October 2023 arXiv papers — page 74

Ryan O'Donnell, Rocco A. Servedio, Pedro Paredes

We give a strongly explicit construction of $\varepsilon$-approximate $k$-designs for the orthogonal group $\mathrm{O}(N)$ and the unitary group $\mathrm{U}(N)$, for $N=2^n$. Our designs are of cardinality $\mathrm{poly}(N^k/\varepsilon)$ (equivalently, they have seed length $O(nk + \log(1/\varepsilon)))$; up to the polynomial, this matches the number of des

Zehai Tu, Ning Ma, Jon Barker

This paper describes two intelligibility prediction systems derived from a pretrained noise-robust automatic speech recognition (ASR) model for the second Clarity Prediction Challenge (CPC2). One system is intrusive and leverages the hidden representations of the ASR model. The other system is non-intrusive and makes predictions with derived ASR uncertainty.

Ziqiang Zheng, Jipeng Zhang, Tuan-Anh Vu, Shizhe Diao

Large language models (LLMs), such as ChatGPT/GPT-4, have proven to be powerful tools in promoting the user experience as an AI assistant. The continuous works are proposing multi-modal large language models (MLLM), empowering LLMs with the ability to sense multiple modality inputs through constructing a joint semantic space (e.g. visual-text space). Though

Nathan Lambert, Thomas Krendl Gilbert, Tom Zick

Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) easier to use and more effective. A core piece of the RLHF process is the training and utilization of a model of human preferences that acts as a reward function for optimization. This approach, which operates at the intersection of many

John Armstrong, India Jackman

We describe a mainstream "universalist" approach to the understanding of mathematics. We then conduct a systematic (but not exhaustive) review of the academic literature on the decolonisation of mathematics and identify how this challenges the universalist view. We examine evidence of whether the experience of mathematics in the UK is systemically racist, ex

Taekyung Kim, Sanghyuk Chun, Byeongho Heo, Dongyoon Han

Masked image modeling (MIM) has become a leading self-supervised learning strategy. MIMs such as Masked Autoencoder (MAE) learn strong representations by randomly masking input tokens for the encoder to process, with the decoder reconstructing the masked tokens to the input. However, MIM pre-trained encoders often exhibit a limited attention span, attributed

Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimatio

Graham Baker, Davide Valentinis, Andrew P. Mackenzie

We discuss various aspects of non-local electrical transport in anisotropic metals. For a metal with circular Fermi surface, the scattering rates entering the local conductivity and viscosity tensors are well-defined, corresponding to eigenfrequencies of the linearized collision operator. For anisotropic metals, we provide generalized formulas for these scat

Yves Dumont, Valaire Ivric Yatat-Djeumen

The sterile insect technique (SIT) is a technique to control pests and vectors of diseases by releasing mainly sterile males. Several challenges need to be solved before large-scale field application in order to guarantee its success. In this paper we intend to focus on two important issues: residual (sterile) male fertility and contamination by sterile fema

Yaorui Shi, An Zhang, Enzhi Zhang, Zhiyuan Liu

Predicting chemical reactions, a fundamental challenge in chemistry, involves forecasting the resulting products from a given reaction process. Conventional techniques, notably those employing Graph Neural Networks (GNNs), are often limited by insufficient training data and their inability to utilize textual information, undermining their applicability in re

Léonard Oest O'Leary, Mathis Laroche, Marc Feeley

The Ribbit system is a compact Scheme implementation running on the Ribbit Virtual Machine (RVM) that has been ported to a dozen host languages. It supports a simple Foreign Function Interface (FFI) allowing extensions to the RVM directly from the program's source code. We have extended the system to offer conformance to the R4RS standard while staying as co

Shoutao Guo, Shaolei Zhang, Yang Feng

Simultaneous machine translation (SiMT) generates translation while reading the whole source sentence. However, existing SiMT models are typically trained using the same reference disregarding the varying amounts of available source information at different latency. Training the model with ground-truth at low latency may introduce forced anticipations, where

Nidhi Adhlakha, Zeinab Ebrahimpour, Paola Di Pietro, Johannes Schmidt

Thanks to its tunable infrared band-gap and to its anisotropic conduction properties, black phosphorus represents a very unique 2D material, whose potential in the engineering of new devices still needs to be fully explored. We investigate here the nonlinear terahertz (THz) electrodynamics of black phosphorus along the more conducting armchair direction. Sim

