nils-herrmann/clean_scirepeval huggingface dataset:
Sampling strategies for information extraction over the deep web

Information extraction systems discover structured information in natural language text. Having information in structured form enables much richer querying and data mining than possible over the natural language text. However, information extraction is a computationally expensive task, and hence improving the efficiency of the extraction process over large text collections is of critical interest. In this paper, we focus on an especially valuable family of text collections, namely, the so-called deep-web text collections, whose contents are not crawlable and are only available via querying. Important steps for efficient information extraction over deep-web text collections (e.g., selecting the collections on which to focus the extraction effort, based on their contents; or learning which documents within these collections—and in which order—to process, based on their words and phrases) require having a representative document sample from each collection. These document samples have to be collected by querying the deep-web text collections, an expensive process that renders impractical the existing sampling approaches developed for other data scenarios. In this paper, we systematically study the space of query-based document sampling techniques for information extraction over the deep web. Specifically, we consider (i) alternative query execution schedules, which vary on how they account for the query effectiveness, and (ii) alternative document retrieval and processing schedules, which vary on how they distribute the extraction effort over documents. We report the results of the first large-scale experimental evaluation of sampling techniques for information extraction over the deep web. Our results show the merits and limitations of the alternative query execution and document retrieval and processing strategies, and provide a roadmap for addressing this critically important building block for efficient, scalable information extraction.
Multiple-Criteria Decision Analysis-Based Multifactor Dimensionality Reduction for Detecting Gene–Gene Interactions

Gene–gene interactions (GGIs) are important markers for determining susceptibility to a disease. Multifactor dimensionality reduction (MDR) is a popular algorithm for detecting GGIs and primarily adopts the correct classification rate (CCR) to assess the quality of a GGI. However, CCR measurement alone may not successfully detect certain GGIs because of potential model preferences and disease complexities. In this study, multiple-criteria decision analysis (MCDA) based on MDR was named MCDA-MDR and proposed for detecting GGIs. MCDA facilitates MDR to simultaneously adopt multiple measures within the two-way contingency table of MDR to assess GGIs; the CCR and rule utility measure were employed. Cross-validation consistency was adopted to determine the most favorable GGIs among the Pareto sets. Simulation studies were conducted to compare the detection success rates of the MDR-only-based measure and MCDA-MDR, revealing that MCDA-MDR had superior detection success rates. The Wellcome Trust Case Control Consortium dataset was analyzed using MCDA-MDR to detect GGIs associated with coronary artery disease, and MCDA-MDR successfully detected numerous significant GGIs (p < 0.001). MCDA-MDR performance assessment revealed that the applied MCDA successfully enhanced the GGI detection success rate of the MDR-based method compared with MDR alone.
A multisource and multivariate dataset for indoor localization methods based on WLAN and geo-magnetic field fingerprinting

Indoor localization is a key topic for the Ambient Intelligence (AmI) research community. In this scenarios, recent advancements in wearable technologies, particularly smartwatches with built-in sensors, and personal devices, such as smartphones, are being seen as the breakthrough for making concrete the envisioned Smart Environment (SE) paradigm. In particular, scenarios devoted to indoor localization represent a key challenge to be addressed. Many works try to solve the indoor localization issue, but the lack of a common dataset or frameworks to compare and evaluate solutions represent a big barrier to be overcome in the field. The unavailability and uncertainty of public datasets hinders the possibility to compare different indoor localization algorithms. This constitutes the main motivation of the proposed dataset described herein. We collected Wi-Fi and geo-magnetic field fingerprints, together with inertial sensor data during two campaigns performed in the same environment. Retrieving sincronized data from a smartwatch and a smartphone worn by users at the purpose of create and present a public available dataset is the goal of this work.
Precision grasping based on probabilistic models of unknown objects

Reliable precision grasping for unknown objects is a prerequisite for robots that work in the field of logistics, manufacturing and household tasks. The nature of this task requires a simultaneous solution of a mixture of sub-problems. These include estimating object properties, finding viable grasps and executing grasps without displacement. We propose to explicitly take perceptual uncertainty into account during grasp execution. The underlying object representation is a probabilistic signed distance field, which includes both signed distances to the surface and spatially interpretable variances. Based on this representation, we propose a two-stage grasp generation method, which is specifically designed for generating precision grasps. In order to evaluate the whole approach, we perform extensive real world grasping experiments on a set of hard-to-grasp objects. Our approach achieves 78% success rate and shows robustness to the placement orientation.
Semi Supervised Semantic Segmentation Using Generative Adversarial Network

