Miracle-dz/UniFAR-FTUs huggingface dataset:
Market Effects of Industrial Demand Response and Flexibility Potential from Wastewater Treatment Facilities

Demand response (DR) is widely seen as a valuable tool to introduce more flexibility into power markets. For the implementation of DR programs, industrial customers seem to offer the greatest potential, given that they are often large single electricity consumers with a certain degree of flexibility. One industrial sector that has received little attention from the literature so far, but shows a large potential for DR, is the wastewater treatment sector. This study examines industrial DR in general and then presents the opportunities for DR from wastewater treatment plants (WWTP) in particular. Three main sources of flexibility can be identified: a potential for variable electricity use in aeration, the time-shifting operation of pumps and the exploitation of overcapacity in the system. In order to quantify system and market effects, DR from WWTPs should be examined by means of an integrated energy systems model.
Definition and Evaluation of Model-Free Coordination of Electrical Vehicle Charging With Reinforcement Learning

Demand response (DR) becomes critical to manage the charging load of a growing electric vehicle (EV) deployment. Initial DR studies mainly adopt model predictive control, but models are largely uncertain for the EV scenario (e.g., customer behavior). Model-free approaches, based on reinforcement learning (RL), are an attractive alternative. We propose a new Markov decision process (MDP) formulation in the RL framework, to jointly coordinate a set of charging stations. State-of-the-art algorithms either focus on a single EV, or control an aggregate of EVs in multiple steps (e.g., 1) make aggregate load decisions and 2) translate the aggregate decision to individual EVs). In contrast, our RL approach jointly controls the whole set of EVs at once. We contribute a new MDP formulation with a scalable state representation independent of the number of charging stations. Using a batch RL algorithm, fitted $Q$ -iteration, we learn an optimal charging policy. With simulations using real-world data, we: 1) differentiate settings in training the RL policy (e.g., the time span covered by training data); 2) compare its performance to an oracle all-knowing benchmark (providing an upper performance bound); 3) analyze performance fluctuations throughout a full year; and 4) demonstrate generalization capacity to larger sets of charging stations.
An Overview of Schema Extraction and Matching Techniques

W383 contains huge amount of data both in structured and unstructured form. One of the forms for the structured data is HTML tables for which metadata are not explicitly stored/available. As a result, data in such tables cannot be queried accurately and users cannot get exact results to their queries through search engines. Schema extraction establishes schema for data found in different form of web tables. Once the schema of web tables has been extracted, the tables can be created and populated against this schema which can be queried using SQL resulting much better results than traditional search engines. Schema Matching determines number of correspondences which identifies the similar elements from two different schemas. Columns and data values are compared one after the other to match schema. In this paper, different ways of extracting data from tables are mentioned and different tools used for schema extraction are named. Two techniques for Schema Matching are briefly explained.
Probabilistic Forecasting of Electricity Prices Using Kernel Regression

Electricity price forecasting has become crucial for energy companies due to its fundamental importance for decision making processes and operational management. Electricity price time series exhibit variable means, significant volatility and spikes, which places high demands on forecasting models. Moreover, in recent years researchers and practitioners have come to understand the limitations of point forecasts and require models to generate probabilistic forecasts. In contrast to point forecasts, the probabilistic forecasts takes the form of a predictive probability distribution over future quantities or events of interest. In the paper the probabilistic forecasting model based on Nadaraya- Watson estimator is proposed. The model generates the point forecasts as 24-component vectors representing day-ahead electricity prices. The probabilistic forecasts are calculated as quantiles based on the residual distribution for historical data forecasts. The performance of the proposed model is validated by testing on data from the Polish electricity market.
Memristor-Based Circuit Design for Neuron With Homeostatic Plasticity

