February 2024 arXiv papers — page 86

Siyin Wang, Shimin Li, Tianxiang Sun, Jinlan Fu

In the realm of Large Language Models (LLMs), users commonly employ diverse decoding strategies and adjust hyperparameters to control the generated text. However, a critical question emerges: Are LLMs conscious of the existence of these decoding strategies and capable of regulating themselves? The current decoding generation process often relies on empirical

Dingquan Li, Kede Ma, Jing Wang, Ge Li

The Geometry-based Point Cloud Compression (G-PCC) has been developed by the Moving Picture Experts Group to compress point clouds. In its lossy mode, the reconstructed point cloud by G-PCC often suffers from noticeable distortions due to the na\"{i}ve geometry quantization (i.e., grid downsampling). This paper proposes a hierarchical prior-based super resol

Daniil Kozhemiachenko, Liubov Vashentseva

We study an extension of First Degree Entailment (FDE) by Dunn and Belnap with a non-contingency operator $\blacktriangle\phi$ which is construed as "$\phi$ has the same value in all accessible states" or "all sources give the same information on the truth value of $\phi$". We equip this logic dubbed $\mathbf{K}^\blacktriangle_\mathbf{FDE}$ with frame semant

Byung-Kwan Lee, Beomchan Park, Chae Won Kim, Yong Man Ro

The remarkable success of Large Language Models (LLMs) and instruction tuning drives the evolution of Vision Language Models (VLMs) towards a versatile general-purpose model. Yet, it remains unexplored whether current VLMs genuinely possess quality object-level image understanding capabilities determined from 'what objects are in the image?' or 'which object

Guorong Qu, Xing Wu, Yu Xiao

For the Fornberg-Whitham equation, the local well-posedness in the critical Besov space $B_{p, 1}^{1+\frac{1}{p}}(\mathbb{R})$ with $1\leq p <\infty$ has been studied in (Guo, Nonlinear Anal. RWA., 2023). However, for the endpoint case $p=\infty$, whether it is locally well-posed or ill-posed in $B_{\infty, 1}^{1}(\mathbb{R})$ is still unknown. In this paper

Hans Wenzel, Eduard Kuhn, Ben King, Paul Crump

A general theory for the intrinsic (Lorentzian) linewidth of photonic--crystal surface--emitting lasers (PCSELs) is presented. The effect of spontaneous emission is modeled by a classical Langevin force entering the equation for the slowly varying waves. The solution of the coupled--wave equations, describing the propagation of four basic waves within the pl

Liming Huang, Yulei Wu, Juan Marcelo Parra-Ullauri, Reza Nejabati

The adoption of Artificial Intelligence (AI) based Virtual Network Functions (VNFs) has witnessed significant growth, posing a critical challenge in orchestrating AI models within next-generation 6G networks. Finding optimal AI model placement is significantly more challenging than placing traditional software-based VNFs, due to the introduction of numerous

Manfred Küker, Silva Järvinen, Swetlana Hubrig, Ilya Ilyin

The magnetic field in the O9.7 V star HD54879 has been monitored for almost a decade. Spectropolarimetric observations reveal a rather strong mean longitudinal magnetic field that varies with a period of about 7.41 yr. Observations in the H$\alpha$ line show a variation with the same period, while the H$\beta$ line shows only little variation. Assuming the p

Deniz Gorur, Antonio Rago, Francesca Toni

Argument mining (AM) is the process of automatically extracting arguments, their components and/or relations amongst arguments and components from text. As the number of platforms supporting online debate increases, the need for AM becomes ever more urgent, especially in support of downstream tasks. Relation-based AM (RbAM) is a form of AM focusing on identi

Huafeng Liu, Mengmeng Sheng, Zeren Sun, Yazhou Yao

Learning with noisy labels has gained increasing attention because the inevitable imperfect labels in real-world scenarios can substantially hurt the deep model performance. Recent studies tend to regard low-loss samples as clean ones and discard high-loss ones to alleviate the negative impact of noisy labels. However, real-world datasets contain not only no

Claire S. Lee, Noelle Lim, Michael Guerzhoy

We present a novel task that can elucidate the connection between anxiety and ADHD; use Transformers to make progress toward solving a task that is not solvable by keyword-based classifiers; and discuss a method for visualization of our classifier illuminating the connection between anxiety and ADHD presentations. Up to approximately 50% of adults with ADHD

