Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the optical properties of monocrystalline silicon with a deposited layer of spherical zinc sulfide nanoparticles

In the present work zinc sulfide (ZnS) nanoparticles (NPs) were synthesized on silicon wafer by femtosecond pulsed ablation processing of ZnS bulk target using an electrostatic field in argon gas atmosphere. The morphology, size distribution and structural characterization of obtained ZnS NPs were investigated using scanning electron microscope (SEM), dynamic light scattering (DLS) technique and X-ray diffraction (XRD). Dominant size of obtained NPs lies in the range 10-20 nm, NPs are in spherical shape, particles of other shape and agglomerates of particles are absent. XRD investigation of synthesized NPs identified hexagonal wurtzite structure. There is a structural phase transition from the sphalerite ZnS bulk (target) structure to the structural phase of wurtzite (obtained NPs). The optical characterization of synthesized by laser ablation ZnS NPs was carried out using a photoluminescence (PL) measurement. ZnS NPs show a strong broad PL emission spectra covering the entire visible electromagnetic spectra region (range from 380 to 800 nm) centered at 513.7 nm.
A statistical analysis of the relationship between Pc4 and Pc5 ULF waves, solar winds and geomagnetic storms for predicting earthquake precursor signatures in low latitude regions

Short-term earthquake forecasting is impossible due to the seismometer’s limited sensitivity in detecting the generation of micro-fractures prior to an earthquake. Therefore, there is a strong desire for a non-seismological approach, and one of the most established methods is geomagnetic disturbance observation. Previous research shows that disturbances in the ground geomagnetic field serves as a potential precursor for earthquake studies. It was discovered that electromagnetic waves (EM) in the Ultra-Low Frequency (ULF) range are a promising tool for studying the seismomagnetic effect of earthquake precursors. This study used a multiple regression approach to analyse the preliminary study on the relationship between Pc4 (6.7-22 mHz) and Pc5 (1.7-6.7 mHz) ULF magnetic pulsations, solar wind parameters and geomagnetic indices for predicting earthquake precursor signatures in low latitude regions. The ground geomagnetic field was collected from Davao station (7.00° N, 125.40° E), in the Philippines, which experiences nearby earthquake events (Magnitude <5.0, depth <100 km and epicentre distance from magnetometer station <100 km). The Pc5 ULF waves show the highest variance with four solar wind parameters, namely SWS, SWP, IMF-Bz, SIE and geomagnetic indices (SYM/H) prior to an earthquake event based on the regression model value of R2 = 0.1510. Furthermore, the IMF-Bz, SWS, SWP, SWE, and SYM/H were found to be significantly correlated with Pc5 ULF geomagnetic pulsation. This Pc5 ULF magnetic pulsation behaviour in solar winds and geomagnetic storms establishes the possibility of using Pc5 to predict earthquakes.
Determining the Rolling Window Size of Deep Neural Network Based Models on Time Series Forecasting

Time series forecasting has always been a significant task in various domains. In this paper, we propose DeepARMA, a LSTM-based recurrent neural network to tackle this problem. DeepARMA is derived from an existing time series forecasting baseline, DeepAR, overcoming two of its weaknesses: (1) rolling window size determination: the way DeepAR determines rolling window size is casual and vulnerable, which may lead to the unnecessary computation and inefficiency of the model;(2) neglect of the noise: pure autoregressive model cannot deal with the condition where data are composed of various kinds of noise, neither do most of time series models including DeepAR. In order to solve these two problems, we first combine a classic information theoretic criterion, AIC, with the network to determine the proper rolling window size. Then, we propose a jointly-learned neural network fusing white Gaussian noise series given by ARIMA models to DeepAR’s input. That is exactly why we name the network ‘DeepARMA’. Our experiments on a real-world dataset demonstrate that our improvement settles those two problems put forward above.
Research on Model Compression for Embedded Platform through Quantization and Pruning

So far, artificial intelligence has gone through decades of development. Although artificial intelligence technology is not yet mature, it has already been applied in many walks of life. With the explosion of IoT technology in 2019, artificial intelligence has ushered in a new climax. It can be said that the development of IoT technology has led to the development of artificial intelligence once again. But the traditional deep learning model is very complex and redundant. The hardware environment of IoT can not afford the time and resources cost by the model which runs on the GPU originally, so model compression without decreasing accuracy rate so much is applicable in this situation. In this paper, we experimented with using two tricks for model compression: Pruning and Quantization. By utilizing these methods, we got a remarkable improvement in model simplification while retaining a relatively close accuracy.
The Sparse Group Log Ridge for the Selection of Variable Groups

