nils-herrmann/clean_scirepeval huggingface dataset:
Twitter knows: Understanding the emergence of topics in social networks

Social networks such as Twitter and Facebook are important and widely used communication environments that exhibit scale, complexity, node interaction, and emergent behavior. In this paper, we analyze emergent behavior in Twitter and propose a definition of emergent behavior focused on the pervasiveness of a topic within a community. We extend an existing stochastic model for user behavior, focusing on advocate-follower relationships. The new user posting model includes retweets, replies, and mentions as user responses. To capture emergence, we propose a RPBS (Rising, Plateau, Burst and Stabilization) topic pervasiveness model with a new metric that captures how frequent and in what form the community is talking about a particular topic. Our initial validation compares our model with four Twitter datasets. Our extensive experimental analysis allows us to explore several “what-if” scenarios with respect to topic and knowledge sharing, showing how a pervasive topic evolves given various popularity scenarios.
Functional Interdependence Theory: An Evolutionary Account of Social Situations.

Social interactions are characterized by distinct forms of interdependence, each of which has unique effects on how behavior unfolds within the interaction. Despite this, little is known about the psychological mechanisms that allow people to detect and respond to the nature of interdependence in any given interaction. We propose that interdependence theory provides clues regarding the structure of interdependence in the human ancestral past. In turn, evolutionary psychology offers a framework for understanding the types of information processing mechanisms that could have been shaped under these recurring conditions. We synthesize and extend these two perspectives to introduce a new theory: functional interdependence theory (FIT). FIT can generate testable hypotheses about the function and structure of the psychological mechanisms for inferring interdependence. This new perspective offers insight into how people initiate and maintain cooperative relationships, select social partners and allies, and identify opportunities to signal social motives.
Beneficial effects of tea catechins on diet-induced obesity: stimulation of lipid catabolism in the liver

Obesity has increased at an alarming rate in recent years and is now a worldwide health problem. We investigated the effects of long-term feeding with tea catechins, which are naturally occurring polyphenolic compounds widely consumed in Asian countries, on the development of obesity in C57BL/6J mice. We measured body weight, adipose tissue mass and liver fat content in mice fed diets containing either low-fat (5% triglyceride (TG)), high-fat (30% TG), or high-fat supplemented with 0.1–0.5% (w/w) tea catechins for 11 months. The β-oxidation activities and related mRNA levels were measured after 1 month of feeding. Supplementation with tea catechins resulted in a significant reduction of high-fat diet-induced body weight gain, visceral and liver fat accumulation, and the development of hyperinsulinemia and hyperleptinemia. Feeding with tea catechins for 1 month significantly increased acyl-CoA oxidase and medium chain acyl-CoA dehydrogenase mRNA expression as well as β-oxidation activity in the liver. The stimulation of hepatic lipid metabolism might be a factor responsible for the anti-obesity effects of tea catechins. The present results suggest that long-term consumption of tea catechins is beneficial for the suppression of diet-induced obesity, and it may reduce the risk of associated diseases including diabetes and coronary heart disease.
Addressing morphological variation in alphabetic languages

The selection of indexing terms for representing documents is a key decision that limits how effective subsequent retrieval can be. Often stemming algorithms are used to normalize surface forms, and thereby address the problem of not finding documents that contain words related to query terms through infectional or derivational morphology. However, rule-based stemmers are not available for every language and it is unclear which methods for coping with morphology are most effective. In this paper we investigate an assortment of techniques for representing text and compare these approaches using data sets in eighteen languages and five different writing systems.
 We find character n-gram tokenization to be highly effective. In half of the languages examined n-grams outperform unnormalized words by more than 25%; in highly infective languages relative improvements over 50% are obtained. In languages with less morphological richness the choice of tokenization is not as critical and rule-based stemming can be an attractive option, if available. We also conducted an experiment to uncover the source of n-gram power and a causal relationship between the morphological complexity of a language and n-gram effectiveness was demonstrated.
THE NUMBER OF REALIZATIONS OF A

Laman graphs model planar frameworks that are rigid for a general choice of distances between the vertices. There are finitely many ways, up to isometries, to realize a Laman graph in the plane. Such realizations can be seen as solutions of systems of quadratic equations prescribing the distances between pairs of points. Using ideas from algebraic and tropical geometry, we provide a recursion formula for the number of complex solutions of such systems.
Unifying Terrain Awareness for the Visually Impaired through Real-Time Semantic Segmentation

Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel-wise semantic segmentation to cover navigation-related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short-range obstacles, fast-approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state-of-the-art methods while maintaining real-time speed. We also present a closed-loop field test involving real visually-impaired users, demonstrating the effectivity and versatility of the assistive framework.
South-South medical tourism and the quest for health in Southern Africa.

