nils-herrmann/clean_scirepeval huggingface dataset:
Multidisciplinary optimization of servodrives for robot manipulators

In this paper, a multiobjective optimization approach for designing a Manipulator Robot by simultaneously considering the mechanism, the controller and the servo drive subsystems is proposed. The integrated design problem is considered as a nonlinear multiobjective dynamic optimization problem, which relates the structural parameters, the robot controller and the selection of the ratio gear-motor from an industry catalog. A three dof manipulator robot and its controller are designed, where the performance design objectives are tracking error, manipulability measure and energy consumption.
Identification and optimization of pteridinone Toll-like receptor 7 (TLR7) agonists for the oral treatment of viral hepatitis.

Pteridinone-based Toll-like receptor 7 (TLR7) agonists were identified as potent and selective alternatives to the previously reported adenine-based agonists, leading to the discovery of GS-9620. Analogues were optimized for the immunomodulatory activity and selectivity versus other TLRs, based on differential induction of key cytokines including interferon α (IFN-α) and tumor necrosis factor α (TNF-α). In addition, physicochemical properties were adjusted to achieve desirable in vivo pharmacokinetic and pharmacodynamic properties. GS-9620 is currently in clinical evaluation for the treatment of chronic hepatitis B (HBV) infection.
hRESTS: An HTML Microformat for Describing RESTful Web Services

The Web 2.0 wave brings, among other aspects, the Programmable Web:increasing numbers of Web sites provide machine-oriented APIs and Web services. However, most APIs are only described with text in HTML documents. The lack of machine-readable API descriptions affects the feasibility of tool support for developers who use these services. We propose a microformat called hRESTS (HTML for RESTful Services) for machine-readable descriptions of Web APIs, backed by a simple service model. The hRESTS microformat describes main aspects of services, such as operations, inputs and outputs. We also present two extensions of hRESTS:SA-REST, which captures the facets of public APIs important for mashup developers, and MicroWSMO, which provides support for semantic automation.
Role of continuous glucose monitoring for type 2 in diabetes management and research.

The advent of continuous glucose monitoring (CGM) is a significant stride forward in our ability to better understand the glycemic status of our patients. Current clinical practice employs two forms of CGM: professional (retrospective or "masked") and personal (real-time) to evaluate and/or monitor glycemic control. Most studies using professional and personal CGM have been done in those with type 1 diabetes (T1D). However, this technology is agnostic to the type of diabetes and can also be used in those with type 2 diabetes (T2D). The value of professional CGM in T2D for physicians, patients, and researchers is derived from its ability to: (1) to discover previously unknown hyper- and hypoglycemia (silent and symptomatic); (2) measure glycemic control directly rather than through the surrogate metric of hemoglobin A1C (HbA1C) permitting the observation of a wide variety of metrics that include glycemic variability, the percent of time within, below and above target glucose levels, the severity of hypo- and hyperglycemia throughout the day and night; (3) provide actionable information for healthcare providers derived by the CGM report; (4) better manage patients on hemodialysis; and (5) effectively and efficiently analyze glycemic effects of new interventions whether they be pharmaceuticals (duration of action, pharmacodynamics, safety, and efficacy), devices, or psycho-educational. Personal CGM has also been successfully used in a small number of studies as a behavior modification tool in those with T2D. This comprehensive review describes the differences between professional and personal CGM and the evidence for the use of each form of CGM in T2D. Finally, the opinions of key professional societies on the use of CGM in T2D are presented.
Spatio-temporal correlations and visual signalling in a complete neuronal population

Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20% more information about the visual scene than decoding under the assumption of independence, and preserves 40% more visual information than optimal linear decoding. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.
Improved Regularization Techniques for End-to-End Speech Recognition

Regularization is important for end-to-end speech models, since the models are highly flexible and easy to overfit. Data augmentation and dropout has been important for improving end-to-end models in other domains. However, they are relatively under explored for end-to-end speech models. Therefore, we investigate the effectiveness of both methods for end-to-end trainable, deep speech recognition models. We augment audio data through random perturbations of tempo, pitch, volume, temporal alignment, and adding random noise. We further investigate the effect of dropout when applied to the inputs of all layers of the network. We show that the combination of data augmentation and dropout give a relative performance improvement on both Wall Street Journal (WSJ) and LibriSpeech dataset of over 20%. Our model performance is also competitive with other end-to-end speech models on both datasets.
Design and analysis of a head-mounted parallel kinematic device for skull surgery

