April 2024 arXiv papers — page 166

Andrej Kolar-Požun, Mitja Jančič, Gregor Kosec

Radial Basis Function-generated Finite Differences (RBF-FD) is a meshless method that can be used to numerically solve partial differential equations. The solution procedure consists of two steps. First, the differential operator is discretised on given scattered nodes and afterwards, a global sparse matrix is assembled and inverted to obtain an approximate

Alp Eren Sari, Francesco Locatello, Paolo Favaro

We present two practical improvement techniques for unsupervised segmentation learning. These techniques address limitations in the resolution and accuracy of predicted segmentation maps of recent state-of-the-art methods. Firstly, we leverage image post-processing techniques such as guided filtering to refine the output masks, improving accuracy while avoid

Banik Rai, Sandip Kumar Kuila, Rana Saha, Sankalpa Hazra

Trigonal Cr$_5$Te$_8$, a self-intercalated van der Waals ferromagnet with an out-of-plane magnetic anisotropy, has long been known to crystallize in a centrosymmetric structure. However, optical second harmonic generation experiments, together with comprehensive structural analysis, indicate that this compound rather adopts a non-centrosymmetric structure. L

Sjoerd Stendahl, Naureen Ghafoor, Anton Zubayer, Marcus Lorentzon

State-of-the-art Ni/Ti supermirror neutron optics have limited reflected intensity and a restricted neutron energy range due to the interface width. Incorporating low-neutron-absorbing 11B4C enhances reflectivity and allows for thinner layers to be deposited, with which more efficient supermirrors with higher m-values can be realized. However, incorporating

Helvio Carvalho Junior

In the current digital security ecosystem, where threats evolve rapidly and with complexity, companies developing Endpoint Detection and Response (EDR) solutions are in constant search for innovations that not only keep up but also anticipate emerging attack vectors. In this context, this article introduces the HookChain, a look from another perspective at w

Alexander Hock, Johannes Thürigen

Matrix field theory is a combinatorially non-local field theory which has recently been found to be a non-trivial but solvable QFT example. To generalize such non-perturbative structures to other models, a more combinatorial understanding of Dyson-Schwinger equations and their solutions is of high interest. To this end we consider combinatorial Dyson-Schwing

Edgar Assing

In this note we prove a quantitative stability result for the $\epsilon$-factors associated to generic irreducible representations of $\textrm{GL}_n(F)$ under twists by highly ramified characters, where $F$ is a non-archimedean local field.

Benedict Schlüter, Supraja Sridhara, Mark Kuhne, Andrin Bertschi

Hardware-based Trusted execution environments (TEEs) offer an isolation granularity of virtual machine abstraction. They provide confidential VMs (CVMs) that host security-sensitive code and data. AMD SEV-SNP and Intel TDX enable CVMs and are now available on popular cloud platforms. The untrusted hypervisor in these settings is in control of several resourc

Kaichen Huang, Minghao Shao, Shenghua Wan, Hai-Hang Sun

In many real-world visual Imitation Learning (IL) scenarios, there is a misalignment between the agent's and the expert's perspectives, which might lead to the failure of imitation. Previous methods have generally solved this problem by domain alignment, which incurs extra computation and storage costs, and these methods fail to handle the \textit{hard cases

Naoki Sasakura

We compute the signed distribution of the eigenvalues/vectors of the complex order-three random tensor by computing a partition function of a four-fermi theory, where signs are from a Hessian determinant associated to each eigenvector. The issue of the presence of a continuous degeneracy of the eigenvectors is properly treated by a gauge-fixing. The final ex

Yuetian Weng, Mingfei Han, Haoyu He, Xiaojun Chang

Empowered by Large Language Models (LLMs), recent advancements in Video-based LLMs (VideoLLMs) have driven progress in various video understanding tasks. These models encode video representations through pooling or query aggregation over a vast number of visual tokens, making computational and memory costs affordable. Despite successfully providing an overal

Mitsuru Toyoda, Akatsuki Nishioka, Mirai Tanaka

This paper presents an Euler--Lagrange system for a continuous-time model of the accelerated gradient methods in smooth convex optimization and proposes an associated Lyapunov-function-based convergence analysis framework. Recently, ordinary differential equations (ODEs) with dumping terms have been developed to intuitively interpret the accelerated gradient