Semantic segmentation has been a long standing challenging task in computer vision. It aims at assigning a label to each image pixel and needs a significant number of pixel-level annotated data, which is often unavailable. To address this lack of annotations, in this paper, we leverage, on one hand, a massive amount of available unlabeled or weakly labeled data, and on the other hand, non-real images created through Generative Adversarial Networks. In particular, we propose a semi-supervised framework – based on Generative Adversarial Networks (GANs) – which consists of a generator network to provide extra training examples to a multi-class classifier, acting as discriminator in the GAN framework, that assigns sample a label y from the K possible classes or marks it as a fake sample (extra class). The underlying idea is that adding large fake visual data forces real samples to be close in the feature space, which, in turn, improves multiclass pixel classification. To ensure a higher quality of generated images by GANs with consequently improved pixel classification, we extend the above framework by adding weakly annotated data, i.e., we provide class level information to the generator. We test our approaches on several challenging benchmarking visual datasets, i.e. PASCAL, SiftFLow, Stanford and CamVid, achieving competitive performance compared to state-of-the-art semantic segmentation methods.
The CRISPR/Cas Genome-Editing Tool: Application in Improvement of Crops

The Clustered Regularly Interspaced Short Palindromic Repeats associated Cas9/sgRNA system is a novel targeted genome-editing technique derived from bacterial immune system. It is an inexpensive, easy, most user friendly and rapidly adopted genome editing tool transforming to revolutionary paradigm. This technique enables precise genomic modifications in many different organisms and tissues. Cas9 protein is an RNA guided endonuclease utilized for creating targeted double-stranded breaks with only a short RNA sequence to confer recognition of the target in animals and plants. Development of genetically edited (GE) crops similar to those developed by conventional or mutation breeding using this potential technique makes it a promising and extremely versatile tool for providing sustainable productive agriculture for better feeding of rapidly growing population in a changing climate. The emerging areas of research for the genome editing in plants include interrogating gene function, rewiring the regulatory signaling networks and sgRNA library for high-throughput loss-of-function screening. In this review, we have described the broad applicability of the Cas9 nuclease mediated targeted plant genome editing for development of designer crops. The regulatory uncertainty and social acceptance of plant breeding by Cas9 genome editing have also been described. With this powerful and innovative technique the designer GE non-GM plants could further advance climate resilient and sustainable agriculture in the future and maximizing yield by combating abiotic and biotic stresses.
The contribution of arbuscular mycorrhizal fungi in sustainable maintenance of plant health and soil fertility

Beneficial plant–microbe interactions in the rhizosphere are primary determinants of plant health and soil fertility. Arbuscular mycorrhizas are the most important microbial symbioses for the majority of plants and, under conditions of P-limitation, influence plant community development, nutrient uptake, water relations and above-ground productivity. They also act as bioprotectants against pathogens and toxic stresses. This review discusses the mechanism by which these benefits are conferred through abiotic and biotic interactions in the rhizosphere. Attention is paid to the conservation of biodiversity in arbuscular mycorrhizal fungi (AMF). Examples are provided in which the ecology of AMF has been taken into account and has had an impact in landscape regeneration, horticulture, alleviation of desertification and in the bioremediation of contaminated soils. It is vital that soil scientists and agriculturalists pay due attention to the management of AMF in any schemes to increase, restore or maintain soil fertility.
Comparison of Next-Generation Sequencing Systems

With fast development and wide applications of next-generation sequencing (NGS) technologies, genomic sequence information is within reach to aid the achievement of goals to decode life mysteries, make better crops, detect pathogens, and improve life qualities. NGS systems are typically represented by SOLiD/Ion Torrent PGM from Life Sciences, Genome Analyzer/HiSeq 2000/MiSeq from Illumina, and GS FLX Titanium/GS Junior from Roche. Beijing Genomics Institute (BGI), which possesses the world's biggest sequencing capacity, has multiple NGS systems including 137 HiSeq 2000, 27 SOLiD, one Ion Torrent PGM, one MiSeq, and one 454 sequencer. We have accumulated extensive experience in sample handling, sequencing, and bioinformatics analysis. In this paper, technologies of these systems are reviewed, and first-hand data from extensive experience is summarized and analyzed to discuss the advantages and specifics associated with each sequencing system. At last, applications of NGS are summarized.
Pedestrian tracking using Velodyne data — Stochastic optimization for extended object tracking

