October 2023 arXiv papers — page 150

Piotr Z. Stasiak, Andrew W. Baggaley, Giorgio Krstulovic, Carlo F. Barenghi

The reciprocal energy and enstrophy transfers between normal fluid and superfluid components dictate the overall dynamics of superfluid $^4$He including the generation, evolution and coupling of coherent structures, the distribution of energy among lengthscales, and the decay of turbulence. To better understand the essential ingredients of this interaction,

Yuchen Yang

Audiovisual (AV) archives, as an essential reservoir of our cultural assets, are suffering from the issue of accessibility. The complex nature of the medium itself made processing and interaction an open challenge still in the field of computer vision, multimodal learning, and human-computer interaction, as well as in culture and heritage. In recent years, w

Nikolay Bogoychev, Pinzhen Chen

Terminology correctness is important in the downstream application of machine translation, and a prevalent way to ensure this is to inject terminology constraints into a translation system. In our submission to the WMT 2023 terminology translation task, we adopt a translate-then-refine approach which can be domain-independent and requires minimal manual effo

Federico De Lillo, Jishnu Suresh

The recent start of the fourth observing run of the LIGO-Virgo-KAGRA (LVK) collaboration has reopened the hunt for gravitational-wave (GW) signals, with one compact-binary-coalescence (CBC) signal expected to be observed every few days. Among the signals that could be detected for the first time there is the stochastic gravitational-wave background (SGWB) fr

Soumyadeep Kundu, Muktish Acharyya

We study the bacterial respiration through the numerical solution of the Fairen-Velarde coupled nonlinear differential equations. The instantaneous concentrations of the oxygen and the nutrients are computed. The fixed point solution and the stable limit cycle are found in different parameter ranges as predicted by the linearized differential equations. In a

Pablo Cabanas-Molero, Antonio J. Munoz-Montoro, Julio Carabias-Orti, Pedro Vera-Candeas

This paper presents a novel approach to sound source separation that leverages spatial information obtained during the recording setup. Our method trains a spatial mixing filter using solo passages to capture information about the room impulse response and transducer response at each sensor location. This pre-trained filter is then integrated into a multicha

Kurt Schab, Frederick Chen, Lukas Jelinek, Miloslav Capek

Characteristic modes of arbitrary two-dimensional periodic systems are analyzed using scattering parameter data. This approach bypasses the need for periodic integral equations and allows for characteristic modes to be computed from generic simulation or measurement data. Example calculations demonstrate the efficacy of the method through comparison against

Simon Benhamou

This paper provides the mathematical expressions, formulated in classical trigonometric terms, that are required (i) to compute an animal's body orientations and rotations in 3D space from data acquired at high-frequency by on-board accelerometers, magnetometers and gyrometers, (ii) to relate 3D rotations to changes in 3D orientation, (iii) to represent thei

R. Adam, M. Ricci, D. Eckert, P. Ade

The thermodynamical properties of the intracluster medium (ICM) are driven by scale-free gravitational collapse, but they also reflect the rich astrophysical processes at play in galaxy clusters. At low masses ($\sim 10^{14}$ M$_{\odot}$) and high redshift ($z \gtrsim 1$), these properties remain poorly constrained observationally, due to the difficulty in o

Liang Xu, Kangkang Zhao, Lei Zhu, Hang Xue

Large language models (LLMs), like ChatGPT and GPT-4, have demonstrated remarkable abilities in natural language understanding and generation. However, alongside their positive impact on our daily tasks, they can also produce harmful content that negatively affects societal perceptions. To systematically assess the safety of Chinese LLMs, we introduce SuperC

Akira Yoshida, Naoko Nakagawa, Shin-ichi Sasa

We study a liquid-gas coexistence system in a container under gravity with heat flow in the direction opposite to gravity. By molecular dynamics simulation, we find that the liquid buoys up and continues to float steadily. The height at which the liquid floats is determined by a dimensionless parameter related to the ratio of the temperature gradient to grav

CRESST Collaboration, G. Angloher, S. Banik, G. Benato

Diamond operated as a cryogenic calorimeter is an excellent target for direct detection of low-mass dark matter candidates. Following the realization of the first low-threshold cryogenic detector that uses diamond as absorber for astroparticle physics applications, we now present the resulting exclusion limits on the elastic spin-independent interaction cros

