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Open radio access network (ORAN) provides an open architecture to implement radio access network (RAN) of the fifth generation (5G) and beyond mobile communications. As a key technology for the evolution to the sixth generation (6G) systems, cell-free massive multiple-input multiple-output (CF-mMIMO) can effectively improve the spectrum efficiency, peak rate and reliability of wireless communication systems. Starting from scalable implementation of CF-mMIMO, we study a cell-free RAN (CF-RAN) under the ORAN architecture. Through theoretical analysis and numerical simulation, we investigate the uplink and downlink spectral efficiencies of CF-mMIMO with the new architecture. We then discuss the implementation issues of CF-RAN under ORAN architecture, including time-frequency synchronization and over-the-air reciprocity calibration, low layer splitting, deployment of ORAN radio units (O-RU), artificial intelligent based user associations. Finally, we present some representative experimental results for the uplink distributed reception and downlink coherent joint transmission of CF-RAN with commercial off-the-shelf O-RUs.

Event-based cameras are bio-inspired sensors that capture brightness change of every pixel in an asynchronous manner. Compared with frame-based sensors, event cameras have microsecond-level latency and high dynamic range, hence showing great potential for object detection under high-speed motion and poor illumination conditions. Due to sparsity and asynchronism nature with event streams, most of existing approaches resort to hand-crafted methods to convert event data into 2D grid representation. However, they are sub-optimal in aggregating information from event stream for object detection. In this work, we propose to learn an event representation optimized for event-based object detection. Specifically, event streams are divided into grids in the x-y-t coordinates for both positive and negative polarity, producing a set of pillars as 3D tensor representation. To fully exploit information with event streams to detect objects, a dual-memory aggregation network (DMANet) is proposed to leverage both long and short memory along event streams to aggregate effective information for object detection. Long memory is encoded in the hidden state of adaptive convLSTMs while short memory is modeled by computing spatial-temporal correlation between event pillars at neighboring time intervals. Extensive experiments on the recently released event-based automotive detection dataset demonstrate the effectiveness of the proposed method.

Let k be a number field. For a variety X over k that satisfies weak approximation with Brauer-Manin obstruction, we study the same property for smooth projective models of its symmetric products. Based on the same method, we also explore the property of weak approximation with Brauer-Manin obstruction for norm varieties., Comment: 9 pages; improved theorem 1.3

Numerous physics theories are rooted in partial differential equations (PDEs). However, the increasingly intricate physics equations, especially those that lack analytic solutions or closed forms, have impeded the further development of physics. Computationally solving PDEs by classic numerical approaches suffers from the trade-off between accuracy and efficiency and is not applicable to the empirical data generated by unknown latent PDEs. To overcome this challenge, we present KoopmanLab, an efficient module of the Koopman neural operator family, for learning PDEs without analytic solutions or closed forms. Our module consists of multiple variants of the Koopman neural operator (KNO), a kind of mesh-independent neural-network-based PDE solvers developed following dynamic system theory. The compact variants of KNO can accurately solve PDEs with small model sizes while the large variants of KNO are more competitive in predicting highly complicated dynamic systems govern by unknown, high-dimensional, and non-linear PDEs. All variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation in fluid mechanics) and ERA5 (i.e., one of the largest high-resolution global-scale climate data sets in earth physics). These demonstrations suggest the potential of KoopmanLab to be a fundamental tool in diverse physics studies related to equations or dynamic systems.

The lacking of analytic solutions of diverse partial differential equations (PDEs) gives birth to series of computational techniques for numerical solutions. In machine learning, numerous latest advances of solver designs are accomplished in developing neural operators, a kind of mesh-free approximators of the infinite-dimensional operators that map between different parameterization spaces of equation solutions. Although neural operators exhibit generalization capacities for learning an entire PDE family simultaneously, they become less accurate and explainable while learning long-term behaviours of non-linear PDE families. In this paper, we propose Koopman neural operator (KNO), a new neural operator, to overcome these challenges. With the same objective of learning an infinite-dimensional mapping between Banach spaces that serves as the solution operator of target PDE family, our approach differs from existing models by formulating a non-linear dynamic system of equation solution. By approximating the Koopman operator, an infinite-dimensional linear operator governing all possible observations of the dynamic system, to act on the flow mapping of dynamic system, we can equivalently learn the solution of an entire non-linear PDE family by solving simple linear prediction problems. In zero-shot prediction and long-term prediction experiments on representative PDEs (e.g., the Navier-Stokes equation), KNO exhibits notable advantages in breaking the tradeoff between accuracy and efficiency (e.g., model size) while previous state-of-the-art models are limited. These results suggest that more efficient PDE solvers can be developed by the joint efforts from physics and machine learning.

