nils-herrmann/clean_scirepeval huggingface dataset:
Pre-clinical antigenicity studies of an innovative multivalent vaccine for human visceral leishmaniasis

The notion that previous infection by Leishmania spp. in endemic areas leads to robust anti-Leishmania immunity, supports vaccination as a potentially effective approach to prevent disease development. Nevertheless, to date there is no vaccine available for human leishmaniasis. We optimized and assessed in vivo the safety and immunogenicity of an innovative vaccine candidate against human visceral leishmaniasis (VL), consisting of Virus-Like Particles (VLP) loaded with three different recombinant proteins (LJL143 from Lutzomyia longipalpis saliva as the vector-derived (VD) component, and KMP11 and LeishF3+, as parasite-derived (PD) antigens) and adjuvanted with GLA-SE, a TLR4 agonist. No apparent adverse reactions were observed during the experimental time-frame, which together with the normal hematological parameters detected seems to point to the safety of the formulation. Furthermore, measurements of antigen-specific cellular and humoral responses, generally higher in immunized versus control groups, confirmed the immunogenicity of the vaccine formulation. Interestingly, the immune responses against the VD protein were reproducibly more robust than those elicited against leishmanial antigens, and were apparently not caused by immunodominance of the VD antigen. Remarkably, priming with the VD protein alone and boosting with the complete vaccine candidate contributed towards an increase of the immune responses to the PD antigens, assessed in the form of increased ex vivo CD4+ and CD8+ T cell proliferation against both the PD antigens and total Leishmania antigen (TLA). Overall, our immunogenicity data indicate that this innovative vaccine formulation represents a promising anti-Leishmania vaccine whose efficacy deserves to be tested in the context of the "natural infection".
From ICT coordination to ICT integration: a longitudinal case study

This study utilizes a school-improvement perspective to examine the role of curriculum coordination in the integration of information and communication technologies (ICT) into primary schools. The nature and impact of this role is examined in seven primary schools in Australia. These seven schools were drawn from a longitudinal intervention that provided additional ICTrelated resources and personnel to the schools. An instrument, referred to as the Learning Outcomes and Pedagogy Attributes (LOPA) measure, was developed and charted for the seven schools over the 4-year data collection period. The changes in LOPA score over time were then analysed in terms of the conditions at the school with regard to curriculum ICT coordination. The study concludes that the coordinator role and school leadership in general, play critical but varying roles in the complex process of ICT integration into schools. Success appeared to be associated with the support provided for the role, the extent to which the role was connected to school leadership, personal leadership characteristics of those in the role and the strategies employed within the role.
Performance improvement of synchronous reluctance motors: A review

Synchronous Reluctance motors can replace the more commonly used, induction, switched reluctance and permanent magnet motors. They have been shown to outperform similarly dimensioned induction motors in efficiency, torque and power density. They are simple and cheap to construct and require simple control. Such a motor would be ideal for wide spread use in Africa, which currently faces a power deficit, considering that motors account for about 43% of global electric power consumption. The use of synchronous reluctance motors in Electric Vehicles may contribute to the production of more efficient, heavier load and longer range vehicles. This would accelerate adoption of Electric Vehicles and it would help wean the world off fossil fuels. The synchronous reluctance motor has disadvantages of high torque ripple and poor power factor. A radical redesign of the rotor geometry and simultaneous optimisation of the rotor and the stator may significantly improve its performance.
Optimizing Statistical Machine Translation for Text Simplification

Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and quantity of manually simplified corpora, which are expensive to build. In this paper, we conduct an in-depth adaptation of statistical machine translation to perform text simplification, taking advantage of large-scale paraphrases learned from bilingual texts and a small amount of manual simplifications with multiple references. Our work is the first to design automatic metrics that are effective for tuning and evaluating simplification systems, which will facilitate iterative development for this task.
Nine Ways to Reduce Cognitive Load in Multimedia Learning