Mojtaba Moradi-Sepahvand, Turaj Amraee, Saleh Sadeghi Gougheri

In this paper, a multi-stage model for expansion co-planning of transmission lines, Battery Energy Storages (BESs), and Wind Farms (WFs) is presented considering resilience against extreme weather events. In addition to High Voltage Alternating Current (HVAC) lines, Multi-Terminal Voltage Source Converter (MTVSC) based High Voltage Direct Current (HVDC) line

Xiangyu Shi, Yuhao Luo, Li Wang, Haorui He

Replay attack is one of the most effective and simplest voice spoofing attacks. Detecting replay attacks is challenging, according to the Automatic Speaker Verification Spoofing and Countermeasures Challenge 2021 (ASVspoof 2021), because they involve a loudspeaker, a microphone, and acoustic conditions (e.g., background noise). One obstacle to detecting repl

Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb

An emerging new paradigm for solving inverse problems is via the use of deep learning to learn a regularizer from data. This leads to high-quality results, but often at the cost of provable guarantees. In this work, we show how well-posedness and convergent regularization arises within the convex-nonconvex (CNC) framework for inverse problems. We introduce a

Mojtaba Moradi-Sepahvand, Turaj Amraee

In this paper, an integrated multi-period model for long term expansion planning of electric energy transmission grid, power generation technologies, and energy storage devices is introduced. The proposed method gives the type, size and location of generation, transmission and storage devices to supply the electric load demand over the planning horizon. The

Tingting Wu, Matthew D. Lew

Fluorescent molecules are versatile nanoscale emitters that enable detailed observations of biophysical processes with nanoscale resolution. Because they are well-approximated as electric dipoles, imaging systems can be designed to visualize their 3D positions and 3D orientations, so-called dipole-spread function (DSF) engineering, for 6D super-resolution si

Samitha Gunarathne, Thushan Sivalingam, Nurul Huda Mahmood, Nandana Rajatheva

Managing inter-cell interference is among the major challenges in a wireless network, more so when strict quality of service needs to be guaranteed such as in ultra-reliable low latency communications (URLLC) applications. This study introduces a novel intelligent interference management framework for a local 6G network that allocates resources based on inte

Antonin Raffin, Olivier Sigaud, Jens Kober, Alin Albu-Schäffer

In search of a simple baseline for Deep Reinforcement Learning in locomotion tasks, we propose a model-free open-loop strategy. By leveraging prior knowledge and the elegance of simple oscillators to generate periodic joint motions, it achieves respectable performance in five different locomotion environments, with a number of tunable parameters that is a ti

Daniel R. Clarkson, Lawrence A. Bull, Tina A. Dardeno, Chandula T. Wickramarachchi

At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental costs. To counter this, the authors propose a Bayesian hierarchical model to predict surface-roughness measurements for a turning machining process. The hierarchical model is compared

Ethan R. Hansen, Vasileios Niaouris, Bethany E. Matthews, Christian Zimmermann

The shallow donor in zinc oxide (ZnO) is a promising semiconductor spin qubit with optical access. Single indium donors are isolated in a commercial ZnO substrate using plasma focused ion beam (PFIB) milling. Quantum emitters are identified optically by spatial and frequency filtering. The indium donor assignment is based on the optical bound exciton transit

Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister

Modern machine learning methods and the availability of large-scale data have significantly advanced our ability to predict target quantities from large sets of covariates. However, these methods often struggle under distributional shifts, particularly in the presence of hidden confounding. While the impact of hidden confounding is well-studied in causal eff

Haoyu Zhang, Yu Wang, Guanghao Yin, Kejun Liu

Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing rich information from multiple sources (e.g., language, video, and audio), the potential sentiment-irrelevant and conflicting information across modalities may hinder the performance from being further improved. To alleviate this, we present Adaptive Language-guided Multimodal Transform

Prasanna K. Dhani, Germán Rodrigo, German F. R. Sborlini

We analyze in detail the most singular behaviour of processes involving triple-collinear splittings with massive particles in the quasi-collinear limit, and present compact expressions for the splitting amplitudes and the corresponding splitting kernels at the squared-amplitude level. Our expressions fully agree with well-known triple-collinear splittings in

Philipp Korablev

A homologically trivial part of any Turaev-Viro invariant odd order $r$ is a Turaev-Viro type invariant order $\frac{r + 1}{2}$. In this paper we find an explicit formulas for this Turaev -- Viro type invariant, corresponding to the invariant order $r = 7$. Our formulas express $6j$-symbols and colour weights in terms of $\gamma$, where $\gamma$ is a root of

Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac

In this paper, we investigate the behavior of gradient descent algorithms in physics-informed machine learning methods like PINNs, which minimize residuals connected to partial differential equations (PDEs). Our key result is that the difficulty in training these models is closely related to the conditioning of a specific differential operator. This operator

Rajneesh Kashyap, Mohit Kalra, Arti Kashyap

Oil reservoirs around the globe are at their declining phase and in spite of enormous effectiveness of Enhanced Oil Recovery(EOR) in the Tertiary Stage. This process still bypasses some oil reason being surface forces responsible for holding oil inside the rock surface which are not being altered by the application of existing technologies. The processes com

Gerardo Roa Dabike, Scott Bannister, Jennifer Firth, Simone Graetzer

The Cadenza project aims to improve the audio quality of music for those who have a hearing loss. This is being done through a series of signal processing challenges, to foster better and more inclusive technologies. In the first round, two common listening scenarios are considered: listening to music over headphones, and with a hearing aid in a car. The fir

Hongli Ni, Yuhao Yuan, Mingsheng Li, Yifan Zhu

Deep-tissue chemical imaging plays a vital role in biological and medical applications. Here, we present a shortwave infrared photothermal (SWIP) microscope for millimeter-deep vibrational imaging with sub-micron lateral resolution and nanoparticle detection sensitivity. By pumping the overtone transition of carbon-hydrogen bonds and probing the subsequent p

Nicholas Kroeger, Dan Ley, Satyapriya Krishna, Chirag Agarwal

Recent advancements in Large Language Models (LLMs) have demonstrated exceptional capabilities in complex tasks like machine translation, commonsense reasoning, and language understanding. One of the primary reasons for the adaptability of LLMs in such diverse tasks is their in-context learning (ICL) capability, which allows them to perform well on new tasks

Ognjen Tošić

An important result in the theory of harmonic maps is due to Benoist--Hulin: given a quasi-isometry $f:X\to Y$ between pinched Hadamard manifolds, there exists a unique harmonic map at a finite distance from $f$. Here we show existence of harmonic maps under a weaker condition on $f$, that we call non-collapsing -- we require that the following two condition

Wanpeng Tan

A novel chiral electron-hole (CEH) pairing mechanism is proposed to account for non-BCS superconductivity. In contrast to BCS Cooper pairs, CEH pairs exhibit a pronounced affinity to antiferromagnetism for superconductivity. The gap equations derived from this new microscopic mechanism are analyzed for both s- and d-wave superconductivity, revealing marked d

Montserrat Casals-Ruiz, Jone Lopez de Gamiz Zearra

A result by Bridson, Howie, Miller, and Short states that if $S$ is a finitely presented subgroup of the direct product of free groups, then $S$ is virtually a nilpotent extension of a direct product of free groups. Moreover, if $S$ is a subgroup of type $FP_n$ of the direct product of $n$ free groups, then the nilpotent extension is finite, so $S$ is actual

Saulo Queiroz, João P. Vilela, Edmundo Monteiro

In this letter, we introduce the computational-limited (comp-limited) signals, a communication capacity regime in which the signal time computational complexity overhead is the key constraint -- rather than power or bandwidth -- to the overall communication capacity. We present the Spectro-Computational (SC) analysis, a novel mathematical framework that enha

Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu

Diffusion models have gained prominence in generating high-quality sequences of text. Nevertheless, current approaches predominantly represent discrete text within a continuous diffusion space, which incurs substantial computational overhead during training and results in slower sampling speeds. In this paper, we introduce a soft absorbing state that facilit

Haitong Liu, Qiang Li, Hoi-To Wai

This paper studies the performative prediction problem where a learner aims to minimize the expected loss with a decision-dependent data distribution. Such setting is motivated when outcomes can be affected by the prediction model, e.g., in strategic classification. We consider a state-dependent setting where the data distribution evolves according to an und

Juntae Kim, Eunjung Cho, Dongbin Na

The recent program development industries have required problem-solving abilities for engineers, especially application developers. However, AI-based education systems to help solve computer algorithm problems have not yet attracted attention, while most big tech companies require the ability to solve algorithm problems including Google, Meta, and Amazon. Th

