March 2024 arXiv papers — page 61

Rui Xu, Yue Sun, Chao Chen, Parv Venkitasubramaniam

Uncertainty is critical to reliable decision-making with machine learning. Conformal prediction (CP) handles uncertainty by predicting a set on a test input, hoping the set to cover the true label with at least $(1-\alpha)$ confidence. This coverage can be guaranteed on test data even if the marginal distributions $P_X$ differ between calibration and test da

Caio O. da Silva

In the present work we studied a subfield of Applied Mathematics called Riemannian Optimization. The main goal of this subfield is to generalize algorithms, theorems and tools from Mathematical Optimization to the case in which the optimization problem is defined on a Riemannian manifold. As a case study, we implemented some of the main algorithms described

Michaël Fanuel, Antoine Aspeel, Michael T. Schaub, Jean-Charles Delvenne

Due to their flexibility to represent almost any kind of relational data, graph-based models have enjoyed a tremendous success over the past decades. While graphs are inherently only combinatorial objects, however, many prominent analysis tools are based on the algebraic representation of graphs via matrices such as the graph Laplacian, or on associated grap

Tausifa Jan Saleem, Ramanjit Ahuja, Surendra Prasad, Brejesh Lall

Lottery ticket hypothesis for deep neural networks emphasizes the importance of initialization used to re-train the sparser networks obtained using the iterative magnitude pruning process. An explanation for why the specific initialization proposed by the lottery ticket hypothesis tends to work better in terms of generalization (and training) performance has

Xiaoqing Zhang, Xiuying Chen, Shen Gao, Shuqi Li

Information-seeking dialogue systems are widely used in e-commerce systems, with answers that must be tailored to fit the specific settings of the online system. Given the user query, the information-seeking dialogue systems first retrieve a subset of response candidates, then further select the best response from the candidate set through re-ranking. Curren

Adela Krylova, Roman Makarov, Sergei Pasynkov, Yegor Bugayenko

In traditional management, tasks are typically assigned to individuals, with each worker taking full responsibility for the success or failure of a task. In contrast, modern Agile, Lean, and eXtreme Programming practices advocate for shared responsibility, where an entire group is accountable for the outcome of a project or task. Despite numerous studies in

Jingyuan Cui, Kai-Xuan Zhang, Je-Geun Park

Magnetic van der Waals (vdW) materials have attracted massive attention because of their academic interest and application potential for the past few years. Its main advantage is the intrinsic two-dimensionality, enabling much smaller devices of novel concepts. One particular exciting direction lies in the current-driven spin-orbit torque (SOT). Here, we, fo

Jiahao Lu, Jiacheng Deng, Tianzhu Zhang

3D instance segmentation (3DIS) is a crucial task, but point-level annotations are tedious in fully supervised settings. Thus, using bounding boxes (bboxes) as annotations has shown great potential. The current mainstream approach is a two-step process, involving the generation of pseudo-labels from box annotations and the training of a 3DIS network with the

Xin Fang, Ghislain Fourier, Lars Göttgens, Ben Wilop

In this survey, we present a detailed guide on using the computer algebra system OSCAR to compute monomial bases for simple, finite-dimensional modules of simple, complex Lie algebras. We will also demonstrate how to determine monomial bases for the homogeneous coordinate ring of a (partial) flag variety, depending on a chosen birational sequence and a monom

Hamam Mokayed, Rajkumar Saini, Oluwatosin Adewumi, Lama Alkhaled

This paper addresses the critical challenge of vehicle detection in the harsh winter conditions in the Nordic regions, characterized by heavy snowfall, reduced visibility, and low lighting. Due to their susceptibility to environmental distortions and occlusions, traditional vehicle detection methods have struggled in these adverse conditions. The advanced pr

ATLAS Collaboration

A search for heavy Majorana neutrinos in scattering of same-sign $W$ boson pairs in proton-proton collisions at $\sqrt{s}= 13$ TeV at the LHC is reported. The dataset used corresponds to an integrated luminosity of 140 fb$^{-1}$, collected with the ATLAS detector during 2015-2018. The search is performed in final states including a same-sign $ee$ or $e\mu$ p

Toshikazu Abe, Osamu Hatori

We revise a proof of a Mazur-Ulam theorem for generalized gyrovector spaces.

