May 2024 arXiv papers — page 24

Ho Chun Wu, Esther Hoi Shan Lau, Paul Yuen, Kevin Hung

Pedestrian trajectory modelling in an urban complex is challenging because pedestrians can have many possible destinations, such as shops, escalators, and attractions. Moreover, weather and time-of-day may affect pedestrian behavior. In this paper, a new weather-time-trajectory fusion network (WTTFNet) is proposed to improve the performance of baseline deep

Haosheng Xu, Dongheng Qian, Jing Wang

Machine learning has revolutionized many fields, including materials science. However, predicting properties of crystalline materials using machine learning faces challenges in input encoding, output versatility, and interpretability. We introduce CrystalBERT, an adaptable transformer-based framework integrating space group, elemental, and unit cell informat

Hongyu Liu, Catharine W. K. Lo, Shen Zhang

In recent years, mean field games (MFGs) have garnered considerable attention and emerged as a dynamic and actively researched field across various domains, including economics, social sciences, finance, and transportation. The inverse design and decoding of MFGs offer valuable means to extract information from observed data and gain insights into the intric

Linus Jeary, Tom Kuipers, Mehran Hosseini, Nicola Paoletti

Conformal Prediction (CP) is a popular uncertainty quantification method that provides distribution-free, statistically valid prediction sets, assuming that training and test data are exchangeable. In such a case, CP's prediction sets are guaranteed to cover the (unknown) true test output with a user-specified probability. Nevertheless, this guarantee is vio

Nan Li, Bo Kang, Tijl De Bie

Personalized recommendation systems often drive users towards more extreme content, exacerbating opinion polarization. While (content-aware) moderation has been proposed to mitigate these effects, such approaches risk curtailing the freedom of speech and of information. To address this concern, we propose and explore the feasibility of \emph{content-agnostic

Antonio J. Durán

If $A(z)=\sum_{n=0}^\infty a_nz^n$ and $B(z)=\sum_{n=0}^\infty b_nz^n$ are two formal power series, with $a_n,b_n\in \mathbb{R}$, the polynomials $(p_n)_n$ defined by the generating function $$ A(z)B(xz)=\sum_{n=0}^\infty p_n(x)z^n $$ are called the Brenke polynomials generated by $A$ and associated to $B$. We say that $A\in \mathcal{R}_B$ if the Brenke poly

Ludovica Zullo, Grégory Setnikar, Amit Pawbake, Tristan Cren

Misfit layer compounds, heterostructures composed by a regular alternating stacking of rocksalt monochalcogenides bilayers and few-layer transition metal dichalchogenides, are an emergent platform to investigate highly doped transition metal dichalcogenides. Among them, (LaSe)$_{1.14}$(NbSe$_2$)$_2$ displays Ising superconductivity, while the presence of a c

Antonio Briola, Silvia Bartolucci, Tomaso Aste

We introduce a novel large-scale deep learning model for Limit Order Book mid-price changes forecasting, and we name it `HLOB'. This architecture (i) exploits the information encoded by an Information Filtering Network, namely the Triangulated Maximally Filtered Graph, to unveil deeper and non-trivial dependency structures among volume levels; and (ii) guara

Mahmoud Ahmed, Junjie Fei, Jian Ding, Eslam Mohamed Bakr

In this paper, we introduce Part-Aware Point Grounded Description (PaPGD), a challenging task aimed at advancing 3D multimodal learning for fine-grained, part-aware segmentation grounding and detailed explanation of 3D objects. Existing 3D datasets largely focus on either vision-only part segmentation or vision-language scene segmentation, lacking the fine-g

Zoltan Eisler, Johannes Muhle-Karbe

Minimizing execution costs for large orders is a fundamental challenge in finance. Firms often depend on brokers to manage their trades due to limited internal resources for optimizing trading strategies. This paper presents a methodology for evaluating the effectiveness of broker execution algorithms using trading data. We focus on two primary cost componen

Roumaissae Eljazzar, Mohammed Mouniane, Mohamed Rossafi

This paper explores the concept of $K$-$g$-frames in locally $C^*$-algebras, which are shown to be more general than $g$-frames. The authors first introduce the notion of a $g$-orthonormal basis and utilize it to define the $g$-operator, a crucial element for studying the construction of $K$-$g$-frames in locally $C^*$-algebras. The paper establishes a relat

Lorenzo Mirasola, Rodrigo Tenorio

The sensitivity of continuous gravitational-wave (CW) searches for unknown neutron stars (NSs) is limited by their parameter space breadth. To fit within reasonable computing budgets, hierarchical schemes are used to identify interesting candidates using affordable methods. The resulting sensitivity depends on the number of candidates selected to follow-up.

