May 2023 arXiv papers — page 64

Xuewu Lin, Tianwei Lin, Zixiang Pei, Lichao Huang

Sparse algorithms offer great flexibility for multi-view temporal perception tasks. In this paper, we present an enhanced version of Sparse4D, in which we improve the temporal fusion module by implementing a recursive form of multi-frame feature sampling. By effectively decoupling image features and structured anchor features, Sparse4D enables a highly effic

Xun Jiang, Zailei Zhou, Xing Xu, Yang Yang

Video Moment Retrieval (VMR) aims at retrieving the most relevant events from an untrimmed video with natural language queries. Existing VMR methods suffer from two defects: (1) massive expensive temporal annotations are required to obtain satisfying performance; (2) complicated cross-modal interaction modules are deployed, which lead to high computational c

Minwoo Lee, Hyukhun Koh, Kang-il Lee, Dongdong Zhang

Gender bias is a significant issue in machine translation, leading to ongoing research efforts in developing bias mitigation techniques. However, most works focus on debiasing bilingual models without much consideration for multilingual systems. In this paper, we specifically target the gender bias issue of multilingual machine translation models for unambig

Benedict Bauer, Stefan Gerhold

We argue that a classical inequality due to Fan, Taussky and Todd (1955) is equivalent to the dissipativity of a Jordan block. As the latter can be characterised via the zeros of Chebyshev polynomials, we obtain a short new proof of the inequality. Three other inequalities of Fan-Taussky-Todd are reproven similarly. By the Lumer-Phillips theorem, the semigro

Shuai Zhao, Ruijie Quan, Linchao Zhu, Yi Yang

Pre-trained vision-language models~(VLMs) are the de-facto foundation models for various downstream tasks. However, scene text recognition methods still prefer backbones pre-trained on a single modality, namely, the visual modality, despite the potential of VLMs to serve as powerful scene text readers. For example, CLIP can robustly identify regular (horizon

Thilo Krill, Max Pitz

Two graphs are of the same topological type if they can be mutually embedded into each other topologically. We show that there are exactly $\aleph_1$ distinct topological types of countable trees. In general, for any infinite cardinal $\kappa$ there are exactly $\kappa^+$ distinct topological types of trees of size $\kappa$. This solves a problem of van der

Niyati Bafna, Cristina España-Bonet, Josef van Genabith, Benoît Sagot

Most existing approaches for unsupervised bilingual lexicon induction (BLI) depend on good quality static or contextual embeddings requiring large monolingual corpora for both languages. However, unsupervised BLI is most likely to be useful for low-resource languages (LRLs), where large datasets are not available. Often we are interested in building bilingua

Louis Ledoux, Marc Casas

Many scientific computing problems can be reduced to Matrix-Matrix Multiplications (MMM), making the General Matrix Multiply (GEMM) kernels in the Basic Linear Algebra Subroutine (BLAS) of interest to the high-performance computing community. However, these workloads have a wide range of numerical requirements. Ill-conditioned linear systems require high-pre

Hugues Meyer, Heiko Rieger

Chemotaxis, i.e. motion generated by chemical gradients, is a motility mode shared by many living species that has been developed by evolution to optimize certain biological processes such as foraging or immune response. In particular, auto-chemotaxis refers to chemotaxis mediated by a cue produced by the chemotactic particle itself. Here, we investigate the

Xiaolin Zheng, Mengling Hu, Weiming Liu, Chaochao Chen

Short text clustering is challenging since it takes imbalanced and noisy data as inputs. Existing approaches cannot solve this problem well, since (1) they are prone to obtain degenerate solutions especially on heavy imbalanced datasets, and (2) they are vulnerable to noises. To tackle the above issues, we propose a Robust Short Text Clustering (RSTC) model

Wenhao Yu, Meng Jiang, Peter Clark, Ashish Sabharwal

Although counterfactual reasoning is a fundamental aspect of intelligence, the lack of large-scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult to evaluate and improve models on this ability. To address this void, we introduce the first such dataset, named IfQA, where each question is based on a counterfactual presuppositi

Sebastian Pineda Arango, Josif Grabocka

Automated Machine Learning (AutoML) is a promising direction for democratizing AI by automatically deploying Machine Learning systems with minimal human expertise. The core technical challenge behind AutoML is optimizing the pipelines of Machine Learning systems (e.g. the choice of preprocessing, augmentations, models, optimizers, etc.). Existing Pipeline Op