Yasin Abbasi Yadkori, Ilja Kuzborskij, David Stutz, András György

We develop a principled procedure for determining when a large language model (LLM) should abstain from responding (e.g., by saying "I don't know") in a general domain, instead of resorting to possibly "hallucinating" a non-sensical or incorrect answer. Building on earlier approaches that use self-consistency as a more reliable measure of model confidence, w

Kaichen Huang, Hai-Hang Sun, Shenghua Wan, Minghao Shao

Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications. In this work, we focus on the problem of Learning from Noisy Demonstrations (LND), where the imitator is required to learn from data with noise that often occurs during the processes of data collection or tra

Constanza Fierro, Reinald Kim Amplayo, Fantine Huot, Nicola De Cao

The increasing demand for the deployment of LLMs in information-seeking scenarios has spurred efforts in creating verifiable systems, which generate responses to queries along with supporting evidence. In this paper, we explore the attribution capabilities of plan-based models which have been recently shown to improve the faithfulness, grounding, and control

Cai Zhou, Rose Yu, Yusu Wang

Graph transformers have recently received significant attention in graph learning, partly due to their ability to capture more global interaction via self-attention. Nevertheless, while higher-order graph neural networks have been reasonably well studied, the exploration of extending graph transformers to higher-order variants is just starting. Both theoreti

T. Xylakis-Dornbusch, T. T. Hansen, T. C. Beers, N. Christlieb

Context. In recent years, the R-Process Alliance (RPA) has conducted a successful search for stars enhanced in elements produced by the rapid neutron-capture (r-)process. In particular, the RPA has uncovered a number of stars strongly enriched in light r-process elements, such as Sr, Y and Zr, the so-called limited-r stars, in order to investigate the astrop

Qianqian Kang, Der-Chen Chang, Wei Wang

In this paper, we introduce the spectral projection operators $\mathbb{P}_m$ on non-degenerate nilpotent Lie groups $\mathcal{N}$ of step two, associated to the joint spectrum of sub-Laplacian and derivatives in step two. We construct their kernels $P_m(\mathbf{y},\mathbf{t})$ by using Laguerre calculus and find a simple integral representation formula for $

Oskar Fiuk, Emanuel Kieronski

The uniform one-dimensional fragment of first-order logic was introduced a few years ago as a generalization of the two-variable fragment to contexts involving relations of arity greater than two. Quantifiers in this logic are used in blocks, each block consisting only of existential quantifiers or only of universal quantifiers. In this paper we consider the

A. Paliwal, W. Cui, D. de Andrés, M. De Petris

The abundance of galaxy clusters with mass and redshift is a well-known cosmological probe. The cluster mass is a key parameter for studies that aim to constrain cosmological parameters using galaxy clusters, making it critical to understand and properly account for the errors in its estimates. Subsequently, it becomes important to correctly calibrate scalin

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A search for the fully reconstructed $B_s^0 \rightarrow \mu^+\mu^-\gamma$ decay is performed at the LHCb experiment using proton-proton collisions at $\sqrt{s}=13$\,TeV corresponding to an integrated luminosity of $5.4\,\mathrm{fb^{-1}}$. No significant signal is found and upper limits on the branching fraction in intervals of the dimuon mass are set \begin{

Spyridon Chavlis, Panayiota Poirazi

Artificial neural networks (ANNs) are at the core of most Deep learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. However, unlike biological brains who tackle similar problems in a very efficient manner, DL algorithms require a large number of trainable parameters, m

Yun Shi, Wei Wang, Qingyan Wu

A monogenic function of two vector variables is a function annihilated by the operator consisting of two Dirac operators, which are associated to two variables, respectively. We give the explicit form of differential operators in the Dirac complex resolving this operator and prove its ellipticity directly. This open the door to apply the method of several co

M. Cristina Câmara, Gabriel Lopes Cardoso

The Riemann-Hilbert approach, in conjunction with the canonical Wiener-Hopf factorisation of certain matrix functions called monodromy matrices, enables one to obtain explicit solutions to the non-linear field equations of some gravitational theories. These solutions are encoded in the elements of a matrix $M$ depending on the Weyl coordinates $\rho$ and $v$