Yu Feng, Xing Shi, Mengli Cheng, Yun Xiong

As the task of 2D-to-3D reconstruction has gained significant attention in various real-world scenarios, it becomes crucial to be able to generate high-quality point clouds. Despite the recent success of deep learning models in generating point clouds, there are still challenges in producing high-fidelity results due to the disparities between images and poi

Xing-Xing Dong, Shu-Min Zhao, Jia-Peng Huo, Tong-Tong Wang

Charged lepton flavor violation (CLFV) represents a clear new physics (NP) signal beyond the standard model (SM). In this work, we investigate the CLFV decays of vector mesons $V\rightarrow l_i^{\pm}l_j^{\mp}$ with $V\in\{\phi, J/\Psi, \Upsilon(1S), \Upsilon(2S),\Upsilon(3S)\}$ in the $\mathrm{U}(1)_{X} \mathrm{SSM}$. Considering the SM-like Higgs boson mass

Gemb Kaljavesi, Tobias Kerbl, Tobias Betz, Kirill Mitkovskii

Extensive testing is necessary to ensure the safety of autonomous driving modules. In addition to component tests, the safety assessment of individual modules also requires a holistic view at system level, which can be carried out efficiently with the help of simulation. Achieving seamless compatibility between a modular software stack and simulation is comp

Vittorio Cortellessa, Daniele Di Pompeo, Michele Tucci

As organizations increasingly migrate their applications to the cloud, the optimization of microservices architectures becomes imperative for achieving sustainability goals. Nonetheless, sustainable deployments may increase costs and deteriorate performance, thus the identification of optimal tradeoffs among these conflicting requirements is a key objective

Hadi M. Dolatabadi, Sarah M. Erfani, Christopher Leckie

Deep neural networks (DNNs) are vulnerable to shortcut learning: rather than learning the intended task, they tend to draw inconclusive relationships between their inputs and outputs. Shortcut learning is ubiquitous among many failure cases of neural networks, and traces of this phenomenon can be seen in their generalizability issues, domain shift, adversari

Alexey Glutsyuk

We consider a 3-parameter family of linear special double confluent Heun equations introduced and studied by V.M.Buchstaber and S.I.Tertychnyi, which is an equivalent presentation of a model of Josephson junction in superconductivity. Buchstaber and Tertychnyi have shown that the set of those complex parameters for which the Heun equation has a polynomial so

Yuhan Li, Peisong Wang, Zhixun Li, Jeffrey Xu Yu

With the development of foundation models such as large language models, zero-shot transfer learning has become increasingly significant. This is highlighted by the generative capabilities of NLP models like GPT-4, and the retrieval-based approaches of CV models like CLIP, both of which effectively bridge the gap between seen and unseen data. In the realm of

Jhuma Dutta, Pooja Bhatt, Kuljeet Kaur, Daniel E. Gómez

Strong light-matter coupling is a quantum process in which light and matter are coupled together, generating hybridized states. This is similar to the notion of molecular hybridization, but one of the components is light. Here, we utilized the idea and prepared quantum phototransistors using donor-acceptor combinations that can transfer energy via Rabi oscil

Xiangyu Zhang, Hexin Liu, Kaishuai Xu, Qiquan Zhang

Depression is a critical concern in global mental health, prompting extensive research into AI-based detection methods. Among various AI technologies, Large Language Models (LLMs) stand out for their versatility in mental healthcare applications. However, their primary limitation arises from their exclusive dependence on textual input, which constrains their

Yu Chen, Shuai-Xia Xu, Yu-Qiu Zhao

In the paper, we consider the extended Gross-Witten-Wadia unitary matrix model by introducing a logarithmic term in the potential. The partition function of the model can be expressed equivalently in terms of the Toeplitz determinant with the $(i,j)$-entry being the modified Bessel functions of order $i-j-\nu$, $\nu\in\mathbb{C}$. When the degree $n$ is fini

Kun Wang, Jingya Zhu

Motivated by experimental hints and theoretical frameworks indicating the existence of an extended Higgs sector, we explore the feasibility of detecting a 95 GeV light Higgs boson decaying into a diphoton within the minimal dilaton model at the 14 TeV LHC. Initially, we identify the correlations between the production cross section, decay branching ratios, a

Joerg Osterrieder, Stephen Chan, Jeffrey Chu, Yuanyuan Zhang

Blockchain technology, a foundational distributed ledger system, enables secure and transparent multi-party transactions. Despite its advantages, blockchain networks are susceptible to anomalies and frauds, posing significant risks to their integrity and security. This paper offers a detailed examination of blockchain's key definitions and properties, alongs

