Miracle-dz/UniFAR-FTUs huggingface dataset:
Protective Effect of a Lipid-Based Preparation from Mycobacterium smegmatis in a Murine Model of Progressive Pulmonary Tuberculosis

A more effective vaccine against tuberculosis (TB) is urgently needed. Based on its high genetic homology with Mycobacterium tuberculosis (Mtb), the nonpathogenic mycobacteria, Mycobacterium smegmatis (Ms), could be an attractive source of potential antigens to be included in such a vaccine. We evaluated the capability of lipid-based preparations obtained from Ms to provide a protective response in Balb/c mice after challenge with Mtb H37Rv strain. The intratracheal model of progressive pulmonary TB was used to assess the level of protection in terms of bacterial load as well as the pathological changes in the lungs of immunized Balb/c mice following challenge with Mtb. Mice immunized with the lipid-based preparation from Ms either adjuvanted with Alum (LMs-AL) or nonadjuvanted (LMs) showed significant reductions in bacterial load (P < 0.01) compared to the negative control group (animals immunized with phosphate buffered saline (PBS)). Both lipid formulations showed the same level of protection as Bacille Calmette and Guerin (BCG). Regarding the pathologic changes in the lungs, mice immunized with both lipid formulations showed less pneumonic area when compared with the PBS group (P < 0.01) and showed similar results compared with the BCG group. These findings suggest the potential of LMs as a promising vaccine candidate against TB.
Convolutional neural networks based image resampling with noisy training set

A new learning model for image resampling with convolutional neural network is proposed. Its main idea is the dataset preparation method for deep learning. The proposed algorithm can work with noisy and noiseless images and provides good quality for wide noise level range. The method was tested using standard datasets and was also applied for retinal image resampling.
Organizing for Open Innovation: Incorporating the Externality of Control with Diversity of Contribution

With whom should firms join forces (diversity of contributions, DoC) and how should they share decision-making power with external actors (externality of control, EoC)? Despite the growing importance of open innovation, there is no unifying framework explaining the different ways in which firms can organize to acquire external contributions and exploit them. This conceptual article introduces a new framework according to which the relationship between DoC and a firm’s innovative performance is moderated by (1) the characteristics of a project, such as the disparity between a specific problem and the firm’s existing knowledge base, the problem’s modularity, and the tacitness of the anticipated solution; and (2) EoC. With regards to project characteristics we argue that (a) the disparity between a specific problem and the firm’s existing knowledge base moderates the DoC–performance relationship positively; (b) the problem’s modularity moderates the DoC–performance relationship positively; and (c) the tacitness of the anticipated solution moderates the DoC-performance relationship negatively. We argue that EoC moderates DoC’s impact on performance positively.
The working mother-in-law effect on the labour force participation of first and second-generation immigrant women in the UK

ABSTRACT There are clear differences in labour force participation rates of women of immigrant origins, both first and second generation, across a range of European countries. Many of these differences, particularly for women with low participation rates, are not fully explained in existing studies. I argue that to better understand differences in labour force participation, and the role of ‘culture’ in shaping them, we need to take account of the context provided by previous generations of women. While mothers may transmit expectations regarding participation directly to their daughters through intergenerational transmission and childhood socialisation, mothers-in-law may also be important in indirectly influencing their daughters-in-law through their sons’ behaviours and attitudes. Using the UK large-scale household panel study Understanding Society, the contribution of this paper is to investigate the ‘mother-in-law effect’ in the UK. I shed light on the extent to which partnered women’s participation is affected by the work status of their partner’s mother. I find a positive association between a working mother-in-law and female labour force participation, which remains sizeable after accounting for individual-level characteristics. I also show that Pakistani and Bangladeshi women’s participation is more sensitive to the work status of their mothers-in-law than that of other groups.
Visualization of Network Communities and Their Links

People already know the web community and its characteristics, but do not understand the web network. Another problem is that from the network communities we cannot get the links between them clearly although it is sometimes very important for us to find the relationship between network communities. Our study described the network communities and their relationship in a visual way. In the study, we visualize the Community with a maximum of four hierarchies. We used cell squares to visualize the members and strips to visualize Sub-Sub-Communities. We also visualized the links by lines. From the visualization of the network communities and their links, we will be able to get more information which is otherwise hidden behind the data and the web page. The visualization of the network Communities can helps us know the size and the development of a Community and the relationship between the Communities. The method could be used in advertisement and search engines.
CT-PET Landmark-based Registration Using a Dynamic Lung Model

