nils-herrmann/clean_scirepeval huggingface dataset:
Advances in Convection Permitting Tropical Cyclone Analysis and Prediction through EnKF Assimilation of Reconnaissance Aircraft Observations

This article first presents an overview of the recent advances in the analysis and prediction of tropical cyclones through assimilating reconnaissance aircraft observations. Many of these advances have now been implemented in operational and experimental real time hurricane prediction models. These advances are made possible through improved methodologies including more efficient quality control and data thinning, advanced data assimilation techniques that use ensembles to estimate flow dependent error covariances, and improved numerical models running at convection permitting resolutions, along with the availability of massively parallel computing. Impacts of aircraft reconnaissance observations on hurricane prediction are then exemplified using a continuously cycling regional scale convection permitting analysis and forecast system based on the Weather Research and Forecasting (WRF) model and the ensemble Kalman filter (EnKF) In comparison to the non reconnaissance experiment that assimilates only conventional observations, as well as to the WRF forecasts directly initialized with the global operational analysis, the cycling WRF EnKF system with assimilation of aircraft flight level and dropsonde observations can considerably reduce both the mean position and intensity forecast errors for lead times from day 1 to day 5 averaged over a large number of forecast samples including the real time implementation during the 2013 Atlantic hurricane season. These findings reaffirm the added value and need for maintaining and maybe expanding routine airborne reconnaissance missions for better tropical cyclone monitoring and prediction.
Serving More GPU Jobs, with Low Penalty, Using Remote GPU Execution and Migration
Venue: 2016 IEEE International Conference on Cluster Computing (CLUSTER)

Remote GPU execution has been proven to increase GPU occupancy and reduce job waiting time in multi GPU batch queue systems, by allowing jobs to utilize remote GPUs when there are not enough unoccupied local GPUs available. However, for GPU communication intensive applications, remote GPU communication overhead may account for more than 70% of the applications' execution times. The need for using a remote GPU exists when there are not enough local GPUs available on a node assigned to the job, but a local GPU could become available afterward. We propose mrCUDA, a middleware for migrating execution on a remote GPU to a local GPU on demand. Our evaluation shows that for long running jobs mrCUDA overhead accounts for less than 1% of their total execution times. In addition, by applying mrCUDA to the first come first serve (FCFS) job scheduling algorithm, we could reduce job lifetimes (waiting execution times) as much as 30% on average without changing the scheduling policy.
CLAP: Clustered Look Ahead Prefetching for Energy Efficient DRAM System
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems

DRAM is one of the main sources of energy consumption in computer systems. Thus, reducing the energy consumption of DRAM can prolong the lifetime of battery operated embedded/mobile systems. To this end, we propose a DRAM energy aware prefetching scheme to increase row buffer hits and idle periods of DRAM by clustering its accesses. Although prefetching schemes have traditionally been used to improve the system performance, utilizing them for the energy conservation of DRAM has yet to be investigated. For such energy conservation, our scheme accurately predicts and clusters potential future DRAM accesses. Clustered DRAM accesses exploit a popular first ready first come first serve memory request scheduling and a power down mode of DRAM more effectively; the probability of row buffer hits and idle periods is significantly increased by our clustering scheme. As a result, large amounts of row activation and idle energy consumption, which are major energy consumption factors in modern DRAM, can be saved. Our prefetching based memory traffic clustering scheme was shown to reduce the power and energy consumption of DRAM and improve its performance by an average of 0.2% 28.9% and 15.7% respectively, for memory intensive programs.
Automated Decision Making Methods for the Multi objective Optimization Task of Cloud Service Placement
Venue: 2016 28th International Teletraffic Congress (ITC 28)

The network functions virtualization (NFV) paradigm provides advantages with respect to aspects like flexibility, costs, and scalability of networks. However, management and orchestration of the resulting networks also introduce new challenges. The placement of services and virtualized network functions (VNFs) is a multi objective optimization task that confronts operators with a multitude of possible solutions that are incomparable among each other. The goal of this work is to investigate mechanisms that enable automated decision making between such multi dimensional solutions. To this end, we investigate techniques from the domain of multi attribute decision making that aggregate the performance of placements to a single numeric score. A comparison between resulting rankings of placements shows that many techniques produce similar results. Hence, placements that achieve good rankings according to many approaches might be viable candidates in the context of automated decision making.
A scalable neighbor based routing protocol for mobile ad hoc networks
Venue: Int. J. Distributed Sens. Networks

