December 2023 arXiv papers — page 75

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

We present cross sections for the reaction $e^+e^-\to K_S^0K_L^0$ at center-of-mass energies ranging from 3.51 GeV to 4.95 GeV using data samples collected in the BESIII experiment, corresponding to a total integrated luminosity of 26.5 fb$^{-1}$. The ratio of neutral-to-charged kaon form factors at large momentum transfers ($12~{\rm GeV}^2<Q^2<25~{\rm GeV}^

Nuo Chen, Hongguang Li, Baoyuan Wang, Jia Li

This paper investigates the performance of Large Language Models (LLMs) and Tool-augmented LLMs in tackling complex mathematical reasoning tasks. We introduce IMP-TIP: Improving Math Reasoning with Tool-augmented Interleaf Prompting, a framework that combines the strengths of both LLMs and Tool-augmented LLMs. IMP-TIP follows the ``From Good to Great" concep

Jihong Ouyang, Zhiyao Yang, Silong Liang, Bing Wang

Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has received much attention recently. Many works investigate sentiment information through opinion words, such as ''good'' and ''bad''. However, implicit sentiment widely exists in the ABSA dataset, which refers to the sentence containing no distinct opinion words but still

Zhenyu Xie, Yang Wu, Xuehao Gao, Zhongqian Sun

Text-guided motion synthesis aims to generate 3D human motion that not only precisely reflects the textual description but reveals the motion details as much as possible. Pioneering methods explore the diffusion model for text-to-motion synthesis and obtain significant superiority. However, these methods conduct diffusion processes either on the raw data dis

Peng Shen, Xugang Lu, Hisashi Kawai

Multi-talker overlapped speech recognition remains a significant challenge, requiring not only speech recognition but also speaker diarization tasks to be addressed. In this paper, to better address these tasks, we first introduce speaker labels into an autoregressive transformer-based speech recognition model to support multi-speaker overlapped speech recog

Phuoc-Loc Tran, Shen-Ming Lee, Truong-Nhat Le, Chin-Shang Li

We consider logistic regression including two sets of discrete or categorical covariates that are missing at random (MAR) separately or simultaneously. We examine the asymptotic properties of two multiple imputation (MI) estimators, given in the study of Lee at al. (2023), for the parameters of the logistic regression model with both sets of discrete or cate

Ana M. Teixeira

We briefly overview some theoretical aspects of charged lepton flavour violation in rare muon transitions and decays. Relying on the effective field theory approach (model-independent), we discuss the probing power of charged lepton flavour violation processes regarding the new physics scale, especially in what concerns muon-electron conversion in nuclei. We

Dhanyamol Antony, Anita Das, Shirish Gosavi, Dalu Jacob

A bipartite graph $G=(A, B, E)$ is said to be a biconvex bipartite graph if there exist orderings $<_A$ in $A$ and $<_B$ in $B$ such that the neighbors of every vertex in $A$ are consecutive with respect to $<_B$ and the neighbors of every vertex in $B$ are consecutive with respect to $<_A$. A caterpillar is a tree that will result in a path upon deletion of

P. M. Marychev, Yajiang Chen

In this work we investigate the intertype (IT) domain in strongly disordered ferromagnetic superconductors with a Curie temperature lower than the superconducting critical temperature. In such unique materials, the coexistence of superconductivity and ferromagnetism allows for the exploration of both unconventional superconductivity and interplay between mag

Zhurun Ji, Yuzhou Zhao, Yicong Chen, Ziyan Zhu

Studies of moire systems have elucidated the exquisite effect of quantum geometry on the electronic bands and their properties, leading to the discovery of new correlated phases. However, most experimental studies have been confined to a few layers in the 2D limit. The extension of twistronics to its 3D limit, where the twist is extended into the third dimen

Syed Sabanam, Kangujam Priyokumar Singh

This paper has studied five-dimensional FRW cosmological models for k=-1,0,1in the presence of two perfect fluids namely ordinary baryonic fluid and a bizarre creating dark energy within the framework of Lyra's manifold (Lyra 1951). We have obtained the exact solution of the equation assuming the parametrization of the Hubble parameter and the relation betwe

