February 2024 arXiv papers — page 22

Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao, Siyuan Lu

Automatic prompt optimization is an important approach to improving the performance of large language models (LLMs). Recent research demonstrates the potential of using LLMs as prompt optimizers, which can generate improved task prompts via iterative refinement. In this paper, we propose a novel perspective to investigate the design of LLM-based prompt optim

Ling Yang, Haotian Qian, Zhilong Zhang, Jingwei Liu

Diffusion models have demonstrated exceptional efficacy in various generative applications. While existing models focus on minimizing a weighted sum of denoising score matching losses for data distribution modeling, their training primarily emphasizes instance-level optimization, overlooking valuable structural information within each mini-batch, indicative

George Eskandar, Chongzhe Zhang, Abhishek Kaushik, Karim Guirguis

3D Object Detectors (3D-OD) are crucial for understanding the environment in many robotic tasks, especially autonomous driving. Including 3D information via Lidar sensors improves accuracy greatly. However, such detectors perform poorly on domains they were not trained on, i.e. different locations, sensors, weather, etc., limiting their reliability in safety

Mark D. Robinson, Peiying Cai, Martin Emons, Reto Gerber

Computational biologists are frequently engaged in collaborative data analysis with wet lab researchers. These interdisciplinary projects, as necessary as they are to the scientific endeavour, can be surprisingly challenging due to cultural differences in operations and values. In these Ten Simple Rules guide we aim to help dry lab researchers identify sourc

Manuel E. Rodrigues, Marcos V. de S. Silva

In Atazadeh and Hadi (JCAP \textbf{01}, 067 (2024)), the authors proposed that black bounce solutions, such as the Simpson-Visser and the Bardeen-type spacetimes, can be obtained from Rastall gravity. To achieve these spacetimes, the authors consider the presence of a phantom scalar field with nonlinear electrodynamics. However, in this comment, we obtained

Karen Efremyan, Elizaveta Petrova, Evgeny Kaskov, Alexander Kapitanov

A common problem for composite images is the incompatibility of their foreground and background components. Image harmonization aims to solve this problem, making the whole image look more authentic and coherent. Most existing solutions predict lookup tables (LUTs) or reconstruct images, utilizing various attributes of composite images. Recent approaches hav

Mehmet Erbay, Birgit Jacob, Kirsten Morris

The solvability for infinite dimensional differential algebraic equations possessing a resolvent index and a Weierstra{\ss} form is studied. In particular, the concept of integrated semigroups is used to determine a subset on which solutions exist and are unique. This information is later used for a important class of systems, namely, port-Hamiltonian differ

Jonas Dann, Tobias Götz, Daniel Ritter, Jana Giceva

Efficiently finding subgraph embeddings in large graphs is crucial for many application areas like biology and social network analysis. Set intersections are the predominant and most challenging aspect of current join-based subgraph query processing systems for CPUs. Previous work has shown the viability of utilizing FPGAs for acceleration of graph and join

M. Abello, J. Courson, A. Maury, J. Renaud

In this article, we study the behaviour of a looped string launched in ambient air using motorised wheels. We show that the loop, once it reaches its stationary state, is either in the pulley or the air-lifted state. The transition between these two distinct states occurs at the so-called takeoff speed. We prove that this speed differs from one string to ano

Asbjørn Bækgaard Lauritsen, Robert Seiringer

We prove a lower bound on the ground state energy of the dilute spin-polarized Fermi gas capturing the leading correction to the kinetic energy resulting from repulsive interactions. This correction depends on the $p$-wave scattering length of the interaction and matches the corresponding upper bound in [J. Funct. Anal. 286.7 (2024), p. 110320].

Xin Chen, Xixiang Zhang, Jie-Xiang Yu, Jiadong Zang

Ionic conductors have great potential for interesting tunable physical properties via ionic liquid gating and novel energy storage applications such as all-solid-state lithium batteries. In particular, low migration barriers and high hopping attempt frequency are the keys to achieve fast ion diffusion in solids. Taking advantage of the oxygen-vacancy channel

Alessandro Bacchetta, Francesco Giovanni Celiberto, Marco Radici

We present a model calculation of T-odd transverse-momentum-dependent distributions of gluons in the nucleon. The model is based on the assumption that a nucleon can emit a gluon, and what remains after the emission is treated as a single spectator particle. This spectator particle is considered to be on-shell, but its mass is allowed to take a continuous ra