William Bergeron, Michael Jones, Chase Barber, Kale DeYoung

Low-power small form factor data processing units (DPUs) enable offloading and acceleration of a broad range of networking and security services. DPUs have accelerated the transition to programmable networking by enabling the replacement of FPGAs/ASICs in a wide range of network oriented devices. The GraphBLAS sparse matrix graph open standard math library i

Yue Zhou, David Grass, Warren S. Warren, Martin C. Fischer

Cadmium sulfide (CdS) pigments have degraded in several well-known paintings, but the mechanisms of degradation have yet to be fully understood. Traditional non-destructive analysis techniques primarily focus on macroscopic degradation, whereas microscopic information is typically obtained with invasive techniques that require sample removal. Here, we demons

A. R. Counsell, F. Gittins, N. Andersson

Mature neutron stars are expected exhibit gravity g-modes due to stratification caused by varying composition. These modes are affected by nuclear reactions, leading to complex (damped) mode frequencies and the suppression of high order g-modes, in contrast with the common non-dissipative analysis which leads to an infinite g-mode spectrum. Focusing on the t

Elahe Vahdani, Yingli Tian

This paper tackles the challenge of point-supervised temporal action detection, wherein only a single frame is annotated for each action instance in the training set. Most of the current methods, hindered by the sparse nature of annotated points, struggle to effectively represent the continuous structure of actions or the inherent temporal and semantic depen

José Villa-Morales

The article provides upper bounds for the blow-up time of a system of fractional differential equations in the Caputo sense. Furthermore, concrete examples of blow-up time estimation are given using a numerical algorithm of the predictor-corrector type.

Ofir Arviv, Dmitry Nikolaev, Taelin Karidi, Omri Abend

Despite the impressive growth of the abilities of multilingual language models, such as XLM-R and mT5, it has been shown that they still face difficulties when tackling typologically-distant languages, particularly in the low-resource setting. One obstacle for effective cross-lingual transfer is variability in word-order patterns. It can be potentially mitig

T. Yontan, R. Canbay

In this study, we have used the {\it Gaia} Third Data Release (Gaia DR3) to investigate an intermediate-age open cluster Collinder 74. Taking into account the stars with membership probabilities over 0.5 and inside the limiting radius of the cluster, we identified 102 most likely cluster members. The mean proper-motion components of Collinder 74 are estimate

Benjamin Hilprecht, Kristian Kersting, Carsten Binnig

While there has been extensive work on deep neural networks for images and text, deep learning for relational databases (RDBs) is still a rather unexplored field. One direction that recently gained traction is to apply Graph Neural Networks (GNNs) to RBDs. However, training GNNs on large relational databases (i.e., data stored in multiple database tables) is

Shijie Zhou, Jonathan R. Bradley

In public health applications, spatial data collected are often recorded at different spatial scales and over different correlated variables. Spatial change of support is a key inferential problem in these applications and have become standard in univariate settings; however, it is less standard in multivariate settings. There are several existing multivaria

Ankush Agarwal, Andrea Amato, Goncalo dos Reis, Stefano Pagliarani

We propose a novel approach to numerically approximate McKean-Vlasov stochastic differential equations (MV-SDE) using stochastic gradient descent (SGD) while avoiding the use of interacting particle systems (IPS) {and the associated simulation costs required to achieve the ``propagation of chaos'' limit}. The SGD technique is deployed to solve a Euclidean mi

Angelica Albertini, Rossella Gamba, Alessandro Nagar, Sebastiano Bernuzzi

We present a first complete implementation of an effective-one-body (EOB) model for extreme-mass-ratio inspirals (EMRIs) that incorporates aligned spins (on both the primary and the secondary) as well as orbital eccentricity. The model extends TEOBResumS-Dal\'i for these binaries by (i) recasting conservative first-order gravitational self-force (1GSF) infor

Ying Zhang, Meng Yue

Under voltage load shedding (UVLS) for power grid emergency control builds the last defensive perimeter to prevent cascade outages and blackouts in case of contingencies. This letter proposes a novel cooperative multi-agent deep reinforcement learning (MADRL)-based UVLS algorithm in an adaptive decentralized way. With well-designed input signals reflecting t

Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi

Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics. In this work, we propose CD-UCT, a model-based reinforcement learning method for causal discovery based on tree search that builds directed acyclic graphs incrementally. We also formalize and prove the correctness of an efficient algorithm

Ben Eyal, Amir Bachar, Ophir Haroche, Moran Mahabi

Text-to-SQL semantic parsing faces challenges in generalizing to cross-domain and complex queries. Recent research has employed a question decomposition strategy to enhance the parsing of complex SQL queries. However, this strategy encounters two major obstacles: (1) existing datasets lack question decomposition; (2) due to the syntactic complexity of SQL, m

