Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical clustering of DNA k-mer counts in RNA-seq fastq files reveals batch effects

Batch effects are artificial sources of variation due to experimental design. Batch effect is a widespread phenomenon in high througput data which can be minimized but not always be avoided. Therefore mechanisms for detection of batch effects are needed which requires comparison of multiple samples. Due to large data volumes 1e12 Bytes this can be technical challenging. We describe the application of hierarchical clustering (HC) on DNA k-mer profiles of multiple fastq files which creates tree structures based on similarity of sequence content. Ideally, HC tree adjacency reflects experimental treatment groups but the algorithm may also agglomerate according to sample preparation groups which then indicates the presence of batch effects. This introduces a new perspective in quality control. In order to perform fast analysis on large data sets we implemented a new algorithm. The algorithm and user interface are implemented a new R-package (seqTools). The result is now available on R-Forge for Linux and Windows. Using this implementation, we compared the transcriptome sequence content of 61 human tissue samples on 8 different flowcells. We pairwise compared flowcells where at least 4 samples from the same tissue type (fibroblasts or jurkat cells) were present and 30) does not remove the identified batch effects.
Runtime Protocol Binding: Flexible Service Integration by Means of Flexible Service Interactions

Dynamic and flexible service composition and interactionare a must in service oriented computing (SOC) scenarios. In this paper the authors present an extension to a previous work, the DENEB platform for the development and execution of Web processes, allowing Web processes to acquire and execute new interaction protocols at runtime. This makes DENEB a well adapted and flexible platform for service integration, useful to work in complex and evolving inter-organisational environments. The proposed solution is exemplified by means of the development of a real use case.
A Based State Machine Controller to Achieve Dynamic Walk – Standup - Walk Cycle of Biped Robot

— In this paper, we show how it is possible to obtain a walk – Standup - walk cycle with a biped robot using only a state machine controller. This work is a continuation of another in which the same approach was used to control the walking gait of the same robot. We introduce four critical angles that affect robot speed and step length. Our control approach consists of two control levels. The low level is a PID controller and the high level is a Dynamic Walking Algorithm, inspired from human locomotion, which tunes the PD controllers and impose the reference signals based on the walking which increases walk stability. This method could be easily implemented in real time because it needs acceptable calculation time. We validated the control approach to a dynamic simulation of our 14DOF biped called ROBIAN. We prove that we can maintain robot walking and stopping stability and walk cycle’s repetition without referencing a predefined trajectory or detecting the center of pressure. Results show that the walk of the biped is very similar to human one.
Relational Schemas as a Source of If–Then Self-Inference Procedures

It is generally accepted that the sense of self is constructed rather than directly perceived or experienced. The hypothesis is advanced here that people's rules of self-inference derive in large part from if–then expectancies about the contingencies of interpersonal interaction; that is, expectancies about how other people will react to one's behaviors. If so, a central type of cognitive structure contributing to self-construal is the relational schema, representing regularities in interaction. Research examining the cognitive representation of interpersonal expectancies, the activation of those representations, and the effects on self-experience is described.
HPI Question Answering System in the BioASQ 2015 Challenge

I describe my participation on the 2015 edition of the BioASQ challenge in which I submitted results for the concept matching, docu- ment retrieval, passage retrieval, exact answer and ideal answer sub- tasks. My approach relies on a in-memory based database (IMDB) and its built-in text analysis features, as well as on PubMed for retrieving relevant citations, and on predened ontologies and terminologies nec- essary for matching concepts to the questions. Although results are far below the ones obtained by other groups, I present an novel approach for answer extraction based on sentiment analysis.
BPMN for knowledge acquisition and anomaly handling in CPS for smart factories

In this paper, we describe a real-time knowledge acquisition and anomaly handling architecture in a maintenance scenario of an industrial cyber-physical production environment. We use the Business Process Model and Notation (BPMN) for modeling and controlling of the maintenance procedure. Automatic handwriting and pen gesture recognition is combined with a networked smart pen that is used on semantically structured paper forms. Here, we discuss our architecture with BPMN-modeled workflows for real-time data processing. All detected anomalies are automatically associated to the corresponding knowledge sources such as the process model, structure model, business process model and user models; linked Semantic Media-Wiki (SMW) pages are built up accordingly. Our architecture provides for a seamless integration of real-time knowledge sources by smart pen technology and real-time CPS processing by a BPMN engine handling the execution and coordination of the modeled interactions.
S Matrix versus ABCD Chain Matrix Formulation in Probe-tip Calibrations

