October 2024 arXiv papers — page 42

Panos Fitsilis, Vyron Damasiotis, Charalampos Dervenis, Vasileios Kyriatzis

The significance of open data in higher education stems from the changing tendencies towards open science, and open research in higher education encourages new ways of making scientific inquiry more transparent, collaborative and accessible. This study focuses on the critical role of open data stewards in this transition, essential for managing and dissemina

Hongwen Zhang, Long Ma, Chao Zhang, Yinghua Qiu

Bipolar nanopores, with asymmetric charge distributions, can induce significant ionic current rectification (ICR) at ultra-short lengths, finding potential applications in nanofluidic devices, energy conversion, and other related fields. Here, with simulations, we investigated the characteristics of ion transport and modulation of ICR inside bipolar nanopore

Yongkang Cheng, Mingjiang Liang, Shaoli Huang, Gaoge Han

Audio-driven simultaneous gesture generation is vital for human-computer communication, AI games, and film production. While previous research has shown promise, there are still limitations. Methods based on VAEs are accompanied by issues of local jitter and global instability, whereas methods based on diffusion models are hampered by low generation efficien

Mingjiang Liang, Yongkang Cheng, Hualin Liang, Shaoli Huang

We present RopeTP, a novel framework that combines Robust pose estimation with a diffusion Trajectory Prior to reconstruct global human motion from videos. At the heart of RopeTP is a hierarchical attention mechanism that significantly improves context awareness, which is essential for accurately inferring the posture of occluded body parts. This is achieved

Xilun Zhang, Shiqi Liu, Peide Huang, William Jongwon Han

Sim-to-real transfer remains a significant challenge in robotics due to the discrepancies between simulated and real-world dynamics. Traditional methods like Domain Randomization often fail to capture fine-grained dynamics, limiting their effectiveness for precise control tasks. In this work, we propose a novel approach that dynamically adjusts simulation en

Junran Wu, Xueyuan Chen, Shangzhe Li

Graph contrastive learning has achieved great success in pre-training graph neural networks without ground-truth labels. Leading graph contrastive learning follows the classical scheme of contrastive learning, forcing model to identify the essential information from augmented views. However, general augmented views are produced via random corruption or learn

Skund Tewari, Sagnik Chatterjee, Deepak Kumar, Ritam Mallick

The recent mass ($0.77 \pm ^{0.20}_{0.17}M_{\odot}$) and radius ($10.4\pm^{0.86}_{0.78} \text{km}$) measurement of HESS J1731-347 made it one of the most fascinating object if it is indeed a neutron star. In this work, we examine the current status of the dense matter equation of states in the context of this compact object being a neutron star. We use three

Zaixi Zhang, Ruofan Jin, Kaidi Fu, Le Cong

Protein structure is key to understanding protein function and is essential for progress in bioengineering, drug discovery, and molecular biology. Recently, with the incorporation of generative AI, the power and accuracy of computational protein structure prediction/design have been improved significantly. However, ethical concerns such as copyright protecti

Masayuki Miyamoto

The class of graphs that do not contain a path on $k$ nodes as an induced subgraph ($P_k$-free graphs) has rich applications in the theory of graph algorithms. This paper explores the problem of deciding $P_k$-freeness from the viewpoint of distributed computing. For specific small values of $k$, we present the \textit{first} $\mathsf{CONGEST}$ algorithms sp

Loc Bao Pham, Huong Hoang Luong, Phu Thien Tran, Phuc Hoang Ngo

Melody stuck in your head, also known as "earworm", is tough to get rid of, unless you listen to it again or sing it out loud. But what if you can not find the name of that song? It must be an intolerable feeling. Recognizing a song name base on humming sound is not an easy task for a human being and should be done by machines. However, there is no research

Jinze Wang, Jiong Jin, Tiehua Zhang, Boon Xian Chai

The accurate diagnosis of machine breakdowns is crucial for maintaining operational safety in smart manufacturing. Despite the promise shown by deep learning in automating fault identification, the scarcity of labeled training data, particularly for equipment failure instances, poses a significant challenge. This limitation hampers the development of robust

Hyewon Kim, Woocheol Shin, Dahee Kim, Junghoon Kim

Hypergraphs serve as a powerful tool for modeling complex relationships across domains like social networks, transactions, and recommendation systems. The (k,g)-core model effectively identifies cohesive subgraphs by assessing internal connections and co-occurrence patterns, but it is susceptible to inflated cohesiveness due to trivial hyperedges. To address

