Miracle-dz/UniFAR-FTUs huggingface dataset:
English Verb Classes and Alternations: A Preliminary Investigation

In this rich reference work, Beth Levin classifies over 3,000 English verbs according to shared meaning and behavior. Levin starts with the hypothesis that a verb's meaning influences its syntactic behavior and develops it into a powerful tool for studying the English verb lexicon. She shows how identifying verbs with similar syntactic behavior provides an effective means of distinguishing semantically coherent verb classes, and isolates these classes by examining verb behavior with respect to a wide range of syntactic alternations that reflect verb meaning. The first part of the book sets out alternate ways in which verbs can express their arguments. The second presents classes of verbs that share a kernel of meaning and explores in detail the behavior of each class, drawing on the alternations in the first part. Levin's discussion of each class and alternation includes lists of relevant verbs, illustrative examples, comments on noteworthy properties, and bibliographic references. The result is an original, systematic picture of the organization of the verb inventory. Easy to use, "English Verb Classes and Alternations" sets the stage for further explorations of the interface between lexical semantics and syntax. It will prove indispensable for theoretical and computational linguists, psycholinguists, cognitive scientists, lexicographers, and teachers of English as a second language. Beth Levin is associate professor of linguistics at Northwestern University.
Progress in forecasting by neural networks

The forecasting of electricity consumption by means of neural networks is reported. The neural model used is a multilayer perceptron. Learning is accomplished with a backpropagation algorithm. The neural network forecasts are made directly from the observations without any corrections. Exogeneous variables, such as temperature and nebulosity, are introduced directly as a network input. The output is always one neuron which provides forecast consumption one step ahead. The neural network results are judged to be no better than those of traditional models. Its advantages are its ability to forecast more than one step ahead and the possibility of introducing economic characteristics in the minimization criteria. <<ETX>>
Radio frequency (RF) transient classification using sparse representations over learned dictionaries

Automatic classification of transitory or pulsed radio frequency (RF) signals is of particular interest in persistent surveillance and remote sensing applications. Such transients are often acquired in noisy, cluttered environments, and may be characterized by complex or unknown analytical models, making feature extraction and classification difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. We compare two dictionary learning methods from the image analysis literature, the K-SVD algorithm and Hebbian learning, and extend them for use with RF data. Both methods allow us to learn discriminative RF dictionaries directly from data without relying on analytical constraints or additional knowledge about the expected signal characteristics. We then use a pursuit search over the learned dictionaries to generate sparse classification features in order to identify time windows that contain a target pulse. In this paper we compare the two dictionary learning methods and discuss how their performance changes as a function of dictionary training parameters. We demonstrate that learned dictionary techniques are suitable for pulsed RF analysis and present results with varying background clutter and noise levels.
Hadoop-Based Model of Mass Data Storage

Aiming at more and more data produced by network, it is extremely important to manage and store these data by using mass data storage platform. This paper presents a method of managing rationally and storing mass data based on distributed computing technique. It is based on Hadoop distributed platforms, mainly using the HDFS distributed file system, MapReduce parallel computing models and Hbase distributed database technology as massive data processing methods, to achieve the efficient storage. The model can overcome the existing deficiencies of the current means of storage and solve the problems of mass data in storage, which has good scalability and reliability, thus the efficiency of storage can be further improved.
Semi-Supervised Word Sense Disambiguation via Context Weighting

Word sense disambiguation as a central research topic in natural language processing can promote the development of many applications such as information retrieval, speech synthesis, machine translation, summarization and question answering. Previous approaches can be grouped into three categories: supervised, unsupervised and knowledge-based. The accuracy of supervised methods is the highest, but they suffer from knowledge acquisition bottleneck. Unsupervised method can avoid knowledge acquisition bottleneck, but its effect is not satisfactory. With the built-up of large-scale knowledge, knowledge-based approach has attracted more and more attention. This paper introduces a new context weighting method, and based on which proposes a novel semi-supervised approach for word sense disambiguation. The significant contribution of our method is that thesaurus and machine learning techniques are integrated in word sense disambiguation. Compared with the state of the art on the test data of the English all words disambiguation task in Sensaval-3, our method yields obvious improvements over existing methods in nouns, adjectives and verbs disambiguation.
Globally Networked Learning Environments in Professional Communication: Challenging Normalized Ways of Learning, Teaching, and Knowing

Even a cursory glance at the daily news will provide ample testimony to the importance for professional communication of the contributions to this special issue of Journal of Business and Technical Communication (JBTC). Indeed, as recent events have made abundantly clear, the most pressing challenges and crises we face—be these economic or environmental crises or social justice issues—are global. And yet, despite their global nature and their far-reaching consequences for local communities, much deliberation and decision making about these issues has been shifted to global economic
18.5: Late‐News Paper: WUXGA Resolution 3D Stereoscopic Head Mounted Full Color AMOLED Microdisplay

