April 2023 arXiv papers — page 124

Chuan-Chi Lai, Ang-Hsun Tsai, Chia-Wei Ting, Ko-Han Lin

In this letter, we study the deployment of Unmanned Aerial Vehicle mounted Base Stations (UAV-BSs) in multi-UAV cellular networks. We model the multi-UAV deployment problem as a user satisfaction maximization problem, that is, maximizing the proportion of served ground users (GUs) that meet a given minimum data rate requirement. We propose an interference-aw

Hongwen Zhang, Siyou Lin, Ruizhi Shao, Yuxiang Zhang

Creating animatable avatars from static scans requires the modeling of clothing deformations in different poses. Existing learning-based methods typically add pose-dependent deformations upon a minimally-clothed mesh template or a learned implicit template, which have limitations in capturing details or hinder end-to-end learning. In this paper, we revisit p

Georg Linden

We describe the locally analytic $\mathrm{GL}_d(K)$-representations which arise as the global sections of homogeneous vector bundles on the projective space restricted to the Drinfeld upper half space over a non-archimedean local field $K$. We thereby generalize work of Orlik (2008) for $p$-adic fields to the effect that it becomes applicable to local fields

A. R. G. Santos, S. Mathur, R. A. García, A. -M. Broomhall

The photometric time series of solar-like stars can exhibit rotational modulation due to active regions co-rotating with the stellar surface, allowing us to constrain stellar rotation and magnetic activity. In this work we investigate the behavior, particularly the variability, of the photometric magnetic activity of Kepler solar-like stars and compare it wi

Håkon Hukkelås, Frank Lindseth

We address the task of in-the-wild human figure synthesis, where the primary goal is to synthesize a full body given any region in any image. In-the-wild human figure synthesis has long been a challenging and under-explored task, where current methods struggle to handle extreme poses, occluding objects, and complex backgrounds. Our main contribution is TriA-

André F. V. Matias, Daniel F. Valente-Matias, Nuno R. Neng, José M. F. Nogueira

Several natural and industrial processes involve the extraction or retention of a solute by a fluid invading a network of channels. Examples include aquifer contamination, chemical filtration, and coffee extraction. We propose a continuum equation to model these processes, parametrized by the P\'eclet number and the rate of mass transfer between the solid an

G. Franciolini, F. Iacovelli, M. Mancarella, M. Maggiore

Primordial Black Holes (PBHs) have recently attracted much attention as they may explain some of the LIGO/Virgo/KAGRA observations and significantly contribute to the dark matter in our universe. The next generation of Gravitational Wave (GW) detectors will have the unique opportunity to set stringent bounds on this putative population of objects. Focusing o

Haocheng Dai, Michael Penwarden, Robert M. Kirby, Sarang Joshi

Neural operator learning as a means of mapping between complex function spaces has garnered significant attention in the field of computational science and engineering (CS&E). In this paper, we apply Neural operator learning to the time-of-flight ultrasound computed tomography (USCT) problem. We learn the mapping between time-of-flight (TOF) data and the het

Zhichao Duan, Xiuxing Li, Zhengyan Zhang, Zhenyu Li

Question Answering (QA) is the task of automatically answering questions posed by humans in natural languages. There are different settings to answer a question, such as abstractive, extractive, boolean, and multiple-choice QA. As a popular topic in natural language processing tasks, extractive question answering task (extractive QA) has gained extensive att

Q. -P. Ding, J. Babu, K. Rana, Y. Lee

139La nuclear magnetic resonance (NMR) and nuclear quadrupole resonance (NQR) measurements have been performed to investigate the magnetic properties of the itinerant magnet La2Ni7 which shows a series of antiferromagnetic (AFM) phase transitions at $T_{N1}$=61 K, $T_{N2}$=56 K, and $T_{N3}$=42 K under zero magnetic field. Two distinct La NMR signals were ob

Sri Charan Kattamuru, Kshitij Agrawal, Shyam Prasad Adhikari, Abhishek Bose

Images captured through smartphone cameras often suffer from degradation, blur being one of the major ones, posing a challenge in processing these images for downstream tasks. In this paper we propose low-compute lightweight patch-wise features for image quality assessment. Using our method we can discriminate between blur vs sharp image degradation. To this

Alexander Teretenkov, Oleg Lychkovskiy

We establish a duality between open many-body systems governed by the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) equation and satisfying the detailed balance condition on the one side, and closed bilayer systems with a self-adjoint Hamiltonian on the other side. Under this duality, the identity operator on the open system side maps to a quantum many-body s

Sebastian Monnet

Given a $2$-adic field $K$, we give formulae for the number of totally ramified quartic field extensions $L/K$ with a given discriminant valuation and Galois closure group. We use these formulae to prove a refinement of Serre's mass formula, which will have applications to the arithmetic statistics of number fields.