Jigme Zangpo, Hirokazu Kobayashi

Microscopy is an essential tool in imaging research, and the edge-enhanced microscope by using the vortex filter is of particular interest as an optical information processing that highlights amplitude and phase edges of object in all directions. The application of this technique is not limited to the visible range, but edge enhancement of object in invisibl

Minsuk Chang, Seokhyeon Park, Hyeon Jeon, Aeri Cho

In image classification, a significant problem arises from bias in the datasets. When it contains only specific types of images, the classifier begins to rely on shortcuts - simplistic and erroneous rules for decision-making. This leads to high performance on the training dataset but inferior results on new, varied images, as the classifier's generalization

Tuija Leinonen, David Wong, Antti Vasankari, Ali Wahab

Traditionally, machine learning-based clinical prediction models have been trained and evaluated on patient data from a single source, such as a hospital. Cross-validation methods can be used to estimate the accuracy of such models on new patients originating from the same source, by repeated random splitting of the data. However, such estimates tend to be h

Timo Kaiser, Maximilian Schier, Bodo Rosenhahn

Cell tracking and segmentation assist biologists in extracting insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches often suffer from a lack of long-term consistency and the ability to reconstruct lineage trees correctly. To address this issue, we introduce an uncertainty estimation technique for

Dazhong Rong, Guoyao Yu, Shuheng Shen, Xinyi Fu

To gather a significant quantity of annotated training data for high-performance image classification models, numerous companies opt to enlist third-party providers to label their unlabeled data. This practice is widely regarded as secure, even in cases where some annotated errors occur, as the impact of these minor inaccuracies on the final performance of t

Jinbo Wu, Xing Liu, Chenming Wu, Xiaobo Gao

This paper presents TexRO, a novel method for generating delicate textures of a known 3D mesh by optimizing its UV texture. The key contributions are two-fold. We propose an optimal viewpoint selection strategy, that finds the most miniature set of viewpoints covering all the faces of a mesh. Our viewpoint selection strategy guarantees the completeness of a

Zhiqiang Yan, Yuankai Lin, Kun Wang, Yupeng Zheng

Depth completion is a vital task for autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. However, most existing methods either rely only on 2D depth representations or directly incorporate raw 3D point clouds for compensation, which are still insufficient to capture the fine-grained 3

Jacobus Dijkman, Marjolein Dijkstra, René van Roij, Max Welling

The intrinsic Helmholtz free-energy functional, the centerpiece of classical density functional theory, is at best only known approximately for 3D systems. Here we introduce a method for learning a neuralnetwork approximation of this functional by exclusively training on a dataset of radial distribution functions, circumventing the need to sample costly hete

H. Dawson, S. Geier, U. Heber, I. Pelisoli

We present the first volume-limited sample of spectroscopically confirmed hot subluminous stars out to 500 pc, defined using the accurate parallax measurements from the {\em Gaia} space mission data release 3 (DR3). The sample comprises a total of 397 members, with 305 ($\sim 77\%$) identified as hot subdwarf stars, including 83 newly discovered systems. Of

Giuseppe Cannizzaro, Fabio Toninelli

The goal of these lecture notes is to present recent results regarding the large-scale behaviour of critical and super-critical non-linear stochastic PDEs, that fall outside the realm of the theory of Regularity Structures. These include the two-dimensional Anisotropic KPZ equation, the stochastic Burgers equation in dimension $d\ge 2$ and the stochastic Nav

G. P. Veldes, N. Lazarides, D. J. Frantzeskakis, I. Kourakis

The interaction between two co-propagating electromagnetic pulses in a magnetized plasma is considered, from first principles, relying on a fluid-Maxwell model. Two circularly polarized wavepackets by same group velocities are considered, characterized by opposite circular polarization, to be identified as left-hand- or right hand circularly polarized (i.e.

Novendra Setyawan, Ghufron Wahyu Kurniawan, Chi-Chia Sun, Jun-Wei Hsieh

Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable success in computer vision tasks. However, their deep architectures often lead to high computational redundancy, making them less suitable for resource-constrained environments, such as edge devices. This paper introduces ParFormer, a novel vision transformer that addresses this c

Carlo Marzo

The rigorous conditions to obtain sensible predictions in non (proper) renormalizable Quantum Field Theories were derived a long time ago, most notably in the works of Steven Weinberg. In this paper we explicitly illustrate the challenges met in carrying this program within the Affine Gravity framework, in particular when attempting to pinpoint viable partic

