nils-herrmann/clean_scirepeval huggingface dataset:
miRNAs and apoptosis: RNAs to die for

MicroRNAs (miRNAs) are small non-coding RNAs of about 18–24 nucleotides in length that negatively regulate gene expression. Discovered only recently, it has become clear that they are involved in many biological processes such as developmental timing, differentiation and cell death. Data that connect miRNAs to various kinds of diseases, particularly cancer, are accumulating. miRNAs can influence cancer development in many ways, including the regulation of cell proliferation, cell transformation, and cell death. In this review, we focus on miRNAs that have been shown to play a role in the regulation of apoptosis. We first describe in detail how Drosophila has been utilized as a model organism to connect several miRNAs with the cell death machinery. We discuss the genetic approaches that led to the identification of those miRNAs and subsequent work that helped to establish their function. In the second part of the review article, we focus on the involvement of miRNAs in apoptosis regulation in mammals. Intriguingly, many of the miRNAs that regulate apoptosis have been shown to affect cancer development. In the end, we discuss a virally encoded miRNA that influences the cell death response in the mammalian host cell. In summary, the data gathered over the recent years clearly show the potential and important role of miRNAs to regulate apoptosis at various levels and in several organisms.
Markov Chain Monte Carlo Maximum Likelihood

Markov chain Monte Carlo (e. g., the Metropolis algorithm and Gibbs sampler) is a general tool for simulation of complex stochastic processes useful in many types of statistical inference. The basics of Markov chain Monte Carlo are reviewed, including choice of algorithms and variance estimation, and some new methods are introduced. The use of Markov chain Monte Carlo for maximum likelihood estimation is explained, and its performance is compared with maximum pseudo likelihood estimation.
DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning

Microphones are remarkably powerful sensors of human behavior and context. However, audio sensing is highly susceptible to wild fluctuations in accuracy when used in diverse acoustic environments (such as, bedrooms, vehicles, or cafes), that users encounter on a daily basis. Towards addressing this challenge, we turn to the field of deep learning; an area of machine learning that has radically changed related audio modeling domains like speech recognition. In this paper, we present DeepEar -- the first mobile audio sensing framework built from coupled Deep Neural Networks (DNNs) that simultaneously perform common audio sensing tasks. We train DeepEar with a large-scale dataset including unlabeled data from 168 place visits. The resulting learned model, involving 2.3M parameters, enables DeepEar to significantly increase inference robustness to background noise beyond conventional approaches present in mobile devices. Finally, we show DeepEar is feasible for smartphones by building a cloud-free DSP-based prototype that runs continuously, using only 6% of the smartphone's battery daily.
Instant messaging in teen life

Instant Messaging (IM) is being widely adopted by teenagers. In a study of 16 teenage IM users, we explore IM as an emerging feature of teen life, focusing our questions on its support of interpersonal communication and its role and salience in everyday life. We qualitatively describe the teens' IM use interpersonally, as well as its place in the domestic ecology. We also identify technology adoption conditions and discuss behaviors around privacy management. In this initial investigation, we found differences in the nature of use between high school and college teens, differences we propose are accounted for by teens' degree of autonomy as a function of domestic and scholastic obligations, the development of independent work practices, Internet connectivity access, and even transportation access. Moreover, while teen IM use is in part characterized as an optimizing choice between multiple communications media, practice is also tied to concerns around peer pressure, peer group membership and creating additional opportunities to socialize.
The price of power: power seeking and backlash against female politicians.

Two experimental studies examined the effect of power-seeking intentions on backlash toward women in political office. It was hypothesized that a female politician's career progress may be hindered by the belief that she seeks power, as this desire may violate prescribed communal expectations for women and thereby elicit interpersonal penalties. Results suggested that voting preferences for female candidates were negatively influenced by her power-seeking intentions (actual or perceived) but that preferences for male candidates were unaffected by power-seeking intentions. These differential reactions were partly explained by the perceived lack of communality implied by women's power-seeking intentions, resulting in lower perceived competence and feelings of moral outrage. The presence of moral-emotional reactions suggests that backlash arises from the violation of communal prescriptions rather than normative deviations more generally. These findings illuminate one potential source of gender bias in politics.
TOMOHA: TOpic model-based HAshtag recommendation on twitter

