Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Missing Data Imputation Methods for Improving Detection of Obstructive Sleep Apnea

Sleep apnea is a common sleep disorder where the breathing is paused or reduced during sleep, which forces awakening due to less oxygen in the blood. We employ the four data mining methods K-Nearest Neighbor, Support Vector Machine, Arti cial Neural Network, and Decision Tree, to analyze datasets containing the four non-invasive sensor signals chest respiration, abdominal respiration, nasal respiration, and oxygen saturation. Good results for sleep apnea analysis using these signals as input data for the data mining methods already exist. We examine how using the European Data Format Plus (EDF+) a ects the data mining results, because it is a standardised data format used for storing sleep data, and is used by the sensors and Resmed tool NOX which we use for data acquisition. We also examine how pre-processing input data with imputation methods to handle missing data a ects the data mining results, as we want to support usage of sensors of all qualities, in which we have to assume missing data will occur. There are two tasks in this thesis. First, we check how well the data mining algorithms works with our signals in the EDF+ data format. We conclude that EDF+ is as good as the most common data format used in PhysioNet, as we could store and read data without any problems, or it might be even better since the information is stored in a single le instead of several les. By converting data to EDF+, we con rmed that signals and annotations may be stored in the same le. We con rm that our data mining algorithms and all signal combinations, except the sole use of respiration from the chest, may be used for o -line classi cation of sleep apnea. In the second task, we examine how imputation methods work and how pre-processing our signal data with imputation methods a ect our data mining methods. For the missing data challenge, we discovered that the only imputation method that may be used for all percentages of missing values, 5%, 10%, 20%, 30% and 50%, and the four data mining methods, is Self-Organising Maps. This is the overall best method, and the only method that should be used for datasets containing 30% or more missing data, because the others do not maintain the data structure of the dataset. Imputing with the mean and median of each class of normal or disrupted breathing should not be applied as an imputation method, and we assume that separating between classes when imputing is bad practice. Multiple Linear Regression and K-Nearest Neighbor are better at maintaining the data structure than mean and median imputation, but both have a deviation of about 8% compared to the results of the complete dataset. Self-Organising Maps has at most a deviation of 1.25% from the classi cation of the complete dataset. Mean and median imputation may be used if the imputation time is important, as they are better than handling the missing values by replacing them with zeros and the fastest methods, using only a few milliseconds when imputing.
Do macro variables, asset markets, or surveys forecast inflation better?

Surveys do! We examine the forecasting power of four alternative methods of forecasting U.S. inflation out-of-sample: time series ARIMA models; regressions using real activity measures motivated from the Phillips curve; term structure models that include linear, non-linear, and arbitrage-free specifications; and survey-based measures. We also investigate several methods of combining forecasts. Our results show that surveys outperform the other forecasting methods and that the term structure specifications perform relatively poorly. We find little evidence that combining forecasts produces superior forecasts to survey information alone. When combining forecasts, the data consistently places the highest weights on survey information.
Rapid communication The conjunction of feature and depth information

By inducing feature-contingent depth aftereffects, we show that the human visual system combines feature information with depth information. These contingent aftereffects were revealed through the use of a novel selective adaptation paradigm whose stimuli required the combination of feature and depth information in order to segment two interleaved, transparent surfaces. We argue that this combined processing exemplifies the remarkable resourcefulness of a visual system that has adapted to exploit conjunctions of cues that can aid in the segmentation of visual surfaces. 2002 Elsevier Science Ltd. All rights reserved.
Developing an Risk Signal Detection System Based on Opinion Mining for Financial Decision Support

Companies have long sought to detect financial risks and prevent crises in their business activities. Investors also have a great need to identify risks and utilize them for investment. Thus, several studies have attempted to detect financial risk. However, these studies had limitations in that various data were not exploited and diverse perspectives of the firm were not reflected. This can lead to wrong choices for investment. Thus, the purpose of this study was to propose risk signal prediction models based on firm data and opinion mining, reflecting both the perspectives of firms and investors. Furthermore, we developed a process to obtain real time firm related data and convenience visualization. To develop this process, a credit event was defined as an event that led to a critical risk of the firm. In the next step, the firm risk score was calculated for a firm having a possible credit event. This score was calculated by combining the firm activity score and opinion mining score. The firm activity score was calculated based on a financial statement and disclosure data indicator, while the opinion mining score was calculated based on a sentiment analysis of news and social data. As a result, the total firm risk grade was derived, and the risk level was proposed. These processes were developed into a system and illustrated by real firm data. The results of this study demonstrate that it is possible to derive risk signals through integrated monitoring indicators and provide useful information to users. This study can help users make decisions. It also provides users an opportunity to identify new investment momentums.
Insurers’ Investment Strategies: Pro- or Countercyclical?

