nils-herrmann/clean_scirepeval huggingface dataset:
Impact of Sagittal Balance on Clinical Outcomes in Surgically Treated T12 and L1 Burst Fractures: Analysis of Long-Term Outcomes after Posterior-Only and Combined Posteroanterior Treatment

Long-term radiological and clinical outcome retrospective study of surgical treatment for T12 and L1 burst fractures in perspective of sagittal balance measures.  Patients with age of 16-60 years, complete radiographs, early surgical treatment surgery, and follow-up (F/U) > 18 months were included and strict exclusion criteria applied. Regional and thoracolumbar kyphosis angles (RKA and TLA) were measured preoperatively and at final F/U, as were parameters of the spinopelvic sagittal alignment. Clinical outcomes were assessed using validated measures.  36 patients with age mean age of 39 years and F/U of 69 months were included. 61% of patients were treated with bisegmental posterior instrumentation (POST-I) and 39% with combined posteroanterior instrumented fusion (PA-F). At F/U, several indicators for clinical outcomes showed a significant correlation with radiographic measures in the overall cohort with inferior clinical outcomes corresponding with increasing residual deformity and sagittal malalignment. Statistical analysis failed to reach level of significance for the differences between POST-I and PA-F group at final F/U. Only a strong trend towards better restoration of the thoracolumbar alignment was observed for the PA-F group in terms of the RKA and TLA.  Results in a surgically treated cohort of T12 and L1 burst fracture patients indicate that superior clinical outcomes depend on restoration of sagittal alignment.
Drug addiction: the neurobiology of behaviour gone awry

Drug addiction manifests as a compulsive drive to take a drug despite serious adverse consequences. This aberrant behaviour has traditionally been viewed as bad 'choices' that are made voluntarily by the addict. However, recent studies have shown that repeated drug use leads to long-lasting changes in the brain that undermine voluntary control. This, combined with new knowledge of how environmental, genetic and developmental factors contribute to addiction, should bring about changes in our approach to the prevention and treatment of addiction.
Kabbalah Logic and Semantic Foundations for a Postmodern Fuzzy Set and Fuzzy Logic Theory

Despite half a century of fuzzy sets and fuzzy logic progress, as fuzzy sets address complex and uncertain information through the lens of human knowledge and subjectivity, more progress is needed in the semantics of fuzzy sets and in exploring the multi-modal aspect of fuzzy logic due to the different cognitive, emotional and behavioral angles of assessing truth. We lay here the foundations of a postmodern fuzzy set and fuzzy logic theory addressing these issues by deconstructing fuzzy truth values and fuzzy set membership functions to re-capture the human knowledge and subjectivity structure in membership function evaluations. We formulate a fractal multi-modal logic of Kabbalah which integrates the cognitive, emotional and behavioral levels of humanistic systems into epistemic and modal, deontic and doxastic and dynamic multi-modal logic. This is done by creating a fractal multi-modal Kabbalah possible worlds semantic frame of Kripke model type. The Kabbalah possible worlds semantic frame integrates together both the multi-modal logic aspects and their Kripke possible worlds model. We will not focus here on modal operators and axiom sets. We constructively define a fractal multi-modal Kabbalistic L-fuzzy set as the central concept of the postmodern fuzzy set theory based on Kabbalah logic and semantics.
Learning a Generative Adversarial Network for High Resolution Artwork Synthesis

Artwork is a mode of creative expression and this paper is particularly interested in investigating if machine can learn and synthetically create artwork that are usually nonfigurative and structured abstract. To this end, we propose an extension to the Generative Adversarial Network (GAN), namely as the ArtGAN to synthetically generate high quality artwork. This is in contrast to most of the current solutions that focused on generating structural images such as birds, flowers and faces. The key innovation of our work is to allow back-propagation of the loss function w.r.t. the labels (randomly assigned to each generated images) to the generator from the categorical autoencoder-based discriminator that incorporates an autoencoder into the categorical discriminator for additional complementary information. In order to synthesize a high resolution artwork, we include a novel magnified learning strategy to improve the correlations between neighbouring pixels. Based on visual inspection and Inception scores, we demonstrate that ArtGAN is able to draw high resolution and realistic artwork, as well as generate images of much higher quality in four other datasets (i.e. CIFAR-10, STL-10, Oxford-102 and CUB-200).
Working with evaluation stakeholders: A rationale, step-wise approach and toolkit.

