nils-herrmann/clean_scirepeval huggingface dataset:
Single-tuned filter design for harmonic mitigation and optimization with capacitor banks

Ideally, an electricity supply should invariably show a perfectly sinusoidal voltage signal at every customer location. However, for a number of reasons, utilities often find it hard to preserve such desirable conditions. The deviation of the voltage and Current waveforms from sinusoidal is expressed as harmonic distortion. The harmonic distortion in the power system is increasing with wide use of nonlinear loads. Thus, it is important to analyze and evaluate the various harmonic problems in the power system and introduce the appropriate solution techniques. A single-tuned filter design is illustrated and implemented in a model which created in Electromagnetic Transient Analysis Program (ETAP). The system was constructed for this simulation and does not represent one particular real life system Furthermore, the model was used to test the effects of injecting harmonic current by variable speed drive (VFD) into 3 situations. Firstly, the scheme with capacitor banks only, then, using a single-tuned filter and finally both schemes coherently.
CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing

Functional turnover of transcription factor binding sites (TFBSs), such as whole-motif loss or gain, are common events during genome evolution. Conventional probabilistic phylogenetic shadowing methods model the evolution of genomes only at nucleotide level, and lack the ability to capture the evolutionary dynamics of functional turnover of aligned sequence entities. As a result, comparative genomic search of non-conserved motifs across evolutionarily related taxa remains a difficult challenge, especially in higher eukaryotes, where the cis-regulatory regions containing motifs can be long and divergent; existing methods rely heavily on specialized pattern-driven heuristic search or sampling algorithms, which can be difficult to generalize and hard to interpret based on phylogenetic principles. We propose a new method: Conditional Shadowing via Multi-resolution Evolutionary Trees, or CSMET, which uses a context-dependent probabilistic graphical model that allows aligned sites from different taxa in a multiple alignment to be modeled by either a background or an appropriate motif phylogeny conditioning on the functional specifications of each taxon. The functional specifications themselves are the output of a phylogeny which models the evolution not of individual nucleotides, but of the overall functionality (e.g., functional retention or loss) of the aligned sequence segments over lineages. Combining this method with a hidden Markov model that autocorrelates evolutionary rates on successive sites in the genome, CSMET offers a principled way to take into consideration lineage-specific evolution of TFBSs during motif detection, and a readily computable analytical form of the posterior distribution of motifs under TFBS turnover. On both simulated and real Drosophila cis-regulatory modules, CSMET outperforms other state-of-the-art comparative genomic motif finders.
Broadband circularly polarized aperture-coupled microstrip antenna with dual-offset feedlines

A new feeding technique for a broadband circularly polarized aperture-coupled patch antenna is proposed operating at X-band. The stacked microstrip antennas are used for broad bandwidth and flat gain. These broadband antennas fed by slot-coupled quadrature hybrid have dual-offset feedlines for low cross-polarization level. The quadrature hybrid has multi-section with multi-layers. The grounds of coupler and antenna are connected by via. The simulated 10 dB return loss bandwidth is 35.5% from 8.1 to 11.6 GHz and the 3 dB axial ratio (AR) bandwidth is 35%.
Faster isosurface ray tracing using implicit KD-trees

The visualization of high-quality isosurfaces at interactive rates is an important tool in many simulation and visualization applications. Today, isosurfaces are most often visualized by extracting a polygonal approximation that is then rendered via graphics hardware or by using a special variant of preintegrated volume rendering. However, these approaches have a number of limitations in terms of the quality of the isosurface, lack of performance for complex data sets, or supported shading models. An alternative isosurface rendering method that does not suffer from these limitations is to directly ray trace the isosurface. However, this approach has been much too slow for interactive applications unless massively parallel shared-memory supercomputers have been used. In this paper, we implement interactive isosurface ray tracing on commodity desktop PCs by building on recent advances in real-time ray tracing of polygonal scenes and using those to improve isosurface ray tracing performance as well. The high performance and scalability of our approach will be demonstrated with several practical examples, including the visualization of highly complex isosurface data sets, the interactive rendering of hybrid polygonal/isosurface scenes, including high-quality ray traced shading effects, and even interactive global illumination on isosurfaces.
Feature-Rich Error Detection in Scientific Writing Using Logistic Regression

The goal of the Automatic Evaluation of Scientific Writing (AESW) Shared Task 2016 is to identify sentences in scientific articles which need editing to improve their correctness and readability or to make them better fit within the genre at hand. We encode many different types of errors occurring in the dataset by linguistic features. We use logistic regression to assign a probability indicating whether a sentence needs to be edited. We participate in both tracks at AESW 2016: binary prediction and probabilistic estimation. In the former track, our model (HITS) gets the fifth place and in the latter one, it ranks first according to the evaluation metric.
A Data Imputation Method Based on Deep Belief Network

Accurately filling missing values is an important step to enhance the usability of Big Data. However current incomplete data imputation algorithms are of high time complexity and low accuracy. To address this problem, we propose a novel algorithm to impute incomplete data. Firstly, a deep belief network model with denoising is designed to remove the noise brought by incomplete data and extract high quality features. Then, we utilize softmax for data classification. Finally, according to the classification results, partial distance and sequence imputation strategies are proposed to measure the correlation between records and improve filling accuracy, respectively. Compared with different algorithms, the experimental results confirm the effectiveness and efficiency of the proposed method in data imputation.
An overview of augmented and virtual reality applications in radiotherapy and future developments enabled by modern tablet devices

We review augmented (AR) and virtual reality (VR) applications in radiotherapy as found in the scientific literature and highlight future developments enabled by the use of small mass-produced devices and portability of techniques developed in other fields to radiotherapy.  The application of AR and VR within radiotherapy is still in its infancy, with the notable exception of training and teaching applications. The relatively high cost of equipment needed to generate a realistic 3D effect seems one factor that has slowed down its use, but also the sheer amount of image data is relatively recent, were radiotherapy professionals are only beginning to explore how to use this to its full potential. This increased availability of 3D data in radiotherapy will drive the application of AR and VR in radiotherapy to efficiently recognise and extract key features in the data to act on in clinical decision making.  The development of small mass-produced tablet devices coming on the market will allow the user to interact with computer-generated information more easily, facilitating the application of AR and VR. The increased connectivity enabling virtual presence of remote multidisciplinary team meetings heralds significant changes to how radiotherapy professionals will work, to the benefit of our patients.
Application and Performance Analysis of DSDV Routing Protocol in Ad-Hoc Wireless Sensor Network with Help of NS 2 Knowledge

Wireless Sensor Networks (WSNs) are charac-terized by multi-hop wireless connectivity, frequently changing network topology and need for efficient routing protocols. The purpose of this paper is to evaluate performance of routing protocol DSDV in wireless sensor network (WSN) scales regarding the End-to-End delay and throughput PDR with mobility factor .Routing protocols are a critical aspect to performance in mobile wireless networks and play crucial role in determining network performance in terms of packet delivery fraction, end-to-end delay and packet loss. Destination-sequenced distance vector (DSDV) protocol is a proactive protocol depending on routing tables which are maintained at each node. The routing protocol should detect and maintain optimal route(s) between source and destination nodes. In this paper, we present application of DSDV in WSN as extend to our pervious study to the design and impleme-ntation the details of the DSDV routing protocol in MANET using the ns-2 network simulator.
Dungeons and DQNs : Toward Reinforcement Learning Agents that Play Tabletop Roleplaying Games

Game playing has been an important testbed for artificial intelligence. Board games, first-person shooters, and real-time strategy games have well-defined win conditions and rely on strong feedback from a simulated environment. Text adventures require natural language understanding to progress through the game but still have an underlying simulated environment. In this paper, we propose tabletop roleplaying games as a challenge due to an infinite action space, multiple (collaborative) players and models of the world, and no explicit reward signal. We present an approach for reinforcement learning agents that can play tabletop roleplaying games.
Association Between Palliative Care and Patient and Caregiver Outcomes: A Systematic Review and Meta-analysis.

The use of palliative care programs and the number of trials assessing their effectiveness have increased.



To determine the association of palliative care with quality of life (QOL), symptom burden, survival, and other outcomes for people with life-limiting illness and for their caregivers.



MEDLINE, EMBASE, CINAHL, and Cochrane CENTRAL to July 2016.


Study Selection
Randomized clinical trials of palliative care interventions in adults with life-limiting illness.



Two reviewers independently extracted data. Narrative synthesis was conducted for all trials. Quality of life, symptom burden, and survival were analyzed using random-effects meta-analysis, with estimates of QOL translated to units of the Functional Assessment of Chronic Illness Therapy-palliative care scale (FACIT-Pal) instrument (range, 0-184 [worst-best]; minimal clinically important difference [MCID], 9 points); and symptom burden translated to the Edmonton Symptom Assessment Scale (ESAS) (range, 0-90 [best-worst]; MCID, 5.7 points).



Quality of life, symptom burden, survival, mood, advance care planning, site of death, health care satisfaction, resource utilization, and health care expenditures.



