LLukas22/scidocs huggingface dataset:
A method for automatic stock trading combining technical analysis and nearest neighbor classification

In this paper we propose and analyze a novel method for automatic stock trading which combines technical analysis and the nearest neighbor classification. Our first and foremost objective is to study the feasibility of the practical use of an intelligent prediction system exclusively based on the history of daily stock closing prices and volumes. To this end we propose a technique that consists of a combination of a nearest neighbor classifier and some well known tools of technical analysis, namely, stop loss, stop gain and RSI filter. For assessing the potential use of the proposed method in practice we compared the results obtained to the results that would be obtained by adopting a buy-and-hold strategy. The key performance measure in this comparison was profitability. The proposed method was shown to generate considerable higher profits than buy-and-hold for most of the companies, with few buy operations generated and, consequently, minimizing the risk of market exposure. 2010 Elsevier Ltd. All rights reserved.
Price forecasting using wavelet transform and LSE based mixed model in Australian electricity market

Purpose – Price forecasting is essential for risk management in deregulated electricity markets. The purpose of this paper is to propose a hybrid technique using wavelet transform (WT) and multiple linear regression (MLR) to forecast price profile in electricity markets. Design/methodology/approach – Price series is highly volatile and non-stationary in nature. In this work, initially complete price series has been decomposed into separate 48 half-hourly series and then these series have been categorized into different segments for price forecasting. For some segments, WT based MLR has been applied and for the other segments, simple MLR model has been applied. The model is general in nature and has been implemented for one day-ahead price forecasting in National Electricity Market (NEM) of Australia. Participants can use the technique practically, since it predicts price well before submission of bids. Findings – Forecasting performance of the proposed WT and MLR based mixed model has been compared with the three other models, an analytical model, a MLR model and an artificial neural network (ANN) based model. The proposed model was found to be better. Performance evaluation for different wavelets was performed, and it has been observed that for improving forecasting accuracy using WT, Daubechies wavelet of order two gives the best performance. Originality/value – Forecasting accuracy improvement of an established technique by incorporating time domain and wavelet domain variables of the same time series into one set has been implemented in this work. The paper also attempts to explain how non-stationarity can be removed from a non-stationary time series by applying WT after appropriate statistical investigation. Moreover, real time electricity markets are highly unpredictable and yet under investigated. The model has been applied to NEM for the same reason.
Survey on Opinion Mining and Summarization of User Reviews on Web

Large amount of user generated data is present on web as blogs, reviews tweets, comments etc. This data involve user’s opinion, view, attitude, sentiment towards particular product, topic, event, news etc. Opinion mining (sentiment analysis) is a process of finding users’ opinion from user-generated content. Opinion summarization is useful in feedback analysis, business decision making and recommendation systems. In recent years opinion mining is one of the popular topics in Text mining and Natural Language Processing. This paper presents the methods for opinion extraction, classification, and summarization. This paper also explains different approaches, methods and techniques used in process of opinion mining and summarization, and comparative study of these different methods. Keywords— Natural Language Processing, Opinion Mining, Opinion Summarization.
Predicting elections from social media : a three-country , three-method comparative study

This study introduces and evaluates the robustness of different volumetric, sentiment, and social network approaches to predict the elections in three Asian countries – Malaysia, India, and Pakistan from Twitter posts. We find that predictive power of social media performs well for India and Pakistan but is not effective for Malaysia. Overall, we find that it is useful to consider the recency of Twitter posts while using it to predict a real outcome, such as an election result. Sentiment information mined using machine learning models was the most accurate predictor of election outcomes. Social network information is stable despite sudden surges in political discussions, for e.g. around electionsrelated news events. Methods combining sentiment and volume information, or sentiment and social network information, are effective at predicting smaller vote shares, for e.g. vote shares in the case of independent candidates and regional parties. We conclude with a detailed discussion on the caveats of social media analysis for predicting real-world outcomes and recommendations for future work. ARTICLE HISTORY Received 1 August 2017 Revised 12 February 2018 Accepted 12 March 2018
Simplified Morasses with Linear Limits

