nils-herrmann/clean_scirepeval huggingface dataset:
Universal risk factors for multifactorial diseases

The risk for multifactorial diseases is determined by risk factors that frequently apply across disorders (universal risk factors). To investigate unresolved issues on etiology of and individual’s susceptibility to multifactorial diseases, research focus should shift from single determinant-outcome relations to effect modification of universal risk factors. We present a model to investigate universal risk factors of multifactorial diseases, based on a single risk factor, a single outcome measure, and several effect modifiers. Outcome measures can be disease overriding, such as clustering of disease, frailty and quality of life. “Life course epidemiology” can be considered as a specific application of the proposed model, since risk factors and effect modifiers of multifactorial diseases typically have a chronic aspect. Risk factors are categorized into genetic, environmental, or complex factors, the latter resulting from interactions between (multiple) genetic and environmental factors (an example of a complex factor is overweight). The proposed research model of multifactorial diseases assumes that determinant-outcome relations differ between individuals because of modifiers, which can be divided into three categories. First, risk-factor modifiers that determine the effect of the determinant (such as factors that modify gene-expression in case of a genetic determinant). Second, outcome modifiers that determine the expression of the studied outcome (such as medication use). Third, generic modifiers that determine the susceptibility for multifactorial diseases (such as age). A study to assess disease risk during life requires phenotype and outcome measurements in multiple generations with a long-term follow up. Multiple generations will also enable to separate genetic and environmental factors. Traditionally, representative individuals (probands) and their first-degree relatives have been included in this type of research. We put forward that a three-generation design is the optimal approach to investigate multifactorial diseases. This design has statistical advantages (precision, multiple-informants, separation of non-genetic and genetic familial transmission, direct haplotype assessment, quantify genetic effects), enables unique possibilities to study social characteristics (socioeconomic mobility, partner preferences, between-generation similarities), and offers practical benefits (efficiency, lower non-response). LifeLines is a study based on these concepts. It will be carried out in a representative sample of 165,000 participants from the northern provinces of the Netherlands. LifeLines will contribute to the understanding of how universal risk factors are modified to influence the individual susceptibility to multifactorial diseases, not only at one stage of life but cumulatively over time: the lifeline.
’ s repository of research publications and other research outputs Presuppositions in Context : Constructing Bridges Book Section

Traditionally, a distinction is made between that what is as serted by uttering a sentence and that what is presupposed. Presuppositions are characte rized as those propositions which persist even if the sentence which triggers them is neg ated. Thus ‘The king of France is bald’ presupposes that there is a king of France , since this follows from both ‘The king of France is bald’ and ‘It is not the case that the kin g of France is bald’. Stalnaker (1974) put forward the idea that a presupposition of an asserted sentence is a piece of information which is assumed by the speaker to be part of the common background of the speaker and interpreter. The presuppositions as anaphors theory of Van der Sandt (1992) — currently the best theory of presuppos ition as far as empirical predictions are concerned (Beaver 1997:983)— can be seen as o e advanced realization of Stalnaker’s basic idea. The main insight of Van der Sa ndt is that there is an interesting correspondence between the behaviour of anaph oric pronouns in discourse and the projection of presuppositions (i.e., whether and ho w presuppositions survive in complex sentences). Like most research in this area, Van d er Sandt’s work concentrates on the interaction between presuppositions and the linguistic context (i.e., the preceding sentences). However, not only linguistic contex t interacts with presuppositions.
Artificial intelligence and human decision making

It's quite difficult to define precisely the word decision, but each of us generally agrees that he has already experienced the concept. Every human being, right or wrong, think that in many occasions he has made a choice between different alternatives. Whether he exercises his free will or bends to some kind of causal necessity, is another (philosophical) question we do not enter into. The intuitive notion of human free will in choosing between various alternatives will suffice for our talk.
Regulation of Initial Coin Offerings ∗

We analyze the regulation of initial coin offerings (ICOs), in which a company raises funds by pre-selling access to a later product or service. We start by presenting a model that rationalizes the use of ICOs for launching peer-to-peer platforms: by adding dynamics to a platform launch, ICOs can 1) solve a coordination failure problem inherent in many projects with network effects (strategic complementarity in consumers’ participation); and 2) harness the “wisdom of the crowd” by aggregating dispersed signals about project quality. Through either mechanism, an ICO increases platform value, makes the launch of a valuable platform more likely, and enhances social welfare. However, the dynamics of ICOs are also subject to manipulation without strong enforcement on disclosures of off-chain compensation. We use our model to analyze under what circumstances ICOs should be banned or allowed, and highlight the importance of the disclosure requirement.
A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors

We designed and evaluated an assumption-free, deep learning-based methodology for animal health monitoring, specifically for the early detection of respiratory disease in growing pigs based on environmental sensor data. Two recurrent neural networks (RNNs), each comprising gated recurrent units (GRUs), were used to create an autoencoder (GRU-AE) into which environmental data, collected from a variety of sensors, was processed to detect anomalies. An autoencoder is a type of network trained to reconstruct the patterns it is fed as input. By training the GRU-AE using environmental data that did not lead to an occurrence of respiratory disease, data that did not fit the pattern of "healthy environmental data" had a greater reconstruction error. All reconstruction errors were labelled as either normal or anomalous using threshold-based anomaly detection optimised with particle swarm optimisation (PSO), from which alerts are raised. The results from the GRU-AE method outperformed state-of-the-art techniques, raising alerts when such predictions deviated from the actual observations. The results show that a change in the environment can result in occurrences of pigs showing symptoms of respiratory disease within 1⁻7 days, meaning that there is a period of time during which their keepers can act to mitigate the negative effect of respiratory diseases, such as porcine reproductive and respiratory syndrome (PRRS), a common and destructive disease endemic in pigs.
A biterm topic model for short texts

Uncovering the topics within short texts, such as tweets and instant messages, has become an important task for many content analysis applications. However, directly applying conventional topic models (e.g. LDA and PLSA) on such short texts may not work well. The fundamental reason lies in that conventional topic models implicitly capture the document-level word co-occurrence patterns to reveal topics, and thus suffer from the severe data sparsity in short documents. In this paper, we propose a novel way for modeling topics in short texts, referred as biterm topic model (BTM). Specifically, in BTM we learn the topics by directly modeling the generation of word co-occurrence patterns (i.e. biterms) in the whole corpus. The major advantages of BTM are that 1) BTM explicitly models the word co-occurrence patterns to enhance the topic learning; and 2) BTM uses the aggregated patterns in the whole corpus for learning topics to solve the problem of sparse word co-occurrence patterns at document-level. We carry out extensive experiments on real-world short text collections. The results demonstrate that our approach can discover more prominent and coherent topics, and significantly outperform baseline methods on several evaluation metrics. Furthermore, we find that BTM can outperform LDA even on normal texts, showing the potential generality and wider usage of the new topic model.
THE INFLUENCE OF SOCIAL FORCES : EVIDENCE FROM THE BEHAVIOR OF FOOTBALL REFEREES

Analyzing the neutrality of referees during 12 German premier league (1. Bundesliga) soccer seasons, this paper documents evidence that social forces influence agents’ decisions. Referees, who are appointed to be impartial, tend to favor the home team by systematically awarding more stoppage time in close matches in which the home team is behind. They also favor the home team in decisions to award goals and penalty kicks. Crowd composition affects the size and the direction of the bias, and the crowd’s proximity to the field is related to the quality of refereeing.
Distributed TensorFlow with MPI

Machine Learning and Data Mining (MLDM) algorithms are becoming increasingly important in analyzing large volume of data generated by simulations, experiments and mobile devices. With increasing data volume, distributed memory systems (such as tightly connected supercomputers or cloud computing systems) are becoming important in designing in-memory and massively parallel MLDM algorithms. Yet, the majority of open source MLDM software is limited to sequential execution with a few supporting multi-core/manycore execution. In this paper, we extend recently proposed Google TensorFlow for execution on large scale clusters using Message Passing Interface (MPI). Our approach requires minimal changes to the TensorFlow runtime – making the proposed implementation generic and readily usable to increasingly large users of TensorFlow. We evaluate our implementation using an InfiniBand cluster and several well known datasets. Our evaluation indicates the efficiency of our proposed implementation.
The tech writer's survival guide: A comprehensive handbook for aspiring technical writers [Book Review]

Book Publisher: New York: Checkmark Books/ An Imprint of Facts on File, Inc., 2001, 269 pp. with index. The technical communication profession is more than 50 years old, so it is somewhat surprising that only a few book publishers provide sources of career information and advice for curious readers who are considering entering the profession. Four books currently in print provide good general introductions to technical writing as a career choice: Opportunities in Technical Writing Careers , a comprehensive but slightly outdated guide; Technical Writing for Dummies , a wide-ranging book with a strong emphasis on business communication; The Complete Idiot’s Guide to Technical Writing , an effective introduction to technical writing (for those who like the breezy writing style associated with the Idiot’s imprint) with emphasis on software documentation; and Janet Van Wicklen’s book, first published in 1992 with the title The Tech Writing Game , now republished in a revised edition as The Tech Writer’s Survival Guide.
Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation

As the world moves toward greenhouse gas reduction, there is increasingly active work around Li-ion chemistry-based batteries as an energy source for electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids. In these applications, the battery management system (BMS) requires an accurate online estimation of the state of charge (SOC) in a battery pack. This estimation is difficult, especially after substantial battery aging. In order to address this problem, this paper utilizes SOC estimation of Li-ion battery packs using a fuzzy-improved extended Kalman filter (fuzzy-IEKF) for Li-ion cells, regardless of their age. The proposed approach introduces a fuzzy method with a new class and associated membership function that determines an approximate initial value applied to SOC estimation. Subsequently, the EKF method is used by considering the single unit model for the battery pack to estimate the SOC for following periods of battery use. This approach uses an adaptive model algorithm to update the model for each single cell in the battery pack. To verify the accuracy of the estimation method, tests are done on a LiFePO4 aged battery pack consisting of 120 cells connected in series with a nominal voltage of 432 V.
Acquiring Predicate Paraphrases from News Tweets

We present a simple method for evergrowing extraction of predicate paraphrases from news headlines in Twitter. Analysis of the output of ten weeks of collection shows that the accuracy of paraphrases with different support levels is estimated between 60-86%. We also demonstrate that our resource is to a large extent complementary to existing resources, providing many novel paraphrases. Our resource is publicly available, continuously expanding based on daily news.
A Mosaic Approach to Touchless Fingerprint Image with Multiple Views

Touch-based fingerprint technology causes distortions to the fingerprint features due to contact between finger and sensor device. Touch-less fingerprint technique is introduced in an effort to solve this problem by avoiding contact between the finger and the surface of the sensor. However, single contact-less images of the finger leads to less captured features and less overlap between the different views of the fingerprint. In this paper, a new touchless approach for fingerprints based on multiple views images is proposed. Three fingerprint images are captured from the left, center and right side of finger using mobile camera. These three images are combined together using the mosaic method in order to construct a large usable area and increase the overlap area. The proposed method has been compared with other proposed touchless methods. Our touchless mosaic method has offered better performance and achieves more fingerprint features compare to single view touchless method. The proposed method has been evaluated using our touchless database that consists of 480 fingerprint images.
Monte Carlo Tree Search in Lines of Action

The success of Monte Carlo tree search (MCTS) in many games, where αβ-based search has failed, naturally raises the question whether Monte Carlo simulations will eventually also outperform traditional game-tree search in game domains where αβ -based search is now successful. The forte of αβ-based search are highly tactical deterministic game domains with a small to moderate branching factor, where efficient yet knowledge-rich evaluation functions can be applied effectively. In this paper, we describe an MCTS-based program for playing the game Lines of Action (LOA), which is a highly tactical slow-progression game exhibiting many of the properties difficult for MCTS. The program uses an improved MCTS variant that allows it to both prove the game-theoretical value of nodes in a search tree and to focus its simulations better using domain knowledge. This results in simulations superior in both handling tactics and ensuring game progression. Using the improved MCTS variant, our program is able to outperform even the world's strongest αβ-based LOA program. This is an important milestone for MCTS because the traditional game-tree search approach has been considered to be the better suited for playing LOA.
Boundary-Aware Instance Segmentation

We address the problem of instance-level semantic segmentation, which aims at jointly detecting, segmenting and classifying every individual object in an image. In this context, existing methods typically propose candidate objects, usually as bounding boxes, and directly predict a binary mask within each such proposal. As a consequence, they cannot recover from errors in the object candidate generation process, such as too small or shifted boxes. In this paper, we introduce a novel object segment representation based on the distance transform of the object masks. We then design an object mask network (OMN) with a new residual-deconvolution architecture that infers such a representation and decodes it into the final binary object mask. This allows us to predict masks that go beyond the scope of the bounding boxes and are thus robust to inaccurate object candidates. We integrate our OMN into a Multitask Network Cascade framework, and learn the resulting boundary-aware instance segmentation (BAIS) network in an end-to-end manner. Our experiments on the PASCAL VOC 2012 and the Cityscapes datasets demonstrate the benefits of our approach, which outperforms the state-of-the-art in both object proposal generation and instance segmentation.
Exploiting hybrid contexts for Tweet segmentation

