Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding

We formalize and study the multi-goal task assignment and path finding (MG-TAPF) problem from theoretical and algorithmic perspectives. The MG-TAPF problem is to compute an assignment of tasks to agents, where each task consists of a sequence of goal locations, and collision-free paths for the agents that visit all goal locations of their assigned tasks in sequence. Theoretically, we prove that the MG-TAPF problem is NP-hard to solve optimally. We present algorithms that build upon algorithmic techniques for the multi-agent path finding problem and solve the MG-TAPF problem optimally and bounded-suboptimally. We experimentally compare these algorithms on a variety of different benchmark domains.Comment: ICRA 202

Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization

The advent of large-scale pre-trained language models has contributed greatly to the recent progress in natural language processing. Many state-of-the-art language models are first trained on a large text corpus and then fine-tuned on downstream tasks. Despite its recent success and wide adoption, fine-tuning a pre-trained language model often suffers from overfitting, which leads to poor generalizability due to the extremely high complexity of the model and the limited training samples from downstream tasks. To address this problem, we propose a novel and effective fine-tuning framework, named Layerwise Noise Stability Regularization (LNSR). Specifically, we propose to inject the standard Gaussian noise or In-manifold noise and regularize hidden representations of the fine-tuned model. We first provide theoretical analyses to support the efficacy of our method. We then demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L2-SP, Mixout and SMART. While these previous works only verify the effectiveness of their methods on relatively simple text classification tasks, we also verify the effectiveness of our method on question answering tasks, where the target problem is much more difficult and more training examples are available. Furthermore, extensive experimental results indicate that the proposed algorithm can not only enhance the in-domain performance of the language models but also improve the domain generalization performance on out-of-domain data.Comment: Accepted by TNNL

Based on MIPv6 with Support to Improve the Mobile Commerce Transaction

Mobile Commerce is anticipated to be the next business revolution. Under the trend of mobile age, a person begins to realize the benefits of transaction by mobility operations. We can access information, shop and bank on line, work from home and speak and send messages via mobile appliances throughout all over the world. The research that is mobile transaction managing on database has begun since 1950 and skips the Link and Network Layer with support to improve mobile commerce. This paper focus on how effectually to make the new generation of mobile network protocol apply on mobile commerce and improve the mainly four properties required by mobile transactions. The four properties are respectively atomicity, consistency, isolation and durability. The purpose based on the mobile commerce environment and making mobile transactions complete and personal by means of the Destination Extension Header based on IPv6 and the Java Transaction Service. After experiment and testing, this paper verify that we improve the mobile commerce environment and make the mobile transaction more complete with the optimization of the Destination Extension Header based on IPv6 and the Java Transaction Service under the comparison with the environment on IPv4

More on volume dependence of spectral weight function

Spectral weight functions are easily obtained from two-point correlation functions and they might be used to distinguish single-particle from multi-particle states in a finite-volume lattice calculation, a problem crucial for many lattice QCD simulations. In previous studies, it is shown that the spectral weight function for a broad resonance shares the typical volume dependence of a two-particle scattering state i.e. proportional to $1/L^3$ in a large cubic box of size $L$ while the narrow resonance case requires further investigation. In this paper, a generalized formula is found for the spectral weight function which incorporates both narrow and broad resonance cases. Within L\"uscher's formalism, it is shown that the volume dependence of the spectral weight function exhibits a single-particle behavior for a extremely narrow resonance and a two-particle behavior for a broad resonance. The corresponding formulas for both $A^+_1$ and $T^-_1$ channels are derived. The potential application of these formulas in the extraction of resonance parameters are also discussed

Document Clustering Method Based on Frequent Co-occurring Words

PACLIC 20 / Wuhan, China / 1-3 November, 200

Radiative transitions in charmonium from Nf=2N_f=2 twisted mass lattice QCD

We present a study for charmonium radiative transitions: $J/\psi\rightarrow\eta_c\gamma$, $\chi_{c0}\rightarrow J/\Psi\gamma$ and $h_c\rightarrow\eta_c\gamma$ using $N_f=2$ twisted mass lattice QCD gauge configurations. The single-quark vector form factors for $\eta_c$ and $\chi_{c0}$ are also determined. The simulation is performed at a lattice spacing of $a= 0.06666$ fm and the lattice size is $32^3\times 64$. After extrapolation of lattice data at nonzero $Q^2$ to 0, we compare our results with previous quenched lattice results and the available experimental values.Comment: typeset with revtex, 15 pages, 11 figures, 4 table

Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence

Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world. For this purpose, a desirable solution should properly balance memory stability with learning plasticity, and acquire sufficient compatibility to capture the observed distributions. Existing advances mainly focus on preserving memory stability to overcome catastrophic forgetting, but remain difficult to flexibly accommodate incremental changes as biological intelligence (BI) does. By modeling a robust Drosophila learning system that actively regulates forgetting with multiple learning modules, here we propose a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity, and accordingly coordinates a multi-learner architecture to ensure solution compatibility. Through extensive theoretical and empirical validation, our approach not only clearly enhances the performance of continual learning, especially over synaptic regularization methods in task-incremental settings, but also potentially advances the understanding of neurological adaptive mechanisms, serving as a novel paradigm to progress AI and BI together