Miracle-dz/UniFAR-FTUs huggingface dataset:
QuickEval: An Interactive Tool for Coverage Based Testing of Haskell Programs

We present QuickEval, a tool for Haskell programs that can be used to evaluate and test the programs based on the coverage. QuickEval combines random testing and program coverage. QuickEval can be used to execute a function with random or user given input and finds out the unevaluated or unexecuted expressions of function. This information can be used to generate new tests to cover unexecuted/uncovered expressions until all the subexpressions of the function are covered. Thus, QuickEval can be used to perform the unit testing and to achieve the desired coverage of program. QuickEval can also be used effectively to perform the assignment evaluation process as it can create the test suite automatically and also helps to evaluate the function with all possible test cases which guarantees the proper evaluation of assignments. Coverage based testing also helps the developers to debug the programs easily.
Interferon-alpha therapy for chronic hepatitis B in children: a meta-analysis.

A meta-analysis of six randomized clinical trials involving 240 children with chronic hepatitis B treated with recombinant interferon-alpha (IFN-alpha) was performed. IFN-alpha treatment was effective in blocking viral replication. Clearance of hepatitis B virus (HBV) DNA from sera occurred in 44 of 127 treated patients (P < .00001), and clearance of hepatitis B e antigen (HBeAg) occurred in seven of 74 treated patients (P = .099). IFN-alpha normalized serum levels of alanine aminotransferase (ALT) in 33 of 85 treated patients (P = .017). At the end of the follow-up period, viral replication was still reduced in IFN-alpha-treated patients, HBV DNA clearance occurred in 36 of 126 patients (P = .014), and HBeAg clearance occurred in 29 of 126 patients (P = .026). Regarding these virological and biochemical endpoints, we found that prolonged therapy (> 6 months) was associated with a better response, whereas high dosages of IFN-alpha were not. These findings could be biased by limited follow-up. Children with high ALT levels had a better response. However, these randomized clinical trials had some methodological flaws, including the lack of information on histologic response to IFN-alpha treatment by pediatric patients and the absence of "hard outcomes" (such as survival or development of cirrhosis or hepatocellular carcinoma).
INTERFACING WITH BUILDING DATA : TOWARD AN INTEGRATED MOBILE AUGMENTED ENVIRONMENT

Communications between buildings and their occupants through multimodal Human Computer Interactions (HCI) can dramatically enhance the way buildings are experienced. Although building performance data is becoming more readily available, no research has been established to enable visualization of and interaction with this information in a robust way. This paper will present a method that will allow users to visualize and interact with building performance data in real space. It extends our research efforts to enable users to visualize such data for multiroom settings by developing methods to enable such environments to become mobile and being able to communicate with readily available building data.
Localized matching model for plant prediction using incremental clustering

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Clustering is a data mining technique, which is used to place data elements into related groups without advance knowledge of the group definitions. Here, we propose an incremental clustering technique for managing knowledge in edaphology, a study concerned with the influence of soils on living things, particularly plants. The soil information along with the appropriate plants to be cultivated on it for better yield, collected by edaphologists, are utilized in the proposed system. Initially, an incremental DBSCAN algorithm is applied to a dynamic database where, the data may be frequently updated. Then, the data available in the soil database is grouped into clusters and every new element is added into it without the need of rerunning process. Finally, we have performed the plant prediction using regression model. The experimentation is carried out in soil database to analyze the performance of the proposed system in plant prediction.
Fast and Accurate Refined Nyström-Based Kernel SVM

In this paper, we focus on improving the performance of the Nyström based kernel SVM. Although the Nyström approximation has been studied extensively and its application to kernel classification has been exhibited in several studies, there still exists a potentially large gap between the performance of classifier learned with the Nyström approximation and that learned with the original kernel. In this work, we make novel contributions to bridge the gap without increasing the training costs too much by proposing a refined Nyström based kernel classifier. We adopt a two-step approach that in the first step we learn a sufficiently good dual solution and in the second step we use the obtained dual solution to construct a new set of bases for the Nyström approximation to re-train a refined classifier. Our approach towards learning a good dual solution is based on a sparse-regularized dual formulation with the Nyström approximation, which can be solved with the same time complexity as solving the standard formulation. We justify our approach by establishing a theoretical guarantee on the error of the learned dual solution in the first step with respect to the optimal dual solution under appropriate conditions. The experimental results demonstrate that (i) the obtained dual solution by our approach in the first step is closer to the optimal solution and yields improved prediction performance; and (ii) the second step using the obtained dual solution to re-train the model further improves the performance.
Detecting abnormal state of elderly for service robot with H-FCM

