nils-herrmann/clean_scirepeval huggingface dataset:
Underestimation of cognitive impairment by Mini-Mental State Examination versus the Montreal Cognitive Assessment in patients with transient ischemic attack and stroke: a population-based study.

The Mini-Mental State Examination (MMSE) is insensitive to mild cognitive impairment and executive function. The more recently developed Montreal Cognitive Assessment (MoCA), an alternative, brief 30-point global cognitive screen, might pick up more cognitive abnormalities in patients with cerebrovascular disease.



In a population-based study (Oxford Vascular Study) of transient ischemic attack and stroke, the MMSE and MoCA were administered to consecutive patients at 6-month or 5-year follow-up. Accepted cutoffs of MMSE <27 and MoCA <26 were taken to indicate cognitive impairment.



Of 493 patients, 413 (84%) were testable. Untestable patients were older (75.5 versus 69.9 years, P<0.001) and often had dysphasia (24%) or dementia (15%). Although MMSE and MoCA scores were highly correlated (r(2)=0.80, P<0.001), MMSE scores were skewed toward higher values, whereas MoCA scores were normally distributed: median and interquartile range 28 (26 to 29) and 23 (20 to 26), respectively. Two hundred ninety-one of 413 (70%) patients had MoCA <26 of whom 162 had MMSE > or =27, whereas only 5 patients had MoCA > or =26 and MMSE <27 (P<0.0001). In patients with MMSE > or =27, MoCA <26 was associated with higher Rankin scores (P=0.0003) and deficits in delayed recall, abstraction, visuospatial/executive function, and sustained attention.



The MoCA picked up substantially more cognitive abnormalities after transient ischemic attack and stroke than the MMSE, demonstrating deficits in executive function, attention, and delayed recall.
Risk factors for peripheral intravenous catheter failure: a multivariate analysis of data from a randomized controlled trial.

To assess the relative importance of independent risk factors for peripheral intravenous catheter (PIVC) failure.



Secondary data analysis from a randomized controlled trial of PIVC dwell time. The Prentice, Williams, and Peterson statistical model was used to identify and compare risk factors for phlebitis, occlusion, and accidental removal.



Three acute care hospitals in Queensland, 
The trial included 3,283 adult medical and surgical patients (5,907 catheters) with a PIVC with greater than 4 days of expected use.



Modifiable risk factors for occlusion included hand, antecubital fossa, or upper arm insertion compared with forearm (hazard ratio [HR], 1.47 [95% confidence interval (CI), 1.28-1.68], 1.27 [95% CI, 1.08-1.49], and 1.25 [95% CI, 1.04-1.50], respectively); and for phlebitis, larger diameter PIVC (HR, 1.48 [95% CI, 1.08-2.03]). PIVCs inserted by the operating and radiology suite staff had lower occlusion risk than ward insertions (HR, 0.80 [95% CI, 0.67-0.94]). Modifiable risks for accidental removal included hand or antecubital fossa insertion compared with forearm (HR, 2.45 [95% CI, 1.93-3.10] and 1.65 [95% CI, 1.23-2.22], respectively), clinical staff insertion compared with intravenous service (HR, 1.69 [95% CI, 1.30-2.20]); and smaller PIVC diameter (HR, 1.29 [95% CI, 1.02-1.61]). Female sex was a nonmodifiable factor associated with an increased risk of both phlebitis (HR, 1.64 [95% CI, 1.28-2.09]) and occlusion (HR, 1.44 [95% CI, 1.30-1.61]).



PIVC survival is improved by preferential forearm insertion, selection of appropriate PIVC diameter, and insertion by intravenous teams and other specialists.
Computational Intelligence in Games

Video games provide an opportunity and challenge for the soft computational intelligence methods like the symbolic games did for “good old-fashioned artificial intelligence.” This article reviews the achievements and future prospects of one particular approach, that of evolving neural networks, or neuroevolution. This approach can be used to construct adaptive characters in existing video games, and it can serve as a foundation for a new genre of games based on machine learning. Evolution can be guided by human knowledge, allowing the designer to control the kinds of solutions that emerge and encouraging behaviors that appear visibly intelligent to the human player. Such techniques may allow building video games that are more engaging and entertaining than current games, and those that can serve as training environments for people. Techniques developed in these games may also be widely applicable in other fields, such as robotics, resource optimization, and intelligent assistants.
Count-Based Exploration in Feature Space for Reinforcement Learning