?1. In a recent series of papers Kanamori ([4], [5], and [6]) defines generalizations of several combinatorial principles known to follow from the existence of morasses. Kanamori proves the consistency of his generalizations by forcing arguments which come close to satisfying the hypotheses of the Martin's Axiomtype characterizations of morasses developed independently by Shelah and Stanley [9] and the author [12]. A similar "almost application" of morasses appears in [11], in which Todorcevic uses forcing to prove the consistency of the existence of Kurepa trees with no Aronszajn or Cantor subtrees. In all cases the attempted proofs using morasses fail for the same reason: the partial orders involved do not have strong enough closure properties. In an attempt to solve this problem Shelah and Stanley strengthened their characterization of morasses to allow applications to what they called "good canonical limit" partial orders. However, for rather subtle reasons even this strengthened forcing axiom is not good enough for the proposed applications. The problem this time is that Shelah and Stanley's "weak commutativity of Lim and restriction" requirement (see [9, 3.9(iv)]) is not satisfied. Furthermore, there is reason to believe that an ordinary morass is just not good enough for these applications, since in L morasses exist at all regular uncountable cardinals, but even a weak form of Todorcevic's conclusion cannot hold at ineffable cardinals (see the end of ?4). A possible solution to this problem is suggested by the fact that FLK is equivalent to a forcing axiom which applies to partial orders satisfying precisely the kind of weak closure conditions involved in the examples described above (see [13]). What is needed to make the proposed morass applications work is something which will do for morass constructions what Lb. does for ordinary transfinite recursion constructions. In this paper we show how extra structure can be built into a morass to accomplish this goal. All of our results are stated in terms of the simplification of morasses introduced in [14]. For the convenience of the reader we list below the relevant definitions and theorems concerning simplified morasses. For the motivation behind the definitions and proofs of the theorems we refer the reader to [14].
Association of body mass with pulmonary function in the Childhood Asthma Management Program (CAMP).

BACKGROUND
While increases in body mass index (BMI) have been associated with the incidence and prevalence of asthma, the mechanisms behind this association are unclear.


METHODS
We hypothesised that BMI would be independently associated with measures of asthma severity in a population of children with mild to moderate asthma enrolled in the Childhood Asthma Management Program (CAMP). A multivariable baseline cross sectional analysis of BMI with our outcomes of interest was performed.


RESULTS
BMI was generally not associated with symptoms, nor was it associated with atopy. While BMI was positively associated with the methacholine concentration that causes a 20% fall in forced expiratory volume in 1 second (PC(20)FEV(1)), this association did not persist after adjustment for FEV(1). Increasing BMI was associated with increasing FEV(1) (beta = 0.006 l, 95% CI (0.001 to 0.01)) and forced vital capacity (FVC) (beta = 0.012 l, 95% CI (0.007 to 0.017)). However, decrements in the FEV(1)/FVC ratio were noted with increasing BMI (beta = -0.242, 95% CI (-0.118 to -0.366)). Thus, an increase in BMI of 5 units was associated with a decrease in FEV(1)/FVC of over 1%.


CONCLUSIONS
Although the association of FEV(1) and FVC with BMI did not support our initial hypothesis, the decrease noted in the FEV(1)/FVC ratio has potential relevance in the relationship between BMI and asthma severity.
The Language of Mathematics

Mohan Ganesalingam. The Language of Mathematics: A Linguistic and Philosophical Investigation. FoLLI Publications on Logic, Language and Information. Springer, 2013. ISBN: 978-3-642-37011-3 (hbk); 978-3-642-37012-0 (ebook). Pp. xx + 260. This ambitious book sets out to provide a linguistic analysis of the language used in written mathematics, both textual and symbolic. It is a revised version of the author’s Cambridge Ph.D. thesis, a worthy recipient of FoLLI’s E. W. Beth dissertation award for 2011. Mohan Ganesalingam is a linguist with a Ph.D. in computer science, and his work combines insights from these disciplines with a substantial grasp of mathematics. However, there is much in the book that should interest philosophers of mathematics. Firstly, Ganesalingam’s project leads him to confront some significant issues in the foundations of mathematics, for which he proposes a response that is, in part, novel. Secondly, and perhaps more importantly, he demonstrates something which is often discussed but seldom attempted: he shows how his account of mathematics can be applied to a significant body of actual mathematical practice. The book is very clearly structured. Chapter 1 begins with a defence of Ganesalingam’s methodological presuppositions. Critically, and in distinction from earlier projects of more modest scope, notably the work of Aarne Ranta (Ranta, 1997), he insists on sufficient breadth to encompass all of pure mathematics and on what he calls ‘full adaptivity’, that any mathematical content be extracted from the text under analysis, and not baked into the analytic system (p. 3). The latter constraint prevents him from, for example, building set theory into his linguistic model. Although his account is intended to provide an analysis of the content of ‘rigorous, careful textbooks’ he confines it to what he calls the ‘formal mode’ of the language found therein: the statements exclusively concerning mathematical objects and mathematical properties (p. 7). Characteristically, such textbooks also contain much that is in an informal mode—remarks about the context, or value, or interest, of the mathematical results, say—but, as Ganesalingam notes, analysis of these comments would require a full analysis of natural language (p. 8). Conversely, one of the attractions mathematics in the formal mode holds for the linguist is its comparative simplicity. In Chapter 2, Ganesalingam surveys the problem that he has set himself, identifying some of the distinctive linguistic features of mathematics that his analysis, or any comparable rival, should account for. These include the interdependency of text and symbols, the extensive use of stipulative definition to expand the language as it is being used, and some idiomatic features of symbolic notation that lack counterparts in natural language. In particular, Ganesalingam observes that the syntax of mathematics is type-dependent: for example, an expression such as α → (β)n may be syntactically well-defined only if α and β are ordinals and
Getting at Systemic Risk via an Agent-Based Model of the Housing Market †