Guanghui Hu, Andreas Kirsch

The paper is concerned with well-posedness of TE and TM polarizations of time-harmonic electromagnetic scattering by perfectly conducting periodic surfaces and periodically arrayed obstacles with local perturbations. The classical Rayleigh Expansion radiation condition does not always lead to well-posedness of the Helmholtz equation even in unperturbed perio

Yunjing Gao, Xiao Wang, Ning Xi, Yunfeng Jiang

Emergent Ising$_h^2$ integrability is anticipated in a quantum Ising ladder composed of two weakly-coupled critical transverse field Ising chains. The system is remarkable for including eight types of massive relativistic particles, with their scattering matrix and mass spectrum characterized by the $\mathcal{D}_8^{(1)}$ Lie algebra. In this article, by comp

Yuqian Zhang, Weijie Ji, Jelena Bradic

In this paper, we propose a new random forest algorithm that constructs the trees using a novel adaptive split-balancing method. Rather than relying on the widely-used random feature selection, we propose a permutation-based balanced splitting criterion. The adaptive split balancing forest (ASBF), achieves minimax optimality under the Lipschitz class. Its lo

Jonathan Oliver, Jue Mo, Susmit Yenkar, Raghav Batta

Similarity has been applied to a wide range of security applications, typically used in machine learning models. We examine the problem posed by masquerading samples; that is samples crafted by bad actors to be similar or near identical to legitimate samples. We find that these samples potentially create significant problems for machine learning solutions. T

Tian Jin, Zhongjian Zhu

In this paper, we calculate the $n+3$, $n+4$ dimensional homotopy groups of indecomposable $\mathbf{A}_n^2$-complexes after localization at 2. This job is seen as a sequel to P.J. Hilton's work on the $n+1,n+2$ dimensional homotopy groups of $\mathbf{A}_n^2$-complexes (1950-1951). The main technique used is analysing the homotopy property of $J(X,A)$, define

Michael Bildhauer, Martin Fuchs

Using a Caccioppoli-type inequality involving negative exponents for a directional weight we establish variants of Bernstein's theorem for variational integrals with linear and nearly linear growth. We give some mild conditions for entire solutions of the equation \[ {\rm div} \Big[Df(\nabla u)\Big] = 0 \, , \] under which solutions have to be affine functio

Chi Zhang, Jingjing Fan, Man Ho Au, Siu Ming Yiu

Replacing non-polynomial functions (e.g., non-linear activation functions such as ReLU) in a neural network with their polynomial approximations is a standard practice in privacy-preserving machine learning. The resulting neural network, called polynomial approximation of neural network (PANN) in this paper, is compatible with advanced cryptosystems to enabl

Yang Ni, Zhuowen Zou, Wenjun Huang, Hanning Chen

Drawing inspiration from the outstanding learning capability of our human brains, Hyperdimensional Computing (HDC) emerges as a novel computing paradigm, and it leverages high-dimensional vector presentation and operations for brain-like lightweight Machine Learning (ML). Practical deployments of HDC have significantly enhanced the learning efficiency compar

Clément Dallard, Matjaž Krnc, O-joung Kwon, Martin Milanič

Many recent works address the question of characterizing induced obstructions to bounded treewidth. In 2022, Lozin and Razgon completely answered this question for graph classes defined by finitely many forbidden induced subgraphs. Their result also implies a characterization of graph classes defined by finitely many forbidden induced subgraphs that are $(tw

Daegyu Lim, Myeong-Ju Kim, Junhyeok Cha, Jaeheung Park

Momentum observer (MOB) can estimate external joint torque without requiring additional sensors, such as force/torque or joint torque sensors. However, the estimation performance of MOB deteriorates due to the model uncertainty which encompasses the modeling errors and the joint friction. Moreover, the estimation error is significant when MOB is applied to h

V. Temlyakov

Sampling recovery on some function classes is studied in this paper. Typically, function classes are defined by imposing smoothness conditions. It was understood in nonlinear approximation that structural conditions in the form of control of the number of big coefficients of an expansion of a function with respect to a given system of functions plays an impo

L. Napolitano, L. Pentericci, P. Santini, A. Calabrò

The resonant scattering interaction between Ly$\alpha$ photons and neutral hydrogen implies that a partially neutral IGM can significantly impact the detectability of Ly$\alpha$ emission in galaxies. The redshift evolution of the Ly$\alpha$ equivalent width distribution of galaxies thus offers a key probe of the degree of ionization during the Epoch of Reion

