December 2023 arXiv papers — page 94

Lingqiang Chen, Qinglin Zhao, Guanghui Li, Mengchu Zhou

Deep graph convolution networks (GCNs) have recently shown excellent performance in traffic prediction tasks. However, they face some challenges. First, few existing models consider the influence of auxiliary information, i.e., weather and holidays, which may result in a poor grasp of spatial-temporal dynamics of traffic data. Second, both the construction o

Krishna Rajagopal, Bruno Scheihing-Hitschfeld, Rachel Steinhorst

The adiabatic hydrodynamization framework is a promising framework within which to describe and characterize pre-hydrodynamic attractors in a model-independent fashion. Using this framework, we define a procedure to identify a time-dependent change in coordinates which reveals a dynamical reduction in the number of active degrees of freedom. Applying this pr

Nadezda Alexandrovna Knorozova, Alessandro Ronca

Recurrent Neural Cascades (RNCs) are the recurrent neural networks with no cyclic dependencies among recurrent neurons. This class of recurrent networks has received a lot of attention in practice. Besides training methods for a fixed architecture such as backpropagation, the cascade architecture naturally allows for constructive learning methods, where recu

Yu-Cun Xie, Jen-Tsung Hsiang, Bei-Lok Hu

This paper continues the investigation initiated in arXiv:2204.08634 into the quantum thermodynamic properties of space by deriving the vacuum compressibility of a variety of dynamical spacetimes containing massive and massless conformally coupled quantum fields. The quantum processes studied here include particle creation, Casimir effect, and the trace anom

Matteo Capoferri, Mikhail Cherdantsev, Igor Velčić

We develop a qualitative homogenisation and spectral theory for elliptic systems of partial differential equations in divergence form with highly contrasting (i.e., non uniformly elliptic) random coefficients. The focus of the paper is on the behaviour of the spectrum as the heterogeneity parameter tends to zero; in particular, we show that in general one do

Marco Piva

We study a class of renormalizable quantum field theories with purely virtual particles that exhibits nonrenormalizable behavior in the high-energy limit of scattering cross sections, which grow as powers of the center-of-mass energy squared and seems to violate unitarity bounds. We point out that the problem should be viewed as a violation of perturbativity

Nicolas Dauphas, Andy W. Heard, Eric Siciliano Rego, Olivier Rouxel

This chapter investigates the complexities surrounding the iron biogeochemical cycle from the Archean to present, with a focus on assessing the balance between iron sources and sinks during long periods of Earth's history with relatively invariable redox conditions, when steady state can be safely assumed. Currently, the residence time of iron in the ocean m

Alessandro Castelnovo, Riccardo Crupi, Nicolò Mombelli, Gabriele Nanino

The remarkable success of Artificial Intelligence in advancing automated decision-making is evident both in academia and industry. Within the plethora of applications, ranking systems hold significant importance in various domains. This paper advocates for the application of a specific form of Explainable AI -- namely, contrastive explanations -- as particul

Maximilian Wegge, Roman Klinger

Emotion corpora are typically sampled based on keyword/hashtag search or by asking study participants to generate textual instances. In any case, these corpora are not uniform samples representing the entirety of a domain. We hypothesize that this practice of data acquisition leads to unrealistic correlations between overrepresented topics in these corpora t

Edward W. Kolb, Andrew J. Long

Cosmological gravitational particle production (CGPP) is the creation of particles in an expanding universe due solely to their gravitational interaction. These particles can play an important role in the cosmic history through their connection to various cosmological relics including dark matter, gravitational wave radiation, dark radiation, and the baryon

Jingwei Guo, Kaizhu Huang, Xinping Yi, Rui Zhang

Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the identical filter weights to mine their local contexts. Despite the success, existing spectral GNNs usually fail to deal with complex networks (e.g., WWW) due to such homogeneous sp

Kangwook Jang, Sungnyun Kim, Hoirin Kim

Albeit great performance of Transformer-based speech selfsupervised learning (SSL) models, their large parameter size and computational cost make them unfavorable to utilize. In this study, we propose to compress the speech SSL models by distilling speech temporal relation (STaR). Unlike previous works that directly match the representation for each speech f

Yuan Sui, Jiaru Zou, Mengyu Zhou, Xinyi He

Table reasoning tasks have shown remarkable progress with the development of large language models (LLMs), which involve interpreting and drawing conclusions from tabular data based on natural language (NL) questions. Existing solutions mainly tested on smaller tables face scalability issues and struggle with complex queries due to incomplete or dispersed da

