March 2024 arXiv papers — page 60

Anton V. Saetchnikov, Elina A. Tcherniavskaia, Vladimir A. Saetchnikov, Andreas Ostendorf

In water monitoring, environmental analysis, cell culture stability, and biomedical applications, precise pH control is demanded. Traditional methods like pH strips and meters have limitations: pH strips lack precision, while electrochemical meters, though more accurate, are fragile, prone to drift, and unsuitable for small volumes. In this paper, we propose

Robin Strässer, Marc Seidel, Felix Brändle, David Meister

In this paper, we propose a collision avoidance safety filter for autonomous electric scooters to enable safe operation of such vehicles in pedestrian areas. In particular, we employ multiple low-cost ultrasonic sensors to detect a wide range of possible obstacles in front of the e-scooter. Based on possibly faulty distance measurements, we design a filter t

Erik Miehling, Manish Nagireddy, Prasanna Sattigeri, Elizabeth M. Daly

Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings. We claim that many of the observed shortcomings can be attributed to violation of one or more conversational principles. By drawing upon extensive research from both the social science and AI communities, we propose a set of maxims -- qua

Eneko Osaba, Esther Villar-Rodriguez, Antón Asla

Research focused on the conjunction between quantum computing and routing problems has been very prolific in recent years. Most of the works revolve around classical problems such as the Traveling Salesman Problem or the Vehicle Routing Problem. The real-world applicability of these problems is dependent on the objectives and constraints considered. Anyway,

Jaeill Kim, Wonseok Lee, Moonjung Eo, Wonjong Rhee

Class Incremental Learning (CIL) constitutes a pivotal subfield within continual learning, aimed at enabling models to progressively learn new classification tasks while retaining knowledge obtained from prior tasks. Although previous studies have predominantly focused on backward compatible approaches to mitigate catastrophic forgetting, recent investigatio

Maxime Zagar, Luc Meyer, Michel Kieffer, Hélène Piet-Lahanier

This paper addresses the problem of target search and tracking using a fleet of cooperating UAVs evolving in some unknown region of interest containing an a priori unknown number of moving ground targets. Each drone is equipped with an embedded Computer Vision System (CVS), providing an image with labeled pixels and a depth map of the observed part of its en

Alina Petukhova, João P. Matos-Carvalho, Nuno Fachada

Text clustering is an important method for organising the increasing volume of digital content, aiding in the structuring and discovery of hidden patterns in uncategorised data. The effectiveness of text clustering largely depends on the selection of textual embeddings and clustering algorithms. This study argues that recent advancements in large language mo

Irene Aldridge

This paper proposes a fast Markov Matrix-based methodology for computing Top Trading Cycles (TTC) that delivers O(1) computational speed, that is speed independent of the number of agents and objects in the system. The proposed methodology is well suited for complex large-dimensional problems like housing choice. The methodology retains all the properties of

Barbara Linek, Marta Łuszczak, Wolfgang Schäfer, Antoni Szczurek

We calculate several differential distributions for diffractive dijets production in $e p \to e' {\rm jet \, jet} p$ in the pQCD dipole approach using off diagonal unintegrated gluon distributions (GTMDs). Different models from the literature are used. We concentrate on the contribution from exclusive $q \bar q$ dijets. Results of our calculations are compar

Chien-Hua Chen

In this paper, we compute the natural density of rank-$1$ Drinfeld module over $\mathbb{F}_q[T]$ with surjective adelic Galois representation; and the natural density of rank-$2$ Drinfeld modules over $\mathbb{F}_q[T]$ whose $\mathfrak{l}$-adic Galois image containing the special linear subgroup for finitely many prime ideal $\mathfrak{l}$.

Benjamin Bobbia, Matthias Picard

This paper addresses a new active learning strategy for regression problems. The presented Wasserstein active regression model is based on the principles of distribution-matching to measure the representativeness of the labeled dataset. The Wasserstein distance is computed using GroupSort Neural Networks. The use of such networks provides theoretical foundat

Adrian Röfer, Nick Heppert, Abdallah Ayad, Eugenio Chisari

Tactile sensing is vital for human dexterous manipulation, however, it has not been widely used in robotics. Compact, low-cost sensing platforms can facilitate a change, but unlike their popular optical counterparts, they are difficult to deploy in high-fidelity tasks due to their low signal dimensionality and lack of a simulation model. To overcome these ch