Li Wang, Lihui Wang, Zixiang Kuai, Lei Tang

To promote the generalization ability of breast tumor segmentation models, as well as to improve the segmentation performance for breast tumors with smaller size, low-contrast and irregular shape, we propose a progressive dual priori network (PDPNet) to segment breast tumors from dynamic enhanced magnetic resonance images (DCE-MRI) acquired at different cent

Kexin Zhu, Bo Lin, Yang Qiu, Adam Yule

We introduce a high-performance fingerprint liveness feature extraction technique that secured first place in LivDet 2023 Fingerprint Representation Challenge. Additionally, we developed a practical fingerprint recognition system with 94.68% accuracy, earning second place in LivDet 2023 Liveness Detection in Action. By investigating various methods, particul

Yufei Gu, Xiaoqing Zheng, Tomaso Aste

Double descent presents a counter-intuitive aspect within the machine learning domain, and researchers have observed its manifestation in various models and tasks. While some theoretical explanations have been proposed for this phenomenon in specific contexts, an accepted theory to account for its occurrence in deep learning remains yet to be established. In

Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek, Jun Wang

This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting. We investigate how LLMs can be effectively induced to generate a coherent chain of thoughts. To achieve this, we introduce a two-level hierarchical graphical model tailored for natural langua

Alexandros Xenos, Themos Stafylakis, Ioannis Patras, Georgios Tzimiropoulos

This paper is on the problem of Knowledge-Based Visual Question Answering (KB-VQA). Recent works have emphasized the significance of incorporating both explicit (through external databases) and implicit (through LLMs) knowledge to answer questions requiring external knowledge effectively. A common limitation of such approaches is that they consist of relativ

Nicola Fusco, Francesco Maggi, Massimiliano Morini, Michael Novack

A comparison theorem by Choe, Ghomi and Ritor\'e states that the exterior isoperimetric profile $I_\mathcal{C}$ of any convex body $\mathcal{C}$ in $\mathbb{R}^N$ lies above that of any half-space $H$. We characterize convex bodies such that $I_\mathcal{C}\equiv I_H$ in terms of a notion of "maximal affine dimension at infinity'', briefly called the asymptot

Emanuele Maggio, Andriy Smolyanyuk, Jan M. Tomczak

We present an algorithm for the determination of the local symmetry group for arbitrary k-points in 3D Brillouin zones. First, we test our implementation against tabulated results available for standard high-symmetry points (given by universal fractional coordinates). Then, to showcase the general applicability of our methodology, we produce the irreducible

Dorothee D. Haroske, Leszek Skrzypczak, Hans Triebel

The paper deals with continuous and compact mappings generated by the Fourier transform between distinguished Besov spaces $B^s_p(\mathbb{R}^n) = B^s_{p,p}(\mathbb{R}^n)$, $1\le p \le \infty$, and between Sobolev spaces $H^s_p(\mathbb{R}^n)$, $1<p< \infty$. In contrast to the paper {\em H. Triebel, Mapping properties of Fourier transforms. Z. Anal. Anwend. 4

Nicholas Thomas Walker, Stefan Ultes, Pierre Lison

Constructing responses in task-oriented dialogue systems typically relies on information sources such the current dialogue state or external databases. This paper presents a novel approach to knowledge-grounded response generation that combines retrieval-augmented language models with logical reasoning. The approach revolves around a knowledge graph represen

Elizabeth Bates, Vasilios Mavroudis, Chris Hicks

As machine learning models become more capable, they have exhibited increased potential in solving complex tasks. One of the most promising directions uses deep reinforcement learning to train autonomous agents in computer network defense tasks. This work studies the impact of the reward signal that is provided to the agents when training for this task. Due

Zhaonan Dong, Lorenzo Mascotto

We prove hp-optimal error estimates for the original DG method when approximating solutions to first-order hyperbolic problems with constant convection fields in the L2 and DG norms. The main theoretical tools used in the analysis are novel hp-optimal approximation properties of the special projector introduced in [Cockburn, Dong, Guzman, SINUM, 2008]. We as

Sascha Kurz

A code $C \subseteq \{0, 1, 2\}^n$ of length $n$ is called trifferent if for any three distinct elements of $C$ there exists a coordinate in which they all differ. By $T(n)$ we denote the maximum cardinality of trifferent codes with length. $T(5)=10$ and $T(6)=13$ were recently determined. Here we determine $T(7)=16$, $T(8)=20$, and $T(9)=27$. For the latter