We introduce a new count-based optimistic exploration algorithm for reinforcement learning (RL) that is feasible in environments with highdimensional state-action spaces. The success of RL algorithms in these domains depends crucially on generalisation from limited training experience. Function approximation techniques enable RL agents to generalise in order to estimate the value of unvisited states, but at present few methods enable generalisation regarding uncertainty. This has prevented the combination of scalable RL algorithms with efficient exploration strategies that drive the agent to reduce its uncertainty. We present a new method for computing a generalised state visit-count, which allows the agent to estimate the uncertainty associated with any state. Our φ-pseudocount achieves generalisation by exploiting the same feature representation of the state space that is used for value function approximation. States that have less frequently observed features are deemed more uncertain. The φ-ExplorationBonus algorithm rewards the agent for exploring in feature space rather than in the untransformed state space. The method is simpler and less computationally expensive than some previous proposals, and achieves near state-of-the-art results on highdimensional RL benchmarks.
Grammatical Error Correction: Machine Translation and Classifiers

We focus on two leading state-of-the-art approaches to grammatical error correction – machine learning classification and machine translation. Based on the comparative study of the two learning frameworks and through error analysis of the output of the state-of-the-art systems, we identify key strengths and weaknesses of each of these approaches and demonstrate their complementarity. In particular, the machine translation method learns from parallel data without requiring further linguistic input and is better at correcting complex mistakes. The classification approach possesses other desirable characteristics, such as the ability to easily generalize beyond what was seen in training, the ability to train without human-annotated data, and the flexibility to adjust knowledge sources for individual error types. Based on this analysis, we develop an algorithmic approach that combines the strengths of both methods. We present several systems based on resources used in previous work with a relative improvement of over 20% (and 7.4 F score points) over the previous state-of-the-art.
Learning Unsupervised Learning Rules

A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this goal is approached by minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect. In this work, we propose instead to directly target a later desired task by meta-learning an unsupervised learning rule, which leads to representations useful for that task. Here, our desired task (meta-objective) is the performance of the representation on semi-supervised classification, and we meta-learn an algorithm – an unsupervised weight update rule – that produces representations that perform well under this meta-objective. Additionally, we constrain our unsupervised update rule to a be a biologically-motivated, neuron-local function, which enables it to generalize to novel neural network architectures. We show that the meta-learned update rule produces useful features and sometimes outperforms existing unsupervised learning techniques. We show that the metalearned unsupervised update rule generalizes to train networks with different widths, depths, and nonlinearities. It also generalizes to train on data with randomly permuted input dimensions and even generalizes from image datasets to a text task.
Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.
Logistic Regression in Rare Events Data

We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.
Issuer Quality and Corporate Bond Returns

We show that the credit quality of corporate debt issuers deteriorates during credit booms, and that this deterioration forecasts low excess returns to corporate bondholders. The key insight is that changes in the pricing of credit risk disproportionately affect the financing costs faced by low quality firms, so the debt issuance of low quality firms is particularly useful for forecasting bond returns. We show that a significant decline in issuer quality is a more reliable signal of credit market overheating than rapid aggregate credit growth. We use these findings to investigate the forces driving time-variation in expected corporate bond returns.  For helpful suggestions, we are grateful to Malcolm Baker, Effi Benmelech, Dan Bergstresser, John Campbell, Sergey Chernenko, Lauren Cohen, Ian Dew-Becker, Martin Fridson, Victoria Ivashina, Chris Malloy, Andrew Metrick, Jun Pan, Erik Stafford, Luis Viceira, Jeff Wurgler, seminar participants at the 2012 AEA Annual Meetings, Columbia GSB, Dartmouth Tuck, Federal Reserve Bank of New York, Federal Reserve Board of Governors, Harvard Business School, MIT Sloan, NYU Stern, Ohio State Fisher, University of Chicago Booth, University of Pennsylvania Wharton, Washington University Olin, Yale SOM, and especially David Scharfstein, Andrei Shleifer, Jeremy Stein, and Adi Sunderam. We thank Annette Larson and Morningstar for data on bond returns and Mara Eyllon and William Lacy for research assistance. The Division of Research at the Harvard Business School provided funding.
Training principles for fascial connective tissues: scientific foundation and suggested practical applications.