On Twitter, hashtags are used to summarize topics of the tweet content and to help to categorize and search tweets. However, hashtags are created in a free style and thus heterogeneous, increasing difficulty of their usage. We propose TOMOHA, a supervised TOpic MOdel-based solution for HAshtag recommendation on Twitter. We treat hashtags as labels of topics, and develop a supervised topic model to discover relationship among words, hashtags and topics of tweets. We also novelly add user following relationship into the model. We infer the probability that a hashtag will be contained in a new tweet, and recommend k most probable ones. We propose parallel computing and pruning techniques to speed up model training and recommendation process. Experiments show that our method can properly and efficiently recommend hashtags.
Global Aesthetics Consensus: Avoidance and Management of Complications from Hyaluronic Acid Fillers—Evidence- and Opinion-Based Review and Consensus Recommendations

Although the safety profile of hyaluronic acid fillers is favorable, adverse reactions can occur. Clinicians and patients can benefit from ongoing guidance on adverse reactions to hyaluronic acid fillers and their management.



A multinational, multidisciplinary group of experts in cosmetic medicine convened the Global Aesthetics Consensus Group to review the properties and clinical uses of Hylacross and Vycross hyaluronic acid products and develop updated consensus recommendations for early and late complications associated with hyaluronic acid fillers.



The consensus panel provided specific recommendations focusing on early and late complications of hyaluronic acid fillers and their management. The impact of patient-, product-, and technique-related factors on such reactions was described. Most of these were noted to be mild and transient. Serious adverse events are rare. Early adverse reactions to hyaluronic acid fillers include vascular infarction and compromise; inflammatory reactions; injection-related events; and inappropriate placement of filler material. Among late reactions are nodules, granulomas, and skin discoloration. Most adverse events can be avoided with proper planning and technique. Detailed understanding of facial anatomy, proper patient and product selection, and appropriate technique can further reduce the risks. Should adverse reactions occur, the clinician must be prepared and have tools available for effective treatment.



Adverse reactions with hyaluronic acid fillers are uncommon. Clinicians should take steps to further reduce the risk and be prepared to treat any complications that arise.
A Novel Method for Accurate and Efficient Barcode Detection with Morphological Operations

Barcode technology is the pillar of automatic identification, that is used in a wide range of real-time applications with various types of codes. The different types of codes and applications impose special problems, so there is a continuous need for solutions with improved effectiveness. There are several methods for barcode localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need automatic barcode localization, e.g. conveyor belts, automated production, where missed detections cause loss of profit. In this paper, we mainly deal with segmentation of images with 1D barcode, but also analyze the operation of different methods for 2D barcode images as well. Our goal is to detect automatically, rapidly and accurately the barcode location by the help of extracted features. We compare some published method from the literature, which basically rely on the contrast between the background and the shape that represent the code. We also propose a novel algorithm, that outperforms the others in both accuracy and efficiency in detecting 1D codes.
Beyond equilibrium: predicting human behaviour in normal form games

It is standard in multi-agent settings to assume that agents will adopt Nash equilibrium strategies. However, studies in experimental economics demonstrate that Nash equilibrium is a poor description of human players' actual behavior. In this study, we consider a wide range of widely-studied models from behavioral game theory. For what we believe is the first time, we evaluate each of these models in a meta-analysis, taking as our data set large-scale and publicly-available experimental data from the literature. We then propose a modified model that we believe is more suitable for practical prediction of human behavior.
Improving Multi-class Text Classification with Naive Bayes

There are numerous text documents available in electronic form. More and more are becoming available every day. Such documents represent a massive amount of information that is easily accessible. Seeking value in this huge collection requires organization; much of the work of organizing documents can be automated through text classification. The accuracy and our understanding of such systems greatly influences their usefulness. In this paper, we seek 1) to advance the understanding of commonly used text classification techniques, and 2) through that understanding, improve the tools that are available for text classification. We begin by clarifying the assumptions made in the derivation of Naive Bayes, noting basic properties and proposing ways for its extension and improvement. Next, we investigate the quality of Naive Bayes parameter estimates and their impact on classification. Our analysis leads to a theorem which gives an explanation for the improvements that can be found in multiclass classification with Naive Bayes using Error-Correcting Output Codes. We use experimental evidence on two commonly-used data sets to exhibit an application of the theorem. Finally, we show fundamental flaws in a commonly-used feature selection algorithm and develop a statistics-based framework for text feature selection. Greater understanding of Naive Bayes and the properties of text allows us to make better use of it in text classification.  Tommi Jaakkola Title: Assistant Professor of Electrical Engineering and Computer Science
A 0.02-to-6GHz SDR balun-LNA using a triple-stage inverter-based amplifier

This paper describes a software-defined-radio (SDR) balun low-noise amplifier (LNA) with no explicit bias circuit, inductor or ac-coupling network. It relies on a triple-stage inverter-based amplifier with resistive feedback to maximize the bandwidth and realize single-to-differential conversion. RC degeneration applied at the last gain stage enhances both linearity and output gain-phase balancing. Optimized in 65-nm CMOS the balun-LNA covers the 0.02-to-6-GHz band with S11>;-11 dB, voltage gain of 21.2-dB and noise figure below 3.2-dB. The in-band IIP2 (IIP3) is +36 to +51 dBm (-5.6 to -5.3 dBm). The power consumption is 7.9 mW at 1.2 V.
A Helmet-Mounted Pedestrian Dead Reckoning System

Since most persons in emergency service wear some sort of headgear, we investigate how basic navigation sensors could be incorporated into a safety helmet and thereby be made truly wearable. It is well known that the key to solving the generic ubiquitous localization problem is the combination of different positioning techniques and sensor modalities. In this paper, a combined approach of pedestrian dead reckoning (PDR) and GPS positioning is followed. An novel combination of neural-network step length predictions and helmet-mounted sensors is presented. The experimental system shows low accumulated error over an extended walk and indoor/outdoor navigation is demonstrated.
Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification

Sentiment Classification seeks to identify a piece of text according to its author’s general feeling toward their subject, be it positive or negative. Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic. This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time.
Detecting product review spammers using rating behaviors

This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic behaviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their impact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a subset of highly suspicious reviewers for further scrutiny by our user evaluators with the help of a web based spammer evaluation software specially developed for user evaluation experiments. Our results show that our proposed ranking and supervised methods are effective in discovering spammers and outperform other baseline method based on helpfulness votes alone. We finally show that the detected spammers have more significant impact on ratings compared with the unhelpful reviewers.
Central pattern generators for locomotion control in animals and robots: A review

The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, low-dimensional, input signals. The review will first cover neurobiological observations concerning locomotor CPGs and their numerical modelling, with a special focus on vertebrates. It will then cover how CPG models implemented as neural networks or systems of coupled oscillators can be used in robotics for controlling the locomotion of articulated robots. The review also presents how robots can be used as scientific tools to obtain a better understanding of the functioning of biological CPGs. Finally, various methods for designing CPGs to control specific modes of locomotion will be briefly reviewed. In this process, I will discuss different types of CPG models, the pros and cons of using CPGs with robots, and the pros and cons of using robots as scientific tools. Open research topics both in biology and in robotics will also be discussed.
A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data

Recent approaches in classifying evolving data streams are based on supervised learning algorithms, which can be trained with labeled data only. Manual labeling of data is both costly and time consuming. Therefore, in a real streaming environment, where huge volumes of data appear at a high speed, labeled data may be very scarce. Thus, only a limited amount of training data may be available for building the classification models, leading to poorly trained classifiers. We apply a novel technique to overcome this problem by building a classification model from a training set having both unlabeled and a small amount of labeled instances. This model is built as micro-clusters using semi-supervised clustering technique and classification is performed with kappa-nearest neighbor algorithm. An ensemble of these models is used to classify the unlabeled data. Empirical evaluation on both synthetic data and real botnet traffic reveals that our approach, using only a small amount of labeled data for training, outperforms state-of-the-art stream classification algorithms that use twenty times more labeled data than our approach.
Hybrid graphene-metal reconfigurable terahertz antenna

The concept and analysis of a graphene-metal reconfigurable terahertz dipole antenna are presented. The hybrid graphene-metal implementation is proposed to combine i) dynamic frequency reconfiguration using graphene field effect and ii) high radiation efficiency enabled by metal. The good direct matching to terahertz continuous wave sources, the large tuning range, and the improved efficiency demonstrate that graphene-metal structures are very promising for reconfigurable terahertz antennas.
Obtaining personal data and asking for erasure: Do app vendors and website owners honour your privacy rights?

EU Directive 95/46/EC and the upcoming EU General Data Protection Regulation grant Europeans the right of access to data pertaining to them. Consumers can approach their service providers to obtain all personal data stored and processed there. Furthermore, they can demand erasure (or correction) of their data. We conducted an undercover field study to determine whether these rights can be exerted in practice. We assessed the behaviour of the vendors of 150 smartphone apps and 120 websites that are popular in Germany. Our deletion requests were fulfilled in 52 to 57 % of the cases and less than half of the data provision requests were answered satisfactorily. Further, we observed instances of carelessness: About 20 % of website owners would have disclosed our personal data to impostors. The results indicate that exerting privacy rights that have been introduced two decades ago is still a frustrating endeavour most of the time.
Feature-based similarity search in 3D object databases

The development of effective content-based multimedia search systems is an important research issue due to the growing amount of digital audio-visual information. In the case of images and video, the growth of digital data has been observed since the introduction of 2D capture devices. A similar development is expected for 3D data as acquisition and dissemination technology of 3D models is constantly improving. 3D objects are becoming an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in methods for 3D similarity search has arisen, and a growing number of competing algorithms for content-based retrieval of 3D objects have been proposed. We survey feature-based methods for 3D retrieval, and we propose a taxonomy for these methods. We also present experimental results, comparing the effectiveness of some of the surveyed methods.
Development of the Perceived Stress Questionnaire: a new tool for psychosomatic research.

A 30-question Perceived Stress Questionnaire (PSQ) was validated, in Italian and English, among 230 subjects. Test-retest reliability was 0.82 for the General (past year or two) PSQ, while monthly Recent (past month) PSQs varied by a mean factor of 1.9 over 6 months; coefficient alpha > 0.9. General and/or Recent PSQ scores were associated with trait anxiety (r = 0.75), Cohen's Perceived Stress Scale (r = 0.73), depression (r = 0.56), self-rated stress (r = 0.56), and stressful life events (p < 0.05). The General PSQ was higher in in-patients than in out-patients (p < 0.05); both forms were correlated with a somatic complaints scale in a non-patient population (r > 0.5), and were higher, among 27 asymptomatic ulcerative colitis patients, in the seven who had rectal inflammation than in those with normal proctoscopy (p = 0.03). Factor analysis yielded seven factors, of which those reflecting interpersonal conflict and tension were significantly associated with health outcomes. The Perceived Stress Questionnaire may be a valuable addition to the armamentarium of psychosomatic researchers.
Maximin affinity learning of image segmentation

Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation. Machine learning has been applied to the affinity classifier to produce affinity graphs that are good in the sense of minimizing edge misclassification rates. However, this error measure is only indirectly related to the quality of segmentations produced by ultimately partitioning the affinity graph. We present the first machine learning algorithm for training a classifier to produce affinity graphs that are good in the sense of producing segmentations that directly minimize the Rand index, a well known segmentation performance measure. The Rand index measures segmentation performance by quantifying the classification of the connectivity of image pixel pairs after segmentation. By using the simple graph partitioning algorithm of finding the connected components of the thresholded affinity graph, we are able to train an affinity classifier to directly minimize the Rand index of segmentations resulting from the graph partitioning. Our learning algorithm corresponds to the learning of maximin affinities between image pixel pairs, which are predictive of the pixel-pair connectivity.
A field study of two online dry-out methods for power transformers

Dry-out methods for transformers, using cellulose cartridges and molecular sieves, were compared. The latter are preferable for keeping a dry transformer dry, whereas the former are more efficient in drying a wet transformer. In this article, we compare the effectiveness of the cellulose cartridge filter and molecular sieve methods. Two identical transformers with similar insulation wetness were used. We estimated the water content of the paper (WCP) insulation from (1) the water activity of the oil, (2) the dielectric response of the transformer, and (3) the water content of individual paper samples determined by Karl Fischer titration. We also measured the acid number of the oil, its interfacial tension, dielectric strength, furan content, dissolved gas content, and particle count.