arXiv subjects

Jing Hou

Publications and source records attributed to Jing Hou.

8 recordsLinked to original sources

arXiv2025

Long-term behavior of nonlocal reaction-diffusion equation under small random perturbations

In this paper, we investigate the nonlocal reaction-diffusion equation driven by stationary noise, which is a regular approximation to white noise and satisfies certain properties. We show the existence of random attractor for the equation. When stochastic nonlocal reaction-diffusion equation is driven by additive and multiplicative noise, we prove that the solution converges to the corresponding deterministic equation and establish the upper semicontinuity of the attractors as the perturbation parameter \delta and \epsilon both approaches zero.

math.DS↗

arXiv2025

Generative AI and Organizational Structure in the Knowledge Economy

Generative AI (GenAI) is rapidly transforming knowledge work, yet its implications for organizational hierarchies remain poorly understood. Unlike earlier automation technologies, GenAI can both perform tasks autonomously and assist human workers, while its intrinsic fallibility, the tendency to produce confident but incorrect outputs, demands continuous human oversight. We develop a theoretical model to study how GenAI reshapes workforce composition and organizational structure in knowledge-based hierarchies. Our analysis highlights two deployment dimensions, namely mode (automation vs.\ augmentation) and location (worker vs.\ expert layer), which generate a 2X2 design space whose organizational implications are not predicted by traditional technology adoption theories. We obtain three main findings. First, GenAI's effect on entry-level skill requirements is critically mode-dependent. Worker-level automation leads firms to hire fewer but more skilled workers who validate AI outputs and limit costly escalation to experts. Worker-level augmentation, by contrast, expands workers' effective capability, allowing firms to relax entry-level knowledge requirements while sustaining performance. The decline in junior employment documented in recent studies therefore reflects deployment choices favoring automation over augmentation, not an inevitable consequence of GenAI itself. Second, expert-level deployment uniformly lowers entry-level skill requirements, regardless of whether GenAI automates or augments. By expanding experts' capacity to support downstream workers, it enables organizations to employ a broader base of less specialized workers, thereby broadening entry-level access to knowledge work. Third, organizational structure evolves non-monotonically as GenAI improves: across all four deployment architectures, the span of control initially contracts before eventually expanding.

econ.TH↗

arXiv2024

Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities across many challenging real-world tasks. As the extension of RL in the multi-agent system domain, multi-agent RL (MARL) not only need to learn the control policy but also requires consideration regarding interactions with all other agents in the environment, mutual influences among different system components, and the distribution of computational resources. This augments the complexity of algorithmic design and poses higher requirements on computational resources. Simultaneously, simulators are crucial to obtain realistic data, which is the fundamentals of RL. In this paper, we first propose a series of metrics of simulators and summarize the features of existing benchmarks. Second, to ease comprehension, we recall the foundational knowledge and then synthesize the recently advanced studies of MARL-related autonomous driving and intelligent transportation systems. Specifically, we examine their environmental modeling, state representation, perception units, and algorithm design. Conclusively, we discuss open challenges as well as prospects and opportunities. We hope this paper can help the researchers integrate MARL technologies and trigger more insightful ideas toward the intelligent and autonomous driving.

cs.AI↗

arXiv2023

Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation

Building embodied agents on integrating Large Language Models (LLMs) and Reinforcement Learning (RL) have revolutionized human-AI interaction: researchers can now leverage language instructions to plan decision-making for open-ended tasks. However, existing research faces challenges in meeting the requirement of open-endedness. They typically either train LLM/RL models to adapt to a fixed counterpart, limiting exploration of novel skills and hindering the efficacy of human-AI interaction. To this end, we present OpenPAL, a co-training framework comprising two stages: (1) fine-tuning a pre-trained LLM to translate human instructions into goals for planning, and goal-conditioned training a policy for decision-making; (2) co-training to align the LLM and policy, achieving instruction open-endedness. We conducted experiments using Contra, an open-ended FPS game, demonstrating that an agent trained with OpenPAL not only comprehends arbitrary instructions but also exhibits efficient execution. These results suggest that OpenPAL holds the potential to construct open-ended embodied agents in practical scenarios.

