nils-herrmann/clean_scirepeval huggingface dataset:
Global Changes and Drivers of the Water Footprint of Food Consumption : A Historical Analysis

Water is one of the most important limiting resources for food production. How much water is needed for food depends on the size of the population, average food consumption patterns and food production per unit of water. These factors show large differences around the world. This paper analyzes sub-continental dynamics of the water footprint of consumption (WFcons) for the prevailing diets from 1961 to 2009 using data from the Food and Agriculture Organization (FAO). The findings show that, in most regions, the water needed to feed one person decreased even if diets became richer, because of the increase in water use efficiency in food production during the past half-century. The logarithmic mean Divisia index (LMDI) decomposition approach is used to analyze the contributions of the major drivers of WFcons for food: population, diet and agricultural practices (output per unit of water). We compare the contributions of these drivers through different subcontinents, and find that population growth still was the major driver behind increasing WFcons for food until now and that potential water savings through agricultural practice improvements were offset by population growth and diet change. The changes of the factors mentioned above were the largest in most developing areas with rapid economic development. With the development of globalization, the international food trade has brought more and more water savings in global water use over time. The results indicate that, in the near future and in many regions, diet change is likely to override population growth as the major driver behind WFcons for food.
A novel softplus linear unit for deep convolutional neural networks

Current improvements in the performance of deep neural networks are partly due to the proposition of rectified linear units. A ReLU activation function outputs zero for negative component, inducing the death of some neurons and a bias shift of the outputs, which causes oscillations and impedes learning. According to the theory that “zero mean activations improve learning ability”, a softplus linear unit (SLU) is proposed as an adaptive activation function that can speed up learning and improve performance in deep convolutional neural networks. Firstly, for the reduction of the bias shift, negative inputs are processed using the softplus function, and a general form of the SLU function is proposed. Secondly, the parameters of the positive component are fixed to control vanishing gradients. Thirdly, the rules for updating the parameters of the negative component are established to meet back- propagation requirements. Finally, we designed deep auto-encoder networks and conducted several experiments with them on the MNIST dataset for unsupervised learning. For supervised learning, we designed deep convolutional neural networks and conducted several experiments with them on the CIFAR-10 dataset. The experiments have shown faster convergence and better performance for image classification of SLU-based networks compared with rectified activation functions.
An Active Suspension System for a Planetary Rover

This paper reviews the design of the hybrid wheeled-leg rover Sherpa. Focus is set on the mechanical design of the suspension system that is constituted by four independently controllable legs with a wheel mounted at each leg. Achievements and drawbacks of the current design are outlined and lead together with the new application range to a revised design of the suspension. The new design and its modular actuation components are presented in this paper.
College students ' social networking experiences on Facebook ☆

Available online 11 January 2009 Millions of contemporary young adults use social networking sites. However, little is known about how much, why, and how they use these sites. In this study, 92 undergraduates completed a diary-likemeasure each day for aweek, reporting daily time use and responding to an activities checklist to assess their use of the popular social networking site, Facebook. At the end of the week, they also completed a follow-up survey. Results indicated that students use Facebook approximately 30 min throughout the day as part of their daily routine. Students communicated on Facebook using a one-to-many style, in which they were the creators disseminating content to their friends. Even so, they spent more time observing content on Facebook than actually posting content. Facebook was used most often for social interaction, primarily with friends with whom the students had a pre-established relationship offline. In addition to classic identity markers of emerging adulthood, such as religion, political ideology, and work, young adults also used media preferences to express their identity. Implications of social networking site use for the development of identity and peer relationships are discussed.
Through-Wall Opportunistic Sensing System Utilizing a Low-Cost Flat-Panel Array

