arXiv subjects

Rui Chen

Publications and source records attributed to Rui Chen.

At least 217 records · Page 12Linked to original sources

arXiv2025

THOR: A Generic Energy Estimation Approach for On-Device Training

Battery-powered mobile devices (e.g., smartphones, AR/VR glasses, and various IoT devices) are increasingly being used for AI training due to their growing computational power and easy access to valuable, diverse, and real-time data. On-device training is highly energy-intensive, making accurate energy consumption estimation crucial for effective job scheduling and sustainable AI. However, the heterogeneity of devices and the complexity of models challenge the accuracy and generalizability of existing estimation methods. This paper proposes THOR, a generic approach for energy consumption estimation in deep neural network (DNN) training. First, we examine the layer-wise energy additivity property of DNNs and strategically partition the entire model into layers for fine-grained energy consumption profiling. Then, we fit Gaussian Process (GP) models to learn from layer-wise energy consumption measurements and estimate a DNN's overall energy consumption based on its layer-wise energy additivity property. We conduct extensive experiments with various types of models across different real-world platforms. The results demonstrate that THOR has effectively reduced the Mean Absolute Percentage Error (MAPE) by up to 30%. Moreover, THOR is applied in guiding energy-aware pruning, successfully reducing energy consumption by 50%, thereby further demonstrating its generality and potential.

cs.LG↗

arXiv2025

Probing $k$-Space Alternating Spin Polarization via the Anomalous Hall Effect

Altermagnets represent a recently discovered class of collinear magnets, characterized by antiparallel neighboring magnetic moments and alternating-sign spin polarization in momentum-space($k$-space). However, experimental methods for probing the $k$-space spin polarization in altermagnets remain limited. In this work, we propose an approach to address this challenge by interfacing an altermagnet with the surface of a topological insulator. The massless Dirac fermions on the topological insulator surface acquire a mass due to the time-reversal symmetry breaking. The local $k$-space magnetic moment at the Dirac point directly determines both the sign and magnitude of this Dirac mass, resulting in an anomalous Hall effect. By measuring the Hall conductance, we can extract the local $k$-space magnetic moment. Moreover, we can map the global magnetic moment distribution by tuning the Dirac point position using an in-plane magnetic field, thereby revealing the $k$-space spin density of the altermagnet. This work establishes the Dirac fermion on the topological insulator surface as a sensitive probe for unveiling spin characters of altermagnets and those of other unconventional antiferromagnets.

cond-mat.mes-hall↗

arXiv2025

Safety Index Synthesis with State-dependent Control Space

This paper introduces an approach for synthesizing feasible safety indices to derive safe control laws under state-dependent control spaces. The problem, referred to as Safety Index Synthesis (SIS), is challenging because it requires the existence of feasible control input in all states and leads to an infinite number of constraints. The proposed method leverages Positivstellensatz to formulate SIS as a nonlinear programming (NP) problem. We formally prove that the NP solutions yield safe control laws with two imperative guarantees: forward invariance within user-defined safe regions and finite-time convergence to those regions. A numerical study validates the effectiveness of our approach.

eess.SY↗

arXiv2025

ThinTact:Thin Vision-Based Tactile Sensor by Lensless Imaging

Vision-based tactile sensors have drawn increasing interest in the robotics community. However, traditional lens-based designs impose minimum thickness constraints on these sensors, limiting their applicability in space-restricted settings. In this paper, we propose ThinTact, a novel lensless vision-based tactile sensor with a sensing field of over 200 mm2 and a thickness of less than 10 mm.ThinTact utilizes the mask-based lensless imaging technique to map the contact information to CMOS signals. To ensure real-time tactile sensing, we propose a real-time lensless reconstruction algorithm that leverages a frequency-spatial-domain joint filter based on discrete cosine transform (DCT). This algorithm achieves computation significantly faster than existing optimization-based methods. Additionally, to improve the sensing quality, we develop a mask optimization method based on the generic algorithm and the corresponding system matrix calibration algorithm.We evaluate the performance of our proposed lensless reconstruction and tactile sensing through qualitative and quantitative experiments. Furthermore, we demonstrate ThinTact's practical applicability in diverse applications, including texture recognition and contact-rich object manipulation. The paper will appear in the IEEE Transactions on Robotics: https://ieeexplore.ieee.org/document/10842357. Video: https://youtu.be/YrOO9BDMAHo

cs.RO↗

arXiv2025

Five-dimensional Floquet topological semimetals with emergent Yang monopoles and linked Weyl surfaces