Xinliang Zhang, Lei Zhu, Hangzhou He, Lujia Jin

Scribble-based weakly-supervised semantic segmentation using sparse scribble supervision is gaining traction as it reduces annotation costs when compared to fully annotated alternatives. Existing methods primarily generate pseudo-labels by diffusing labeled pixels to unlabeled ones with local cues for supervision. However, this diffusion process fails to exp

Sunghyeon Woo, Baeseong Park, Byeongwook Kim, Minjung Jo

Large language models (LLMs) have achieved significant success across various domains. However, training these LLMs typically involves substantial memory and computational costs during both forward and backward propagation. While parameter-efficient fine-tuning (PEFT) considerably reduces the training memory associated with parameters, it does not address th

Lorenzo Peracchio, Giovanna Nicora, Enea Parimbelli, Tommaso Mario Buonocore

Applying Artificial Intelligence (AI) and Machine Learning (ML) in critical contexts, such as medicine, requires the implementation of safety measures to reduce risks of harm in case of prediction errors. Spotting ML failures is of paramount importance when ML predictions are used to drive clinical decisions. ML predictive reliability measures the degree of

Raghav Kapoor, Yash Parag Butala, Melisa Russak, Jing Yu Koh

For decades, human-computer interaction has fundamentally been manual. Even today, almost all productive work done on the computer necessitates human input at every step. Autonomous virtual agents represent an exciting step in automating many of these menial tasks. Virtual agents would empower users with limited technical proficiency to harness the full poss

Maximiliano Contino

Three approximation problems in Krein spaces are studied, namely the indefinite weighted least squares problem and the related problems of indefinite abstract splines and smoothing. In every case, we analyze if the problem has a solution for every point of the Krein space, the existence of a linear and continuous operator that maps each data point to its sol

Sabi Biswas, Nipen Saikia

In the paper, we give partition-theoretic results for the coefficients of some mock theta functions and prove their congruence properties. Some recurrence relations connecting the coefficients of the mock theta functions with certain restricted partition functions are also established.

Shaolei Zhang, Tian Yu, Yang Feng

Large Language Models (LLMs) sometimes suffer from producing hallucinations, especially LLMs may generate untruthful responses despite knowing the correct knowledge. Activating the truthfulness within LLM is the key to fully unlocking LLM's knowledge potential. In this paper, we propose TruthX, an inference-time intervention method to activate the truthfulne

Zeyu Tian, Lianming Xu, Liang Li, Li Wang

Many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing. In this study, we propose a three-layer architecture of emergency caching networks focusing on data collection and reliable transmission, by leveraging efficient perception and edge caching technologies. Based on this

Charles Masson, Homin K. Lee

Consistent range-hashing is a technique used in distributed systems, either directly or as a subroutine for consistent hashing, commonly to realize an even and stable data distribution over a variable number of resources. We introduce FlipHash, a consistent range-hashing algorithm with constant time complexity and low memory requirements. Like Jump Consisten

Yu. G. Nikonorov

The paper is devoted to the study of geodesic orbit Riemannian metrics on nilpotent Lie groups. The main result is the construction of continuous families of pairwise non-isomorphic connected and simply connected nilpotent Lie groups, every of which admits geodesic orbit metrics. The minimum dimension of groups in the constructed families is $10$.

Vojtěch Dvořák, Adva Mond, Victor Souza

The $(m,b)$ Maker-Breaker percolation game on $(\mathbb{Z}^2)_p$, introduced by Day and Falgas-Ravry, is played in the following way. Before the game starts, each edge of $\mathbb{Z}^2$ is removed independently with probability $1-p$. After that, Maker chooses a vertex $v_0$ to protect. Then, in each round Maker and Breaker claim respectively $m$ and $b$ unc

Suyeon Lee, Jieun Kang, Harim Kim, Kyoung-Mee Chung

The demand for conversational agents that provide mental health care is consistently increasing. In this work, we develop a psychological counseling agent, referred to as CoCoA, that applies Cognitive Behavioral Therapy (CBT) techniques to identify and address cognitive distortions inherent in the client's statements. Specifically, we construct a memory syst

Asaad R. Sakhel, Robert J. Ragan, William J. Mullin

The Gross-Pitaevskii equation (GPE) in a double well potential produces solutions that break the symmetry of the underlying non-interacting Hamiltonian, i.e., asymmetric solutions. The GPE is derived from the more general second-quantized Fock Schroedinger equation (FSE). We investigate whether such solutions appear in the more general case or are artifacts

