December 2023 arXiv papers — page 39

Shrigyan Brahmachari, Roberto Rubboli, Marco Tomamichel

We develop a fixed-point iterative algorithm that computes the matrix projection with respect to the Bures distance on the set of positive definite matrices that are invariant under some symmetry. We prove that the fixed-point iteration algorithm converges exponentially fast to the optimal solution in the number of iterations. Moreover, it numerically shows

Francesco Cireddu, Milan Wils, Isaac C. F. Wong, Peter T. H. Pang

The Einstein Telescope faces a critical data analysis challenge with correlated noise, often overlooked in current parameter estimation analyses. We address this issue by presenting the statistical formulation of the likelihood that includes correlated noise for the Einstein Telescope or any detector network. By considering varying degrees of correlation, we

Norman Zerbe, Lars Ole Schwen, Christian Geißler, Katja Wiesemann

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and ve

Pedro D. Alvarez, Benjamin Koch, Ali Riahinia, Angel Rincon

In this paper, we investigate the theoretical possibility that a Lagrangian fixed point, when applied to cosmological models, can drive dynamical evolution towards a bouncing universe. We analyze the physics of a Lagrangian fixed point within the context of a gravitational average effective action featuring scale-dependent couplings. To explore this concept,

Xiaoyue Duan, Shuhao Cui, Guoliang Kang, Baochang Zhang

Consistent editing of real images is a challenging task, as it requires performing non-rigid edits (e.g., changing postures) to the main objects in the input image without changing their identity or attributes. To guarantee consistent attributes, some existing methods fine-tune the entire model or the textual embedding for structural consistency, but they ar

Zanqiu Shen, Jianshe Ma, Ping Su

Linear minimum mean square error (LMMSE) receivers are often applied in practical communication scenarios for single-input-multiple-output (SIMO) systems owing to their low computational complexity and competitive performance. However, their performance is only the best among all the linear receivers, as they minimize the bit mean square error (MSE) alone in

Atsunori Ogawa, Naohiro Tawara, Takatomo Kano, Marc Delcroix

Confidence estimation, in which we estimate the reliability of each recognized token (e.g., word, sub-word, and character) in automatic speech recognition (ASR) hypotheses and detect incorrectly recognized tokens, is an important function for developing ASR applications. In this study, we perform confidence estimation for end-to-end (E2E) ASR hypotheses. Rec

Siqi Chen, Bin Shan, Ye Li

Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning. However, PINNs face training difficulties for evolutionary PDEs, particularly for dynamical systems whose solutions exhibit multi-scale or turbulent behavior over time. The reason is that PINNs may violate the tempora

Gaëtan Michelet, Frank Breitinger

Generative AIs, especially Large Language Models (LLMs) such as ChatGPT or Llama, have advanced significantly, positioning them as valuable tools for digital forensics. While initial studies have explored the potential of ChatGPT in the context of investigations, the question of to what extent LLMs can assist the forensic report writing process remains unres

Till Beemelmanns, Wassim Zahr, Lutz Eckstein

Vision Transformers (ViTs) have achieved state-of-the-art results on various computer vision tasks, including 3D object detection. However, their end-to-end implementation also makes ViTs less explainable, which can be a challenge for deploying them in safety-critical applications, such as autonomous driving, where it is important for authorities, developers

Marcelino Cabrera, Carlos Cruz, Pavel Novoa-Hernández, David A. Pelta

Automated Decision-Making Systems (ADS) have become pervasive across various fields, activities, and occupations, to enhance performance. However, this widespread adoption introduces potential risks, including the misuse of ADS. Such misuse may manifest when ADS is employed in situations where it is unnecessary or when essential requirements, conditions, and

Mahnaz Khalili, Bojan Brodic, Peter Göransson, Suvi Heinonen

As global groundwater levels continue to decline rapidly, there is a growing need for advanced techniques to monitor and manage aquifers effectively. This study focuses on validating a numerical model using seismic data from a small-scale experimental setup designed to estimate water volume in a porous reservoir. Expanding on previous work with synthetic dat

Keerthana S Kumar, Ajit Kumar Dash, Hasna Sabreen H, Manvi Verma

An intense low-energy broad luminescence peak (L-peak) is usually observed in 2D transition metal dichalcogenides (TMDs) at low temperatures. L-peak has earlier been attributed to bound excitons, but its origins are widely debated with direct consequences on optoelectronic properties. To decouple the contributions of physisorbed and chemisorbed oxygen, organ

Milutin Blagojević, James M. Nester

It is shown that the Lorentz invariant $f(T)$ gravity, defined by the coframe-connection-multiplier form of the Lagrangian, can be gauge-fixed to the pure coframe form. After clarifying basic aspects of the problem in the Lagrangian formalism, a more detailed analysis of this gauge equivalence is given relying on the Dirac Hamiltonian approach.