Kansei Inamura, Xiao-Gang Wen

A generalized symmetry (defined by the algebra of local symmetric operators) can go beyond group or higher group description. A theory of generalized symmetry (up to holo-equivalence) was developed in terms of symmetry-TO -- a bosonic topological order (TO) with gappable boundary in one higher dimension. We propose a general method to compute the 2+1D symmet

Carla Vairetti, José Luis Assadi, Sebastián Maldonado

Imbalanced classification is a well-known challenge faced by many real-world applications. This issue occurs when the distribution of the target variable is skewed, leading to a prediction bias toward the majority class. With the arrival of the Big Data era, there is a pressing need for efficient solutions to solve this problem. In this work, we present a no

Oleg Verbitsky, Maksim Zhukovskii

It is well known that almost all graphs are canonizable by a simple combinatorial routine known as color refinement, also referred to as the 1-dimensional Weisfeiler-Leman algorithm. With high probability, this method assigns a unique label to each vertex of a random input graph and, hence, it is applicable only to asymmetric graphs. The strength of combinat

Afonso S. Bandeira, Antoine Maillard

We consider the problem $(\rm P)$ of exactly fitting an ellipsoid (centered at $0$) to $n$ standard Gaussian random vectors in $\mathbb{R}^d$, as $n, d \to \infty$ with $n / d^2 \to \alpha > 0$. This problem is conjectured to undergo a sharp transition: with high probability, $(\rm P)$ has a solution if $\alpha < 1/4$, while $(\rm P)$ has no solutions if $\a

Bartolomeo Fiorini, Kazuya Koyama, Tessa Baker

We test the convergence of fast simulations based on the COmoving Lagrangian Acceleration (COLA) method for predictions of the matter power spectrum, specialising our analysis in the redshift range $1 \le z \le 1.65$, relevant to high-redshift spectroscopic galaxy surveys. We then focus on the enhancement of the matter power spectrum in modified gravity (MG)

Irene Cortés, Jorge Beltrán, Arturo de la Escalera, Fernando García

In recent years, the field of autonomous driving has witnessed remarkable advancements, driven by the integration of a multitude of sensors, including cameras and LiDAR systems, in different prototypes. However, with the proliferation of sensor data comes the pressing need for more sophisticated information processing techniques. This research paper introduc

Hans L. Bodlaender, Isja Mannens, Jelle J. Oostveen, Sukanya Pandey

The Integer Multicommodity Flow problem has been studied extensively in the literature. However, from a parameterised perspective, mostly special cases, such as the Disjoint Paths problem, have been considered. Therefore, we investigate the parameterised complexity of the general Integer Multicommodity Flow problem. We show that the decision version of this

Weibiao Wang, Zhongzi Wang

It is known that an automorphism of $F_g$, the oriented closed surface of genus $g$, is extendable over the 4-sphere $S^4$ if and only if it has a bounding invariant spin structure \cite{WsWz}. We show that each automorphism of $F_g$ has an invariant spin structure, and obtain a stably extendable result: Each automorphism of $F_g$ is extendable over $S^4$ af

Jiashuo Wang, Haozhao Wang, Shichao Sun, Wenjie Li

In the quest to advance human-centric natural language generation (NLG) systems, ensuring alignment between NLG models and human preferences is crucial. For this alignment, current popular methods leverage a reinforcement learning (RL) approach with a reward model trained on feedback from humans. However, inherent disagreements due to the subjective nature o

Andronikos Paliathanasis

We address the group classification problem for gravitational field equations within the context of brane-world cosmology, considering the presence of a bulk scalar field. Our investigation revolves around a five-dimensional spacetime, with the four-dimensional Friedmann--Lema\^{\i}tre--Robertson--Walker geometry embedded within it. Additionally, we assume t

Lucie-Aimée Kaffee, Arnav Arora, Isabelle Augenstein

The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and the editors are encouraged to use the content moderation policies as explanations for making moderation decisions. Currently, only a few comments explicitly mention those policies -- 20% of the English ones, but as few

Faical Ndairou, Delfim F. M. Torres

We introduce a new optimal control problem where the controlled dynamical system depends on multi-order (incommensurate) fractional differential equations. The cost functional to be maximized is of Bolza type and depends on incommensurate Caputo fractional-orders derivatives. We establish continuity and differentiability of the state solutions with respect t

Chan Wu, Hanxiao Zhang, Lin Ju, Jinjing Huang

Recently, various distributed strategies for large language model training have been proposed. However, these methods provided limited solutions for the trade-off between memory consumption and communication cost. In this paper, we rethink the impact of memory consumption and communication costs on the training speed of large language models, and propose a m