H. Burak Dogaroglu, A. Burakhan Koyuncu, Atanas Boev, Elena Alshina

As learned image codecs (LICs) become more prevalent, their low coding efficiency for out-of-distribution data becomes a bottleneck for some applications. To improve the performance of LICs for screen content (SC) images without breaking backwards compatibility, we propose to introduce parameterized and invertible linear transformations into the coding pipel

Yuliang Wang

This paper presents an innovative approach to computational acoustic imaging of biperiodic surfaces, exploiting the capabilities of an acoustic superlens to overcome the diffraction limit. We address the challenge of imaging physical entities in complex environments by considering the partial differential equations that govern the physics and solving the cor

Paul-Amaury Matt, Marco Roth

We introduce a novel quantum computing heuristic for solving the irregular strip packing problem, a significant challenge in optimizing material usage across various industries. This problem involves arranging a set of irregular polygonal pieces within a fixed-height, rectangular container to minimize waste. Traditional methods heavily rely on manual optimiz

Magnus Perninge

We consider partial differential equations (PDEs) characterized by an upper barrier that depends on the solution itself and a fixed lower barrier, while accommodating a non-local driver. First, we show a Feynman-Kac representation for the PDE when the driver is local. Specifically, we relate the non-linear Snell envelope for an optimal stopping problem, wher

Guoliang Wang

This paper addresses the stabilization problem of stochastic jump systems (SJSs) closed by a generally sampled controller. Because of the controller's switching and state both sampled, it is challenging to study its stabilization. A new stabilizing method deeply depending on the mode classifications is proposed to deal with the above sampling situation, whos

Huiyong Zheng, Wenning Jiang, Xiao Liu

This paper presents the design of a time-division multiplexed capacitively-coupled chopper analog front-end (AFE) with a novel impedance boost loop (IBL) and a novel DC servo loop (DSL). The proposed IBL has two impedance booting loops for compensating leakage current due to parasitic capacitance from the ESD pad and external interconnections, and the choppe

J. -B. Marceau, D. -M Ta, A. Aguilar, A. Loiseau

Driving molecular assembly into micrometer-scale patterns is key for defining advanced materials of interest in various fields, including life sciences, photovoltaics, and quantum photonics. However, the driving process competes with other forces, such as Brownian motion, ripening phenomena, capillary forces, and non-specific adsorption. Here we report on a

Katharina Kaiser, Anna Rosławska, Michelangelo Romeo, Fabrice Scheurer

Controlling electrically-stimulated quantum light sources (QLS) is key for developing integrated and low-scale quantum devices. The mechanisms leading to quantum emission are complex, as a large number of electronic states of the system impacts the emission dynamics. Here, we use a scanning tunneling microscope (STM) to excite a model QLS, namely a single mo

Thong Nguyen, Mariya Hendriksen, Andrew Yates, Maarten de Rijke

Learned sparse retrieval (LSR) is a family of neural methods that encode queries and documents into sparse lexical vectors that can be indexed and retrieved efficiently with an inverted index. We explore the application of LSR to the multi-modal domain, with a focus on text-image retrieval. While LSR has seen success in text retrieval, its application in mul

Nahuel Barrios, Marcelo S. Guimaraes, Bruno W. Mintz, Letícia F. Palhares

Correlation functions are important probes for the behavior of quantum field theories. Already at tree-level, the Refined Gribov Zwanziger (RGZ) effective action for Yang-Mills theories provides a good approximation for the gluon propagator, as compared to that calculated by nonperturbative methods such as Lattice Field Theory and Dyson-Schwinger Equations.

Jak Chakhalian

A prime goal for this lecture is to provide a reader with a reasonably self-sufficient answer to the question of what interesting effects can happen if you join two dissimilar materials with correlated carriers to construct a sandwich with the interface across those layers. Through the lens of physical phenomena, we will delve into the design ideas that lead

Yu Ran, Ao-Xiang Zhang, Mingjie Li, Weixuan Tang

The goal of No-Reference Image Quality Assessment (NR-IQA) is to predict the perceptual quality of an image in line with its subjective evaluation. To put the NR-IQA models into practice, it is essential to study their potential loopholes for model refinement. This paper makes the first attempt to explore the black-box adversarial attacks on NR-IQA models. S

