nils-herrmann/clean_scirepeval huggingface dataset:
Managing personal health records using meta-data and cloud storage

Patient generated data or personal clinical data in general is considered an important aspect in improving patient outcomes. However, personal clinical data is difficult to collect and manage due to their distributed nature, i.e., located over multiple places such as doctor's office, radiology center, hospitals, or some clinics, and heterogeneous data types such as text, image, chart, or paper based documents. In case of emergency, this situation makes necessary personal clinical data retrieval almost impossible. In addition, since the amount and types of personal clinical data continue to grow, finding relevant clinical data when needed is getting more difficult if no actions are taken. In response to such scenarios, we propose an approach that manages personal health data by utilizing meta-data for organization and easy retrieval of clinical data and cloud storage for easy access and sharing with caregivers to implement the continuity of care and evidence-based treatment. In case of emergency, we make critical medical information such as current medication and allergies available to relevant caregivers with valid license numbers only.
Mastering 2048 with Delayed Temporal Coherence Learning, Multi-Stage Weight Promotion, Redundant Encoding and Carousel Shaping

2048 is an engaging single-player nondeterministic video puzzle game, which, thanks to the simple rules and hard-to-master gameplay, has gained massive popularity in recent years. As 2048 can be conveniently embedded into the discrete-state Markov decision processes framework, we treat it as a testbed for evaluating existing and new methods in reinforcement learning. With the aim to develop a strong 2048 playing program, we employ temporal difference learning with systematic n-tuple networks. We show that this basic method can be significantly improved with temporal coherence learning, multi-stage function approximator with weight promotion, carousel shaping, and redundant encoding. In addition, we demonstrate how to take advantage of the characteristics of the n-tuple network, to improve the algorithmic effectiveness of the learning process by delaying the (decayed) update and applying lock-free optimistic parallelism to effortlessly make advantage of multiple CPU cores. This way, we were able to develop the best known 2048 playing program to date, which confirms the effectiveness of the introduced methods for discrete-state Markov decision problems.
A report on sound event detection with different binaural features

In this paper, we compare the performance of using binaural audio features in place of single channel features for sound event detection. Three different binaural features are studied and evaluated on the publicly available TUT Sound Events 2017 dataset of length 70 minutes. Sound event detection is performed separately with single channel and binaural features using stacked convolutional and recurrent neural network and the evaluation is reported using standard metrics of error rate and F-score. The studied binaural features are seen to consistently perform equal to or better than the single-channel features with respect to error rate metric.
Design of miniaturized ultra-wideband Quasi-Yagi antenna

A novel Quasi-Yagi antenna was proposed with ultra-wideband and miniaturization characteristics. The butterfly dipoles and parasitic branches are adopting to improve the bandwidth of the Quasi-Yagi antenna. Affections of antenna structure parameters on antenna was analyzed in this paper. After optimization, the operating frequency ranges from 3.3 to 10.2 GHz. Meanwhile, gain is more than 5.1 dB, and the maximum is 9.8 dB.
Stable dynamic walking over uneven terrain

We propose a constructive control design for stabilization of non-periodic trajectories of underactuated robots. An important example of such a system is an underactuated “dynamic walking” biped robot traversing rough or uneven terrain. The stabilization problem is inherently challenging due to the nonlinearity, open-loop instability, hybrid (impact) dynamics, and target motions which are not known in advance. The proposed technique is to compute a transverse linearization about the desired motion: a linear impulsive system which locally represents “transversal” dynamics about a target trajectory. This system is then exponentially stabilized using a modified receding-horizon control design, providing exponential orbital stability of the target trajectory of the original nonlinear system. The proposed method is experimentally verified using a compass-gait walker: a two-degree-of-freedom biped with hip actuation but pointed stilt-like feet. The technique is, however, very general and can be applied to a wide variety of hybrid nonlinear systems.
Neural networks for document image preprocessing: state of the art

Neural network are most popular in the research community due to its generalization abilities. Additionally, it has been successfully implemented in biometrics, features selection, object tracking, document image preprocessing and classification. This paper specifically, clusters, summarize, interpret and evaluate neural networks in document Image preprocessing. The importance of the learning algorithms in neural networks training and testing for preprocessing is also highlighted. Finally, a critical analysis on the reviewed approaches and the future research guidelines in the field are suggested.
Beyond set disjointness: the communication complexity of finding the intersection

