security and privacy is used to decouple the

Benjamin Kenwright

Sound is a fundamental and rich source of information; playing a key role in many areas from humanities and social sciences through to engineering and mathematics. Sound is more than just data 'signals'. It encapsulates physical, sensorial and emotional, as well as social, cultural and environmental factors. Sound contributes to the transformation of our exp

Tolga Altinoluk, Nestor Armesto, Guillaume Beuf

We study the production, in Deep Inelastic Scattering at high energy, of a quark-gluon dijet induced by $t$-channel quark exchange with the target, which goes beyond the eikonal approximation. Throughout this study we follow the Color Glass Condensate approach, keep full dependence on the quark mass and consider the target to be unpolarized. We focus on the

Rafael Alves, Diego S. de Brito, Marcelo C. Medeiros, Ruy M. Ribeiro

We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This re

Cameron R. D. Bunney, Jorma Louko

The Unruh effect states that a uniformly linearly accelerated observer with proper acceleration $a$ experiences the Minkowski vacuum as a thermal state at temperature $T_U=a/(2\pi)$. An observer in uniform circular motion experiences a similar effective temperature, operationally defined in terms of excitation and de-excitation rates, and physically interpre

Mark McClure

This is the most exciting time in my lifetime for geothermal. There are many, many innovative things happening. To name a few, promising new approaches to Enhanced Geothermal Systems, geothermal projects in sedimentary and lower enthalpy formations, new approaches for geothermal exploration, lithium extraction from produced brines, geothermal energy storage,

Duygu Ceylan, Chun-Hao Paul Huang, Niloy J. Mitra

Image diffusion models, trained on massive image collections, have emerged as the most versatile image generator model in terms of quality and diversity. They support inverting real images and conditional (e.g., text) generation, making them attractive for high-quality image editing applications. We investigate how to use such pre-trained image models for te

Pawel Morzywolek, Johan Decruyenaere, Stijn Vansteelandt

Motivated by applications in personalized medicine and individualized policymaking, there is a growing interest in techniques for quantifying treatment effect heterogeneity in terms of the conditional average treatment effect (CATE). Some of the most prominent methods for CATE estimation developed in recent years are T-Learner, DR-Learner and R-Learner. The

Francesco Camilloni, Werner Becker, Peter Predehl, Konrad Dennerl

The Vela supernova remnant complex is a region containing at least three supernova remnants: Vela, Puppis A, and Vela Jr. With the launch of the spectro-imaging X-ray telescope eROSITA on board the Spectrum Roentgen Gamma (SRG) mission, it became possible to observe the one degree wide Vela Jr in its entirety. Although several previous pointed Chandra and XM

Tarry Singh

The rapid advancements in artificial intelligence, particularly in large language multi-modal models like GPT-4, have raised concerns about the potential displacement of human workers in various industries. This position paper aims to analyze the current state of job replacement by AI models and explores potential implications and strategies for a balanced c

K. -Y. Huang, S. Viti, J. Holdship, J. G. Mangum

HNCO and SiO are well known shock tracers and have been observed in nearby galaxies, including the nearby (D=3.5 Mpc) starburst galaxy NGC 253. The simultaneous detection of these two species in regions where the star formation rate is high may be used to study the shock history of the gas. We perform a multi-line molecular study using these two shock tracer

G. Martínez-Solaeche, Carolina Queiroz, R. M. González Delgado, Natália V. N. Rodrigues

This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were observed in 60 optical bands over $\sim$ 1 deg$^2$ in the AEGIS field. We developed two algorithms based on artificial neural networks (ANN) to classify objects into four categories: stars, galaxies, quasars at low redshift ($z <

Sabina J. Sloman, Daniel Cavagnaro, Stephen B. Broomell

Adaptive design optimization (ADO) is a state-of-the-art technique for experimental design (Cavagnaro, Myung, Pitt, & Kujala, 2010). ADO dynamically identifies stimuli that, in expectation, yield the most information about a hypothetical construct of interest (e.g., parameters of a cognitive model). To calculate this expectation, ADO leverages the modeler's

Henning Samtleben

We review the structure of maximal $D=11$ and $D=10$ supergravities. Upon dimensional reduction, these theories give rise to the unique maximal supergravities in all lower spacetime dimensions $D<10$. In $D$ dimensions, maximal supergravity exhibits the exceptional global symmetry group E$_{11-D}$, part of which is realized as hidden symmetries and only mani

