nils-herrmann/clean_scirepeval huggingface dataset:
Streamline Variability Plots for Characterizing the Uncertainty in Vector Field Ensembles

We present a new method to visualize from an ensemble of flow fields the statistical properties of streamlines passing through a selected location. We use principal component analysis to transform the set of streamlines into a low-dimensional Euclidean space. In this space the streamlines are clustered into major trends, and each cluster is in turn approximated by a multivariate Gaussian distribution. This yields a probabilistic mixture model for the streamline distribution, from which confidence regions can be derived in which the streamlines are most likely to reside. This is achieved by transforming the Gaussian random distributions from the low-dimensional Euclidean space into a streamline distribution that follows the statistical model, and by visualizing confidence regions in this distribution via iso-contours. We further make use of the principal component representation to introduce a new concept of streamline-median, based on existing median concepts in multidimensional Euclidean spaces. We demonstrate the potential of our method in a number of real-world examples, and we compare our results to alternative clustering approaches for particle trajectories as well as curve boxplots.
Update on the use of immunoglobulin in human disease: A review of evidence.

Human immunoglobulin preparations for intravenous or subcutaneous administration are the cornerstone of treatment in patients with primary immunodeficiency diseases affecting the humoral immune system. Intravenous preparations have a number of important uses in the treatment of other diseases in humans as well, some for which acceptable treatment alternatives do not exist. We provide an update of the evidence-based guideline on immunoglobulin therapy, last published in 2006. Given the potential risks and inherent scarcity of human immunoglobulin, careful consideration of its indications and administration is warranted.
Evolutionary Policy Transfer and Search Methods for Boosting Behavior Quality: RoboCup Keep-Away Case Study

This study evaluates various evolutionary search methods to direct neural controller evolution in company with policy (behavior) transfer across increasingly complex collective robotic (RoboCup keep-away) tasks. Robot behaviors are first evolved in a source task and then transferred for further evolution to more complex target tasks. Evolutionary search methods tested include objective-based search (fitness function), behavioral and genotypic diversity maintenance, and hybrids of such diversity maintenance and objective-based search. Evolved behavior quality is evaluated according to effectiveness and efficiency. Effectiveness is the average task performance of transferred and evolved behaviors, where task performance is the average time the ball is controlled by a keeper team. Efficiency is the average number of generations taken for the fittest evolved behaviors to reach a minimum task performance threshold given policy transfer. Results indicate that policy transfer coupled with hybridized evolution (behavioral diversity maintenance and objective-based search) addresses the bootstrapping problem for increasingly complex keep-away tasks. That is, this hybrid method (coupled with policy transfer) evolves behaviors that could not otherwise be evolved. Also, this hybrid evolutionary search was demonstrated as consistently evolving topologically simple neural controllers that elicited high-quality behaviors.
Ontology driven design of EMF metamodels and well-formedness constraints

Ontologies provide high-level means for capturing requirements of systems with precise semantics and automated meta-level reasoning techniques to identify specification flaws early in the design even if certain parts of the system is underspecified. Domain-specific modeling environments effectively support domain engineers for designing the system by providing efficient means for instance-level validation of well-formedness constraints. In the current paper, we aim at a combined use of ontologies and DSM techniques where domain requirements captured in textual ontology languages like OWL2/SWRL will drive the development of a DSM environment. More specifically, we provide (i) an automated mapping from OWL2 ontologies to metamodels defined by the industry-standard EMF platform, which is extended by a (ii) mapping of requirements and constraints (captured in OWL2 and SWRL) into a textual graph pattern language efficiently evaluated by the EMF-IncQuery incremental model query technology.
Differentiation in MALDI-TOF MS and FTIR spectra between two closely related species Acidovorax oryzae and Acidovorax citrulli