Alvari Seppänen, Risto Ojala, Kari Tammi

Adverse weather can cause noise to light detection and ranging (LiDAR) data. This is a problem since it is used in many outdoor applications, e.g. object detection and mapping. We propose the task of multi-echo denoising, where the goal is to pick the echo that represents the objects of interest and discard other echoes. Thus, the idea is to pick points from

Jingwei Ni, Zhijing Jin, Qian Wang, Mrinmaya Sachan

Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work -- sometimes negative transfer occurs between tasks, especially when aggregating loosely related skills, leaving it an open question when MTL works. Previous studies show that MTL performance can be improved by al

Jiangyi Lin, Yaxin Fan, Xiaomin Chu, Peifeng Li

The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change. Previous work focused on detecting topic shifts using pre-trained models to encode the utterance, failing to delve into the various levels of topic granularity in the dialogue and understand dialogue contents. To address the a

Derek Harland, Paul Leask, Martin Speight

The crystalline structure of nuclear matter is investigated in the standard Skyrme model with massive pions. A semi-analytic method is developed to determine local minima of the static energy functional with respect to variations of both the field and the period lattice of the crystal. Four distinct Skyrme crystals are found. Two of these were already known

Ayush Maheshwari, Ashim Gupta, Amrith Krishna, Atul Kumar Singh

We release S\={a}mayik, a dataset of around 53,000 parallel English-Sanskrit sentences, written in contemporary prose. Sanskrit is a classical language still in sustenance and has a rich documented heritage. However, due to the limited availability of digitized content, it still remains a low-resource language. Existing Sanskrit corpora, whether monolingual

Silvia Cingolani, Marco Gallo, Kazunaga Tanaka

In this paper we study the following nonlinear fractional Choquard-Pekar equation \begin{equation}\label{eq_abstract} (-\Delta)^s u + \mu u =(I_\alpha*F(u)) F'(u) \quad \hbox{in}\ \mathbb{R}^N, \tag{$*$} \end{equation} where $\mu>0$, $s \in (0,1)$, $N \geq 2$, $\alpha \in (0,N)$, $I_\alpha \sim \frac{1}{|x|^{N-\alpha}}$ is the Riesz potential, and $F$ is a g

Wenhao Yu, Zhihan Zhang, Zhenwen Liang, Meng Jiang

Large language models (LLMs) exhibit remarkable performance across various NLP tasks. However, they often generate incorrect or hallucinated information, which hinders their practical applicability in real-world scenarios. Human feedback has been shown to effectively enhance the factuality and quality of generated content, addressing some of these limitation

Dongliang Jing, Andrew W. Eckford

Channel memory and inter-symbol interference (ISI) are harmful factors in diffusion-based molecular communication (DBMC) between bionanosensors. To tackle these problems, this paper proposes a lightweight ISI-mitigating coding scheme to improve the system performance by shaping the signal using a constrained code. To characterize the proposed coding scheme t

Keke Huang, Jing Tang, Juncheng Liu, Renchi Yang

Graph Neural Networks (GNNs) have shown superior performance for semi-supervised learning of numerous web applications, such as classification on web services and pages, analysis of online social networks, and recommendation in e-commerce. The state of the art derives representations for all nodes in graphs following the same diffusion (message passing) mode

Zeyu Leo Liu, Tim Dettmers, Xi Victoria Lin, Veselin Stoyanov

Large and sparse feed-forward layers (S-FFN) such as Mixture-of-Experts (MoE) have proven effective in scaling up Transformers model size for \textit{pretraining} large language models. By only activating part of the FFN parameters conditioning on input, S-FFN improves generalization performance while keeping training and inference costs (in FLOPs) fixed. In

Paul Saves, Remi Lafage, Nathalie Bartoli, Youssef Diouane

The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. This paper presents SMT 2.0, a major new release of SMT that introduces significant upgrades and new features to the toolbox. This release adds the capability to handle mixed-variable

A. Hayashi

The Aharonov-Bohm (AB) phase is usually associated with a line integral of the electromagnetic vector potential generated by an external current source, such as a solenoid. According to this interpretation, the AB phase of a nonclosed path cannot be observed, as the integral depends on the gauge choice of the vector potential. Recent attempts to explain the

Mitradeep Sarkar, Michael T. Enders, Mehrdad Shokooh-Saremi, Kenji Watanabe

High-quality low-dimensional layered and van der Waals materials are typically exfoliated, with sample cross sectional areas on the order of tens to hundreds of microns. The small size of flakes makes the experimental characterization of their dielectric properties unsuitable with conventional spectroscopic ellipsometry, due to beam-sample size mismatch and