Conventional sports training emphasizes adequate training of muscle fibres, of cardiovascular conditioning and/or neuromuscular coordination. Most sports-associated overload injuries however occur within elements of the body wide fascial net, which are then loaded beyond their prepared capacity. This tensional network of fibrous tissues includes dense sheets such as muscle envelopes, aponeuroses, as well as specific local adaptations, such as ligaments or tendons. Fibroblasts continually but slowly adapt the morphology of these tissues to repeatedly applied challenging loading stimulations. Principles of a fascia oriented training approach are introduced. These include utilization of elastic recoil, preparatory counter movement, slow and dynamic stretching, as well as rehydration practices and proprioceptive refinement. Such training should be practiced once or twice a week in order to yield in a more resilient fascial body suit within a time frame of 6-24 months. Some practical examples of fascia oriented exercises are presented.
Deep Neural Network Initialization With Decision Trees.

In this paper, a novel, automated process for constructing and initializing deep feedforward neural networks based on decision trees is presented. The proposed algorithm maps a collection of decision trees trained on the data into a collection of initialized neural networks with the structures of the networks determined by the structures of the trees. The tree-informed initialization acts as a warm-start to the neural network training process, resulting in efficiently trained, accurate networks. These models, referred to as ``deep jointly informed neural networks'' (DJINN), demonstrate high predictive performance for a variety of regression and classification data sets and display comparable performance to Bayesian hyperparameter optimization at a lower computational cost. By combining the user-friendly features of decision tree models with the flexibility and scalability of deep neural networks, DJINN is an attractive algorithm for training predictive models on a wide range of complex data sets.
Activity Analysis as a Method for Information Systems Development: General Introduction and Experiments from Nigeria and Finland

This paper investigates if the Activity-Theoretical methods of work development used by Engeström and others can be transformed into a day-to-day methodology for information systems practitioners. We first present and justify our theoretical framework of Activity Analysis and Development fairly extensively. In the second part we compare work development with information systems development and argue that in its less technological areas, the latter can potentially use the same methodologies as the former. In the third part, small experiments on using Activity Analysis during the earliest phases of information systems development in Nigeria and Finland are reported. In conclusion, we argue that the experiments were encouraging, but the methodology needs to be supported by further illustrative examples and training material. We argue that compared to currently used methods in the earliest and latest “phases” of systems development, Activity Analysis and Development is comprehensive, theoretically well founded, detailed and practicable.
Causal Inference for Recommendation

We develop a causal inference approach to recommender systems. Observational recommendation data contains two sources of information: which items each user decided to look at and which of those items each user liked. We assume these two types of information come from differentmodels—the exposure data comes from a model by which users discover items to consider; the click data comes from a model by which users decide which items they like. Traditionally, recommender systems use the click data alone (or ratings data) to infer the user preferences. But this inference is biased by the exposure data, i.e., that users do not consider each item independently at random. We use causal inference to correct for this bias. On real-world data, we demonstrate that causal inference for recommender systems leads to improved generalization to new data.
Predicting Generated Story Quality with Quantitative Measures

The ability of digital storytelling agents to evaluate their output is important for ensuring high-quality humanagent interactions. However, evaluating stories remains an open problem. Past evaluative techniques are either model-specific—which measure features of the model but do not evaluate the generated stories—or require direct human feedback, which is resource-intensive. We introduce a number of story features that correlate with human judgments of stories and present algorithms that can measure these features. We find this approach results in a proxy for human-subject studies for researchers evaluating story generation systems.
Large-Scale Taxonomy Mapping for Restructuring and Integrating Wikipedia

We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category disambiguation and taxonomy restructuring perform with high accuracy. Besides, we assess these methods on automatically generated datasets and show that we are able to effectively enrich WordNet with a large number of instances from Wikipedia. Our approach produces an integrated resource, thus bringing together the fine-grained classification of instances in Wikipedia and a wellstructured top-level taxonomy from WordNet.
Security and Privacy Vulnerabilities of In-Car Wireless Networks: A Tire Pressure Monitoring System Case Study