Environment perception is a key enabling technology in autonomous vehicles, and multiple object tracking is an important part of this. High resolution sensors, such as automotive radar and lidar, leads to the so called extended target tracking problem, in which there are multiple detections per tracked object. For computationally feasible multiple extended target tracking, the data association problem must be handled. Previous work has relied on the use of clustering algorithms, together with assignment algorithms, to achieve this. In this paper we present a stochastic optimisation method that directly maximises the desired likelihood function, and solves the problem in a single step, rather than two steps (clustering+assignment). The proposed method is evaluated against previous work in an experiment where Velodyne data is used to track pedestrians, and the results clearly show that the proposed method achieves the best performance, especially in challenging scenarios.
50GHz Double-Balanced Up-Conversion Mixer Using CMOS 90nm Process

This paper reports the first millimeter-wave double-balanced up-conversion mixer that is realized using a standard CMOS 90nm process. The circuit integrates passive on-chip baluns for single-ended to differential-ended conversions at the high frequency LO and RF ports and an active balun at the IF port. The passive baluns use a stacked configuration and also provide impedance matching to improve conversion gain. The active balun employs the quasi-symmetric properties of the drain and the source of the MOSFET to provide a balanced output. The mixer has a measured 11dB conversion loss and a LO rejection of 26.5dB. The power consumption is 13.2mW.
A comparison of Fractal Koch Antenna for UHF band applications

The design of Log Periodic Fractal Koch Antennas (LPFKA) for Ultra High Frequency (UHF) band applications for three different iterations is discussed in this paper. A size reduction is achieved by using Fractal Koch technique. In this works, 0th, 1st and series iteration have been applied to the antennas and the performances of the antennas have been discussed. The antennas have been designed and simulated using Computer Simulation Technology (CST) software and fabricated using wet etching technique. The Flame Retardant-4 (FR4) laminated boards which have dielectric constant 4.6 and tangent loss 0.019 have been used as a substrate material. The simulated and measured result in term of return loss, radiation patterns and current distribution are compared and analyzed. The results shows a good agreement for both simulated and measured and 26 % of size reductions of the antennas have been achieved without degraded the performance of the antennas.
DEVELOPING THREE-DIMENSIONAL (3D) TOPOLOGICAL MODEL FOR 3D GIS

3D spatial data modeling is one of the key research problems in 3D GIS. More and more applications depend on these 3D spatial data. Mostly, these data are stored in Geo-DBMSs. However, recent Geo-DBMSs do not support 3D primitives modeling, it only able to describe a single-attribute of the third-dimension, i.e. modeling 2.5D datasets that used 2D primitives (plus a single z-coordinate) such as polygons in 3D space. This research focuses on 3D topological model based on space partition for 3D GIS, for instance, 3D polygons or tetrahedron form a solid3D object. Firstly, this report discusses formal definitions of 3D spatial objects, and then all the properties of each object primitives will be elaborated in detailed. The author also discusses methods for constructing the topological properties to support object semantics is introduced. The formal framework to describe the spatial model, database using Oracle Spatial is also given in this report. All related topological structures that forms the object features are discussed in detail. All related features are tested using real 3D spatial dataset of 3D building. Finally, the report concludes the experiment via visualization of using AutoDesk Map 3D.
Diabetic retinopathy classification using deeply supervised ResNet

Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population all over the world. Classifying DR is a time-consuming work, which requires an experienced ophthalmologist to evaluate color fundus photographs of retina. In this paper, we propose a deeply supervised ResNet approach to classify the severity of DR automatically. In this new convolutional neural networks (CNN) architecture, we add three sets of additional side-output layers to intermediate hidden layers of a 11-layer ResNet. By introducing these deeply supervised layers, we can provide additional regularization during training network. More importantly, we can perform multi-scale learning by leveraging the predictions of intermediate supervised layers, thus improving the final performance. Furthermore, to combat the issue of class-imbalance in the dataset, we adopt cost sensitive learning and an oversampling method of cropping images. We train and evaluate our network on the publicly available Kaggle dataset, and the results show that our method outperforms the state-of-the-art method.
Integrating Spatial Working Memory and Remote Memory: Interactions between the Medial Prefrontal Cortex and Hippocampus

In recent years, two separate research streams have focused on information sharing between the medial prefrontal cortex (mPFC) and hippocampus (HC). Research into spatial working memory has shown that successful execution of many types of behaviors requires synchronous activity in the theta range between the mPFC and HC, whereas studies of memory consolidation have shown that shifts in area dependency may be temporally modulated. While the nature of information that is being communicated is still unclear, spatial working memory and remote memory recall is reliant on interactions between these two areas. This review will present recent evidence that shows that these two processes are not as separate as they first appeared. We will also present a novel conceptualization of the nature of the medial prefrontal representation and how this might help explain this area's role in spatial working memory and remote memory recall.
Proposal and Synthesis Design of Wideband Phase Shifters on Multimode Resonator

