nils-herrmann/clean_scirepeval huggingface dataset:
The Sharing Economy: The End of Employment and the Rise of Crowd Based Capitalism

Sharing isn't new. Giving someone a ride, having a guest in your spare room, running errands for someone, participating in a supper club these are not revolutionary concepts. What is new, in the "sharing economy," is that you are not helping a friend for free; you are providing these services to a stranger for money. In this book, Arun Sundararajan, an expert on the sharing economy, explains the transition to what he describes as "crowd based capitalism" a new way of organizing economic activity that may supplant the traditional corporate centered model. As peer to peer commercial exchange blurs the lines between the personal and the professional, how will the economy, government regulation, what it means to have a job, and our social fabric be affected? Drawing on extensive research and numerous real world examples including Airbnb, Lyft, Uber, Etsy, TaskRabbit, France's BlaBlaCar, China's Didi Kuaidi, and India's Ola, Sundararajan explains the basics of crowd based capitalism. He describes the intriguing mix of "gift" and "market" in its transactions, demystifies emerging blockchain technologies, and clarifies the dizzying array of emerging on demand platforms. He considers how this new paradigm changes economic growth and the future of work. Will we live in a world of empowered entrepreneurs who enjoy professional flexibility and independence? Or will we become disenfranchised digital laborers scurrying between platforms in search of the next wedge of piecework? Sundararajan highlights the important policy choices and suggests possible new directions for self regulatory organizations, labor law, and funding our social safety net.
Transplantation of bone marrow derived mesenchymal stem cells expressing elastin alleviates pelvic floor dysfunction
Venue: Stem Cell Research Therapy

BackgroundPelvic floor dysfunction (PFD) is a group of clinical conditions including stress urinary incontinence (SUI) and pelvic organ prolapse (POP) The abnormality of collagen and elastin metabolism in pelvic connective tissues is implicated in SUI and POP.MethodsTo reconstitute the connective tissues with normal distribution of collagen and elastin, we transduced elastin to bone marrow derived mesenchymal stem cells (BMSC) Elastin expressing BMSCs were then differentiated to fibroblasts using bFGF, which produced collagen and elastin. To achieve the sustained release of bFGF, we formulated bFGF in poly (lactic co glycolic acid) (PLGA) nanoparticles (NP).ResultsIn an in vitro cell culture system of 7 days, when no additional bFGF was administrated, the initial PLGA loaded bFGF NP induced prolonged production of collagen and elastin from elastin expressing BMSCs. In vivo, co injection of PLGA loaded bFGF NP and elastin expressing BMSCs into the PFD rats significantly improved the outcome of urodynamic tests. Together, these results provided an efficient model of connective tissue engineering using BMSC and injectable PLGA loaded growth factors.ConclusionsOur results provided the first instance of a multidisciplinary approach, combining both stem cell and nanoparticle technologies, for the treatment of PFD.
Xanthomonas euvesicatoria Causes Bacterial Spot Disease on Pepper Plant in Korea
Venue: The plant pathology journal

In 2004, bacterial spot causing xanthomonads (BSX) were reclassified into 4 species Xanthomonas euvesicatoria, X. vesicatoria, X. perforans, and X. gardneri. Bacterial spot disease on pepper plant in Korea is known to be caused by both X. axonopodis pv. vesicatoria and X. vesicatoria. Here, we reidentified the pathogen causing bacterial spots on pepper plant based on the new classification. Accordingly, 72 pathogenic isolates were obtained from the lesions on pepper plants at 42 different locations. All isolates were negative for pectolytic activity. Five isolates were positive for amylolytic activity. All of the Korean pepper isolates had a 32 kDa protein unique to X. euvesicatoria and had the same band pattern of the rpoB gene as that of X. euvesicatoria and X. perforans as indicated by PCR restriction fragment length polymorphism analysis. A phylogenetic tree of 16S rDNA sequences showed that all of the Korean pepper plant isolates fit into the same group as did all the reference strains of X. euvesicatoria and X. perforans. A phylogenetic tree of the nucleotide sequences of 3 housekeeping genes gapA, gyrB, and lepA showed that all of the Korean pepper plant isolates fit into the same group as did all of the references strains of X. euvesicatoria. Based on the phenotypic and genotypic characteristics, we identified the pathogen as X. euvesicatoria. Neither X. vesicatoria, the known pathogen of pepper bacterial spot, nor X. perforans, the known pathogen of tomato plant, was isolated. Thus, we suggest that the pathogen causing bacterial spot disease of pepper plants in Korea is X. euvesicatoria.
Eyeriss: a spatial architecture for energy efficient dataflow for convolutional neural networks
Venue: CARN

Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity. The complexity comes from the need to simultaneously process hundreds of filters and channels in the high dimensional convolutions, which involve a significant amount of data movement. Although highly parallel compute paradigms, such as SIMD/SIMT, effectively address the computation requirement to achieve high throughput, energy consumption still remains high as data movement can be more expensive than computation. Accordingly, finding a dataflow that supports parallel processing with minimal data movement cost is crucial to achieving energy efficient CNN processing without compromising accuracy. In this paper, we present a novel dataflow, called row stationary (RS) that minimizes data movement energy consumption on a spatial architecture. This is realized by exploiting local data reuse of filter weights and feature map pixels, i.e. activations, in the high dimensional convolutions, and minimizing data movement of partial sum accumulations. Unlike dataflows used in existing designs, which only reduce certain types of data movement, the proposed RS dataflow can adapt to different CNN shape configurations and reduces all types of data movement through maximally utilizing the processing engine (PE) local storage, direct inter PE communication and spatial parallelism. To evaluate the energy efficiency of the different dataflows, we propose an analysis framework that compares energy cost under the same hardware area and processing parallelism constraints. Experiments using the CNN configurations of AlexNet show that the proposed RS dataflow is more energy efficient than existing dataflows in both convolutional (1.4x to 2.5x) and fully connected layers (at least 1.3x for batch size larger than 16) The RS dataflow has also been demonstrated on a fabricated chip, which verifies our energy analysis.
14.5 Eyeriss: An energy efficient reconfigurable accelerator for deep convolutional neural networks
Venue: 2016 IEEE International Solid State Circuits Conference (ISSCC)

Deep learning using convolutional neural networks (CNN) gives state of the art accuracy on many computer vision tasks (e.g. object detection, recognition, segmentation) Convolutions account for over 90% of the processing in CNNs for both inference/testing and training, and fully convolutional networks are increasingly being used. To achieve state of the art accuracy requires CNNs with not only a larger number of layers, but also millions of filters weights, and varying shapes (i.e. filter sizes, number of filters, number of channels) as shown in Fig. 14.5.1. For instance, AlexNet  uses 2.3 million weights (4.6MB of storage) and requires 666 million MACs per 227x227 image (13kMACs/pixel) VGG16  uses 14.7 million weights (29.4MB of storage) and requires 15.3 billion MACs per 224x224 image (306kMACs/pixel) The large number of filter weights and channels results in substantial data movement, which consumes significant energy.
14.5 Eyeriss: An energy efficient reconfigurable accelerator for deep convolutional neural networks
Venue: ISSCC

Deep learning using convolutional neural networks (CNN) gives state of the art accuracy on many computer vision tasks (e.g. object detection, recognition, segmentation) Convolutions account for over 90% of the processing in CNNs for both inference/testing and training, and fully convolutional networks are increasingly being used. To achieve state of the art accuracy requires CNNs with not only a larger number of layers, but also millions of filters weights, and varying shapes (i.e. filter sizes, number of filters, number of channels) as shown in Fig. 14.5.1. For instance, AlexNet  uses 2.3 million weights (4.6MB of storage) and requires 666 million MACs per 227x227 image (13kMACs/pixel) VGG16  uses 14.7 million weights (29.4MB of storage) and requires 15.3 billion MACs per 224x224 image (306kMACs/pixel) The large number of filter weights and channels results in substantial data movement, which consumes significant energy.
Eyeriss: A Spatial Architecture for Energy Efficient Dataflow for Convolutional Neural Networks
Venue: 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)

Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity. The complexity comes from the need to simultaneously process hundreds of filters and channels in the high dimensional convolutions, which involve a significant amount of data movement. Although highly parallel compute paradigms, such as SIMD/SIMT, effectively address the computation requirement to achieve high throughput, energy consumption still remains high as data movement can be more expensive than computation. Accordingly, finding a dataflow that supports parallel processing with minimal data movement cost is crucial to achieving energy efficient CNN processing without compromising accuracy. In this paper, we present a novel dataflow, called row stationary (RS) that minimizes data movement energy consumption on a spatial architecture. This is realized by exploiting local data reuse of filter weights and feature map pixels, i.e. activations, in the high dimensional convolutions, and minimizing data movement of partial sum accumulations. Unlike dataflows used in existing designs, which only reduce certain types of data movement, the proposed RS dataflow can adapt to different CNN shape configurations and reduces all types of data movement through maximally utilizing the processing engine (PE) local storage, direct inter PE communication and spatial parallelism. To evaluate the energy efficiency of the different dataflows, we propose an analysis framework that compares energy cost under the same hardware area and processing parallelism constraints. Experiments using the CNN configurations of AlexNet show that the proposed RS dataflow is more energy efficient than existing dataflows in both convolutional (1.4x to 2.5x) and fully connected layers (at least 1.3x for batch size larger than 16) The RS dataflow has also been demonstrated on a fabricated chip, which verifies our energy analysis.
Prophylactic L arginine and ibuprofen delay the development of tactile allodynia and suppress spinal miR 155 in a rat model of diabetic neuropathy.
Venue: Translational research the journal of laboratory and clinical medicine

