Miracle-dz/UniFAR-FTUs huggingface dataset:
Input 1-layer linear 2-layer linear 2-layer ReLU 6-layer linear 6-layer ReLU

We study the interplay between memorization and generalization of overparameterized networks in the extreme case of a single training example and an identitymapping task. We examine fully-connected and convolutional networks (FCN and CNN), both linear and nonlinear, initialized randomly and then trained to minimize the reconstruction error. The trained networks stereotypically take one of two forms: the constant function (memorization) and the identity function (generalization). We formally characterize generalization in single-layer FCNs and CNNs. We show empirically that different architectures exhibit strikingly different inductive biases. For example, CNNs of up to 10 layers are able to generalize from a single example, whereas FCNs cannot learn the identity function reliably from 60k examples. Deeper CNNs often fail, but nonetheless do astonishing work to memorize the training output: because CNN biases are location invariant, the model must progressively grow an output pattern from the image boundaries via the coordination of many layers. Our work helps to quantify and visualize the sensitivity of inductive biases to architectural choices such as depth, kernel width, and number of channels.
Localized surface plasmon resonance of dielectrically-coated gold nanoparticle arrays

In this thesis, I study the localized surface plasmon resonance phenomenon in dielectrically coated, closely-spaced gold nanoparticles. I examine the effect of a dielectric coating (that models the sensing of a biomolecular analyte layer) on the optical absorption of these particles. The extinction spectra and the electric field around the particles are calculated. The particles are chosen to be either spheres or hemispheres to be representative of solution phase (3D) or surface (2D) experiments. Calculations are based on the Discrete Dipole Approximation method. In particular, I study the effect of a dielectric coating on the localized surface plasmon spectra around clusters of coated gold nanohemispheres. Based on this study, I propose a new sensing mechanism for detecting biomolecules attached onto a linear array of closely-spaced gold nanohemispheres immobilized on a waveguide surface.
BRAIN ELECTRICAL SIGNATURES OF THE NOVEL WORD LEXICALIZATION

There is currently an increasing in terest in the acquisition of reading fluency. This is characterized as autom ated reading w ith higher rates of speed and accuracy th a t enables the reader to carry out processes of higher-level comprehension. A key factor in reading fluency is the establishment of word representations in the reader’s lexicon, which allow the direct visual recognition of words. It is widely accepted th a t a repeated visual exposure to novel words is needed to construct these mental representations. However, the nature of memory traces reached after this training is a question hotly debated in behavioral literature. W hile some authors argue tha t a simple visual training enables the formation of lexical traces for novel words, others argue tha t a training not only in orthographic but also in other word features (such as phonology or meaning) is required for the acquisition of high quality lexical representations. The use of more suitable measures for exploring the brain response during this process could contribute to solve this question. In this sense, the ERP approach emerges as a powerful tool to study the neurophysiological mechanisms underlying the acquisition of the lexical reading, and particularly the train ing conditions under which the formation of high quality lexical representations is possible. In this paper, the main contributions from the ERP literature to the understanding of the novel word lexicalization are reviewed.
BOTANY IN SECONDARY EDUCATION: STUDENTS' VIEWS ON NATIVE FLORA

This research aimed to assess students' perceptions regarding the study of the Brazilian flora and the use of technology in the learning of botany. It was mainly motivated by the necessity for improving the teaching of botany in Brazil, in an attempt to make the learning more dynamic and contextualized, as well as to motivate both students and teachers. The methodology encompassed the application of a questionnaire, a botanical game, and interviews. Most students were found to have a limited understanding about the Brazilian flora and its history. In addition, the students pointed out that the inclusion of technology would be beneficial for their learning process, and ‘perceived usefulness’ was observed to be a determining factor of their opinions. During the game, they proved to be interested and involved. The interviews revealed their necessity to know more about the native flora in order to preserve it.
Understanding Influence of Psychological Capital on Student's Engagement and Academic Motivation

This study examined the influence of psychological capital on engagement and motivation of college students. The study was conducted on 230 commerce and business students in Bhutan. Using random sampling approach, data were collected on a standard questionnaire all the variables of the study. Correlations and regressions were used to analyze obtained data. Results revealed positive and significant association among psychological capital, engagement and motivation. Regression analysis indicated significant influence of psychological capital on student's engagement and on their motivation. The implications of the study have been explicated in the research.
Inclusive Education in Nigeria—A Myth or Reality?

