October 2024 arXiv papers — page 83

Xabier Pérez-Couto, Lara Pallas-Quintela, Minia Manteiga, Eva Villaver

The identification of new white dwarfs (WDs) polluted with heavy elements is important since they provide a valuable tool for inferring chemical properties of putative planetary systems accreting material on their surfaces. The Gaia space mission has provided us with an unprecedented amount of astrometric, photometric, and low resolution (XP) spectroscopic d

Rozita Konstantinou, Ihsan Kanbaz, Okan Yurduseven, Michail Matthaiou

We propose a novel approach for boosting the realized gain in enhanced directivity arrays with both active and parasitic dipoles as radiating elements. The optimization process involves two main objectives: maximizing the end-fire gain and minimizing the reflection coefficient to ensure high realized gain. In the first step, the current excitation vector of

Raghav Bongole, Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering

We study agents acting in an unknown environment where the agent's goal is to find a robust policy. We consider robust policies as policies that achieve high cumulative rewards for all possible environments. To this end, we consider agents minimizing the maximum regret over different environment parameters, leading to the study of minimax regret. This resear

Abhijeet Kumar, Unnati Singh, Rajdeep Chatterjee, Tathagata Bandyopadhyay

An efficient system of a queue control and regulation in public spaces is very important in order to avoid the traffic jams and to improve the customer satisfaction. This article offers a detailed road map based on a merger of intelligent systems and creating an efficient systems of queues in public places. Through the utilization of different technologies i

Xi Xu, Wenda Xu, Siqi Ouyang, Lei Li

Simultaneous speech translation (SimulST) systems must balance translation quality with response time, making latency measurement crucial for evaluating their real-world performance. However, there has been a longstanding belief that current metrics yield unrealistically high latency measurements in unsegmented streaming settings. In this paper, we investiga

Bernardo D'Auria, Carlos Escudero

We study the anticipating version of the classical portfolio optimization problem in a financial market with the presence of a trader who possesses privileged information about the future (insider information), but who is also subjected to a delay in the information flow about the market conditions; hence this trader possesses an asymmetric information with

Rohit Das, Tzung-Han Lin, Ko-Chih Wang

Geometry and texture estimation from a single face image is an ill-posed problem since there is very little information to work with. The problem further escalates when the face is rotated at a different angle. This paper tries to tackle this problem by introducing a novel method for texture estimation from a single image by first using StyleGAN and 3D Morph

Seyed Hamed Haghshenas, Mia Naeini

State Estimation is a crucial task in power systems. Graph Neural Networks have demonstrated significant potential in state estimation for power systems by effectively analyzing measurement data and capturing the complex interactions and interrelations among the measurements through the system's graph structure. However, the information about the system's gr

Charles Lovering, Michael Krumdick, Viet Dac Lai, Seth Ebner

Some statements have one well-defined continuation (e.g., "the Eiffel Tower is in [Paris]"), whereas others have a natural distribution over multiple options (e.g., "the weighted coin flip was [Heads/Tails].") We argue that language model (LM) outputs should capture these natural distributions. Our work specifically tests whether LM output probabilities are

Divyanshu Aggarwal, Sankarshan Damle, Navin Goyal, Satya Lokam

A common challenge towards the adaptability of Large Language Models (LLMs) is their ability to learn new languages over time without hampering the model's performance on languages in which the model is already proficient (usually English). Continual fine-tuning (CFT) is the process of sequentially fine-tuning an LLM to enable the model to adapt to downstrea

Philip Boeken, Patrick Forré, Joris M. Mooij

Faithfulness is a common assumption in causal inference, often motivated by the fact that the faithful parameters of linear Gaussian and discrete Bayesian networks are typical, and the folklore belief that this should also hold for other classes of Bayesian networks. We address this open question by showing that among all Bayesian networks over a given DAG,

Behzad Eslam Panah

Motivated by a new interesting nonlinear electrodynamics (NLED) model which is known as Modification Maxwell (ModMax) theory, we obtain an exact analytic BTZ black hole solution in the presence of a new NLED model and the cosmological constant. Then, by considering the obtained solution, we obtain Hawking temperature, entropy, electric charge, mass, and elec

Nicolas Lenzing, David Krüger, Michael Potthoff

The real-time dynamics of local magnetic moments exchange coupled to a metallic system of conduction electrons is subject to dissipative friction even in the absence of spin-orbit coupling. Phenomenologically, this is usually described by a local Gilbert damping constant. Here, we use both linear response theory and adiabatic response theory to derive the sp

