nils-herrmann/clean_scirepeval huggingface dataset:
Prospective detection of open spina bifida at 11-13 weeks by assessing intracranial translucency and posterior brain.

We describe a case series of six fetuses with open spina bifida (OSB) from four different prenatal units, where the anomaly was detected at the routine 11-13-week ultrasound examination. Crown-rump length ranged from 49 to 78 mm. All cases were first suspected during nuchal translucency thickness measurement in the mid-sagittal plane of the face. OSB was lumbosacral in five fetuses and cervical in one. The intracranial translucency (IT) was obliterated in two cases, but some fluid was found in the other four cases. However, in all cases the typical landmarks of a normal posterior brain and normal IT were absent. In all six cases the ratio of brainstem diameter to brainstem-occipital bone distance was increased (≥ 1). This detection of an abnormal posterior brain led to a targeted examination and detection of the spinal lesion during the same examination in five cases, whereas in one suspicious case the patient was recalled at 17 weeks, when the abnormality was detected. Two fetuses had both multiple anomalies and trisomy 18. These prospective cases demonstrate the feasibility of using the standard mid-sagittal plane commonly used for NT measurement to assess the IT and the posterior brain and to determine the presence of OSB during NT screening.
Optical interconnection networks in data centers: recent trends and future challenges

Warehouse-scale data center operators need much-higher-bandwidth intra-data center networks (DCNs) to sustain the increase of network traffic due to cloud computing and other emerging web applications. Current DCNs based on commodity switches require excessive amounts of power to face this traffic increase. Optical intra-DCN interconnection networks have recently emerged as a promising solution that can provide higher throughput while consuming less power. This article provides an update on recent developments in the field of ultra-highcapacity optical interconnects for intra-DCN communication. Several recently proposed architectures and technologies are examined and compared, while future trends and research challenges are outlined.
The Italian Metaphor Database

This paper describes the main features of the Italian Metaphor Database, buing built at the University of Perugia (Italy). The database is being developed as a resource to be used both as a knowledge base on conceptual metaphors in Italian and their lexical expressions, and to enrich general lexical resources. The reason to develop such a database is that most NLP systems have to deal with metaphorical expressions sooner or later but, as previous research has shown, existing lexical resources for Italian do not contain complete and consistent data on metaphors, empirically derived but theoretically motivated. Thus, by referring to the Cognitive Theory of metaphor, conceptual metaphors instantiated in Italian are being represented in the resource, together with data on the way they are expressed in the language (i.e., through lexical units or multiword expressions), examples of them found within a corpus, and data on metaphorical linguistic expressions encoded/missing within ItalWordNet.
Data fusion

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.
 This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.
Named entity recognition in vietnamese text using label propagation

This paper presents our named entity recognition system for Vietnamese text using labeled propagation. In here we propose: (i) a method of choosing noun phrases as the named entity candidates; (ii) a method to measure the word similarity; and (iii) a method of decreasing the effect of high frequency labels in labeled documents. Experimental results show that our labeled propagate method achieves higher accuracy than the old one . In addition, when the number of the labeled data is small, its accuracy is higher than when using conditional random fields.
From contours to 3D object detection and pose estimation

This paper addresses view-invariant object detection and pose estimation from a single image. While recent work focuses on object-centered representations of point-based object features, we revisit the viewer-centered framework, and use image contours as basic features. Given training examples of arbitrary views of an object, we learn a sparse object model in terms of a few view-dependent shape templates. The shape templates are jointly used for detecting object occurrences and estimating their 3D poses in a new image. Instrumental to this is our new mid-level feature, called bag of boundaries (BOB), aimed at lifting from individual edges toward their more informative summaries for identifying object boundaries amidst the background clutter. In inference, BOBs are placed on deformable grids both in the image and the shape templates, and then matched. This is formulated as a convex optimization problem that accommodates invariance to non-rigid, locally affine shape deformations. Evaluation on benchmark datasets demonstrates our competitive results relative to the state of the art.
Leveraging Procedural Knowledge for Task-oriented Search