The nonvolatility and resistance variability of memristor make it a candidate for emulating complex dynamic behaviors in the neural system. In this paper, the memristive neuron with homeostatic plasticity is implemented. The memristor is applied to the neuron circuit, and its memristance represents the membrane sensibility of neurons. Composed of a trigger module, a pulse generation module and a feedback module, the memristor-based neuron can adaptively adjust the firing rate of neuron and maintain it in the inherent firing rate range, which shows similarities to its biological counterpart. The proposed design can mimic firing rate behaviors of the electrophysiological experiment performed in rodent, where the homeostatic plasticity of neuron is inhibited by sleep signal and promoted by wake signal. Furthermore, all the simulations are conducted in Cadence PSPICE, and the functionality of the design is proved with the simulation.
Toxocara sero-prevalence and its relationship with allergic asthma in asthmatic patients in north-eastern Iran

Abstract Toxocariasis is an important neglected tropical disease that has been suggested as a possible etiologic agent of asthma. The objective of the present study was to investigate possible significant association between Toxocara seroprevalence and asthma in a clinic-based case-control study. Blood samples were collected from human subjects aged 5–70 years, 50 of whom had signs of asthma and 50 of whom had no signs of asthma. Risk factors for asthma and Toxocara spp. infection were assessed by a questionnaire given to each patient. Blood samples were analysed to measure levels of anti-Toxocara spp. immunoglobulin G (IgG). Patients with bronchial asthma were observed to have higher Toxocara spp. seropositivity than that of the non-asthmatic controls (6 vs 2%, P = 0.47). The mean anti-Toxocara spp. antibody titre was not significantly higher in patients with bronchial asthma than in individuals without asthma (P = 0.395, 95% CI = 0.579–1.45). There was no significant difference in the mean age, sex, social class, exposure to smoking and presence of domestic dog or cat at home between the two groups (P ≥ 0.05). The presence of anti-Toxocara spp. IgG was statistically associated with higher blood eosinophils, but it was not associated with asthma (P ≥ 0.05). The observed relationship between exposure to Toxocara spp. infection and bronchial asthma in Iranian patients warrants further evaluation. An understanding of any potential influence on the pathogenesis of human asthma provides a potential avenue for prevention.
A systematic approach to searching: an efficient and complete method to develop literature searches

Creating search strategies for systematic reviews, finding the best balance between sensitivity and specificity, and translating search strategies between databases is challenging. Several methods describe standards for systematic search strategies, but a consistent approach for creating an exhaustive search strategy has not yet been fully described in enough detail to be fully replicable. The authors have established a method that describes step by step the process of developing a systematic search strategy as needed in the systematic review. This method describes how single-line search strategies can be prepared in a text document by typing search syntax (such as field codes, parentheses, and Boolean operators) before copying and pasting search terms (keywords and free-text synonyms) that are found in the thesaurus. To help ensure term completeness, we developed a novel optimization technique that is mainly based on comparing the results retrieved by thesaurus terms with those retrieved by the free-text search words to identify potentially relevant candidate search terms. Macros in Microsoft Word have been developed to convert syntaxes between databases and interfaces almost automatically. This method helps information specialists in developing librarian-mediated searches for systematic reviews as well as medical and health care practitioners who are searching for evidence to answer clinical questions. The described method can be used to create complex and comprehensive search strategies for different databases and interfaces, such as those that are needed when searching for relevant references for systematic reviews, and will assist both information specialists and practitioners when they are searching the biomedical literature.
On the hardness of the strongly dependent decision problem

We present necessary and sufficient conditions for solving the strongly dependent decision (SDD) problem in various distributed systems. Our main contribution is a novel characterization of the SDD problem based on point-set topology. For partially synchronous systems, we show that any algorithm that solves the SDD problem induces a set of executions that is closed with respect to the point-set topology. We also show that the SDD problem is not solvable in the asynchronous system augmented with any arbitrarily strong failure detectors.
Measurement Method for Resistive Current Components of Metal Oxide Surge Arrester in Service

