Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward a Local Perspective on Online Collaboration

We study the structural properties of large scale collaboration in online communities of innovation and the role that position in the community plays in determining knowledge contribution. Contrary to previous research, we argue for a more local perspective when examining online collaboration. We demonstrate that a member's centrality and spanning within his/her local neighborhood is a better predictor of contribution than global centrality and spanning within the whole community. We contribute both theoretically and methodologically to research on large scale collaboration. On the theoretical front, a local view of position implies a more confined and local organization of work in online communities than previously thought. From a methodological perspective, evaluating the local structure of large networks involves radically different algorithms that have only recently become feasible with the increase of processing power.
Dysregulated Autophagy Contributes to Podocyte Damage in Fabry’s Disease

Fabry’s disease results from an inborn error of glycosphingolipid metabolism that is due to deficiency of the lysosomal hydrolase α-galactosidase A. This X-linked defect results in the accumulation of enzyme substrates with terminally α-glycosidically bound galactose, mainly the neutral glycosphingolipid Globotriaosylceramide (Gb3) in various tissues, including the kidneys. Although end-stage renal disease is one of the most common causes of death in hemizygous males with Fabry’s disease, the pathophysiology leading to proteinuria, hematuria, hypertension, and kidney failure is not well understood. Histological studies suggest that the accumulation of Gb3 in podocytes plays an important role in the pathogenesis of glomerular damage. However, due to the lack of appropriate animal or cellular models, podocyte damage in Fabry’s disease could not be directly studied yet. As murine models are insufficient, a human model is needed. Here, we developed a human podocyte model of Fabry’s disease by combining RNA interference technology with lentiviral transduction of human podocytes. Knockdown of α-galactosidase A expression resulted in diminished enzymatic activity and slowly progressive accumulation of intracellular Gb3. Interestingly, these changes were accompanied by an increase in autophagosomes as indicated by an increased abundance of LC3-II and a loss of mTOR kinase activity, a negative regulator of the autophagic machinery. These data suggest that dysregulated autophagy in α-galactosidase A-deficient podocytes may be the result of deficient mTOR kinase activity. This finding links the lysosomal enzymatic defect in Fabry’s disease to deregulated autophagy pathways and provides a promising new direction for further studies on the pathomechanism of glomerular injury in Fabry patients.
Improving distributed space-time coding through combining at the multiple-antenna relay

Distributed space-time coding (DSTC) is a cooperative scheme proposed for wireless relay networks that achieves full diversity when the transmit power is infinitely high. In this paper, we propose to employ maximum-ratio combining (MRC) at the multiple-antenna relay to improve the performance of DSTC. Simulations and analysis show that the new scheme achieves a much better performance in relay networks than DSTC. In some network scenarios, it can even improve the scaling of the error rate with the transmit power. Furthermore, the new scheme requires a shorter training interval than DSTC.
Expression Profiles and Biological Roles of miR-196a in Swine

MicroRNAs (miRNAs) are a class of small non-coding RNA molecules, which play important roles in animals by targeting mRNA transcripts for translational repression. Recent studies have demonstrated that miRNAs are involved in regulation of adipocyte development. The expression of miR-196a in different porcine tissues and developing fat tissues was detected, and gene ontology (GO) term enrichment was then used to predict the expression profiles and potential biological roles of miR-196a in swine. To further verify the roles of miR-196a in porcine adipocyte development, a recombinant adenovirus encoding miR-196a gene (Ad-miR-196a) was constructed and used to study the effect of miR-196a on preadipocyte proliferation and differentiation. Here, our data demonstrate that miR-196a displays a tissue-specific expression pattern and has comprehensive biological roles in swine, especially in adipose development. In addition, overexpression of miR-196a had no effect on preadipocyte proliferation, but induced preadipocyte differentiation by increasing expression of adipocyte specific markers, lipid accumulation and triglyceride content. These data represent the first demonstration of miR-196a expression profiles and roles in swine, thereby providing valuable insight into the functions of miR-196a in adipocyte biology.
Tracking points on deformable objects with ranklets

We present a robust algorithm for point tracking on deformable objects. The key elements are the use of orientation selective rank features (ranklets), local filter adaptation and dynamic model update. A multi-scale vector of ranklets is used to encode a neighbourhood of each tracked point. The shape of the filters is optimised for each neighbourhood independently. Substantial appearance variations are catered for by maintaining a stack of models for each tracked point. This enables the system to recalibrate whenever the object reverts to its original appearance.
Handwritten Character Recognition In Malayalam Scripts- A Review

Handwritten character recognition is one of the most challenging and ongoing areas of research in the field of pattern recognition. HCR research is matured for foreign languages like Chinese and Japanese but the problem is much more complex for Indian languages. The problem becomes even more complicated for South Indian languages due to its large character set and the presence of vowels modifiers and compound characters. This paper provides an overview of important contributions and advances in offline as well as online handwritten character recognition of Malayalam scripts.
An Empirical Mode Decomposition Approach to Peak Load Demand Forecasting