Recently, Floquet topological matter has attracted significant attention for its potential to reveal novel topological phases inaccessible in static systems. In this paper, we investigate the effect of a time-periodic driving on the five-dimensional (5D) normal insulators. We show that the time-periodic driving can induce a topological phase transition from a \textbf{TP} (time reversal combined with space inversion) symmetry-preserving normal insulator to a 5D Floquet topological semimetal with emergent Yang monopoles characterized by the second Chern number. Additionally, we show that this time-periodic driving can also lead to a topological phase transition from a \textbf{TP} symmetry-breaking normal insulator to a 5D Floquet topological semimetal with a Hopf link formed by Weyl surfaces. Besides, further increasing the strength of the time-periodic driving, the two 5D Floquet topological semimetal phases are transformed into the 5D Floquet Chern insulators. Our paper is helpful for future studies in higher dimensional Floquet systems.

cond-mat.mes-hall↗

arXiv2025

Large upper critical fields and strong coupling superconductivity in the medium-entropy alloy (Ti1/3Hf1/3Ta1/3)1-xNbx

Since the discovery of high-entropy superconductors in 2014, superconductivity has remained a focal point of interest in medium- and high-entropy alloys (MEAs-HEAs). Here, we report a series of (Ti0.33Hf0.33Ta0.33)1-xNbx MEA superconductors crystallized in the BCC structure, whose superconductivity was characterized by resistivity, magnetization, and specific heat measurements. The study found that the (Ti0.33Hf0.33Ta0.33)1-xNbx MEAs exhibit bulk superconductivity. With the doping of Nb, the superconducting transition temperature (Tc) increases from 5.31 K to 9.11 K, and the normalized Cel jumps at Tc, and the logarithmically averaged characteristic phonon frequency exhibit dome-shaped curves. Results from specific heat measurements indicate that the superconductivity is of a strongly coupled s-wave type observed. Furthermore, at low Nb content, the upper critical field of the samples is larger than the Pauli paramagnetic limit. The strongly coupling behavior and large upper critical field in s-wave type (Ti0.33Hf0.33Ta0.33)1-xNbx MEA superconductors are unusual, as they typically occur in other unconventional superconductors. Thus, (Ti0.33Hf0.33Ta0.33)1-xNbx may have significant potential in the research and understanding of physical mechanisms.

cond-mat.supr-con↗

arXiv2024

Towards Precise Scaling Laws for Video Diffusion Transformers

Achieving optimal performance of video diffusion transformers within given data and compute budget is crucial due to their high training costs. This necessitates precisely determining the optimal model size and training hyperparameters before large-scale training. While scaling laws are employed in language models to predict performance, their existence and accurate derivation in visual generation models remain underexplored. In this paper, we systematically analyze scaling laws for video diffusion transformers and confirm their presence. Moreover, we discover that, unlike language models, video diffusion models are more sensitive to learning rate and batch size, two hyperparameters often not precisely modeled. To address this, we propose a new scaling law that predicts optimal hyperparameters for any model size and compute budget. Under these optimal settings, we achieve comparable performance and reduce inference costs by 40.1% compared to conventional scaling methods, within a compute budget of 1e10 TFlops. Furthermore, we establish a more generalized and precise relationship among validation loss, any model size, and compute budget. This enables performance prediction for non-optimal model sizes, which may also be appealed under practical inference cost constraints, achieving a better trade-off.