Detecting abnormal state of elder from sensor is very important for high-level activity inference of service robot. This paper proposes a model to solve this problem using location information. Firstly, the feature can be extracted from location information using the SLAM Map, and the vector based on l,θ is calculated. Then approach of clustering the feature vector based on H-FCM algorithm. Experiment results demonstrate that the model is feasible to learn human state habit and can afford a judging gist to detect persons' abnormal state.
Planner-aided Design of Ladder Climbing Capabilities for a DARPA Robotics Challenge Humanoid

This paper describes preliminary steps toward providing humanoid robots with ladder climbing capabilities. Although seemingly straightforward for humans, ladder climbing is quite challenging for humanoid robots due to differences from human kinematics, significant physical stresses, simultaneous coordination of four limbs in contact, and limited motor torques. We present a planning strategy for the the Hubo-II+ humanoid that automatically optimizes motion primitives offline and adapts them on-line to novel ladders. The adapted motions are guaranteed to satisfy contact, collision, quasi-static balance, and torque limit constraints. This method is used to automatically test a number of strategies, including forward and backward climbing, on a variety of ladders in simulation. This planner-aided design paradigm has proven useful for hardware design because it permits the rapid testing, verification, and optimization of hardware changes by automatically generating novel climbing strategies.
Cloning SO(N) level 2

For each $N$ an infinite number of Conformal Field Theories is presented that has the same fusion rules as SO(N) level 2. These new theories are obtained as extensions of the chiral algebra of $SO(NM^2)$ level 2, and correspond to new modular invariant partition functions of these theories.
Dynamic Federalism: Competition, Cooperation and Securities Enforcement

The concept of competition between the federal government and the states was central to the framers' vision of our constitutional structure. In the framers' view, federal-state regulatory competition ensured an alternative regime to citizens dissatisfied with the dominant regulator's performance. Recently, the dynamics of federalism have shifted power in the securities enforcement field from the SEC to certain state securities regulators. The states, rather than the SEC, have led enforcement efforts in the Wall Street analyst conflicts and the mutual fund trading investigations. This shift in authority has prompted renewed debate over whether a uniform national system of securities regulation is preferable to the current dual system. The rising profile of state officials, and calls from certain quarters to curtail states' enforcement powers, presents a paradigm through which to assess how a dynamic federalist system helps to ensure optimal regulatory policies and practices. This Essay explores the complex dynamics of federal-state regulatory competition in securities enforcement and concludes that efforts to curtail the states' enforcement power are misguided. Contrary to the claims of state regulators' critics, the recent move by states to exert influence in securities enforcement demonstrates the vibrancy and health of our political and constitutional structure.
Simultaneous input vector selection and dual threshold voltage assignment for static leakage minimization

Dual Vt assignment and input vector control are two tightly coupled leakage reduction techniques. We study how to apply them effectively to a circuit to minimize the static leakage power. We argue that simply combining them in a serial fashion will not reach their full potential in leakage reduction. To show this, we propose a heuristic algorithm that integrates them into a single optimization loop by assigning the value for primary inputs and Vt for logic gates simultaneously. Our algorithm leverages the fact that both input vector and threshold voltage Vt have great impact on a gate's leakage at standby mode and avoids to assign a gate both low Vt and input vector that results high leakage. The selection of input vector and the assignment of Vt are integrated seamlessly through the concepts of leakage observability, worst leakage state, and path factor. The proposed algorithm has a low run time complexity and achieves an average 15% leakage reduction on all the ISCAS and MCNC benchmarks over the serial combination of input vector selection and dual Vt assignment.
Mechanism of Human Nucleocytoplasmic Hexosaminidase D

Mammalian β-hexosaminidases have been shown to play essential roles in cellular physiology and health. These enzymes are responsible for the cleavage of the monosaccharides N-acetylglucosamine (GlcNAc) and N-acetylgalactosamine (GalNAc) from cellular substrates. One of these β-hexosaminidases, hexosaminidase D (HexD), encoded by the HEXDC gene, has received little attention. No mechanistic studies have focused on the role of this unusual nucleocytoplasmically localized β-hexosaminidase, and its cellular function remains unknown. Using a series of kinetic and mechanistic investigations into HexD, we define the precise catalytic mechanism of this enzyme and establish the identities of key enzymic residues. The preparation of synthetic aryl N-acetylgalactosaminide substrates for HexD in combination with measurements of kinetic parameters for wild-type and mutant enzymes, linear free energy analyses of the enzyme-catalyzed hydrolysis of these substrates, evaluation of the reaction by nuclear magnetic resonance, and inhibition studies collectively reveal the detailed mechanism of action employed by HexD. HexD is a retaining glycosidase that operates using a substrate-assisted catalytic mechanism, has a preference for galactosaminide over glucosaminide substrates, and shows a pH optimum in its second-order rate constant at pH 6.5–7.0. The catalytically important residues are Asp148 and Glu149, with Glu149 serving as the general acid/base residue and Asp148 as the polarizing residue. HexD is inhibited by Gal-NAG-thiazoline (Ki = 420 nM). The fundamental insights gained from this study will aid in the development of potent and selective probes for HexD, which will serve as useful tools to improve our understanding of the physiological role played by this unusual enzyme.
First principle study of the stability of H atoms in SiN layers on MONOS-type memories during program/erase operations

