nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of smart grid architectures for monitoring and analyzing power grid data via Modbus and REST

Smart grid, smart metering, electromobility, and the regulation of the power network are keywords of the transition in energy politics. In the future, the power grid will be smart. Based on different works, this article presents a data collection, analyzing, and monitoring software for a reference smart grid. We discuss two possible architectures for collecting data from energy analyzers and analyze their performance with respect to real-time monitoring, load peak analysis, and automated regulation of the power grid. In the first architecture, we analyze the latency, needed bandwidth, and scalability for collecting data over the Modbus TCP/IP protocol and in the second one over a RESTful web service. The analysis results show that the solution with Modbus is more scalable as the one with RESTful web service. However, the performance and scalability of both architectures are sufficient for our reference smart grid and
Targeting adenosine for cancer immunotherapy

Immune checkpoint antagonists (CTLA-4 and PD-1/PD-L1) and CAR T-cell therapies generate unparalleled durable responses in several cancers and have firmly established immunotherapy as a new pillar of cancer therapy. To extend the impact of immunotherapy to more patients and a broader range of cancers, targeting additional mechanisms of tumor immune evasion will be critical. Adenosine signaling has emerged as a key metabolic pathway that regulates tumor immunity. Adenosine is an immunosuppressive metabolite produced at high levels within the tumor microenvironment. Hypoxia, high cell turnover, and expression of CD39 and CD73 are important factors in adenosine production. Adenosine signaling through the A2a receptor expressed on immune cells potently dampens immune responses in inflamed tissues. In this article, we will describe the role of adenosine signaling in regulating tumor immunity, highlighting potential therapeutic targets in the pathway. We will also review preclinical data for each target and provide an update of current clinical activity within the field. Together, current data suggest that rational combination immunotherapy strategies that incorporate inhibitors of the hypoxia-CD39-CD73-A2aR pathway have great promise for further improving clinical outcomes in cancer patients.
Redefining innovation — eco-innovation research and the contribution from ecological economics

While innovation processes toward sustainable development (eco-innovations) have received increasing attention during the past years, theoretical and methodological approaches to analyze these processes are poorly developed. Against this background, the term eco-innovation is introduced in this paper addressing explicitly three kinds of changes towards sustainable development: technological, social and institutional innovation. Secondly, the potential contribution of neoclassical and (co-)evolutionary approaches from environmental and innovation economics to eco-innovation research is discussed. Three peculiarities of eco-innovation are identified: the double externality problem, the regulatory push/pull effect and the increasing importance of social and institutional innovation. While the first two are widely ignored in innovation economics, the third is at the least not elaborated appropriately. The consideration of these peculiarities may help to overcome market failure by establishing a specific eco-innovation policy and to avoid a ‘technology bias’ through a broader understanding of innovation. Finally, perspectives for a specific contribution of ecological economics to eco-innovation research are drawn. It is argued that methodological pluralism as established in ecological economics would be very beneficial for eco-innovation research. A theoretical framework integrating elements from both neoclassical and evolutionary approaches should be pursued in order to consider the complexity of factors influencing innovation decisions as well as the specific role of regulatory instruments. And the experience gathered in ecological economics integrating ecological, social and economic aspects of sustainable development is highly useful for opening up innovation research to social and institutional changes.
A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion

We address the speaker-independent acoustic inversion (AI) problem, also referred to as acoustic-to-articulatory mapping. The scarce availability of multi-speaker articulatory data makes it difficult to learn a mapping which generalizes from a limited number of training speakers and reliably reconstructs the articulatory movements of unseen speakers. In this paper, we propose a Multi-task Learning (MTL)-based approach that explicitly separates the modeling of each training speaker AI peculiarities from the modeling of AI characteristics that are shared by all speakers. Our approach stems from the well known Regularized MTL approach and extends it to feed-forward deep neural networks (DNNs). Given multiple training speakers, we learn for each an acoustic-to-articulatory mapping represented by a DNN. Then, through an iterative procedure, we search for a canonical speaker-independent DNN that is “similar” to all speaker-dependent DNNs. The degree of similarity is controlled by a regularization parameter. We report experiments on the University of Wisconsin X-ray Microbeam Database under different training/testing experimental settings. The results obtained indicate that our MTL-trained canonical DNN largely outperforms a standardly trained (i.e., single task learning-based) speaker independent DNN.
Comparative analysis of power density in Si MOSFET and GaN HEMT based flyback converters

Gallium Nitride (GaN) based power devices have the potential to achieve higher efficiency and higher switching frequency than those possible with Silicon (Si) power devices. In literature, GaN based converters are claimed to offer higher power density. However, a detailed comparative analysis on the power density of GaN and Si based low power dc-dc flyback converter is not reported. In this paper, comparison of a 100 W, dc-dc flyback converter based on GaN and Si is presented. Both the converters are designed to ensure an efficiency of 80%. Based on this, the switching frequency for both the converters are determined. The analysis shows that the GaN based converter can be operated at approximately ten times the switching frequency of Si-based converter. This leads to a reduction in the area product of the flyback transformer required in GaN based converter. It is found that the volume of the flyback transformer can be reduced by a factor of six for a GaN based converter as compared to a Si based converter. Further, it is observed that the value of output capacitance used in the GaN based converter reduces by a factor of ten as compared to the Si based converter, implying a reduction in the size of the output capacitors. Therefore, a significant improvement in the power density of the GaN based converter as compared to the Si based converter is seen.
Unsupervised Camera Motion Estimation and Moving Object Detection in Videos

In this article, we consider the robust estimation of a location parameter using Mestimators. We propose here to couple this estimation with the robust scale estimate proposed in [Dahyot and Wilson, 2006]. The resulting procedure is then completely unsupervised. It is applied to camera motion estimation and moving object detection in videos. Experimental results on different video materials show the adaptability and the accuracy of this new robust approach.
Battery Model Parameters Estimation with the Sigma Point Kalman Filter

Accurate estimation of the State of Charge (SOC) of the battery is one of the key problems to the battery management system. The SOC should be obtained indirectly according to some algorithms under a mathematical model, along with some measurable quantities. A Sigma Point Kalman Filter based battery model parameters estimation method is proposed. The parameters can be estimated accurately while efficiently with the proposed method. Compared to the classical least squares method, the proposed method consumes much less memory and calculation time, which makes it suitable for embedded applications.
QoE Doctor: Diagnosing Mobile App QoE with Automated UI Control and Cross-layer Analysis

Smartphones have become increasingly prevalent and important in our daily lives. To meet users' expectations about the Quality of Experience (QoE) of mobile applications (apps), it is essential to obtain a comprehensive understanding of app QoE and identify the critical factors that affect it. However, effectively and systematically studying the QoE of popular mobile apps such as Facebook and YouTube still remains a challenging task, largely due to a lack of a controlled and reproducible measurement methodology, and limited insight into the complex multi-layer dynamics of the system and network stacks.
 In this paper, we propose QoE Doctor, a tool that supports accurate, systematic, and repeatable measurements and analysis of mobile app QoE. QoE Doctor uses UI automation techniques to replay QoE-related user behavior, and measures the user-perceived latency directly from UI changes. To better understand and analyze QoE problems involving complex multi-layer interactions, QoE Doctor supports analysis across the application, transport, network, and cellular radio link layers to help identify the root causes. We implement QoE Doctor on Android, and systematically quantify various factors that impact app QoE, including the cellular radio link layer technology, carrier rate-limiting mechanisms, app design choices and user-side configuration options.
High-strength porous biomaterials for bone replacement: A strategy to assess the interplay between cell morphology, mechanical properties, bone ingrowth and manufacturing constraints.

High-strength fully porous biomaterials built with additive manufacturing provide an exciting opportunity for load-bearing orthopedic applications. While factors controlling their mechanical and biological response have recently been the subject of intense research, the interplay between mechanical properties, bone ingrowth requirements, and manufacturing constraints, is still unclear. In this paper, we present two high-strength stretch-dominated topologies, the Tetrahedron and the Octet truss, as well as an intuitive visualization method to understand the relationship of cell topology, pore size, porosity with constraints imposed by bone ingrowth requirements and additive manufacturing. 40 samples of selected porosities are fabricated using Selective Laser Melting (SLM), and their morphological deviations resulting from SLM are assessed via micro-CT. Mechanical compression testing is used to obtain stiffness and strength properties, whereas bone ingrowth is assessed in a canine in vivo model at four and eight weeks. The results show that the maximum strength and stiffness ranged from 227.86±10.15 to 31.37±2.19MPa and 4.58±0.18 to 1.23±0.40GPa respectively, and the maximum 0.2% offset strength is almost 5 times stronger than that of tantalum foam. For Tetrahedron samples, bone ingrowth after four and eight weeks is 28.6%±11.6%, and 41.3%±4.3%, while for the Octet truss 35.5%±1.9% and 56.9%±4.0% respectively. This research is the first to demonstrate the occurrence of bone ingrowth into high-strength porous biomaterials which have higher structural efficiency than current porous biomaterials in the market.