Can Wan, Yupeng Ren, Ping Ju

The large-scale integration of intermittent renewable energy has brought serious challenges to the frequency security of power systems. In this paper, a novel nonparametric stochastic analysis method of system dynamic frequency is proposed to accurately analyze the impact of renewable energy uncertainty on power system frequency security, independent of any

Yuhao Zhang, Kaiqi Kou, Bei Li, Chen Xu

End-to-end Speech Translation (ST) aims to convert speech into target text within a unified model. The inherent differences between speech and text modalities often impede effective cross-modal and cross-lingual transfer. Existing methods typically employ hard alignment (H-Align) of individual speech and text segments, which can degrade textual representatio

Abhinav Thorat, Ravi Kolla, Niranjan Pedanekar, Naoyuki Onoe

We study the problem of estimation of Individual Treatment Effects (ITE) in the context of multiple treatments and networked observational data. Leveraging the network information, we aim to utilize hidden confounders that may not be directly accessible in the observed data, thereby enhancing the practical applicability of the strong ignorability assumption.

Joseph S. Schafer, Annie Denton, Chloe Seelhoff, Jordyn Vo

When designing multi-stakeholder privacy systems, it is important to consider how different groups of social media users have different goals and requirements for privacy. Additionally, we must acknowledge that it is important to keep in mind that even a single creator's needs can change as their online visibility and presence shifts, and that robust multi-s

Hanwen Yao, Waleed Abu Laban, Christian Häger, Alexandre Graell i Amat

Quantum low-density parity-check (QLDPC) codes have emerged as a promising technique for quantum error correction. A variety of decoders have been proposed for QLDPC codes and many of them utilize belief propagation (BP) decoding in some fashion. However, the use of BP decoding for degenerate QLDPC codes is known to have issues with convergence. These issues

David Hason Rudd, Huan Huo, Guandong Xu

Speech Emotion Recognition (SER) affective technology enables the intelligent embedded devices to interact with sensitivity. Similarly, call centre employees recognise customers' emotions from their pitch, energy, and tone of voice so as to modify their speech for a high-quality interaction with customers. This work explores, for the first time, the effects

Jinli Duan, Haoyu Ding, Sung Kim

This paper presents a novel multi modal deep learning framework for enhanced agricultural pest detection, combining tiny-BERT's natural language processing with R-CNN and ResNet-18's image processing. Addressing limitations of traditional CNN-based visual methods, this approach integrates textual context for more accurate pest identification. The R-CNN and R

Yimeng Bai, Yang Zhang, Jing Lu, Jianxin Chang

Short video recommendations often face limitations due to the quality of user feedback, which may not accurately depict user interests. To tackle this challenge, a new task has emerged: generating more dependable labels from original feedback. Existing label generation methods rely on manual rules, demanding substantial human effort and potentially misaligni

Juntao Hu, Yuan Wu

The most successful multi-domain text classification (MDTC) approaches employ the shared-private paradigm to facilitate the enhancement of domain-invariant features through domain-specific attributes. Additionally, they employ adversarial training to align marginal feature distributions. Nevertheless, these methodologies encounter two primary challenges: (1)

Bin-Bin Hu, Hai-Tao Zhang, Bin Liu, Jianing Ding

This paper proposes a distributed guiding-vector-field (DGVF) controller for cross-domain unmanned systems (CDUSs) consisting of heterogeneous unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs), to achieve coordinated navigation whereas maneuvering along their prescribed paths. In particular, the DGVF controller provides a hierarchical arch

Nilakshan Kunananthaseelan, Jing Zhang, Mehrtash Harandi

We introduce a language-grounded visual prompting method to adapt the visual encoder of vision-language models for downstream tasks. By capitalizing on language integration, we devise a parameter-efficient strategy to adjust the input of the visual encoder, eliminating the need to modify or add to the model's parameters. Due to this design choice, our algori

Omar S. M. El Nahhas, Marko van Treeck, Georg Wölflein, Michaela Unger

Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratiz

Zhihao Zhu, Chenwang Wu, Rui Fan, Yi Yang

Recent research demonstrates that GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. However, they mainly focus on node classification tasks, neglecting the potential threats entailed within the domain of graph classification tasks. Furthermore, their practicality is quest

Wei-Yao Wang, Wen-Chih Peng, Wei Wang, Philip S. Yu

Agent forecasting systems have been explored to investigate agent patterns and improve decision-making in various domains, e.g., pedestrian predictions and marketing bidding. Badminton represents a fascinating example of a multifaceted turn-based sport, requiring both sophisticated tactic developments and alternate-dependent decision-making. Recent deep lear