Intra-regional South-South medical tourism is a vastly understudied subject despite its significance in many parts of the Global South. This paper takes issue with the conventional notion of South Africa purely as a high-end "surgeon and safari" destination for medical tourists from the Global North. It argues that South-South movement to South Africa for medical treatment is far more significant, numerically and financially, than North-South movement. The general lack of access to medical diagnosis and treatment in SADC countries has led to a growing temporary movement of people across borders to seek help at South African institutions in border towns and in the major cities. These movements are both formal (institutional) and informal (individual) in nature. In some cases, patients go to South Africa for procedures that are not offered in their own countries. In others, patients are referred by doctors and hospitals to South African facilities. But the majority of the movement is motivated by lack of access to basic healthcare at home. The high demand and large informal flow of patients from countries neighbouring South Africa has prompted the South African government to try and formalise arrangements for medical travel to its public hospitals and clinics through inter-country agreements in order to recover the cost of treating non-residents. The danger, for 'disenfranchised' medical tourists who fall outside these agreements, is that medical xenophobia in South Africa may lead to increasing exclusion and denial of treatment. Medical tourism in this region and South-South medical tourism in general are areas that require much additional research.
k-Shape: Efficient and Accurate Clustering of Time Series

The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data mining methods, not only due to its exploratory power, but also as a preprocessing step or subroutine for other techniques. In this paper, we present k-Shape, a novel algorithm for time-series clustering. k-Shape relies on a scalable iterative refinement procedure, which creates homogeneous and well-separated clusters. As its distance measure, k-Shape uses a normalized version of the cross-correlation measure in order to consider the shapes of time series while comparing them. Based on the properties of that distance measure, we develop a method to compute cluster centroids, which are used in every iteration to update the assignment of time series to clusters. To demonstrate the robustness of k-Shape, we perform an extensive experimental evaluation of our approach against partitional, hierarchical, and spectral clustering methods, with combinations of the most competitive distance measures. k-Shape outperforms all scalable approaches in terms of accuracy. Furthermore, k-Shape also outperforms all non-scalable (and hence impractical) combinations, with one exception that achieves similar accuracy results. However, unlike k-Shape, this combination requires tuning of its distance measure and is two orders of magnitude slower than k-Shape. Overall, k-Shape emerges as a domain-independent, highly accurate, and highly efficient clustering approach for time series with broad applications.
Diagnosis, management and treatment of orbital and periorbital cellulitis in children.

Children with red swollen eyes frequently present to emergency departments. Some patients will have orbital cellulitis, a condition that requires immediate diagnosis and treatment. Orbital cellulitis can be confused with the less severe, but more frequently encountered, periorbital cellulitis, which requires less aggressive management. Delayed recognition of the signs and symptoms of orbital cellulitis can lead to serious complications such as blindness, meningitis and cerebral abscess. This article describes the clinical features, epidemiology and outcomes of the condition, and discusses management and treatment. It also includes a case study.
noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance from Python Scripts

We present noWorkflow, an open-source tool that systematically and transparently collects provenance from Python scripts, including data about the script execution and how the script evolves over time. During the demo, we will show how noWorkflow collects and manages provenance, as well as how it supports the analysis of computational experiments. We will also encourage attendees to use noWorkflow for their own scripts.
Security and Privacy in IoT Era

Trends in miniaturization have resulted in an explosion of small, low power devices with network connectivity. Welcome to the era of Internet of Things (IoT), wearable devices, and automated home and industrial systems. These devices are loaded with sensors, collect information from their surroundings, process it, and relay it to remote locations for further analysis. Pervasive and seeminly harmless, this new breed of devices raise security and privacy concerns. In this chapter, we evaluate the security of these devices from an industry point of view, concentrating on the design flow, and catalogue the types of vulnerabilities we have found. We also present an in-depth evaluation of the Google Nest Thermostat, the Nike+ Fuelband SE Fitness Tracker, the Haier SmartCare home automation system, and the Itron Centron CL200 electric meter. We study and present an analysis of the effects of these compromised devices in an every day setting. We then finish by discussing design flow enhancements, with security mechanisms that can be efficiently added into a device in a comparative way.
Wireless Secrecy Regions With Friendly Jamming

