arXiv subjects

Yi Sun

Publications and source records attributed to Yi Sun.

At least 73 records · Page 4Linked to original sources

arXiv2024

Cross-Category Functional Grasp Transfer

Generating grasps for a dexterous hand often requires numerous grasping annotations. However, annotating high DoF dexterous hand poses is quite challenging. Especially for functional grasps, requiring the hand to grasp the object in a specific pose to facilitate subsequent manipulations. This prompts us to explore how people achieve manipulations on new objects based on past grasp experiences. We find that when grasping new items, people are adept at discovering and leveraging various similarities between objects, including shape, layout, and grasp type. Considering this, we analyze and collect grasp-related similarity relationships among 51 common tool-like object categories and annotate semantic grasp representation for 1768 objects. These objects are connected through similarities to form a knowledge graph, which helps infer our proposed cross-category functional grasp synthesis. Through extensive experiments, we demonstrate that the grasp-related knowledge indeed contributed to achieving functional grasp transfer across unknown or entirely new categories of objects.

cs.RO↗

arXiv2024

CoActionGraphRec: Sequential Multi-Interest Recommendations Using Co-Action Graphs

There are unique challenges to developing item recommender systems for e-commerce platforms like eBay due to sparse data and diverse user interests. While rich user-item interactions are important, eBay's data sparsity exceeds other e-commerce sites by an order of magnitude. To address this challenge, we propose CoActionGraphRec (CAGR), a text based two-tower deep learning model (Item Tower and User Tower) utilizing co-action graph layers. In order to enhance user and item representations, a graph-based solution tailored to eBay's environment is utilized. For the Item Tower, we represent each item using its co-action items to capture collaborative signals in a co-action graph that is fully leveraged by the graph neural network component. For the User Tower, we build a fully connected graph of each user's behavior sequence, with edges encoding pairwise relationships. Furthermore, an explicit interaction module learns representations capturing behavior interactions. Extensive offline and online A/B test experiments demonstrate the effectiveness of our proposed approach and results show improved performance over state-of-the-art methods on key metrics.

cs.IR↗

arXiv2024

Behavior evolution-inspired approach to walking gait reinforcement training for quadruped robots

Reinforcement learning method is extremely competitive in gait generation techniques for quadrupedal robot, which is mainly due to the fact that stochastic exploration in reinforcement training is beneficial to achieve an autonomous gait. Nevertheless, although incremental reinforcement learning is employed to improve training success and movement smoothness by relying on the continuity inherent during limb movements, challenges remain in adapting gait policy to diverse terrain and external disturbance. Inspired by the association between reinforcement learning and the evolution of animal motion behavior, a self-improvement mechanism for reference gait is introduced in this paper to enable incremental learning of action and self-improvement of reference action together to imitate the evolution of animal motion behavior. Further, a new framework for reinforcement training of quadruped gait is proposed. In this framework, genetic algorithm is specifically adopted to perform global probabilistic search for the initial value of the arbitrary foot trajectory to update the reference trajectory with better fitness. Subsequently, the improved reference gait is used for incremental reinforcement learning of gait. The above process is repeatedly and alternatively executed to finally train the gait policy. The analysis considering terrain, model dimensions, and locomotion condition is presented in detail based on simulation, and the results show that the framework is significantly more adaptive to terrain compared to regular incremental reinforcement learning.

cs.RO↗

arXiv2024

Adaptive Motion Planning for Multi-fingered Functional Grasp via Force Feedback

Enabling multi-fingered robots to grasp and manipulate objects with human-like dexterity is especially challenging during the dynamic, continuous hand-object interactions. Closed-loop feedback control is essential for dexterous hands to dynamically finetune hand poses when performing precise functional grasps. This work proposes an adaptive motion planning method based on deep reinforcement learning to adjust grasping poses according to real-time feedback from joint torques from pre-grasp to goal grasp. We find the multi-joint torques of the dexterous hand can sense object positions through contacts and collisions, enabling real-time adjustment of grasps to generate varying grasping trajectories for objects in different positions. In our experiments, the performance gap with and without force feedback reveals the important role of force feedback in adaptive manipulation. Our approach utilizing force feedback preliminarily exhibits human-like flexibility, adaptability, and precision.

cs.RO↗

arXiv2024

The Family of LML Detectors and the Family of LAS Detectors for Massive MIMO Communications