Lei Wang, Ee-Peng Lim

Large language models (LLMs) have achieved impressive zero-shot performance in various natural language processing (NLP) tasks, demonstrating their capabilities for inference without training examples. Despite their success, no research has yet explored the potential of LLMs to perform next-item recommendations in the zero-shot setting. We have identified tw

Y. Boujakhrout, E. H Saidi, R. Ahl Laamara, L. B Drissi

Using results on topological line defects of 4D Chern-Simons theory and the algebra/cycle homology correspondence in complex surfaces $\mathcal{S}$ with ADE singularities, we study the graded properties of the $sl(m|n)$ chain and its embedding in string theory. Because of the $\mathbb{Z}_{2}$-grading of $ sl(m|n)$, we show that the $\left( m+n\right) !/m!n!$

Gaël Guennebaud, Aurélie Bugeau, Antoine Dudouit

Assessing the energy consumption or carbon footprint of data distribution of video streaming services is usually carried out through energy or carbon intensity figures (in Wh or gCO2e per GB). In this paper, we first review the reasons why such approaches are likely to lead to misunderstandings and potentially to erroneous conclusions. To overcome those shor

Juhan Aru, Titus Lupu, Avelio Sepúlveda

In this note we show that the 2D continuum Gaussian free field (GFF) admits an excursion decomposition that is on the one hand similar to the classical excursion decomposition of the Brownian motion, and on the other hand can be seen as an FK representation of the continuum GFF. In particular, 2D continuum GFF can be written as an infinite sum of disjoint po

Taoufiq Ouaj, Leonard Kramme, Marvin Metzelaars, Jiahan Li

In this work, we report on the growth of hexagonal boron nitride (hBN) crystals from an iron flux at atmospheric pressure and high temperature and demonstrate that (i) the entire sheet of hBN crystals can be detached from the metal in a single step using hydrochloric acid and that (ii) these hBN crystals allow the fabrication of high carrier mobility graphen

Jaeyoun You, Jinhan Choi, Ho-Jae Shin, Bongwon Suh

Predicting athletes' performance has relied mostly on statistical data. Besides the traditional data, various types of data, including video, have become available. However, it is challenging to use them for deep learning, especially when the size of the athletes' dataset is small. This research proposes a feature-selection strategy based on the criteria use

Jia-Hong Huang, Modar Alfadly, Bernard Ghanem, Marcel Worring

Deep neural networks have been critical in the task of Visual Question Answering (VQA), with research traditionally focused on improving model accuracy. Recently, however, there has been a trend towards evaluating the robustness of these models against adversarial attacks. This involves assessing the accuracy of VQA models under increasing levels of noise in

Fateme Abbasi, Sandip Banerjee, Jarosław Byrka, Parinya Chalermsook

This paper considers the well-studied algorithmic regime of designing a $(1+\epsilon)$-approximation algorithm for a $k$-clustering problem that runs in time $f(k,\epsilon)poly(n)$ (sometimes called an efficient parameterized approximation scheme or EPAS for short). Notable results of this kind include EPASes in the high-dimensional Euclidean setting for $k$

Clemencia Siro, Tunde Oluwaseyi Ajayi

Question answering (QA) models have shown compelling results in the task of Machine Reading Comprehension (MRC). Recently these systems have proved to perform better than humans on held-out test sets of datasets e.g. SQuAD, but their robustness is not guaranteed. The QA model's brittleness is exposed when evaluated on adversarial generated examples by a perf

Jun Wu, Xuesong Ye, Yanyuet Man

The rapid and accurate identification of bot accounts in online social networks is an ongoing challenge. In this paper, we propose BOTTRINET, a unified embedding framework that leverages the textual content posted by accounts to detect bots. Our approach is based on the premise that account personalities and habits can be revealed through their contextual co

Gilles Dowek, François Thiré

Libraries of formal proofs are an important part of our mathematical heritage, but their usability and sustainability is poor. Indeed, each library is specific to a proof system, sometimes even to some version of this system. Thus, a library developed in one system cannot, in general, be used in another and when the system is no more maintained, the library

