December 2023 arXiv papers — page 103

Tian-Yang Sun, Chun-Yu Xiong, Shang-Jie Jin, Yu-Xin Wang

Glitches represent a category of non-Gaussian and transient noise that frequently intersects with gravitational wave (GW) signals, exerting a notable impact on the processing of GW data. The inference of GW parameters, crucial for GW astronomy research, is particularly susceptible to such interference. In this study, we pioneer the utilization of temporal an

Bharath Roy Choudhury

Consider a navigation rule defined on a graph that maps every vertex of the graph to a vertex in such a way that the navigation rule commutes with every automorphism of the graph. It is to say that the navigation rule applied to the vertices remains the same after taking any automorphism of the graph. Such a navigation rule is said to have the covariance pro

Peter Borgen Sørensen, Anders Nielsen

Valid mass load predictions of nutrients, in particular nitrogen (N) and phosphorus (P), are needed for the limnological understanding of single lake ecosystems as well as larger river/lake ecosystems. The mass of N and P that enters a lake will determine the ecological state of the lake, and the mass release from the lake will determine the ecological state

Yu. A. Sakhratov, L. E. Svistov, A. P. Reyes

The magnetic H - T phase diagram of a quasi-two-dimensional easy plane antiferromagnet RbFe(MoO$_4$)$_2$ (S = 5/2) with an equilateral triangular lattice structure is studied with $^{87}$Rb NMR technique for field directed along hard axis C3. The studies confirm the two step transition from the low field umbrella-like incommensurate magnetic phase to the par

Heechan Yoon, Seungkyu Lee

Unlike opaque object, novel view synthesis of transparent object is a challenging task, because transparent object refracts light of background causing visual distortions on the transparent object surface along the viewpoint change. Recently introduced Neural Radiance Fields (NeRF) is a view synthesis method. Thanks to its remarkable performance improvement,

Kyra Ahrens, Hans Hergen Lehmann, Jae Hee Lee, Stefan Wermter

We address the Continual Learning (CL) problem, wherein a model must learn a sequence of tasks from non-stationary distributions while preserving prior knowledge upon encountering new experiences. With the advancement of foundation models, CL research has pivoted from the initial learning-from-scratch paradigm towards utilizing generic features from large-sc

David Monniaux

Current compilers implement security features and optimizations that require nontrivial semantic reasoning about pointers and memory allocation: the program after the insertion of the security feature, or after applying the optimization, must simulate the original program despite a different memory layout. In this article, we illustrate such reasoning on poi

Feiyu Li, Ning Guo, Qiang Zheng, Yang Shen

Advancement of technologies relies on discovery of new materials with emerging physical properties that are determined by their crystal structures. Ruddlesden-Popper (R-P) phases with formula of $A_{n+1}$$B_n$$X_{3n+1}$ (n=1,2,3...) are among one of the most widely studied class of materials due to their electrical, optical, magnetic, thermal properties and

John Lloyd, Nathan F. Lepora

Tactile servoing is an important technique because it enables robots to manipulate objects with precision and accuracy while adapting to changes in their environments in real-time. One approach for tactile servo control with high-resolution soft tactile sensors is to estimate the contact pose relative to an object surface using a convolutional neural network

Y. M. Cho, Franklin H. Cho

We propose the ultra high energy cosmic ray recently detected by Telescope Array to be the electroweak monopole, and present theoretical arguments which support this. This strongly motivates the necessity for the ``cosmic" MoEDAL experiment which could back up our proposal. To confirm this we propose Telescope Array to measure the magnetic charge of the ultr

Joshua Males

In a recent paper, Bringmann, Craig, Ono, and the author showed that the number of $t$-hooks ($t\geq2$) among all partitions of $n$ is not always asymptotically equidistributed on congruence classes $a \pmod{b}$. In this short note, we clarify the situation of $t=1$, i.e. all hook lengths, and show that this case does give asymptotic equidistribution, closin

Naoya Suda

Giving explicit parametrizations of discrete constant Gaussian curvature surfaces of revolution that are defined from an integrable systems approach, we study Ricci flow for discrete surfaces, and see how discrete surfaces of revolution have a geometric realization for the Ricci flow that approaches the constant Gaussian curvature surfaces we have parametriz

M. Miyamoto, R. Kobayashi, G. Kuwano, M. Tsujimoto

Communication using terahertz (~10^12 Hz) electromagnetic waves is critical for developing 6th-generation wireless network infrastructures. Conflictions between stable radiation and the modulation frequency of terahertz sources impede the superposing of transmitting signals on carrier waves. The Josephson junctions included in a cuprate superconductor radiat