First, we propose a theory of multimedia learning based on the assumptions that humans possess separate systems for processing pictorial and verbal material (dual-channel assumption), each channel is limited in the amount of material that can be processed at one time (limited-capacity assumption), and meaningful learning involves cognitive processing including building connections between pictorial and verbal representations (active-processing assumption). Second, based on the cognitive theory of multimedia learning, we examine the concept of cognitive overload in which the learner’s intended cognitive processing exceeds the learner’s available cognitive capacity. Third, we examine five overload scenarios. For each overload scenario, we offer one or two theory-based suggestions for reducing cognitive load, and we summarize our research results aimed at testing the effectiveness of each suggestion. Overall, our analysis shows that cognitive load is a central consideration in the design of multimedia instruction.
Modeling of Overhead Transmission Lines for Lightning Studies

The aim of this paper is to analyze the influence of the models proposed for representing overhead transmission lines in lightning calculations. A full model of a transmission line for lightning overvoltage calculations can be split into several parts: wires (shield wires and phase conductors), towers, footing impedances and insulators. An additional component, the arrester, should be added if the study is aimed at selecting the arrester ratings needed to achieve a given performance of the line. This paper presents a sensitivity study whose main goal is to determine the effect that the model and the parameters selected for representing the main parts of a transmission line can have on the flashover rate.
Familial hypertrichosis cubiti: hairy elbows syndrome.

Genetically determined hypertrichosis is uncommon, but several forms of familial hairiness have been described. In some kindreds the unusual hairiness has been generalized, while in others, the hypertrichosis has been confined to a specific site on the body. The purpose of this paper is to report the occurrence of undue hairiness of the elbow regions in members of an Amish family, and to discuss the genetic significance of the condition. No previous descriptions of hypertrichosis of this distribution could be found in the literature, and it is therefore suggested that the condition be termed familial hypertrichosis cubiti or the hairy elbows syndrome.
System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems

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Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics

As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.
Nivolumab in previously untreated melanoma without BRAF mutation.

Nivolumab was associated with higher rates of objective response than chemotherapy in a phase 3 study involving patients with ipilimumab-refractory metastatic melanoma. The use of nivolumab in previously untreated patients with advanced melanoma has not been tested in a phase 3 controlled study.



We randomly assigned 418 previously untreated patients who had metastatic melanoma without a BRAF mutation to receive nivolumab (at a dose of 3 mg per kilogram of body weight every 2 weeks and dacarbazine-matched placebo every 3 weeks) or dacarbazine (at a dose of 1000 mg per square meter of body-surface area every 3 weeks and nivolumab-matched placebo every 2 weeks). The primary end point was overall survival.



At 1 year, the overall rate of survival was 72.9% (95% confidence interval [CI], 65.5 to 78.9) in the nivolumab group, as compared with 42.1% (95% CI, 33.0 to 50.9) in the dacarbazine group (hazard ratio for death, 0.42; 99.79% CI, 0.25 to 0.73; P<0.001). The median progression-free survival was 5.1 months in the nivolumab group versus 2.2 months in the dacarbazine group (hazard ratio for death or progression of disease, 0.43; 95% CI, 0.34 to 0.56; P<0.001). The objective response rate was 40.0% (95% CI, 33.3 to 47.0) in the nivolumab group versus 13.9% (95% CI, 9.5 to 19.4) in the dacarbazine group (odds ratio, 4.06; P<0.001). The survival benefit with nivolumab versus dacarbazine was observed across prespecified subgroups, including subgroups defined by status regarding the programmed death ligand 1 (PD-L1). Common adverse events associated with nivolumab included fatigue, pruritus, and nausea. Drug-related adverse events of grade 3 or 4 occurred in 11.7% of the patients treated with nivolumab and 17.6% of those treated with dacarbazine.