Julien Allasia

In this paper, we study random walks evolving on Z in a dynamic random environment that we assume to have time correlations that decrease polynomially fast. We show a law of large numbers by generalizing methods already used for the nearest-neighbor framework to the finite-range one. This requires some new ideas to get around the absence of a monotonicity pr

Chantal Pitte, Quentin Baghi, Sylvain Marsat, Marc Besançon

Supermassive black hole binaries (SMBHBs) are expected to be detected by the future space-based gravitational-wave detector LISA with a large signal-to-noise ratio (SNR). This prospect enhances the possibility of differentiating higher harmonics in the inspiral-merger-ringdown (IMR) waveform. In this study, we test the ability of LISA to identify the presenc

Matthew Weidner, Ria Pradeep, Benito Geordie, Heather Miller

Conflict-free Replicated Data Types (CRDTs) allow collaborative access to an app's data. We describe a novel CRDT operation, for-each on the list of CRDTs, and demonstrate its use in collaborative apps. Our for-each operation applies a given mutation to each element of a list, including elements inserted concurrently. This often preserves user intention in a

Changsheng Lu, Hao Zhu, Piotr Koniusz

Unlike current deep keypoint detectors that are trained to recognize limited number of body parts, few-shot keypoint detection (FSKD) attempts to localize any keypoints, including novel or base keypoints, depending on the reference samples. FSKD requires the semantically meaningful relations for keypoint similarity learning to overcome the ubiquitous noise a

Jingyan Xu, Danila A. Barskiy

In this work, we describe essential tools of linear algebra necessary for calculating the effect of chemical exchange on spin dynamics and polarization transfer in various nuclear magnetic resonance (NMR) experiments. We show how to construct Hamiltonian, relaxation, and chemical exchange superoperators in the Liouville space, as well as demonstrate correspo

Igor Poboiko, Paul Pöpperl, Igor V. Gornyi, Alexander D. Mirlin

We develop an analytical approach to the study of one-dimensional free fermions subject to random projective measurements of local site occupation numbers, based on the Keldysh path-integral formalism and replica trick. In the limit of rare measurements, $\gamma / J \ll 1$ (where $\gamma$ is measurement rate per site and $J$ is hopping constant in the tight-

Judd Harrison, Christine T. H. Davies

We compute the complete set of SM and tensor $B_{(s)}\to D_{(s)}^*\ell\bar{\nu}$ semileptonic form factors across the full kinematic range of the decay using second generation MILC $n_f=2+1+1$ HISQ gluon field configurations and HISQ valence quarks, with the heavy-HISQ method. Lattice spacings range from $0.09\mathrm{fm}$ to $0.044\mathrm{fm}$ with pion mass

Max van Meer, Emre Deniz, Gert Witvoet, Tom Oomen

Sensors in high-precision mechatronic systems require accurate calibration, which is achieved using test beds that, in turn, require even more accurate calibration. The aim of this paper is to develop a cascaded calibration method for position sensors of mechatronic systems while taking into account the variance of the calibration model of the test bed. The

Mengyin Liu, Jie Jiang, Chao Zhu, Xu-Cheng Yin

Detecting pedestrians accurately in urban scenes is significant for realistic applications like autonomous driving or video surveillance. However, confusing human-like objects often lead to wrong detections, and small scale or heavily occluded pedestrians are easily missed due to their unusual appearances. To address these challenges, only object regions are

Diego Chamorro, Oscar Jarrín

In this article we consider a damped version of the incompressible Navier-Stokes equations in the whole three-dimensional space with a divergence-free and time-independent external force. Within the framework of a well-prepared force and with a particular choice of the damping parameter, when the Grashof numbers are large enough, we are able to prove some es

Kevin PT. Haughn, Christina Harvey, Daniel J. Inman

There is a growing need for uncrewed aerial vehicles (UAVs) to operate in cities. However, the uneven urban landscape and complex street systems cause large-scale wind gusts that challenge the safe and effective operation of UAVs. Current gust alleviation methods rely on traditional control surfaces and computationally expensive modeling to select a control

Jaeyoun You, Sojeong Park, Seok-Kyeong Hong, Bongwon Suh

Selfies represent people's desires, and social media platforms like Instagram have been flooded with them. This study uses selfie data to examine how peoples' desires for ideal facial representations vary by region, particularly in East Asia. Through the analysis, we aim to refute the "all Asians prefer identical visuals," which is a subset of the prevalent

