nils-herrmann/clean_scirepeval huggingface dataset:
A Machine Learning System to Improve Heart Failure Patient Assistance

In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a “supervised database” suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.
60-GHz Millimeter-Wave Life Detection System (MLDS) for Noncontact Human Vital-Signal Monitoring

A first reported experimental study of a 60 GHz millimeter-wave life detection system (MLDS) for noncontact human vital-signal monitoring is presented. This detection system is constructed by using V-band millimeter-wave waveguide components. A clutter canceller is implemented in the system with an adjustable attenuator and phase shifter. It performs clutter cancellation for the transmitting power leakage from the circulator and background reflection to enhance the detecting sensitivity of weak vital signals. The noncontact vital signal measurements have been conducted on a human subject in four different physical orientations from distances of 1 and 2 m. The time-domain and spectrum waveforms of the measured breathing and heartbeat are presented. This prototype system will be useful for the development of the 60-GHz CMOS MLDS detector chip design.
A 3-Month, Randomized, Double-Blind, Placebo-Controlled Study Evaluating the Ability of an Extra-Strength Marine Protein Supplement to Promote Hair Growth and Decrease Shedding in Women with Self-Perceived Thinning Hair

An oral marine protein supplement (MPS) is designed to promote hair growth in women with temporary thinning hair (Viviscal Extra Strength; Lifes2good, Inc., Chicago, IL). This double-blind, placebo-controlled study assessed the ability of MPS to promote terminal hair growth in adult women with self-perceived thinning hair associated with poor diet, stress, hormonal influences, or abnormal menstrual cycles. Adult women with thinning hair were randomized to receive MPS (N = 30) or placebo (N = 30) twice daily for 90 days. Digital images were obtained from a 4 cm(2) area scalp target area. Each subject's hair was washed and shed hairs were collected and counted. After 90 days, these measures were repeated and subjects completed Quality of Life and Self-Assessment Questionnaires. MPS-treated subjects achieved a significant increase in the number of terminal hairs within the target area (P < 0.0001) which was significantly greater than placebo (P < 0.0001). MPS use also resulted in significantly less hair shedding (P = 0.002) and higher total Self-Assessment (P = 0.006) and Quality of Life Questionnaires scores (P = 0.035). There were no reported adverse events. MPS promotes hair growth and decreases hair loss in women suffering from temporary thinning hair.
Pl@ntNet mobile app

Pl@ntNet is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.
Self-Supervised Feature Learning for Semantic Segmentation of Overhead Imagery

Overhead imageries play a crucial role in many applications such as urban planning, crop yield forecasting, mapping, and policy making. Semantic segmentation could enable automatic, efficient, and large-scale understanding of overhead imageries for these applications. However, semantic segmentation of overhead imageries is a challenging task, primarily due to the large domain gap from existing research in ground imageries, unavailability of large-scale dataset with pixel-level annotations, and inherent complexity in the task. Readily available vast amount of unlabeled overhead imageries share more common structures and patterns compared to the ground imageries, therefore, its large-scale analysis could benefit from unsupervised feature learning techniques. In this work, we study various self-supervised feature learning techniques for semantic segmentation of overhead imageries. We choose image semantic inpainting as a self-supervised task [36] for our experiments due to its proximity to the semantic segmentation task. We (i) show that existing approaches are inefficient for semantic segmentation, (ii) propose architectural changes towards self-supervised learning for semantic segmentation, (iii) propose an adversarial training scheme for self-supervised learning by increasing the pretext task’s difficulty gradually and show that it leads to learning better features, and (iv) propose a unified approach for overhead scene parsing, road network extraction, and land cover estimation. Our approach improves over training from scratch by more than 10% and ImageNet pre-trained network by more than 5% mIOU.
The Media and Technology Usage and Attitudes Scale: An empirical investigation