We consider the following fundamental communication problem - there is data that is distributed among servers, and the servers want to compute the intersection of their data sets, e.g., the common records in a relational database. They want to do this with as little communication and as few messages (rounds) as possible. They are willing to use randomization, and fail with a tiny probability. Given a protocol for computing the intersection, it can also be used to compute the exact Jaccard similarity, the rarity, the number of distinct elements, and joins between databases. Computing the intersection is at least as hard as the set disjointness problem, which asks whether the intersection is empty. Formally, in the two-server setting, the players hold subsets S, T ⊆ [n]. In many realistic scenarios, the sizes of S and T are significantly smaller than n, so we impose the constraint that |S|, |T| ≤ k. We study the minimum number of bits the parties need to communicate in order to compute the intersection set S ∩ T, given a certain number r of messages that are allowed to be exchanged. While O(k log (n/k)) bits is achieved trivially and deterministically with a single message, we ask what is possible with more than one message and with randomization. We give a smooth communication/round tradeoff which shows that with O(log* k) rounds, O(k) bits of communication is possible, which improves upon the trivial protocol by an order of magnitude. This is in contrast to other basic problems such as computing the union or symmetric difference, for which Ω(k log(n/k)) bits of communication is required for any number of rounds. For two players, known lower bounds for the easier problem of set disjointness imply our algorithms are optimal up to constant factors in communication and number of rounds. We extend our protocols to $m$-player protocols, obtaining an optimal O(mk) bits of communication with a similarly small number of rounds.
Extendible Hashing - A Fast Access Method for Dynamic Files

Extendible hashing is a new access technique, in which the user is guaranteed no more than two page faults to locate the data associated with a given unique identifier, or key. Unlike conventional hashing, extendible hashing has a dynamic structure that grows and shrinks gracefully as the database grows and shrinks. This approach simultaneously solves the problem of making hash tables that are extendible and of making radix search trees that are balanced. We study, by analysis and simulation, the performance of extendible hashing. The results indicate that extendible hashing provides an attractive alternative to other access methods, such as balanced trees.
The conduct of drug metabolism studies considered good practice (II): in vitro experiments.

In vitro drug metabolism studies, which are inexpensive and readily carried out, serve as an adequate screening mechanism to characterize drug metabolites, elucidate their pathways, and make suggestions for further in vivo testing. This publication is a sequel to part I in a series and aims at providing a general framework to guide designs and protocols of the in vitro drug metabolism studies considered good practice in an efficient manner such that it would help researchers avoid common pitfalls and misleading results. The in vitro models include hepatic and non-hepatic microsomes, cDNA-expressed recombinant human CYPs expressed in insect cells or human B lymphoblastoid, chemical P450 inhibitors, S9 fraction, hepatocytes and liver slices. Important conditions for conducting the in vitro drug metabolism studies using these models are stated, including relevant concentrations of enzymes, co-factors, inhibitors and test drugs; time of incubation and sampling in order to establish kinetics of reactions; appropriate control settings, buffer selection and method validation. Separate in vitro data should be logically integrated to explain results from animal and human studies and to provide insights into the nature and consequences of in vivo drug metabolism. This article offers technical information and data and addresses scientific rationales and practical skills related to in vitro evaluation of drug metabolism to meet regulatory requirements for drug development.
EFNS guidelines on the diagnosis and management of European Lyme neuroborreliosis.

Lyme neuroborreliosis (LNB) is a nervous system infection caused by Borrelia burgdorferi sensu lato (Bb).



To present evidence-based recommendations for diagnosis and treatment.



Data were analysed according to levels of evidence as suggested by EFNS.


RECOMMENDATIONS
The following three criteria should be fulfilled for definite LNB, and two of them for possible LNB: (i) neurological symptoms; (ii) cerebrospinal fluid (CSF) pleocytosis; (iii) Bb-specific antibodies produced intrathecally. PCR and CSF culture may be corroborative if symptom duration is <6 weeks, when Bb antibodies may be absent. PCR is otherwise not recommended. There is also not enough evidence to recommend the following tests for diagnostic purposes: microscope-based assays, chemokine CXCL13, antigen detection, immune complexes, lymphocyte transformation test, cyst formation, lymphocyte markers. Adult patients with definite or possible acute LNB (symptom duration <6 months) should be offered a single 14-day course of antibiotic treatment. Oral doxycycline (200 mg daily) and intravenous (IV) ceftriaxone (2 g daily) are equally effective in patients with symptoms confined to the peripheral nervous system, including meningitis (level A). Patients with CNS manifestations should be treated with IV ceftriaxone (2 g daily) for 14 days and late LNB (symptom duration >6 months) for 3 weeks (good practice points). Children should be treated as adults, except that doxycycline is contraindicated under 8 years of age (nine in some countries). If symptoms persist for more than 6 months after standard treatment, the condition is often termed post-Lyme disease syndrome (PLDS). Antibiotic therapy has no impact on PLDS (level A).
RTSeg: Real-Time Semantic Segmentation Comparative Study