Jinghong Li, Wen Gu, Koichi Ota, Shinobu Hasegawa

With the rapid development of the internet in the past decade, it has become increasingly important to extract valuable information from vast resources efficiently, which is crucial for establishing a comprehensive digital ecosystem, particularly in the context of research surveys and comprehension. The foundation of these tasks focuses on accurate extractio

Michael Jungo, Lars Vögtlin, Atefeh Fakhari, Nathan Wegmann

Handwriting recognition is a key technology for accessing the content of old manuscripts, helping to preserve cultural heritage. Deep learning shows an impressive performance in solving this task. However, to achieve its full potential, it requires a large amount of labeled data, which is difficult to obtain for ancient languages and scripts. Often, a trade-

Saul Gonzalez, Parfait Atchade-Adelomou

Quantum algorithms have begun to surpass classical ones in several computation fields, yet practical application remains challenging due to hardware and software limitations. Here, we introduce a quantum algorithm that quadratically improves spatial discretization within these constraints. Implemented in the quantum software library Pennylane, our algorithm

Paulo Amorim, Jean-Baptiste Casteras, João Paulo Dias

In this paper, following the studies of Amorim and al. in Partial Differ.Equ. Appl. '23, we consider some new aspects of the motion of the director field of a nematic liquid crystal submitted to a magnetic field and to a laser beam. In particular, we study the existence and partial orbital stability of special standing waves, in the spirit of Cazenave and Li

Davide Berghi, Peipei Wu, Jinzheng Zhao, Wenwu Wang

Sound event localization and detection (SELD) combines two subtasks: sound event detection (SED) and direction of arrival (DOA) estimation. SELD is usually tackled as an audio-only problem, but visual information has been recently included. Few audio-visual (AV)-SELD works have been published and most employ vision via face/object bounding boxes, or human po

Akash Ratheesh, Ofer Dagan, Nisar R. Ahmed, Jay McMahon

This paper considers the problem of evaluating an autonomous system's competency in performing a task, particularly when working in dynamic and uncertain environments. The inherent opacity of machine learning models, from the perspective of the user, often described as a `black box', poses a challenge. To overcome this, we propose using a measure called the

Aksel Samuelsberg, Per Kristen Jakobsen

In this paper, we solve a North-type Energy Balance Model (EBM) using an analytical method, the Boundary Integral Method. This approach is discussed in light of existing analytical techniques for this type of equation. We use the method to demonstrate that the placement of a zonally symmetric continent, with an altered ice-albedo feedback dynamic, introduces

Yuan Sun, Xuan Wang, Yunfan Zhang, Jie Zhang

We present a method named iComMa to address the 6D camera pose estimation problem in computer vision. Conventional pose estimation methods typically rely on the target's CAD model or necessitate specific network training tailored to particular object classes. Some existing methods have achieved promising results in mesh-free object and scene pose estimation

Yuqing Wang, Zhenyu Weng, Zhaokun Zhou, Shuaijian Ji

Over the past years, Printed Mathematical Expression Recognition (PMER) has progressed rapidly. However, due to the insufficient context information captured by Convolutional Neural Networks, some mathematical symbols might be incorrectly recognized or missed. To tackle this problem, in this paper, a Dual Branch transformer-based Network (DBN) is proposed to

Erik Christensen

Haagerup's proof of the non commutative little Grothendieck inequality raises some questions on the commutative little inequality, and it offers a new result on scalar matrices with non negative entries. The theory of completely bounded maps implies that the commutative Grothendieck inequality follows from the little commutative inequality, and that this pas

Oliver Grainge, Michael Milford, Indu Bodala, Sarvapali D. Ramchurn

Visual Place Recognition (VPR) is a critical task for performing global re-localization in visual perception systems. It requires the ability to accurately recognize a previously visited location under variations such as illumination, occlusion, appearance and viewpoint. In the case of robotic systems and augmented reality, the target devices for deployment

Ranyang Zhou, Sabbir Ahmed, Arman Roohi, Adnan Siraj Rakin

In this work, we propose DRAM-Locker as a robust general-purpose defense mechanism that can protect DRAM against various adversarial Deep Neural Network (DNN) weight attacks affecting data or page tables. DRAM-Locker harnesses the capabilities of in-DRAM swapping combined with a lock-table to prevent attackers from singling out specific DRAM rows to safeguar

