ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes

Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete(e.g., binary) object goal states, which poses challenges for the learning of complex tasks and transferring learned policy from simulated environments to the real world. Furthermore, state discretization limits a robot's ability to follow human instructions based on the grounding of actions and states. To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes. ARNOLD is comprised of 8 language-conditioned tasks that involve understanding object states and learning policies for continuous goals. To promote language-instructed learning, we provide expert demonstrations with template-generated language descriptions. We assess task performance by utilizing the latest language-conditioned policy learning models. Our results indicate that current models for language-conditioned manipulations continue to experience significant challenges in novel goal-state generalizations, scene generalizations, and object generalizations. These findings highlight the need to develop new algorithms that address this gap and underscore the potential for further research in this area. See our project page at: https://arnold-benchmark.github.ioComment: The first two authors contributed equally; 20 pages; 17 figures; project availalbe: https://arnold-benchmark.github.io

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance

Performance of the CMS muon trigger system in proton-proton collisions at √s = 13 TeV

The muon trigger system of the CMS experiment uses a combination of hardware and software to identify events containing a muon. During Run 2 (covering 2015-2018) the LHC achieved instantaneous luminosities as high as 2 × 10 cm s while delivering proton-proton collisions at √s = 13 TeV. The challenge for the trigger system of the CMS experiment is to reduce the registered event rate from about 40 MHz to about 1 kHz. Significant improvements important for the success of the CMS physics program have been made to the muon trigger system via improved muon reconstruction and identification algorithms since the end of Run 1 and throughout the Run 2 data-taking period. The new algorithms maintain the acceptance of the muon triggers at the same or even lower rate throughout the data-taking period despite the increasing number of additional proton-proton interactions in each LHC bunch crossing. In this paper, the algorithms used in 2015 and 2016 and their improvements throughout 2017 and 2018 are described. Measurements of the CMS muon trigger performance for this data-taking period are presented, including efficiencies, transverse momentum resolution, trigger rates, and the purity of the selected muon sample. This paper focuses on the single- and double-muon triggers with the lowest sustainable transverse momentum thresholds used by CMS. The efficiency is measured in a transverse momentum range from 8 to several hundred GeV

Resistive switching behaviors of oxygen-rich TaOx films prepared by reactive magnetron sputtering

ABSTRACTIn this work, Ta2O5 films were first deposited on Si substrates by reactive magnetron sputtering of a Ta metal target at various substrate temperatures, RF powers and sputtering pressures. The crystal characteristics of these films can be effectively tailored by controlling the sputtering process. Based on the optimized process parameters, tantalum oxide (TaOx) films with different oxygen component content were sputtered on ITO buffered Si substrates and comparatively investigated. The results show that the film with Ta/TaOx/ITO structure has a resistance switching (RS) behavior and its conduction mechanism is closely related to the O2-/O concentration related to the oxygen partial pressure at the dielectric layer and electrode interface. This study provides an in-depth understanding of the component/structure design and structure-activity relationship for high-performance TaOx-based resistive memory

A Modified Impact-Increment-Based State Enumeration Method and its Application in Power Distribution Systems

Reliable planning and operation of power distribution systems are of great significance. In this paper, the impact-increment based state enumeration (IIBSE) method is modified to adapt to the features of distribution systems. With the proposed method, the expectation, probabilistic, and duration reliability indices can be accurately obtained with a lower enumerated order of contingency states. In addition, the time-consuming optimal power flow (OPF) calculation can be replaced by a simple matrix operation for both independent and radial series failure states. Therefore, the accuracy and efficiency of the assessment process are improved comprehensively. The case of RBTS bus 6 system and IEEE 123 node test feeder system are utilized to test the performance of the modified IIBSE. The results show the superiority of the proposed method over Monte Carlo (MC) sampling and state enumeration (SE) methods in distribution systems

SwitchFuzz: Switch Short-Term Goals in Directed Grey-Box Fuzzing

In recent years, fuzzing has become a powerful tool for security researchers to uncover security vulnerabilities. It is used to discover software vulnerabilities by continuously generating malformed inputs to trigger bugs. Directed grey-box fuzzing has also been widely used in the verification of patch testing and in vulnerability reproduction. For directed grey-box fuzzing, the core problem is to make test cases reach the target and trigger vulnerabilities faster. Selecting seeds that are closer to the target site to be mutated first is an effective method. For this purpose, the DGF calculates the distance between the execution path and the target site by a specific algorithm. However, as time elapses in the execution process, the seeds covering a larger amount of basic blocks may be overlooked due to their long distances. At the same time, directed fuzzing often ignores the impact of coverage on test efficiency, resulting in a local optimum problem without accumulating enough valuable test cases. In this paper, we analyze and discuss these problems and propose SwitchFuzz, a fuzzer that can switch short-term goals during execution. SwitchFuzz keeps shortening the distance of test cases to reach the target point when it performs well and prioritizes reaching the target point. When positive feedback is not achieved over a period of time, SwitchFuzz tries to explore more possibilities. We compared the efficiency of SwitchFuzz with that of AFLGO in setting single target and multiple targets for crash recurrence in our experiments, respectively. The results show that SwitchFuzz produces a significant improvement over AFLGO in both the speed and the probability of triggering a specified crash. SwitchFuzz can discover more edges than AFLGO in the same amount of time and can generate seeds with smaller distances