In the broad field of evaluation, the importance of stakeholders is often acknowledged and different categories of stakeholders are identified. Far less frequent is careful attention to analysis of stakeholders' interests, needs, concerns, power, priorities, and perspectives and subsequent application of that knowledge to the design of evaluations. This article is meant to help readers understand and apply stakeholder identification and analysis techniques in the design of credible evaluations that enhance primary intended use by primary intended users. While presented using a utilization-focused-evaluation (UFE) lens, the techniques are not UFE-dependent. The article presents a range of the most relevant techniques to identify and analyze evaluation stakeholders. The techniques are arranged according to their ability to inform the process of developing and implementing an evaluation design and of making use of the evaluation's findings.
Creating Human-like AI Movement in Games Using Imitation Learning

The way characters move and behave in computer and video games are important factors in their believability, which has an impact on the player’s experience. This project explores Imitation Learning using limited amounts of data as an approach to creating human-like AI behaviour in games, and through a user study investigates what factors determine if a character is human-like, when observed through the characters first-person perspective. The idea is to create or shape AI behaviour by recording one’s own actions. The implemented framework uses a Nearest Neighbour algorithm with a KD-tree as the policy which maps a state to an action. Results showed that the chosen approach was able to create human-like AI behaviour while respecting the performance constraints of a modern 3D game.
Cell biology of adhesive setae in gecko lizards.

Adhesive devices of digital pads of gecko lizards are formed by microscopic hair-like structures termed setae that derive from the interaction between the oberhautchen and the clear layer of the epidermis. The two layers form the shedding complex and permit skin shedding in lizards. Setae consist of a resistant but flexible corneous material largely made of keratin-associated beta-proteins (KA beta Ps, formerly called beta-keratins) of 8-22 kDa and of alpha-keratins of 45-60 kDa. In Gekko gecko, 19 sauropsid keratin-associated beta-proteins (sKAbetaPs) and at least two larger alpha-keratins are expressed in the setae. Some sKA beta Ps are rich in cysteine (111-114 amino acids), while others are rich in glycine (169-219 amino acids). In the entire genome of Anolis carolinensis 40 Ka beta Ps are present and participate in the formation of all types of scales, pad lamellae and claws. Nineteen sKA beta Ps comprise cysteine-rich 9.2-14.4 kDa proteins of 89-142 amino acids, and 19 are glycine-rich 16.5-22.0 kDa proteins containing 162-225 amino acids, and only two types of sKA beta Ps are cysteine- and glycine-poor proteins. Genes coding for these proteins contain an intron in the 5'-non-coding region, a typical characteristic of most sauropsid Ka beta Ps. Gecko KA beta Ps show a central amino acid region of high homology and a beta-pleated conformation that is likely responsible for the polymerization of Ka beta Ps into long and resistant filaments. The association of numerous filaments, probably over a framework of alpha-keratins, permits the formation of bundles of corneous material for the elongation of setae, which may be over 100 microm long. The terminals branching off each seta may derive from the organization of the cytoskeleton and from the mechanical separation of keratin bundles located at the terminal apex of setae.
Real-Time Simulation of a Wind Turbine Generator Coupled With a Battery Supercapacitor Energy Storage System