The family of local maximum likelihood (LML) detectors, including the global maximum likelihood (GML) detector, and the family of likelihood ascent search (LAS) detectors are akin to each other and possess common properties significant in both theory and practical multi-input multi-output (MIMO) communications. It is proved that a large MIMO channel possesses the LML characteristic, implying and predicting that a local search detector with likelihood ascent, like a wide-sense sequential LAS (WSLAS) detector, can approach the GML detection. By the replica method, the bit error rate (BER) of an LML detector in the large MIMO channel is obtained. The BER indicates that in the high signal-to-noise ratio (SNR) regime, both the LML and GML detectors achieve the AWGN channel performance when the channel load is as high as up to 1.5086 bits/dimension with an equal-energy distribution, and the channel load can be higher with an unequal-energy distribution. The analytical result is verified by simulation in the equal-energy distribution that the sequential LAS (SLAS) detector, a linear-complexity LML detector, can approach the BER of the NP-hard GML detector. The LML and LAS detectors in the two families are successfully applied to symbol detection in massive antenna MIMO communications and demonstrate the performance near the GML detection. This book chapter reviews the LML and LAS detectors in a unified framework. The focus is on their formulation, relationships, properties, and GML performance in BER and spectral efficiency in large MIMO channels.

eess.SP↗

arXiv2024

Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education

Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domain-specific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks. These alignment challenges are critical in education because of the need for an intelligent visualization system to support beginners' self-regulated learning. Therefore, we apply the framework to education and introduce Tailor-Mind, an interactive visualization system designed to facilitate self-regulated learning for artificial intelligence beginners. Drawing on insights from a preliminary study, we identify self-regulated learning tasks and fine-tuning objectives to guide visualization design and tuning data construction. Our focus on aligning visualization with fine-tuned LLM makes Tailor-Mind more like a personalized tutor. Tailor-Mind also supports interactive recommendations to help beginners better achieve their learning goals. Model performance evaluations and user studies confirm that Tailor-Mind improves the self-regulated learning experience, effectively validating the proposed framework.

cs.HC↗

arXiv2024

Second-order topological insulator in Bilayer borophene

As the novel topological states, the higher-order topological insulators have attracted great attentions in the past years. However, their realizations in realistic materials, in particular in two dimensional systems, remains the big challenge due to the lack of adequate candidates. Here, based on the first-principle calculation and tight-binding model simulations, we identify the currently \emph{existing} bilayer $α_{5}$-phase borophenes as the two-dimensional second-order topological insulators, protected by the $C_{2}$-rotational symmetry. The formation of interlayer B-B covalent bonds, stabilizing the bilayer borophenes and opening the large direct bulk gaps ($\sim 0.55-0.62$ eV) at Fermi level, plays the key roles. The second-order topology is characterized by the bulk quantized quadrupole momentum. Our results enriches the candidates for the second-order topological insulators, and also provide a way to study topological states in borophenes.

cond-mat.mtrl-sci↗

arXiv2024

On the estimation rate of Bayesian PINN for inverse problems

Solving partial differential equations (PDEs) and their inverse problems using Physics-informed neural networks (PINNs) is a rapidly growing approach in the physics and machine learning community. Although several architectures exist for PINNs that work remarkably in practice, our theoretical understanding of their performances is somewhat limited. In this work, we study the behavior of a Bayesian PINN estimator of the solution of a PDE from $n$ independent noisy measurement of the solution. We focus on a class of equations that are linear in their parameters (with unknown coefficients $θ_\star$). We show that when the partial differential equation admits a classical solution (say $u_\star$), differentiable to order $β$, the mean square error of the Bayesian posterior mean is at least of order $n^{-2β/(2β+ d)}$. Furthermore, we establish a convergence rate of the linear coefficients of $θ_\star$ depending on the order of the underlying differential operator. Last but not least, our theoretical results are validated through extensive simulations.