Tamme Wollweber, Kartik Ayyer

This paper introduces Spectral Incoherent Diffractive Imaging (SIDI) as a novel method for achieving dark-field imaging of nanostructures with heterogeneous oxidation states. With SIDI, shifts in photoemission profiles can be spatially resolved, enabling the independent imaging of the underlying emitter distributions contributing to each spectral line. In th

Simon Nik, Christian H. Weiß

For parameter estimation of continuous and discrete distributions, we propose a generalization of the method of moments (MM), where Stein identities are utilized for improved estimation performance. The construction of these Stein-type MM-estimators makes use of a weight function as implied by an appropriate form of the Stein identity. Our general approach a

Greta Coraglia, Jacopo Emmenegger

Most categorical models for dependent types have traditionally been heavily set based: contexts form a category, and for each we have a set of types in said context -- and for each type a set of terms of said type. This is the case for categories with families, categories with attributes, and natural models; in particular, all of them can be traced back to c

Kehinde Aruleba, Ismaila Temitayo Sanusi, George Obaido, Blessing Ogbuokiri

The integration of artificial intelligence tools such as ChatGPT in the education system has gained attention in recent years. This experience report explores students' perceptions and suggestions for integrating ChatGPT in a computer science course. Following a ChatGPT activity which includes code completion and analysis, seven students participated in in-d

Jacek Cyranka, Piotr B. Mucha

We present an opinion model founded upon the principles of the bounded confidence interaction among agents. Our objective is to explain the polarization effects inherent to vector-valued opinions. The evolutionary process adheres to the rule where each agent aspires to increase polarization through communication with a single friend during each discrete time

Nikita Kavokine, Markus Müller, Antoine Georges, Olivier Parcollet

We present the mean field solution of the quantum and classical Heisenberg spin glasses, using the combination of a high precision numerical solution of the Parisi full replica symmetry breaking equations and a continuous time Quantum Monte Carlo. We characterize the spin glass order and its low-energy excitations down to zero temperature. The Heisenberg spi

Jorge Arvesú Carballo, Alejandro J. Quintero Roba

This paper studies a new family of Angelesco multiple orthogonal polynomials with shared orthogonality conditions with respect to a system of weight functions, which are complex analogues of Pascal distributions on a legged star-like set. The emphasis is placed on the algebraic properties such as the raising operators, the Rodrigues-type formula, the explici

R. T. Ovsiannikov, A. Yu. Korchin

An important problem in the particle physics is interaction of the Higgs boson with the fermions. It is in the processes, which involve $h f f $ interaction, the manifestations of $CP$ violation are possible. This in turn can be helpful in solving the problem of the dominance of matter over antimatter in the Universe. In this connection, in the present paper

Nicolai Amann, Hannes Leeb, Lukas Steinberger

Recently, there has been substantial interest in statistical guarantees for cross-validation (CV) methods of uncertainty quantification in statistical learning (cf. Barber et al. 2021a, Liang and Barber 2024, Steinberger and Leeb 2023). These guarantees should hold under minimal assumptions on the data generating process and conditional on the training data,

André Nitze

This study explores the impact of Large Language Models (LLMs) in higher education, focusing on an automated oral examination simulation using a prototype. The design considerations of the prototype are described, and the system is evaluated with a select group of educators and students. Technical and pedagogical observations are discussed. The prototype pro

Fritz Colonius, Alexandre J. Santana, Eduardo C. Viscovini

For linear control systems with bounded control range, chain controllability properties are analyzed. It is shown that there exists a unique chain control set and that it equals the sum of the control set around the origin and the center Lyapunov space of the homogeneous part. For the proof, the linear control system is extended to a bilinear control system

Sergio Barrera Cabodevila, Carlos A. Salgado, Bin Wu

We first assemble a full set of the Boltzmann Equation in Diffusion Approximation (BEDA) for studying thermalization/hydrodynamization and quark production in out of equilibrium systems. We then discuss thermalization and the production of three flavors of massless quarks in spatially homogeneous systems initially filled only with gluons. A complete parametr