Elise Mills, Graeme F. Clark, Matthew J. Simpson, Mark Baird

Sigmoid growth models are often used to study population dynamics. The size of a population at equilibrium commonly depends explicitly on the availability of resources, such as an energy or nutrient source, which is not explicit in standard sigmoid growth models. A simple generalised extension of sigmoid growth models is introduced that can explicitly accoun

Mawei Wu

Let $\mathcal{C}$ be a small category. In this paper, we mainly study the category of modules $\mathfrak{M}\mbox{od-}\mathfrak{R}$ on ringed sites $(\mathbf{C},\mathfrak{R})$. We firstly reprove the Theorem A of the paper (M. Wu and F. Xu. Skew category algebras and modules on ringed finite sites. J. A. 631, 2023), then we characterize $\mathfrak{M}\mbox{od-

M. C. Gordillo, J. Segovia

Multiquark systems appear less frequently than mesons and baryons despite the enormous world-wide experimental effort that has been made during the last two decades. In this work, we will propose a possible explanation for that fact, restricting ourselves to the case of sets including only $c$ and $\bar{c}$ quarks. We will show that those multiquarks can be

Yoshihide Sawada, Ryuji Saiin, Kazuma Suetake

Recently, the number of parameters in DNNs has explosively increased, as exemplified by LLMs (Large Language Models), making inference on small-scale computers more difficult. Model compression technology is, therefore, essential for integration into products. In this paper, we propose a method of quantization-aware training. We introduce a novel normalizati

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

Using the $e^+e^-$ collision data collected with the BESIII detector operating at the BEPCII collider, at center-of-mass energies from the threshold to $4.95$~GeV, we present precise measurements of the cross sections for the process $e^+e^-\to D_s^+D_s^-$ using a single tag method. The resulting cross section lineshape exhibits several new structures, there

Bhargav Jha, Shaunak Bopardikar, Alexander Von Moll, David Casbeer

This paper presents joint motion planning of a vehicle with an attached rotating turret. The turret has a limited range as well as the field of view. The objective is capture a maneuvering target such that at the terminal time it is withing the field-of-view and range limits. Catering to it, we present a minimum effort guidance law that commensurate for the

Agnieszka Janiuk, Joseph Saji, Gerardo Urrutia

Compact binary mergers are sources of gravitational waves, and can be accompanied by electromagnetic signals. We discuss the possible features in the kilonova emissions which may help distinguish the black hole - neutron star mergers from the binary neutron stars. In addition, the amount of ejected material may depend on whether the system undergoes the crea

Swarup Ranjan Behera, Vijaya V Saradhi

Sports visualization focuses on the use of structured data, such as box-score data and tracking data. Unstructured data sources pertaining to sports are available in various places such as blogs, social media posts, and online news articles. Sports visualization methods either not fully exploited the information present in these sources or the proposed visua

Wenlve Zhou, Zhiheng Zhou, Tianlei Wang, Delu Zeng

Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a source domain to perform well on a target domain without requiring additional annotations. In the context of domain adaptive semantic segmentation, which tackles UDA for dense prediction, the goal is to circumvent the need for costly pixel-level annotations. Typically, various preva

Zhuo Chen, Chien-Min Kao, Hsin-Hsiung Huang, Lingling An

Positron Emission Tomography (PET) is a crucial tool in medical imaging, particularly for diagnosing diseases like cancer and Alzheimer's. The advent of Positronium Lifetime Imaging (PLI) has opened new avenues for assessing the tissue micro-environment, which is vital for early-stage disease detection. In this study, we introduce a two-component reconstruct

Xunye Cai, Jingshu Li, Ran Ding, Meng Lu

Interest in searches for Charged Lepton Flavor Violation (CLFV) has continued in the past few decades since the observation of CLFV will indicate new physics beyond the Standard Model (BSM). As several future lepton colliders with high luminosity have been proposed, the search for CLFV will reach an unprecedented level of precision. Many BSM models allow CLF

Naratip Nunchot, Ryusuke Ikeda

Superconducting transition, defined as vanishing of the resistivity, under a magnetic field in a clean bulk type II superconductor with weak sample disorder is believed to be a reflection of freezing of the vortex liquid to a kind of vortex solids. This fundamental issue on superconductivity is examined in detail. Based on the Ginzburg-Landau fluctuation the