math.ST↗

arXiv2024

The verification of periodicity with the use of recurrent neural networks

The ability to automatically and robustly self-verify periodicity present in time-series astronomical data is becoming more important as data sets rapidly increase in size. The age of large astronomical surveys has rendered manual inspection of time-series data less practical. Previous efforts in generating a false alarm probability to verify the periodicity of stars have been aimed towards the analysis of a constructed periodogram. However, these methods feature correlations with features that do not pertain to periodicity, such as light curve shape, slow trends and stochastic variability. The common assumption that photometric errors are Gaussian and well determined is also a limitation of analytic methods. We present a novel machine learning based technique which directly analyses the phase folded light curve for its false alarm probability. We show that the results of this method are largely insensitive to the shape of the light curve, and we establish minimum values for the number of data points and the amplitude to noise ratio.

astro-ph.IM↗

arXiv2024

High-Temperature Anomalous Metal States in Iron-Based Interface Superconductors

The nature of the anomalous metal state has been a major puzzle in condensed matter physics for more than three decades. Here, we report systematic investigation and modulation of the anomalous metal states in high-temperature interface superconductor FeSe films on SrTiO3 substrate. Remarkably, under zero magnetic field, the anomalous metal state persists up to 20 K in pristine FeSe films, an exceptionally high temperature standing out from previous observations. In stark contrast, for the FeSe films with nano-hole arrays, the characteristic temperature of the anomalous metal state is considerably reduced. We demonstrate that the observed anomalous metal states originate from the quantum tunneling of vortices adjusted by the Ohmic dissipation. Our work offers a perspective for understanding the origin and modulation of the anomalous metal states in two-dimensional bosonic systems.

cond-mat.supr-con↗

arXiv2024

Transparent Object Depth Completion

The perception of transparent objects for grasp and manipulation remains a major challenge, because existing robotic grasp methods which heavily rely on depth maps are not suitable for transparent objects due to their unique visual properties. These properties lead to gaps and inaccuracies in the depth maps of the transparent objects captured by depth sensors. To address this issue, we propose an end-to-end network for transparent object depth completion that combines the strengths of single-view RGB-D based depth completion and multi-view depth estimation. Moreover, we introduce a depth refinement module based on confidence estimation to fuse predicted depth maps from single-view and multi-view modules, which further refines the restored depth map. The extensive experiments on the ClearPose and TransCG datasets demonstrate that our method achieves superior accuracy and robustness in complex scenarios with significant occlusion compared to the state-of-the-art methods.

cs.CV↗

arXiv2024

Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming to help users efficiently obtain information and execute tasks, and provide users with more intelligent, convenient, and rich interaction experiences. With the development of smartphones and IoT, computing and sensing devices have become ubiquitous, greatly expanding the boundaries of IPAs. However, due to the lack of capabilities such as user intent understanding, task planning, tool using, and personal data management etc., existing IPAs still have limited practicality and scalability. Recently, the emergence of foundation models, represented by large language models (LLMs), brings new opportunities for the development of IPAs. With the powerful semantic understanding and reasoning capabilities, LLM can enable intelligent agents to solve complex problems autonomously. In this paper, we focus on Personal LLM Agents, which are LLM-based agents that are deeply integrated with personal data and personal devices and used for personal assistance. We envision that Personal LLM Agents will become a major software paradigm for end-users in the upcoming era. To realize this vision, we take the first step to discuss several important questions about Personal LLM Agents, including their architecture, capability, efficiency and security. We start by summarizing the key components and design choices in the architecture of Personal LLM Agents, followed by an in-depth analysis of the opinions collected from domain experts. Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.

cs.HC↗

arXiv2024

VulMCI : Code Splicing-based Pixel-row Oversampling for More Continuous Vulnerability Image Generation

In recent years, the rapid development of deep learning technology has brought new prospects to the field of vulnerability detection. Many vulnerability detection methods involve converting source code into images for detection, yet they often overlook the quality of the generated images. Due to the fact that vulnerability images lack clear and continuous contours, unlike images used in object detection, Convolutional Neural Networks (CNNs) tend to lose semantic information during the convolution and pooling processes. Therefore, this paper proposes a pixel row oversampling method based on code line concatenation to generate more continuous code features, addressing the issue of discontinuity in code image coloration.Building upon these contributions, we propose the vulnerability detection system VulMCI and conduct tests on the SARD and NVD datasets. Experimental results demonstrate that VulMCI outperforms seven state-of-the-art vulnerability detectors (namely Checkmarx, FlawFinder, RATS, VulDeePecker, SySeVR, VulCNN, and Devign). Compared to other image-based methods, VulMCI shows improvements in various metrics, including a 2.877\% increase in True Positive Rate (TPR), a 5.446\% increase in True Negative Rate (TNR), and a 5.91\% increase in Accuracy (ACC). On the NVD real-world dataset, VulMCI achieves an average accuracy of 5.162\%, confirming its value in practical vulnerability detection applications.