Tire Pressure Monitoring Systems represent, to the best of our knowledge, the first in-car wireless network mandated for every new automobile. The security and privacy implications of such incar wireless sensor networks are not fully understood— in particular, it is unclear whether the metal car body sufficiently shields these low-power transceivers from eavesdropping and outside interference. To address these questions, this paper presents a privacy and security evaluation of two tire pressure monitoring systems using both laboratory experiments with isolated tire pressure sensor modules and experiments with a complete vehicle system. We find that the sensor messages can be sniffed and decoded up to 40m from a passing vehicle with a basic low-noise amplifier and the openly available GNU radio platform. The proprietary protocols contain a static 32 bit identifier, and can be relatively easily reverse-engineered. In addition, a sensor message can be triggered through a wireless activation signal. This raises location privacy risks because vehicles could potentially be tracked through these identifiers and drivers do not have any option to disable the system. Furthermore, current protocols do not employ authentication mechanisms and vehicle implementation do not appear to perform basic input validation or filtering of messages. This allows straightforward spoofing of sensor messages. One of our experiments demonstrates this by triggering the tire pressure warning message in a moving vehicle through a spoofed message from another nearby vehicle. The paper concludes with a set of recommendations and a key management protocol that could significantly improve privacy and security of the tire pressure monitoring system and other forthcoming in-car wireless sensor networks.
Backpropagating through Structured Argmax using a SPIGOT

We introduce the structured projection of intermediate gradients optimization technique (SPIGOT), a new method for backpropagating through neural networks that include hard-decision structured predictions (e.g., parsing) in intermediate layers. SPIGOT requires no marginal inference, unlike structured attention networks (Kim et al., 2017) and some reinforcement learning-inspired solutions (Yogatama et al., 2017). Like socalled straight-through estimators (Hinton, 2012), SPIGOT defines gradient-like quantities associated with intermediate nondifferentiable operations, allowing backpropagation before and after them; SPIGOT’s proxy aims to ensure that, after a parameter update, the intermediate structure will remain well-formed. We experiment on two structured NLP pipelines: syntactic-then-semantic dependency parsing, and semantic parsing followed by sentiment classification. We show that training with SPIGOT leads to a larger improvement on the downstream task than a modularly-trained pipeline, the straight-through estimator, and structured attention, reaching a new state of the art on semantic dependency parsing.
A Study of Android Application Security

The fluidity of application markets complicate smartphone security. Although recent efforts have shed light on particular security issues, there remains little insight into broader security characteristics of smartphone applications. This paper seeks to better understand smartphone application security by studying 1,100 popular free Android applications. We introduce the ded decompiler, which recovers Android application source code directly from its installation image. We design and execute a horizontal study of smartphone applications based on static analysis of 21 million lines of recovered code. Our analysis uncovered pervasive use/misuse of personal/phone identifiers, and deep penetration of advertising and analytics networks. However, we did not find evidence of malware or exploitable vulnerabilities in the studied applications. We conclude by considering the implications of these preliminary findings and offer directions for future analysis.
A Neural Knowledge Language Model

Current language models have significant limitation in the ability to encode and decode factual knowledge. This is mainly because they acquire such knowledge from statistical co-occurrences although most of the knowledge words are rarely observed. In this paper, we propose a Neural Knowledge Language Model (NKLM) which combines symbolic knowledge provided by the knowledge graph with the RNN language model. By predicting whether the word to generate has an underlying fact or not, the model can generate such knowledge-related words by copying from the description of the predicted fact. In experiments, we show that the NKLM significantly improves the performance while generating a much smaller number of unknown words.
Sample size estimation and statistical power analyses

The concept of sample size and statistical power estimation is now something that Optometrists that want to perform research, whether it be in practice or in an academic institution, cannot simply hide away from. Ethics committees, journal editors and grant awarding bodies are now increasingly requesting that all research be backed up with sample size and statistical power estimation in order to justify any study and its ! This article presents a step-by-step guide of the process for determining sample size and statistical power. It builds on statistical concepts presented in earlier articles in Optometry Today by Richard Armstrong and Frank Eperjesi.2-7