Jiacai Liu, Wenye Li, Ke Wei

Projected policy gradient under the simplex parameterization, policy gradient and natural policy gradient under the softmax parameterization, are fundamental algorithms in reinforcement learning. There have been a flurry of recent activities in studying these algorithms from the theoretical aspect. Despite this, their convergence behavior is still not fully

Kazuhiro Sato, Masato Suzuki

We address the problem of finding the nearest graph Laplacian to a given matrix, with the distance measured using the Frobenius norm. Specifically, for the directed graph Laplacian, we propose two novel algorithms by reformulating the problem as convex quadratic optimization problems with a special structure: one based on the active set method and the other

Goro Akagi, Ulisse Stefanelli, Riccardo Voso

We investigate the Weighted Energy-Dissipation variational approach to semilinear gradient flows with state-dependent dissipation. A family of parameter-dependent functionals defined over entire trajectories is introduced and proved to admit global minimizers. These global minimizers correspond to solutions of elliptic-in-time regularizations of the limiting

ES Buhl, E Maae, LW Matthiessen, MH Nielsen

The aim of this study was to investigate the difference in output, when training a model in three different scenarios: a large clinical delineated data set (with 700/78 patients for training/testing, from the Danish Breast Cancer Group (DBCG) RT Nation Study), a clinical but curated dataset (with 328/36 patients for training/testing, from the DBCG RT Nation

Raffael Theiler, Olga Fink

Pumped-storage hydropower plants (PSH) actively participate in grid power-frequency control and therefore often operate under dynamic conditions, which results in rapidly varying system states. Predicting these dynamically changing states is essential for comprehending the underlying sensor and machine conditions. This understanding aids in detecting anomali

Jacquiline Romero, Gerard Milburn

Photonic quantum computation refers to quantum computation that uses photons as the physical system for doing the quantum computation. The field is largely divided between discrete-variable (DV) and continuous-variable (CV) photonic quantum computation. In the former, quantum information is represented by one or more modal properties (e.g. polarisation) that

Jesus M. Alvarez Llorente, Vicente P. Guerrero-Bote, Felix de Moya-Anegon

This paper presents and evaluates a set of methods to classify individual Scopus publications using their references back to the second generation, where each publication can be assigned fractionally into up to five ASJC (All Science Journal Classifications) categories, excluding the Multidisciplinary area and the miscellaneous categories. Based on proposals

Dijana Milosavljević, Helge Rosner, Annika Johansson

We present a detailed theoretical study of the electronic structure of hafnium tin HfSn crystallizing in a B$20$ structure, renowned for the diversity of physical and peculiar topological properties. The chiral crystal structure of these materials protects multifold band crossings located at high symmetry points. We employ density functional methods to revea

H. V. Ovcharenko, O. B. Zaslavskii

We consider head-on collisions of two particles near the event horizon. Particle 1 is outgoing, particle 2 is ingoing. We elucidate, in which case the energy $E_{c.m.}$ in the center of mass frame can grow unbounded. If the proper time between the horizon and an arbitrary point outside it for particle 1 is finite, we deal with a white hole. If it is infinite

Shamas Ul Ebad Khan, Rejin John Varghese, Panagiotis Kassanos, Dario Farina

Space poses significant challenges for humans, leading to physiological adaptations in response to an environment vastly different from Earth. A comprehensive understanding of these physiological adaptations is needed to devise effective countermeasures to support human life in space. This narrative review first focuses on the impact of the environment in sp

M. Koussour, N. Myrzakulov, M. K. M. Ali

In this article, we introduce an innovative parametric representation of the Hubble parameter, providing a model-independent means to explore the dynamics of an accelerating cosmos. The model's parameters are rigorously constrained through a Markov Chain Monte Carlo (MCMC) approach, leveraging a comprehensive dataset consisting of 31 data points from cosmic

Nicolay Rusnachenko, Huizhi Liang

Emotion expression is one of the essential traits of conversations. It may be self-related or caused by another speaker. The variety of reasons may serve as a source of the further emotion causes: conversation history, speaker's emotional state, etc. Inspired by the most recent advances in Chain-of-Thought, in this work, we exploit the existing three-hop rea

William J. Leigh

Requiring physical consistency in a classical flat spacetime geometrisation of fermions is shown to suggest the introduction of torsion. A resulting simple model for that torsion produces a localised quantum-like particle as a solution of a spinor identity that closely resembles the Dirac equation of quantum electrodynamics. All relevant integrals involving

