Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-wide assessment of genetic risk for systemic lupus erythematosus and disease severity

Abstract Using three European and two Chinese genome-wide association studies (GWAS), we investigated the performance of genetic risk scores (GRSs) for predicting the susceptibility and severity of systemic lupus erythematosus (SLE), using renal disease as a proxy for severity. We used four GWASs to test the performance of GRS both cross validating within the European population and between European and Chinese populations. The performance of GRS in SLE risk prediction was evaluated by receiver operating characteristic (ROC) curves. We then analyzed the polygenic nature of SLE statistically. We also partitioned patients according to their age-of-onset and evaluated the predictability of GRS in disease severity in each age group. We found consistently that the best GRS in the prediction of SLE used SNPs associated at the level of P < 1e−05 in all GWAS data sets and that SNPs with P-values above 0.2 were inflated for SLE true positive signals. The GRS results in an area under the ROC curve ranging between 0.64 and 0.72, within European and between the European and Chinese populations. We further showed a significant positive correlation between a GRS and renal disease in two independent European GWAS (Pcohort1 = 2.44e−08; Pcohort2 = 0.00205) and a significant negative correlation with age of SLE onset (Pcohort1 = 1.76e−12; Pcohort2 = 0.00384). We found that the GRS performed better in the prediction of renal disease in the ‘later onset’ compared with the ‘earlier onset’ group. The GRS predicts SLE in both European and Chinese populations and correlates with poorer prognostic factors: young age-of-onset and lupus nephritis.
The End of the Rainbow: In search of crossing points between organizations and play

Against the backdrop of an unstable economic and social environment, managers and scholars of organization have been taking an increasing interest in computer games as a source of inspiration. This paper reviews three perspectives that have been taken when attempting to enrich organizations with elements of computer games. We consider the design of computer games to be the most interesting of the three perspectives and present two case studies in which game design principles were applied in an organizational setting. The studies show the value of such a design process as an instrument for exploring a complex organizational system. Furthermore, the use of isolated game elements in a finite organizational context was shown to be an effective way to create effects such as transparency and curiosity.
An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data

MOTIVATION Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data pose a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target. RESULTS In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine. In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature.
WAVELET REGRESSION IN RANDOM DESIGN WITH HETEROSCEDASTIC DEPENDENT ERRORS

We investigate function estimation in nonparametric regression models with random design and heteroscedastic correlated noise. Adaptive properties of warped wavelet nonlinear approximations are studied over a wide range of Besov scales, f ∈ B S π,r , and for a variety of L P error measures. We consider error distributions with Long-Range-Dependence parameter α, 0 2, it is seen that there are three rate phases, namely the dense, sparse and long range dependence phase, depending on the relative values of s, p, π and α. Furthermore, we show that long range dependence does not come into play for shape estimation f - ∫ f. The theory is illustrated with some numerical examples.
Towards Automatic Parallelisation for Multi-Processor DSPs

This paper describes a preliminary compiler based approach to achieving high performance DSP applications by automatically mapping C programs to multi-processor DSP systems. DSP programs typically contain pointer based memory accesses making automatic parallelisation difficult. This paper presents a new method to convert a restricted class of pointer-based memory accesses into array accesses with explicit index functions suitable for parallelisation. Different parallelisation approaches suitable for multi-processor DSPs are considered. We implemented our pointer conversion algorithm in the prototype Octave compiler where experimental results demonstrated that our technique increases the number of parallelisable loops from 6 to 24 for 11 of the DSPstone benchmarks. Furthermore our technique is shown to also improve the actual performance of DSP codes on single processor systems decreasing execution time by up to 33%.
Information Retrieval Applications in Software Development

Information retrieval (IR) extracts and organizes natural language information found in unstructured text. Many of the challenges faced by software engineers can be addressed using IR techniques on the unstructured text provided by source code and its associated documents. A survey of IR-based techniques applied to software engineering challenges during the initial development process is presented. In particular, the following problems are considered: requirements discovery, maintaining software repositories, establishing traceability links, efficient software reuse, and effective software metrics. These techniques highlight the bright future that IR brings to addressing SE problems.
Toward Hierarchical Mobile Edge Computing: An Auction-Based Profit Maximization Approach

