Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial simulation of hydrocarbon generation in coal using hydrous pyrolysis techniques

Work by Brooks and Smith, 1967; Connan, 1974; Durand and Paratte, 1982; Thompson et al, 1985; and MacGregor and McKenzie, 1986 suggests that certain coals can be considered as source rocks for liquid hydrocarbons. Their conclusions are based on the close association of coal with liquid hydrocarbon occurrences and promising source potential supported by various geochemical analyses. However, the liquid hydrocarbon potential of coal cannot easily be evaluated because of its variable maceral composition, its unique occurrence compared to most source rocks, and difficulty in quantifying liquid hydrocarbon expulsion. The overall objective of this study is to complete hydrous pyrolysis experiments to evaluate the liquid hydrocarbon potential of coal. Specific aspects of this study include: (1) a quantitative assessment of the generative potential of coal, and (2) an evaluation of the composition of liquid hydrocarbons generated from coal at various temperatures.
An Approach to Improving Corpus Quality for Indonesian-English Statistical Machine Translation

—This paper discusses one of the fundamental issues in statistical machine translation (SMT) : the corpus quality. Corpus is a reference system based on an electronic collection of texts composed in a specific language used for building language model, translation model, or factor model. The quality of a SMT system depends heavily on the quality and quantity of the bilingual language resource. However, previous related work mainly focuses on the quantity and tries to collect more bilingual data. Checking the source sentences and their translations manually in a parallel corpus is a very difficult task and requires large resources. In this paper, to optimize the bilingual corpus to improve the quality of the translation system, we propose some approaches to processing the parallel corpus by filtering bad translation sentences from the parallel corpus. Filter we use is the minimum value of each sentence that is tested by the Bilingual Evaluation Understudy (BLEU) method. The experimental results show that at an optimal point of minimum value, MPS quality can be improved by increasing quality at the expense of quantity of a corpus.
Image manipulation detection

Techniques and methodologies for validating the authenticity of digital images and testing for the presence of doctoring and manipulation operations on them has recently attracted attention. We review three categories of forensic features and discuss the design of classifiers between doctored and original images. The performance of classifiers with respect to selected controlled manipulations as well as to uncontrolled manipulations is analyzed. The tools for image manipulation detection are treated under feature fusion and decision fusion scenarios.
CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms

In this paper, we present a new video database: CVD2014-Camera Video Database. In contrast to previous video databases, this database uses real cameras rather than introducing distortions via post-processing, which results in a complex distortion space in regard to the video acquisition process. CVD2014 contains a total of 234 videos that are recorded using 78 different cameras. Moreover, this database contains the observer-specific quality evaluation scores rather than only providing mean opinion scores. We have also collected open-ended quality descriptions that are provided by the observers. These descriptions were used to define the quality dimensions for the videos in CVD2014. The dimensions included sharpness, graininess, color balance, darkness, and jerkiness. At the end of this paper, a performance study of image and video quality algorithms for predicting the subjective video quality is reported. For this performance study, we proposed a new performance measure that accounts for observer variance. The performance study revealed that there is room for improvement regarding the video quality assessment algorithms. The CVD2014 video database has been made publicly available for the research community. All video sequences and corresponding subjective ratings can be obtained from the CVD2014 project page (<;uri xlink:type="simple">http://www.helsinki.fi/psychology/groups/visualcognition/<;/uri>).
Requirements Engineering as a Success Factor in Software Projects

Requirements engineering might be the single most important part of any software project. Based on their field study of 15 RE teams in nine software organizations, the authors identify the RE practices that clearly contribute to project success, particularly in terms of team knowledge, resource allocation, and process. They then summarize the best practices exhibited by the most successful teams.
Inhibitory effects of erythrocyte membrane proteins on the in vitro invasion of the human malarial parasite (Plasmodium falciparum) into its host cell