Traditionally, insurers are seen as stabilisers of financial markets that act countercyclically by buying assets whose price falls. Recent studies challenge this view by providing empirical evidence of procyclicality. This paper sheds new light on the underlying reasons for these opposing views. Our model predicts procyclicality when prices fall due to increasing risk premia, and countercyclicality in response to rises in the risk-free rate. Using granular data on insurers’ government bond holdings, we validate these predictions empirically. Our findings contribute to the current policy discussion on macroprudential measures beyond banking. JEL Classification: G01, G11, G12, G22, G23
Global Second Language Proficiency Predicts Self-Perceptions of General Sarcasm Use Among Bilingual Adults

Each culture has a distinct set of features that contribute to a unique communication style. For example, bilinguals often balance multiple social contexts and may undergo cognitive changes that consequently support different communication styles. The present work examines how individual differences in bilingual experience affect one form of communication style: sarcastic and indirect language. A diverse sample of largely bilingual adults (first language English) rated their likelihood of using sarcastic and indirect language across different daily settings. They also rated their second language experience. There were two key findings: Bilinguals use sarcasm for similar social functions as do monolinguals (general sarcasm, frustration diffusion, and embarrassment diffusion) and greater global second language proficiency linked to greater usage of general sarcasm in daily life. These results suggest that bilinguals may use sarcasm to achieve various communicative goals and bilingual experience may affect general cognitive capacities that support sarcasm use across real-world contexts.
Intersectorial contagion risk in Morocco

This paper proposes an evaluation of intersectorial contagion risk through the analysis of the network of the intersectorial expositions, on the one hand, and the implementation of the approach of the contingent claims analysis (CCA) on the other hand. From this point of view, the matrix of the intersectorial expositions was approximated and of the indicators of centrality, resulting from the network analysis, were estimated. Then, using the CCA, which rises from the pricing theory of the options, the indicators of risk were calculated for each sector, in particular, the distance to the default, probability of default and the leverage ratio. The results obtained confirm that the financial and non-financial companies are the most systemic institutional sectors being able to constitute a principal channel of contagion. Lastly, an estimate of the joint and the conditional probabilities of default of the economic sectors was carried out, while taking as a starting point the work by Goodhart and al (2009) and by using the Archimedean copulas.
It's Not A Lie If You Believe It: On Norms, Lying, and Self-Serving Belief Distortion

This paper focuses on belief distortion in the context of lying decisions. We employ a twostage variant of the â€œdice under the cupâ€ paradigm, in which subjectsâ€™ beliefs are elicited in stage 1 before performing the dice task in stage 2. In stage 1, we elicit the subjectsâ€™ beliefs about (i) majoritarian behavior or (ii) majoritarian normative beliefs in a previous session, and, in order to identify self-serving belief distortion, we vary whether participants are aware or unaware of the upcoming opportunity to lie in the dice task. We find that belief distortion occurs, but only with a specific kind of beliefs. When subjects are aware of the dice task ahead, they convince themselves that lying behavior is widespread in order to justify their lying. In contrast with beliefs about majority behavior, we find that beliefs about the extent to which lying is disapproved of are not distorted. Believing that the majority disapproves of lying does not inhibit own lying. These findings are consistent with a model where agents are conditional norm-followers, and where honest behavior is a strong indicator of disapproval of lying, but disapproval of lying is not a strong indicator of honest behavior
Governance Matters More than Development Constraints for Growth in Sub-Saharan African Countries

<br>The purpose of this study is to identify a series of development constraints for Sub-Saharan African (SSA) countries and to revisit the importance of governance quality that offsets the negative impacts of development constraints on economic growth in SSA. Over the last decade, SSA countries have gained momentum through significant economic growth. Some countries in the region, however, still struggle to upgrade their industrial structures and suffer from unfavorable initial conditions and external shocks. This study sheds light on the development challenges, especially faced by SSA, and draws implications regarding the role of good governance. This study revisits the importance of the governance factor for economic growth in SSA. By systematically reviewing the literature to categorize development constraints into two groups, we identify variables or conditions are either exogenously or endogenously determined. Employing the Hausman-Taylor models and the Fixed Effects method, we found that SSA countries with better governance perform better in terms of economic growth, regardless of unfavorable development conditions. The results show that, regardless of various development constraints, governance quality remains critical to growth in SSA. Thus, for SSA countries to effectively overcome their development constraints, an effort to improve governance is highly recommended.
Quantification of training and match-load distribution across a season in elite English Premier League soccer players