This paper deals with the problem of non-linear landmark-based registration of CT and PET images of thoracic regions. We propose a general method to introduce a breathing model in a registration procedure in order to guarantee physiologically plausible deformations. Initial results are very promising and demonstrate the interest of this method to improve the combination of anatomical and functional images for diagnosis and oncology applications.
A Streamlined Architecture For 3-D Discrete Wavelet Transformation And Inverse Discrete Wavelet Transform

This paper presents an architecture of the lifting based running 3-D discrete wavelet transform (DWT), which is a powerful image and video compression algorithm. The proposed design is one of the first lifting based complete 3DDWT architectures without group of pictures restriction. The new computing technique based on analysis of lifting signal flow graph minimizes the storage requirement. This architecture enjoys reduced memory referencing and related low power consumption, low latency, and high throughput compared to those of earlier reported works. Further, the digital data can be retrieved using Inverse Discrete Wavelet Transform (IDWT). The images need to be retrieved without loosing of information. The proposed architecture has been successfully implemented on Xilinx Spartan series field-programmable gate array, offering a speed of 40 MHz, making it suitable for realtime compression even with large frame dimensions. Moreover, the architecture is fully scalable beyond the present coherent Daubechies filterbank (9, 7).
MEG decoding across subjects

Brain decoding is a data analysis paradigm for neuroimaging experiments that is based on predicting the stimulus presented to the subject from the concurrent brain activity. In order to make inference at the group level, a straightforward but sometimes unsuccessful approach is to train a classifier on the trials of a group of subjects and then to test it on unseen trials from new subjects. The extreme difficulty is related to the structural and functional variability across the subjects. We call this approach decoding across subjects. In this work, we address the problem of decoding across subjects for magnetoen-cephalographic (MEG) experiments and we provide the following contributions: first, we formally describe the problem and show that it belongs to a machine learning sub-field called transductive transfer learning (TTL). Second, we propose to use a simple TTL technique that accounts for the differences between train data and test data. Third, we propose the use of ensemble learning, and specifically of stacked generalization, to address the variability across subjects within train data, with the aim of producing more stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we compare the standard approach of not modelling the differences across subjects, to the proposed one of combining TTL and ensemble learning. We show that the proposed approach is consistently more accurate than the standard one.
An Inducible and Reversible Mouse Genetic Rescue System

Inducible and reversible regulation of gene expression is a powerful approach for uncovering gene function. We have established a general method to efficiently produce reversible and inducible gene knockout and rescue in mice. In this system, which we named iKO, the target gene can be turned on and off at will by treating the mice with doxycycline. This method combines two genetically modified mouse lines: a) a KO line with a tetracycline-dependent transactivator replacing the endogenous target gene, and b) a line with a tetracycline-inducible cDNA of the target gene inserted into a tightly regulated (TIGRE) genomic locus, which provides for low basal expression and high inducibility. Such a locus occurs infrequently in the genome and we have developed a method to easily introduce genes into the TIGRE site of mouse embryonic stem (ES) cells by recombinase-mediated insertion. Both KO and TIGRE lines have been engineered for high-throughput, large-scale and cost-effective production of iKO mice. As a proof of concept, we have created iKO mice in the apolipoprotein E (ApoE) gene, which allows for sensitive and quantitative phenotypic analyses. The results demonstrated reversible switching of ApoE transcription, plasma cholesterol levels, and atherosclerosis progression and regression. The iKO system shows stringent regulation and is a versatile genetic system that can easily incorporate other techniques and adapt to a wide range of applications.
Counting of Frozen Semen Straws Using Image Processing

The proposed work is to verify the count of the semen straws which are kept in a compact bunch by using image processing techniques. These straws are kept in liquid nitrogen for the straw content preservation purpose. Due to the liquid nitrogen characteristics and safety requirements for both semen straws as well as workers working in such an environment, a non-harming, rapid process for straw counting is required. So, straw counting using image processing algorithm is carried out. The counting problem is solved by using several image enhancement, image segmentation and object counting techniques. For the problem solving, many traditional methods like contrast stretching, thresholding, morphological processing as well as advanced methods like color based information removal and image enhancement using intensity factor are used.
Sentence segmentation of aphasic speech

Automatic analysis of impaired speech for screening or diagnosis is a growing research field; however there are still many barriers to a fully automated approach. When automatic speech recognition is used to obtain the speech transcripts, sentence boundaries must be inserted before most measures of syntactic complexity can be computed. In this paper, we consider how language impairments can affect segmentation methods, and compare the results of computing syntactic complexity metrics on automatically and manually segmented transcripts. We find that the important boundary indicators and the resulting segmentation accuracy can vary depending on the type of impairment observed, but that results on patient data are generally similar to control data. We also find that a number of syntactic complexity metrics are robust to the types of segmentation errors that are typically made.
DT-CWT Feature Based Face Recognition Using Supervised Kernel ONPP