Yutian Wang, Yuan-Hua Ni, Xun Li

This paper aims to extend the BML method proposed in Wang et al. [22] to make it applicable to more general coupled nonlinear FBSDEs. We interpret BML from the fixed-point iteration perspective and show that optimizing BML is equivalent to minimizing the distance between two consecutive trial solutions in a fixed-point iteration. Thus, this paper provides a

Guillem Ramírez, Matthias Lindemann, Alexandra Birch, Ivan Titov

Large-scale deployment of generative AI tools often depends on costly API calls to a Large Language Model (LLM) to fulfil user queries. To curtail the frequency of these calls, one can employ a smaller language model -- a student -- which is continuously trained on the responses of the LLM. This student gradually gains proficiency in independently handling a

Shosaku Matsuzaki, Tomo Murao

We introduce (co)homology theory for multiple group racks and construct cocycle invariants of compact oriented surfaces in the 3-sphere using their 2-cocycles, where a multiple group rack is a rack consisting of a disjoint union of groups. We provide a method to find new rack cocycles and multiple group rack cocycles from existing rack cocycles and quandle c

Tomoaki Abuku, Hikaru Manabe, Richard J. Nowakowski, Carlos P. Santos

The class of Poset Take-Away games includes many interesting and difficult games. Playing on an $n$-dimensional positive quadrant (the origin being the bottom of the poset) gives rise to nim, wythoff's nim and chomp. These are impartial games. We introduce a partisan game motivated by chomp and the recent chocolate-bar version. Our game is played on a chocol

Luca Becchetti, Andrea Clementi, Francesco Pasquale, Luca Trevisan

We study the minority-opinion dynamics over a fully-connected network of $n$ nodes with binary opinions. Upon activation, a node receives a sample of opinions from a limited number of neighbors chosen uniformly at random. Each activated node then adopts the opinion that is least common within the received sample. Unlike all other known consensus dynamics, we

Mohammadhasan Faghihi, Meysam Yadegar, Mohammadhosein Bakhtiaridoust, Nader Meskin

This paper introduces a novel approach to solve the coverage optimization problem in multi-agent systems. The proposed technique offers an optimal solution with a lower cost with respect to conventional Voronoi-based techniques by effectively handling the issue of agents remaining stationary in regions void of information using a ranking function. The propos

Hannes Kern, Terry Lyons

In 2015, Bailleul presented a mechanism to solve rough differential equations by constructing flows, using the log-ODE method. We extend this notion in two ways: On the one hand, we localize Bailleul's notion of an almost-flow to solve RDEs on manifolds. On the other hand, we extend his results to non-geometric rough paths, living in any connected, cocommuta

Zheng-Qiao Duan, Ju-Ying Zhao, Ke Yang

We derive the exact solutions for electrically charged black holes both in the absence and presence of a cosmological constant in the gravitational theory with Lorentz violation induced by a background Kalb-Ramond (KR) field. The corresponding thermodynamic properties are investigated. It is found that the standard first law of thermodynamics and the Smarr f

Giuliano Basso

In this mostly expository article, we give streamlined proofs of several well-known Lipschitz extension theorems. We pay special attention to obtaining statements with explicit expressions for the extension constants. One of our main results is an explicit version of a very general Lipschitz extension theorem of Lang and Schlichenmaier. A special case of the

Fateme Jamshidi, Luca Ganassali, Negar Kiyavash

Motivated by conditional independence testing, an essential step in constraint-based causal discovery algorithms, we study the nonparametric Von Mises estimator for the entropy of multivariate distributions built on a kernel density estimator. We establish an exponential concentration inequality for this estimator. We design a test for conditional independen

Jinyuan Wang, Junlong Li, Hai Zhao

In open-domain question-answering (ODQA), most existing questions require single-hop reasoning on commonsense. To further extend this task, we officially introduce open-domain multi-hop reasoning (ODMR) by answering multi-hop questions with explicit reasoning steps in open-domain setting. Recently, large language models (LLMs) have found significant utility

Peng Jiang, Srikanth Saripalli

As the demand for autonomous navigation in off-road environments increases, the need for effective solutions to understand these surroundings becomes essential. In this study, we confront the inherent complexities of semantic segmentation in RADAR data for off-road scenarios. We present a novel pipeline that utilizes LIDAR data and an existing annotated off-