Jose L. Goity, Christian Weiss, Cintia Willemyns

The target normal single-spin asymmetry in inclusive electron-nucleon scattering is studied in the low-energy regime that includes the $\Delta$ resonance. The particular interest in the asymmetry resides in that it is driven by two-photon exchange effects. It probes the spin-dependent absorptive part of the two-photon exchange amplitude, which is free of inf

Daniele Nantes-Sobrinho, Pascal Fontaine

This volume contains the post-proceedings of the Seventeenth Logical and Semantic Frameworks with Applications (LSFA 2022), organised by the Universidade Federal de Minas Gerais, Brasil. Because of the COVID-19 pandemic, the meeting was held hybridly, on September 23-24, 2022. LSFA aims to bring together researchers and students interested in theoretical and

Andrés Aranda, Samuel Braunfeld, David Chodounský, Jan Hubička

We give an infinitary extension of the Ne\v{s}et\v{r}il-R\"{o}dl theorem for category of relational structures with special type-respecting embeddings.

Yijun Yuan, Andreas Nuechter

We present Uni-Fusion, a universal continuous mapping framework for surfaces, surface properties (color, infrared, etc.) and more (latent features in CLIP embedding space, etc.). We propose the first universal implicit encoding model that supports encoding of both geometry and different types of properties (RGB, infrared, features, etc.) without requiring an

Maoyu Zhang, Biao Cai, Wenlin Dai, Dehan Kong

Dynamic networks have been increasingly used to characterize brain connectivity that varies during resting and task states. In such characterizations, a connectivity network is typically measured at each time point for a subject over a common set of nodes representing brain regions, together with rich subject-level information. A common approach to analyzing

C. Spingola

Current cosmological controversies can be solved if a sufficient level of precision is achieved by observations. Future surveys with the next generation of telescopes will offer significantly improved depth and angular resolution with respect to existing observations, opening the so-called "era of precision cosmology". But, that era can be considered already

Jürgen Struckmeier

The Vlasov equation embodies the smooth field approximation of the self-consistent equation of motion for charged particle beams. This framework is fundamentally altered if we include the fluctuating forces that originate from the actual charge granularity. We thereby perform the transition from a reversible description to a statistical mechanics' descriptio

Zeqing Xia, Bojun Xiong, Zhouhui Lian

Font design is of vital importance in the digital content design and modern printing industry. Developing algorithms capable of automatically synthesizing vector fonts can significantly facilitate the font design process. However, existing methods mainly concentrate on raster image generation, and only a few approaches can directly synthesize vector fonts. T

Cláudio Nassif Cruz, A. C. Amaro de Faria

In this paper we will show that a new structure of space-time with a minimum speed reveals a connection with Weyl geometry in the approximation of weak-field Newtonian limit. Symmetrical Special Relativity (SSR) has a minimum speed $V$ that plays the role of a preferred reference frame $S_V$ of vacuum that leads to the cosmological constant $\Lambda$. In ord

José Ferreira

In this dissertation, we study two cosmological models based on $f(Q)$ gravity. We resort to mock catalogs of standard siren (SS) events to see whether data from future gravitational wave (GWs) observatories will be able to distinguish these models from $\Lambda$CDM. The first model is the most general $f(Q)$ formulation that replicates a $\Lambda$CDM backgr

E. I. Zakharov, V. V. Barinov, R. A. Burenin, D. S. Gorbunov

Dark matter sterile neutrinos radiatively decay in the Milky Way, which can be tested with searches for almost monochromatic photons in the X-ray cosmic spectrum. We analyse the data of SRG/ART-XC telescope operated for two years in the all-sky survey mode. With no significant hints in the Galactic diffuse X-ray spectrum we explore models with sterile neutri

Diana Davis, Serge Troubetzkoy

We study the set of lengths of the horizontal chords of a continuous function. We give a new proof of Hopf's characterization of this set, and show that it implies that no matter which function we choose, at least half of the possible lengths occur. We prove several results about functions for which all the possible lengths occur.

