nils-herrmann/clean_scirepeval huggingface dataset:
Big Data, Big Knowledge: Big Data for Personalized Healthcare

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the “physiological envelope” during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval

The huge variance of human pose and the misalignment of detected human images significantly increase the difficulty of person Re-Identification (Re-ID). Moreover, efficient Re-ID systems are required to cope with the massive visual data being produced by video surveillance systems. Targeting to solve these problems, this work proposes a Global-Local-Alignment Descriptor (GLAD) and an efficient indexing and retrieval framework, respectively. GLAD explicitly leverages the local and global cues in human body to generate a discriminative and robust representation. It consists of part extraction and descriptor learning modules, where several part regions are first detected and then deep neural networks are designed for representation learning on both the local and global regions. A hierarchical indexing and retrieval framework is designed to eliminate the huge redundancy in the gallery set, and accelerate the online Re-ID procedure. Extensive experimental results show GLAD achieves competitive accuracy compared to the state-of-the-art methods. Our retrieval framework significantly accelerates the online Re-ID procedure without loss of accuracy. Therefore, this work has potential to work better on person Re-ID tasks in real scenarios.
Semi-Implicit Variational Inference

Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution. This mixing distribution can assume any density function, explicit or not, as long as independent random samples can be generated via reparameterization. Not only does SIVI expand the variational family to incorporate highly flexible variational distributions, including implicit ones that have no analytic density functions, but also sandwiches the evidence lower bound (ELBO) between a lower bound and an upper bound, and further derives an asymptotically exact surrogate ELBO that is amenable to optimization via stochastic gradient ascent. With a substantially expanded variational family and a novel optimization algorithm, SIVI is shown to closely match the accuracy of MCMC in inferring the posterior in a variety of Bayesian inference tasks.
The "push-down" technique using a novel micro-forceps for microvascular venous coupling.

A microvascular coupling system was developed and introduced for clinical application to facilitate fast and safe anastomosis of small vessels. However, operators often encounter some difficulty, particularly in pinning the vascular wall onto the ring-pins. To overcome the difficulty, the authors developed the "push down" technique and made newly-designed micro-forceps. These forceps have been used in 111 venous couplings involving 96 critical anastomoses. This study reports herein the patency results showing effectiveness and safety of the "push down" technique using a prototype micro-forceps in the pinning procedure in a microvascular coupling system.
Finite element analysis of composite overwrapped pressure vessel for hydrogen storage

This paper investigates the effect of winding angle on composite overwrapped pressure vessel (COPV) manufactured by filament winding process where continuous fibers impregnated in resin and wound over a liner. Three dimensional shell model is considered for the structural analysis of pressure vessel. The study on COPV is carried out by considering carbon T300/epoxy material. The thickness of composite vessel is calculated by using netting analysis. The study focused on optimum winding angle, total deformation, stress generation and failure analysis of composite pressure vessel. The failure of COPV is predicted by using Tsai-Wu failure criteria. The classical laminate theory (CLT) and failure criteria is considered for analytical method and obtained results are compared with numerical results which are obtained from ANSYS workbench (ACP) for validation. This comparison further helps in predicting behavior of COPV for change in winding angle and operating internal pressure.
Database Servers on Chip Multiprocessors: Limitations and Opportunities

Prior research shows that database system performance is dominated by off-chip data stalls, resulting in a concerted effort to bring data into on-chip caches. At the same time, high levels of integration have enabled the advent of chip multiprocessors and increasingly large (and slow) on-chip caches. These two trends pose the imminent technical and research challenge of adapting high-performance data management software to a shifting hardware landscape. In this paper we characterize the performance of a commercial database server running on emerging chip multiprocessor technologies. We find that the major bottleneck of current software is data cache stalls, with L2 hit stalls rising from oblivion to become the dominant execution time component in some cases. We analyze the source of this shift and derive a list of features for future database designs to attain maximum
Mobile Robot Navigation Using Active Vision