M. Xiao, F. Ostiguy, D. Johnson

PIP-II beam transfer line (BTL) to transport the beam from PIP-II Linac to the Booster ring at Fermilab. One crucial aspect of the BTL design involved the implementation of collimators. These collimators play a vital role in removing large amplitude particles from the beamline that might otherwise miss the horizontal and vertical edge of the foil at the poin

Benedikt Künzel, Jonathan Rollin

Simultaneous Geometric Embedding (SGE) asks whether, for a given collection of graphs on the same vertex set V, there is an embedding of V in the plane that admits a crossing-free drawing with straightline edges for each of the given graphs. It is known that SGE is $\exists\mathbb{R}$-complete, that is, the problem is polynomially equivalent to deciding whet

Feng Yichang, Wang Jin, Zhang Haiyun, Lu Guodong

This paper proposes a Bayes Net based Monte Carlo optimization for motion planning (BN-MCO). Primarily, we adjust the potential fields determined by goal and start constraints to progressively guide the sampled clusters toward the goal and start points. Then, we utilize the Gaussian mixed modal (GMM) to perform the Monte Carlo optimization, confronting these

William Leeney, Ryan McConville

Federated Learning is machine learning in the context of a network of clients whilst maintaining data residency and/or privacy constraints. Community detection is the unsupervised discovery of clusters of nodes within graph-structured data. The intersection of these two fields uncovers much opportunity, but also challenge. For example, it adds complexity due

Erik T. K. Mau, Oleh E. Omel'chenko, Michael Rosenblum

We present an extension of the Kuramoto-Sakaguchi model for networks, deriving the second-order phase approximation for a paradigmatic model of oscillatory networks - an ensemble of non-identical Stuart-Landau oscillators coupled pairwisely via an arbitrary coupling matrix. We explicitly demonstrate how this matrix translates into the coupling structure in t

Qiankun Zuo, Ling Chen, Yanyan Shen, Michael Kwok-Po Ng

Effective connectivity estimation plays a crucial role in understanding the interactions and information flow between different brain regions. However, the functional time series used for estimating effective connectivity is derived from certain software, which may lead to large computing errors because of different parameter settings and degrade the ability

Thomas F. Bloom, Vivian Kuperberg

We prove near-optimal upper bounds for the odd moments of the distribution of coprime residues in short intervals, confirming a conjecture of Montgomery and Vaughan. As an application we prove near-optimal upper bounds for the average of the refined singular series in the Hardy-Littlewood conjectures concerning the number of prime $k$-tuples for $k$ odd. The

Kai Qiu, Huishuai Zhang, Zhirong Wu, Stephen Lin

Training foundation models on extensive datasets and then finetuning them on specific tasks has emerged as the mainstream approach in artificial intelligence. However, the model robustness, which is a critical aspect for safety, is often optimized for each specific task rather than at the pretraining stage. In this paper, we propose a method for pretraining

Yanlong Hao

Let $(X,d)$, $(Y, d')$ be two roughly geodesically complete Gromov hyperbolic spaces under comparable isometric actions of $\Gamma$. Assume that the limit set $\Lambda \Gamma=\partial X\partial Y$. If spaces $X$ and $Y$ have the same asymptotic marked length spectrum, meaning that $$\lim_{{l_{d}([\gamma])\to \infty}}\frac{l_d(\gamma)}{l_{d'}(\gamma)}=1.$$ Th

Alessio Dallabona, Mogens Blanke, Henrik C. Pedersen, Dimitrios Papageorgiou

Wind energy is the leading non-hydro renewable technology. Increasing reliability is a key factor in reducing the downtime of high-power wind turbines installed in remote off-shore places, where maintenance is costly and less reactive. Defects in the pitch system are responsible for up to 20% of a wind turbine downtime.Thus, monitoring such defects is essent

Sathish Kumar Paramasivam, Shakhil Ponnarassery Gangadharan, Milorad V. Milošević, Andrea Perali

In the pursuit of higher critical temperature of superconductivity, quasi-flat electronic bands and van Hove singularities in two dimensions (2D) have emerged as a potential approach to enhance Cooper pairing on the basis of mean-field expectations. However, these special electronic features suppress the superfluid stiffness and, hence, the Berezinskii-Koste

Sékou-Oumar Kaba, Siamak Ravanbakhsh

Using symmetry as an inductive bias in deep learning has been proven to be a principled approach for sample-efficient model design. However, the relationship between symmetry and the imperative for equivariance in neural networks is not always obvious. Here, we analyze a key limitation that arises in equivariant functions: their incapacity to break symmetry