Forty-three RCTs provided data on 12 731 patients (mean age, 67 years) and 2479 caregivers. Thirty-five trials used usual care as the control, and 14 took place in the ambulatory setting. In the meta-analysis, palliative care was associated with statistically and clinically significant improvements in patient QOL at the 1- to 3-month follow-up (standardized mean difference, 0.46; 95% CI, 0.08 to 0.83; FACIT-Pal mean difference, 11.36] and symptom burden at the 1- to 3-month follow-up (standardized mean difference, -0.66; 95% CI, -1.25 to -0.07; ESAS mean difference, -10.30). When analyses were limited to trials at low risk of bias (n = 5), the association between palliative care and QOL was attenuated but remained statistically significant (standardized mean difference, 0.20; 95% CI, 0.06 to 0.34; FACIT-Pal mean difference, 4.94), whereas the association with symptom burden was not statistically significant (standardized mean difference, -0.21; 95% CI, -0.42 to 0.00; ESAS mean difference, -3.28). There was no association between palliative care and survival (hazard ratio, 0.90; 95% CI, 0.69 to 1.17). Palliative care was associated consistently with improvements in advance care planning, patient and caregiver satisfaction, and lower health care utilization. Evidence of associations with other outcomes was mixed.



In this meta-analysis, palliative care interventions were associated with improvements in patient QOL and symptom burden. Findings for caregiver outcomes were inconsistent. However, many associations were no longer significant when limited to trials at low risk of bias, and there was no significant association between palliative care and survival.
The cerebellar cognitive affective/Schmahmann syndrome scale.

Cerebellar cognitive affective syndrome (CCAS; Schmahmann's syndrome) is characterized by deficits in executive function, linguistic processing, spatial cognition, and affect regulation. Diagnosis currently relies on detailed neuropsychological testing. The aim of this study was to develop an office or bedside cognitive screen to help identify CCAS in cerebellar patients. Secondary objectives were to evaluate whether available brief tests of mental function detect cognitive impairment in cerebellar patients, whether cognitive performance is different in patients with isolated cerebellar lesions versus complex cerebrocerebellar pathology, and whether there are cognitive deficits that should raise red flags about extra-cerebellar pathology. Comprehensive standard neuropsychological tests, experimental measures and clinical rating scales were administered to 77 patients with cerebellar disease-36 isolated cerebellar degeneration or injury, and 41 complex cerebrocerebellar pathology-and to healthy matched controls. Tests that differentiated patients from controls were used to develop a screening instrument that includes the cardinal elements of CCAS. We validated this new scale in a new cohort of 39 cerebellar patients and 55 healthy controls. We confirm the defining features of CCAS using neuropsychological measures. Deficits in executive function were most pronounced for working memory, mental flexibility, and abstract reasoning. Language deficits included verb for noun generation and phonemic > semantic fluency. Visual spatial function was degraded in performance and interpretation of visual stimuli. Neuropsychiatric features included impairments in attentional control, emotional control, psychosis spectrum disorders and social skill set. From these results, we derived a 10-item scale providing total raw score, cut-offs for each test, and pass/fail criteria that determined 'possible' (one test failed), 'probable' (two tests failed), and 'definite' CCAS (three tests failed). When applied to the exploratory cohort, and administered to the validation cohort, the CCAS/Schmahmann scale identified sensitivity and selectivity, respectively as possible exploratory cohort: 85%/74%, validation cohort: 95%/78%; probable exploratory cohort: 58%/94%, validation cohort: 82%/93%; and definite exploratory cohort: 48%/100%, validation cohort: 46%/100%. In patients in the exploratory cohort, Mini-Mental State Examination and Montreal Cognitive Assessment scores were within normal range. Complex cerebrocerebellar disease patients were impaired on similarities in comparison to isolated cerebellar disease. Inability to recall words from multiple choice occurred only in patients with extra-cerebellar disease. The CCAS/Schmahmann syndrome scale is useful for expedited clinical assessment of CCAS in patients with cerebellar disorders.awx317media15678692096001.
A high precision reference data set for pedestrian navigation using foot-mounted inertial sensors

In this paper we present a reference data set that we are making publicly available to the indoor navigation community . This reference data is intended for the analysis and verification of algorithms based on foot mounted inertial sensors. Furthermore, we describe our data collection methodology that is applicable to the analysis of a broad range of indoor navigation approaches. We employ a high precision optical reference system that is traditionally being used in the film industry for human motion capturing and in applications such as analysis of human motion in sports and medical rehabilitation. The data set provides measurements from a six degrees of freedom foot mounted inertial MEMS sensor array, as well as synchronous high resolution data from the optical tracking system providing ground truth for location and orientation. We show the use of this reference data set by comparing the performance of algorithms for an essential part of pedestrian dead reckoning systems for positioning, namely identification of the rest phase during the human gait cycle.
Cybersecurity Information Sharing Governance Structures: An Ecosystem of Diversity, Trust, and Tradeoffs