Current approaches to measuring people's everyday usage of technology-based media and other computer-related activities have proved to be problematic as they use varied outcome measures, fail to measure behavior in a broad range of technology-related domains and do not take into account recently developed types of technology including smartphones. In the present study, a wide variety of items, covering a range of up-to-date technology and media usage behaviors. Sixty-six items concerning technology and media usage, along with 18 additional items assessing attitudes toward technology, were administered to two independent samples of individuals, comprising 942 participants. Factor analyses were used to create 11 usage subscales representing smartphone usage, general social media usage, Internet searching, e-mailing, media sharing, text messaging, video gaming, online friendships, Facebook friendships, phone calling, and watching television in addition to four attitude-based subscales: positive attitudes, negative attitudes, technological anxiety/dependence, and attitudes toward task-switching. All subscales showed strong reliabilities and relationships between the subscales and pre-existing measures of daily media usage and Internet addiction were as predicted. Given the reliability and validity results, the new Media and Technology Usage and Attitudes Scale was suggested as a method of measuring media and technology involvement across a variety of types of research studies either as a single 60-item scale or any subset of the 15 subscales.
Beyond Wise et al . : Neuroleptic-induced “ anhedonia ” in rats : Pimozide blocks reward quality of food

O all the neurotransmitters in the brain the one that is best known to the lay public, by far, is dopamine (DA). To the public, and much of the media, DA is the brain’s “pleasure transmitter.” By this popular view, it is a burst of DA that produces the pleasure associated with the receipt or anticipation of natural rewards – a chocolate cake, sex, even social rewards – as well as the euphoria produced by addictive drugs, such as amphetamine, cocaine or heroin. In the popular media it is often suggested that it is pursuit of this DA “rush” that leads to a variety of impulse control disorders, such as over-eating, gambling and addiction. Of all the ideas, in all of neuroscience, this is one of the most widely known, and its origin can be traced to the 1978 paper by Roy Wise, Joan Spindler, Harriet deWit and Gary Gerber, that is the topic of this chapter. This paper is an excellent example of how the interpretation of a seemingly simple experimental finding can profoundly influence the thinking and trajectory of an entire field, and the public imagination, for decades – even if it is wrong. But before getting to that conclusion, we should step back and consider the time leading up to the publication of this extremely influential paper. It was only a little over a decade earlier that DA was first recognized as a neurotransmitter in its own right (rather than just being a precursor of norepinephrine). This came about from a series of important studies by researchers in Sweden who developed a histofluorescence method to identify and map monoamine neurotransmitters in the brain (including DA). They thus effectively created the field of chemical neuroanatomy, as well as vividly mapping the location of dopamine in the brain. They also produced useful methods to selectively lesion monoamine-containing cells with neurotoxins, such as 6-hydroxydopamine (6-OHDA), which would help reveal dopamine’s psychological functions via behavioral consequences. As a result of these studies it became clear that DA-producing neurons located in the midbrain (the substantia nigra and ventral tegmental area) send dense projections to the dorsal and ventral striatum (the caudate-putamen and nucleus accumbens) in the
Probabilistic Extraction of Beat Positions from a Beat Activation Function

We present a probabilistic way to extract beat positions from the output (activations) of the neural network that is at the heart of an existing beat tracker. The method can serve as a replacement for the greedy search the beat tracker currently uses for this purpose. Our experiments show improvement upon the current method for a variety of data sets and quality measures, as well as better results compared to other state-of-the-art algorithms.
The Theory Behind Mp3

Since the MPEG-1 Layer III encoding technology is nowadays widely used it might be interesting to gain knowledge of how this powerful compression/decompression scheme actually functions. How come the MPEG-1 Layer III is capable of reduc ing the bit rate with a factor of 12 without almost any audible degradation? Would it be fairly easy to implement this encoding algorithm? This paper will answer these questions and give further additional detailed information.
Associative Embedding: End-to-End Learning for Joint Detection and Grouping

We introduce associative embedding, a novel method for supervising convolutional neural networks for the task of detection and grouping. A number of computer vision problems can be framed in this manner including multi-person pose estimation, instance segmentation, and multi-object tracking. Usually the grouping of detections is achieved with multi-stage pipelines, instead we propose an approach that teaches a network to simultaneously output detections and group assignments. This technique can be easily integrated into any state-of-the-art network architecture that produces pixel-wise predictions. We show how to apply this method to multi-person pose estimation and report state-of-the-art performance on the MPII and MS-COCO datasets.
Social Attractiveness in Dialogs