Ana Isabel Garcia Guerra, Teng Sung Shiuan, Paul Hibbard, Yap Jing Yew

A Technical Reference for Autonomous Vehicles (AVs), with part 1 focusing on basic behaviour guidelines (TR68-1) is published with the intent to be a reference for evaluation of appropriated behaviour on Autonomous Vehicles for Singapore. This is based on applicability from Basic Theory of Driving (BTD) and Final Theory of Driving (FTD) which are the traffic

Bing Xiao, Hang Wong, Kam Man Shum

An isotropic antenna radiates and receives electromagnetic wave uniformly in magnitude in 3D space. A multi-frequency quasi-isotropic antenna can serve as a practically feasible solution to emulate an ideal multi-frequency isotropic radiator. It is also an essential technology for mobile smart devices for massive IoT in the upcoming 6G. However, ever since t

Man-Chun Lee, Luen-Fai Tam, Jingbo Wan

In this work, we consider the area non-increasing map between manifolds with positive curvature. By exploring the strong maximum principle along the graphical mean curvature flow, we show that an area non-increasing map between certain positively curved manifolds is either homotopy trivial, Riemannian submersion, local isometry or isometric immersion. This i

Zhihao Zhu, Rui Fan, Chenwang Wu, Yi Yang

Recent studies have demonstrated the vulnerability of recommender systems to data privacy attacks. However, research on the threat to model privacy in recommender systems, such as model stealing attacks, is still in its infancy. Some adversarial attacks have achieved model stealing attacks against recommender systems, to some extent, by collecting abundant t

Yongqiang Liu, Wentao Xie

In this paper, we study the first homology group of finite cyclic covering of complex line arrangement complement. We show that this first integral homology group is torsion-free under certain condition similar to the one used by Cohen-Dimca-Orlik. In particular, this includes the case of the Milnor fiber, which generalizes the previous results obtained by W

S. C. Hou, G. Q. Shuai, X. Y. Zhang, J. Shen

The initial state of a quantum system can significantly influence its future dynamics, especially in non-Markovain quantum processes due to the environmental memory effects. Based on a previous work of ours, we propose a method to quantify the memory effects of a non-Markovian quantum process conditioned on a particular system initial state. We apply our met

David Hason Rudd, Huan Huo, Guandong Xu

Emotion recognition (ER) from speech signals is a robust approach since it cannot be imitated like facial expression or text based sentiment analysis. Valuable information underlying the emotions are significant for human-computer interactions enabling intelligent machines to interact with sensitivity in the real world. Previous ER studies through speech sig

Amna Batool, Didar Zowghi, Muneera Bano

As artificial intelligence transforms a wide range of sectors and drives innovation, it also introduces complex challenges concerning ethics, transparency, bias, and fairness. The imperative for integrating Responsible AI (RAI) principles within governance frameworks is paramount to mitigate these emerging risks. While there are many solutions for AI governa

Francesca Gandini, Shubhra Mishra, Douglas Shaw

A Harris Graph is a tough, Eulerian, non-Hamiltonian graph. Several approaches to creating new Harris graphs from existing ones are explored, including creating families of Harris graphs and combining Harris graphs. Pictures of all Harris Graphs through order 9 and the number of Harris graphs through order 12 are included. We also prove a result about barnac

Ji Liu, Tianshi Che, Yang Zhou, Ruoming Jin

Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exchanges between devices and the centralized server in the standard FL paradigm suffer from severe efficiency bottlenecks on the server. While enabling collaborative training without

Chengran Yang, Jiakun Liu, Bowen Xu, Christoph Treude

API documentation is often the most trusted resource for programming. Many approaches have been proposed to augment API documentation by summarizing complementary information from external resources such as Stack Overflow. Existing extractive-based summarization approaches excel in producing faithful summaries that accurately represent the source content wit

Sourajit Saha, Shubhashis Roy Dipta

Semantic Segmentation is a significant research field in Computer Vision. Despite being a widely studied subject area, many visualization tools do not exist that capture segmentation quality and dataset statistics such as a class imbalance in the same view. While the significance of discovering and introspecting the correlation between dataset statistics and

Chong Zhang

Take $S \subset \mathrm{SL}_2(\mathbb{Z}) \times \mathrm{SL}_2(\mathbb{Z})\times \mathrm{SL}_2(\mathbb{Z})$ be finite symmetric and assume $S$ generates a group $G$ which is Zariski-dense in $\mathrm{SL}_2 \times \mathrm{SL}_2\times \mathrm{SL}_2(\mathbb{Z})$. This paper proves that the Cayley graphs $$ \{\mathcal{C} a y(G(\bmod q), S(\bmod q))\}_{q \in \mat

Fangqing Chen

This paper uses clustering algorithms to introduce a shape framework for deformable objects. Until now, the shape detection of the deformable objects has faced several challenges: 1) unable to form a unified framework for multiple shapes; 2) the calculation burden as a large number of calculations; 3) the inability to solve the 3D point-cloud case. A novel s

M. M. McKinnon

A statistical model is used to determine how stochastic fluctuations in the intensities of orthogonal polarization modes contribute to the modulation and depolarization of pulsar radio emission. General expressions for the distributions of the Stokes parameters, linear polarization, polarization position angle, and fractional polarization are derived when th

Shuailei Ma, Chen-Wei Xie, Ying Wei, Siyang Sun

Prompt learning has emerged as an efficient alternative for fine-tuning foundational models, such as CLIP, for various downstream tasks. However, there is no work that provides a comprehensive explanation for the working mechanism of the multi-modal prompts. In this paper, we conduct a direct analysis of the multi-modal prompts by asking the following questi

David Ahmedt-Aristizabal, Mohammad Ali Armin, Zeeshan Hayder, Norberto Garcia-Cairasco

Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded

Xiaoguang Wang, Fei Yang

Let $\theta$ be an irrational number of bounded type. We prove that all capture components in the parameter space of cubic polynomials $f_a(z)=e^{2\pi i\theta}z+a z^2+z^3$, where $a\in\mathbb{C}$, are Jordan domains.

Ionel-Dumitrel Ghiba, Peter Lewintan, Adam Sky, Patrizio Neff

It has become commonplace for the stored energy function of any realistic shell model to align ``within first order" with the classical Koiter membrane-bending (flexural) shell model. In this paper, we assess whether certain extended Cosserat shell models are consistent with the classical linear Koiter model. In doing this, we observe that there are numerous

Ritu Ramakrishnan, Tianxiang Xing, Tianfeng Chen, Ming-Hao Lee

In healthcare, artificial intelligence (AI) has been changing the way doctors and health experts take care of people. This paper will cover how AI is making major changes in the health care system, especially with nutrition. Various machine learning and deep learning algorithms have been developed to extract valuable information from healthcare data which he

Minoru Eto, Kentaro Nishimura, Muneto Nitta

The ground state of QCD with two flavors at a finite baryon chemical potential under rapid rotation is a chiral soliton lattice (CSL) of the $\eta$ meson, consisting of a stack of sine-Gordon solitons carrying a baryon number, due to the anomalous coupling of the $\eta$ meson to the rotation. In a large parameter region, the ground state becomes a non-Abelia

Wenhao Cao, Saonli Basu

Recent advances in genotyping technology have delivered a wealth of genetic data, which is rapidly advancing our understanding of the underlying genetic architecture of complex diseases. Mendelian Randomization (MR) leverages such genetic data to estimate the causal effect of an exposure factor on an outcome from observational studies. In this paper, we util

Md Zesun Ahmed Mia, Malyaban Bal, Abhronil Sengupta

Preliminary attempts at incorporating the critical role of astrocytes - cells that constitute more than 50\% of human brain cells - in brain-inspired neuromorphic computing remain in infancy. This paper seeks to delve deeper into various key aspects of neuron-synapse-astrocyte interactions to mimic self-attention mechanisms in Transformers. The cross-layer p

Francisco X. Linares Cedeño, Gabriel Germán, Juan Carlos Hidalgo

We study a generalization of the the Starobinsky model adding a term of the form $R^{2p}$ to the Einstien-Hilbert action. We take the power $p$ as a parameter of the model and explore the constraints from CMB plus BAO data through a Bayesian analysis, thus exploring a range of values for the exponent parameter. We incorporate a reheating phase to the model t

Massoud Amini, Mahdi Moosazadeh

Let $X$ be a product system over a quasi-lattice ordered groupoid $(G,P)$. Under mild hypotheses, we associate to $X$ a $C^*$-algebra which is couniversal for injective Nica covariant Toeplitz representations of $X$ which preserve the gauge coaction. When $(G,P)$ is a quasi-lattice ordered group this couniversal $C^*$-algebra coincides with the Cuntz-Nica-Pi

Md Ahmed Al Muzaddid, William J. Beksi

In agriculture, automating the accurate tracking of fruits, vegetables, and fiber is a very tough problem. The issue becomes extremely challenging in dynamic field environments. Yet, this information is critical for making day-to-day agricultural decisions, assisting breeding programs, and much more. To tackle this dilemma, we introduce NTrack, a novel multi

Dongze Li, Kang Zhao, Wei Wang, Bo Peng

Audio-driven talking head synthesis is a promising topic with wide applications in digital human, film making and virtual reality. Recent NeRF-based approaches have shown superiority in quality and fidelity compared to previous studies. However, when it comes to few-shot talking head generation, a practical scenario where only few seconds of talking video is

Zhaozhong Yang, Liangjie Gou, Chao Min, Duo Yi

Effective prediction of shale gas production is crucial for strategic reservoir development. However, in new shale gas blocks, two main challenges are encountered: (1) the occurrence of negative transfer due to insufficient data, and (2) the limited interpretability of deep learning (DL) models. To tackle these problems, we propose a novel transfer learning

Chiu Chun Chan, Sheeraz A. Alvi, Xiangyun Zhou, Salman Durrani

The threat posed by wildfires or bushfires has become a severe global issue due to the increase in human activities in forested areas and the impact of climate change. Consequently, there is a surge in the development of automatic wildfire detection methods. Approaches based on long-distance imagery from satellites or watchtowers encounter limitations, such

Jorge Ocampo Jimenez, Wael Suleiman

The representation of a Configuration Space C plays a vital role in accelerating the finding of a collision-free path for sampling-based motion planners where the majority of computation time is spent in collision checking of states. Traditionally, planners evaluate C's representations through limited evaluations of collision-free paths using the collision c

Guangjie Zeng, Hao Peng, Angsheng Li, Zhiwei Liu

Semi-supervised clustering techniques have emerged as valuable tools for leveraging prior information in the form of constraints to improve the quality of clustering outcomes. Despite the proliferation of such methods, the ability to seamlessly integrate various types of constraints remains limited. While structural entropy has proven to be a powerful cluste

Hirotaka Kobayashi

We obtain asymptotic formulae for the second discrete moments of the Riemann zeta function over arithmetic progressions $\frac{1}{2} + i(a n + b)$. It reveals noticeable relation between the discrete moments and the continuous moment of the Riemann zeta function. Especially, when $a$ is a positive integer, main terms of the formula are equal to those for the

Zahra Ghasemi, Mehdi Nesht, Chris Aldrich, John Karageorgos

Semi-autogenous grinding (SAG) mills play a pivotal role in the grinding circuit of mineral processing plants. Accurate prediction of SAG mill throughput as a crucial performance metric is of utmost importance. The potential of applying genetic programming (GP) for this purpose has yet to be thoroughly investigated. This study introduces an enhanced GP appro

Tuowei Chen, Jiequan Li

In this paper, we introduce a hyperbolic model for entropy dissipative system of viscous conservation laws via a flux relaxation approach. We develop numerical schemes for the resulting hyperbolic relaxation system by employing the finite-volume methodology used in the community of hyperbolic conservation laws, e.g., the generalized Riemann problem method. F

Ta Sheng Tan, Wen Chean Teh

Graph burning is a natural discrete graph algorithm inspired by the spread of social contagion. Despite its simplicity, some open problems remain steadfastly unsolved, notably the burning number conjecture, which says that every connected graph of order $m^2$ has burning number at most $m$. Earlier, we showed that the conjecture also holds for a path forest,

Nisal Ranasinghe, Damith Senanayake, Sachith Seneviratne, Malin Premaratne

Traditional machine learning is generally treated as a black-box optimization problem and does not typically produce interpretable functions that connect inputs and outputs. However, the ability to discover such interpretable functions is desirable. In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the

C. Huang, M. Mootz, L. Luo, D. Cheng

Nonlinearities in quantum systems are fundamentally characterized by the interplay of phase coherences, their interference, and state transition amplitudes. Yet the question of how quantum coherence and interference manifest in transient, massive Higgs excitations, prevalent within both the quantum vacuum and superconductors, remains elusive. One hallmark ex

Yacine Izza, Joao Marques-Silva

Robustness is widely regarded as a fundamental problem in the analysis of machine learning (ML) models. Most often robustness equates with deciding the non-existence of adversarial examples, where adversarial examples denote situations where small changes on some inputs cause a change in the prediction. The perceived importance of ML model robustness explain

Eve Y. Y. Yang

The paper aims to establish the Tur\'an inequalities, the Laguerre inequalities (order $2$), and the determinantal inequalities (order $3$) for $\Delta p(n)$ and $\Delta \bar{p}(n)$, where $\Delta f(n)$ is the first-order forward difference of a sequence $f(n)$. The functions $p(n)$ and $\bar{p}(n)$ denote the partition function and overpartition function, r

Zhi Gao, Yuntao Du, Xintong Zhang, Xiaojian Ma

Utilizing large language models (LLMs) to compose off-the-shelf visual tools represents a promising avenue of research for developing robust visual assistants capable of addressing diverse visual tasks. However, these methods often overlook the potential for continual learning, typically by freezing the utilized tools, thus limiting their adaptation to envir

Tuowei Chen, Qiangchang Ju

In this paper, we study the global existence and low Mach number limit of strong solutions to the 2-D full compressible Navier-Stokes equations around the plane Couette flow in a horizontally periodic layer with non-slip and isothermal boundary conditions. It is shown that the plane Couette flow is asymptotically stable for sufficiently small initial perturb

Jinwen Hu, Huan Hu

In order to characterize the common feature of the general Lorentz violation models that the local speed of light is variable at ultrahigh energy scale, we introduced a parameter n to characterize the variation of the speed of light between inertial systems. And in order not to violate some fundamental principles and experiments' results, some constraints we

Grace Zhang

An application of optimal control theory results in a lower bound on the speed $|\dot{\lambda}(t)|$ that must be attained at least once by any external forcing function that induces tipping in the asymptotically autonomous scalar ODE $\dot{x} = f(x+\lambda(t))$. The value of this critical speed depends on the total arclength $\int_{-\infty}^{\infty} |\dot{\l

Grant Rosario, David Noever

With the growing capabilities of modern object detection networks and datasets to train them, it has gotten more straightforward and, importantly, less laborious to get up and running with a model that is quite adept at detecting any number of various objects. However, while image datasets for object detection have grown and continue to proliferate (the curr

Sabrina Toro, Anna V Anagnostopoulos, Sue Bello, Kai Blumberg

Background: Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curator

Jun Zhuang, Mohammad Al Hasan

Node representation learning by using Graph Neural Networks (GNNs) has been widely explored. However, in recent years, compelling evidence has revealed that GNN-based node representation learning can be substantially deteriorated by delicately-crafted perturbations in a graph structure. To learn robust node representation in the presence of perturbations, va

Ziqian Li, Tanay Roy, Yao Lu, Eliot Kapit

Reservoir engineering is a powerful technique to autonomously stabilize a quantum state. Traditional schemes involving multi-body states typically function for discrete entangled states. In this work, we enhance the stabilization capability to a continuous manifold of states with programmable stabilized state selection using multiple continuous tuning parame

Ovchenkov Y. A., Chareev D. A., Gippius A. A., Presnov D. E.

In this work, we present the studies of structural phase transitions in Fe(Se,Te) crystals in the range of about 30% selenium substitution by tellurium. We found a significant change in the properties of the ordered state of these compositions compared to the case of pure FeSe. The resistivity at low temperatures for the studied Fe(Se,Te) is proportional to

Yemin Yu, Luotian Yuan, Ying Wei, Hanyu Gao

Machine learning-assisted retrosynthesis prediction models have been gaining widespread adoption, though their performances oftentimes degrade significantly when deployed in real-world applications embracing out-of-distribution (OOD) molecules or reactions. Despite steady progress on standard benchmarks, our understanding of existing retrosynthesis predictio

Bingyuan Wang, Hengyu Meng, Zeyu Cai, Lanjiong Li

Visual storytelling often uses nontypical aspect-ratio images like scroll paintings, comic strips, and panoramas to create an expressive and compelling narrative. While generative AI has achieved great success and shown the potential to reshape the creative industry, it remains a challenge to generate coherent and engaging content with arbitrary size and con

Yifei Qi, Longqun Ni, Zhenyu Ye, Jiaojiao Zhang

Spin glass theory, as a paradigm for describing disordered magnetic systems, constitutes a prominent subject of study within statistical physics. Replica symmetry breaking (RSB), as one of the pivotal concepts for the understanding of spin glass theory, means that, under identical conditions disordered systems can yield distinct states with nontrivial correl

Ratul Ali, Aktarul Islam, Md. Shohel Rana, Saila Nasrin

Acoustic data serves as a fundamental cornerstone in advancing scientific and engineering understanding across diverse disciplines, spanning biology, communications, and ocean and Earth science. This inquiry meticulously explores recent advancements and transformative potential within the domain of acoustics, specifically focusing on machine learning (ML) an

Wenbin An, Wenkai Shi, Feng Tian, Haonan Lin

Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only known categories. Due to the lack of supervision and category information, current methods usually perform poorly on novel categories and struggle to reveal semantic meanings of t

Roy Long

The Market for Lemons is a classic model of asymmetric information first studied by Nobel Prize economist George Akerlof. It shows that information asymmetry between the seller and buyer may result in market collapse or some sellers leaving the market. "Lemons" in the used car market are cars of poor quality. The information asymmetry present is that the buy

Jianan Wang, Chuixiong Wu, Fen Zuo

A cut of a graph can be represented in many different ways. Here we propose to represent a cut through a ``relation tree'', which is a spanning tree with signed edges. We show that this picture helps to classify the main greedy heuristics for the maximum cut problem, in analogy with the minimum spanning tree problem. Namely, all versions of the Sahni-Gonzale

Dongyan Huo, Yudong Chen, Qiaomin Xie

In this paper, we study the effectiveness of using a constant stepsize in statistical inference via linear stochastic approximation (LSA) algorithms with Markovian data. After establishing a Central Limit Theorem (CLT), we outline an inference procedure that uses averaged LSA iterates to construct confidence intervals (CIs). Our procedure leverages the fast

Lev Tankelevitch, Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott

Generative AI (GenAI) systems offer unprecedented opportunities for transforming professional and personal work, yet present challenges around prompting, evaluating and relying on outputs, and optimizing workflows. We argue that metacognition$\unicode{x2013}$the psychological ability to monitor and control one's thoughts and behavior$\unicode{x2013}$offers a

Yanting Yang, Yiren Zhang, Zongyu Li, Jeffery Siyuan Tian

Deep complex-valued neural networks (CVNNs) provide a powerful way to leverage complex number operations and representations and have succeeded in several phase-based applications. However, previous networks have not fully explored the impact of complex-valued networks in the frequency domain. Here, we introduce a unified complex-valued deep learning framewo

Xuehua Li, Cairong Chen, Deren Han

By incorporating a new matrix splitting and the momentum acceleration into the relaxed-based matrix splitting (RMS) method \cite{soso2023}, a generalization of the RMS (GRMS) iterative method for solving the generalized absolute value equations (GAVEs) is proposed. On the one hand, unlike some existing methods, by using the Cauchy's convergence principle we

Ruian He, Shili Zhou, Yuqi Sun, Ri Cheng

With the rise of real-time rendering and the evolution of display devices, there is a growing demand for post-processing methods that offer high-resolution content in a high frame rate. Existing techniques often suffer from quality and latency issues due to the disjointed treatment of frame supersampling and extrapolation. In this paper, we recognize the sha

Alex Zheng

I investigate bias in terms of ChatGPT's college major recommendations for students with various profiles, looking at demographic disparities in factors such as race, gender, and socioeconomic status, as well as educational disparities such as score percentiles. By constructing prompts for the ChatGPT API, allowing the model to recommend majors based on high

Feihu Liu

We propose a class of generating functions denoted by $\textrm{RGF}_p(x)$, which is related to the Sylvester denumerant for the quotients of numerical semigroups. Using MacMahon's partition analysis, we can obtain $\textrm{RGF}_p(x)$ by extracting the constant term of a rational function. We use $\textrm{RGF}_p(x)$ to give a system of generators of the quoti