Olmo Piana, Hung-Yi Pu, Kinwah Wu

In this work we study the co-evolution of central black holes (BHs) and host galaxies by utilizing an advanced iteration of the DELPHI semi-analytical model of galaxy formation and evolution. Based on dark matter halo merger trees spanning the redshift range from $z=20$ to $z=4$, it now incorporates essential components such as gas heating and cooling, cold

Xiaoqing Zhang, Yuhan Liu, Jianzhou Wang, Zhenxing Hu

Influencer marketing campaign success heavily depends on identifying key opinion leaders who can effectively leverage their credibility and reach to promote products or services. The selecting influencers process is vital for boosting brand visibility, fostering consumer trust, and driving sales. While traditional research often simplifies complex factors li

Wang Yufeng, Chen Chao, Yang Zhou, Wang Shuhui

Empathetic response generation endeavors to empower dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. Psychological research demonstrates that emotion, as an essential factor in empathy, encompasses trait emotions, which are static and context-independent, and state emotions, which are dynamic and context-dependen

U. Bekbaev

This paper deals with $n$-dimensional algebras, over any field, which have only trivial derivation (automorphism) and simple algebras. It is shown that the corresponding sets of algebras are not empty and, in algebraically closed field case, they are dense subsets of the variety of $n$-dimensional algebras with respect to the Zariski topology. Moreover, an i

Vladyslav Zalevskyi, Thomas Sanchez, Margaux Roulet, Jordina Aviles Verddera

Segmentation of fetal brain tissue from magnetic resonance imaging (MRI) plays a crucial role in the study of in utero neurodevelopment. However, automated tools face substantial domain shift challenges as they must be robust to highly heterogeneous clinical data, often limited in numbers and lacking annotations. Indeed, high variability of the fetal brain m

Flavia Sofia Acerbo, Jan Swevers, Tinne Tuytelaars, Tong Duy Son

This paper proposes DriViDOC: a framework for Driving from Vision through Differentiable Optimal Control, and its application to learn autonomous driving controllers from human demonstrations. DriViDOC combines the automatic inference of relevant features from camera frames with the properties of nonlinear model predictive control (NMPC), such as constraint

Armen Beck, Jonathan Fine, Gaurav Chopra

Optimization of chemical systems and processes have been enhanced and enabled by the guidance of algorithms and analytical approaches. While many methods will systematically investigate how underlying variables govern a given outcome, there is often a substantial number of experiments needed to accurately model these relations. As chemical systems increase i

Haoyu Wang, Xiaoyu Tan, Xihe Qiu, Chao Qu

Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases. However, many existing methods struggle to account for the intricate dependencies between joints. We introduce CoordiGraph, a novel architecture that leverages subequivariant principles from physics to enhance

Muyan Jiang, Ying Chen, Xin Chen, Javad Lavaei

A large fraction of total healthcare expenditure occurs due to end-of-life (EOL) care, which means it is important to study the problem of more carefully incentivizing necessary versus unnecessary EOL care because this has the potential to reduce overall healthcare spending. This paper introduces a principal-agent model that integrates a mixed payment system

Lan Feng, Mohammadhossein Bahari, Kaouther Messaoud Ben Amor, Éloi Zablocki

Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability to scale to different data domains and the impact of larger dataset sizes on their generalization remain under-explored. While these questions can be studied by employing multiple datasets, it is challenging due to several discrepancies, e.g., in data formats, m

I-Hung Hsu, Zihan Xue, Nilay Pochh, Sahil Bansal

Event linking connects event mentions in text with relevant nodes in a knowledge base (KB). Prior research in event linking has mainly borrowed methods from entity linking, overlooking the distinct features of events. Compared to the extensively explored entity linking task, events have more complex structures and can be more effectively distinguished by exa

Gastón Andrés García, Fabio Gavarini

In this paper we describe the effect on quantum groups -- namely, both QUEA's and QFSHA's -- of deformations by twist and by 2-cocycles, showing how such deformations affect the semiclassical limit. As a second, more important task, we discuss how these deformation procedures can be "stretched" to a new extent, via a formal variation of the original recipes,

Jack Muir, Gerrit Olivier, Anthony Reid

This paper presents an innovative end-to-end workflow for mineral exploration, integrating ambient noise tomography (ANT) and artificial intelligence (AI) to enhance the discovery and delineation of mineral resources essential for the global transition to a low carbon economy. We focus on copper as a critical element, required in significant quantities for r