A UWB through-wall imaging system is proposed based on a planar low profile aperture array operating from 0.9 GHz to 2.3 GHz. The goal is to provide a lightweight, fixed array to serve as an alternative to synthetic aperture radars (SAR) that require continuous array movement while collecting data. The proposed system consists of 12 dual-linear printed elements arranged within a triangular lattice, each forming a “flower” shape and backed by a ground plane. The array delivers half-space radiation with wideband performance, necessary for imaging applications. UWB capability is realized by suppressing grating lobes via the introduction of virtual phase centers interwoven within the actual array feeds. The proposed system is demonstrated for through-wall imaging via a non-coherent process. Distinctively, several coherent images are forged from various fixed aperture locations (referred to as “snapshot” locations) and appropriately combined to create a composite scene image. In addition to providing a unique wideband imaging capability (as an alternative to SAR), the system is portable and inexpensive for collecting/storing scattering data. The array design and data collection system is described, and several through-wall images are presented to demonstrate functionality.
Discovery of inference rules for question-answering

One of the main challenges in question-answering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as X writes Y implies X is the author of Y in answering questions, such rules are generally unavailable to question-answering systems due to the inherent difficulty in constructing them. In this paper, we present an unsupervised algorithm for discovering inference rules from text. Our algorithm is based on an extended version of Harris Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus. Essentially, if two paths tend to link the same set of words, we hypothesize that their meanings are similar. We use examples to show that our system discovers many inference rules easily missed by humans.
Mapping underwater ship hulls using a model-assisted bundle adjustment framework

This paper reports on a model-assisted bundle adjustment (BA) framework in which visually-derived features are fused with an underlying three-dimensional (3D) mesh provided a priori. By using an approach inspired by the expectationmaximization (EM) class of algorithms, we introduce a hidden binary label for each visual feature that indicates if that feature is considered part of the nominal model, or if the feature corresponds to 3D structure that is absent from the model. Therefore, in addition to improved estimates of the feature locations, we can identify visual features that correspond to foreign structure on the ship hull. We show that this framework is a special case of the Gaussian maxmixtures framework, which can be efficiently incorporated into state-of-the-art graph-based simultaneous localization and mapping (SLAM) solvers. In addition, the precision of our bundle adjustment framework allows the identification of structural deviations between 3D structure inferred from bundle-adjusted camera imagery and the prior model. These structural deviations are clustered into shapes, which allow us to fuse camera-derived structure back into the 3D mesh. This augmented model can be used within a 3D photomosaicing pipeline, providing a visually intuitive 3D reconstruction of the ship hull. We evaluate our pipeline using the Bluefin Robotics hovering au∗Corresponding author Email addresses: paulozog@umich.edu (Paul Ozog), mattjr@umich.edu (Matthew Johnson-Roberson), eustice@umich.edu (Ryan M. Eustice) 1Electrical Engineering and Computer Science Department 2Naval Architecture and Marine Engineering Department  2017 tonomous underwater vehicle (HAUV) surveying the SS Curtiss, where a 3D mesh derived from computer aided design (CAD) drawings serves as the prior model. In addition to more consistent visual reconstructions, we can update the prior mesh with 3D information corresponding to underwater structure, such as biofouling or manually-placed cylindrical shapes with known dimensions.
Movie recommendation system using clustering and pattern recognition network

As the world is going global, people have a lot to choose from when it comes to movies. There are different genres, cultures and languages to choose from in the world of movies. This arises the issue of recommending movies to users in automated systems. So far, a decent number of works has been done in this field. But there is always room for renovation. This paper proposed a machine learning approach to recommend movies to users using K-means clustering algorithm to separate similar users and creating a neural network for each cluster.
5G cellular network integration with SDN: Challenges, issues and beyond

The tremendous growth in communication technology is shaping a hyper-connected network where billions or connected devices are producing a huge volume of data. Cellular and mobile network is a major contributor towards this technology shift and require new architectural paradigm to provide low latency, high performance in a resource constrained environment. 5G technology deployment with fully IP-based connectivity is anticipated by 2020. However, there is no standard established for 5G technology and many efforts are being made to establish a unified 5G stander. In this context, variant technology such as Software Defined Network (SDN) and Network Function virtualization (NFV) are the best candidate. SDN dissociate control plane from data plane and network management is done on the centralized control plane. In this paper, a survey on state of the art on the 5G integration with the SDN is presented. A comprehensive review is presented for the different integrated architectures of 5G wireless network and the generalized solutions over the period 2010–2016. This comparative analysis of the existing solutions of SDN-based cellular network (5G) implementations provides an easy and concise view of the emerging trends by 2020.
Deep Texture Features for Robust Face Spoofing Detection

Biometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioral traits of valid users, process known as spoofing attack. In this context, robust countermeasure methods must be developed and integrated with the traditional biometric applications in order to prevent such frauds. Despite face being a promising trait due to its convenience and acceptability, face recognition systems can be easily fooled with common printed photographs. Most of state-of-the-art antispoofing techniques for face recognition applications extract handcrafted texture features from images, mainly based on the efficient local binary patterns (LBP) descriptor, to characterize them. However, recent results indicate that high-level (deep) features are more robust for such complex tasks. In this brief, a novel approach for face spoofing detection that extracts deep texture features from images by integrating the LBP descriptor to a modified convolutional neural network is proposed. Experiments on the NUAA spoofing database indicate that such deep neural network (called LBPnet) and an extended version of it (n-LBPnet) outperform other state-of-the-art techniques, presenting great results in terms of attack detection.
DTN: an architectural retrospective

We review the rationale behind the current design of the Delay/Disruption Tolerant Networking (DTN) Architecture and highlight some remaining open issues. Its evolution, from a focus on deep space to a broader class of heterogeneous networks that may suffer disruptions, affected design decisions spanning naming and addressing, message formats, data encoding methods, routing, congestion management and security. Having now achieved relative stability with the design, additional experience is required in long-running operational environments in order to fine tune our understanding of DTN concepts and the types of capabilities that are worth the investment in implementation complexity. We expect key management, handling of congestion, multicasting capability, and routing to remain active areas of research and development, and that DTN may continue to be an active research endeavor for at least the next few years.
Country-wide rainfall maps from cellular communication networks.

Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km(2)), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent.
An unusual case of predation: dog pack or cougar attack?

Injuries produced by animals are capable of leaving severe patterns and in some cases may result in the death of the attacked individual. Law enforcement authorities may come to erroneous conclusions about the source of the bites based on their awareness of animals present and similarities of the injuries to the untrained eye, with dreadful consequences. Expertise of a carnivore biologist and an odontologist that indentifies the particularities of bite marks may be useful for identifying the attacking species. We present the investigation of a fatal dog pack attack involving a 43-year-old man in Bell Ville (Argentina) where the evidence provided by a forensic dentist and a biologist was categorical for establishing the animal species involved. Because of the unusual characteristics of the wounds and the initial hypothesis made by local authorities of a cougar attack, habits and specific patterns of both dog pack and cougar predation on humans are discussed.
An Empirical Exploration of Curriculum Learning for Neural Machine Translation

Machine translation systems based on deep neural networks are expensive to train. Curriculum learning aims to address this issue by choosing the order in which samples are presented during training to help train better models faster. We adopt a probabilistic view of curriculum learning, which lets us flexibly evaluate the impact of curricula design, and perform an extensive exploration on a German-English translation task. Results show that it is possible to improve convergence time at no loss in translation quality. However, results are highly sensitive to the choice of sample difficulty criteria, curriculum schedule and other hyperparameters.
A subjective study on QoE of 360 video for VR communication

Currently, more and more 360-degree videos (or 360 videos for short) are being provided via the Internet. This kind of videos can render a virtual reality (VR) environment via a head-mounted display (HMD). However, understanding the quality of experience (QoE) of 360 videos is a big challenge because user experience in VR is a very complex phenomenon. In this paper, the QoE of 360 videos is considered in terms of four aspects, namely perceptual quality, presence, acceptability, and cybersickness. Subjective tests are designed to investigate the influences of important factors including encoding parameters, content characteristics, and device types on QoE aspects. In addition, a comparison of perceptual quality and acceptability between VR and non-VR rendering modes, which refer to watching 360 videos with and without using an HMD respectively, is also made in this study. To the best of our knowledge, this is the first study that covers these four QoE aspects and a large number of influence factors.
A bibliometric analysis on rural studies in human geography and related disciplines

