Comprehensive meta-analysis of integrity test validities: Findings and implications for personnel selection and theories of job performance.

Conducted a comprehensive meta-analysis based on 665 validity coefficients across 576,460 data points to investigate whether integrity test validities are generalizable and to estimate differences in validity due to potential moderating influences. Results indicate that integrity test validities are substantial for predicting job performance and counterproductive behaviors on the job, such as theft, disciplinary problems, and absenteeism. The estimated mean operational predictive validity of integrity test for predicting supervisory ratings of job performance is .41. Results from predictive validity studies conducted on applicants and using external criterion measures (i.e., excluding self-reports) indicate that integrity tests predict the broad criterion of organizationally disruptive behaviors better than they predict employee theft alone. Despite the influence of moderators, integrity test validities are positive across situations and settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Information cut for clustering using a gradient descent approach

We introduce a new graph cut for clustering which we call the Information Cut. It is derived using Parzen windowing to estimate an information theoretic distance measure between probability density functions. We propose to optimize the Information Cut using a gradient descent-based approach. Our algorithm has several advantages compared to many other graph-based methods in terms of determining an appropriate affinity measure, computational complexity, memory requirements and coping with different data scales. We show that our method may produce clustering and image segmentation results comparable or better than the state-of-the art graph-based methods.     

A combo technology of autotrophic and heterotrophic denitrification processes for groundwater treatment

In this study,a sequential process (heterotrophic up-flow column and completely mixed membrane bioreactors) was proposed combining advantages of the both processes.The system was operated for 249 days with simulated and real groundwater for nitrate removal at concentrations varying from 25 to 145 mg·L-1 NO3-N.The contribution of heterotrophic process to total nitrate removal in the system was controlled by dozing the ethanol considering the nitrate concentration.By this way,sulfur based autotrophic denitrification rate was decreased and the effluent sulfate concentrations were controlled.The alkalinity requirement in the autotrophic process was produced in the heterotrophic reactor,and the system was operated without alkalinity supplementation.Throughout the study,the chemical oxygen demand in the heterotrophic reactor effluent was (23.7 ± 22) mg·L-1 and it was further decreased to(7.5 ± 7.2) mg·L-1 in the system effluent,corresponding to a 70％ reduction.In the last period of the study,the real groundwater containing 145 mg·L-1 NO3-N was completely removed.Membrane was operated without chemical washing in the first 114 days.Between days 115-249 weekly chemical washing was required.     

Leaching of a copper flotation concentrate with ammonium persulfate in an autoclave system

The leaching behavior of a copper flotation concentrate was investigated using ammonium persulfate （APS） in an autoclave systee. The decomposition products of APS, active oxygen, and acidic medium were used to extract metals from the concentrate. Leaching experiments were performed to compare the availability of APS as an oxidizing agent for leaching of the concentrate under atmospheric conditions and in an autoclave system. Leaching temperature and APS concentration were found to be important parameters in both leaching systems. Atmospheric leaching studies showed that the metal extractions increased with the increase in APS concentration and temperature （up to 333 K）. A similar tendency was determined in the autoclave studies up to 423 K. It was also determined that the metal extractions decreased at temperatures above 423 K due to the passivation of the particle surface by molten elemental sulfur. The results showed that higher copper extractions could be achieved using an autoclave system.     

Classification of semantic relations between nominals

The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of semantic relations in text. We present the development and evaluation of a semantic analysis task: automatic recognition of relations between pairs of nominals in a sentence. The task was part of SemEval-2007, the fourth edition of the semantic evaluation event previously known as SensEval. Apart from the observations we have made, the long-lasting effect of this task may be a framework for comparing approaches to the task. We introduce the problem of recognizing relations between nominals, and in particular the process of drafting and refining the definitions of the semantic relations. We show how we created the training and test data, list and briefly describe the 15 participating systems, discuss the results, and conclude with the lessons learned in the course of this exercise.     

Subnanosecond switching, plasma electrode pockels cell for high-contrast imaging with partially coherent light

A high-contrast optical switch for imaging partially coherent light (~150 times the diffraction limit) requires that it have a large angular acceptance. We describe the development of a high-speed Pockels cell that uses a thin crystal to simultaneously achieve high-contrast (greater than 1800:1) and large-angular acceptance (greater than 7 mrad for a 5-mm aperture). A KD*P crystal was used in a longitudinal-mode configuration with plasma discharges forming low-resistance, high optical transmission electrodes to couple the switching voltage. Rise times of the switched optical pulse of the order of 500 ps were observed. Characterization of the device in the near and far fields was also performed.     

Dielectric properties of sol-gel derived Ta2O5 thin films

The dielectric constant and the dielectric loss of tantalum pentoxide (Ta₂O₅) thin films, produced by sol-gel spin-coated process on Corning glass substrates, have been investigated in the frequency range of 20-10⁵ Hz and the temperature range of 183-403 K, using ohmic Al electrodes. The frequency and temperature dependence of relaxation time has also been determined. The capacitance and loss factor were found to decrease with increasing frequency and increase with increasing temperature. The activation energy values were evaluated and a good agreement between the activation energy values obtained from capacitance and dielectric loss factor measurements were observed.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.