Lilang Lin, Lehong Wu, Jiahang Zhang, Jiaying Liu

Generative models, as a powerful technique for generation, also gradually become a critical tool for recognition tasks. However, in skeleton-based action recognition, the features obtained from existing pre-trained generative methods contain redundant information unrelated to recognition, which contradicts the nature of the skeleton's spatially sparse and te

Runshi Zhang, Hao Mo, Junchen Wang, Bimeng Jie

Complicated image registration is a key issue in medical image analysis, and deep learning-based methods have achieved better results than traditional methods. The methods include ConvNet-based and Transformer-based methods. Although ConvNets can effectively utilize local information to reduce redundancy via small neighborhood convolution, the limited recept

Shun Shimomura

For the fourth Painlev\'e transcendents we derive elliptic asymptotic representations, which were announced by late Professor Kapaev without proofs. Then we newly obtain related results including the correction function.

Jingyi Zhang, Jiaxing Huang, Xiaoqin Zhang, Ling Shao

Test-time prompt tuning, which learns prompts online with unlabelled test samples during the inference stage, has demonstrated great potential by learning effective prompts on-the-fly without requiring any task-specific annotations. However, its performance often degrades clearly along the tuning process when the prompts are continuously updated with the tes

Mohammadreza Doostmohammadian, Sérgio Pequito

Distributed optimization finds many applications in machine learning, signal processing, and control systems. In these real-world applications, the constraints of communication networks, particularly limited bandwidth, necessitate implementing quantization techniques. In this paper, we propose distributed optimization dynamics over multi-agent networks subje

Xiao Tang, Yudan Jiang, Jinxin Liu, Qinghe Du

This paper reveals the potential of movable antennas in enhancing anti-jamming communication. We consider a legitimate communication link in the presence of multiple jammers and propose deploying a movable antenna array at the receiver to combat jamming attacks. We formulate the problem as a signal-to-interference-plus-noise ratio maximization, by jointly op

Sakaé Fuchino

We examine the Zermelo Fraenkel set theory with Choice (ZFC) enhanced by one of the (structural) reflection principles down to a small cardinal and/or Recurrence Axioms defined below. The strongest forms of reflection principles spotlight the three scenarios in which the size of the continuum is either $\aleph_1$, or $\aleph_2$, or very large, while the maxi

Liangxun Li

Assume that the generalized Ramanujan conjecture holds on the automorphic $L$-function $L(s, \pi)$ on $\GL_d$ over $\mathbb{Q}$ with $d\geq 3$, we can obtain a small log-saving non-trivial bound on the second integral moment of $L(1/2+it, \pi)$. Specifically the bound \[ \int_{T}^{2T}\Big|L\big(\frac{1}{2}+it, \pi\big)\Big |^2 \dd t\ll_{\pi} \frac{T^{\frac{d

Manami Hosoi, Yumiko Umegaki

Let $\chi$ be a primitive Dirichlet character whose conductor $q$ is a prime number. For the certain averages of values of $\log |L(s, \chi)|$ in $q$-aspect at a fixed $s=\sigma>1/2$, under Generalized Riemann Hypothesis (GRH), we explain it can be written as integrals involving the same density function ($M$-function) for the average of values of the differ

Jiemin Wu, Songning Lai, Ruiqiang Xiao, Tianlang Xue

Large Language Models (LLMs) are powerful tools for text generation, translation, and summarization, but they often suffer from hallucinations-instances where they fail to maintain the fidelity and coherence of contextual information during decoding, sometimes overlooking critical details due to their sampling strategies and inherent biases from training dat

A. S. Abay Krishna, K. K. Naseeda, N. C. Randeep

We present a method for bidirectional teleportation of a single qubit using quantum walks on two independent one dimensional lattices and two independent cycles with four vertices, employing nearest neighbor jumps with coin outcomes. In addition, we discuss two different methods for two qubit teleportation by employing nearest neighbor jumps and next nearest

Chunlei Zhang, Mun Kim, Yi-Hui Zhang, Yi-Pu Wang

Achieving oscillations with small dimensions, high power, high coherence, and low phase noise has been a long-standing goal in wave physics, driving innovations across classical electromagnetic theory and quantum physics. Key applications include electronic oscillators, lasers, and spin-torque oscillations. In recent decades, physicists have increasingly foc