Nabajit Talukdar, Kukil Kalpa Rajkhowa

If $G$ be a finite $p$-group and $\chi$ is a non-linear irreducible character of $G$, then $\chi(1)\leq |G/Z(G)|^{\frac{1}{2}}$. In \cite{fernandez2001groups}, Fern\'{a}ndez-Alcober and Moret\'{o} obtained the relation between the character degree set of a finite $p$-group $G$ and its normal subgroups depending on whether $|G/Z(G)|$ is a square or not. In th

ATLAS Collaboration

This paper presents a measurement of the production cross-section of a $Z$ boson in association with $b$- or $c$-jets, in proton-proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS experiment at the Large Hadron Collider using data corresponding to an integrated luminosity of 140 fb$^{-1}$. Inclusive and differential cross-sections are measured for event

Rubén Calvo, Carles Martorell, Guillermo B. Morales, Serena Di Santo

Recent analyses combining advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons provide strong support for the hypothesis that neural dynamics operate near the edge of instability across regions in the brain. However, these analyses, as well as related studies, often fail to capture the intricate temporal str

Esmaeel Mohammadi, Daniel Ortiz-Arroyo, Mikkel Stokholm-Bjerregaard, Aviaja Anna Hansen

Even though Deep Reinforcement Learning (DRL) showed outstanding results in the fields of Robotics and Games, it is still challenging to implement it in the optimization of industrial processes like wastewater treatment. One of the challenges is the lack of a simulation environment that will represent the actual plant as accurately as possible to train DRL p

K. B. Yogendra, Suman Karmakar, Tanmoy Das

Classical magnets exhibit exotic ground state properties such as spin liquids and fractionalization, promising a manifestation of superposition and projective symmetry construction in classical theory. While system-specific spin-ice or soft-spin models exist, a formal theory for general classical magnets remains elusive. Here, we introduce a generic symmetry

Kazushi Kobayashi

Let $(X^n,\check{X}^n)$ be a mirror pair of an $n$-dimensional complex torus $X^n$ and its mirror partner $\check{X}^n$. Then, by SYZ transform, we can construct a holomorphic line bundle with an integrable connection from each pair of a Lagrangian section of $\check{X}^n\to \mathbb{R}^n/\mathbb{Z}^n$ and a unitary local system along it, and those holomorphi

Shreyas Chandgothia, Ardhendu Sekhar, Amit Sethi

Training a computer vision system to segment a novel class typically requires collecting and painstakingly annotating lots of images with objects from that class. Few-shot segmentation techniques reduce the required number of images to learn to segment a new class, but careful annotations of object boundaries are still required. On the other hand, interactiv

Xuemei Tang, Zekun Deng, Qi Su, Hao Yang

Natural Language Processing (NLP) plays a pivotal role in the realm of Digital Humanities (DH) and serves as the cornerstone for advancing the structural analysis of historical and cultural heritage texts. This is particularly true for the domains of named entity recognition (NER) and relation extraction (RE). In our commitment to expediting ancient history

Ali M. Hasan, Walid J. Azzam

Gamma-ray bursts (GRBs) are extremely powerful explosions that have been traditionally classified into two categories: long bursts (LGRBs) with an observed duration T90 > 2 s, and short bursts (SGRBs) with an observed duration T90 < 2 s, where T90 is the time interval during which 90% of the fluence is detected. LGRBs are believed to emanate from the core-co

Yuta Nishimura

In this paper, we construct generating functions of alternating sums for the Arakawa-Kaneko zeta values. From the expressions, we show alternating sum formulas for them. Based on these results, we apply the same method to other zeta values.