As an important power system protection device, the condition of metal oxide surge arresters (MOSAs) in service could be monitored, and online monitoring might be considered in a few critical installations. In this paper, a new resistive components measurement method termed as the remote nonsimultaneous resistive current measurement method (RNS) for MOSAs online condition monitoring is proposed. The RNS remotely and nonsimultaneously measures all types of resistive current parameters of MOSAs by employing the remote nonsimultaneous phase difference measurement method and noninteger harmonic analysis algorithm. The simulation and application results indicate that the proposed method accurately measures various parameters of MOSAs under complicated field circumstances.
Stage-Wise Learning of Reaching Using Little Prior Knowledge

In some manipulation robotics environments, because of the difficulty of precisely modeling dynamics and computing features which describe well the variety of scene appearances, hand-programming a robot behavior is often intractable. Deep reinforcement learning methods partially alleviate this problem in that they can dispense with hand-crafted features for the state representation and do not need pre-computed dynamics. However, they often use prior information in the task definition in the form of shaping rewards which guide the robot toward goal state areas but require engineering or human supervision and can lead to sub-optimal behavior. In this work we consider a complex robot reaching task with a large range of initial object positions and initial arm positions and propose a new learning approach with minimal supervision. Inspired by developmental robotics, our method consists of a weakly-supervised stage-wise procedure of three tasks. First, the robot learns to fixate the object with a 2-camera system. Second, it learns hand-eye coordination by learning to fixate its end-effector. Third, using the knowledge acquired in the previous steps, it learns to reach the object at different positions and from a large set of initial robot joint angles. Experiments in a simulated environment show that our stage-wise framework yields similar reaching performances, compared with a supervised setting without using kinematic models, hand-crafted features, calibration parameters or supervised visual modules.
A Multi-Feature Multi-Classifier System for Speech Emotion Recognition

Recently, Speech Emotion Recognition (SER) has attracted the interest of researchers because of its broad appli- cations, such as human-machine interface and health care. In this paper, we propose a multi-feature, multi-classifier system for SER. Four Convolutional Neural Networks (CNNs) and a traditional Support Vector Machine (SVM) classifier are used to exploit emotional information in multiple features, including MFCC, PLP, prosodic feature, raw waveform and i-vector. Moreover, a decision level fusion strategy is proposed to combine predictions from all classifiers. In Multimodal Emotion Recog- nition Challenge (MEC) 2017, the experimental results showed that our system achieved 40.75% accuracy and 45.08% macro average precision on the testing set of Chinese Natural Audio- Visual Emotion Database 2.0 (CHEVAD 2.0), which demonstrates the effectiveness of our system.
Downstream Targets of Cyclic Nucleotides in Plants

Efficient integration of various external and internal signals is required to maintain adaptive cellular function. Numerous distinct signal transduction systems have evolved to allow cells to receive these inputs, to translate their codes and, subsequently, to expand and integrate their meanings. Two of these, cyclic AMP and cyclic GMP, together referred to as the cyclic nucleotide signaling system, are between them. The cyclic nucleotides regulate a vast number of processes in almost all living organisms. Once synthesized by adenylyl or guanylyl cyclases, cyclic nucleotides transduce signals by acting through a number of cellular effectors. Because the activities of several of these effectors are altered simultaneously in response to temporal changes in cyclic nucleotide levels, agents that increase cAMP/cGMP levels can trigger multiple signaling events that markedly affect numerous cellular functions. In this mini review, we summarize recent evidence supporting the existence of cNMP effectors in plant cells. Specifically, we highlight cAMP-dependent protein kinase A (PKA), cGMP-dependent kinase G (PKG), and cyclic nucleotide phosphodiesterases (PDEs). Essentially this manuscript documents the progress that has been achieved in recent decades in improving our understanding of the regulation and function of cNMPs in plants and emphasizes the current gaps and unanswered questions in this field of plant signaling research.
25-Gb/s Clocked Pluggable Optics for High-Density Data Center Interconnections

