Miracle-dz/UniFAR-FTUs huggingface dataset:
State-of-health prediction of lithium-ion battery based on improved gate recurrent unit

State of health (SOH) prediction of lithium-ion batteries is still a very important issue in evaluating the safety and reliability of battery-powered systems. This paper uses the lithiumion battery data in NASA Ames Research Center for research, analyzes the correlation between the relevant feature data, selects the data the most relevant to the capacity through the threshold, and uses it as the input of the neural network. We combine Particle Swarm Optimization (PSO) algorithm and Gate Recurrent Unit (GRU) to form PSO-GRU, and use the PSO-GRU method to find the time step and the number of neurons for the best prediction effect. The experimental results show that, compared with the LSTM method, the PSO-GRU method has higher prediction accuracy and has fewer weight parameters for the neural network training model.
A Queuing Based Technique for Efficient Task Scheduling in Fog

In recent years, with the rapid growth of the IoT devices, Fog computing also known as Edge computing has gained a lot of attention and is emerging as a popular computing technology for performing the IoT applications which requires immediate response in real-time. Fog as a virtualized layer of distributed network present between the Cloud and the end-users aims to process a large amount of data locally and send only the necessary information to the Cloud with a goal of reducing delay and cost. Fog having a limited amount of resources requires an efficient and systematic approach for scheduling of tasks. The sequence of scheduling the resources plays a crucial role in satisfying the deadlines of the requests. Hence, a Queuing based Technique (QBT) for efficient task scheduling in Fog has been proposed in this paper to support the rapidly increasing amount of data, enhance the performance and decrease the response time. Regarding this, an overall architecture is presented along with the flow of activities. Then corresponding node reservation and queuing mechanism with the algorithms are described. Experimental evaluation and results comparison is conducted to test the efficiency of the proposed QBT model.
Feature Fusion Network for Low-Light Image Enhancement

With the popularity of electronic cameras, we can get pictures under various lighting conditions in our daily life. The images which obtained in low illumination environment will lose contrast, colour, and content details which will bring obstacles for understanding the image. To tackle this problem, a method is proposed in this paper to tackle low-light image enhancement. This method fuses the high level and the low level feature, uses an attention module to model the context information and selects the critical feature to supplemented the high level feature to reconstruct the enhanced image. A number of experiments demonstrate that our method can get images with improved and reduced noise.
The Limits of onetary Economics: On Money as a Constraint on Market Power

We formulate a generalization of the traditional medium-of-exchange function of money in contexts where there is imperfect competition in the intermediation of credit, settlement, or payment services used to conduct transactions. We find that the option to settle transactions with money strengthens the stance of sellers of goods and services vis-a-vis intermediaries, and show this mechanism is operative even for sellers who never exercise the option to sell for cash. These latent money demand considerations imply that in general, in contrast to current conventional wisdom in policy-oriented research in monetary economics, monetary policy remains effective through medium-of-exchange transmission channels—even in highly developed credit economies where the share of monetary transactions is negligible.
Lack of evidence for correlation between COVID-19 infodemic and vaccine acceptance

How information consumption affects behaviour is an open and widely debated research question. A popular hypothesis states that the so-called infodemic has a substantial impact on orienting individual decisions. A competing hypothesis stresses that exposure to vast amounts of even contradictory information has little effect on personal choices. The COVID-19 pandemic offered an opportunity to investigate this relationship, analysing the interplay between COVID-19 related information circulation and the propensity of users to get vaccinated. We analyse the vaccine infodemics on Twitter and Facebook by looking at 146M contents produced by 20M accounts between 1 January 2020 and 30 April 2021. We find that vaccine-related news triggered huge interest through social media, affecting attention patterns and the modality in which information was spreading. However, we observe that such a tumultuous information landscape translated only in minimal variations in overall vaccine acceptance as measured by Facebook's daily COVID-19 Trends and Impact Survey (previously known as COVID-19 World Symptoms Survey) on a sample of 1.6M users. Notably, the observation period includes the European Medicines Agency (EMA) investigations over blood clots cases potentially related to vaccinations, a series of events that could have eroded trust in vaccination campaigns. We conclude the paper by investigating the numerical correlation between various infodemics indices and vaccine acceptance, observing strong compatibility with a null model. This finding supports the hypothesis that altered information consumption patterns are not a reliable predictor of collective behavioural change. Instead, wider attention on social media seems to resolve in polarisation, with the vaccine-prone and the vaccine-hesitant maintaining their positions.
Fundamental groups and group presentations with bounded relator lengths