William Leeney, Ryan McConville

(1) The enhanced capability of Graph Neural Networks (GNNs) in unsupervised community detection of clustered nodes is attributed to their capacity to encode both the connectivity and feature information spaces of graphs. The identification of latent communities holds practical significance in various domains, from social networks to genomics. Current real-wo

Marli R. Cantarino, K. R. Pakuszewski, Björn Salzmann, Pedro H. A. Moya

Superconductivity (SC) is absent in Cr-substituted BaFe$_{2}$As$_{2}$ (CrBFA), a well-established but poorly understood topic. Additionally, the suppression of the spin density wave transition temperature ($T_{\text{SDW}}$) in CrBFA and Mn-substituted BaFe$_{2}$As$_{2}$ (MnBFA) coincides as a function of Cr/Mn content, despite the distinct electronic effects

James D. Green, Timothy J. H. Hele

Recent years have seen an explosion of interest in organic radicals due to their promise for highly efficient organic light-emitting diodes (OLEDs) and molecular qubits. However, accurately and inexpensively computing their electronic structure has been challenging, especially for excited states, due to the spin-contamination problem. Furthermore, while alte

Rakesh Munagala, Dheeraj Naidu Amudala, Rohit Budhiraja

We consider the uplink of a hardware-impaired intelligent reflective surfaces (IRS) aided multi-cell massive multiple-input multiple-output (mMIMO) system with mobile user equipments, whose channel age with time. For this system, we analyze the distortion due to hardware impairments, and the effect of channel aging, and propose a novel distortion-and-aging-a

Craig R. Walton, Oliver Shorttle

Understanding the co-evolution of complex life with Earth's geology is an enduring challenge. The rock record evidences remarkable correlations between changes in biology and the wider Earth system, yet cause and effect remain unclear. Here, we link the evolutionary history of eukaryotes with the rise and fall of carbonate rock fraction within continental cr

Francisco J. de Anda, Stephen F. King

We discuss an $SU(5)$ Grand Unified Theory (GUT) based on the 10d orbifold $(\mathbb{T}^2)^3/(\mathbb{Z}_4\times\mathbb{Z}_2)$ plus three modular $S_4$ groups with moduli at respective fixed points $i,i+2,\omega=e^{2i\pi/3}$. The resulting model has hierarchical quark and charged lepton mass matrices, arising from a double weighton mechanism, and reproduces

Dapeng Li, Na Lou, Bin Zhang, Zhiwei Xu

Parameter sharing, as an important technique in multi-agent systems, can effectively solve the scalability issue in large-scale agent problems. However, the effectiveness of parameter sharing largely depends on the environment setting. When agents have different identities or tasks, naive parameter sharing makes it difficult to generate sufficiently differen

Hongwei Cui, Yuyang Du, Qun Yang, Yulin Shao

Task-oriented communications are an important element in future intelligent IoT systems. Existing IoT systems, however, are limited in their capacity to handle complex tasks, particularly in their interactions with humans to accomplish these tasks. In this paper, we present LLMind, an LLM-based task-oriented AI agent framework that enables effective collabor

Liping Yi, Han Yu, Zhuan Shi, Gang Wang

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system heterogeneity. Model-Heterogeneous Personalized FL (MHPFL) has emerged to address this challenge. Existi

Zhuoyifan Zhang, Lu Zhang, Liang Wang, Haoming Wu

The research on neural radiance fields for new view synthesis has experienced explosive growth with the development of new models and extensions. The NERF algorithm, suitable for underwater scenes or scattering media, is also evolving. Existing underwater 3D reconstruction systems still face challenges such as extensive training time and low rendering effici

Victor Sabanza Gil, Andres M. Bran, Malte Franke, Remi Schlama

The prediction of chemical reactions has gained significant interest within the machine learning community in recent years, owing to its complexity and crucial applications in chemistry. However, model evaluation for this task has been mostly limited to simple metrics like top-k accuracy, which obfuscates fine details of a model's limitations. Inspired by pr

Tim Davis

This thesis studies modular forms from a classical and adelic viewpoint. We use this interplay to obtain results about the arithmetic of the Fourier coefficients of modular forms and their generalisations. In Chapter 2, we compute lower bounds for the $p$-adic valuation of local Whittaker newforms with non-trivial central character. We obtain these bounds by