Inspired by recent results on information-theoretic security, we consider the transmission of confidential messages over wireless networks, in which the legitimate communication partners are aided by friendly jammers. We characterize the security level of a confined region in a quasi-static fading environment by computing the probability of secrecy outage in connection with two new measures of physical-layer security: the jamming coverage and the jamming efficiency. Our analysis for various jamming strategies based on different levels of channel state information provides insight into the design of optimal jamming configurations and shows that a single jammer is not sufficient to maximize both figures of merit simultaneously. Moreover, a single jammer requires full channel state information to provide security gains in the vicinity of the legitimate receiver.
Xandra: An Autonomous Cyber Battle System for the Cyber Grand Challenge

On 4 August 2016, DARPA conducted the final event of the Cyber Grand Challenge (CGC). The challenge in CGC was to build an autonomous system capable of playing in a capture-the-flag hacking competition. The final event pitted the systems from seven finalists against each other, with each system attempting to defend its own network services while proving vulnerabilities in other systems’ defended services. Xandra, our automated cyber reasoning system, took second place overall in the final event. Xandra placed first in security (preventing exploits), second in availability (keeping services operational and efficient), and fourth in evaluation (proving vulnerabilities in competitor services). Xandra also drew the least power of any of the competitor systems. In this article, we describe the high-level strategies applied by Xandra, their realization in Xandra’s architecture, the synergistic interplay between offense and defense, and finally, lessons learned via post-mortem analysis of the final event.
A gas sensor using double split-ring resonator coated with conducting polymer at microwave frequncies

In this research, a gas sensor using double split-ring resonator (DSRR) incorporated with conducting polymer (CP) is proposed at microwave frequencies (Ku-band). The DSRR fabricated on printed circuit board (PCB) is excited by a high-impedance microstrip line, and the CP is coated inside of an inner circle of the DSRR. Electrical characteristics of the CP can be deviated by an interaction between CP and a target gas, and then this deviation of electrical characteristic is demonstrated by S21 frequency response of the DSRR. To examine the performance of the proposed sensor, 100 ppm ethanol (C2H5OH) gas is exposed at room temperature. According to the measured result, the S21 resonance frequency of the DSRR is shifted by 220 MHz and simultaneously, the resonance amplitude is changed by 0.79 dB level. It is clearly found that the DSRR with CP material can be a good candidate for a sensitive gas sensor operating at microwave frequencies.
A dynamic pole-phase modulation induction machine model

Pole-phase modulation can provide performance benefits in highly dynamic applications through the electronic control of induction machine pole count. An applicable dynamic model is developed, and verified using finite element analysis. The proposed model can describe machines with an arbitrary number of electrical inputs and pole count configurations. The available degrees of freedom in polyphase vector space decomposition are used to develop decoupled energy conversion subspaces for each available pole count, allowing conventional control techniques to be directly applied. Simulation results for a smooth 2-pole/6-pole transition using field-oriented control techniques are provided.
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns

Stock prices are formed based on short and/or long-term commercial and trading activities that reflect different frequencies of trading patterns. However, these patterns are often elusive as they are affected by many uncertain political-economic factors in the real world, such as corporate performances, government policies, and even breaking news circulated across markets. Moreover, time series of stock prices are non-stationary and non-linear, making the prediction of future price trends much challenging. To address them, we propose a novel State Frequency Memory (SFM) recurrent network to capture the multi-frequency trading patterns from past market data to make long and short term predictions over time. Inspired by Discrete Fourier Transform (DFT), the SFM decomposes the hidden states of memory cells into multiple frequency components, each of which models a particular frequency of latent trading pattern underlying the fluctuation of stock price. Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion. Modeling multi-frequency trading patterns can enable more accurate predictions for various time ranges: while a short-term prediction usually depends on high frequency trading patterns, a long-term prediction should focus more on the low frequency trading patterns targeting at long-term return. Unfortunately, no existing model explicitly distinguishes between various frequencies of trading patterns to make dynamic predictions in literature. The experiments on the real market data also demonstrate more competitive performance by the SFM as compared with the state-of-the-art methods.
Effects of Four Formulations of Prostaglandin Analogs on Eye Surface Cells. A Comparative Study