Hiromichi Takagi

We construct a $13$-dimensional affine variety $\mathscr{H}_{\mathbb{A}}^{13}$ associated with $\mathbb{P}^{2}\times\mathbb{P}^{2}$-fibrations of relative Picard number $1$. The construction is modelled on the fact that the affine cone over the Segre-embedded $\mathbb{P}^{2}\times\mathbb{P}^{2}$ is the null locus of the $\sharp$-map of the $9$-dimensional no

Dhiman Goswami, Sadiya Sayara Chowdhury Puspo, Md Nishat Raihan, Al Nahian Bin Emran

This paper presents the MasonTigers entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches across 14 different languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches a

Sadiya Sayara Chowdhury Puspo, Md Nishat Raihan, Dhiman Goswami, Al Nahian Bin Emran

This paper presents the MasonTigers entry to the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. The task encompasses Binary Human-Written vs. Machine-Generated Text Classification (Track A), Multi-Way Machine-Generated Text Classification (Track B), and Human-Machine Mixed Text Detection (Track

Bahareh Harandizadeh, Abel Salinas, Fred Morstatter

This paper explores the pressing issue of risk assessment in Large Language Models (LLMs) as they become increasingly prevalent in various applications. Focusing on how reward models, which are designed to fine-tune pretrained LLMs to align with human values, perceive and categorize different types of risks, we delve into the challenges posed by the subjecti

Xulu Zhang, Wengyu Zhang, Xiao-Yong Wei, Jinlin Wu

This paper presents a pilot study that explores the application of active learning, traditionally studied in the context of discriminative models, to generative models. We specifically focus on image synthesis personalization tasks. The primary challenge in conducting active learning on generative models lies in the open-ended nature of querying, which diffe

Juliette Woodrow, Ali Malik, Chris Piech

Teaching students how to write code that is elegant, reusable, and comprehensible is a fundamental part of CS1 education. However, providing this "style feedback" in a timely manner has proven difficult to scale. In this paper, we present our experience deploying a novel, real-time style feedback tool in Code in Place, a large-scale online CS1 course. Our to

Minghui Xu, Jiahao Zhang, Hechuan Guo, Xiuzhen Cheng

Decentralized Storage Network (DSN) is an emerging technology that challenges traditional cloud-based storage systems by consolidating storage capacities from independent providers and coordinating to provide decentralized storage and retrieval services. However, current DSNs face several challenges associated with data privacy and efficiency of the proof sy

Jaegon Um, Hyunsuk Hong, Hyunggyu Park

This study investigates the suitability of the annealed approximation in high-dimensional systems characterized by dense networks with quenched link disorder, employing models of coupled oscillators. We demonstrate that dynamic equations governing dense-network systems converge to those of the complete-graph version in the thermodynamic limit, where link dis

Junya Wang, Yi-Jiao Zhang, Cong Xu, Jiaze Li

The evolution processes of complex systems carry key information in the systems' functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process ha

Md Nishat Raihan, Dhiman Goswami, Al Nahian Bin Emran, Sadiya Sayara Chowdhury Puspo

Our paper presents team MasonTigers submission to the SemEval-2024 Task 9 - which provides a dataset of puzzles for testing natural language understanding. We employ large language models (LLMs) to solve this task through several prompting techniques. Zero-shot and few-shot prompting generate reasonably good results when tested with proprietary LLMs, compare

Roman Emelyanov, Andrey Tikhomirov, Aleksandr Beznosikov, Alexander Gasnikov

Variational inequalities offer a versatile and straightforward approach to analyzing a broad range of equilibrium problems in both theoretical and practical fields. In this paper, we consider a composite generally non-monotone variational inequality represented as a sum of $L_q$-Lipschitz monotone and $L_p$-Lipschitz generally non-monotone operators. We appl

Takeshi Sato, Souta Miyamoto, Shota Kato

Rheology plays a pivotal role in understanding the flow behavior of fluids by discovering governing equations that relate deformation and stress, known as constitutive equations. Despite the importance of these equations, current methods for deriving them lack a systematic methodology, often relying on sense of physics and incurring substantial costs. To ove

Zemin Cai, Zhengyuan Fan, Tianshu Liu

Traditionally, deriving aerodynamic parameters for an airfoil via Computational Fluid Dynamics requires significant time and effort. However, recent approaches employ neural networks to replace this process, it still grapples with challenges like lack of end-to-end training and interpretability. A novel and more efficient neural network is proposed in this p