Xiaoyu Xu

We prove that any compact, orientable 3-manifold with empty or toral boundary is profinitely almost rigid among all compact, orientable 3-manifolds. In other words, the profinite completion of its fundamental group determines its homeomorphism type to finitely many possibilities. Moreover, the profinite completion of the fundamental group of a mixed 3-manifo

Hana Mizerová

We define a dissipative measure-valued (DMV) solution to the system of equations governing the motion of a general compressible, viscous, electrically and heat conducting fluid driven by non-conservative boundary conditions. We show the stability of strong solutions to the full compressible magnetohydrodynamic system in a large class of these DMV solutions.

P. V. Sreenivasa Reddy, Guang-Yu Guo

The CoSi-family of materials (CoSi, CoGe, RhSi and RhGe) forms a cubic chiral structure and hosts unconventional multifold chiral fermions, such as spin-1 and spin-3/2 fermions, leading to intriguing phenomena like long Fermi arc surface states and exotic transport properties. Recent interest on the phonon behavior in chiral materials is growing due to their

Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du

Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context -- this phenomenon, known as \emph{context-memory knowledge conflicts}, can lead to undesirable model behaviour, such as reliance on outdated or incorrect informati

Ram Mohan Rao Kadiyala, M. V. P. Chandra Sekhara Rao

Social media is a great source of data for users reporting information and regarding their health and how various things have had an effect on them. This paper presents various approaches using Transformers and Large Language Models and their ensembles, their performance along with advantages and drawbacks for various tasks of SMM4H'24 - Classifying texts on

Shiyan Hu, Kai Zhao, Xiangfei Qiu, Yang Shu

Many methods have been proposed for unsupervised time series anomaly detection. Despite some progress, research on predicting future anomalies is still relatively scarce. Predicting anomalies is particularly challenging due to the diverse reaction time and the lack of labeled data. To address these challenges, we propose MultiRC to integrate reconstructive a

Flavio Figueiredo, Tales Panoutsos, Nazareno Andrade

Analyzing musical influence networks, such as those formed by artist influence or sampling, has provided valuable insights into contemporary Western music. Here, computational methods like centrality rankings help identify influential artists. However, little attention has been given to how influence changes over time. In this paper, we apply Bayesian Surpri

Pavan Kumar Gadamsetty, K. V. S. Hari, Lajos Hanzo

Reconfigurable holographic surfaces (RHS) are intrinsically amalgamated with reconfigurable intelligent surfaces (RIS), for beneficially ameliorating the signal propagation environment. This potent architecture significantly improves the system performance in non-line-of-sight scenarios at a low power consumption. Briefly, the RHS technology integrates ultra

Yue Yang, Bryce Ikeda, Gedas Bertasius, Daniel Szafir

Robot Imitation Learning (IL) is a crucial technique in robot learning, where agents learn by mimicking human demonstrations. However, IL encounters scalability challenges stemming from both non-user-friendly demonstration collection methods and the extensive time required to amass a sufficient number of demonstrations for effective training. In response, we

Benedikt Eisenhuth, Martin Grothaus

We consider a degenerate infinite dimensional stochastic Hamiltonian system with multiplicative noise and establish the essential m-dissipativity on $L^2(\mu^{\Phi})$ of the corresponding Kolmogorov (backwards) operator. Here, $\Phi$ is the potential and $\mu^{\Phi}$ the invariant measure with density $e^{-\Phi}$ with respect to an infinite dimensional non-d

Guillermo Gordillo-Núñez

We review the so-called Nikiforov-Uvarov method along with some basic results about classical orthogonal polynomials and hypergeometric functions related to the hypergeometric differential equation. The method is employed to address certain eigenvalue problems that appear in quantum mechanics, namely, time-independent Schr\"odinger equation, paying special a

Moritz Wolf, Lars O. Stietz, Patrick L. S. Connor, Peter Schleper

The availability of precise and accurate simulation is a limiting factor for interpreting and forecasting data in many fields of science and engineering. Often, one or more distinct simulation software applications are developed, each with a relative advantage in accuracy or speed. The quality of insights extracted from the data stands to increase if the acc