Haoran Xu, Weiting Tan, Shuyue Stella Li, Yunmo Chen

Incorporating language-specific (LS) modules is a proven method to boost performance in multilingual machine translation. This approach bears similarity to Mixture-of-Experts (MoE) because it does not inflate FLOPs. However, the scalability of this approach to hundreds of languages (experts) tends to be unmanageable due to the prohibitive number of parameter

Simon Kothe, Peter Kirton

Neural network quantum states as ansatz wavefunctions have shown a lot of promise for finding the ground state of spin models. Recently, work has been focused on extending this idea to mixed states for simulating the dynamics of open systems. Most approaches so far have used a purification ansatz where a copy of the system Hilbert space is added which when t

Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar

In order to train networks for verified adversarial robustness, it is common to over-approximate the worst-case loss over perturbation regions, resulting in networks that attain verifiability at the expense of standard performance. As shown in recent work, better trade-offs between accuracy and robustness can be obtained by carefully coupling adversarial tra

Keyi Wu, Samuel A. Prieto, Eyob Mengiste, Borja García de Soto

The formwork system belonging to the temporary structure plays an important role in the smooth progress and successful completion of a construction project. Ensuring that the formwork system is installed as designed is essential for construction safety and quality. The current way to measure the spacing between formwork system members is mostly done using ma

Danilo Chamorro, Jiahua Zhao, Claire Birnie, Myrna Staring

Multi-channel Analysis of Surface Waves (MASW) is a seismic method employed to obtain useful information about shear-wave velocities in the near surface. A fundamental step in this methodology is the extraction of dispersion curves from dispersion spectra, which are obtained after applying specific processing algorithms onto the recorded shot gathers. Whilst

Cheikh M. Bamba Dione, David Adelani, Peter Nabende, Jesujoba Alabi

In this paper, we present MasakhaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the UD (universal dependencies) guidelines. We conducted extensive POS baseline experiments using conditional random field and several multilingual pre-trained lan

Luca Schaufelberger, Maximilian E. Merkel, Aria Mansouri Tehrani, Nicola A. Spaldin

We present a method to constrain local charge multipoles within density-functional theory. Such multipoles quantify the anisotropy of the local charge distribution around atomic sites and can indicate potential hidden orders. Our method allows selective control of specific multipoles, facilitating a quantitative exploration of the energetic landscape outside

Wenhao Zhu, Tianyu Wen, Guojie Song, Liang Wang

Graph Transformer has recently received wide attention in the research community with its outstanding performance, yet its structural expressive power has not been well analyzed. Inspired by the connections between Weisfeiler-Lehman (WL) graph isomorphism test and graph neural network (GNN), we introduce \textbf{SEG-WL test} (\textbf{S}tructural \textbf{E}nc

Claudia Bandiera, Richard D. Connors, Francesco Viti

A Mathematical Program with Equilibrium Constraints (MPEC) is formulated to capture the relationships between multiple Mobility Service Providers (MSPs) and the users of a multi-modal transport network. The network supply structure is defined through a novel supernetwork approach where users' daily trip chains are represented to model the mobility services u

Dusan Jakovetic, Natasa Krejic, Greta Malaspina

We consider two formulations for distributed optimization wherein $N$ agents in a generic connected network solve a problem of common interest: distributed personalized optimization and consensus optimization. A new method termed DINAS (Distributed Inexact Newton method with Adaptive Stepsize) is proposed. DINAS employs large adaptively computed step-sizes,

Asuka Ito, Kazunori Kohri, Kazunori Nakayama

We study graviton-photon conversion in magnetosphere of a pulsar and explore the possibility of detecting high frequency gravitational waves with pulsar observations. It is shown that conversion of one polarization mode of photons can be enhanced significantly due to strong magnetic fields around a pulsar. We also constrain stochastic gravitational waves in

A A Zhukov, I E Batov

We report on the low temperature measurements of the magnetotransport in Si-doped InAs quantum wire in the presence of a charged tip of an atomic force microscope serving as a mobile gate, i.e. scanning gate microscopy (SGM). By altering the carrier concentration with back gate voltage, we transfer the wire through several transport regimes: from residual Co

Yang Qi, Zhichao Zhu, Yiming Wei, Lu Cao

The highly irregular spiking activity of cortical neurons and behavioral variability suggest that the brain could operate in a fundamentally probabilistic way. Mimicking how the brain implements and learns probabilistic computation could be a key to developing machine intelligence that can think more like humans. In this work, we propose a theory of stochast

