nils-herrmann/clean_scirepeval huggingface dataset:
Results of a pivotal phase II study of brentuximab vedotin for patients with relapsed or refractory Hodgkin's lymphoma.

Brentuximab vedotin is an antibody-drug conjugate (ADC) that selectively delivers monomethyl auristatin E, an antimicrotubule agent, into CD30-expressing cells. In phase I studies, brentuximab vedotin demonstrated significant activity with a favorable safety profile in patients with relapsed or refractory CD30-positive lymphomas.



In this multinational, open-label, phase II study, the efficacy and safety of brentuximab vedotin were evaluated in patients with relapsed or refractory Hodgkin's lymphoma (HL) after autologous stem-cell transplantation (auto-SCT). Patients had histologically documented CD30-positive HL by central pathology review. A total of 102 patients were treated with brentuximab vedotin 1.8 mg/kg by intravenous infusion every 3 weeks. In the absence of disease progression or prohibitive toxicity, patients received a maximum of 16 cycles. The primary end point was the overall objective response rate (ORR) determined by an independent radiology review facility.



The ORR was 75% with complete remission (CR) in 34% of patients. The median progression-free survival time for all patients was 5.6 months, and the median duration of response for those in CR was 20.5 months. After a median observation time of more than 1.5 years, 31 patients were alive and free of documented progressive disease. The most common treatment-related adverse events were peripheral sensory neuropathy, nausea, fatigue, neutropenia, and diarrhea.



The ADC brentuximab vedotin was associated with manageable toxicity and induced objective responses in 75% of patients with relapsed or refractory HL after auto-SCT. Durable CRs approaching 2 years were observed, supporting study in earlier lines of therapy.
Towards gameplay analysis via gameplay metrics

User-oriented research in the game industry is undergoing a change from relying on informal user-testing methods adapted directly from productivity software development to integrating modern approaches to usability- and user experience testing. Gameplay metrics analysis form one of these techniques, being based on instrumentation methods in HCI. Gameplay metrics are instrumentation data about the user behavior and user-game interaction, and can be collected during testing, production and the live period of the lifetime of a digital game. The use of instrumentation data is relatively new to commercial game development, and remains a relatively unexplored method of user research. In this paper, the focus is on utilizing game metrics for informing the analysis of gameplay during commercial game production as well as in research contexts. A series of case studies are presented, focusing on the major commercial game titles Kane & Lynch and Fragile Alliance.
Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features

Indexing and classification tools for Content Based Visual Information Retrieval (CBVIR) have been penetrating the universe of medical image analysis. They have been recently investigated for Alzheimer’s disease (AD) diagnosis. This is a normal “knowledge diffusion” process, when methodologies developed for multimedia mining penetrate a new application area. The latter brings its own specificities requiring an adjustment of methodologies on the basis of domain knowledge. In this paper, we develop an automatic classification framework for AD recognition in structural Magnetic Resonance Images (MRI). The main contribution of this work consists in considering visual features from the most involved region in AD (hippocampal area) and in using a late fusion to increase precision results. Our approach has been first evaluated on the baseline MR images of 218 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and then tested on a 3T weighted contrast MRI obtained from a subsample of a large French epidemiological study: “Bordeaux dataset”. The experimental results show that our classification of patients with AD versus NC (Normal Control) subjects achieves the accuracies of 87 % and 85 % for ADNI subset and “Bordeaux dataset” respectively. For the most challenging group of subjects with the Mild Cognitive Impairment (MCI), we reach accuracies of 78.22 % and 72.23 % for MCI versus NC and MCI versus AD respectively on ADNI. The late fusion scheme improves classification results by 9 % in average for these three categories. Results demonstrate very promising classification performance and simplicity compared to the state-of-the-art volumetric AD diagnosis methods.
Web-scale information extraction in knowitall: (preliminary results)

Manually querying search engines in order to accumulate a large bodyof factual information is a tedious, error-prone process of piecemealsearch. Search engines retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse information from multiple documents. This paperintroduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner.The paper describes preliminary experiments in which an instance of KnowItAll, running for four days on a single machine, was able to automatically extract 54,753 facts. KnowItAll associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KnowItAll's architecture and reports on lessons learned for the design of large-scale information extraction systems.
Covalsa: Covariance estimation from compressive measurements using alternating minimization