Recently, hydrogen has been recognized as an important heteroatom in Metal-Oxide-Nitride-Oxide-Semiconductor (MONOS) -type memories for its quality improvement. However, if stability of H atoms in SiN layers changes by applying high voltage, memory characteristics of MONOS-type memories change during program/erase (P/E) operations, resulting in memory degradation. Therefore, in this paper, we have investigated the stability of H atoms in SiN charge trapping layers of MONOS-type memories during P/E operations by using first principles calculations. Our calculations show that H atoms in Si or N vacancies in SiN layers become unstable and migrate when high voltage is applied, leading to memory degradation with changes of memory characteristics. This result clearly indicates that suppression of H atoms in SiN layers is necessary for realization of high quality MONOS-type memories.
Characterization and Transferable Utility of Microsatellite Markers in the Wild and Cultivated Arachis Species

Microsatellite or simple sequence repeat (SSR) is one of the most widely distributed molecular markers that have been widely utilized to assess genetic diversity and genetic mapping for important traits in plants. However, the understanding of microsatellite characteristics in Arachis species and the currently available amount of high-quality SSR markers remain limited. In this study, we identified 16,435 genome survey sequences SSRs (GSS-SSRs) and 40,199 expressed sequence tag SSRs (EST-SSRs) in Arachis hypogaea and its wild relative species using the publicly available sequence data. The GSS-SSRs had a density of 159.9–239.8 SSRs/Mb for wild Arachis and 1,015.8 SSR/Mb for cultivated Arachis, whereas the EST-SSRs had the density of 173.5–384.4 SSR/Mb and 250.9 SSRs/Mb for wild and cultivated Arachis, respectively. The trinucleotide SSRs were predominant across Arachis species, except that the dinucleotide accounted for most in A. hypogaea GSSs. From Arachis GSS-SSR and EST-SSR sequences, we developed 2,589 novel SSR markers that showed a high polymorphism in six diverse A. hypogaea accessions. A genetic linkage map that contained 540 novel SSR loci and 105 anchor SSR loci was constructed by case of a recombinant inbred lines F6 population. A subset of 82 randomly selected SSR markers were used to screen 39 wild and 22 cultivated Arachis accessions, which revealed a high transferability of the novel SSRs across Arachis species. Our results provided informative clues to investigate microsatellite patterns across A. hypogaea and its wild relative species and potentially facilitate the germplasm evaluation and gene mapping in Arachis species.
Preference Based Multiobjective Evolutionary Algorithm for Constrained Optimization Problems

Constrained optimization problems (COPs) are converted into a bi-objective optimization problem first, and a novel fitness function based on achievement scalarizing function (ASF) is presented. The fitness function adopts the valuable properties of ASF and can measure the merits of individuals by the weighting distance from the ndividuals to the reference point, where the reference point and the weighting vector reflect the preference of decision makers. In the initial stage of the evolution, the main preference should be put in generating more feasible solutions, and in the later stage of the evolution, the main preference should be put in improving the objective function. For this purpose, the proper reference point and weighting vector are chosen adaptively to realize the preference in different evolutionary stages. Then a new preference based multiobjective evolutionary algorithm is proposed based on all these. The numerical experiments for four standard test functions with different characteristic illustrate that the new proposed algorithm is effective and efficient.
ESD protection design for broadband RF circuits with decreasing-size distributed protection scheme

The resulting capacitive load, from a large electrostatic discharge (ESD) protection device for high ESD robustness, has an adverse effect on the performance of broadband RF circuits due to the impedance mismatch and bandwidth degradation. The conventional distributed ESD protection scheme, using equal four-stage ESD protection can achieve a better impedance match, but degrades the ESD performance. A new distributed ESD protection structure is proposed in this work, to achieve both good ESD robustness and RF performance. The proposed ESD protection circuit is constructed by arranging ESD protection stages with decreasing device size, named decreasing-size distributed ESD (DS-DESD) protection scheme, which is beneficial to the ESD level. The experimental results have shown a human-body-model (HBM) ESD robustness of up to 8 kV.
Multiple Viewpoints a Strategy for Multi-Step Queries Based EMD in CBIR System

