nils-herrmann/clean_scirepeval huggingface dataset:
On the risk of upward lightning initiated from wind turbines

In recent years the number of wind turbines installed in Europe and other continents has increase dramatically. Appropriate lightning protection is required in order to avoid costly replacements of lightning damaged turbine blades, components of the electronic control system, and/or temporary loss of energy production. Depending on local site conditions elevated objects with heights of 100 m and more can frequently initiate upward lightning. From the 100 m high and instrumented radio tower on Gaisberg in Austria more than 50 flashes per year are initiated and measured. Also lightning location systems or video studies in Japan ,  or in the US [3] show frequent occurrence of lightning initiated from wind turbines, especially during cold season. Up to now no reliable method exists to estimate the expected frequency of upward lightning for a given structure and location. About half of the flashes observed at the GBT are of ICCOnly type. Unfortunately this type of discharge is not detected by lightning location systems as its current waveform does not show any fast rising and high peak current pulses as typical for first or subsequent return strokes in downward lightning (cloud-to-ground). Nevertheless some of this ICCOnly type discharges transferred the highest amount of charge, exceeding the 300 C specified in IEC 62305 for lightning protection level LPL I.
Credit Scoring and Disparate Impact

We analyze the problem of disparate impact in credit scoring and evaluate three approaches to identifying and correcting the problem, namely: 1) post-development univariate test with variable elimination, 2) postdevelopment multivariate test with variable elimination, 3) control variable approach with coefficient adjustment. The third approach is a new innovation developed by the authors. Results are illustrated with simulation data calibrated to actual distributions of typical variables used in score development. Results show that controlling disparate impact by eliminating variables may have unintended and undesirable consequences.
Automatic Construction of Morphologically Motivated Translation Models for Highly Inflected , Low-Resource Languages

Statistical Machine Translation (SMT) of highly inflected, low-resource languages suffers from the problem of low bitext availability, which is exacerbated by large inflectional paradigms. When translating into English, rich source inflections have a high chance of being poorly estimated or out-of-vocabulary (OOV). We present a source language-agnostic system for automatically constructing phrase pairs from foreign-language inflections and their morphological analyses using manually constructed datasets, including Wiktionary. We then demonstrate the utility of these phrase tables in improving translation into English from Finnish, Czech, and Turkish in simulated low-resource settings, finding substantial gains in translation quality. We report up to +2.58 BLEU in a simulated low-resource setting and +1.65 BLEU in a moderateresource setting. We release our morphologically-motivated translation models, with tens of thousands of inflections in each of 8 languages.
Modified HOG Descriptor-Based Banknote Recognition System

15 August, 2018  11 October, 2018 Online: 18 October, 2018 This research presents a banknote recognition and counterfeit detection system that proposes a pre-processing approach before creating a new feature set by extracting Speeded-Up Robust Feature, creating image patches from its vertices and then computing the Histogram of Gradient descriptors from cells within the patch boundaries. A feature reduction approach is also designed based on a high correlation and Low variance filter to reduce the feature set by calculating the intra-feature value variability to ascertain the similarity between the elements to prune out highly correlated values and variances using a fixed threshold. For experimental evaluation, additional analysis was conducted using Principal Component Analysis, Bag of words, Low Variance filter, and High Correlation Filter method. Furthermore, the classification was performed using 10-fold crossvalidation based One-Versus-One Support Vector Machine. Additionally, the developed technique was tested on counterfeit banknotes to authenticate genuine and fake currencies. Lastly, point matching method was applied to detect our suggested feature vectors to match images containing multiple notes to distinguish and determine the value of the bills.
LaneNet: Real-Time Lane Detection Networks for Autonomous Driving