Ana Lokmer, Charanraj Goud Alladi, Réjane Troudet, Delphine Bacq-Daian

Background: The choice of efficient antipsychotic therapy for schizophrenia relies on a time-consuming trial-and-error approach, whereas the social and economic burdens of the disease call for faster alternatives. Material \& methods: In a search for predictive biomarkers of antipsychotic response, blood methylomes of 28 patients were analyzed before and 4 w

Alan B. Whiting

The geocentric universe, in its most developed form as set out by Ptolemy, was a remarkably successful and coherent theory. It did not, however, specify the order of the planets, that is, which was closer to Earth and which farther away. One would naively think that seeing one planet pass in front of another would settle the matter. In practice such mutual p

Enzo A. Saavedra, Federico García, Federico A. Fogantini, Mariano Méndez

We analysed a dedicated NuSTAR observation of the neutron-star low-mass X-ray binary Z-source GX 13+1 to study the timing and spectral properties of the source. From the colour-colour diagram, we conclude that during that observation the source transitioned from the normal branch to the flaring branch. We fitted the spectra of the source in each branch with

Liwen hu, Lei Ma, Zhaofei Yu, Boxin Shi

As a neuromorphic sensor with high temporal resolution, the spike camera shows enormous potential in high-speed visual tasks. However, the high-speed sampling of light propagation processes by existing cameras brings unavoidable noise phenomena. Eliminating the unique noise in spike stream is always a key point for spike-based methods. No previous work has a

Eugenio Mauri, Simona Cocco, Rémi Monasson

We study transition paths in energy landscapes over multi-categorical Potts configurations using the mean-field approach introduced by Mauri et al., {\em Phys Rev Lett 130, 158402 (2023)}. Paths interpolate between two fixed configurations or are anchored at one extremity only. We characterize the properties of `good' transition paths realizing a trade-off b

James Carzon, Bruno R. de Abreu, Leighton Regayre, Kenneth Carslaw

Atmospheric aerosols influence the Earth's climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these processes could help improve model-based climate predictions. We propose a scalable statistical framework for constraining para

Yi Guo, Nan Cao, Ligan Cai, Yanqiu Wu

Datamation is designed to animate an analysis pipeline step by step, which is an intuitive and effective way to interpret the results from data analysis. However, creating a datamation is not easy. A qualified datamation needs to not only provide a correct analysis result but also ensure that the data flow and animation are coherent. Existing animation autho

Baudouin Saintyves, Matthew Spenko, Heinrich M. Jaeger

Designing robotic systems that can change their physical form factor as well as their compliance to adapt to environmental constraints remains a major conceptual and technical challenge. To address this, we introduce the Granulobot, a modular system that blurs the distinction between soft, modular, and swarm robotics. The system consists of gear-like units t

Paritosh Meihar, Rowtu Srinu, Vivek Saraswat, Sandip Lashkare

HfO2-based Ferroelectric field-effect transistor (FeFET) has become a center of attraction for non-volatile memory applications because of their low power, fast switching speed, high scalability, and CMOS compatibility. In this work, we show an n-channel FeFET-based Multibit memory, termed MirrorBit, which effectively doubles the chip density via programming

Mayara Antunes, Bernardo Carvalho

We introduce first-time sensitivity for a homeomorphism of a compact metric space, that is a condition on the first increasing times of open balls of the space. Continuum-wise expansive homeomorphisms, the shift map on the Hilbert cube, and also some partially hyperbolic diffeomorphisms satisfy this condition. We prove the existence of local unstable continu

Mohammed Al-Rawi

Deep Neural Networks (DNNs) are built using artificial neural networks. They are part of machine learning methods that are capable of learning from data that have been used in a wide range of applications. DNNs are mainly handcrafted and they usually contain numerous layers. Research frontier has emerged that concerns automated construction of DNNs via evolu

Nikos Frantzikinakis, Mariusz Lemańczyk, Thierry de la Rue

We prove structural results for measure preserving systems, called Furstenberg systems, naturally associated with bounded multiplicative functions. We show that for all pretentious multiplicative functions these systems always have rational discrete spectrum and, as a consequence, zero entropy. We obtain several other refined structural and spectral results,

Ko Sanders

We initiate an investigation into separable, but physically reasonable, states in relativistic quantum field theory. In particular we will consider the minimum amount of energy density needed to ensure the existence of separable states between given spacelike separated regions. This is a first step towards improving our understanding of the balance between e