Clemens Seibold, Anna Hilsmann, Peter Eisert

Automatic generation of morphed face images often produces ghosting artifacts due to poorly aligned structures in the input images. Manual processing can mitigate these artifacts. However, this is not feasible for the generation of large datasets, which are required for training and evaluating robust morphing attack detectors. In this paper, we propose a met

Daniel Abode, Ramoni Adeogun, Lou Salaün, Renato Abreu

In this paper, we present an unsupervised approach for frequency sub-band allocation in wireless networks using graph-based learning. We consider a dense deployment of subnetworks in the factory environment with a limited number of sub-bands which must be optimally allocated to coordinate inter-subnetwork interference. We model the subnetwork deployment as a

Maximilian Scheurer, Gian-Luca R. Anselmetti, Oumarou Oumarou, Christian Gogolin

We propose to use wavefunction overlaps obtained from a quantum computer as inputs for the classical split-amplitude techniques, tailored and externally corrected coupled cluster, to achieve balanced treatment of static and dynamic correlation effects in molecular electronic structure simulations. By combining insights from statistical properties of matchgat

Ashutosh Singh, Priyanka Sharma, Om Prakash

For a prime $p$ and a positive integer $m$, let $\mathbb{F}_{p^m}$ be the finite field of characteristic $p$, and $\mathfrak{R}_l:=\mathbb{F}_{p^m}[v]/\langle v^l-v\rangle$ be a non-chain ring. In this paper, we study the $(\sigma,\delta)$-cyclic codes over $\mathfrak{R}_l$. Further, we study the application of these codes in finding DNA codes. Towards this,

S. A. Pustilnik, A. L. Tepliakova, A. S. Vinokurov

Cas I is a LV dIrr with a wide range of suggested distances. Tikhonov (2019), using the HST images and the TRGB method, places Cas I at D = 1.6+-0.1 Mpc. Besides, he estimates the stellar metallicity of Cas I at the level of z ~ 0.0004 (Z ~ Zo/50). Such a nearby extremely low-metallicity dwarf, if real, would be a very valuable object for detailed studies. A

Yorgos Felekis, Fabio Massimo Zennaro, Nicola Branchini, Theodoros Damoulas

Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them. These maps have significant relevance for real-world challenges such as synthesizing causal evidence from multiple experimental environments, learning causally consistent representa

Rui Mao, Guojing Tian, Xiaoming Sun

In quantum chemistry, the variational quantum eigensolver (VQE) is a promising algorithm for molecular simulations on near-term quantum computers. However, VQEs using hardware-efficient circuits face scaling challenges due to the barren plateau problem. This raises the question of whether chemically inspired circuits from unitary coupled cluster (UCC) method

Carlos Balado Sánchez, Rebeca P. Díaz Redondo, Ana Fernández Vilas, Angel M. Sánchez Bermúdez

In this paper we introduce a proposal to provide students in labs with an alternative to the traditional visible range spectrophotometers, whose acquisition and maintenance entails high costs, based on smartphones. Our solution faced two aspects. On the one hand, the software for the smartphone, able to perform the typical functionalities of the traditional

Wenjie Wu, Changjun Fan, Jincai Huang, Zhong Liu

The Bin Packing Problem (BPP) is a well-established combinatorial optimization (CO) problem. Since it has many applications in our daily life, e.g. logistics and resource allocation, people are seeking efficient bin packing algorithms. On the other hand, researchers have been making constant advances in machine learning (ML), which is famous for its efficien

Mauricio Bustamante

The most energetic astrophysical sources in the Milky Way, cosmic accelerators capable of producing high-energy cosmic rays, have resisted discovery for over a century. Up to now, astrophysicists sought these sources mainly by scouring the Galaxy for the gamma rays they are expected to emit. In 2023, the IceCube Neutrino Observatory discovered high-energy ne

Rebeca P. Díaz Redondo, Ana Fernández Vilas, Gabriel Fernández dos Reis

Smart meters are of the basic elements in the so-called Smart Grid. These devices, connected to the Internet, keep bidirectional communication with other devices in the Smart Grid structure to allow remote readings and maintenance. As any other device connected to a network, smart meters become vulnerable to attacks with different purposes, like stealing dat

Sarah Peluse

About twenty years ago, Green wrote a survey article on the utility of looking at toy versions over finite fields of problems in additive combinatorics. This article was extremely influential, and the rapid development of additive combinatorics necessitated a follow-up survey ten years later, which was written by Wolf. Since the publication of Wolf's article

