nils-herrmann/clean_scirepeval huggingface dataset:
SloMo: Downclocking WiFi Communication

As manufacturers continue to improve the energy efficiency of battery-powered wireless devices, WiFi has become one of—if not the—most significant power draws. Hence, modern devices fastidiously manage their radios, shifting into low-power listening or sleep states whenever possible. The fundamental limitation with this approach, however, is that the radio is incapable of transmitting or receiving unless it is fully powered. Unfortunately, applications found on today’s wireless devices often require frequent access to the channel. We observe, however, that many of these same applications have relatively low bandwidth requirements. Leveraging the inherent sparsity in Direct Sequence Spread Spectrum (DSSS) modulation, we propose a transceiver design based on compressive sensing that allows WiFi devices to operate their radios at lower clock rates when receiving and transmitting at low bit rates, thus consuming less power. We have implemented our 802.11b-based design in a software radio platform, and show that it seamlessly interacts with existing WiFi deployments. Our prototype remains fully functional when the clock rate is reduced by a factor of five, potentially reducing power consumption by over 30%.
An Authoring Tool for Location-Based Mobile Games with Augmented Reality Features

This paper presents an authoring tool for building location-based mobile games, enhanced with augmented reality capabilities. These games are a subclass of pervasive games in which the gameplay evolves and progresses according to player's location. We have conducted a literature review on authoring tools and pervasive games to (i) collect the common scenarios of current location-based mobile games, and (ii) features of authoring tools for these games. Additionally, we have also used the focus groups methodology to find new scenarios for location-based mobile games, in particular regarding how augmented reality can be used in these games. Both literature review and focus groups provide us a set of requirements to design a software architecture for our authoring tool, a web-based application where games are created and executed. In our approach, games are designed as a set of missions that can be ordered or not. Players use mobile devices to perform these missions in order to complete each game. Our main objective is to provide a software solution to enable non-programmers users to design, build and run location-based mobile games. In order to evaluate our tool, we present a game design called "Battle for Fortaleza", and how this game is implemented in our solution.
Online gaming and risks predict cyberbullying perpetration and victimization in adolescents

The present study examined factors associated with the emergence and cessation of youth cyberbullying and victimization in Taiwan. A total of 2,315 students from 26 high schools were assessed in the 10th grade, with follow-up performed in the 11th grade. Self-administered questionnaires were collected in 2010 and 2011. Multiple logistic regression was conducted to examine the factors. Multivariate analysis results indicated that higher levels of risk factors (online game use, exposure to violence in media, internet risk behaviors, cyber/school bullying experiences) in the 10th grade coupled with an increase in risk factors from grades 10 to 11 could be used to predict the emergence of cyberbullying perpetration/victimization. In contrast, lower levels of risk factors in the 10th grade and higher levels of protective factors coupled with a decrease in risk factors predicted the cessation of cyberbullying perpetration/victimization. Online game use, exposure to violence in media, Internet risk behaviors, and cyber/school bullying experiences can be used to predict the emergence and cessation of youth cyberbullying perpetration and victimization.
Soft robotics: Technologies and systems pushing the boundaries of robot abilities

The proliferation of soft robotics research worldwide has brought substantial achievements in terms of principles, models, technologies, techniques, and prototypes of soft robots. Such achievements are reviewed here in terms of the abilities that they provide robots that were not possible before. An analysis of the evolution of this field shows how, after a fewpioneeringworks in the years 2009 to 2012, breakthrough resultswere obtainedby taking seminal technological and scientific challenges related to soft robotics from actuation and sensing to modeling and control. Further progress in soft robotics research hasproduced achievements that are important in termsof robot abilities—that is, from the viewpoint of what robots can do today thanks to the soft robotics approach. Abilities such as squeezing, stretching, climbing, growing, andmorphingwouldnotbepossiblewith anapproachbasedonly on rigid links. The challengeahead for soft robotics is to further develop theabilities for robots togrow, evolve, self-heal, develop, andbiodegrade,which are the ways that robots can adapt their morphology to the environment.
Lipid-induced insulin resistance: unravelling the mechanism

Insulin resistance has long been associated with obesity. More than 40 years ago, Randle and colleagues postulated that lipids impaired insulin-stimulated glucose use by muscles through inhibition of glycolysis at key points. However, work over the past two decades has shown that lipid-induced insulin resistance in skeletal muscle stems from defects in insulin-stimulated glucose transport activity. The steatotic liver is also resistant to insulin in terms of inhibition of hepatic glucose production and stimulation of glycogen synthesis. In muscle and liver, the intracellular accumulation of lipids-namely, diacylglycerol-triggers activation of novel protein kinases C with subsequent impairments in insulin signalling. This unifying hypothesis accounts for the mechanism of insulin resistance in obesity, type 2 diabetes, lipodystrophy, and ageing; and the insulin-sensitising effects of thiazolidinediones.
Epidemiology of adolescent idiopathic scoliosis.

Adolescent idiopathic scoliosis is a common disease with an overall prevalence of 0.47-5.2 % in the current literature. The female to male ratio ranges from 1.5:1 to 3:1 and increases substantially with increasing age. In particular, the prevalence of curves with higher Cobb angles is substantially higher in girls than in boys: The female to male ratio rises from 1.4:1 in curves from 10° to 20° up to 7.2:1 in curves >40°. Curve pattern and prevalence of scoliosis is not only influenced by gender, but also by genetic factors and age of onset. These data obtained from school screening programs have to be interpreted with caution, since methods and cohorts of the different studies are not comparable as age groups of the cohorts and diagnostic criteria differ substantially. We do need data from studies with clear standards of diagnostic criteria and study protocols that are comparable to each other.
Efficacy of peer support interventions for depression: a meta-analysis.

To assess the efficacy of peer support for reducing symptoms of depression.



Medline, PsycINFO, CINAHL and CENTRAL databases were searched for clinical trials published as of April 2010 using Medical Subject Headings and free text terms related to depression and peer support. Two independent reviewers selected randomized controlled trials (RCTs) that compared a peer support intervention for depression to usual care or a psychotherapy control condition. Meta-analyses were conducted to generate pooled standardized mean differences (SMD) in the change in depressive symptoms between study conditions.



Seven RCTs of peer support vs. usual care for depression involving 869 participants were identified. Peer support interventions were superior to usual care in reducing depressive symptoms, with a pooled SMD of -0.59 (95% CI, -0.98 to -0.21; P=.002). Seven RCTs with 301 total participants compared peer support to group cognitive behavioral therapy (CBT). There was no statistically significant difference between group CBT and peer interventions, with a pooled SMD of 0.10 (95% CI, -0.20 to 0.39, P=.53).



Based on the available evidence, peer support interventions help reduce symptoms of depression. Additional studies are needed to determine effectiveness in primary care and other settings with limited mental health resources.
Pose-and-illumination-invariant face representation via a triplet-loss trained deep reconstruction model

Face recognition under variable pose and illumination is a challenging problem in computer vision tasks. In this paper, we solve this problem by proposing a new residual based deep face reconstruction neural network to extract discriminative pose-and-illumination-invariant (PII) features. Our deep model can change arbitrary pose and illumination face images to the frontal view with standard illumination. We propose a new triplet-loss training method instead of Euclidean loss to optimize our model, which has two advantages: a) The training triplets can be easily augmented by freely choosing combinations of labeled face images, in this way, overfitting can be avoided; b) The triplet-loss training makes the PII features more discriminative even when training samples have similar appearance. By using our PII features, we achieve 83.8% average recognition accuracy on MultiPIE face dataset which is competitive to the state-of-the-art face recognition methods.
Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database

The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations.



We investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes.



We considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning.



The unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean.



Models that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series.
Low level laser therapy/photobiomodulation in the management of side effects of chemoradiation therapy in head and neck cancer: part 1: mechanisms of action, dosimetric, and safety considerations

There is a large body of evidence supporting the efficacy of low level laser therapy (LLLT), more recently termed photobiomodulation (PBM), for the management of oral mucositis (OM) in patients undergoing radiotherapy for head and neck cancer (HNC). Recent advances in PBM technology, together with a better understanding of mechanisms involved, may expand the applications for PBM in the management of other complications associated with HNC treatment. This article (part 1) describes PBM mechanisms of action, dosimetry, and safety aspects and, in doing so, provides a basis for a companion paper (part 2) which describes the potential breadth of potential applications of PBM in the management of side-effects of (chemo)radiation therapy in patients being treated for HNC and proposes PBM parameters. This study is a narrative non-systematic review. We review PBM mechanisms of action and dosimetric considerations. Virtually, all conditions modulated by PBM (e.g., ulceration, inflammation, lymphedema, pain, fibrosis, neurological and muscular injury) are thought to be involved in the pathogenesis of (chemo)radiation therapy-induced complications in patients treated for HNC. The impact of PBM on tumor behavior and tumor response to treatment has been insufficiently studied. In vitro studies assessing the effect of PBM on tumor cells report conflicting results, perhaps attributable to inconsistencies of PBM power and dose. Nonetheless, the biological bases for the broad clinical activities ascribed to PBM have also been noted to be similar to those activities and pathways associated with negative tumor behaviors and impeded response to treatment. While there are no anecdotal descriptions of poor tumor outcomes in patients treated with PBM, confirming its neutrality with respect to cancer responsiveness is a critical priority. Based on its therapeutic effects, PBM may have utility in a broad range of oral, oropharyngeal, facial, and neck complications of HNC treatment. Although evidence suggests that PBM using LLLT is safe in HNC patients, more research is imperative and vigilance remains warranted to detect any potential adverse effects of PBM on cancer treatment outcomes and survival.
SECTION ll 5 RETRIEVAL AS A MEMORY MODIFIER : AN INTERPRETATION OF NEGATIVE RECENCY AND RELATED PHENOMENA

Although it is commonplace to assume that the type or level of processing during the input of a verbal item determines the representation of that item in memory, which in turn influences later attempts to store, recognize, or recall that item or similar items, it is much less common to assume that the way in which an item is retrieved from memory is also a potent determiner of that item's subsequent representation in memory. Retrieval from memory is often assumed, implicitly or explicitly, as a process analogous to the way in which the contents of a memory location in a computer are read out, that is, as a process that does not, by itself, modify the state of the retrieved item in memory. In my opinion, however, there is ample evidence for a kind of Heisenberg principle with respect to retrieval processes: an item can seldom, if ever, be retrieved from memory without modifying the representation of that item in memory in significant ways. It is both appropriate and productive, I think, to analyze retrieval processes within the same kind of levels-of-processing framework formulated by Craik and Lockhart ( 1972) with respect to input processes; this chapter is an attempt to do so. In the first of the two main sections below, I explore the extent to which negative-recency phenomena in the long-term recall of a list of items is attributable to differences in levels of retrieval during initial recall. In the second section I present some recent results from ex-
Detecting memory leaks through introspective dynamic behavior modelling using machine learning

This paper expands staleness-based memory leak detection by presenting a machine learning-based framework. The proposed framework is based on an idea that object staleness can be better leveraged in regard to similarity of objects; i.e., an object is more likely to have leaked if it shows significantly high staleness not observed from other similar objects with the same allocation context. 
 A central part of the proposed framework is the modeling of heap objects. To this end, the framework observes the staleness of objects during a representative run of an application. From the observed data, the framework generates training examples, which also contain instances of hypothetical leaks. Via machine learning, the proposed framework replaces the error-prone user-definable staleness predicates used in previous research with a model-based prediction. 
 The framework was tested using both synthetic and real-world examples. Evaluation with synthetic leakage workloads of SPEC2006 benchmarks shows that the proposed method achieves the optimal accuracy permitted by staleness-based leak detection. Moreover, by incorporating allocation context into the model, the proposed method achieves higher accuracy than is possible with object staleness alone. Evaluation with real-world memory leaks demonstrates that the proposed method is effective for detecting previously reported bugs with high accuracy.
Heart rate monitoring from wrist-type PPG based on singular spectrum analysis with motion decision

Heart rate (HR) monitoring is necessary for daily healthcare. Wrist-type photoplethsmography (PPG) is a convenient and non-invasive technique for HR monitoring. However, motion artifacts (MA) caused by subjects' movements can extremely interfere the results of HR monitoring. In this paper, we propose a high accuracy method using motion decision, singular spectrum analysis (SSA) and spectral peak searching for daily HR estimation. The proposed approach was evaluated on 8 subjects under a series of different motion states. Compared with electrocardiogram (ECG) recorded simultaneously, the experimental results indicated that the averaged absolute estimation error was 2.33 beats per minute (BPM).
Shilajit: A Natural Phytocomplex with Potential Procognitive Activity

Shilajit is a natural substance found mainly in the Himalayas, formed for centuries by the gradual decomposition of certain plants by the action of microorganisms. It is a potent and very safe dietary supplement, restoring the energetic balance and potentially able to prevent several diseases. Recent investigations point to an interesting medical application toward the control of cognitive disorders associated with aging, and cognitive stimulation. Thus, fulvic acid, the main active principle, blocks tau self-aggregation, opening an avenue toward the study of Alzheimer's therapy. In essence, this is a nutraceutical product of demonstrated benefits for human health. Considering the expected impact of shilajit usage in the medical field, especially in the neurological sciences, more investigations at the basic biological level as well as clinical trials are necessary, in order to understand how organic molecules of shilajit and particularly fulvic acid, one of the active principles, and oligoelements act at both the molecular and cellular levels and in the whole organism.
Research and realization of reflective memory network based on QNX

In this paper, the working principle of reflective memory network is introduced, reflective memory network is designed and realized, and real-time, delay determinacy and reliability of reflective memory network are tested under QNX real-time operating system. The performance tests indicate that the reflective memory network meets the demands of the real-time and dependability and improves the stability of the power-supply control system greatly.
Effect Of Spoiler Position On Aerodynamic Characteristics Of An Airfoil

In this paper the effect of spoilers on aerodynamic characteristics of an airfoil were observed by CFD.As the experimental airfoil NACA 2415 was choosen and spoiler was extended from five different positions based on the chord length C. Airfoil section is designed with a spoiler extended at an angle of 7 degree with the horizontal.The spoiler extends to 0.15C.The geometry of 2-D airfoil without spoiler and with spoiler was designed in GAMBIT.The numerical simulation was performed by ANS YS Fluent to observe the effect of spoiler position on the aerodynamic characteristics of this particular airfoil. The results obtained from the computational process were plotted on graph and the conceptual assumptions were verified as the lift is reduced and the drag is increased that obeys the basic function of a spoiler. Coefficient of drag.  An airplane wing has a special shape called an airfoil. As a wing moves through air, the air is split and passes above and below the wing. The wing's upper surface is shaped so the air rushing over the top speeds up and stretches out. This decreases the air pressure above the wing. The air flowing below the wing moves in a straighter line, so its speed and air pressure remains the same. Since high air pressure always moves toward low air pressure, the air below the wing pushes upward toward the air above the wing. The wing is in the middle, and the whole wing is ―lifted‖. The faster an airplane moves, the more lift there is and when the force of lift is greater than the force of gravity, the airplane is able to fly.  A spoiler, sometimes called a lift dumper is a device intended to reduce lift in an aircraft. Spoilers are plates on the top surface of a wing which can be extended upward into the airflow and spoil it. By doing so, the spoiler creates a carefully controlled stall over the portion of the wing behind it, greatly reducing the lift of that wing section. Spoilers are designed to reduce lift also making considerable increase in drag. Spoilers increase drag and reduce lift on the wing. If raised on only one wing, they aid roll control, causing that wing to drop. If the spoilers rise symmetrically in flight, the aircraft can either be slowed in level flight or can descend rapidly without an increase in airspeed. When the …
Design of a view based approach for Bengali Character recognition

This paper presents a method to use View based approach in Bangla Optical Character Recognition (OCR) system providing reduced data set to the ANN classification engine rather than the traditional OCR methods. It describes how Bangla characters are processed, trained and then recognized with the use of a Backpropagation Artificial neural network. This is the first published account of using a segmentation-free optical character recognition system for Bangla using a view based approach. The methodology presented here assumes that the OCR pre-processor has presented the input images to the classification engine described here. The size and the font face used to render the characters are also significant in both training and classification. The images are first converted into greyscale and then to binary images; these images are then scaled to a fit a pre-determined area with a fixed but significant number of pixels. The feature vectors are then formed extracting the characteristics points, which in this case is simply a series of 0s and 1s of fixed length. Finally, a Artificial neural network is chosen for the training and classification process. Although the steps are simple, and the simplest network is chosen for the training and recognition process.
Improving Neural Language Models with Weight Norm Initialization and Regularization

Embedding and projection matrices are commonly used in neural language models (NLM) as well as in other sequence processing networks that operate on large vocabularies. We examine such matrices in fine-tuned language models and observe that a NLM learns word vectors whose norms are related to the word frequencies. We show that by initializing the weight norms with scaled log word counts, together with other techniques, lower perplexities can be obtained in early epochs of training. We also introduce a weight norm regularization loss term, whose hyperparameters are tuned via a grid search. With this method, we are able to significantly improve perplexities on two word-level language modeling tasks (without dynamic evaluation): from 54.44 to 53.16 on Penn Treebank (PTB) and from 61.45 to 60.13 on WikiText-2 (WT2).