Yufei Zhao, Shiduo Wang, Hua Duan

Existing heterogeneous graph neural network algorithms (HGNNs) mostly rely on meta-paths to capture the rich semantic information contained in heterogeneous graphs (also known as heterogeneous information networks (HINs)), but most of these HGNNs focus on different ways of feature aggre gation and ignore the properties of the meta-paths themselves. This pape

Gaoxiang Zhao, Lu Wang, Xiaoqiang Wang

The effectiveness of anomaly signal detection can be significantly undermined by the inherent uncertainty of relying on one specified model. Under the framework of model average methods, this paper proposes a novel criterion to select the weights on aggregation of multiple models, wherein the focal loss function accounts for the classification of extremely i

Shohei Enomoto

Deep neural networks (DNNs) struggle to generalize to out-of-distribution domains that are different from those in training despite their impressive performance. In practical applications, it is important for DNNs to have both high standard accuracy and robustness against out-of-distribution domains. One technique that achieves both of these improvements is

Ben David Normann, Kenny Solevåg-Hoti, Hans Georg Schaathun

The distinction between weak and strong lensing is somewhat arbitrary, and both regimes are manifestations of the same physical phenomenon: gravity bending the path of light. Nevertheless, these two regimes have to a large extent been treated separately, since they require different approaches. This review traces the development of methods combining weak-len

Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuuki Yamanaka

Semi-supervised anomaly detection, which aims to improve the anomaly detection performance by using a small amount of labeled anomaly data in addition to unlabeled data, has attracted attention. Existing semi-supervised approaches assume that most unlabeled data are normal, and train anomaly detectors by minimizing the anomaly scores for the unlabeled data w

Belle II Collaboration, I. Adachi, L. Aggarwal, H. Ahmed

We report measurements of the $e^+e^- \to B\bar{B}$, $B\bar{B}{}^*$, and $B^*\bar{B}{}^*$ cross sections at four energies, 10653, 10701, 10746 and 10805 MeV, using data collected by the Belle~II experiment. We reconstruct one $B$ meson in a large number of hadronic final states and use its momentum to identify the production process. In the first $2-5$ MeV a

Jin-Tao Bu, Jian-Qi Zhang, Ge-Yi Ding, Jia-Chong Li

Quantum heat engines and refrigerators are open quantum systems, whose dynamics can be well understood using a non-Hermitian formalism. A prominent feature of non-Hermiticity is the existence of exceptional points (EPs), which has no counterpart in closed quantum systems. It has been shown in classical systems that dynamical encirclement in the vicinity of a

Slavomír Hanzely, Farshed Abdukhakimov, Martin Takáč

This paper investigates the global convergence of stepsized Newton methods for convex functions with H\"older continuous Hessians or third derivatives. We propose several simple stepsize schedules with fast global convergence guarantees, up to $\mathcal {O}\left(k^{-3} \right)$. For cases with multiple plausible smoothness parameterizations or an unknown smo

Giuseppe Serra, Florian Buettner

Given the ability to model more realistic and dynamic problems, Federated Continual Learning (FCL) has been increasingly investigated recently. A well-known problem encountered in this setting is the so-called catastrophic forgetting, for which the learning model is inclined to focus on more recent tasks while forgetting the previously learned knowledge. The

Miguel A. Ferrer, Abhijit Das, Moises Diaz, Aythami Morales

Script identification plays a vital role in applications that involve handwriting and document analysis within a multi-script and multi-lingual environment. Moreover, it exhibits a profound connection with human cognition. This paper provides a new database for benchmarking script identification algorithms, which contains both printed and handwritten documen

Paul J. Groot, S. Bloemen, P. Vreeswijk, J. van Roestel

The main science aim of the BlackGEM array is to detect optical counterparts to gravitational wave mergers. Additionally, the array will perform a set of synoptic surveys to detect Local Universe transients and short time-scale variability in stars and binaries, as well as a six-filter all-sky survey down to ~22nd mag. The BlackGEM Phase-I array consists of

Chenze Shao, Fandong Meng, Jiali Zeng, Jie Zhou

Neural Machine Translation (NMT) has made remarkable progress over the past years. However, under-translation and over-translation remain two challenging problems in state-of-the-art NMT systems. In this work, we conduct an in-depth analysis on the underlying cause of under-translation in NMT, providing an explanation from the perspective of decoding objecti

