Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers

Linear models in machine learning are extremely computational efficient but they have high representation bias due to non-linear nature of many real-world datasets. In this article, we show that this representation bias can be greatly reduced by discretization. Discretization is a common procedure in machine learning that is used to convert a quantitative attribute into a qualitative one. It is often motivated by the limitation of some learners to handle qualitative data. Since discretization looses information (as fewer distinctions among instances are possible using discretized data relative to undiscretized data) – where discretization is not essential, it might appear desirable to avoid it, and typically, it is avoided. However, in the past, it has been shown that discretization can leads to superior performance on generative linear models, e.g., naive Bayes. This motivates a systematic study of the effects of discretizing quantitative attributes for discriminative linear models, as well. In this article, we demonstrate that, contrary to prevalent belief, discretization of quantitative attributes, for discriminative linear models, is a beneficial pre-processing step, as it leads to far superior classification performance, especially on bigger datasets, and surprisingly, much better convergence, which leads to better training time. We substantiate our claims with an empirical study on 52 benchmark datasets, using three linear models optimizing different objective functions.
Segmentation of Regions of Interest Using Active Contours with SPF Function

Segmentation of regions of interest is a well-known problem in image segmentation. This paper presents a region-based image segmentation technique using active contours with signed pressure force (SPF) function. The proposed algorithm contemporaneously traces high intensity or dense regions in an image by evolving the contour inwards. In medical image modalities these high intensity or dense regions refer to tumor, masses, or dense tissues. The proposed method partitions an image into an arbitrary number of subregions and tracks down salient regions step by step. It is implemented by enforcing a new region-based SPF function in a traditional edge-based level set model. It partitions an image into subregions and then discards outer subregion and partitions inner region into two more subregions; this continues iteratively until a stopping condition is fulfilled. A Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed segmentation algorithm has been applied to different images in order to demonstrate the accuracy, effectiveness, and robustness of the algorithm.
Effect of core geometry and size on concrete compressive strength.

ii Acknowledgments. iv List of Tables . xi List of Figures . xiv List of Abbreviations and Symbols.xx Chapter 1 Introduction . 1 1.1 Background . 1 1.2 Project Objectives . 4 1.3 Research Approach . 5 1.4 Organization of Thesis . 6 Chapter 2 Literature Review . 7 2.1 Introduction . 7 2.2 Core Preparation Effects on Strength . 7 2.2.1 Aggregate Type and Size . 8 2.2.2 Compressive Strength Testing Conditions . 9 2.2.2.1 Consolidation . 9 2.2.2.2 Curing Conditions . 10 2.2.2.3 Moisture Conditioning . 13 2.2.2.4 Sulfur Capping . 15
From type checking by recursive descent to type checking with an abstract machine

Modern type systems for programming languages usually incorporate additional information useful for program analysis, e.g., effects, control flow, non-interference, strictness etc. When designing a typing predicate for such systems, a form of logical derivation rules is normally taken. Despite the expressivity of this approach, the straightforward implementation of an appropriate type checker is usually inefficient in terms of stack consumption and further optimisations. This leads to a significant gap between an analysis and program implementing the analysis. In this paper we demonstrate an application of techniques investigated by Danvy et al. to derive an abstract machine for typing from the traditional recursive descent approach. All used techniques are off-the-shelf and no appropriate correspondence theorems between an initial type system and the derived abstract machine needs to be proven: they are instead corollaries of the correctness of inter-derivation and of the initial specification. Whereas a recursive descent is something straightforward to implement based on declarative typing rules, the derived abstract machine exposes behaviour similar to Landin's SECD machine and gives a solid basis for further optimizations using abstract interpretation.
SwiftGPU: Fostering energy efficiency in a near-threshold GPU through a tactical performance boost

In this paper, we investigate the challenges of preserving energy-efficiency in a Near-Threshold Computing (NTC) GPU. Two key factors can significantly undermine the efficacy of GPUs at NTC: (a) elongated delays at NTC make the GPU applications severely sensitive to Multi-cycle Latency Datapaths (MLDs) within the GPU pipeline; and (b) process variation (PV) at NTC induces a substantial performance variance. To address these emerging challenges, we propose SwiftGPU - an energy-efficient GPU design paradigm at NTC. SwiftGPU dynamically adjusts the degree of parallelization, and the speed of the MLDs within each stream core of the GPU. The proposed scheme achieves an average of ~15% improvement in energy-efficiency over an ideal PV-free GPU, operating at the Super-Threshold regime. SwiftGPU incurs marginal area, wire-length and power overheads of 0.65%, 2.6% and 3.7%, respectively.
Association between β2 adrenergic receptor polymorphisms and rheumatoid arthritis in conjunction with human leukocyte antigen (HLA)-DRB1 shared epitope