Jiayi Guo, Chaofei Wang, You Wu, Eric Zhang

Recently, CLIP-guided image synthesis has shown appealing performance on adapting a pre-trained source-domain generator to an unseen target domain. It does not require any target-domain samples but only the textual domain labels. The training is highly efficient, e.g., a few minutes. However, existing methods still have some limitations in the quality of gen

Lorenzo Baldassari, Maarten V. de Hoop, Elisa Francini, Sergio Vessella

Through coupled physics, we study an early-warning inverse source problem for the elasto-gravitational equations. It consists of a mixed hyperbolic-elliptic system of partial differential equations describing elastic wave displacement and gravity perturbations produced by a source in a homogeneous bounded medium. Within the Cowling approximation, we prove un

Alan Nawzad Amin, Eli Nathan Weinstein, Debora Susan Marks

Applying machine learning to biological sequences - DNA, RNA and protein - has enormous potential to advance human health, environmental sustainability, and fundamental biological understanding. However, many existing machine learning methods are ineffective or unreliable in this problem domain. We study these challenges theoretically, through the lens of ke

Jörg Feldvoss, Friedrich Wagemann

This paper is a sequel to our article [Feldvoss-Wagemann], where we mainly considered semi-simple Leibniz algebras. It turns out that the analogue of the Hochschild-Serre spectral sequence for Leibniz cohomology cannot be applied to many ideals, and therefore this spectral sequence seems not to be applicable for computing the cohomology of non-semi-simple Le

Rupert L. Frank

These notes are an extended version of a series of lectures given at the CIME Summer School in Cetraro in June 2022. The goal is to explain questions about optimal functional inequalities on the example of the sharp Sobolev inequality and its fractional generalizations. Topics covered include compactness theorems for optimizing sequences, characterization of

Aurélien Deya, Reika Fukuizumi, Laurent Thomann

The study is devoted to the interpretation and wellposedness of the stochastic NLS model \begin{equation*} (\imath \partial_t-\Delta)u=|u|^2+\dot{B}, \quad u_0=0,\quad \quad t\in \mathbb{R}, \ x\in \mathbb{T}, \end{equation*} where $\dot{B}$ stands for a space-time fractional noise with index $H=(H_0,H_1)$ in a subset of $(0,1)^{2}$. We first establish that

Christoph Luther, Gunnar König, Moritz Grosse-Wentrup

The Shapley Additive Global Importance (SAGE) value is a theoretically appealing interpretability method that fairly attributes global importance to a model's features. However, its exact calculation requires the computation of the feature's surplus performance contributions over an exponential number of feature sets. This is computationally expensive, parti

Andreea Iana, Goran Glavaš, Heiko Paulheim

The advent of personalized news recommendation has given rise to increasingly complex recommender architectures. Most neural news recommenders rely on user click behavior and typically introduce dedicated user encoders that aggregate the content of clicked news into user embeddings (early fusion). These models are predominantly trained with standard point-wi

Maxim Mostovoy

Topological magnetic defects in multiferroic materials acquire an electric charge or dipole moment due to the inverse Dzyaloshinskii-Moriya mechanism. This magnetoelectric coupling makes possible to excite large-amplitude collective motion of topological magnetic textures with an oscillating electric field. Here, I discuss electric excitation of a polar opti

Mohammed Al-Rawi

Zernike moments can be used to generate invariant features that are applied in various machine vision applications. They, however, suffer from slow implementation and numerical stability problems. We propose a novel method for computing Zernike using Fast Fourier Transform (FFT) and GPU computing. The method can be used to generate accurate moments up to hig

Yaoyao Liu, Bernt Schiele, Andrea Vedaldi, Christian Rupprecht

Incremental object detection (IOD) aims to train an object detector in phases, each with annotations for new object categories. As other incremental settings, IOD is subject to catastrophic forgetting, which is often addressed by techniques such as knowledge distillation (KD) and exemplar replay (ER). However, KD and ER do not work well if applied directly t

Omar Abou El Kheir, Luigi Bonati, Michele Parrinello, Marco Bernasconi

The phase change compound Ge$_2$Sb$_2$Te$_5$ (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible transition between the crystalline and amorphous phases induced by Joule heating. The crystallization kinetics of GST225 is a key functional feature for the operation of these dev

Cyrill Krähenbühl, Marc Wyss, David Basin, Vincent Lenders

In its current state, the Internet does not provide end users with transparency and control regarding on-path forwarding devices. In particular, the lack of network device information reduces the trustworthiness of the forwarding path and prevents end-user applications requiring specific router capabilities from reaching their full potential. Moreover, the i