The intracellular development of the erythrocytic stage of the malarial parasite (merozoite) is initiated by the attachment of the parasite to the erythrocyte surface. This paper describes an assay system to investigate Plasmodium falciparum merozoite entry into the host cell and reports on three observations regarding this interaction. (a) Merozoites do not invade human erythrocytes treated with either trypsin or neuraminidase, and both enzymes partially cleave glycophorin A, the major erythrocyte surface sialoglycoprotein. (b) A membrane protein fraction containing glycophorin A will, at low concentrations, inhibit the invasion of isolated merozoites into erythrocytes; no other fractions of membrane proteins have appreciable effects on the reinvasion. (c) Merozoites do not reinvade erythrocytes preincubated with F ab' fragments of antibody prepared against glycophorin A. Together, these three observations imply a role for glycophorin A in the attachment of the malarial parasite to the erythrocyte surface.
A hierarchical key management scheme for wireless sensor networks based on identity-based encryption

Limited resources (such as energy, computing power, storage, and so on) make it impractical for wireless sensor networks (WSNs) to deploy traditional security schemes. In this paper, a hierarchical key management scheme is proposed on the basis of identity-based encryption (IBE). This proposed scheme not only converts the distributed flat architecture of the WSNs to a hierarchical architecture for better network management but also ensures the independence and security of the sub-networks. This paper firstly reviews the identity-based encryption, particularly, the Boneh-Franklin algorithm. Then a novel hierarchical key management scheme based on the basic Boneh-Franklin and Diffie-Hellman (DH) algorithms is proposed. At last, the security and efficiency of our scheme is discussed by comparing with other identity-based schemes for flat architecture of WSNs.
Management of medical images using wavelets-based multi-watermarking algorithm

Medical images of all types are produced in hospitals and health centers more than ever. The ease of production of medical images has been facilitated by the advancements in information and communication technologies. Traditional methods used to manage medical images fall short in coping with the great amount of information inherited in medical images. Therefore, there remains immediate need to develop efficient management strategies that work well with all types of medical images. In this paper, we apply a wavelet-based image multi-watermarking algorithm to implement three issues of great importance to the management of medical images. These issues are; image source authentication, image annotation, and image retrieval. Performance of the proposed algorithm was evaluated using four types of medical images; Ultrasound, X-ray, CT and MRI.
Error rate analysis and optimal power allocation in multiple access relay channels with Analog Network Coding

In this paper, we examine multi-source multi-relay systems that employ Analog Network Coding for which we provide a two-fold contribution: i) We derive a closed-form upper bound of the average symbol error rate (SER) per source of the system, which is shown to be tight in the high-Signal-to-Noise Ratio (SNR) region, especially for an adequate number of relays, and ii) based on this bound, for a given total power budget to be distributed among the source and relay nodes we formulate the power allocation optimization problem with the aim of minimizing the SER per source. Results show that for a common target SER among the sources, an increase in their number results in an increase in the expected energy gains over the equal power allocation for all the nodes policy.
Extended lapped transforms: properties, applications, and fast algorithms

The family of lapped orthogonal transforms is extended to include basis functions of arbitrary length. Within this new family, the extended lapped transform (ELT) is introduced, as a generalization of the previously reported modulated lapped transform (MLT). Design techniques and fast algorithms for the ELT are presented, as well as examples that demonstrate the good performance of the ELT in signal coding applications. Therefore, the ELT is a promising substitute for traditional block transforms in transform coding systems, and also a good substitute for less efficient filter banks in subband coding systems. >
Differences in Affinity of Binding of Lymphocytic Choriomeningitis Virus Strains to the Cellular Receptor α-Dystroglycan Correlate with Viral Tropism and Disease Kinetics