Twitter has attracted hundred millions of users to share and disseminate most up-to-date information. However, the noisy and short nature of tweets makes many applications in information retrieval (IR) and natural language processing (NLP) challenging. Recently, segment-based tweet representation has demonstrated effectiveness in named entity recognition (NER) and event detection from tweet streams. To split tweets into meaningful phrases or segments, the previous work is purely based on external knowledge bases, which ignores the rich local context information embedded in the tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg. HybridSeg incorporates local context knowledge with global knowledge bases for better tweet segmentation. HybridSeg consists of two steps: learning from off-the-shelf weak NERs and learning from pseudo feedback. In the first step, the existing NER tools are applied to a batch of tweets. The named entities recognized by these NERs are then employed to guide the tweet segmentation process. In the second step, HybridSeg adjusts the tweet segmentation results iteratively by exploiting all segments in the batch of tweets in a collective manner. Experiments on two tweet datasets show that HybridSeg significantly improves tweet segmentation quality compared with the state-of-the-art algorithm. We also conduct a case study by using tweet segments for the task of named entity recognition from tweets. The experimental results demonstrate that HybridSeg significantly benefits the downstream applications.
Performance comparison of LQR and ANFIS controller for stabilizing double inverted pendulum system

In this paper performance of LQR and ANFIS control for a Double Inverted Pendulum system is compared. The double inverted pendulum system is highly unstable and nonlinear. Mathematical model is presented by linearizing the system about its vertical position. The analysis of the system is performed for its stability, controllability and observability. Furthermore, the LQR controller and ANFIS controller based on the state variable fusion is proposed for the control of the double inverted pendulum system and simulation results show that ANFIS controller has better tracking performance and disturbance rejecting performance as compared to LQR controller.
A look at challenges and opportunities of Big Data analytics in healthcare

Big Data analytics can revolutionize the healthcare industry. It can improve operational efficiencies, help predict and plan responses to disease epidemics, improve the quality of monitoring of clinical trials, and optimize healthcare spending at all levels from patients to hospital systems to governments. This paper provides an overview of Big Data, applicability of it in healthcare, some of the work in progress and a future outlook on how Big Data analytics can improve overall quality in healthcare systems.
Real-time Distracted Driver Posture Classification

In this paper, we present a new dataset for “distracted driver” posture estimation. 1 In addition, we propose a novel system that achieves 95.98% driving posture 2 estimation classification accuracy. The system consists of a genetically-weighted 3 ensemble of Convolutional Neural Networks (CNNs). We show that a weighted 4 ensemble of classifiers using a genetic algorithm yields in better classification 5 confidence. We also study the effect of different visual elements (i.e. hands and face) 6 in distraction detection and classification by means of face and hand localizations. 7 Finally, we present a thinned version of our ensemble that could achieve a 94.29% 8 classification accuracy and operate in a realtime environment.
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires realtime inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment.,,,,,, In this work, we propose SqueezeDet, a fully convolutional neural network for object detection that aims to simultaneously satisfy all of the above constraints. In our network we use convolutional layers not only to extract feature maps, but also as the output layer to compute bounding boxes and class probabilities. The detection pipeline of our model only contains a single forward pass of a neural network, thus it is extremely fast. Our model is fullyconvolutional, which leads to small model size and better energy efficiency. Finally, our experiments show that our model is very accurate, achieving state-of-the-art accuracy on the KITTI [10] benchmark. The source code of SqueezeDet is open-source released.
Neural Machine Translation with Decoding History Enhanced Attention

Neural Machine Translation (NMT) with source side attention have achieved remarkable performance. however, there has been little work exploring to attend to the target side which can potentially enhance the memory capbility of NMT. We reformulate a Decoding-History Enhanced Attention mechanism (DHEA) to render NMT model better at selecting both source side and target side information. DHEA enables a dynamic control on the ratios at which source and target contexts contribute to the generation of target words, offering a way to weakly induce structure relations among both source and target tokens. It also allows training errors to be directly back-propagated through short-cut connections and effectively alleviates the gradient vanishing problem. The empirical study on Chinese-English translation shows that our model with proper configuration can improve by 0.9 BLEU upon Transformer and achieve the best reported results in the same dataset. On WMT14 English-German task and a larger WMT14 English-French task, our model achieves comparable results with the state-of-the-art NMT systems.