nils-herrmann/clean_scirepeval huggingface dataset:
Shift work to balance everyday life - a salutogenic nursing perspective in home help service in Sweden

Nurses in Sweden have a high absence due to illness and many retire before the age of sixty. Factors at work as well as in private life may contribute to health problems. To maintain a healthy work-force there is a need for actions on work-life balance in a salutogenic perspective. The aim of this study was to explore perceptions of resources in everyday life to balance work and private life among nurses in home help service.



Thirteen semi-structured individual interviews and two focus group interviews were conducted with home help service nurses in Sweden. A qualitative content analysis was used for the analyses.



In the analyses, six themes of perceptions of recourses in everyday life emerged; (i) Reflecting on life. (ii) Being healthy and taking care of yourself. (iii) Having a meaningful job and a supportive work climate. (iv) Working shifts and part time. (v) Having a family and a supporting network. (vi) Making your home your castle.



The result points out the complexity of work-life balance and support that the need for nurses to balance everyday life differs during different phases and transitions in life. In this salutogenic study, the result differs from studies with a pathogenic approach. Shift work and part time work were seen as two resources that contributed to flexibility and a prerequisite to work-life balance. To have time and energy for both private life and work was seen as essential. To reflect on and discuss life gave inner strength to set boundaries and to prioritize both in private life and in work life. Managers in nursing contexts have a great challenge to maintain and strengthen resources which enhance the work-life balance and health of nurses. Salutogenic research is needed to gain an understanding of resources that enhance work-life balance and health in nursing contexts.
mD3DOCKxb: An Ultra-Scalable CPU-MIC Coordinated Virtual Screening Framework

Molecular docking is an important method in computational drug discovery. In large-scale virtual screening, millions of small drug-like molecules (chemical compounds) are compared against a designated target protein (receptor). Depending on the utilized docking algorithm for screening, this can take several weeks on conventional HPC systems. However, for certain applications including large-scale screening tasks for newly emerging infectious diseases such high runtimes can be highly prohibitive. In this paper, we investigate how the massively parallel neo-heterogeneous architecture of Tianhe-2 Supercomputer consisting of thousands of nodes comprising CPUs and MIC coprocessors that can efficiently be used for virtual screening tasks. Our proposed approach is based on a coordinated parallel framework called mD3DOCKxb in which CPUs collaborate with MICs to achieve high hardware utilization. mD3DOCKxb comprises a novel efficient communication engine for dynamic task scheduling and load balancing between nodes in order to reduce communication and I/O latency. This results in a highly scalable implementation with parallel efficiency of over 84% (strong scaling) when executing on 8,000 Tianhe-2 nodes comprising 192,000 CPU cores and 1,368,000 MIC cores.
GraphIn: An Online High Performance Incremental Graph Processing Framework

The massive explosion in social networks has led to a significant growth in graph analytics and specifically in dynamic, time-varying graphs. Most prior work processes dynamic graphs by first storing the updates and then repeatedly running static graph analytics on saved snapshots. To handle the extreme scale and fast evolution of real-world graphs, we propose a dynamic graph analytics framework, GraphIn, that incrementally processes graphs on-the-fly using fixed-sized batches of updates. As part of GraphIn, we propose a novel programming model called I-GAS (based on gather-apply-scatter programming paradigm) that allows for implementing a large set of incremental graph processing algorithms seamlessly across multiple CPU cores. We further propose a property-based, dual-path execution model to choose between incremental or static computation. Our experiments show that for a variety of graph inputs and algorithms, GraphIn achieves up to 9.3 million updates/sec and over 400× speedup when compared to static graph recomputation.
Soft Material Characterization for Robotic Applications

In this article we present mechanical measurements of three representative elastomers used in soft robotic systems: Sylgard 184, Smooth-Sil 950, and EcoFlex 00-30. Our aim is to demonstrate the effects of the nonlinear, time-dependent properties of these materials to facilitate improved dynamic modeling of soft robotic components. We employ uniaxial pull-to-failure tests, cyclic loading tests, and stress relaxation tests to provide a qualitative assessment of nonlinear behavior, batch-to-batch repeatability, and effects of prestraining, cyclic loading, and viscoelastic stress relaxation. Strain gauges composed of the elastomers embedded with a microchannel of conductive liquid (eutectic gallium–indium) are also tested to quantify the interaction between material behaviors and measured strain output. It is found that all of the materials tested exhibit the Mullins effect, where the material properties in the first loading cycle differ from the properties in all subsequent cycles, as well as response sensitivity to loading rate and production variations. Although the materials tested show stress relaxation effects, the measured output from embedded resistive strain gauges is found to be uncoupled from the changes to the material properties and is only a function of strain.
How to Make a Digital Currency on a Blockchain Stable