Nick Fischer, Marvin Künnemann, Mirza Redzic

We revisit $k$-Dominating Set, one of the first problems for which a tight $n^k-o(1)$ conditional lower bound (for $k\ge 3$), based on SETH, was shown (P\u{a}tra\c{s}cu and Williams, SODA 2007). However, the underlying reduction creates dense graphs, raising the question: how much does the sparsity of the graph affect its fine-grained complexity? We first se

Mirosław Firlej, Tomasz Fiutowski, Marek Idzik, Jakub Moroń

The design and measurement results of ultra-low power, fast 10-bit Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) prototypes in 65 nm CMOS technology are presented. Eight prototype ADCs were designed using two different switching schemes of capacitive Digital-to-Analog Converters (DACs), based on MIM or MOM capacitors, and controll

Weiwen Xu, Deng Cai, Zhisong Zhang, Wai Lam

As humans, we consistently interact with our peers and receive feedback in the form of natural language. This language feedback allows us to maintain appropriate behavior, and rectify potential errors. The question arises naturally: can we use language feedback to align large language models (LLMs)? In contrast to previous research that aligns LLMs with scal

Zhenlin Su, Liyan Xu, Jin Xu, Jiangnan Li

Identifying speakers of quotations in narratives is an important task in literary analysis, with challenging scenarios including the out-of-domain inference for unseen speakers, and non-explicit cases where there are no speaker mentions in surrounding context. In this work, we propose a simple and effective approach SIG, a generation-based method that verbal

Stefano Peluchetti

The scope of this paper is generative modeling through diffusion processes. An approach falling within this paradigm is the work of Song et al. (2021), which relies on a time-reversal argument to construct a diffusion process targeting the desired data distribution. We show that the time-reversal argument, common to all denoising diffusion probabilistic mode

Ernesto Lozano Calvo, Bernardo Taveira, Fredrik Kahl, Niklas Gustafsson

Object detection applied to LiDAR point clouds is a relevant task in robotics, and particularly in autonomous driving. Single frame methods, predominant in the field, exploit information from individual sensor scans. Recent approaches achieve good performance, at relatively low inference time. Nevertheless, given the inherent high sparsity of LiDAR data, the

Max Hahn-Klimroth, Remco van der Hofstad, Noela Müller, Connor Riddlesden

The pooled data problem asks to identify the unknown labels of a set of items from condensed measurements. More precisely, given $n$ items, assume that each item has a label in $\cbc{0,1,\ldots, d}$, encoded via the ground-truth $\SIGMA$. We call the pooled data problem sparse if the number of non-zero entries of $\SIGMA$ scales as $k \sim n^{\theta}$ for $\

Sergio Abriola, Simon Halfon, Aliaume Lopez, Sylvain Schmitz

The complexity of a well-quasi-order (wqo) can be measured through three ordinal invariants: the width as a measure of antichains, height as a measure of chains, and maximal order type as a measure of bad sequences. We study these ordinal invariants for the finitary powerset, i.e., the collection Pf(A) of finite subsets of a wqo A ordered with the Hoare embe

Eloi Moliner, Leonardo Fierro, Alec Wright, Matti Hämäläinen

This letter introduces an innovative method to enhance the quality of audio time stretching by precisely decomposing a sound into sines, transients, and noise and by improving the processing of the latter component. While there are established methods for time-stretching sines and transients with high quality, the manipulation of noise or residual components

S. Demirci, T. Lappi, S. Schlichting

We explore the consequences of gluonic hot spots inside the proton for the initial eccentricities in a proton-nucleus collision, and the constraints on the parameters describing these hot spots from coherent and incoherent exclusive vector meson production cross sections in deep inelastic scattering. We show that geometric fluctuations of hot spots inside th

Roberto Pereira, Xavier Mestre, David Gregoratti

This work considers the asymptotic behavior of the distance between two sample covariance matrices (SCM). A general result is provided for a class of functionals that can be expressed as sums of traces of functions that are separately applied to each covariance matrix. In particular, this class includes very conventional metrics, such as the Euclidean distan

Jan-Ole Koslik, Carlina C. Feldmann, Sina Mews, Rouven Michels

Over the last decade, hidden Markov models (HMMs) have become increasingly popular in statistical ecology, where they constitute natural tools for studying animal behavior based on complex sensor data. Corresponding analyses sometimes explicitly focus on - and in any case need to take into account - periodic variation, for example by quantifying the activity