Miguel Albaladejo, Juan Nieves, Enrique Ruiz-Arriola

We predict femtoscopy correlation functions for $S$--wave $D_{(s)}\phi$ pairs of lightest pseudoscalar open charm mesons and Goldstone bosons from next-to-leading order unitarized heavy-meson chiral perturbation theory amplitudes. The effect of the two-state structure around $2300\,\text{MeV}$ can be clearly seen in the $(S,I)=(0,1/2)$ $D\pi$, $D\eta$, $D_s

Robert S. Whitney

This is a review of the theory of quantum thermodynamic demons; these are quantum systems that look like they violate the laws of thermodynamics, in analogy with Maxwell's demon. It concentrates on autonomous demons that can be made using nanoelectronics. Here ``autonomous'' means that the demon operates without any external measurement or driving, making it

Linyan Huang, Huijie Wang, Jia Zeng, Shengchuan Zhang

Multi-camera 3D object detection for autonomous driving is a challenging problem that has garnered notable attention from both academia and industry. An obstacle encountered in vision-based techniques involves the precise extraction of geometry-conscious features from RGB images. Recent approaches have utilized geometric-aware image backbones pretrained on d

Karishma Mohiuddin, Mirza Ariful Alam, Mirza Mohtashim Alam, Pascal Welke

Skilled employees are the most important pillars of an organization. Despite this, most organizations face high attrition and turnover rates. While several machine learning models have been developed to analyze attrition and its causal factors, the interpretations of those models remain opaque. In this paper, we propose the HR-DSS approach, which stands for

Florian Henning, Christof Külske, Niklas Schubert

We consider the i.i.d. Bernoulli field $\mu_p$ with occupation density $p \in (0,1)$ on a possibly non-regular countably infinite tree with bounded degrees. For large $p$, we show that the quasilocal Gibbs property, i.e. compatibility with a suitable quasilocal specification, is lost under the deterministic transformation which removes all isolated ones and

Lydia A. Schoenpflug, Maxime W. Lafarge, Anja L. Frei, Viktor H. Koelzer

Automating tissue segmentation and tumor detection in histopathology images of colorectal cancer (CRC) is an enabler for faster diagnostic pathology workflows. At the same time it is a challenging task due to low availability of public annotated datasets and high variability of image appearance. The semi-supervised learning for CRC detection (SemiCOL) challe

Shu Cai, Jing Guo, Haiyun Shu, Liuxiang Yang

A material described as lutetium-hydrogen-nitrogen (Lu-H-N in short) was recently claimed to have near-ambient superconductivity[Gammon et al, Nature 615, 244, 2023]. If the results could be reproduced by other teams, it would be a major scientific breakthrough. Here, we report our results of transport and structure measurements on a material prepared using

Dimitris Saraidaris, Jheng-Wei Li, Andreas Weichselbaum, Jan von Delft

SU(N) symmetry is incompatible with the many-body localized (MBL) phase, even when strong disorder is present. However, recent studies have shown that finite-size SU(2) systems exhibit non-ergodic, subthermal behavior, characterized by the breakdown of the eigenstate thermalization hypothesis, and by the excited eigenstates entanglement entropy that is inter

Matteo Muffo, Roberto Tedesco, Licia Sbattella, Vincenzo Scotti

The introduction of embedding techniques has pushed forward significantly the Natural Language Processing field. Many of the proposed solutions have been presented for word-level encoding; anyhow, in the last years, new mechanism to treat information at an higher level of aggregation, like at sentence- and document-level, have emerged. With this work we addr

Qinqi Wu

Let $d\in\mathbb{Z}$ and $p_i$ be an integral polynomial with $p_i(0)=0,1\leq i\leq d$. It is shown that if $S$ is thickly syndetic in $\mathbb{Z}$, then $\{(m,n)\in\mathbb{Z}^2:m+p_i(n),m+p_2(n),\ldots,m+p_d(n)\in S\}$ is thickly syndetic in $\mathbb{Z}^2$. Meanwhile, we construct a transitive, strong mixing and non-minimal topological dynamical system $(X,

Edric Tam, David Dunson

We introduce Fiedler regularization, a novel approach for regularizing neural networks that utilizes spectral/graphical information. Existing regularization methods often focus on penalizing weights in a global/uniform manner that ignores the connectivity structure of the neural network. We propose to use the Fiedler value of the neural network's underlying