Ruiquan Huang, Yuan Cheng, Jing Yang, Vincent Tan

In multi-task reinforcement learning (RL) under Markov decision processes (MDPs), the presence of shared latent structures among multiple MDPs has been shown to yield significant benefits to the sample efficiency compared to single-task RL. In this paper, we investigate whether such a benefit can extend to more general sequential decision making problems, su

Dorian Quelle, Alexandre Bovet

Automated fact-checking, using machine learning to verify claims, has grown vital as misinformation spreads beyond human fact-checking capacity. Large Language Models (LLMs) like GPT-4 are increasingly trusted to write academic papers, lawsuits, and news articles and to verify information, emphasizing their role in discerning truth from falsehood and the imp

Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud

Human feedback is commonly utilized to finetune AI assistants. But human feedback may also encourage model responses that match user beliefs over truthful ones, a behaviour known as sycophancy. We investigate the prevalence of sycophancy in models whose finetuning procedure made use of human feedback, and the potential role of human preference judgments in s

Armando J. Cabrera Pacheco, Markus Wolff

Motivated by solving the constraint equations in the evolutionary form suggested by R\'acz, we propose a family of asymptotically flat initial data sets which are "asymptotically spherically symmetric" at infinity. Within this family, we obtain Penrose-like energy estimates and establish the existence of solutions for the constraint equations in the spherica

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

The $\Xi_b^0 \rightarrow \Xi_c^+ D_s^-$ and $\Xi_b^- \rightarrow \Xi_c^0 D_s^-$ decays are observed for the first time using proton-proton collision data collected by the LHCb experiment at a centre-of-mass energy of $\sqrt{s}=13\mathrm{TeV}$, corresponding to an integrated luminosity of $5.1\mathrm{fb}^{-1}$. The relative branching fractions times the beaut

Zhongzhan Huang, Pan Zhou, Shuicheng Yan, Liang Lin

In diffusion models, UNet is the most popular network backbone, since its long skip connects (LSCs) to connect distant network blocks can aggregate long-distant information and alleviate vanishing gradient. Unfortunately, UNet often suffers from unstable training in diffusion models which can be alleviated by scaling its LSC coefficients smaller. However, th

Shehzaad Dhuliawala, Vilém Zouhar, Mennatallah El-Assady, Mrinmaya Sachan

In a human-AI collaboration, users build a mental model of the AI system based on its reliability and how it presents its decision, e.g. its presentation of system confidence and an explanation of the output. Modern NLP systems are often uncalibrated, resulting in confidently incorrect predictions that undermine user trust. In order to build trustworthy AI,

H. Thuruthipilly, Junais, A. Pollo, U. Sureshkumar

Low surface brightness galaxies (LSBGs) which are defined as galaxies that are fainter than the night sky, play a crucial role in understanding galaxy evolution and cosmological models. Upcoming large-scale surveys like Rubin Observatory Legacy Survey of Space and Time (LSST) and Euclid are expected to observe billions of astronomical objects. In this contex

Seok-Young Chung, Sujin Lee, Young Woong Park

This paper investigates a generalized interlacing property between Bessel functions, particularly $J_\nu$ and $J_\mu$, where the difference $|\nu-\mu|$ exceeds $2$. This interlacing phenomenon is marked by a compensatory interaction with the zeros of Lommel polynomials, extending our understanding beyond the traditional $|\nu-\mu| \le 2$ regime. The paper ex

Liangze Jiang, Zheng-Guang Wu, Lei Wang

This paper studies distributed continuous-time optimization for time-varying quadratic cost functions with uncertain parameters. We first propose a centralized adaptive optimization algorithm using partial information of the cost function. It can be seen that even if there are uncertain parameters in the cost function, exact optimization can still be achieve

Zekai Qu, Ruobing Xie, Chaojun Xiao, Yuan Yao

With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual information in PLM with the personalized behavioral information in user historical behavior sequences to enhance sequential recommendation (SR). However, despite the commonalities of

Hansi Yang, Yongqi Zhang, Quanming Yao, James Kwok

Node classification on graphs is an important research problem with many applications. Real-world graph data sets may not be balanced and accurate as assumed by most existing works. A challenging setting is positive-unlabeled (PU) node classification, where labeled nodes are restricted to positive nodes. It has diverse applications, e.g., pandemic prediction

Mimi Dai, Qirui Peng

We construct non-unique weak solutions $\theta\in C_t^0C_x^{0-}$ for forced surface quasi-geostrophic (SQG) equation. This is achieved through a convex integration scheme adapted to the sum-difference system of two distinct solutions. Without external forcing, non-unique weak solutions $\theta$ in space $C_t^0C_x^{\alpha}$ with $\alpha<-\frac15$ were constru