ABSTRACT Objective: To examine training and match loads undertaken by soccer players competing in the English Premier League. Methods: Using a retrospective design, external (GPS) and internal training loads (sessions ratings of perceived exertion [sRPE-TL]) were examined in 26 players across the competition phase of the 2012–2013 English Premier League season. Within-subject linear mixed-models estimated the mean effects (95% confidence interval [CI]) for load data across 6-week mesocycles and 1-week microcycles. Results: Daily sRPE-TL (95% CI range, 15 to 111 AU) and total distance (95% CI range, 179 to 949 AU) were higher during the early stages (mesocycle 1 and 2) of the competition period. Overall, high-speed activity was similar between mesocycles. Across the training week, load was greater on match day and lower pre match-day (G-1) vs. all other days, respectively (p < 0.001). sRPE-TL (~70–90 AU per day) and total distance (~700–800 m per day) progressively declined over the 3 days before a match (p < 0.001). High-speed distance was greater 3 days (G-3) before a game vs. G-1 (95% CI, 140 to 336 m) while very high-speed distance was greater on G-3 and G-2 than G-1 (95% CI range, 8 to 62 m; p < 0.001). Conclusion: Periodisation of in-season training load is mainly evident across the weekly microcycle reflecting the recovery and preparation for matches.
Programming by Demonstration of Object Grasping Using Gaussian Mixture Model Based Techniques

A programming by demonstration techniques combination that allows object grasping is presented. The techniques are the task-parameterized Gaussian mixture model and the Gaussian mixture models. With the first technique, a path hand to the new position and orientation of the object is estimated. Nevertheless, the final value obtained of position and orientation is not always accurate, so it encourages to apply a second method or step correction using Gaussian mixture models. A real experiment allow us to obtain a database of different grasps to subsequently validate the proposed algorithm. The obtained results by using a grasp quality subjective evaluation and graphics error show an improvement to the result generated only by using the task-parameterized Gaussian mixture model
Designing for the Mobile Device: Experiences, Challenges, and Methods

The inlet air temperature and the exhaust gas temperature of a gas turbine power plant are monitored and are utilized as a determining characteristic in a power output stabilization control circuit. The control circuit is designed to change its response upon the attainment of a particular inlet air temperature threshold to conform to differing high and low temperature operating characteristics of the gas turbine power plant.
Purification and characterization of a unique, potent, peptidyl probe for the high conductance calcium-activated potassium channel from venom of the scorpion Buthus tamulus.

Abstract An inhibitor of the high conductance, Ca2(+)-activated K+ channel (PK,Ca) has been purified to homogeneity from venom of the scorpion Buthus tamulus by a combination of ion exchange and reversed-phase chromatography. This peptide, which has been named iberiotoxin (IbTX), is one of two minor components of the crude venom which blocks PK,Ca. IbTX consists of a single 4.3-kDa polypeptide chain, as determined by polyacrylamide gel electrophoresis, analysis of amino acid composition, and Edman degradation. Its complete amino acid sequence has been defined. IbTX displays 68% sequence homology with charybdotoxin (ChTX), another scorpion-derived peptidyl inhibitor of PK,Ca, and, like this latter toxin, its amino terminus contains a pyroglutamic acid residue. However, IbTX possesses 4 more acidic and 1 less basic amino acid residue than does ChTX, making this toxin much less positively charged than the other peptide. In single channel recordings, IbTX reversibly blocks PK,Ca in excised membrane patches from bovine aortic smooth muscle. It acts exclusively at the outer face of the channel and functions with an IC50 of about 250 pM. Block of channel activity appears distinct from that of ChTX since IbTX decreases both the probability of channel opening as well as the channel mean open time. IbTX is a selective inhibitor of PK,Ca; it does not block other types of voltage-dependent ion channels, especially other types of K+ channels that are sensitive to inhibition by ChTX. IbTX is a partial inhibitor of 125I-ChTX binding in bovine aortic sarcolemmal membrane vesicles (Ki = 250 pM). The maximal extent of inhibition that occurs is modulated by K+, decreasing as K+ concentration is raised, but K+ does not affect the absolute inhibitory potency of IbTX. A Scatchard analysis indicates that IbTX functions as a noncompetitive inhibitor of ChTX binding. Taken together, these data suggest that IbTX interacts at a distinct site on the channel and modulates ChTX binding by an allosteric mechanism. Therefore, IbTX defines a new class of peptidyl inhibitor of PK,Ca with unique properties that make it useful for investigating the characteristics of this channel in target tissues.
Price Discovery, High-Frequency Trading and Jumps in Bitcoin Markets