Bowen Cao, Deng Cai, Leyang Cui, Xuxin Cheng

Standard language models generate text by selecting tokens from a fixed, finite, and standalone vocabulary. We introduce a novel method that selects context-aware phrases from a collection of supporting documents. One of the most significant challenges for this paradigm shift is determining the training oracles, because a string of text can be segmented in v

Kaikai An, Fangkai Yang, Junting Lu, Liqun Li

Effective incident management is pivotal for the smooth operation of enterprises-level cloud services. In order to expedite incident mitigation, service teams compile troubleshooting knowledge into Troubleshooting Guides (TSGs) accessible to on-call engineers (OCEs). While automated pipelines are enabled to resolve the most frequent and easy incidents, there

Arthur Touati

Given a regular solution $\mathbf{g}_0$ of the Einstein-null dusts system without restriction on the number of dusts, we construct families of solutions $(\mathbf{g}_\lambda)_{\lambda\in(0,1]}$ of the Einstein vacuum equations such that $\mathbf{g}_\lambda-\mathbf{g}_0$ and $\partial(\mathbf{g}_\lambda-\mathbf{g}_0)$ converges respectively strongly and weakl

Leigh Lapworth

Three block encoding methods are evaluated for solving linear systems of equations using QSVT (Quantum Singular Value Transformation). These are ARCSIN, FABLE and PREPARE-SELECT. The performance of the encoders is evaluated using a suite of 30 test cases including 1D, 2D and 3D Laplacians and 2D CFD matrices. A subset of cases is used to characterise how the

Gary Greaves, Sho Suda

We provide a method to construct $t$-designs from weighing matrices and association schemes. One instance of our method can produce a $3$-design from any (symmetric or skew-symmetric) conference matrix, thereby providing a partial answer to a question of Gunderson and Semeraro JCTB 2017. We explore variations of our method on some matrices that satisfy certa

Evgenia Ilia, Wilker Aziz

Language models (LMs) are statistical models trained to assign probability to human-generated text. As such, it is reasonable to question whether they approximate linguistic variability exhibited by humans well. This form of statistical assessment is difficult to perform at the passage level, for it requires acceptability judgements (i.e., human evaluation)

Yasuko Kawahata

We explore the state of information disruption caused by the cocktail party effect within the framework of non-perfect information games and evolutive games with multiple werewolves. In particular, we mathematically model and analyze the effects on the gain of each strategy choice and the formation process of evolutionary stable strategies (ESS) under the as

Simon Lodato, Christos Mavridis, Federico Vaccari

What role do non-elected bureaucrats play when elections provide imperfect accountability and create incentives for pandering? We develop a model where politicians and bureaucrats interact to implement policy. Both can either be good, sharing the voters' preferences over policies, or bad, intent on enacting policies that favor special interests. Our analysis

Philip Ndikum, Serge Ndikum

This research paper delves into the application of Deep Reinforcement Learning (DRL) in asset-class agnostic portfolio optimization, integrating industry-grade methodologies with quantitative finance. At the heart of this integration is our robust framework that not only merges advanced DRL algorithms with modern computational techniques but also emphasizes

Yi Huang, Jiancheng Huang, Yifan Liu, Mingfu Yan

Denoising diffusion models have emerged as a powerful tool for various image generation and editing tasks, facilitating the synthesis of visual content in an unconditional or input-conditional manner. The core idea behind them is learning to reverse the process of gradually adding noise to images, allowing them to generate high-quality samples from a complex

Zishun Zheng, Yihan Wang, Yuan Lin

To enable autonomous vehicles to perform discretionary lane change amidst the random traffic flow on highways, this paper introduces a decision-making and control method for vehicle lane change based on Model Predictive Control (MPC). This approach divides the driving control of vehicles on highways into two parts: lane-change decision and lane-change contro

Mehmet Caner, Qingliang Fan

This paper explores the statistical properties of forming constrained optimal portfolios within a high-dimensional set of assets. We examine portfolios with tracking error constraints, those with simultaneous tracking error and weight restrictions, and portfolios constrained solely by weight. Tracking error measures portfolio performance against a benchmark

Navaneeth Krishnan Mohan, Rikteem Bhowmick, Devesh Kumar, Rohit Chaurasiya

Digital quantum simulation is the process of simulating the dynamics of a physical system by a programmable quantum computer. The universality of quantum computers makes it possible to simulate any quantum system, whether fermionic or bosonic. In this work, we discuss the application of digital quantum simulations to simulate a ubiquitous bosonic system, a b