Lukas Weissitsch, Franziska Staab, Karsten Durst, Andrea Bachmaier

Magnets are key materials for the electrification of mobility and also for the generation and transformation of electric energy. Research and development in recent decades lead to high performance magnets, which require a finely tuned microstructure to serve applications with ever increasing requirements. Besides optimizing already known materials and the se

Max Auer, Roland Zweimüller

We establish abstract local limit theorems for hitting times and return-times of suitable sequences (A_{l}) of asymptotically rare events in ergodic probability preserving dynamical systems, including versions for tuples of consecutive times and positions of the hits. These results are shown to apply in the setup of Gibbs-Markov systems.

Lukas Weissitsch, Stefan Wurster, Martin Stueckler, Timo Mueller

Bulk composites using powder blends of Mn and Bi with equal atomic ratios are consolidated and severely deformed by high-pressure torsion (HPT). Subsequent annealing treatments lead to the formation of the ferromagnetic and rare-earth free hard magnetic {\alpha}-MnBi phase. The initial phase formation is studied by in-situ high-energy XRD annealing experimen

Mirela Ostrek, Carol O'Sullivan, Michael J. Black, Justus Thies

We present ESP, a novel method for context-aware full-body generation, that enables photo-realistic synthesis and inpainting of people wearing clothing that is semantically appropriate for the scene depicted in an input photograph. ESP is conditioned on a 2D pose and contextual cues that are extracted from the photograph of the scene and integrated into the

Christian Pilato, Francesca Palumbo

These proceedings contain the contributions to the CPS workshop 2023 (http://www.cpsschool.eu/cps-workshop/). The CPS Workshop 2023 is an initiative of the CPS Summer School 2023 community to offer participants close contact with leading experts in the field and the opportunity to present and discuss their ideas in a dynamic and friendly setting.

Neha Sengar, Indra Kumari, Jihui Lee, Dongsoo Har

Driver distraction is a principal cause of traffic accidents. In a study conducted by the National Highway Traffic Safety Administration, engaging in activities such as interacting with in-car menus, consuming food or beverages, or engaging in telephonic conversations while operating a vehicle can be significant sources of driver distraction. From this viewp

Petr Gregor, Jaka Kranjc, Borut Lužar, Kenny Štorgel

A {\em packing coloring} of a graph $G$ is a mapping assigning a positive integer (a color) to every vertex of $G$ such that every two vertices of color $k$ are at distance at least $k+1$. The least number of colors needed for a packing coloring of $G$ is called the {\em packing chromatic number} of $G$. In this paper, we continue the study of the packing ch

Jack Y. Araz, Andy Buckley, Gregor Kasieczka, Jan Kieseler

With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different en

Liang Peng, Songyue Cai, Zongqian Wu, Huifang Shang

Prompt learning has demonstrated impressive efficacy in the fine-tuning of multimodal large models to a wide range of downstream tasks. Nonetheless, applying existing prompt learning methods for the diagnosis of neurological disorder still suffers from two issues: (i) existing methods typically treat all patches equally, despite the fact that only a small nu

Ivano Ciardelli, Martin Otto

Inquisitive modal logic, InqML, in its epistemic incarnation, extends standard epistemic logic to capture not just the information that agents have, but also the questions that they are interested in. We use the natural notion of bisimulation equivalence in the setting of InqML, as introduced in [Ciardelli/Otto: JSL 2021], to characterise the expressiveness

Junyu Chen, Binh T. Nguyen, Shang Hui Koh, Yong Sheng Soh

The Gromov-Wasserstein (GW) distance is an extension of the optimal transport problem that allows one to match objects between incomparable spaces. At its core, the GW distance is specified as the solution of a non-convex quadratic program and is not known to be tractable to solve. In particular, existing solvers for the GW distance are only able to find loc

Zhangyin Feng, Runyi Hu, Liangxin Liu, Fan Zhang

Autoregressive and diffusion models drive the recent breakthroughs on text-to-image generation. Despite their huge success of generating high-realistic images, a common shortcoming of these models is their high inference latency - autoregressive models run more than a thousand times successively to produce image tokens and diffusion models convert Gaussian n

Marco Bjarne Schuster, Boris Wiegand, Jilles Vreeken

Although event logs are a powerful source to gain insight about the behavior of the underlying business process, existing work primarily focuses on finding patterns in the activity sequences of an event log, while ignoring event attribute data. Event attribute data has mostly been used to predict event occurrences and process outcome, but the state of the ar