Loukas Kavouras, Eleni Psaroudaki, Konstantinos Tsopelas, Dimitrios Rontogiannis

The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods that operate effectively. Global counterfactual explanations, expressed as actions to offer recourse, aim to provide succinct explanations and insights applicable to large populati

Anastasios Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas, Dimitra I. Kaklamani

Stacked intelligent metasurface (SIM) is an emerging design that consists of multiple layers of metasurfaces. A SIM enables holographic multiple-input multiple-output (HMIMO) precoding in the wave domain, which results in the reduction of energy consumption and hardware cost. On the ground of multiuser beamforming, this letter focuses on the downlink achieva

Qian Chen, Chenyu Wu, Shuai Han, Weixiao Meng

The rapid development of the aviation Internet of Things (IoT) has positioned in-flight connectivity (IFC) as one of its critical applications. Space-air-ground integrated networks (SAGIN) are essential for ensuring the performance of IFC by enabling seamless and reliable connectivity. However, most existing research treats satellites merely as transparent f

Giacomo Acciarini, Laurent Beauregard, Dario Izzo

Low-thrust trajectories play a crucial role in optimizing scientific output and cost efficiency in asteroid belt missions. Unlike high-thrust transfers, low-thrust trajectories require solving complex optimal control problems. This complexity grows exponentially with the number of asteroids visited due to orbital mechanics intricacies. In the literature, met

Núria Armengol Urpí, Marco Bagatella, Marin Vlastelica, Georg Martius

Offline data are both valuable and practical resources for teaching robots complex behaviors. Ideally, learning agents should not be constrained by the scarcity of available demonstrations, but rather generalize beyond the training distribution. However, the complexity of real-world scenarios typically requires huge amounts of data to prevent neural network

Flavio Tagliaferri, Emmanuel Blazquez, Giacomo Acciarini, Dario Izzo

This study addresses optimal impulsive trajectory design within the Circular Restricted Three-Body Problem (CR3BP), presenting a global optimization-based approach to identify minimum $\Delta V$ transfers between periodic orbits, including heteroclinic connections. By combining a Monotonic Basin Hopping (MBH) algorithm with a sequential quadratic solver in a

Jiachun Li, Pengfei Cao, Yubo Chen, Jiexin Xu

Chain-of-thought (CoT) prompting demonstrates varying performance under different reasoning tasks. Previous work attempts to evaluate it but falls short in providing an in-depth analysis of patterns that influence the CoT. In this paper, we study the CoT performance from the perspective of effectiveness and faithfulness. For the former, we identify key facto

Anna Ciechan, Piotr Dluzewski, Slawomir Kret, Katarzyna Gas

High-resolution transmission electron microscopy and superconducting quantum interference device magnetometry shows that epitaxial CuMnSb films exhibit a coexistence of two magnetic phases, coherently intertwined in nanometric scales. The dominant $\alpha$~phase is half-Heusler cubic antiferromagnet with the N\'{e}el temperature of 62~K, the equilibrium stru

Boje Deforce, Bart Baesens, Estefanía Serral Asensio

The recent surge in foundation models for natural language processing and computer vision has fueled innovation across various domains. Inspired by this progress, we explore the potential of foundation models for time-series forecasting in smart agriculture, a field often plagued by limited data availability. Specifically, this work presents a novel applicat

Long-Bin Chen, Jian Wang, Yefan Wang

We present the analytic total cross section of top quark pair production in electron-positron annihilation at next-to-next-to-leading order (NNLO) in Quantum Chromodynamics (QCD). By utilizing the optical theorem, the NNLO corrections are related to the imaginary parts of three-loop self-energy Feynman diagrams, of which the master integrals are calculated w

Yushu Li, Yongyi Su, Xulei Yang, Kui Jia

Existing test-time adaptation (TTA) approaches often adapt models with the unlabeled testing data stream. A recent attempt relaxed the assumption by introducing limited human annotation, referred to as Human-In-the-Loop Test-Time Adaptation (HILTTA) in this study. The focus of existing HILTTA studies lies in selecting the most informative samples to label, a

Dario Izzo, Giacomo Acciarini, Francesco Biscani

We introduce a novel neural architecture termed thermoNET, designed to represent thermospheric density in satellite orbital propagation using a reduced amount of differentiable computations. Due to the appearance of a neural network on the right-hand side of the equations of motion, the resulting satellite dynamics is governed by a NeuralODE, a neural Ordina

Huaiwu Zhang, Yutong Xia, Siru Zhong, Kun Wang

The increasing number of vehicles highlights the need for efficient parking space management. Predicting real-time Parking Availability (PA) can help mitigate traffic congestion and the corresponding social problems, which is a pressing issue in densely populated cities like Singapore. In this study, we aim to collectively predict future PA across Singapore