Bitcoin and other similar digital currencies on blockchains are not ideal means for payment, because their prices tend to go up in the long term (thus people are incentivized to hoard those currencies), and to fluctuate widely in the short term (thus people would want to avoid risks of losing values). The reason why those blockchain currencies based on proof of work are unstable may be found in their designs that the supplies of currencies do not respond to their positive and negative demand shocks, as the authors have formulated in our past work. Continuing from our past work, this paper proposes minimal changes to the design of blockchain currencies so that their market prices are automatically stabilized, absorbing both positive and negative demand shocks of the currencies by autonomously controlling their supplies. Those changes are: 1) limiting re-adjustment of proof-of-work targets, 2) making mining rewards variable according to the observed over-threshold changes of block intervals, and 3) enforcing negative interests to remove old coins in circulation. We have made basic design checks of these measures through simple simulations. In addition to stabilization of prices, the proposed measures may have effects of making those currencies preferred means for payment by disincentivizing hoarding, and improving sustainability of the currency systems by making rewards to miners perpetual.
Absorbable polymeric surgical clips for appendicular stump closure: A randomized control trial of laparoscopic appendectomy with lapro-clips

A randomized control trial was performed to evaluate the effectiveness and safety of absorbable polymeric clips for appendicular stump closure in laparoscopic appendectomy (LA). Patients were randomly enrolled into an experimental group (ligation of the appendicular base with Lapro-Clips, L-C group) or control group (ligation of the appendicular base with Hem-o-lok Clips, H-C group). We identified 1,100 patients who underwent LA between April 1, 2012 and February 3, 2015. Overall, 99 patients (9.0%, 99/1,100) developed a complication following LA (47 [8.5%] in the L-C group and 52 [9.5%] in the H-C group (P = 0.598). No statistically significant differences were observed in intra-abdominal abscesses, stump leakage, superficial wound infections, post-operative abdominal pain, overall adverse events, or the duration of the operations and hospital stays between the groups (all p > 0.05). Adverse risk factors associated with the use of absorbable clips in LA included body mass index ≥ 27.5 kg/m2, diabetes, American Society of Anesthesiologists degree ≥ III, gangrenous appendicitis, severe inflammation of the appendix base, appendix perforation, and the absence of peritoneal drainage. The results indicate that the Lapro-Clip is a safe and effective device for closing the appendicular stump in LA in select patients with appendicitis.
Urdu Nasta’liq text recognition system based on multi-dimensional recurrent neural network and statistical features

Character recognition for cursive script like Arabic, handwritten English and French is a challenging task which becomes more complicated for Urdu Nasta’liq text due to complexity of this script over Arabic. Recurrent neural network (RNN) has proved excellent performance for English, French as well as cursive Arabic script due to sequence learning property. Most of the recent approaches perform segmentation-based character recognition, whereas, due to the complexity of the Nasta’liq script, segmentation error is quite high as compared to Arabic Naskh script. RNN has provided promising results in such scenarios. In this paper, we achieved high accuracy for Urdu Nasta’liq using statistical features and multi-dimensional long short-term memory. We present a robust feature extraction approach that extracts feature based on right-to-left sliding window. Results showed that selected features significantly reduce the label error. For evaluation purposes, we have used Urdu printed text images dataset and compared the proposed approach with the recent work. The system provided 94.97 % recognition accuracy for unconstrained printed Nasta’liq text lines and outperforms the state-of-the-art results.
Using fleets of electric-drive vehicles for grid support

bstract Electric-drive vehicles can provide power to the electric grid when they are parked (vehicle-to-grid power). We evaluated the economic potential f two utility-owned fleets of battery electric-drive vehicles to provide power for a specific electricity market, regulation, in four US regional egulation services markets. The two battery-electric fleet cases are: (a) 100 Th!nk City vehicle and (b) 252 Toyota RAV4. Important variables re: (a) the market value of regulation services, (b) the power capacity (kW) of the electrical connections and wiring, and (c) the energy capacity kWh) of the vehicle’s battery. With a few exceptions when the annual market value of regulation was low, we find that vehicle-to-grid power for egulation services is profitable across all four markets analyzed. Assuming now more than current Level 2 charging infrastructure (6.6 kW) the
Low power H.264 deblocking filter hardware implementations