Nivolumab was associated with significant improvements in overall survival and progression-free survival, as compared with dacarbazine, among previously untreated patients who had metastatic melanoma without a BRAF mutation.
RayCore: A Ray-Tracing Hardware Architecture for Mobile Devices

We present RayCore, a mobile ray-tracing hardware architecture. RayCore facilitates high-quality rendering effects, such as reflection, refraction, and shadows, on mobile devices by performing real-time Whitted ray tracing. RayCore consists of two major components: ray-tracing units (RTUs) based on a unified traversal and intersection pipeline and a tree-building unit (TBU) for dynamic scenes. The overall RayCore architecture offers considerable benefits in terms of die area, memory access, and power consumption. We have evaluated our architecture based on FPGA and ASIC evaluations and demonstrate its performance on different benchmarks. According to the results, our architecture demonstrates high performance per unit area and unit energy, making it highly suitable for use in mobile devices.
LambdaMerge: merging the results of query reformulations

Search engines can automatically reformulate user queries in a variety of ways, often leading to multiple queries that are candidates to replace the original. However, selecting a replacement can be risky: a reformulation may be more effective than the original or significantly worse, depending on the nature of the query, the source of reformulation candidates, and the corpus. In this paper, we explore methods to mitigate this risk by issuing several versions of the query (including the original) and merging their results. We focus on reformulations generated by random walks on the click graph, a method that can produce very good reformulations but is also variable and prone to topic drift. Our primary contribution is λ-Merge, a supervised merging method that is trained to directly optimize a retrieval metric (such as NDCG or MAP) using features that describe both the reformulations and the documents they return. In experiments on Bing data and GOV2, λ-Merge outperforms the original query and several unsupervised merging methods. λ-Merge also outperforms a supervised method to predict and select the best single formulation, and is competitive with an oracle that always selects the best formulation.
Scalable Iterative Classification for Sanitizing Large-Scale Datasets

Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aim to share such data while obscuring features that could disclose personally identifiable information. Much of this data exhibits weak structure (e.g., text), such that machine learning approaches have been developed to detect and remove identifiers from it. While learning is never perfect, and relying on such approaches to sanitize data can leak sensitive information, a small risk is often acceptable. Our goal is to balance the value of published data and the risk of an adversary discovering leaked identifiers. We model data sanitization as a game between 1) a publisher who chooses a set of classifiers to apply to data and publishes only instances predicted as non-sensitive and 2) an attacker who combines machine learning and manual inspection to uncover leaked identifying information. We introduce a fast iterative greedy algorithm for the publisher that ensures a low utility for a resource-limited adversary. Moreover, using five text data sets we illustrate that our algorithm leaves virtually no automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93 percent of the original data, and completes after at most five iterations.
Viewing artworks: Contributions of cognitive control and perceptual facilitation to aesthetic experience

When we view visual images in everyday life, our perception is oriented toward object identification. In contrast, when viewing visual images as artworks, we also tend to experience subjective reactions to their stylistic and structural properties. This experiment sought to determine how cognitive control and perceptual facilitation contribute to aesthetic perception along with the experience of emotion. Using functional MRI, we show that aesthetic perception activated bilateral insula which we attribute to the experience of emotion. Moreover, while adopting the aesthetic orientation activated the left lateral prefrontal cortex, paintings that facilitated visuospatial exploration activated the left superior parietal lobule. The results suggest that aesthetic experience is a function of the interaction between top-down orienting of attention and bottom-up perceptual facilitation.
Modeling of GSM-Based Energy Recharge Scheme for Prepaid Meter

This paper presents the design and modeling of a GSM-based Energy Recharge System for prepaid metering. It is observed that one of the faulty subsystems contributing to the huge revenue loss in Nigerian Power Sector is the metering and billing system. Errors get introduced at every stage of energy billing, like: errors with electro-mechanical meters, human errors while noting down the meter reading; and error while processing the paid bills and the due bills. The remedy for this drawback is a prepaid energy billing. There are clear results from many countries, where prepaid system has reduced the revenue loss by a large amount. This paper models a GSM-based Energy Recharge Interface which contains a prepaid card equivalent to a mobile SIM card. The prepaid card communicates with the power utility using GSM communication network. Once the prepaid card is out of balance, the consumer load is disconnected from the utility supply by the latching Relay (contactor). The power utility can recharge the prepaid card remotely through GSM/SMS mode base on customer requests. GSM-Based Recharge System(GBRS) for single phase prepaid meter has been modeled and simulated in Matlab/Simulink environment. The results obtained shows good system performance. A prior billing is bound to do away with the problems of unpaid bills and human error in meter readings, thereby ensuring justified revenue for the utility.
Listwise approach to learning to rank: theory and algorithm