We study the geometry of compact geodesic spaces with trivial first Betti number admitting large finite groups of isometries. We show that if a finite group $G$ acts by isometries on a compact geodesic space $X$ whose first Betti number vanishes, then diam$(X) / $diam$(X / G ) \leq 4 \sqrt{ \vert G \vert }$. For a group $G$ and a finite symmetric generating set $S$, $P_k(\Gamma (G, S))$ denotes the 2-dimensional CW-complex whose 1-skeleton is the Cayley graph $\Gamma$ of $G$ with respect to $S$ and whose 2-cells are $m$-gons for $0 \leq m \leq k$, defined by the simple graph loops of length $m$ in $\Gamma$, up to cyclic permutations. Let $G$ be a finite abelian group with $\vert G \vert \geq 3$ and $S$ a symmetric set of generators for which $P_k(\Gamma (G,S))$ has trivial first Betti number. We show that the first nontrivial eigenvalue $-\lambda_1$ of the Laplacian on the Cayley graph satisfies $\lambda_1 \geq 2 - 2 \cos ( 2 \pi / k ) $. We also give an explicit upper bound on the diameter of the Cayley graph of $G$ with respect to $S$ of the form $O (k^2 \vert S \vert \log \vert G \vert )$. Related explicit bounds for the Cheeger constant and Kazhdan constant of the pair $(G,S)$ are also obtained.
π-π Interaction Between Metal-Organic Framework and Reduced Graphene Oxide for Visible Light Photocatalytic H2 Production

Solar water splitting provides a promising path for sustainable hydrogen production and solar energy storage. In recent times, metal–organic frameworks (MOFs) have received considerable attention as promising materials for diverse solar energy conversion applications. However, their photocatalytic performance is poor and rarely explored due to rapid electron–hole recombination. Herein, we have developed a material MOF@rGO that exhibits highly enhanced visible-light photocatalytic activity. A real-time investigation reveals that a strong π–π interaction between MOF and rGO is responsible for efficient separation of electron–hole pairs, and thereby enhances the photocatalytic hydrogen production activity. Surprisingly, MOF@rGO showed ∼9.1-fold enhanced photocatalytic hydrogen production activity compared to that of pristine MOF. In addition, it is worth mentioning here that remarkable apparent quantum efficiency (0.66%) is achieved by π–π interaction mediated charge carrier separation.
Discriminating Equivalent Algorithms via Relative Performance

In scientific computing, it is common that a mathematical expression can be computed by many different algorithms (sometimes over hundreds), each identifying a specific sequence of library calls. Although mathematically equivalent, those algorithms might exhibit significant differences in terms of performance. However in practice, due to fluctuations, there is not one algorithm that consistently performs noticeably better than the rest. For this reason, with this work we aim to identify not the one best algorithm, but the subset of algorithms that are reliably faster than the rest. To this end, instead of using the usual approach of quantifying the performance of an algorithm in absolute terms, we present a measurement-based clustering approach to sort the algorithms into equivalence (or performance) classes using pair-wise comparisons. We show that this approach, based on relative performance, leads to robust identification of the fastest algorithms even under noisy system conditions. Furthermore, it enables the development of practical machine learning models for automatic algorithm selection.
Computer Vision-assisted Single-antenna and Single-anchor RSSI Localization Harnessing Dynamic Blockage Events

This paper demonstrates the feasibility of single-antenna and single-RF (radio frequency)- anchor received power strength indicator (RSSI) localization (SARR-LOC) with the assistance of the computer vision (CV) technique. Generally, to perform radio frequency (RF)-based device localization, either 1) fine-grained channel state information or 2) RSSIs from multiple antenna elements or multiple RF anchors (e.g., access points) is required. Meanwhile, owing to deficiency of single-antenna and single-anchor RSSI, which only indicates a coarse-grained distance information between a receiver and a transmitter, realizing localization with single-antenna and single-anchor RSSI is challenging. Our key idea to address this challenge is to leverage CV technique and to estimate the most likely first Fresnel zone (FFZ) between the receiver and transmitter, where the role of the RSSI is to detect blockage timings. Specifically, historical positions of an obstacle that dynamically blocks the FFZ are detected by the CV technique, and we estimate positions at which a blockage starts and ends via a time series of RSSI. These estimated obstacle positions, in principle, coincide with points on the FFZ boundaries, enabling the estimation of the FFZ and localization of the transmitter. The experimental evaluation revealed that the proposed SARR-LOC achieved the localization error less than 1.0 m in an indoor environment, which is comparable to that of a conventional triangulation-based RSSI localization with multiple RF anchors.
Study on Brake Control for High-Speed Train Based on Optimal Distribution of Braking Force