cs.CV↗

arXiv2024

From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking

Intuitively, an ideal collaborative filtering (CF) model should learn from users' full rankings over all items to make optimal top-K recommendations. Due to the absence of such full rankings in practice, most CF models rely on pairwise loss functions to approximate full rankings, resulting in an immense performance gap. In this paper, we provide a novel analysis using the multiple ordinal classification concept to reveal the inevitable gap between a pairwise approximation and the ideal case. However, bridging the gap in practice encounters two formidable challenges: (1) none of the real-world datasets contains full ranking information; (2) there does not exist a loss function that is capable of consuming ranking information. To overcome these challenges, we propose a pseudo-ranking paradigm (PRP) that addresses the lack of ranking information by introducing pseudo-rankings supervised by an original noise injection mechanism. Additionally, we put forward a new ranking loss function designed to handle ranking information effectively. To ensure our method's robustness against potential inaccuracies in pseudo-rankings, we equip the ranking loss function with a gradient-based confidence mechanism to detect and mitigate abnormal gradients. Extensive experiments on four real-world datasets demonstrate that PRP significantly outperforms state-of-the-art methods.

cs.IR↗

arXiv2024

Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data

Collaborative filtering (CF) stands as a cornerstone in recommender systems, yet effectively leveraging the massive unlabeled data presents a significant challenge. Current research focuses on addressing the challenge of unlabeled data by extracting a subset that closely approximates negative samples. Regrettably, the remaining data are overlooked, failing to fully integrate this valuable information into the construction of user preferences. To address this gap, we introduce a novel positive-neutral-negative (PNN) learning paradigm. PNN introduces a neutral class, encompassing intricate items that are challenging to categorize directly as positive or negative samples. By training a model based on this triple-wise partial ranking, PNN offers a promising solution to learning complex user preferences. Through theoretical analysis, we connect PNN to one-way partial AUC (OPAUC) to validate its efficacy. Implementing the PNN paradigm is, however, technically challenging because: (1) it is difficult to classify unlabeled data into neutral or negative in the absence of supervised signals; (2) there does not exist any loss function that can handle set-level triple-wise ranking relationships. To address these challenges, we propose a semi-supervised learning method coupled with a user-aware attention model for knowledge acquisition and classification refinement. Additionally, a novel loss function with a two-step centroid ranking approach enables handling set-level rankings. Extensive experiments on four real-world datasets demonstrate that, when combined with PNN, a wide range of representative CF models can consistently and significantly boost their performance. Even with a simple matrix factorization, PNN can achieve comparable performance to sophisticated graph neutral networks.

cs.IR↗

arXiv2024

Fast inverse lithography based on a model-driven block stacking convolutional neural network

In the realm of lithography, Optical Proximity Correction (OPC) is a crucial resolution enhancement technique that optimizes the transmission function of photomasks on a pixel-based to effectively counter Optical Proximity Effects (OPE). However, conventional pixel-based OPC methods often generate patterns that pose manufacturing challenges, thereby leading to the increased cost in practical scenarios. This paper presents a novel inverse lithographic approach to OPC, employing a model-driven, block stacking deep learning framework that expedites the generation of masks conducive to manufacturing. This method is founded on vector lithography modelling and streamlines the training process by eliminating the requirement for extensive labeled datasets. Furthermore, diversity of mask patterns is enhanced by employing a wave function collapse algorithm, which facilitates the random generation of a multitude of target patterns, therefore significantly expanding the range of mask paradigm. Numerical experiments have substantiated the efficacy of the proposed end-to-end approach, highlighting its superior capability to manage mask complexity within the context of advanced OPC lithography. This advancement is anticipated to enhance the feasibility and economic viability of OPC technology within actual manufacturing environments.

physics.optics↗

arXiv2024

Formation of Fe-6.5wt%Si High Silicon Steel by Double Glow Plasma Surface Metallurgy Technology

High silicon steel with 6.5% silicon content is the best because of its excellent magnetic properties, such as high saturation magnetization, high resistivity, low iron loss and near zero magnetostriction. High silicon steel can greatly save energy, and reduce the weight and size of electrical appliances. This has a very important application prospect for energy and aerospace industry. The high brittleness of high silicon steel makes its production and processing very difficult. For more than 30 years, many steel companies and research institutions around the world have adopted various technical means to study the industrialization of high silicon steel, but they have not been successful . JFE-NKK steel company in Japan has realized the small batch production of high silicon steel by using SiCl4-CVD technology. However, due to the complex process, corrosion and pollution, high cost, its production scale is greatly limited. So far, large-scale production of high silicon steel is still a major challenge in the world. This paper will introduce the experimental results of successfully preparing high silicon steel by Double Glow Plasma Surface Metallurgy Technology. The process is simple and easy without any corrosion or pollution, which may provide a new way for the world to achieve large-scale production of high silicon steel. The large-scale production and wide application of high silicon steel is likely to change the pattern of the world's energy and electric power industry, save a lot of energy for mankind, and create huge economic benefits.

