Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing of N-stage pipelined ADC using test input regeneration and sliding window techniques

Pipelined Analog-to-Digital Converters (PADCs) are one of the main building blocks in communication systems. It is of big interest to find a simple low cost test for the PADC. The purpose of this paper is to find a simple low cost DC test for an N-stage PADC without the need of complex additional circuitry. In order to reach this objective, a new testing technique called Test Input Regeneration (TIR) is proposed. The TIR technique selects the test inputs that can be applied to the first stage of the N-stage and can be regenerated at the input of other stages. Based on the TIR technique, the sliding window technique is introduced in order to test the whole N-stage (including the time alignment and Digital Error Correction (DEC) circuits) achieving a high catastrophic fault coverage. The test was verified using the Eldo simulator provided by Mentor Graphics Corp. on 90nm CMOS model provided from MOSIS.
A dynamic forecasting method for small scale residential electrical demand

Small scale electrical demand forecasting is an emerging field motivated by the penetration of renewable energy sources and the growth of microgrids and virtual power plants. These advances pose more complex forecasting challenges compared to the already established large scale forecasting approaches. Current short term load forecasting methods deal with two types of day, normal and anomalous, which are predicted separately. Anomalous days are classified as such ahead of time, based on key calendar events such as public holidays. However, there are some anomalous days which are not always predictable on a day ahead basis. Due to unforeseen events, a seemingly normal day can progress towards an anomalous case causing high errors in prediction. We propose a new dynamic forecasting mechanism that actively monitors residential electrical demand along a forecasted day, and detects anomalous pattern changes from a previously predicted demand of the day. A self-organising map is employed to detect anomalous days as they progress. Once an anomaly is detected, a neural network based prediction system changes its input neurons according to a previously detected and recorded match found in a database of anomalous days, in order to accommodate the anomalous day prediction. Results are based on measured power demands recorded in Ireland from domestic smart-meters between 2009-2011, and focus on small scale residential electrical demands of up to 350 kWh. During anomalous days our dynamic prediction approach achieves forecasting results within 3.63% of the real load, down from the 7.37% obtained by the initial prediction algorithm and the 5.41% achieved by standalone re-prediction, without pattern matching.
An Optimized Illumination Normalization Method for Face Recognition

Differences in illumination conditions cause significant challenges for any 2-D face recognition algorithm. One of the methods to counter these effects is image preprocessing before feature extraction. In this paper we present a new preprocessing approach that uses custom filters obtained through an optimization procedure striving for most suitable preprocessing filters for the selected feature extractor and distance measure. We experiment with it using Local Binary Pattern texture features and X2 histogram distance metric. Results are provided for Face Recognition Grand Challenge (FRGC) 1.0. 4 dataset.
Rosencrantz and Guildenstern are Devalued? How Alliance Announcements Change the Stock Market Valuation of Rivals

Joanne E. Oxley Rotman School of Management University of Toronto Rachelle C. Sampson R.H. Smith School of Business University of Maryland Brian S. Silverman Rotman School of Management University of Toronto August, 2005 Revised April, 2006 PRELIMINARY AND INCOMPLETE PLEASE DO NOT CITE WITHOUT PERMISSION Acknowledgements: We would like to thank Benjamin Cole, Klara Gotz, P.K. Toh, Brad van Wijk, and Maggie Zhou for able assistance in collecting, cleaning and compiling the various data elements for this project.
Regulation of autophagy and chloroquine sensitivity by oncogenic RAS in vitro is context-dependent