In recent years, the sparse model is a research hotspot in the field of artificial intelligence. Since the Lasso model ignores the group structure among variables, and can only achieve the selection of scattered variables. Besides, Group Lasso can only select groups of variables. To address this problem, the Sparse Group Log Ridge model is proposed, which can select both groups of variables and variables in one group. Then the MM algorithm combined with the block coordinate descent algorithm can be used for solving. Finally, the advantages of the model in terms of variables selection and prediction are shown through the experiment.
IEPet: A Lightweight Multiscale Infrared Environmental Perception Network

In recent years, the development of unmanned driving technology requires continuous progress in environment perception technology. Aiming at the key research direction of infrared environment perception in unmanned driving technology, a lightweight real-time detection network model for infrared environment perception, IEPet, is proposed. The model backbone adds the BottleneckCSP module and the proposed DCAP attention module, which can significantly improve the detection ability and spatial position perception ability while maintaining light weight. At the same time, the model improves the detection accuracy by using a 3-scales detection head. Comparative experiments on the unmanned driving data set show that compared with the lightweight model YOLOv4-tiny, the model proposed in this paper has an increase in F1 Score by 1.48% and an average detection accuracy by 6.37% to reach 84.31%. And the model is lighter. It shows that the proposed IEPet model can better meet the excellent performance required for infrared environment perception.
Small-signal modeling and analysis of current-mode control for multiple-output forward converters

A small-signal model for a multiple-output forward converter with current model control is derived. The model can accurately predict the small-signal characteristics for current mode control. Both theoretical and experimental results show that the power stage pole-zero relative positions, which are critical to the compensator design, are not affected by introducing current mode control. <<ETX>>
Ecological and economic management tools in forestry

The importance of this article is related to the development of new policies for the management of the forestry business. The emergence of new business practices requires a revision of existing business management systems or the acquisition of new unique management skills. This article highlights the key ecological and economic management tools in forestry, identified as a result of the study of the works of domestic and foreign authors, as well as the provisions of regulatory legal acts. The developed environmental and financial forestry management tools make it possible to consider both industries and economic entities for the impact on the environment, aimed at the prospects for the creation of a certain entity. Upon completion of this work, important environmental and economic management tools were adopted that will contribute to the formation of a management idea for forestry. When modeling the ecological and economic management of forestry, a rational option for regulating the instruments was chosen, which would bring the system to the desired state. It was concluded that it is necessary to maximize the coefficients regulating the instruments of environmental and economic management of forestry. It is also necessary to improve the environmental audit and insurance, the legislative mechanism, the introduction of trade rights in certain territories.
Development of the Electroencephalograph-based Brain Computer Interface System

A practical BCI-based application design contains a variety of design stages are needed to be considered. The design challenges are majorly present in 3 major stages: brain signal acquisition, signal processing unit, and signal classification. Combinations of different approaches have to be employed to achieve the functional and accurate performance of the overall design. Those design choices, algorithms, and methodologies that are meant to solve design challenges presented in the previously mentioned three stages have become a hot subject of a number of studies. This paper aims at providing a thorough overview of existing methodologies for BCI-based application design, comparing their principles and performance and recommending suitable design choices that would yield an objective result for the application.
Performance analysis of vortex acoustic wave based on uniform circular array

As a carrier for information and energy, acoustic waves have been applied in underwater communication widely, however, the narrow band and low transmission speed are the main problems. Whether in the field of optics or electromagnetic waves, the orbital angular momentum (OAM) represents the natural properties of the spiral phase structure. By introducing the OAM into the acoustics field, the transmission capacity and spectrum efficiency of the underwater acoustic communication system can be expanded. Based on the analysis and detection of the vortex acoustic wave generated by the circular array of transducers, we studied the array generation method of the spiral acoustic beam, and gave the characteristics of the vortex acoustic beam when propagating under the water. In the direction of the main axis, the uniform circular array was used to generate different topological acoustic vortex. To determine the relationship between the OAM topology mode and the transducer array, the spiral acoustic waves in different topology modes were generated, and the number of array elements, array radius, transmission frequency, etc. were investigated to give the effects on OAM acoustic vortex.
Target Tracking Using Kalman Filter Based Algorithms

Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.
Control Strategy of Pipeline Robot

The detection of pipeline in service is very important, but the existing manual detection has blind spots, long cycle, high risk, low precision and high cost. Therefore, it is very important to develop a mobile detection robot to replace manual inspection. This paper mainly introduces the control strategy of pipeline robot. In this paper, the overall control structure of the pipeline robot system is built, including the design of hardware framework and the design of software implementation process. Hardware design includes the selection design of microcontroller ARM, sensor, motor driver and so on. The software design is mainly the process design of action realization and the frame design of man-machine interactive operation panel structure. After the system construction is completed, the motion control experiment of the pipeline robot control system is carried out to test the walking performance of the pipeline robot prototype in the vertical variable diameter pipeline, and the motion control experiment is carried out to verify the rationality of the mechanical structure of the pipeline robot and the practicability of the control system.
Investigation of wet etching solutions and method for thicker stand alone type of mesa structures of Bi2212 single crystals