In this paper, we consider the channel modeling of a heterogeneous vehicular integrated sensing and communication (ISAC) system, where a dual-functional multi-antenna base station (BS) intends to communicate with a multi-antenna vehicular receiver (MR) and sense the surrounding environments simultaneously. The time-varying complex channel impulse responses (CIRs) of the sensing and communication channels are derived, respectively, in which the sensing and communication channels are correlated with shared clusters. The proposed models show great generality for the capability in covering both monostatic and bistatic sensing scenarios, and as well for considering both static clusters/targets and mobile clusters/targets. Important channel statistical characteristics, including time-varying spatial cross-correlation function (CCF) and temporal auto-correlation function (ACF), are derived and analyzed. Numerically results are provided to show the propagation characteristics of the proposed ISAC channel model. Finally, the proposed model is validated via the agreement between theoretical and simulated as well as measurement results., Comment: 6 pages, 3 figures. This work has been submitted to IEEE for possible publication

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown great success in achieving high performance on classification tasks with very few time steps. However, how to design a directly-trained SNN for the regression task of object detection still remains a challenging problem. To address this problem, we propose EMS-YOLO, a novel directly-trained SNN framework for object detection, which is the first trial to train a deep SNN with surrogate gradients for object detection rather than ANN-SNN conversion strategies. Specifically, we design a full-spike residual block, EMS-ResNet, which can effectively extend the depth of the directly-trained SNN with low power consumption. Furthermore, we theoretically analyze and prove the EMS-ResNet could avoid gradient vanishing or exploding. The results demonstrate that our approach outperforms the state-of-the-art ANN-SNN conversion methods (at least 500 time steps) in extremely fewer time steps (only 4 time steps). It is shown that our model could achieve comparable performance to the ANN with the same architecture while consuming 5.83 times less energy on the frame-based COCO Dataset and the event-based Gen1 Dataset., Comment: Accepted by ICCV2023

Due to limited resources on edge and different characteristics of deep neural network (DNN) models, it is a big challenge to optimize DNN inference performance in terms of energy consumption and end-to-end latency on edge devices. In addition to the dynamic voltage frequency scaling (DVFS) technique, the edge-cloud architecture provides a collaborative approach for efficient DNN inference. However, current edge-cloud collaborative inference methods have not optimized various compute resources on edge devices. Thus, we propose DVFO, a novel DVFS-enabled edge-cloud collaborative inference framework, which co-optimizes DVFS and offloading parameters via deep reinforcement learning (DRL). Specifically, DVFO automatically co-optimizes 1) the CPU, GPU and memory frequencies of edge devices, and 2) the feature maps to be offloaded to cloud servers. In addition, it leverages a thinking-while-moving concurrent mechanism to accelerate the DRL learning process, and a spatial-channel attention mechanism to extract DNN feature maps of secondary importance for workload offloading. This approach improves inference performance for different DNN models under various edge-cloud network conditions. Extensive evaluations using two datasets and six widely-deployed DNN models on three heterogeneous edge devices show that DVFO significantly reduces the energy consumption by 33% on average, compared to state-of-the-art schemes. Moreover, DVFO achieves up to 28.6%-59.1% end-to-end latency reduction, while maintaining accuracy within 1% loss on average.

In this paper, we present our solutions for the 5th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW), which includes four sub-challenges of Valence-Arousal (VA) Estimation, Expression (Expr) Classification, Action Unit (AU) Detection and Emotional Reaction Intensity (ERI) Estimation. The 5th ABAW competition focuses on facial affect recognition utilizing different modalities and datasets. In our work, we extract powerful audio and visual features using a large number of sota models. These features are fused by Transformer Encoder and TEMMA. Besides, to avoid the possible impact of large dimensional differences between various features, we design an Affine Module to align different features to the same dimension. Extensive experiments demonstrate that the superiority of the proposed method. For the VA Estimation sub-challenge, our method obtains the mean Concordance Correlation Coefficient (CCC) of 0.6066. For the Expression Classification sub-challenge, the average F1 Score is 0.4055. For the AU Detection sub-challenge, the average F1 Score is 0.5296. For the Emotional Reaction Intensity Estimation sub-challenge, the average pearson's correlations coefficient on the validation set is 0.3968. All of the results of four sub-challenges outperform the baseline with a large margin.

In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges. The MuSe 2022 focuses on humor detection, emotional reactions and multimodal emotional stress utilizing different modalities and data sets. In our work, different kinds of multimodal features are extracted, including acoustic, visual, text and biological features. These features are fused by TEMMA and GRU with self-attention mechanism frameworks. In this paper, 1) several new audio features, facial expression features and paragraph-level text embeddings are extracted for accuracy improvement. 2) we substantially improve the accuracy and reliability of multimodal sentiment prediction by mining and blending the multimodal features. 3) effective data augmentation strategies are applied in model training to alleviate the problem of sample imbalance and prevent the model from learning biased subject characters. For the MuSe-Humor sub-challenge, our model obtains the AUC score of 0.8932. For the MuSe-Reaction sub-challenge, the Pearson's Correlations Coefficient of our approach on the test set is 0.3879, which outperforms all other participants. For the MuSe-Stress sub-challenge, our approach outperforms the baseline in both arousal and valence on the test dataset, reaching a final combined result of 0.5151., 8 pages, 2 figures, to appear in MuSe 2022 (ACM MM2022 co-located workshop)

Background: although solution-processed small molecular organic semiconductors have attracted great attention for organic electronic applications, the intrinsic crystal misorientation of the organic semiconductors still remains a challenging issue. Objective: two benzoic acid-based additives, i.e. 4-propylbenzoic acid (RBA) and 4-octylbenzoic acid (OBA), were employed to regulate the crystal growth and charge transport of organic semiconductors. Methods: RBA and OBA were mixed with a π-conjugated organic semiconductor 6,13- bis(triisopropylsilylethynyl) pentacene (TIPS pentacene), respectively. Organic thin-film transistors (OTFTs) with different bottom-gate, top-contact and bottom-gate, bottom-contact configurations were fabricated to investigate charge transport. Results: RBA and OBA share a similar benzoic acid structure with the same hydrophilic head but differ in the length of the hydrophobic tail. The benzoic acid-based additive forms a stratified selfassembled interfacial layer and maneuvers nucleation seed distribution via synergetic interactions with the silanol groups on silicon dioxide and with the bulky side chains of the semiconductor. The TIPS pentacene film with OBA additive exhibited a 10-fold reduction in misorientation angle as compared to the counterpart with RBA. Conclusion: Distinct thin film morphology in terms of crystal alignment and grain width was observed and correlated to the hydrophobic tail length. In particular, OTFTs incorporating the TIPS pentacene/benzoic acid mixture as the active layer showed a mobility of up to 0.15 cm2/Vs.