Ji Qi, Chuchun Zhang, Xiaozhi Wang, Kaisheng Zeng

The robustness to distribution changes ensures that NLP models can be successfully applied in the realistic world, especially for information extraction tasks. However, most prior evaluation benchmarks have been devoted to validating pairwise matching correctness, ignoring the crucial measurement of robustness. In this paper, we present the first benchmark t

Ruiqi Sun, Siwei Ye, Jie Zhao, Xin He

The inherent diversity of computation types within the deep neural network (DNN) models often requires a variety of specialized units in hardware processors, which limits computational efficiency, increasing both inference latency and power consumption, especially when the hardware processor needs to support and execute different neural networks. In this stu

Alexander E. Holroyd

We prove the existence of a finitely dependent proper colouring of the integer lattice Z^d that is fully isometry-invariant in law, for all dimensions d. Previously this was known only for d=1, while only translation-invariant examples were known for higher d. Moreover we show that four colours suffice, and that the colouring can be expressed as an isometry-

Simon Walker-Samuel

Magnetic resonance imaging (MRI) is a highly versatile and widely used clinical imaging tool. The content of MRI images is controlled by an acquisition sequence, which coordinates the timing and magnitude of the scanner hardware activations, which shape and coordinate the magnetisation within the body, allowing a coherent signal to be produced. The use of de

J. Magnusson, T. G. Blackburn, E. Gerstmayr, E. E. Los

Current experiments investigating radiation reaction employ high energy electron beams together with tightly focused laser pulses in order to reach the quantum regime, as expressed through the quantum nonlinearity parameter $\chi$. Such experiments are often complicated by the large number of latent variables, including the precise structure of the electron

Huajun Long, Jie Li, Rui Li, Xinfeng Liu

Stroke can lead to the impaired motor ability of the patient's lower limbs and hemiplegia. Accurate assessment of the lower limbs' motor ability is important for diagnosis and rehabilitation. To digitalize such assessment so that each test can be traced back any time and subjectivity can be avoided, we test how dual-modality smart shoes equipped with pressur

Fumihiro Naokawa, Toshiya Namikawa

We calculate the effect of gravitational lensing on the parity-odd power spectrum of the cosmic microwave background (CMB) polarization induced by axionlike particles (ALPs). Several recent works have reported a tantalizing hint of cosmic birefringence, a rotation of the linear polarization plane of CMB, which ALPs can explain. In future CMB observations, we

Zeqi Zhang, Ravindra T. Desai, Oleg Shebanits, Fredrik L. Johansson

The Cassini spacecraft's Grand Finale flybys through Saturn's ionosphere provided unprecedented insight into the composition and dynamics of the gas giant's upper atmosphere and a novel and complex spacecraft-plasma interaction. In this article, we further study Cassini's interaction with Saturn's ionosphere using three dimensional Particle-in-Cell simulatio

Giovanni Gandolfi, Balakrishna Sandeep Haridasu, Stefano Liberati, Andrea Lapi

We look for possible evidence of a non-minimal coupling (NMC) between dark matter (DM) and gravity using data from the X-COP compilation of galaxy clusters. We consider a theoretically motivated NMC that may dynamically arise from the collective behavior of the coarse-grained DM field (e.g., via Bose-Einstein condensation) with averaging/coherence length $\L

Eunbi Choi, Kyoung-Woon On, Gunsoo Han, Sungwoong Kim

Open-domain conversation systems integrate multiple conversation skills into a single system through a modular approach. One of the limitations of the system, however, is the absence of management capability for external memory. In this paper, we propose a simple method to improve BlenderBot3 by integrating memory management ability into it. Since no trainin

Chuanyuan Tan, Yuehe Chen, Wenbiao Shao, Wenliang Chen

Question answering over knowledge bases (KBQA) aims to answer factoid questions with a given knowledge base (KB). Due to the large scale of KB, annotated data is impossible to cover all fact schemas in KB, which poses a challenge to the generalization ability of methods that require a sufficient amount of annotated data. Recently, LLMs have shown strong few-

Saibo Geng, Martin Josifoski, Maxime Peyrard, Robert West

Despite their impressive performance, large language models (LMs) still struggle with reliably generating complex output structures when not finetuned to follow the required output format exactly. To address this issue, grammar-constrained decoding (GCD) can be used to control the generation of LMs, guaranteeing that the output follows a given structure. Mos