In this paper, a new class of wideband phase shifters on multimode resonator is proposed and developed. Compared with its conventional counterparts, these new phase shifters have a few advantageous features such as simple structure, intrinsic wideband characteristic, large phase-shift value, and easy adjustment in phase shift. The phase properties of multimode resonator are at first studied to derive the quantitative relationship between the phase slope and their respective resonant frequencies. On the one hand, a prescribed wideband phase shift can be achieved by adjusting the impedance ratio Rz of the multimode resonator and the electrical length of the reference line. On the other hand, the wide operating band can be intrinsically obtained over a frequency range covered by multiple resonant frequencies in the multimode resonator. In this context, the transmission-line models of the proposed phase shifters are deduced and synthesized to design these phase shifters with a prescribed phase shift value and return loss within the operating band. Moreover, the synthesized model can be directly mapped into all the physical dimensions, thus allowing for a quick design process. Finally, two 180° wideband phase shifters are designed, fabricated, and tested to verify the proposed design method and predicted frequency responses.
FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions

Machine learning as a service has been widely deployed to utilize deep neural network models to provide prediction services. However, this raises privacy concerns since clients need to send sensitive information to servers. In this paper, we focus on the scenario where clients want to classify private images with a convolutional neural network model hosted in the server, while both parties keep their data private. We present FALCON, a fast and secure approach for CNN predictions based on Fourier Transform. Our solution enables linear layers of a CNN model to be evaluated simply and efficiently with fully homomorphic encryption. We also introduce the first efficient and privacy-preserving protocol for softmax function, which is an indispensable component in CNNs and has not yet been evaluated in previous works due to its high complexity. We implemented the FALCON and evaluated the performance on real-world CNN models. The experimental results show that FALCON outperforms the best known works in both computation and communication cost.
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures—arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithms that are provably robust against such failures, with a focus on achieving optimal statistical performance. A main result of this work is a sharp analysis of two robust distributed gradient descent algorithms based on median and trimmed mean operations, respectively. We prove statistical error rates for three kinds of population loss functions: strongly convex, nonstrongly convex, and smooth non-convex. In particular, these algorithms are shown to achieve order-optimal statistical error rates for strongly convex losses. To achieve better communication efficiency, we further propose a median-based distributed algorithm that is provably robust, and uses only one communication round. For strongly convex quadratic loss, we show that this algorithm achieves the same optimal error rate as the robust distributed gradient descent algorithms.
Condylomata gigantea in anal and perianal region: surgical and CO2 laser treatment

Case report.. We present a case of 28-year-old female patient with condylomata gigantea (Buschke-Lowenstein tumor) in anal and perianal region with propagation on vulva and vagina. The local surgical excision and CO2 laser treatment were performed. Histological examination showed presence of HPV type 11 without malignant potential.  Three months later, there was no recurrence.
Potential pitfalls of process modeling: part A

This paper summarizes typical pitfalls as they can be observed in larger process modeling projects.  The identified pitfalls have been derived from a series of focus groups and semi-structured interviews with business process analysts and managers of process management and modeling projects.  The paper provides a list of typical characteristics of unsuccessful process modeling. It covers six pitfalls related to strategy and governance (1-3) and the involved stakeholders (4-6). Further issues related to tools and related requirements (7-10), the practice of modeling (11-16), the way we design to-be models (17-19), and how we deal with success of modeling and maintenance issues (19-21) will be discussed in the second part of this paper.  This paper is a personal viewpoint, and does not report on the outcomes of a structured qualitative research project.  The provided list of total 22 pitfalls increases the awareness for the main challenges related to process modeling and helps to identify common mistakes.  This paper is one of the very few contributions in the area of challenges related to process modeling.
An Experimental Study on Capsule Networks

In this work we perform experiments with the recently published work on Capsule Networks. Capsule Networks have been shown to deliver state of the art performance for MNIST and claim to have greater discriminative power than Convolutional Neural Networks for special tasks, such as recognizing overlapping digits. The authors of Capsule Networks have evaluated datasets with low number of categories, viz. MNIST, CIFAR-10, SVHN among others. We evaluate capsule networks on two datasets viz. Traffic Signals, Food101, and CIFAR10 with less number of iterations, making changes to the architecture to account for RGB images. Traditional techniques like dropout, batch normalization were applied to capsule networks for performance evaluation.