nils-herrmann/clean_scirepeval huggingface dataset:
Transmission of Extensively Drug-Resistant Tuberculosis in South Africa.

Drug-resistant tuberculosis threatens recent gains in the treatment of tuberculosis and human immunodeficiency virus (HIV) infection worldwide. A widespread epidemic of extensively drug-resistant (XDR) tuberculosis is occurring in South Africa, where cases have increased substantially since 2002. The factors driving this rapid increase have not been fully elucidated, but such knowledge is needed to guide public health interventions.



We conducted a prospective study involving 404 participants in KwaZulu-Natal Province, South Africa, with a diagnosis of XDR tuberculosis between 2011 and 2014. Interviews and medical-record reviews were used to elicit information on the participants' history of tuberculosis and HIV infection, hospitalizations, and social networks. Mycobacterium tuberculosis isolates underwent insertion sequence (IS)6110 restriction-fragment-length polymorphism analysis, targeted gene sequencing, and whole-genome sequencing. We used clinical and genotypic case definitions to calculate the proportion of cases of XDR tuberculosis that were due to inadequate treatment of multidrug-resistant (MDR) tuberculosis (i.e., acquired resistance) versus those that were due to transmission (i.e., transmitted resistance). We used social-network analysis to identify community and hospital locations of transmission.



Of the 404 participants, 311 (77%) had HIV infection; the median CD4+ count was 340 cells per cubic millimeter (interquartile range, 117 to 431). A total of 280 participants (69%) had never received treatment for MDR tuberculosis. Genotypic analysis in 386 participants revealed that 323 (84%) belonged to 1 of 31 clusters. Clusters ranged from 2 to 14 participants, except for 1 large cluster of 212 participants (55%) with a LAM4/KZN strain. Person-to-person or hospital-based epidemiologic links were identified in 123 of 404 participants (30%).



The majority of cases of XDR tuberculosis in KwaZulu-Natal, South Africa, an area with a high tuberculosis burden, were probably due to transmission rather than to inadequate treatment of MDR tuberculosis. These data suggest that control of the epidemic of drug-resistant tuberculosis requires an increased focus on interrupting transmission. (Funded by the National Institute of Allergy and Infectious Diseases and others.).
The effects of magnetic fields on plant growth and health

De Souza et al. showed that the growth and yield of lettuce could be improved by treatment of its seeds before they were grown, using rectified sinusoidal non-uniform electromagnetic fields.[1] It was observed that magnetism has effects on lettuce at the nursery, vegetative, and maturity stages, including a significant increase in root length and shoot height, a greater growth rate, and a significant increase in plant height, leaf area, and fresh mass. Positive biological effects of magnetism on sunflower and wheat seedlings weights were reported.[2] Further data show that the magnetic field induced by the voltage of a specific waveform enhanced or inhibited mung bean growth, depending on the frequencies,[3] which suggests that the magnetic field on plant growth may be sensitive to the waveform and frequency of the source electrical voltage. The effect of static magnetic field on plant growth has also been studied. Cakmak et al. found that static magnetic field accelerated germination and early growth of wheat and bean seeds.[4] Vashisth et al. obtained similar results with chickpeas; furthermore, they found that the responses of the plant to static magnetic field varied with field strength and duration of exposure with no particular trend.[5] However, as indicated by a literature review, weak magnetic field exhibited negative effects on plant growth, such as inhibition of primary root growth, in some cases.[6] For instance, exposure to magnetic field inhibited early growth of radish seedlings with decrease in the weight and leaf area.[7] An interesting result is that the biological effect of a magnetic field is different between the south and north poles, as illustrated by a study, which showed that radish seedlings had a significant tropic response to the south pole of the magnet, but insignificant response to the north pole.  It is theorized that the south pole of the magnet enhances plant and bacterial A study was conducted to test the hypothesis that a magnetic field can affect plant growth and health. The study divided plants into three groups. The first group of plant seeds grew in a low magnetic field. The second group grew in a high magnetic field. The third group grew in the absence of a magnetic field, serving as a control group. Several growth parameters were measured, including the germination rate, plant height, and leaf size. In addition, the health status was measured by leaf color, spots, the stem curvature, and the death rate. Plant growth was observed continuously for four weeks. The results showed that magnetism had a significant positive effect on plant growth. Plant seeds under the influence of the magnetic field had a higher germination rate, and these plants grew taller, larger, and healthier than those in the control group. No adverse effects of magnetism on plant growth were noticed. However, the removal of the magnetic field weakened the plant stem, suggesting the role of magnetism in supplying plants with energy. Edward Fu
A kernel method for canonical correlation analysis