This paper aims to conduct a study on the listwise approach to learning to rank. The listwise approach learns a ranking function by taking individual lists as instances and minimizing a loss function defined on the predicted list and the ground-truth list. Existing work on the approach mainly focused on the development of new algorithms; methods such as RankCosine and ListNet have been proposed and good performances by them have been observed. Unfortunately, the underlying theory was not sufficiently studied so far. To amend the problem, this paper proposes conducting theoretical analysis of learning to rank algorithms through investigations on the properties of the loss functions, including consistency, soundness, continuity, differentiability, convexity, and efficiency. A sufficient condition on consistency for ranking is given, which seems to be the first such result obtained in related research. The paper then conducts analysis on three loss functions: likelihood loss, cosine loss, and cross entropy loss. The latter two were used in RankCosine and ListNet. The use of the likelihood loss leads to the development of a new listwise method called ListMLE, whose loss function offers better properties, and also leads to better experimental results.
How much can behavioral targeting help online advertising?

Behavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia when looking at how much BT can truly help online advertising in commercial search engines. To answer this question, in this paper we provide an empirical study on the click-through log of advertisements collected from a commercial search engine. From the comprehensively experiment results on the sponsored search log of the commercial search engine over a period of seven days, we can draw three important conclusions: (1) Users who clicked the same ad will truly have similar behaviors on the Web; (2) Click-Through Rate (CTR) of an ad can be averagely improved as high as 670% by properly segmenting users for behavioral targeted advertising in a sponsored search; (3) Using the short term user behaviors to represent users is more effective than using the long term user behaviors for BT. The statistical t-test verifies that all conclusions drawn in the paper are statistically significant. To the best of our knowledge, this work is the first empirical study for BT on the click-through log of real world ads.
The Computational Rise and Fall of Fairness

The fair division of indivisible goods has long been an important topic in economics and, more recently, computer science. We investigate the existence of envy-free allocations of indivisible goods, that is, allocations where each player values her own allocated set of goods at least as highly as any other player’s allocated set of goods. Under additive valuations, we show that even when the number of goods is larger than the number of agents by a linear fraction, envy-free allocations are unlikely to exist. We then show that when the number of goods is larger by a logarithmic factor, such allocations exist with high probability. We support these results experimentally and show that the asymptotic behavior of the theory holds even when the number of goods and agents is quite small. We demonstrate that there is a sharp phase transition from nonexistence to existence of envy-free allocations, and that on average the computational problem is hardest at that transition.
Understanding modern device drivers

Device drivers are the single largest contributor to operating-system kernel code with over 5 million lines of code in the Linux kernel, and cause significant complexity, bugs and development costs. Recent years have seen a flurry of research aimed at improving the reliability and simplifying the development of drivers. However, little is known about what constitutes this huge body of code beyond the small set of drivers used for research.
 In this paper, we study the source code of Linux drivers to understand what drivers actually do, how current research applies to them and what opportunities exist for future research. We determine whether assumptions made by most driver research, such as that all drivers belong to a class, are indeed true. We also analyze driver code and abstractions to determine whether drivers can benefit from code re-organization or hardware trends. We develop a set of static-analysis tools to analyze driver code across various axes. Broadly, our study looks at three aspects of driver code (i) what are the characteristics of driver code functionality and how applicable is driver research to all drivers, (ii) how do drivers interact with the kernel, devices, and buses, and (iii) are there similarities that can be abstracted into libraries to reduce driver size and complexity?
 We find that many assumptions made by driver research do not apply to all drivers. At least 44% of drivers have code that is not captured by a class definition, 28% of drivers support more than one device per driver, and 15% of drivers do significant computation over data. From the driver interactions study, we find USB bus offers an efficient bus interface with significant standardized code and coarse-grained access, ideal for executing drivers in isolation. We also find that drivers for different buses and classes have widely varying levels of device interaction, which indicates that the cost of isolation will vary by class. Finally, from our driver similarity study, we find 8% of all driver code is substantially similar to code elsewhere and may be removed with new abstractions or libraries.