nils-herrmann/clean_scirepeval huggingface dataset:
An Efficient Proximal-Gradient Method for Single and Multi-task Regression with Structured Sparsity

We consider the optimization problem of learning regressio n models with a mixed-norm penalty that is defined over overlapping groups to achieve structure d sparsity. It has been previously shown that such penalty can encode prior knowledge on the input or o utput structure to learn an structuredsparsity pattern in the regression parameters. However, be caus of the non-separability of the parameters of the overlapping groups, developing an efficie nt optimization method has remained a challenge. An existing method casts this problem as a secon d-order cone programming (SOCP) and solves it by interior-point methods. However, this appr oach is computationally expensive even for problems of moderate size. In this paper, we propose an ef ficient proximal-gradient method that achieves a faster convergence rate and is much more efficient and scalable than solving the SOCP formulation. Our method exploits the structure of the non-s mooth structured-sparsity-inducing norm, introduces its smooth approximation, and solves this approximation function instead of optimizing the original objective function directly. We de monstrate the efficiency and scalability of our method on simulated datasets and show that our method c an be successfully applied to a very large-scale dataset in genetic association analysis.
LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated.
Extensional Versus Intuitive Reasoning : The Conjunction Fallacy in Probability Judgment

Perhaps the simplest and the most basic qualitative law of probability is the conjunction rule: The probability of a conjunction, P(A&B), cannot exceed the probabilities of its constituents, P(A) and .P(B), because the extension (or the possibility set) of the conjunction is included in the extension of its constituents. Judgments under uncertainty, however, are often mediated by intuitive heuristics that are not bound by the conjunction rule. A conjunction can be more representative than one of its constituents, and instances of a specific category can be easier to imagine or to retrieve than instances of a more inclusive category. The representativeness and availability heuristics therefore can make a conjunction appear more probable than one of its constituents. This phenomenon is demonstrated in a variety of contexts including estimation of word frequency, personality judgment, medical prognosis, decision under risk, suspicion of criminal acts, and political forecasting. Systematic violations of the conjunction rule are observed in judgments of lay people and of experts in both between-subjects and within-subjects comparisons. Alternative interpretations of the conjunction fallacy are discussed and attempts to combat it are explored.
An investigation into the applicability of building information models in geospatial environment in support of site selection and fire response management processes

1474-0346/$ see front matter 2008  A doi:10.1016/j.aei.2008.06.001 * Corresponding author.  +90 312 266 00 5 (U. Isikdag); tel.: +44 161 295 62 90; fax: +44 016 tel.: +44 0161 295 4992; fax: +44 0161 295 3173 (G. E-mail addresses: egetera@superonline.com, uisik j.underwood@salford.ac.uk (J. Underwood), g.aoauad@ Some tasks in the construction industry and urban management field such as site selection and fire response management are usually managed by using a Geographical Information System (GIS), as the tasks in these processes require a high level and amount of integrated geospatial information. Recently, a key element of this integrated geospatial information to emerge is detailed geometrical and semantic information about buildings. In parallel, Building Information Models (BIMs) of today have the capacity for storing and representing such detailed geometrical and semantic information. In this context, the research aimed to investigate the applicability of BIMs in geospatial environment by focusing specifically on these two domains; site selection and fire response management. In the first phase of the research two use case scenarios were developed in order to understand the processes in these domains in a more detailed manner and to establish the scope of a possible software development for transferring information from BIMs into the geospatial environment. In the following phase of the research two data models were developed – a Schema-Level Model View and a geospatial data model. The Schema-Level Model View was used in simplifying the information acquired from the BIM, while the geospatial data model acted as the template for creating physical files and databases in the geospatial environment. Following this, three software components to transfer building information into the geospatial environment were designed, developed, and validated. The first component served for acquiring the building information from the BIM, while the latter two served for transforming the information into the geospatial environment. The overall research demonstrated that it is possible to transfer (high level of geometric and semantic) information acquired from BIMs into the geospatial environment. The results also demonstrated that BIMs provide a sufficient level and amount of (geometric and semantic) information (about the building) for the seamless automation of data management tasks in the site selection and fire response management processes.
OntoDM: An Ontology of Data Mining