ABSTRACT :To solve the problem of train sliding caused by unreasonable braking force distribution in highspeed train braking process, it presented a vehicle braking force distribution algorithm in the paper. Firstly, a high-speed train dynamic model of the coupling force between trains and running resistance was established. Secondly, a sliding mode controller was designed to solve the longitudinal target braking force of the train. Finally, quadratic programming was adopted to solve the objective function further by establishing the objective optimization function using adhesion utilization, taking the two constraints (i.e., adhesion limit and passenger ride comfort) into consideration. It could be concluded based on the results of the simulation experiment that, the optimized control algorithm in this paper could ensure that each car was running well after the braking force was distributed.
Responses to referee # 2 Interactive comment on “ A comparison between Envisat and ICESat sea ice thickness in the Antarctic ” by Jinfei Wang

Dear Reviewer: We would like to thank you for the helpful comments to improve this manuscript. We made major revisions on this study as suggested. Specifically, we added more description about the importance of Antarctic sea ice thickness in the Introduction part. We added the discussion of possible uncertainties induced by comparing large-footprint satellite remote sensing data (Envisat and ICESat) with point measurements (ULS observations). We also corrected the mistake that Point 3 referred to by considering the sea ice thickness growth during the freezing season, rather than the melting season. In Section 4, we added some quantitative analyses to avoid the speculative and weak statements.
Some effective tricks are used to improve Soft Actor Critic

The development of artificial intelligence is becoming more intelligent, and Reinforcement learning (RL) plays an important role. RL has been widely used in recommendation systems, smart cars, game AI, and investment transactions. Since the emergence of DQN, DRL improvement methods have been proposed continuously. Soft Actor-Critic (SAC) is the current state-of-the-art RL algorithm. In this paper, some tricks are used to improve SAC. Firstly, the baseline is added to the policy gradient algorithm to reduce variance and speed up training. This paper adopts a new method to implement it in SAC. Because the temperature coefficient is a super parameter, which is difficult to adjust, so I propose a novel method to set it. These tricks proved to be effective on the MUJOCO.
Research on image classification algorithm based on depth residuals shrinkage network in Commercial Image Library

With the advent of the age of multimedia technology and image data, commercial image library services have emerged. We need to use image classification to parse into content that the computer can understand. But when recognizing image features for classification, image data will inevitably produce noise, so in order to make image classification more accurate, we need to remove information that is irrelevant to noise. The Deep Residual Shrinkage Network (DRSN) proposed in this paper can effectively denoise images, and then learn features to classify images. The Deep Residual Shrinkage Network (DRSN) combines the Deep Residual Network (ResNet) and the SENet commonly used in the attention mechanism, and adds a soft threshold function to it. The image data set used in this experiment was downloaded from Panorama Network and Baidu Pictures and then processed. In the experiment, noise was first manually added to the data set, and the deep residual network and the deep residual shrinkage network were used for comparison experiments. It is concluded through experiments that the improved DRSN network has higher accuracy when applied to image classification.
Research on the Emotional Polarity Classification of Barrage Texts

With the widespread application of multimedia technology, video barrage has become an important form of expressing opinions and emotions. The classification of Chinese barrage texts according to emotions is of great significance for mining users’ emotions. However, the large number of Internet buzzwords in the barrage texts brings certain difficulties to sentiment analysis. Therefore, in order to address the ambiguity problem of Internet buzzwords in the barrage texts and effectively classifying the video barrage texts, the BERT pre-training language model and Support Vector Machine are combined to classify the barrage texts of positive and negative emotions. In this paper, the word vectors of the barrage texts are firstly generated by the BERT model. The SVM model is used as a classifier, and Particle Swarm Optimization is introduced to optimize the parameters of the SVM model. This method is compared with the traditional methods on the barrage data sets of different video types. The experimental results show that the precision, recall and F1-score of the proposed method are higher than those of the traditional models.
Improving Distant Supervised Relation Extraction using shortest dependency path

Distant supervised relation extraction is the problem of classifying the semantic relation between a pair of entities in a given sentence, where data is created by aligning unlabelled text with knowledge base automatically. Prior researches indicated that most of the sentences in the distant supervised relation extraction setting are very long and benefit from the attention over words. We note that the shortest dependency path between the entity pair in the syntax analysis tree of a sentence can help word based attention. In this paper, we propose a noval distant supervised neural relation extraction method, which makes use of the shortest dependency path to supervise the learning of word attention. Through extensive experiments on benchmark datasets, we demonstrate effectiveness of our model.
Improved unsupervised domain adaptation network based on category attention

Domain adaptation method can significantly reduce the distribution difference between images in variant domains, which plays an important role in unsupervised medical image segmentation. In this paper, an improved unsupervised domain adaptation framework is proposed based on category attention mechanism. The framework considers both image-level and feature-level alignment, and realizes semantic segmentation in different domains through a shared encoder-decoder. A category attention based classifier is proposed to compute the category attention feature and refine the semantic segmentation prediction. Sufficient experiments on MMWHS-2017 dataset indicate that the proposed method achieves the best segmentation performance among all comparasion algorithms.
Enhancing security in Internet of Things using authentication techniques: A review survey