Arie Bodek, M. E. Christy, Zihao Lin, Giulia-Maria Bulugean

We present a brief report (at the Nufact-2024 conference) summarizing a global extraction of the ${\rm ^{12}C}$ longitudinal (${\cal R}_L$) and transverse (${\cal R}_T$) nuclear electromagnetic response functions from an analysis of all available electron scattering data on carbon. Since the extracted response functions cover a large kinematic range they can

Ram Mohan Rao Kadiyala, Siddartha Pullakhandam, Kanwal Mehreen, Subhasya Tippareddy

This paper presents our system description and error analysis of our entry for NLLP 2024 shared task on Legal Natural Language Inference (L-NLI) \citep{hagag2024legallenssharedtask2024}. The task required classifying these relationships as entailed, contradicted, or neutral, indicating any association between the review and the complaint. Our system emerged

Hadi Madanian, Li-Jen Chen, Jonathan Ng, Michael J. Starkey

The magnetic cloud within an interplanetary coronal mass ejection (ICME) is characterized by high magnetic field intensities. In this study, we investigate the interaction of a magnetic cloud carrying a density structure with the Earth's bow shock during the ICME event on 24 April 2023. Elevated abundances of cold protons and heavier ions, namely alpha parti

Jaeyong Bae, Yongjoo Baek, Hawoong Jeong

While deep learning has been successfully applied to the data-driven classification of anomalous diffusion mechanisms, how the algorithm achieves the feat still remains a mystery. In this study, we use a well-known technique aimed at achieving explainable AI, namely the Gradient-weighted Class Activation Map (Grad-CAM), to investigate how deep learning (impl

Shiming Zhang, Zhen Lu, Yue Yang

We simulated the intermittent boundary-layer flashback (BLF) of hydrogen-enriched swirling flames using large-eddy simulation (LES) with the flame-surface-density (FSD) method. Three cases of intermittent BLF, characterized by periodic flame entry and exit of the mixing tube, are presented. The intermittent BLF characteristics varied with the hydrogen volume

Matthias Bitzer, Reinis Cimurs, Benjamin Coors, Johannes Goth

Simulation plays a crucial role in the rapid development and safe deployment of autonomous vehicles. Realistic traffic agent models are indispensable for bridging the gap between simulation and the real world. Many existing approaches for imitating human behavior are based on learning from demonstration. However, these approaches are often constrained by foc

Morenikeji Neri, Thomas Powell

The Robbins-Siegmund theorem is one of the most important results in stochastic optimization, where it is widely used to prove the convergence of stochastic algorithms. We provide a quantitative version of the theorem, establishing a bound on how far one needs to look in order to locate a region of \emph{metastability} in the sense of Tao. Our proof involves

Alessandro Ottaviano, Robert Balas, Tim Fischer, Thomas Benz

The growing complexity of real-time control algorithms with increasing performance demands, along with the shift to 2.5D technology, drive the need for scalable controllers to manage chiplets' coupled operation in 2.5D systems-in-package. These controllers must offer real-time computing capabilities, as well as System-in-package (SiP) compatible IO interface

Riccardo Zanella, Federico Califano, Antonio Franchi, Stefano Stramigioli

In this letter, we propose a control scheme for rigid bodies designed to optimise transient behaviors. The search space for the optimal control input is parameterized to yield a passive, specifically lossless, nonlinear feedback controller. As a result, it can be combined with other stabilizing controllers without compromising the stability of the closed-loo

S. M. Yousuf Iqbal Tomal, Abdullah Al Shafin, Afrida Afaf, Debojit Bhattacharjee

This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for cla

Peter Morfe, Felix Otto, Christian Wagner

In this note, we connect two seemingly unrelated objects: On the one hand is a two-dimensional drift-diffusion process $X$ with divergence-free and time-independent drift $b$. The drift is given by a stationary Gaussian ensemble, and we focus on the critical case where a small-scale cut-off is necessary for well-posedness and the large-scale cancellations le

Mohammad Farazmand

Sparse Discrete Empirical Interpolation Method (S-DEIM) was recently proposed for state estimation in dynamical systems when only a sparse subset of the state variables can be observed. The S-DEIM estimate involves a kernel vector whose optimal value is inferred through a data assimilation algorithm. This data assimilation step suffers from two drawbacks: (i

Hongkuan Zhou, Lavdim Halilaj, Sebastian Monka, Stefan Schmid

Despite the remarkable success of deep neural networks (DNNs) in computer vision, they fail to remain high-performing when facing distribution shifts between training and testing data. In this paper, we propose Knowledge-Guided Visual representation learning (KGV) - a distribution-based learning approach leveraging multi-modal prior knowledge - to improve ge

