Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust machine learning by median-of-means: Theory and practice

We introduce new estimators for robust machine learning based on median-of-means (MOM) estimators of the mean of real valued random variables. These estimators achieve optimal rates of convergence under minimal assumptions on the dataset. The dataset may also have been corrupted by outliers on which no assumption is granted. We also analyze these new estimators with standard tools from robust statistics. In particular, we revisit the concept of breakdown point. We modify the original definition by studying the number of outliers that a dataset can contain without deteriorating the estimation properties of a given estimator. This new notion of breakdown number, that takes into account the statistical performances of the estimators, is non-asymptotic in nature and adapted for machine learning purposes. We proved that the breakdown number of our estimator is of the order of (number of observations)*(rate of convergence). For instance, the breakdown number of our estimators for the problem of estimation of a d-dimensional vector with a noise variance sigma^2 is sigma^2d and it becomes sigma^2 s log(d/s) when this vector has only s non-zero component. Beyond this breakdown point, we proved that the rate of convergence achieved by our estimator is (number of outliers) divided by (number of observation). Besides these theoretical guarantees, the major improvement brought by these new estimators is that they are easily computable in practice. In fact, basically any algorithm used to approximate the standard Empirical Risk Minimizer (or its regularized versions) has a robust version approximating our estimators. As a proof of concept, we study many algorithms for the classical LASSO estimator. A byproduct of the MOM algorithms is a measure of depth of data that can be used to detect outliers.
Is IVOL Puzzle a Result of Overreaction or Underreaction?

Reasons behind IVOL puzzle remain largely unexplained. With a method misidentifying return reversal, Huang et al. (2010) claim that return reversal, a result of overreaction, leads to IVOL puzzle. We demonstrate that IVOL puzzle is a result of underreaction rather than of overreaction from both portfolio level and firm level. The negative relation between idiosyncratic volatility and stock returns exists only among underreaction stocks. And idiosyncratic volatility is positive related to stock returns for overreaction stocks.
Samverkan på pappret : en studie om hur primärvård och socialtjänst samverkar med samordnade individuella planer

Collaboration on paper - a study of how primary health care and social services collaborate by using coordinated individual plans The aim of this study was to analyze how primary health care and social services collaborate by using coordinated individual plans (SIP). Since 2010 health care and social services in Sweden are assigned responsibility to establish coordinated individuals plans for clients and patients in need of coordination of interventions. This study was based on eight semi-structured interviews with coworkers in primary health care and social services. New institutional theory has been used as theoretical framework for the analysis. The result of this study shows that the cowor-kers have a positive attitude towards coordinated individual plans. Nevertheless, it seems like the collaboration with these plans is limited between primary health care and social services. Several obstacles have been identified based on following aspects/circumstances in this work: resources, knowledge, the imperative element of SIP and organizational perspectives. In con-clusion, the study shows that the intentions of the coordinated individual plans are positive, even though there is a lot to be done before primary health care and social services are ready to use these the plans according to the legislator's intentions. (Less)
HOSUR: A Novel Measure for Evaluation of Image Segmentation Quality

Image segmentation is one of the vital tasks in image processing. Nevertheless, no universally accepted quality measure for evaluating the performance of various segmentation algorithms or even different parameterizations of the same algorithm exists. In this paper, we propose a new segmentation evaluation measure, based on the fusion of HOG and SURF features. We call it the HOSUR. HOSUR exploits the local shape and corner information to evaluate the similarity between a given segmentation and its respective ground truth. It thus belongs to the category of supervised evaluation measures. Experimental results show accuracy of up to 85%
Transformational Leadership, Organizational Climate and Job Satisfaction in Work Teams

Purpose: The purpose of this paper is to analyze the role of transformational leadership and its relationship with organizational climate, job satisfaction and work team performance. Design/Methodology/Approach: An empirical study was conducted with questionnaires applied to a sample of 185 leaders and work team collaborators from Colombian companies belonging to the construction sector. The hypotheses were tested using Structural Equation Modeling (SEM). Findings: The results showed that transformational leadership positively influences job satisfaction and organizational climate in work teams. It was also found that job satisfaction has a positive influence on organizational climate, and that both constructs positively influence teamwork performance. Practical Implications: This study analyzes the strategic role transformational leadership plays in organizational results stemming from work teams, to the extent that such leadership style promotes a positive organizational climate and high employee satisfaction levels. Organizations could enhance their performance through practices that promote this leadership style in their managers. Originality/Value: This research expands the literature on transformational leadership by presenting empirical evidences on the relationship between this leadership style, organizational climate, job satisfaction, and team performance in the particular context of an emerging economy such as Colombia.
A Greedy Optimization Algorithm Based on QoS in UAV Cluster Network