Many search engine users attempt to satisfy an information need by issuing multiple queries, with the expectation that each result will contribute some portion of the required information. Previous research has shown that structured or semi-structured descriptive knowledge bases (such as Wikipedia) can be used to improve search quality and experience for general or entity-centric queries. However, such resources do not have sufficient coverage of procedural knowledge, i.e. what actions should be performed and what factors should be considered to achieve some goal; such procedural knowledge is crucial when responding to task-oriented search queries. This paper provides a first attempt to bridge the gap between two evolving research areas: development of procedural knowledge bases (such as wikiHow) and task-oriented search. We investigate whether task-oriented search can benefit from existing procedural knowledge (search task suggestion) and whether automatic procedural knowledge construction can benefit from users' search activities (automatic procedural knowledge base construction). We propose to create a three-way parallel corpus of queries, query contexts, and task descriptions, and reduce both problems to sequence labeling tasks. We propose a set of textual features and structural features to identify key search phrases from task descriptions, and then adapt similar features to extract wikiHow-style procedural knowledge descriptions from search queries and relevant text snippets. We compare our proposed solution with baseline algorithms, commercial search engines, and the (manually-curated) wikiHow procedural knowledge; experimental results show an improvement of +0.28 to +0.41 in terms of Precision@8 and mean average precision (MAP).
A longitudinal study of the association between violent video game play and aggression among adolescents.

In the past 2 decades, correlational and experimental studies have found a positive association between violent video game play and aggression. There is less evidence, however, to support a long-term relation between these behaviors. This study examined sustained violent video game play and adolescent aggressive behavior across the high school years and directly assessed the socialization (violent video game play predicts aggression over time) versus selection hypotheses (aggression predicts violent video game play over time). Adolescents (N = 1,492, 50.8% female) were surveyed annually from Grade 9 to Grade 12 about their video game play and aggressive behaviors. Nonviolent video game play, frequency of overall video game play, and a comprehensive set of potential 3rd variables were included as covariates in each analysis. Sustained violent video game play was significantly related to steeper increases in adolescents' trajectory of aggressive behavior over time. Moreover, greater violent video game play predicted higher levels of aggression over time, after controlling for previous levels of aggression, supporting the socialization hypothesis. In contrast, no support was found for the selection hypothesis. Nonviolent video game play also did not predict higher levels of aggressive behavior over time. Our findings, and the fact that many adolescents play video games for several hours every day, underscore the need for a greater understanding of the long-term relation between violent video games and aggression, as well as the specific game characteristics (e.g., violent content, competition, pace of action) that may be responsible for this association.
Design and Optimization of Ultrasonic Wireless Power Transmission Links for Millimeter-Sized Biomedical Implants

Ultrasound has been recently proposed as an alternative modality for efficient wireless power transmission (WPT) to biomedical implants with millimeter (mm) dimensions. This paper presents the theory and design methodology of ultrasonic WPT links that involve mm-sized receivers (Rx). For given load <inline-formula><tex-math notation="LaTeX">$(R_{L})$</tex-math></inline-formula> and powering distance <inline-formula><tex-math notation="LaTeX">$(d)$</tex-math></inline-formula>, the optimal geometries of transmitter (Tx) and Rx ultrasonic transducers, including their diameter and thickness, as well as the optimal operation frequency <inline-formula><tex-math notation="LaTeX">$(f_{c})$</tex-math></inline-formula> are found through a recursive design procedure to maximize the power transmission efficiency (PTE). First, a range of realistic <inline-formula><tex-math notation="LaTeX">$f_{c}$</tex-math></inline-formula>s is found based on the Rx thickness constrain. For a chosen <inline-formula><tex-math notation="LaTeX">$f_{c}$</tex-math></inline-formula> within the range, the diameter and thickness of the Rx transducer are then swept together to maximize PTE. Then, the diameter and thickness of the Tx transducer are optimized to maximize PTE. Finally, this procedure is repeated for different <inline-formula><tex-math notation="LaTeX">$f_{c}$</tex-math></inline-formula>s to find the optimal <inline-formula><tex-math notation="LaTeX">$f_{c}$</tex-math></inline-formula> and its corresponding transducer geometries that maximize PTE. A design example of ultrasonic link has been presented and optimized for WPT to a 1 mm<sup>3</sup> implant, including a disk-shaped piezoelectric transducer on a silicon die. In simulations, a PTE of 2.11% at <inline-formula><tex-math notation="LaTeX">$f_{c}$</tex-math></inline-formula> of 1.8 MHz was achieved for <inline-formula><tex-math notation="LaTeX">$R_{L}$</tex-math></inline-formula> of 2.5 <inline-formula><tex-math notation="LaTeX">$\text{k}\Omega$</tex-math></inline-formula> at <inline-formula><tex-math notation="LaTeX">$d = 3\ \text{cm}$</tex-math></inline-formula>. In order to validate our simulations, an ultrasonic link was optimized for a 1 mm<sup>3</sup> piezoelectric transducer mounted on a printed circuit board (PCB), which led to simulated and measured PTEs of 0.65% and 0.66% at <inline-formula><tex-math notation="LaTeX">$f_{c}$</tex-math></inline-formula> of 1.1 MHz for <inline-formula><tex-math notation="LaTeX">$R_{L}$</tex-math></inline-formula> of 2.5 <inline-formula><tex-math notation="LaTeX">$\text{k}\Omega$</tex-math></inline-formula> at <inline-formula><tex-math notation="LaTeX">$d = 3\ \text{cm}$</tex-math></inline-formula>, respectively.
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video