Anton Dmytriiev, Markus Boettcher, Thabiso O. Machipi

Despite numerous studies, the origin of the gamma-ray emission from blazars is still debated, in particular whether it is produced by leptonic or hadronic processes. In this study, we are testing the leptonic scenario for the Flat Spectrum Radio Quasar (FSRQ) 3C 279, assuming that the gamma-ray emission is generated by inverse Compton scattering of external

Triet Minh Thai, Son T. Luu

Visual Question Answering (VQA) is a task that requires computers to give correct answers for the input questions based on the images. This task can be solved by humans with ease but is a challenge for computers. The VLSP2022-EVJVQA shared task carries the Visual Question Answering task in the multilingual domain on a newly released dataset: UIT-EVJVQA, in w

Wei Li, Jiahao Xie, Chen Change Loy

We introduce Correlational Image Modeling (CIM), a novel and surprisingly effective approach to self-supervised visual pre-training. Our CIM performs a simple pretext task: we randomly crop image regions (exemplars) from an input image (context) and predict correlation maps between the exemplars and the context. Three key designs enable correlational image m

Paul Gavrikov, Janis Keuper, Margret Keuper

Neural networks have a number of shortcomings. Amongst the severest ones is the sensitivity to distribution shifts which allows models to be easily fooled into wrong predictions by small perturbations to inputs that are often imperceivable to humans and do not have to carry semantic meaning. Adversarial training poses a partial solution to address this issue

Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad

The R-package GeoAdjust https://github.com/umut-altay/GeoAdjust-package implements fast empirical Bayesian geostatistical inference for household survey data from the Demographic and Health Surveys Program (DHS) using Template Model Builder (TMB). DHS household survey data is an important source of data for tracking demographic and health indicators, but pos

Christophe Bruneel-Zupanc

This paper proposes semi-instrumental variables (semi-IVs) as an alternative to instrumental variables (IVs) to identify the causal effect of a binary (or discrete) endogenous treatment. A semi-IV is a less restrictive form of instrument: it affects the selection into treatment but is excluded only from one, not necessarily both, potential outcomes. Having t

Yue Li, Carolina Scarton

Considering a conversation thread, rumour stance classification aims to identify the opinion (e.g. agree or disagree) of replies towards a target (rumour story). Although the target is expected to be an essential component in traditional stance classification, we show that rumour stance classification datasets contain a considerable amount of real-world data

Andrzej Rozkosz, Leszek Slominski

We consider Neumann problem for linear elliptic equations involving integro-differential operators of Levy-type. We show that suitably defined viscosity solutions have probabilistic representations given in terms of the reflected stochastic Skorokhod equation associated with an Ito process and an independent pure-jump Levy process. As an application of the r

Marios Papachristou, M. Amin Rahimian, Arash Azadegan

Modern production systems are increasingly defined by dense networks of multi-tier sourcing dependencies, where localized upstream disruptions can cascade into system-wide collapses. While supply chain resilience has garnered significant managerial attention, we still lack theoretically-grounded, reliable, analytical metrics that can distinguish inherently r

Francesco Paissan, Cem Subakan, Mirco Ravanelli

In this paper, we introduce a new approach, called Posthoc Interpretation via Quantization (PIQ), for interpreting decisions made by trained classifiers. Our method utilizes vector quantization to transform the representations of a classifier into a discrete, class-specific latent space. The class-specific codebooks act as a bottleneck that forces the interp

Xunguang Wang, Jiawang Bai, Xinyue Xu, Xiaomeng Li

Deep hashing has been extensively applied to massive image retrieval due to its efficiency and effectiveness. Recently, several adversarial attacks have been presented to reveal the vulnerability of deep hashing models against adversarial examples. However, existing attack methods suffer from degraded performance or inefficiency because they underutilize the

Samuel I. Watson

We describe the \proglang{R} package \pkg{glmmrBase} and an extension \pkg{glmmrOptim}. \pkg{glmmrBase} provides a flexible approach to specifying, fitting, and analysing generalised linear mixed models. We use an object-orientated class system within \proglang{R} to provide methods for a wide range of covariance and mean functions, including specification o

Mohamed Ould El Hadj

We investigate the problem of scattering and conversion of monochromatic planar gravitational and electromagnetic waves impinging upon a Reissner-Nordstr\"om black hole using a Regge pole description, i.e., a complex angular momentum approach. For this purpose, we first compute numerically the Regge pole spectrum for various charge-to-mass ratio configuratio

Bo Wei, Kean Ming Tan, Xuming He

Estimating the causal effect of a treatment or exposure for a subpopulation is of great interest in many biomedical and economical studies. Expected shortfall, also referred to as the super-quantile, is an attractive effect-size measure that can accommodate data heterogeneity and aggregate local information of effect over a certain region of interest of the

Wen Cheng, Shichen Dong, Wei Wang

This paper describes our submission to ICASSP 2023 MUG Challenge Track 4, Keyphrase Extraction, which aims to extract keyphrases most relevant to the conference theme from conference materials. We model the challenge as a single-class Named Entity Recognition task and developed techniques for better performance on the challenge: For the data preprocessing, w