Giacomo Acciarini, Nicola Baresi, David J. B. Lloyd, Dario Izzo

Numerical continuation techniques are powerful tools that have been extensively used to identify particular solutions of nonlinear dynamical systems and enable trajectory design in chaotic astrodynamics problems such as the Circular Restricted Three-Body Problem. However, the applicability of equilibrium points and periodic orbits may be questionable in real

Lucrezia Guiotto Nai Fovino, Anita Zanella, Luca Di Mascolo, Michele Ginolfi

Science, Technology, Engineering, and Mathematics classes are mainly taught using visual supports. However, the advancement of technology and the increasing eforts to equip schools with digital instrumentation have opened up the possibility of exploring new teaching avenues, such as sonifcation. We explored the efcacy of sonifcation in education using a nove

Toshiya Iwata, Kouichi Hagino, Hirokazu Odaka, Tsubasa Tamba

We are developing an imaging polarimeter by combining a fine-pixel CMOS image sensor with a coded aperture mask as part of the cipher project, aiming to achieve X-ray polarimetry in the energy range of $10$$\unicode{x2013}$$30~\mathrm{keV}$. A successful proof-of-concept experiment was conducted using a fine-pixel CMOS sensor with a $2.5~\mathrm{\mu m}$ pixe

Chenze Shao, Fandong Meng, Yijin Liu, Jie Zhou

Language generation based on maximum likelihood estimation (MLE) has become the fundamental approach for text generation. Maximum likelihood estimation is typically performed by minimizing the log-likelihood loss, also known as the logarithmic score in statistical decision theory. The logarithmic score is strictly proper in the sense that it encourages hones

Ateeb Naseer, Achintya Priydarshi, Pritam Ghosh, Raihan Ahammed

Two-dimensional ferroelectric monolayers are promising candidates for compact memory devices and flexible electronics. Here, through first-principles calculations, we predict room temperature ferroelectricity in AB-type monolayers comprising group III (A = Al, In, Ga) and group V (B = As, P, Sb) elements. We show that their spontaneous polarization, oriented

Boštjan Brešar, Jasmina Ferme, Přemysl Holub, Marko Jakovac

For a non-decreasing sequence $S=(s_1,s_2,\ldots)$ of positive integers, a partition of the vertex set of a graph $G$ into subsets $X_1,\ldots, X_\ell$, such that vertices in $X_i$ are pairwise at distance greater than $s_i$ for every $i\in\{1,\ldots,\ell\}$, is called an $S$-packing $\ell$-coloring of $G$. The minimum $\ell$ for which $G$ admits an $S$-pack

Yuni Susanti, Nina Holsmoelle

This study explores the capability of Large Language Models (LLMs) to evaluate causality in causal graphs generated by conventional statistical causal discovery methods-a task traditionally reliant on manual assessment by human subject matter experts. To bridge this gap in causality assessment, LLMs are employed to evaluate the causal relationships by determ

Laurent Beauregard, Dario Izzo, Giacomo Acciarini

In this study, we investigate trajectories involving multiple impulses within the framework of a generic spacecraft dynamics. Revisiting the age-old query of "How many impulses?", we present novel manipulations heavily leveraging on the properties ofthe state transition matrix. Surprisingly, we are able to rediscover classical results leading to the introduc

Filippo Palomba, Andrea Pugnana, José Manuel Alvarez, Salvatore Ruggieri

Deferring systems extend supervised Machine Learning (ML) models with the possibility to defer predictions to human experts. However, evaluating the impact of a deferring strategy on system accuracy is still an overlooked area. This paper fills this gap by evaluating deferring systems through a causal lens. We link the potential outcomes framework for causal

Armand Leclerc, Guillaume Laibe, Nicolas Perez

Context: Discrete symmetries have found numerous applications in photonics and quantum mechanics, but remain little studied in fluid mechanics, particularly in astrophysics. Aims: We aim to show how PT and anti-PT symmetries determine the behaviour of linear perturbations in a wide class of astrophysical problems. They set the location of Exceptional Points

Abdul Aziz A. B, A. B Abdul Rahim

This research presents a comprehensive assessment of pan-sharpening techniques for satellite imagery, focusing on the critical aspects of spectral fidelity and spatial enhancement. Motivated by the need for informed algorithm selection in remote sensing, A novel cascaded and structured evaluation framework has been proposed with a detailed comparative analys