We report a WUXGA (1920 × 1200) resolution head mounted OLED microdisplay with luminance of 2000cd/m in monochrome and 240cd/m2 in full color. We believe this is the highest resolution OLED microdisplay based headset ever reported. The sub‐pixel size for this 0.86‐inch diagonal microdisplay is 2.45 μm × 8.5 μm.
Spectral response and surface layer thickness of GaAs:Cs-O negative electron affinity photocathode

On-line spectral response curves of GaAs:Cs-O NEA photo- cathode of reflection model is first presented and the relation between spectral response curves and the thickness of Cs-O layer is discussed. When Cs and O is deposited on the surface of cleaning GaAs wafer, photo-cathode's spectral response rises sharply and long-wavelength threshold tends to a fixed value at the beginning of the activation. But, as Cs and O are deposited continually, spectral response rises slowly and the long-wavelength threshold tends to be shortened. When a fine thickness of Cs-O layer is reached, the optimum spectral response is obtained. As a quantity Cs-O is further increased, both the spectral response and the long-wavelength threshold decrease. The thickness of GaAs photo-cathode surface layer that consists of Cs-O layer and GaAs-O layer is researched by take-off angle XPS technology. Thickness of Cs-O layer is approximately equal to 0.7 nm, and the GaAs-O layer is approximately 0.2nm. Our experiments show when the thickness of Cs-O layer is approximately equal to 0.7nm, and the GaAs-O layer is approximately 0.2mm. Our experiments show when the thickness of Cs-O layer is 0.7nm or so and the GaAs-O layer tend to be disappeared, NEA photo-cathode with the optimum spectral response is achieved which can be used widely in low-light level imaging detectors.
Withanolide Compounds from the Flower of Datura metel L.

Three new withanolide compounds named baimantuoluoline A (1), B (2), and C (3) and the two known withanolides withafastuosin E (4) and withametelin C (5) were isolated from the fraction exhibiting activity for psoriasis in the flower of Datura metel L. The three new structures were determined as (5α,6α,7α,12β,15β,22R)-6,7-epoxy-5,12,15-trihydroxy-1-oxowitha-2,24-dienolide (1), (5α,6β,15β,22R)- 5,6,15,21-tetrahydroxy-1-oxowith-24-enolide (2), and (5α,6β,12β,22R)-5,6,12,21-tetrahydroxy-27-methoxy-1-oxowitha-2,24-dienolide (3) on the basis of extensive spectroscopic data (HR-ESI-MS, 1H- and 13C-NMR, 1H,1H-COSY, HSQC, HMBC, and NOESY) (withanolide=22-hydroxyergostan-26-oic acid δ-lactone).
Indigenous Methods in Preserving Bush Mango Kernels in Cameroon

Traditional practices for preserving Irvingia wombolu and Irvingia gabonensis (bush mango) kernels were assessed in a survey covering twelve villages (Dongo, Bouno, Gribi [East], Elig-Nkouma, Nkom I, Ngoumou [Centre], Bidjap, Nko’ovos, Ondodo [South], Besong-Abang, Ossing and Kembong [Southwest]), in the humid lowland forest zone of Cameroon. All the interviewed households that own trees of species were found to preserve kernels in periods of abundance, excluding Elig-Nkouma (87.5%). Eighty nine and 85% did so in periods of scarcity for I. wombolu and I. gabonensis respectively. Seventeen and twenty-nine kernel preservation practices were recorded for I. wombolu and I. gabonensis respectively. Most were based on continuous heating of the kernels or kernel by-products (cakes). The most commonly involved keeping the sun-dried kernels in a plastic bag on a bamboo rack hung above the fireplace in the kitchen. A 78% of interviews households reported preserving I. wombolu kernels for less than one year while 22% preserved it for more than one year with 1.9% for two years, the normal length of the off-season period for trees in the wild. Cakes wrapped with leaves and kept on a bamboo rack hung over the fireplace were reported by households in the East and South provinces to store Irvingia gabonensis longer (more than one year). Further studies on the utilization of heat for preserving and canning bush mango kernels are recommended.
Parallel power flow calculation in electric distribution networks

This paper develops a parallel method for fast distribution power flow studies using multi-processors. The method belongs to the class of coarse-grain and exhibits good convergence characteristics over wide range of r/x ratio of distribution lines. The method can be applied to radial distribution systems. It uses only active and reactive power injections at the main feeder and at laterals as state variables; thus reducing the number of state variables. Radial distribution networks are mapped into the tree structure of multi-processor system for allocating each node to processes. Forward/backward sweep approach is realized by communication between the root processor and leaf processors. The proposed method is evaluated on various distribution systems with promising results.
Vehicle Logo Recognition Based on Local Feature Descriptor

In this paper, we present a comparison of the scale-invariant feature transforms (SIFT)-based feature-matching scheme and the speeded up robust features (SURF)-based feature-matching scheme in the field of vehicle logo recognition. We capture a set of logo images which are varied in illumination, blur, scale, and rotation. Six kinds of vehicle logo training set are formed using 25 images in average and the rest images are used to form the testing set. The Logo Recognition system that we programmed indicates a high recognition rate of the same kind of query images through adjusting different parameters.
The seeing passenger car 'VaMoRs-P'

A passenger car Mercedes 500 SEL has been equipped with vision in the framework of the EUREKA-project 'Pometheus III'. Road and object recognition is performed both in a look-ahead and in a look-back region; this allows an internal servo-maintained representation of the entire situation around the vehicle using the 4D approach to dynamic machine vision. Obstacles are detected and tracked both in the forward and in the backward viewing range up to about 100 meters distance; depending on the computing power available for this purpose up to 4 or 5 objects may be tracked in parallel in each hemisphere. A fixation type viewing direction control with the capability of saccadic shifts of viewing direction for attention focussing has been developed. The overall system comprises about 60 transputers of T-222 and T-800. Beside a PC as transputer host all other processors in VaMoRs-P are transputers. A description of the parallel processing architecture is given; the system integration follows the well proven paradigm of orientation towards 4D physical objects and expectations with prediction error feedback. This allows frequent data driven bottom-up and model driven top-down integration steps for efficient and robust object tracking.
Causal discourse analyzer: improving automated feedback on academic ESL writing

Expressing causal relations plays a central role in academic writing. While it is important that writing instructors assess and provide feedback on learners’ causal discourse, it could be a very time-consuming task. In this respect, automated writing evaluation (AWE) tools may be helpful. However, to date, there have been no AWE tools capable of evaluating causal discourse. The authors of the present study attempt to fill in this gap by (1) developing an automated causal discourse analyzer and (2) investigating how accurately the analyzer processes learners’ causal discourse in academic writing. The accuracy of the analyzer is evaluated on cause-and-effect essays written by 17 non-native undergraduate students. The results indicate precision of . 93, recall of . 71, and accuracy of . 76, which is promising for pedagogical applications of the analyzer, that is, providing learners with automated formative feedback specific to causal discourse.
FINITE ELEMENT BEAM PROPAGATION METHOD FOR NONLINEAR OPTICAL WAVEGUIDES

A new beam propagation method based on the finite element method (FE-BPM) has been developed for the analysis of nonlinear optical waveguides. A formulation for the FE-BPM that is applicable not only to the TE mode but also to the TM mode is presented. Various techniques for enhancing the performance of the FE-BPM are introduced, including the Pade equation of wide-angle beam propagation, the transparent boundary condition, the perfect matched layer condition for prevention of spurious reflection from the edge of the analysis region, and an algorithm for adaptive updating of the reference index of refraction and the finite-element grids. Beam propagation analysis of spatial soliton emission in a nonlinear optical waveguide is performed in order to investigate the performance of the new FE-BPM. © 1999 Scripta Technica, Electron Comm Jpn Pt 2, 82(4): 47–53, 1999
On the realization of NPN-only log-domain chaotic oscillators

Two new chaotic oscillators with double-scroll dynamics based on the log-domain circuit-design technique are presented. As the circuits operate in log domain, they are suitable for low-voltage, high-frequency operation. The important key parameters of the oscillators are electronically tunable, thus, they are well suited for integrated circuit implementation. Furthermore, the circuits do not employ any PNP transistors, hence they can be implemented in cheap bipolar processes, which do not offer high-quality vertical PNPs. Experimental results verifying the feasibility of the circuits are given.
Hidden Markov models and neural networks

In this contribution, we discuss the application of machine learning methods, particularly hidden Markov models and artificial neural networks, to bioinformatics tasks. We begin with a brief summary of the two paradigms as computational and learning systems and describe the nature of the problems to which these two approaches can be applied. Then, we give some examples of their application to bioinformatics, briefly discuss the strengths and weaknesses of these two approaches, and provide some avenues for further reading. Keywords: hidden Markov models; neural networks; machine learning
Hardware circuit implementation of computer-generated holographic algorithm

This paper presents a novel computer-generated hologram algorithm fit to be implemented on hardware circuit, called recurrence formula method. In order to improve the speed of the hologram computation, the computation units based on this algorithm in FPGA chip were implemented by programming the chip with the Very-hight-speed Integrated Circuit Hardware Description Language. The number of the units is determined by the resources of the FPGA chip. Much more computation units can also be implemented by paralleling more FPGA chips to work together. This article will promote the implementation of the computer-generated holographic display technology.
Dynamic Fast Issue (DFI) Mechanism for Dynamic Scheduled Processors