Recording fast motion in a high FPS (frame-per-second) requires expensive high-speed cameras. As an alternative, interpolating low-FPS videos from commodity cameras has attracted significant attention. If only low-FPS videos are available, motion assumptions (linear or quadratic) are necessary to infer intermediate frames, which fail to model complex motions. Event camera, a new camera with pixels producing events of brightness change at the temporal resolution of $\mu s$ $(10^{-6}$ second $)$, is a game-changing device to enable video interpolation at the presence of arbitrarily complex motion. Since event camera is a novel sensor, its potential has not been fulfilled due to the lack of processing algorithms. The pioneering work Time Lens introduced event cameras to video interpolation by designing optical devices to collect a large amount of paired training data of high-speed frames and events, which is too costly to scale. To fully unlock the potential of event cameras, this paper proposes a novel TimeReplayer algorithm to interpolate videos captured by commodity cameras with events. It is trained in an unsupervised cycle-consistent style, canceling the necessity of high-speed training data and bringing the additional ability of video extrapolation. Its state-of-the-art results and demo videos in supplementary reveal the promising future of event-based vision., Comment: Accepted to CVPR 2022, project page https://sites.google.com/view/timereplayer/

Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field of computer vision tasks. Without inductive bias, MLPs perform well on feature extraction and achieve amazing results. However, due to the simplicity of their structures, the performance highly depends on the local features communication machenism. To further improve the performance of MLP, we introduce information communication mechanisms from brain-inspired neural networks. Spiking Neural Network (SNN) is the most famous brain-inspired neural network, and achieve great success on dealing with sparse data. Leaky Integrate and Fire (LIF) neurons in SNNs are used to communicate between different time steps. In this paper, we incorporate the machanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNN-MLP model achieves 81.9%, 83.3% and 83.5% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively, which are state-of-the-art results as far as we know., Comment: This paper is accepted by CVPR 2022

Spatially modulated full-polarization imaging systems can obtain full Stokes parameter information by single imaging, with strong characteristics of real-time execution and stability. Since all the polarization information is modulated into an interferogram, demodulation becomes an essential step. As the resolution of the obtained image continuously increases, the data to be calculated also increase. Therefore, a higher speed for the demodulation process is requisite. This article proposes a local filtering method and bypasses the frequency centralization method in the frequency domain to improve the speed of demodulation. The demodulation time of an interferogram of size 1024×1024 pixels was reduced from 3.027 473 s to 0.134 637 s, increasing the speed of demodulation by 97.72%. Meanwhile, the demodulation time of an interferogram of size 6144×6144 pixels was reduced from 444.329 92 s to 4.637 069 s, increasing the speed of demodulation by 98.75%.

Video frame interpolation is a challenging task due to the ever-changing real-world scene. Previous methods often calculate the bi-directional optical flows and then predict the intermediate optical flows under the linear motion assumptions, leading to isotropic intermediate flow generation. Follow-up research obtained anisotropic adjustment through estimated higher-order motion information with extra frames. Based on the motion assumptions, their methods are hard to model the complicated motion in real scenes. In this paper, we propose an end-to-end training method A^2OF for video frame interpolation with event-driven Anisotropic Adjustment of Optical Flows. Specifically, we use events to generate optical flow distribution masks for the intermediate optical flow, which can model the complicated motion between two frames. Our proposed method outperforms the previous methods in video frame interpolation, taking supervised event-based video interpolation to a higher stage., Comment: Accepted to ECCV2022; Fix a few typos in the equation and figure

Event cameras are considered to have great potential for computer vision and robotics applications because of their high temporal resolution and low power consumption characteristics. However, the event stream output from event cameras has asynchronous, sparse characteristics that existing computer vision algorithms cannot handle. Spiking neural network is a novel event-based computational paradigm that is considered to be well suited for processing event camera tasks. However, direct training of deep SNNs suffers from degradation problems. This work addresses these problems by proposing a spiking neural network architecture with a novel residual block designed and multi-dimension attention modules combined, focusing on the problem of depth prediction. In addition, a novel event stream representation method is explicitly proposed for SNNs. This model outperforms previous ANN networks of the same size on the MVSEC dataset and shows great computational efficiency.

Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face videos with continuously controllable expressions, which is a strong demand in the industry. The challenge is that speech-related expressions and emotion-related expressions are often highly coupled. Meanwhile, traditional image-to-image translation methods cannot work well in our application due to the coupling of expressions with other attributes such as poses, i.e., translating the expression of the character in each frame may simultaneously change the head pose due to the bias of the training data distribution. In this paper, we propose a high-quality facial expression editing method for talking face videos, allowing the user to control the target emotion in the edited video continuously. We present a new perspective for this task as a special case of motion information editing, where we use a 3DMM to capture major facial movements and an associated texture map modeled by a StyleGAN to capture appearance details. Both representations (3DMM and texture map) contain emotional information and can be continuously modified by neural networks and easily smoothed by averaging in coefficient/latent spaces, making our method simple yet effective. We also introduce a mouth shape preservation loss to control the trade-off between lip synchronization and the degree of exaggeration of the edited expression. Extensive experiments and a user study show that our method achieves state-of-the-art performance across various evaluation criteria., Comment: Demo video: https://youtu.be/WD-bNVya6kM