Hongcheng Yang, Dingkang Liang, Dingyuan Zhang, Zhe Liu

The recent advancements in point cloud learning have enabled intelligent vehicles and robots to comprehend 3D environments better. However, processing large-scale 3D scenes remains a challenging problem, such that efficient downsampling methods play a crucial role in point cloud learning. Existing downsampling methods either require a huge computational burd

Frank Phillipson

The work explores the integration of quantum computing into logistics and supply chain management, emphasising its potential for use in complex optimisation problems. The discussion introduces quantum computing principles, focusing on quantum annealing and gate-based quantum computing, with the Quantum Approximate Optimisation Algorithm and Quantum Annealing

Christian Carrick

Using Ravenel's Thom spectrum $X(n)$, we introduce the concept of chromatic defect, which measures how far a spectrum is from being complex-orientable. We compute the chromatic defect of various examples of interest, such as finite spectra, the Real Johnson--Wilson spectra $ER(n)$, fixed points of Morava $E$-theories (with respect to finite subgroups of the

David Albandea, Guilherme Catumba, Alberto Ramos

Recent studies have claimed that the strong $CP$ problem does not occur in QCD, proposing a new order of limits in volume and topological sectors when studying observables on the lattice. In order to shed light on this issue, we study the effect of the topological $\theta$-term on a simple quantum mechanical rotor that allows a lattice description. The topol

Byeonghu Na, Yeongmin Kim, HeeSun Bae, Jung Hyun Lee

Conditional diffusion models have shown remarkable performance in various generative tasks, but training them requires large-scale datasets that often contain noise in conditional inputs, a.k.a. noisy labels. This noise leads to condition mismatch and quality degradation of generated data. This paper proposes Transition-aware weighted Denoising Score Matchin

Jamie Duell, Monika Seisenberger, Hsuan Fu, Xiuyi Fan

Deep Neural Networks (DNNs) stand out as one of the most prominent approaches within the Machine Learning (ML) domain. The efficacy of DNNs has surged alongside recent increases in computational capacity, allowing these approaches to scale to significant complexities for addressing predictive challenges in big data. However, as the complexity of DNN models r

René Pollmann, Franz Roeder, Victor Quiring, Raimund Ricken

Broadband quantum light is a vital resource for quantum metrology and spectroscopy applications such as quantum optical coherence tomography or entangled two photon absorption. For entangled two photon absorption in particular, very high photon flux combined with high time-frequency entanglement is crucial for observing a signal. So far these conditions coul

Victor Luder, Sizhen Bian, Michele Magno

Cycling power measurement is an indispensable metric with profound implications for cyclists' performance and fitness levels. It empowers riders with real-time feedback, supports precise training regimen planning, mitigates injury risks, and enhances muscular development. Despite these advantages, the widespread adoption of cycling power meters has been hamp

Jia Wan, Qiangqiang Wu, Wei Lin, Antoni B. Chan

The existing crowd counting models require extensive training data, which is time-consuming to annotate. To tackle this issue, we propose a simple yet effective crowd counting method by utilizing the Segment-Everything-Everywhere Model (SEEM), an adaptation of the Segmentation Anything Model (SAM), to generate pseudo-labels for training crowd counting models

Yoshihito Kuno

Quantum non-locality is an important concept in quantum physics. In this work, we study the quantum non-locality in a fermion many-body system under quasi-periodic disorders. The Clauser-Horne-Shimony-Holt (CHSH) inequality is systematically investigated, which quantifies quantum non-locality between two sites. We find that the quantum non-locality explicitl

Rares Dolga, Lucas Maystre, Marius Cobzarenco, David Barber

Transformers have achieved state-of-the-art results across a range of domains, but their quadratic attention mechanism poses significant challenges for long-sequence modelling. Recent efforts to design linear-time attention mechanisms have yielded more scalable alternatives, yet often at the cost of performance, particularly on discrete data such as language

Zhaoxun Ju, Chao Yang, Hongbo Wang, Yu Qiao

Language-conditioned robot behavior plays a vital role in executing complex tasks by associating human commands or instructions with perception and actions. The ability to compose long-horizon tasks based on unconstrained language instructions necessitates the acquisition of a diverse set of general-purpose skills. However, acquiring inherent primitive skill

Maurits Bleeker, Mariya Hendriksen, Andrew Yates, Maarten de Rijke

Vision-language models (VLMs) mainly rely on contrastive training to learn general-purpose representations of images and captions. We focus on the situation when one image is associated with several captions, each caption containing both information shared among all captions and unique information per caption about the scene depicted in the image. In such ca