As democracy entails freedom of association, so should it be for inclusive education in Nigeria. This paper therefore jettisoned the current practice of segregational effort on special education for the challenged and thereby recommended total inclusive education as a better practice for Nigeria classrooms. Practically, it recommended a policy twist in favour of inclusive education as part of the National Policy on Education as well as adequate retraining of teachers in inclusive education pedagogies and provision of abundant resources to support inclusive education learning.
Treatment of Severe Plasmodium falciparum Malaria With Artesunate: Report of the First Case in the Western United States

Severe malaria, caused by Plasmodium falciparum, is a life-threatening disease that causes 1 to 3 million deaths worldwide each year. The standard treatment in the United States for severe malaria is intravenous quinidine, which has many adverse effects, including cardiotoxicity. In addition, quinidine is not always available in pharmacies. Artesunate, an artemisinin derivative, is used extensively outside of the United States and is shown to clear parasitemia effectively, dissipate fever quickly, and also decrease mortality. Recently, artesunate is available through the Centers for Disease Control and Prevention by physician request. In this study, we report the first case of severe P. falciparum malaria in the western United States successfully treated with artesunate. The patient's initial level of parasitemia was 30% to 40%. Three days after the artesunate treatment, she had complete resolution of her symptoms and parasitemia without adverse effects. Artesunate should be considered the drug of choice for P. falciparum malaria in the United States.
ANALYSIS OF HUMAN DYNAMICS IN MICRO-TELEOPERATION SYSTEM

Microsurgery and microassembly are some high precision tasks that require the assistance of a micromanipulator. However, in order to manipulate micro objects it is inevitable to consider the scaling effect problem between the worlds with different physical characteristics. Some scaling methods have been proposed, but how the scaling gains affect the human operator performance is still a remaining problem. In this paper, when different transfer function parameters are selected, the effects on the performance and the dynamic characteristics of the human operator are analyzed.
Nonlinear inversion for earthquake rupture parameters

The spatial distribution of rupture velocity, rise time, and intensity of highfrequency radiation on the fault strongly influence the ground motion. Because It is practically impossible to simulate deterministically high-frequency (frequency > 1.0 Hz) phase changes in ground acceleration, we invert the envelope of rootmean-square acceleration to determine these kinematic source parameters. The envelope inversion, a highly nonlinear problem, is solved by a hybrid global search algorithm. The synthetic acceleration waveforms are calculated by using empirical Green's functions. The Northridge earthquake inversions show the strongest radiation sources associated primarily with the beginning of the rupture (first 4 s) associated with the breaking of a high-stress-drop asperity. The rupture velocity is nearly uniform, about 3.0 km/s. The rise time is poorly resolved. Introduction The acceleration recordings from large earthquake contain abundant information of the dynamic rupture process on the fault. These recordings are generally dominated by amplitudes in the frequency range between 1.0-20 Hz. At these frequencies, it is practically impossible to reproduce the acceleration waveform analytically or numerically due to the small-scale complexity of source, path and site. Consequently, waveform inversions are not available in this frequency band. Waveform modeling has been successfully applied to determine the spatial distribution of earthquake fault rupturing process from strong motion records that are low passed at 2.0 Hz or less (e.g., Archuleta, 1984[1]; Hartzell and Heaton, 1983[2]; Wald et al., 1991[3]). Attempts have been made to extract information from strong motion recordings related to the high-frequency radiation process: Zeng et al . (1993)[4] used the envelope of the squared high-frequency (>5 Hz) displacement seismograms for inversion; Cocco and Boatwright (1993)[5] deconvolved the squared acceleration envelope; Kakehi and Irikura (1996[6], 1997[7]) and Hartzell et al.(1996)[8] inverted the envelope of root-mean-squared acceleration using small earthquake recordings as empirical Green's functions. If recordings of small earthquakes located within the rupture area
A Review of Construction Waste Cause Factors