Transfer learning aims to leverage knowledge from pre-trained models to benefit the target task. Prior transfer learning work mainly transfers from a single model. However, with the emergence of deep models pre-trained from different resources, model hubs consisting of diverse models with various architectures, pre-trained datasets and learning paradigms are available. Directly applying single-model transfer learning methods to each model wastes the abundant knowledge of the model hub and suffers from high computational cost. In this paper, we propose a Hub-Pathway framework to enable knowledge transfer from a model hub. The framework generates data-dependent pathway weights, based on which we assign the pathway routes at the input level to decide which pre-trained models are activated and passed through, and then set the pathway aggregation at the output level to aggregate the knowledge from different models to make predictions. The proposed framework can be trained end-to-end with the target task-specific loss, where it learns to explore better pathway configurations and exploit the knowledge in pre-trained models for each target datum. We utilize a noisy pathway generator and design an exploration loss to further explore different pathways throughout the model hub. To fully exploit the knowledge in pre-trained models, each model is further trained by specific data that activate it, which ensures its performance and enhances knowledge transfer. Experiment results on computer vision and reinforcement learning tasks demonstrate that the proposed Hub-Pathway framework achieves the state-of-the-art performance for model hub transfer learning., Comment: Accepted by NeurIPS 2022

Supplemental Document

Supplemental Document

This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars. DRL methods are popular approaches for autonomous vehicle (AV) agents. However, most existing methods are sample- and time-inefficient and lack a natural communication channel with the human expert. In this paper, how new human drivers learn from human coaches motivates us to study new ways of human-in-the-loop learning and a more natural and approachable training interface for the agents. We propose incorporating natural language voice instructions (NLI) in model-based deep reinforcement learning to train self-driving cars. We evaluate the proposed method together with a few state-of-the-art DRL methods in the CARLA simulator. The results show that NLI can help ease the training process and significantly boost the agents' learning speed., Comment: NeurIPS 2021 Workshop on Machine Learning for Autonomous Driving

With the increasing penetration of renewable energy power generation such as wind power in the grid, the randomness and fluctuating power of wind power generation have adversely affected the safe and stable operation of the grid. The synchronous generator has the function of regulating the power balance of the system and maintaining the stability of the grid voltage. Therefore, a hybrid energy storage system ( HESS) can be configured for the wind farm, and a control strategy based on a synchronous generator model is adopted for the power conversion system(PCS). Therefore, the energy storage can be equivalent to a virtual synchronous generator (VSG). By controlling the output of hybrid energy storage, the power of the grid-connected tie line is smoother. At the same time, according to load fluctuations, energy storage can absorb or emit power, maintain system frequency and voltage stability, and effectively improve the grid-connected performance of the wind farm. Finally, a system simulation model consisting of wind turbines, hybrid energy storage and micro-turbine generators was established, and the simulation results verified the feasibility of the control strategy.

2. Criotettix pananensis sp. nov. (Figs. 7���13) Holotype ♀, paratype ♂ Panan, Zhejiang, 2015-7-27, leg. Zhao Xue-Qian & Du Xue-Dong. Female (Figs. 7-10) Size small, thickset. Head exserted above the pronotal surface. Width of vertex narrower than diameter of eye, anterior margin of vertex nearly straight, not protruding beyond eyes, lateral ridge slightly raised, median carina indistinctly, in profile, vertex and frontal ridge forming an obtuse round shape. Frontal ridge gradually widened downward. Antenna filiform, 15-segmented, length of a segment in middle about 4.5 times longer than width, inserted the lower margin of eyes. Eyes globose, lateral ocelli placed on middle of anterior margins of eyes. Disc of pronotum coarsely, with numerous small tubercles, anterior margin of pronotum curved, midkeel of pronotum interrupted, upper margin of pronotum curved slightly in profile. Lateral keels in prozona indistinct, parallel, without a pair short longitudinal keels between shoulder, humeral angle widely rounded, caudal end of pronotum narrow, long cone-shaped, surpassing the top of hind femora distinctly. Lateral lobes of pronotum turning outwardly, posterior angles directed distinctly backwards, apex of posterior angles strident, posterior margin of each lateral lobe with two bisinuate. Tegmina ovate, length 2 times width, apex rounded. Hind wings extending over the end of pronotum. Upper and lower margins of fore and middle femora straight, the width of tegmina 1.7 times wider than width of midfemur. Length of hind femur 3.2 times width, without projection in upper keel, genicular denticles right angle. Outer side of hind tibia with 8 spines, inner side with 7 spines. Length of first segment of hind tarsus longer than third. Ovipositor narrow and long, length of upper valvulae 4.8 times its width, upper and lower valvulae with slender saw-like teeth. Length of subgenital plate longer its width, middle of posterior margin of subgenital plate with a triangular convex. Body dark brown. Wings black. Hind femur and tibia dark brown. Measurement (in mm): Length of body: ♂ 9.7, ♀ 12.5. Length of Pronotum: ♂ 13.3, ♀ 16.4. Length of hind femur: ♂ 7.4, ♀ 7.7. Male (Figs. 11���13): Body size smaller than female, feature of structure similar to female. Subgenital plate conical, apex narrow. Diagnosis. The new species is similar to Criotettix transpinoides Zheng, Bai & Xu, 2012. The major differences are listed in Table 1. Etymology. The new species name was from the type locality, Panan., Published as part of Zhang, Ziyang, Liu, Zhixing & Yin, Hong, 2021, Two new species of the genus Criotettix Bolivar (Orthoptera: Tetrigoidea Scelimenidae) from Zhejiang, China, pp. 187-192 in Zootaxa 4975 (1) on pages 189-191, DOI: 10.11646/zootaxa.4975.1.8, http://zenodo.org/record/4806197, {"references":["Zheng, Z. M., Bai, Y. & Xu, S. Q. (2012) Survey of grasshoppers from Linhai Sanjiang wetland, Zhejiang province (Orthoptera). Journal of Shaanxi Normal University, 40 (6), 58 - 60."]}

