Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores

Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of large amount of interactive feedback. This paper presents a new method that uses scores provided by humans, instead of pairwise preferences, to improve the feedback efficiency of interactive reinforcement learning. Our key insight is that scores can yield significantly more data than pairwise preferences. Specifically, we require a teacher to interactively score the full trajectories of an agent to train a behavioral policy in a sparse reward environment. To avoid unstable scores given by human negatively impact the training process, we propose an adaptive learning scheme. This enables the learning paradigm to be insensitive to imperfect or unreliable scores. We extensively evaluate our method on robotic locomotion and manipulation tasks. The results show that the proposed method can efficiently learn near-optimal policies by adaptive learning from scores, while requiring less feedback compared to pairwise preference learning methods. The source codes are publicly available at https://github.com/SSKKai/Interactive-Scoring-IRL.Comment: Accepted by IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

Network Pruning via Feature Shift Minimization

Channel pruning is widely used to reduce the complexity of deep network models. Recent pruning methods usually identify which parts of the network to discard by proposing a channel importance criterion. However, recent studies have shown that these criteria do not work well in all conditions. In this paper, we propose a novel Feature Shift Minimization (FSM) method to compress CNN models, which evaluates the feature shift by converging the information of both features and filters. Specifically, we first investigate the compression efficiency with some prevalent methods in different layer-depths and then propose the feature shift concept. Then, we introduce an approximation method to estimate the magnitude of the feature shift, since it is difficult to compute it directly. Besides, we present a distribution-optimization algorithm to compensate for the accuracy loss and improve the network compression efficiency. The proposed method yields state-of-the-art performance on various benchmark networks and datasets, verified by extensive experiments. Our codes are available at: https://github.com/lscgx/FSM

Impact of wind farm wake steering control on blade root load

Yaw misalignment is known to affect blade root loads on wind turbines. Most of previous studies concentrate on yaw misalignment in the context of wake steering control, aiming at increasing the total output power of the wind farm. There, wake steering is compared with greedy control, in which yaw misalignment is considered to be 0. In reality, yaw misalignment also occurs in greedy control due to changes in wind direction arising from varying inflow conditions (e.g. turbulence). This paper aims at comparing these two sources of yaw misalignment-naturally changing wind direction versus active yaw in wake steering-in terms of blade root loads. To this end, SCADA data from a real wind farm is used to get yaw misalignment statistics in actual greedy control conditions. FAST.Farm is used to simulate three wind turbines arranged in series, to study maximum and damage-equivalent loads corresponding to in-plane and out-of-plane bending moments on the blades. The results show that compared with actual greedy control, wake steering control reduces the maximum load from the upstream wind turbine, but increases it from other wind turbines. Concerning the damage-equivalent loads from all wind turbines, the blade's in-plane moment is reduced, but the blade's out-of-plane moment is increased.Impact of wind farm wake steering control on blade root loadacceptedVersio

Knowledge, attitude, and practice toward ultrasound screening for breast cancer among women

BackgroundSeveral obstacles can hinder breast cancer screening. This study aimed to investigate the knowledge, attitude, and practice (KAP) toward ultrasound screening for breast cancer in women.MethodsThis cross-sectional study recruited women who visited the breast specialist clinic of Zhongshan City People’s Hospital (a tertiary hospital) between August 2022 and April 2023 through convenience sampling. KAP scores ≥70% were considered adequate.ResultsThis study enrolled 501 participants. The mean knowledge, attitude, and practice levels were 8.56 ± 1.81/12 (possible range 0–12, 71.33%), 29.80 ± 2.71 (possible range 8–40, 74.50%), and 32.04 ± 3.09 (possible range 8–40, 80.10%). Senior high school education (vs. junior high school and below, coefficient = 1.531, 95%CI: 1.013–2.312, p = 0.044), bachelor’s education and above (vs. junior high school and below, coefficient = 5.315, 95%CI: 3.546–7.966, p < 0.001), housewife or unemployed (vs. employed, coefficient = 0.671, 95%CI: 0.466–0.966, p = 0.032), and a history of breast ultrasound (vs. no, coefficient = 1.466, 95%CI: 1.121–1.917, p = 0.005) were independently and positively associated with knowledge. Knowledge (coefficient = 1.303, 95%CI: 1.100–1.544, p = 0.002) and monthly income >10,000 (vs. <5,000, coefficient = 4.364, 95%CI: 1.738–10.956, p = 0.002) were independently and positively associated with attitude. Only attitude (coefficient = 1.212, 95%CI: 1.096–1.340, p < 0.001) was independently and positively associated with the practice. A structural equation modeling (SEM) analysis was used to estimate causality among KAP dimensions, showing that knowledge directly influenced attitude (β = −1.090, p = 0.015), knowledge did not directly influence practice (β = −0.117, p = 0.681) but had an indirect influence (β = 0.826, p = 0.028), and attitude directly influenced practice (β = −0.757, p = 0.016).ConclusionWomen in Zhongshan City had good knowledge, favorable attitudes, and active practice toward breast ultrasound screening for breast cancer. Women’s characteristics associated with a poorer KAP were identified, allowing for more targeted interventions