Chloroquine (CQ) is an antimalarial drug and late-stage inhibitor of autophagy currently FDA-approved for use in the treatment of rheumatoid arthritis and other autoimmune diseases. Based primarily on its ability to inhibit autophagy, CQ and its derivative, hydroxychloroquine, are currently being investigated as primary or adjuvant therapy in multiple clinical trials for cancer treatment. Oncogenic RAS has previously been shown to regulate autophagic flux, and cancers with high incidence of RAS mutations, such as pancreatic cancer, have been described in the literature as being particularly susceptible to CQ treatment, leading to the hypothesis that oncogenic RAS makes cancer cells dependent on autophagy. This autophagy “addiction” suggests that the mutation status of RAS in tumors could identify patients who would be more likely to benefit from CQ therapy. Here we show that RAS mutation status itself is unlikely to be beneficial in such a patient selection because oncogenic RAS does not always promote autophagy addiction. Moreover, oncogenic RAS can have opposite effects on both autophagic flux and CQ sensitivity in different cells. Finally, for any given cell type, the positive or negative effect of oncogenic RAS on autophagy does not necessarily predict whether RAS will promote or inhibit CQ-mediated toxicity. Thus, although our results confirm that different tumor cell lines display marked differences in how they respond to autophagy inhibition, these differences can occur irrespective of RAS mutation status and, in different contexts, can either promote or reduce chloroquine sensitivity of tumor cells.
Dynamics in highly skilled Migration: A European Perspective

This paper analyzes the differences in earnings among college-educated immigrants from different European countries in the United States. While most studies treat European immigrants as a single successful group in the United States labor market, this paper suggests that there is reason to assume differences in labor market integration among migrants from European countries. Indeed, idiosyncratic sociopolitical structures and heterogeneous education systems in European countries lead to significantly different regional results. Moreover, economic linkages between European countries as well as the US immigration system seem to affect immigrant earnings. Along the lines of economic assimilation theory, this analysis of 1993 microdata from the National Survey of College Graduates (National Science Foundation) in combination with various data sources on education, trade and the US immigration policy finds that immigrants educated in Eastern or Southern European countries earn less than native-born Americans. In contrast, and controlling for relevant variables, immigrants from Western or Northern Europe earn up to 10 and 19 percent more than comparable native-born Americans. These findings suggest differences in integration of European immigrants due to regionally diverse education systems, economic ties with the US and immigrants’ legal status when entering the US. The results highlight new aspects on the transferability of foreign skills and how European higher education is valued in international labor markets. Thus, it may provide valuable support for shaping US policy towards highly skilled immigrants as well as inform European education policy about international competitiveness of their graduates. 1 This paper was published in the Georgetown Public Policy Review Winter/Spring 2007.
A High Performance Application Development Environment for Large-Scale Scientific Computations

Effective high-level data management is becoming an import ant issue with more and more scientific applications manipulating huge amounts of secon dary-storage and tertiary-storage data using parallel processors. A major problem facing the c urrent solutions to this data management problem is that these solutions either require a dee p understanding of specific data storage architectures and file layouts to obtain the best per formance (as in high-performance storage management systems and parallel file systems) or the y sacrifice significant performance in exchange for ease-of-use and portability (as in tra ditional database management systems). In this paper, we discuss the design, implementation, and ev aluation of a novel application development environment for scientific computations. This environment includes a number of components that make it easy for the programmers to code and r un their applications without much programming effort, and at the same time, to harness the available computational and storage power on parallel architectures.
Context-aware scheduling of workforce for multiple urgent events

Workforce management is an important issue for human involved collaboration. When multiple urgent events simultaneously happen at different places with unexpected requirements, administrators should schedule workforce under the consideration of both system and user context, such as event requirements, user qualifications, etc. In this paper, we tackle this challenging problem of scheduling workforce for multiple urgent events. We study the Feasible Workforce Assignment Problem (WAP) that determines whether a system state satisfies the requirements of a given multiple-event scenario. Then, we present an efficient polynomial solution to solve it, with a case study to illustrate the effectiveness of our method.
The image segmentation algorithm based on 2-D maximum entropy