Rapid growth in construction activities increases construction waste problems around the world. Construction waste gives a negative impact to the environment, costs, time, productivity and social of country. To reduce these negative impacts, it needs a comprehensive understanding of the construction waste generation and management. This paper identifies various causative factors of construction waste existing in construction field activities. The common causes of construction waste were identified from past research papers. The causes of construction waste are matrix and found that 63 waste factors existed in construction activities. The waste causes were grouped into the seven categories: Design, Handling, Worker, Management, Site condition, Procurement and External. The frequency of the causative factor in each category was determined based on the deduction from past research works that were considered. The highest frequency as indicated by most researchers is a frequent design change that contributes to large generation of construction waste. Meanwhile, factors from each category are also determined. Through identifying the important causes, construction industries players can avoid and alert about how the waste was generated. It will gives a great benefit for country in term of economic, social and protection of the environment. Keyword: Construction waste, Causes of waste, Mapping Matrix
A HYBRID FILTER-WRAPPER FEATURE SELECTION APPROACH FOR AUTHORSHIP ATTRIBUTION

Many criminals make use of the convenience of anonymity in the cyber world to conduct inappropriate or illegal activities. Authorship attribution aims to identify the most likely author from potential suspects for evidence collection and forensic investigation. Authorship attribution is typically achieved by employing classification algorithms to identify the author based on various writing-style features. However, not all features are useful (relevant) and irrelevant or redundant features may even deteriorate the classification accuracy and slow down the processing time. Feature selection as important data processing techniques can solve this problem, but they have not been investigated in authorship attribution. In this paper, we propose a novel hybrid filter-wrapper feature selection approach to authorship attribution tasks, where a rich set of writing-style features, including syntactic features, lexical features, and structural features, is extracted in order to include all available useful information. In the proposed approach, a correlation based filter feature selection method is used to filter out irrelevant features and then a particle swarm optimization based wrapper method is proposed for feature selection to further remove redundant features, select only relevant features. Experiments on real-life Blog and E-mail datasets show that the proposed approach can improve the classification performance by selecting only a small subset of features, and achieve better classification performance than filter only, wrapper only, and a commonly used wrapper method (linear forward selection).
Under review as a conference paper at ICLR 2020 uses the mathematically appealing tensor methods

Graph-based semi-supervised learning (SSL) assigns labels to initially unlabelled vertices in a graph. Graph neural networks (GNNs), esp. graph convolutional networks (GCNs), inspired the current-state-of-the art models for graph-based SSL problems. GCNs inherently assume that the labels of interest are numerical or categorical variables. However, in many real-world applications such as coauthorship networks, recommendation networks, etc., vertex labels can be naturally represented by probability distributions or histograms. Moreover, real-world network datasets have complex relationships going beyond pairwise associations. These relationships can be modelled naturally and flexibly by hypergraphs. In this paper, we explore GNNs for graph-based SSL of histograms. Motivated by complex relationships (those going beyond pairwise) in real-world networks, we propose a novel method for directed hypergraphs. Our work builds upon existing works on graph-based SSL of histograms derived from the theory of optimal transportation. A key contribution of this paper is to establish generalisation error bounds for a one-layer GNN within the framework of algorithmic stability. We also demonstrate our proposed methods’ effectiveness through detailed experimentation on real-world data. We have made the code available.
InP Backside Via Formation Using High Etch Rate and Low Temperature HI-Based ICP Etching