physics.app-ph↗

arXiv2024

Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills

The requirements for real-world manipulation tasks are diverse and often conflicting; some tasks require precise motion while others require force compliance; some tasks require avoidance of certain regions, while others require convergence to certain states. Satisfying these varied requirements with a fixed state-action representation and control strategy is challenging, impeding the development of a universal robotic foundation model. In this work, we propose Meta-Control, the first LLM-enabled automatic control synthesis approach that creates customized state representations and control strategies tailored to specific tasks. Our core insight is that a meta-control system can be built to automate the thought process that human experts use to design control systems. Specifically, human experts heavily use a model-based, hierarchical (from abstract to concrete) thought model, then compose various dynamic models and controllers together to form a control system. Meta-Control mimics the thought model and harnesses LLM's extensive control knowledge with Socrates' "art of midwifery" to automate the thought process. Meta-Control stands out for its fully model-based nature, allowing rigorous analysis, generalizability, robustness, efficient parameter tuning, and reliable real-time execution.

cs.RO↗

arXiv2024

Projection Method for Steady states of Cahn-Hilliard Equation with the Dynamic Boundary Condition

The Cahn-Hilliard equation is a fundamental model that describes phase separation processes of two-phase flows or binary mixtures. In recent years, the dynamic boundary conditions for the Cahn-Hilliard equation have been proposed and analyzed. Our first goal in this article is to present a projection method to locate the steady state of the CH equation with dynamic boundary conditions. The main feature of this method is that it only uses the variational derivative in the metric $L^2$ and not that in the metric $H^{-1}$, thus significantly reducing the computational cost. In addition, the projected dynamics fulfill the important physical properties: mass conservation and energy dissipation. In the temporal construction of the numerical schemes, the convex splitting method is used to ensure a large time step size. Numerical experiments for the two-dimensional Ginzburg-Landau free energy, where the surface potential is the double well potential or the moving contact line potential, are conducted to demonstrate the effectiveness of this projection method.

math.DS↗

arXiv2024

Superconductivity in the medium entropy alloys Nb2TiW and Nb2TiMo

This study describes the synthesis and characterization of Nb2TiW and Nb2TiMo medium entropy alloys (MEAs). The Nb2TiW and Nb2TiMo MEAs can be successfully synthesized by an arc melting method. Their structures and superconducting properties are investigated by detailed characterization of X ray diffraction (XRD), resistivity, magnetization, and specific heat measurements. XRD results confirm that the obtained Nb2TiW and Nb2TiMo compounds have the same body-centered cubic (BCC) structures. Experimental results show that the superconducting transition temperatures Tcs of Nb2TiW and Nb2TiMo are around 4.86 K and 3.22 K, respectively. The upper and lower critical fields of Nb2TiW are 3.52(2) T and 53.36(2) Oe, respectively, and those of Nb2TiMo are 2.11(2) T and 68.23(3) Oe, respectively. First-principles calculations reveal that the d electrons of Nb, Ti, and Mo or W are the dominant contribution of the density of states near the Fermi level. Specific heat measurement results indicate that Nb2TiW and Nb2TiMo display BCS full-gap s-wave superconductivity.