Wenshuai Xu, Zhenbo Xu

The key technology to overcome the drawbacks of hyperspectral imaging (expensive, high capture delay, and low spatial resolution) and make it widely applicable is to select only a few representative bands from hundreds of bands. However, current band selection (BS) methods face challenges in fair comparisons due to inconsistent train/validation settings, inc

Piotr Bilinski, Thomas Merritt, Abdelhamid Ezzerg, Kamil Pokora

Creating realistic and natural-sounding synthetic speech remains a big challenge for voice identities unseen during training. As there is growing interest in synthesizing voices of new speakers, here we investigate the ability of normalizing flows in text-to-speech (TTS) and voice conversion (VC) modes to extrapolate from speakers observed during training to

Martijn Gösgens, Remco van der Hofstad, Nelly Litvak

We present the class of projection methods for community detection that generalizes many popular community detection methods. In this framework, we represent each clustering (partition) by a vector on a high-dimensional hypersphere. A community detection method is a projection method if it can be described by the following two-step approach: 1) the graph is

Rui Pan, Yuxing Liu, Xiaoyu Wang, Tong Zhang

Heavy-ball momentum with decaying learning rates is widely used with SGD for optimizing deep learning models. In contrast to its empirical popularity, the understanding of its theoretical property is still quite limited, especially under the standard anisotropic gradient noise condition for quadratic regression problems. Although it is widely conjectured tha

Aaron Brunk, Oliver Habrich, Timileyin David Oyedeji, Yangyiwei Yang

A Cahn-Hilliard-Allen-Cahn phase-field model coupled with a heat transfer equation, particularly with full non-diagonal mobility matrices, is studied. After reformulating the problem w.r.t. the inverse of temperature, we proposed and analysed a structure-preserving approximation for the semi-discretisation in space and then a fully discrete approximation usi

Johann Laux, Fabian Stephany, Alice Liefgreen

The global surge in AI applications is transforming industries, leading to displacement and complementation of existing jobs, while also giving rise to new employment opportunities. Data annotation, encompassing the labelling of images or annotating of texts by human workers, crucially influences the quality of a dataset directly influences the quality of AI

Enikő Kevi, Kim-Thang Nguyen

Designing online algorithms with machine learning predictions is a recent technique beyond the worst-case paradigm for various practically relevant online problems (scheduling, caching, clustering, ski rental, etc.). While most previous learning-augmented algorithm approaches focus on integrating the predictions of a single oracle, we study the design of onl

Hanxiang He, Han Hu, Xintao Huan, Heng Liu

Deep learning has significantly advanced wireless sensing technology by leveraging substantial amounts of high-quality training data. However, collecting wireless sensing data encounters diverse challenges, including unavoidable data noise, limited data scale due to significant collection overhead, and the necessity to reacquire data in new environments. Tak

Simon Brown

Ethereum is undergoing significant changes to its architecture as it evolves. These changes include its switch to PoS consensus and the introduction of significant infrastructural changes that do not require a change to the core protocol, but that fundamentally affect the way users interact with the network. These changes represent an evolution toward a more

Supriya Sarker, Bibek Poudel, Michael Villarreal, Weizi Li

This paper explores the intricacies of traffic behavior at unsignalized intersections through the lens of a novel dataset, combining manual video data labeling and advanced traffic simulation in SUMO. This research involved recording traffic at various unsignalized intersections in Memphis, TN, during different times of the day. After manually labeling video

He Gao, Ying Li, Yanjun Xie, Daxin Liang

Technologies enabling passive daytime radiative cooling and daylight harvesting are highly relevant for energy-efficient buildings. Despite recent progress demonstrated with passively cooling polymer coatings, however, it remains challenging to combine also a passive heat gain mechanism into a single substrate for all-round thermal management. Herein, we dev

Johannes Schleischitz

Recently Koivusalo, Levesley, Ward and Zhang introduced the set of simultaneously $\Phi$-badly approximable real vectors of $\mathbb{R}^m$ with respect to an approximation function $\Phi$, and determined its Hausdorff dimension for the special class of power functions $\Phi(t)=t^{-\tau}$. We refine this by naturally extending the formula to arbitrary decreas