Vyas Raina, Samson Tan, Volkan Cevher, Aditya Rawal

Deep learning-based Natural Language Processing (NLP) models are vulnerable to adversarial attacks, where small perturbations can cause a model to misclassify. Adversarial Training (AT) is often used to increase model robustness. However, we have discovered an intriguing phenomenon: deliberately or accidentally miscalibrating models masks gradients in a way

Lili Ding, Elke Beyreuther, Boris Koppitz, Konrad Kempf

Ferroelectric domain wall conductivity (DWC) is an intriguing functional property, that can be controlled through external stimuli such as electric and mechanical fields. Optical-field control, as a non-invasive flexible handle, has rarely been applied so far, but significantly expands the possibility for both tuning and probing DWC. On the one hand, as know

Hankyul Kang, Ming-Hsuan Yang, Jongbin Ryu

We propose an efficient interactive method for multi-head self-attention via decomposition. For existing methods using multi-head self-attention, the attention operation of each head is computed independently. However, we show that the interactions between cross-heads of the attention matrix enhance the information flow of the attention operation. Considerin

Carlos Bermejo-Barbanoj, Beatriz Moya, Alberto Badías, Francisco Chinesta

We present a method to increase the resolution of measurements of a physical system and subsequently predict its time evolution using thermodynamics-aware neural networks. Our method uses adversarial autoencoders, which reduce the dimensionality of the full order model to a set of latent variables that are enforced to match a prior, for example a normal dist

Ruiyang Ren, Peng Qiu, Yingqi Qu, Jing Liu

Due to the excellent capacities of large language models (LLMs), it becomes feasible to develop LLM-based agents for reliable user simulation. Considering the scarcity and limit (e.g., privacy issues) of real user data, in this paper, we conduct large-scale user simulation for web search, to improve the analysis and modeling of user search behavior. Speciall

Zhaoying Wang, Wei Dong

The collaborative visual perception of multiple Unmanned Aerial Vehicles (UAVs) has increasingly become a research hotspot. Compared to a single UAV equipped with a short-baseline stereo camera, multi-UAV collaborative vision offers a wide and variable baseline, providing potential benefits in flexible and large-scale depth perception. In this paper, we prop

Kelei Tian, Song Li, Ge Yi, Ying Xu

In this paper, we study two generalized constrained integrable hierarchies, which are called the $c$-$k$ constrained KP and BKP hierarchies. The Fermionic picture of the $c$-$k$ constrained KP hierarchy is given. We give some solutions for the $c$-$k$ constrained KP hierarchy by using the free Fermion operators and define its additional symmetries. Its addit

Li Lin, Yixiang Liu, Jiewei Wu, Pujin Cheng

Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-center data, especially in the face of significant data heterogeneity, notably in medical contexts. In the realm of

Vadim Liventsev, Tobias Fritz

Reinforcement Learning in Healthcare is typically concerned with narrow self-contained tasks such as sepsis prediction or anesthesia control. However, previous research has demonstrated the potential of generalist models (the prime example being Large Language Models) to outperform task-specific approaches due to their capability for implicit transfer learni

Sean Dewar, Georg Grasegger, Eleftherios Kastis, Anthony Nixon

We explore the rigidity of generic frameworks in 3-dimensions whose underlying graph is close to being planar. Specifically we consider apex graphs, edge-apex graphs and their variants and prove independence results in the generic 3-dimensional rigidity matroid adding to the short list of graph classes for which 3-dimensional rigidity is understood. We then

Jianbo Lu, Shining Yang, Yan Liu, Yuying Zhang

In the framework of the theory of general relativity, in order to obtain stable traversable wormholes, matter needs to violate the null energy condition. It is well known that the violation of the energy condition (EC) of matter leads to various physical problems. To address this issue, researchers have turned their attention to exploring modified theories o

Yuhao Wang, Ruiyang Ren, Junyi Li, Wayne Xin Zhao

Considering the limited internal parametric knowledge, retrieval-augmented generation (RAG) has been widely used to extend the knowledge scope of large language models (LLMs). Despite the extensive efforts on RAG research, in existing methods, LLMs cannot precisely assess the relevance of retrieved documents, thus likely leading to misleading or even incorre