1. Criotettix jinningensis sp. nov. (Figs. 1–6) Holotype ♀, paratype ♂ Jinning, Zhejiang, 2016-8-8, leg. Zhao Xue-Qian & Zhang Kai-Jian. Female (Figs. 1–4) Size small, thickset. Head exserted above the pronotal surface. Width of vertex narrower than diameter of eye, anterior margin of vertex straight, not protruding beyond eyes, median carina indistinctly, in profile, vertex and frontal ridge forming an obtuse round shape. Frontal ridge gradually widened downward. Antenna filiform, 15-segmented, length of a segment in middle about 4.2 times longer than width, inserted the lower margin of eyes. Eyes globose, lateral ocelli placed on middle of anterior margins of eyes. Disc of pronotum coarsely, with numerous small tubercles, anterior margin of pronotum almost straight, midkeel of pronotum interrupted, upper margin of pronotum undulated slightly in profile, apex not reaching the end of hind tibiae, lateral keels in prozona almost parallel, humeral angle widely rounded, caudal end of pronotum narrow, long cone-shaped, lateral lobes of pronotum turning outwardly, posterior angles directed distinctly backwards Tegmina long, ovate, apex narrowly rounded, width 1.3 times width of mid leg femur. Hind wings not reaching the top of hind process. Upper and lower margins of fore and middle femora straight, the width of tegmina 1.3 times wider than width of midfemur. Length of hind femur 3.3 times width, genicular denticles right angle. Outer side of hind tibia with 8 spines, inner side with 7 spines. Length of first segment of hind tarsus longer than third. Ovipositor narrow and long, length of upper valvulae 5.2 times its width, upper and lower valvulae with slender saw-like teeth. Length of subgenital plate longer its width. Body dark brown. Wings dark. Hind femur and tibia dark brown. Male (Figs. 5–6): Body size smaller than female, feature of structure similar to female. Subgenital plate conical, apex sharp. Measurement (in mm): Length of body: ♂ 10.6, ♀ 12.6. Length of Pronotum: ♂ 13.8, ♀ 15.4. Length of hind femur: ♂ 7.1, ♀ 9.2. Diagnosis. The new species is similar to Criotettix yunnanensis Zheng & Ou, 2003. The major differences are listed in Table 1. Etymology. The new species name was from the type locality, Jinning.

1. Criotettix jinningensis sp. nov. (Figs. 1���6) Holotype ♀, paratype ♂ Jinning, Zhejiang, 2016-8-8, leg. Zhao Xue-Qian & Zhang Kai-Jian. Female (Figs. 1���4) Size small, thickset. Head exserted above the pronotal surface. Width of vertex narrower than diameter of eye, anterior margin of vertex straight, not protruding beyond eyes, median carina indistinctly, in profile, vertex and frontal ridge forming an obtuse round shape. Frontal ridge gradually widened downward. Antenna filiform, 15-segmented, length of a segment in middle about 4.2 times longer than width, inserted the lower margin of eyes. Eyes globose, lateral ocelli placed on middle of anterior margins of eyes. Disc of pronotum coarsely, with numerous small tubercles, anterior margin of pronotum almost straight, midkeel of pronotum interrupted, upper margin of pronotum undulated slightly in profile, apex not reaching the end of hind tibiae, lateral keels in prozona almost parallel, humeral angle widely rounded, caudal end of pronotum narrow, long cone-shaped, lateral lobes of pronotum turning outwardly, posterior angles directed distinctly backwards Tegmina long, ovate, apex narrowly rounded, width 1.3 times width of mid leg femur. Hind wings not reaching the top of hind process. Upper and lower margins of fore and middle femora straight, the width of tegmina 1.3 times wider than width of midfemur. Length of hind femur 3.3 times width, genicular denticles right angle. Outer side of hind tibia with 8 spines, inner side with 7 spines. Length of first segment of hind tarsus longer than third. Ovipositor narrow and long, length of upper valvulae 5.2 times its width, upper and lower valvulae with slender saw-like teeth. Length of subgenital plate longer its width. Body dark brown. Wings dark. Hind femur and tibia dark brown. Male (Figs. 5���6): Body size smaller than female, feature of structure similar to female. Subgenital plate conical, apex sharp. Measurement (in mm): Length of body: ♂ 10.6, ♀ 12.6. Length of Pronotum: ♂ 13.8, ♀ 15.4. Length of hind femur: ♂ 7.1, ♀ 9.2. Diagnosis. The new species is similar to Criotettix yunnanensis Zheng & Ou, 2003. The major differences are listed in Table 1. Etymology. The new species name was from the type locality, Jinning., Published as part of Zhang, Ziyang, Liu, Zhixing & Yin, Hong, 2021, Two new species of the genus Criotettix Bolivar (Orthoptera: Tetrigoidea Scelimenidae) from Zhejiang, China, pp. 187-192 in Zootaxa 4975 (1) on pages 187-189, DOI: 10.11646/zootaxa.4975.1.8, http://zenodo.org/record/4806197, {"references":["Zheng, Z. M. & Ou, X. H. (2003) Four new species of Scelimenidae from Yunnan province (Orthoptera, Tetrigoidea). Acta Zootaxonomica Sinica, 28 (4), 673 - 678."]}