Canonical correlation analysis is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features because of its linearity. On the other hand, kernel method used in support vector machine is an efficient approach to improve such a linear method. In this paper, we investigate the effectiveness of applying kernel method to canonical correlation analysis. Keyword: multivariate analysis, multimodal data, kernel method, regularization
Image denoising using group sparsity residual and external nonlocal self-similarity prior

Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, due to a majority of reconstruction methods only exploit the nonlocal self-similarity (NSS) prior of the degraded observation image, it is very challenging to reconstruct the latent clean image directly from the noisy observation. In this paper we propose a novel model for image denoising via group sparsity residual and external NSS prior. To boost the performance of image denoising, the concept of group sparsity residual is proposed, and thus the problem of image denoising is transformed into one that reduces the group sparsity residual. Due to the fact that the groups contain a large amount of NSS information of natural images, we obtain a good estimation of the group sparse coefficients of the original image by the external NSS prior based on Gaussian Mixture model (GMM) learning and the group sparse coefficients of noisy image are used to approximate the estimation. Experimental results demonstrate that the proposed approach not only outperforms many state-of-the-art methods, but also delivers the best qualitative denoising results with finer details and less ringing artifacts.
Embeddings for Word Sense Disambiguation: An Evaluation Study

Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-ofthe-art WSD system that incorporates several standard WSD features.
Neural Episodic Control

Deep reinforcement learning methods attain super-human performance in a wide range of environments. Such methods are grossly inefficient, often taking orders of magnitudes more data than humans to achieve reasonable performance. We propose Neural Episodic Control: a deep reinforcement learning agent that is able to rapidly assimilate new experiences and act upon them. Our agent uses a semi-tabular representation of the value function: a buffer of past experience containing slowly changing state representations and rapidly updated estimates of the value function. We show across a wide range of environments that our agent learns significantly faster than other state-of-the-art, general purpose deep reinforcement learning agents.
Digital business strategy: toward a next generation of insights

Over the last two decades, IT strategy has been framed as a functional-level strategy that must be aligned with the firm’s business strategy (Henderson and Venkatraman 1993). This thinking is also reflected in research studies in business process redesign, intraand interorganizational systems, business value of IT, and IT outsourcing. However, during the last decade, the digital infrastructure of business and society has undergone radical shifts and unleashed a new digital era. Today’s digital technologies are fundamentally reshaping traditional business processes as modular, distributed, cross-functional, and global processes that enable work to be carried out across boundaries of time, distance, and function (e.g., Sambamurthy et al. 2003; Straub and Watson 2001; Wheeler 2002). They are also enabling different forms of dynamic capabilities suitable for turbulent environments (Pavlou and El Sawy 2006). Digital technologies are also transforming the structure of social relationships in both the consumer and the enterprise space. Furthermore, products and services increasingly have embedded digital technologies, and it is becoming more difficult to disentangle business processes from their underlying IT infrastructures (e.g., El Sawy 2003; Orlikowski 2009). Digital platforms are enabling cross-boundary industry disruptions, and thus inducing new forms of business strategies (e.g., Burgelman and Grove 2007). Furthermore, theoretical structures for strategy making in nonlinear dynamic environments are also emerging (e.g., Davis et al. 2009; Meyer et al. 2005; Pavlou and El Sawy 2010). Consequently, as exponential advancements in the price/performance capability of computing, storage, bandwidth, and software applications drives the next generation of digital technologies to be delivered through cloud computing, it is time to rethink the role of IT strategy, from that of a functional-level strategy aligned but essentially subordinate to business strategy, to a fusion between IT strategy and business strategy into an overarching digital business strategy.
Privacy and security in library RFID: issues, practices, and architectures