Kenneth Cecire, Shane Wood

From November 26 to December 12, 2022, Shane Wood and Kenneth Cecire, QuarkNet staff members under the University of Notre Dame, traveled to South Africa as Lecturers in the African School of Fundamental Physics and Applications (ASP) 2022, held at Nelson Mandela University in Gqeberha (Port Elizabeth) within the same calendar period. ASP is held every other

Alexia Jolicoeur-Martineau, Emy Gervais, Kilian Fatras, Yan Zhang

Ensemble methods combine the predictions of multiple models to improve performance, but they require significantly higher computation costs at inference time. To avoid these costs, multiple neural networks can be combined into one by averaging their weights. However, this usually performs significantly worse than ensembling. Weight averaging is only benefici

Cheng-Long Wang, Mengdi Huai, Di Wang

As a way to implement the "right to be forgotten" in machine learning, \textit{machine unlearning} aims to completely remove the contributions and information of the samples to be deleted from a trained model without affecting the contributions of other samples. Recently, many frameworks for machine unlearning have been proposed, and most of them focus on im

Shengjie Wang, Yanfei Kang, Fotios Petropoulos

In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, with little exploration into probabilistic intermittent demand forecasting. This is despite the fact that probabilistic forecasting is crucial for effective decision-making under unc

Jakob Möller, Norbert J. Mauser

We present the self-consistent Pauli equation, a semi-relativistic model for charged spin-$1/2$-particles with self-interaction with the electromagnetic field. The Pauli equation arises as the $O(1/c)$ approximation of the relativistic Dirac equation. The fully relativistic self-consistent model is the Dirac-Maxwell equation where the description of spin and

Khulood D. Alazwary, Ahmad Adnan Qidan, T. E. H. El-Gorashi, Jaafar M. H. Elmirghani

This paper evaluates the performance of rate splitting (RS), a robust interference management scheme, in an optical wireless communication (OWC) network that uses infrared lasers referred to as vertical-cavity surface-emitting lasers (VCSELs) as optical transmitters. In 6G OWC, providing high spectral and energy efficiency requires advanced multiple access s

Nicolas Lazzari, Andrea Poltronieri, Valentina Presutti

This paper describes a pattern to formalise context-free grammars in OWL and its use for sequence classification. The proposed approach is compared to existing methods in terms of computational complexity as well as pragmatic applicability, with examples in the music domain.

Jannik Thuemmel, Matthias Karlbauer, Sebastian Otte, Christiane Zarfl

Deep learning has gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the last few years, achieving forecast skills comparable to established numerical weather prediction models with com

Johannes Teutsch, Sebastian Kerz, Tim Brüdigam, Dirk Wollherr

In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise. The strategy uses only past measured, potentially noisy data in a non-parametric system representation and does not require any prior model identification. The approximation of chance

Bowen Zhang, Xianghua Fu, Daijun Ding, Hu Huang

Stance detection predicts attitudes towards targets in texts and has gained attention with the rise of social media. Traditional approaches include conventional machine learning, early deep neural networks, and pre-trained fine-tuning models. However, with the evolution of very large pre-trained language models (VLPLMs) like ChatGPT (GPT-3.5), traditional me

Yunlu Gong, Shiting Tian, Liancheng Zhou, Tingfeng Yi

In this work, we report periodicity search analyses in the gamma-ray light curve of the blazar S4 0954+658 monitoring undertaken by the Fermi Large Area Telescope (LAT). Four analytical methods and a tool are adopted to detect any periodic flux modulation and corresponding significance level, revealing that (i) a 66 d quasi-periodic oscillation (QPO) with th

Andrew R. Warwick, Rhys Thomas, Max Boleininger, Ömer Koç

Zirconium alloys are widely used as the fuel cladding material in pressurised water reactors, accumulating a significant population of defects and dislocations from exposure to neutrons. We present and interpret synchrotron microbeam X-ray diffraction measurements of proton-irradiated Zircaloy-4, where we identify a transient peak and the subsequent saturati

Andrea Sacchetti

In this paper we show that the typical effects of quantum resonances, namely, the exponential-type decay of the survival amplitude, continue to exist even when a nonlinear perturbative term is added to the time-dependent Schroedinger equation. The difficulty in giving a rigorous and appropriate definition of quantum resonances by means of the notions already

Nicolò Drago, Christiaan J. F. van de Ven

The Dobrushin-Lanford-Ruelle (DLR) condition and the classical Kubo-Martin-Schwinger (KMS) condition are considered in the context of classical lattice systems. In particular, we prove that these conditions are equivalent for the case of a lattice spin system with values in a compact symplectic manifold by showing that infinite volume Gibbs states are in bij