We present some advantageous features of the chain ABCD matrix formulation in comparison with S-matrix formulation in characteristic impedance extraction with probetip calibrations. The procedure is based on the known measurement technique of two different on-wafer line standards [1]. An initial off-wafer LRM or TRL calibration with standard calibration substrate is assumed. The first feature is the formulation of the whole extraction problem in terms of the ABCD chain matrix. It allows us to omit one of the limitations of the true traveling waves based Smatrix [3] asymmetry of a general reciprocal transition between two different waveguides, in particular probe-tip-line junction. This limitation is a difference between complex characteristic impedances of both waveguides. On the contrary, the symmetry of its admittance matrix or equivalently the determinant AD-BC of its chain matrix is not influenced by this effect. If one models the probe-tip-line transition by use of only a shunt admittance or cascade of a shunt admittance and a series impedance, the A element of chain matrix is not influenced by these parasitics and is equal to one. This allows immediate characteristic impedance extraction without analyzing any of these transition models. Moreover, one is able to choose the valid model and verify if any of the possible equivalent parasitic elements can be neglected. The next feature is that the chain matrix formulation modified in a specific way allows the extraction of the equivalent position of the reference planes at the probe-tips. Equivalent means that their locations are equal to their physical locations in case of negligible influence of the series parasitic impedance (usually parasitic inductance). This is very interesting property if we take into consideration that the exact position of the probe tips and reference planes are not known. The last feature allows us to extract the characteristic impedance of the lines without modeling the transition structure under assumption that the condition A=D of its chain ABCD matrix (the model of a reciprocal structure is symmetric) is approximated with good accuracy. Such a formulation takes automatically into consideration even a distributed nature of the transition, which can be of importance at mm-wave frequencies. All the equivalent elements values of the models are extracted from analytical equations for every frequency point. Thus their frequency behavior can be investigated and possible validity of the model (constant equivalent element values) proved. I General Waveguide Theory With the increasing use of planar transmission lines, junctions between waveguides supporting lossy hybrid modes have become common. In this case the classical microwave circuit theories fail. The classical waveguide theory fails to
Emotion detection using relative grid based coefficients through human facial expressions

Facial expressions are important in human computer interaction, because the machine can thereby understand human reactions and act accordingly. Facial expressions act like a nonverbal communication cues in human-human or human computer interactions. In noises environment, getting visual data is difficult. According to the relative bin sub-image based studies, high dimensionality is affecting the system which consequently affects the performance of the emotion detection system. Due to these reasons, a new approach using relative grid coefficient feature extraction through visual data is proposed. Support vector machine with radial basis kernel is used for the classification of emotions. Preliminary results showed that an average of 89% accuracy was obtained for relative grid based features.
Translating topics to words for image annotation

One of the classic techniques for image annotation is the language translation model. It views an image as a document, i.e., a set of visual words which are obtained by vector quatitizing the image regions generated by unsupervised image segmentation. Annotating images are achieved by translating visual words to textual words, just like translating a document in English to a document in French. In this paper, we also view an image as a document, but we view the annotation processes as two consecutive processes, i.e., document summarization and translation. In the document summarization process, an image document is firstly summarized into its own visual language, which we called visual topics. The translation process translates these visual topics to textual words. Compared to the original translation model, our visual topics learned by the probabilistic latent semantic analysis (PLSA) approach provide an intermediate abstract level of visual description. We show improved annotation performance on the Corel image dataset.
Part-Time Prospects: An International Comparison of Part-Time Work in Europe, North America and the Pacific Rim

The growth in part-time employment has been one of the most striking features in industrialized economies over the past forty years. Part-Time Prospects presents for the first time a systematically comparative analysis of the common and divergent patterns in the use of part-time work in Europe, America and the Pacific Rim. It brings together sociologists and economists in this wide-ranging and comprehensive survey. It tackles such areas as gender issues, ethnic questions and the differences between certain national economies including low pay, pensions and labour standards.
A rare case of infectious colitis

Methicillin-resistant Staphylococcus aureus (MRSA) is responsible for numerous infectious processes. Gastrointestinal tract involvement is rather rare and only a handful of cases of MRSA colitis have been reported in North America. We present a case of MRSA colitis in an adult without apparent risk factors. Abdominal computed tomography (CT) showed thickening of the sigmoid colon, indicative of colitis, and empiric therapy with ciprofloxacin and metronidazole was started. Initial work-up for infection—including blood and stool cultures, and stool Clostridium difficile toxin assay—was negative. The patient’s clinical status improved but his diarrhea did not abate. Repetition of stool culture demonstrated luxuriant growth of MRSA sensitive to vancomycin. Oral vancomycin was administered and the patient’s symptoms promptly ceased.
Foraging behavior can influence dispersal of marine organisms

It has become increasingly apparent that widely varying behaviors of organisms (e.g., ontogenetic depth preference, selective tidal stream transport, position maintenance) can significantly influence dispersal and connectivity between nearby populations. However, more common day‐to‐day behaviors, such as foraging, are often assumed to have little effect on dispersal at larger scales (tens to thousands of kilometers). In this review, we (1) place foraging within the context of current knowledge on behaviors that influence dispersal and transport, (2) provide evidence for foraging behavior as a driving mechanism of distributions, and (3) present a meta‐analysis of dispersal distances estimated for three dispersal hypotheses (passive, swimming directly against currents, and foraging behavior) from 59 studies that document oceanographic conditions and in situ organism distributions. Results of the analyses reveal that when foraging and aggregative behaviors are considered, dispersal distances for larvae and zooplankton could be hundreds of kilometers less than many current dispersal estimates. Reduced transport because of foraging implies that individual populations might be more isolated than currently accepted. These findings suggest that fine‐scale organism behaviors should be considered in models aimed to improve conservation efforts.
An Abnormal Crowd Behavior Detection Algorithm Based on Fluid Mechanics

Abnormal crowd behavior detection is an advanced topic researched in fields of computer vision and digital image processing. The problems such as diversity of monitoring scene, different crowd density and mutual occlusion among crowds etc result in a low recognition rate for abnormal crowd behavior detection. In order to solve these problems, this paper combines a streakline model based on fluid dynamics with an abnormal behavior detection method presented by Hassner et al., and proposes a modified algorithm to improve the recognition accuracy of abnormal crowd behavior. Finally, the validity and accuracy of the algorithm are verified via a large amount of challenging real-world surveillance videos.
A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing

Neural probabilistic parsers are attractive for their capability of automatic feature combination and small data sizes. A transition-based greedy neural parser has given better accuracies over its linear counterpart. We propose a neural probabilistic structured-prediction model for transition-based dependency parsing, which integrates search and learning. Beam search is used for decoding, and contrastive learning is performed for maximizing the sentence-level log-likelihood. In standard Penn Treebank experiments, the structured neural parser achieves a 1.8% accuracy improvement upon a competitive greedy neural parser baseline, giving performance comparable to the best linear parser.
Cloud Hadoop Map Reduce For Remote Sensing Image Analysis

Image processing algorithms related to remote sensing have been tested and utilized on the Hadoop MapReduce parallel platform by using an experimental 112-core high-performance cloud computing system that is situated in the Environmental Studies Center at the University of Qatar. Although there has been considerable research utilizing the Hadoop platform for image processing rather than for its original purpose of text processing, it had never been proved that Hadoop can be successfully utilized for high-volume image files. Hence, the successful utilization of Hadoop for image processing has been researched using eight different practical image processing algorithms. We extend the file approach in Hadoop to regard the whole TIFF image file as a unit by expanding the file format that Hadoop uses. Finally, we apply this to other image formats such as the JPEG, BMP, and GIF formats. Experiments have shown that the method is scalable and efficient in processing multiple large images used mostly for remote sensing applications, and the difference between the single PC runtime and the Hadoop runtime is clearly noticeable.
Dietary intake increases serum levels of carboxymethil-lysine (CML) in diabetic patients.

INTRODUCTION Advanced glycation end products are produced endogenously, in association with hyperglycemia and oxidative stress. They can also be generated during cooking or food processing and, once absorbed, alter protein function and promote inflammation. METHODS We selected 40 healthy male subjects, 17 patients with type 2 diabetes of both sexes and 15 patients with type 1 diabetes of both sexes. Each participant underwent both a food frequency questionnaire (FFQ) and 24-hour dietary recall specially adapted for measuring CML intake, anthropometry, measurement of blood pressure and biochemical parameters in blood and urine. RESULTS Serum CML levels were significantly higher in patients with diabetes compared to healthy subjects (p 0.04), showing a direct relationship between dietary intake and serum levels of CML in T2D patients (r 0.53 p 0.03). sCML levels correlated positively with length of diabetes mellitus, and inversely with body mass index (BMI). The most important dietary factor contributing to raise CML levels in these patients with diabetes was the consumption of milk powder. CONCLUSION Serum levels of CML were found to be higher among diabetic subjects, associated to length of diabetes as expected, but also with the ingestion of foods containing higher amounts of ML. The consumption of milk powder in this group is a major determinant of increased serum levels.
A Review of Multilevel Selective Harmonic Elimination PWM: Formulations, Solving Algorithms, Implementation and Applications

Selective harmonic elimination pulse width modulation (SHE-PWM) offers tight control of the harmonic spectrum of a given voltage and/or current waveform generated by a power electronics converter. Owing to its formulation and focus on elimination of low-order harmonics, it is highly beneficial for high-power converters operating with low switching frequencies. Over the last decade, the application of SHE-PWM has been extended to include multilevel converters. This paper provides a comprehensive review of the SHE-PWM modulation technique, aimed at its application to multilevel converters. This review focuses on various aspects of multilevel SHE-PWM, including different problem formulations, solving algorithms, and implementation in various multilevel converter topologies. An overview of current and future applications of multilevel SHE-PWM is also provided.
What test oracle should I use for effective GUI testing?