Cheng Guo, Lorenzo Rapetti, Kourosh Darvish, Riccardo Grieco

This paper proposes a framework that combines online human state estimation, action recognition and motion prediction to enable early assessment and prevention of worker biomechanical risk during lifting tasks. The framework leverages the NIOSH index to perform online risk assessment, thus fitting real-time applications. In particular, the human state is ret

Zhongze Zhang, Tao Jiang, Wei Yu

This paper addresses an uplink localization problem in which a base station (BS) aims to locate a remote user with the help of reconfigurable intelligent surfaces (RISs). We propose a strategy in which the user transmits pilots sequentially and the BS adaptively adjusts the sensing vectors, including the BS beamforming vector and multiple RIS reflection coef

C. Leloup, G. Patanchon, J. Errard, C. Franceschet

We present a study of the impact of an uncertainty in the beam far side-lobe knowledge on the measurement of the Cosmic Microwave Background $B$-mode signal at large scale. It is expected to be one of the main source of systematic effects in future CMB observations. Because it is crucial for all-sky survey missions to take into account the interplays between

Dang Van Thin, Duong Ngoc Hao, Ngan Luu-Thuy Nguyen

The ComOM shared task aims to extract comparative opinions from product reviews in Vietnamese language. There are two sub-tasks, including (1) Comparative Sentence Identification (CSI) and (2) Comparative Element Extraction (CEE). The first task is to identify whether the input is a comparative review, and the purpose of the second task is to extract the qui

Julia A. Meister, Khuong An Nguyen

With the proliferation of increasingly complicated Deep Learning architectures, data synthesis is a highly promising technique to address the demand of data-hungry models. However, reliably assessing the quality of a 'synthesiser' model's output is an open research question with significant associated risks for high-stake domains. To address this challenge,

Ting Zhu, Shufei Duan, Huizhi Liang, Wei Zhang

The lack of an available emotion pathology database is one of the key obstacles in studying the emotion expression status of patients with dysarthria. The first Chinese multimodal emotional pathological speech database containing multi-perspective information is constructed in this paper. It includes 29 controls and 39 patients with different degrees of moto

Maryam Nowroozi, Samir Siksek

Let $E$ be an elliptic curve over the rationals given by an integral Weierstrass model and let $P$ be a rational point of infinite order. The multiple $nP$ has the form $(A_n/B_n^2,C_n/B_n^3)$ where $A_n$, $B_n$, $C_n$ are integers with $A_n C_n$ and $B_n$ coprime, and $B_n$ positive. The sequence $(B_n)$ is called the elliptic divisibility sequence generate

Aditi Dudeja

In this paper, we consider the problem of maintaining a $(1-\varepsilon)$-approximate maximum weight matching in a dynamic graph $G$, while the adversary makes changes to the edges of the graph. In the fully dynamic setting, where both edge insertions and deletions are allowed, Gupta and Peng gave an algorithm for this problem with an update time of $\tilde{

Markus Reiter-Haas, Beate Klösch, Markus Hadler, Elisabeth Lex

Revealing the framing of news articles is an important yet neglected task in information seeking and retrieval. In the present work, we present FrameFinder, an open tool for extracting and analyzing frames in textual data. FrameFinder visually represents the frames of text from three perspectives, i.e., (i) frame labels, (ii) frame dimensions, and (iii) fram

Qijun Zhang, Shiyu Li, Guanglei Zhou, Jingyu Pan

Power efficiency is a critical design objective in modern microprocessor design. To evaluate the impact of architectural-level design decisions, an accurate yet efficient architecture-level power model is desired. However, widely adopted data-independent analytical power models like McPAT and Wattch have been criticized for their unreliable accuracy. While s

Bagas Prabowo, Jurgen Dijkema, Xiao Xue, Fabio Sebastiano

In semiconductor spin quantum bits (qubits), the radio-frequency (RF) gate-based readout is a promising solution for future large-scale integration, as it allows for a fast, frequency-multiplexed readout architecture, enabling multiple qubits to be read out simultaneously. This paper introduces a theoretical framework to evaluate the effect of various parame

Carlos Minervino, Claudio Campelo, Maxwell Oliveira, Salatiel Silva

The Spatial Pattern Matching (SPM) query allows for the retrieval of Points of Interest (POIs) based on spatial patterns defined by keywords and distance criteria. However, it does not consider the connectivity between POIs. In this study, we introduce the Qualitative and Quantitative Spatial Pattern Matching (QQ-SPM) query, an extension of the SPM query tha

Lorenzo Lamberti, Elia Cereda, Gabriele Abbate, Lorenzo Bellone

Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles' perception, planning, and control skills. The recent emergence of autonomous nano-sized drone racing imposes new challenges, as their ~10cm form factor heavily restricts the resources available onboard, including memory, computation, and sensors. This paper describes the me

Jovial Cheukam-Ngouonou, Ramiz Gindullin, Nicolas Beldiceanu, Rémi Douence

We present the proofs of the conjectures mentioned in the paper published in the proceedings of the 2024 AAAI conference [1], and discovered by the decomposition methods presented in the same paper.