Lucía Gómez Zaragozá, Rocío del Amor, Elena Parra Vargas, Valery Naranjo

Emotional Voice Messages (EMOVOME) is a spontaneous speech dataset containing 999 audio messages from real conversations on a messaging app from 100 Spanish speakers, gender balanced. Voice messages were produced in-the-wild conditions before participants were recruited, avoiding any conscious bias due to laboratory environment. Audios were labeled in valenc

Richard Zhang, Emőke-Ágnes Horvát

Twitter is a common site of offensive language. Prior literature has shown that the emotional content of tweets can heavily impact their diffusion when discussing political topics. We extend prior work to look at offensive tweets that link to academic articles. Using a mixed methods approach, we identify three findings: firstly, offensive language is common

Gerard Pelegrí, Stuart Flannigan, Andrew J. Daley

We investigate the properties of few interacting bosons in a Creutz ladder, which has become a standard model for topological systems, and which can be realised in experiments with cold atoms in optical lattices. At the single-particle level, this system may exhibit a completely flat energy landscape with non-trivial topological properties. In this scenario,

Charles Alba, Bing Xue, Joanna Abraham, Thomas Kannampallil

Clinical notes recorded during a patient's perioperative journey holds immense informational value. Advances in large language models (LLMs) offer opportunities for bridging this gap. Using 84,875 pre-operative notes and its associated surgical cases from 2018 to 2021, we examine the performance of LLMs in predicting six postoperative risks using various fin

Deaglan J. Bartlett, Benjamin D. Wandelt, Matteo Zennaro, Pedro G. Ferreira

Rapid and accurate evaluation of the nonlinear matter power spectrum, $P(k)$, as a function of cosmological parameters and redshift is of fundamental importance in cosmology. Analytic approximations provide an interpretable solution, yet current approximations are neither fast nor accurate relative to numerical emulators. We use symbolic regression to obtain

M. Ben Khalifa, B. Darna, J. Loreau

A detailed interpretation of the detected emission lines of environments in which propyne (or methyl acetylene, CH$_3$CCH) is observed requires a knowledge of its collisional rate coefficients with the most abundant species in the interstellar medium, He or H$_2$. We present the first three-dimensional potential energy surface (3D-PES) for the CH$_3$CCH-He m

Dejan Grba

The extensive industrialization of artificial intelligence (AI) since the mid-2010s has increasingly motivated artists to address its economic and sociopolitical consequences. In this chapter, I discuss interrelated art practices that thematize creative agency, crowdsourced labor, and delegated artmaking to reveal the social rootage of AI technologies and un

Meng Liu, Shuai Li, Fei Xiao, Ruijie Wang

The ever-expanding scale of integrated circuits has brought about a significant rise in the design risks associated with radiation-resistant integrated circuit chips. Traditional single-particle experimental methods, with their iterative design approach, are increasingly ill-suited for the challenges posed by large-scale integrated circuits. In response, thi

Paul Jimenez, Raphael Cardoso, Maurìcio Gomes de Queiroz, Mohab Abdalla

The study of regularity in signals can be of great importance, typically in medicine to analyse electrocardiogram (ECG) or electromyography (EMG) signals, but also in climate studies, finance or security. In this work we focus on security primitives such as Physical Unclonable Functions (PUFs) or Pseudo-Random Number Generators (PRNGs). Such primitives must

Panqi Jia, A. Burakhan Koyuncu, Jue Mao, Ze Cui

The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks. Unlike the hand-engineered transforms in the classical frameworks, NN-based models learn the non-linear transforms providing more compact bit representations, and achieve faster coding speed on parallel devices over their c

Xuan Wang, Zeshan Pang, Yuliang Lu, Xuehu Yan

To provide a foundation for the research of deep learning models, the construction of model pool is an essential step. This paper proposes a Training-Free and Efficient Model Generation and Enhancement Scheme (MGE). This scheme primarily considers two aspects during the model generation process: the distribution of model parameters and model performance. Exp

Linrui Tian, Qi Wang, Bang Zhang, Liefeng Bo

In this work, we tackle the challenge of enhancing the realism and expressiveness in talking head video generation by focusing on the dynamic and nuanced relationship between audio cues and facial movements. We identify the limitations of traditional techniques that often fail to capture the full spectrum of human expressions and the uniqueness of individual

Tomoro Mochida

For a given group $G$, we construct an invariant of flat $G$-connections on 4-manifolds from a finite type involutory quasitriangular Hopf $G$-algebra. Hopf $G$-algebras are generalizations of Hopf algebras, equipped with gradings by $G$. In our construction, we color the dotted components of a Kirby diagram with elements of $G$ and employ the Hennings-type