In this paper, we present two efficient and low power H.264 deblocking filter (DBF) hardware implementations that can be used as part of an H.264 video encoder or decoder for portable applications. The first implementation (DBF_4times4) starts filtering the available edges as soon as a new 4times4 block is ready by using a novel edge filtering order to overlap the execution of DBF module with other modules in the H.264 encoder/decoder. Overlapping the execution of DBF hardware with the execution of the other modules in the H.264 encoder/decoder improves the performance of the H.264 encoder/decoder. The second implementation (DBF_16times16) starts filtering the available edges after a new 16times16 macroblock is ready. Both DBF hardware architectures are implemented in Verilog HDL and both implementations are synthesized to 0.18 mum UMC standard cell library. Both DBF implementations can work at 200 MHz and they can process 30 VGA (640times480) frames per second. DBF_4times4 and DBF_16times16 hardware implementations, excluding on-chip memories, are synthesized to 7.4 K and 5.3 K gates respectively. These gate counts are the lowest among the H.264 DBF hardware implementations presented in the literature. Our hardware implementations are more cost effective solutions for portable applications. DBF_16times16 has 36% less power consumption than DBF_4times4 on a Xilinx Virtex II FPGA on an Arm Versatile PB926EJ-S development board. Therefore, DBF_4times4 hardware can be used in an H.264 encoder or decoder for which the performance is more important, whereas DBF_16times16 hardware can be used in an H.264 encoder or decoder for which the power consumption is more important.
Basic Income : A simple and powerful idea for the twenty-first century *

7 Give all citizens a modest, yet unconditional income, and let them top it up at will with income from other sources. This exceedingly simple idea has a surprisingly diverse pedigree. In the course of the last two centuries, it has been independently thought up under a variety of names – “territorial dividend” and “state bonus,” for example, “demogrant” and “citizen’s wage,” “universal benefit” and “basic income” – in most cases without much success. In the late sixties and early seventies, it enjoyed a sudden popularity in the United States and was even put forward by a presidential candidate, but it was soon shelved and just about forgotten. In the last two decades, however, it has gradually become the subject of an unprecedented and fast expanding public discussion throughout the European Union. Some see it as a crucial remedy for many social ills, including unemployment and poverty. Others denounce it as a crazy, economically flawed, ethically objectionable proposal, to be forgotten as soon as possible, to be dumped once and for all into the dustbin of the history of ideas. To shed light on this debate, I start off saying more about what basic income is and what it is not, and about what distinguishes it from existing guaranteed income schemes. On this background, it 1
Comet assay in reconstructed 3D human epidermal skin models—investigation of intra- and inter-laboratory reproducibility with coded chemicals

Reconstructed 3D human epidermal skin models are being used increasingly for safety testing of chemicals. Based on EpiDerm™ tissues, an assay was developed in which the tissues were topically exposed to test chemicals for 3h followed by cell isolation and assessment of DNA damage using the comet assay. Inter-laboratory reproducibility of the 3D skin comet assay was initially demonstrated using two model genotoxic carcinogens, methyl methane sulfonate (MMS) and 4-nitroquinoline-n-oxide, and the results showed good concordance among three different laboratories and with in vivo data. In Phase 2 of the project, intra- and inter-laboratory reproducibility was investigated with five coded compounds with different genotoxicity liability tested at three different laboratories. For the genotoxic carcinogens MMS and N-ethyl-N-nitrosourea, all laboratories reported a dose-related and statistically significant increase (P < 0.05) in DNA damage in every experiment. For the genotoxic carcinogen, 2,4-diaminotoluene, the overall result from all laboratories showed a smaller, but significant genotoxic response (P < 0.05). For cyclohexanone (CHN) (non-genotoxic in vitro and in vivo, and non-carcinogenic), an increase compared to the solvent control acetone was observed only in one laboratory. However, the response was not dose related and CHN was judged negative overall, as was p-nitrophenol (p-NP) (genotoxic in vitro but not in vivo and non-carcinogenic), which was the only compound showing clear cytotoxic effects. For p-NP, significant DNA damage generally occurred only at doses that were substantially cytotoxic (>30% cell loss), and the overall response was comparable in all laboratories despite some differences in doses tested. The results of the collaborative study for the coded compounds were generally reproducible among the laboratories involved and intra-laboratory reproducibility was also good. These data indicate that the comet assay in EpiDerm™ skin models is a promising model for the safety assessment of compounds with a dermal route of exposure.
Impact of Data and Task Characteristics on Design of Spatio-Temporal Data Visualization Tools

It is widely recognized that data visualization may be a powerful methodology for exploratory analysis. In order to fulfill this claim, visualization software must be carefully designed taking into account two principal aspects: characteristics of the data to be visualized and the exploratory tasks to be supported. The tasks that may potentially arise in data exploration are, in their turn, dependent on the data. In the chapter, we present visualization software tools for three different types of spatio-temporal data developed using a task-driven approach to design. We demonstrate that different exploratory tasks may be anticipated in these three cases and that different techniques are required to properly support exploration of the data. Prior to the consideration of the examples, we briefly describe the typologies of data and tasks we use in our work. 10.1 Scope and Perspective This chapter offers a view on geovisualization from the perspective of computer scientists with an extensive experience in developing software tools for exploratory analysis of spatial data. Our tools are mostly based on combination of familiar techniques from various disciplines: Cartography, Statistical Graphics, Information Visualization, and Human–Computer Interaction. Traditional mapping and graphing techniques are enhanced with interactivity and manipulability. Typically, the ideas concerning useful technique combinations and enhancements come to us when we examine some specific datasets received from people interested in exploring these data. It is commonly recognized that techniques used for graphical representation of data must correspond to characteristics of the data (Bertin, 1983), and the same applies to software tools for visual data exploration. However, as we have learned from our Exploring Geovisualization J. Dykes, A.M. MacEachren, M.-J. Kraak (Editors) q 2005   November 2004 do not redistribute. J. Dykes, A.M. MacEachren, M-J. Kraak (2005), Exploring Geovisualization, Pergamon, 732pp. 0-08-044531-4 experience, the route from data characteristics to the development of appropriate tools consists of two parts: first, data characteristics determine the potential questions (tasks) that may emerge in the process of the data exploration; second, the tasks make requirements of the tools and thereby define the space of possible design options. In this chapter, we advocate the task – analytical approach to the selection of appropriate visualization techniques and design of tools for the exploratory analysis of geographically referenced data. For this purpose, we offer three examples of geovisualization tool design for different types of spatio-temporal data. Prior to the consideration of the examples, we introduce the typological framework we use for revealing the set of potential tasks from the characteristics of datasets to analyze. We hope this material will be useful both for designers of geovisualization tools and for analysts applying existing tools to their data.
Portfolio Selection Algorithm with Regret Logarithmic in Price Variation

We present a novel efficient algorithm for portfolio selection which theoretically attains two desirable properties: 1. Worst-case guarantee: the algorithm is universal in the sense that it asymptotically performs almost as well as the best constant rebalanced portfolio determined in hindsight from the realized market prices. Furthermore, it attains the tightest known bounds on the regret, or the log-wealth difference relative to the best constant rebalanced portfolio. We prove that the regret of algorithm is bounded by O(logQ), where Q is the quadratic variation of the stock prices. This is the first improvement upon Cover’s [Cov91] seminal work that attains a regret bound of O(log T ), where T is the number of trading iterations. 2. Average-case guarantee: in the Geometric Brownian Motion (GBM) model of stock prices, our algorithm attains tighter regret bounds, which are provably impossible in the worst-case. Hence, when the GBM model is a good approximation of the behavior of market, the new algorithm has an advantage over previous ones, albeit retaining worst-case guarantees. We derive this algorithm as a special case of a novel and more general method for online convex optimization with exp-concave loss functions.
Deep Reinforcement Learning with an Unbounded Action Space

In this paper, we propose the deep reinforcement relevance network (DRRN), a novel deep architecture, for handling an unbounded action space with applications to language understanding for text-based games. For a particular class of games, a user must choose among a variable number of actions described by text, with the goal of maximizing long-term reward. In these games, the best action is typically that which fits the best to the current situation (modeled as a state in the DRRN), also described by text. Because of the exponential complexity of natural language with respect to sentence length, there is typically an unbounded set of unique actions. Therefore, it is very difficult to pre-define the action set as in the deep Q-network (DQN). To address this challenge, the DRRN extracts high-level embedding vectors from the texts that describe states and actions, respectively, and computes the inner products between the state and action embedding vectors to approximate the Q-function. We evaluate the DRRN on two popular text games, showing superior performance over the DQN.