Internet of Things (IoT) is an interrelation of heterogeneous physical devices over the network. It can assist the people by utilizing the latest technology through machine-to-machine (M2M) communication everywhere throughout the world. In general, IoT is connected with millions of individuals and computers and also provides the services among these with the help of sensors and actuators, etc. Actually, providing security to all users and its supported devices is a daunting task because as technology continues to grow, similarly various types of security threat and challenges are developing. The main objective is to enhance the security in the Internet of Things so that the existing system can be made more reliable as per user perspectives. To make more secure systems, have to use reliable security techniques and latest standards as per present era development since the old security standard is not sufficient for the user which uses the equipment’s according to the latest technology in IoT. So, biometric-based authentication is considered as the solution from preventing the new security threats in the IoT system. In any IoT system, biometric-based authentication can be implemented by using two approaches; one physical-based biometric authentication and another is behavioural-based authentication. In this paper, behavioral-based biometrics is the estimation and recording of personal behaviour patterns and also their utilization to check and validate an individual computer user using some machine learning algorithms to authenticate the IoT devices.
Semantic Segmentation of Parking Lot Scene Based on Surround View System

Semantic segmentation of parking lot scenes is the prerequisite of environment perception for automatic parking technology. It provides environmental semantic information for automatic parking of vehicles. However, due to the dim light of the parking lot environment, unclear ground signs, road reflections and other factors, semantic segmentation such as FCN The method is not yet able to segment ground signs such as background and lane lines to meet the perception needs of automatic parking. This paper proposes a parking lot scene semantic segmentation method based on a surround view system. The surround view system consists of 4 fisheye cameras. The images acquired by each camera are subjected to distortion correction, inverse perspective transformation and image stitching fusion to obtain a ring view. Based on the ring view, a semantic segmentation algorithm based on attention and feature fusion is proposed. Experiments are carried out on a self-made parking lot dataset with a size of 1280x960 pixels. The results show that the method proposed in this paper improves the mIoU of the FCN model by 12.3%.
Complex-valued U-Net for PolSAR Image Semantic Segmentation

As an image semantic segmentation network, U-Net has the advantage of simple structure which is suitable for semantic segmentation of PolSAR images with small datasets. However, the original U-Net is a real-valued (RV) network, whose input must be RV. If it is directly used in the segmentation of PolSAR image, the complex-valued (CV) input must be converted into RV, which results in the loss of information. In this paper, a CV U-Net, which is mathematically strict, is proposed for semantic segmentation of PolSAR images. Considering that the PolSAR dataset is small, the structure and parameters of CV U-Net are furtherly simplified based on the original U-Net to prevent overfitting. Experimental results on the Flevoland dataset show that the proposed CV U-Net has better segmentation performance than the original RV U-Net and some other semantic segmentation networks.
Perspectives and challenges of global cattle and sheep meat and milk production

Cattle and sheep meat and milk production is an imperative of animal husbandry in the world. Statistics show a constant trend of increasing the number of inhabitants of the planet, and thus the need for livestock products. Scientists are trying to find a balance between food needs and sustainable development. Special emphasis must be placed on respecting the differences in the understanding and choice of food, and thus milk and meat. Not all cultures have the same habits, on the other hand, the conditions for the development of animal husbandry differ significantly in terms of the natural potentials of individual countries. With the development of new technologies, it is possible to find optimal models for the successful development of animal husbandry in the future, taking into account the conservation of biodiversity.
Activated carbon fibers as biological supports and redox mediators in the biotic and abiotic anaerobic transformation of nitroaromatic compounds

xvii CHAPTER 1.INTRODUCTION . 1 1.1. Nitroaromatic compounds – Uses, environmental issues, and remediation technologies . 1 1.2. Biotransformation of nitroaromatic compounds and the role of redox mediators . 4 1.3. State of the art in the use of redox mediators in anaerobic systems . 6 1.3. 1. Immobilization of redox mediators in the biotransformation of compounds with electron withdrawing groups . 10 1.3. 2. Physicochemical properties of activated carbon relevant for redox processes . 10 1.4. Motivation of this research . 12 1.5. Hypothesis . 13 1.6. Objectives . 13 1.6. 1. General objectives . 13 1.6. 2. Specific objectives . 13 1.7. Scope and structure of thesis . 14 References . 16 CHAPTER 2.ACTIVATED CARBON FIBERS AS REDOX MEDIATORS FOR THE INCREASED REDUCTION OF NITROAROMATICS . 23 ABSTRACT . 23. 23 2.