Lorenzo De Stefani, Vedant Gupta

Asymptotically tight lower bounds are derived for the Input/Output (I/O) complexity of a class of dynamic programming algorithms including matrix chain multiplication, optimal polygon triangulation, and the construction of optimal binary search trees. Assuming no recomputation of intermediate values, we establish an $\Omega\left(\frac{n^3}{\sqrt{M}B}\right)$

Maohao Shen, Shun Zhang, Jilong Wu, Zhiping Xiu

Large language models (LLMs) have revolutionized natural language processing (NLP) with impressive performance across various text-based tasks. However, the extension of text-dominant LLMs to with speech generation tasks remains under-explored. In this work, we introduce a text-to-speech (TTS) system powered by a fine-tuned Llama model, named TTS-Llama, that

M. Tanveer, A. Quadir

One of the major difficulties in machine learning methods is categorizing datasets that are imbalanced. This problem may lead to biased models, where the training process is dominated by the majority class, resulting in inadequate representation of the minority class. Universum twin support vector machine (UTSVM) produces a biased model towards the majority

Enshi Zhang, Christian Poellabauer

Speech Emotion Recognition (SER) focuses on identifying emotional states from spoken language. The 2024 IEEE SLT-GenSEC Challenge on Post Automatic Speech Recognition (ASR) Emotion Recognition tasks participants to explore the capabilities of large language models (LLMs) for emotion recognition using only text data. We propose a novel approach that first ref

Chun-Hung Liu, Sergey Norin, David R. Wood

This paper explores the structure of graphs defined by an excluded minor or an excluded odd minor through the lens of graph products and tree-decompositions. We prove that every graph excluding a fixed odd minor is contained in the strong product of two graphs each with bounded treewidth. For graphs excluding a fixed minor, we strengthen the result by showin

Yongxiang Li, Shiqing Wang, Zhong Sun

There have been a plethora of research on multi-level memory devices, where the resistive random-access memory (RRAM) is a prominent example. Although it is easy to write an RRAM device into multiple (even quasi-continuous) states, it suffers from the inherent variations that should limit the storage capacity, especially in the open-loop writing scenario. Th

Yiming Fan, Habib Najm, Yue Yu, Stewart Silling

Nonlocal, integral operators have become an efficient surrogate for bottom-up homogenization, due to their ability to represent long-range dependence and multiscale effects. However, the nonlocal homogenized model has unavoidable discrepancy from the microscale model. Such errors accumulate and propagate in long-term simulations, making the resultant predict

Shang-Ling Shih, Chao-Kai Wen, Chau Yuen, Shi Jin

This paper introduces a novel smartphone-enabled localization technology for ambient Internet of Things (IoT) devices, leveraging the widespread use of smartphones. By utilizing the passive movement of a smartphone, we create a virtual large array that enables direct localization using only angle-of-arrival (AoA) information. Unlike traditional two-step loca

Frankie Chan, Lindsey Styron

This work provides an effective algorithm for distinguishing finite quotients between two non-isomorphic finitely generated Fuchsian groups $\Gamma$ and $\Lambda$. It will suffice to take a finite quotient which is abelian, dihedral, a subgroup of $\mathrm{PSL}(2,\mathbf{F}_q)$, or an abelian extension of one of these 3. We will develop an approach for creat

Pramod Padmanabhan, Vladimir Korepin

Yang-Baxter equations define quantum integrable models. The tetrahedron and higher simplex equations are multi-dimensional generalizations. Finding the solutions of these equations is a formidable task. In this work we develop a systematic method - constructing higher simplex operators [solutions of corresponding simplex equations] from lower simplex ones. W

Xupeng Chen, Zhixin Lai, Kangrui Ruan, Shichu Chen

Artificial intelligence has made significant strides in medical visual question answering (Med-VQA), yet prevalent studies often interpret images holistically, overlooking the visual regions of interest that may contain crucial information, potentially aligning with a doctor's prior knowledge that can be incorporated with minimal annotations (e.g., bounding

Hongchao Zhang, Zhizhen Qin, Sicun Gao, Andrew Clark

Neural Control Barrier Functions (NCBFs) have shown significant promise in enforcing safety constraints on nonlinear autonomous systems. State-of-the-art exact approaches to verifying safety of NCBF-based controllers exploit the piecewise-linear structure of ReLU neural networks, however, such approaches still rely on enumerating all of the activation region

Sharadind Peddiraju, Srini Rajagopal

The challenge of creating domain-centric embeddings arises from the abundance of unstructured data and the scarcity of domain-specific structured data. Conventional embedding techniques often rely on either modality, limiting their applicability and efficacy. This paper introduces a novel modeling approach that leverages structured data to filter noise from