Lane detection is to detect lanes on the road and provide the accurate location and shape of each lane. It severs as one of the key techniques to enable modern assisted and autonomous driving systems. However, several unique properties of lanes challenge the detection methods. The lack of distinctive features makes lane detection algorithms tend to be confused by other objects with similar local appearance. Moreover, the inconsistent number of lanes on a road as well as diverse lane line patterns, e.g. solid, broken, single, double, merging, and splitting lines further hamper the performance. In this paper, we propose a deep neural network based method, named LaneNet, to break down the lane detection into two stages: lane edge proposal and lane line localization. Stage one uses a lane edge proposal network for pixel-wise lane edge classification, and the lane line localization network in stage two then detects lane lines based on lane edge proposals. Please note that the goal of our LaneNet is built to detect lane line only, which introduces more difficulties on suppressing the false detections on the similar lane marks on the road like arrows and characters. Despite all the difficulties, our lane detection is shown to be robust to both highway and urban road scenarios method without relying on any assumptions on the lane number or the lane line patterns. The high running speed and low computational cost endow our LaneNet the capability of being deployed on vehicle-based systems. Experiments validate that our LaneNet consistently delivers outstanding performances on real world traffic scenarios.
Rooting the Domain Archaea by Phylogenomic Analysis Supports the Foundation of the New Kingdom Proteoarchaeota

The first 16S rRNA-based phylogenies of the Archaea showed a deep division between two groups, the kingdoms Euryarchaeota and Crenarchaeota. This bipartite classification has been challenged by the recent discovery of new deeply branching lineages (e.g., Thaumarchaeota, Aigarchaeota, Nanoarchaeota, Korarchaeota, Parvarchaeota, Aenigmarchaeota, Diapherotrites, and Nanohaloarchaeota) which have also been given the same taxonomic status of kingdoms. However, the phylogenetic position of some of these lineages is controversial. In addition, phylogenetic analyses of the Archaea have often been carried out without outgroup sequences, making it difficult to determine if these taxa actually define lineages at the same level as the Euryarchaeota and Crenarchaeota. We have addressed the question of the position of the root of the Archaea by reconstructing rooted archaeal phylogenetic trees using bacterial sequences as outgroup. These trees were based on commonly used conserved protein markers (32 ribosomal proteins) as well as on 38 new markers identified through phylogenomic analysis. We thus gathered a total of 70 conserved markers that we analyzed as a concatenated data set. In contrast with previous analyses, our trees consistently placed the root of the archaeal tree between the Euryarchaeota (including the Nanoarchaeota and other fast-evolving lineages) and the rest of archaeal species, which we propose to class within the new kingdom Proteoarchaeota. This implies the relegation of several groups previously classified as kingdoms (e.g., Crenarchaeota, Thaumarchaeota, Aigarchaeota, and Korarchaeota) to a lower taxonomic rank. In addition to taxonomic implications, this profound reorganization of the archaeal phylogeny has also consequences on our appraisal of the nature of the last archaeal ancestor, which most likely was a complex organism with a gene-rich genome.
Convolutional neural network compression for natural language processing

Convolutional neural networks are modern models that are very efficient in many classification tasks. They were originally created for image processing purposes. Then some trials were performed to use them in different domains like natural language processing. The artificial intelligence systems (like humanoid robots) are very often based on embedded systems with constraints on memory, power consumption etc. Therefore convolutional neural network because of its memory capacity should be reduced to be mapped to given hardware. In this paper, results are presented of compressing the efficient convolutional neural networks for sentiment analysis. The main steps are quantization and pruning processes. The method responsible for mapping compressed network to FPGA and results of this implementation are presented. The described simulations showed that 5-bit width is enough to have no drop in accuracy from floating point version of the network. Additionally, significant memory footprint reduction was achieved (from 85% up to 93%).
End-to-end learning of latent edge weights for Graph Convolutional Networks