Two important plant pathogenic bacteria Acidovorax oryzae and Acidovorax citrulli are closely related and often not easy to be differentiated from each other, which often resulted in a false identification between them based on traditional methods such as carbon source utilization profile, fatty acid methyl esters, and ELISA detection tests. MALDI-TOF MS and Fourier transform infrared (FTIR) spectra have recently been successfully applied in bacterial identification and classification, which provide an alternate method for differentiating the two species. Characterization and comparison of the 10 A. oryzae strains and 10 A. citrulli strains were performed based on traditional bacteriological methods, MALDI-TOF MS, and FTIR spectroscopy. Our results showed that the identity of the two closely related plant pathogenic bacteria A. oryzae and A. citrulli was able to be confirmed by both pathogenicity tests and species-specific PCR, but the two species were difficult to be differentiated based on Biolog and FAME profile as well as 16 S rRNA sequence analysis. However, there were significant differences in MALDI-TOF MS and FTIR spectra between the two species of Acidovorax. MALDI-TOF MS revealed that 22 and 18 peaks were specific to A. oryzae and A. citrulli, respectively, while FTIR spectra of the two species of Acidovorax have the specific peaks at 1738, 1311, 1128, 1078, 989 cm-1 and at 1337, 968, 933, 916, 786 cm-1, respectively. This study indicated that MALDI-TOF MS and FTIR spectra may give a new strategy for rapid bacterial identification and differentiation of the two closely related species of Acidovorax.
"Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data

The success of applications that process data critically depends on the quality of the ingested data. Completeness of a data source is essential in many cases. Yet, most missing value imputation approaches suffer from severe limitations. They are almost exclusively restricted to numerical data, and they either offer only simple imputation methods or are difficult to scale and maintain in production. Here we present a robust and scalable approach to imputation that extends to tables with non-numerical values, including unstructured text data in diverse languages. Experiments on public data sets as well as data sets sampled from a large product catalog in different languages (English and Japanese) demonstrate that the proposed approach is both scalable and yields more accurate imputations than previous approaches. Training on data sets with several million rows is a matter of minutes on a single machine. With a median imputation F1 score of 0.93 across a broad selection of data sets our approach achieves on average a 23-fold improvement compared to mode imputation. While our system allows users to apply state-of-the-art deep learning models if needed, we find that often simple linear n-gram models perform on par with deep learning methods at a much lower operational cost. The proposed method learns all parameters of the entire imputation pipeline automatically in an end-to-end fashion, rendering it attractive as a generic plugin both for engineers in charge of data pipelines where data completeness is relevant, as well as for practitioners without expertise in machine learning who need to impute missing values in tables with non-numerical data.
A literature review of recommender systems in the television domain

Recommender Systems (RSs) are software tools and techniques providing suggestions of relevant items to users. These systems have received increasing attention from both academy and industry since the 90’s, due to a variety of practical applications as well as complex problems to solve. Since then, the number of research papers published has increased significantly in many application domains (books, documents, images, movies, music, shopping, TV programs, and others). One of these domains has our attention in this paper due to the massive proliferation of televisions (TVs) with computational and network capabilities and due to the large amount of TV content and TV-related content available on the Web. With the evolution of TVs and RSs, the diversity of recommender systems for TV has increased substantially. In this direction, it is worth mentioning that we consider “recommender systems for TV” as those that make recommendations of both TV-content and any content related to TV. Due to this diversity, more investigation is necessary because research on recommender systems for TV domain is still broader and less mature than in other research areas. Thus, this literature review (LR) seeks to classify, synthesize, and present studies according to different perspectives of RSs in the television domain. For that, we initially identified, from the scientific literature, 282 relevant papers published from 2003 to May, 2015. The papers were then categorized and discussed according to different research and development perspectives: recommended item types, approaches, algorithms, architectural models, output devices, user profiling and evaluation. The obtained results can be useful to reveal trends and opportunities for both researchers and practitioners in the area.
Tilt displays: designing display surfaces with multi-axis tilting and actuation

We present a new type of actuatable display, called Tilt Displays, that provide visual feedback combined with multi-axis tilting and vertical actuation. Their ability to physically mutate provides users with an additional information channel that facilitates a range of new applications including collaboration and tangible entertainment while enhancing familiar applications such as terrain modelling by allowing 3D scenes to be rendered in a physical-3D manner. Through a mobile 3x3 custom built prototype, we examine the design space around Tilt Displays, categorise output modalities and conduct two user studies. The first, an exploratory study examines users' initial impressions of Tilt Displays and probes potential interactions and uses. The second takes a quantitative approach to understand interaction possibilities with such displays, resulting in the production of two user-defined gesture sets: one for manipulating the surface of the Tilt Display, the second for conducting everyday interactions.
Image demosaicing: a systematic survey