Objective: In the present work, the frequency of inherited polymorphisms of the β2 adrenergic receptor (β2AR) gene and their association with rheumatoid arthritis (RA) as well as human leukocyte antigen (HLA)-DRB1 alleles was examined. Methods: An allele-specific polymerase chain reaction was used to determine the common variants of the β2AR at positions 16, 27 and 164 in patients with RA (n = 310) and ethnically matched healthy controls (n = 305) from Germany. HLA-DRB1 genotyping was performed by oligonucleotide hybridisation of enzymatically amplified DNA allowing low-resolution HLA-DRB1 genotyping comprising specificities DRB1*01 to DRB1*17. Results: Arginine (Arg) at codon 16 was present in 278 patients with RA (89.7%) compared to 202 controls (66.2%; odds ratio (OR) 4.43, 95% CI 2.81 to 7.02, p<0.001). Homozygosity for Arg16 was found in 107 patients with RA (34.5%) compared to 14 controls (4.6%; OR 10.9, CI 5.9 to 20.5, p<0.001). Stratifying patients for their HLA-DR status revealed that homozygosity for Arg16 exhibited the greatest risk for RA in combination with HLA-DRB1*04 (OR 17.1, 95% CI 1.71 to 414.4, p = 0.004). Interestingly, patients with the Arg16 allele have a younger mean (SD) age at disease onset compared to patients without Arg16 (46.1 (2.0) vs 53.1 (2.7) respectively, p<0.05). Furthermore, 93.3% patients with homozygosity for Arg16 were positive for anti-cyclic citrullinated peptide (CCP) antibodies vs 75% patients with homozygosity for Gly16 (p<0.05). Conclusion: There was a highly significant distortion between patients with RA and controls in the distribution of β2AR polymorphisms at codon 16, contributing (together with the HLA-DR alleles) to the genetic background of RA.
Learning Common Grammar from Multilingual Corpus

We propose a corpus-based probabilistic framework to extract hidden common syntax across languages from non-parallel multilingual corpora in an unsupervised fashion. For this purpose, we assume a generative model for multilingual corpora, where each sentence is generated from a language dependent probabilistic context-free grammar (PCFG), and these PCFGs are generated from a prior grammar that is common across languages. We also develop a variational method for efficient inference. Experiments on a non-parallel multilingual corpus of eleven languages demonstrate the feasibility of the proposed method.
A new scale invariant feature detector and modified SURF descriptor

Extracting distinctive scale invariant features from images of the same scene or object is very important in many computer vision applications, and there has been significant research into the scale invariant feature detectors and descriptors. Some of these methods have emphasized on computational speed and accuracy, so that they can enable lots of real-time applications with reduced computational requirements and better performance. The purpose of this paper is to introduce a new scale invariant octagonal center-surround detector, named OCT, and give a modified SURF descriptor named I-SURF descriptor. OCT detector computes responses at every pixel and all scale, and could be implemented efficiently by using integral image and slanted integral image for image convolutions. I-SURF modifies the SURF descriptor by considering the boundary effect of the adjacent subregions, and introduces index vector to speed up matching. The evaluation system provided by Mikolajczyk is applied to OCT and I-SURF. Experiments of the repeatability score, matching accuracy and timing proved that our detector and descriptor have better performance than SURF and SIFT.
Anthropometry and esophageal cancer risk in the European prospective investigation into cancer and nutrition.

BACKGROUND Increasing evidence suggests that general obesity [measured by body mass index (BMI)] is positively associated with risk of esophageal adenocarcinoma (EAC). In contrast, previous studies have shown inverse relations with esophageal squamous cell carcinoma (ESCC). However, it is still unclear whether body fat distribution, particularly abdominal obesity, is associated with each type of esophageal cancer. METHODS We applied multivariable adjusted Cox proportional hazards regression to investigate the association between anthropometric measures and risk of EAC and ESCC among 346,554 men and women participating in the European Prospective Investigation into Cancer and Nutrition. All statistical tests were two sided. RESULTS During 8.9 years of follow-up, we documented 88 incident cases of EAC and 110 cases of ESCC. BMI, waist circumference, and waist-to-hip ratio (WHR) were positively associated with EAC risk [highest versus lowest quintile; relative risk (RR), 2.60; 95% confidence interval (95% CI), 1.23-5.51; P(trend) < 0.01; RR, 3.07; 95% CI, 1.35-6.98; P(trend) < 0.003; and RR, 2.12; 95% CI, 0.98-4.57; P(trend) < 0.004]. In contrast, BMI and waist circumference were inversely related to ESCC risk, whereas WHR showed no association with ESCC. In stratified analyses, BMI and waist circumference were significantly inversely related to ESCC only among smokers but not among nonsmokers. However, when controlled for BMI, we found positive associations for waist circumference and WHR with ESCC, and these associations were observed among smokers and nonsmokers. CONCLUSION General and abdominal obesity were associated with higher EAC risk. Further, our study suggests that particularly an abdominal body fat distribution might also be a risk factor for ESCC.
Chronic Intake of Sucrose Accelerates Sarcopenia in Older Male Rats through Alterations in Insulin Sensitivity and Muscle Protein Synthesis.