Casper Farret Jentink, Francesco Pepe, Christophe Lovis, Sébastien Bovay

This paper provides a comprehensive overview of the subsystems of the NIGHT instrument. NIGHT (the Near Infrared Gatherer of Helium Transits) is a narrowband, high-resolution spectrograph, marking the first dedicated survey instrument for exoplanetary atmosphere observations. Developed through a collaboration between the Observatory of Geneva and the Univers

Laurent Baulieu, Tom Wetzstein

This paper develops an enlarged BRST framework to treat the large gauge transformations of a given quantum field theory. It determines the associated infinitely many Noether charges stemming from a gauge fixed and BRST invariant Lagrangian, a result that cannot be obtained from Noether's second theorem. The geometrical significance of this result is highligh

Junjie Wang, Guangjing Yang, Wentao Chen, Huahui Yi

In response to the challenges posed by the extensive parameter updates required for full fine-tuning of large-scale pre-trained models, parameter-efficient fine-tuning (PEFT) methods, exemplified by Low-Rank Adaptation (LoRA), have emerged. LoRA simplifies the fine-tuning process but may still struggle with a certain level of redundancy in low-rank matrices

Julia Reuter, Viktor Martinek, Roland Herzog, Sanaz Mostaghim

When developing empirical equations, domain experts require these to be accurate and adhere to physical laws. Often, constants with unknown units need to be discovered alongside the equations. Traditional unit-aware genetic programming (GP) approaches cannot be used when unknown constants with undetermined units are included. This paper presents a method for

Vladimir Petrov Kostov

We consider polynomials $Q:=\sum _{j=0}^da_jx^j$, $a_j\in \mathbb{R}^*$, with all roots real. When the {\em sign pattern} $\sigma (Q):=({\rm sgn}(a_d),{\rm sgn}(a_{d-1})$, $\ldots$, ${\rm sgn}(a_0))$ has $\tilde{c}$ sign changes, the polynomial $Q$ has $\tilde{c}$ positive and $d-\tilde{c}$ negative roots. We suppose the moduli of these roots distinct. The {

Soyed Tuhin Ahmed, Mehdi Tahoori

The performance of deep learning algorithms such as neural networks (NNs) has increased tremendously recently, and they can achieve state-of-the-art performance in many domains. However, due to memory and computation resource constraints, implementing NNs on edge devices is a challenging task. Therefore, hardware accelerators such as computation-in-memory (C

Aleksandar Mikovic

We show how the path integral for gravity and matter on a piecewise flat spacetime can be used to define the physical quantum gravity states and the related transition amplitudes. The physical states are given by the path integrals for open manifolds from a certain topological class, while the corresponding transition amplitudes are obtained by gluing two su

Anzhong Hu, Lise Aabel, Giuseppe Durisi, Sven Jacobsson

We analyze the uplink performance of a distributed massive multiple-input multiple-output (MIMO) architecture in which the remotely located access points (APs) are connected to a central processing unit via a fiber-optical fronthaul carrying a dithered and 1-bit quantized version of the received radio-frequency (RF) signal. The innovative feature of the prop

Bruno Deprez, Toon Vanderschueren, Bart Baesens, Tim Verdonck

Money laundering presents a pervasive challenge, burdening society by financing illegal activities. The use of network information is increasingly being explored to effectively combat money laundering, given it involves connected parties. This led to a surge in research on network analytics for anti-money laundering (AML). The literature is, however, fragmen

Zhilong Song, Linfeng Fan, Shuaihua Lu, Qionghua Zhou

Directly generating material structures with optimal properties is a long-standing goal in material design. One of the fundamental challenges lies in how to overcome the limitation of traditional generative models to efficiently explore the global chemical space rather than a small localized space. Herein, we develop a framework named MAGECS to address this

Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu

In federated learning (FL), the multi-step update and data heterogeneity among clients often lead to a loss landscape with sharper minima, degenerating the performance of the resulted global model. Prevalent federated approaches incorporate sharpness-aware minimization (SAM) into local training to mitigate this problem. However, the local loss landscapes may

Roman Denkin

This report investigates the perceptions of teaching staff on the prevalence of student cheating and the impact of Generative AI on academic integrity. Data was collected via an anonymous survey of teachers at the Department of Information Technology at Uppsala University and analyzed alongside institutional statistics on cheating investigations from 2004 to

Ruichang Zhang, Youcheng Sun, Mustafa A. Mustafa

Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns. Recent scholarly efforts have focused on developing battery-aided load-shaping techniques to protect user privacy while balancing costs. This paper pr