In recent years the cybersecurity policy debate in Washington has been dominated by calls for greater information sharing within the private sector, and between the private sector and the federal government. The passage of the Cybersecurity Information Sharing Act (CISA) (signed into law under the Cybersecurity Act of 2015) underscored federal efforts to collect information from the private sector, and assuaged some concerns regarding private sector liability in sharing activities. However, the law lacked specificity on how continued federal efforts would work with existing information sharing networks, and failed to address other challenges associated with sharing including trust building, privacy and propriety interests, reciprocation, and quality control. This paper aims to bring granularity to implementations of information sharing initiatives by creating a taxonomy of the governance and policy models within each of these organizations. The research shows how this diverse ecosystem of sharing models work together and separately, and the impact governance and policy have on key components critical to sharing infrastructure.
Report on the 11 th IWSLT Evaluation Campaign , IWSLT 2014

The paper overviews the 11th evaluation campaign organized by the IWSLT workshop. The 2014 evaluation offered multiple tracks on lecture transcription and translation based on the TED Talks corpus. In particular, this year IWSLT included three automatic speech recognition tracks, on English, German and Italian, five speech translation tracks, from English to French, English to German, German to English, English to Italian, and Italian to English, and five text translation track, also from English to French, English to German, German to English, English to Italian, and Italian to English. In addition to the official tracks, speech and text translation optional tracks were offered, globally involving 12 other languages: Arabic, Spanish, Portuguese (B), Hebrew, Chinese, Polish, Persian, Slovenian, Turkish, Dutch, Romanian, Russian. Overall, 21 teams participated in the evaluation, for a total of 76 primary runs submitted. Participants were also asked to submit runs on the 2013 test set (progress test set), in order to measure the progress of systems with respect to the previous year. All runs were evaluated with objective metrics, and submissions for two of the official text translation tracks were also evaluated with human post-editing.
Single Image Layer Separation via Deep Admm Unrolling

Single image layer separation aims to divide the observed image into two independent components according to special task requirements and has been widely used in many vision and multimedia applications. Because this task is fundamentally ill-posed, most existing approaches tend to design complex priors on the separated layers. However, the cost function with complex prior regularization is hard to optimize. The performance is also compromised by fixed iteration schemes and less data fitting ability. More importantly, it is also challenging to design a unified framework to separate image layers for different applications. To partially mitigate the above limitations, we develop a flexible optimization unrolling technique to incorporate deep architectures into iterations for adaptive image layer separation. Specifically, we first design a general energy model with implicit priors and adopt the widely used alternating direction method of multiplier (ADMM) to establish our basic iteration scheme. By unrolling with residual convolution architectures, we successfully obtain a simple, flexible, and data-dependent image separation method. Extensive experiments on the tasks of rain streak removal and reflection removal validate the effectiveness of our approach.
Multipoint and Multi-Objective Aerodynamic Shape Optimization

A gradient-based Newton–Krylov algorithm is presented for the aerodynamic shape optimization of singleand multi-element airfoil configurations. The flow is governed by the compressible Navier–Stokes equations in conjunction with a one-equation transport turbulence model. The preconditioned generalized minimal residual method is applied to solve the discrete-adjoint equation, which leads to a fast computation of accurate objective function gradients. Optimization constraints are enforced through a penalty formulation, and the resulting unconstrained problem is solved via a quasi-Newton method. The new algorithm is evaluated for several design examples, including the lift enhancement of a takeoff configuration and a lift-constrained drag minimization at multiple transonic operating points. Furthermore, the new algorithm is used to compute a Pareto front based on competing objectives, and the results are validated using a genetic algorithm. Overall, the new algorithm provides an efficient approach for addressing the issues of complex aerodynamic design.
A novel 3-D printed loop antenna using flexible NinjaFlex material for wearable and IoT applications

A stretchable, flexible loop antenna working at 2.4GHz ISM band was fabricated by the additive manufacturing (AM) 3-D printing technology. NinjaFlex, a flexible 3-D printable material was utilized for the first time as a 3-D hemi-sphere substrate for the loop antenna. A 3-D printer based on the Fused Diffusion Modelling (FDM) technology was employed to 3-D print the substrate material. The resonance frequency of the antenna shifts in response to the applied force which makes the configuration suitable for the wireless strain sensing application. The proposed antenna was designed for wearable electronics application such as health monitoring earrings. Hence it was designed in such a way that it maintains the Specific Absorption Rate (SAR) of the human head tissues within the assigned standard limits when placed near human replicating head. The proposed antenna system could be useful in the additively manufactured wearable packaging and IoT applications.