Donghyuk Kim, Jae-Young Kim, Wontak Han, Jongsoon Won

Processing-in-memory (PIM) architecture is an inherent match for data analytics application, but we observe major challenges to address when accelerating it using PIM. In this paper, we propose Darwin, a practical LRDIMM-based multi-level PIM architecture for data analytics, which fully exploits the internal bandwidth of DRAM using the bank-, bank group-, ch

Matej Novosad, Robert Penicka, Vojtech Vonasek

In this paper, we propose a new method called Clustering Topological PRM (CTopPRM) for finding multiple homotopically distinct paths in 3D cluttered environments. Finding such distinct paths, e.g., going around an obstacle from a different side, is useful in many applications. Among others, using multiple distinct paths is necessary for optimization-based tr

Raghad Al-Bakri, Bassam Shehadeh

In this paper, we use Yukawa theory to calculate differential and total cross-sections for elastic and inelastic scattering in nucleon-nucleon interactions. We start from the fundamental Lagrangian and derive the $T$-matrix and hence the invariant scattering matrix leading towards the differential and total cross section. We perform calculations utilizing tw

Damodar Panigrahi, William Anderson, Joshua Whitman, Sudip Mittal

Automated Intelligent Cyberdefense Agents (AICAs) that are part Intrusion Detection Systems (IDS) and part Intrusion Response Systems (IRS) are being designed to protect against sophisticated and automated cyber-attacks. An AICA based on the ideas of Self-Adaptive Autonomic Computing Systems (SA-ACS) can be considered as a managing system that protects a man

Michelle L. M. Collins, Noushin Karim, David Martinez-Delgado, Matteo Monelli

Pisces VII/Triangulum III (Pisc~VII) was discovered in the DESI Legacy Imaging Survey and was shown to be a Local Group dwarf galaxy with follow-up imaging from the 4-m Telescopio Nazionale Galileo. However, this imaging was unable to reach the horizontal branch of Pisc VII, preventing a precision distance measurement. The distance bound from the red giant b

Byungjoon Min, Maxi San Miguel

We study the coevolutionary dynamics of network topology and social complex contagion using a threshold cascade model. Our coevolving threshold model incorporates two mechanisms: the threshold mechanism for the spreading of a minority state such as a new opinion, idea, or innovation and the network plasticity implemented as rewiring of links to cut the conne

Taehyeong Kim, Jeong-Hoon Ju, Yeongrak Kim

In this paper, we suggest a new method for a given tensor to find CP decompositions using a less number of rank $1$ tensors. The main ingredient is the Least Absolute Shrinkage and Selection Operator (LASSO) by considering the decomposition problem as a sparse optimization problem. As applications, we design experiments to find some CP decompositions of the

Callum Hunter, Michael Kenna-Allison

In this work we investigate the most general non-minimally coupled $\mathbb{Z}_2$ symmetric scalar-tensor effective field theory (EFT) of gravity up to dimension six in the operator expansion. The most general action is presented along with its equations of motion both in the covariant form and also in the coordinate form resulting from an FLRW analysis. The

Jingning Xu, Benlai Tang, Mingjie Wang, Minghao Li

Recently, talking face generation has drawn ever-increasing attention from the research community in computer vision due to its arduous challenges and widespread application scenarios, e.g. movie animation and virtual anchor. Although persevering efforts have been undertaken to enhance the fidelity and lip-sync quality of generated talking face videos, there

Isabel Funke, Dominik Rivoir, Stefanie Speidel

Surgical phase recognition is a basic component for different context-aware applications in computer- and robot-assisted surgery. In recent years, several methods for automatic surgical phase recognition have been proposed, showing promising results. However, a meaningful comparison of these methods is difficult due to differences in the evaluation process a

Ferdinand Mütsch, Helen Gremmelmaier, Nicolas Becker, Daniel Bogdoll

Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions. Even though simulations come with a series of benefits compared to real-world experiments, various challenges still prevent virtual testing from entirely replacing physical test-drives. Our work pro

Nils Hemmingsson

In this paper, we analyze a certain family of holomorphic correspondences on $\hat{\mathbb C}\times\hat{\mathbb C}$ and prove their equidistribution properties. In particular, for any correspondence in this family we prove that the naturally associated multivalued map $F$ is such that for any $a\in \mathbb C$, we have that $(F^n)_*(\delta_a)$ converges to a