BACKGROUND Today, high chronic intake of added sugars is frequent, which leads to inflammation, oxidative stress, and insulin resistance. These 3 factors could reduce meal-induced stimulation of muscle protein synthesis and thus aggravate the age-related loss of muscle mass (sarcopenia). OBJECTIVES Our aims were to determine if added sugars could accelerate sarcopenia and to assess the capacity of antioxidants and anti-inflammatory agents to prevent this. METHODS For 5 mo, 16-mo-old male rats were starch fed (13% sucrose and 49% wheat starch diet) or sucrose fed (62% sucrose and 0% wheat starch diet) with or without rutin (5 g/kg diet), vitamin E (4 times), vitamin A (2 times), vitamin D (5 times), selenium (10 times), and zinc (+44%) (R) supplementation. We measured the evolution of body composition and inflammation, plasma insulin-like growth factor 1 (IGF-I) concentration and total antioxidant status, insulin sensitivity (oral-glucose-tolerance test), muscle weight, superoxide dismutase activity, glutathione concentration, and in vivo protein synthesis rates. RESULTS Sucrose-fed rats lost significantly more lean body mass (-8.1% vs. -5.4%, respectively) and retained more fat mass (+0.2% vs. -33%, respectively) than starch-fed rats. Final muscle mass was 11% higher in starch-fed rats than in sucrose-fed rats. Sucrose had little effect on inflammation, oxidative stress, and plasma IGF-I concentration but reduced the insulin sensitivity index (divided by 2). Meal-induced stimulation of muscle protein synthesis was significantly lower in sucrose-fed rats (+7.3%) than in starch-fed rats (+22%). R supplementation slightly but significantly reduced oxidative stress and increased muscle protein concentration (+4%) but did not restore postprandial stimulation of muscle protein synthesis. CONCLUSIONS High chronic sucrose intake accelerates sarcopenia in older male rats through an alteration of postprandial stimulation of muscle protein synthesis. This effect could be explained by a decrease of insulin sensitivity rather than by changes in plasma IGF-I, inflammation, and/or oxidative stress.
Combining Textual and Speech Features in the NLI Task Using State-of-the-Art Machine Learning Techniques

We summarize the involvement of our CEMI team in the ”NLI Shared Task 2017”, which deals with both textual and speech input data. We submitted the results achieved by using three different system architectures; each of them combines multiple supervised learning models trained on various feature sets. As expected, better results are achieved with the systems that use both the textual data and the spoken responses. Combining the input data of two different modalities led to a rather dramatic improvement in classification performance. Our best performing method is based on a set of feed-forward neural networks whose hidden-layer outputs are combined together using a softmax layer. We achieved a macro-averaged F1 score of 0.9257 on the evaluation (unseen) test set and our team placed first in the main task together with other three teams.
pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry

SUMMARY Research in proteomics requires powerful database-searching software to automatically identify protein sequences in a complex protein mixture via tandem mass spectrometry. In this paper, we describe a novel database-searching software system called pFind (peptide/protein Finder), which employs an effective peptide-scoring algorithm that we reported earlier. The pFind server is implemented with the C++ STL, . Net and XML technologies. As a result, high speed and good usability of the software are achieved.
Present experience with Campath‐1H in organ transplantation and its potential use in pediatric recipients

Abstract: Campath‐1H is a humanized monoclonal antibody directed at CD52 expressed on lymphocytes and other cells of the immune system. It has been tested extensively in lymphoid malignancies, autoimmune diseases including rheumatoid arthritis, multiple sclerosis, and organ transplantation. Although its use in children has been limited to date, so far it appears to be well tolerated in children. Currently, studies are being implemented to further assess its safety and efficacy in pediatric organ transplantation. Immune cell depletion using Campath‐1H appears to be particularly useful in organ transplantation in that lower doses of maintenance immunosuppressive drugs are needed. This feature is particularly attractive in children.
Modeling and Optimal Design of an Eddy Current Coupling for Slip-Synchronous Permanent Magnet Wind Generators