A novel DT-CWT feature-kernel face recognition method is proposed in this paper. This involves transforming a face image with DT-CWT on feature extraction stage, and supervised kernel Orthogonal Neighborhood Preserving Projections (ONPP) being then automatically applied to the feature vectors. Numerical experiments are reported to illustrate the performance of our algorithm and to compare it with a few competing methods.
The Study of Lexical Features in English Press Registers

Register is a variety of a language used for a particular purpose or situation and different registers are marked by a variety of linguistics features. This research aimed to investigate the lexical features in two types of press registers that are news reportage and editorial commentary. This study applied a text-based approach in which 10 news reportage articles and 10 editorial commentary articles under the political theme during 2012 to 2016 were selected. To analyse the data, both content and function words were examined. In the news reportage, it was used to serve a narrative function in reporting facts and concise and factual information. On the contrary, argumentation and critique characterized the register of editorial commentary. Based on the findings, the research showed that most of the content words classified by parts of speech were proper noun, reporting verbs and describing adjectives and adverbs while common nouns, cognitive verbs, evaluating adjectives and adverbs were commonly found in the editorial commentary. For the function words, it was found that first personal pronouns, modal verbs of prediction and suggestion, and conjunctions of addition and suggestion were the word forms which were found significant in the register of news reportage. In the research, the types of word forms in each register type were markedly different. However, the functional feature of each register played a very important role in their use of the lexical forms and affected the variation of the linguistics features in the two types of the register in press.
A spectral method for confidence interval generation and run length control in simulations

This paper discusses a method for placing confidence limits on the steady state mean of an output sequence generated by a discrete event simulation. An estimate of the variance is obtained by estimating the spectral density at zero frequency. This estimation is accomplished through a regression analysis of the logarithm of the averaged periodogram. By batching the output sequence the storage and computational requirements of the method remain low. A run length control procedure is developed that uses the relative width of the generated confidence interval as a stopping criterion. Experimental results for several queueing models of an interactive computer system are reported.
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification

This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query instance and the prototype vector of the support set for each relation candidate independently. On the contrary, our proposed MLMAN model encodes the query instance and each support set in an interactive way by considering their matching information at both local and instance levels. The final class prototype for each support set is obtained by attentive aggregation over the representations of support instances, where the weights are calculated using the query instance. Experimental results demonstrate the effectiveness of our proposed methods, which achieve a new state-of-the-art performance on the FewRel dataset.
Extracellular-to-intracellular signal transfer via G-proteins

We look at the problem of signal transduction by extracellular agonist binding to a receptor protein at the membrane (sensor) via binding of G-proteins (effectors) to a highly integrative target molecule, such as the second messenger cAMP (target). We explore the effects of binding times, effector assignment and effector pool size on the shape of the output signal under different input scenarios. We conclude that low rates of information transfer may sometimes coincide with a high probability or efficiency of plasticity induction.
Nonisolated Multiport Converters Based on Integration of PWM Converter and Phase-Shift-Switched Capacitor Converter

Photovoltaic (PV) systems having rechargeable batteries are prone to be complex and costly because multiple converters are necessary to individually regulate a load, PV panel, and battery. This paper proposes novel nonisolated multiport converters (MPCs) integrating a bidirectional pulsewidth modulation (PWM) converter and phase-shift-switched capacitor converter (PS-SCC) for standalone PV systems. A PWM converter and PS-SCC are integrated by reducing the total switch count, realizing the simplified system and circuit. In the proposed MPCs, two control freedoms of duty cycle and phase shift angle are manipulated to individually regulate the load, PV panel, and/or battery. The detailed operation analysis was performed to mathematically derive gain characteristics and zero voltage switching operation boundaries. For the battery discharging mode, in which the PV panel is not available and the MPC behaves as a single-input–single-output converter with two control freedoms available, the optimized control scheme achieving the lowest rms current is also proposed to maximize power conversion efficiencies. Various kinds of experimental verification tests using a 200-W prototype were performed to verify the theoretical analysis and to demonstrate the performance of the proposed MPC.
A Cluster-Based Passive Direction Finding Cross Location Method