Himadri Mukherjee, Gunja Sachdeva

This paper discusses the generalized congruence equation $X^tAX=B$, for $X \in M_n(k)$ over any field $k$, through the action of monoid $Sol_A \times Sol_B := \{X \ | \ X^tAX = A\} \times \{X \ | \ X^tBX = B\}$. We have completely characterized for what matrices $A$, the monoid $Sol_A$ is a Lie group. We have given the structure of the Lie group $Sol_A$ and

Qiushi Zhu, Xiaoying Zhao, Jie Zhang, Yu Gu

Recently, many efforts have been made to explore how the brain processes speech using electroencephalographic (EEG) signals, where deep learning-based approaches were shown to be applicable in this field. In order to decode speech signals from EEG signals, linear networks, convolutional neural networks (CNN) and long short-term memory networks are often used

Noelia Juarez, Paola B. Manasero, Jorge Oviedo

In a many-to-one matching market, we analyze the matching game induced by a stable rule when firms' choice function satisfy substitutability. We show that any stable rule implements the individually rational correspondence in Nash equilibrium when both sides of the market play strategically. Moreover, when only workers play strategically and firms' choice fu

Viktor Losert

For a connected Lie group G it was shown by Lee, Ludwig, Samei and Spronk that its Fourier algebra A(G) is weakly amenable only if G is abelian. We extend this result to general connected locally compact groups, extending an approach developed in special cases by Choi and Ghandehari.

Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao

Large Language Model (LLM) has demonstrated significant ability in various Natural Language Processing tasks. However, their effectiveness is highly dependent on the phrasing of the task prompt, leading to research on automatic prompt optimization using labeled task data. We reveal that these prompt optimization techniques are vulnerable to distribution shif

M. Yama, M. Matsuo, T. Kato

The inverse Rashba-Edelstein effect (IREE) in a two-dimensional electron gas (2DEG) induced by spin pumping from an adjacent ferromagnetic insulator (FI) is investigated theoretically. In particular, spin and current densities in the 2DEG in which both Rashba and Dresselhaus spin-orbit interactions coexist are formulated, and their dependencies on ferromagne

G. P. Papari, V. M. Fomin

We analyze the origin of the parabolic background of magnetoresistance oscillations measured in finite-width superconducting mesoscopic rings with input and output stubs and in patterned films. The transmission model explaining the sinusoidal oscillation of magnetoresistance is extended to address the parabolic background as a function of the magnetic field.

Xuhang Jiang, Xing Wang, Li Lin Yang, Jingbang Zhao

In this paper, we investigate two-loop non-planar triangle Feynman integrals involving elliptic curves. In contrast to the Sunrise and Banana integral families, the triangle families involve non-trivial sub-sectors. We show that the methodology developed in the context of Banana integrals can also be extended to these cases and obtain $\varepsilon$-factorize

Timo Baumgärtner, Benjamin J. Mittmann, Till Malzacher, Johannes Roßkopf

In the event of stroke, a catheter-guided procedure (thrombectomy) is used to remove blood clots. Feasibility of machine learning based automatic classifications for thrombus detection on digital substraction angiography (DSA) sequences has been demonstrated. It was however not used live in the clinic, yet. We present an open-source tool for automatic thromb

P. Sinha, V. Bairathi, K. Gopal, C. Jena

We report first study of transverse momentum ($p_\mathrm{T}$) spectra for $\pi^{\pm}$, $K^{\pm}$, $p$, and $\bar{p}$ in isobar, $^{96}_{44}$Ru+$^{96}_{44}$Ru and $^{96}_{40}$Zr+$^{96}_{40}$Zr, collisions at $\sqrt{s_{\mathrm{NN}}} = 200$ GeV using a multi-phase transport (AMPT) model. Particle yields ($dN/dy$), average transverse momenta ($\langle p_\mathrm{

Thibault Cavalié, Jonathan Lunine, Olivier Mousis

Jupiter's deep abundances help to constrain the formation history of the planet and the environment of the protoplanetary nebula. Juno recently measured Jupiter's deep oxygen abundance near the equator to be 2.2$_{-2.1}^{+3.9}$ times the protosolar value (2$\sigma$ uncertainties). Even if the nominal value is supersolar, subsolar abundances cannot be ruled o

Qiankun Zuo, Baiying Lei, Ning Zhong, Yi Pan

Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically. However, it remains a challenge to effectively fuse structural and functional features in exploring the brain network. In this paper, a novel brain structure-function fusing-representati