An accurate and reliable electric load forecasting model is very essential for efficient and effective operation of the Electricity Supply Industry (ESI). Several single models have been developed for electric load forecast for ESI but it is becoming increasingly difficult to obtain accurate forecast by these models because of the volatility coupled with the nonlinear and non- stationary nature of electric load series. In this paper, we propose a novel Electric Peak load forecasting model that combines empirical mode decomposition (EMD) and artificial neural network (ANN). The propose model involves three stages of development. In the first stage, the historical load data obtained from Power holding company of Nigeria (PHCN), Bida is decomposed into several intrinsic mode functions and a residue component using the EMD sifting process. The second stage involves building separate neural network models for each of these IMFS and residue component and the last stage involves combining the predictions from these models and making forecast. When the forecast from this model is compared with that obtained from a conventional neural network model, it was observed that the proposed model outperforms the conventional neural network model, by 2.3% for the whole year model and by 1.8% for the weekday model, judging by the forecast accuracy of both models.
DCAS-based concurrent deques supporting bulk allocation

We present a lock-free implementation of a dynamically sized double-ended queue (deque) that is based on the double compare-and-swap (DCAS) instruction. This implementation improves over the best previous one by allowing storage to be allocated and freed in bulk when the size of the deque changes significantly, and to avoid invocation of the storage allocator at all while the size remains relatively stable. We achieved this implementation in two steps by first solving the easier problem of implementing the deque for a garbage-collected environment, and then applying the Lock-Free Reference Counting methodology we recently proposed in order to achieve a version independent of garbage collection.
A novel low voltage and high speed CMOS charge pump circuit

A novel low voltage, high speed CMOS charge pump circuit for PLL is designed in the paper. The new structure refrains from the common spurious jump phenomenon, and is designed in N well mixed-signal Chartered 0. 35μm CMOS process, simulated using Hspice. The results show the new circuit can operate under a 1V power supply, does not suffer from any waveform jitter. The output voltage has a relatively wide range, from 120mV up to 980mV. The operating frequency is 500MHz, and power consumption is low to 150μW.
Financial Engineering Estimation of Minimum Risk Hedge Ratio

In this paper, the financial engineering minimum risk-based portfolio hedging model is first analyzed. It is then followed by the investigation on various major estimation methods for the minimum risk hedge ratio. The results revealed in the current study show that the HR obtained by the ordinary least squares (OLS) model is maximal and the out-of-sample hedging performance is the best; however, the hedging effectiveness is not sufficiently stable for both the out-of-sample and in-sample estimation. © 2011. Selection and peer-review under responsibility of Desheng Dash Wu Copyright transferred and reserved with RiskLab.
An Enhanced Approach for Cases Indexing and Retrieval in Case-Based Reasoning

-Case-Based Reasoning (CBR) is one of most successful applied AI technologies of recent years. Commercial and industrial applications can be developed rapidly and existing corporate databases can be used as knowledge resources. Helpdesks and diagnostic systems are the most common applications. CBR techniques can be applied to large and complex datasets. In this paper we are aiming to find each unique feature for each case in a Case-Base using a technique to build Power Set Tree (PST) using adding feature, Each possible reduct is added to a node in the tree, After building the PST we will perform a Breadth-First search to prune this tree and reduce the number of possibilities by deleting each unique feature node and its children, that would help in getting the most accurate unique features with minimum number of comparisons. Key-Words: -Case-Based Reasoning – Refinement – Indexing – Cases Representation
Nearby Construction Impedes the Progression to Overt Autoimmune Diabetes in NOD Mice

Construction nearby animal houses has sporadically been reported to affect various aspects of animal health. Most of the reports have focussed on the impact on stress hormone levels and the hypersensitivity of animals relative to humans. There has also been an anecdotal report on the impact of construction on autoimmune diabetes in NOD mice. Here, we describe that nearby construction significantly impedes the progression to overt diabetes in female NOD mice offspring. We demonstrate that this was not due to a genetic drift or to particularities associated with our specific mouse colony. Interestingly, although the glycemia levels remained low in mice born from mothers subject to construction stress during gestation, we detected an active autoimmune reaction towards pancreatic islet cells, as measured by both the degree of insulitis and the presence of insulin autoantibody levels in the serum. These results suggest that the external stress imposed during embryonic development does not prevent but significantly delays the autoimmune process. Together, our findings emphasize the impact of surrounding factors during in vivo studies and are in agreement with the hypothesis that both environmental and genetic cues contribute to autoimmune diabetes development.
3D MEMS Simulation Modeling Using Modified Nodal Analysis