ATLAS Collaboration

Proton-proton data recorded by the ATLAS detector in 2011, at a centre-of-mass energy of 7 TeV, have been used for an improved determination of the W-boson mass and a first measurement of the W-boson width at the LHC. Recent fits to the proton parton distribution functions are incorporated in the measurement procedure and an improved statistical method is us

F. P. Callan, S. A. Sim, C. E. Collins, L. J. Shingles

Type Iax supernovae (SNe Iax) are proposed to arise from deflagrations of Chandrasekhar mass white dwarfs (WDs). Previous deflagration simulations have achieved good agreement with the light curves and spectra of intermediate-luminosity and bright SNe Iax. However, the model light curves decline too quickly after peak, particularly in red optical and near-in

Rocio Gonzalez-Diaz, Miguel A. Gutiérrez-Naranjo, Eduardo Paluzo-Hidalgo

In this paper, we present SIMAP, a novel layer integrated into deep learning models, aimed at enhancing the interpretability of the output. The SIMAP layer is an enhanced version of Simplicial-Map Neural Networks (SMNNs), an explainable neural network based on support sets and simplicial maps (functions used in topology to transform shapes while preserving t

Zhonghua Zhai, Chen Ju, Jinsong Lan, Shuai Xiao

In this work, we propose Cell Variational Information Bottleneck Network (cellVIB), a convolutional neural network using information bottleneck mechanism, which can be combined with the latest feedforward network architecture in an end-to-end training method. Our Cell Variational Information Bottleneck Network is constructed by stacking VIB cells, which gene

Andrew Williams, Aaron Boley, Giuliana Rotola, Richard Green

The rapid launch of hundreds of thousands of satellites into Low Earth Orbit will significantly alter our view of the sky and raise concerns about the sustainability of Earth's orbital space. A new framework for sustainable space development must balance technological advancement, protection of space environments, and our capacity to explore the Universe.

Andre Büttner, Nils Gruschka

Nowadays, most online services offer different authentication methods that users can set up for multi-factor authentication but also as a recovery method. This configuration must be done thoroughly to prevent an adversary's access while ensuring the legitimate user does not lose access to their account. This is particularly important for fundamental everyday

Daulet Baimukashev, Gokhan Alcan, Ville Kyrki

Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations. Its use avoids the difficult and tedious procedure of manual reward specification while retaining the generalization power of reinforcement learning. In IRL, the reward is usually represented as a linear combination of features. In

Mounia Hamidouche, Biniam Fisseha Demissie, Bilel Cherif

As more devices connect to the internet, it becomes crucial to address their limitations and basic security needs. While much research focuses on utilizing ML and DL to tackle security challenges, there is often a tendency to overlook the practicality and feasibility of implementing these methods in real-time settings. This oversight stems from the constrain

Sukhdeep Singh, Anuj Sharma, Vinod Kumar Chauhan

Graph Neural Networks (GNN) have emerged as a popular and standard approach for learning from graph-structured data. The literature on GNN highlights the potential of this evolving research area and its widespread adoption in real-life applications. However, most of the approaches are either new in concept or derived from specific techniques. Therefore, the

Connor Beveridge, Sanjay Iyer, Caitlin E. Randolph, Matthew Muhoberac

Lipidomics generates large data that makes manual annotation and interpretation challenging. Lipid chemical and structural diversity with structural isomers further complicates annotation. Although, several commercial and open-source software for targeted lipid identification exists, it lacks automated method generation workflows and integration with statist

Jiaheng Yu, Jing Li, Yue He, Kai Zhu

Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks. However, they ignore that the difference relation density of nodes between the user- and item-side causes the adaptability of graphs on bilateral nodes to be different a

Arindam Misra

The Financial system has witnessed rapid technological changes. The rise of Bitcoin and other crypto assets based on Distributed Ledger Technology mark a fundamental change in the way people transact and transmit value over a decentralized network, spread across geographies. This has created regulatory and tax policy blind spots, as governments and tax admin

Guillem Simeon, Antonio Mirarchi, Raul P. Pelaez, Raimondas Galvelis

Most state-of-the-art neural network potentials do not account for molecular attributes other than atomic numbers and positions, which limits its range of applicability by design. In this work, we demonstrate the importance of including additional electronic attributes in neural network potential representations with a minimal architectural change to TensorN

Zhiyuan Xie, Gorm Bruun Andresen

To move towards a low-carbon society by 2050, understanding the intricate dynamics of energy systems is critical. Our study examines these interactions through the lens of hydrogen storage, dividing it into 'direct' and 'indirect' hydrogen storage. Direct hydrogen storage involves electrolysis-produced hydrogen being stored before use, while indirect storage

Ronald Remmerswaal, Roman Hatzky, Eric Sonnendrücker

The use of gyrokinetics, wherein phase-space coordinate transformations result in a phase-space dimensionality reduction as well as the removal of fast time scales, has enabled the simulation of microturbulence in fusion devices. The state-of-the-art gyrokinetic models used in practice are parallel-only models wherein the perpendicular part of the vector pot