Siyuan Pan, Linna Zhang, Jie Zhang, Xiaoshuang Li

Structured pruning can simplify network architecture and improve inference speed. Combined with the underlying hardware and inference engine in which the final model is deployed, better results can be obtained by using latency collaborative loss function to guide network pruning together. Existing pruning methods that optimize latency have demonstrated leadi

Jinyan Su, Terry Yue Zhuo, Di Wang, Preslav Nakov

With the rapid progress of large language models (LLMs) and the huge amount of text they generated, it becomes more and more impractical to manually distinguish whether a text is machine-generated. Given the growing use of LLMs in social media and education, it prompts us to develop methods to detect machine-generated text, preventing malicious usage such as

Jiequan Cui, Zhuotao Tian, Zhisheng Zhong, Xiaojuan Qi

In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists of 1) a weighted Mean Square Error (wMSE) loss and 2) a Cross-Entropy loss incorporating soft labels. Thanks to the decomposed formulation of DKL loss, we have ident

Xiao Gong, Wei Chen, Bo Ai, Geert Leus

CANDECOMP/PARAFAC (CP) decomposition is the mostly used model to formulate the received tensor signal in a massive MIMO system, as the receiver generally sums the components from different paths or users. To achieve accurate and low-latency channel estimation, good and fast CP decomposition (CPD) algorithms are desired. The CP alternating least squares (CPAL

Miya Nakajima, Takahiro Saitoh, Tsuyoshi Kato

The importance of ultrasonic nondestructive testing has been increasing in recent years, and there are high expectations for the potential of laser ultrasonic visualization testing, which combines laser ultrasonic testing with scattered wave visualization technology. Even if scattered waves are visualized, inspectors still need to carefully inspect the image

Chung-En Tsai, Ying-Ting Lin, Yen-Huan Li

This work introduces the first small-loss and gradual-variation regret bounds for online portfolio selection, marking the first instances of data-dependent bounds for online convex optimization with non-Lipschitz, non-smooth losses. The algorithms we propose exhibit sublinear regret rates in the worst cases and achieve logarithmic regrets when the data is "e

Puyu Yang, Ahad Shoaib, Robert West, Giovanni Colavizza

Wikipedia is a well-known platform for disseminating knowledge, and scientific sources, such as journal articles, play a critical role in supporting its mission. The open access movement aims to make scientific knowledge openly available, and we might intuitively expect open access to help further Wikipedia's mission. However, the extent of this relationship

Kosuke Yamada, Ryohei Sasano, Koichi Takeda

The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill. In this paper, we address the latter task of argument clustering, which aims to acquire frame element knowledge, and propose a method that applies deep metric

G. Csörnyei, R. I. Anderson, C. Vogl, S. Taubenberger

Despite being one of the best-known galaxies, the distance to the Whirlpool Galaxy, M 51, is still debated. Current estimates range from 6.02 to 9.09 Mpc, and different methods yield discrepant results. No Cepheid distance has been published for M 51 to date. We aim to estimate a more reliable distance to M 51 through two independent methods: Cepheid variabl

Paolo Gandini

In this talk, we present the latest experimental results on heavy flavour spectroscopy at the LHCb detector. The first observation of two baryonic resonances is reported in the $\Xi_b^{(-,0)} \pi^+\pi^-$ final states and a study of charmonium decays to $K_s^0 K \pi$ in $B \rightarrow (K_s^0 K \pi) K$ decays is presented.

Amnon Aharony

This paper will be published in ``50 years of the renormalization group", dedicated to the memory of Michael E. Fisher, edited by Amnon Aharony, Ora Entin-Wohlman, David Huse, and Leo Radzihovsky, World Scientific. I start with a review of my personal and scientific interactions with Michael E. Fisher, who was my post-doc mentor in 1972-1974. I then describe

Juan Mera Menéndez, Jose Emilio Labra Gayo, Enrique Riesgo Canal, Aitor Echevarría Fernández

Serverless technologies, also known as FaaS (Function as a Service), are promoted as solutions that provide dynamic scalability, speed of development, cost-per-consumption model, and the ability to focus on the code while taking attention away from the infrastructure that is managed by the vendor. A microservices architecture is defined by the interaction an

Niccolò Di Marco, Andrea Frosini, Christophe Picouleau

Given a 3-uniform hypergraph H, its 2-intersection graph G has for vertex set the hyperedges of H and ee' is an edge of G whenever e and e' have exactly two common vertices in H. Di Marco et al. prove that deciding wether a graph G is the 2-intersection graph of a 3-uniform hypergraph is NP-complete. The main problems we study concern the class of claw-free