ABSTRACT α-Dystroglycan (α-DG) was recently identified as a receptor for lymphocytic choriomeningitis virus (LCMV) and several other arenaviruses, including Lassa fever virus (W. Cao, M. D. Henry, P. Borrow, H. Yamada, J. H. Elder, E. V. Ravkov, S. T. Nichol, R. W. Compans, K. P. Campbell, and M. B. A. Oldstone, Science 282:2079–2081, 1998). Data presented in this paper indicate that the affinity of binding of LCMV to α-DG determines viral tropism and the outcome of infection in mice. To characterize this relationship, we evaluated the interaction between α-DG and several LCMV strains, variants, and reassortants. These viruses could be divided into two groups with respect to affinity of binding to α-DG, dependence on this protein for cell entry, viral tropism, and disease course. Viruses that exhibited high-affinity binding to α-DG displayed a marked dependence on α-DG for cell entry and were blocked from infecting mouse 3T6 fibroblasts by 1 to 4 nM soluble α-DG. In addition, high-affinity binding to α-DG correlated with an ability to infiltrate the white pulp (T-dependent) area of the spleen, cause ablation of the cytotoxic T-lymphocyte (CTL) response by day 7 postinfection, and establish a persistent infection. In contrast, viruses with a lower affinity of binding to α-DG were only partially inhibited from infecting α-DG−/− embryonic stem cells and required a concentration of soluble α-DG higher than 100 nM to prevent infection of mouse 3T6 fibroblasts. These viruses that bound at low affinity were mainly restricted to the splenic red pulp, and the host generated an effective CTL response that rapidly cleared the infection. Reassortants of viruses that bound to α-DG at high and low affinities were used to map genes responsible for the differences described to the S RNA, containing the virus attachment protein glycoprotein 1.
Computational modeling of RNA 3D structures, with the aid of experimental restraints

In addition to mRNAs whose primary function is transmission of genetic information from DNA to proteins, numerous other classes of RNA molecules exist, which are involved in a variety of functions, such as catalyzing biochemical reactions or performing regulatory roles. In analogy to proteins, the function of RNAs depends on their structure and dynamics, which are largely determined by the ribonucleotide sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore, the majority of known RNAs remain structurally uncharacterized. To address this problem, computational structure prediction methods were developed that simulate either the physical process of RNA structure formation (“Greek science” approach) or utilize information derived from known structures of other RNA molecules (“Babylonian science” approach). All computational methods suffer from various limitations that make them generally unreliable for structure prediction of long RNA sequences. However, in many cases, the limitations of computational and experimental methods can be overcome by combining these two complementary approaches with each other. In this work, we review computational approaches for RNA structure prediction, with emphasis on implementations (particular programs) that can utilize restraints derived from experimental analyses. We also list experimental approaches, whose results can be relatively easily used by computational methods. Finally, we describe case studies where computational and experimental analyses were successfully combined to determine RNA structures that would remain out of reach for each of these approaches applied separately.
Plant Electrical Signal Classification Based on Waveform Similarity

(1) Background: Plant electrical signals are important physiological traits which reflect plant physiological state. As a kind of phenotypic data, plant action potential (AP) evoked by external stimuli—e.g., electrical stimulation, environmental stress—may be associated with inhibition of gene expression related to stress tolerance. However, plant AP is a response to environment changes and full of variability. It is an aperiodic signal with refractory period, discontinuity, noise, and artifacts. In consequence, there are still challenges to automatically recognize and classify plant AP; (2) Methods: Therefore, we proposed an AP recognition algorithm based on dynamic difference threshold to extract all waveforms similar to AP. Next, an incremental template matching algorithm was used to classify the AP and non-AP waveforms; (3) Results: Experiment results indicated that the template matching algorithm achieved a classification rate of 96.0%, and it was superior to backpropagation artificial neural networks (BP-ANNs), supported vector machine (SVM) and deep learning method; (4) Conclusion: These findings imply that the proposed methods are likely to expand possibilities for rapidly recognizing and classifying plant action potentials in the database in the future.
SYSBIONS: nested sampling for systems biology