The modeling, simulation, and experimental verification of several MEMS devices are presented. Simulated results include 3D mode analysis, residual stress effects, thermal expansion, nonlinear deflections, time-varying electrostatic forces, process sensitivities, induced currents, and the transient performance in accelerated reference frames. To simulate the performance of these MEMS devices a modified nodal analysis approach is used to formulate a system of ODEs that is solved by static, steady state, and transient solvers. Index Terms SUGAR, modified nodal analysis, static, steady state, transient, accelerating frames, sensitivity.
Domain-Specific QA for the Construction Sector

Previous research on Question-Answering (QA) has focused on general domain questions. The general approach is to first recognize Named Entities (NE) in both texts and questions; then the most relevant answers (or passages) are selected (after an IR selection) according to the type of question and the NEs included in the possible answers. In this paper, we extend this general approach to domain-specific questions in the construction sector. This extension allows us to answer questions such as “What material is appropriate for . ”, which cannot be answered by a general QA system. Our approach is based on a domain-specific thesaurus, which contains a large set of domain-specific concepts organized into a hierarchy. Generic concepts such as “material” are considered as semantic categories. Our experiments on a technical corpus in construction show that this approach is effective: using our extension, we can obtain improvements on Mean Reciprocal Rank (MRR) of about 10%.
In Vivo Effects of Histone H3 Depletion on Nucleosome Occupancy and Position in Saccharomyces cerevisiae

Previous studies in Saccharomyces cerevisiae established that depletion of histone H4 results in the genome-wide transcriptional de-repression of hundreds of genes. To probe the mechanism of this transcriptional de-repression, we depleted nucleosomes in vivo by conditional repression of histone H3 transcription. We then measured the resulting changes in transcription by RNA–seq and in chromatin organization by MNase–seq. This experiment also bears on the degree to which trans-acting factors and DNA–encoded elements affect nucleosome position and occupancy in vivo. We identified ∼60,000 nucleosomes genome wide, and we classified ∼2,000 as having preferentially reduced occupancy following H3 depletion and ∼350 as being preferentially retained. We found that the in vivo influence of DNA sequences that favor or disfavor nucleosome occupancy increases following histone H3 depletion, demonstrating that nucleosome density contributes to moderating the influence of DNA sequence on nucleosome formation in vivo. To identify factors important for influencing nucleosome occupancy and position, we compared our data to 40 existing whole-genome data sets. Factors associated with promoters, such as histone acetylation and H2A.z incorporation, were enriched at sites of nucleosome loss. Nucleosome retention was linked to stabilizing marks such as H3K36me2. Notably, the chromatin remodeler Isw2 was uniquely associated with retained occupancy and altered positioning, consistent with Isw2 stabilizing histone–DNA contacts and centering nucleosomes on available DNA in vivo. RNA–seq revealed a greater number of de-repressed genes (∼2,500) than previous studies, and these genes exhibited reduced nucleosome occupancy in their promoters. In summary, we identify factors likely to influence nucleosome stability under normal growth conditions and the specific genomic locations at which they act. We find that DNA–encoded nucleosome stability and chromatin composition dictate which nucleosomes will be lost under conditions of limiting histone protein and that this, in turn, governs which genes are susceptible to a loss of regulatory fidelity.
On the Interactions of Reward, Time, and Effort in Human Movement

The goal of our actions should be to maximize reward while minimizing effort and time. An individual’s movements in response to changing costs and rewards can reveal how they value these contributing variables. The motivation of this dissertation is to better understand how reward, effort, and time interact to determine an individual’s movement preferences. The first study used an objective measure of effort, metabolic cost, to quantify subjective effort valuation in healthy young adults in a low-effort reaching task. Participants were idiosyncratic in how they valued effort with the group as a whole representing effort objectively. The second study investigated the costs of effort and time in gait choice by probing preferences to walk or run across changing relative distances. I found that effort alone could not explain choices. Participants also considered time, with their tendency to discount time predicting running velocity. These first two studies demonstrate that effort is essential in explaining movement behaviors with its exact contribution varying across individuals. In the third and fourth studies I investigated the role of reward in discounting effort costs in movements within individuals. The third study consisted of young adults reaching towards alternating quadrants where some quadrants were paired with reward. Expectation of reward led participants to react earlier and move faster, discounting the cost of effort to arrive at the reward sooner. Movements toward reward were also less variable, violating the traditional speed-accuracy trade-off. For the final study, I probed whether aging would influence an individual’s willingness to alter reaching behavior in response to reward. Older adults exhibited a reduced response to reward. While reward led to faster reaction times, I did not observe an increase in movement speed. I found that metabolic cost of reaching was not elevated in older adults compared to young, suggesting that the reduced response in older adults could not be explained by an increased effort cost, but rather a reduced sensitivity to reward. Collectively, these studies advance our understanding of how reward, time, and effort interact in explaining movement preferences in changing cost and reward landscapes both within and across human populations.
Perceptions of Newly Registered Thai Nurses about Workplace Bullying