Although the world has experienced rapid urbanization, rural areas have always been and are still an important research field in human geography. This paper performed a bibliometric analysis on rural geography studies based on the peer-reviewed articles concerning rural geography published in the SSCI-listed journals from 1990 to 2012. Our analysis examines publication patterns (document types and publishing languages, article outputs and their categories, major journals and their publication, most productive authors, geographic distribution and international collaboration) and demonstrates the evolution of intellectual development of rural geography by studying highly cited papers and their citation networks and temporal evolution of keywords. Our research findings include: The article number has been increasing since the 1900s, and went through three phases, and the rural geography research is dominated in size by UK and USA. The USA is the most productive in rural geography, but the UK had more impact than other countries in the terms of the average citation of articles. Three distinct but loosely linked research streams of rural geography were identified and predominated by the UK rural geographers. The keywords frequencies evolved according to contexts of rural development and academic advances of human geography, but they were loosely and scattered since the rural researches in different regions or different systems faced with different problems.
Automated Robot Picking System for E-Commerce Fulfillment Warehouse Application

In e-commerce fulfillment warehouses, manual item picking is a labor-intensive and tedious task. Therefore, automation of item picking can improve efficiency and save cost for e-commerce businesses. This paper presents an automated robot picking solution that meets the requirements of automating the picking of items from shelves. The hardware of the proposed system comprises a lightweight robot manipulator, a low-cost commercially available 3D camera system and a custom-built robotic gripper. The software of the proposed system is modular comprising the task planning module, item identification and registration modules, grasp planning module and the motion planning modules. Simulations are carried to illustrate full cycles of the automated picking of items. Experiments were conducted using a prototype of the proposed system. The test results show the system is capable of picking several regularly-shaped and irregularly-shaped items from the bins of the shelf. Such preliminary observations prove the system to be flexible and versatile. It has the potential to be used in real e-commerce fulfillment warehouses.
TRAUMA, DISSOCIATION, AND DISORGANIZED ATTACHMENT: THREE STRANDS OF A SINGLE BRAID

During the past decade, research findings, theoretical reflections, and clinical experiences have woven together the themes of attachment disorganization, dissociative processes, and vulnerability to trauma-related emotional disorders. The resulting unitary perspective is captured in this article by an overview of inquiries on unresolved traumatic memories based on the Adult Attachment Interview (AAI) and of studies on the sequelae of early disorganized attachments. To illustrate the intriguing clinical implications of this unitary perspective, the author considers such topics as vulnerability to complex trauma-related disorders, delayed dissociative responses to past traumatic memories, and the definition of psychological trauma. Some psychotherapeutic implications of the interplay between trauma-related disorders and attachment disorganization are briefly addressed in the concluding section. Overview of Research on Attachment in Infants and Adults
DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level

Traffic congestion causes huge economic loss worldwide in every year due to wasted fuel, excessive air pollution, lost time, and reduced productivity. Understanding how humans move and select the transportation mode throughout a large-scale transportation network is vital for urban congestion prediction and transportation scheduling. In this study, we collect big and heterogeneous data (e.g., GPS records and transportation network data), and we build an intelligent system, namely DeepTransport, for simulating and predicting human mobility and transportation mode at a citywide level. The key component of DeepTransport is based on the deep learning architecture that that aims to understand human mobility and transportation patterns from big and heterogeneous data. Based on the learning model, given any time period, specific location of the city or people’s observed movements, our system can automatically simulate or predict the persons’ future movements and their transportation mode in the large-scale transportation network. Experimental results and validations demonstrate the efficiency and superior performance of our system, and suggest that human transportation mode may be predicted and simulated more easily than previously thought.
Twin Networks: Using the Future as a Regularizer

Being able to model long-term dependencies in sequential data, such as text, has been among the long-standing challenges of recurrent neural networks (RNNs). This issue is strictly related to the absence of explicit planning in current RNN architectures, more explicitly, the network is trained to predict only the next token given previous ones. In this paper, we introduce a simple way of biasing the RNNs towards planning behavior. Particularly, we introduce an additional neural network which is trained to generate the sequence in reverse order, and we require closeness between the states of the forward RNN and backward RNN that predict the same token. At each step, the states of the forward RNN are required to match the future information contained in the backward states. We hypothesize that the approach eases modeling of long-term dependencies thus helping in generating more globally consistent samples. The model trained with conditional generation achieved 4% relative improvement (CER of 7.3 compared to a baseline of 7.6).