Diabetic neuropathy (DN) is a common complication of diabetes mellitus that is hardly reversible at the late stages. Since treatment of neuropathic pain is predominantly symptomatic, a prophylactic measure would be useful. Both ibuprofen and L arginine exert antiallodynic effects on chronic constriction injury (CCI) induced cold allodynia. Furthermore, ibuprofen is effective in CCI induced mechanical allodynia. The aim of the study was to assess the antiallodynic effect of prophylactic ibuprofen and L arginine in streptozotocin induced DN in rats and to further investigate the role of spinal miR 155 and nitric oxide (NO) in this effect. Tactile allodynia was assessed weekly by von Frey filaments. Oral daily administration of ibuprofen, L arginine and their combination, for 4 weeks starting 1 week after streptozotocin injection (ie, before the development of tactile allodynia) resulted in a significant decrease of tactile allodynia compared with the control diabetic group. This was evident in the fifth week of the experiment. The 3 treatments prevented the decrease in muscle fiber diameter and epidermal thickness, seen in the control diabetic group. Furthermore, ibuprofen, L arginine and their combination prevented the increase in the spinal NO level and miRNA 155, seen in the control diabetic group. In conclusion, both ibuprofen and L arginine delayed the development of behavioral and histologic changes of DN, with concomitant suppression of spinal miR 155 and NO level. L arginine being tolerable may be useful prophylactically in diabetic patients.
Calculating Transition Energy Barriers and Characterizing Activation States for Steps of Fusion.
Venue: Biophysical journal

We use continuum mechanics to calculate an entire least energy pathway of membrane fusion, from stalk formation, to pore creation, and through fusion pore enlargement. The model assumes that each structure in the pathway is axially symmetric. The static continuum stalk structure agrees quantitatively with experimental stalk architecture. Calculations show that in a stalk, the distal monolayer is stretched and the stored stretching energy is significantly less than the tilt energy of an unstretched distal monolayer. The string method is used to determine the energy of the transition barriers that separate intermediate states and the dynamics of two bilayers as they pass through them. Hemifusion requires a small amount of energy independently of lipid composition, while direct transition from a stalk to a fusion pore without a hemifusion intermediate is highly improbable. Hemifusion diaphragm expansion is spontaneous for distal monolayers containing at least two lipid components, given sufficiently negative diaphragm spontaneous curvature. Conversely, diaphragms formed from single component distal monolayers do not expand without the continual injection of energy. We identify a diaphragm radius, below which central pore expansion is spontaneous. For larger diaphragms, prior studies have shown that pore expansion is not axisymmetric, and here our calculations supply an upper bound for the energy of the barrier against pore formation. The major energy requiring deformations in the steps of fusion are: widening of a hydrophobic fissure in bilayers for stalk formation, splay within the expanding hemifusion diaphragm, and fissure widening initiating pore formation in a hemifusion diaphragm.
Exploring the influence of social media on employee work performance
Venue: Internet Res.

The purpose of this paper is to investigate the influence of social media on employees' work performance, as well as the underlying mechanism for how they create value at work.  Based on media synchronicity and social capital theories, the authors propose that social media can foster employees' social capital and subsequently facilitate knowledge transfer. Both social capital and knowledge transfer help promote work performance. Specifically, the authors adopt shared vision, network ties and trust to represent, respectively, the cognitive, structural and relational dimensions of social capital. The research model is tested using data collected from 379 Chinese working professionals.  The empirical results reveal that social media can promote the formation of employees' social capital indicated by network ties, shared vision and trust, which, in turn, can facilitate knowledge transfer. Shared vision and knowledge transfer positively influence work performanc.
An Observer Based Robust Fuzzy Stabilization Control Design for Switched Nonlinear Systems with Immeasurable Premise Variables
Venue: Int. J. Fuzzy Syst.

This paper investigates the output feedback robust stabilization problem for a class of switched nonlinear fuzzy systems, in which the premise variables depend on the state variables and do not measured directly. A switched state observer is designed to obtain the estimation of the immeasurable states. By using the parallel distributed compensation (PDC) design method and the multiple Lyapunov function approach, an output feedback controller and the switching laws are developed. To obtain the feasible solutions of the control and observer gain matrixes, a novel decoupled method is proposed, and the sufficient conditions of guaranteeing the stability of the control system conditions can be transformed into some linear matrix inequalities (LMIs) which can be easily solved. Two simulation examples are provided to show the effectiveness of the suggested theoretical results.
EPA 9 Collaborate as a Member of an Interprofessional Team: a Short Communication from the AAMC Core EPAs for Entering Residency Pilot Schools