Dylan M. H. Leung, Kenny C. Y. Ng

The particle nature of dark matter (DM) has been a long-lasting mystery. Many models suggest that DM could decay or self annihilate into standard model particles, and thus could be a source of gamma rays in the sky. The High Altitude Water Cherenkov (HAWC) observatory has yielded some of the strongest limits in searches of DM decay or annihilation. Building

Daniel Max Hoffmann, Piotr Kowalski, Chieu-Minh Tran, Jinhe Ye

We show that if G is a split semisimple algebraic group over a model complete field K, then the groups G(K) and G(K)' (the commutator group which is a ``Chevalley group'' as for example the group PSL_2(K)) are model complete as well.

Junbo Shen, Qinze Yu, Shenyang Chen, Qingxiong Tan

Signal peptide (SP) is a short peptide located in the N-terminus of proteins. It is essential to target and transfer transmembrane and secreted proteins to correct positions. Compared with traditional experimental methods to identify signal peptides, computational methods are faster and more efficient, which are more practical for analyzing thousands or even

Sanghun Lee, Eunseo Kim, Junho Bang, Jongho Park

Solids undergoing a transition from order to disorder experience the proliferation of topological defects. The melting process generates transient quantum states. However, their dynamical nature with femtosecond lifetime hinders exploration with atomic precision. Here, we suggest an alternative approach to the dynamical melting process by focusing on the int

Han Liang, Jiacheng Bao, Ruichi Zhang, Sihan Ren

We have recently seen tremendous progress in realistic text-to-motion generation. Yet, the existing methods often fail or produce implausible motions with unseen text inputs, which limits the applications. In this paper, we present OMG, a novel framework, which enables compelling motion generation from zero-shot open-vocabulary text prompts. Our key idea is

Yabing Wang, Fan Wang, Jianfeng Dong, Hao Luo

Cross-lingual cross-modal retrieval has garnered increasing attention recently, which aims to achieve the alignment between vision and target language (V-T) without using any annotated V-T data pairs. Current methods employ machine translation (MT) to construct pseudo-parallel data pairs, which are then used to learn a multi-lingual and multi-modal embedding

Yunze Liu, Changxi Chen, Li Yi

We focus on the human-humanoid interaction task optionally with an object. We propose a new task named online full-body motion reaction synthesis, which generates humanoid reactions based on the human actor's motions. The previous work only focuses on human interaction without objects and generates body reactions without hand. Besides, they also do not consi

Ts. B. Georgiev, R. K. Zamanov, S. Y. Stefanov

We analyzed TESS photometric data of the flickering-active cataclysmic star AQ Men in 2018--2019. We processed 7 sectors with 14 light curves (LCs) inside them, with a time resolution of 2 min. Aiming to study the "macro-flickering", with quasi periods (QPs) between 10 and 100 hours, we processed LCs after 55 time reduced, with a time resolution of 1.83 hour

Charlie Dickens, Eric Bax

Data sketching has emerged as a key infrastructure for large-scale data analysis on streaming and distributed data. Merging sketches enables efficient estimation of cardinalities and frequency histograms over distributed data. However, merging sketches can require that each sketch stores hash codes for identifiers in different data sets or partitions, in ord

Frederik Ravn Klausen

This PhD thesis deals with a number of different problems in mathematical physics with the common thread that they have probabilistic aspects. The problems all stem from mathematical studies of lattice systems in statistical and quantum physics; however beyond that, the selection of the concrete problems is to a certain extent arbitrary. This thesis consists

George Close, William Ravenscroft, Thomas Hain, Stefan Goetze

Neural network based approaches to speech enhancement have shown to be particularly powerful, being able to leverage a data-driven approach to result in a significant performance gain versus other approaches. Such approaches are reliant on artificially created labelled training data such that the neural model can be trained using intrusive loss functions whi