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a grave threat to public health and the global economy. SARS-CoV-2 is closely related to the more lethal but less transmissible coronaviruses SARS-CoV-1 and Middle East respiratory syndrome coronavirus (MERS-CoV). Here, we have carried out comparative viral-human protein-protein interaction and viral protein localization analyses for all three viruses. Subsequent functional genetic screening identified host factors that functionally impinge on coronavirus proliferation, including Tom70, a mitochondrial chaperone protein that interacts with both SARS-CoV-1 and SARS-CoV-2 ORF9b, an interaction we structurally characterized using cryo-electron microscopy. Combining genetically validated host factors with both COVID-19 patient genetic data and medical billing records identified molecular mechanisms and potential drug treatments that merit further molecular and clinical study.

A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease2019 (COVID-19), has infected over 2.3million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein-protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66druggable human proteins or host factors targeted by 69compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.

The power system inertia is gradually decreasing with the growing share of variable renewable energy (VRE). This may jeopardize the frequency dynamics and challenges the secure operation of power systems. In this paper, the concept of frequency security margin is proposed to quantify the system frequency regulation ability under contingency. It is defined as the maximum power imbalance that the system can tolerate while keeping frequency within the tolerable frequency range. A frequency constrained unit commitment (FCUC) model considering frequency security margin is proposed. Firstly, the analytical formulation of system frequency nadir is derived while considering both the frequency regulation characteristics of the thermal generators and the frequency support from VRE plants. Then, the frequency security margin is analytically formulated and piecewise linearized. A novel FCUC model is proposed by incorporating linear frequency security constraints into the traditional unit commitment model. Case studies on a modified IEEE RTS-79 system and HRP-38 system are provided to verify the effectiveness of the proposed FCUC model. The impacts of VRE penetration on system frequency security are analyzed using frequency security margin.

Here we report the design, synthesis, and characterization of bifunctional chemical ligands that induce the association of Ras with ubiquitously expressed immunophilin proteins such as FKBP12 and cyclophilin A. We show this approach is applicable to two distinct Ras ligand scaffolds, and that both the identity of the immunophilin ligand and the linker chemistry affect compound efficacy in biochemical and cellular contexts. These ligands bind to Ras in an immunophilin-dependent fashion and mediate the formation of tripartite complexes of Ras, immunophilin, and the ligand. The recruitment of cyclophilin A to GTP-bound Ras blocks its interaction with B-Raf in biochemical assays. Our study demonstrates the feasibility of ligand-induced association of Ras with intracellular proteins and suggests it as a promising therapeutic strategy for Ras-driven cancers.

Spatially modulated full polarization imaging is the method of modulating the polarization information of a target into a single interferogram, which has a good real-time feature. Since polarization modulation lenses are made of birefringent crystals, they will produce chromatic dispersion. Therefore, the interference fringes on the interferogram will become blurred, affecting the subsequent demodulation processing. It is necessary to determine the bandwidth limit of the incident light. Based on these considerations, this paper analyses the distribution of the polarization information in the frequency domain and proposes a method of determining the bandwidth limit based on the pixel size in the frequency domain.

Since wind turbines or photovoltaic (PV) panels are generally connected to the power grid by power electronic inverters, the power system inertia is gradually decreasing along with the growing share of renewable energy. This jeopardizes the system frequency response dynamics so that the corresponding frequency security issue is becoming the bottle-neck factor that restricts the development of high renewable energy penetration. Consequently, power system scheduling models need to incorporate frequency dynamics. The difficulty lies in how to formulate the frequency security constraints from the perspective of hourly load-generation balance since the frequency dynamics have a shorter time scale (5~30 s). Several modeling methods have been proposed based on different assumptions and simplifications. However, their accuracy is not clear. We first propose a novel method to formulate linear frequency security constraints, which considers more details of frequency response dynamics. Then, an evaluation methodology is designed to quantify the accuracy of those frequency constraints. Using this evaluation method, we compare two typical methods in recent literature with the proposed method. The results show the effectiveness and superiority of our proposed method.

The objective of this study was to investigate the measurement of serum fibroblast growth factor-21 (FGF-21), a protein mainly synthesized by the liver, as a sensitive biomarker for diagnosis of ketosis in dairy cows. Ninety Holstein-Friesian dairy cows (60 healthy and 30 ketosis cases) were selected and divided into a Ketosis group (K), and a Control group (C). We measured serum FGF-21 and other biochemical parameters by commercial ELISA kits. In a combined population of all 90 cows, we found that serum FGF-21 level was lower (P < 0.001) in cows suffering from ketosis. When the β-hydroxybutyric acid (BHBA) level increased over 1.2 mmol/L, the FGF-21 level tended to decline below 300.85 pg/ml. The area under the receiver operating characteristic curve (AUC-ROC) for serum FGF-21 for diagnosis of fatty liver was 0.952-0.025 [95% confidence interval (CI) 0.904, 1.000] which was higher than the AUC-ROC for glucose (Glc) and other tested parameters. We concluded that FGF-21 could be a diagnostic parameter in the evaluation and auxiliary diagnosis of changes in the energy metabolism state, and serum FGF-21 measurement would have a considerable clinical impact and lead to greater profitability in the dairy industry.