Mengjiang Sun, Peng Chen, Zhenxin Cao

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar differs from the traditional phased array (PA) radar, and can form range-angle-dependent beampattern and differentiate between closely spaced targets sharing the same angle but occupying distinct range cells. In the FDA-MIMO radar, target range estimation is achieved by employing a subtl

Shubhang Bhatnagar, Narendra Ahuja

Unsupervised deep metric learning (UDML) focuses on learning a semantic representation space using only unlabeled data. This challenging problem requires accurately estimating the similarity between data points, which is used to supervise a deep network. For this purpose, we propose to model the high-dimensional data manifold using a piecewise-linear approxi

Yitwah Cheung, Anthony Quas

The BCZ map was introduced in 2001 by Boca, Cobeli and Zaharescu as a tool to study the statistical properties of Farey sequences, whose relation to Riemann Hypothesis dates back to Franel and Landau. Later, J. Athreya and the first author observed that the BCZ map arises as a Poincare section of horocycle flow, establishing both ergodicity as well as zero m

Snehashis Mukherjee

In this article the right nilpotent $\mathbb{F}_p$-braces of cardinality $p^5$ has been classified. We use the connection between nilpotent $\mathbb{F}_p$-braces of cardinality $p^5$ and nilpotent pre-Lie algebras of the same order, building on the known relationship between pre-Lie algebras and braces. Leveraging insights from the classification of nilpoten

Rui Wang, Dengpan Ye, Long Tang, Yunming Zhang

With the continuous improvements of deepfake methods, forgery messages have transitioned from single-modality to multi-modal fusion, posing new challenges for existing forgery detection algorithms. In this paper, we propose AVT2-DWF, the Audio-Visual dual Transformers grounded in Dynamic Weight Fusion, which aims to amplify both intra- and cross-modal forger

Jiayun Wang, Yubei Chen, Stella X. Yu

Learning visual features from unlabeled images has proven successful for semantic categorization, often by mapping different $views$ of the same object to the same feature to achieve recognition invariance. However, visual recognition involves not only identifying $what$ an object is but also understanding $how$ it is presented. For example, seeing a car fro

Changmeng Zheng, Dayong Liang, Wengyu Zhang, Xiao-Yong Wei

This paper presents a pilot study aimed at introducing multi-agent debate into multimodal reasoning. The study addresses two key challenges: the trivialization of opinions resulting from excessive summarization and the diversion of focus caused by distractor concepts introduced from images. These challenges stem from the inductive (bottom-up) nature of exist

Ali Malik, Juliette Woodrow, Chris Piech

We propose and carry-out a novel method of formative assessment called Assessment via Teaching (AVT), in which learners demonstrate their understanding of CS1 topics by tutoring more novice students. AVT has powerful benefits over traditional forms of assessment: it is centered around service to others and is highly rewarding for the learners who teach. More

Shu-Ze Wang, Xue-Qing Yu, Li-Xuan Wei, Li Wang

Magnetic impurities in superconductors are of increasing interest due to emergent Yu-Shiba-Rusinov (YSR) states and Majorana zero modes for fault-tolerant quantum computation. However, a direct relationship between the YSR multiple states and magnetic anisotropy splitting of quantum impurity spins remains poorly characterized. By using scanning tunneling mic

Quanli Ji, Ranchao Wu, Tonghua Zhang

In this work, we study the dynamics of a spatially heterogeneous single population model with the memory effect and nonlinear boundary condition. By virtue of the implicit function theorem and Lyapunov-Schmidt reduction, spatially nonconstant positive steady state solutions appear from two trivial solutions, respectively. By using bifurcation analysis, the H

Rui Chen, Weiye Zhao, Ruixuan Liu, Weiyang Zhang

Safety Index Synthesis (SIS) is critical for deriving safe control laws. Recent works propose to synthesize a safety index (SI) via nonlinear programming and derive a safe control law such that the system 1) achieves forward invariant (FI) with some safe set and 2) guarantees finite time convergence (FTC) to that safe set. However, real-world system dynamics

Xiaozhou Pan, Tanjung Krisnanda, Andrea Duina, Kimin Park

Quantum metrology offers the potential to surpass its classical counterpart, pushing the boundaries of measurement precision toward the ultimate Heisenberg limit. This enhanced precision is normally attained by utilizing large squeezed states or multi-particle entangled quantum states, both of which are often challenging to implement and prone to decoherence