The multitiered concept of Internet of Things (IoT) devices, cloudlets, and clouds is facilitating a user-centric IoT. However, in such three tier network, it is still desirable to investigate efficient strategies to offer the computing, storage, and communications resources to the users. To this end, this paper proposes a new hierarchical model by introducing the concept of field, shallow, and deep cloudlets where the cloudlet tier itself is designed in three hierarchical levels based on the principle of LTE-advanced backhaul network. Accordingly, we explore a two time scale approach in which the computing resources are offered in an auction-based profit maximization manner and then the communications resources are allocated to satisfy the users’ quality of service.
Improving Sentiment Analysis in an Online Cancer

Online Health Communities is a major source for patients and their family members in the process of gathering information and seeking social support. The American Cancer Society Cancer Survivors Network has many users and presents a large number of users' interactions with regards to coping with cancer. Sentiment analysis is an important step in understanding participants' needs and concerns and the impact of users' responses on other members. We present an automated approach for sentiment analysis in an online cancer survivor community and compare it with a previous sentiment analysis approach. Both approaches are machine learning based and are tested on the same dataset. However, this work uses features derived from a dynamic sentiment lexicon, whereas the previous work uses a general sentiment lexicon to extract features. Tested on several classifiers, with only six features (versus thirteen), our results show 2.3% improvement on average, in terms of accuracy, and greater improvement in F-measure and AUC. An additional experiment was conducted that showed a positive impact of dimensionality reduction by extracting abstract features, instead of using term frequency (TF) vector space as attribute values. Keywords—sentiment analysis; dynamic sentiment lexicon; abstract features.
Tutorial on VLSI Partitioning

The tutorial introduces the partitioning with applications to VLSI circuit designs. The problem formulations include two-way, multiway, and multi-level partitioning, partitioning with replication, and performance driven partitioning. We depict the models of multiple pin nets for the partitioning processes. To derive the optimum solutions, we describe the branch and bound method and the dynamic programming method for a special case of circuits. We also explain several heuristics including the group migration algorithms, network flow approaches, programming methods, Lagrange multiplier methods, and clustering methods. We conclude the tutorial with research directions.
Sensitivity of meteoric smoke distribution to microphysical properties and atmospheric conditions

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Sensitivity of meteoric smoke distribution to microphysical properties and atmospheric conditions L. Megner, M. Rapp, J. Gumbel
A particle swarm optimized Fuzzy Neural Network for bankruptcy prediction

Since the excellent performances of treating nonlinear data with self-learning capability, the neural networks (NNs) are wildly use in financial prediction problem. But the NNs more or less suffer from the slow convergence, “black-box” i.e., it is almost impossible to analysis them for how they work. The Fuzzy Neural Networks(FNN) allow to add rules to neural networks. This avoids the black-box but lacks of effective learning capability. To overcome these drawbacks, in this study a particle swarm optimization algorithm is proposed first, then combined with the fuzzy neural network to predict corporation bankruptcy. The results indicate that the predictive accuracies obtained from PSO-FNN are much higher than the ones obtained from NNs. To make this clearer, an illustrative example is also demonstrated in this study.
Family Control and the Rent–Seeking Society

A high level of trust within a small elite, like a low level of trust in society at large, may be a serious impediment to economic development. This is because such concentrated high trust among the elite promotes political rent seeking, known to retard growth. We propose that entrusting the governance of a country's great corporations to a few wealthy families promotes this undesirable distribution of trust. Preliminary empirical evidence and arguments grounded in game theory support this view.
Sustainable Skills for the World of Work in the Digital Age

The skills that small and medium‐sized enterprises will need in the near future are an essential research topic that has not yet been appropriately addressed. The current paper aims to fill this gap in the literature. Explicitly, the study aimed to identify the critical skills for organizations and the disruptive technological skills. To this end, a literature review was conducted, and an online survey explored the main critical skills for the future and the disruptive technological skills contributing to achieving sustainable employability. The critical skills identified in the study were complex problem‐solving, critical thinking, creativity, people management, coordinating with others, emotional intelligence, judgement and decision making, service orientation and negotiating and cognitive flexibility. As for the disruptive technological skills, these were artificial intelligence, nanotechnology, robotization, internet of things, augmented reality and digitalization. Findings may assist companies to develop the skills needed to respond to the challenges of a continuously changing business environment. © 2018 John Wiley & Sons, Ltd.
The Analysis of Amil Zakat Institution/Lembaga Amil Zakat (LAZ) Accountability toward Public Satisfaction and Trust