Laura K. Rogers, John Debes, Richard J. Anslow, Amy Bonsor

This work combines spectroscopic and photometric data of the polluted white dwarf WD0141-675 which has a now retracted astrometric super-Jupiter candidate and investigates the most promising ways to confirm Gaia astrometric planetary candidates and obtain follow-up data. Obtaining precise radial velocity measurement for white dwarfs is challenging due to the

Jie Fan

Notions of unknown truths and unknowable truths are important in formal epistemology, which are related to each other in e.g. Fitch's paradox of knowability. Although there have been some logical research on the notion of unknown truths and some philosophical discussion on the two notions, there seems to be no logical research on unknowable truths. In this p

Mojtaba Moradi-Sepahvand, Turaj Amraee, Farrokh Aminifar, Amirhossein Akbari

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition

Lisa Sauermann, Zixuan Xu

We prove a new lower bound for the almost 20 year old problem of determining the smallest possible size of an essential cover of the $n$-dimensional hypercube $\{\pm 1\}^n$, i.e. the smallest possible size of a collection of hyperplanes that forms a minimal cover of $\{\pm 1\}^n$ and such that furthermore every variable appears with a non-zero coefficient in

C. Boersma, L. J. Allamandola, V. J. Esposito, A. Maragkoudakis

A first look is taken at the NIRSpec 1-5 $\mu$m observations from JWST program 1591 that targets 7 objects along the low-mass stellar life cycle with PAH emission. Spectra extracted from a 1.5$^{\prime\prime}$ radius sized circular aperture are explored, showing a wealth of features, including the 3 $\mu$m PAH complex, the PAH-continuum, and atomic and molec

Ziyao Guo, Kai Wang, George Cazenavette, Hui Li

The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a model trained on this synthetic set will perform equally well as a model trained on the full, real dataset. Until now, no method of Dataset Distillation has reached this completely lossless goal, in part due to the fact that previous methods only remain effective

Ruben Queiros, Luis Ferreira, Helder Fontes, Rui Campos

The increasing complexity of recent Wi-Fi amendments is making the use of traditional algorithms and heuristics unfeasible to address the Rate Adaptation (RA) problem. This is due to the large combination of configuration parameters along with the high variability of the wireless channel. Recently, several works have proposed the usage of Reinforcement Learn

Weikai Yang, Mengchen Liu, Zheng Wang, Shixia Liu

Recent studies have indicated that foundation models, such as BERT and GPT, excel in adapting to a variety of downstream tasks. This adaptability has established them as the dominant force in building artificial intelligence (AI) systems. As visualization techniques intersect with these models, a new research paradigm emerges. This paper divides these inters

Oskar A. Sultanov

Time-decaying perturbations of nonlinear oscillatory systems in the plane are considered. It is assumed that the unperturbed systems are non-isochronous and the perturbations oscillate with an asymptotically constant frequency. Resonance effects and long-term asymptotic regimes for solutions are investigated. In particular, the emergence of stable states clo

Nilanjan Chaudhuri, Snigdha Ghosh, Pradip Roy, Sourav Sarkar

We calculate, in a systematic way, the general structure of the self-energy of light massive fermions and the effective propagator in a thermomagnetic medium with the inclusion of anomalous magnetic moment (AMM) of the fermion in the weak field approximation. It is found that the self-energy of a massive fermion in this case consists of five non-trivial stru

Md Sohag Mia, Abdullah Al Bary Voban, Abu Bakor Hayat Arnob, Abdu Naim

Efficient and accurate detection of small objects in manufacturing settings, such as defects and cracks, is crucial for ensuring product quality and safety. To address this issue, we proposed a comprehensive strategy by synergizing Faster R-CNN with cutting-edge methods. By combining Faster R-CNN with Feature Pyramid Network, we enable the model to efficient

Arne Wolf, Anthea Monod

We propose a model for network community detection using topological data analysis, a branch of modern data science that leverages theory from algebraic topology to statistical analysis and machine learning. Specifically, we use cellular sheaves, which relate local to global properties of various algebraic topological constructions, to propose three new algo

Julian Gaal, Thomas Wiemann, Alexander Mock, Mario Porrmann

This paper presents FeatSense, a feature-based GPU-accelerated SLAM system for high resolution LiDARs, combined with a map generation algorithm for real-time generation of large Truncated Signed Distance Fields (TSDFs) on embedded hardware. FeatSense uses LiDAR point cloud features for odometry estimation and point cloud registration. The registered point cl