Wind power generation studies of slow phenomena using a detailed model can be difficult to perform with a conventional offline simulation program. Due to the computational power and high-speed input and output, a real-time simulator is capable of conducting repetitive simulations of wind profiles in a short time with detailed models of critical components and allows testing of prototype controllers through hardware-in-the-loop (HIL). This paper discusses methods to overcome the challenges of real-time simulation of wind systems, characterized by their complexity and high-frequency switching. A hybrid flow-battery supercapacitor energy storage system (ESS), coupled in a wind turbine generator to smooth wind power, is studied by real-time HIL simulation. The prototype controller is embedded in one real-time simulator, while the rest of the system is implemented in another independent simulator. The simulation results of the detailed wind system model show that the hybrid ESS has a lower battery cost, higher battery longevity, and improved overall efficiency over its reference ESS.
A Survey on Software Defined Networking: Architecture for Next Generation Network

The evolution of software defined networking (SDN) has played a significant role in the development of next-generation networks (NGN). SDN as a programmable network having “service provisioning on the fly” has induced a keen interest both in academic world and industry. In this article, a comprehensive survey is presented on SDN advancement over conventional network. The paper covers historical evolution in relation to SDN, functional architecture of the SDN and its related technologies, and OpenFlow standards/protocols, including the basic concept of interfacing of OpenFlow with network elements (NEs) such as optical switches. In addition a selective architecture survey has been conducted. Our proposed architecture on software defined heterogeneous network, points towards new technology enabling the opening of new vistas in the domain of network technology, which will facilitate in handling of huge internet traffic and helps infrastructure and service providers to customize their resources dynamically. Besides, current research projects and various activities as being carried out to standardize SDN as NGN by different standard development organizations (SODs) have been duly elaborated to judge how this technology moves towards standardization.
More Robust Doubly Robust Off-policy Evaluation

We study the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of a policy from the data generated by another policy(ies). In particular, we focus on the doubly robust (DR) estimators that consist of an importance sampling (IS) component and a performance model, and utilize the low (or zero) bias of IS and low variance of the model at the same time. Although the accuracy of the model has a huge impact on the overall performance of DR, most of the work on using the DR estimators in OPE has been focused on improving the IS part, and not much on how to learn the model. In this paper, we propose alternative DR estimators, called more robust doubly robust (MRDR), that learn the model parameter by minimizing the variance of the DR estimator. We first present a formulation for learning the DR model in RL. We then derive formulas for the variance of the DR estimator in both contextual bandits and RL, such that their gradients w.r.t. the model parameters can be estimated from the samples, and propose methods to efficiently minimize the variance. We prove that the MRDR estimators are strongly consistent and asymptotically optimal. Finally, we evaluate MRDR in bandits and RL benchmark problems, and compare its performance with the existing methods.
Concealed weapon detection from images using SIFT and SURF

Nowadays the global attacks of terrorism continues to escalate, so it is exigent to find the different techniques to ensure the safety of individuals at the public places like airports, railways and sensitive buildings etc. This paper presents proposed method for feature extraction from images to detect weapons especially different shapes of guns. Most effective algorithms SIFT and SURF used for feature extraction applied on database of images of luggage. Both algorithms give results that are invariant to scale, rotation, occlusion, change in illumination and noise. In the proposed method, input image is divided into parts in order to detect multiple guns of same or different type. Experimentally proposed method shows 90% efficiency and SURF is faster than SIFT. Algorithms are tested on the database of images containing weapons.
An Accurate, Continuous, and Lossless Self-Learning CMOS Current-Sensing Scheme for Inductor-Based DC-DC Converters