In this paper, the possibility of using an eddy current slip coupling to remove cogging torque and torque ripple in a slip-synchronous permanent magnet wind generator is investigated. Two different topologies are proposed. It is shown that two-dimensional (2-D) finite-element methods are inaccurate compared to three-dimensional (3-D) finite-element methods when solving eddy currents in eddy current couplings. The 3-D finite-element transient simulations are validated using a manufactured prototype 15-kW eddy current coupling. An analytical approximation is developed and used for the design optimization of two eddy current coupling topologies. Using the optimally designed eddy current coupling, it is shown that the analytical and 3-D finite-element solutions compare very well and that the proposed eddy current coupling topology has no torque ripple. The analytical approximation is well suited to the rapid design optimization of eddy current couplings that use a conductive material in the air gap.
A neural network based space vector PWM controller for a three-level voltage-fed inverter induction motor drive

A neural network based implementation of space vector modulation (SVM) of a three-level voltage-fed inverter has been proposed in this paper that fully covers the linear undermodulation region. The neural network has the advantage of very fast implementation of SVM algorithm, particularly when a dedicated ASIC chip is used instead of a digital signal processor. A three-level inverter has a large number of switching states compared to a two-level inverter and therefore the SVM algorithm to be implemented in a neural network is considerably more complex. In the proposed scheme, a three-layer feedforward neural network receives the command voltage and angle information at the input and generates symmetrical PWM waves for the three phases with the help of a single timer and simple logic circuits. The ANN based modulator distributes switching states such that neutral point voltage is balanced in open loop manner. The frequency and voltage can be varied from zero to full value in the whole undermodulation range. A DSP based modulator generates the data which are used to train the network by backpropagation algorithm in MATLAB/Neural Network Toolbox. The performance of an open loop volts/Hz speed controlled induction motor drive has been evaluated with the ANN based modulator and compared with that of conventional DSP based modulator, and shows excellent performance. The modulator can be easily applied to a vector-controlled drive, and its performance can be extended to overmodulation region.
WEIGHTED ASSOCIATION RULE MINING- A REVIEW

Traditional association rule mining consider support confident measures to find out frequent item sets, it assumes all items are having equal significance. Where as weighted association rule mining assigns weights to items based on different aspects. Because researchers are more concerned with qualitative aspects of attributes (e.g. significance), as compared to considering only quantitative ones (e.g. number of appearances in a database etc). Because qualitative properties are required in order to fully exploit the attributes present in the dataset. In last few years a number of weighted associative rule mining algorithms have been proposed, i.e. WAR, WARM, WFIM, WIP, WUARM, FWARM and others. These algorithms employ different rule discovery, rule ranking, rule pruning, rule prediction methods. This paper focuses on surveying and comparing the weighted associative rule mining techniques with regards to the above criteria.
nReader: reading news quickly, deeply and vividly

In this paper we present our design of a novel system, named nReader, to help people read online news. According to researches on news recommendation and a newly deployed survey on user's feeling and requirement about current news reading style, we build our system by adding extra feature to the framework of the popular RSS (Rich Site Summary) system.We designed corresponding views in our reading tools to support browsing mode and intensively reading mode. After a preliminary user testing, the feedback is encouraging. A wider and more delicate user study will be performed to boost our system and interface to give user a more convenient and comfortable online news reading experience.
Steganographic Image Sharing App

With the advent of smartphones, many people have the ability to take a photo and upload it to the internet whenever they want. Most smartphones will include basic metadata in the image file; however, this is usually limited to information about the camera itself, and not the content of the photo. This data is also easy to access, as there are many websites and applications that will extract the metadata from an image, thus it is unsuitable for personal information.While there are many applications out there which will allow a user to take and upload photos for their friends or the public to view, such as Facebook or Instagram, these applications rely on information provided along side the image for privacy settings, commenting, and image searching. Other search engines would have difficulty categorising the pictures they find on various sites, and if the image is saved and re-uploaded somewhere else, the information about them would likely be lost.Steganography provides a feasible and viable solution to these problems. By embedding information directly into the image, the information about the image will not be lost, if it is separated from its original source. The aim of the capstone is to demonstrate this aspect via an image sharing app that allows users to exchange messages and personalised information that is embedded in the image such that it is inaccessible without knowing their keys, and metadata that can be used by search engines to categorise the image and its contents. The developed application will provide a platform for users to create an account, share images, embed information within the image, and search for images other users have uploaded.