cs.AI↗

arXiv2023

Spreeze: High-Throughput Parallel Reinforcement Learning Framework

The promotion of large-scale applications of reinforcement learning (RL) requires efficient training computation. While existing parallel RL frameworks encompass a variety of RL algorithms and parallelization techniques, the excessively burdensome communication frameworks hinder the attainment of the hardware's limit for final throughput and training effects on a single desktop. In this paper, we propose Spreeze, a lightweight parallel framework for RL that efficiently utilizes a single desktop hardware resource to approach the throughput limit. We asynchronously parallelize the experience sampling, network update, performance evaluation, and visualization operations, and employ multiple efficient data transmission techniques to transfer various types of data between processes. The framework can automatically adjust the parallelization hyperparameters based on the computing ability of the hardware device in order to perform efficient large-batch updates. Based on the characteristics of the "Actor-Critic" RL algorithm, our framework uses dual GPUs to independently update the network of actors and critics in order to further improve throughput. Simulation results show that our framework can achieve up to 15,000Hz experience sampling and 370,000Hz network update frame rate using only a personal desktop computer, which is an order of magnitude higher than other mainstream parallel RL frameworks, resulting in a 73% reduction of training time. Our work on fully utilizing the hardware resources of a single desktop computer is fundamental to enabling efficient large-scale distributed RL training.

cs.LG↗

arXiv2023

Dynamic of Time Varying Weighted Backward Shift Operator

we study the hypercyclic and chaotic properties of the time varying weighted backward shift operator $(Tx)(t)=w(t)x(t+a)$ in $L_p(0,\infty)(1\leq p<\infty)$ and $C_0[0,\infty)$. And we also analyse the spectral structure of the operators if the spaces are complex.

math.FA↗

arXiv2022

Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition

Named entity recognition (NER) task aims at identifying entities from a piece of text that belong to predefined semantic types such as person, location, organization, etc. The state-of-the-art solutions for flat entities NER commonly suffer from capturing the fine-grained semantic information in underlying texts. The existing span-based approaches overcome this limitation, but the computation time is still a concern. In this work, we propose a novel span-based NER framework, namely Global Pointer (GP), that leverages the relative positions through a multiplicative attention mechanism. The ultimate goal is to enable a global view that considers the beginning and the end positions to predict the entity. To this end, we design two modules to identify the head and the tail of a given entity to enable the inconsistency between the training and inference processes. Moreover, we introduce a novel classification loss function to address the imbalance label problem. In terms of parameters, we introduce a simple but effective approximate method to reduce the training parameters. We extensively evaluate GP on various benchmark datasets. Our extensive experiments demonstrate that GP can outperform the existing solution. Moreover, the experimental results show the efficacy of the introduced loss function compared to softmax and entropy alternatives.

cs.CL↗

arXiv2015

Mid-infrared ultra-short mode-locked fiber laser utilizing topological insulator Bi2Te3 nano-sheets as the saturable absorber

The newly-emergent two-dimensional topological insulators (TIs) have shown their unique electronic and optical properties, such as good thermal management, high nonlinear refraction index and ultrafast relaxation time. Their narrow energy band gaps predict their optical absorption ability further into the mid-infrared region and their possibility to be very broadband light modulators ranging from the visible to the mid-infrared region. In this paper, a mid-infrared mode-locked fluoride fiber laser with TI Bi2Te3 nano-sheets as the saturable absorber is presented. Continuous wave lasing, Q-switched and continuous-wave mode-locking (CW-ML) operations of the laser are observed sequentially by increasing the pump power. The observed CW-ML pulse train has a pulse repetition rate of 10.4 MHz, a pulse width of ~6 ps, and a center wavelength of 2830 nm. The maximum achievable pulse energy is 8.6 nJ with average power up to 90 mW. This work forcefully demonstrates the promising applications of two-dimensional TIs for ultra-short laser operation and nonlinear optics in the mid-infrared region.

physics.optics↗