Motivated by the need for unification of the field of data mining and the growing demand for formalized representation of outcomes of research, we address the task of constructing an ontology of data mining. The proposed ontology, named OntoDM, is based on a recent proposal of a general framework for data mining, and includes definitions of basic data mining entities, such as datatype and dataset, data mining task, data mining algorithm and components thereof (e.g., distance function), etc. It also allows for the definition of more complex entities, e.g., constraints in constraint-based data mining, sets of such constraints (inductive queries) and data mining scenarios (sequences of inductive queries). Unlike most existing approaches to constructing ontologies of data mining, OntoDM is a deep/heavy-weight ontology and follows best practices in ontology engineering, such as not allowing multiple inheritance of classes, using a predefined set of relations and using a top level ontology.
Chapter 0 Immersion in Digital Games : a Review of Gaming Experience Research

  Immersion is a widely valued experience when playing digital games however it is only one component of the gaming experience. In this chapter, we review our specific approach to immersion in relation to other concepts that are used to describe gaming experiences. These include: concepts that are not specific to games such as flow and attention; generic conceptualizations of the gaming experience of which immersion may form part, such as incorporation; and specific concepts around immersion, engagement and involvement such as presence and other formulations of immersion. To illustrate the sorts of studies being done in this area, we describe one experiment in detail which aims to position immersion in relation to presence. The chapter makes three contributions: a clear formulation of immersion as one aspect of gaming experience; an overview of the state-of-the-art of gaming experience research; and a demonstration of the possibility to achieve an empirically founded understanding of these rich, subjective experiences.
A Questionnaire Approach based on the Technology Acceptance Model for Mobile tracking on Patient progress Applications

Healthcare professionals spend much of their time wandering between patients and offices, while the supportive technology stays stationary. Therefore, mobile applications has adapted for healthcare industry. In spite of the advancement and variety of available mobile based applications, there is an eminent need to investigate the current position of the acceptance of those mobile health applications that are tailored towards the tracking patients condition, share patients information and access. Consequently, in this study Technology Acceptance Model has designed to investigate the user acceptance of mobile technology application within healthcare industry. The purpose of this study is to design a quantitative approach based on the technology acceptance model questionnaire as its primary research methodology. It utilized a quantitative approach based a Technology Acceptance Model (TAM) to evaluate the system mobile tracking Model. The related constructs for evaluation are: Perceived of Usefulness, Perceived Ease of Use, User Satisfaction and Attribute of Usability. All these constructs are modified to suit the context of the study. Moreover, this study outlines the details of each construct and its relevance toward the research issue. The outcome of the study represents series of approaches that will apply for checking the suitability of a mobile tracking on patient progress application for health care industry and how well it achieves the aims and objectives of the design.
Tadalafil therapy for pulmonary arterial hypertension.

Treatment options for pulmonary arterial hypertension target the prostacyclin, endothelin, or nitric oxide pathways. Tadalafil, a phosphodiesterase type-5 inhibitor, increases cGMP, the final mediator in the nitric oxide pathway.



In this 16-week, double-blind, placebo-controlled study, 405 patients with pulmonary arterial hypertension (idiopathic or associated), either treatment-naive or on background therapy with the endothelin receptor antagonist bosentan, were randomized to placebo or tadalafil 2.5, 10, 20, or 40 mg orally once daily. The primary end point was the change from baseline to week 16 in the distance walked in 6 minutes. Changes in World Health Organization functional class, clinical worsening, and health-related quality of life were also assessed. Patients completing the 16-week study could enter a long-term extension study. Tadalafil increased the distance walked in 6 minutes in a dose-dependent manner; only the 40-mg dose met the prespecified level of statistical significance (P<0.01). Overall, the mean placebo-corrected treatment effect was 33 m (95% confidence interval, 15 to 50 m). In the bosentan-naive group, the treatment effect was 44 m (95% confidence interval, 20 to 69 m) compared with 23 m (95% confidence interval, -2 to 48 m) in patients on background bosentan therapy. Tadalafil 40 mg improved the time to clinical worsening (P=0.041), incidence of clinical worsening (68% relative risk reduction; P=0.038), and health-related quality of life. The changes in World Health Organization functional class were not statistically significant. The most common treatment-related adverse events reported with tadalafil were headache, myalgia, and flushing.