Image demosaicing is a problem of interpolating full-resolution color images from so-called color-filter-array (CFA) samples. Among various CFA patterns, Bayer pattern has been the most popular choice and demosaicing of Bayer pattern has attracted renewed interest in recent years partially due to the increased availability of source codes/executables in response to the principle of “reproducible research”. In this article, we provide a systematic survey of over seventy published works in this field since 1999 (complementary to previous reviews ). Our review attempts to address important issues to demosaicing and identify fundamental differences among competing approaches. Our findings suggest most existing works belong to the class of sequential demosaicing i.e., luminance channel is interpolated first and then chrominance channels are reconstructed based on recovered luminance information. We report our comparative study results with a collection of eleven competing algorithms whose source codes or executables are provided by the authors. Our comparison is performed on two data sets: Kodak PhotoCD (popular choice) and IMAX high-quality images (more challenging). While most existing demosaicing algorithms achieve good performance on the Kodak data set, their performance on the IMAX one (images with varying-hue and high-saturation edges) degrades significantly. Such observation suggests the importance of properly addressing the issue of mismatch between assumed model and observation data in demosaicing, which calls for further investigation on issues such as model validation, test data selection and performance evaluation.
A Naive Clustering Algorithm for Text Mining

Predefined categories can be assigned to the natural language text using for text classification. It is a “bag-of-word” representation, previous documents have a word with values, it represents how frequently this word appears in the document or not. But large documents may face many problems because they have irrelevant or abundant information is there. This paper explores the effect of other types of values, which express the distribution of a word in the document. These values are called distributional features. All features are calculated by tfidf style equation and these features are combined with machine learning techniques. Term frequency is one of the major factor for distributional features it holds weighted item set. When the need is to minimize a certain score function, discovering rare data correlations is more interesting than mining frequent ones. This paper tackles the issue of discovering rare and weighted item sets, i.e., the infrequent weighted item set mining problem. The classifier which gives the more accurate result is selected for categorization. Experiments show that the distributional features are useful for text categorization.
GST: GPU-decodable supercompressed textures

Modern GPUs supporting compressed textures allow interactive application developers to save scarce GPU resources such as VRAM and bandwidth. Compressed textures use fixed compression ratios whose lossy representations are significantly poorer quality than traditional image compression formats such as JPEG. We present a new method in the class of supercompressed textures that provides an additional layer of compression to already compressed textures. Our texture representation is designed for endpoint compressed formats such as DXT and PVRTC and decoding on commodity GPUs. We apply our algorithm to commonly used formats by separating their representation into two parts that are processed independently and then entropy encoded. Our method preserves the CPU-GPU bandwidth during the decoding phase and exploits the parallelism of GPUs to provide up to 3X faster decode compared to prior texture supercompression algorithms. Along with the gains in decoding speed, our method maintains both the compression size and quality of current state of the art texture representations.
A novel and provably secure authentication and key agreement scheme with user anonymity for global mobility networks

Ubiquitous networks support the roaming service for mobile communication devices. The mobile user can use the services in the foreign network with the help of the home network. Mutual authentication plays an important role in the roaming services, and researchers put their interests on the authentication schemes. Recently, in 2016, Gope and Hwang found that mutual authentication scheme of He et al. for global mobility networks had security disadvantages such as vulnerability to forgery attacks, unfair key agreement, and destitution of user anonymity. Then, they presented an improved scheme. However, we find that the scheme cannot resist the off-line guessing attack and the de-synchronization attack. Also, it lacks strong forward security. Moreover, the session key is known to HA in that scheme. To get over the weaknesses, we propose a new two-factor authentication scheme for global mobility networks. We use formal proof with random oracle model, formal verification with the tool Proverif, and informal analysis to demonstrate the security of the proposed scheme. Compared with some very recent schemes, our scheme is more applicable.
Sybil-Resistant Pseudonymization and Pseudonym Change without Trusted Third Parties

The issuing of pseudonyms is an established approach for protecting the privacy of users while limiting access and preventing sybil attacks. To prevent pseudonym deanonymization through continuous observation and correlation, frequent and unlinkable pseudonym changes must be enabled. Existing approaches for realizing sybil-resistant pseudonymization and pseudonym change (PPC) are either inherently dependent on trusted third parties (TTPs) or involve significant computation overhead at end-user devices. In this paper, we investigate a novel, TTP-independent approach towards sybil-resistant PPC. Our proposal is based on the use of cryptocurrency block chains as general-purpose, append-only bulletin boards. We present a general approach as well as BitNym, a specific design based on the unmodified Bitcoin network. We discuss and propose TTP-independent mechanisms for realizing sybil-free initial access control, pseudonym validation and pseudonym mixing. Evaluation results demonstrate the practical feasibility of our approach and show that anonymity sets encompassing nearly the complete user population are easily achievable.
Google Scholar : the democratization of citation analysis ?