Kyungmin Lee, Kihyuk Sohn, Jinwoo Shin

Recent progress in text-to-3D generation has been achieved through the utilization of score distillation methods: they make use of the pre-trained text-to-image (T2I) diffusion models by distilling via the diffusion model training objective. However, such an approach inevitably results in the use of random timesteps at each update, which increases the varian

Shanthi Karpurapu, Sravanthy Myneni, Unnati Nettur, Likhit Sagar Gajja

Behavior-driven development (BDD) is an Agile testing methodology fostering collaboration among developers, QA analysts, and stakeholders. In this manuscript, we propose a novel approach to enhance BDD practices using large language models (LLMs) to automate acceptance test generation. Our study uses zero and few-shot prompts to evaluate LLMs such as GPT-3.5

Todor Milanov

We found an interesting application of the K-theoretic Heisenberg algebras of Weiqiang Wang to the foundations of permutation equivariant K-theoretic Gromov--Witten theory. We also found an explicit formula for the genus 0 correlators in the permutation equivariant Gromov--Witten theory of the point. In the non-equivariant limit our formula reduces to a well

Taekkyung Oh, Sangwook Bae, Junho Ahn, Yonghwa Lee

In cellular networks, authorities may need to physically locate user devices to track criminals or illegal equipment. This process involves authorized agents tracing devices by monitoring uplink signals with cellular operator assistance. However, tracking uncooperative uplink signal sources remains challenging, even for operators and authorities. Three key c

Pascal Naidon

This work investigates how the closed channel of a Feshbach resonance is characterised by experimental observables. Surprisingly, it is found that the two-body observables associated with the Feshbach resonance can be insensitive to the properties of the closed channel. In particular, it is impossible in this situation to determine the energy of the bound st

Ziyuan Tang, Tianshi Xu, Huan He, Yousef Saad

Anderson Acceleration (AA) is a popular algorithm designed to enhance the convergence of fixed-point iterations. In this paper, we introduce a variant of AA based on a Truncated Gram-Schmidt process (AATGS) which has a few advantages over the classical AA. In particular, an attractive feature of AATGS is that its iterates obey a three-term recurrence in the

Lindon Roberts

We develop a new approximation theory for linear and quadratic interpolation models, suitable for use in convex-constrained derivative-free optimization (DFO). Most existing model-based DFO methods for constrained problems assume the ability to construct sufficiently accurate approximations via interpolation, but the standard notions of accuracy (designed fo

Nikola Kovačević

In this paper, we prove that the variety $C_m(L)$ of commuting $m$-tuples of elements of simple Lie algebra $L$ is often reducible. Explicitely, we prove it is reducible for all simple Lie algebra $L$ not isomorphic to $\mathfrak{sl}_2$ and $\mathfrak{sl})_3$, and all $m \geq 4$. We also prove it is reducible for $C_3(L)$ for $L$ of types $B_k,C_k,E_7,E_8,F_

Pengxiang Zhao, Ping Li, Yingjie Gu, Yi Zheng

As deep learning models exponentially increase in size, optimizers such as Adam encounter significant memory consumption challenges due to the storage of first and second moment data. Current memory-efficient methods like Adafactor and CAME often compromise accuracy with their matrix factorization techniques. Addressing this, we introduce Adapprox, a novel a

Xiaofei Guan, Hang Qi, Zhiwei Sun

This paper discusses the theory and numerical method of two-scale analysis for the multiscale Landau-Lifshitz-Gilbert equation in composite ferromagnetic materials. The novelty of this work can be summarized in three aspects: Firstly, the more realistic and complex model is considered, including the effects of the exchange field, anisotropy field, stray fiel

Junhong Xu, Kai Yin, Jason M. Gregory, Kris Hauser

Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that describe the borders of any infeasible, non-navigable, or unsafe regions. We propose a principled boundary-aware safe stochastic planning framework with promising results. Our meth

Katsuya O. Akamatsu, Naoki Kawashima

The behavior of $b=2$ real-space renormalization group (RSRG) maps like the majority rule and the decimation map was examined by numerically applying RSRG steps to critical $q=2,3,4$ Potts spin configurations. While the majority rule is generally believed to work well, a more thorough investigation of the action of the map has yet to be considered in the lit

Kyohoon Jin, Junho Lee, Juhwan Choi, Sangmin Song

Efforts to leverage deep learning models in low-resource regimes have led to numerous augmentation studies. However, the direct application of methods such as mixup and cutout to text data, is limited due to their discrete characteristics. While methods using pretrained language models have exhibited efficiency, they require additional considerations for rob