Due to the cellular instability, the premixed methane/air flame will generate self-acceleration which can increase the explosion intensity. This work explored the suppression of CO2 or/and H2O on the cellular instability to mitigate the explosion intensity. The cellular instability was analyzed qualitatively and quantitatively in the viewpoint of the flame schlieren images and cellular instability parameters. The inhibition mechanism of CO2 or/and H2O on the cellular instability was also analyzed by numerical simulation. One important result is that CO2 can decrease the number of cellular structures obviously while H2O can increase the size of cellular structures, indicating that CO2 or/and H2O can inhibit the destabilization of cellular instability to spherical flame significantly. Meanwhile, CO2 enhances the intensity of diffusional-thermal instability while H2O decreases the intensity of diffusional-thermal instability. However, the intensity of hydrodynamic instability is inhibited by CO2 or/and H2O significantly due to an increase of flame thickness and a decrease of thermal expansion ratio. The inhibition mechanism analysis show that the key free radicals and adiabatic flame temperature are important to cellular instability. Both the peak ROP of H/O/OH and the mole fraction of H/O/OH can be decreased noticeably by CO2 or/and H2O. The adiabatic flame temperature decreases monotonously with increasing CO2 or/and H2O volume fraction. The most elementary reaction of enhancing adiabatic flame temperature is R38 while the first inhibiting reaction is R52.

To solve the influence of residual buoyancy on underactuated autonomous underwater vehicles(AUVs) at the time of operation with great depth, this paper uses the recursive least square(RLS) method to design the residual buoyancy observer and monitor the residual buoyancy in real time. A depth controller is designed later based on the value obtained by the observer. For this depth controller the vertical plane motion is decoupled and decomposed into forward speed control and depth control. By adopting the mature S-plane controller to ensure the stability of speed control, the problem of depth control is selectively solved. The depth deviation with the influence of residual buoyancy is transformed into the desired pitch angle, and the control variables are divided into the feedforward items overcoming the remaining buoyancy and the linear feedback items so that the desired pitch angle can be tracked to achieve the purpose of depth control. By simulating an AUV model of Harbin Engineering University, the validity of the residual buoyancy observer and the depth controller is verified.

We demonstrate for the first time, a new type of aperiodic phase mask (APM) for fabricating multi-channel aperiodic fiber Bragg gratings. The mask is made of individual diffraction phase gratings with discrete unequal phase-steps incorporated at periodic locations. The diffraction at the discrete phase-steps in the phase mask produces corresponding half phase-steps at periodic locations along the fiber. The accumulated phase, along with index modulation, generates the desired multinotch reflection spectrum. Complex fiber Bragg grating filters fabricated using APM, in a standard phase mask based fabrication setup, can be used to simultaneously suppress multiple aperiodic OH emission wavelengths in near infrared (NIR) existing in upper atmosphere, and increase the sensitivity of ground based telescopes., Comment: To be submitted to Optics Letters. 3 pages, 4 figures

Benefited from the flexibility of Smart Grids, various dynamic lightning protection and Artificial Intelligence-based lightning protection methods become available. Based on Artificial Intelligence technologies, the paper proposes a method of lightning-caused faults forecasting for Smart Grid distribution systems. A self-balancing dynamic lightning protection, which assists the self-healing function of a Smart Grid distribution network, is also presented in the paper. The Artificial Intelligence-based Lightning Protection, which is defined and described in the paper, provides more comprehensive lightning protection solutions to improve system reliability for the Smart Grids.

A novel electrocatalyst based on nitrogen-doped carbon nanocapsules containing metallic cobalt nanoparticle (Co–N–C) tetragonal microstructures was firstly synthesized., A facile non-template solid-state reaction method has been developed for the first time preparation of a tetragonal microstructure self-assembled from nitrogen-doped carbon nanocapsules containing metallic cobalt nanoparticles (Co–N–C) by using graphitic carbon nitride (g-C 3 N 4 ) and Co(CH 3 COO) 2 ·4H 2 O as the only reactants, in which g-C 3 N 4 acts as the effective source of nitrogen and carbon elements in the N-doped carbon nanocapsules and Co(CH 3 COO) 2 ·4H 2 O controls the microscopic structure of the self-assembled product. The as-prepared Co–N–C tetragonal microstructures exhibit enhanced electrocatalytic activities for both the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). The synergistic effect of the chemical compositions and the robust microstructure made of the interconnected N-doped carbon nanocapsules accounts for the superior ORR and OER activity and stability to the commercial Pt/C electrocatalyst. The synthetic strategy presented in this work demonstrates a new avenue for developing highly active carbon-based electrocatalysts for electrochemical energy storage and conversion.

Latin America belongs to one of the most emerging regions in the world. It plays a crucial part in China’s overall diplomacy in this new era, especially concerning the emerging markets and developing countries. We have reasons to believe that the strategic importance of Latin America in the overall layout of China’s diplomacy will become more prominent within the foreseeable future.