Quentin Gontier, Charles Wiame, Joe Wiart, François Horlin

Existing studies analyzing electromagnetic field (EMFE) in wireless networks have primarily considered downlink communications. In the uplink, the EMFE caused by the user's smartphone is usually the only considered source of radiation, thereby ignoring contributions caused by other active neighboring devices. In addition, the network coverage and EMFE are ty

Imad Eddine Marouf, Subhankar Roy, Enzo Tartaglione, Stéphane Lathuilière

In this work, we study the problem of continual learning (CL) where the goal is to learn a model on a sequence of tasks, such that the data from the previous tasks becomes unavailable while learning on the current task data. CL is essentially a balancing act between being able to learn on the new task (i.e., plasticity) and maintaining the performance on the

Anton Shapkin, Denis Litvinov, Yaroslav Zharov, Egor Bogomolov

Current state-of-the-art large language models are effective in generating high-quality text and encapsulating a broad spectrum of world knowledge. These models, however, often hallucinate and lack locally relevant factual data. Retrieval-augmented approaches were introduced to overcome these problems and provide more accurate responses. Typically, the retri

Qilong Li, Ji Liu, Yifan Sun, Chongsheng Zhang

In recent years, Deep Neural Networks (DNN) have emerged as a practical method for image recognition. The raw data, which contain sensitive information, are generally exploited within the training process. However, when the training process is outsourced to a third-party organization, the raw data should be desensitized before being transferred to protect se

Alex Rutar

We provide a self-contained exposition of the well-known multifractal formalism for self-similar measures satisfying the strong separation condition. At the heart of our method lies a pair of quasiconvex optimization problems which encode the parametric geometry of the Lagrange dual associated with the constrained variational principle. We also give a direct

George D. Tsibidis, Emmanuel Stratakis

A key issue in the use of high-power mid-infrared (Mid-IR) laser sources for a plethora of applications is the investigation of the exciting laser driven physical phenomena taking place in materials coated with dielectric films. Here, we present a theoretical investigation of the ultrafast processes and thermal response upon excitation of two-layered complex

Andy Regensky, André Kaup

The large amounts of data associated with 360-degree video require highly effective compression techniques for efficient storage and distribution. The development of improved motion models for 360-degree motion compensation has shown significant improvements in compression efficiency. A geodesic motion model representing translational camera motion proved to

Alessandro Cotellucci, Agostino Patella

We present the analysis of two recently proposed noise reduction techniques, Hutch++ and XTrace, both based on inexact deflation. These methods were proven to have a better asymptotic convergence to the solution than the classical Girard-Hutchinson stochastic method. We applied these methods to the computation of the trace of the inverse of the Dirac operato

Caroline Jonas, George Lavrelashvili, Jean-Luc Lehners

We study the perturbative stability of Euclidean axion-dilaton wormholes that asymptotically approach flat space, both with a massless and a massive dilaton, and focussing on homogeneous perturbations. We find massless wormholes to always be perturbatively stable. The phenomenologically more relevant case of a massive dilaton presents us with a wide variety

Jinhan Liang, Zuowei Liu, Lan Yang

A small component of dark matter (DM) that is strongly interacting with the standard model sector is consistent with various experimental observations. Despite the small abundance, strongly-interacting DM can lead to pronounced signals in DM direct detection experiments. We study Belle II sensitivity on strongly-interacting DM that has a MeV-GeV mass and cou

Ana Mucalica, Dmitry E. Pelinovsky

We present a new exact solution to the defocusing modified Korteweg-de Vries equation to describe the interaction of a dark soliton and a traveling periodic wave. The solution (which we refer to as to the dark breather) is obtained by using the Darboux transformation with the eigenfunctions of the Lax system expressed in terms of the Jacobi theta functions.

Maria Milkova, Maksim Rudnev, Lidia Okolskaya

Basic values are concepts or beliefs which pertain to desirable end-states and transcend specific situations. Studying personal values in social media can illuminate how and why societal values evolve especially when the stimuli-based methods, such as surveys, are inefficient, for instance, in hard-to-reach populations. On the other hand, user-generated cont

Marco Klotz, Richard Waltrich, Niklas Lettner, Viatcheslav Agafonov

The search for long-lived quantum memories, which can be efficiently interfaced with flying qubits is longstanding. One possible solution is to use the electron spin of a color center in diamond to mediate interaction between a long-lived nuclear spin and a photon. Realizing this in a nanodiamond furthermore facilitates the integration into photonic devices