Due to the strong mobility of nodes, the possibility and randomness of network holes were increasing in UAV network, and the limited energy-carrying capacity of nodes leads to the limitation of cruising ability. The general geographic assistant routing protocols often failed to satisfy demand in the network. A greedy optimization algorithm based on Quality of Service (QoS) was proposed in this paper. The mechanism set segment points based on the route forwarding strategy, in order to optimize the detour path part, and guarantees to response the existing network holes or optimize a new network hole duly. Considering the actual situation of the stability of the UAV communication links and the limited energy-carrying capacity, comprehensive criteria based on QoS was provided for selection of nodes according to the actual application requirements. The simulation shows that compared with the GPSR algorithm and BOPF algorithm the algorithm can optimize the transmission path, reduce end-to-end delay, and improve the performance of the network.
The peculiarities of securitising cyberspace: a multi-actor analysis of the construction of cyber threats in the US (2003-2016)

The rapid development of information and communication technologies rendered cybersecurity an integral aspect of contemporary security discourses and practices in different fields. Yet, despite the obvious intellectual demands of the field, most academic literature on cybersecurity in international relations and security studies remain policy-oriented and under-theorised. One of the few exceptions are studies utilising the Copenhagen school’s securitisation theory to studying discourses and practices of cybersecurity, particularly in the US. Nevertheless, the cyber securitisation literature is still limited in its engagement with the complexity of cybersecurity. One important aspect of this limitation is their focus on official and government’s discourses; an approach that is not applicable with a multi-stakeholder, privately-dominated cyberspace. This state-centric approach does not reflect the diversity of cybersecurity discourses by highlighting only the militarised, geopolitical narratives, adopted by some policy makers. Besides, it overlooks the nuances in threat perceptions, not just between the private and the public sectors, but also among different agencies inside the government. Therefore, focusing on the US as a case study, this paper will employ the securitisation theory’s sectoral analysis for studying the process of securitisation in the field of cybersecurity, using a multi-actor approach which considers the role of several state and nonstate actors in producing and managing cybersecurity discourses, and how complex public-private relationships influence cybersecurity policies and practices. The paper uses the method of discourse analysis in studying cybersecurity discourses of the government, private sector, and media in the US, by examining multiple resources, including official policy documents, congressional hearings, and opinion articles. The analysis covers the period from 2003, when the first cybersecurity strategy was announced, until the end of the Obama administration in 2016. The arguments presented by this paper contribute to the theorisation of the complex conceptual and policy problems of cybersecurity and of cyber securitisation processes through an inductive approach that develops an understanding of the logics and politics of security and risk as contextuallybound and sector-dependent.
Will monetary policy become more of a science: a speech at Monetary Policy over Fifty Years, a conference to mark the fiftieth anniversary of the Deutsche Bundesbank, Frankfurt, Germany, September 21, 2007

This paper reviews the progress that the science of monetary policy has made over recent decades. This progress has significantly expanded the degree to which the practice of monetary policy reflects the application of a core set of "scientific" principles. However, there remains, and will likely always remain, elements of art in the conduct of monetary policy
An Efficient Distributed Database Clustering Algorithm for Big Data Processing

This paper proposes a distributed data clustering technique based on deep neural network. First, each record in the distributed database is taken as an input vector, and its characteristics are extracted and input to the input layer of the depth neural network. The weight of the connection is trained by BP algorithm, and the training of depth neural network output is realized by adjusting the weight. Finally, the data clustering results are judged according to the similarity of the current vector corresponding to the output data. Experimental results based on small-scale distributed systems show that this method has better test set accuracy than traditional k-means clustering method, and is more suitable for large-scale data clustering in the distributed environments.
Teachers' Navigation of Mathematical Representations in Argumentation