Shizhen Zhao, Xin Wen, Jiahui Liu, Chuofan Ma

Balancing training on long-tail data distributions remains a long-standing challenge in deep learning. While methods such as re-weighting and re-sampling help alleviate the imbalance issue, limited sample diversity continues to hinder models from learning robust and generalizable feature representations, particularly for tail classes. In contrast to existing

Johannes Heeg, Yunlong Song, Davide Scaramuzza

The sample inefficiency of reinforcement learning (RL) remains a significant challenge in robotics. RL requires large-scale simulation and can still cause long training times, slowing research and innovation. This issue is particularly pronounced in vision-based control tasks where reliable state estimates are not accessible. Differentiable simulation offers

João Pedro Fernandes Torres, Catherine Mulligan, Joaquim Jorge, Catarina Moreira

The growing volume of academic publications poses significant challenges for researchers conducting timely and accurate Systematic Literature Reviews, particularly in fast-evolving fields like artificial intelligence. This growth of academic literature also makes it increasingly difficult for lay people to access scientific knowledge effectively, meaning aca

Zhehui Wang, Tao Luo, Cheng Liu, Weichen Liu

Large language models (LLMs) have garnered substantial attention due to their promising applications in diverse domains. Nevertheless, the increasing size of LLMs comes with a significant surge in the computational requirements for training and deployment. Memristor crossbars have emerged as a promising solution, which demonstrated a small footprint and rema

Adam Brandenburger, Pierfrancesco La Mura

Phase-space versions of quantum mechanics -- from Wigner's original distribution to modern discrete-qudit constructions -- represent some states with negative quasi-probabilities. Conventional Shannon and R\'enyi entropies become complex-valued in this setting and lose their operational meaning. Building on the axiomatic treatments of R\'enyi (1961) and Dar\

Zong-guo Si, Hong-xin Wang, Lei Wang, Yang Zhang

Multiple electroweak phase transitions occurring sequentially in the early universe can give rise to intriguing phenomenology, compared to the typical single-step electroweak phase transition. In this work, we investigate this scenario within the framework of the two-Higgs-doublet model with a pseudoscalar, utilizing the complete one-loop finite-temperature

Ram Mohan Rao Kadiyala

This paper presents a detailed system description of our entry for the WASSA 2024 Task 2, focused on cross-lingual emotion detection. We utilized a combination of large language models (LLMs) and their ensembles to effectively understand and categorize emotions across different languages. Our approach not only outperformed other submissions with a large marg

Shreya Arvind, Rishabh Pomaje, Rajshekhar V Bhat

This paper presents a novel approach to solving convex optimization problems by leveraging the fact that, under certain regularity conditions, any set of primal or dual variables satisfying the Karush-Kuhn-Tucker (KKT) conditions is necessary and sufficient for optimality. Similar to Theory-Trained Neural Networks (TTNNs), the parameters of the convex optimi

Nanyan Xu, Yunhe Sheng

The purpose of this paper is to clarify the relations between various constructions of solutions of the Yang-Baxter equation from Leibniz algebras, racks, 3-Leibniz algebras, 3-racks, linear racks, trilinear racks, and give new constructions of solutions of the Yang-Baxter equation. First we show that a 3-Leibniz algebra naturally gives rise to a 3-rack on t

Junsheng Zhou, Yu-Shen Liu, Zhizhong Han

Large language and vision models have been leading a revolution in visual computing. By greatly scaling up sizes of data and model parameters, the large models learn deep priors which lead to remarkable performance in various tasks. In this work, we present deep prior assembly, a novel framework that assembles diverse deep priors from large models for scene

Longxuan Ma, Jiapeng Li, Mingda Li, Wei-Nan Zhang

Document-grounded dialogue (DGD) uses documents as external knowledge for dialogue generation. Correctly understanding the dialogue context is crucial for selecting knowledge from the document and generating proper responses. In this paper, we propose using a dialogue policy to help the dialogue understanding in DGD. Our dialogue policy consists of two kinds

Barbara Hof

The European Organization for Nuclear Research (CERN) in Geneva is renowned for operating the world`s largest particle accelerator and is often regarded as a model of high-profile international collaboration. Less well known, however, is a key episode from the late 1950s, when CERN was confronted with the research priorities of similar organisations. The iss

Giampiero Marra, Rosalba Radice

Motivated by empirical studies investigating treatment effects in survival analysis, we propose a bivariate transformation model to quantify the impact of a binary treatment on a time-to-event outcome. The model equations are connected through a bivariate Gaussian distribution, with the dependence parameter capturing unobserved confounding, and are specified

D. K. He, Z. Song

Exceptional points (EPs), a unique feature of non-Hermitian systems, represent degeneracies in non-Hermitian operators that likely do not occur in Hermitian systems. Nevertheless, unlike its fermionic counterpart, a Hermitian bosonic Kitaev model supports a non-Hermitian core matrix, involving a quantum phase transition (QPT) when an exceptional point appear

Lishui Fan, Mouxiang Chen, Zhongxin Liu

Large language models (LLMs) have achieved impressive performance in code generation. However, due to the long-tail distribution of LLMs' training data, low-frequency terms are typically underrepresented in the training process. Consequently, LLMs often misunderstand or overlook problem-specific, low-frequency keywords during code generation, compromising th

Tomas André, Emiliano De Santis, Nicusor Timneanu, Carl Caleman

Single Particle Imaging techniques at X-ray lasers have made significant strides, yet the challenge of determining the orientation of freely rotating molecules during delivery remains. In this study, we propose a novel method to partially retrieve the relative orientation of proteins exposed to ultrafast X-ray pulses by analyzing the fragmentation patterns r

David MacTaggart, Tom Williams, OPM Aslam

In the first half of May 2024, the solar active region (AR)13664 was responsible for generating the strongest geomagnetic storm in over 20 years, through an enhanced production of X-class flares and coronal mass ejections (CMEs). A key factor in this production was the complex magnetic topology of AR13664. In this work, we investigate the region's magnetic t

Alexander Fleming, Jens Deussen, Uwe Naumann

A sequential solver for differential-algebraic equations with embedded optimization criteria (DAEOs) was developed to take advantage of the theoretical work done by Deussen et al. Solvers of this type separate the optimization problem from the differential equation and solve each individually. The new solver relies on the reduction of a DAEO to a sequence of

Yugandhar Reddy Gogireddy, Jithendra Reddy Gogireddy

Reproducibility in scientific research, particularly within the realm of natural language processing (NLP), is essential for validating and verifying the robustness of experimental findings. This paper delves into the reproduction and evaluation of dialogue summarization models, focusing specifically on the discrepancies observed between original studies and

Daniel Keren, Margarita Osadchy, Roi Poranne

Sum of squares (SOS) optimization is a powerful technique for solving problems where the positivity of a polynomials must be enforced. The common approach to solve an SOS problem is by relaxation to a Semidefinite Program (SDP). The main advantage of this transormation is that SDP is a convex problem for which efficient solvers are readily available. However

Mahir Shahriar Dhrubo, Samira Akter, Anwarul Bashir Shuaib, Md Toki Tahmid

Efficient vectorization of hand-drawn cadastral maps, such as Mouza maps in Bangladesh, poses a significant challenge due to their complex structures. Current manual digitization methods are time-consuming and labor-intensive. Our study proposes a semi-automated approach to streamline the digitization process, saving both time and human resources. Our method

Animesh Kumar

The world has witnessed rapid technological transformation, past couple of decades and with Advent of Cloud computing the landscape evolved exponentially leading to efficient and scalable application development. Now, the past couple of years the digital ecosystem has brought in numerous innovations with integration of Artificial Intelligence commonly known

Zhi Hou, Tianyi Zhang, Yuwen Xiong, Hengjun Pu

Recent large vision-language-action models pretrained on diverse robot datasets have demonstrated the potential for generalizing to new environments with a few in-domain data. However, those approaches usually predict individual discretized or continuous action by a small action head, which limits the ability in handling diverse action spaces. In contrast, w

Shunsuke Inenaga, Dmitry Kosolobov

The compact directed acyclic word graph (CDAWG) of a string $T$ is an index occupying $O(\mathsf{e})$ space, where $\mathsf{e}$ is the number of right extensions of maximal repeats in $T$. For highly repetitive datasets, the measure $\mathsf{e}$ typically is small compared to the length $n$ of $T$ and, thus, the CDAWG serves as a compressed index. Unlike oth

Guangcong Zheng, Teng Li, Rui Jiang, Yehao Lu

Recent advancements have integrated camera pose as a user-friendly and physics-informed condition in video diffusion models, enabling precise camera control. In this paper, we identify one of the key challenges as effectively modeling noisy cross-frame interactions to enhance geometry consistency and camera controllability. We innovatively associate the qual

Ming Guan, Michael Bartokos, Kjeld Beeks, Yuta Fukunaga

Thorium-229, with its exceptionally low-energy nuclear excited state, is a key candidate for developing nuclear clocks. $^{229}$Th-doped CaF$_2$ crystals, benefiting from calcium fluoride's wide band gap, show great promise as solid-state nuclear clock materials. These crystals are excited by vacuum ultraviolet (VUV) lasers, which over time cause radiation d

Yanzhu Guo, Simone Conia, Zelin Zhou, Min Li

Current Large Language Models (LLMs) are predominantly designed with English as the primary language, and even the few that are multilingual tend to exhibit strong English-centric biases. Much like speakers who might produce awkward expressions when learning a second language, LLMs often generate unnatural outputs in non-English languages, reflecting English

Paul A. Jenkins

The Wright-Fisher diffusion is a fundamentally important model of evolution encompassing genetic drift, mutation, and natural selection. Suppose you want to infer the parameters associated with these processes from an observed sample path. Then to write down the likelihood one first needs to know the mutual arrangement of two path measures under different pa

Jiaxu Li, Kejia Fan, Songning Lai, Linpu Lv

Time series classification underpins critical applications such as healthcare diagnostics and gesture-driven interactive systems in multimedia scenarios. However, time series class-incremental learning (TSCIL) faces two major challenges: catastrophic forgetting and intra-class variations. Catastrophic forgetting occurs because gradient-based parameter update

Gunnar Wilken

Building on Buchholz' assignment for ordinals below Bachmann-Howard ordinal, see Buchholz 2003, we introduce systems of fundamental sequences for two kinds of relativized $\vartheta$-function-based notation systems of strength $\Pi^1_1{\operatorname{-CA}_0}$ and prove Bachmann property for these systems, which is essential for monotonicity properties of subr

Szymon Bobek, Paloma Korycińska, Monika Krakowska, Maciej Mozolewski

This study is located in the Human-Centered Artificial Intelligence (HCAI) and focuses on the results of a user-centered assessment of commonly used eXplainable Artificial Intelligence (XAI) algorithms, specifically investigating how humans understand and interact with the explanations provided by these algorithms. To achieve this, we employed a multi-discip

Animesh Kumar

With rapid transformation of technologies, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) in finance is disrupting the entire ecosystem and operations which were followed for decades. The current landscape is where decisions are increasingly data-driven by financial institutions with an appetite for automation while mitigating risks. Th

Anton Svirsky, Anna Frishman

We study the self-organization of turbulence in a geophysically motivated two-dimensional fluid with local interactions. Using simulations and theory, we show that the out-of-equilibrium flux to small scales imposes a constraint on the large-scale emergent flow. Consequently, a rich phase diagram of large-scale configurations emerges, replacing the unique st

Michal Novák, Barbora Dohnalová, Miloslav Konopík, Anna Nedoluzhko

The paper presents an overview of the third edition of the shared task on multilingual coreference resolution, held as part of the CRAC 2024 workshop. Similarly to the previous two editions, the participants were challenged to develop systems capable of identifying mentions and clustering them based on identity coreference. This year's edition took another s

Lorenzo Varrassi, Florian Ellinger, Espen Flage-Larsen, Michael Wolloch

The GW approximation represents the state-of-the-art ab-initio method for computing excited-state properties. Its execution requires control over a larger number of (often interdependent) parameters, and therefore its application in high-throughput studies is hindered by the intricate and time-consuming convergence process across a multi-dimensional paramete

Yuki Hagiwara, Octavia-Andreea Ciora, Maureen Monnet, Gino Lancho

The diagnosis of glaucoma plays a critical role in the management and treatment of this vision-threatening disease. Glaucoma is a group of eye diseases that cause blindness by damaging the optic nerve at the back of the eye. Often called "silent thief of sight", it exhibits no symptoms during the early stages. Therefore, early detection is crucial to prevent

Ao Jin, Chenhao Li, Qinyi Wang, Ya Liu

Aerial robotics for transporting suspended payloads as the form of freely-floating manipulator are growing great interest in recent years. However, the force/torque caused by payload and residual dynamics will introduce unmodeled perturbations to the aerial robotics, which negatively affects the closed-loop performance. Different from estimation-like methods

Guy Blachar

We show that the lamplighter groups $(\mathbb{Z}/p\mathbb{Z})^n\wr\mathbb{Z}$, where $p$ is prime and $n\ge 1$ is a positive integer, are profinitely rigid.