Brian M. Andersen, Andreas Kreisel, P. J. Hirschfeld

A growing number of superconducting materials display evidence for spontaneous time-reversal symmetry breaking (TRSB) below their critical transition temperatures. Precisely what this implies for the nature of the superconducting ground state of such materials, however, is often not straightforward to infer. We review the experimental status and survey diffe

Xianghua Zeng, Hao Peng, Angsheng Li

The importance of effective detection is underscored by the fact that socialbots imitate human behavior to propagate misinformation, leading to an ongoing competition between socialbots and detectors. Despite the rapid advancement of reactive detectors, the exploration of adversarial socialbot modeling remains incomplete, significantly hindering the developm

Zizhen Zhou, Qianqian Zhang, Jungang Ge, Ying-Chang Liang

In space-air-ground integrated networks (SAGINs), cognitive spectrum sharing has been regarded as a promising solution to improve spectrum efficiency by enabling a secondary network to access the spectrum of a primary network. However, different networks in SAGIN may have different quality of service (QoS) requirements, which can not be well satisfied with t

Ruonan Dong, Hui Xu, Han Zhang, GuoPeng Zhang

Federated Learning (FL) is a distributed machine learning paradigm that addresses privacy concerns in machine learning and still guarantees high test accuracy. However, achieving the necessary accuracy by having all clients participate in FL is impractical, given the constraints of client local computing resource. In this paper, we introduce a multi-user col

S. Pochinda, T. Gessey-Jones, H. T. J. Bevins, A. Fialkov

The early Universe, spanning 400,000 to 400 million years after the Big Bang ($z\approx1100-11$), has been left largely unexplored as the light from luminous objects is too faint to be observed directly. While new experiments are pushing the redshift limit of direct observations, measurements in the low-frequency radio band promise to probe early star and bl

Antoine Schnepf, Flavian Vasile, Ugo Tanielian

The recent advances in text and image synthesis show a great promise for the future of generative models in creative fields. However, a less explored area is the one of 3D model generation, with a lot of potential applications to game design, video production, and physical product design. In our paper, we present 3DGEN, a model that leverages the recent work

Soumita Pramanick

A mechanism of radiative generation of realistic neutrino mixing at one-loop level with $D5\times Z_2$ is presented in this paper. The process is demonstrated in two set-ups using $D5\times Z_2$ symmetry viz. Model 1 and Model 2. Two right-handed neutrinos are present in both the models. In both Model 1 and Model 2, when mixing between these two right-handed

Rebeca P. Díaz-Redondo, Carlos Garcia-Rubio, Ana Fernández Vilas, Celeste Campo

Undoubtedly, Location-based Social Networks (LBSNs) provide an interesting source of geo-located data that we have previously used to obtain patterns of the dynamics of crowds throughout urban areas. According to our previous results, activity in LBSNs reflects the real activity in the city. Therefore, unexpected behaviors in the social media activity are a

Martin Lellep, Moritz Linkmann, Alexander Morozov

Solutions of long, flexible polymer molecules are complex fluids that simultaneously exhibit fluid-like and solid-like behaviour. When subjected to an external flow, dilute polymer solutions exhibit elastic turbulence - a unique, chaotic flow state absent in Newtonian fluids, like water. Unlike its Newtonian counterpart, elastic turbulence is caused by polym

Jonas Oppenlaender, Tahir Abbas, Ujwal Gadiraju

Pilot studies are an essential cornerstone of the design of crowdsourcing campaigns, yet they are often only mentioned in passing in the scholarly literature. A lack of details surrounding pilot studies in crowdsourcing research hinders the replication of studies and the reproduction of findings, stalling potential scientific advances. We conducted a systema

Yinlin Guo, Haofan Huang, Xi Chen, He Zhao

With the rapid development of speech synthesis and voice conversion technologies, Audio Deepfake has become a serious threat to the Automatic Speaker Verification (ASV) system. Numerous countermeasures are proposed to detect this type of attack. In this paper, we report our efforts to combine the self-supervised WavLM model and Multi-Fusion Attentive classif

Henrique M. Borges, Vítor V. Vasconcelos, Flávio L. Pinheiro

Recently, social debates have been marked by increased polarization of social groups. Such polarization not only implies that groups cannot reach a consensus on fundamental questions but also materializes in more modular social spaces/networks that further amplify the risks of polarization in less polarizing topics. How can network adaptation bridge differen