Direction finding cross location is a widely used passive location method, featuring omni-direction and fast speed. However, with the increase in station number, false location points are seen to be rapidly increased among the direction finding lines. In order to accurately and rapidly locate radiation sources, how to rapidly eliminate false points is an important issue urgent to be solved. This paper proposes a cluster-based passive direction finding cross location method, which can obtain location estimates of radiation sources without acquiring any prior information about the radiation sources other than the azimuth information of the radiation sources measured by the stations. This method proves extensively applicable.
Iterative mapreduce based heuristic algorithm for solving N Puzzle

Mapreduce Programming paradigm provides an elegant and efficacious parallel implementation of Heuristic Search Algorithm. We present here a Parallel Breadth First Heuristic Search (PBFHS) Algorithm for solving very large combinatorial problems. Using N Puzzle as our application domain we found that scalability of Breadth First Search (BFS) and Iterative Deepening A* (IDA*) is limited on a single machine due to hardware constraints. We generated a highly restrictive, large search space using combination of three efficient and admissible heuristics namely Manhattan Distance, N-Max Swaps and Tile out of row and column. A Hadoop cluster comprising of 12 processors, solves the hardest 15 Puzzle in 2 hours, and 24 Puzzle in 6 hours of computing time.
A human forearm and wrist motion assist exoskeleton robot with EMG-based Fuzzy-neuro control

In this paper, an EMG-based fuzzy-neuro control method is proposed for a three degree of freedom (3 DOF) human forearm and wrist motion assist exoskeleton robot (W-EXOS). The W-EXOS assists human forearm pronation/supination motion, wrist flexion/extension motion and ulnar/radial deviation. The paper presents the EMG-based fuzzy-neuro control method with multiple fuzzy-neuro controllers and the adaptation method of controllers. The skin surface electromyography (EMG) signals of muscles in forearm of the exoskeleton users' and the hand force/forearm torque are used as input information for the controllers. Fuzzy-neuro control method, which is a combination of flexible fuzzy control and adaptive neural network control, has been applied to realize the natural and flexible motion assist. In the control method, multiple fuzzy-neuro controllers are applied, since the muscles activation levels change in accordance with the angles of motions. The control method is able to adapt according the changing EMG signal levels of different users. Experiments have been performed to evaluate the proposed EMG-based fuzzy-neuro control method.
Causes of government construction projects failure in an emerging economy: Evidence from Ghana

The purpose of this paper is to investigate the factors that cause government construction projects failure in a developing economy. ,The study used the sequential data collection approach through an in-depth semi-structured interview (16 participants) and questionnaire survey (230 participants) to solicit their perceptions from project management practitioners (PMP), contractors and client (government officials) about the factors that lead to Ghanaian Government construction projects. The relative importance index was used to determine the relative importance of the factors identified. This was followed by Spearman rank correlation coefficient and Kendall’s coefficient of concordance to measure the degree of agreement among the participants on their perceptions. ,In total, 34 factors were identified as the main factors that lead into Ghanaian Government construction projects failure. The top ten most important factors that cause Ghanaian Government construction projects failure are: political interferences, delays in payment, partisan politics, bureaucracy, corruption, poor supervision, lack of commitment by project leaders, poor planning, starting more projects than the government can fund and change in government. The failure factors were grouped into four main themes and found that the most important failure factors are leadership. This is followed by management and administrative practices, resources and external forces, respectively. ,This study is limited to only the public sector, and therefore the findings may not be applicable in the private sector. ,Policy makers and construction PMP would be able to use the findings as a guide during the implementation of government projects in order to reduce and/or avoid government construction projects failure. ,Construction projects failure in developing countries is high. Accordingly, the extant literature has been devoted to identifying the factors that lead to failure; however, they have mainly been discussed from a generic point of view or individual case studies. Researches that focus exclusively on government construction projects in developing countries are rare despite the dynamics in which these projects are implemented. This research extends the construction project management literature by focussing on government construction projects in a developing economy, where there are weak public institutional systems coupled with partisanship politics and bad cultural orientation towards government sector work inherited from a colonial rule.
Embedding Foreign Code [ DRAFT ]

Special purpose embedded languages facilitate generating high-performance code from purely functional high-level code; for example, we want to program highly parallel GPUs without the usual high barrier to entry and the time-consuming development process. We previously demonstrated the feasibility of a skeleton-based, generative approach to compiling such embedded languages. In this paper, we (a) describe our solution to some of the practical problems with skeleton-based code generation and (b) introduce our approach to enabling interoperability with native code. In particular, we show, in the context of a functional embedded language for GPU programming, how template meta programming simplifies code generation and optimisation. Furthermore, we present our design for a foreign function interface for an embedded language.