Fernando Fonseca, Maurício Sousa, Daniel Mendes, Alfredo Ferreira

Three-dimensional immersive sketching for content creation and modeling has been studied for some time. However, research in this domain mainly focused on CAVE-like scenarios. These setups can be expensive and offer a narrow interaction space. Building more affordable setups using head-mounted displays is possible, allowing greater immersion and a larger spa

Rajarshi Saha, Naomi Sagan, Varun Srivastava, Andrea J. Goldsmith

The prohibitive sizes of Large Language Models (LLMs) today make it difficult to deploy them on memory-constrained edge devices. This work introduces $\rm CALDERA$ -- a new post-training LLM compression algorithm that harnesses the inherent low-rank structure of a weight matrix $\mathbf{W}$ by approximating it via a low-rank, low-precision decomposition as $

Serge Bouc, Ivo Dell'Ambrogio, Rubén Martos

In equivariant topology, Greenlees and May used Mackey functors to show that, rationally, the stable homotopy category of $G$-spectra over a finite group $G$ splits as a product of simpler module categories. We extend the algebraic part (also independently proved by Th\'evenaz and Webb) of this classical result to Mackey modules over an arbitrary Green funct

Manoj K. Joshi, Milena Guevara-Bertsch, Florian Kranzl, Rainer Blatt

The oscillating magnetic field produced by unbalanced currents in radio-frequency ion traps induces transition frequency shifts and sideband transitions that can be harmful to precision spectroscopy experiments. Here, we describe a methodology, based on two-photon spectroscopy, for determining both the strength and direction of rf-induced magnetic fields wit

Yuguang Yang, Runtang Guo, Sheng Wu, Yimi Wang

Interpreting complex deep networks, notably pre-trained vision-language models (VLMs), is a formidable challenge. Current Class Activation Map (CAM) methods highlight regions revealing the model's decision-making basis but lack clear saliency maps and detailed interpretability. To bridge this gap, we propose DecomCAM, a novel decomposition-and-integration me

Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang, Mingyi Hong

In this work, we focus on the alignment problem of diffusion models with a continuous reward function, which represents specific objectives for downstream tasks, such as increasing darkness or improving the aesthetics of images. The central goal of the alignment problem is to adjust the distribution learned by diffusion models such that the generated samples

Yiting Dong, Xiang He, Guobin Shen, Dongcheng Zhao

Dynamic Vision Sensors (DVS) capture event data with high temporal resolution and low power consumption, presenting a more efficient solution for visual processing in dynamic and real-time scenarios compared to conventional video capture methods. Event data augmentation serve as an essential method for overcoming the limitation of scale and diversity in even

Andrea Esposito

SERENE (uSer ExpeRiENce dEtector), also known as UX-SAD (User eXperience-Smells Automatic Detector), is a research project born in 2020, which comprises different components. As its name suggests, its primary goal is to provide a way to quickly and (semi-) automatically detect problems in the user experience of websites and web-based systems. Through a set o

Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo

Graph Neural Networks (GNNs) have advanced spatiotemporal forecasting by leveraging relational inductive biases among sensors (or any other measuring scheme) represented as nodes in a graph. However, current methods often rely on Recurrent Neural Networks (RNNs), leading to increased runtimes and memory use. Moreover, these methods typically operate within 1

Julia Jentsch, Ali Burak Ünal, Şeyma Selcan Mağara, Mete Akgün

Handling missing data is crucial in machine learning, but many datasets contain gaps due to errors or non-response. Unlike traditional methods such as listwise deletion, which are simple but inadequate, the literature offers more sophisticated and effective methods, thereby improving sample size and accuracy. However, these methods require accessing the whol

Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo

Processing multidomain data defined on multiple graphs holds significant potential in various practical applications in computer science. However, current methods are mostly limited to discrete graph filtering operations. Tensorial partial differential equations on graphs (TPDEGs) provide a principled framework for modeling structured data across multiple in

Johnnatan Messias Peixoto Afonso

Blockchains revolutionized centralized sectors like banking and finance by promoting decentralization and transparency. In a blockchain, information is transmitted through transactions issued by participants or applications. Miners crucially select, order, and validate pending transactions for block inclusion, prioritizing those with higher incentives or fee

Tom Bewley, Salim I. Amoukou, Saumitra Mishra, Daniele Magazzeni

We introduce T-CREx, a novel model-agnostic method for local and global counterfactual explanation (CE), which summarises recourse options for both individuals and groups in the form of human-readable rules. It leverages tree-based surrogate models to learn the counterfactual rules, alongside 'metarules' denoting their regions of optimality, providing both a