Philipp Altmann, Céline Davignon, Maximilian Zorn, Fabian Ritz

To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT). In contrast to the prevalent practice of validating RL models based on their optimal behavior learned during training, we posit that considering a range of edge-case trajectories provides a more comprehensive understandin

Zechun Niu, Zhilin Zhang, Jiaxin Mao, Qingyao Ai

Counterfactual learning to rank (CLTR) has attracted extensive attention in the IR community for its ability to leverage massive logged user interaction data to train ranking models. While the CLTR models can be theoretically unbiased when the user behavior assumption is correct and the propensity estimation is accurate, their effectiveness is usually empiri

Arnau Dòria-Cerezo, Pau Boira, Víctor Repecho, Domingo Biel

This paper presents two methods for implementing complex-valued sliding mode controllers in three-phase power converters. The paper includes the description of the algorithms and a detailed analysis of the proposed implementations. The methods, that are easy to code and have a low computational burden, retain the sliding mode properties of robustness and fas

G. Moza, O. Brandibur, E. A. Kokovics, L. F. Vesa

Generic results for degenerate Chenciner (generalized Neimark-Sacker) bifurcation are obtained in the present work. The bifurcation arises in two-dimensional discrete-time systems with two independent parameters. We define in this work a new transformation of parameters, which enables the study of the bifurcation when the degeneracy occurs. By the four bifur

Rebekah Rousi

This study set out to examine the relationship between expressed social emotions (i.e. that what people say they are feeling) and physical sensations, the connection between emotion and bodily experience. It additionally provided the opportunity to investigate how the neurological findings of gender differences can be observed in practice, what difference do

Bin Liu, Julio H. Braslavsky

Robust dynamic operating envelopes (RDOEs) solve the problem of secure allocation of latent network capacity to flexible distributed energy resources (DER) in unbalanced distribution networks. As the computational complexity of RDOEs is much higher than that of dynamic operating envelopes (DOEs), which disregard uncertainties in network parameters and DER ca

Xubin Ren, Wei Wei, Lianghao Xia, Chao Huang

Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep learning techniques, such as RNNs, GNNs, and Transformer architectures, have significantly propelled the advancement of recommender systems by enhancing their comprehension of user beh

Pol G. Recasens, Yue Zhu, Chen Wang, Eun Kyung Lee

Large language models (LLMs) have revolutionized the state-of-the-art of many different natural language processing tasks. Although serving LLMs is computationally and memory demanding, the rise of Small Language Models (SLMs) offers new opportunities for resource-constrained users, who now are able to serve small models with cutting-edge performance. In thi

Maximilian Mahlein, Chiara Pinto, Laura Fabbietti

The study of antinuclei in cosmic rays provides a unique opportunity to probe physics beyond the Standard Model. Antinuclei in our Galaxy may stem either from annihilation or decay of dark matter, or from collisions of cosmic rays with the interstellar medium, which constitute the background of indirect dark matter searches. Understanding the formation mecha

Peng-Fei Han, Ru-Suo Ye, Yi Zhang

The challenge of solving the initial value problem for the coupled Lakshmanan Porsezian Daniel equation, while considering nonzero boundary conditions at infinity, is addressed through the development of a suitable inverse scattering transform. Analytical properties of the Jost eigenfunctions are examined, along with the analysis of scattering coefficient ch

Hossein Askari, Fred Roosta, Hongfu Sun

In the realm of medical imaging, inverse problems aim to infer high-quality images from incomplete, noisy measurements, with the objective of minimizing expenses and risks to patients in clinical settings. The Diffusion Models have recently emerged as a promising approach to such practical challenges, proving particularly useful for the zero-shot inference o

G. Moza, S. Lugojan, L. Ciurdariu

Degenerate Chenciner bifurcation in generic discrete-time dynamical systems is studied in this work. While the non-degenerate Chenciner bifurcation can be described by 2 bifurcation diagrams, the degeneracy we studied in this work gives rise to 32 different bifurcation diagrams.