Abstract Indonesia has a high potency of zakat fund but the result of its collection shows the great gap compared with the potency. The current issue of Amil Zakat Institution/Lembaga Amil Zakat (LAZ) is about their public trust and public satisfaction. The aim of the research is to examine an impact of LAZ’s accountability on public satisfaction and public trust among muzakki in Indonesia. This research collects data using questionnaire from muzakki who paid their zakat in LAZ’s. This study uses SEM-PLS to analyze data. The result shows (1) significant influence on the LAZ’s accountability to public satisfaction, (2) public satisfaction on LAZ has a significant influence to public trust.Abstrak Indonesia memiliki potensi dana zakat yang tinggi, namun hasil dari pengumpulannya menunjukkan ada perbedaan yang besar dibandingkan dengan potensinya. Permasalahan yang dihadapi oleh lembaga amil zakat (LAZ) sekarang adalah terkait dengan kepercayaan publik dan kepuasan publik. Tujuan penelitian ini adalah menguji pengaruh akuntabilitas LAZ terhadap kepuasan publik dan kepercayaan publik. Penelitian ini mengumpulkan data menggunakan kuisioner dari muzakki yang membayar zakat di LAZ. Penelitian ini menganalisa data yang terkumpul menggunakan SEM-PLS. Hasil penelitian menunjukkan (1) adanya pengaruh yang signifikan dari akuntabilitas lembaga amil zakat terhadap kepuasan publik, (2) kepuasan publik atas lembaga amil zakat mempunyai pengaruh signifikan atas kepercayaan publik.
Efficient Alignment Between Event Logs and Process Models

The aligning of event logs with process models is of great significance for process mining to enable conformance checking, process enhancement, performance analysis, and trace repairing. Since process models are increasingly complex and event logs may deviate from process models by exhibiting redundant, missing, and dislocated events, it is challenging to determine the optimal alignment for each event sequence in the log, as this problem is NP-hard. Existing approaches utilize the cost-based <inline-formula><tex-math notation="LaTeX">$A^*$</tex-math><alternatives> <inline-graphic xlink:href="song-ieq1-2601094.gif"/></alternatives></inline-formula> algorithm to address this problem. However, scalability is often not considered, which is especially important when dealing with industrial-sized problems. In this paper, by taking advantage of the structural and behavioral features of process models, we present an efficient approach which leverages effective heuristics and trace replaying to significantly reduce the overall search space for seeking the optimal alignment. We employ real-world business processes and their traces to evaluate the proposed approach. Experimental results demonstrate that our approach works well in most cases, and that it outperforms the state-of-the-art approach by up to 5 orders of magnitude in runtime efficiency.
Uncertainty about the Bank ' s target for policy

The aim of the present analysis is to shed light on the question whether Central Banks should publish their macroeconomic forecasts, and what could possibly be gained in monetary policy if they did so. We show that disclosing the Central Bank’s assessment of the prevailing inflationary pressures in the form of a forecast improves macroeconomic performance even if this assessment is imprecise. We are also interested in finding out the useful content of the forecasts, if published, and answering the question whether it makes a difference if these official forecasts are "unconditional" in the sense of incorporating the Central Bank's forecasts of its own policy as well, or "conditional" on some other policy assumption. Possible conditional alternatives may include assuming unchanged instruments, however specified, or assuming the kind of policy that the private sector is estimated to expect. The analysis comes out in favour of publishing unconditional forecasts, which reveal the intended results of monetary policy. A discussion of some practical issues related to publishing official macroeconomic forecasts is also provided.
Neuropsychological correlates of a right unilateral lacunar thalamic infarction

OBJECTIVES To report on a patient with a lacunar infarction in the right intralaminar nuclei of the thalamus. The role of the thalamic intralaminar nuclei in cognitive function is as yet insufficiently known. The patient described has shown signs of apathy and loss of initiative, in combination with cognitive deficits, which have persisted essentially unaltered up to the present day since an abrupt onset 17 years ago. METHODS High resolution MRI was performed to show the extent of the lesion; a combination of published and experimental neuropsychological techniques was administered to show the nature of the cognitive defects; Single photon emission computed tomography (SPECT) was employed to obtain a measure of cortical perfusion. RESULTS Brain MRI disclosed an isolated lacunar infarction in the dorsal caudal intralaminar nuclei of the thalamus. Neuropsychological evaluation indicated problems with attention and concentration, executive disturbances, and memory deficits both in the visual and verbal domains. The memory deficits could not be attributed to problems in the early stages of information processing, and are hence regarded as resulting from a failure of retrieval rather than encoding or storage. Brain SPECT disclosed a hypoperfusion of the right frontal cortex. CONCLUSION The data indicate that the cognitive profile is the result of a dysfunction of executive functions. This is corroborated by the finding of decreased blood flow in the right frontal cortex, and by evidence from the neuroanatomical literature. Thus the dysexecutive symptoms are thought to be caused by disconnection of the prefrontal cortex from the brainstem activating nuclei through the strategic localisation of the right thalamic infarction.
Arabic version geriatric depressive scale-15 for Iraqi elderly dwellers in Al-Najaf city: Validity and reliability