Rob Axtell (George Mason University) Ernesto Carrella (George Mason University) Ben Conlee (Ellington Capital Management) Doyne Farmer (Oxford University) John Geanakoplos (Yale University) Jon Goldstein (George Mason University) Matthew Hendrey (George Mason University) Peter Howitt (Brown University) Philip Kalikman (Yale University) David Masad (George Mason University) Nathan Palmer (George Mason University) Chun-Yi Yang (George Mason University)
Mechanisms for the intracellular manipulation of organelles by conventional electroporation.

Conventional electroporation (EP) changes both the conductance and molecular permeability of the plasma membrane (PM) of cells and is a standard method for delivering both biologically active and probe molecules of a wide range of sizes into cells. However, the underlying mechanisms at the molecular and cellular levels remain controversial. Here we introduce a mathematical cell model that contains representative organelles (nucleus, endoplasmic reticulum, mitochondria) and includes a dynamic EP model, which describes formation, expansion, contraction, and destruction for the plasma and all organelle membranes. We show that conventional EP provides transient electrical pathways into the cell, sufficient to create significant intracellular fields. This emerging intracellular electrical field is a secondary effect due to EP and can cause transmembrane voltages at the organelles, which are large enough and long enough to gate organelle channels, and even sufficient, at some field strengths, for the poration of organelle membranes. This suggests an alternative to nanosecond pulsed electric fields for intracellular manipulations.
Distinct Representations of Cognitive and Motivational Signals in Midbrain Dopamine Neurons

Dopamine is essential to cognitive functions. However, despite abundant studies demonstrating that dopamine neuron activity is related to reinforcement and motivation, little is known about what signals dopamine neurons convey to promote cognitive processing. We therefore examined dopamine neuron activity in monkeys performing a delayed matching-to-sample task that required working memory and visual search. We found that dopamine neurons responded to task events associated with cognitive operations. A subset of dopamine neurons were activated by visual stimuli if the monkey had to store the stimuli in working memory. These neurons were located dorsolaterally in the substantia nigra pars compacta, whereas ventromedial dopamine neurons, some in the ventral tegmental area, represented reward prediction signals. Furthermore, dopamine neurons monitored visual search performance, becoming active when the monkey made an internal judgment that the search was successfully completed. Our findings suggest an anatomical gradient of dopamine signals along the dorsolateral-ventromedial axis of the ventral midbrain.
UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem

In the stochastic multi-armed bandit problem we consider a modification of the UCB algorithm of Auer et al. [4]. For this modified algorithm we give an improved bound on the regret with respect to the optimal reward. While for the original UCB algorithm the regret in Karmed bandits after T trials is bounded by const · K log(T ) ∆ , where ∆ measures the distance between a suboptimal arm and the optimal arm, for the modified UCB algorithm we show an upper bound on the regret of const · K log(T∆ 2) ∆ .
A Computational Model of "Active Vision" for Visual Search in Human-Computer Interaction

Tim Halverson is a cognitive scientist with an interest in human-computer interaction, cognitive modeling, eye movements, and fatigue; he is a post-doctoral research associate in the Performance and Learning Model Team of the Air Force Research Laboratory. Anthony J. Hornof is a computer scientist with an interest in human-computer interaction, cognitive modeling, visual search, and eye tracking; he is an Associate Professor in the Department of Computer and Information Science at the University of Oregon.
Women, Wives and Land Rights in Africa: Situating Gender Beyond the Household in the Debate Over Land Policy and Changing Tenure Systems