Leo Segre, Shai Avidan

3D scene registration is a fundamental problem in computer vision that seeks the best 6-DoF alignment between two scenes. This problem was extensively investigated in the case of point clouds and meshes, but there has been relatively limited work regarding Neural Radiance Fields (NeRF). In this paper, we consider the problem of rigid registration between two

Fatima Ezzeddine, Omran Ayoub, Silvia Giordano

In recent years, there has been a notable increase in the deployment of machine learning (ML) models as services (MLaaS) across diverse production software applications. In parallel, explainable AI (XAI) continues to evolve, addressing the necessity for transparency and trustworthiness in ML models. XAI techniques aim to enhance the transparency of ML models

Armand Despons

Autocatalysis, the ability of a chemical system to make more of itself, is a crucial feature in metabolism and is speculated to have played a decisive role in the origin of life. Nevertheless, how autocatalytic systems behave far from equilibrium remains unexplored. In this work, we elaborate on recent advances regarding the stoichiometric characterization o

Ángel Felipe, María Jaenada, Pedro Miranda, Leandro Pardo

In this paper we introduce a new family of estimators for the parameters of shape and scale of the log-logistic distribution being robust when rank set sample method is used to select the data. Rank set sampling arises as a way to reduce the impact of extremal data. Log-logistic distribution is an important distribution suitable for modeling many different s

Aneta Wojnar, Débora Aguiar Gomes

We examine the relationship between Palatini-like theories of gravity and models incorporating linear generalized uncertainty principles. Additionally, we delve into the thermodynamics of systems comprising both Bose and Fermi gases. Our analysis encompasses the equations of state for various systems, including general Fermi gases, degenerate Fermi gases, Bo

Juri Opitz

The Area Under Curve measure (AUC) seems apt to evaluate and compare diverse models, possibly without calibration. An important example of AUC application is the evaluation and benchmarking of models that predict faithfulness of generated text. But we show that the AUC yields an academic and optimistic notion of accuracy that can misalign with the actual acc

Luigi Montoro, Luigi Muglia, Berardino Sciunzi

We provide the classification of all the positive solutions to $-\Delta u=\frac{1}{u^\gamma}$ in the half space, under minimal assumption.

Ivan Kovalyov, Stefan Kunis

We study a truncated two-dimensional moment problem in terms of the Stieltjes transform. The set of the solutions is described by the Schur step-by-step algorithm, which is based on the continued fraction expansion of the solution. In particular, the obtained results are applicable to the two-dimensional moment problem for atomic measures.

Rui Wang, Chuanfu Shen, Manuel J. Marin-Jimenez, George Q. Huang

Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigat

Piotr Pokora

In this note we focus on the defect of singular plane curve that was recently introduced by Dimca. Roughly speaking, the defect of a reduced plane curve measures the discrepancy from the property of being a free curve. We find some lower-bound on the defect for certain classes of irreducible plane curves admitting nodes, ordinary cusps and ordinary triple po

Ibtissam Issa, Abdelaziz Soufyane, Octavio Vera Villagran

In this paper, we examine the stability of a heat-conducting copper rod and a magnetizable piezoelectric beam, with fractional damping affecting the longitudinal displacement of the piezoelectric material's centerline. We establish a polynomial stability result that is dependent on the order of the fractional derivative.

Lei Zhang, Yuhang Zhou, Yi Yang, Xinbo Gao

Despite providing high-performance solutions for computer vision tasks, the deep neural network (DNN) model has been proved to be extremely vulnerable to adversarial attacks. Current defense mainly focuses on the known attacks, but the adversarial robustness to the unknown attacks is seriously overlooked. Besides, commonly used adaptive learning and fine-tun

Joan Giner-Miguelez, Abel Gómez, Jordi Cabot

Recent regulatory initiatives like the European AI Act and relevant voices in the Machine Learning (ML) community stress the need to describe datasets along several key dimensions for trustworthy AI, such as the provenance processes and social concerns. However, this information is typically presented as unstructured text in accompanying documentation, hampe

Huaqing Guan, Hanwen Cui, Ning Ding, Kuo Yang

Manipulating point defects for tailored macroscopic properties remains a formidable challenge in materials science. This study demonstrates a proof-of-principle for a universal law involving element Mn, significantly enhancing vacancy diffusion through an unprecedented anomalous Friedel Oscillations phenomenon, across most metals in the periodic table. The c