2D maximum entropy algorithm is of quite good effect in image segmentation, but it requires long time on the complex calculation. Considering CGApsilas (chaos genetic algorithm) ability to retain the species diversity and great astringencypsila a new 2-D maximum entropy method based on CGA was put forward. It has been proved that the new algorithm is of better capacity to search for the best , performs more steadily and results in better segmentation effect.
Identification of Lactobacillus plantarum, Lactobacillus pentosus and Lactobacillus fermentum from honey stomach of honeybee

This study aimed to isolate and identify Lactobacillus in the honey stomach of honeybee Apis dorsata. Samples of honeybee were collected from A. dorsata colonies in different bee trees and Lactobacillus bacteria isolated from honey stomachs. Ninety two isolates were Gram-stained and tested for catalase reaction. By using bacterial universal primers, the 16S rDNA gene from DNA of bacterial colonies amplified with polymerase chain reaction (PCR). Forty-nine bacterial 16S rDNA gene were sequenced and entrusted in GenBank. Phylogenetic analysis showed they were different phylotypes of Lactobacillus. Two of them were most closely relevant to the previously described species Lactobacillus plantarum. Other two phylotypes were identified to be closely related to Lactobacillus pentosus. However, only one phylotype was found to be distantly linked to the Lactobacillus fermentum. The outcomes of the present study indicated that L. plantarum, L. pentosus, and L. fermentum were the dominant lactobacilli in the honey stomach of honeybee A. dorsata collected during the dry season from Malaysia forest area - specifically “Melaleuca in Terengganu”.
Optimal Lateral Row Anchor Positioning in Posterior-Superior Transosseous Equivalent Rotator Cuff Repair

Background: The optimal placement of suture anchors in transosseous-equivalent (TOE) double-row rotator cuff repair remains controversial. Purpose: A 3-dimensional (3D) high-resolution micro–computed tomography (micro-CT) histomorphometric analysis of cadaveric proximal humeral greater tuberosities (GTs) was performed to guide optimal positioning of lateral row anchors in posterior-superior (infraspinatus and supraspinatus) TOE rotator cuff repair. Study Design: Descriptive laboratory study. Methods: Thirteen fresh-frozen human cadaveric proximal humeri underwent micro-CT analysis. The histomorphometric parameters analyzed in the standardized volumes of interest included cortical thickness, bone volume, and trabecular properties. Results: Analysis of the cortical thickness of the lateral rows demonstrated that the entire inferior-most lateral row, 15 to 21 mm from the summit of the GT, had the thickest cortical bone (mean, 0.79 mm; P = .0001), with the anterior-most part of the GT, 15 to 21 mm below its summit, having the greatest cortical thickness of 1.02 mm (P = .008). There was a significantly greater bone volume (BV; posterior, 74.5 ± 27.4 mm3; middle, 55.8 ± 24.9 mm3; anterior, 56.9 ± 20.7 mm3; P = .001) and BV as a percentage of total tissue volume (BV/TV; posterior, 7.3% ± 2.7%, middle, 5.5% ± 2.4%; anterior, 5.6% ± 2.0%; P = .001) in the posterior third of the GT than in intermediate or anterior thirds. In terms of both BV and BV/TV, the juxta-articular medial row had the greatest value (BV, 87.3 ± 25.1 mm3; BV/TV, 8.6% ± 2.5%; P = .0001 for both) followed by the inferior-most lateral row 15 to 21 mm from the summit of the GT (BV, 62.0 ± 22.7 mm3; BV/TV, 6.1% ± 2.2%; P = .0001 for both). The juxta-articular medial row had the greatest value for both trabecular number (0.3 ± 0.06 mm–1; P = .0001) and thickness (0.3 ± 0.08 μm; P = .0001) with the lowest degree of trabecular separation (1.3 ± 0.4 μm; P = .0001). The structure model index (SMI) has been shown to strongly correlate with bone strength, and this was greatest at the inferior-most lateral row 15 to 21 mm from the summit of the GT (2.9 ± 0.9; P = .0001). Conclusion: The inferior-most lateral row, 15 to 21 mm from the tip of the GT, has good bone stock, the greatest cortical thickness, and the best SMI for lateral row anchor placement. The anterior-most part of the GT 15 to 21 mm below its summit had the greatest cortical thickness of all zones. The posterior third of the GT also has good bone stock parameters, second only to the medial row. The best site for lateral row cortical anchor placement is 15 to 21 mm below the summit of the GT. Clinical Relevance: Optimal lateral anchor positioning is 15 to 21 mm below the summit of the greater tuberosity in TOE.
Efficient Kernel Sparse Coding Via First-Order Smooth Optimization