Erwan Escudie, Matthia Sabatelli, Jilles Dibangoye

A recent method for solving zero-sum partially observable stochastic games (zs-POSGs) embeds the original game into a new one called the occupancy Markov game. This reformulation allows applying Bellman's principle of optimality to solve zs-POSGs. However, improving a current solution requires solving a linear program with exponentially many potential constr

Riccardo Rende, Luciano Loris Viteritti

The dot product attention mechanism, originally designed for natural language processing tasks, is a cornerstone of modern Transformers. It adeptly captures semantic relationships between word pairs in sentences by computing a similarity overlap between queries and keys. In this work, we explore the suitability of Transformers, focusing on their attention me

Carter T. Butts

The biased net paradigm was the first general and empirically tractable scheme for parameterizing complex patterns of dependence in networks, expressing deviations from uniform random graph structure in terms of latent ``bias events,'' whose realizations enhance reciprocity, transitivity, or other structural features. Subsequent developments have introduced

Chiara Ferri

The entry of new technological infrastructures into the financial markets poses serious concerns about the misuse of the economic system for illicit purposes, such as money laundering and financing of terrorism. Although there are cases in which this connection has already been discovered by malicious actors, distributed ledger technologies can nevertheless

Qizhou Chen, Qing Shao

Convolutional Neural Networks (CNNs) have been widely employed for image Super-Resolution (SR) in recent years. Various techniques enhance SR performance by altering CNN structures or incorporating improved self-attention mechanisms. Interestingly, these advancements share a common trait. Instead of explicitly learning high-frequency details, they learn an i

Daniele Dell'Erba, Yong Li, Sven Schewe

We propose DFAMiner, a passive learning tool for learning minimal separating deterministic finite automata (DFA) from a set of labelled samples. Separating automata are an interesting class of automata that occurs generally in regular model checking and has raised interest in foundational questions of parity game solving. We first propose a simple and linear

Winnie Street, John Oliver Siy, Geoff Keeling, Adrien Baranes

This paper examines the extent to which large language models (LLMs) have developed higher-order theory of mind (ToM); the human ability to reason about multiple mental and emotional states in a recursive manner (e.g. I think that you believe that she knows). This paper builds on prior work by introducing a handwritten test suite -- Multi-Order Theory of Min

Vid Hanžel, Blaž Bertalanič, Carolina Fortuna

Due to growing population and technological advances, global electricity consumption, and consequently also CO2 emissions are increasing. The residential sector makes up 25% of global electricity consumption and has great potential to increase efficiency and reduce CO2 footprint without sacrificing comfort. However, a lack of uniform consumption data at the

Soham Sen, Sunandan Gangopadhyay, Sukanta Bhattacharyya

In this work, we consider a general gravitational wave detector of gravitational wave interacting with an incoming gravitational wave carrying plus polarization only placed inside a harmonic trap. This model can be well acquainted with the description of a resonant detector of gravitational wave as well. The well known detector-gravitational wave interaction

Abdul Aziz A. B, A. B Abdul Rahim

Recent strides in multimodal model development have ignited a paradigm shift in the realm of text-to-image generation. Among these advancements, CLIP stands out as a remarkable achievement which is a sophisticated autoencoder adept at encoding both textual and visual information within a unified latent space. This paper delves into a comparative analysis bet

Matthias Bentert, Leon Kellerhals, Rolf Niedermeier

We study the parameterized complexity of finding shortest s-t-paths with an additional fairness requirement. The task is to compute a shortest path in a vertex-colored graph where each color appears (roughly) equally often in the solution. We provide a complete picture of the parameterized complexity landscape of the problem with respect to structural parame

Ryszard Deszcz, Małgorzata Głogowska, Jan Jełowicki, Miroslava Petrović-Torgašev

Let (M,g) be a 2-quasi-Einstein non-conformally flat semi-Riemannian manifold of dimension > 3. We prove that if its Riemann-Christoffel curvature tensor R is a linear combination of some Kulkarni-Nomizu tensors formed by the metric tensor g, the Ricci tensor S and its square S^2, then some pseudosymmetry type curvature conditions are satisfied. Certain non-

Pier Luigi Silvestrelli, Matteo Tessarolo, Abdolvahab Seif, Alberto Ambrosetti

Recently we investigated from first principles screening properties in systems where small molecules, characterized by a finite electronic dipole moment, are encapsulated into different nanocages. The most relevant result was the observation of an antiscreening effect in alkali-halide nanocages characterized by ionic bonds. Here we extend the study to anothe

Zijie Jiang, Yusuke Monno, Masatoshi Okutomi, Sho Suzuki

Enabling the synthesis of arbitrarily novel viewpoint images within a patient's stomach from pre-captured monocular gastroscopic images is a promising topic in stomach diagnosis. Typical methods to achieve this objective integrate traditional 3D reconstruction techniques, including structure-from-motion (SfM) and Poisson surface reconstruction. These methods