This study investigates how acoustic and lexical properties of spontaneous speech in dialogs affect perceived social attractiveness in terms of speaker likeability, friendliness, competence, and self-confidence. We analyze a database of longer spontaneous dialogs between German female speakers and the mutual ratings that dialog partners assigned to one another after every conversation. Thus the ratings reflect long-term impressions based on dialog behavior. Using linear mixed models, we investigate both classical acoustic-prosodic and lexical parameters as well as parameters that capture the degree of speakers’ adaptation, or “convergence”, of these parameters to each other. Specifically we find that likeability is correlated with the speaker’s lexical convergence as well as with her convergence in f0 peak height. Friendliness is significantly related to variation in intensity. For competence, the proportion of positive words in the dialog, variation in shimmer, and overall phonetic convergence are significant correlates. Self-confidence finally is related to several prosodic, phonetic, and lexical adaptation parameters. In some cases, the effect depends on whether interlocutors also had eye contact during their conversation. Taken together, these findings provide evidence that in addition to classical parameters, convergence parameters play an important role in the mutual perception of social attractiveness.
Classification of human activity by using a Stacked Autoencoder

This paper investigates the application of a deep neural network architecture that consists of stackted autoencoder with two autoencoders and a softmax layer for the purpose of human activity classification. Th performance of the proposed architecture is tested on a commonly used data set known as Human Activity Recognition Using Smartphones. It is observed that the proposed method yields better classification results than the representative state-of-the-art methods provided that the parameters of the deep network are suitably optimized.
Design, Fabrication, and Visual Servo Control of an XY Parallel Micromanipulator With Piezo-Actuation

This paper presents a complete design and development procedure of a new XY micromanipulator for two-dimensional (2-D) micromanipulation applications. The manipulator possesses both a nearly decoupled motion and a simple structure, which is featured with parallel-kinematic architecture, flexure hinge-based joints, and piezoelectric actuation. Based on pseudo-rigid-body (PRB) simplification approach, the mathematical models predicting kinematics, statics, and dynamics of the XY stage have been obtained, which are verified by the finite-element analysis (FEA) and then integrated into dimension optimization via the particle swarm optimization (PSO) method. Moreover, a prototype of the micromanipulator is fabricated and calibrated using a microscope vision system, and visual servo control employing a modified PD controller is implemented for the accuracy improvement. The experiments discover that a workspace size of 260 mum times 260 mum with a 2-D positioning accuracy and repeatability around 0.73 and 1.02 mum, respectively, can be achieved by the micromanipulator.
Expansion Planning of Active Distribution Networks With Centralized and Distributed Energy Storage Systems

A multistage active distribution network (ADN) planning model that is integrated with the application of energy storage system (ESS) is presented in this paper. Both the long-term investment cost and short-term operation conditions of ADN are considered in the proposed model. The power supply reliability improvement brought by ESS is also analyzed. At each planning stage, the operation conditions are divided into several typical day scenarios and an extreme condition scenario which are based on the load forecast data. The long-term expansion planning decisions including those for replacing and adding circuits, introducing ESS, and considering ADN short-term operation strategies for the ESS charging and discharging are optimized together in the proposed model. The ESS benefits pertaining to peak load shaving and power reliability enhancement are demonstrated using numerical cases, in which the centralized and the distributed ESS are considered as options for the ADN implementation. The effectiveness of the proposed model is demonstrated through various discussions in the paper.
Assumptions of multiple regression: Correcting two misconceptions

In 2002, an article entitled “Four assumptions of multiple regression that researchers should always test” by Osborne and Waters was published in PARE. This article has gone on to be viewed more than 275,000 times (as of August 2013), and it is one of the first results displayed in a Google search for “regression assumptions”. While Osborne and Waters’ efforts in raising awareness of the need to check assumptions when using regression are laudable, we note that the original article contained at least two fairly important misconceptions about the assumptions of multiple regression: Firstly, that multiple regression requires the assumption of normally distributed variables; and secondly, that measurement errors necessarily cause underestimation of simple regression coefficients. In this article, we clarify that multiple regression models estimated using ordinary least squares require the assumption of normally distributed errors in order for trustworthy inferences, at least in small samples, but not the assumption of normally distributed response or predictor variables. Secondly, we point out that regression coefficients in simple regression models will be biased (toward zero) estimates of the relationships between variables of interest when measurement error is uncorrelated across those variables, but that when correlated measurement error is present, regression coefficients may be either upwardly or downwardly biased. We conclude with a brief corrected summary of the assumptions of multiple regression when using ordinary least squares.
Identifying Harm Events in Clinical Care through Medical Narratives

Preventable medical errors are estimated to be among the leading causes of injury and death in the United States. To prevent such errors, healthcare systems have implemented patient safety and incident reporting systems. These systems enable clinicians to report unsafe conditions and cases where patients have been harmed due to errors in medical care. These reports are narratives in natural language and while they provide detailed information about the situation, it is non-trivial to perform large scale analysis for identifying common causes of errors and harm to the patients. In this work, we present a method for identifying harm events in patient care and categorize the harm event types based on their severity level. We show that our method which is based on convolutional and recurrent networks with an attention mechanism is able to significantly improve over the existing methods on two large scale datasets of patient reports.
Relationship between procrastination and academic performance among a group of undergraduate dental students in India.