We consider the problem of dictionary learning and sparse coding, where the task is to find a concise set of basis vectors that accurately represent the observation data with only small numbers of active bases. Typically formulated as an L1-regularized least-squares problem, the problem incurs computational difficulty originating from the nondifferentiable objective. Recent approaches to sparse coding thus have mainly focused on acceleration of the learning algorithm. In this paper, we propose an even more efficient and scalable sparse coding algorithm based on the first-order smooth optimization technique. The algorithm finds the theoretically guaranteed optimal sparse codes of the epsilon-approximate problem in a series of optimization subproblems, where each subproblem admits analytic solution, hence very fast and scalable with large-scale data. We further extend it to nonlinear sparse coding using kernel trick by showing that the representer theorem holds for the kernel sparse coding problem. This allows us to apply dual optimization, which essentially results in the same linear sparse coding problem in dual variables, highly beneficial compared with the existing methods that suffer from local minima and restricted forms of kernel function. The efficiency of our algorithms is demonstrated for natural stimuli data sets and several image classification problems.
Towards the Automatic Recommendation of Musical Parameters based on Algorithm for Extraction of Linguistic Rules

In the present article the authors describe an analysis of data associated to the emotional responses to fractal generated music. This analysis is done via discovery of rules, and it constitutes the basis to elevate computer-assisted creativity: Our ultimate goal is to cre­ ate musical pieces by retrieving the right set of parame­ ters associated to a target emotion. This paper contains the description of (i) variables associated to fractal music and emotions; (ii) the data gathering method to obtain the tuples relating input parameters and emotional re­ sponses; (iii) the rules that where discovered by using an algorithm LR-FIR. Even though similar experiments whose intention is to elucidate emotional responses from music have been reported, this study stands because a connection is appointed between fractal-generated mu­ sic and emotional responses, all with the purpose of advancing in computer-assisted creativity.
Pattern Recognition with Localized Gabor Wavelet Grids

This paper introduces Gabor wavelet grids localized within the region of importance (ROI) for a robust pattern recognition system that is capable of recognizing various image classes. The representation of the patterns is based on Gabor wavelet transforms. The features are extracted as a vector of values using an optimally chosen multiple frequency, multiple orientation 2D symmetrical Gabor wavelet grid that spans the region of importance (ROI). This technique of feature extraction is biologically motivated. The purpose of this study is to demonstrate that localized Gabor wavelet grids with a MLP classifier can be effectively used for recognition of various types of patterns. Recognition of close to 100% is demonstrated for some real world patterns like faces, currency notes, postage stamps and typed characters.
Perfectly matched layer implementation using bilinear transform for microwave device applications

This paper presents an extensive study on the perfectly matched layer (PML) implementation using bilinear transform in the finite-difference time-domain (FDTD) simulation. The bilinear transform is used to implement both the stretched coordinate PML (SC-PML) and the uniaxial PML (UPML) with the complex frequency-shifted (CFS) equations. It is shown that with the CFS factor, the implemented SC-PML and UPML attain significantly lower relative reflection error over wide frequency range with both superior in performance to the split-field PML. The FDTD algorithm incorporating these PMLs is applied to analyze wide-band responses of some complex microwave devices, including RF microelectromechanical systems switch and coplanar waveguide crossover junction.
IB Programme: Theory of Knowledge (TOK): Exploring Learning Outcomes, Benefits, and Perceptions