Active cameras provide a navigating vehicle with the ability to fixate and track features over extended periods of time, and wide fields of view. While it is relatively straightforward to apply fixating vision to tactical, short-term navigation tasks, using serial fixation on a succession of features to provide global information for strategic navigation is more involved. However, active vision is seemingly well-suited to this task: the ability to measure features over such a wide range means that the same ones can be used as a robot makes a wide range of movements. This has advantages for map-building and localisation. The core work of this thesis concerns simultaneous localisation and map-building for a robot with a stereo active head, operating in an unknown environment and using point features in the world as visual landmarks. Importance has been attached to producing maps which are useful for extended periods of navigation. Many map-building methods fail on extended runs because they do not have the ability to recognise previously visited areas as such and adjust their maps accordingly. With active cameras, it really is possible to re-detect features in an area previously visited, even if the area is not passed through along the original trajectory. Maintaining a large, consistent map requires detailed information to be stored about features and their relationships. This information is computationally expensive to maintain, but a sparse map of landmark features can be handled successfully. We also present a method which can dramatically increase the efficiency of updates in the case that repeated measurements are made of a single feature, permitting continuous real-time tracking of features irrespective of the total map size. Active sensing requires decisions to be made about where resources can best be applied. A strategy is developed for serially fixating on different features during navigation, making the measurements where most information will be gained to improve map and localisation estimates. A useful map is automatically maintained by adding and deleting features to and from the map when necessary. What sort of tasks should an autonomous robot be able to perform? In most applications, there will be at least some prior information or commands governing the required motion and we will look at how this information can be incorporated with map-building techniques designed for unknown environments. We will make the distinction between position-based navigation, and so-called context-based navigation, where a robot manoeuvres with respect to locally observed parts of the surroundings. A fully automatic, real-time implementation of the ideas developed is presented, and a variety of detailed and extended experiments in a realistic environment are used to evaluate algorithms and make ground-truth comparisons.
Fast and Accurate Image Super-Resolution Using a Combined Loss

Recently, several methods for single image super-resolution(SISR) based on deep neural networks have obtained high performance with regard to reconstruction accuracy and computational performance. This paper details the methodology and results of the New Trends in Image Restoration and Enhancement (NTIRE) challenge. The task of this challenge is to restore rich details (high frequencies) in a high resolution image for a single low resolution input image based on a set of prior examples with low and corresponding high resolution images. The challenge has two tracks. We present a super-resolution (SR) method, which uses three losses assigned with different weights to be regarded as optimization target. Meanwhile, the residual blocks are also used for obtaining significant improvement in the evaluation. The final model consists of 9 weight layers with four residual blocks and reconstructs the low resolution image with three color channels simultaneously, which shows better performance on these two tracks and benchmark datasets.
Sustainability Metrics

As more companies embrace the concepts of sustainable development, there is a need to bring the ideas inherent in eco-efficiency and the " triple-bottom line " thinking down to a practical implementation level. Putting this concept into operation requires an understanding of the key indicators of sustainability and how they can be measured to determine if, in fact, progress is being made. Sustainability metrics are intended as simple yardsticks that are applicable across industry. The primary objective of this approach is to improve internal management decision-making with respect to the sustainability of processes, products and services. This approach can be used to make better decisions at any stage of the stage-gate process: from identification of an innovation to design to manufacturing and ultimately to exiting a business. More specifically, sustainability metrics can assist decision makers in setting goals, benchmarking, and comparing alternatives such as different suppliers, raw materials, and improvement options from the sustainability perspective. This paper provides a review on the early efforts and recent progress in the development of sustainability metrics. The experience of BRIDGES to Sustainability™, a not-for-profit organization, in testing, adapting, and refining the sustainability metrics are summarized. Basic and complementary metrics under six impact categories: material, energy, water, solid wastes, toxic release, and pollutant effects, are discussed. The development of BRIDGESworks™ Metrics, a metrics management software tool, is also presented. The software was designed to be both easy to use and flexible. It incorporates a base set of metrics and their heuristics for calculation, as well as a robust set of impact assessment data for use in identifying pollutant effects. While providing a metrics management starting point, the user has the option of creating other metrics defined by the user. The sustainability metrics work at BRIDGES to Sustainability™ was funded partially by the U.S. Department of Energy through a subcontract with the American Institute of Chemical Engineers and through corporate pilots.
XAHM: An XML-Based Adaptive Hypermedia Model and Its Implementation