cs.CR↗

arXiv2024

Trainable Joint Channel Estimation, Detection and Decoding for MIMO URLLC Systems

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximum a posteriori (MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities.

eess.SP↗

arXiv2024

Text2Grasp: Grasp synthesis by text prompts of object grasping parts

The hand plays a pivotal role in human ability to grasp and manipulate objects and controllable grasp synthesis is the key for successfully performing downstream tasks. Existing methods that use human intention or task-level language as control signals for grasping inherently face ambiguity. To address this challenge, we propose a grasp synthesis method guided by text prompts of object grasping parts, Text2Grasp, which provides more precise control. Specifically, we present a two-stage method that includes a text-guided diffusion model TextGraspDiff to first generate a coarse grasp pose, then apply a hand-object contact optimization process to ensure both plausibility and diversity. Furthermore, by leveraging Large Language Model, our method facilitates grasp synthesis guided by task-level and personalized text descriptions without additional manual annotations. Extensive experiments demonstrate that our method achieves not only accurate part-level grasp control but also comparable performance in grasp quality.

cs.AI↗

arXiv2024

Trustless Audits without Revealing Data or Models

There is an increasing conflict between business incentives to hide models and data as trade secrets, and the societal need for algorithmic transparency. For example, a rightsholder wishing to know whether their copyrighted works have been used during training must convince the model provider to allow a third party to audit the model and data. Finding a mutually agreeable third party is difficult, and the associated costs often make this approach impractical. In this work, we show that it is possible to simultaneously allow model providers to keep their model weights (but not architecture) and data secret while allowing other parties to trustlessly audit model and data properties. We do this by designing a protocol called ZkAudit in which model providers publish cryptographic commitments of datasets and model weights, alongside a zero-knowledge proof (ZKP) certifying that published commitments are derived from training the model. Model providers can then respond to audit requests by privately computing any function F of the dataset (or model) and releasing the output of F alongside another ZKP certifying the correct execution of F. To enable ZkAudit, we develop new methods of computing ZKPs for SGD on modern neural nets for simple recommender systems and image classification models capable of high accuracies on ImageNet. Empirically, we show it is possible to provide trustless audits of DNNs, including copyright, censorship, and counterfactual audits with little to no loss in accuracy.

cs.CR↗

arXiv2024

The Calculation of the Rate Constants via a Master Equation approach -- A Comprehensive Tutorial

Intermolecular hydrogen transfer free radical reactions are common in the combustion process and in a number of organic chemistry reactions. Therefore, evaluating the pressure and temperature-dependent rate constants of them is of great importance. Here, we present a tutorial on how a Master Equation model can be constructed to evaluate the rate constants of reversible hydrogen transfer reactions that involve tunnelling, hindered rotor effects, as well as source and depletion.

physics.chem-ph↗

arXiv2024

Self-injection-locked optical parametric oscillator based on microcombs

Narrow-linewidth yet tunable laser oscillators are one of the most important tools for precision metrology, optical atomic clocks, sensing and quantum computing. Commonly used tunable coherent oscillators are based on stimulated emission or stimulated Brillouin scattering; as a result, the operating wavelength band is limited by the gain media. Based on nonlinear optical gain, optical parametric oscillators (OPOs) enable coherent signal generation within the whole transparency window of the medium used. However, the demonstration of OPO-based Hertz-level linewidth and tunable oscillators has remained elusive. Here, we present a tunable coherent oscillator based on a multimode coherent OPO in a high-Q microresonator, i.e., a microcomb. Single-mode coherent oscillation is realized through self-injection locking (SIL) of one selected comb line. We achieve coarse tuning up to 20 nm, and an intrinsic linewidth down to sub-Hertz level, which is three orders of magnitude lower than the pump. Furthermore, we demonstrate that this scheme results into repetition rate stabilization of the microcomb. These results open exciting possibilities for generating tunable coherent radiation where stimulated emission materials are difficult to obtain, and the stabilization of microcomb sources beyond the limits imposed by the thermorefractive noise in the cavity.

physics.optics↗