nils-herrmann/clean_scirepeval huggingface dataset:
Augmented reality: an application of heads-up display technology to manual manufacturing processes

We describe the design and prototyping steps we have taken toward the implementation of a heads-up, see-through, head-mounted display (HUDSET). Combined with head position sensing and a real world registration system, this technology allows a computer-produced diagram to be superimposed and stabilized on a specific position on a real-world object. Successful development of the HUDset technology will enable cost reductions and efficiency improvements in many of the human-involved operations in aircraft manufacturing, by eliminating templates, formboard diagrams, and other masking devices. The Manufacturing Problem Someone once said that a Boeing 747 is not really an airplane, but five million parts flying in close formation. Modern airliners are complex machines that require a tremendous amount of manual effort to manufacture and assembly. The production of an airliner does not lend itself to complete automation for two reasons: first, because of the small lot size of parts. In many cases the average lot size for a part is less than ten. Second, automation is not practical in many cases because of the skills required to perform a task. Robots can not today provide the dexterity and perception of a human required to assemble many of the components of this product. In addition, the reprogramming costs for robotic systems are exacerbated by the small lot sizes. For these reasons, people continue to play a major role in the manufacture and assembly of modem aircraft. Even so, aircraft are becoming more and more complex, as are their manufacturing processes. People in the factories are required to use an increasing amount of information in their jobs. Much of this information is derived from engineering designs for parts and processes stored in CAD systems. In many cases, this information comes to the factory floor in the form of assembly guides, templates, mylar drawings, wiring lists, and location markings on sheet metal. Occasionally, a PC screen will be used to indicate the next step in a process, or the next location to be connected. A significant source of expense and delay in aircraft manufacturing comes from the requirement to mirror changes in the engineering design in the guides, templates and so on used to control the manufacturing processes. If manufacturing workers were able to directly access digital CAD 659 0073-1 129-1192 $3.00
Globally Normalized Transition-Based Neural Networks

We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-ofspeech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models. We discuss the importance of global as opposed to local normalization: a key insight is that the label bias problem implies that globally normalized models can be strictly more expressive than locally normalized models.
Probabilistic Model-Agnostic Meta-Learning

Meta-learning for few-shot learning entails acquiring a prior over previous tasks and experiences, such that new tasks be learned from small amounts of data. However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be too ambiguous to acquire a single model (e.g., a classifier) for that task that is accurate. In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution. Our approach extends model-agnostic meta-learning, which adapts to new tasks via gradient descent, to incorporate a parameter distribution that is trained via a variational lower bound. At meta-test time, our algorithm adapts via a simple procedure that injects noise into gradient descent, and at meta-training time, the model is trained such that this stochastic adaptation procedure produces samples from the approximate model posterior. Our experimental results show that our method can sample plausible classifiers and regressors in ambiguous few-shot learning problems. We also show how reasoning about ambiguity can also be used for downstream active learning problems.
Sparse Model-based Image Alignment Feature Alignment Pose & Structure Refinement Motion Estimation Thread New Image Last Frame Map Frame Queue Feature Extraction Initialize Depth-Filters

Direct methods for Visual Odometry (VO) have gained popularity due to their capability to exploit information from all intensity gradients in the image. However, low computational speed as well as missing guarantees for optimality and consistency are limiting factors of direct methods, where established feature-based methods instead succeed at. Based on these considerations, we propose a Semi-direct VO (SVO) that uses direct methods to track and triangulate pixels that are characterized by high image gradients but relies on proven feature-based methods for joint optimization of structure and motion. Together with a robust probabilistic depth estimation algorithm, this enables us to efficiently track pixels lying on weak corners and edges in environments with little or high-frequency texture. We further demonstrate that the algorithm can easily be extended to multiple cameras, to track edges, to include motion priors, and to enable the use of very large field of view cameras, such as fisheye and catadioptric ones. Experimental evaluation on benchmark datasets shows that the algorithm is significantly faster than the state of the art while achieving highly competitive accuracy.  Video of the experiments: https://youtu.be/hR8uq1RTUfA
Efficient Processing of Joins on Set-valued Attributes