Precision skull surgery requires specialized instrumentation to satisfy demanding requirements in cochlear array implantation, deep brain stimulation electrode placement, and related applications. A miniaturized reconfigurable parallel kinematic mechanism which can be directly mounted on a patient’s skull was designed, built, and tested for precision skull surgery. A Stewart–Gough platform is attached to a patient’s skull so no optical tracking affecting the overall accuracy in keyhole surgery is required. Six bone anchors comprising the mechanism base joints are implanted at positions with sufficient skull thickness. Since no fixation frame is required, intervention planning flexibility is increased. The centers of the spherical shaped bone anchors can be localized accurately in the image space. An implicit registration to the physical space is performed by assembling the kinematics. Registration error is minimized compared to common optical tracker-based approaches. Due to the reconfigurable mechanism, an optimization of the hexapod’s configuration is needed to maximize accuracy and mechanical stability during the incision. Mathematical simulation was conducted to estimate system performance. Simulation results revealed significant improvement in accuracy and stability when exploiting the redundant degrees of freedom and the implemented reconfigurability. Inaccurate localization of base points in the image data set was identified as the main source of error. A first prototype with passive prismatic actuators, i.e. micrometer calipers, was successfully built. A head-mounted parallel kinematic device for high precision skull surgery was developed that provides submillimetric accuracy in straight-line incisions. The system offers enhanced flexibility due to the absence of a rigid fixation frame.
Collaborative filtering and the generalized vector space model

Collaborative filtering is a technique for recommending documents to users based on how similar their tastes are to other users. If two users tend to agree on what they like, the system will recommend the same documents to them. The generalized vector space model of information retrieval represents a document by a vector of its similarities to all other documents. The process of collaborative filtering is nearly identical to the process of retrieval using GVSM in a matrix of user ratings. Using this observation, a model for filtering collaboratively using document content is possible.
Detecting medley of iris spoofing attacks using DESIST

Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. However, similar to other biometric modalities, iris recognition systems are also vulnerable to presentation attacks (commonly called spoofing) that attempt to conceal or impersonate identity. Examples of typical iris spoofing attacks are printed iris images, textured contact lenses, and synthetic creation of iris images. It is critical to note that majority of the algorithms proposed in the literature are trained to handle a specific type of spoofing attack. These algorithms usually perform very well on that particular attack. However, in real-world applications, an attacker may perform different spoofing attacks. In such a case, the problem becomes more challenging due to inherent variations in different attacks. In this paper, we focus on a medley of iris spoofing attacks and present a unified framework for detecting such attacks. We propose a novel structural and textural feature based iris spoofing detection framework (DESIST). Multi-order dense Zernike moments are calculated across the iris image which encode variations in structure of the iris image. Local Binary Pattern with Variance (LBPV) is utilized for representing textural changes in a spoofed iris image. The highest classification accuracy of 82.20% is observed by the proposed framework for detecting normal and spoofed iris images on a combined iris spoofing database.
PBFT vs Proof-of-Authority: Applying the CAP Theorem to Permissioned Blockchain

Permissioned blockchains are arising as a solution to federate companies prompting accountable interactions. A variety of consensus algorithms for such blockchains have been proposed, each of which has different benefits and drawbacks. Proof-of-Authority (PoA) is a new family of Byzantine fault-tolerant (BFT) consensus algorithms largely used in practice to ensure better performance than traditional Practical Byzantine Fault Tolerance (PBFT). However, the lack of adequate analysis of PoA hinders any cautious evaluation of their effectiveness in real-world permissioned blockchains deployed over the Internet, hence on an eventually synchronous network experimenting Byzantine nodes. In this paper, we analyse two of the main PoA algorithms, named Aura and Clique, both in terms of provided guarantees and performances. First, we derive their functioning including how messages are exchanged, then we weight, by relying on the CAP theorem, consistency, availability and partition tolerance guarantees. We also report a qualitative latency analysis based on message rounds. The analysis advocates that PoA for permissioned blockchains, deployed over the Internet with Byzantine nodes, do not provide adequate consistency guarantees for scenarios where data integrity is essential. We claim that PBFT can fit better such scenarios, despite a limited loss in terms of performance.
The Influence of the Severity of Fading and Shadowing on the Statistical Properties of the Capacity of Nakagami-Lognormal Channels