Procrastination, generally defined as a voluntary, irrational delay of behavior, is a prevalent phenomenon among college students throughout the world and occurs at alarmingly high rates. For this study, a survey was conducted of 209 second-, third-, and fourth-year undergraduate dental students of Bapuji Dental College and Hospital, Davangere, India, to identify the relationship between their level of procrastination and academic performance. A sixteen-item questionnaire was used to assess the level of procrastination among these students. Data related to their academic performance were also collected. Spearman's correlation coefficient test was used to assess the relationship between procrastination and academic performance. It showed a negative correlation of -0.63 with a significance level of p<0.01 (two-tailed test), indicating that students who showed high procrastination scores performed below average in their academics. In addition, analysis with the Mann-Whitney U test found a significant difference in procrastination scores between the two gender groups (p<0.05). Hence, among the Indian undergraduate dental students evaluated in this study, it appeared that individuals with above average and average academic performance had lower scores of procrastination and vice versa.
EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text

Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale. In this paper we present EUROSENSE, a multilingual sense-annotated resource based on the joint disambiguation of the Europarl parallel corpus, with almost 123 million sense annotations for over 155 thousand distinct concepts and entities from a languageindependent unified sense inventory. We evaluate the quality of our sense annotations intrinsically and extrinsically, showing their effectiveness as training data for Word Sense Disambiguation.
The Role of the Gut Microbiota in Childhood Obesity.

Childhood and adolescent obesity has reached epidemic proportions worldwide. The pathogenesis of obesity is complex and multifactorial, in which genetic and environmental contributions seem important. The gut microbiota is increasingly documented to be involved in the dysmetabolism associated with obesity.



We conducted a systematic search for literature available before October 2015 in the PubMed and Scopus databases, focusing on the interplay between the gut microbiota, childhood obesity, and metabolism.



The review discusses the potential role of the bacterial component of the human gut microbiota in childhood and adolescent-onset obesity, with a special focus on the factors involved in the early development of the gut bacterial ecosystem, and how modulation of this microbial community might serve as a basis for new therapeutic strategies in combating childhood obesity. A vast number of variables are influencing the gut microbial ecology (e.g., the host genetics, delivery method, diet, age, environment, and the use of pre-, pro-, and antibiotics); but the exact physiological processes behind these relationships need to be clarified.



Exploring the role of the gut microbiota in the development of childhood obesity may potentially reveal new strategies for obesity prevention and treatment.
Boosting Moving Average Reversion Strategy for Online Portfolio Selection: A Meta-learning Approach

In this paper, we study the online portfolio selection problem from the perspective of meta learning for mean reversion. The online portfolio selection problem aims to maximize the final accumulated wealth by rebalancing the portfolio at each time period based on the portfolio prices announced before. Mean Reversion is a typical principle in portfolio theory and strategies that utilize this principle achieve the superior empirical performances so far. However there are some important limits of existing Mean Reversion strategies: First, the mean reversion strategies have to set a fixed window size, where the optimal window size can only be chosen in hindsight. Second, most existing mean reversion techniques ignore the temporal heterogeneity of historical price relatives from different periods. Moreover, most mean reversion methods suffer from noises and outliers in the data, which greatly affects the performances. In order to tackle the limits of previous approaches, we exploit mean reversion principle from a meta learning perspective and propose a boosting method for price relative prediction. More specifically, we generate several experts where each expert follows a specific mean reversion policy and predict the final price relatives with meta learning techniques. The sampling of multiple experts involves mean reversion strategies with various window sizes; while the meta learning technique brings temporal heterogeneity and stronger robustness for prediction. We adopt online passive-aggressive learning for portfolio optimization with the predicted price relatives. Extensive experiments have been conducted on real-world datasets and our approach outperforms the state-of-the-art approaches significantly.