Ayman Asad Khan, Md Toufique Hasan, Kai Kristian Kemell, Jussi Rasku

This paper presents an experience report on the development of Retrieval Augmented Generation (RAG) systems using PDF documents as the primary data source. The RAG architecture combines generative capabilities of Large Language Models (LLMs) with the precision of information retrieval. This approach has the potential to redefine how we interact with and augm

Timo Jakumeit, Lukas Brand, Jens Kirchner, Robert Schober

The notion of synthetic molecular communication (MC) refers to the transmission of information via molecules and is largely foreseen for use within the human body, where traditional electromagnetic wave (EM)-based communication is impractical. MC is anticipated to enable innovative medical applications, such as early-stage tumor detection, targeted drug deli

Eva Luvison, Sylvain Chatel, Justinas Sukaitis, Vincent Graf Narbel

Humanitarian organizations distribute aid to people affected by armed conflicts or natural disasters. Digitalization has the potential to increase the efficiency and fairness of aid-distribution systems, and recent work by Wang et al. has shown that these benefits are possible without creating privacy harms for aid recipients. However, their work only provid

Jiayi Song, Weidong Yang, Zhijun Li, Wen-Ming Chen

The task of point cloud upsampling (PCU) is to generate dense and uniform point clouds from sparse input captured by 3D sensors like LiDAR, holding potential applications in real yet is still a challenging task. Existing deep learning-based methods have shown significant achievements in this field. However, they still face limitations in effectively handling

Sören von Bülow, Giulio Tesei, Kresten Lindorff-Larsen

Recent years have seen tremendous developments in the use of machine learning models to link amino acid sequence, structure and function of folded proteins. These methods are, however, rarely applicable to the wide range of proteins and sequences that comprise intrinsically disordered regions. We here review developments in the study of sequence-ensemble-fun

Ivaxi Sheth, Bahare Fatemi, Mario Fritz

Causality is essential in scientific research, enabling researchers to interpret true relationships between variables. These causal relationships are often represented by causal graphs, which are directed acyclic graphs. With the recent advancements in Large Language Models (LLMs), there is an increasing interest in exploring their capabilities in causal rea

M. V. Tamm, M. Oiva, K. D. Mukhina, M. Mets

Cultural data typically contains a variety of biases. In particular, geographical locations are unequally portrayed in media, creating a distorted representation of the world. Identifying and measuring such biases is crucial to understand both the data and the socio-cultural processes that have produced them. Here we suggest to measure geographical biases in

R. J. Shalloo, A. Ferran Pousa, M. Mewes, S. Jalas

We propose a novel scheme for controlling the injection of a high-quality electron bunch into a channel-guided laser plasma accelerator. This all-optical technique, constricted waveguide injection, creates a highly tunable controlled injection structure natively within a plasma waveguide, a key requirement for efficient acceleration of high-quality multi-GeV

Yukihiro Okamoto

For any compact connected submanifold $K$ of $\mathbb{R}^n$, let $\Lambda_K$ denote its unit conormal bundle, which is a Legendrian submanifold of the unit cotangent bundle of $\mathbb{R}^n$. In this paper, we give examples of pairs $(K_0,K_1)$ of compact connected submanifolds of $\mathbb{R}^n$ such that $\Lambda_{K_0}$ is not Legendrian isotopic to $\Lambd

Irene De Blasi

This work presents some results regarding three-dimensional billiards having a non-constant potential of Keplerian type inside a regular domain $D\subset \mathcal R^3$. Two models will be analysed: in the first one, only an inner Keplerian potential is present, and every time the particle encounters the boundary of $D$ is reflected back by keeping constant i

Tomas André, Ibrahim Dawod, Sebastian Cardoch, Emiliano De Santis

We simulated the Coulomb explosion dynamics due to the fast ionization induced by high-intensity X-rays in six proteins that share similar atomic content and shape. We followed and projected the trajectory of the fragments onto a virtual detector, providing a unique explosion footprint. After collecting 500 explosion footprints for each protein, we utilized

I. C. Jebaraj, O. V. Agapitov, M. Gedalin, L. Vuorinen

In this study, we present the first-ever direct measurements of synchrotron-emitting heliospheric traveling shocks, intercepted by the Parker Solar Probe (PSP) during its close encounters. Given that much of our understanding of powerful astrophysical shocks is derived from synchrotron radiation, these observations by PSP provide an unprecedented opportunity

