arXiv subjects

Jing

Publications and source records attributed to Jing.

9 recordsLinked to original sources

arXiv2026

LERA: Reinstating Judgment as a Structural Precondition for Execution in Automated Systems

As automated systems increasingly transition from decision support to direct execution, the problem of accountability shifts from decision quality to execution legitimacy. While optimization, execution, and feedback mechanisms are extensively modeled in contemporary AI and control architectures, the structural role of judgment remains undefined. Judgment is typically introduced as an external intervention rather than a native precondition to execution. This work does not propose a new decision-making algorithm or safety heuristic, but identifies a missing structural role in contemporary AI and control architectures. This paper identifies this absence as a missing Judgment Root Node and proposes LERA (Judgment-Governance Architecture) , a structural framework that enforces judgment as a mandatory, non-bypassable prerequisite for execution. LERA is founded on two axioms: (1) execution is not a matter of system capability, but of structural permission, and (2) execution is not the chronological successor of judgment, but its structural consequence. Together, these axioms decouple execution legitimacy from computational capacity and bind it to judgment completion through a governance gate. LERA does not aim to optimize decisions or automate judgment. Instead, it institutionalizes judgment as a first-class architectural component, ensuring that execution authority remains accountable. By reinstating judgment at the execution boundary, LERA establishes a foundational architecture for judgment-governed automation.

cs.CY↗

arXiv2025

DexCanvas: Bridging Human Demonstrations and Robot Learning for Dexterous Manipulation

We present DexCanvas, a large-scale hybrid real-synthetic human manipulation dataset containing 7,000 hours of dexterous hand-object interactions seeded from 70 hours of real human demonstrations, organized across 21 fundamental manipulation types based on the Cutkosky taxonomy. Each entry combines synchronized multi-view RGB-D, high-precision mocap with MANO hand parameters, and per-frame contact points with physically consistent force profiles. Our real-to-sim pipeline uses reinforcement learning to train policies that control an actuated MANO hand in physics simulation, reproducing human demonstrations while discovering the underlying contact forces that generate the observed object motion. DexCanvas is the first manipulation dataset to combine large-scale real demonstrations, systematic skill coverage based on established taxonomies, and physics-validated contact annotations. The dataset can facilitate research in robotic manipulation learning, contact-rich control, and skill transfer across different hand morphologies.

cs.RO↗

arXiv2024

Some Critical Thinking on EV Battery Reliability: from Enhancement to Optimization -- comprehensive perspectives, lifecycle innovation, system cognation, and strategic insights

In the era of sustainable transportation, the significance of electric vehicles (EVs) and their battery technology is becoming increasingly paramount. This study addresses the critical aspect of EV battery reliability, an essential factor in the vehicles' sustainability, performance, and longevity. Current efforts to enhance EV battery reliability tend to focus on isolated areas, often missing the broader, interconnected challenges within the system. This research investigates these challenges across micro, meso, and macro levels, presenting a novel lifecycle framework that includes "Zero"-Life reliability and phases such as use, reuse, repurpose, and recycling. By adopting a holistic approach and delving into system cognition, the study aims to bridge the gap between isolated improvements and comprehensive system optimization, aligning with global sustainability goals and contributing to the advancement of sustainable transportation and EV technology.

eess.SY↗

arXiv2023

A Cross-direction Task Decoupling Network for Small Logo Detection

Logo detection plays an integral role in many applications. However, handling small logos is still difficult since they occupy too few pixels in the image, which burdens the extraction of discriminative features. The aggregation of small logos also brings a great challenge to the classification and localization of logos. To solve these problems, we creatively propose Cross-direction Task Decoupling Network (CTDNet) for small logo detection. We first introduce Cross-direction Feature Pyramid (CFP) to realize cross-direction feature fusion by adopting horizontal transmission and vertical transmission. In addition, Multi-frequency Task Decoupling Head (MTDH) decouples the classification and localization tasks into two branches. A multi frequency attention convolution branch is designed to achieve more accurate regression by combining discrete cosine transform and convolution creatively. Comprehensive experiments on four logo datasets demonstrate the effectiveness and efficiency of the proposed method.