Chanho Lee, Jinsu Son, Hyounguk Shon, Yunho Jeon

Rotation-equivariance is an essential yet challenging property in oriented object detection. While general object detectors naturally leverage robustness to spatial shifts due to the translation-equivariance of the conventional CNNs, achieving rotation-equivariance remains an elusive goal. Current detectors deploy various alignment techniques to derive rotat

Paul Norbury

The super Weil-Petersson metric defined over the moduli space of smooth super curves produces a natural measure over the moduli space of smooth curves. The construction of the measure uses the extra data of a spin structure on each smooth curve. When we allow marked points, the construction produces a collection of measures indexed by the behaviour of the sp

Rongsheng Wang, Haoming Chen, Ruizhe Zhou, Yaofei Duan

Existing research has demonstrated that refining large language models (LLMs) through the utilization of machine-generated instruction-following data empowers these models to exhibit impressive zero-shot capabilities for novel tasks, without requiring human-authored instructions. In this paper, we systematically investigate, preprocess, and integrate three C

Rohith Peddi, Shivvrat Arya, Bharath Challa, Likhitha Pallapothula

Following step-by-step procedures is an essential component of various activities carried out by individuals in their daily lives. These procedures serve as a guiding framework that helps to achieve goals efficiently, whether it is assembling furniture or preparing a recipe. However, the complexity and duration of procedural activities inherently increase th

Krishna Hanumanthu, Cyril J. Jacob, Suhas B. N., Amit Kumar Singh

Let $e,r \ge 0$ be integers and let $\mathbb{F}_e : = \mathbb{P}(\mathcal{O}_{\mathbb{P}^1} \oplus \mathcal{O}_{\mathbb{P}^1}(-e))$ denote the Hirzebruch surface with invariant $e$. We compute the Seshadri constants of an ample line bundle at an arbitrary point of the $r$-point blow-up of $\mathbb{F}_e$ when $r \leq e-1$ and at a very general point when $r=e

Niklas Wingren, Daniel Sjöberg, Mats Gustafsson, Johan Lundgren

The scattering formulation of characteristic mode decomposition is utilized to extend modal analysis to lossless scatterers breaking time-reversal symmetry. This enables characteristic modes analysis on devices containing gyrotropic or moving media. The resulting nonreciprocity introduces features not observed in reciprocal scenarios, such as asymmetric phas

Jin-Hong Park, Jun-Won Rhim

The localized nature of a flat band is understood by the existence of a compact localized eigenstate. However, the localization properties of a partially flat band, ubiquitous in surface modes of topological semimetals, have been unknown. We show that the partially flat band is characterized by a non-normalizable compact localized state(NCLS). The partially

Lukas Morand, Tarek Iraki, Johannes Dornheim, Stefan Sandfeld

In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to take into account manufacturing processes and tailor materials design approaches to support downstream process design approaches. As a major step into this direction, we present a ho

Yan Wang, Tongtong Su, Yusen Li, Jiuwen Cao

Recent research on deep convolutional neural networks (CNNs) has provided a significant performance boost on efficient super-resolution (SR) tasks by trading off the performance and applicability. However, most existing methods focus on subtracting feature processing consumption to reduce the parameters and calculations without refining the immediate feature

Jussi Behrndt, Dale Frymark, Markus Holzmann, Christian Stelzer-Landauer

For a family of self-adjoint Dirac operators $-i c (\alpha \cdot \nabla) + \frac{c^2}{2}$ subject to generalized MIT bag boundary conditions on domains in $\mathbb R^3$ it is shown that the nonrelativistic limit in the norm resolvent sense is the Dirichlet Laplacian. This allows to transfer spectral geometry results for Dirichlet Laplacians to Dirac operator

Munir Hiabu, Emil Hofman, Gabriele Pittarello

We introduce new approaches for forecasting IBNR (Incurred But Not Reported) frequencies by leveraging individual claims data, which includes accident date, reporting delay, and possibly additional features for every reported claim. A key element of our proposal involves computing development factors, which may be influenced by both the accident date and oth

Christos G. Tsinos, Alexandros-Apostolos A. Boulogeorgos, Theodoros A. Tsiftsis

Reconfigurable intelligent surfaces (RISs) operate similarly to electromagnetic (EM) mirrors and remarkably go beyond Snell law to generate an applicable EM environment allowing for flexible adaptation and fostering sustainability in terms of economic deployment and energy efficiency. However, the conventional RIS is controlled through high-latency field pro