Recently, the grid-connected photovoltaics power stations are extensively installed. Nevertheless, to save the maintenance time, improve the safety performance, solve the complexities of traditional overhaul methods and eliminate restrictions further research is needed. Therefore, in this paper, a method of self-diagnose control for photovoltaic system connected grid based on micro-inverter was proposed. The smartPV software used to monitor the parameters of each module and the whole system. Subsequently, we develop a new software to self-diagnose the specific location of the fault or shadow. The feasibility and rationality of the self-diagnose method to the system state were verified by a simulation model and experiment. Results showed that this method can provide a number of functions: such as, the sub-system grouping contrast, the system self-started, found the specific position of the battery plate, divide the degree of the shielding, and protect the system and the system self-closed. In conclusion, this method makes the maintenance of the photovoltaic connected grid more safe and efficient. Moreover, it can be applied to on-line maintenance and autonomous maintenance for the household and the large scale grid-connected photovoltaics systems.

This article presents a robust composite neural-based dynamic surface control design for the path following of unmanned marine surface vessels in the presence of nonlinearly parameterized uncertainties and unknown time-varying disturbances. Compared with the existing neural network-based dynamic surface control methods where only the tracking errors are commonly used for the neural network weight updating, the proposed scheme employs both the tracking errors and the prediction errors to construct the adaption law. Therefore, faster identification of the system dynamics and improved tracking accuracy are achieved. In particular, an outstanding advantage of the proposed neural network structure is simplicity. No matter how many neural network nodes are utilized, only one adaptive parameter that needs to be tuned online, which effectively reduces the computational burden and facilitates to implement the proposed controller in practice. The uniformly ultimate boundedness stability of the closed-loop system is established via Lyapunov analysis. Comparison studies are presented to demonstrate the effectiveness of the proposed composite neural-based dynamic surface control architecture.

We study the dependence of the formation energies of oxygen and metal induced defects in Ta2O5, TaO2, TaO, TiO2 and Ti4O7 on the chemical potential of electron and atomic constitutes. In the study of single defect, metal induced defects are found to be preferable to oxygen induced defects. This is against the experimental fact of the dominant role of oxygen induced defects in the RS process. A simple multiple defects picture without correlated atomic rearrangement does not cure this problem. The problem is resolved under the correlated multiple defect picture where the multiple defects result in correlated atomic rearrangement and the final products show certain atomic ordering.

The paper addresses the multiobjective optimization of a simulated moving-bed chromatographic reactor (SCMCR). The selection of the operating parameters such as the length and number of columns, switching time, and liquid flow rates in different sections is not straightforward in an SCMCR. In most cases, conflicting requirements and constraints govern the optimal choice of the decision (operating or design) variables. A mathematical model that predicts single-column experimental results well was modified for MTBE synthesis in an SCMCR. Thereafter, a few simple multiobjective optimization problems were solved that included both existing and designstage SCMCRs using a nondominated sorting genetic algorithm (NSGA). Optimal design and operating conditions for three cases of practical relevance are studied in this work, namely, (a) simultaneous maximization of the purity and yield of MTBE; (b) simultaneous maximization of the purity and yield of MTBE, together with minimization of the total amount of adsorbent/ catalyst required; and (c) maximization of the purity of MTBE with simultaneous minimization of the eluent consumption, to illustrate the procedures and interpret the results obtained. Paretooptimal solutions were obtained in all cases, and moreover, it was found that the performance of the SCMCR could be improved significantly under optimal operating conditions.

Methyl tert -butyl ether (MTBE), a high-octane blending agent for motor gasoline, is produced by reacting directly tert -butyl alcohol (TBA) with methanol using Amberlyst 15 ion-exchange resin, which acts as both catalyst and adsorbent. Experiments were carried out in a fixed-bed reactor in the temperature range 318–328 K using rectangular pulse input and measuring the elution profiles of TBA, MTBE, and H 2 O. A mathematical model based on a quasi-homogeneous kinetics was developed, which assumes the reaction in the polymer phase to be homogeneous. The kinetic parameters, as well as the adsorption equilibrium constants of water, TBA, and MTBE in methanol together with their dependence on temperature, were determined by tuning the simulation results to fit the experimental data using a state-of-the-art optimization technique, the genetic algorithm. The model was further validated using the tuned adsorption and rate parameters to predict other experimental results. The kinetics reported in the present study were obtained under conditions free of both external and internal mass transfer resistance. The enthalpy and entropy of adsorption obtained from Arrhenius plots were found to be consistent with thermodynamics.

Detecting ultra-high frequency (UHF) signal method is becoming more and more popular in the area of monitoring partial discharge (PD) in power transformers. In order to learn the characteristics of the signal, we should pay attention to the frequency spectrum of electromagnetic (EM) wave, which may be influenced by power transformers' inner structures, such as core and winding. In this paper, both with obstacle standing in the propagation way or not, the frequency spectrum of UHF signals radiated from PD source was simulated and compared. It's concluded that, for EM waves radiated from the same PD source, their frequency spectrums are different because of obstacle. In addition, the inner structure of transformer changes the cavity resonance frequency, which may influence frequency spectrum of UHF signals. In order to investigate the mechanism, the resonance frequencies both in situations of removing and setting the core are simulated and compared. Simulation results indicate that resonance frequency changes when there is one core in tank.

Due to the development of the worldwide smart grid, IEC 61850, as a major standard in the future smart grid area, will bear more and more important role. This paper introduces the traditional development method of IEC 61850 and its main problems, such as complex modeling, long development cycle and high development cost. In order to solve these problems, this paper proposes a new development method of IEC 61850 protocol, and skips some complex parts in the traditional development method of IEC 61850. In this paper the messages meeting IEC 61850 protocol are obtained, after they are analyzed and tested, the client-side and server-side software based on IEC 61850 protocol are developed, which makes the base for embedding the IEC61850 into intelligent electronic devices.