Kristin Kirchner, Joshua Willems

We define various higher-order Markov properties for stochastic processes $(X(t))_{t\in \mathbb{T}}$, indexed by an interval $\mathbb{T} \subseteq \mathbb{R}$ and taking values in a real and separable Hilbert space $U$. We furthermore investigate the relations between them. In particular, for solutions to the stochastic evolution equation $\mathcal{L} X = \d

Erin Compaan, Wangseok Shin, Nikolaos Tzirakis

In this paper we obtain improved local well-posedness results for the Schr\"odinger-KdV system on the half-line. We employ the Laplace-Fourier method in conjunction with the restricted norm method of Bourgain appropriately modified in order to accommodate the bounded operators of the half-line problem. Our result extends the previous local results in [6], [7

Huaijin Wang, Chuanju Xu

A novel recurrence formula for moments with respect to M\"{u}ntz-Legendre polynomials is proposed and applied to construct a numerical method for solving generalized Gauss quadratures with power function weight for M\"{u}ntz systems. These quadrature rules exhibit several properties similar to the classical Gaussian quadratures for polynomial systems, includ

Damian Sójka, Yuyang Liu, Dipam Goswami, Sebastian Cygert

The goal of the challenge is to develop a test-time adaptation (TTA) method, which could adapt the model to gradually changing domains in video sequences for semantic segmentation task. It is based on a synthetic driving video dataset - SHIFT. The source model is trained on images taken during daytime in clear weather. Domain changes at test-time are mainly

Veronica Chatrath, Oluwanifemi Bamgbose, Shaina Raza

As the use of large language models (LLMs) increases within society, as does the risk of their misuse. Appropriate safeguards must be in place to ensure LLM outputs uphold the ethical standards of society, highlighting the positive role that artificial intelligence technologies can have. Recent events indicate ethical concerns around conventionally trained L

Chen Xing, Guillaume Aulanier, Brigitte Schmieder, Xin Cheng

The properties of pre-eruptive structures and coronal mass ejections (CMEs) are characterized by those of their footpoints, the latter of which thus attract great interest. However, how to identify the footpoints of pre-eruptive structures and how to identify the footpoints with ground-based instruments, still remain elusive. In this work, we study an arc-sh

V. V. Savchuk, S. O. Chaichenko, A. L. Shidlich

We calculated the exact value and found the polynomial of the best weighted polynomial approximation of the kernels of the form $\frac {A+Bx}{(x^2+\lambda^2)^2}$, where $A,B\in {\mathbb R}$, $\lambda>0$ in the mean-square metric.

Zehua Tian, Jiliang Jing, Jiangfeng Du

Herein, we propose a novel strategy for implementing a direct readout of the symmetric characteristic function of the quantum states of quantum fields without the involvement of idealized measurements, an aspect that has always been deemed ill-defined in quantum field theory. This proposed scheme relies on the quantum control and measurements of an auxiliary

Minggang Luo, Youssef Jeyar, Brahim Guizal, Junming Zhao

In this work we analyze the near-field radiative heat transfer (NFRHT) between finite-thickness planar fused silica slabs coated with graphene gratings. We go beyond the effective medium approximation by using an exact Fourier Modal Method (FMM) equipped with specific Local Basis Functions (LBF), and this is needed for realistic experimental analysis. In gen

Maurantonio Caprolu, Savio Sciancalepore, Aleksandar Grigorov, Velyan Kolev

People Nearby is a service offered by Telegram that allows a user to discover other Telegram users, based only on geographical proximity. Nearby users are reported with a rough estimate of their distance from the position of the reference user, allowing Telegram to claim location privacy In this paper, we systematically analyze the location privacy provided

Jorge Andres Robinson Arrieta

Laudenbach proved that the mapping class group of the connect sum of $n$ copies of $S^2 \times S^1$ is an extension of $Out(F_n)$ by a finite group. Brendle-Broaddus-Putman proved that this exact sequence splits. We provide an explicit section $s$ of this split exact sequence.

Tobias Deußer, Cong Zhao, Wolfgang Krämer, David Leonhard

During the pre-training step of natural language models, the main objective is to learn a general representation of the pre-training dataset, usually requiring large amounts of textual data to capture the complexity and diversity of natural language. Contrasting this, in most cases, the size of the data available to solve the specific downstream task is ofte

Patrick G. J. Irwin, Jack Dobinson, Arjuna James, Michael H. Wong

Spectral observations of Neptune made in 2019 with the MUSE instrument at the Very Large Telescope in Chile have been analysed to determine the spatial variation of aerosol scattering properties and methane abundance in Neptune's atmosphere. The darkening of the South Polar Wave (SPW) at $\sim$ 60$^\circ$S, and dark spots such as the Voyager 2 Great Dark Spo

Arunava Majumder, Marius Krumm, Tina Radkohl, Lukas J. Fiderer

Measurement-based quantum computation (MBQC) offers a fundamentally unique paradigm to design quantum algorithms. Indeed, due to the inherent randomness of quantum measurements, the natural operations in MBQC are not deterministic and unitary, but are rather augmented with probabilistic byproducts. Yet, the main algorithmic use of MBQC so far has been to com

Caroline Ostermann

Understanding the role of Biogenic Volatile Organic Compounds (BVOCs) in atmospheric chemistry and their impact on oxidants and aerosol formation is essential for global environmental research. BVOC emissions, primarily sourced from biogenic processes, pose a challenge due to the vast diversity of vegetation and ecosystems, as well as temporal changes in lan

Xiao Yu, Baolin Peng, Michel Galley, Jianfeng Gao

The self-improving ability of large language models (LLMs), enabled by prompting them to analyze and revise their own outputs, has garnered significant interest in recent research. However, this ability has been shown to be absent and difficult to learn for smaller models, thus widening the performance gap between state-of-the-art LLMs and more cost-effectiv

S. Taibi, M. S. Pawlowski, S. Khoperskov, M. Steinmetz

It has been observed that several Milky Way (MW) satellite dwarf galaxies are distributed along a coherent planar distribution known as the Vast Polar Structure (VPOS). Here we investigate whether MW satellites located on the VPOS have different physical and orbital properties from those not associated with it. Using the proper motion measurements of the MW

D. Edstrom, A. Romanov, D. Broemmelsiek, K. Carlson

The IOTA Proton Injector (IPI), currently under installation at the Fermilab Accelerator Science and Technology facility (FAST), is a machine capable of delivering 20 mA pulses of protons at 2.5 MeV to the Integrable Optics Test Accelerator (IOTA) ring. First beam in the IPI beamline is anticipated in the first half of 2024, when it will operate alongside th

Vítězslav Štembera

This paper investigates the applicability of the DK and DKM shell element classes for the first time within the framework of the standard (and sequential) finite-element-based limit analysis, which is a direct method used for determining the plastic collapse (and post-collapse) behaviour of structures. Despite these elements not being able to guarantee a str

Shengcheng Yu, Chunrong Fang, Ziyuan Tuo, Quanjun Zhang

Graphical User Interface (GUI) has become one of the most significant parts of mobile applications (apps). It is a direct bridge between mobile apps and end users, which directly affects the end user's experience. Neglecting GUI quality can undermine the value and effectiveness of the entire mobile app solution. Significant research efforts have been devoted

Omer F. Dayi

Two actions which are functionals of different variables but describing the same dynamical system can be shown to possess the same origin by constructing a master action which generates both of them. We first present the master action which produces an action depending on the fluid variables and a gauge theory action whose equations of motion are equivalent

Jyothis Chandran, Mathieu Remazeilles, R. B. Barreiro

In 2015, the Planck Collaboration released an all-sky map of the thermal Sunyaev-Zeldovich (SZ) effect, obtained by implementing the Needlet Internal Linear Combination (NILC) method on the Planck PR2 data. The quality of the Planck data has significantly improved since then. The Planck PR4 data release offers upgraded full-sky maps in the LFI and HFI freque

C. A. J. Hanselaar, E. Silvas, A. Terechko, W. P. M. H. Heemels

To enable highly automated vehicles where the driver is no longer a safety backup, the vehicle must deal with various Functional Insufficiencies (FIs). Thus-far, there is no widely accepted functional architecture that maximizes the availability of autonomy and ensures safety in complex vehicle operational design domains. In this paper, we present a survey o

Andrei Boiarov, Dmitry Bleklov, Pavlo Bredikhin, Nikita Koritsky

This paper presents RaceLens, a novel application utilizing advanced deep learning and computer vision models for comprehensive analysis of racing photos. The developed models have demonstrated their efficiency in a wide array of tasks, including detecting racing cars, recognizing car numbers, detecting and quantifying car details, and recognizing car orient