This paper presents an XML-based Adaptive Hypermedia Model (XAHM) and its modular architecture, for modelling and supporting Adaptive Hypermedia Systems, i.e. hypertext-based multimedia systems that allow user-driven access to information and content personalization. We propose a graph-based layered model for the description of the logical structure of the hypermedia, and XML-based models for the description of i) metadata about basic information fragments and ii) "neutral" pages to be adapted. Furthermore, we describe a modular architecture, which allows the design of the hypermedia and its run-time support. We introduce a multidimensional approach to model different aspects of the adaptation process, which is based on three different "adaptivity dimensions": user’s behaviour (preferences and browsing activity), technology (network and user’s terminal) and external environment (time, location, language, socio-political issues, etc.). An Adaptive Hypermedia is modelled with respect to such dimensions, and a view over it corresponds to each potential position of the user in the "adaptation space"; the model supports the adaptation of both contents and link structure of the hypermedia.
A time-efficient rendezvous algorithm with a full rendezvous degree for heterogeneous cognitive radio networks

Channel rendezvous is a prerequisite for secondary users (SUs) to set up communications in cognitive radio networks (CRNs). It is expected that the rendezvous can be achieved within a short finite time for delay-sensitive applications and over all available channels to increase the robustness to unstable channels. Some existing works suffer from a small number of rendezvous channels and can only guarantee rendezvous under the undesired requirements such as synchronous clock, homogeneous available channels, predetermined roles and explicit SUs' identifiers (IDs). In this paper, to address these limitations, we employ the notion of Disjoint Set Cover (DSC) and propose a DSC-based Rendezvous (DSCR) algorithm. We first present an approximation algorithm to construct one DSC. The variant permutations of elements in the ingeniously constructed DSC are then utilized to regulate the order of accessing channels, enabling SUs to rendezvous on all available channels within a short duration. We derive the theoretical maximum and expected rendezvous latency and prove the full rendezvous degree of the DSCR algorithm. Extensive simulations show that the DSCR algorithm can significantly reduce the rendezvous latency compared to existing algorithms.
Bacterial Nitric Oxide Extends the Lifespan of C. elegans

Nitric oxide (NO) is an important signaling molecule in multicellular organisms. Most animals produce NO from L-arginine via a family of dedicated enzymes known as NO synthases (NOSes). A rare exception is the roundworm Caenorhabditis elegans, which lacks its own NOS. However, in its natural environment, C. elegans feeds on Bacilli that possess functional NOS. Here, we demonstrate that bacterially derived NO enhances C. elegans longevity and stress resistance via a defined group of genes that function under the dual control of HSF-1 and DAF-16 transcription factors. Our work provides an example of interspecies signaling by a small molecule and illustrates the lifelong value of commensal bacteria to their host.
Multiscale image registration.

A multiscale image registration technique is presented for the registration of medical images that contain significant levels of noise. An overview of the medical image registration problem is presented, and various registration techniques are discussed. Experiments using mean squares, normalized correlation, and mutual information optimal linear registration are presented that determine the noise levels at which registration using these techniques fails. Further experiments in which classical denoising algorithms are applied prior to registration are presented, and it is shown that registration fails in this case for significantly high levels of noise, as well. The hierarchical multiscale image decomposition of E. Tadmor, S. Nezzar, and L. Vese [20] is presented, and accurate registration of noisy images is achieved by obtaining a hierarchical multiscale decomposition of the images and registering the resulting components. This approach enables successful registration of images that contain noise levels well beyond the level at which ordinary optimal linear registration fails. Image registration experiments demonstrate the accuracy and efficiency of the multiscale registration technique, and for all noise levels, the multiscale technique is as accurate as or more accurate than ordinary registration techniques.
Negotiating Privacy Concerns and Social Capital Needs in a Social Media Environment

Social network sites (SNSs) are becoming an increasingly popular resource for both students and adults, who use them to connect with and maintain relationships with a variety of ties. For many, the primary function of these sites is to consume and distribute personal content about the self. Privacy concerns around sharing information in a public or semi-public space are amplified by SNSs’ structural characteristics, which may obfuscate the true audience of these disclosures due to their technical properties (e.g., persistence, searchability) and dynamics of use (e.g., invisible audiences, context collapse) (boyd, 2008b). Early work on the topic focused on the privacy pitfalls of Facebook and other SNSs (e.g., Acquisti & Gross, 2006; Barnes, 2006; Gross & Acquisti, 2005) and argued that individuals were (perhaps inadvertently) disclosing information that might be inappropriate for some audiences, such as future employers, or that might enable identity theft or other negative outcomes.
Plan Stability: Replanning versus Plan Repair