Umme Ruman, Md. Shafiqul Islam

To extract the approximate solutions in the case of nonlinear fractional order differential equations with the homogeneous and nonhomogeneous boundary conditions, the weighted residual method is embedded here. We exploit three methods such as Galerkin, Least Square, and Collocation for the efficient numerical solution of nonlinear two-point boundary value pr

Tanja Kojić, Maurizio Vergari, Simon Knuth, Maximilian Warsinke

Inside-out tracking is growing popular in consumer VR, enhancing accessibility. It uses HMD camera data and neural networks for effective hand tracking. However, limited user experience studies have compared this method to traditional controllers, with no consensus on the optimal control technique. This paper investigates the impact of control methods and ga

Asad Ali Shahid, Yashraj Narang, Vincenzo Petrone, Enrico Ferrentino

In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks. However, this comes at the cost of an enormous amount of experience required for training, exacerbated by the sensitivity of learning efficiency and the policy performance to hyperparameter selection, which often requires numerous trials

Vaibhav Kumar Jena, Abu Sufian

In this article, we study the uniform null controllability problem for a system of coupled parabolic equations with an oscillating coefficient. This is done in three steps -- first, we study the spectral properties of an elliptic operator; second, we allow the system to evolve freely and obtain the required decay; third, we use a Carleman estimate to prove a

David San Andrés, Víctor M. Rivilla, Laura Colzi, Izaskun Jiménez-Serra

We report the first detection in the interstellar medium of $N$-cyanomethanimine (H$_2$CNCN), the stable dimer of HCN of highest energy, and the most complex organic molecule identified in space containing the prebiotically relevant NCN backbone. We have identified a plethora of $a$-type rotational transitions with 3 $\leq J_\text{up} \leq$ 11 and $K_\text{a

Wei Zhong, Cong Yu, Shi Jia, Shang-Fei Liu

The planet's mass loss is important for the planet's formation and evolution. The radius valley (RV) is believed to be triggered by evaporation-induced mass loss. As an alternative mechanism for the RV, the mass loss of post-impact planets is thoroughly investigated in this work. The impact energy is converted to the planet's internal energy, enhancing its c

Vilhelm Agdur

Community detection in graphs is a problem that is likely to be relevant whenever network data appears, and consequently the problem has received much attention with many different methods and algorithms applied. However, many of these methods are hard to study theoretically, and they optimise for somewhat different goals. A general and rigorous account of t

Yan Yang, Bin Gao, Ya-xiang Yuan

Bilevel optimization, with broad applications in machine learning, has an intricate hierarchical structure. Gradient-based methods have emerged as a common approach to large-scale bilevel problems. However, the computation of the hyper-gradient, which involves a Hessian inverse vector product, confines the efficiency and is regarded as a bottleneck. To circu

Fabian A. Braeu, Stéphane Avril, Michaël J. A. Girard

$\bf{Purpose}$: To assess whether Growth & Remodeling (G&R) theory could explain staphyloma formation from a local scleral weakening. $\bf{Methods}$: A finite element model of a healthy eye was reconstructed, including the following connective tissues: the lamina cribrosa, the peripapillary sclera, and the peripheral sclera. The scleral shell was modelled as

J. Schmitt, C. Adami, M. Dennefeld, F. Agneray

MISTRAL is the new Faint Object Spectroscopic Camera mounted at the folded Cassegrain focus of the 1.93m telescope of Haute-Provence Observatory. We describe the design and components of the instrument and give some details about its operation. We emphasise in particular the various observing modes and the performances of the detector. A short description is

Juan Du, Yukun Xie, Chen Zhang

In modern manufacturing, most products are conforming. Few products are nonconforming with different defect types. The identification of defect types can help further root cause diagnosis of production lines. With the sensing technology development, process variables evolved as time changes, which can be collected in high resolution as multichannel functiona

Chen Yuan, Qing-Guo Huang

In the recent second part of the third observation run by the LIGO-Virgo-KAGRA collaboration, a candidate with sub-solar mass components was reported, which we labelled as SSM200308. This study investigates the premise that primordial black holes (PBHs), arising from Gaussian perturbation collapses, could explain SSM200308. Through Bayesian analysis, we obta