The estimation of covariance matrices from compressive measurements has recently attracted considerable research efforts in various fields of science and engineering. Owing to the small number of observations, the estimation of the covariance matrices is a severely ill-posed problem. This can be overcome by exploiting prior information about the structure of the covariance matrix. This paper presents a class of convex formulations and respective solutions to the high-dimensional covariance matrix estimation problem under compressive measurements, imposing either Toeplitz, sparseness, null-pattern, low rank, or low permuted rank structure on the solution, in addition to positive semi-definiteness. To solve the optimization problems, we introduce the Co-Variance by Augmented Lagrangian Shrinkage Algorithm (CoVALSA), which is an instance of the Split Augmented Lagrangian Shrinkage Algorithm (SALSA). We illustrate the effectiveness of our approach in comparison with state-of-the-art algorithms.
Compact solid-State switched pulsed power and its applications

Power semiconductor devices, such as insulated-gate bipolar transistors, metal-oxide-semiconductor field-effect transistors, and static-induction thyristors, are used in different kinds of pulsed power generators developed for different applications. In addition, the semiconductor opening switch is found to have very effective applications in pulsed power generation by inductive energy storage. Semiconductor switches have greatly extended the scales of pulsed power parameters, especially in repetition rate and lifetime. They have also enabled new areas of pulsed power applications, such as accelerators, flue-gas treatment, and gas lasers.
End-to-End Communication Architecture for Smart Grids

Smart grids heavily depend on communication in order to coordinate the generation, distribution, and consumption of energy-even more so if distributed power plants based on renewable energies are taken into account. Given the variety of communication partners, a heterogeneous network infrastructure consisting of IP-based and suitable field-level networks is the most appropriate solution. This paper investigates such a two-tier infrastructure and possible field-level networks with particular attention to metering and supervisory control and data acquisition applications. For the problem of network integration, a combination of gateway and tunneling solutions is proposed which allows a semitransparent end-to-end connection between application servers and field nodes. The feasibility of the approach and implementation details are discussed at the example of powerline communication and IP-based networks investigated in the European research project on real-time energy management via powerlines and internet. Nevertheless, it is shown that the communication architecture is versatile enough to serve as a generic solution for smart grids.
Realtime High-Speed Network Traffic Monitoring Using ntopng

Monitoring network traffic has become increasingly challenging in terms of number of hosts, protocol proliferation and probe placement topologies. Virtualised environments and cloud services shifted the focus from dedicated hardware monitoring devices to virtual machine based, software traffic monitoring applications.   This paper covers the design and implementation of ntopng, an open-source traffic monitoring application designed for highspeed networks. ntopng’s key features are large networks realtime analytics and the ability to characterise application protocols and user traffic behaviour. ntopng was extensively validated in various monitoring environments ranging from small networks to .it ccTLD traffic analysis. Traffic monitoring, real-time analytics, open-source software, monitoring of virtual and cloud environments.
A pre-collision control strategy for human-robot interaction based on dissipated energy in potential inelastic impacts

Enabling human-robot collaboration raises new challenges in safety-oriented robot design and control. Indices that quantitatively describe human injury due to a human-robot collision are needed to propose suitable pre-collision control strategies. This paper presents a novel model-based injury index built on the concept of dissipated kinetic energy in a potential inelastic impact. This quantity represents the fracture energy lost when a human-robot collision occurs, modeling both clamped and unclamped cases. It depends on the robot reflected mass and velocity in the impact direction. The proposed index is expressed in analytical form suitable to be integrated in a constraint-based pre-collision control strategy. The exploited control architecture allows to perform a given robot task while simultaneously bounding our injury assessment and minimizing the reflected mass in the direction of the impact. Experiments have been performed on a lightweight robot ABB FRIDA to validate the proposed injury index as well as the pre-collision control strategy.
A review of soft computing applications in supply chain management