Shovito Barua Soumma, Daniel Peterson, Shyamal Mehta, Hassan Ghasemzadeh

Parkinson's disease (PD) is a progressive neurological disorder that impacts the quality of life significantly, making in-home monitoring of motor symptoms such as Freezing of Gait (FoG) critical. However, existing symptom monitoring technologies are power-hungry, rely on extensive amounts of labeled data, and operate in controlled settings. These shortcomin

Yonghong Bai, Zhiyuan Yan

Physical Unclonable Functions (PUFs) are widely used in key generation, with each PUF cell typically producing one bit of data. To enable the extraction of longer keys, a new non-binary response generation scheme based on the one-probability of PUF bits is proposed. Instead of using PUF bits directly as keys, non-binary responses are first derived by compari

Leyun Gao, Zijian Wang, Cheng-en Liu, Jinning Li

This work proposes a new yet economical experiment to probe the charged lepton flavor violation (CLFV) process mediated by an extra massive neutron gauge boson $Z^\prime$ beyond the standard model, by extending a recently proposed muon dark matter project in the Peking University Muon (PKMuon) Experiment. The devices used originally for light mass dark matte

Fei Fei

This paper introduces a new asymptotic-preserving Monte Carlo (APMC) method for simulating multi-species gas flows. This method decomposes the collision operator of the traditional APMC methods into macro and micro collision parts: the macro collision part corresponds to the first-order Chapman-Enskog (CE) expansion, solved with a second-order scheme; In con

Xingrui Zhuo, Jiapu Wang, Gongqing Wu, Shirui Pan

Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To enhance the generalization of KGQE models, recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries. Th

Tianlin Guo, Lingling Zhang, Jiaxin Wang, Yuokuo Lei

Few-shot relation extraction with none-of-the-above (FsRE with NOTA) aims at predicting labels in few-shot scenarios with unknown classes. FsRE with NOTA is more challenging than the conventional few-shot relation extraction task, since the boundaries of unknown classes are complex and difficult to learn. Meta-learning based methods, especially prototype-bas

Tiangang Cui, Alex Gorodetsky

We present a new sampling-based approach for enabling efficient computation of low-rank Bayesian matrix completion and quantifying the associated uncertainty. Firstly, we design a new prior model based on the singular-value-decomposition (SVD) parametrization of low-rank matrices. Our prior is analogous to the seminal nuclear-norm regularization used in non-

Siddharth Viswanath, Dhananjay Bhaskar, David R. Johnson, Joao Felipe Rocha

Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Prot

Yuly Billig, Kathlyn Dykes

For an affine algebraic variety, we introduce algebraic Gelfand-Fuks cohomology of polynomial vector fields with coefficients in differentiable $AV$-modules. Its complex is given by cochains that are differential operators in the sense of Grothendieck. Using the jets of vector fields, we compute this cohomology for varieties with uniformizing parameters. We

Ming Zhong, Zhizhi Wu, Nanako Honda

Dense retrievers have achieved state-of-the-art performance in various information retrieval tasks, but their robustness against tokenizer poisoning remains underexplored. In this work, we assess the vulnerability of dense retrieval systems to poisoned tokenizers by evaluating models such as BERT, Dense Passage Retrieval (DPR), Contriever, SimCSE, and ANCE.

Junhao Tan, Songwen Pei, Wei Qin, Bo Fu

Frequency information (e.g., Discrete Wavelet Transform and Fast Fourier Transform) has been widely applied to solve the issue of Low-Light Image Enhancement (LLIE). However, existing frequency-based models primarily operate in the simple wavelet or Fourier space of images, which lacks utilization of valid global and local information in each space. We found

Xinpeng Li, Vinooth Kulkarni, Daniel T. Chen, Qiang Guan

Current quantum devices face challenges when dealing with large circuits due to error rates as circuit size and the number of qubits increase. The circuit wire-cutting technique addresses this issue by breaking down a large circuit into smaller, more manageable subcircuits. However, the exponential increase in the number of subcircuits and the complexity of

Jing Zhang, Linjiajie Fang, Kexin Shi, Wenjia Wang

``Distribution shift'' is the main obstacle to the success of offline reinforcement learning. A learning policy may take actions beyond the behavior policy's knowledge, referred to as Out-of-Distribution (OOD) actions. The Q-values for these OOD actions can be easily overestimated. As a result, the learning policy is biased by using incorrect Q-value estimat