Simon Brezovnik, Zhongyuan Che, Niko Tratnik, Petra Žigert Pleteršek

Let $G$ be a plane elementary bipartite graph whose infinite face is forcing. We provide a bijection between the set of maximal hypercubes of its resonance graph and the set of maximal resonant sets of $G$, which generalizes a main result in [MATCH Commun. Math. Comput. Chem. 68 (2012) 65-77], where $G$ was only considered as an elementary benzenoid graph wi

Ruipeng Zhang, Ziqing Fan, Jiangchao Yao, Ya Zhang

This paper presents a Domain-Inspired Sharpness-Aware Minimization (DISAM) algorithm for optimization under domain shifts. It is motivated by the inconsistent convergence degree of SAM across different domains, which induces optimization bias towards certain domains and thus impairs the overall convergence. To address this issue, we consider the domain-level

Tianle Zhang, Dongjiang Li, Yihang Li, Zecui Zeng

The advancements in embodied AI are increasingly enabling robots to tackle complex real-world tasks, such as household manipulation. However, the deployment of robots in these environments remains constrained by the lack of comprehensive bimanual-mobile robot manipulation data that can be learned. Existing datasets predominantly focus on single-arm manipulat

Dominik Semmler, Benedikt Fesl, Michael Joham, Wolfgang Utschick

We compare dirty paper coding (DPC) and linear precoding methods in a reconfigurable intelligent surface (RIS)- aided high-signal-to-noise ratio (SNR) scenario, where the channel between the base station (BS) and the RIS is dominated by a line-of-sight (LOS) component. Furthermore, we consider two groups of users where one group can be efficiently served by

Yutong He, Jie Hu, Xinmeng Huang, Songtao Lu

Stochastic bilevel optimization tackles challenges involving nested optimization structures. Its fast-growing scale nowadays necessitates efficient distributed algorithms. In conventional distributed bilevel methods, each worker must transmit full-dimensional stochastic gradients to the server every iteration, leading to significant communication overhead an

Yiming Cui, Cheng Han, Dongfang Liu

Visual-based perception is the key module for autonomous driving. Among those visual perception tasks, video object detection is a primary yet challenging one because of feature degradation caused by fast motion or multiple poses. Current models usually aggregate features from the neighboring frames to enhance the object representations for the task heads to

Jiahui Xin, Hanzhong Liu, Wei Ma

Covariate-adaptive randomization is widely employed to balance baseline covariates in interventional studies such as clinical trials and experiments in development economics. Recent years have witnessed substantial progress in inference under covariate-adaptive randomization with a fixed number of strata. However, concerns have been raised about the impact o

Dhaneesh Kumar, Cornelius Krull, Yuefeng Yin, Nikhil V. Medhekar

Quantum dots (QD) with electric-field-controlled charge state are promising for electronics applications, e.g., digital information storage, single-electron transistors and quantum computing. Inorganic QDs consisting of semiconductor nanostructures or heterostructures often offer limited control on size and composition distribution, as well as low potential

Futoshi Takahashi

In this paper, we study the asymptotic behavior of radial solutions for several weighted elliptic equations with power type or exponential type nonlinearities on an annulus.

Chuanbiao Song, Yan Hong, Jun Lan, Huijia Zhu

This study reveals a cutting-edge re-balanced contrastive learning strategy aimed at strengthening face anti-spoofing capabilities within facial recognition systems, with a focus on countering the challenges posed by printed photos, and highly realistic silicone or latex masks. Leveraging the HySpeFAS dataset, which benefits from Snapshot Spectral Imaging te

Nikhil Gosala, Kürsat Petek, B Ravi Kiran, Senthil Yogamani

Semantic Bird's Eye View (BEV) maps offer a rich representation with strong occlusion reasoning for various decision making tasks in autonomous driving. However, most BEV mapping approaches employ a fully supervised learning paradigm that relies on large amounts of human-annotated BEV ground truth data. In this work, we address this limitation by proposing t

Xinghu Jin, Guodong Pang, Yu Wang, Lihu Xu

Piecewise $\alpha$-stable Ornstein-Uhlenbeck (OU) processes arising in queue networks usually do not have an explicit dissipation, which makes the related numerical methods such as Euler-Maruyama (EM) scheme more difficult to analyze. We develop an EM scheme with decreasing step size $\Lambda=(\eta_n)_{n\in \mathbb{N}}$ to approximate their ergodic measures.