SwitchFuzz: Switch Short-Term Goals in Directed Grey-Box Fuzzing

In recent years, fuzzing has become a powerful tool for security researchers to uncover security vulnerabilities. It is used to discover software vulnerabilities by continuously generating malformed inputs to trigger bugs. Directed grey-box fuzzing has also been widely used in the verification of patch testing and in vulnerability reproduction. For directed grey-box fuzzing, the core problem is to make test cases reach the target and trigger vulnerabilities faster. Selecting seeds that are closer to the target site to be mutated first is an effective method. For this purpose, the DGF calculates the distance between the execution path and the target site by a specific algorithm. However, as time elapses in the execution process, the seeds covering a larger amount of basic blocks may be overlooked due to their long distances. At the same time, directed fuzzing often ignores the impact of coverage on test efficiency, resulting in a local optimum problem without accumulating enough valuable test cases. In this paper, we analyze and discuss these problems and propose SwitchFuzz, a fuzzer that can switch short-term goals during execution. SwitchFuzz keeps shortening the distance of test cases to reach the target point when it performs well and prioritizes reaching the target point. When positive feedback is not achieved over a period of time, SwitchFuzz tries to explore more possibilities. We compared the efficiency of SwitchFuzz with that of AFLGO in setting single target and multiple targets for crash recurrence in our experiments, respectively. The results show that SwitchFuzz produces a significant improvement over AFLGO in both the speed and the probability of triggering a specified crash. SwitchFuzz can discover more edges than AFLGO in the same amount of time and can generate seeds with smaller distances

Bridge Node Detection between Communities Based on GNN

In a complex network, some nodes are relatively concentrated in topological structure, thus forming a relatively independent node group, which we call a community. Usually, there are multiple communities on a network, and these communities are interconnected and exchange information with each other. A node that plays an important role in the process of information exchange between communities is called an inter-community bridge node. Traditional methods of defining and detecting bridge nodes mostly quantify the bridging effect of nodes by collecting local structural information of nodes and defining index operations. However, on the one hand, it is often difficult to capture the deep topological information in complex networks based on a single indicator, resulting in inaccurate evaluation results; on the other hand, for networks without community structure, such methods may rely on community partitioning algorithms, which require significant computing power. In this paper, considering the multi-dimensional attributes and structural characteristics of nodes, a deep learning-based framework named BND is designed to quickly and accurately detect bridge nodes. Considering that the bridging function of nodes between communities is abstract and complex, and may be related to the multi-dimensional information of nodes, we construct an attribute graph on the basis of the original graph according to the features of the five dimensions of the node to meet our needs for extracting bridging-related attributes. In the deep learning model, we overlay graph neural network layers to process the input attribute graph and add fully connected layers to improve the final classification effect of the model. Graph neural network algorithms including GCN, GAT, and GraphSAGE are compatible with our proposed framework. To the best of our knowledge, our work is the first application of graph neural network techniques in the field of bridge node detection. Experiments show that our designed framework can effectively capture network topology information and accurately detect bridge nodes in the network. In the overall model effect evaluation results based on indicators such as Accuracy and F1 score, our proposed graph neural network model is generally better than baseline methods. In the best case, our model has an Accuracy of 0.9050 and an F1 score of 0.8728

Experimental Investigation on Vibro-Acoustic Characteristics of Stiffened Plate Structures with Different Welding Parameters

Varied welding process parameters result in different welding energy inputs and welding residual stresses, significantly impacting the vibro-acoustic characteristics. This work investigated the influence of different welding energy inputs on the vibro-acoustic characteristics of the stiffened plate structure. Several experiments on the stiffened plate structure with different welding energy inputs were conducted regarding modal, underwater vibration, and acoustic radiation. The results revealed that welding energy input had the most significant impact on the first-order natural frequency, and the impact first becomes higher and subsequently decreases as welding energy input increases. The welding energy input had relatively little effect on the peak point distribution of vibration and acoustic radiation curves but could affect the peak point amplitude. With the increase in welding energy input, the overall vibration acceleration level and sound radiation level in each frequency band decreased and then increased. The best result was obtained when the welding energy input was 167 J/cm with a welding current of 200 A, a welding voltage of 25 V, and a welding speed of 3.02–3.06 mm/s. Based on construction technology, this research can provide some instructive insights for enhancing the acoustic stealth performance of ships and marine structures

A fast reliability assessment method for power system using self-supervised learning and feature reconstruction

Under the global zero-carbon campaign (United Nations Climate Change, 2021), more stochastic renewable generations are being integrated into the system. This trend significantly expands the state space and increases the computational burden for the model-driven reliability assessment. Data-driven approaches are developed to improve efficiency based on artificial intelligence. However, the requirement of large-scale samples limits it in applications. To address that, this paper adopts a self-supervised stage to avoid the high cost of labeling, while ensuring the efficiency and accuracy of reliability assessment. The training process of this method is split into two stages. In the first stage, feature reconstruction and unsupervised learning are used to provide the initial network parameters. Thereafter, the second learning stage can be trained in a task-agnostic way with fewer labels. The results of case study demonstrate the effectiveness of the proposed approach