Fangming Zhao, Nikolaos Pappas, Chuan Ma, Xinghua Sun

We optimize the Age of Information (AoI) in mobile networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reache

Marcus Gozon, Jingjin Yu

In the $15$-puzzle game, $15$ labeled square tiles are reconfigured on a $4\times 4$ board through an escort, wherein each (time) step, a single tile neighboring it may slide into it, leaving the space previously occupied by the tile as the new escort. We study a generalized sliding-tile puzzle (GSTP) in which (1) there are $1+$ escorts and (2) multiple tile

Shingo Haruna, Takuji Nomura, Hirono Kaneyasu

We explore the pairing state and gap structure of UTe$_2$ using a six-orbital model which we call the $f$-$d$-$p$ model. Our model accurately reproduces the quasi-two-dimensional Fermi surfaces consistent with recent de Haas-van Alphen oscillation measurements and the $(0, \pm \pi, 0)$ antiferromagnetic spin fluctuations observed by neutron scattering. We in

Yong Niu, Xing Xing, Zhichun Jia, Ruidi Liu

Sequential recommendation aims to infer user preferences from historical interaction sequences and predict the next item that users may be interested in the future. The current mainstream design approach is to represent items as fixed vectors, capturing the underlying relationships between items and user preferences based on the order of interactions. Howeve

David Cole, Himanshu Sharma, Wei Wang

We propose a framework for applying reinforcement learning to contextual two-stage stochastic optimization and apply this framework to the problem of energy market bidding of an off-shore wind farm. Reinforcement learning could potentially be used to learn close to optimal solutions for first stage variables of a two-stage stochastic program under different

Masaaki Fukasawa, Takashi Sato, Jun Sekine

We study backward stochastic difference equations (BS{\Delta}E) driven by a d-dimensional stochastic process on a lattice whose increments have only d + 1 possible values that generates the lattice. Regarding the driving process as a d dimensional asset price process, we give applications to an optimal investment problem and a market equilibrium analysis, wh

Mikihiro Fujii

In this paper, we consider the inhomogeneous Dirichlet boundary value problem for the stationary Navier--Stokes equations in $n$-dimensional half spaces $\mathbb{R}^n_+= \{ x=(x',x_n)\ ;\ x' \in \mathbb{R}^{n-1}, x_n > 0 \}$ with $n \geq 3$ and prove the well-posedness in the scaling critical Besov spaces. Our approach is to regard the system as an evolution

Nicolas Delporte, Saswato Sen, Reiko Toriumi

The study of matter fields on an ensemble of random geometries is a difficult problem still in need of new methods and ideas. We will follow a point of view inspired by probability theory techniques that relies on an expansion of the two point function as a sum over random walks. An analogous expansion for Fermions on non-Euclidean geometries is still lackin

Sanghoon Oh, Qi Chen, H. Eric Tseng, Gaurav Pandey

A continuous motion planning method for connected automated vehicles is considered for generating feasible trajectories in real-time using three consecutive clothoids. The proposed method reduces path planning to a small set of nonlinear algebraic equations such that the generated path can be efficiently checked for feasibility and collision. After path plan

Reek Majumder, Jacquan Pollard, M Sabbir Salek, David Werth

The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affect

Mikihiro Fujii

Let us consider the incompressible Navier--Stokes equations with the time-periodic external forces in the whole space $\mathbb{R}^n$ with $n\geq 2$ and investigate the existence and non-existence of time-periodic solutions. In the higher dimensional case $n \geq 3$, we construct a unique small solution for given small time-periodic force in the scaling criti

Hao Hu, Xuan Wang, Jingxiang Sun, Yanbo Fan

High-fidelity and efficient audio-driven talking head generation has been a key research topic in computer graphics and computer vision. In this work, we study vector image based audio-driven talking head generation. Compared with directly animating the raster image that most widely used in existing works, vector image enjoys its excellent scalability being

Hui Fu, Zeqing Wang, Ke Gong, Keze Wang

Speech-driven 3D facial animation aims to synthesize vivid facial animations that accurately synchronize with speech and match the unique speaking style. However, existing works primarily focus on achieving precise lip synchronization while neglecting to model the subject-specific speaking style, often resulting in unrealistic facial animations. To the best