Performance of traditional content-based image retrieval systems is far from user’s expectation due to the “semantic gap” between low-level visual features and the richness of human semantics. In attempt to reduce the “semantic gap”, this paper introduces a new effective image retrieval approach-the multi-step queries strategy-that facilitates retrieval of semantically similar images from multi layers in the feature space. In the retrieval process, images are segmented three layers, raw pixel layer, blob feature layer and global image layer, and retrieval process is refined gradually. Moreover similarity between images is measured with an approximation of the earth mover’s distance (EMD), which quickly computes minimal-cost correspondences between sets of signatures. Experimental results in medical image database show that the proposed method in this paper is good enough to describe image information, and significantly improves the performance of the retrieval system.
Analyzing Soccer Goalkeeper Performance Using a Metaphor-Based Visualization

The dimensionality of soccer statistics relating to goalkeeper specific data can be difficult to interpret. Leveraging our existing statistical analytics tool, Soccer Scoop, we developed a goalkeeper visualization add-on that can assist a team manager. With the newly developed goalkeeper visualization tool, a team manager can compare a single goalkeeper between two games, measure the overall performance of the goalkeeper both for games played at home or away, as well as to devise the appropriate training exercises needed to strengthen any visible weakness. To keep with the continuity of the visualization styles of our first tool, Soccer Scoop, the goalkeeper visualization applies similar techniques, such as glyphs, details on demand, color, and Gestalt principles.
Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection

In this paper, we evaluate convolutional neural network (CNN) features using the AlexNet architecture and very deep convolutional network (VGGNet) architecture. To date, most CNN researchers have employed the last layers before output, which were extracted from the fully connected feature layers. However, since it is unlikely that feature representation effectiveness is dependent on the problem, this study evaluates additional convolutional layers that are adjacent to fully connected layers, in addition to executing simple tuning for feature concatenation (e.g., layer 3 + layer 5 + layer 7) and transformation, using tools such as principal component analysis. In our experiments, we carried out detection and classification tasks using the Caltech 101 and Daimler Pedestrian Benchmark Datasets.
BiPS, a Photocleavable, Isotopically Coded, Fluorescent Cross-linker for Structural Proteomics *

Cross-linking combined with mass spectrometry is an emerging approach for studying protein structure and protein-protein interactions. However, unambiguous mass spectrometric identification of cross-linked peptides derived from proteolytically digested cross-linked proteins is still challenging. Here we describe the use of a novel cross-linker, bimane bisthiopropionic acid N-succinimidyl ester (BiPS), that overcomes many of the challenges associated with other cross-linking reagents. BiPS is distinguished from other cross-linkers by a unique combination of properties: it is photocleavable, fluorescent, homobifunctional, amine-reactive, and isotopically coded. As demonstrated with a model protein complex, RNase S, the fluorescent moiety of BiPS allows for sensitive and specific monitoring of the different cross-linking steps, including detection and isolation of cross-linked proteins by gel electrophoresis, determination of in-gel digestion completion, and fluorescence-based separation of cross-linked peptides by HPLC. The isotopic coding of BiPS results in characteristic ion signal “doublets” in mass spectra, thereby permitting ready detection of cross-linker-containing peptides. Under MALDI-MS conditions, partial photocleavage of the cross-linker occurs, releasing the cross-linked peptides. This allows differentiation between dead-end, intra-, and interpeptide cross-links based on losses of specific mass fragments. It also allows the use of the isotope doublets as mass spectrometric “signatures. ” A software program was developed that permits automatic cross-link identification and assignment of the cross-link type. Furthermore photocleavage of BiPS assists in cross-link identification by allowing separate tandem mass spectrometry sequencing of each peptide comprising the original cross-link. By combining the use of BiPS with MS, we have provided the first direct evidence for the docking site of a phosphorylated G-protein-coupled receptor C terminus on the multifunctional adaptor protein β-arrestin, clearly demonstrating the broad potential and application of this novel cross-linker in structural and cellular biology.
A 3 D Model Alignment and Retrieval System

Techniques for 3D model alignment and retrieval are proposed in this paper. Since the techniques of 3D modeling and digitizing tools are in great demand, the expectations of 3D models alignment and retrieval are increasingly. We propose an algorithm for 3D model alignment, which gets the affine transformation between two 3D models. The main idea of our 3D alignment algorithm in rotation is to search the similarity of projected 2D shapes from each viewing angle between two models. Then, we apply the technique to match two 3D models after recovering the affine transformation. Our database has more than 10,000 3D models, and a query interface of drawing is easy to use to retrieval 3D models from 2D shapes.