This brief presents a clocked pluggable optics suitable for high-density data center interconnections. The proposed architecture performs a SERDES function at the module side by exploiting a forwarded clock from the ASIC. Due to the relaxed channel loss of the ASIC-to-module interface, the use of power-hungry equalizers can be avoided. Based on an 850-nm multi-mode fiber interface, a 25-Gb/s link operation is demonstrated. A vertical-cavity surface-emitting laser-based transmitter outputs an optical modulation power of 0.6 mW. The optical receiver sensitivity is measured to be −7.5 dBm at 21.2 Gb/s with an optical excitation, and $120~{\mu }\text{A}_{\text{pp}}$ at 25 Gb/s with an electrical excitation. The jitter tracking capability of the implemented clock and data recovery is evaluated in the presence of ±100-ppm frequency offsets and the measured jitter tolerance complies with the 100 GbE specification well. The optical transceiver is implemented in 65-nm CMOS technology and consumes 281 mW at 25 Gb/s, corresponding to the energy efficiency of 11.2 pJ/b.
Consumption of Spinacia Oleracea (spinach) and aerobic exercise controls obesity in rats by an inhibitory action on pancreatic lipase

Abstract Context: Spinaciaoleracea (spinach) is abundant in antioxidant phytoconstituents, termed as the natural antioxidant mixture (NAO). Objective: This study evaluates the anti-hyperlipidemicand anti-obesity effects of an antioxidant-rich extract of Spinaciaoleracea (NAOE) and aerobic exercise (AE) in rats fed with high fat diet (HFD). Methods: Rats received NAOE (200 and 400 mg/kg), the standard drug orlistat (10 mg/kg), AE and NAOEAE (NAOE 400 mg/kg + AE) daily with HFD for 21 d. Results: Orlistat, NAOE and NAOEAE treatments to HFD-fed rats significantly reduced the HFD-elevated food intake, weight gain, pancreatic lipase activity and lipid peroxidation, and successfully restored the HFD-skewed lipid profile and antioxidant levels. Conclusions: It may be concluded that NAOE exhibited a promising anti-hyperlipidemic effect by its inhibitory action on pancreatic lipase. The combination treatment NAOEAE produced the best results indicating the essential role of exercise along with consumption of antioxidant-rich foods in maintaining a normal lipid profile and controlling obesity.
Exploiting Syntactic and Semantic Kernels for Target-Polarity Word Collocation Extraction

Target-polarity word (T-P) collocation extraction is a basic sentiment analysis task, which aims to extract the targets and their modifying polarity words by analyzing the relationships between them. Recent studies rely primarily on syntactic rule matching. However, this kind of method has two disadvantages: (1) the syntactic rules are limited and hard matching is always used during the matching procedure that can result in the low recall value, and (2) this method omits the effect of the latent lexical semantic information on the T-P collocation extraction task. To solve the structured syntactic information loss and the lexical semantic information loss problems, in this paper we study the impact of syntactic and semantic information and incorporate them into kernels to automatically extract T-P collocations. We define a syntactic kernel based on the tree structures between targets and polarity words, and a semantic kernel based on the word semantic representations of them to exploit the good way to represent the two kinds of information with support vector machines. The experiments on four types of product reviews show that based on the basic features, a suitable combination of the syntactic and semantic kernels is very promising for T-P collocation extraction.
Multimodal Facial Expression Recognition Based on Dempster-Shafer Theory Fusion Strategy

In this paper, we present our facial expression recognition framework for the Multimodal Emotion Recognition Challenge 2017. The task of the challenge is to recognize one of eight facial emotions in short video clips extracted from Chinese films, TV plays and talk shows. Considering the fact that each video is given only one video-level label, while each frame of the video is unlabeled, we improved the unsupervised auto-coder and used it for feature extraction. In addition, we have extracted other features, including traditional hand-craft features and deep learning features. Finally, the random forest algorithm was used to classify different emotional labels, and followed by a decision level fusion method based on Dempster-Shafer evidence theory. The macro average precision(MAP) of our best submission result achieves 59.68% on the testing set, which significantly outperforms the baseline of 30.63%.
A New Multitasking Malware Classification Model Based on Feature Fusion