Shangquan Sun, Wenqi Ren, Jingyang Peng, Fenglong Song

Many existing methods for low-light image enhancement (LLIE) based on Retinex theory ignore important factors that affect the validity of this theory in digital imaging, such as noise, quantization error, non-linearity, and dynamic range overflow. In this paper, we propose a new expression called Digital-Imaging Retinex theory (DI-Retinex) through theoretica

Zixuan Yi, Xi Wang, Iadh Ounis

In real-world recommender systems, implicitly collected user feedback, while abundant, often includes noisy false-positive and false-negative interactions. The possible misinterpretations of the user-item interactions pose a significant challenge for traditional graph neural recommenders. These approaches aggregate the users' or items' neighbours based on im

Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Fakhreddine Zayer, Jorge Dias

Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies have been deployed in a wide range of applications, ranging from personal to industrial use-cases. However, curr

Stephen Meisenbacher, Nihildev Nandakumar, Alexandra Klymenko, Florian Matthes

The application of Differential Privacy to Natural Language Processing techniques has emerged in relevance in recent years, with an increasing number of studies published in established NLP outlets. In particular, the adaptation of Differential Privacy for use in NLP tasks has first focused on the $\textit{word-level}$, where calibrated noise is added to wor

Andrei Semenov, Vladimir Ivanov, Aleksandr Beznosikov, Alexander Gasnikov

We propose a novel architecture and method of explainable classification with Concept Bottleneck Models (CBMs). While SOTA approaches to Image Classification task work as a black box, there is a growing demand for models that would provide interpreted results. Such a models often learn to predict the distribution over class labels using additional descriptio

Colin Petitjean

In this short note, we prove that the set of elementary tensors is weakly closed in the projective tensor product of two Banach spaces. As a result, we are able to answer a question from the literature proving that if $(x_n) \subset X$ and $(y_n) \subset Y$ are two weakly null sequences such that $(x_n \otimes y_n)$ converges weakly in $X \widehat{\otimes}_\

Guangyuan Liu, Hongyang Du, Dusit Niyato, Jiawen Kang

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of

Abhishek Duttagupta, Jin Zhao, Shanker Shreejith

Federated Learning (FL) is a distributed learning scheme that enables deep learning to be applied to sensitive data streams and applications in a privacy-preserving manner. This paper focuses on the use of FL for analyzing smart energy meter data with the aim to achieve comparable accuracy to state-of-the-art methods for load forecasting while ensuring the p

Artem Kraevskiy, Artem Prokhorov, Evgeniy Sokolovskiy

Financial markets of emerging economies are vulnerable to extreme and cascading information spillovers, surges, sudden stops and reversals. With this in mind, we develop a new online early warning system (EWS) to detect what is referred to as `concept drift' in machine learning, as a `regime shift' in economics and as a `change-point' in statisti

Keizo Hasegawa, Hisashi Kasuya

In this paper we determine all the simply connected non-degenerate CR Lie groups, which are flat with respect to the Cartan connection: in terms of associated Lie algebras, we assert that the only Cartan flat non-degenerate CR Lie algebras are $\mathfrak{su}(2)$, $\mathfrak{sl}(2,\mathbb{R})$, $\mathfrak{aff}(\mathbb{R}) \oplus \mathbb{R}$, and $\mathfrak{h}

D. Sicilia, G. Scandariato, G. Guilluy, M. Esposito

HAT-P-67 b is one of the lowest-density gas giants known to date, making it an excellent target for atmospheric characterization through the transmission spectroscopy technique. In the framework of the GAPS large programme, we collected four transit events, with the aim of studying the exoplanet atmosphere and deriving the orbital projected obliquity. We exp

G. Moza, D. Constantinescu, R. Efrem, L. Bucur

A generalized two-dimensional cubic Lotka-Volterra model with infinitesimal parameters is studied. Three different cases have been considered, one non-degenerate and two degenerate. The local behavior of the model has been studied in the three cases. Six bifurcation diagrams with thirty different regions have been obtained in the non-degenerate case, respect

Philipp Lücke

We present an overview of results on the question of whether the non-stationary ideal of an uncountable regular cardinal $\kappa$ can be defined by a $\Pi_1$-formula using parameters of hereditary cardinality at most $\kappa$. These results show that this question is deeply connected to several central topics of current research in set theory.