Masahiro Ibe, Yuhei Nakayama, Satoshi Shirai

Higgsinos and Winos in the supersymmetric Standard Model are prime candidates for dark matter due to their weakly interacting nature. The mass differences between their charged components (charginos) and neutral components (neutralinos) become degenerate when other superparticles are heavy, resulting in long-lived charginos. In the case of the Winos, the mas

Reza Rahaeimehr, Marten van Dijk

A robust authentication and authorization mechanism is imperative in modular system development, where modularity and modular thinking are pivotal. Traditional systems often employ identity modules responsible for authentication and token issuance. Tokens, representing user credentials, offer advantages such as reduced reliance on passwords, limited lifespan

Maximilian Dinkel, Carolin M. Geitner, Gil Robalo Rei, Jonas Nitzler

Solving inverse problems using Bayesian methods can become prohibitively expensive when likelihood evaluations involve complex and large scale numerical models. A common approach to circumvent this issue is to approximate the forward model or the likelihood function with a surrogate model. But also there, due to limited computational resources, only a few tr

Tianshuo Peng, Zuchao Li, Ping Wang, Lefei Zhang

Multi-modal aspect-based sentiment analysis (MABSA) has recently attracted increasing attention. The span-based extraction methods, such as FSUIE, demonstrate strong performance in sentiment analysis due to their joint modeling of input sequences and target labels. However, previous methods still have certain limitations: (i) They ignore the difference in th

Lucas Luttner

This paper introduces the "Uncertainty-aware Mixture of Experts" (uMoE), a novel solution aimed at addressing aleatoric uncertainty within Neural Network (NN) based predictive models. While existing methodologies primarily concentrate on managing uncertainty during inference, uMoE uniquely embeds uncertainty into the training phase. Employing a "Divide and C

Wen-Lei Zhao, Guanling Li, Jie Liu

We investigate both theoretically and numerically the wavepacket's dynamics in momentum space for a Floquet non-Hermitian system with a periodically-kicked driven potential. We have deduced the exact expression of a time-evolving wavepacket under the condition of quantum resonance. With this analytical expression, we can investigate thoroughly the temporal b

Michal Jex, František Štampach

We prove necessary and sufficient conditions for lattice Schr\"{o}dinger operators to have a zero energy bound state in arbitrary dimension. The two criteria are sharp, complementary, and depend crucially on both the dimension and asymptotic behaviour of the potential. The method relies on a discrete variant of Agmon's comparison principle which is also prov

J. Alfonso-Garzón, J. van den Eijnden, N. P. M. Kuin, F. Fürst

From late September 2017 to February 2018, the Be X-ray binary (BeXB) Swift J0243.6+6124 underwent an unprecedently bright giant outburst. The reported X-ray luminosities were so high that the system was classified as an Ultraluminous X-ray source (ULX). It was also the first BeXB pulsar showing radio jet emission. The source was not only bright in X-rays an

R. Arun, R. Gopal, V. K. Chandrasekar, M. Lakshmanan

We investigate the influence of field-like torque and the direction of the external magnetic field on a one-dimensional array of serially connected spin-torque nano oscillators, having free layers with perpendicular anisotropy, to achieve complete synchronization between them by analyzing the associated Landau-Lifshitz-Gilbert-Slonczewski equation. The obtai

Hao Ma, Zhiyuan Peng, Mingjie Shao, Jing Li

Target-speaker automatic speech recognition (ASR) aims to transcribe the desired speech of a target speaker from multi-talker overlapped utterances. Most of the existing target-speaker ASR (TS-ASR) methods involve either training from scratch or fully fine-tuning a pre-trained model, leading to significant training costs and becoming inapplicable to large fo

Wenting Chen, Linlin Shen, Jingyang Lin, Jiebo Luo

To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process. AdaMatch exploits the fine-grained relation between adaptive patches and words to provide explanations

Alexander V. Kolesnikov

In this survey paper we present classical and recent results relating the auction design and the optimal transportation theory. In particular, we discuss in details the seminal result of Daskalakis, Deckelbaum and Tzamos \cite{DDT} about duality between auction design with $1$ bidder and the weak transportation problem. Later investigations revealed the conn

Sebastian Mair, Matthias Althoff

Cooperative Adaptive Cruise Control (CACC) is a well-studied technology for forming string-stable vehicle platoons. Ensuring collision avoidance is particularly difficult in CACC due to the small desired inter-vehicle spacing. We propose a safety protocol preventing collisions in a provably-correct manner while still maintaining a small distance to the prece