Jian Yao, Hu Chen, Ya-Li Mao, Zheng-Da Li

The question of whether complex numbers play a fundamental role in quantum theory has been debated since the inception of quantum mechanics. Recently, a feasible proposal to differentiate between real and complex quantum theories based on the technique of testing Bell nonlocalities has emerged [Nature 600, 625-629 (2021)]. Based on this method, the real quan

Vincent David, Sébastien Galtier, Romain Meyrand

The breakdown of scale invariance in turbulent flows, known as multifractal scaling, is considered a cornerstone of turbulence. In solar wind turbulence, a monofractal behavior can be observed at electron scales, in contrast to larger scales where multifractality always prevails. Why scale invariance appears at electron scales is a challenging theoretical pu

Vladimir A. Zolotarev

Scattering problem for a self-adjoint integro-differential operator, which is the sum of the operator of second derivative and of a finite-dimensional self-adjoint operator, is studied. Jost solutions are found and it is shown that the scattering function has a multiplicative structure, besides, each of the multipliers is a scattering coefficient for a pair

Hongliu Cao, Minh Nhat Do, Alexis Ravanel, Eoin Thomas

Face recognition has been used more and more in real world applications in recent years. However, when the skin color bias is coupled with intra-personal variations like harsh illumination, the face recognition task is more likely to fail, even during human inspection. Face normalization methods try to deal with such challenges by removing intra-personal var

Grzegorz Kapustka, Michał Kapustka

We study twisted derived equivalences of hyper-K\"ahler fourfolds. We describe when two hyper-K\"ahler fourfolds of $K3^{[2]}$-type of Picard rank $1$ with isomorphic transcendental lattices are derived equivalent. Then we present new constructions of pairs of twisted derived equivalent hyper-K\"ahler manifolds of Picard rank $\geq 2$.

Nikhilesh Bhatnagar, Ashok Urlana, Vandan Mujadia, Pruthwik Mishra

Cross-lingual summarization involves the summarization of text written in one language to a different one. There is a body of research addressing cross-lingual summarization from English to other European languages. In this work, we aim to perform cross-lingual summarization from English to Hindi. We propose pairing up the coverage of newsworthy events in te

Liwei Ni, Zonglin Yang, Jiaxi Zhang, Changhong Feng

Technology mapping is an essential step in EDA flow. However, the function of the circuit may be changed after technology mapping, and equivalence checking (EC) based verification is highly necessary. The traditional EC method has significant time and resource constraints, making it only feasible to carry out at a coarse-grained level. To make it efficient f

Jangho Kim, Pratitee Pattanaik, Wolfgang Unger

We investigate the chiral transition of $U(3)$ lattice gauge theory based on the strong coupling expansion. A generalized vertex model with vertices and weights derived from the tensor network approach of the dual representation of lattice QCD with staggered fermions is used and the configurations are sampled by the Metropolis algorithm. We study the chiral

Tommy Albing, Rikard Nelander

Rather than sending used containers and materials to the landfill, recycling can help lower the human impact on the environment. However, manually sorting the mixture of incoming material can be both costly and potentially harmful to the person carrying out the task. In many cases, manual sorting could be replaced with automation, where a container is sorted

S. Y. Lou

Dark equations are defined as some kinds of integrable couplings with some fields being homogeneously and linearly coupled to others. In this paper, dark equations are extended in several aspects. Taking the Korteweg-de Vrise (KdV) equation as an example, the dark KdV systems are extended to nonhomogenous forms, nonlinear couplings and graded linear cases. T

Hengsong Yue, Nuo Chen, Tao Chu

This study presents an effective optical equalization technique for generating ultrahigh baud rate signals. The equalization technique was demonstrated using a dual-drive Mach-Zehnder modulator (DDMZM) with two-phase shifters having different bandwidths, which can be achieved by adjusting the structural design of the modulator or by incorporating varying bia

Liwei Ni, Zonglin Yang, Jiaxi Zhang, Junfeng Liu

Rewriting is a common procedure in logic synthesis aimed at improving the performance, power, and area (PPA) of circuits. The traditional reconvergence-driven And-Inverter Graph (AIG) rewriting method focuses solely on optimizing the reconvergence cone through Boolean algebra minimization. However, there exist opportunities to incorporate other node-rewritin

Junwei He, Qianqian Xu, Yangbangyan Jiang, Zitai Wang

Graph anomaly detection is crucial for identifying nodes that deviate from regular behavior within graphs, benefiting various domains such as fraud detection and social network. Although existing reconstruction-based methods have achieved considerable success, they may face the \textit{Anomaly Overfitting} and \textit{Homophily Trap} problems caused by the a