Marcelo Matheus Gauy, Marcelo Finger

An acoustic model, trained on a significant amount of unlabeled data, consists of a self-supervised learned speech representation useful for solving downstream tasks, perhaps after a fine-tuning of the model in the respective downstream task. In this work, we build an acoustic model of Brazilian Portuguese Speech through a Transformer neural network. This mo

Oleg S. Ugolnikov

We suggest the procedure of building the maps of noctilucent clouds (NLC) zonal and meridional velocity, mean altitude and particle size based on three-color photometry by identical all-sky RGB-cameras separated by 115 km in a close-meridional direction. The procedure is applied to the bright NLC event on July 3, 2023. The altitude is measured by precise tri

Joel Yeo, Benedikt J. Daurer, Dari Kimanius, Deepan Balakrishnan

Ewald sphere curvature correction, which extends beyond the projection approximation, stretches the shallow depth of field in cryo-EM reconstructions of thick particles. Here we show that even for previously assumed thin particles, reconstruction artifacts which we refer to as ghosts can appear. By retrieving the lost phases of the electron exitwaves and acc

René Wittmann

Grain boundaries in extremely confined colloidal smectics possess a topological fine structure with coexisting nematic and tetratic symmetry of the director field. An alternative way to approach the problem of smectic topology is via the layer structure, which is typically more accessible in experiments on molecular liquid crystals. Here, we consider exempla

Yuhang Yang, Wei Zhai, Hongchen Luo, Yang Cao

Learning 3D human-object interaction relation is pivotal to embodied AI and interaction modeling. Most existing methods approach the goal by learning to predict isolated interaction elements, e.g., human contact, object affordance, and human-object spatial relation, primarily from the perspective of either the human or the object. Which underexploit certain

Zhiyuan You, Zheyuan Li, Jinjin Gu, Zhenfei Yin

We introduce a Depicted image Quality Assessment method (DepictQA), overcoming the constraints of traditional score-based methods. DepictQA allows for detailed, language-based, human-like evaluation of image quality by leveraging Multi-modal Large Language Models (MLLMs). Unlike conventional Image Quality Assessment (IQA) methods relying on scores, DepictQA

Gijeong Kim, Dongyun Kang, Joon-Ha Kim, Seungwoo Hong

This paper presents a contact-implicit model predictive control (MPC) framework for the real-time discovery of multi-contact motions, without predefined contact mode sequences or foothold positions. This approach utilizes the contact-implicit differential dynamic programming (DDP) framework, merging the hard contact model with a linear complementarity constr

Simone DAgostino, Filippo Moro, Tristan Torchet, Yigit Demirag

An increasing number of neuroscience studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the brain for supporting non-linear computation through localized synaptic integration. In particular, dendritic branches play a key role in temporal signal processing and feature detection, using coincidence detection (CD) mech

A. Amvrosiadis, J. L. Wardlow, J. E. Birkin, I. Smail

We present a new method for modelling the kinematics of galaxies from interferometric observations by performing the optimization of the kinematic model parameters directly in visibility-space instead of the conventional approach of fitting velocity fields produced with the CLEAN algorithm in real-space. We demonstrate our method on ALMA observations of $^{1

Taewook Nam, Juyong Lee, Jesse Zhang, Sung Ju Hwang

We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human feedback. In our framework, the agent receives task instructions grounded in a training environment from large language models. Then, a vision-language model guides the agent in learning the mul

Sarah Schwindt-Drews, Kai Storms, Steven Peters, Bettina Abendroth

This study investigates the impact of initial contact of drivers with an SAE Level 3 Automated Driving System (ADS) under real traffic conditions, focusing on the Mercedes-Benz Drive Pilot in the EQS. It examines Acceptance, Trust, Usability, and User Experience. Although previous studies in simulated environments provided insights into human-automation inte

Suzanne Lancaster, Thomas Mikolajick, Stefan Slesazeck

A wakeup scheme for ferroelectric thin Hf$_{0.5}$Zr$_{0.5}$O$_2$ films is presented, based on a gradual switching approach using multiple short pulses with a voltage amplitude roughly equal to the coercive voltage. This enables the on-chip wakeup and switching operation of ferroelectric devices such as tunnel junctions (FTJs) with identical pulses. After wak

Jussi Behrndt

The notion of quasi boundary triples and their Weyl functions from extension theory of symmetric operators is extended to the general framework of adjoint pairs of operators under minimal conditions on the boundary maps. With the help of the corresponding abstract Titchmarsh-Weyl $M$-functions sufficient conditions for the unique solvability of the related b