In order to develop terahertz emitting devices based on high temperature superconductor Bi2212, we explored a simple and easy fabrication process of the stand alone type of mesa structures (SAMs) by using a wet etching method. We underscored a fact that a thicker mesa structure up to about 10 μm can be made by finding an appropriate etching process comparing four different solutions in the wet etching processes. In addition, we have also improved a method to produce free standing SAM chips which are not fixed on any substrates by glues. We measured the characteristics of the device fabricated using this wet etching method and observed clear emission from a chip with the dimensions of 170 × 170 × 8.3 μm3 without the local heating signature of back bending behavior in the current–voltage characteristics. This mesa chip is several times thicker than those used previously. Thus, the method shown here enables us to make many thicker SAMs at a time and would be useful to improve the emission power of superconducting terahertz emitters.
Brain Connectivity Analysis in Anesthetized and Awake States: an ECoG Study in Monkeys

Increasingly, studies have shown that changes in brain network topology accompany loss of consciousness such that the functional connectivity of the prefrontal-parietal network differs significantly in anesthetized and awake states. In this work, anesthetized and awake segments of electrocorticography were selected from two monkeys. Using phase lag index, functional connectivity matrices were built in multiple frequency bands. Quantifying topological changes in brain network through graph-theoretic properties revealed significant differences between the awake and anesthetized states. Compared to the awake state, there were distinct increases in overall and Delta prefrontal–frontal connectivity, and decreases in Alpha, Beta1 and Beta2 prefrontal–frontal connectivity during the anesthetized state, which indicate a change in the topology of the small-world network. Using functional connectivity features we achieved a satisfactory classification accuracy (93.68%). Our study demonstrates that functional connectivity features are of sufficient power to distinguish awake versus anesthetized state.Clinical Relevance— This explores the brain network topology in awake and anesthetized states, and provides new ideas for clinical depth of anesthesia monitoring.
ANOMALY DETECTION FOR ABNORMAL ACTIVITY WITH VIDEO SURVEILLANCE USING DEEP LEARNING

: Detection of abnormal activities is essential but demanding research problem in computer vision. Anomaly detection in video surveillance involves monitoring of the designated area of interest to detect anomalies. Video surveillance is extensively used for a variety of fields, for instance, traffic monitoring, medical monitoring, security guarding, etc. Amid these various research fields, the detection of anomalous events plays a significant role. The goal is to identify abnormal behavior through the use of reliable features by utilizing deep learning methods. To address that, a fusion approach is proposed in videos based on three-dimensional convolutional neural networks (C3D) for extracting spatiotemporal features and convolutional long-short-term-memory (ConvLSTM) to aggregate the features that enhances the accuracy and efficiency of the model. The popular and open source UCSD dataset consisting of two sub datasets UCSD Ped1 and UCSD Ped2 has been used to evaluate the performance of the proposed method. The result demonstrates that we get high accuracy as compared to other approaches which is 94.3% and 92.6% for the first and second datasets respectively.
An atrial fibrillation detection system based on machine learning algorithm with mix-domain features and hardware acceleration *

This paper presents a real-time electrocardiogram (ECG) analysis system that can detect atrial fibrillation (AF) using machine learning algorithms without a cloud server. The system takes advantage of the heterogeneous structure of the Zynq system-on-chip (SoC) to optimize the tasks of local implementation of AF detection. The features extraction is based on multi-domain features including entropy features and RR interval features, which is conducted using the embedded micro controller to generate significant features for AF detection. An AF classifier based on artificial neural network (ANN) algorithm is then implemented in the programmable logic of the SoC for acceleration. The validation of the proposed system is performed by using the real-world ECG data from MIT-BIH database and CPSC 2018 database. The experimental results show an accuracy 93.60% and 97.78% when tested on these two databases respectively. The AF detection performance of the embedded algorithm is majorly identical to that of the PC-based algorithm, indicating a robust performance of hardware implementation of the AF detection.
Theory of the reactant-stationary kinetics for a coupled auxiliary enzyme assay