We present Latent-Graph Convolutional Networks (L-GCN), an approach for machine learning on any kind of graph structure, including directed graphs, multi-graphs and knowledge graphs. Our approach extends Graph Convolutional Networks (Kipf and Welling, 2016) by allowing for end-to-end training and by supporting any kind of data available on the edges in the network, such as numerous transactions within a company network, different relations between entities in a knowledge graph or different flights between airports. The edge features are mapped to latent graphs which are in turn used in the GCN's localpooling operation. Taking this approach allows us to use the predictive power that is contained within the edges of the graph. We achieve competitive results on four graph datasets (CORA, RITA, AIFB and NELL).
Deep Sequential Context Networks for Action Prediction

This paper proposes efficient and powerful deep networks for action prediction from partially observed videos containing temporally incomplete action executions. Different from after-the-fact action recognition, action prediction task requires action labels to be predicted from these partially observed videos. Our approach exploits abundant sequential context information to enrich the feature representations of partial videos. We reconstruct missing information in the features extracted from partial videos by learning from fully observed action videos. The amount of the information is temporally ordered for the purpose of modeling temporal orderings of action segments. Label information is also used to better separate the learned features of different categories. We develop a new learning formulation that enables efficient model training. Extensive experimental results on UCF101, Sports-1M and BIT datasets demonstrate that our approach remarkably outperforms state-of-the-art methods, and is up to 300x faster than these methods. Results also show that actions differ in their prediction characteristics, some actions can be correctly predicted even though only the beginning 10% portion of videos is observed.
Human Motion Segmentation via Robust Kernel Sparse Subspace Clustering

Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem. Under the framework of sparse subspace clustering, we propose to use the geodesic exponential kernel to model the Riemannian manifold structure, use correntropy to measure the reconstruction error, use the triangle constraint to guarantee temporal continuity in each cluster and use multi-view reconstruction to extract the relations between different joints. Therefore, exploiting some special characteristics of motion capture data, we propose a new segmentation method, which is robust to non-Gaussian noise, since correntropy is a localized similarity measure. We also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model. Our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem. Extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method.
Geometric Considerations for the Design of Rigid Origami Structures

Transformable polyhedral surfaces with rigid facets, i.e., rigid origami, are useful for designing kinetic and deployable structures. In order to apply rigid origami to various architectural and other engineering design purposes, it is essential to consider the geometry of origami in kinetic motion and provide sufficiently generalized methods to produce controlled variations of shapes that suit the given design conditions. In this paper, we introduce the author’s recent studies and their extensions on the geometry of rigid origami for designing transformable and deployable structures.
SoK: Cryptographically Protected Database Search

Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly, systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions:1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms.2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in ) An analysis of attacks against protected search for different base  A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search.
Autophagy in acute brain injury

Autophagy is an evolutionarily ancient mechanism that ensures the lysosomal degradation of old, supernumerary or ectopic cytoplasmic entities. Most eukaryotic cells, including neurons, rely on proficient autophagic responses for the maintenance of homeostasis in response to stress. Accordingly, autophagy mediates neuroprotective effects following some forms of acute brain damage, including methamphetamine intoxication, spinal cord injury and subarachnoid haemorrhage. In some other circumstances, however, the autophagic machinery precipitates a peculiar form of cell death (known as autosis) that contributes to the aetiology of other types of acute brain damage, such as neonatal asphyxia. Here, we dissect the context-specific impact of autophagy on non-infectious acute brain injury, emphasizing the possible therapeutic application of pharmacological activators and inhibitors of this catabolic process for neuroprotection.
The impact and response to electronic bullying and traditional bullying among adolescents