Sensing current is a fundamental function in power supply circuits, especially as it generally applies to protection and feedback control. Emerging state-of-the-art switching supplies, in fact, are now exploring ways to use this sensed-current information to improve transient response, power efficiency, and compensation performance by appropriately self-adjusting, on the fly, frequency, inductor ripple current, switching configuration (e.g., synchronous to/from asynchronous), and other operating parameters. The discontinuous, non-integrated, and inaccurate nature of existing lossless current-sensing schemes, however, impedes their widespread adoption, and lossy solutions are not acceptable. Lossless, filter-based techniques are continuous, but inaccurate when integrated on-chip because of the inherent mismatches between the filter and the power inductor. The proposed GM-C filter-based, fully integrated current-sensing CMOS scheme circumvents this accuracy limitation by introducing a self-learning sequence to start-up and power-on-reset. During these seldom-occurring events, the gain and bandwidth of the internal filter are matched to the response of the power inductor and its equivalent series resistance (ESR), effectively measuring their values. A 0.5 mum CMOS realization of the proposed scheme was fabricated and applied to a current-mode buck switching supply, achieving overall DC and AC current-gain errors of 8% and 9%, respectively, at 0.8 A DC load and 0.2 A ripple currents for 3.5 muH-14 muH inductors with ESRs ranging from 48 mOmega to 384 mOmega (other lossless, state-of-the-art solutions achieve 20%-40% error, and only when the nominal specifications of the power MOSFET and/or inductor are known). Since the self-learning sequence is non-recurring, the power losses associated with the foregoing solution are minimal, translating to a 2.6% power efficiency savings when compared to the more traditional but accurate series-sense resistor (e.g., 50 mOmega) technique.
Block Copolymer Electrolytes Polymers for Solid-State Lithium Batteries

Bergfelt, A. 2018. Block Copolymer Electrolytes. Polymers for Solid-State Lithium Batteries. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1630. 68  Acta Universitatis Upsaliensis. ISBN 978-91-513-0233-1. The use of solid polymer electrolytes (SPEs) for lithium battery devices is a rapidly growing research area. The liquid electrolytes that are used today are inflammable and harmful towards the battery components. The adoption of SPEs could drastically improve this situation, but they still suffer from a too low performance at ambient temperatures for most practical applications. However, by increasing the operating temperature to between 60 °C and 90 °C, the electrolyte performance can be drastically increased. The drawback of this approach, partly, is that parasitic side reactions become noticeable at these elevated temperatures, thus affecting battery lifetime and performance. Furthermore, the ionically conductive polymer loses its mechanical integrity, thus triggering a need for an external separator in the battery device. One way of combining both mechanical properties and electrochemical performance is to design block copolymer (BCP) electrolytes, that is, polymers that are tailored to combine one ionic conductive block with a mechanical block, into one polymer. The hypothesis is that the BCP electrolytes should self-assemble into well-defined microphase separated regions in order to maximize the block properties. By varying monomer composition and structure of the BCP, it is possible to design electrolytes with different battery device performance. In Paper I and Paper II two types of methacrylate-based triblock copolymers with different mechanical blocks were synthesized, in order to evaluate morphology, electrochemical performance, and battery performance. In Paper III and Paper IV a different strategy was adopted, with a focus on diblock copolymers. In this strategy, the ethylene oxide was replaced by poly(e-caprolactone) and poly(trimethylene carbonate) as the lithium-ion dissolving group. The investigated mechanical blocks in these studies were poly(benzyl methacrylate) and polystyrene. The battery performance for these electrolytes was superior to the methacrylatebased battery devices, thus resulting in stable battery cycling at 40 °C and 30 °C. Andreas Bergfelt, Department of Chemistry Ångström, Polymer Chemistry, Box 538, Uppsala University, SE-751 21 Uppsala, Sweden.
Seasonal variation in phenolic composition, antibacterial and antioxidant activities of Ulva rigida (Chlorophyta) and assessment of antiacetylcholinesterase potential