Joshua A. M. Kaste, Yair Shachar-Hill

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both of these methods use metabolic reaction network models of metabolism operating at steady state, so that reaction rates (fluxes) and the levels of metabolic intermedia

Christopher Wright

In this paper, we study the uniqueness of weak solutions of the Plateau flow, which was first introduced by Wettstein as a half-Laplacian heat flow and recently studied by Struwe using alternative methods. This geometric gradient flow is of interest due to its links with free boundary minimal surfaces and the Plateau problem. We obtain uniqueness of weak sol

Yuan Bi, Zhongliang Jiang, Ricarda Clarenbach, Reza Ghotbi

Generalization capabilities of learning-based medical image segmentation across domains are currently limited by the performance degradation caused by the domain shift, particularly for ultrasound (US) imaging. The quality of US images heavily relies on carefully tuned acoustic parameters, which vary across sonographers, machines, and settings. To improve th

Prantika Bhowmik, Jie Jiang, Lisa Upton, Alexandre Lemerle

The dynamic activity of stars such as the Sun influences (exo)planetary space environments through modulation of stellar radiation, plasma wind, particle and magnetic fluxes. Energetic stellar phenomena such as flares and coronal mass ejections act as transient perturbations giving rise to hazardous space weather. Magnetic fields -- the primary driver of ste

Chinmay Kulkarni, Stefania Druga, Minsuk Chang, Alex Fiannaca

Recent advances in Machine-Learning have led to the development of models that generate images based on a text description.Such large prompt-based text to image models (TTIs), trained on a considerable amount of data, allow the creation of high-quality images by users with no graphics or design training. This paper examines the role such TTI models can playi

Matteo Lorenzo, Paolo Pezzutto, Filippo De Lillo, Francesco Michele Ventrella

We report on an experimental study of a device composed by an array of submerged, reversed and periodic cylindrical pendula (resonators), whose objective is the attenuation of surface gravity waves. The idea is inspired by the concept of metamaterials, i.e. engineered structures designed to interact with waves and manipulate their propagation properties. The

Hadrien Reynaud, Mengyun Qiao, Mischa Dombrowski, Thomas Day

Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, causal modelling, and operator training become approachable through synthetic data. Especially, heavily operator-dependant modalities like Ultrasound imaging require robust framework

Lokesh Chandra Das

Predicting traffic volume in real-time can improve both traffic flow and road safety. A precise traffic volume forecast helps alert drivers to the flow of traffic along their preferred routes, preventing potential deadlock situations. Existing parametric models cannot reliably forecast traffic volume in dynamic and complex traffic conditions. Therefore, in o

Elizabeth Seger, Aviv Ovadya, Ben Garfinkel, Divya Siddarth

Numerous parties are calling for the democratisation of AI, but the phrase is used to refer to a variety of goals, the pursuit of which sometimes conflict. This paper identifies four kinds of AI democratisation that are commonly discussed: (1) the democratisation of AI use, (2) the democratisation of AI development, (3) the democratisation of AI profits, and

Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin

State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applications like skin cancer detection. To tackle this problem, we propose Reveal to Revise (R2R), a framework entailing the entire eXplainable Artificial In

Bowen Zhao, Wei-Neng Chen, Xiaoguo Li, Ximeng Liu

Recently, evolutionary computation (EC) has been promoted by machine learning, distributed computing, and big data technologies, resulting in new research directions of EC like distributed EC and surrogate-assisted EC. These advances have significantly improved the performance and the application scope of EC, but also trigger privacy leakages, such as the le

Tanja Auge, Gunnar Bali, Meike Klettke, Bertram Ludäscher

We present a provenance model for the generic workflow of numerical Lattice Quantum Chromodynamics (QCD) calculations, which constitute an important component of particle physics research. These calculations are carried out on the largest supercomputers worldwide with data in the multi-PetaByte range being generated and analyzed. In the Lattice QCD community

Pavel Okun, Antonio C. Cancio, Kieron Burke

Using principles of asymptotic analysis, we derive the exact leading corrections to the Thomas-Fermi kinetic energy approximation for Kohn-Sham electrons for slabs. This asymptotic expansion approximation includes crucial quantum oscillations missed by standard semilocal density functionals. Because these account for the derivative discontinuity, chemical ac