With adolescents’ frequent use of social media, electronic bullying has emerged as a powerful platform for peer victimization. The present two studies explore how adolescents perceive electronic vs. traditional bullying in emotional impact and strategic responses. In Study 1, 97 adolescents (mean age = 15) viewed hypothetical peer victimization scenarios, in parallel electronic and traditional forms, with female characters experiencing indirect relational aggression and direct verbal aggression. In Study 2, 47 adolescents (mean age = 14) viewed the direct verbal aggression scenario from Study 1, and a new scenario, involving male characters in the context of direct verbal aggression. Participants were asked to imagine themselves as the victim in all scenarios and then rate their emotional reactions, strategic responses, and goals for the outcome. Adolescents reported significant negative emotions and disruptions in typical daily activities as the victim across divergent bullying scenarios. In both studies few differences emerged when comparing electronic to traditional bullying, suggesting that online and off-line bullying are subtypes of peer victimization. There were expected differences in strategic responses that fit the medium of the bullying. Results also suggested that embarrassment is a common and highly relevant negative experience in both indirect relational and direct verbal aggression among
Maintaining Knowledge about Temporal Intervals

The research described in this paper was supported in part by the National Science Foundation under Grants IST-g0-12418 and IST-82-10564. and in part by the Office of Naval Research under Grant N00014-80-C-0197. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission.
Quantitative learning strategies based on word networks

Yue-Tian-Yi Zhao a, Zi-Yang Jia b, Yong Tang c,d,*, Jason Jie Xiong e, Yi-Cheng Zhang d a School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, 610054, China b Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA c School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China d Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland e Department of Computer Information Systems and Supply Chain Management, Walker College of Business, Appalachian State University, Boone, NC 28608, USA
Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences

Recent results suggest that it is possible to grasp a variety of singulated objects with high precision using Convolutional Neural Networks (CNNs) trained on synthetic data. This paper considers the task of bin picking, where multiple objects are randomly arranged in a heap and the objective is to sequentially grasp and transport each into a packing box. We model bin picking with a discrete-time Partially Observable Markov Decision Process that specifies states of the heap, point cloud observations, and rewards. We collect synthetic demonstrations of bin picking from an algorithmic supervisor uses full state information to optimize for the most robust collision-free grasp in a forward simulator based on pybullet to model dynamic object-object interactions and robust wrench space analysis from the Dexterity Network (Dex-Net) to model quasi-static contact between the gripper and object. We learn a policy by fine-tuning a Grasp Quality CNN on Dex-Net 2.1 to classify the supervisor’s actions from a dataset of 10,000 rollouts of the supervisor in the simulator with noise injection. In 2,192 physical trials of bin picking with an ABB YuMi on a dataset of 50 novel objects, we find that the resulting policies can achieve 94% success rate and 96% average precision (very few false positives) on heaps of 5-10 objects and can clear heaps of 10 objects in under three minutes. Datasets, experiments, and supplemental material are available at http://berkeleyautomation.github.io/dex-net.
PDE-Net: Learning PDEs from Data

Partial differential equations (PDEs) play a prominent role in many disciplines of science and engineering. PDEs are commonly derived based on empirical observations. However, with the rapid development of sensors, computational power, and data storage in the past decade, huge quantities of data can be easily collected and efficiently stored. Such vast quantity of data offers new opportunities for data-driven discovery of physical laws. Inspired by the latest development of neural network designs in deep learning, we propose a new feed-forward deep network, called PDENet, to fulfill two objectives at the same time: to accurately predict dynamics of complex systems and to uncover the underlying hidden PDE models. Comparing with existing approaches, our approach has the most flexibility by learning both differential operators and the nonlinear response function of the underlying PDE model. A special feature of the proposed PDE-Net is that all filters are properly constrained, which enables us to easily identify the governing PDE models while still maintaining the expressive and predictive power of the network. These constrains are carefully designed by fully exploiting the relation between the orders of differential operators and the orders of sum rules of filters (an important concept originated from wavelet theory). Numerical experiments show that the PDE-Net has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment. Equal contribution School of Mathematical Sciences, Peking University, Beijing, China Beijing Computational Science Research Center, Beijing, China Beijing International Center for Mathematical Research, Peking University, Beijing, China Center for Data Science, Peking University Laboratory for Biomedical Image Analysis, Beijing Institute of Big Data Research.  Dong <dongbin@math.pku.edu.cn>. Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018.