InP-based devices have shown great potential in realizing high-speed Monolithic Millimeter-wave Integrated Circuits (MMICs). Backside via is one of the most important elements for high-speed MMICs. We have demonstrated high etch rate and low temperature InP etching using HI-based Inductively Coupled Plasma (ICP). A Dry Film Resist (DFR) with high temperature tolerance was employed as an etching mask. Using these technologies, InP backside via formation process was simplified. After via formation, electric properties were evaluated over a 3-inch diameter wafer. High uniformity and reproducibility enough for practical applications were obtained.
Chapter 2 Analytical Approaches to River Control

This chapter discusses in detail the analytical framework used in this work. A discussion of the World Commission on Dams process and response is used to identify the gaps in existing approaches. This is put into context using the Bhakra project as a case study. The combination of political ecology approaches and Foucauldian discourse analysis is proposed for an examination of discursive and political functions of river control, with a special focus on the role of expert knowledge.
Problem of Multiplicity in Clinical Studies and Inferences Made When It Is Present: Response

Address correspondence to David C. Flanigan, MD (email: david.flanigan@osumc.edu). Submitted September 6, 2019; accepted September 11, 2019. One or more of the authors has declared the following potential conflict of interest or source of funding: D.C.F. is a consultant for Vericel, Conmed-MTF, DePuy Mitek, Zimmer, Smith & Nephew, and Ceterix and has received research support from Vericel, Zimmer, MTF, Smith & Nephew, Histogenics, Aesculap, Cartiheal, Anika Therapeutics, and Moximed. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto.
Microring lasers based on Si3N4 optical waveguides cladded with perovskite quantum-dot film

We demonstrate microring lasers based on Si3N4 optical waveguides cladded with MAPbBr3 quantum dots composite film. Si3N4 microrings are designed and fabricated with electron beam (E-beam) lithography and inductively-coupled plasma reactive-ion-etching followed by spin coating a MAPbBr3 quantum dots composite film on them. We clearly observe the lasing modes with narrow linewidths for both TE- and TM-polarization modes when the microrings are optically pumped with a nanosecond laser. The experimental results show that the laser has a typical linewidth of 0.23 nm and a minimum pump power of 8.46 μJ/cm2.
A novel improved CNN algorithm via denoising approach

Address-event-based Dynamic Vision Sensor(DVS) and Convolutional Neural Network(CNN) have been widely researched in recent years. However, the collected data of DVS are easily affected by some noise, which makes it difficult to identify the target during the classification processing. In order to solve the problem of misclassification, a novel improved CNN(NI-CNN) technique is proposed in this paper. Firstly, the appropriate number of event pulses are chosen and mapped to the frame domain, then the optimization denosing approach is utilized to the whole classification system. Secondly, reducing intra-class spacing and enlarging inter-class divergence by joint loss function which is adjusted regularization parameters. Numerical comparisons between our proposed approach and some state-of-the-art solvers, on several accessible databases, are presented to demonstrate its efficiency and effectiveness.
Limited propagation and nanofocusing of radially polarized light through hybrid plasmonic waveguide

Hybrid plasmonic waveguides (HPWs) have become a hot topic in nanophotonic due to their excellent optical field constraints and low propagation loss. Based on the polarization dependence of surface plasmon polariton (SPP) excitation the symmetry of cylindrical vector beams, a cylindrical hybrid plasmonic waveguide (CHPW) realizing limited propagation and two tapered hybrid plasmonic waveguides (THPWs) achieving nanofocusing are presented. CHPW supports radial polarization mode and well compensate mode propagation loss by adjusting the structural parameters of the waveguide. Splendid mode limitation and long transmission distance with low loss can be achieved simultaneously. On the basis of CHPW, the structure is tapered to realize nanofocusing. And periodic grooves are constructed on the metallic surface of the tapered hybrid plasmonic waveguide (THPW) to meet the phase matching condition and maximize the coupling of light energy from inside to outside. Meanwhile, the low index layer of THPW is replaced with two different index layers, which is broadened to gather more energy efficiently and the energy is converged on the apex of the waveguide to form the ultrahigh field enhancement, which is another optional way to improve the performance of THPW. The results offer vital reference value for designing and manufacture related photonic devices.
Radiation-induced degradation of optoelectronic sensors