This paper deals with the study of the statistical properties of the capacity of Nakagami-lognormal (NLN) channels for various fading environments. Specifically, the impact of shadowing and the severity of fading on the channel capacity is investigated. We have derived analytical expressions for the probability density function (PDF), cumulative distribution function (CDF), level-crossing rate (LCR), and average duration of fades (ADF) of the channel capacity. These results are analyzed for different levels of shadowing and for various fading conditions, corresponding to different terrestrial environments. It is observed that the severity of fading and shadowing has a significant influence on the spread and the maximum value of the PDF and LCR of the channel capacity. Moreover, it is also observed that if the fading gets less severe as compared to the Rayleigh fading, the mean channel capacity increases. However, the shadowing effect has no impact on the mean channel capacity. The validity of all analytical results is confirmed by simulations.
Good manufacturing practices production of a purification-free oral cholera vaccine expressed in transgenic rice plants

The first Good Manufacturing Practices production of a purification-free rice-based oral cholera vaccine (MucoRice-CTB) from transgenic plants in a closed cultivation system yielded a product meeting regulatory requirements. Despite our knowledge of their advantages, plant-based vaccines remain unavailable for human use in both developing and industrialized countries. A leading, practical obstacle to their widespread use is producing plant-based vaccines that meet governmental regulatory requirements. Here, we report the first production according to current Good Manufacturing Practices of a rice-based vaccine, the cholera vaccine MucoRice-CTB, at an academic institution. To this end, we established specifications and methods for the master seed bank (MSB) of MucoRice-CTB, which was previously generated as a selection-marker-free line, evaluated its propagation, and given that the stored seeds must be renewed periodically. The production of MucoRice-CTB incorporated a closed hydroponic system for cultivating the transgenic plants, to minimize variations in expression and quality during vaccine manufacture. This type of molecular farming factory can be operated year-round, generating three harvests annually, and is cost- and production-effective. Rice was polished to a ratio of 95 % and then powdered to produce the MucoRice-CTB drug substance, and the identity, potency, and safety of the MucoRice-CTB product met pre-established release requirements. The formulation of MucoRice-CTB made by fine-powdering of drug substance and packaged in an aluminum pouch is being evaluated in a physician-initiated phase I study.
Microservices and Their Design Trade-Offs: A Self-Adaptive Roadmap

Migrating to microservices (microservitization) enables optimising the autonomy, replaceability, decentralised governance and traceability of software architectures. Despite the hype for microservitization , the state of the art still lacks consensus on the definition of microservices, their properties and their modelling techniques. This paper summarises views of microservices from informal literature to reflect on the foundational context of this paradigm shift. A strong foundational context can advance our understanding of microservitization and help guide software architects in addressing its design problems. One such design problem is finalising the optimal level of granularity of a microservice architecture. Related design trade-offs include: balancing the size and number of microservices in an architecture and balancing the nonfunctional requirement satisfaction levels of the individual microservices as well as their satisfaction for the overall system. We propose how self-adaptivity can assist in addressing these design trade-offs and discuss some of the challenges such a selfadaptive solution. We use a hypothetical online movie streaming system to motivate these design trade-offs. A solution roadmap is presented in terms of the phases of a feedback control loop.
A time-series compression technique and its application to the smart grid

Time-series data is increasingly collected in many domains. One example is the smart electricity infrastructure, which generates huge volumes of such data from sources such as smart electricity meters. Although today these data are used for visualization and billing in mostly 15-min resolution, its original temporal resolution frequently is more fine-grained, e.g., seconds. This is useful for various analytical applications such as short-term forecasting, disaggregation and visualization. However, transmitting and storing huge amounts of such fine-grained data are prohibitively expensive in terms of storage space in many cases. In this article, we present a compression technique based on piecewise regression and two methods which describe the performance of the compression. Although our technique is a general approach for time-series compression, smart grids serve as our running example and as our evaluation scenario. Depending on the data and the use-case scenario, the technique compresses data by ratios of up to factor 5,000 while maintaining its usefulness for analytics. The proposed technique has outperformed related work and has been applied to three real-world energy datasets in different scenarios. Finally, we show that the proposed compression technique can be implemented in a state-of-the-art database management system.
Automatic device driver synthesis with termite