Koji Aoshima, Martin Servin

We investigate how well a physics-based simulator can replicate a real wheel loader performing bucket filling in a pile of soil. The comparison is made using field test time series of the vehicle motion and actuation forces, loaded mass, and total work. The vehicle was modeled as a rigid multibody system with frictional contacts, driveline, and linear actuat

Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola

A significant amount of protein function requires binding small molecules, including enzymatic catalysis. As such, designing binding pockets for small molecules has several impactful applications ranging from drug synthesis to energy storage. Towards this goal, we first develop HarmonicFlow, an improved generative process over 3D protein-ligand binding struc

Shaun Laing, James Bateman

We explore the information which proposed matterwave interferometry experiments with large test masses can provide about parameterizable extensions to quantum mechanics, such as have been proposed to explain the apparent quantum to classical transition. Specifically, we consider a matterwave near-field Talbot interferometer and Continuous Spontaneous Localis

Sandro Costa Magalhães, Filipe Neves dos Santos, António Paulo Moreira, Jorge Dias

Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects' spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. So, in this study, we state to answer the research question: "How can we use and control monocular sensors to perceive objects' position in the 3D t

Joan Alegre, Juan Carlos Escanciano

This paper proposes minimum distance inference for a structural parameter of interest, which is robust to the lack of identification of other structural nuisance parameters. Some choices of the weighting matrix lead to asymptotic chi-squared distributions with degrees of freedom that can be consistently estimated from the data, even under partial identificat

C. Reinhoffer, Sven Esser, Sebastian Esser, E. A. Mashkovich

We report on time-resolved nonlinear terahertz spectroscopy of a strongly correlated ruthenate, CaRuO$_3$, as a function of temperature, frequency and terahertz field strength. Third-harmonic radiation for frequencies up to 2.1 THz is observed evidently at low temperatures below 80 K, where the low-frequency linear dynamical response deviates from the Drude

Rocio Diaz Martin, Ivan Medri, Yikun Bai, Xinran Liu

The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning. In this paper, we focus on circular probability measures, i.e., probability measures supported on the unit circle, and introduce a new computationally efficient metric for these measures, d

Rubén Rodríguez-Romero, Víctor Compán, Andrés Sáez, Enrique García-Macías

This paper presents an innovative hierarchical meta-modelling approach for predicting the effective elastic properties of stone injected with composite cement grouts containing Carbon NanoTubes (CNTs) for structural rehabilitation purposes. The study first addresses the homogenization of the nano- and micro-scales through numerical representative volume elem

Bartholomew Andrews, Mathi Raja, Nimit Mishra, Michael P. Zaletel

The stability of fractional Chern insulators is widely believed to be predicted by the resemblance of their single-particle spectra to Landau levels. We investigate the scope of this geometric stability hypothesis by analyzing the stability of a set of fractional Chern insulators that explicitly do not have a Landau level continuum limit. By computing the ma

Yuanhang Shao, Xiuwen Liu

Graph-based semi-supervised learning (GSSL) has been used successfully in various applications. Existing methods leverage the graph structure and labeled samples for classification. Label Propagation (LP) and Graph Neural Networks (GNNs) both iteratively pass messages on graphs, where LP propagates node labels through edges and GNN aggregates node features f

Daniel Ketover, Yevgeny Liokumovich

We give a new proof of the Smale conjecture for $\mathbb{RP}^3$ and all lens spaces using minimal surfaces and min-max theory. For $\mathbb{RP}^3$, the conjecture was first proved in 2019 by Bamler-Kleiner using Ricci flow.

Yegor Klochkov, Jean-Francois Ton, Ruocheng Guo, Yang Liu

We address the problem of concept removal in deep neural networks, aiming to learn representations that do not encode certain specified concepts (e.g., gender etc.) We propose a novel method based on adversarial linear classifiers trained on a concept dataset, which helps to remove the targeted attribute while maintaining model performance. Our approach Deep

Yuhe Ding, Bo Jiang, Lijun Sheng, Aihua Zheng

Transferability estimation aims to provide heuristics for quantifying how suitable a pre-trained model is for a specific downstream task, without fine-tuning them all. Prior studies have revealed that well-trained models exhibit the phenomenon of Neural Collapse. Based on a widely used neural collapse metric in existing literature, we observe a strong correl