A new opportunity for the emerging tellurium semiconductor: making resistive switching devices

Abstract: The development of the resistive switching cross-point array as the next-generation platform for high-density storage, in-memory computing and neuromorphic computing heavily relies on the improvement of the two component devices, volatile selector and nonvolatile memory, which have distinct operating current requirements. The perennial current-volatility dilemma that has been widely faced in various device implementations remains a major bottleneck. Here, we show that the device based on electrochemically active, low-thermal conductivity and low-melting temperature semiconducting tellurium filament can solve this dilemma, being able to function as either selector or memory in respective desired current ranges. Furthermore, we demonstrate one-selector-one-resistor behavior in a tandem of two identical Te-based devices, indicating the potential of Te-based device as a universal array building block. These nonconventional phenomena can be understood from a combination of unique electrical-thermal properties in Te. Preliminary device optimization efforts also indicate large and unique design space for Te-based resistive switching devices

Anomalous stopping of laser-accelerated intense proton beam in dense ionized matter

Ultrahigh-intensity lasers (10$^{18}$-10$^{22}$W/cm$^{2}$) have opened up new perspectives in many fields of research and application [1-5]. By irradiating a thin foil, an ultrahigh accelerating field (10$^{12}$ V/m) can be formed and multi-MeV ions with unprecedentedly high intensity (10$^{10}$A/cm$^2$) in short time scale ($\sim$ps) are produced [6-14]. Such beams provide new options in radiography [15], high-yield neutron sources [16], high-energy-density-matter generation [17], and ion fast ignition [18,19]. An accurate understanding of the nonlinear behavior of beam transport in matter is crucial for all these applications. We report here the first experimental evidence of anomalous stopping of a laser-generated high-current proton beam in well-characterized dense ionized matter. The observed stopping power is one order of magnitude higher than single-particle slowing-down theory predictions. We attribute this phenomenon to collective effects where the intense beam drives an decelerating electric field approaching 1GV/m in the dense ionized matter. This finding will have considerable impact on the future path to inertial fusion energy.Comment: 8 pages, 4 figure

Energy loss enhancement of very intense proton beams in dense matter due to the beam-density effect

Thoroughly understanding the transport and energy loss of intense ion beams in dense matter is essential for high-energy-density physics and inertial confinement fusion. Here, we report a stopping power experiment with a high-intensity laser-driven proton beam in cold, dense matter. The measured energy loss is one order of magnitude higher than the expectation of individual particle stopping models. We attribute this finding to the proximity of beam ions to each other, which is usually insignificant for relatively-low-current beams from classical accelerators. The ionization of the cold target by the intense ion beam is important for the stopping power calculation and has been considered using proper ionization cross section data. Final theoretical values agree well with the experimental results. Additionally, we extend the stopping power calculation for intense ion beams to plasma scenario based on Ohm's law. Both the proximity- and the Ohmic effect can enhance the energy loss of intense beams in dense matter, which are also summarized as the beam-density effect. This finding is useful for the stopping power estimation of intense beams and significant to fast ignition fusion driven by intense ion beams