In patients with pulmonary arterial hypertension, tadalafil 40 mg was well tolerated and improved exercise capacity and quality of life measures and reduced clinical worsening.
Teaching Distributed Artificial Intelligence with RoboRally

Teaching Artificial Intelligence (AI) or multi-agent systems is a challenging task as algorithms are in question which are advantageous in highly complex and dynamic environments. Explaining multi-agent systems (MAS) in lectures requires interactive approaches accompanied by exercises. The key challenge in using practical exercises within lectures on MAS is to establish an environment for testing which is extremely time consuming. It is not reasonable that students do this work as they do have not enough time focussing on the important aspects. In this paper, we introduce a system which supports experimenting with AI and Distributed Artificial Intelligence (DAI) algorithms concurrently to the lecture. Our system is based on a board game called RoboRally. Different issues from the field of AI and DAI can be implemented and tested in a kind of challenge.
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic

While significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic. In fact, businesses are interested in learning a fine-grained representation of how users are feeling towards their products or services. In this work, we describe the methods used by the team Emotion Mining in Arabic (EMA), as part of the SemEval-2018 Task 1 for Affect Mining for Arabic tweets. EMA participated in all 5 subtasks. For the five tasks, several preprocessing steps were evaluated and eventually the best system included diacritics removal, elongation adjustment, replacement of emojis by the corresponding Arabic word, character normalization and light stemming. Moreover, several features were evaluated along with different classification and regression techniques. For the 5 subtasks, word embeddings feature turned out to perform best along with Ensemble technique. EMA achieved the 1st place in subtask 5, and 3rd place in subtasks 1 and 3.
MetaLDA: A Topic Model that Efficiently Incorporates Meta Information

Besides the text content, documents and their associated words usually come with rich sets of meta information, such as categories of documents and semantic/syntactic features of words, like those encoded in word embeddings. Incorporating such meta information directly into the generative process of topic models can improve modelling accuracy and topic quality, especially in the case where the word-occurrence information in the training data is insufficient. In this paper, we present a topic model, called MetaLDA, which is able to leverage either document or word meta information, or both of them jointly. With two data argumentation techniques, we can derive an efficient Gibbs sampling algorithm, which benefits from the fully local conjugacy of the model. Moreover, the algorithm is favoured by the sparsity of the meta information. Extensive experiments on several real world datasets demonstrate that our model achieves comparable or improved performance in terms of both perplexity and topic quality, particularly in handling sparse texts. In addition, compared with other models using meta information, our model runs significantly faster.
A multimodal parallel architecture: A cognitive framework for multimodal interactions

Human communication is naturally multimodal, and substantial focus has examined the semantic correspondences in speech-gesture and text-image relationships. However, visual narratives, like those in comics, provide an interesting challenge to multimodal communication because the words and/or images can guide the overall meaning, and both modalities can appear in complicated "grammatical" sequences: sentences use a syntactic structure and sequential images use a narrative structure. These dual structures create complexity beyond those typically addressed by theories of multimodality where only a single form uses combinatorial structure, and also poses challenges for models of the linguistic system that focus on single modalities. This paper outlines a broad theoretical framework for multimodal interactions by expanding on Jackendoff's (2002) parallel architecture for language. Multimodal interactions are characterized in terms of their component cognitive structures: whether a particular modality (verbal, bodily, visual) is present, whether it uses a grammatical structure (syntax, narrative), and whether it "dominates" the semantics of the overall expression. Altogether, this approach integrates multimodal interactions into an existing framework of language and cognition, and characterizes interactions between varying complexity in the verbal, bodily, and graphic domains. The resulting theoretical model presents an expanded consideration of the boundaries of the "linguistic" system and its involvement in multimodal interactions, with a framework that can benefit research on corpus analyses, experimentation, and the educational benefits of multimodality.
A low cost wireless sensor node for building monitoring