The Geriatric Depression Scale (GDS-15) is regarded as the most widely used screening tools and easy to administer. The Geriatric Depressive Scale-15 short version was translated to the Arabic language by a researcher and was assessed by psychiatric specialists, then retranslated to the English language by an English language specialist, and after acceptance of the last form, and elderly was selected (samples size was (n=279) elderly persons (138 male and 141 female) form hospitals and general community setting, each elderly was assessed by using GDS-15 and then assessed by using the ICD-10 criteria for major depressive episode to detect presence or absence of depression. The average age of study group were (71.84) years, diagnosis of major depression according to Arabic GDS-15 short version for Iraqi elderly (44.4% for positive and 55.6 for negative) and according to ICD-10 criteria for depression (38% for positive and 62% for negative). Also, the study found that Arabic GDS-15 for Iraqi elderly had high specificity and sensitivity and interrater reliability (k= 0.72). This type of Arabic version GDS-15 is valid and reliable in the assessment of depression among Iraqi elderly dwellers in Alnajaf city for different marital status, educational levels, residencies, and occupations.
Discovery of an alien species of mayfly in South America (Ephemeroptera)

Abstract Despite its wide, almost worldwide distribution, the mayfly genus Cloeon Leach, 1815 (Ephemeroptera: Baetidae) is restricted in the Western hemisphere to North America, where a single species is reported. In the Neotropics, except for some species wrongly attributed to the genus in the past, there are no records of Cloeon. Recently, however, specimens of true Cloeon were collected along the coast of Espírito Santo, Southeastern Brazil. In order to verify the hypothesis that this species was recently introduced to Brazil, our aim was to identify the species based on morphological and molecular characters and to confirm the presence of true representatives of the genus in the Neotropics. Our results revealed that the specimens found in Brazil belong to the Afrotropical species C. smaeleni Lestage, 1924. The identity of the species, its distribution, along with its previous absence in regularly sampled sites, is a clear sign that the specimens of C. smaeleni found in Espírito Santo are introduced, well established, and that the colonization took place very recently.
LP decoding of LDPC codes in HARQ systems

The combination of low density parity check (LDPC) Codes and Automatic Repeat reQuest (ARQ) has shown to be a promising option to increase the throughput of a communication system. A recently proposed method uses the decoding result to request unreliable bits for retransmission (reliability-based Hybrid ARQ). Commonly, Belief Propagation is used to decode LDPC codes. The result of this algorithm naturally provides an estimate for the probability of a bit to be erroneous. Another possibility to decode LDPC codes is via linear programming (LP) decoding. Despite some drawbacks (e.g. in terms of implementation complexity), linear programming decoding provides an interesting alternative to Belief Propagation. This work investigates the feasibility of combining LP decoding with ARQ. In lack of a probability measure for the decoded bits, when using LP decoding, request criteria using the integrality of the values in the LP solution are proposed. We present statistical investigations on the proposed criteria and the results of throughput simulations demonstrating the gains achievable by the proposed methods.
Augmented letters: mnemonic gesture-based shortcuts

We propose Augmented Letters, a new technique aimed at augmenting gesture-based techniques such as Marking Menus [9] by giving them natural, mnemonic associations. Augmented Letters gestures consist of the initial of command names, sketched by hand in the Unistroke style, and affixed with a straight tail. We designed a tentative touch device interaction technique that supports fast interactions with large sets of commands, is easily discoverable, improves user's recall at no speed cost, and supports fluid transition from novice to expert mode. An experiment suggests that Augmented Letters outperform Marking Menu in terms of user recall.
Generalized scaling for models with multiple absorbing states

At a continuous transition into a non-unique absorbing state, particle systems may exhibit non-universal critical behaviour, in apparent violation of hyperscaling. We propose a generalized scaling theory for dynamic critical behaviour at a transition to an absorbing state, which is capable of describing exponents which vary according to the initial configuration. The resulting hyperscaling relation is supported by simulations of two lattice models.