Broadcasting is an essential and effective mechanism used to disseminate data on several types of networks such as mobile ad hoc network. Such method is adopted by a considerable number of routing protocols, in particular, in the route discovery stage since the broadcasting is easy to implement and certainly provides an ideal connectivity among nodes. However, the broadcasting increases the routing overhead, packet delay, which negativity affects the throughput due to the excessive use of the redundant Route REQuest message. In addition, the network is susceptible to so called broadcast storm problem. Therefore, developing a new routing protocol, which is able to relieve the unnecessary Route REQuest messages while boosting the performance of the network, is required. In this article, a novel routing protocol for mobile ad hoc network, called scalable neighbor based mobile routing, is proposed. The broadcasting in this protocol is governed by the inverse relation between the number of neighbors and the probability of the rebroadcasted Route REQuest messages. Extensive simulation experiments are carried out to evaluate the performance of the proposed protocol and to compare the state of the art protocols, namely, neighbor coverage based probabilistic rebroadcast, with the latest version of ad hoc on demand distance vector protocol. Simulation results show that scalable neighbor based mobile routing outperforms both protocols, the neighbor coverage based probabilistic rebroadcast and the ad hoc on demand distance vector, in terms of routing overhead, medium access control collision, end to end delay, packet delivery ratio, and energy consumption.
High efficiency WSi superconducting nanowire single photon detectors for quantum state engineering in the near infrared.
Venue: Optics letters

We report on high efficiency superconducting nanowire single photon detectors based on amorphous tungsten silicide and optimized at 1064 nm. At an operating temperature of 1.8 K, we demonstrated a 93% system detection efficiency at this wavelength with a dark noise of a few counts per second. Combined with cavity enhanced spontaneous parametric downconversion, this fiber coupled detector enabled us to generate narrowband single photons with a heralding efficiency greater than 90% and a high spectral brightness of 0.6x104 photons/(s*mW*MHz) Beyond single photon generation at large rate, such high efficiency detectors open the path to efficient multiple photon heralding and complex quantum state engineering.
Superconducting Nanowire Single Photon Detector With a System Detection Efficiency Over 80% at 940 nm Wavelength
Venue: IEEE Photonics Journal

Implementations of quantum information require single photon detectors (SPDs) with high detection efficiency (DE) at a wavelength of 940 nm, which is a challenge for the available semiconducting SPDs. Superconducting nanowire SPDs (SNSPDs) are capable of detecting visible and near infrared single photons with high DE. However, these detection capabilities place stringent design requirements on the cavity and nanowire geometry structures. We design, fabricate, and measure SNSPDs with high DE optimized for the 940 nm wavelength. The NbN SNSPDs were fabricated on 1 D photonic crystals for high optical absorptance. By tuning the filling factor of the nanowire through numerical simulations and experiments, we were able to obtain an SNSPD (7 nm thick, 125 nm width, and 0.57 filling factor, as well as active area of 18 18 mm) with a saturated system DE of 83.6 3.7% at a dark count rate of 10 Hz, and a low polarization dependence of 1.17 0.02. To our best knowledge, this is the highest value reported for NbN SNSPDs at 940 nm wavelength. The availability of an SNSPD with high system DE at 940 nm may have a profound impact in the field of photonic quantum technologies, such as multiphoton entanglement.
Social media brand building strategies in B2B companies

The purpose of this paper is to provide a unique overview of business to business (B2B) companies engagement with and strategic approach to use of social media in brand building. This research complements the much more extensive knowledge base regarding social media use in business to consumer (B2C) contexts.  Since social media marketing is a relatively new activity for B2B companies an interpretivist stance that is inductive in nature is adopted. Semi structured interviews were conducted with marketing professionals involved in managing social media programmes in France, Ireland, the UK and the USA.  The study found that the level of enagement with social media marketing varied, as summarised in the B2B Social Media Engagement Taxonomy. Enhancing brand image, extending brand awareness and facilitating customer engagement were the most common social media objectives. There was no evidence to suggest that companies saw social media as heralding a paradigm shift in brand management and control of the kind discussed and experienced in B2C social media contexts. The B2B social media strategy framework is proposed; this identifies the following six components of a social media strategy: monitoring and listening, empowering and enagaging employees, creating compelling content, stimulating electronic word of mouth, evaluating and selecting channels, and enhacning brand presence through integrating social media.  The research contributes to the knowledge base associated with social media marketing by offering insights into and a framework summarising B2B social media strategy.
Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach

This study presents a research approach using data mining for predicting the performance metrics of posts published in brands' Facebook pages. Twelve posts' performance metrics extracted from a cosmetic company's page including 790 publications were modeled, with the two best results achieving a mean absolute percentage error of around 27% One of them, the "Lifetime Post Consumers" model, was assessed using sensitivity analysis to understand how each of the seven input features influenced it (category, page total likes, type, month, hour, weekday, paid) The type of content was considered the most relevant feature for the model, with a relevance of 36% A status post captures around twice the attention of the remaining three types (link, photo, video) We have drawn a decision process flow from the "Lifetime Post Consumers" model, which by complementing the sensitivity analysis information may be used to support manager's decisions on whether to publish a post.
The political nature of brand governance: a discourse analysis approach to a regional brand building process

The present paper takes the empirical phenomenon of place branding as an appropriate point of view to understand the communicative process of brand governance in the realm of the public.The paper explores the modalities through which a brand governance emerges as a negotiated and contested mechanism reproduced through language. By drawing on the analytical approach of interpretative repertoires, the analysis demonstrates that a process of governance can be seen as a 'text' in a constant state of negotiation in which the level of involvement in the brand building process can be discursively contested, with language illustrating the ways in which different actors express their positionalities (hegemonic or subalternate) The analysis suggests that this can be seen as a power political process in which politics of space and time are expressed linguistically by the different actors involved in brand governance.
Brand building efforts in high and low performing small and medium sized enterprises (SMEs)

The purpose of this paper is to examine brand building efforts within small and medium sized enterprises (SMEs) in an attempt to understand the extent to which such efforts are undertaken in high and low performing enterprises.  The study uses data from 430 SMEs in an emerging market. After evaluating scale measures with exploratory and confirmatory factor analyses, multiple stage analyses involving cluster analysis, ANOVA and hierarchical regression with sub group analysis were used to examine the theoretical relationships among the constructs.  Findings from the study identify four composite brand building efforts that influence SMEs' performance. These are implemented in varying extents, culminating into proportional levels of performance within high and low performing SMEs. Significant differences with regard to profit margin, market share, sales volume, customer base and new product success are also exhibited in the two enterprise segments.  The results provide evidence to issues of research and managerial interest, offering insightful implications to the academic and practitioner communities. The paper offers a preliminary attempt to progress literature by synthesizing various brand building efforts under four broader theorizations in SMEs setting.
The corporate brand and strategic direction: Senior business school managers' cognitions of corporate brand building and management

This revelatory study focusses on top Financial Times (FT) ranked British business school managers' cognitions of corporate brand building and management. The study insinuates that there is a prima facie bilateral link between corporate branding and strategic direction. The data revealed that, among this genus of business schools, corporate brand building entailed an ongoing concern along with strategic management, stakeholder management, corporate communications, service focus, leadership and commitment. These empirical findings chime with the early conceptual scholarship on corporate brand management dating back to the mid 1990s. These foundational articles stressed the multi disciplinary and strategic nature of corporate brand management and stressed the significant role of the CEO. As such, this research adds further credence to the above in terms of best practice vis a vis corporate brand management. Curiously, although senior managers espouse a corporate brand orientation, corporate brand management is seemingly not accorded a similar status in the curriculum. Drawing on a general embedded case study methodological approach, data was collected from eight leading (FT ranked) business schools in Great Britain at Oxford, Cambridge, Durham, Bradford, Cranfield, Warwick, Lancaster and City (London) Universities. Each of these eight British business schools can be deemed 'top' business schools by virtue of their inclusion in the influential FT worldwide list of top business schools. The primary mode of qualitative data collection comprised the 37 in depth interviews with business school Deans, Associate Deans and other senior faculty members and other managers.
Triad theory of hotel managerial leadership, employee brand building behavior, and guest images of luxury hotel brands

This paper aims to investigate the brand building behavior in the luxury hotel industry from the perspective of frontline employees. In particular, this study addresses the importance and relevance of supportive leadership, brand building behavior and customers' perceived brand image in the hotel industry.  The research uses data from four luxury class (4 and 5 star) hotels in the Pearl River Delta of China. Contact with frontline employees yielded employee and customer data, with 243 of 369 employee questionnaires having one or more matches with 1,158 customer questionnaires. Hierarchical linear modeling was adopted to test the research model.  Luxury hotels benefit from managers who provide supportive leadership that encourages employee brand building behavior. In turn, employee brand building behavior influences customers' positive perception of brand image. Practical implications Brand building behavior is a top to bottom process. Luxury hotels need to pay attention to internal brand building orientation, while managers should reinforce the organization's cultural orientation and provide appropriate job skills training to improve employees' willingness and ability to build the company's brand.  Findings of this study contribute to the brand management literature and the hotel management literature by addressing important matters affecting the frontline employees' brand building behavior.
Iterative l1 minimization for non convex compressed sensing
Venue: ArXiv