Arega Getaneh Abate, Dorsa Majdi, Jalal Kazempour, Maryam Kamgarpour

It is a common practice in the current literature of electricity markets to use game-theoretic approaches for strategic price bidding. However, they generally rely on the assumption that the strategic bidders have prior knowledge of rival bids, either perfectly or with some uncertainty. This is not necessarily a realistic assumption. This paper takes a diffe

Shingo Takemoto, Kazuki Naganuma, Shunsuke Ono

This paper proposes a novel regularization method, named Spatio-Spectral Structure Tensor Total Variation (S3TTV), for denoising and destriping of hyperspectral (HS) images. HS images are inevitably contaminated by various types of noise, during acquisition process, due to the measurement equipment and the environment. For HS image denoising and destriping t

Sayed Muddashir Hossain, Jan Alexandersson, Philipp Müller

Accurate utterance classification in motivational interviews is crucial to automatically understand the quality and dynamics of client-therapist interaction, and it can serve as a key input for systems mediating such interactions. Motivational interviews exhibit three important characteristics. First, there are two distinct roles, namely client and therapist

Matteo Acclavio, Gianluca Curzi, Giulio Guerrieri

In this paper we investigate the complexity-theoretical aspects of cyclic and non-wellfounded proofs in the context of parsimonious logic, a variant of linear logic where the exponential modality ! is interpreted as a constructor for streams over finite data. We present non-wellfounded parsimonious proof systems capturing the classes $\mathbf{FP}$ and $\math

MengMeng Han, Tennessee Leeuwenburg, Brad Murphy

Site-specific weather forecasts are essential to accurate prediction of power demand and are consequently of great interest to energy operators. However, weather forecasts from current numerical weather prediction (NWP) models lack the fine-scale detail to capture all important characteristics of localised real-world sites. Instead they provide weather infor

Naram Mhaisen, George Iosifidis

This paper brings the concept of ``optimism" to the new and promising framework of online Non-stochastic Control (NSC). Namely, we study how NSC can benefit from a prediction oracle of unknown quality responsible for forecasting future costs. The posed problem is first reduced to an optimistic learning with delayed feedback problem, which is handled through

Loizos Hadjiloizou, Frank J. Jiang, Amr Alanwar, Karl H. Johansson

In this paper, we introduce a hybrid zonotope-based approach for formally verifying the behavior of autonomous systems operating under Linear Temporal Logic (LTL) specifications. In particular, we formally verify the LTL formula by constructing temporal logic trees (TLT)s via backward reachability analysis (BRA). In previous works, TLTs are predominantly con

Luis Sigcha, Luigi Borzì, Ignacio Pavón, Nélson Costa

Freezing of gait (FOG) is one of the most incapacitating symptoms in Parkinsons disease, affecting more than 50 percent of patients in advanced stages of the disease. The presence of FOG may lead to falls and a loss of independence with a consequent reduction in the quality of life. Wearable technology and artificial intelligence have been used for automatic

Amith Manoharan, Aditya Sharma, Himani Belsare, Kaustab Pal

Navigation of wheeled vehicles on uneven terrain necessitates going beyond the 2D approaches for trajectory planning. Specifically, it is essential to incorporate the full 6dof variation of vehicle pose and its associated stability cost in the planning process. To this end, most recent works aim to learn a neural network model to predict the vehicle evolutio

Wenqing Fang, Chaopu Yang, Taiyang Chen, Youming Zhang

Chinese people are talking about a national birth rate of 6.4 per 1,000 people in 2023, the lowest in the world. Whether this is related to the wrong use of various artificial night light rays passing through our eyes is worth reflection. Non-visual effects of light are the effects of the blue component of light, which effectively inhibits the melatonin secr

Dini Wang, Peng Yi, Yiguang Hong, Jie Chen

Cooperation plays a fundamental role in societal and biological domains, and the population structure profoundly shapes the dynamics of evolution. Practically, individuals behave either altruistically or egoistically in multiple groups, such as relatives, friends and colleagues, and feedbacks from these groupwise interactions will contribute to one's cogniti

Chen Huang, Peixin Qin, Yang Deng, Wenqiang Lei

The conversational recommendation system (CRS) has been criticized regarding its user experience in real-world scenarios, despite recent significant progress achieved in academia. Existing evaluation protocols for CRS may prioritize system-centric factors such as effectiveness and fluency in conversation while neglecting user-centric aspects. Thus, we propos