SUMMARY Superscalar processors can achieve increased performance by issuing instructions Out-of-Order (OoO) from the original instruction stream. Implementing an OoO instruction scheme requires a hardware mechanism to prevent incorrectly executed instructions from updating registers values. In addition, performance decreases if data dependencies, a branch or a trap among instructions appears. To this end we propose a new mechanism named Dynamic Fast Issue (DFI) mechanism to issue instructions in an OoO fashion to multiple parallel functional units without considerable hardware complexity. The above system, which will be implemented in our Superscalar Functional Assignments Register Microprocessor (FARM) [1], solves data dependencies, supports precise interrupt and branch prediction, which are the main problems associated with the dynamic scheduling of instructions in superscalar machines. Results are written only once, , directly into the register file (RF). To ensure that results are written in order in their appropriate output registers, a record of instruction order and state is maintained by a status buffer (STB). A 64 entries integrated register file is implemented to hold both renamed and logical registers. To recover the processor state from an interrupt or a branch missprediction, a status buffer (STB) and a recovery list table (RLT) are implemented. Novel aspects of the above system architecture as well as the principle underlying this process and the constraints that must be met is presented. Performance evaluation results are performed through full-pipelined-level architectural simulator and SPECint95 benchmark programs.
Single event upset injection simulation and fault-tolerant design for image compression applications

This paper describes a SEU fault injection framework. Based on the assumption of SEU effects and SEU distribution, the quantitative analysis between measured data and simulation model is investigated. By adjusting some parameters in the simulation-based framework, the proposed framework can be very possibly close to the published data and some accelerated radiation experiments. Furthermore, how the JPEG2000 based hardware architecture is sensitive to SEUs can be found out. In terms of hardware resources and operating frequencies, some fault-tolerant techniques can be introduced to the more sensitive parts, which show the framework's effectiveness in fault-tolerant design for image compression applications.
Neural network implementation of the SMSE filter for imaging processing

This paper presents an implementation and enhancement of the SMSE (scaled mean square error) filter, using a Hopfield neural network based algorithm. We show the development of the original SMSE filter from the MMSE (minimum mean square error) filter and the PMSE (parametric mean square error) filter, both of which suffer from the oversmooth phenomena. The SMSE filter is more efficient than the PMSE filter in terms of noise removal as it does not take into account all the correlation factors used for image restoration. An adaptive SMSE filter is also presented. The adaptive SMSE filter uses a mask operation technique. A user- defined mask is moved across the image and the filtering parameters are computed based on the local image statistics of the region below the mask. The original and adaptive SMSE filters are implemented using a Hopfield neural network based algorithm. A number of experiments were performed to test the filter characteristics.
A rapid parallelization of cone-beam projection and back-projection operator based on texture fetching interpolation

Projection and back-projection are the most computational consuming parts in Computed Tomography (CT) reconstruction. Parallelization strategies using GPU computing techniques have been introduced. We in this paper present a new parallelization scheme for both projection and back-projection. The proposed method is based on CUDA technology carried out by NVIDIA Corporation. Instead of build complex model, we aimed on optimizing the existing algorithm and make it suitable for CUDA implementation so as to gain fast computation speed. Besides making use of texture fetching operation which helps gain faster interpolation speed, we fixed sampling numbers in the computation of projection, to ensure the synchronization of blocks and threads, thus prevents the latency caused by inconsistent computation complexity. Experiment results have proven the computational efficiency and imaging quality of the proposed method.
An Improved Direction-of-Arrival Estimation Method Based on Sparse Reconstruction

In this paper, we account for efficient approach of direction-of-arrival estimation based on sparse reconstruction of sensor measurements with an overcomplete basis. MSD-FOCUSS ( MMV Synchronous Descending FOCal Underdetermined System Solver) algorithm is developed against to sparse reconstruction in multiple-measurement-vectors (MMV) system where noise perturbations exist in both the measurements and sensing matrix. The paper shows how sparse-signal model of DOA estimation is established and MSD-FOCUSS is derived, then the simulation results illustrate the advantage of MSD-FOCUSS when it is used to solve the problem of DOA estimation.
Evaluation of Handheld Computers for Direct Systematic Classroom Observation

Through this study the authors evaluate outcomes associated with the use of handheld computers by interventionists in improving the efficiency of direct systematic classroom observation. Information from observations is used by interventionists for treatment planning and evaluation. In this study, interventionists were trained to use personal digital assistants with classroom observational software for use with students who displayed low levels of academic engagement. Results indicated that the personal digital assistants and observational software were perceived as user-friendly, increased computer self-efficacy, and facilitated treatment planning and evaluation. Discussion focuses on implications for use of handheld computers and mobile devices by interventionists.