A later version of this paper has been published in the Journal of Financial Stability (Volume 50, October 2020, Article Number 100776). When trading volumes are high, bitcoin futures on the Chicago Mercantile Exchange (CME) and -- until recently -- the Chicago Board Options Exchange (CBOE) lead high-frequency bitcoin price formation. During periods of low trading volumes, the futures become decoupled from prices on centralised bitcoin spot exchanges. However, the trading volume on CME futures has been trending upwards and their price discovery role is now highly significant. Basis risk increases following jumps in either spot or futures prices but these are rapidly arbitraged away by high-frequency institutional traders in the futures.
Generic Brain-computer Interface for Social and Human-computer Interaction

After suffering a more severe disease like spinal cord injury or stroke patients are often not able to interact or even communicate with their environment anymore, especially at the beginning of rehabilitation. Brain-Computer Interfaces (BCIs) can substitute this temporarily lost communication channels and might support rehabilitation by providing an alternative way for controlling a computer only by thoughts without any muscle activity. This enables the patient to communicate by writing letters on the screen, to stay socially in contact with friends or people outside the rehabilitation facility by participating in games like Second Life, where they may appear as healthy persons. Another application isto control items in their room connected to the BCI system like the lights which can be turned off and on as it can be done in a virtual smart home without leaving the bed. In this paper, the technology of such BCIs and the mentioned applications are described utilizing the P300 approach. A generic BCI interface is presented, which allows controlling them and concurrently and transparently switching among them. The results of a recent study show that a BCI can be used by patients suffering from cervical spinal cord injury almost as well as by healthy people which encourages us to think it may assist rehabilitation regarding the social aspect. A variety of BCI can be implemented with the aim of provide them a more active and accessible lives. Keywords-Brain-Computer Interface; BCI; P300; Visual Evoked Potentials; speller; Second Life; Virtual Smart Home
Cognitive Emotional Coping Strategies in Women abused in Their Childhood

In this article we investigated the presence of child abuse in women; the comparative evaluation of coping strategies, cognitive-emotional in two groups of women: abused and non-abused (N = 175). For this purpose, we used the Child Abuse and Trauma Scale (Sanders, Becher-Lausen, 1995), as well as the Cognitive Emotion Regulation Questionnaire (CERQ, Garnefski, Kraaij, Spinhoven, 2001). The results show that generally there are differences in which coping strategies are used by non-abused and abused women when it comes to slow adaptable strategies. Such strategies as Self-blaming, Blaming others and Catastrophizing are more frequently utilized in case of women who experienced child-abuse. Non-abused women adopt high adaptable strategists in stress management, especially Refocusing on Planning and Positive Reevaluation, where they got high scores. Regarding correlations of cognitive-emotional inter-strategies for women who were abused in childhood, we remark that there are associations between slow adatable strategies, Self-blaming, Catastrophizing and Acceptance; moderated correlations between Acceptance and Rumination.
A HYBRID METHOD PROPOSED FOR BEHAVIOURAL ANALYSIS ON TWITTER OPINION DATA USING DICTIONARY AND SEMANTIC BASED APPROACH

he world of technology is growing by leaps and bounds and the arena in technology that is going to be explored is Data Mining. It is estimated that till 2025, most of the world's trade will be based on Data Mining [1]. There is vast availability of people opinion data on twitter for almost every product and service. The challenge is to interpret this data and to extract the information which can lead a decision maker to take better decisions. In dictionary-based approach every word with some positive, negative or neutral value is mapped but opinions are not always direct, hence the sense of the sentence or sub-sentence doesn't agree with its numeral weight. This short coming of this approach lead us to come up with some strategies to increase the accuracy of this method by multiplying the weights together and using some fundamental semantic rule to classify sarcastic tweets. Hence in this paper a hybrid approach is implemented which ensures the sign of total weight of the sentence according to its indirect sense. The positive outcome is that opinions which were earlier treated as neutral are now retaining their sense and add up to our decisions. The hybrid approach is using the concepts of dictionary-based approach and semantic-based approach i.e. matching words from the dictionary and assigning their sentimental value and also using some specific semantic rules used for analyzing sarcastic or neutral tweets for gaining more information about the opinions. The proposed mining of opinions has become easier and more accurate that can be utilized for product's sale forecasting.