We present two stretch-dominated cell topologies for porous biomaterials that can be used for load-bearing orthopaedic applications, and prove that they encourage bone ingrowth in a canine model. We also introduce an intuitive method to visualize and understand the relationship of cell topology, pore size, porosity with constraints imposed by bone ingrowth requirements and additive manufacturing. We show this strategy helps to gain insight into the interaction of exogenous implant factors and endogenous system factors that can affect the success of load-bearing orthopaedic devices.
Natural Scene Character Recognition Using Robust PCA and Sparse Representation

Natural scene character recognition is challenging due to the cluttered background, which is hard to separate from text. In this paper, we propose a novel method for robust scene character recognition. Specifically, we first use robust principal component analysis (PCA) to denoise character image by recovering the missing low-rank component and filtering out the sparse noise term, and then use a simple Histogram of oriented Gradient (HOG) to perform image feature extraction, and finally, use a sparse representation based classifier for recognition. In experiments on four public datasets, namely the Char74K dataset, ICADAR 2003 robust reading dataset, Street View Text (SVT) dataset and IIIT5K-word dataset, our method was demonstrated to be competitive with the state-of-the-art methods.
Vortioxetine for major depressive disorder: An indirect comparison with duloxetine, escitalopram, levomilnacipran, sertraline, venlafaxine, and vilazodone, using number needed to treat, number needed to harm, and likelihood to be helped or harmed.

Vortioxetine is approved for the treatment of major depressive disorder and differs from other antidepressants in terms of its pharmacodynamic profile. Given the limited number of head-to-head studies comparing vortioxetine with other antidepressants, indirect comparisons using effect sizes observed in other trials can be helpful to discern potential differences in clinical outcomes.



Data sources were the clinical trial reports for the pivotal short-term double-blind trials for vortioxetine and from publicly available sources for the pivotal short-term double-blind trials for two commonly used generic serotonin specific reuptake inhibitor antidepressants (sertraline, escitalopram), two commonly used generic serotonin-norepinephrine reuptake inhibitor antidepressants (venlafaxine, duloxetine), and two recently introduced branded antidepressants (vilazodone, levomilnacipran). Response was the efficacy outcome of interest, defined as a≥50% reduction from baseline on the Montgomery-Asberg Depression Rating Scale or Hamilton Depression Rating Scale. The tolerability outcome of interest was discontinuation because of an adverse event. Number needed to treat (NNT) and number needed to harm (NNH) for these outcomes vs. placebo were calculated, as well as likelihood to be helped or harmed (LHH) to contrast efficacy vs. tolerability.



The analysis included 8 studies for duloxetine, 3 studies for escitalopram, 5 studies for levomilnacipran, 1 study for sertraline, 4 studies for venlafaxine, 2 studies for vilazodone, and 11 studies for vortioxetine. NNTs for response vs. placebo were 6 (95% CI 5-8), 7 (5-11), 10 (8-16), 6 (4-13), 6 (5-9), 8 (6-16), and 9 (7-11), respectively. NNHs for discontinuation because of an adverse event vs. placebo were 25 (17-51), 31 (19-92), 19 (14-27), 7 (5-12), 8 (7-11), 27 (15-104), and 43 (28-91), respectively. LHH values contrasting response vs. discontinuation because of an adverse event were 4.3, 4.6, 1.8, 1.2, 1.4, 3.3, and 5.1 respectively.



Subjects were all participants in carefully designed and executed clinical trials and may not necessarily reflect patients in clinical settings who may have complex psychiatric and non-psychiatric comorbidities. The measured outcomes come from different studies and thus comparisons are indirect.



Vortioxetine demonstrates similar efficacy to that observed for duloxetine, escitalopram, levomilnacipran, sertraline, venlafaxine, and vilazodone, however overall tolerability as measured by discontinuation because of an adverse event differs. Vortioxetine is 5.1 times more likely to be associated with response than discontinuation because of an adverse event when compared to placebo.
AthenaCloud: A cloud-based platform for multi-device educational software generation

Nowadays, information technologies play an important role in education. In education, mobile and TV applications can be considered a support tool in the teaching learning process, however, relevant and appropriate mobile and TV applications are not always available; teachers can only judge applications by reviews or anecdotes instead of testing them. These reasons lead to the needs and benefits for creating one’s own mobile application for teaching and learning. In this work, we present a cloud-based platform for multi-device educational software generation (smartphones, tablets, Web, Android-based TV boxes, and smart TV devices) called AthenaCloud. It is important to mention that an open cloud-based platform allows teachers to create their own multi-device software by using a personal computer with Internet access. The goal of this platform is to provide a software tool to help educators upload their electronic contents – or use existing contents in an open repository – and package them in the desired setup file for one of the supported devices and operating systems.
Argument Mining from Speech: Detecting Claims in Political Debates