Members of the Association of American Medical Colleges Core Entrustable Professional Activities (EPAs) for Entering Residency Pilot worked to conceptualize how graduates might be entrusted for EPA 9: Collaborate as a member of an interprofessional team. Through an iterative group process informed by the literature and application to local curriculum and clinical experiences, we drafted a developmental framework and curriculum mapping tool. Ultimately, entrustment requires assessment in clinical settings. Nonetheless, teamwork and communication skills that are relevant to future entrustment can be taught and assessed in pre clinical contexts such as small groups to ensure students are on an entrustment trajectory.
Large scale strategic games and adversarial machine learning
Venue: 2016 IEEE 55th Conference on Decision and Control (CDC)

Decision making in modern large scale and complex systems such as communication networks, smart electricity grids, and cyber physical systems motivate novel game theoretic approaches. This paper investigates big strategic (non cooperative) games where a finite number of individual players each have a large number of continuous decision variables and input data points. Such high dimensional decision spaces and big data sets lead to computational challenges, relating to efforts in non linear optimization scaling up to large systems of variables. In addition to these computational challenges, real world players often have limited information about their preference parameters due to the prohibitive cost of identifying them or due to operating in dynamic online settings. The challenge of limited information is exacerbated in high dimensions and big data sets. Motivated by both computational and information limitations that constrain the direct solution of big strategic games, our investigation centers around reductions using linear transformations such as random projection methods and their effect on Nash equilibrium solutions. Specific analytical results are presented for quadratic games and approximations. In addition, an adversarial learning game is presented where random projection and sampling schemes are investigated.
A cross cultural comparative analysis of small group collaboration using mobile twitter
Venue: Comput. Hum. Behav.

This study was undertaken to determine the distinctive user behaviors and patterns of participants communicating using Twitter on a mobile device in a small group collaborative setting. Participants were from Western and Eastern cultures (the United States and Korea) Tweets were coded and classified and the results analyzed. Several cultural dimensions were utilized to detect subtle differences between participants from these two different cultures in their dynamic mobile exchanges. Systematic differences in microblogging behaviors were found mostly in the direction predicted by the findings of previous studies on differences in national culture; however, we also report new and interesting findings that contribute to the knowledge base. We discuss the theoretical and practical implications of our findings and how this mobile communication information technology can be leveraged to the advantage of individuals and organizations. This study extends cultural studies in CMC to mobile communications.This study develops a framework about cultural differences in mobile communications.This study provides a guideline for mobile service design in different cultures.
Differing association of alcohol consumption with different stroke types: a systematic review and meta analysis
Venue: BMC Medicine

BackgroundWhether light to moderate alcohol consumption is protective against stroke, and whether any association differs by stroke type, is controversial. We conducted a meta analysis to summarize the evidence from prospective studies on alcohol drinking and stroke types.MethodsStudies were identified by searching PubMed to September 1, 2016, and reference lists of retrieved articles. Additional data from 73,587 Swedish adults in two prospective studies were included. Study specific results were combined in a random effects model.ResultsThe meta analysis included 27 prospective studies with data on ischemic stroke (25 studies) intracerebral hemorrhage (11 studies) and/or subarachnoid hemorrhage (11 studies) Light and moderate alcohol consumption was associated with a lower risk of ischemic stroke, whereas high and heavy drinking was associated with an increased risk; the overall RRs were 0.90 (95 CI, 0.85 0.95) for less than 1 drink/day, 0.92 (95 CI, 0.87 0.97) for 1 2 drinks/day, 1.08 (95 CI, 1.01 1.15) for more than 2 4 drinks/day, and 1.14 (95 CI, 1.02 1.28) for more than 4 drinks/day. Light and moderate alcohol drinking was not associated with any hemorrhagic stroke subtype. High alcohol consumption >2 4 drinks/day) was associated with a non significant increased risk of both hemorrhagic stroke subtypes, and the relative risk for heavy drinking >4 drinks/day) were 1.67 (95 CI, 1.25 2.23) for intracerebral hemorrhage and 1.82 (95 CI, 1.18 2.82) for subarachnoid hemorrhage.ConclusionLight and moderate alcohol consumption was inversely associated only with ischemic stroke, whereas heavy drinking was associated with increased risk of all stroke types with a stronger association for hemorrhagic strokes.
Semilinear elliptic equations of the Henon type in hyperbolic space

This paper deals with a class of the semilinear elliptic equations of the Henon type in hyperbolic space. The problem involves a logarithm weight in the Poincare ball model, bringing singularities on the boundary. Considering radial functions, a compact Sobolev embedding result is proved, which extends a former Ni result made for a unit ball in RN. Combining this compactness embedding with the Mountain Pass Theorem, a result of the existence of positive solution is established.