Tao Tang, Guangrun Wang, Yixing Lao, Peng Chen

Neural implicit fields have been a de facto standard in novel view synthesis. Recently, there exist some methods exploring fusing multiple modalities within a single field, aiming to share implicit features from different modalities to enhance reconstruction performance. However, these modalities often exhibit misaligned behaviors: optimizing for one modalit

Saja Al Ani, Joanne Cleland, Ahmed Zoha

Speech sound disorder (SSD) is defined as a persistent impairment in speech sound production leading to reduced speech intelligibility and hindered verbal communication. Early recognition and intervention of children with SSD and timely referral to speech and language therapists (SLTs) for treatment are crucial. Automated detection of speech impairment is re

Steven Walton, Minh-Binh Tran

In our previous work, numerical schemes for a simplified version of 3-wave kinetic equations, in which only the simple forward-cascade terms of the collision operators are kept, have been successfully designed, especially to capture the long time dynamics of the equation given the multiple blow-up time phenomenon. In this second work in the series, we propos

Timothy S. Groves, Susan Perkin

Experimental measurements of interactions in ionic liquids and concentrated electrolytes over the past decade or so have revealed simultaneous monotonic and oscillatory decay modes. These observations have been hard to interpret using classical theories, which typically allow for just one electrostatic decay mode in electrolytes. Meanwhile, substantial progr

F. R. Donnan, I. García-Bernete, D. Rigopoulou, M. Pereira-Santaella

We present an analysis of the combined NIRSpec and MIRI spectra of dusty galaxies between 1.5 - 28 $\mu$m restframe by implementing a differential extinction model, where the strength of extinction varies across the spectrum as different layers of the obscuring dust are probed. Our model is able to recover a 2D distribution of dust temperature and extinction

Maram Hasanain, Fatema Ahmed, Firoj Alam

The use of propaganda has spiked on mainstream and social media, aiming to manipulate or mislead users. While efforts to automatically detect propaganda techniques in textual, visual, or multimodal content have increased, most of them primarily focus on English content. The majority of the recent initiatives targeting medium to low-resource languages produce

Weiqing Cao, Jiaqun Wei

We introduce the notion of (strongly) $n$-AIR-tilting modules, which is a high dimension version of support $\tau$-tilting modules. The relations between them and $n$-silting modules and $n$-quasi-tilting modules, as well as generalized two-term silting complexes, are investigated. Our results particularly suggest a way to negate the rank question for siltin

Giovanni Molica Bisci, Paolo Malanchini, Simone Secchi

We consider the boundary value problem $$ \cases{ -\Delta_\gamma u = \lambda u + \left\vert u \right\vert^{2^*_\gamma-2}u &in $\Omega$\cr u = 0 &on $\partial\Omega$,\cr } $$ where $\Omega$ is an open bounded domain in $\mathbb{R}^N$, $N \geq 3$, while $\Delta_\gamma$ is the Grushin operator $$ \Delta_ \gamma u(z) = \Delta_x u(z) + \vert x \vert^{2\gamma} \De

Yuming Fu, Yanhua Zhang

We prove that a quadratic polynomial with a bounded type Siegel disk and a quadratic post-critically finite polynomial are always mateable.

Rowel Gündlach, Ivo V. Stoepker, Stella Kapodistria, Jacques A. C. Resing

Bitcoin is currently subject to a significant pay-for-speed trade-off. This is caused by lengthy and highly variable transaction confirmation times, especially during times of congestion. Users can reduce their transaction confirmation times by increasing their transaction fee. In this paper, based on the inner workings of Bitcoin, we propose a model-based a

Mahdi Ebrahimi

A transversal matroid whose dual is also transversal is called bi-transversal. Let $G$ be an undirected graph with vertex set $V$. In this paper, for every subset $W$ of $V$, we associate a bi-transversal matroid to the pair $(G,W)$. We also derive an explicit formula for counting bases of this matroid.

Haolin Li, Shuyang Jiang, Lifeng Zhang, Siyuan Du

Fraud detection remains a challenging task due to the complex and deceptive nature of fraudulent activities. Current approaches primarily concentrate on learning only one perspective of the graph: either the topological structure of the graph or the attributes of individual nodes. However, we conduct empirical studies to reveal that these two types of featur