Objective: To explore the perceptions of workplace bullying among newly registered nurses (RNs) who had the practice experience of 1 – 3 years in Thailand. Method: A cross-sectional study was conducted using multi-stage random sampling of nursing staff working in eight regional hospitals. Data were collected using the Negative Acts Questionnaire-Revised with a Cronbach’s alpha of 0.90. Descriptive statistics was used to analyze the data. Results: The response rate was 91.7% (N = 220). It was found that 18.2% of the new RNs (n = 40) indicated that they were occasionally bullied and 12.3% (n = 27) were bullied every day. Some RNs (20%) acknowledged that they had experienced workplace bullying. The most perceived perpetrators for bullying were their own colleagues. Specific types of negative behaviors were identified. Conclusion: Workplace bullying towards newly registered nurses existed, although less frequently than expected. Specific negative behaviors related to colleagues’ bullying could be used as a guidance for nurse managers to solve this important problem. Keywords: workplace bullying, newly registered nurses, Thailand
New insights into the promoterless transcription of DNA coligo templates by RNA polymerase III

Chemically synthesized DNA can carry small RNA sequence information but converting that information into small RNA is generally thought to require large double-stranded promoters in the context of plasmids, viruses and genes. We previously found evidence that circularized oligodeoxynucleotides (coligos) containing certain sequences and secondary structures can template the synthesis of small RNA by RNA polymerase III in vitro and in human cells. By using immunoprecipitated RNA polymerase III we now report corroborating evidence that this enzyme is the sole polymerase responsible for coligo transcription. The immobilized polymerase enabled experiments showing that coligo transcripts can be formed through transcription termination without subsequent 3′ end trimming. To better define the determinants of productive transcription, a structure-activity relationship study was performed using over 20 new coligos. The results show that unpaired nucleotides in the coligo stem facilitate circumtranscription, but also that internal loops and bulges should be kept small to avoid secondary transcription initiation sites. A polymerase termination sequence embedded in the double-stranded region of a hairpin-encoding coligo stem can antagonize transcription. Using lessons learned from new and old coligos, we demonstrate how to convert poorly transcribed coligos into productive templates. Our findings support the possibility that coligos may prove useful as chemically synthesized vectors for the ectopic expression of small RNA in human cells.
Collaborative Sensing for Urban Transportation

In this paper, we overview the current status of research in the field of collaborative sensing for urban transportation. We first classify the different types of sensors that can be relevant in collaborative urban sensing and then we analyze three different topics: parking spaces, traffic, and trajectories. We discuss issues regarding the sensing and data sharing approaches as well as relevant use cases. Finally, we identify some research challenges and trends.
Video compression complexity reduction with adaptive down-sampling

Encoding video sequences is a computation-demanding task in high-performance codecs. Optimizing this stage may result in a substantial encoding speed-up. In this paper, we propose a faster approach to encode sequences with the H.264/AVC codec, using mixed-resolution. By having some of the frames down-sampled, the overall computation is reduced, without greatly affecting the rate-distortion performance, which remains evaluated using the sequence's original resolution. In addition to this approach, a method which regards only the most frequent prediction modes of a frame is incorporated, so that the combined complexity reduction can be significant with negligible performance losses.
Named Entity Recognition Using a New Fuzzy Support Vector Machine

Recognizing and extracting exact name entities, like Persons, Locations, Organizations, Dates and Times are very useful to mining information from electronics resources and text. Learning to extract these types of data is called Named Entity Recognition(NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering and Summarization Systems, Information Retrieval and Information Extraction, Machine Translation, Video Annotation, Semantic Web Search and Bioinformatics, especially Gene identification, proteins and DNAs names. Nowadays more researchers use three type of approaches namely, Rule-base NER, Machine Learning-base NER and Hybrid NER to identify names. Machine learning method is more famous and applicable than others, because it’s more portable and domain independent. Some of the Machine learning algorithms used in NER methods are, support vector machine(SVM), Hidden Markov Model, Maximum Entropy Model (MEM) and Decision Tree. In this paper, we review these methods and compare them based on precision in recognition and also portability using the Message Understanding Conference(MUC) named entity definition and its standard data set to find their strength and weakness of each these methods. We have improved the precision in NER from text using the new proposed method that calls FSVM for NER. In our method we have employed Support Vector Machine as one of the best machine learning algorithm for classification and we contribute a new fuzzy membership function thus removing the Support Vector Machine’s weakness points in NER precision and multi classification. The design of our method is a kind of One-Against-All multi classification technique to solve the traditional binary classifier in SVM.