Alec J. Coffman, Zuxin Jin, Junhan Chen, Joseph E. Subotnik

We implement a rare-event sampling scheme for quantifying the rate of thermally-activated nonadiabatic transitions in the condensed phase. Our QM/MM methodology uses the recently developed INAQS package to interface between an elementary electronic structure package and a popular open-source molecular dynamics software (GROMACS) to simulate an electron trans

Kianoosh Sattari, Lucas Eddy, Jacob L. Beckham, Kevin M. Wyss

Flash Joule heating (FJH) is a far-from-equilibrium (FFE) processing method for converting low-value carbon-based materials to flash graphene (FG). Despite its promise in scalability and performance, attempts to explore the reaction mechanism have been limited due to complexity involved in the FFE process. Data-driven machine learning (ML) models effectively

Ludovic Pricoupenko

Non Efimovian $N$-body resonances are investigated in the regime of a large two-body s wave scattering length. In view of a universal description of low-energy bound and quasi-bound states, a contact model is introduced. The modeling requires two parameters in addition to the scattering length. Using a modified scalar product, the contact model provides a no

Benoît Cerutti, Gwenael Giacinti

Extragalactic plasma jets are some of the few astrophysical environments able to confine ultra-high-energy cosmic rays, but whether they are capable of accelerating these particles is unknown. In this work, we revisit particle acceleration at relativistic magnetized shocks beyond the local uniform field approximation, by considering the global transverse str

Antoine Dizet, Ubbo Visser, Cedric Buche

This article offers a literature review of goalkeeper robots in the context of the RoboCupSoccer competition. The latter is one of the various league categories hosted by the RoboCup Federation, which fosters AI and Robotics with their landmark challenges. Despite the number of articles on the subject of the goalkeeper, there is a lack of studies offering a

Shravan Kumar Sajja, Sumanta Mukherjee, Satyam Dwivedi

Counterfactual explanations for machine learning models are used to find minimal interventions to the feature values such that the model changes the prediction to a different output or a target output. A valid counterfactual explanation should have likely feature values. Here, we address the challenge of generating counterfactual explanations that lie in the

Yi-Nan Wang, Yi Zhang

In this paper, we explore a new type of global symmetries$-$the fermionic higher-form symmetries. They are generated by topological operators with fermionic parameter, which act on fermionic extended objects. We present a set of field theory examples with fermionic higher-form symmetries, which are constructed from fermionic tensor fields. They include the f

Dieter Rautenbach, Florian Werner

Albertson defined the irregularity of a graph $G$ as $irr(G)=\sum\limits_{uv\in E(G)}|d_G(u)-d_G(v)|$. For a graph $G$ with $n$ vertices, $m$ edges, maximum degree $\Delta$, and $d=\left\lfloor \frac{\Delta m}{\Delta n-m}\right\rfloor$, we show $$irr(G)\leq d(d+1)n+\frac{1}{\Delta}\left(\Delta^2-(2d+1)\Delta-d^2-d\right)m.$$

István Blahota

In this paper, we consider norm convergence for a special matrix-based de la Vall\'ee Poussin-like mean of Fourier series for the Walsh system. We estimate the difference between the named mean above and the corresponding function in norm, and the upper estimation is given by the modulus of continuity of the function.

D. Hernandez-Rajkov, N. Grani, F. Scazza, G. Del Pace

At the interface between two fluid layers in relative motion, infinitesimal fluctuations can be exponentially amplified, inducing vorticity and the breakdown of the laminar flow. This process, known as the Kelvin-Helmholtz instability, is responsible for many familiar phenomena observed in the atmosphere and in the oceans, as well as in astrophysical objects

K. J. Eskola, V. Guzey, T. Löytäinen, H. Paukkunen

We make predictions for the cross section of coherent $J/\psi$ photoproduction in Pb-Pb and O-O ultraperipheral collisions (UPCs) at the LHC as a function of the $J/\psi$ rapidity $y$ in the framework of collinear factorization and next-to-leading order (NLO) perturbative QCD. We quantify the strong scale dependence and significant uncertainties due to nucle

Zhi-Wei Sun

Let $n>1$ be an integer, and let $T$ be a tree with $n+1$ vertices $v_1,\ldots,v_{n+1}$, where $v_1$ and $v_{n+1}$ are two leaves of $T$. For each edge $e$ of $T$, assign a complex number $w(e)$ as its weight. We obtain that $$\det[x+d(v_{j+1},v_k)]_{1\le j,k\le n}=2^{n-2}\prod_{e\in E(T)}w(e),$$ where $d(v_{j+1},v_k)$ is the weighted distance between $v_{j+