We expose privacy issues related to Radio Frequency Identification (RFID) in libraries, describe current deployments, and suggest novel architectures for library RFID. Libraries are a fast growing application of RFID; the technology promises to relieve repetitive strain injury, speed patron self-checkout, and make possible comprehensive inventory. Unlike supply-chain RFID, library RFID requires item-level tagging, thereby raising immediate patron privacy issues. Current conventional wisdom suggests that privacy risks are negligible unless an adversary has access to library databases. We show this is not the case. In addition, we identify private authentication as a key technical issue: how can a reader and tag that share a secret efficiently authenticate each other without revealing their identities to an adversary? Previous solutions to this problem require reader work linear in the number of tags. We give a general scheme for building private authentication with work logarithmic in the number of tags, given a scheme with linear work as a sub protocol. This scheme may be of independent interest beyond RFID applications. We also give a simple scheme that provides security against a passive eavesdropper using XOR alone, without pseudo-random functions or other heavy crypto operations.
Coaxial silicon nanowires as solar cells and nanoelectronic power sources

Solar cells are attractive candidates for clean and renewable power; with miniaturization, they might also serve as integrated power sources for nanoelectronic systems. The use of nanostructures or nanostructured materials represents a general approach to reduce both cost and size and to improve efficiency in photovoltaics. Nanoparticles, nanorods and nanowires have been used to improve charge collection efficiency in polymer-blend and dye-sensitized solar cells, to demonstrate carrier multiplication, and to enable low-temperature processing of photovoltaic devices. Moreover, recent theoretical studies have indicated that coaxial nanowire structures could improve carrier collection and overall efficiency with respect to single-crystal bulk semiconductors of the same materials. However, solar cells based on hybrid nanoarchitectures suffer from relatively low efficiencies and poor stabilities. In addition, previous studies have not yet addressed their use as photovoltaic power elements in nanoelectronics. Here we report the realization of p-type/intrinsic/n-type (p-i-n) coaxial silicon nanowire solar cells. Under one solar equivalent (1-sun) illumination, the p-i-n silicon nanowire elements yield a maximum power output of up to 200 pW per nanowire device and an apparent energy conversion efficiency of up to 3.4 per cent, with stable and improved efficiencies achievable at high-flux illuminations. Furthermore, we show that individual and interconnected silicon nanowire photovoltaic elements can serve as robust power sources to drive functional nanoelectronic sensors and logic gates. These coaxial silicon nanowire photovoltaic elements provide a new nanoscale test bed for studies of photoinduced energy/charge transport and artificial photosynthesis, and might find general usage as elements for powering ultralow-power electronics and diverse nanosystems.
Human Pose Estimation with Iterative Error Feedback

Hierarchical feature extractors such as Convolutional Networks (ConvNets) have achieved impressive performance on a variety of classification tasks using purely feedforward processing. Feedforward architectures can learn rich representations of the input space but do not explicitly model dependencies in the output spaces, that are quite structured for tasks such as articulated human pose estimation or object segmentation. Here we propose a framework that expands the expressive power of hierarchical feature extractors to encompass both input and output spaces, by introducing top-down feedback. Instead of directly predicting the outputs in one go, we use a self-correcting model that progressively changes an initial solution by feeding back error predictions, in a process we call Iterative Error Feedback (IEF). IEF shows excellent performance on the task of articulated pose estimation in the challenging MPII and LSP benchmarks, matching the state-of-the-art without requiring ground truth scale annotation.
Information diffusion and external influence in networks

Social networks play a fundamental role in the diffusion of information. However, there are two different ways of how information reaches a person in a network. Information reaches us through connections in our social networks, as well as through the influence external out-of-network sources, like the mainstream media. While most present models of information adoption in networks assume information only passes from a node to node via the edges of the underlying network, the recent availability of massive online social media data allows us to study this process in more detail.
 We present a model in which information can reach a node via the links of the social network or through the influence of external sources. We then develop an efficient model parameter fitting technique and apply the model to the emergence of URL mentions in the Twitter network. Using a complete one month trace of Twitter we study how information reaches the nodes of the network. We quantify the external influences over time and describe how these influences affect the information adoption. We discover that the information tends to "jump" across the network, which can only be explained as an effect of an unobservable external influence on the network. We find that only about 71% of the information volume in Twitter can be attributed to network diffusion, and the remaining 29% is due to external events and factors outside the network.
Mitigation of post-traumatic stress symptoms by Cannabis resin: a review of the clinical and neurobiological evidence.

