Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Bayes factors for testing hypotheses about intervention effectiveness in addictions research

Abstract Background and Aims It has been proposed that more use should be made of Bayes factors in hypothesis testing in addiction research. Bayes factors are the ratios of the likelihood of a specified hypothesis (e.g. an intervention effect within a given range) to another hypothesis (e.g. no effect). They are particularly important for differentiating lack of strong evidence for an effect and evidence for lack of an effect. This paper reviewed randomized trials reported in Addiction between January and June 2013 to assess how far Bayes factors might improve the interpretation of the data. Methods Seventy‐five effect sizes and their standard errors were extracted from 12 trials. Seventy‐three per cent (n = 55) of these were non‐significant (i.e. P > 0.05). For each non‐significant finding a Bayes factor was calculated using a population effect derived from previous research. In sensitivity analyses, a further two Bayes factors were calculated assuming clinically meaningful and plausible ranges around this population effect. Results Twenty per cent (n = 11) of the non‐significant Bayes factors were < ⅓ and 3.6% (n = 2) were > 3. The other 76.4% (n = 42) of Bayes factors were between ⅓ and 3. Of these, 26 were in the direction of there being an effect (Bayes factor > 1 and < 3); 12 tended to favour the hypothesis of no effect (Bayes factor < 1 and > ⅓); and for four there was no evidence either way (Bayes factor = 1). In sensitivity analyses, 13.3% of Bayes Factors were < ⅓ (n = 20), 62.7% (n = 94) were between ⅓ and 3 and 24.0% (n = 36) were > 3, showing good concordance with the main results. Conclusions Use of Bayes factors when analysing data from randomized trials of interventions in addiction research can provide important information that would lead to more precise conclusions than are obtained typically using currently prevailing methods.
Penal Mediation Policy of Land Criminal Offense Resolution in the Outer Courts of Indonesia

A land was a masterpiece that was created by the Almighty God had been given to mankind who lives at starting their born, their activities in the world until death. Therefore, the land was a basic human need, which required a land for living, to grow a crops, investing to be a source of life. Cosmological, the land was wherein the people lived, to work and live, activity places, and a rest after hard working. The land was a natural resource that was needed by human, the human needs for land always increasing, and land availability will be limited. Due it’s limitation, it was impacted an economic value was higher based on increasing demand for land, therefore, the land was true for residential shifted into business items that led to the competition was very tight and raised a legal issues unlike the land disputed in aspects Civil law, the law of the State Administration to Criminal Offense. The criminal policy towards a land criminal offense resolution resolved through various means that penal policies and non-penal policy / mediation penal, penal mediation policy as one of the best ways for settling disputes on the outer courts in Indonesia based on the philosophical basis of Pancasila state solution based on the precepts 4 th , democracy led by the policy wisdom in the consultative / representative of the essence of each problems resolved by discussion and consensus, especially, on criminal cases land in the form of crime / offense against the annexation of land rights, occupying land without the landowner's permission, forging documents of land ownership, doubling certificate of property rights to land, embezzlement rights over immovable unlike a land, houses, and fields. The criminal offense other than intentional, negligent as well as was structured, due to the circumstances and the need that was conducted by any individual or legal entity offender.
A case of paradoxical brain embolism presenting as a typical lacunar stroke

A 59 year old white male presented with a clinical picture typical of lacunar stroke. However, a thorough diagnostic work up showed that the most probable mechanism of stroke was paradoxical brain embolism through a patent foramen ovale. The clinical-neuroradiological mismatch that ultimately lead to the correct interpretation is discussed.
Home Energy Simulation for Non-Intrusive Load Monitoring Applications

Home Energy Management (HEM) is a vital component of smart grid, which can be considered as a distributed cyber physical system. HEM involves appropriate management of home appliance usage through deliberate efforts from the end-user. This can enable a stable operation of the grid as well as reduce energy usage and bills for the end-user. The installation of smart meter has led to a number of analytics and applications developed on top of its data. However, the algorithms are evaluated over a very small subset of experimental or open dataset. To mitigate this problem, a bottom-up data generation approach is proposed in this paper. The appliances are considered as combination of fundamental electrical components. The appliance characteristics and operations are modeled through stochastic parameters, which are available as prior information or through learning from existing meter data. Preliminary results of generating data for the application of Non-Intrusive Load Monitoring is presented.
Climate Change Effects on Heat Waves and Future Heat Wave-Associated IHD Mortality in Germany

The influence of future climate change on the occurrence of heat waves and its implications for heat wave-related mortality due to ischemic heart diseases (IHD) in Germany is studied. Simulations of 19 regional climate models with a spatial resolution of 0.25° × 0.25° forced by the moderate climate change scenario A1B are analyzed. Three model time periods of 30 years are evaluated, representing present climate (1971–2000), near future climate (2021–2050), and remote future climate (2069–2098). Heat waves are defined as periods of at least three consecutive days with daily mean air temperature above the 97.5th percentile of the all-season temperature distribution. Based on the model simulations, future heat waves in Germany will be significantly more frequent, longer lasting and more intense. By the end of the 21st century, the number of heat waves will be tripled compared to present climate. Additionally, the average duration of heat waves will increase by 25%, accompanied by an increase of the average temperature during heat waves by about 1 K. Regional analyses show that stronger than average climate change effects are observed particularly in the southern regions of Germany. Furthermore, we investigated climate change impacts on IHD mortality in Germany applying temperature projections from 19 regional climate models to heat wave mortality relationships identified in a previous study. Future IHD excess deaths were calculated both in the absence and presence of some acclimatization (i.e., that people are able to physiologically acclimatize to enhanced temperature levels in the future time periods by 0% and 50%, respectively). In addition to changes in heat wave frequency, we incorporated also changes in heat wave intensity and duration into the future mortality evaluations. The results indicate that by the end of the 21st century the annual number of IHD excess deaths in Germany attributable to heat waves is expected to rise by factor 2.4 and 5.1 in the acclimatization and non-acclimatization approach, respectively. Even though there is substantial variability across the individual model simulations, it is most likely that the future burden of heat will increase considerably. The obtained results point to public health interventions to reduce the vulnerability of the population to heat waves.
An ontology design pattern for IoT device tagging systems

Modeling devices has become a crucial task in the Internet of Things (IoT) and Semantic Web technologies are seen as a promising tool for this purpose. However, as it may be arduous to manipulate semantic models, industrial solutions often re-define non-standard, simplified semantics. This is the case with Project Haystack, a framework to tag devices with labels from a predefined vocabulary in the field of Building Automation. In order to make Project Haystack standard and fully semantic, we wrapped its vocabulary in an ontology. In this paper, we present the general structure of this ontology, along with a method to turn tag sets into a Semantic Web model and back. The whole results in a reusable ontology design pattern. We aligned our Haystack tagging ontology with the wide-spreaded Semantic Sensor Network upper ontology and we designed a configuration environment for Building Automation systems based on semantic data to illustrate, so as to discuss the added-value of semantics in automation.
Learning Global-to-Local Discrete Components with Nonparametric Bayesian Feature Construction

Finding common latent components from data is an important step in many data mining applications. These latent variables are typically categorical and there are many sources of categorical variables, including dichotomous, nominal, ordinal, and cardinal values. Thus it is important to be able to represent the discrete components (categories) in a flexible way. Here we propose a nonparametric Bayesian approach to learning "plastic" discrete components by considering the uncertainty of the number of components with the Indian buffet processes (IBP). As observation models, we use the product of experts (PoE) to utilize sharper representation power and sparse over-completeness. We apply the proposed method to optical hand-written digit datasets and demonstrate its capability of finding flexible global-to-local components that can be used to describe and generate the observed digit images faithfully.
Concerns in a requirements model-a small case study

Concerns are introduced into the software development process with the beginning of requirements engineering. Even simple requirements statements and use-case descriptions can give rise to substantial concern models. These models can exhibit features relating to the occurrence and distribution of concerns of different domains and types, of shared versus unique concerns, and of explicit versus implicit concerns. In the way that concerns are organized, shared, and viewed, these models exhibit aspectual features and multiple dimensions. Early-stage concern modeling has many potential applications. It can lead to better understanding and more systematic treatment of concerns from the first stages of development and it may help to identify reusable elements during requirements analysis. It can facilitate the formulation of alternative views and representations of requirements, and it should support the development of compositional technologies for requirements. Concernmodeling of requirements can also support the more systematic and effective use of concern modeling and aspect-oriented technologies downstream in the life cycle.
Blind spectrum sensing method for OFDM signal detection in Cognitive Radio communications

Cognitive Radio is an enabling technology for accessing the unused spectrum. It may need to work in blind scenarios where it is unaware of the received signal parameters. In real-time military applications, the cyclostationary analysis of OFDM signals involves high computational complexity and requires additional processing and detection time. In order to detect the active carrier frequencies in such a scenario, we propose a blind two stage spectrum sensing scheme where the sequential sliding window energy detection is followed by cyclostationary feature detection that extracts the underlying periodic properties of the OFDM signal. The second-order cyclostationarity due to the equally spaced pilot subcarriers and due to the preamble with cyclic extension is explored. The peaks due to pilots and due to the preamble and cyclic extension are captured. The cyclostationary feature detection is performed over a selected cyclic spectrum instead of exploring the entire spectrum. The blind energy/cyclostationary detection of OFDM signals is compared with the matched filter based spectrum sensing algorithm of detecting OFDM signals. Simulations demonstrate the reliable and highly robust performance of the proposed non-parametric spectrum sensing method in Gaussian environment.
Global topological features of cancer proteins in the human interactome

MOTIVATION The study of interactomes, or networks of protein-protein interactions, is increasingly providing valuable information on biological systems. Here we report a study of cancer proteins in an extensive human protein-protein interaction network constructed by computational methods. RESULTS We show that human proteins translated from known cancer genes exhibit a network topology that is different from that of proteins not documented as being mutated in cancer. In particular, cancer proteins show an increase in the number of proteins they interact with. They also appear to participate in central hubs rather than peripheral ones, mirroring their greater centrality and participation in networks that form the backbone of the proteome. Moreover, we show that cancer proteins contain a high ratio of highly promiscuous structural domains, i.e., domains with a high propensity for mediating protein interactions. These observations indicate an underlying evolutionary distinction between the two groups of proteins, reflecting the central roles of proteins, whose mutations lead to cancer. CONTACT paul.bates@cancer.org. uk SUPPLEMENTARY INFORMATION The interactome data are available though the PIP (Potential Interactions of Proteins) web server at http://bmm.cancerresearchuk. org/servers/pip. Further additional material is available at http://bmm.cancerresearchuk. org/servers/pip/bioinformatics/
Pterostilbene-Induced Tumor Cytotoxicity: A Lysosomal Membrane Permeabilization-Dependent Mechanism

The phenolic phytoalexin resveratrol is well known for its health-promoting and anticancer properties. Its potential benefits are, however, limited due to its low bioavailability. Pterostilbene, a natural dimethoxylated analog of resveratrol, presents higher anticancer activity than resveratrol. The mechanisms by which this polyphenol acts against cancer cells are, however, unclear. Here, we show that pterostilbene effectively inhibits cancer cell growth and stimulates apoptosis and autophagosome accumulation in cancer cells of various origins. However, these mechanisms are not determinant in cell demise. Pterostilbene promotes cancer cell death via a mechanism involving lysosomal membrane permeabilization. Different grades of susceptibility were observed among the different cancer cells depending on their lysosomal heat shock protein 70 (HSP70) content, a known stabilizer of lysosomal membranes. A375 melanoma and A549 lung cancer cells with low levels of HSP70 showed high susceptibility to pterostilbene, whereas HT29 colon and MCF7 breast cancer cells with higher levels of HSP70 were more resistant. Inhibition of HSP70 expression increased susceptibility of HT29 colon and MCF7 breast cancer cells to pterostilbene. Our data indicate that lysosomal membrane permeabilization is the main cell death pathway triggered by pterostilbene.
Automatic visualization of two-dimensional cellular complexesRober

A two-dimensional cellular complex is a partition of a surface into a nite number of elements|faces (open disks), edges (open arcs), and vertices (points). The topology of a cellular complex consists of the abstract incidence and adjacency relations among its elements. Here we describe a program that, given only the topology of a cellular complex, computes a geometric realization of the same|that is, a speciic partition of a speciic surface in three-space|guided by various aesthetic and presentational criteria.
A unified approach for solving bottleneck k-bipartition problems

G(V,.E) is an undirected , connected graph with [ V I = n and I EI = m. Each edge ei of E is associated with a cost c(ei) . For all non-empty vertex set S strictly contained in V, let T = V S. (S, T) is called a bipartition of V. The set of edges with one end in S and the other in T is call the disconnecting set of and T, denoted by E(S,T). If I Sl = k, then , T) is called a k--bipartition of V. Let E' be a non-empty set of E, use max(E') and ~ . n ! E ' ) to denote the maximum and rmmmum edge cost of edges in E' respectively. There are four variations of bottleneck k-bipartition problems. All of them are to find a k-bipartition (S, T) such that: (1) min(E(S, T))is maximized. ~2) max(E IS, T))is minimized. [3) min(E--E(S, T)) is maximized. (4) max( E---E( S,T)) is minimized. In this paper, we present efficient algorithms for solving above four variations of bottleneck k-bipartition problems in O(Tmst + nlogn + kn) time where Trust is the time complexity of the most efficient algorithm for constructing a minimum spanning tree or a maximum spanning tree. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. © 1991 A C M 089791-382-5/91/0003/0039 $ 1 . 5 0 Section
Reply: Depression, Stroke Diagnosis, and SSRI Discontinuation Syndrome