D. Martínez, P. A. Orellana, L. Rosales, J. Dolado

Bound states in the continuum (BICs) are exotic, localized states even though their energy lies in the continuum spectra. Since its discovery in 1929, the quest to unveil these exotic states in charge transport experiments remains an active pursuit in condensed matter physics. Here, we study charge transport in InSb nanowire networks in the ballistic regime

Mengke Ge, Junpeng Wang, Binhan Chen, Yingjian Zhong

The advent of Transformers has revolutionized computer vision, offering a powerful alternative to convolutional neural networks (CNNs), especially with the local attention mechanism that excels at capturing local structures within the input and achieve state-of-the-art performance. Processing in-memory (PIM) architecture offers extensive parallelism, low dat

Victor Ibañez, Przemyslaw Szostak, Quincy Wong, Konstanty Korski

Graph convolutional networks (GCNs) have emerged as a powerful alternative to multiple instance learning with convolutional neural networks in digital pathology, offering superior handling of structural information across various spatial ranges - a crucial aspect of learning from gigapixel H&E-stained whole slide images (WSI). However, graph message-passing

Kabirat Olayemi, Mien Van, Sean McLoone, Yuzhu Sun

Autonomous ground vehicle (UGV) navigation has the potential to revolutionize the transportation system by increasing accessibility to disabled people, ensure safety and convenience of use. However, UGV requires extensive and efficient testing and evaluation to ensure its acceptance for public use. This testing are mostly done in a simulator which result to

Carlos Fernandes, Rafael Wagner, Leonardo Novo, Ernesto F. Galvão

Quantum theory is traditionally formulated using complex numbers. This imaginarity of quantum theory has been quantified as a resource with applications in discrimination tasks, pseudorandomness generation, and quantum metrology. Here we propose witnesses for imaginarity that are basis-independent, relying on measurements of unitary-invariant properties of s

Quentin Mazouni, Helge Spieker, Arnaud Gotlieb, Mathieu Acher

Reinforcement Learning is the premier technique to approach sequential decision problems, including complex tasks such as driving cars and landing spacecraft. Among the software validation and verification practices, testing for functional fault detection is a convenient way to build trustworthiness in the learned decision model. While recent works seek to m

Raza Yunus, Jan Eric Lenssen, Michael Niemeyer, Yiyi Liao

Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision. It enables the synthesizing of photorealistic novel views, useful for the movie industry and AR/VR applications. It also facilitates the content creation necessary in computer games and AR/VR by avoidi

Heng Guo, Jianfeng Zhang, Jiaxing Huang, Tony C. W. Mok

Segment anything model (SAM) demonstrates strong generalization ability on natural image segmentation. However, its direct adaptation in medical image segmentation tasks shows significant performance drops. It also requires an excessive number of prompt points to obtain a reasonable accuracy. Although quite a few studies explore adapting SAM into medical ima

Masanori Hirano

With the recent development of large language models (LLMs), models that focus on certain domains and languages have been discussed for their necessity. There is also a growing need for benchmarks to evaluate the performance of current LLMs in each domain. Therefore, in this study, we constructed a benchmark comprising multiple tasks specific to the Japanese

Abhinau K. Venkataramanan, Alan C. Bovik

High Dynamic Range (HDR) videos are able to represent wider ranges of contrasts and colors than Standard Dynamic Range (SDR) videos, giving more vivid experiences. Due to this, HDR videos are expected to grow into the dominant video modality of the future. However, HDR videos are incompatible with existing SDR displays, which form the majority of affordable

Mohammad Haidar

In this study, we discuss the observation of spin wave interference generated by magnetic oscillators. We employ micromagnetic simulations for two coherent spin Hall nanowire oscillators positioned nearby, horizontally or vertically. The two nanowires produce circular waves with short wavelengths on the order of 100 nm, which interfere with each other. In th

Yuhi Kamio

We have identified some necessary conditions for the existence of rigid sphere designs. In particular, we have successfully resolved the conjecture proposed by [Ban87]; Given fixed positive integers t and d, we show that there exist only finitely many rigid t-designs on Sd, up to orthogonal transformations.

Zhichao Wei, Qingkun Su, Long Qin, Weizhi Wang

Recent advances in tuning-free personalized image generation based on diffusion models are impressive. However, to improve subject fidelity, existing methods either retrain the diffusion model or infuse it with dense visual embeddings, both of which suffer from poor generalization and efficiency. Also, these methods falter in multi-subject image generation d

Harold R. L. Yang, Philip B. Zhang

In this paper, we introduce stable multivariate generalizations of Narayana polynomials of type A and type B. We give an insertion algorithm for labeled plane trees and introduce the notion of improper edges. Our polynomials are multivariate generating polynomials of labeled plane trees and can be generated by a grammatical labeling based on a context-free g

M. Lanza de Cristoforis

We present a nonvariational setting for the Neumann problem for harmonic functions that are H\"{o}lder continuous and that may have infinite Dirichlet integral. Then we introduce a space of distributions on the boundary (a space of first order traces for H\"{o}lder continuous harmonic functions), we analyze the properties of the corresponding distributional

Simone Göttlich, Manuel Schaller, Karl Worthmann

For linear-quadratic optimal control problems (OCPs) governed by elliptic and parabolic partial differential equations (PDEs), we investigate the impact of perturbations on optimal solutions. Local perturbations may occur, e.g., due to discretization of the optimality system or {disturbed} problem data. Whereas these perturbations may exhibit global effects

Takeshi Fukao, Ulisse Stefanelli, Riccardo Voso

We consider a general optimal control problem in the setting of gradient flows. Two approximations of the problem are presented, both relying on the variational reformulation of gradient-flow dynamics via the Weighted-Energy-Dissipation variational approach. This consists in the minimization of global-in-time functionals over trajectories, combined with a li

Pengwei Xie, Siang Chen, Wei Tang, Dingchang Hu

Robotic grasping is a primitive skill for complex tasks and is fundamental to intelligence. For general 6-Dof grasping, most previous methods directly extract scene-level semantic or geometric information, while few of them consider the suitability for various downstream applications, such as target-oriented grasping. Addressing this issue, we rethink 6-Dof

Kálmán Liptai, László Németh, Tamás Szakács, László Szalay

One of the most popular and studied recursive series is the Fibonacci sequence. It is challenging to see how Fibonacci numbers can be used to generate other recursive sequences. In our article, we describe some families of integer recurrence sequences as rational polynomial linear combinations of Fibonacci numbers.

Tianyang Chen, Peng Xu, Stjepan Picek, Bo Luo

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user's secret key is leaked or stolen), which threatens al

Sergiu Busuioc

We derive the Enskog equation utilizing orthonormal vielbein fields, enabling the utilization of arbitrary coordinate systems to characterize spatial geometry. Additionally, we employ an adapted coordinate system in the momentum space, connected to the physical space through vielbeins. Within this framework, the momentum component perpendicular to a curved b

Rasoul Ghadimi, Chiranjit Mondal, Sunje Kim, Bohm-Jung Yang

The quantum valley Hall effect (QVHE) is characterized by the valley Chern number (VCN) in a way that one-dimensional (1D) chiral metallic states are guaranteed to appear at the domain walls (DW) between two domains with opposite VCN for a given valley. Although in the case of QVHE, the total BC of the system is zero, the BC distributed locally around each v

Seongjun Jeong, Gi-Cheon Kang, Seongho Choi, Joochan Kim

Developing Vision-and-Language Navigation (VLN) agents typically assumes a \textit{train-once-deploy-once} strategy, which is unrealistic as deployed agents continually encounter novel environments. To address this, we propose the Continual Vision-and-Language Navigation (CVLN) paradigm, where agents learn and adapt incrementally across multiple \textit{scen

Bumsoo Kim, Wonseop Shin, Kyuchul Lee, Yonghoon Jung

Leveraging large-scale Text-to-Image (TTI) models have become a common technique for generating exemplar or training dataset in the fields of image synthesis, video editing, 3D reconstruction. However, semantic structural visual hallucinations involving perceptually severe defects remain a concern, especially in the domain of non-photorealistic rendering (NP

Toke Vibel, Mikkel Berg Christensen, Rasmus Malthe Fiil Andersen, Laurits Nikolaj Stokholm

The investigation of the fluctuations in interacting quantum systems at finite temperatures showcases the ongoing challenges in understanding complex quantum systems. Recently, atom number fluctuations in weakly interacting Bose-Einstein condensates were observed, motivating an investigation of the thermal component of partially condensed Bose gases. Here, w

Ulrich Ellwanger, Margarete Muehlleitner, Nikolaos Rompotis, Nausheen R. Shah

A number of benchmark scenarios for NMSSM Higgs boson searches via Higgs-to-Higgs decays at the LHC have been proposed by the NMSSM Subgroup of the LHC HWG3. Some of them are already in use by the ATLAS and CMS collaborations for the interpretation of their results from Run 2. In this document we summarize the theory setup, the underlying procedures and repr

Aadirupa Saha, Hilal Asi

We consider the well-studied dueling bandit problem, where a learner aims to identify near-optimal actions using pairwise comparisons, under the constraint of differential privacy. We consider a general class of utility-based preference matrices for large (potentially unbounded) decision spaces and give the first differentially private dueling bandit algorit

Zhuofan Wen, Fengyu Zhang, Siyuan Zhang, Haiyang Sun

Multimodal fusion is a significant method for most multimodal tasks. With the recent surge in the number of large pre-trained models, combining both multimodal fusion methods and pre-trained model features can achieve outstanding performance in many multimodal tasks. In this paper, we present our approach, which leverages both advantages for addressing the t

Matteo Ferrari, Sara Fraschini

We consider a family of conforming space-time finite element discretizations for the wave equation based on splines of maximal regularity in time. Traditional techniques may require a CFL condition to guarantee stability. Recent works by O. Steinbach and M. Zank (2018), and S. Fraschini, G. Loli, A. Moiola, and G. Sangalli (2023), have introduced uncondition

Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam

Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still require fine-tuning to reach satisfactory levels of performance, many of them are in the low-data regime, making fine-tuning challenging. To address this, we propose LLM2LLM, a tar

Elena N. Govorun, Julien Dupré de Baubigny, Patrick Perrin, Mathilde Reyssat

Polymer association at liquid-liquid interfaces is a promising way to spontaneously obtain soft self-healing membranes. In the case of reversible bonding between two polymers, the macromolecules are mobile everywhere within the membrane and they can be absorbed into it at both boundaries due to binding to macromolecules of the other type. In this work, we de

Hyo Jeong Yun, Chanyoung Kim, Moonjeong Hahm, Kyuri Kim

Environmental, social, and governance (ESG) factors are widely adopted as higher investment return indicators. Accordingly, ongoing efforts are being made to automate ESG evaluation with language models to extract signals from massive web text easily. However, recent approaches suffer from a lack of training data, as rating agencies keep their evaluation met

Guillaume Broux-Quemerais, Sarah Kaakaï, Anis Matoussi, Wissal Sabbagh

In this paper, we present a probabilistic numerical method for a class of forward utilities in a stochastic factor model. For this purpose, we use the representation of dynamic consistent utilities with mean of ergodic Backward Stochastic Differential Equations (eBSDEs) introduced by Liang and Zariphopoulou in [27]. We establish a connection between the solu

Gilles Blanchard, Jean-Baptiste Fermanian, Hannah Marienwald

We endeavour to estimate numerous multi-dimensional means of various probability distributions on a common space based on independent samples. Our approach involves forming estimators through convex combinations of empirical means derived from these samples. We introduce two strategies to find appropriate data-dependent convex combination weights: a first on

Stephen R. Clark, Craig McGregor

South Africa is currently facing a critical situation in its power generation landscape, which is plagued by frequent power outages and the need to move from fossil fuels to renewable energy sources. This period emphasizes the importance of having firm-dispatchable power to balance out the intermittent nature of wind and solar energy sources. The paper propo

Tao Zhu, Yong-Mei Zhang, Jian-Sheng Wang

Using the nonequilibrium Green's function formalism, we propose a general microscopic framework to investigate the radiative heat transfer (RHT) between coplanar objects with a square lattice. We employ the obtained formulas to two-dimensional (2D) metal configurations with a tight-binding model and the Drude model. Our results reveal that the RHT between co

Alessio Monti, Stefano Vellucci, Mirko Barbuto, Luca Stefanini

In this study, we investigate the feasibility of designing reconfigurable transmitting metasurfaces through the use of Drude-like scatterers with purely electric response. Theoretical and numerical analyses are provided to demonstrate that the response of spherical Drude-like scatterers can be tailored to achieve complete transmission, satisfying a generaliz

Guankun Wang, Long Bai, Wan Jun Nah, Jie Wang

Recent advancements in Surgical Visual Question Answering (Surgical-VQA) and related region grounding have shown great promise for robotic and medical applications, addressing the critical need for automated methods in personalized surgical mentorship. However, existing models primarily provide simple structured answers and struggle with complex scenarios du

Mitsunori Araki, Valerio Lattanzi, Christian P. Endres, Paula Caselli

Seven rotational and fine-structure transitions of the deuterated molecular ion SD+ in the X 3S- ground electronic state have been measured in the 271-863 GHz region in the laboratory. This ion has been produced by DC-glow discharge using a mixture of D2S and argon in a free space cell in a temperature range of -140 to -160C. The rotational, centrifugal dist

G. Rauw, S. Lizin, S. Rosu, E. Mossoux

We investigated the X-ray emission of HD 149404, a 9.81-day period O-star binary in a post-Roche lobe overflow evolutionary stage. X-ray emission of O-star binaries consists of the intrinsic emission of the individual O stars and a putative additional component arising from the wind-wind interaction. Phase-locked variations in the X-ray spectra can be used t

Qiaoqiao Jin, Xuanhong Chen, Meiguang Jin, Ying Chen

Contemporary makeup approaches primarily hinge on unpaired learning paradigms, yet they grapple with the challenges of inaccurate supervision (e.g., face misalignment) and sophisticated facial prompts (including face parsing, and landmark detection). These challenges prohibit low-cost deployment of facial makeup models, especially on mobile devices. To solve

Zilin Xie, Kangning Li, Jinbao Jiang, Jinzhong Yang

Open-pit mine change detection (CD) in high-resolution (HR) remote sensing images plays a crucial role in mineral development and environmental protection. Significant progress has been made in this field in recent years, largely due to the advancement of deep learning techniques. However, existing deep-learning-based CD methods encounter challenges in effec

Paul San Sebastian, Mikel Cañizo, Román Orús

Variational quantum algorithms are gaining attention as an early application of Noisy Intermediate-Scale Quantum (NISQ) devices. One of the main problems of variational methods lies in the phenomenon of Barren Plateaus, present in the optimization of variational parameters. Adding geometric inductive bias to the quantum models has been proposed as a potentia

Akira Sakurai, Kazuyuki Shudo

In the area of blockchain, numerous methods have been proposed for suppressing intentional forks by attackers more effectively than the random rule. However, all of them, except for the random rule, require major updates, rely on a trusted third party, or assume strong synchrony. Hence, it is challenging to apply these methods to existing systems such as Bit

Shuai Lu, Jiayi Ding, Mingji Chen, Wei Gu

This letter first explores the solution uniqueness of the data-driven modeling of price-responsive flexible loads (PFL). The PFL on the demand side is critical in modern power systems. An accurate PFL model is fundamental for system operations. However, whether the PFL model can be uniquely and correctly identified from operational data remains unclear. To a

Sébastien Dam, Jean-Marie Batail, Gabriel H Robert, Dominique Drapier

Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions,adver

Hongsu Kim, Jae Sok Oh

It is suggested that the original, minimal Kaluza-Klein theory should be extended by adding a 5-dimensional version of the Gibbons-Hawking gravitational surface term. It is then demonstrated that the usual dimensional reduction of the newly added surface (boundary) term leads to the emergence of the famous Abelian Chern-Simons term. It is stressed that the a

Tor Ole B. Odden, Benjamin Zwickl

Computation has revolutionized science and is gradually making its way into science teaching and learning. However, we currently lack theoretical frameworks to make sense of how students learn to use computation as a disciplinary tool. In this study, we propose disciplinary computational literacy as a productive theoretical lens on this subject. This theoret

Wanli Xie, Ruibin Zhao, Zhenguo Xu, Tingting Liang

Neural network models have shown outstanding performance and successful resolutions to complex problems in various fields. However, the majority of these models are viewed as black-box, requiring a significant amount of data for development. Consequently, in situations with limited data, constructing appropriate models becomes challenging due to the lack of

Chenyao Yu, Yingfeng Cai, Jiaxin Zhang, Hui Kong

As a part of the perception results of intelligent driving systems, static object detection (SOD) in 3D space provides crucial cues for driving environment understanding. With the rapid deployment of deep neural networks for SOD tasks, the demand for high-quality training samples soars. The traditional, also reliable, way is manual labelling over the dense L