It is known from clinical studies that some patients attempt to cope with the symptoms of post-traumatic stress disorder (PTSD) by using recreational drugs. This review presents a case report of a 19-year-old male patient with a spectrum of severe PTSD symptoms, such as intense flashbacks, panic attacks, and self-mutilation, who discovered that some of his major symptoms were dramatically reduced by smoking cannabis resin. The major part of this review is concerned with the clinical and preclinical neurobiological evidence in order to offer a potential explanation of these effects on symptom reduction in PTSD. This review shows that recent studies provided supporting evidence that PTSD patients may be able to cope with their symptoms by using cannabis products. Cannabis may dampen the strength or emotional impact of traumatic memories through synergistic mechanisms that might make it easier for people with PTSD to rest or sleep and to feel less anxious and less involved with flashback memories. The presence of endocannabinoid signalling systems within stress-sensitive nuclei of the hypothalamus, as well as upstream limbic structures (amygdala), point to the significance of this system for the regulation of neuroendocrine and behavioural responses to stress. Evidence is increasingly accumulating that cannabinoids might play a role in fear extinction and antidepressive effects. It is concluded that further studies are warranted in order to evaluate the therapeutic potential of cannabinoids in PTSD.
Web-Scale Distributional Similarity and Entity Set Expansion

Computing the pairwise semantic similarity between all words on the Web is a computationally challenging task. Parallelization and optimizations are necessary. We propose a highly scalable implementation based on distributional similarity, implemented in the MapReduce framework and deployed over a 200 billion word crawl of the Web. The pairwise similarity between 500 million terms is computed in 50 hours using 200 quad-core nodes. We apply the learned similarity matrix to the task of automatic set expansion and present a large empirical study to quantify the effect on expansion performance of corpus size, corpus quality, seed composition and seed size. We make public an experimental testbed for set expansion analysis that includes a large collection of diverse entity sets extracted from Wikipedia.
First impressions: making up your mind after a 100-ms exposure to a face.

People often draw trait inferences from the facial appearance of other people. We investigated the minimal conditions under which people make such inferences. In five experiments, each focusing on a specific trait judgment, we manipulated the exposure time of unfamiliar faces. Judgments made after a 100-ms exposure correlated highly with judgments made in the absence of time constraints, suggesting that this exposure time was sufficient for participants to form an impression. In fact, for all judgments-attractiveness, likeability, trustworthiness, competence, and aggressiveness-increased exposure time did not significantly increase the correlations. When exposure time increased from 100 to 500 ms, participants' judgments became more negative, response times for judgments decreased, and confidence in judgments increased. When exposure time increased from 500 to 1,000 ms, trait judgments and response times did not change significantly (with one exception), but confidence increased for some of the judgments; this result suggests that additional time may simply boost confidence in judgments. However, increased exposure time led to more differentiated person impressions.
Use of selected waste materials in concrete mixes.

A modern lifestyle, alongside the advancement of technology has led to an increase in the amount and type of waste being generated, leading to a waste disposal crisis. This study tackles the problem of the waste that is generated from construction fields, such as demolished concrete, glass, and plastic. In order to dispose of or at least reduce the accumulation of certain kinds of waste, it has been suggested to reuse some of these waste materials to substitute a percentage of the primary materials used in the ordinary portland cement concrete (OPC). The waste materials considered to be recycled in this study consist of glass, plastics, and demolished concrete. Such recycling not only helps conserve natural resources, but also helps solve a growing waste disposal crisis. Ground plastics and glass were used to replace up to 20% of fine aggregates in concrete mixes, while crushed concrete was used to replace up to 20% of coarse aggregates. To evaluate these replacements on the properties of the OPC mixes, a number of laboratory tests were carried out. These tests included workability, unit weight, compressive strength, flexural strength, and indirect tensile strength (splitting). The main findings of this investigation revealed that the three types of waste materials could be reused successfully as partial substitutes for sand or coarse aggregates in concrete mixtures.
Adversarial Removal of Demographic Attributes from Text Data

Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in—and can be recovered from—the intermediate representations learned by text-based neural classifiers. The implication is that decisions of classifiers trained on textual data are not agnostic to—and likely condition on—demographic attributes. When attempting to remove such demographic information using adversarial training, we find that while the adversarial component achieves chance-level development-set accuracy during training, a post-hoc classifier, trained on the encoded sentences from the first part, still manages to reach substantially higher classification accuracies on the same data. This behavior is consistent across several tasks, demographic properties and datasets. We explore several techniques to improve the effectiveness of the adversarial component. Our main conclusion is a cautionary one: do not rely on the adversarial training to achieve invariant representation to sensitive features.