Zhuoyi Zhang, Yunchen Zhang, Gonglei Shi, Yu Shen

Neural network quantization is widely used to reduce model inference complexity in real-world deployments. However, traditional integer quantization suffers from accuracy degradation when adapting to various dynamic ranges. Recent research has focused on a new 8-bit format, FP8, with hardware support for both training and inference of neural networks but lac

Zehua Xia, Qi Gou, Bowen Yu, Haiyang Yu

This paper addresses the problem of generating questions from a given context and an answer, specifically focusing on questions that require multi-hop reasoning across an extended context. Previous studies have suggested that key phrase selection is essential for question generation (QG), yet it is still challenging to connect such disjointed phrases into me

Donggyu Kim, Minseog Oh

This paper introduces a dynamic minimum variance portfolio (MVP) model using nonlinear volatility dynamic models, based on high-frequency financial data. Specifically, we impose an autoregressive dynamic structure on MVP processes, which helps capture the MVP dynamics directly. To evaluate the dynamic MVP model, we estimate the inverse volatility matrix usin

Timothee Derkenne, Annie Colin, Corentin Tregouet

Blue energy represents a large reservoir of renewable osmotic energy that can be converted into electricity by reverse electrodialysis (RED). This method is based on ion-exchange membrane. Before large scale production, these membranes are compared on very small samples on the basis of the power they enable to produce per unit area. Through a systematic stud

Lhoussain El Fadil, Omar Kchit

In this paper, for any nonic number field $K$ defined by a monic irreducible trinomial $F(x)=x^9+ax^2+b \in \mathbb{Z}[x]$, we calculate $\nu_p(i(K))$ for every rational prime $p$. In particular, we characterize the index $i(K)$ of this family of number fields. As an application of our results, if $i(K)\neq1$, then $K$ is not monogenic. We illustrate our res

Manith Adikari, Angelo Cangelosi, Randy Gomez

A social robot acting as a 'mediator' can enhance interactions between humans, for example, in fields such as education and healthcare. A particularly promising area of research is the use of a social robot mediator in a multiparty setting, which tends to be the most applicable in real-world scenarios. However, research in social robot mediation for multipar

Maria Gorelik, Vladimir Hinich, Vera Serganova

We present a proof of a generalization of the theorem of H.~Matsumoto on Coxeter groups. Our generalized version is applicable to "graphs admitting geometric realization". The original version of the theorem for Coxeter groups is a special case when applied to the Cayley graph and the geometric representation of a Coxeter group. Our version of Matsumoto theo

Sagnik Ray Choudhury, Pepa Atanasova, Isabelle Augenstein

Reasoning over spans of tokens from different parts of the input is essential for natural language understanding (NLU) tasks such as fact-checking (FC), machine reading comprehension (MRC) or natural language inference (NLI). However, existing highlight-based explanations primarily focus on identifying individual important tokens or interactions only between

Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum

Models for conversational question answering (ConvQA) over knowledge graphs (KGs) are usually trained and tested on benchmarks of gold QA pairs. This implies that training is limited to surface forms seen in the respective datasets, and evaluation is on a small set of held-out questions. Through our proposed framework REIGN, we take several steps to remedy t

Liangchen Li, Chong Li, Rong Luo, Cun-Quan Zhang

A triangle-path in a graph $G$ is a sequence of distinct triangles $T_1,T_2,\ldots,T_m$ in $G$ such that for any $i, j$ with $1\leq i < j \leq m$, $|E(T_i)\cap E(T_{i+1})|=1$ and $E(T_i)\cap E(T_j)=\emptyset$ if $j > i+1$. A connected graph $G$ is triangularly connected if for any two nonparallel edges $e$ and $e'$ there is a triangle-path $T_1T_2\cdots T_m$

Juan L. A. Abranches, Antonio D. Pereira, Reiko Toriumi

We introduce a dually-weighted multi-matrix model that for a suitable choice of weights reproduce two-dimensional Causal Dynamical Triangulations (CDT) coupled to the Ising model. When Ising degrees of freedom are removed, this model corresponds to the CDT-matrix model introduced by Benedetti and Henson [Phys. Lett. B 678, 222 (2009)]. We present exact as we

Arnaud Mayeux, Simon Riche

We review the construction (due to Brenner--Schr\"oer) of the Proj scheme associated with a ring graded by a finitely generated abelian group. This construction generalizes the well-known Grothendieck Proj construction for $\mathbb{N}$-graded rings; we extend some classical results (in particular, regarding quasi-coherent sheaves on such schemes) from the $\