In this paper a medium-high performance wireless sensor node based on low-cost Micro Electro-Mechanical Systems (MEMS) accelerometers for Structural Health Monitoring (SHM) applications is proposed. The objective of the work is the estimation of the performances of the low cost MEMS system for SHM with a possible application on a pervasive permanent monitoring of buildings in seismic prone regions often affected by earthquakes. The work aims at improving the sensing technology involved in the dynamic identification and SHM, with the intent to reduce the overall cost of acquisition's system and to make faster the tests procedure. Preliminary experimental tests performed to compare the performance of the proposed system with those of a typical high-performance and expensive wired system, are presented and discussed.
Android Security Permissions - Can We Trust Them?

The popularity of the Android System in combination with the lax market approval process may attract the injection of malicious applications (apps) into the market. Android features a permission system allowing a user to review the permissions an app requests and grant or deny access to resources prior to installation. This system conveys a level of trust due to the fact that an app only has access to resources granted by the stated permissions. Thereby, not only the meaning of single permissions, but especially their combination plays an important role for understanding the possible implications. In this paper we present a method that circumvents the permission system by spreading permissions over two or more apps that communicate with each other via arbitrary communication channels. We discuss relevant details of the Android system, describe the permission spreading process, possible implications and countermeasures. Furthermore, we present three apps that demonstrate the problem and a possible detection method.
Brain Tumor Detection and Classification Using Deep Learning Classifier on MRI Images

Magnetic Resonance Imaging (MRI) has become an effective tool for clinical research in recent years and has found itself in applications such as brain tumour detection. In this study, tumor classification using multiple kernel-based probabilistic clustering and deep learning classifier is proposed. The proposed technique consists of three modules, namely segmentation module, feature extraction module and classification module. Initially, the MRI image is pre-processed to make it fit for segmentation and de-noising process is carried out using median filter. Then, pre-processed image is segmented using Multiple Kernel based Probabilistic Clustering (MKPC). Subsequently, features are extracted for every segment based on the shape, texture and intensity. After features extraction, important features will be selected using Linear Discriminant Analysis (LDA) for classification purpose. Finally, deep learning classifier is employed for classification into tumor or non-tumor. The proposed technique is evaluated using sensitivity, specificity and accuracy. The proposed technique results are also compared with existing technique which uses Feed-Forward Back Propagation Network (FFBN). The proposed technique achieved an average sensitivity, specificity and accuracy of 0.88, 0.80 and 0.83, respectively with the highest values as about 1, 0.85 and 0.94. Improved results show the efficiency of the proposed technique.
The MOS 36-Item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs.

Cross-sectional data from the Medical Outcomes Study (MOS) were analyzed to test the validity of the MOS 36-Item Short-Form Health Survey (SF-36) scales as measures of physical and mental health constructs. Results from traditional psychometric and clinical tests of validity were compared. Principal components analysis was used to test for hypothesized physical and mental health dimensions. For purposes of clinical tests of validity, clinical criteria defined mutually exclusive adult patient groups differing in severity of medical and psychiatric conditions. Scales shown in the components analysis to primarily measure physical health (physical functioning and role limitations-physical) best distinguished groups differing in severity of chronic medical condition and had the most pure physical health interpretation. Scales shown to primarily measure mental health (mental health and role limitations-emotional) best distinguished groups differing in the presence and severity of psychiatric disorders and had the most pure mental health interpretation. The social functioning, vitality, and general health perceptions scales measured both physical and mental health components and, thus, had the most complex interpretation. These results are useful in establishing guidelines for the interpretation of each scale and in documenting the size of differences between clinical groups that should be considered very large.
A mesoscale connectome of the mouse brain

Comprehensive knowledge of the brain’s wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.