The rapid increase in multimedia data transmission over the Internet necessitates the multi-modal summarization (MMS) from collections of text, image, audio and video. In this work, we propose an extractive multi-modal summarization method that can automatically generate a textual summary given a set of documents, images, audios and videos related to a specific topic. The key idea is to bridge the semantic gaps between multi-modal content. For audio information, we design an approach to selectively use its transcription. For visual information, we learn the joint representations of text and images using a neural network. Finally, all of the multimodal aspects are considered to generate the textual summary by maximizing the salience, non-redundancy, readability and coverage through the budgeted optimization of submodular functions. We further introduce an MMS corpus in English and Chinese, which is released to the public1. The experimental results obtained on this dataset demonstrate that our method outperforms other competitive baseline methods.
Nonnegative Tucker decomposition with alpha-divergence

Nonnegative tucker decomposition (NTD) is a recent multiway extension of nonnegative matrix factorization (NMF), where nonnega- tivity constraints are incorporated into Tucker model. In this paper we consider alpha-divergence as a discrepancy measure and derive multiplicative updating algorithms for NTD. The proposed multiplicative algorithm includes some existing NMF and NTD algorithms as its special cases, since alpha-divergence is a one-parameter family of divergences which accommodates KL-divergence, Hellinger divergence, X2 divergence, and so on. Numerical experiments on face images show how different values of alpha affect the factorization results under different types of noise.
An improved self-powered switching interface for piezoelectric energy harvesting

In piezoelectric energy harvesting (PEH), with the use of the nonlinear technique named synchronized switching harvesting on inductor (SSHI), the harvesting efficiency can be greatly enhanced. Furthermore, the introduction of its self-powered feature makes this technique more applicable for standalone systems. In this article, a modified circuitry and an improved analysis for self-powered SSHI are proposed. With the modified circuitry, direct peak detection and better isolation among different units within the circuit can be achieved, both of which result in further removal on dissipative components. In the improved analysis, details in open circuit voltage, switching phase lag, and voltage inversion factor are discussed, all of which lead to a better understanding to the working principle of the self-powered SSHI. Both analyses and experiments show that, in terms of harvesting power, the higher the excitation level, the closer between self-powered and ideal SSHI; at the same time, the more beneficial the adoption of self-powered SSHI treatment in piezoelectric energy harvesting, compared to the standard energy harvesting (SEH) technique.
An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches

The anchor mechanism of Faster R-CNN and SSD framework is considered not effective enough to scene text detection, which can be attributed to its IoU based matching criterion between anchors and ground-truth boxes. In order to better enclose scene text instances of various shapes, it requires to design anchors of various scales, aspect ratios and even orientations manually, which makes anchor-based methods sophisticated and inefficient. In this paper, we propose a novel anchor-free region proposal network (AF-RPN) to replace the original anchor-based RPN in the Faster R-CNN framework to address the above problem. Compared with a vanilla RPN and FPN-RPN, AF-RPN can get rid of complicated anchor design and achieve higher recall rate on large-scale COCO-Text dataset. Owing to the high-quality text proposals, our Faster R-CNN based two-stage text detection approach achieves state-of-the-art results on ICDAR-2017 MLT, ICDAR-2015 and ICDAR-2013 text detection benchmarks when using single-scale and single-model (ResNet50) testing only.
Kraken: ultrafast metagenomic sequence classification using exact alignments

Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/ .
Online Supermarkets: Emerging Strategies And Business Models In The UK

The Internet has facilitated the emergence of new strategies and business models in several industries. In the UK, significant changes are happening in supermarket retailing with the introduction of online shopping, especially in terms of channel development and coordination, business scope redefinition, the development of fulfilment centre model and core processes, new ways of customer value creation, and online partnerships. In fact the role of online supermarket itself has undergone some significant changes in the last few years. Based on recent empirical evidence gathered in the UK, this paper will illustrate current developments in the strategies and business models of online supermarket retailing. The main evidence has been collected through an online survey of 6 online supermarkets and in-depth case studies of two leading players. Some of the tendencies are comparable to what happened in retail banking with the introduction of Internet banking, but other tendencies are unique to the supermarket retailing industry. This is a rapidly evolving area and further studies are clearly needed.
Model Learning with Local Gaussian Process Regression

Precise models of the robot inverse dynamics allow the design of significantly more accurate, energy-efficient and more compliant robot control. However, in some cases the accuracy of rigidbody models does not suffice for sound control performance due to unmodeled nonlinearities arising from hydraulic cable dynamics, complex friction or actuator dynamics. In such cases, estimating the inverse dynamics model from measured data poses an interesting alternative. Nonparametric regression methods, such as Gaussian process regression (GPR) or locally weighted projection regression (LWPR), are not as restrictive as parametric models and, thus, offer a more flexible framework for approximating unknown nonlinearities. In this paper, we propose a local approximation to the standard GPR, called local GPR (LGP), for real-time model online-learning by combining the strengths of both regression methods, i.e., the high accuracy of GPR and the fast speed of LWPR. The approach is shown to have competitive learning performance for high-dimensional data while being sufficiently fast for real-time learning. The effectiveness of LGP is exhibited by a comparison with the state-of-the-art regression techniques, such as GPR, LWPR and ν-SVR. The applicability of the proposed LGP method is demonstrated by real-time online-learning of the inverse dynamics model for robot model-based control on a Barrett WAM robot arm.
Perturb and observe MPPT algorithm for solar PV systems-modeling and simulation

The following paper validates an algorithm for Maximum Power Point Tracking using Perturb and Observe technique. The algorithm starts by setting the computed maximum power PMAX to an initial value (usually zero). Next the actual PV voltage and current are measured at specific intervals and the instantaneous value of PV power, PACT is computed. PMAX and PACT are compared. PMAX and PACT are compared. If PACT is greater than PMAX, it is set as the new value of PMAX. At every instant the PACT is calculated, and the comparison is continuously executed. Hence the final value of PMAX will be the point at which maximum power can be delivered to the load. For maximum power transfer across the load, the input impedance should be equal to the load impedance. Based on the mechanism of load matching the duty cycle of the converter is varied so that the output power will almost be equal to the input in practical systems. The system will be modelled with the help of MATLAB/SIMULINK.
Introduction to the Special Issue on Meta-Learning

Recent advances in meta-learning are providing the foundations to construct meta-learning assistants and task-adaptive learners. The goal of this special issue is to foster an interest in meta-learning by compiling representative work in the field. The contributions to this special issue provide strong insights into the construction of future meta-learning tools. In this introduction we present a common frame of reference to address work in meta-learning through the concept of meta-knowledge. We show how meta-learning can be simply defined as the process of exploiting knowledge about learning that enables us to understand and improve the performance of learning algorithms.
Energy harvesting technologies for wireless sensors in rotating environments

Using sensors to measure parameters of interest in rotating environments and communicating the measurements in real-time over wireless links, requires a reliable power source. In this paper, we have investigated the possibility to generate electric power locally by evaluating six different energy-harvesting technologies. The applicability of the technology is evaluated by several parameters that are important to the functionality in an industrial environment. All technologies are individually presented and evaluated, a concluding table is also summarizing the technologies strengths and weaknesses. To support the technology evaluation on a more theoretical level, simulations has been performed to strengthen our claims. Among the evaluated and simulated technologies, we found that the variable reluctance-based harvesting technology is the strongest candidate for further technology development for the considered use-case.
Factorization tricks for LSTM networks

We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is ”matrix factorization by design” of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and states into the independent groups. Both approaches allow us to train large LSTM networks significantly faster to the near state-of the art perplexity while using significantly less RNN parameters.