Mathematical argumentation has recently received more prominent attention in K-12 classrooms which has immediate consequences in the undergraduate mathematics classroom, including the critical intersection with representing mathematical concepts. Educators' perceptions of this intersection is important to understand as they have a signi cant impact on the skills undergraduates bring to their mathematics classrooms. This qualitative study investigated (1) how K-5 elementary educators conceptualized argumentation, (2) the role(s) and purpose(s) they attribute to representations within argumentation, and (3) the criteria for representations they use/o er when arguing claims of generality. Eight elementary educators participated in this study. Each completed two interviews and a classroom observation. The ndings indicate that (1) arguments are to be produced in a prose format, they help support and learn mathematical content, and allow for di ering perspectives; (2) there are no roles for representations within arguments but they were purposed to help navigate concepts involved in a claim statement as well as supplement arguments; and (3) representations called forth to determine the truth value of a claim of generality are relevant to the claim but are not universal instantiations of those Steven LeMay, University of Connecticut, 2017 relevant ideas, and thus are unwarranted. Some implications of this research upon K16 mathematics teaching and learning are discussed. This study's ndings contribute to the literature about mathematical argumentation in the classroom, its relationships with representing mathematical concepts, and how elementary educators perceive both. Future lines of research to strengthen this area are o ered. Teachers' Navigation of Mathematical Representations in Argumentation
Perception of the Z Generation Regarding the Promotion Way of Romanian Army Institution

This article approaches the attitude of Z Generation concerning the promoting of the Romanian Army Institution in social media. This domain is a rigorous one, based on serosity and there are not a multitude of common values with this generation. Technology is a world-known aspect and the IGeneration is known as being born with new technological culture and they do not require to adjust to it. They prefer socializing in online, capture some meaningful general knowledge from their intercommunications. The Internet proposes various techniques of enticing youngsters to consume a lot of time online, on their smart devices.
Examination of Exercise Dependency Levels and State of Loneliness in Students of Sports Sciences Faculty

The aim of this study is to investigate the dependence levels and loneliness status of students studying in sports sciences. The study group of the study consisted of 204 sports science students, 68 women and 136 men with an average age of 20.75 ± 2.47. For the purpose of collecting data, “Exercise Dependancy Scale” developed by Tekkurşun-Demir, Hazar and Cicioğlu (2018) and the“Loneliness Scale” Turkish adaptation of the scale was made by Demir (1989) were used. Descriptive statistics such as frequency and percentage and Mann Whitney U, Kruskall Wallis and Spearman Correlation analysis were used for data analysis. According to the findings of the study, it was observed that students were in the dependent group rarely experienced loneliness. A significant difference was found in the loneliness score of the students according to gender. Significant differences were found in the postponement of exercise dependence scale, individual and social needs and conflict sub-dimension according to students’ were being licensed. According to the frequency of participation in the exercise, there was a significant difference between the postponement of individual and social needs of the exercise dependence scale and the development of the conflict, tolerance and passion. In the correlation analysis of the scales, no significant relationship was found between loneliness and exercise dependence. As a result, in our study, although it was seen that the loneliness levels of the students studying in sports sciences were low, it was concluded that male students were lonelier than female students. In addition, it can be said that students are included in the exercise dependent group, because of the fact that these students are kneaded with sports and due to their sports background, they can adjust the dose and duration of the exercise correctly, and thus they do not postpone their social and individual needs as a part of their lives.
How Did Unconventional Monetary Policy Affect Economic Forecasts?

We study how unconventional monetary policy announcements affected professional forecasters' predictions of bond rates, gross domestic product growth and inflation using data from the monthly survey by the Wall Street Journal. We find that unconventional monetary policy (UMP) announcements moved predicted bond rates in the direction the Fed intended. UMP announcements had differential impacts on forecasters' predictions; they also tended to move growth and inflation predictions in directions opposite those the Fed intended due to Fed information effects. A policy implication of our study is that the Fed should communicate economic projections to the public separately from monetary policy announcements to mitigate Fed information effects.
Do Primary Emotions Predict Psychopathological Symptoms? A Multigroup Path Analysis