Koji Tsukuda, Shun Matsuura

In a regression model with multiple response variables and multiple explanatory variables, if the difference of the mean vectors of the response variables for different values of explanatory variables is always in the direction of the first principal eigenvector of the covariance matrix of the response variables, then it is called a multivariate allometric r

Xun Liang, Hanyu Wang, Shichao Song, Mengting Hu

Controlled Text Generation (CTG) aims to produce texts that exhibit specific desired attributes. In this study, we introduce a pluggable CTG framework for Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled text generation (DATG). This framework utilizes an attribute scorer to evaluate the attributes of sentences generated by LLMs an

Naoya Arakawa

This article presents a simple model of the cortex-basal ganglia-thalamus loop, which is thought to serve for action selection and executions, and reports the results of its implementation. The model is based on the hypothesis that the cerebral cortex predicts actions, while the basal ganglia use reinforcement learning to decide whether to perform the action

Jie Liu, Wenxuan Wang, Yihang Su, Jingyuan Huan

The significant breakthroughs of Medical Multi-Modal Large Language Models (Med-MLLMs) renovate modern healthcare with robust information synthesis and medical decision support. However, these models are often evaluated on benchmarks that are unsuitable for the Med-MLLMs due to the complexity of real-world diagnostics across diverse specialties. To address t

Gloria Dal Santo, Karolina Prawda, Sebastian J. Schlecht, Vesa Välimäki

A common bane of artificial reverberation algorithms is spectral coloration in the synthesized sound, typically manifesting as metallic ringing, leading to a degradation in the perceived sound quality. In delay network methods, coloration is more pronounced when fewer delay lines are used. This paper presents an optimization framework in which a tiny differe

Tim Tsz-Kit Lau, Han Liu, Mladen Kolar

The choice of batch size in minibatch stochastic gradient optimization is critical for both optimization and generalization performance in large-scale model training. Although large-batch training is arguably the dominant paradigm in large-scale deep learning because of hardware advances, model generalization often deteriorates relative to small-batch traini

Shuai-Xia Xu, Shu-Quan Zhao, Yu-Qiu Zhao

We consider the determinantal point process with the confluent hypergeometric kernel. This process is a universal point process in random matrix theory and describes the distribution of eigenvalues of large random Hermitian matrices near the Fisher-Hartwig singularity. Applying the Riemann-Hilbert method, we study the generating function of this process on a

Anton Lipin

We investigate connections between resolvability and different forms of tightness. This study is adjacent to [1,2]. We construct a non-regular refinement $\tau^*$ of the natural topology of the real line $\mathbb{R}$ with properties such that the space $(\mathbb{R}, \tau^*)$ has a hereditary nowhere dense tightness and it has no $\omega_1$-resolvable subspac

Massoud Amini, Qing Meng

We define an equivariant and equicovariant versions of the notion of module nuclearity. More precisely, for a discrete group $\Gamma$ and operator $\mathcal A$-$\Gamma$-(co)module $\mathcal B$, $\mathcal E$ over a $\Gamma$-C$^*$-algebra $\mathcal A$, we define $\mathcal E$-$\Gamma$-nuclearity of $\mathcal B$, as an equivariant version of the notion of $\math

Hongye Zeng, Ke Zou, Zhihao Chen, Yuchong Gao

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we pro

Qiang Zhai, Xin-Yuan Gao, Chun-Shing Lee, Chin-Yuan Ong

Confining glassy polymer into films can substantially modify their local and film-averaged properties. We present a lattice model of film geometry with void-mediated facilitation behaviors but free from any elasticity effect. We analyze the spatially varying viscosity to delineate the transport property of glassy films. The film mobility measurements reporte

Devansh Jalota, Michael Ostrovsky, Marco Pavone

Fraud is ubiquitous across applications and involve users bypassing the rule of law, often with the strategic aim of obtaining some benefit that would otherwise be unattainable within the bounds of lawful conduct. However, user fraud can be detrimental. To mitigate the harms of user fraud, we study the problem of policing fraud as a security game between an

Wenkai Yang, Xiaohan Bi, Yankai Lin, Sishuo Chen

Driven by the rapid development of Large Language Models (LLMs), LLM-based agents have been developed to handle various real-world applications, including finance, healthcare, and shopping, etc. It is crucial to ensure the reliability and security of LLM-based agents during applications. However, the safety issues of LLM-based agents are currently under-expl

Zhiyuan Zeng, Qipeng Guo, Zhaoye Fei, Zhangyue Yin

Sparse Mixture of Experts (MoE) models are popular for training large language models due to their computational efficiency. However, the commonly used top-$k$ routing mechanism suffers from redundancy computation and memory costs due to the unbalanced routing. Some experts are overflow, where the exceeding tokens are dropped. While some experts are vacant,

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using a data sample of $e^+e^-$ collision data corresponding to an integrated luminosity of 19 fb$^{-1}$ collected with the BESIII detector at the BEPCII collider, we search for the production of deuterons and antideuterons via $e^+e^-\to pp\pi^-\bar{d}+c.c.$ for the first time at center-of-mass energies between 4.13 and 4.70 GeV. No significant signal is ob

Asish Bera, Debotosh Bhattacharjee, Mita Nasipuri

Digital forensic is now an unavoidable part for securing the digital world from identity theft. Higher order of crimes, dealing with a massive database is really very challenging problem for any intelligent system. Biometric is a better solution to win over the problems encountered by digital forensics. Many biometric characteristics are playing their signif

Diyi Liu, Weijie Du, Lin Lin, James P. Vary

We present an efficient quantum circuit for block encoding pairing Hamiltonian often studied in nuclear physics. Our block encoding scheme does not require mapping the creation and annihilation operators to the Pauli operators and representing the Hamiltonian as a linear combination of unitaries. Instead, we show how to encode the Hamiltonian directly using

Hassan Gashmard, Hamideh Shakeripour, Mojtaba Alaei

Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces the challenge of achieving superconductivity at room temperature. In recent years, Artificial Intelligence (AI) approache

Yizheng Huang, Jimmy Huang

The rapid advancement of artificial intelligence (AI) has highlighted ChatGPT as a pivotal technology in the field of information retrieval (IR). Distinguished from its predecessors, ChatGPT offers significant benefits that have attracted the attention of both the industry and academic communities. While some view ChatGPT as a groundbreaking innovation, othe

Xiaohua Wu, Lin Li, Xiaohui Tao, Frank Xing

Happiness computing based on large-scale online web data and machine learning methods is an emerging research topic that underpins a range of issues, from personal growth to social stability. Many advanced Machine Learning (ML) models with explanations are used to compute the happiness online assessment while maintaining high accuracy of results. However, do

Ziqi Zhang, Yupin Huang, Quan Deng, Jinghui Xiao

Customer behavioral data significantly impacts e-commerce search systems. However, in the case of less common queries, the associated behavioral data tends to be sparse and noisy, offering inadequate support to the search mechanism. To address this challenge, the concept of query reformulation has been introduced. It suggests that less common queries could u

Jiwon Yoo, Jangwon Lee, Gyeonghwan Kim

Multi-scale architecture, including hierarchical vision transformer, has been commonly applied to high-resolution semantic segmentation to deal with computational complexity with minimum performance loss. In this paper, we propose a novel decoding scheme for semantic segmentation in this regard, which takes multi-level features from the encoder with multi-sc

Amedeo Roberto Esposito, Marco Mondelli

We develop a novel framework for bounding the contraction of information divergences, using duality and associated norms in Orlicz spaces. By working in the dual space, we obtain a principled approach to bounding both distribution-dependent strong data-processing inequality (SDPI) constants and \(F_φ\)-curves of divergences. Our bounds are either available i

Minh-Vuong Nguyen, Linhao Luo, Fatemeh Shiri, Dinh Phung

Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers. However, previous research on evaluating LLMs has solely focused on answer accuracy, neglecting the correctness of the generated CoT. In this paper, we delve deeper into the CoT reasoning capabilities of LLMs in

Xiaolu Wang, Zijian Li, Shi Jin, Jun Zhang

Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients. The Federated Averaging (FedAvg)-based algorithms have gained substantial popularity in FL to reduce the communication overhead, where each client conducts mult

Navid Mohammadi Foumani, Geoffrey Mackellar, Soheila Ghane, Saad Irtza

Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which challenges the quality of the representation learned, (2) The wide range of amplitudes from very small to relatively large due to factors such as the inter-subject variability, risks

Hiroyuki Deguchi, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe

Minimum Bayes risk (MBR) decoding achieved state-of-the-art translation performance by using COMET, a neural metric that has a high correlation with human evaluation. However, MBR decoding requires quadratic time since it computes the expected score between a translation hypothesis and all reference translations. We propose centroid-based MBR (CBMBR) decodin

Xiaolei Ru, Xiaowei Cao, Zijia Liu, Jack Murdoch Moore

Adversarial robustness is essential for security and reliability of machine learning systems. However, adversarial robustness enhanced by defense algorithms is easily erased as the neural network's weights update to learn new tasks. To address this vulnerability, it is essential to improve the capability of neural networks in terms of robust continual learni

Gyeo-Re Han, Artem Goncharov, Merve Eryilmaz, Hyou-Arm Joung

Successful integration of point-of-care testing (POCT) into clinical settings requires improved assay sensitivity and precision to match laboratory standards. Here, we show how innovations in amplified biosensing, imaging, and data processing, coupled with deep learning, can help improve POCT. To demonstrate the performance of our approach, we present a rapi

Pragya Srivastava, Manuj Malik, Vivek Gupta, Tanuja Ganu

Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with an amalgamation of structured tables and unstructured text is uncertain. This study explores LLMs' mathematical reasoning on four financial tabular question-answering datasets: TATQA, FinQA, ConvFinQA, and Multihiertt. Through e

Leonardo Horn Iwaya, Ala Sarah Alaqra, Marit Hansen, Simone Fischer-Hübner

Privacy Impact Assessments (PIAs) offer a systematic process for assessing the privacy impacts of a project or system. As a privacy engineering strategy, PIAs are heralded as one of the main approaches to privacy by design, supporting the early identification of threats and controls. However, there is still a shortage of empirical evidence on their uptake an

Xuan Ren, Biao Wu, Lingqiao Liu

This paper explores an intriguing observation: fine-tuning a large language model (LLM) with responses generated by a LLM often yields better results than using responses generated by humans, particularly in reasoning tasks. We conduct an in-depth investigation to understand why this occurs. Contrary to the common belief that these instances is due to the mo

Yang Cao, Xinyi Chen, Xin Zhang, Siying Li

In this paper, we present a novel method for automatically generating sports news, which employs a unique algorithm that extracts pivotal moments from live text broadcasts and uses them to create an initial draft of the news. This draft is further refined by incorporating key details and background information from a specially designed sports knowledge graph

Xiangjue Dong, Yibo Wang, Philip S. Yu, James Caverlee

Large Language Models (LLMs) can generate biased responses. Yet previous direct probing techniques contain either gender mentions or predefined gender stereotypes, which are challenging to comprehensively collect. Hence, we propose an indirect probing framework based on conditional generation. This approach aims to induce LLMs to disclose their gender bias e

Xin Hao, Phee Lep Yeoh, Changyang She, Yao Yu

Network slicing (NS) is a promising technology that supports diverse requirements for next-generation low-latency wireless communication networks. However, the tampering attack is a rising issue of jeopardizing NS service-provisioning. To resist tampering attacks in NS networks, we propose a novel optimization framework for reliable NS resource allocation in

Soumya Roy, Durgesh Tripathi

The Mg II k \& h line intensity ratios can be used to probe the characteristics of the plasma in the solar atmosphere. In this study, using the observations recorded by the Interface Region Imaging Spectrometer (IRIS), we study the variation of the Mg II k \& h intensity ratio for three flares belonging to X-class, M-class, and C-class, throughout their evol

Yoshiki Toba, Aoi Hashiguchi, Naomi Ota, Masamune Oguri

Herein, we present the statistical properties of active galactic nuclei (AGNs) for approximately 1 million member galaxies of galaxy groups and clusters, with 0.1 $<$ cluster redshift ($z_{\rm cl}$) $<$ 1.4, selected using Subaru Hyper Suprime-Cam, the so-called CAMIRA clusters. In this research, we focused on the AGN power fraction ($f_{\rm AGN}$), which is

Yifei Yang, Zouying Cao, Hai Zhao

Large language models (LLMs) based on transformer are witnessing a notable trend of size expansion, which brings considerable costs to both model training and inference. However, existing methods such as model quantization, knowledge distillation, and model pruning are constrained by various issues, including hardware support limitations, the need for extens

Mian Qin, Junhao Ding, Shuo Qu, Xu Song

Laser powder bed fusion (LPBF) is a widely used metal additive manufacturing technology. However, the accumulation of internal residual stress during printing can cause significant distortion and potential failure. Although various scan patterns have been studied to reduce possible accumulated stress, such as zigzag scanning vectors with changing directions

Feng Wang, Renfang Wang, Hong Qiu

Low-Dose computer tomography (LDCT) is an ideal alternative to reduce radiation risk in clinical applications. Although supervised-deep-learning-based reconstruction methods have demonstrated superior performance compared to conventional model-driven reconstruction algorithms, they require collecting massive pairs of low-dose and norm-dose CT images for neur

Zimeng Lyu, Pujan Thapa, Travis Desell

Identifying phases of flight is important in the field of general aviation, as knowing which phase of flight data is collected from aircraft flight data recorders can aid in the more effective detection of safety or hazardous events. General aviation flight data for phase of flight identification is usually per-second data, comes on a large scale, and is cla

Hongyu Liu, Shen Zhang

By following the study in [24], we consider an inverse boundary problem for the mean field game system where a probability density constraint is enforced on the game agents. That is, we consider the case that reflective boundary conditions are enforced and hence the population distribution of the game agents should be treated as a probability measure which p

Owe Axelsson, Dovod Khojasteh Slakuyeh

We present a modified version of the PRESB preconditioner for two-by-two block system of linear equations with the coefficient matrix $$\textbf{A}=\left(\begin{array}{cc} F & -G^* G & F \end{array}\right),$$ where $F\in\mathbb{C}^{n\times n}$ is Hermitian positive definite and $G\in\mathbb{C}^{n\times n}$ is positive semidefinite. Spectral analysis of the pr

Mona Sloane, Emanuel Moss, Susan Kennedy, Matthew Stewart

Artificial intelligence (AI) systems connected to sensor-laden devices are becoming pervasive, which has notable implications for a range of AI risks, including to privacy, the environment, autonomy and more. There is therefore a growing need for increased accountability around the responsible development and deployment of these technologies. Here we highlig

Mayank Narang, P. Manoj, Ishwara Chandra, Bihan Banerjee

In this work, we present the results from a study using the Giant Meterwave Radio Telescope (GMRT) to search for radio {emission} from planets around three evolved stars namely $\alpha$~Tau, $\beta$~UMi, and $\beta$~Gem. Both $\alpha$~Tau and $\beta$~UMi host massive $\sim$ 6 $M_J$ mass planets at about $\sim$1.4 au from the central star, while $\beta$~Gem i

Mijie Shi, Bo Li, Shengju Yuan

Kink oscillations are ubiquitously observed in solar coronal loops, their understanding being crucial in the contexts of coronal seismology and atmospheric heating. We study kink modes supported by a straight coronal loop embeded in an asymmetric environment using three-dimensional magnetohydrodynamic (MHD) simulations. We implement the asymmetric effect by

Omar Hammad, Md Rezwanur Rahman, Nicholas Clements, Shivakant Mishra

Planned disruptions such as highway constructions are commonplace nowadays and the communities living near these disruptions generally tend to be environmental justice communities -- low socioeconomic status with disproportionately high and adverse human health and environmental effects. A major concern is that such activities negatively impact people's well

Jeremiah Hauth, Cosmin Safta, Xun Huan, Ravi G. Patel

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural network models have proved to be adept at modeling processes with spatial-temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error

Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng

Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts. Remediating norm violations requires social awareness and cultural sensitivity of the nuances at play. To equip interactive AI systems with a remediation ability, we offer ReNoVi - a large-scale corpus of 9,258 multi-turn dialogues

Huaiyuan Ying, Sheng Yu

Electronic health records (EHRs) hold significant value for research and applications. As a new way of information extraction, question answering (QA) can extract more flexible information than conventional methods and is more accessible to clinical researchers, but its progress is impeded by the scarcity of annotated data. In this paper, we propose a novel

Yougang Lyu, Lingyong Yan, Shuaiqiang Wang, Haibo Shi

Despite their success at many natural language processing (NLP) tasks, large language models still struggle to effectively leverage knowledge for knowledge-intensive tasks, manifesting limitations such as generating incomplete, non-factual, or illogical answers. These limitations stem from inadequate knowledge awareness of LLMs during vanilla fine-tuning. To

Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su

The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its potential misuse and societal implications. The need to identify and differentiate such content from genuine human-generated text is critical in combating disinformation, preserving t

Bang-Xian Han, Andrea Pinamonti, Zhefeng Xu, Kilian Zambanini

We find a surprising link between Maz'ya-Shaposhnikova's well-known asymptotic formula concerning fractional Sobolev seminorms and the generalized Bishop-Gromov inequality. In the setting of abstract metric measure spaces we prove the validity of a large family of asymptotic formulas concerning non-local energies. Important examples which are covered by our

Andrew Lowy, Jonathan Ullman, Stephen J. Wright

We provide a simple and flexible framework for designing differentially private algorithms to find approximate stationary points of non-convex loss functions. Our framework is based on using a private approximate risk minimizer to "warm start" another private algorithm for finding stationary points. We use this framework to obtain improved, and sometimes opt

Qingjun Liu, Chaowei Jiang, Xinkai Bian, Xueshang Feng

There is a heated debate regarding the specific roles played by ideal magnetohydrodynamic (MHD) instability and magnetic reconnection in the causes of solar eruptions. In the context with a pre-existing magnetic flux rope (MFR) before an eruption, it is widely believed that an ideal MHD instability, in particular, the torus instability, is responsible for tr

Stephen Sanderson, Shern R. Tee, Debra J. Searles

Constraining molecules in simulations (such as with constant bond lengths and/or angles) reduces their degrees of freedom (DoF), which in turn affects temperature calculations in those simulations. When local temperatures are measured, e.g. from a set of atoms in a subvolume or from velocities in one Cartesian direction, the result can appear to unphysically

Shou-Jyun Zou

We introduce a spin field approach, that is compatible with the Cartan moving frame method, to describe the submanifold in a flat space. In fact, we consider a kind of spin field $\psi$, that satisfies a Killing spin field equation (analogous to a Killing spinor equation) written in terms of the Clifford algebra, and we use the spin field to locally rotate t

Tao Yan, Shengnan Li, Benjamin Kraner, Luyao Zhang

Ethereum 2.0, as the preeminent smart contract blockchain platform, guarantees the precise execution of applications without third-party intervention. At its core, this system leverages the Proof-of-Stake (PoS) consensus mechanism, which utilizes a stochastic process to select validators for block proposal and validation, consequently rewarding them for thei

Amit Dhurandhar, Swagatam Haldar, Dennis Wei, Karthikeyan Natesan Ramamurthy

Given the black box nature of machine learning models, a plethora of explainability methods have been developed to decipher the factors behind individual decisions. In this paper, we introduce a novel problem of black box (probabilistic) explanation certification. We ask the question: Given a black box model with only query access, an explanation for an exam

Fan Huang, Haewoon Kwak, Jisun An

The robustness of AI-content detection models against sophisticated adversarial strategies, such as paraphrasing or word switching, is a rising concern in natural language generation (NLG) applications. This study proposes ToBlend, a novel token-level ensemble text generation method to challenge the robustness of current AI-content detection approaches by ut

Jian Wu, Linyi Yang, Yuliang Ji, Wenhao Huang

Multi-hop QA (MHQA) involves step-by-step reasoning to answer complex questions and find multiple relevant supporting facts. However, Existing large language models'(LLMs) reasoning ability in multi-hop question answering remains exploration, which is inadequate in answering multi-hop questions. Moreover, it is unclear whether LLMs follow a desired reasoning

Connor Pryce, Lanyu Li, Jared P. Whitehead, Zhao Pan

We propose a simple boundary condition regularization strategy to reduce error propagation in pressure field reconstruction from corrupted image velocimetry data. The core idea is to replace the canonical Neumann boundary conditions with Dirichlet ones obtained by integrating the tangential part of the pressure gradient along the boundaries. Rigorous analysi

Gaocheng Ma, Yinfeng Chai, Tianhao Jiang, Ming Lu

Image compression has been the subject of extensive research for several decades, resulting in the development of well-known standards such as JPEG, JPEG2000, and H.264/AVC. However, recent advancements in deep learning have led to the emergence of learned image compression methods that offer significant improvements in coding efficiency compared to traditio

Jinhao Jiang, Kun Zhou, Wayne Xin Zhao, Yang Song

In this paper, we aim to improve the reasoning ability of large language models (LLMs) over knowledge graphs (KGs) to answer complex questions. Inspired by existing methods that design the interaction strategy between LLMs and KG, we propose an autonomous LLM-based agent framework, called KG-Agent, which enables a small LLM to actively make decisions until f

Klichchupong Dabsamut, Kaito Takahashi, Walter R. L. Lambrecht

Recently, LiGa$_5$O$_8$ was identified as a cubic spinel type ultra-wide-band-gap semiconductor with a gap of about 5.36 eV and reported to be unintentionally p-type. Here we present first-principles calculations of the native defects and various of their complexes to try to explain the occurrence of p-type doping. Although we find Li-vacancies to be somewha