An algorithmic framework, based on the difference of convex functions algorithm, is proposed for minimizing a class of concave sparse metrics for compressed sensing problems. The resulting algorithm iterates a sequence of l1 minimization problems. An exact sparse recovery theory is established to show that the proposed framework always improves on the basis pursuit (l1 minimization) and inherits robustness from it. Numerical examples on success rates of sparse solution recovery illustrate further that, unlike most existing non convex compressed sensing solvers in the literature, our method always out performs basis pursuit, no matter how ill conditioned the measurement matrix is. Moreover, the iterative l1 algorithms lead by a wide margin the state of the art algorithms on l1/2 and logarithimic minimizations in the strongly coherent (highly ill conditioned) regime, despite the same objective functions.
Soft Recovery Through l1, 2 Minimization with Applications in Recovery of Simultaneously Sparse and Low Rank Matrice
Venue: ArXiv

This article provides a new type of analysis of a compressed sensing based technique for recovering column sparse matrices, namely minimization of the \ell_{1,2} norm. Rather than providing conditions on the measurement matrix which guarantees the solution of the program to be exactly equal to the ground truth signal (which already has been thoroughly investigated) it presents a condition which guarantees that the solution is approximately equal to the ground truth. Soft recovery statements of this kind are to the best knowledge of the author a novelty in Compressed Sensing. Apart from the theoretical analysis, we present two heuristic proposes how this property of the \ell_{1,2} program can be utilized to design algorithms for recovery of matrices which are sparse and have low rank at the same time.
A robust Bayesian inference based channel estimation in power line communication systems contaminated by impulsive noise

Broadband power line communication based on an orthogonal frequency division multiplexing scheme is considered in this paper. In addition to multipath fading and frequency selectivity, the power line communication channel is inuenced by impulsive noise. More bit errors in power line communications are caused by these effects. In this paper, a relevance vector machine (R VM) is applied to the received complex data in order to estimate the power line communication channel impulse response in a baseband model. The bit error rate and mean squared error (MSE) performances under the impulsive noise and multipath effects are analyzed. It is shown that introducing a new kernel can increase the robustness of the proposed method against the critical effects of impulsive noise and multipath with low complexity. It is also shown that applying proper values to hyperparameters based on the minimum mean square error criterion can increase convergence speed and decrease MSE in our proposed method. Experimental results show the better performance of proposed algorithm compared to recently reported methods such as the Huang method and conventional R VM with Gaussian kernel function in complex mode, namely complex R VM.
Fabrication of silver nanoparticle/polyvinyl alcohol/polycaprolactone hybrid nanofibers nonwovens by two nozzle electrospinning for wound dressing
Venue: Fibers and Polymers

In this study, two biodegradable polymers, polycaprolactone (PCL) and polyvinyl alcohol (PVA) were used to fabricate nanofiber nonwovens (NFNs) Also, the silver nanoparticles (AgNPs) successfully reduced by using tea polyphenols (TP) and incorporated in the NFNs via electrospinning. The morphologies of the NFNs and AgNPs were analyzed by scanning electron microscopy (SEM) transmission electron microscopy (TEM) respectively. The PCL nanofibers and PVA nanofibers interweaved each other, and AgNPs with average diameter 1.53+ 0.15 nm were embedded in the PVA nanofibers. The properties of electrospun NFNs were characterized by pore property, swelling/weight loss, water contact angle, mechanical property, and antibacterial activity. The nanofibers cross linked to each other forming the 3Dnetwork porous structure with diameter about 1 1.5 mm. Although the hydrophobic PCL was added in the hybrid NFNs, the NFNs still showed hydrophilic propriety, high swelling degree (i.e. swelling degree is 330 for 48 h) and low weight loss (i.e. weight loss is 22.4 for 48 h) Also, the hybrid PCL/PVA/AgNPs NFNs exhibited a suitable mechanical property for wound dressings (i.e. tensile strength is 4.27 MPa, and breaking elongation is 88 Moreover, the hybrid NFNs effectively inhibited growth of Escherichia coli and Staphylococcus aureus. In summary, this PCL/PVA/AgNPs NFNs may provide a promising candidate for accelerating wound healing.
Benchmarking Deep Reinforcement Learning for Continuous Control
Venue: ICML

Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart pole swing up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at this https URL in order to facilitate experimental reproducibility and to encourage adoption by other researchers.