Motivation: Model selection is a fundamental part of the scientific process in systems biology. Given a set of competing hypotheses, we routinely wish to choose the one that best explains the observed data. In the Bayesian framework, models are compared via Bayes factors (the ratio of evidences), where a model’s evidence is the support given to the model by the data. A parallel interest is inferring the distribution of the parameters that define a model. Nested sampling is a method for the computation of a model’s evidence and the generation of samples from the posterior parameter distribution. Results: We present a C-based, GPU-accelerated implementation of nested sampling that is designed for biological applications. The algorithm follows a standard routine with optional extensions and additional features. We provide a number of methods for sampling from the prior subject to a likelihood constraint. Availability and implementation: The software SYSBIONS is available from http://www.theosysbio. bio.ic. ac.uk/resources/sysbions/ Contact: m.stumpf@imperial.ac.uk, robert.johnson11@imperial.ac.uk
Thermal Instability and the Formation of Clumpy Gas Clouds

The radiative cooling of optically thin gaseous regions and the formation of a two-phase medium and of cold gas clouds with a clumpy substructure is investigated. We demonstrate how clumpiness can emerge as a result of thermal instability. In optically thin clouds, the growth rate of small density perturbations is independent of their length scale as long as the perturbations can adjust to an isobaric state. However, the growth of a perturbation is limited by its transition from isobaric to isochoric cooling when the cooling timescale is reduced below the sound crossing timescale across its length scale. The temperature at which this transition occurs decreases with the length scale of the perturbation. Consequently, small-scale perturbations have the potential to reach higher amplitudes than large-scale perturbations. When the amplitude becomes nonlinear, advection overtakes the pressure gradient in promoting the compression, resulting in an accelerated growth of the disturbance. The critical temperature for transition depends on the initial amplitude. The fluctuations that can first reach nonlinearity before their isobaric to isochoric transition will determine the characteristic size and mass of the cold dense clumps that would emerge from the cooling of an initially nearly homogeneous region of gas. Thermal conduction is, in general, very efficient in erasing isobaric, small-scale fluctuations, thus suppressing a cooling instability. A weak, tangled magnetic field, however, can reduce the conductive heat flux enough for low-amplitude fluctuations to grow isobarically and become nonlinear if their length scales are of order 10-2 pc. If the amplitude of the initial perturbations is a decreasing function of the wavelength, the size of the emerging clumps will decrease with increasing magnetic field strength. Finally, we demonstrate how a two-phase medium, with cold clumps being pressure confined in a diffuse hot residual background component, would be sustained if there is adequate heating to compensate the energy loss.
Patent Document Classification Based on Mutual Information Feature Selection

We describe a supervised text classification approach based on a greedy feature selection method, which uses a support vector machine (SVM) classifier. As feature selection method we use the mutual information. This measures the quantity of information about the categories contained by the words. To train and test the algorithm we used patent documents from the US Patent Classification System. Average break-even point (BEP) for some US Classes is reported as conclusion.
WCET-driven cache-aware code positioning

Code positioning is a well-known compiler optimization aiming at the improvement of the instruction cache behavior. A contiguous mapping of code fragments in memory avoids overlapping of cache sets and thus decreases the number of cache conflict misses. We present a novel cache-aware code positioning optimization driven by worst-case execution time (WCET) information. For this purpose, we introduce a formal cache model based on a conflict graph which is able to capture a broad class of cache architectures. This cache model is combined with a formal WCET timing model, resulting in a cache conflict graph weighted with WCET data. This conflict graph is then exploited by heuristics for code positioning of both basic blocks and entire functions. Code positioning is able to decrease the accumulated cache misses for a total of 18 real-life benchmarks by 15.5% on average for an automotive processor featuring a 2-way set-associative cache. These cache miss reductions translate to average WCET reductions by 6.1%. For direct-mapped caches, even larger savings of 18.8% (cache misses) and 9.0% (WCET) were achieved.
Addressing Class Imbalance in Grammatical Error Detection with Evaluation Metric Optimization