Background: Research involving animal models has repeatedly proposed dysregulations in subcortically rooted affective systems as a crucial etiological factor in the development of a variety of psychiatric disorders. However, empirical studies with human participants testing these hypotheses have been sparse. Associations between primary emotions systems and different psychiatric symptoms were investigated in order to gain insights into the influence of evolutionary-rooted primary emotions on psychopathology. Material and Methods: The community sample included 616 adults (61.9% female). 243 reported a psychiatric lifetime diagnosis. By applying path analysis, we estimated paths between SEEKING, ANGER, FEAR, SADNESS, CARE, and PLAY (Affective Neuroscience Personality Scales; ANPS) and symptoms of substance abuse (Alcohol, Smoking, and Substance Involvement Screening Test; ASSIST) as well as depression, anxiety, and somatization (Brief Symptom Inventory; BSI-18). To examine the moderator effects of gender and psychiatric lifetime diagnosis, multigroup analysis was applied. Results: Substance abuse was associated with male sex (β = −.25), SADNESS (β = .25), and ANGER (β = .10). Depression was associated with SADNESS (β = .53), FEAR (β = .10), SEEKING (β = −.10), and PLAY (β = −.15). Anxiety was linked to SADNESS (β = .33), FEAR (β = .21) and PLAY (β = −.10). Somatization was associated with SADNESS (β = .26) and PLAY (β = −.12; all p < .001). Multigroup analysis revealed no differences in paths between tested groups (all p > .01). The model explained 14% of the variance of substance abuse, 52% of depression, 32% of anxiety, and 14% of somatization. Conclusions: The results further our understanding of the differential role of primary emotions in the development of psychopathology. In this, the general assumption that primary emotion functioning might be a valuable target in mental health care is underlined.
The Use of Artificial Intelligence as a Tool Supporting Sustainable Development Local Policy

This paper addresses the problem of noise in spa protection areas. Its aim is to determine the delimitation of the areas that exceed a permissible noise level around the sanatorium on the example of a health resort in Inowrocław. The determination of the exceedance of permissible noise levels allows us to develop directly effective local policy tools to be included in planning documents. In order to reduce noise infiltration, it is important to define environmental priorities. Taking into account their impact on the health of users in the protection area, environmental priorities enable us to introduce additional elements to street architecture. In order to properly manage space, in accordance with the idea of sustainable development, zones of environmental sensitivity—and their socio-environmental vulnerability—have been designated for assessing damage (exceeding permissible noise in health facilities) and defining methods of building resilience (proper management). This has provided the basis for a natural balance optimized for the people living in these areas. To achieve the goal above, non-linear support vector machine (SVM) networks were used. This technique allows us to classify the linearly inseparable data and to determine the optimal separation margin. The boundaries of the areas which exceeded permissible noise levels (separation margin) were estimated on the basis of noise pollution maps, created by means of the SVM technique. Thus, the study results in establishing buffer zones where it is possible to use varied land utilization in terms of form and function, as described in the planning documents. Such an activity would limit the spread of noise.
An investigation of the relationship between college student development and alcohol consumption patterns

The purpose of this study was to examine the relationships between college student development and alcohol consumption patterns. Student development was investigated in the domains of moral development through the use of the Defining Issues Test, identity development through the use of the Erwin Identity Scale, and intellectual development through the use of the Scale oflntellectual Development. Alcohol consumption patterns were studied in terms of binge drinking frequency according to responses on the Core Alcohol and Drug Survey. All participants were classified into one of four drinking pattern categories: Abstainers. Drinkers. Singers. and Frequent Singers. The study also used a discriminant analysis to determine the linear combination of student developmental and demographic variables which best predicts student drinking category membership. The study was conducted through a randomized mailing to 400 undergraduate students at the College of William and Mary. The 114 respondents were residential, traditionally-aged students. Results showed that there was a significant effect for the Commitment subscale of the Scale oflntellectual Development by alcohol consumption category. Students who were Non-Singers were more likely to have higher scores on this measure of commitment to a value system and personal method of processing information. There was also a significant effect for Greek membership by alcohol consumption category, as Frequent Singers and Singers \Vere more likely to be members
In Situ Ferroelectric Domain Dynamics Probed with Differential Phase Contrast Imaging

Unlike classical ferroelectrics, relaxor ferroelectrics are characterized by their diffuse phase transitions and dielectric properties. They can also exhibit a low coercive field and giant piezoelectric coefficients [1]. These properties are thought to arise from the existence of polar nanoregions in a non-polar matrix or a slush-like response of a nanoscale multi-domain state, but the models are fully predictive [2, 3]. If relaxor ferroelectric materials are to be designed rather than discovered, a complete fundamental understanding of their response to electric field is required – from the micro to atomic length scale. Recent advances in in situ microscopy and STEM detector technologies, such as 4D STEM and differential phase contrast (DPC) imaging [4], have the potential to unlock the structural details needed to explain the remaining mysteries in these materials.