Jiawei Zhao, Qixing Jiang, Xuede Li, Junfeng Luo

Birds-Eye-View (BEV) segmentation aims to establish a spatial mapping from the perspective view to the top view and estimate the semantic maps from monocular images. Recent studies have encountered difficulties in view transformation due to the disruption of BEV-agnostic features in image space. To tackle this issue, we propose a novel FocusBEV framework con

Henri Funk, Ralf Ludwig, Helmut Kuechenhoff, Thomas Nagler

Climate model large ensembles are an essential research tool for analysing and quantifying natural climate variability and providing robust information for rare extreme events. The models simulated representations of reality are susceptible to bias due to incomplete understanding of physical processes. This paper aims to correct the bias of five climate vari

Hadeel Saadany, Swapnil Bhosale, Samarth Agrawal, Diptesh Kanojia

This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to users' search queries. Ambiguity and complexity of user queries often lead to a mismatch between the user's intent and retrieved product titles or documents. Recent approaches have proposed the use of Transformer-based models, whic

Koji Inoue, Divesh Lala, Gabriel Skantze, Tatsuya Kawahara

In human conversations, short backchannel utterances such as "yeah" and "oh" play a crucial role in facilitating smooth and engaging dialogue. These backchannels signal attentiveness and understanding without interrupting the speaker, making their accurate prediction essential for creating more natural conversational agents. This paper proposes a novel metho

Soumitra Kolya, Puneet Pareek, Saroj Kumar Nandi

Glassy dynamics in a dense system of active particles with self-propulsion force $f_0$ and persistence time $\tau_p$ are crucial for many biological processes. Recent studies have shown that, unlike relaxation dynamics, dynamic heterogeneity (DH) in active glasses exhibits nontrivial behavior. However, the mechanism by which activity affects DH remains unkno

Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam, Karlo Serbetar

Reliable facial expression learning (FEL) involves the effective learning of distinctive facial expression characteristics for more reliable, unbiased and accurate predictions in real-life settings. However, current systems struggle with FEL tasks because of the variance in people's facial expressions due to their unique facial structures, movements, tones,

Yun Xing, Yiheng Li, Ivan Laptev, Shijian Lu

Recent Large Vision Language Models (LVLMs) present remarkable zero-shot conversational and reasoning capabilities given multimodal queries. Nevertheless, they suffer from object hallucination, a phenomenon where LVLMs are prone to generate textual responses not factually aligned with image inputs. Our pilot study reveals that object hallucination is closely

Dario Lucente, Marco Baldovin, Fabio Cecconi, Massimo Cencini

Current research in statistical mechanics mostly concerns the investigation of out-of-equilibrium, irreversible processes, which are ubiquitous in nature and still far from being theoretically understood. Even the precise characterization of irreversibility is the object of an open debate: while in the context of Hamiltonian systems the one-century-old propo

Maoyin Lv, Hao Wu

We consider a class of nonlocal Cahn-Hilliard equations in a bounded domain $\Omega\subset\mathbb{R}^{d}$ $(d\in\{2,3\})$, subject to a nonlocal kinetic rate dependent dynamic boundary condition. This diffuse interface model describes phase separation processes with possible long-range interactions both within the bulk material and on its boundary. The kinet

Sheheryar Mehmood, Peter Ochs

Numerous Optimization Algorithms have a time-varying update rule thanks to, for instance, a changing step size, momentum parameter or, Hessian approximation. In this paper, we apply unrolled or automatic differentiation to a time-varying iterative process and provide convergence (rate) guarantees for the resulting derivative iterates. We adapt these converge

A. V. Mazaev

This paper is devoted to the exploration of rectangular finite elements' ability to model the stress-strain state of isotropic and orthotropic materials with a negative Poisson's ratio, known as auxetic materials. By employing linear elasticity in the plane stress formulation, the research evaluates the linear compatible and the quadratic incompatible shape

Jesus Bautista, Antonio Acuaviva, Jose Hinojosa, Weijia Yao

Existing source-seeking algorithms for robot swarms typically require either direct gradient measurements or rigid geometric formations, limiting their flexibility and resilience to robot failures. We propose a fully distributed solution that overcomes these limitations by computing an ascending direction through local field measurements and distributed esti