We address the problem of class imbalance in supervised grammatical error detection (GED) for non-native speaker text, which is the result of the low proportion of erroneous examples compared to a large number of error-free examples. Most learning algorithms maximize accuracy which is not a suitable objective for such imbalanced data. For GED, most systems address this issue by tuning hyperparameters to maximize metrics like Fβ . Instead, we show that learning classifiers that directly learn model parameters by optimizing evaluation metrics like F1 and F2 score deliver better performance on these metrics as compared to traditional sampling and cost-sensitive learning solutions for addressing class imbalance. Optimizing these metrics is useful in recall-oriented grammar error detection scenarios. We also show that there are inherent difficulties in optimizing precision-oriented evaluation metrics like F0.5. We establish this through a systematic evaluation on multiple datasets and different GED tasks.
Opportunities and challenges for location aware computing in the construction industry

This paper describes the opportunities for location aware computing to enhance information capture and use within the construction industry. The construction industry is characterized as being slow to take up innovative mobile ICT, despite the highly mobile workforce who must collaborate with a range of on and off-site personnel, and make use of large volumes of information. Based on fieldwork and workshop activities within COMIT (a large-scale mobile IT project within the construction industry), the information used within two key business processes - health and safety audits, and site design problem resolution - is outlined, and the opportunities for support by location aware computing discussed. Some potential challenges are also identified, as is the need to understand how to provide real value (as opposed to just information) to the end user.
A non-expert Kaldi recipe for Vietnamese Speech Recognition System

In this paper we describe a non-expert setup for Vietnamese speech recognition system using Kaldi toolkit. We collected a speech corpus over fifteen hours from about fifty Vietnamese native speakers and using it to test the feasibility of our setup. The essential linguistic components for the Automatic Speech Recognition (ASR) system was prepared basing on the written form of the language instead of expertise knowledge on linguistic and phonology as commonly seen in rich resource languages like English. The modeling of tones by integrating them into the phoneme and using the phonetic decision tree is also discussed. Experimental results showed this setup for ASR systems does yield competitive results while still have potentials for further improvements.
Functional analysis of Cdk7-interacting proteins Mat1 and Hint in model organisms

7 REVIEW OF THE LITERATURE 9 1 THE CELL DIVISION CYCLE 9 1.1 Cyclin-dependent kinases (Cdks) 9 1.2 Regulation of Cdks 10 2 CDK-ACTIVATING KINASES 11 2.1 Menage a trois (MAT1) 12 2.2 The Cdk-activating kinase – from mammals to yeast 13 3 CDK ACTIVATION MEETS BASAL TRANSCRIPTION 13 3.1 Transcription factor IIH (TFIIH) 14 3.2 Substrates of the TFIIH kinase 15 4 HISTIDINE TRIAD (HIT) GENE FAMILY 17 4.1 The origin of Hint 18 4.2 FHIT 18 4.3 GalT 19 4.4 Novel members of the HIT family: aprataxin, Hint2, and Hint3 19 4.5 HIT proteins – from structure to function 20 4.6 Other Hint-interacting cellular proteins 22 AIMS OF THE STUDY 23 MATERIALS AND METHODS 24 Generation of genetically engineered Mat1 (I, II) and Hint mice (IV) 24 Generation and analysis of Hint;Fhit mice (IV) 24 Generation of S. cerevisiae HNT1 disruptant (III) 24 PCR genotyping (I, II, III, IV) 25 Protein analysis (I, II, III, IV) 26 Immunofluorescence and immunohistochemistry (I, II, III) 26 BrdU labeling (I, II) 26 Blastocyst outgrowths (I) 27 Microinjection (I) 27 Histology (II, IV) 27 Transmission electron microscopy (II) 27 s-galactosidase and alkaline phosphatase staining (II) 27 Yeast strains and culture conditions (III) 28 Yeast transformation (III) 28 Production of GST proteins and solution binding assay (III) 28 Transfection (III) 29 In vitro kinase assay (III, IV) 29 In situ hybridization (IV) 29 Modeling of three dimensional structure (IV) 30