Space system undergo particularly hard natural radiation environment, but can also potentially be subject to the radiations injected in low earth orbit by the explosion of a nuclear weapons. The increasing use of optoelectronic components in space systems makes the risk assessment regarding the radiation effects of an increasing interest. This paper presents recent results about the degradation of optoelectronic devices in term of atomic displacements. This paper Most of this work has been developed under the EDA contract JIP-ICET2 A-1341-RT-GP within the CapTech Technologies for Components and Modules’ (TCM) in EDA. (Tracking #: SD102-11)
IFPSS: intelligence fire point sensing systems in AIoT environments

The situation at the scene of the fire is changing rapidly. How to collect and analyze the most immediate fire information, providing the most effective information for disaster decision-making has always been an important issue. This paper proposes an Intelligent Fire Point Sensing System (IFPSS), which proposes fire condition prediction based on artificial intelligence technology as well as large amounts of gas and temperature data in fire scenes collected by IoT devices. The IFPSS collected actual gas and temperature data from the simulation room where the actual fire test was conducted. Taking carbon monoxide (CO) and hydrogen sulfide (H2S) data as an example, the artificial intelligence analysis of IFPSS uses linear regression algorithm to establish artificial intelligence model. After training and testing the model, an accuracy of up to 84.4% predicts whether the fire process is in the very early stages of a fire.
A silicon-graphene hybrid waveguide photodetector with a 3dB-bandwidth of 17 GHz

A graphene photodetector based on ultra-thin silicon waveguide at 1. 55μm is proposed. By reducing the silicon core thickness, the fundamental TE waveguide mode is less confined and light-graphene interaction is enhanced. Benefiting from the ultrathin silicon waveguide and reflector structure, the graphene absorption coefficient reaches 0.36 dB/μm. A 10nm-thick CVD-grown hexagonal boron nitride is covered on the graphene to improve the device performance. With the help of metal-graphene-metal structure, the contact resistance is reduced dramatically. The devices have shown a responsivity of 1.4 mA/W at 0 V bias and 23.1 mA/W at 0.3 V bias with 0.24 mW input optical power. The measured 3-dB bandwidth is 17GHz under 0V bias voltage at 1550 nm.
Detection and fine 3D pose estimation of texture-less objects

This paper presents a method for real-time detection and accurate estimation pose of the object. We start from the LINEMOD which be proposed by Hinterstoisser et al. However, it show typical problems such as not being robust to clutter and occlusions. In this paper, we propose a method, namely Block-Matching. Firstly, extracting multi-angle templates for each object. Then cutting each template into many small blocks, and treat each block as a template to match the object in the scene. If the matching score of a small block in a template exceeds a certain threshold, it indicates that there is an object which we want in the scene. The ICP algorithm is the most common choice for fine 3D pose estimation. But ICP can easily get stuck in local minima and its performance largely depends on the quality of initialization. Particle swarm optimization is employed to refine the 6D pose of the target object in this paper to avoid these defects. Extensive experimental results demonstrate the superior performance of the approach compared to the state of the art.
Research on autonomous driving perception based on deep learning algorithm

Multiple source sensor fusion is the foundation of motion planning for autonomous driving system, which is the crucial part in improving the performances for unmanned operational system. In this article, based on the deep learning platform CATARC constructed, applied with Udacity’s Lincoln MKZ multiple sensor data, implemented with Robotic Operation System, Computer Vision, PointCloud Library, Deep Neural Networks and Extended Kalman Filter, constructed a low-cost object pose estimation data fusion solution, aiming at technic support for the industrialization of autonomous driving technologies.