Aiden Price, Kerrie Mengersen, Michael Rigby, Paula Fiévez

Extreme natural hazards are increasing in frequency and intensity. These natural changes in our environment, combined with man-made pollution, have substantial economic, social and health impacts globally. The impact of the environment on human health (environmental health) is becoming well understood in international research literature. However, there are

Chengye Cao, Zhao-Yu Li, Ralph Schönrich, Teresa Antoja

Decoding the key dynamical processes that shape the Galactic disk structure is crucial for reconstructing the Milky Way's evolution history. The second Gaia data release unveils a novel wave pattern in the $L_Z-\langle V_R\rangle$ space, but its formation mechanism remains elusive due to the intricate nature of involved perturbations and the challenges in di

Zhenrui Yue, Huimin Zeng, Yimeng Lu, Lanyu Shang

The proliferation of online misinformation has posed significant threats to public interest. While numerous online users actively participate in the combat against misinformation, many of such responses can be characterized by the lack of politeness and supporting facts. As a solution, text generation approaches are proposed to automatically produce counter-

Zhenbang Xiao, Yu Wang, Shunyu Liu, Huiqiong Wang

The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed graph for use on the large-scale original graph. Existing methods primarily focus on aligning key metrics between the condensed and original graphs, such as gradients, output dist

Xindi Luo, Zequn Sun, Jing Zhao, Zhe Zhao

Parameter-efficient finetuning (PEFT) is a key technique for adapting large language models (LLMs) to downstream tasks. In this paper, we study leveraging knowledge graph embeddings to improve the effectiveness of PEFT. We propose a knowledgeable adaptation method called KnowLA. It inserts an adaptation layer into an LLM to integrate the embeddings of entiti

YiFan Zhang, Weiqi Chen, Zhaoyang Zhu, Dalin Qin

Online updating of time series forecasting models aims to tackle the challenge of concept drifting by adjusting forecasting models based on streaming data. While numerous algorithms have been developed, most of them focus on model design and updating. In practice, many of these methods struggle with continuous performance regression in the face of accumulate

J. A. Rengifo, J. L. Bazo

In recent years, muon tomography has turned into a powerful and innovative technique for non-invasive imaging of large and small structures with applications in different areas like geology, archaeology, security, etc. We present the design and simulation of a transportable and easy to construct detector based on plastic scintillator and Silicon photomultipl

Haoxuan Qu, Ziyan Guo, Jun Liu

Recently, while text-driven human motion generation has received massive research attention, most existing text-driven motion generators are generally only designed to generate motion sequences in a blank background. While this is the case, in practice, human beings naturally perform their motions in 3D scenes, rather than in a blank background. Considering

Hwichan Kim, Shota Sasaki, Sho Hoshino, Ukyo Honda

Low-Rank Adaptation (LoRA) is a widely used Parameter-Efficient Fine-Tuning (PEFT) method that updates an initial weight matrix $W_0$ with a delta matrix $\Delta W$ consisted by two low-rank matrices $A$ and $B$. A previous study suggested that there is correlation between $W_0$ and $\Delta W$. In this study, we aim to delve deeper into relationships between

Aloka Ranjan Sahoo, Sharat Chandra

With help of ab initio density functional theory calculation, DFT+U, and hybrid functional HSE06, we revisit the layer dependent electronic structure and magnetic properties of pristine and 3d transition metal Cr doped MoS$_2$ monolayer and multilayers. Our results show that the dopant Cr atoms prefer to stay at nearest neighbor distances. In the multilayers

Seungdae Han, Joohee Kim

There has been a significant progress in text conditional image generation models. Recent advancements in this field depend not only on improvements in model structures, but also vast quantities of text-image paired datasets. However, creating these kinds of datasets is very costly and requires a substantial amount of labor. Famous face datasets don't have c

Bin Lyu, Hao Liu, Wenqing Hong, Shimin Gong

In this paper, we propose a movable antenna (MA) empowered scheme for symbiotic radio (SR) communication systems. Specifically, multiple antennas at the primary transmitter (PT) can be flexibly moved to favorable locations to boost the channel conditions of the primary and secondary transmissions. The primary transmission is achieved by the active transmissi

Peng-Cheng Hang, Min-Jie Luo

Recently, Wald and Henkel (2018) derived the leading-order estimate of the Humbert functions $\Phi_2$, $\Phi_3$ and $\Xi_2$ for two large arguments, but their technique cannot handle the Humbert function $\Psi_1$. In this paper, we establish the leading asymptotic behavior of the Humbert function $\Psi_1$ for two large arguments. Our proof is based on a conn