Test designers widely believe that the overall effectiveness and cost of software testing depends largely on the type and number of test cases executed on the software. In this paper we show that the test oracle used during testing also contributes significantly to test effectiveness and cost. A test oracle is a mechanism that determines whether software executed correctly for a test case. We define a test oracle to contain two essential parts: oracle information that represents expected output; and an oracle procedure that compares the oracle information with the actual output. By varying the level of detail of oracle information and changing the oracle procedure, a test designer can create different types of test oracles. We design 11 types of test oracles and empirically compare them on four software systems. We seed faults in software to create 100 faulty versions, execute 600 test cases on each version, for all 11 types of oracles. In all, we report results of 660,000 test runs on software. We show (1) the time and space requirements of the oracles, (2) that faults are detected early in the testing process when using detailed oracle information and complex oracle procedures, although at a higher cost per test case, and (3) that employing expensive oracles results in detecting a large number of faults using relatively smaller number of test cases.
Excessive Heat and Respiratory Hospitalizations in New York State: Estimating Current and Future Public Health Burden Related to Climate Change

Background: Although many climate-sensitive environmental exposures are related to mortality and morbidity, there is a paucity of estimates of the public health burden attributable to climate change. Objective: We estimated the excess current and future public health impacts related to respiratory hospitalizations attributable to extreme heat in summer in New York State (NYS) overall, its geographic regions, and across different demographic strata. Methods: On the basis of threshold temperature and percent risk changes identified from our study in NYS, we estimated recent and future attributable risks related to extreme heat due to climate change using the global climate model with various climate scenarios. We estimated effects of extreme high apparent temperature in summer on respiratory admissions, days hospitalized, direct hospitalization costs, and lost productivity from days hospitalized after adjusting for inflation. Results: The estimated respiratory disease burden attributable to extreme heat at baseline (1991–2004) in NYS was 100 hospital admissions, US$644,069 in direct hospitalization costs, and 616 days of hospitalization per year. Projections for 2080–2099 based on three different climate scenarios ranged from 206–607 excess hospital admissions, US$26–$76 million in hospitalization costs, and 1,299–3,744 days of hospitalization per year. Estimated impacts varied by geographic region and population demographics. Conclusions: We estimated that excess respiratory admissions in NYS due to excessive heat would be 2 to 6 times higher in 2080–2099 than in 1991–2004. When combined with other heat-associated diseases and mortality, the potential public health burden associated with global warming could be substantial.
Coping with Unpredictability: Dopaminergic and Neurotrophic Responses to Omission of Expected Reward in Atlantic Salmon (Salmo salar L.)

Comparative studies are imperative for understanding the evolution of adaptive neurobiological processes such as neural plasticity, cognition, and emotion. Previously we have reported that prolonged omission of expected rewards (OER, or ‘frustrative nonreward’) causes increased aggression in Atlantic salmon (Salmo salar). Here we report changes in brain monoaminergic activity and relative abundance of brain derived neurotrophic factor (BDNF) and dopamine receptor mRNA transcripts in the same paradigm. Groups of fish were initially conditioned to associate a flashing light with feeding. Subsequently, the expected food reward was delayed for 30 minutes during two out of three meals per day in the OER treatment, while the previously established routine was maintained in control groups. After 8 days there was no effect of OER on baseline brain stem serotonin (5-HT) or dopamine (DA) activity. Subsequent exposure to acute confinement stress led to increased plasma cortisol and elevated turnover of brain stem DA and 5-HT in all animals. The DA response was potentiated and DA receptor 1 (D1) mRNA abundance was reduced in the OER-exposed fish, indicating a sensitization of the DA system. In addition OER suppressed abundance of BDNF in the telencephalon of non-stressed fish. Regardless of OER treatment, a strong positive correlation between BDNF and D1 mRNA abundance was seen in non-stressed fish. This correlation was disrupted by acute stress, and replaced by a negative correlation between BDNF abundance and plasma cortisol concentration. These observations indicate a conserved link between DA, neurotrophin regulation, and corticosteroid-signaling pathways. The results also emphasize how fish models can be important tools in the study of neural plasticity and responsiveness to environmental unpredictability.