Traditionally, the most commonly used source of bibliometric data is Thomson ISI Web of Knowledge, in particular the (Social) Science Citation Index and the Journal Citation Reports (JCR), which provide the yearly Journal Impact Factors (JIF). This paper presents an alternative source of data (Google Scholar, GS) as well as three alternatives to the JIF to assess journal impact (the h-index, g-index and the number of citations per paper). Because of its broader range of data sources, the use of GS generally results in more comprehensive citation coverage in the area of Management and International Business. The use of GS particularly benefits academics publishing in sources that are not (well) covered in ISI. Among these are: books, conference papers, non-US journals, and in general journals in the field of Strategy and International Business. The three alternative GS-based metrics showed strong correlations with the traditional JIF. As such, they provide academics and universities committed to JIFs with a good alternative for journals that are not ISI-indexed. However, we argue that these metrics provide additional advantages over the JIF and that the free availability of GS allows for a democratization of citation analysis as it provides every academic access to citation data regardless of their institution’s financial means.
The Gut Microbiota and Alzheimer's Disease.

The gut microbiota comprises a complex community of microorganism species that resides in our gastrointestinal ecosystem and whose alterations influence not only various gut disorders but also central nervous system disorders such as Alzheimer's disease (AD). AD, the most common form of dementia, is a neurodegenerative disorder associated with impaired cognition and cerebral accumulation of amyloid-β peptides (Aβ). Most notably, the microbiota-gut-brain axis is a bidirectional communication system that is not fully understood, but includes neural, immune, endocrine, and metabolic pathways. Studies in germ-free animals and in animals exposed to pathogenic microbial infections, antibiotics, probiotics, or fecal microbiota transplantation suggest a role for the gut microbiota in host cognition or AD-related pathogenesis. The increased permeability of the gut and blood-brain barrier induced by microbiota dysbiosis may mediate or affect AD pathogenesis and other neurodegenerative disorders, especially those associated with aging. In addition, bacteria populating the gut microbiota can secrete large amounts of amyloids and lipopolysaccharides, which might contribute to the modulation of signaling pathways and the production of proinflammatory cytokines associated with the pathogenesis of AD. Moreover, imbalances in the gut microbiota can induce inflammation that is associated with the pathogenesis of obesity, type 2 diabetes mellitus, and AD. The purpose of this review is to summarize and discuss the current findings that may elucidate the role of the gut microbiota in the development of AD. Understanding the underlying mechanisms may provide new insights into novel therapeutic strategies for AD.
Generative-Discriminative Variational Model for Visual Recognition

The paradigm shift from shallow classifiers with hand-crafted features to endto-end trainable deep learning models has shown significant improvements on supervised learning tasks. Despite the promising power of deep neural networks (DNN), how to alleviate overfitting during training has been a research topic of interest. In this paper, we present a Generative-Discriminative Variational Model (GDVM) for visual classification, in which we introduce a latent variable inferred from inputs for exhibiting generative abilities towards prediction. In other words, our GDVM casts the supervised learning task as a generative learning process, with data discrimination to be jointly exploited for improved classification. In our experiments, we consider the tasks of multi-class classification, multi-label classification, and zero-shot learning. We show that our GDVM performs favorably against the baselines or recent generative DNN models.
The Symbol Grounding Problem

There has been much discussion recently about the scope and limits of purely symbolic models of the mind and abotlt the proper role of connectionism in cognitive modeling. This paper describes the "symbol grounding problem": How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their (arbitrary) shapes, be grounded in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: (1) iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and (2) categorical representations, which are learned and innate feature detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their (nonsymbolic) categorical representations. Higher-order (3) symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations (e.g. "An X is a Y that is Z "). Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic "module," however; the symbolic functions would emerge as an intrinsically "dedicated" symbol system as a consequence of the bottom-up grounding of categories' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded.