Distributed Consensus Algorithm for Decision-Making in Multi-agent Multi-armed Bandit

We study a structured multi-agent multi-armed bandit (MAMAB) problem in a dynamic environment. A graph reflects the information-sharing structure among agents, and the arms' reward distributions are piecewise-stationary with several unknown change points. The agents face the identical piecewise-stationary MAB problem. The goal is to develop a decision-making policy for the agents that minimizes the regret, which is the expected total loss of not playing the optimal arm at each time step. Our proposed solution, Restarted Bayesian Online Change Point Detection in Cooperative Upper Confidence Bound Algorithm (RBO-Coop-UCB), involves an efficient multi-agent UCB algorithm as its core enhanced with a Bayesian change point detector. We also develop a simple restart decision cooperation that improves decision-making. Theoretically, we establish that the expected group regret of RBO-Coop-UCB is upper bounded by $\mathcal{O}(KNM\log T + K\sqrt{MT\log T})$, where K is the number of agents, M is the number of arms, and T is the number of time steps. Numerical experiments on synthetic and real-world datasets demonstrate that our proposed method outperforms the state-of-the-art algorithms

Cooperative Thresholded Lasso for Sparse Linear Bandit

We present a novel approach to address the multi-agent sparse contextual linear bandit problem, in which the feature vectors have a high dimension $d$ whereas the reward function depends on only a limited set of features - precisely $s_0 \ll d$. Furthermore, the learning follows under information-sharing constraints. The proposed method employs Lasso regression for dimension reduction, allowing each agent to independently estimate an approximate set of main dimensions and share that information with others depending on the network's structure. The information is then aggregated through a specific process and shared with all agents. Each agent then resolves the problem with ridge regression focusing solely on the extracted dimensions. We represent algorithms for both a star-shaped network and a peer-to-peer network. The approaches effectively reduce communication costs while ensuring minimal cumulative regret per agent. Theoretically, we show that our proposed methods have a regret bound of order $\mathcal{O}(s_0 \log d + s_0 \sqrt{T})$ with high probability, where $T$ is the time horizon. To our best knowledge, it is the first algorithm that tackles row-wise distributed data in sparse linear bandits, achieving comparable performance compared to the state-of-the-art single and multi-agent methods. Besides, it is widely applicable to high-dimensional multi-agent problems where efficient feature extraction is critical for minimizing regret. To validate the effectiveness of our approach, we present experimental results on both synthetic and real-world datasets

Advances in Emotion Recognition: Link to Depressive Disorder

Emotion recognition enables real-time analysis, tagging, and inference of cognitive affective states from human facial expression, speech and tone, body posture and physiological signal, as well as social text on social network platform. Recognition of emotion pattern based on explicit and implicit features extracted through wearable and other devices could be decoded through computational modeling. Meanwhile, emotion recognition and computation are critical to detection and diagnosis of potential patients of mood disorder. The chapter aims to summarize the main findings in the area of affective recognition and its applications in major depressive disorder (MDD), which have made rapid progress in the last decade

Detection of Freezing of Gait Using Template-Matching-Based Approaches

Every year, injuries associated with fall incidences cause lots of human suffering and assets loss for Parkinson’s disease (PD) patients. Thereinto, freezing of gait (FOG), which is one of the most common symptoms of PD, is quite responsible for most incidents. Although lots of researches have been done on characterized analysis and detection methods of FOG, large room for improvement still exists in the high accuracy and high efficiency examination of FOG. In view of the above requirements, this paper presents a template-matching-based improved subsequence Dynamic Time Warping (IsDTW) method, and experimental tests were carried out on typical open source datasets. Results show that, compared with traditional template-matching and statistical learning methods, proposed IsDTW not only embodies higher experimental accuracy (92%) but also has a significant runtime efficiency. By contrast, IsDTW is far more available in real-time practice applications

The Influence of Main Bearing Parameters on The Bearing Wear in Rotary Compressor

The main bearing and sub bearing which support the crankshaft rotation often have wear in the rotary compressor, and the exceptional wear will cause a series of problems which contain the vibration, noises, frictional power rising and reliability reduced. The process of improving the actual exceptional wear problems of bearings is analyzed. And based on the finite element method (FEM), the results of the original and improved bearings are compared with each other; contact stress is chosen to be used to evaluate the wear condition of bearings. Then the influence of height, diameter and clearance of main bearing on the wear of the bearing is analyzed by the accelerated life test and the FEM simulation, and the feasibility of the bearing contact stress to evaluate the wear condition of bearings is further verified, at the same time it provides a theoretical basis for the design of compressor bearing

Anisotropic gap structure and sign reversal symmetry in monolayer Fe(Se,Te)

The iron-based superconductors are an ideal platform to reveal the enigma of the unconventional superconductivity and potential topological superconductivity. Among them, the monolayer Fe(Se,Te)/SrTiO3(001), which is proposed to be topological nontrivial, shows interface-enhanced high-temperature superconductivity in the two dimensional limit. However, the experimental studies on the superconducting pairing mechanism of monolayer Fe(Se,Te) films are still limited. Here, by measuring quasiparticle interference in monolayer Fe(Se,Te)/SrTiO3(001), we report the observation of the anisotropic structure of the large superconducting gap and the sign change of the superconducting gap on different electron pockets. The results are well consistent with the 'bonding-antibonding' s+- wave pairing symmetry driven by spin fluctuations in conjunction with spin-orbit coupling. Our work is of basic significance not only for a unified superconducting formalism in the iron-based superconductors, but also for understanding of topological superconductivity in high-temperature superconductors

Modified Wandzura-Wilczek Relation with the Nachtmann Variable

If one retains M^2/Q^2 terms in the kinematics, the Nachtmann variable \xi seems to be more appropriate to describe deep inelastic lepton-nucleon scattering. Up to the first power of M^2/Q^2, a modified Wandzura-Wilczek relation with respect to \xi was derived. Kinematical correction factors are given as functions of \xi and Q^2. A comparison of the modified g_2^WW(\xi) and original g_2^WW(x) with the most recent g_2 data is shown.Comment: 10 pages, 3 figures, revised version with minor correction

Refined Qingkailing Protects MCAO Mice from Endoplasmic Reticulum Stress-Induced Apoptosis with a Broad Time Window

In the current study, we are investigating effect of refined QKL on ischemia-reperfusion-induced brain injury in mice. Methods. Mice were employed to induce ischemia-reperfusion injury of brain by middle cerebral artery occlusion (MCAO). RQKL solution was administered with different doses (0, 1.5, 3, and 6 mL/kg body weight) at the same time of onset of ischemia, and with the dose of 1.5 mL/kg at different time points (0, 1.5, 3, 6, and 9 h after MCAO). Neurological function and brain infarction were examined and cell apoptosis and ROS at prefrontal cortex were evaluated 24 h after MCAO, and western blot and intracellular calcium were also researched, respectively. Results. RQKL of all doses can improve neurological function and decrease brain infarction, and it performed significant effect in 0, 1.5, 3, and 6 h groups. Moreover, RQKL was able to reduce apoptotic process by reduction of caspase-3 expression, or restraint of eIF2a phosphorylation and caspase-12 activation. It was also able to reduce ROS and modulate intracellular calcium in the brain. Conclusion. RQKL can prevent ischemic-induced brain injury with a time window of 6 h, and its mechanism might be related to suppress ER stress-mediated apoptotic signaling