Christian Knoll, Torsten Möller, Kathleen Gregory, Laura Koesten

Charts are used to communicate data visually, but often, we do not know whether a chart's intended message aligns with the message readers perceive. In this mixed-methods study, we investigate how data journalists encode data and how members of a broad audience engage with, experience, and understand these visualizations. We conducted workshops and interview

Esther Taiwo, Ahmed Akinsola, Edward Tella, Kolade Makinde

This study is focused on the ethics of Artificial Intelligence and its application in the United States, the paper highlights the impact AI has in every sector of the US economy and multiple facets of the technological space and the resultant effect on entities spanning businesses, government, academia, and civil society. There is a need for ethical consider

Ioannis Gasteratos, Antoine Jacquier

We study concentration properties for laws of non-linear Gaussian functionals on metric spaces. Our focus lies on measures with non-Gaussian tail behaviour which are beyond the reach of Talagrand's classical Transportation-Cost Inequalities (TCIs). Motivated by solutions of Rough Differential Equations and relying on a suitable contraction principle, we prov

Renan Câmara Pereira, João Moreira, Pedro Costa, Constança Providência

We propose the 3-momentum sphere intersection regularization applied to the one and two fermion line integrals at finite temperature and chemical potential. The quark-antiquark polarization function in this new regularization approach is equivalent to the usual 3-momentum regularization, when the absolute value of the external 3-momentum of the polarization

Pierre Nazé

In this work, I derive the time-dependent probability density function of classical observables using the Hamiltonian mechanics approach, extending the notion of fluctuation theorems for any observables. In particular, the time-dependent probability density function of the thermodynamic work evolving in the switching time is solved as a special case. From su

Jiangjie Chen, Siyu Yuan, Rong Ye, Bodhisattwa Prasad Majumder

Recent advancements in Large Language Models (LLMs) showcase advanced reasoning, yet NLP evaluations often depend on static benchmarks. Evaluating this necessitates environments that test strategic reasoning in dynamic, competitive scenarios requiring long-term planning. We introduce AucArena, a novel evaluation suite that simulates auctions, a setting chose

Maria Mannone, Omar Costa Hamido

The perception and appreciation of beauty in a musical composition appears as being related to the amount of memory and balance between expectation and surprise. In this study, we use a formal computing tool derived from quantum mechanics and adapted to music, to measure the degree of memory of orchestral music, including Liebestod by R. Wagner. We discuss o

F. Tognini, G. Valle, M. Dell'Omodarme, S. Degl'Innocenti

We estimate the impact on the stellar evolution of the uncertainties in the $3\alpha$ and the $^{12}{\rm C}(\alpha,\gamma)^{16}{\rm O}$ reaction rates [...]. We calculated models of low- and intermediate-mass stars for different values of the rates. The $3\alpha$ reaction rate was varied up to $\pm 24\%$, while the $^{12}{\rm C}(\alpha,\gamma)^{16}{\rm O}$ r

S. Moch, B. Ruijl, T. Ueda, J. Vermaseren

We have extended our previous computations of the even-N moments of the flavour-singlet four-loop splitting functions to N = 12 for the pure-singlet quark case and N = 10 for all other cases. These results, obtained using physical quantities in inclusive deep-inelastic scattering, have been and will be used to validate conceptionally much more challenging de

Tousif Islam

We investigate the $\alpha$-$\beta$ mapping, as previously introduced by Islam et al.~\cite{Islam:2022laz}, which relates numerical relativity (NR) and adiabatic point-particle black hole perturbation theory (BHPT) waveforms in the comparable mass regime for quasi-circular, non-spinning binary black holes. This mapping involves scaling the amplitude of indiv

Dean A. Pospisil, Brett W. Larsen, Sarah E. Harvey, Alex H. Williams

Measuring geometric similarity between high-dimensional network representations is a topic of longstanding interest to neuroscience and deep learning. Although many methods have been proposed, only a few works have rigorously analyzed their statistical efficiency or quantified estimator uncertainty in data-limited regimes. Here, we derive upper and lower bou

Chengfeng Shen, Yifan Luo, Zhennan Zhou

This paper considers mean field games with optimal stopping time (OSMFGs) where agents make optimal exit decisions, the coupled obstacle and Fokker-Planck equations in such models pose challenges versus classic MFGs. This paper proposes a generalized fictitious play algorithm that computes OSMFG mixed equilibria by iteratively solving pure strategy systems,