Siow Meng Low, Akshat Kumar, Scott Sanner

In safe MDP planning, a cost function based on the current state and action is often used to specify safety aspects. In the real world, often the state representation used may lack sufficient fidelity to specify such safety constraints. Operating based on an incomplete model can often produce unintended negative side effects (NSEs). To address these challeng

Guodong Pang, Etienne Pardoux

We study a stochastic epidemic model with multiple patches (locations), where individuals in each patch are categorized into three compartments, Susceptible, Infected and Recovered/Removed, and may migrate from one patch to another in any of the compartments. Each individual is associated with a random infectivity function which dictates the force of infecti

Frans Skarman, Oscar Gustafsson

Spade is a new open source hardware description language (HDL) designed to increase developer productivity without sacrificing the low-level control offered by HDLs. It is a standalone language which takes inspiration from modern software languages, and adds useful abstractions for common hardware constructs. It also comes with a convenient set of tooling, s

Peter Samoaa, Linus Aronsson, Antonio Longa, Philipp Leitner

Most machine learning and data analytics applications, including performance engineering in software systems, require a large number of annotations and labelled data, which might not be available in advance. Acquiring annotations often requires significant time, effort, and computational resources, making it challenging. We develop a unified active learning

Yuki Ozawa, Dafang Zhao, Daichi Watari, Ittetsu Taniguchi

The large amount of data collected in buildings makes energy management smarter and more energy efficient. This study proposes a design and implementation methodology of data-driven heating, ventilation, and air conditioning (HVAC) control. Building thermodynamics is modeled using a symbolic regression model (SRM) built from the collected data. Additionally,

Haining Wang

In this article, we study the adjoint Selmer group of a modular form that admits a Jacquet-Langlands transfer to a modular form on a definite quaternion algebra. We show that this Selmer group has length given by the valuation of the Petersson norm of the quaternionic modular form. Our proof is based on an Euler system argument first used by Flach in his stu

Francesco Bardozzo, Andrea Terlizzi, Pietro Liò, Roberto Tagliaferri

This research report introduces ElegansNet, a neural network that mimics real-world neuronal network circuitry, with the goal of better understanding the interplay between connectome topology and deep learning systems. The proposed approach utilizes the powerful representational capabilities of living beings' neuronal circuitry to design and generate improve

Zhao Zan, Leilei Huang, ShuShi Chen, Xiantao Zhang

Versatile Video Coding (VVC) has significantly increased encoding efficiency at the expense of numerous complex coding tools, particularly the flexible Quad-Tree plus Multi-type Tree (QTMT) block partition. This paper proposes a deep learning-based algorithm applied in fast QTMT partition for VVC intra coding. Our solution greatly reduces encoding time by ea

Zbigniew Palmowski, Simon Pojer, Stefan Thonhauser

In this paper we determine bounds and exact asymptotics of the ruin probability for risk process with arrivals given by a linear marked Hawkes process. We consider the light-tailed and heavy-tailed case of the claim sizes. Main technique is based on the principle of one big jump, exponential change of measure, and renewal arguments.

Tomas Samuely, Darshana Wickramaratne, Martin Gmitra, Thomas Jaouen

Low-dimensional materials have remarkable properties that are distinct from their bulk counterparts. A paradigmatic example is Ising superconductivity that occurs in monolayer materials such as NbSe2 which show a strong violation of the Pauli limit. In monolayers, this occurs due to a combination of broken inversion symmetry and spin-orbit coupling that lock

Hugo Martin

We study the classic pure selection integrodifferential equation, stemming from adaptative dynamics, in a measure framework by mean of duality approach. After providing a well posedness result under fairly general assumptions, we focus on the asymptotic behavior of various cases, illustrated by some numerical simulations.

Linus Bergqvist

We give necessary conditions for when a subset of $\mathbb{T}^n$ can contain the support of some non-zero RP-measure. Among other things we show that the support of a positive RP-measure cannot be contained in reflections of inverse images of half-planes by certain functions in $A(\mathbb{D}^n)$, sets of linear measure zero, and when $n=2$, graphs of strictl

Michael Cuntz, Flavien Mabilat

The $\lambda$-quiddities of size $n$ are $n$-tuples of elements of a fixed set, solutions of a matrix equation appearing in the study of Coxeter's friezes. These can be considered on various sets with very different structures from one set to another. The main objective of this text is to obtain explicit formulas giving the number of $\lambda$-quiddities of