Semantic segmentation benefits robotics related applications, especially autonomous driving. Most of the research on semantic segmentation only focuses on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. The few work conducted in this direction does not provide principled methods to evaluate the different design choices for segmentation. In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods. The framework is comprised of different network architectures for feature extraction such as VGG16, Resnet18, MobileNet, and ShuffleNet. It is also comprised of multiple meta-architectures for segmentation that define the decoding methodology. These include SkipNet, UNet, and Dilation Frontend. Experimental results are presented on the Cityscapes dataset for urban scenes. The modular design allows novel architectures to emerge, that lead to 143x GFLOPs reduction in comparison to SegNet. This benchmarking framework is publicly available at 11https://github.com/MSiam/TFSegmentation.
Achieving both high precision and high recall in near-duplicate detection

To find near-duplicate documents, fingerprint-based paradigms such as Broder's shingling and Charikar's simhash algorithms have been recognized as effective approaches and are considered the state-of-the-art. Nevertheless, we see two aspects of these approaches which may be improved. First, high score under these algorithms' similarity measurement implies high probability of similarity between documents, which is different from high similarity of the documents. But how similar two documents are is what we really need to know. Second, there has to be a tradeoff between hash-code length and hash-code multiplicity in fingerprint paradigms, which makes it hard to maintain a satisfactory recall level while improving precision.
 In this paper our contributions are two-folded. First, we propose a framework for implementing the longest common subsequence (LCS) as a similarity measurement in reasonable computing time, which leads to both high precision and recall. Second, we present an algorithm to get a trustable partition from the LCS to reduce the negative impact from templates used in web page design. A comprehensive experiment was conducted to evaluate our method in terms of its effectiveness, efficiency, and quality of result. More specifically, the method has been successfully used to partition a set of 430 million web pages into 68 million subsets of similar pages, which demonstrates its effectiveness. For quality, we compared our method with simhash and a Cosine-based method through a sampling process (Cosine is compared to LCS as an alternative similarity measurement). The result showed that our algorithm reached an overall precision of 0.95 while simhash was 0.71 and Cosine was 0.82. At the same time our method obtains 1.86 times as much recall as simhash and 1.56 times as much recall as Cosine. Comparison experiment was also done for documents in the same web sites. For that, our algorithm, simhash and Cosine find almost the same number of true-positives at a precision of 0.91, 0.50 and 0.63 respectively. In terms of efficiency, our algorithm takes 118 hours to process the whole archive of 430 million topic-type pages on a cluster of six Linux boxes, at the same time the processing time of simhash and Cosine is 94 hours and 68 hours respectively. When considering the need of word segmentation for languages such as Chinese, the processing time of Cosine should be multiplied and in our experiment it is 602 hours.
Fast, Cheap, and Good: Why Animated GIFs Engage Us

Animated GIFs have been around since 1987 and recently gained more popularity on social networking sites. Tumblr, a large social networking and micro blogging platform, is a popular venue to share animated GIFs. Tumblr users follow blogs, generating a feed or posts, and choose to "like' or to "reblog' favored posts. In this paper, we use these actions as signals to analyze the engagement of over 3.9 million posts, and conclude that animated GIFs are significantly more engaging than other kinds of media. We follow this finding with deeper visual analysis of nearly 100k animated GIFs and pair our results with interviews with 13 Tumblr users to find out what makes animated GIFs engaging. We found that the animation, lack of sound, immediacy of consumption, low bandwidth and minimal time demands, the storytelling capabilities and utility for expressing emotions were significant factors in making GIFs the most engaging content on Tumblr. We also found that engaging GIFs contained faces and had higher motion energy, uniformity, resolution and frame rate. Our findings connect to media theories and have implications in design of effective content dashboards, video summarization tools and ranking algorithms to enhance engagement.
DASX: Hardware Accelerator for Software Data Structures