The automatic extraction of arguments from text, also known as argument mining, has recently become a hot topic in artificial intelligence. Current research has only focused on linguistic analysis. However, in many domains where communication may be also vocal or visual, paralinguistic features too may contribute to the transmission of the message that arguments intend to convey. For example, in political debates a crucial role is played by speech. The research question we address in this work is whether in such domains one can improve claim detection for argument mining, by employing features from text and speech in combination. To explore this hypothesis, we develop a machine learning classifier and train it on an original dataset based on the 2015 UK political elections debate.
Imbalance Learning-based Framework for Fear Recognition in the MediaEval Emotional Impact of Movies Task

Fear recognition, which aims at predicting whether a movie segment can induce fear or not, is a promising area in movie emotion recognition. Research in this area, however, has reached a bottleneck. Difficulties may partly result from the imbalanced database. In this paper, we propose an imbalance learning-based framework for movie fear recognition. A data rebalance module is adopted before classification. Several sampling methods, including the proposed softsampling and hardsampling which combine the merits of both undersampling and oversampling, are explored in this module. Experiments are conducted on the MediaEval 2017  Compared with the current state-of-the-art, we achieve an improvement of 8.94% on F1, proving the effectiveness of proposed framework.
Receivables Management and Possible Use of Information Technologies

This paper is focused on receivables management and possibilities how to use available information technologies. The use of information technologies should make receivables management easier on one hand and on the other hand it makes the processes more efficient. Finally it decreases additional costs and losses connected with enforcing receivables when defaulting debts occur. The situation of use of information technologies is different if the subject is financial or nonfinancial institution. In the case of financial institution loans providing is core business and the processes and their technical support are more sophisticated than in the case of non-financial institutions whose loan providing as invoices is just a supplement to their core business activities. The paper shows use of information technologies in individual cases but it also emphasizes the use of general results for further decision making process. Results of receivables management are illustrated on the data of the Czech Republic.
Scalable Overlapping Co-clustering of Word-Document Data

Text clustering is used on a variety of applications such as content-based recommendation, categorization, summarization, information retrieval and automatic topic extraction. Since most pair of documents usually shares just a small percentage of words, the dataset representation tends to become very sparse, thus the need of using a similarity metric capable of a partial matching of a set of features. The technique known as Co-Clustering is capable of finding several clusters inside a dataset with each cluster composed of just a subset of the object and feature sets. In word-document data this can be useful to identify the clusters of documents pertaining to the same topic, even though they share just a small fraction of words. In this paper a scalable co-clustering algorithm is proposed using the Locality-sensitive hashing technique in order to find co-clusters of documents. The proposed algorithm will be tested against other co-clustering and traditional algorithms in well known datasets. The results show that this algorithm is capable of finding clusters more accurately than other approaches while maintaining a linear complexity.
The use of electromyography and magnetic resonance imaging to evaluate a core strengthening exercise programme.

The question that was asked in the study was whether a training routine based on curl-up exercises with a load provided by body mass of the person increases local muscle strength or local muscle endurance.



The aim of this study was to evaluate the effect of 4 weeks training based on a small load and low movement velocity on electrical activity (EMG), cross-sectional area (CSA) of core stabilisers.



The EMG activity was measured in the rectus abdominis (RA), obliquus abdominis externus and erector spinae (ES) muscles. CSA of the muscles: RA, anterolateral abdominal, psoas major, quadratus lumborum, ES, and multifidus at the level of L3-L4 were measured too.



The training increased the CSA and thickness in most of the muscles studied. Statistically significant correlation was found only for the ES circumference (left side) and EMG activity for the right side (r= 0.627, p= 0.022) and left side (r= 0.624, p= 0.023).



The training programme resulted in a increase in the number of curl-up repetitions revealing an endurance increase in abdominal muscles. Furthermore, there was a increase in the EMG activity of the RA. An increase of the CSA of all tested muscles showed an increase of muscle active force.
TRISOMY 8 MOSAICISM WITH ATYPICAL PHENOTYPIC FEATURES

Mosaic trisomy 8 is a relatively common chromosomal abnormality, which shows a great variability in clinical expression, however cases with phenotypic abnormalities tend to present with a distinct, recognizable clinical syndrome with a characteristic facial appearance, a long, slender trunk, limitation of movement in multiple joints, and mild-to-moderate mental retardation; the deep plantar furrows are a typical finding, the agenesis of the corpus callosum occurs frequently. We report a case, which in addition to certain characteristic features of mosaic trisomy 8, presented with craniofacial midline defects, including notched nasal tip, cleft maxillary alveolar ridge, bifid tip of tongue, grooved uvula and left choanal atresia, previously not described in this chromosomal disorder and a severe delay in psychomotor development, uncommon in trisomy 8 mosaicism.