Jin Beniyama, Ryou Ohsawa, Chrysa Avdellidou, Shigeyuki Sako

Studying small near-Earth asteroids is important to understand their dynamical histories and origins as well as to mitigate the damage of the asteroid impact to the Earth. We report the results of multicolor photometry of the tiny near-Earth asteroid 2015 RN$_{35}$ using the 3.8 m Seimei telescope in Japan and the TRAPPIST-South telescope in Chile over 17 ni

Hui Ma, Mingxuan Yang, Jiabin Yin

We consider a partially overdetermined problem for the $p$-Laplace equation in a convex cone $\mathcal{C}$ intersected with the exterior of a smooth bounded domain $\overline{\Omega}$ in $\mathbb{R}^n$($n\geq2$). First, we establish the existence, regularity, and asymptotic behavior of a capacitary potential. Then, based on these properties of the potential,

Mattia Magnabosco

In this work, we generalize the results obtained in (J. Geom. Anal., 32(6):Paper No.173, 32, 2022), presenting some examples of $CD(0,N)$ spaces having different dimensions in different regions, deducing in particular that the topological splitting may fail in $CD(0,N)$ spaces. We also observe that any reasonable non-branching condition may fail in $CD(0,N)$

Lijun Yu, José Lezama, Nitesh B. Gundavarapu, Luca Versari

While Large Language Models (LLMs) are the dominant models for generative tasks in language, they do not perform as well as diffusion models on image and video generation. To effectively use LLMs for visual generation, one crucial component is the visual tokenizer that maps pixel-space inputs to discrete tokens appropriate for LLM learning. In this paper, we

Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang

Large language models (LLMs) have been applied in various applications due to their astonishing capabilities. With advancements in technologies such as chain-of-thought (CoT) prompting and in-context learning (ICL), the prompts fed to LLMs are becoming increasingly lengthy, even exceeding tens of thousands of tokens. To accelerate model inference and reduce

Hamid Abchir, Mohammed Sabak

We give a non-left-orderability criterion for involutory quandles of non-split links. We use this criterion to show that the involutory quandle of any non-trivial alternating link is not left-orderable, thus improving Theorem 8.1. proven by Raundal et al. (Proceedings of the Edinburgh Mathematical Society (2021 64), page 646). We also use the criterion to sh

Charles W. Eaton, Michael Livesey

We classify all $2$-blocks with abelian defect groups of rank $4$ up to Morita equivalence. The classification holds for blocks over a suitable discrete valuation ring as well as for those over an algebraically closed field. An application is that Brou\'{e}'s abelian defect group conjecture holds for all blocks under consideration here.

Phillippe Samer, Phablo F. S. Moura

A connected matching in a graph G consists of a set of pairwise disjoint edges whose covered vertices induce a connected subgraph of G. While finding a connected matching of maximum cardinality is a well-solved problem, it is NP-hard to determine an optimal connected matching in an edge-weighted graph, even in the planar bipartite case. We present two mixed

Christoph Damerius, Peter Kling, Florian Schneider

We consider the following shared-resource scheduling problem: Given a set of jobs $J$, for each $j\in J$ we must schedule a job-specific processing volume of $v_j>0$. A total resource of $1$ is available at any time. Jobs have a resource requirement $r_j\in[0,1]$, and the resources assigned to them may vary over time. However, assigning them less will cause

Davide Basile, Claudio Di Ciccio, Valerio Goretti, Sabrina Kirrane

Decentralization initiatives like Solid enable data owners to control who has access to their data and to stimulate innovation by creating both application and data markets. Once data owners share their data with others, though, it is no longer possible for them to control how their data are used. To address this issue, we propose a usage control architectur

Murat Polat

In the present article, we characterize Clairaut conformal submersions whose total manifolds admit a Ricci soliton and provide a non-trivial example of such Clairaut conformal submersions. We firstly calculate scalar curvature and Ricci tensors of total manifolds of Clairaut conformal submersions and provide necessary conditions for the fibres of such Claira

Nudrat Nawal, Md Rashed Nizam, Priom Das, A K M Monjur Morshed

The freshwater crisis is a growing concern and a pressing problem for the world because of the increasing population, civilization, and rapid industrial growth. The water treatment facilities are able to supply less than 1% of the total water demand. Water desalination can be a potential solution to deal with this alarming issue. Researchers have been explor