This study examined one of the central elements of the International Baccalaureate (IB) Diploma Programme (DP), the theory of knowledge (TOK) course, within an Australian context. Researchers from the University of Western Sydney investigated IB alumni and current DP student ratings of their critical thinking abilities. The study also explored student perceptions of the role of TOK in supporting critical thinking, as well as TOK teacher ratings of their capacity to teach the TOK course.
On the analysis of experimental results in evolutionary computation

Evolutionary computation methods are successfully applied in solving of combinatorial optimization problems. Since the “No Free Lunch” theorem states that there is no single best algorithm to solve all possible problems, throughout the years many algorithms and their modifications have emerged. When a new algorithm is developed, one question that naturally arises is how it compares to other algorithms, whether for some specific problem or in general performance. Because of the stochastic nature of systems involved, usually the only possible way of deriving the answer is to perform extensive experimental analysis. In this paper we provide an overview of possible approaches in the experimental analysis, and describe statistical methods that could be used. Furthermore, we outline similarities and differences between these methods, which lead to a discussion of important issues that need to be resolved when using these methods.
Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo

In the preceding paper (Hodgkin, Huxley & Katz, 1952) we gave a general description of the time course of the current which flows through the membrane of the squid giant axon when the potential difference across the membrane is suddenly changed from its resting value, and held at the new level by a feed-back circuit ('voltage clamp' procedure). This article is chiefly concerned with the identity of the ions which carry the various phases of the membrane current. One of the most striking features of the records of membrane current obtained under these conditions was that when the membrane potential was lowered from its resting value by an amount between about 10 and 100 mV. the initial current (after completion of the quick pulse through the membrane capacity) was in the inward direction, that is to say, the reverse ofthe direction of the current which the same voltage change would have caused to flow in an ohmic resistance. The inward current was of the right order of magnitude, and occurred over the right range of membrane potentials, to be the current responsible for charging the membrane capacity during the rising phase of an action potential. This suggested that the phase of inward current in the voltage clamp records might be carried by sodium ions, since there is much evidence (reviewed by Hodgkin, 1951) that the rising phase of the action potential is caused by the entry of these ions, moving under the influence of concentration and potential differences. To investigate this possibility, we carried out voltage clamp runs with the axon surrounded by solutions with reduced sodium concentration. Choline was used as an inert cation since replacement of sodium with this ion makes the squid axon completely inexcitable, but does not reduce the resting potential (Hodgkin & Katz, 1949; Hodgkin, Huxley & Katz, 1949).
Ground state of a trapped Bose-Einstein condensate in two dimensions: Beyond the mean-field approximation

We consider the ground state of a trapped Bose-Einstein condensate in two dimensions. In the mean-field approximation the ground-state density profile satisfies the Gross-Pitaevskii equation. We compute the leading quantum corrections to the density profile to second order in an expansion around the Thomas-Fermi limit. By summing the ladder diagrams, we obtain a modified equation that includes gradient corrections.
Structural difficulty in grammatical evolution versus genetic programming

Genetic programming (GP) has problems with structural difficulty as it is unable to search effectively for solutions requiring very full or very narrow trees. As a result of structural difficulty, GP has a bias towards narrow trees which means it searches effectively for solutions requiring narrow trees. This paper focuses on the structural difficulty of grammatical evolution (GE). In contrast to GP, GE works on variable-length binary strings and uses a grammar in Backus-Naur Form (BNF) to map linear genotypes to phenotype trees. The paper studies whether and how GE is affected by structural difficulty. For the analysis, we perform random walks through the search space and compare the structure of the visited solutions. In addition, we compare the performance of GE and GP for the Lid problem. Results show that GE representation is biased, this means it has problems with structural difficulty. For binary trees, GE has a bias towards narrow and deep structures; thus GE outperforms standard GP if optimal solutions are composed of very narrow and deep structures. In contrast, problems where optimal solutions require more dense trees are easier to solve for GP than for GE.