It is broadly recognised by global companies that supply chain management is one of the major core competencies for an organisation to compete in the marketplace. Organisational strategies are mainly concentrated on improvement of customer service levels as well as reduction of operational costs in order to maintain profit margins. Therefore supply chain performance has attracted researchers’ attention. A variety of soft computing techniques including fuzzy logic and genetic algorithms have been employed to improve effectiveness and efficiency in various aspects of supply chain management. Meanwhile, an increasing number of papers have been published to address related issues. The aim of this paper is to summarise the findings by a systematic review of existing research papers concerning the application of soft computing techniques to supply chain management. Some areas in supply chain management that have rarely been exposed in existing papers, such as customer relationship management and reverse logistics, are therefore suggested for future research.
A column approximate minimum degree ordering algorithm

Sparse Gaussian elimination with partial pivoting computes the factorization <b>PAQ</b> = <b>LU</b> of a sparse matrix <b>A</b>, where the row ordering <b>P</b> is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, <b>Q</b>, based solely on the nonzero pattern of <b>A</b>, that limits the worst-case number of nonzeros in the factorization. The fill-in also depends on <b>P</b>, but <b>Q</b> is selected to reduce an upper bound on the fill-in for any subsequent choice of <b>P</b>. The choice of <b>Q</b> can have a dramatic impact on the number of nonzeros in <b>L</b> and <b>U</b>. One scheme for determining a good column ordering for <b>A</b> is to compute a symmetric ordering that reduces fill-in in the Cholesky factorization of <b>A<sup>T</sup>A</b>. A conventional minimum degree ordering algorithm would require the sparsity structure of <b>A<sup>T</sup>A</b> to be computed, which can be expensive both in terms of space and time since <b>A<sup>T</sup>A</b> may be much denser than <b>A</b>. An alternative is to compute <b>Q</b> directly from the sparsity structure of <b>A</b>; this strategy is used by MATLAB's COLMMD preordering algorithm. A new ordering algorithm, COLAMD, is presented. It is based on the same strategy but uses a better ordering heuristic. COLAMD is faster and computes better orderings, with fewer nonzeros in the factors of the matrix.
Energy-aware wireless microsensor networks

Self-configuring wireless sensor networks can be invaluable in many civil and military applications for collecting, processing, and disseminating wide ranges of complex environmental data. Because of this, they have attracted considerable research attention in the last few years. The WINS  and SmartDust  projects, for instance, aim to integrate sensing, computing, and wireless communication capabilities into a small form factor to enable low-cost production of these tiny nodes in large numbers. Several other groups are investigating efficient hardware/software system architectures, signal processing algorithms, and network protocols for wireless sensor networks [3]-[5]. Sensor nodes are battery driven and hence operate on an extremely frugal energy budget. Further, they must have a lifetime on the order of months to years, since battery replacement is not an option for networks with thousands of physically embedded nodes. In some cases, these networks may be required to operate solely on energy scavenged from the environment through seismic, photovoltaic, or thermal conversion. This transforms energy consumption into the most important factor that determines sensor node lifetime. Conventional low-power design techniques [6] and hardware architectures only provide point solutions which are insufficient for these highly energy-constrained systems. Energy optimization, in the case of sensor networks, is much more complex, since it involves not only reducing the energy consumption of a single sensor node but also maximizing the lifetime of an entire network. The network lifetime can be maximized
A Bibliometric Study on Culture Research in International Business

National cultures and cultural differences provide a crucial component of the context of international business (IB) research. We conducted a bibliometric study of the articles published in seven leading IB journals, over a period of three decades, to analyze how “national culture” has been impacting in IB research. Co-citation mappings permit us to identify the ties binding works dealing with culture and cultural issues in IB. We identify two main clusters of research each comprising two sub-clusters, with Hofstede’s (1980) work setting much of the conceptual and empirical approach on culture-related studies. One main cluster entails works on the conceptualization of culture and its dimensions and other cluster on cultural distance. This conceptual framework captures the extant IB research incorporating culture-related concepts and influences.
Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges

The development of smart cities and their fast-paced deployment is resulting in the generation of large quantities of data at unprecedented rates. Unfortunately, most of the generated data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards that benefit from the availability of such data. Moreover, the highly dynamic nature of smart cities calls for a new generation of machine learning approaches that are flexible and adaptable to cope with the dynamicity of data to perform analytics and learn from real-time data. In this article, we shed light on the challenge of underutilizing the big data generated by smart cities from a machine learning perspective. In particular, we present the phenomenon of wasting unlabeled data. We argue that semi-supervision is a must for smart cities to address this challenge. We also propose a three-level learning framework for smart cities that matches the hierarchical nature of big data generated by smart cities with a goal of providing different levels of knowledge abstraction. The proposed framework is scalable to meet the needs of smart city services. Fundamentally, the framework benefits from semi-supervised deep reinforcement learning where a small amount of data that has users' feedback serves as labeled data, while a larger amount without such users' feedback serves as unlabeled data. The framework utilizes a mix of labeled and unlabeled data to converge toward better control policies instead of wasting the unlabeled data. This article also explores how deep reinforcement learning and its shift toward semi-supervision can handle the cognitive side of smart city services and improve their performance by providing several use cases spanning the different domains of smart cities. We also highlight several challenges as well as promising future research directions for incorporating machine learning and high-level intelligence into smart city services.
Advocating an ethical memory model for artificial companions from a human-centred perspective

This paper considers the ethical implications of applying three major ethical theories to the memory structure of an artificial companion that might have different embodiments such as a physical robot or a graphical character on a hand-held device. We start by proposing an ethical memory model and then make use of an action-centric framework to evaluate its ethical implications. The case that we discuss is that of digital artefacts that autonomously record and store user data, where this data are used as a resource for future interaction with users.
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit

Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging data by the Shannon–Nyquist sampling theorem. Here, we train a generative adversarial network (GAN) on a sample of 4550 images of nearby galaxies at 0.01 < z < 0.02 from the Sloan Digital Sky Survey and conduct 10× crossvalidation to evaluate the results. We present a method using a GAN trained on galaxy images that can recover features from artificially degraded images with worse seeing and higher noise than the original with a performance that far exceeds simple deconvolution. The ability to better recover detailed features such as galaxy morphology from low signal to noise and low angular resolution imaging data significantly increases our ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope (LSST) and the Hubble and James Webb space telescopes.
Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion

Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately. In this work, we propose a novel framework to embed words, entities and relations into the same continuous vector space. In this model, both entity and relation embeddings are learned by taking knowledge graph and plain text into consideration. In experiments, we evaluate the joint learning model on three tasks including entity prediction, relation prediction and relation classification from text. The experiment results show that our model can significantly and consistently improve the performance on the three tasks as compared with other baselines.
A Deep Denoising Autoencoder Approach to Improving the Intelligibility of Vocoded Speech in Cochlear Implant Simulation

In a cochlear implant (CI) speech processor, noise reduction (NR) is a critical component for enabling CI users to attain improved speech perception under noisy conditions. Identifying an effective NR approach has long been a key topic in CI research.  Recently, a deep denoising autoencoder (DDAE) based NR approach was proposed and shown to be effective in restoring clean speech from noisy observations. It was also shown that DDAE could provide better performance than several existing NR methods in standardized objective evaluations. Following this success with normal speech, this paper further investigated the performance of DDAE-based NR to improve the intelligibility of envelope-based vocoded speech, which simulates speech signal processing in existing CI devices.  We compared the performance of speech intelligibility between DDAE-based NR and conventional single-microphone NR approaches using the noise vocoder simulation. The results of both objective evaluations and listening test showed that, under the conditions of nonstationary noise distortion, DDAE-based NR yielded higher intelligibility scores than conventional NR approaches.  This study confirmed that DDAE-based NR could potentially be integrated into a CI processor to provide more benefits to CI users under noisy conditions.
Development of an Augmented Reality Vehicle for Driver Performance Evaluation

Observing drivers' behaviors by reproducing traffic accidents and conflict situations is important for developing advanced driver assistance systems. For this purpose, driving simulators are frequently used to evaluate the effectiveness of driver assistance systems during product development. However, motion (simulator) sickness can be a serious practical problem with driving simulators. Therefore, an instrumented vehicle, the JARI-ARV (Japan Automobile Research Institute-Augmented Reality Vehicle), was developed to reproduce realistic traffic accident and conflict scenarios without endangering the driver. In this study, we examined level of control in the following cases: a right turn and encounter with a pedestrian by comparing the JARI-ARV with a standard (unaltered) same model vehicle. Results of the experiment indicated that drivers tend to react to virtual traffic participants in the same way as driving a standard vehicle. The study indicates that the JARI-ARV can play a useful role in human factors research.