cs.CV↗

arXiv2013

Bit Level Soft Decision Decoding of Triple Parity Reed Solomon Codes through Automorphism Groups

This paper discusses bit-level soft decoding of triple-parity Reed-Solomon (RS) codes through automorphism permutation. A new method for identifying the automorphism groups of RS binary images is first developed. The new algorithm runs effectively, and can handle more RS codes and capture more automorphism groups than the existing ones. Utilizing the automorphism results, a new bit-level soft-decision decoding algorithm is subsequently developed for general $(n,n-3,4)$ RS codes. Simulation on $(31,28,4)$ RS codes demonstrates an impressive gain of more than 1 dB at the bit error rate of $10^{-5}$ over the existing algorithms.

cs.IT↗

arXiv2011

Efficient Image Transmission Through Analog Error Correction

This paper presents a new paradigm for image transmission through analog error correction codes. Conventional schemes rely on digitizing images through quantization (which inevitably causes significant bandwidth expansion) and transmitting binary bit-streams through digital error correction codes (which do not automatically differentiate the different levels of significance among the bits). To strike a better overall performance in terms of transmission efficiency and quality, we propose to use a single analog error correction code in lieu of digital quantization, digital code and digital modulation. The key is to get analog coding right. We show that this can be achieved by cleverly exploiting an elegant "butterfly" property of chaotic systems. Specifically, we demonstrate a tail-biting triple-branch baker's map code and its maximum-likelihood decoding algorithm. Simulations show that the proposed analog code can actually outperform digital turbo code, one of the best codes known to date. The results and findings discussed in this paper speak volume for the promising potential of analog codes, in spite of their rather short history.

cs.MM↗

arXiv2011

A New Class of MDS Erasure Codes Based on Graphs

Maximum distance separable (MDS) array codes are XOR-based optimal erasure codes that are particularly suitable for use in disk arrays. This paper develops an innovative method to build MDS array codes from an elegant class of nested graphs, termed \textit{complete-graph-of-rings (CGR)}. We discuss a systematic and concrete way to transfer these graphs to array codes, unveil an interesting relation between the proposed map and the renowned perfect 1-factorization, and show that the proposed CGR codes subsume B-codes as their "contracted" codes. These new codes, termed \textit{CGR codes}, and their dual codes are simple to describe, and require minimal encoding and decoding complexity.

cs.IT↗

arXiv2011

Linear Analog Codes: The Good and The Bad

This paper studies the theory of linear analog error correction coding. Since classical concepts of minimum Hamming distance and minimum Euclidean distance fail in the analog context, a new metric, termed the "minimum (squared Euclidean) distance ratio," is defined. It is shown that linear analog codes that achieve the largest possible value of minimum distance ratio also achieve the smallest possible mean square error (MSE). Based on this achievability, a concept of "maximum distance ratio expansible (MDRE)" is established, in a spirit similar to maximum distance separable (MDS). Existing codes are evaluated, and it is shown that MDRE and MDS can be simultaneously achieved through careful design.

cs.IT↗

arXiv2010

Precoded Turbo Equalizer for Power Line Communication Systems

Power line communication continues to draw increasing interest by promising a wide range of applications including cost-free last-mile communication solution. However, signal transmitted through the power lines deteriorates badly due to the presence of severe inter-symbol interference (ISI) and harsh random pulse noise. This work proposes a new precoded turbo equalization scheme specifically designed for the PLC channels. By introducing useful precoding to reshape ISI, optimizing maximum {\it a posteriori} (MAP) detection to address the non-Gaussian pulse noise, and performing soft iterative decision refinement, the new equalizer demonstrates a gain significantly better than the existing turbo equalizers.

cs.IT↗