Faulty device drivers cause significant damage through down time and data loss. The problem can be mitigated by an improved driver development process that guarantees correctness by construction. We achieve this by synthesising drivers automatically from formal specifications of device interfaces, thus reducing the impact of human error on driver reliability and potentially cutting down on development costs.
 We present a concrete driver synthesis approach and tool called Termite. We discuss the methodology, the technical and practical limitations of driver synthesis, and provide an evaluation of non-trivial drivers for Linux, generated using our tool. We show that the performance of the generated drivers is on par with the equivalent manually developed drivers. Furthermore, we demonstrate that device specifications can be reused across different operating systems by generating a driver for FreeBSD from the same specification as used for Linux.
Angle-of-arrival localization based on antenna arrays for wireless sensor networks

Among the large number of contributions concerning the localization techniques for wireless sensor networks (WSNs), there is still no simple, energy and cost efficient solution suitable in outdoor scenarios. In this paper, a technique based on antenna arrays and angle-ofarrival (AoA) measurements is carefully discussed. While the AoA algorithms are rarely considered for WSNs due to the large dimensions of directional antennas, some system configurations are investigated that can be easily incorporated in pocket-size wireless devices. A heuristic weighting function that enables decreasing the location errors is introduced. Also, the detailed performance analysis of the presented system is provided. The localization accuracy is validated through realistic Monte-Carlo simulations that take into account the specificity of propagation conditions in WSNs as well as the radio noise effects. Finally, trade-offs between the accuracy, localization time and the number of anchors in a network are addressed.
Assay of superoxide dismutase activity in animal tissues

Convenient assays for superoxide dismutase have necessarily been of the indirect type. It was observed that among the different methods used for the assay of superoxide dismutase in rat liver homogenate, namely the xanthine-xanthine oxidase ferricytochromec, xanthine-xanthine oxidase nitroblue tetrazolium, and pyrogallol autoxidation methods, a modified pyrogallol autoxidation method appeared to be simple, rapid and reproducible. The xanthine-xanthine oxidase ferricytochromec method was applicable only to dialysed crude tissue homogenates. The xanthine-xanthine oxidase nitroblue tetrazolium method, either with sodium carbonate solution, pH 10.2, or potassium phosphate buffer, pH 7·8, was not applicable to rat liver homogenate even after extensive dialysis. Using the modified pyrogallol autoxidation method, data have been obtained for superoxide dismutase activity in different tissues of rat. The effect of age, including neonatal and postnatal development on the activity, as well as activity in normal and cancerous human tissues were also studied. The pyrogallol method has also been used for the assay of iron-containing superoxide dismutase inEscherichia coli and for the identification of superoxide dismutase on polyacrylamide gels after electrophoresis.
PROFIT-SHARING CONTRACTS IN HOLLYWOOD : EVOLUTION AND ANALYSIS

This article examines the development of profitor revenue-sharing contracts in the motion picture industry. Contrary to much popular belief, such contracts have been in use since the start of the studio era. However, early contracts differed from those seen today. The evolution of the current contract is traced, and evidence regarding the increased use of sharing contracts after 1948 is examined. I examine competing theories of the economic function served by these contracts. I suggest that it is unlikely that these contracts are the result of a standard principal-agent problem.
Antithrombotic therapy in children with venous thromboembolism.

Antithrombotic therapy has recently become more frequent for the treatment of venous thromboembolism (VTE) in the paediatric population. This can be explained by the increased awareness of morbidities and mortalities of VTE in children, as well as the improved survival rate of children with various kinds of serious illnesses. Considering the large number of years a child is expected to survive, associated morbidities such as postthrombotic syndrome and risk of recurrence can significantly impact on the quality of life in children. Therefore, timely diagnosis, evidence-based treatment and prophylaxis strategies are critical to avoid such complications. This review summarizes the current literature about the antithrombotic treatment for VTE in infants and children. It guides the paediatric medical care provider for making a logical and justifiable decision.
Person Re-identification by Deep Learning Attribute-Complementary Information

Automatic person re-identification (re-id) across camera boundaries is a challenging problem. Approaches have to be robust against many factors which influence the visual appearance of a person but are not relevant to the person's identity. Examples for such factors are pose, camera angles, and lighting conditions. Person attributes are a semantic high level information which is invariant across many such influences and contain information which is often highly relevant to a person's identity. In this work we develop a re-id approach which leverages the information contained in automatically detected attributes. We train an attribute classifier on separate data and include its responses into the training process of our person re-id model which is based on convolutional neural networks (CNNs). This allows us to learn a person representation which contains information complementary to that contained within the attributes. Our approach is able to identify attributes which perform most reliably for re-id and focus on them accordingly. We demonstrate the performance improvement gained through use of the attribute information on multiple large-scale datasets and report insights into which attributes are most relevant for person re-id.