Recent research [3,37,38] has proposed compute accelerators to address the energy efficiency challenge. While these compute accelerators specialize and improve the compute efficiency, they have tended to rely on address-based load/store memory interfaces that closely resemble a traditional processor core. The address-based load/store interface is particularly challenging in data-centric applications that tend to access different software data structures. While accelerators optimize the compute section, the address-based interface leads to wasteful instructions and low memory level parallelism (MLP). We study the benefits of raising the abstraction of the memory interface to data structures.
 We propose DASX (, a specialized state machine for data fetch that enables compute accelerators to efficiently access data structure elements in iterative program regions. DASX enables the compute accelerators to employ data structure based memory operations and relieves the compute unit from having to generate addresses for each individual object. DASX exploits knowledge of the program's iteration to i) run ahead of the compute units and gather data objects for the compute unit (i.e., compute unit memory operations do not encounter cache misses) and ii) throttle the fetch rate, adaptively tile the dataset based on the locality characteristics and guarantee cache residency. We demonstrate accelerators for three types of data structures, Vector, Hash) maps, and BTrees. We demonstrate the benefits of DASX on data-centric applications which have varied compute kernels but access few regular data structures. DASX achieves higher energy efficiency by eliminating data structure instructions and enabling energy efficient compute accelerators to efficiently access the data elements. We demonstrate that DASX can achieve 4.4x the performance of a multicore system by discovering more parallelism from the data structure.
Fear and Trembling : Terrorism in Three Religious Traditions

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The Open University ’ s repository of research publications and other research outputs Organizational learning and knowledge in public service organizations : a systematic review of the literature

Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. This paper is a systematic review of the literature on organizational learning and knowledge with relevance to public service organizations. Organizational learning and knowledge are important to public sector organizations, which share complex external challenges with private organizations, but have different drivers and goals for knowledge. The evidence shows that the concepts of organizational learning and knowledge are under-researched in relation to the public sector and, importantly, this raises wider questions about the extent to which context is taken into consideration in terms of learning and knowledge more generally across all sectors. A dynamic model of organizational learning within and across organizational boundaries is developed that depends on four sets of factors: features of the source organization; features of the recipient organization; the characteristics of the relationship between organizations; and the environmental context. The review concludes, first, that defining 'organization' is an important element of understanding organizational learning and knowledge. Second, public organizations constitute an important, distinctive context for the study of organizational learning and knowledge. Third, there continues to be an over-reliance on the private sector as the principal source of theoretical understanding and empirical research and this is conceptually limiting for the understanding of organizational learning and knowledge. Fourth, differences as well as similarities between organizational sectors require conceptualization and research that acknowledge sector-specific aims, values and structures. Finally, it is concluded that frameworks for explaining processes of organizational learning at different levels need to be sufficiently dynamic and complex to accommodate public organizations.
Protective effects of hydroxytyrosol-supplemented refined olive oil in animal models of acute inflammation and rheumatoid arthritis.

Virgin olive oil is the primary source of fat in the Mediterranean diet, and its beneficial health effects have been related with oleic acid and phenolic compounds content. Hydroxytyrosol, a typical virgin olive oil phenolic compound, has beneficial antioxidant and anti-inflammatory properties as previously reported. The aim of this study was to evaluate the effect of hydroxytyrosol-supplemented refined olive oil at 0.5 and 5 mg/kg in a rodent model of rheumatoid arthritis. Rheumatoid arthritis was induced by intradermic administration, in male Wistar rats, of Freund's adjuvant with collagen type II on days 1 and 21. Hydroxytyrosol-supplemented refined olive oils were administrated by gavage from day 23 until day 35. The treatment at 5-mg/kg dose significantly decreased paw edema (P<.01), histological damage, cyclooxygenase-2 and inducible nitric oxide synthase expression, and markedly reduced the degree of bone resorption, soft tissue swelling and osteophyte formation, improving articular function in treated animals. Acute inflammation, induced by carrageenan, was also evaluated for hydroxytyrosol-supplemented refined olive oils at 0.5 and 5 mg/kg. Both doses significantly reduced paw edema (P<.001). Our results suggest that the supplementation of refined olive oil with hydroxytyrosol may be advantageous in rheumatoid arthritis with significant impact not only on chronic inflammation but also on acute inflammatory processes.
A high efficiency solar array simulator implemented by an LLC resonant DC/DC converter

In order to save the cost and energy for PV system testing, a high efficiency solar array simulator (SAS) implemented by an LLC resonant DC/DC converter is proposed. This converter has zero voltage switching (ZVS) operation of the primary switches and zero current switching (ZCS) operation of the rectifier diodes. By frequency modulation control, the output impedance of an LLC converter can be regulated from zero to infinite without shunt or series resistor; hence, the efficiency of the proposed SAS can be significantly increased. According to the provided operation principles and design considerations of an LLC converter, a prototype is implemented to demonstrate the feasibility of the proposed SAS.