If these observations are supported by further studies, we now also have another factor to consider in the selection of antidepressants. Patients who may be at increased risk for cardiovascular or cerebrovascular events may be better served by SSRIs as a drug of first choice. Alternatively, a non-SSRI agent may be the drug of first choice in the treatment of depression for those patients who are on nonsteriodal antiinflammatory drugs.
CHCHD 10 Pro 34 Ser is not a highly penetrant pathogenic variant for amyotrophic lateral sclerosis and frontotemporal dementia

1 Department of Neurodegenerative Disease, University College London Institute of Neurology, Queen Square, London WC1N 3BG, UK 2 Department of Molecular Neuroscience, University College London Institute of Neurology, Queen Square, London WC1N 3BG, UK 3 UCL Genetics Institute, Department of Genetics, Environment and Evolution, UCL, London WC1E 6BT, UK 4 Sobell Department of Motor Neuroscience and Movement Disorders, Queen Square, London, WC1N 3BG, UK 5 Centre for Neuroscience and Trauma, Blizard Institute, Queen Mary University of London, North-East London and Essex Regional MND Care Centre, E1 2AT, UK 6 Medical Research Council Prion Unit, Department of Neurodegenerative Disease, University College London Institute of Neurology, Queen Square, London, WC1N 3BG, UK 7 National Hospital for Neurology and Neurosurgery, Queen Square, London, WC1N 3BG, UK 8 Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, Queen Square, London WC1N 3BG, UK
Prediction of user ’ s intention based on the hand motion recognition

Most methods proposed in the literature for predicting movements involved in a reach-to-grasp action by human being are designed using passive methods, i.e, by using a camera in front of the user. A novel approach to understand the user’s intentions with computer vision methods is proposed in this paper. Our solution performs a hand motion estimation using a wearable system. To realize such system, we employ a camera beneath the wrist to capture motion from the user’s perspective. To predict hand intentions, we characterize a hand-movement trough a Hidden-Markov Model framework. As main result, our system can predict the movement before the hand can reach an object with a performance near to 90% in average.
Fairness of link adaptation techniques in broadband wireless access networks

Link adaptation techniques, such as power control and adaptive coding and modulation, aim at maximizing the resource utilization in wireless networks. However, fair resource allocation must be taken into consideration, particularly in fixed broadband wireless access networks. The low/no mobility of users in such networks leads to location-dependent resource utilization, which can cause a significant variation in the performance from one user to another. For instance, adaptive coding and modulation schemes increase the average throughput in the network; however, they also increase the variation of the throughput. This is because users with good link quality always have high throughput, while users with bad link quality always have low throughput. The fairness and efficiency of various link adaptation techniques are analyzed. The analyzed algorithms are selected as a representative set of different link adaptation techniques with various fairness and efficiency characteristics.
Approximate counting in bounded arithmetic

Abstract We develop approximate counting of sets definable by Boolean circuits in bounded arithmetic using the dual weak pigeonhole principle (dWPHP(PV)), as a generalization of results from [15]. We discuss applications to formalization of randomized complexity classes (such as BPP, APP, MA, AM) in PV1 + dWPHP(PV).
Ovarian Cancer Diagnosis Using Fuzzy Neural Networks Empowered By Evolutionary Clustering Technique

As computational power of modern computer increases exponentially, more efficient computerized solutions are possible for complex real world applications. However, the solutions are usually not interpretable to human beings such as the opaqueness of traditional neural networks. In this paper, we propose a fuzzy neural network that is empowered by genetic algorithm based rough set clustering (GARSC) technique. The system is capable to address real world problems not only with promising accuracy, but also with great interpretabilities. Ovarian cancer diagnosis is exploited to show its superior capabilities.
Sliding window approach based Text Binarisation from Complex Textual images

Text binarisation process classifies individual pixels as text or background in the textual images. Binarization is necessary to bridge the gap between localization and recognition by OCR. This paper presents Sliding window method to binarise text from textual images with textured background. Suitable preprocessing techniques are applied first to increase the contrast of the image and blur the background noises due to textured background. Then Edges are detected by iterative thresholding. Subsequently formed edge boxes are analyzed to remove unwanted edges due to complex background and binarised by sliding window approach based character size uniformity check algorithm. The proposed method has been applied on localized region from heterogeneous textual images and compared with Otsu, Niblack methods and shown encouraging performance of the proposed method.