Nan Jiang, Haitao Yuan, Jianing Si, Minxiao Chen

The next point-of-interest (POI) prediction is a significant task in location-based services, yet its complexity arises from the consolidation of spatial and semantic intent. This fusion is subject to the influences of historical preferences, prevailing location, and environmental factors, thereby posing significant challenges. In addition, the uneven POI di

Shuhao Li, Yue Cui, Jingyi Xu, Libin Li

Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years. With the advancement of Vehicle-to-Everything (V2X) technologies, autonomous driving, and large-scale models in the traffic domain, lane-level traffic prediction has emerged as an indispensable

Yifei Zeng, Yanqin Jiang, Siyu Zhu, Yuanxun Lu

Recent progress in pre-trained diffusion models and 3D generation have spurred interest in 4D content creation. However, achieving high-fidelity 4D generation with spatial-temporal consistency remains a challenge. In this work, we propose STAG4D, a novel framework that combines pre-trained diffusion models with dynamic 3D Gaussian splatting for high-fidelity

Punyajoy Saha, Aalok Agrawal, Abhik Jana, Chris Biemann

With the emergence of numerous Large Language Models (LLM), the usage of such models in various Natural Language Processing (NLP) applications is increasing extensively. Counterspeech generation is one such key task where efforts are made to develop generative models by fine-tuning LLMs with hatespeech - counterspeech pairs, but none of these attempts explor

Phai Vu Dinh, Diep N. Nguyen, Dinh Thai Hoang, Quang Uy Nguyen

While intrusion detection systems (IDSs) benefit from the diversity and generalization of IoT data features, the data diversity (e.g., the heterogeneity and high dimensions of data) also makes it difficult to train effective machine learning models in IoT IDSs. This also leads to potentially redundant/noisy features that may decrease the accuracy of the dete

Jiayi Liu, Manolis Savva, Ali Mahdavi-Amiri

3D modeling of articulated objects is a research problem within computer vision, graphics, and robotics. Its objective is to understand the shape and motion of the articulated components, represent the geometry and mobility of object parts, and create realistic models that reflect articulated objects in the real world. This survey provides a comprehensive ov

Zhonghua Li, Zhenlu Wang

In this paper, we study the evaluation formulas of the interpolated multiple zeta values and the interpolated multiple $t$-values with indices involving $1,2,3$. To get these evaluations, we derive the corresponding algebraic relations in the harmonic algebra.

Yinggui Wang, Wei Huang, Le Yang

Spoken language understanding (SLU), one of the key enabling technologies for human-computer interaction in IoT devices, provides an easy-to-use user interface. Human speech can contain a lot of user-sensitive information, such as gender, identity, and sensitive content. New types of security and privacy breaches have thus emerged. Users do not want to expos

Phai Vu Dinh, Diep N. Nguyen, Dinh Thai Hoang, Marwan Krunz

Knowledge distillation (KD) has been widely used to improve lightweight AI models by transferring soft-label knowledge from a large teacher model to a student model. However, existing KD methods are primarily designed for the image domain rather than lightweight IoT devices, and they often struggle to maintain well-separated feature representations for diffe

Nan Li, Ilya Kolmanovsky, Hong Chen

In this paper, we propose a novel data-driven predictive control approach for systems subject to time-domain constraints. The approach combines the strengths of H-infinity control for rejecting disturbances and MPC for handling constraints. In particular, the approach can dynamically adapt H-infinity disturbance attenuation performance depending on measured

Ty Shedleski, Muhammad Usman

Quantum field theory (QFT) describes the dynamics of quantum particles in the quantum realm in the Minkowski space-time, whereas the General Relativity (GR) is a classical theory describing the nature of dynamical behavior of large bodies in different space-times. This research is a proposal to the proof of concept that through the Einstein-Rosen bridge (als

Melike Sirlanci, George Hripcsak, Cecilia C. Low Wang, J. N. Stroh

Intensive care unit (ICU) patients exhibit erratic blood glucose (BG) fluctuations, including hypoglycemic and hyperglycemic episodes, and require exogenous insulin delivery to keep their BG in healthy ranges. Glycemic control via glycemic management (GM) is associated with reduced mortality and morbidity in the ICU, but GM increases the cognitive load on cl