In this paper, In this paper, we achieved a new Multitask Malware Classification Model Based on DNN (Deep Neural Network), we use the API functions extracted from the dynamic analysis as the main features, the static extracted n-gram, entropy, SYM, Metadata, asm-image and grayscale image as the fusion features to construct a classifier that can deal with the dichotomy problem and the family classification problem. The system was trained in 12 malware families, 3,792 malicious samples and 138 benign samples. The accuracy of the dichotomous problem is 99.61%. The proposed malware family classification model also achieves 99.33% malware family classification error rate.
Silencing of Annexin A1 suppressed the apoptosis and inflammatory response of preeclampsia rat trophoblasts

Preeclampsia (PE) is a disorder that is characterized by pregnancy-induced hypertension. It has been reported that Annexin A1 (ANXA1) is highly expressed in the plasma of women diagnosed with PE. Therefore, the present study aimed to examine the effect of ANXA1 on PE rats. The PE animal model was constructed in rats using Nω-nitro-L-arginine methyl ester (L-NAME), and the blood pressure and urine protein levels of rats were detected. The pathological features of placental tissue, and the levels of inflammatory factors and ANXA1 were respectively measured by hematoxylin-eosin staining, enzyme-linked immunosorbent assay and immunohistochemical assay. The activity of trophoblasts obtained from PE placental tissue was measured using immunofluorescence staining, while cell apoptosis was assessed using flow cytometry. The levels of associated factors were determined by reverse transcription-quantitative polymerase chain reaction and western blot analysis. The results identified that systolic blood pressure, diastolic blood pressure, mean arterial pressure and urine protein levels were enhanced, and that the contents of ANXA1, tumor necrosis factor alpha (TNF-α), interleukin (IL)-1β, IL-6 and IL-8 were increased in the L-NAME group. Transfection with small interfering RNA (siRNA)-ANXA1 markedly decreased the apoptosis and inflammatory response of trophoblasts. In addition, siRNA-ANXA1 upregulated the levels of B-cell lymphoma-2 (Bcl-2) and pro-caspase-3, and downregulated the levels of Bcl-2-associated X protein, cleaved-caspase-3, TNF-α, IL-1β, IL-6 and IL-8. Furthermore, siRNA-ANXA1 repressed the phosphorylation of Janus kinase 2 (JAK2) and signal transducer and activator of transcription 3 (STAT3); however, siRNA-ANXA1 did not alter the levels of JAK2 and STAT3. Therefore, silencing of ANXA1 suppressed the apoptosis and inflammatory response of PE rat trophoblasts, and downregulated JAK2/STAK3 pathway.
NETWORK COMPRESSION USING CORRELATION ANALYSIS OF LAYER RESPONSES

Principal Filter Analysis (PFA) is an easy to implement, yet effective method for neural network compression. PFA exploits the intrinsic correlation between filter responses within network layers to recommend a smaller network footprint. We propose two compression algorithms: the first allows a user to specify the proportion of the original spectral energy that should be preserved in each layer after compression, while the second is a heuristic that leads to a parameter-free approach that automatically selects the compression used at each layer. Both algorithms are evaluated against several architectures and datasets, and we show considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10, CIFAR-100 and ImageNet, PFA achieves a compression rate of 8x, 3x, and 1.4x with an accuracy gain of 0.4%, 1.4% points, and 2.4% respectively. In our tests we also demonstrate that networks compressed with PFA achieve an accuracy that is very close to the empirical upper bound for a given compression ratio. Finally, we show how PFA is an effective tool for simultaneous compression and domain adaptation.