We evaluated the cytotoxic effects of four prostaglandin analogs (PGAs) used to treat glaucoma. First we established primary cultures of conjunctival stromal cells from healthy donors. Then cell cultures were incubated with different concentrations (0, 0.1, 1, 5, 25, 50 and 100%) of commercial formulations of bimatoprost, tafluprost, travoprost and latanoprost for increasing periods (5 and 30 min, 1 h, 6 h and 24 h) and cell survival was assessed with three different methods: WST-1, MTT and calcein/AM-ethidium homodimer-1 assays. Our results showed that all PGAs were associated with a certain level of cell damage, which correlated significantly with the concentration of PGA used, and to a lesser extent with culture time. Tafluprost tended to be less toxic than bimatoprost, travoprost and latanoprost after all culture periods. The results for WST-1, MTT and calcein/AM-ethidium homodimer-1 correlated closely. When the average lethal dose 50 was calculated, we found that the most cytotoxic drug was latanoprost, whereas tafluprost was the most sparing of the ocular surface in vitro. These results indicate the need to design novel PGAs with high effectiveness but free from the cytotoxic effects that we found, or at least to obtain drugs that are functional at low dosages. The fact that the commercial formulation of tafluprost used in this work was preservative-free may support the current tendency to eliminate preservatives from eye drops for clinical use.
The Bacillus anthracis spore.

In response to starvation, Bacillus anthracis can form a specialized cell type called the spore, which is the infectious particle for the disease anthrax. The spore is largely metabolically inactive and can resist a wide range of stresses found in nature. In spite of its dormancy, the spore can sense the presence of nutrient and rapidly return to vegetative growth. These properties help the spore to persist for long periods of time in the environment, survive host defenses after entering the body, and cause disease when the correct location in the host is reached. The anatomy of the spore is unique among bacteria, being comprised of a series of specialized concentric shells, each of which provides specific critical functions. Surrounding the spore core (which houses the chromosome) is a peptidoglycan layer important for spore dormancy, a protein shell that resists a variety of toxic molecules, and finally an exterior protein and glycoprotein layer that, among other functions, mediates interactions with surfaces, including those encountered by the spore within the host. Detailed molecular analysis of these shells has shed considerable light on how each layer determines specific spore properties. Future work, especially on the outermost spore layer, is likely to advance therapeutics, methods for spore decontamination and other critical biodefense technologies.
Analyzing ' human functional failures ' in road accidents

Most safety studies come to the conclusion that human error is the main cause of accidents. Nevertheless, such a conclusion has not proved to be efficient in its capacity to offer adequate means to fight again this error. In a purpose of better qualifying accident causation in TRACE, so-called 'human error' is analyzed here from literature review and in-depth accident data with the aim of going further than such a simple statement. The present report is aimed at investigating the different types of 'errors' with the help of a classification model formalizing typical 'Human Functional Failures' (HFF) involved in road accidents. These failures are not seen as the causes of road accidents, but as the result of the driving system malfunctions which can be found in its components (user/road/vehicle) and their defective interactions (unfitness of an element with another). Such a view tries to extend 'accident causation' analysis toward understanding, not only the causes, but also the processes involved in the accident production. So the purpose is to go further than establishing the facts, toward making a diagnosis on their production process. The usefulness of this diagnosis is to help defining countermeasures suited to the malfunction processes in question. This report D5.1, addressed to human functional failures, is in strong connection with Trace report D5.2 devoted to the factors (human and others) and situations of these failures. Trace deliverable D5.3 stresses the most recurrent typical scenarios in which the human functional failures are found. D5.4 is enlarging the questioning of 'human factors' from the side of sociological and cultural backgrounds determining accidental driving behaviour. All these reports are parts of WP5, whose main objective is to provide operational Work Packages of TRACE project with methodological support concerning 'human factors' aspects involved in road accidents. Keyword list: Human error Human factors Accident study Ergonomics Cognitive Psychology