Ruituo Wu, Jiani Liu, Ce Zhu, Anh-Huy Phan

Efficient probability density estimation is a core challenge in statistical machine learning. Tensor-based probabilistic graph methods address interpretability and stability concerns encountered in neural network approaches. However, a substantial number of potential tensor permutations can lead to a tensor network with the same structure but varying express

Mark Turner, Antonia Chmiela, Thorsten Koch, Michael Winkler

A standard tool for modelling real-world optimisation problems is mixed-integer programming (MIP). However, for many of these problems, information about the relationships between variables is either incomplete or highly complex, making it difficult or even impossible to model the problem directly. To overcome these hurdles, machine learning (ML) predictors

Yangrui Liu, Yun Li, Jing Wu, Xinyu Zhang

The structure and properties of water or ice are of great interest to researchers due to their importance in the biological, cryopreservation and environmental fields. Hexagonal ice (Ih) is a common ice phase in nature and has been extensively studied; however, microstructural investigations at the atomic or molecular scale are still lacking. In this paper,

Jingyuan Li, Wei Liu

Stochastic differential equation (SDE in short) solvers find numerous applications across various fields. However, in practical simulations, we usually resort to using Ito-Taylor series-based methods like the Euler-Maruyama method. These methods often suffer from the limitation of fixed time scales and recalculations for different Brownian motions, which lea

Juan Luis Gonzalez Bello, Munchurl Kim

In this paper, we firstly consider view-dependent effects into single image-based novel view synthesis (NVS) problems. For this, we propose to exploit the camera motion priors in NVS to model view-dependent appearance or effects (VDE) as the negative disparity in the scene. By recognizing specularities "follow" the camera motion, we infuse VDEs into the inpu

Yu Duan, Matthew Eaton, Michael Bluck

In this paper, we introduce an efficient sparse Gaussian process (E-SGP) for the surrogate modelling of fluid mechanics. This novel Bayesian machine learning algorithm allows efficient model training using databases of different structures. It is a further development of the approximated sparse GP algorithm, combining the concept of efficient GP (E-GP) and v

Mohamed Sorour, Pål Johan From

In this paper, a novel tool prototype for harvesting table-top grown strawberries is presented. With robustness against strawberry localization error of 15mm and average cycle time of 8.02 seconds at 50% of maximum operational velocity, it provides a promising contribution towards full automation of strawberry harvesting. In addition, the tool has an overall

Denis Likhachov, Nick Petrovsky, Elias Azarov

The paper presents a method for improving spatial resolution of first-order ambisonic audio. The method is based on time/frequency decomposition of the audio with subsequent extraction of a directed plane wave from each frequency component. The method develops the basic ideas of high angular resolution planewave expansion (HARPEX) and directional audio codin

Rudolf Zeller

The irregular solutions of the stationary Schr\"odinger equation are important for the fundamental formal development of scattering theory. They are also necessary for the analytical properties of the Green function, which in practice can speed up calculations enormously. Despite these facts they are seldom considered in numerical treatments. The reason for

Ariel Neufeld, Philipp Schmocker

We introduce a Banach space-valued extension of random feature learning, a data-driven supervised machine learning technique for large-scale kernel approximation. By randomly initializing the feature maps, only the linear readout needs to be trained, which reduces the computational complexity substantially. Viewing random feature models as Banach space-value

Saad Benjelloun, Salma Lahbabi, Abdelqoddous Moussa

We study the homogenization of the Thomas-Fermi-von Weizsacker (TFW) model for 2D materials. It consists in considering 2D-periodic nuclear densities with periods going to zero. We study the behavior of the corresponding ground state electronic densities and ground state energies. The main result is that these three dimensional problems converge to a limit m

Jouseau Roxane, Salva Sébastien, Samir Chafik

Data quality is a key element for building and optimizing good learning models. Despite many attempts to characterize data quality, there is still a need for rigorous formalization and an efficient measure of the quality from available observations. Indeed, without a clear understanding of the training and testing processes, it is hard to evaluate the intrin

Antoine Michel, Loïc Henriet, Christophe Domain, Antoine Browaeys

We propose a hybrid quantum-classical method to investigate the equilibrium physics and the dynamics of strongly correlated fermionic models with spin-based quantum processors. Our proposal avoids the usual pitfalls of fermion-to-spin mappings thanks to a slave-spin method which allows to approximate the original Hamiltonian into a sum of self-correlated fre

Evdoxia Taka, Yuri Nakao, Ryosuke Sonoda, Takuya Yokota

Fairness is a growing concern for high-risk decision-making using Artificial Intelligence (AI) but ensuring it through purely technical means is challenging: there is no universally accepted fairness measure, fairness is context-dependent, and there might be conflicting perspectives on what is considered fair. Thus, involving stakeholders, often without a ba

Vihari Piratla, Juyeon Heo, Katherine M. Collins, Sukriti Singh

Model explanations can be valuable for interpreting and debugging predictive models. We study a specific kind called Concept Explanations, where the goal is to interpret a model using human-understandable concepts. Although popular for their easy interpretation, concept explanations are known to be noisy. We begin our work by identifying various sources of u

Weiyao Ke, Yi Yin

Quark-gluon plasma's (QGP) properties at non-hydrodynamic and non-perturbative regimes remain largely unexplored. Here, we examine the response functions describing how a QGP-like plasma responds to initial energy-momentum disturbance in both static and Bjorken-expanding plasma at non-hydrodynamic gradient using the Boltzmann equation in the relaxation-time

Yongle Jiang, Xiaoyan Zhou

Let $G$ be $S_{\mathbb{N}}$, the finitary permutation (i.e. permutations with finite support) group on positive integers $\mathbb{N}$. We prove that $G$ has the invariant von Neumann subalgebras rigidity (ISR, for short) property as introduced in Amrutam-Jiang's work. More precisely, every $G$-invariant von Neumann subalgebra $P\subseteq L(G)$ is of the form

Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens

To find the geolocation of a street-view image, cross-view geolocalization (CVGL) methods typically perform image retrieval on a database of georeferenced aerial images and determine the location from the visually most similar match. Recent approaches focus mainly on settings where street-view and aerial images are preselected to align w.r.t. translation or

Xiumin Huang, Yan Luo, Kaicheng Sheng, Yiru Ye

We study the secular effects in the motion of an asteroid with negligible mass in a spatial restricted elliptic three body problem with arbitrary inclination. Averaging over mean anomalies of the asteroid and the planet are applied to obtain the double averaged Hamiltonian system. It admits a two-parameter family of orbits corresponding to the motion of the

Antonia Holzapfel, Paul Brunzema, Sebastian Trimpe

Bayesian optimization (BO) has proven to be a powerful tool for automatically tuning control parameters without requiring knowledge of the underlying system dynamics. Safe BO methods, in addition, guarantee safety during the optimization process, assuming that the underlying objective function does not change. However, in real-world scenarios, time-variation

Fares Fourati, Christopher John Quinn, Mohamed-Slim Alouini, Vaneet Aggarwal

We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for combinatorial multi-armed bandit problems when no extra information other than the joint reward of the selected set of $n$ arms at each time step $t\in [T]$ is observed. SGB adopts an optimized stochastic-explore-then-commit approach and is specifically designed for scenarios with

Hamed Qazanfari, Mohammad M. AlyanNezhadi, Zohreh Nozari Khoshdaregi

Content-based image retrieval (CBIR) systems have emerged as crucial tools in the field of computer vision, allowing for image search based on visual content rather than relying solely on metadata. This survey paper presents a comprehensive overview of CBIR, emphasizing its role in object detection and its potential to identify and retrieve visually similar

Abdelmalek Mouazer, Romain Léguillon, Nada Boudegzdame, Thibaud Levrard

Background: Polypharmacy, i.e. taking five drugs or more, is both a public health and an economic issue. Medication reviews are structured interviews of the patient by the community pharmacist, aiming at optimizing the drug treatment and deprescribing useless, redundant or dangerous drugs. However, they remain difficult to perform and time-consuming. Several

Isabelle Hupont, Marina Wainer, Sam Nester, Sylvie Tissot

Recent publications explore AI biases in detecting objects and people in the environment. However, there is no research tackling how AI examines nature. This case study presents a pioneering exploration into the AI attitudes (ecocentric, anthropocentric and antipathetic) toward nature. Experiments with a Large Language Model (LLM) and an image captioning alg

Shengguang Wu, Zhenglun Chen, Qi Su

Ancient artifacts are an important medium for cultural preservation and restoration. However, many physical copies of artifacts are either damaged or lost, leaving a blank space in archaeological and historical studies that calls for artifact image generation techniques. Despite the significant advancements in open-domain text-to-image synthesis, existing ap

June Sallou, Thomas Durieux, Annibale Panichella

Large Language Models (LLMs) have gained considerable traction within the Software Engineering (SE) community, impacting various SE tasks from code completion to test generation, from program repair to code summarization. Despite their promise, researchers must still be careful as numerous intricate factors can influence the outcomes of experiments involving

Zifan Wang, Zhuorui Ye, Haoran Wu, Junyu Chen

We study a new problem of semantic complete scene forecasting (SCSF) in this work. Given a 4D dynamic point cloud sequence, our goal is to forecast the complete scene corresponding to the future next frame along with its semantic labels. To tackle this challenging problem, we properly model the synergetic relationship between future forecasting and semantic

Jihao Xin, Ivan Ilin, Shunkang Zhang, Marco Canini

In distributed training, communication often emerges as a bottleneck. In response, we introduce Kimad, a solution that offers adaptive gradient compression. By consistently monitoring bandwidth, Kimad refines compression ratios to match specific neural network layer requirements. Our exhaustive tests and proofs confirm Kimad's outstanding performance, establ

Yuanbo Tang, Zhiyuan Peng, Yang Li

Trajectory representation learning on a network enhances our understanding of vehicular traffic patterns and benefits numerous downstream applications. Existing approaches using classic machine learning or deep learning embed trajectories as dense vectors, which lack interpretability and are inefficient to store and analyze in downstream tasks. In this paper

Tomáš Kolárik, Stefan Ratschan, Pavel Surynek

This paper introduces a new approach to solving a continuous-time version of the multi-agent path finding problem. The algorithm translates the problem into an extension of the classical Boolean satisfiability problem, satisfiability modulo theories (SMT), that can be solved by off-the-shelf solvers. This enables the exploitation of conflict generalization t

Máté Kadlicskó, Zsolt Lángi, Shanxiang Lyu

In this paper we investigate the problem of finding the minimum edge density in families of convex, normal mosaics with unit volume cells in $n$-dimensional Euclidean space. In the first part of the paper we solve this problem for mosaics whose cells are Minkowski sums of cells of $1$ or $2$-dimensional mosaics. We show that while for $n=2$ this minimum is a

Aldo Kiem, Sebastian Pokutta, Christoph Spiegel

We study a generalization of a famous result of Goodman and establish that asymptotically at least a $1/256$ fraction of all triangles needs to be monochromatic in any four-coloring of the edges of a complete graph. We also show that any large enough extremal construction must be based on a blow-up of one of the two $R(3,3,3)$ Ramsey-colorings of $K_{16}$. T

Xulu Zhang, Xiao-Yong Wei, Jinlin Wu, Tianyi Zhang

Inversion methods, such as Textual Inversion, generate personalized images by incorporating concepts of interest provided by user images. However, existing methods often suffer from overfitting issues, where the dominant presence of inverted concepts leads to the absence of other desired concepts. It stems from the fact that during inversion, the irrelevant

Pete Schroepfer, Nathalie Schauffel, Jan Gründling, Thomas Ellwart

This paper outlines a roadmap to effectively leverage shared mental models in multi-robot, multi-stakeholder scenarios, drawing on experiences from the BugWright2 project. The discussion centers on an autonomous multi-robot systems designed for ship inspection and maintenance. A significant challenge in the development and implementation of this system is th

Abderrahim El Mouhafid, Mouhamadou Hassane Saley, Ahmed Jellal

Trilayer graphene {(TLG)} consists of three layers of graphene arranged in a particular stacking order. In the case of ABC-ABA-ABC stacking, the layers are arranged in an A-B-C sequence, followed by an A-B-A sequence, and again an A-B-C sequence. This stacking arrangement introduces specific electronic properties and band structures due to the different stac

Antonis Antoniou, Karim P. Y. Thébault

The perturbative treatment of realistic quantum field theories, such as quantum electrodynamics, requires the use of mathematical idealizations in the approximation series for scattering amplitudes. Such mathematical idealisations are necessary to derive empirically relevant models from the theory. Mathematical idealizations can be either controlled or uncon

Daniel Burgarth, Paolo Facchi, Alexander Hahn, Mattias Johnsson

Efficient error estimates for the Trotter product formula are central in quantum computing, mathematical physics, and numerical simulations. However, the Trotter error's dependency on the input state and its application to unbounded operators remains unclear. Here, we present a general theory for error estimation, including higher-order product formulas, wit

Anjitha John William, Priyanka Jalan, Maciej Bilicki, Wojciech A. Hellwing

In cosmological analyses, precise redshift determination remains pivotal for understanding cosmic evolution. However, with only a fraction of galaxies having spectroscopic redshifts (spec-$z$s), the challenge lies in estimating redshifts for a larger number. To address this, photometry-based redshift (photo-$z$) estimation, employing machine learning algorit

Uwe Naumann

We use Algorithmic Differentiation (AD) to implement type-generic tangent and adjoint versions of $$ y=\sum_{i=0}^{n-1} x_{2 i} \cdot x_{2 i+1} $$ in C++. We run an instantiation for char-arithmetic and we print the gradient at $(101~77~114~114~32~121~109~88~115~97)^T$ to std::cout, yielding the output ``Merry Xmas''. Similar instantiations of type-generic s

Ziqi Yuan, Liyuan Wang, Wenbo Ding, Xingxing Zhang

In real-world applications, an object detector often encounters object instances from new classes and needs to accommodate them effectively. Previous work formulated this critical problem as incremental object detection (IOD), which assumes the object instances of new classes to be fully annotated in incremental data. However, as supervisory signals are usua

Anna Pidnebesna, David Hartman, Aneta Pokorná, Matěj Straka

The symmetry of complex networks is a global property that has recently gained attention since MacArthur et al. 2008 showed that many real-world networks contain a considerable number of symmetries. These authors work with a very strict symmetry definition based on the network's automorphism. The potential problem with this approach is that even a slight cha

Nolwenn Bernard

We observe a change in the way users access information, that is, the rise of conversational information access (CIA) agents. However, the automatic evaluation of these agents remains an open challenge. Moreover, the training of CIA agents is cumbersome as it mostly relies on conversational corpora, expert knowledge, and reinforcement learning. User simulati

Nick W. Koning

In traditional hypothesis testing one must pre-specify the significance level $\alpha$ to bound the `size' of the test: its probability to falsely reject the hypothesis. Indeed, a data-dependent selection of $\alpha$ would generally distort the size, possibly making it larger than the specified level $\alpha$. We explore hypothesis testing with a data-depend

Oliver Guest, Michael Aird, Seán Ó hÉigeartaigh

AI alignment work is important from both a commercial and a safety lens. With this paper, we aim to help actors who support alignment efforts to make these efforts as effective as possible, and to avoid potential adverse effects. We begin by suggesting that institutions that are trying to act in the public interest (such as governments) should aim to support

Marco Calautti, Ester Livshits, Andreas Pieris, Markus Schneider

Operational consistent query answering (CQA) is a recent framework for CQA based on revised definitions of repairs, which are built by applying a sequence of operations (e.g., fact deletions) starting from an inconsistent database until we reach a database that is consistent w.r.t. the given set of constraints. It has been recently shown that there are effic

Ivan Ip, Duy Phan

We study cluster algebra of affine type $A_1^{(1)}$ by using two methods including counting the numbers of perfect matchings on snake graphs and compatible pairs on maximal Dyck paths. We find that the sum of coefficients of the terms in the Laurent polynomials of these cluster variables are odd-indexed Fibonacci numbers. In addition, we prove that the numbe

Michael Dyck, Alistair Weld, Julian Klodmann, Alexander Kirst

Intraoperative ultrasound imaging is used to facilitate safe brain tumour resection. However, due to challenges with image interpretation and the physical scanning, this tool has yet to achieve widespread adoption in neurosurgery. In this paper, we introduce the components and workflow of a novel, versatile robotic platform for intraoperative ultrasound tiss

Huaiyuan Ying, Zhengyun Zhao, Yang Zhao, Sihang Zeng

Objective: Biomedical Knowledge Graphs play a pivotal role in various biomedical research domains. Concurrently, term clustering emerges as a crucial step in constructing these knowledge graphs, aiming to identify synonymous terms. Due to a lack of knowledge, previous contrastive learning models trained with Unified Medical Language System (UMLS) synonyms st

Pascal Hémon

We present a study of the hydrodynamic characteristics of sea kayak paddles without taking into account the kayaker. We focus on traditional paddles used in the Arctic, one from Greenland and one from the Aleutian Islands. A basic modern European paddle is included in the study for comparison. First the paddle stroke parameters specific to sea kayaking are i

Mushfiqur Rahman, Runze Liu, Chau-Wai Wong, Huaiyu Dai

In today's digital landscape, journalists urgently require tools to verify the authenticity of facial images and videos depicting specific public figures before incorporating them into news stories. Existing deepfake detectors are not optimized for this detection task when an image is associated with a specific and identifiable individual. This study focuses