The ultimate objective in planning is to construct plans for execution. However, when a plan is executed in a real environment it can encounter differences between the expected and actual context of execution. These differences can manifest as divergences between the expected and observed states of the world, or as a change in the goals to be achieved by the plan. In both cases, the old plan must be replaced with a new one. In replacing the plan an important consideration is plan stability. We compare two alternative strategies for achieving the stable repair of a plan: one is simply to replan from scratch and the other is to adapt the existing plan to the new context. We present arguments to support the claim that plan stability is a valuable property. We then propose an implementation, based on LPG, of a plan repair strategy that adapts a plan to its new context. We demonstrate empirically that our plan repair strategy achieves more stability than replanning and can produce repaired plans more efficiently than replanning.
Digital signal processing for gene prediction

Identification of gene locations in a DNA sequence is one of the important problems in the area of genomics. Nucleotides in exons of a DNA sequence show f = 1/3 periodicity. The period-3 property in exons of eukaryotic gene sequences enables signal processing based time-domain and frequency-domain methods to predict these regions. Identification of the period-3 regions helps in predicting the gene locations within the billions long DNA sequence of eukaryotic cells. Existing non-parametric filtering techniques are less effective in detecting small exons. This paper presents a harmonic suppression filter and parametric minimum variance spectrum estimation technique for gene prediction. We show that both the filtering techniques are able to detect smaller exon regions and adaptive MV filter minimizes the power in introns (non-coding regions) giving more suppression to the intron regions. Furthermore, 2-simplex mapping is used to reduce the computational complexity.
Prosodic phrasing is central to language comprehension

Words, like musical notes, are grouped together into phrases by their rhythmic and durational properties as well as their tonal pitch. This 'prosodic phrasing' affects the understanding of sentences. Many processing studies of prosody have investigated sentences with a single, grammatically required prosodic boundary, which might be interpreted strictly locally, as a signal to end the current syntactic unit. Recent results suggest, however, that the global pattern of prosodic phrasing is what matters in sentence comprehension, not just the occurrence or size of a single local boundary. In this article we claim that the impact of prosodic boundaries depends on the other prosodic choices a speaker has made. We speculate that prosody serves to hold distinct linguistic representations together in memory.
Studying Gamification as a Collaboration Motivator for Virtual Software Teams : Social Issues , Cultural

Gamification is the application of game elements and game design techniques in non-game contexts to engage and motivate people to achieve their goals. Motivation is an essential requirement for effective and efficient collaboration, which is particularly challenging when people work distributedly. In this paper, we discuss the topics of collaboration, motivation, and gamification in the context of software engineering. We then introduce our long-term research goal—building a theoretical framework that defines how gamification can be used as a collaboration motivator for virtual software teams. We also highlight the roles that social and cultural issues might play in understanding the phenomenon. Finally, we give an overview of our proposed research method to foster discussion during the workshop on how to best investigate the topic.
A meta-analysis to determine the dose response for strength development.

The identification of a quantifiable dose-response relationship for strength training is important to the prescription of proper training programs. Although much research has been performed examining strength increases with training, taken individually, they provide little insight into the magnitude of strength gains along the continuum of training intensities, frequencies, and volumes. A meta-analysis of 140 studies with a total of 1433 effect sizes (ES) was carried out to identify the dose-response relationship.



Studies employing a strength-training intervention and containing data necessary to calculate ES were included in the analysis.



ES demonstrated different responses based on the training status of the participants. Training with a mean intensity of 60% of one repetition maximum elicits maximal gains in untrained individuals, whereas 80% is most effective in those who are trained. Untrained participants experience maximal gains by training each muscle group 3 d.wk and trained individuals 2 d.wk. Four sets per muscle group elicited maximal gains in both trained and untrained individuals.



The dose-response trends identified in this analysis support the theory of progression in resistance program design and can be useful in the development of training programs designed to optimize the effort to benefit ratio.