cond-mat.supr-con↗

arXiv2024

Low-power 7-bit hybrid volatile/ nonvolatile tuning of ring resonators

Programmable photonic integrated circuits are expected to play an increasingly important role to enable high-bandwidth optical interconnects, and large-scale in-memory computing as needed to support the rise of artificial intelligence and machine learning technology. To that end, chalcogenide-based non-volatile phase-change materials (PCMs) present a promising solution due to zero static power. However, high switching voltage and small number of operating levels present serious roadblocks to widespread adoption of PCM-programmble units. Here, we demonstrate electrically programmable wide bandgap Sb2S3-clad silicon ring resonator using silicon microheater at CMOS compatible voltage of < 3V. Our device shows low switching energy of 35.33 nJ (0.48 mJ) for amorphization (crystallization) and reversible phase transitions with high endurance (> 2000 switching events) near 1550 nm. Combining volatile thermo-optic effect with non-volatile PCMs, we demonstrate 7-bit (127 levels) operation with excellent repeatability and reduced power consumption. Our demonstration of low-voltage and low-energy operation, combined with the hybrid volatilenonvolatile approach, marks a significant step towards integrating PCM-based programmable units in large-scale optical interconnects.

physics.optics↗

arXiv2024

HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Driving

Generative models have significantly improved the generation and prediction quality on either camera images or LiDAR point clouds for autonomous driving. However, a real-world autonomous driving system uses multiple kinds of input modality, usually cameras and LiDARs, where they contain complementary information for generation, while existing generation methods ignore this crucial feature, resulting in the generated results only covering separate 2D or 3D information. In order to fill the gap in 2D-3D multi-modal joint generation for autonomous driving, in this paper, we propose our framework, \emph{HoloDrive}, to jointly generate the camera images and LiDAR point clouds. We employ BEV-to-Camera and Camera-to-BEV transform modules between heterogeneous generative models, and introduce a depth prediction branch in the 2D generative model to disambiguate the un-projecting from image space to BEV space, then extend the method to predict the future by adding temporal structure and carefully designed progressive training. Further, we conduct experiments on single frame generation and world model benchmarks, and demonstrate our method leads to significant performance gains over SOTA methods in terms of generation metrics.

cs.CV↗

arXiv2024

Comprehensive Performance Evaluation of YOLOv11, YOLOv10, YOLOv9, YOLOv8 and YOLOv5 on Object Detection of Power Equipment

With the rapid development of global industrial production, the demand for reliability in power equipment has been continuously increasing. Ensuring the stability of power system operations requires accurate methods to detect potential faults in power equipment, thereby guaranteeing the normal supply of electrical energy. In this article, the performance of YOLOv5, YOLOv8, YOLOv9, YOLOv10, and the state-of-the-art YOLOv11 methods was comprehensively evaluated for power equipment object detection. Experimental results demonstrate that the mean average precision (mAP) on a public dataset for power equipment was 54.4%, 55.5%, 43.8%, 48.0%, and 57.2%, respectively, with the YOLOv11 achieving the highest detection performance. Moreover, the YOLOv11 outperformed other methods in terms of recall rate and exhibited superior performance in reducing false detections. In conclusion, the findings indicate that the YOLOv11 model provides a reliable and effective solution for power equipment object detection, representing a promising approach to enhancing the operational reliability of power systems.

cs.CV↗

arXiv2024

Symmetric Perception and Ordinal Regression for Detecting Scoliosis Natural Image

Scoliosis is one of the most common diseases in adolescents. Traditional screening methods for the scoliosis usually use radiographic examination, which requires certified experts with medical instruments and brings the radiation risk. Considering such requirement and inconvenience, we propose to use natural images of the human back for wide-range scoliosis screening, which is a challenging problem. In this paper, we notice that the human back has a certain degree of symmetry, and asymmetrical human backs are usually caused by spinal lesions. Besides, scoliosis severity levels have ordinal relationships. Taking inspiration from this, we propose a dual-path scoliosis detection network with two main modules: symmetric feature matching module (SFMM) and ordinal regression head (ORH). Specifically, we first adopt a backbone to extract features from both the input image and its horizontally flipped image. Then, we feed the two extracted features into the SFMM to capture symmetric relationships. Finally, we use the ORH to transform the ordinal regression problem into a series of binary classification sub-problems. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods as well as human performance, which provides a promising and economic solution to wide-range scoliosis screening. In particular, our method achieves accuracies of 95.11% and 81.46% in estimation of general severity level and fine-grained severity level of the scoliosis, respectively.

cs.CV↗