Yinan Sun, Sai Wang

Continuum manipulators have gained significant attention as a promising alternative to rigid manipulators, offering notable advantages in terms of flexibility and adaptability within intricate workspace. However, the broader application of high degree-of-freedom (DoF) continuum manipulators in intricate environments with multiple obstacles necessitates the d

Hwan Kim, Junghoon Kim, Sungsu Lim

While GNN-based detection methods excel at identifying overt outliers, they often struggle with boundary anomalies -- subtly camouflaged nodes that are difficult to distinguish from normal instances. This limitation highlights a fundamental gap in the reasoning capabilities of existing methods. We attribute this issue to the reliance of standard Graph Contra

Xiaofeng Lei, Yih-Chung Tham, Jocelyn Hui Lin Goh, Yangqin Feng

Community vision screening plays a crucial role in identifying individuals with vision loss and preventing avoidable blindness, particularly in rural communities where access to eye care services is limited. Currently, there is a pressing need for a simple and efficient process to screen and refer individuals with significant eye disease-related vision loss

Neda Bagherifard

Let $Y_1$ be a closed, oriented 3-manifold and $\Sigma$ denote a non-separating closed, orientable surface in $Y_1$ which consists of two connected components of the same genus. By cutting $Y_1$ along $\Sigma$ and re-gluing it using an orientation-preserving diffeomorphism of $\Sigma$ we obtain another closed, oriented 3-manifold $Y_2$. When the excision sur

Yusuke Sekikawa, Chingwei Hsu, Satoshi Ikehata, Rei Kawakami

We propose Gumbel-NeRF, a mixture-of-expert (MoE) neural radiance fields (NeRF) model with a hindsight expert selection mechanism for synthesizing novel views of unseen objects. Previous studies have shown that the MoE structure provides high-quality representations of a given large-scale scene consisting of many objects. However, we observe that such a MoE

Franklin Wang, Sumanth Hegde

Offline paired preference optimization algorithms have become a popular approach for fine-tuning on preference data, outperforming traditional supervised fine-tuning in various tasks. However, traditional implementations often involve redundant computations, especially for tasks with long shared prompts. We introduce prefix sharing for preference tuning, a n

Weiche Hsieh, Ziqian Bi, Junyu Liu, Benji Peng

Digital Signal Processing (DSP) and Digital Image Processing (DIP) with Machine Learning (ML) and Deep Learning (DL) are popular research areas in Computer Vision and related fields. We highlight transformative applications in image enhancement, filtering techniques, and pattern recognition. By integrating frameworks like the Discrete Fourier Transform (DFT)

Urmee Maitra, Ashish R. Hota, Philip E. Paré

We consider a susceptible-infected-susceptible (SIS) epidemic model in which a large group of individuals decide whether to adopt partially effective protection without being aware of their individual infection status. Each individual receives a signal which conveys noisy information about its infection state, and then decides its action to maximize its expe

Hao Zhou, Chengming Hu, Dun Yuan, Ye Yuan

Recently, large language models (LLMs) have been successfully applied to many fields, showing outstanding comprehension and reasoning capabilities. Despite their great potential, LLMs usually require dedicated pre-training and fine-tuning for domain-specific applications such as wireless networks. These adaptations can be extremely demanding for computationa

Kanan Mahammadli, Seyda Ertekin

This study introduces SLLMBO, an innovative framework leveraging large language models (LLMs) for hyperparameter optimization (HPO), incorporating dynamic search space adaptability, enhanced parameter space exploitation, and a novel LLM-tree-structured parzen estimator (LLM-TPE) sampler. By addressing limitations in recent fully LLM-based methods and traditi

Michael Iannelli

Conducting comprehensive information retrieval experiments, such as in search or retrieval augmented generation, often comes with high computational costs. This is because evaluating a retrieval algorithm requires indexing the entire corpus, which is significantly larger than the set of (query, result) pairs under evaluation. This issue is especially pronoun

Lucas Steinmetz, Shivam Maheshwari, Garik Kazanjian, Abigail Loyson

Traumatic brain injury (TBI) presents a significant public health challenge, often resulting in mortality or lasting disability. Predicting outcomes such as mortality and Functional Status Scale (FSS) scores can enhance treatment strategies and inform clinical decision-making. This study applies supervised machine learning (ML) methods to predict mortality a

