Comb-shaped Sb₂S₃ nanorod arrays on ZnO nanofibers for thin-film photovoltaics

A hierarchical composite of Sb₂S₃ nanorods grown on zinc oxide (ZnO) nanofiber was prepared, and the formation of comb-shaped Sb₂S₃ nanorod arrays on the ZnO nanofibers was confirmed. It was found that the size of the diameter and the density of the nanorods are regulatable by changing the concentration of polyvinyl pyrrolidone as an additive for the growth of Sb₂S₃ nanorod on ZnO nanofiber. The obtained Sb₂S₃ nanorod arrays were applied as a light absorber for thin-film solar cells composed of glass-fluorine-doped tin oxide/compact ZnO/ZnO nanofibers−ZnS/Sb₂S₃ nanorod arrays/poly(3-hexylthiophene-2, 5-diyl)/MoOx/Ag. The rectification ratio and photocurrent generation efficiency of the comb-shaped Sb₂S₃ nanorod arrays were improved as compared with the heterojunction of randomly stacked Sb₂S₃ nanorods. Smaller series resistance (Rs) of 8.13 Ω cm⁻² and an ideality factor (n) of 2.84 with the comb-shaped Sb₂S₃ nanorod arrays than those of the randomly stacked ones of Rs = 15.01 Ω cm⁻² and n = 3.83 also indicated superior charge extraction property and suppressed recombination of the comb-shaped Sb₂S₃ nanorod arrays at the interface

Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems

Scheduling plays an important role in automated production. Its impact can be found in various fields such as the manufacturing industry, the service industry and the technology industry. A scheduling problem (NP-hard) is a task of finding a sequence of job assignments on a given set of machines with the goal of optimizing the objective defined. Methods such as Operation Research, Dispatching Rules, and Combinatorial Optimization have been applied to scheduling problems but no solution guarantees to find the optimal solution. The recent development of Reinforcement Learning has shown success in sequential decision-making problems. This research presents a Reinforcement Learning approach for scheduling problems. In particular, this study delivers an OpenAI gym environment with search-space reduction for Job Shop Scheduling Problems and provides a heuristic-guided Q-Learning solution with state-of-the-art performance for Multi-agent Flexible Job Shop Problems

RUSH: Robust Contrastive Learning via Randomized Smoothing

Recently, adversarial training has been incorporated in self-supervised contrastive pre-training to augment label efficiency with exciting adversarial robustness. However, the robustness came at a cost of expensive adversarial training. In this paper, we show a surprising fact that contrastive pre-training has an interesting yet implicit connection with robustness, and such natural robustness in the pre trained representation enables us to design a powerful robust algorithm against adversarial attacks, RUSH, that combines the standard contrastive pre-training and randomized smoothing. It boosts both standard accuracy and robust accuracy, and significantly reduces training costs as compared with adversarial training. We use extensive empirical studies to show that the proposed RUSH outperforms robust classifiers from adversarial training, by a significant margin on common benchmarks (CIFAR-10, CIFAR-100, and STL-10) under first-order attacks. In particular, under $\ell_{\infty}$-norm perturbations of size 8/255 PGD attack on CIFAR-10, our model using ResNet-18 as backbone reached 77.8% robust accuracy and 87.9% standard accuracy. Our work has an improvement of over 15% in robust accuracy and a slight improvement in standard accuracy, compared to the state-of-the-arts.Comment: 12 pages, 2 figure

Arbitrating Traffic Contention for Power Saving with Multiple PSM Clients

Data transmission over WiFi quickly drains the batteries of mobile devices. Although the IEEE 802.11 standards provide power save mode (PSM) to help mobile devices conserve energy, PSM fails to bring expected benefits in many real scenarios. In particular, when multiple PSM mobile devices associate to a single access point (AP), PSM does not work well under transmission contention. Optimizing power saving of multiple PSM clients is a challenging task, because each PSM client expects to complete data transmission early so that it can turn to low power mode. In this paper, we define an energy conserving model to describe the general PSM traffic contention problem. We prove that the optimization of energy saving for multiple PSM clients under constraint is an NPcomplete problem. Following this direction, we propose a solution called harmonious power saving mechanism (HPSM) to address one specific case, in which multiple PSM clients associate to a single AP. In HPSM, we first use a basic sociological concept to define the richness of a PSM client based on the link resource it consumes. Then, we separate these poor PSM clients from rich PSM clients in terms of link resource consumption and favor the former to save power when they face PSM transmission contention. We implement prototypes of HPSM based on the open source projects Mad-wifi and NS-2. Our evaluations show that HPSM can help the poor PSM clients effectively save power while only slightly degrading the rich PSM clients\u27 performance in comparison with the existing PSM solutions

Crystal growth and structural analysis of perovskite chalcogenide BaZrS3_3 and Ruddlesden-Popper phase Ba3_3Zr2_2S7_7

Perovskite chalcogenides are gaining substantial interest as an emerging class of semiconductors for optoelectronic applications. High quality samples are of vital importance to examine their inherent physical properties. We report the successful crystal growth of the model system, BaZrS$_3$ and its Ruddlesden-Popper phase Ba$_3$Zr$_2$S$_7$ by flux method. X-ray diffraction analyses showed space group of $Pnma$ with lattice constants of $a$ = 7.056(3) \AA\/, $b$ = 9.962(4) \AA\/, $c$ = 6.996(3) \AA\/ for BaZrS$_3$ and $P4_2/mnm$ with $a$ = 7.071(2) \AA\/, $b$ = 7.071(2) \AA\/, $c$ = 25.418(5) \AA\/ for Ba$_3$Zr$_2$S$_7$. Rocking curves with full-width-at-half-maximum of 0.011$^\circ$ for BaZrS$_3$ and 0.027$^\circ$ for Ba$_3$Zr$_2$S$_7$ were observed. Pole figure analysis, scanning transmission electron microscopy images and electron diffraction patterns also establish high quality of grown crystals. The octahedra tilting in the corner-sharing octahedra network are analyzed by extracting the torsion angles.Comment: 4 Figures, 2 Table

F2^2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns

Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by well-designed perturbations. This could lead to disastrous results on critical applications such as self-driving cars, surveillance security, and medical diagnosis. At present, adversarial training is one of the most effective defenses against adversarial examples. However, traditional adversarial training makes it difficult to achieve a good trade-off between clean accuracy and robustness since spurious features are still learned by DNNs. The intrinsic reason is that traditional adversarial training makes it difficult to fully learn core features from adversarial examples when adversarial noise and clean examples cannot be disentangled. In this paper, we disentangle the adversarial examples into natural and perturbed patterns by bit-plane slicing. We assume the higher bit-planes represent natural patterns and the lower bit-planes represent perturbed patterns, respectively. We propose a Feature-Focusing Adversarial Training (F$^2$AT), which differs from previous work in that it enforces the model to focus on the core features from natural patterns and reduce the impact of spurious features from perturbed patterns. The experimental results demonstrated that F$^2$AT outperforms state-of-the-art methods in clean accuracy and adversarial robustness