The debate over land reform in Africa is embedded in evolutionary models, in which it is assumed landholding systems are evolving into individualized systems of ownership with greater market integration. This process is seen to be occurring even without state protection of private land rights through titling. Gender as an analytical category is excluded in evolutionary models. Women are accommodated only in their dependent position as the wives of landholders in idealized ‘households’. This paper argues that gender relations are central to the organization and transformation of landholding systems. Women have faced different forms of tenure insecurity, both as wives and in their relations with wider kin, as landholding systems have been integrated into wider markets. These cannot be addressed while evolutionary models dominate the policy debate. The paper draws out these arguments from experience of tenure reform in Tanzania and asks how policy-makers might address these issues differently.
A Critical Discourse Analysis of how powerful businesswomen are portrayed in The Economist online

Writer: Julie Risbourg Title: Breaking the ‘glass ceiling’ Subtitle: Language: A Critical Discourse Analysis of how powerful businesswomen are portrayed in The Economist online English Pages: 52 Women still represent a minority in the executive world. Much research has been aimed at finding possible explanations concerning the underrepresentation of women in the male dominated executive sphere. The findings commonly suggest that a patriarchal society and the maintenance of gender stereotypes lead to inequalities and become obstacles for women to break the so-called ‘glass ceiling’. This thesis, however, aims to explore how businesswomen are represented once they have broken the glass ceiling and entered the executive world. Within the Forbes’ list of the 100 most powerful women of 2017, the two first businesswomen on the list were chosen, and their portrayals were analysed through articles published by The Economist online. The theoretical framework of this thesis includes Goffman’s framing theory and takes a cultural feminist perspective on exploring how the media outlet frames businesswomen Sheryl Sandberg and Mary Barra. The thesis also examines how these frames relate to the concepts of stereotyping, commonly used in the coverage of women in the media. More specifically, the study investigates whether negative stereotypes concerning their gender are present in the texts or if positive stereotypes such as idealisation are used to portray them. Those concepts are coupled with the theoretical aspect of the method, which is Critical Discourse Analysis. This method is chosen in order to explore the underlying meanings and messages The Economist chose to refer to these two businesswomen. This is done through the use of linguistic and visual tools, such as lexical choices, word connotations, nomination/functionalisation and gaze. The findings show that they were portrayed positively within a professional environment, and the publication celebrated their success and hard work. Moreover, the results also show that gender related traits were mentioned, showing a subjective representation, which is countered by their idealisation, via their presence in not only the executive world, but also having such high-working titles in male dominated industries.
Model-driven diabetes care: study protocol for a randomized controlled trial

BACKGROUND
People with type 1 diabetes who use electronic self-help tools register a large amount of information about their disease on their participating devices; however, this information is rarely utilized beyond the immediate investigation. We have developed a diabetes diary for mobile phones and a statistics-based feedback module, which we have named Diastat, to give data-driven feedback to the patient based on their own data.


METHOD
In this study, up to 40 participants will be given a smartphone on which is loaded a diabetes self-help application (app), the Few Touch Application (FTA). Participants will be randomized into two groups to be given access to Diastat 4 or 12 weeks, respectively after receiving the smartphone, and will use the FTA with Diastat for 8 weeks after this point. The primary endpoint is the frequency of high and low blood-glucose measurements.


DISCUSSION
The study will investigate the effect of data-driven feedback to patients. Our hypothesis is that this will improve glycemic control and reduce variability. The endpoints are robust indicators that can be assembled with minimal effort by the patient beyond normal routine.


TRIAL REGISTRATION
Clinicaltrials.gov: NCT01774149.
Superhydrophobic tracks for low-friction, guided transport of water droplets.

anti-fogging, [ 6 ] anti-icing, [ 7 ] buoyancy [ 8 ] and drag reduction. [ 9 ] By defi nition, a surface is superhydrophobic if the contact angle between a water drop and the surface at the solid/liquid/air interface is larger than 150 ° , and the contact angle hysteresis is small, i.e., drops readily slide or roll off when the surface is tilted slightly. [ 10–12 ] Here we explore the feasibility of using superhydrophobicity for guided transport of water droplets. We demonstrate a simple yet effi cient approach for droplet transport, in which the droplet is moving on a superhydrophobic surface, using gravity or electrostatic forces as the driving force for droplet transportation and using tracks with vertical walls as gravitational potential barriers to design trajectories. Although the slope of the platform is as small as a few degrees, the drops move at a considerable speed up to 14 cm s − 1 , even in highly curved trajectories. We further demonstrate splitting of a droplet using a superhydrophobic knife and drop-size selection using superhydrophobic tracks. These concepts may fi nd applications in droplet microfl uidics and lab-on-a-chip systems where single droplets with potential analytes are manipulated. [ 13–16 ]