Jiaxing Li, Chi Xu, Lianchen Jia, Feng Wang

Large language models (LLMs) have demonstrated impressive capabilities in language tasks, but they require high computing power and rely on static knowledge. To overcome these limitations, Retrieval-Augmented Generation (RAG) incorporates up-to-date external information into LLMs without extensive fine-tuning. Meanwhile, small language models (SLMs) deployed

Xinhong Xie, Tao Li, Quanyan Zhu

Text detoxification, a variant of style transfer tasks, finds useful applications in online social media. This work presents a fine-tuning method that only uses non-parallel data to turn large language models (LLM) into a detoxification rewritter. We model the fine-tuning process as a Stackelberg game between an LLM (leader) and a toxicity screener (follower

Daniel C. Ruiz, John Sell

In recent years, the widespread adoption of Large Language Models (LLMs) has sparked interest in their potential for application within the military domain. However, the current generation of LLMs demonstrate sub-optimal performance on Army use cases, due to the prevalence of domain-specific vocabulary and jargon. In order to fully leverage LLMs in-domain, m

Eivind Jørstad, Sabrina Pasterski

We revisit the extrapolate dictionary for massless scattering in flat spacetime and identify a soft contribution that is typically dropped from the saddle point approximation. We show how to consistently regulate the extrapolation to include both the soft and hard components and identify the boundary correlation functions as a combination of electric and mag

Xiaoxue Han, Huzefa Rangwala, Yue Ning

Graph Neural Networks (GNNs) are susceptible to distribution shifts, creating vulnerability and security issues in critical domains. There is a pressing need to enhance the generalizability of GNNs on out-of-distribution (OOD) test data. Existing methods that target learning an invariant (feature, structure)-label mapping often depend on oversimplified assum

Rawal Khirodkar, Jyun-Ting Song, Jinkun Cao, Zhengyi Luo

Understanding how humans interact with each other is key to building realistic multi-human virtual reality systems. This area remains relatively unexplored due to the lack of large-scale datasets. Recent datasets focusing on this issue mainly consist of activities captured entirely in controlled indoor environments with choreographed actions, significantly a

Byung-Geel Yu, Kook-Jin Kong, Tae Keun Choi

In the search for the pentaquark $Θ^+$ baryon, the world data on $K^+n\to K^+n$ and $K^+n\to K^0\,p$ reactions on the deuteron target and $K^0\,p\to K^+n$ and $K^0_L\,p\to K^0_S\,p$ reactions on the hydrogen target are revisited to study the isoscalar component of the scattering amplitudes. The determination of the $s$- and $p$-wave phase shifts for the two

Fumiaki Nakai, Takashi Uneyama, Yuto Sasaki, Kiwamu Yoshii

Atomic crystals with dislocations deform plastically at low stresses via dislocation glide. Whether dislocation glide occurs in macroscopic frictional granular media has remained unknown. The discrete element method is employed to simulate the structural and mechanical responses of a granular crystal with an edge dislocation. We find that dislocation glide o

Anubhab Majumder, Kausik Bhattacharya, Amaresh Chakrabarti

Representing systems using the SAPPhIRE causality model is found useful in supporting design-by-analogy. However, creating a SAPPhIRE model of artificial or biological systems is an effort-intensive process that requires human experts to source technical knowledge from multiple technical documents regarding how the system works. This research investigates ho

Mattia Moroder, Matteo Mitrano, Ulrich Schollwöck, Sebastian Paeckel

We introduce phonon state tomography (PST) as a diagnostic probe of electron dynamics in solids whose phonons are optically excited by a laser pulse at initial time. Using a projected-purified matrix-product states algorithm, PST decomposes the exact correlated electron-phonon wavefunction into contributions from purely electronic states corresponding to sta

Yunchong Liu, Xiaorui Shen, Yeyubei Zhang, Zhongyan Wang

Social media platforms like Twitter, Facebook, and Instagram have facilitated the spread of misinformation, necessitating automated detection systems. This systematic review evaluates 36 studies that apply machine learning (ML) and deep learning (DL) models to detect fake news, spam, and fake accounts on social media. Using the Prediction model Risk Of Bias

Beatriz Batista, Márcia Lima, Tayana Conte

Context: Requirements Engineering for AI-based systems (RE4AI) presents unique challenges due to the inherent volatility and complexity of AI technologies, necessitating the development of specialized methodologies. It is crucial to prepare upcoming software engineers with the abilities to specify high-quality requirements for AI-based systems. Goal: This re

Jeremy Conner, Vasilios I. Manousiouthakis

This work presents a novel, dimensionless model that results in a dimensionless algebraic equation that can be used to quantify the pressure drop associated with the steady state, isothermal flow through a straight, horizontal pipeline, of a compressible gas mixture whose thermodynamic behavior is described for comparison purposes by ideal gas (IG) and nonid

Gabriel Bujokas, Anand Patel

We introduce and motivate a conjecture about the existence of complete, 1-dimensional families of covers of an elliptic curve. If the conjecture holds, then it would imply a uniform lower bound of 5 for slope of the moduli space of curves. If the conjecture fails, this would itself be an interesting phenomenon worthy of explanation.

Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang

The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human preferences. Prevalent alignment techniques, such as DPO, PPO and their variants, align LLMs by changing the pre-trained model weights during a phase called post-training. While pre

Giacomo Petrillo

Bayesian Additive Regression Trees (BART) is a nonparametric Bayesian regression technique of rising fame. It is a sum-of-decision-trees model, and is in some sense the Bayesian version of boosting. In the limit of infinite trees, it becomes equivalent to Gaussian process (GP) regression. This limit is known but has not yet led to any useful analysis or appl

Luyao Niu, Hongchao Zhang, Dinuka Sahabandu, Bhaskar Ramasubramanian

Multi-agent cyber-physical systems are present in a variety of applications. Agent decision-making can be affected due to errors induced by uncertain, dynamic operating environments or due to incorrect actions taken by an agent. When an erroneous decision that leads to a violation of safety is identified, assigning responsibility to individual agents is a ke

Shrit Shah, Fatemeh Khoda Parast

This study introduces an innovative approach to automating Cyber Threat Intelligence (CTI) processes in industrial environments by leveraging Microsoft's AI-powered security technologies. Historically, CTI has heavily relied on manual methods for collecting, analyzing, and interpreting data from various sources such as threat feeds. This study introduces an

Mandeep Rathee, Sean MacAvaney, Avishek Anand

Building relevance models to rank documents based on user information needs is a central task in information retrieval and the NLP community. Beyond the direct ad-hoc search setting, many knowledge-intense tasks are powered by a first-stage retrieval stage for context selection, followed by a more involved task-specific model. However, most first-stage ranki

Antonis Antoniades, Albert Örwall, Kexun Zhang, Yuxi Xie

Software engineers operating in complex and dynamic environments must continuously adapt to evolving requirements, learn iteratively from experience, and reconsider their approaches based on new insights. However, current large language model (LLM)-based software agents often follow linear, sequential processes that prevent backtracking and exploration of al

David Benfield, Stefano Coniglio, Martin Kunc, Phan Tu Vuong

Adversarial machine learning concerns situations in which learners face attacks from active adversaries. Such scenarios arise in applications such as spam email filtering, malware detection and fake-image generation, where security methods must be actively updated to keep up with the ever improving generation of malicious data.We model these interactions bet

Abdelrahman Abdelwahab, Akshaj Vishnubhatla, Ayaan Vaswani, Advait Bharathulwar

Inaccuracies in polygraph tests often lead to wrongful convictions, false information, and bias, all of which have significant consequences for both legal and political systems. Recently, analyzing facial micro-expressions has emerged as a method for detecting deception; however, current models have not reached high accuracy and generalizability. The purpose

Zewen Zhang, Pranav Gokhale, Jeffrey M. Larson

Building on previous research on frequency allocation optimization for superconducting circuit quantum processors, this work incorporates several new techniques to improve overall solution quality. New features include tightening constraints, imposing edgewise differences, including edge orientation in the optimization, and integrating multimodule designs wi

Dakotah Tyler, Erik A. Petigura, James Rogers, Jack Lubin

We present an analysis of 126 new radial velocity measurements from the MAROON-X spectrograph to investigate the TOI-1266 system, which hosts two transiting sub-Neptunes at 10.8 and 18.8 days. We measure masses of $M_{b}=4.01~\pm~0.55~M_{\oplus}$ for TOI-1266 b and $M_{c}=2.00~\pm~0.72~M_{\oplus}$ for TOI-1266 c. Our mass measurements agree with existing HAR

Haoxin Liu

Link prediction is a fundamental task in graph analysis. Despite the success of various graph-based machine learning models for link prediction, there lacks a general understanding of different models. In this paper, we propose a unified framework for link prediction that covers matrix factorization and representative network embedding and graph neural netwo