The phenolic composition and antibacterial and antioxidant activities of the green alga Ulva rigida collected monthly for 12 months were investigated. Significant differences in antibacterial activity were observed during the year with the highest inhibitory effect in samples collected during spring and summer. The highest free radical scavenging activity and phenolic content were detected in U. rigida extracts collected in late winter (February) and early spring (March). The investigation of the biological properties of U. rigida fractions collected in spring (April) revealed strong antimicrobial and antioxidant activities. Ethyl acetate and n-hexane fractions exhibited substantial acetylcholinesterase inhibitory capacity with EC50 of 6.08 and 7.6 μg mL−1, respectively. The total lipid, protein, ash, and individual fatty acid contents of U. rigida were investigated. The four most abundant fatty acids were palmitic, oleic, linolenic, and eicosenoic acids.
An effective approach to document retrieval via utilizing WordNet and recognizing phrases

Noun phrases in queries are identified and classified into four types: proper names, dictionary phrases, simple phrases and complex phrases. A document has a phrase if all content words in the phrase are within a window of a certain size. The window sizes for different types of phrases are different and are determined using a decision tree. Phrases are more important than individual terms. Consequently, documents in response to a query are ranked with matching phrases given a higher priority. We utilize WordNet to disambiguate word senses of query terms. Whenever the sense of a query term is determined, its synonyms, hyponyms, words from its definition and its compound words are considered for possible additions to the query. Experimental results show that our approach yields between 23% and 31% improvements over the best-known results on the TREC 9, 10 and 12 collections for short (title only) queries, without using Web data.
Visualization of attributed hierarchical structures in a spatiotemporal context

When visualizing data, spatial and temporal references of these data often have to be considered in addition to the actual data attributes. Nowadays, structural information is becoming more and more important. Hierarchies, for instance, are frequently applied to make large and complex data manageable. Hence, a visual depiction of hierarchical structures in space and time is required. While there are several techniques addressing specific aspects of spatiotemporal visualization, approaches that cope with space, time, data, and structure are rare. With this paper we take a step to fill this gap. By combining various well-established concepts we achieve a reasonably complete visualization of all of the aforementioned aspects, where our focus is on hierarchical structures. We embed hierarchies directly into regions of a map display using variants of the point-based layout. Layering and animation are applied to visualize temporal aspects. Depending on analysis goals, users can switch between representations that emphasize data attributes or hierarchical structures. Interaction techniques support users in navigating the data and their visualization. We demonstrate the usefulness of our approach by adapting it to implement a visualization for spatio-temporal human health data.
Mutation of the gene encoding the ROR2 tyrosine kinase causes autosomal recessive Robinow syndrome

Robinow syndrome is a short-limbed dwarfism characterized by abnormal morphogenesis of the face and external genitalia, and vertebral segmentation. The recessive form of Robinow syndrome (RRS; OMIM 268310), particularly frequent in Turkey, has a high incidence of abnormalities of the vertebral column such as hemivertebrae and rib fusions, which is not seen in the dominant form. Some patients have cardiac malformations or facial clefting. We have mapped a gene for RRS to 9q21–q23 in 11 families. Haplotype sharing was observed between three families from Turkey, which localized the gene to a 4.9-cM interval. The gene ROR2, which encodes an orphan membrane-bound tyrosine kinase, maps to this region. Heterozygous (presumed gain of function) mutations in ROR2 were previously shown to cause dominant brachydactyly type B (BDB; ref. 7). In contrast, Ror2−/− mice have a short-limbed phenotype that is more reminiscent of the mesomelic shortening observed in RRS. We detected several homozygous ROR2 mutations in our cohort of RRS patients that are located upstream from those previously found in BDB. The ROR2 mutations present in RRS result in premature stop codons and predict nonfunctional proteins.
A physical model of power amplifiers AM/AM and AM/PM distortions and their internal relationship

Signal-dependent phase variation, AM/PM, along with amplitude variation, AM/AM, are known to determine nonlinear distortion characteristics of current-mode PAs. However, these distortion effects have been treated separately, putting more weight on the amplitude distortion, while the AM/PM generation mechanisms are yet to be fully understood. Hence, the aim of this work is to present a large-signal physical model that can describe both the AM/AM and AM/PM PA nonlinear distortion characteristics and their internal relationship.