M. Mandolino, D. Scholtes, F. Ferrante, G. Rizzello

Shape Memory Alloys (SMAs) are a class of smart materials that exhibit a macroscopic contraction of up to 5% when heated via an electric current. This effect can be exploited for the development of novel unconventional actuators. Despite having many features such as compactness, lightweight, and high energy density, commercial SMA wires are characterized by

Sudarsun Santhiappan, Nitin Shravan, Balaraman Ravindran

Classification model selection is a process of identifying a suitable model class for a given classification task on a dataset. Traditionally, model selection is based on cross-validation, meta-learning, and user preferences, which are often time-consuming and resource-intensive. The performance of any machine learning classification task depends on the choi

Bokai Xu, Jiayi Zhang, Jiaxun Li, Huahua Xiao

Extremely large-scale multiple-input-multipleoutput (XL-MIMO) has been reviewed as a promising technology for future sixth-generation (6G) networks to achieve higher performance. In practice, various linear precoding schemes, such as zero-forcing (ZF) and regularized ZF (RZF) precoding, are sufficient to achieve near-optimal performance in traditional massiv

Ruiqi Liu, Mengnan Jian, Dawei Chen, Xu Lin

The 6th generation (6G) wireless networks will likely to support a variety of capabilities beyond communication, such as sensing and localization, through the use of communication networks empowered by advanced technologies. Integrated sensing and communication (ISAC) has been recognized as a critical technology as well as an usage scenario for 6G, as widely

Himanshu Sahu, C. M. Chandrashekar

Quantum simulation provides a computationally-feasible approach to model and study many problems in chemistry, condensed-matter physics, or high-energy physics where quantum phenomenon define the systems behaviour. In high-energy physics, quite a few possible applications are investigated in the context of gauge theories and their application to dynamic prob

Diego Alonso-Orán, Ángel Durán, Rafael Granero-Belinchón

In this paper we derive three new asymptotic models for an hyperbolic-hyperbolicelliptic system of PDEs describing the motion of a collision-free plasma in a magnetic field. The first of these models takes the form of a non-linear and non-local Boussinesq system (for the ionic density and velocity) while the second is a non-local wave equation (for the ionic

Ruichen Wang, Zekang Chen, Chen Chen, Jian Ma

Recent text-to-image (T2I) diffusion models show outstanding performance in generating high-quality images conditioned on textual prompts. However, they fail to semantically align the generated images with the prompts due to their limited compositional capabilities, leading to attribute leakage, entity leakage, and missing entities. In this paper, we propose

Savvas Papaioannou, Panayiotis Kolios, Georgios Ellinas

This work proposes a distributed estimation and control approach in which a team of aerial agents equipped with radio jamming devices collaborate in order to intercept and concurrently track-and-jam a malicious target, while at the same time minimizing the induced jamming interference amongst the team. Specifically, it is assumed that the malicious target ma

Jiacheng Ye, Chengzu Li, Lingpeng Kong, Tao Yu

While large language models (LLMs) bring not only performance but also complexity, recent work has started to turn LLMs into data generators rather than task inferencers, where another affordable task model is trained for efficient deployment and inference. However, such an approach has primarily been applied to natural language tasks and has not yet been ex

Conor Houghton

I propose a novel Ising-like model of language evolution. In a simple way, Ising-like models represent the countervailing tendencies towards convergence and change present in language evolution. In the ordinary Ising-model, a node on a graph, in this case representing a language speaker, interacts with all its neighbors. In contrast, in the model proposed he

Kexin Wang, Nils Reimers, Iryna Gurevych

The work of neural retrieval so far focuses on ranking short texts and is challenged with long documents. There are many cases where the users want to find a relevant passage within a long document from a huge corpus, e.g. Wikipedia articles, research papers, etc. We propose and name this task \emph{Document-Aware Passage Retrieval} (DAPR). While analyzing t

Maik Schünemann, Udo Ernst

Selective attention allows to process stimuli which are behaviorally relevant, while attenuating distracting information. However, it is an open question what mechanisms implement selective routing, and how they are engaged in dependence on behavioral need. Here we introduce a novel framework for selective processing by spontaneous synchronization. Input sig

Yun Li, Hongwei Liu, Sihem Mesnager

Subspace codes have important applications in random network coding. It is interesting to construct subspace codes with both sizes, and the minimum distances are as large as possible. In particular, cyclic constant dimension subspaces codes have additional properties which can be used to make encoding and decoding more efficient. In this paper, we construct