Md Kamrul Hasan Chy

As online fraud becomes more sophisticated and pervasive, traditional fraud detection methods are struggling to keep pace with the evolving tactics employed by fraudsters. This paper explores the transformative role of machine learning in addressing these challenges by offering more advanced, scalable, and adaptable solutions for fraud detection and preventi

Shanglin Li, Motoaki Kawanabe, Reinmar J. Kobler

The non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neurotechnology generalization. Without labeled calibration data for target domains, the problem is a source-free unsupervised domain adaptation (SFUDA) problem. For scenarios with cons

Haozhe Liu, Shikun Liu, Zijian Zhou, Mengmeng Xu

We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework. Here, MAR handles temporal planning, while DM focuses on spatial generation in an asymmetric network design: i) a MAR-based planning model containing most of the parameters generates plannin

Xi Huang, Gergely Sóti, Hongyi Zhou, Christoph Ledermann

With the goal of efficiently computing collision-free robot motion trajectories in dynamically changing environments, we present results of a novel method for Heuristics Informed Robot Online Path Planning (HIRO). Dividing robot environments into static and dynamic elements, we use the static part for initializing a deterministic roadmap, which provides a lo

Tamara Ranković, Miloš Simić, Milan Stojkov, Goran Sladić

Proliferation of systems that generate enormous amounts of data and operate in real time has led researchers to rethink the current organization of the cloud. Many proposed solutions consist of a number of small data centers in the vicinity of data sources. That creates a highly complex environment, where strict access control is essential. Recommended acces

A. A. Melnikov, A. A. Sokolik, Yu. G. Selivanov, S. V. Chekalin

We report the observation of the electro-optic effect in Bi$_2$Se$_3$ crystals induced by an intense single-cycle terahertz pulse. The effect reveals itself as a transient change of the polarization state of a femtosecond laser pulse reflected from the crystal that is exposed to the terahertz electric field. The corresponding experimental signal follows the

Tamara Ranković, Ivana Kovačević, Veljko Maksimović, Goran Sladić

Owing to their cost-effectiveness and flexibility, cloud services have been the default choice for the deployment of innumerable software systems over the years. However, novel paradigms are beginning to emerge, as the cloud can't meet the requirements of increasingly many latency- and privacy-sensitive applications. The distributed cloud model, being one of

Ze Hu, Ziqing Zhu, Linghua Zhu, Xiang Wei

Alternative Current Optimal Power Flow (AC-OPF) is essential for efficient power system planning and real-time operation but remains an NP-hard and non-convex optimization problem with significant computational challenges. This paper proposes a novel hybrid classical-quantum deep learning framework for AC-OPF problem, integrating parameterized quantum circui

Ian Berlot-Attwell, Frank Rudzicz, Xujie Si

Advances in Large Language Models (LLMs) have spurred a wave of LLM library learning systems for mathematical reasoning. These systems aim to learn a reusable library of tools, such as formal Isabelle lemmas or Python programs that are tailored to a family of tasks. Many of these systems are inspired by the human structuring of knowledge into reusable and ex

Tamara Ranković, Filip Šiljić, Jovan Tomić, Goran Sladić

Major software failures are reported to be due to misconfiguration. As manual configuration is too error-prone to be deemed a reliable strategy for dynamic and complex systems, automated configuration management has become a standard. Countermeasures against misconfiguration can be focused on prevention or, if failure already occurred, detection. Configurati

Xi Huang, Gergely Sóti, Christoph Ledermann, Björn Hein

Path planning in a changing environment is a challenging task in robotics, as moving objects impose time-dependent constraints. Recent planning methods primarily focus on the spatial aspects, lacking the capability to directly incorporate time constraints. In this paper, we propose a method that leverages a generative model to decompose a complex planning pr

S. Parashar, W. M. Campbell, J. Bourhill, E. N. Ivanov

The coupling between acoustic vibrations in a lithium niobate bulk acoustic wave resonator and microwave photons of a re-entrant microwave cavity was investigated at a temperature close to 4 K. Coupling was achieved by placing the acoustic resonator in the location of the re-entrant cavity electric field maxima, in a symmetric "split-post" configuration, wit

Ichiro Oda

We perform the manifestly covariant quantization of $f(R)$ gravity in the de Donder gauge condition (or harmonic gauge condition) for general coordinate invariance. We explicitly calculate various equal-time commutation relations (ETCRs), in particlular, the ETCR between the metric and its time derivative, and show that it has a nonvanishing and nontrivial e