A theoretical analysis is performed on the nonlinear ordinary diﬀerential equations that govern the dynamics of a coupled auxiliary enzyme catalyzed reaction. The assay consists of a non-observable reaction and an indicator (observable) reaction, where the product of the ﬁrst reaction is the enzyme for the second. Both reactions are governed by the single substrate, single enzyme Michaelis– Menten reaction mechanism. Using singular perturbation methods, we derive asymptotic solutions that are valid under the quasi-steady-state and reactant-stationary assumptions. In particular, we obtain closed form solutions, analogous to the Schnell–Mendoza equation for Michaelis–Menten type reactions, that approximate the evolution of the observable reaction. Conditions for the validity of the asymptotic solutions are also rigorously derived showing that these asymptotic expressions are applicable under the reactant-stationary kinetics.
Title Wafer-scale synthesis of WS 2 films with in-situ controllable p-type doping by atomic layer deposition

Wafer-scale synthesis of p-type TMD films is critical for its commercialization in nextgeneration electro/optoelectronics. In this work, wafer-scale intrinsic n-type WS2 films and in-situ Nb-doped p-type WS2 films were synthesized through atomic layer deposition (ALD) on 8-inch α-Al2O3/Si wafers, 2-inch sapphire and 1 cm 2 GaN substrate pieces. The Nb doping concentration was precisely controlled by altering cycle number of Nb precursor and activated by post-annealing. WS2 n-FETs and Nb-doped p-FETs with different Nb concentrations have been fabricated using CMOS-compatible processes. Xray photoelectron spectroscopy, Raman spectroscopy, and Hall measurements confirmed the effective substitutional doping with Nb. The on/off ratio and electron mobility of WS2 n-FET are as high as 10 5 and 6.85 cm 2 V -1 s -1 , respectively. WS2 p-FET with 15-cycle Nb doping, the on/off ratio and hole mobility are 10 and 0.016 cm 2 V -1 s -1 , respectively. The pn structure based on nand ptype WS2 films was proved with a 10 4 rectifying ratio. The realization of controllable in-situ Nb-doped WS2 films paved a way for fabricating waferscale complementary WS2 FETs.
Creating Domain Specific Embeddings toWork with Imbalanced Datasets in Automatic Extraction of Medication Names in Tweets

—In this work, we introduce two Deep Learning architectures for the identification of drug mentions in tweets. We propose a deep neural approach based on two models: two Bidirectional Long Short-Term Memory (Bi-LSTM) networks and a Conditional Random Field (CRF) network using character and contextualized-word embeddings to deal with the extraction of semantic, syntactic, and morphological features; and a Bidirectional Encoder Representations from Transformers (BERT) using pre-trained contextualized word embeddings created from scratch with FastText with a collection of tweets. Both models have been evaluated on the BioCreative VII Track 3 dataset obtaining an F-measure of 64.2% and 67.7%, respectively.
Development of the micro-patterned 3D neuronal-hydrogel model using soft-lithography for study a 3D neural network on a microelectrode array*

In vitro patterned neuronal models have been studied as one of the strategies to investigate the relationship between structural connectivity and functional activity of neural network. Despite the importance of three-dimensional (3D) cell models, most of these studies have been performed on two-dimensional models. In this study, we present a technique to construct the micro-pattern to 3D neuronal-hydrogel model using a micromolding in capillaries (MIMIC) technique on microelectrode array (MEA). Our technique was suitable to prevent the deformation of micro-patterned collagen model against the neuronal contracted tension during the network formation. The relationship between the growth directions of glial cells and micro-pattern direction was investigated. Lastly, we confirmed that our 3D model had synchronized activity among neurons in 3D. This model is expected to be used as a tool to study the relationship between structural connectivity and functional activity in the 3D environment.
What makes an inference robust?∗

Sentences involving disjunction give rise to ignorance, distributive and free choice inferences. These inferences display certain similarities with regular Scalar Implicatures (SIs) and some researchers have proposed to treat them as such. This proposal, however, faces an important challenge: experimental results have shown that all three inferences are generally more robust, faster to process, and easier to acquire than regular SIs. A common response to this challenge in the literature is to hypothesise that such discrepancies among di erent types of SIs stem from the type of alternatives used to derive them: in contrast to regular SIs, ignorance, distributive and free choice inferences are computed on the basis of sub-constituent alternatives, which are alternatives that are formed without lexical substitution. This paper reports on a series of experiments that tested this hypothesis by comparing positive, disjunctive sentences giving rise to the three inference types of inferences to variants of these sentences involving either negation and conjunction, or negation and disjunction, for which the implicature approach predicts similar inferences on the basis of the same type of alternatives. Our results reveal that, while the three inferences are indeed quite robust in the disjunctive cases, regardless of whether negation is present or not, the inferences that their negative, conjunctive variants give rise to are not. These ndings are challenging for the idea that the type of alternatives involved in SI computation (i.e., sub-constituent vs. lexical) is a major factor responsible for di erences in robustness. We outline two possible alternative explanations of our data. One supplements the implicature approach with an extra assumption about how relevance is calculated for disjunction; the other is based on a non-implicature approach to the cases involving disjunction.