Zheng Cai, Bo Hu, Zhihua Zhu

Online experiments are widely used for improving online services. While doing online experiments, The student t-test is the most widely used hypothesis testing technique. In practice, however, the normality assumption on which the t-test depends on may fail, which resulting in untrustworthy results. In this paper, we first discuss the question of when the t-

Hongliu Cao, Ilias El Baamrani, Eoin Thomas

As the digitization of travel industry accelerates, analyzing and understanding travelers' behaviors becomes increasingly important. However, traveler data frequently exhibit high data sparsity due to the relatively low frequency of user interactions with travel providers. Compounding this effect the multiplication of devices, accounts and platforms while br

Jiaming Lu, Jingqing Ruan, Haoyuan Jiang, Ziyue Li

Reinforcement learning has been revolutionizing the traditional traffic signal control task, showing promising power to relieve congestion and improve efficiency. However, the existing methods lack effective learning mechanisms capable of absorbing dynamic information inherent to a specific scenario and universally applicable dynamic information across vario

D. R. Heath-Brown

We improve the standard Weyl estimate for quartic exponential sums in which the argument is a quadratic irrational. Specifically we show that \[\sum_{n\le N} e(\alpha n^4)\ll_{\ep,\alpha}N^{5/6+\ep}\] for any $\ep>0$ and any quadratic irrational $\alpha\in\R-\Q$. Classically one would have had the exponent $7/8+\ep$ for such $\alpha$. In contrast to the auth

Weiqin Xu, Olivier Curé

In this paper, we tackle the incremental maintenance of Datalog inference materialisation when the rule set can be updated. This is particularly relevant in the context of the Internet of Things and Edge computing where smart devices may need to reason over newly acquired knowledge represented as Datalog rules. Our solution is based on an adaptation of a str

Meghyn Bienvenu, Diego Figueira, Pierre Lafourcade

The Shapley value provides a natural means of quantifying the contributions of facts to database query answers. In this work, we seek to broaden our understanding of Shapley value computation (SVC) in the database setting by revealing how it relates to Fixed-size Generalized Model Counting (FGMC), which is the problem of computing the number of sub-databases

Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas, Elena Hernández-Pereira, Beatriz Pérez-Sánchez

Nowadays, machine learning algorithms continue to grow in complexity and require a substantial amount of computational resources and energy. For these reasons, there is a growing awareness of the development of new green algorithms and distributed AI can contribute to this. Federated learning (FL) is one of the most active research lines in machine learning,

Bojan Nikolić, Marko Djukanović, Milana Grbić, Dragan Matić

The concept of Roman domination has been a subject of intrigue for more than two decades with the fundamental Roman domination problem standing out as one of the most significant challenges in this field. This article studies a practically motivated generalization of this problem, known as the k-strong Roman domination. In this variation, defenders within a

Tom Moussie, Paolo Errante, Marcello Meldi

The Benchmarck on the Aerodynamics of a Rectangular 5:1 Cylinder is studied using a data-driven technique which bridges numerical simulation and available experimental results. Because of intrinsic features of the tools used for investigation, in particular in terms of set-up and boundary conditions, significant discrepancies have been observed in the litera

Alistair Keiller

When designing a power or CPU constrained device where a four-axis robotic arm is required and access to the Robot Operating System (ROS) is not an option, finding an efficient state space controller for a four-axis arm can be an obstacle. In this paper, I explore a method to optimize the computing power required for a computer algebra system (CAS) to comput

Matthew J. Picklo, Jennifer K. Ryan

In this article we consider the extension of the (L)SIAC-MRA enhancement procedure to nonuniform meshes. We demonstrate that error reduction can be obtained on perturbed quadrilateral and Delaunay meshes, and investigate the effect of limited resolution and its impact on the procedure for various function types. We show that utilizing mesh-based localized ke

Edyta Bartnicka, Andrzej Matraś

Let $\Gamma_k(V)$ be the Grassmann graph whose vertex set ${\mathcal G}_{k}(V)$ is formed by all $k$-dimensional subspaces of an $n$-dimensional vector space $V$ over the finite field $F_q$ consisting of $q$ elements. We discuss its subgraph $\Gamma(n,k)_q$ with the vertex set ${\mathcal C}(n,k)_q$ consisting of all non-degenerate linear $[n, k]_q$ codes. %W