Object-oriented and object-relational DBMS support set valued attributes, which are a natural and concise way to model complex information. However, there has been limited research to-date on the evaluation of query operators that apply on sets. In this paper we study the join of two relations on their set-valued attributes. Various join types are considered, namely the set containment, set equality, and set overlap joins. We show that the inverted file, a powerful index for selection queries, can also facilitate the efficient evaluation of most join predicates. We propose join algorithms that utilize inverted files and compare them with signature-based methods for several set-comparison predicates.
Practical Bayesian Optimization of Machine Learning Algorithms

The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a “black art” requiring expert experience, rules of thumb, or sometimes bruteforce search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm’s generalization performance is modeled as a sample from a Gaussian process (GP). We show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance. We describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.
Social Media and Information Overload: Survey Results

A UK-based online questionnaire investigating aspects of usage of usergenerated media (UGM), such as Facebook, LinkedIn and Twitter, attracted 587 participants. Results show a high degree of engagement with social networking media such as Facebook, and a significant engagement with other media such as professional media, microblogs and blogs. Participants who experience information overload are those who engage less frequently with the media, rather than those who have fewer posts to read. Professional users show different behaviours to social users. Microbloggers complain of information overload to the greatest extent. Two thirds of Twitter-users have felt that they receive too many posts, and over half of Twitter-users have felt the need for a tool to filter out the irrelevant posts. Generally speaking, participants express satisfaction with the media, though a significant minority express a range of concerns including information overload and privacy.
Deep Clustering with Convolutional Autoencoders

Deep clustering utilizes deep neural networks to learn feature representation that is suitable for clustering tasks. Though demonstrating promising performance in various applications, we observe that existing deep clustering algorithms either do not well take advantage of convolutional neural networks or do not considerably preserve the local structure of data generating distribution in the learned feature space. To address this issue, we propose a deep convolutional embedded clustering algorithm in this paper. Specifically, we develop a convolutional autoencoders structure to learn embedded features in an end-to-end way. Then, a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment. To avoid feature space being distorted by the clustering loss, we keep the decoder remained which can preserve local structure of data in feature space. In sum, we simultaneously minimize the reconstruction loss of convolutional autoencoders and the clustering loss. The resultant optimization problem can be effectively solved by mini-batch stochastic gradient descent and back-propagation. Experiments on benchmark datasets empirically validate the power of convolutional autoencoders for feature learning and the effectiveness of local structure preservation.
Translating evidence-based decision making into practice: EBDM concepts and finding the evidence.

This is the first of 2 articles that focuses on strategies that can be used to integrate an evidence-based decision making [EBDM] approach into practice. The articles will focus on EBDM methodology and enhancing skills, including how to find valid evidence to answer clinical questions, critically appraise the evidence found and determine if it applies. In addition, online resources will be identified to supplement information presented in each article. The purpose of this article is to define evidence-based decision making and discuss skills necessary for practitioners to efficiently adopt EBDM. It will provide a guide for finding evidence to answer a clinical question using PubMed's specialized searching tools under Clinical Queries.
A Scalable Hierarchical Distributed Language Model

Neural probabilistic language models (NPLMs) have been shown to be competitive with and occasionally superior to the widely-used n-gram language models. The main drawback of NPLMs is their extremely long training and testing times. Morin and Bengio have proposed a hierarchical language model built around a binary tree of words, which was two orders of magnitude faster than the nonhierarchical model it was based on. However, it performed considerably worse than its non-hierarchical counterpart in spite of using a word tree created using expert knowledge. We introduce a fast hierarchical language model along with a simple feature-based algorithm for automatic construction of word trees from the data. We then show that the resulting models can outperform non-hierarchical neural models as well as the best n-gram models.
Deep Reinforcement Learning for Conversational AI

Deep reinforcement learning is revolutionizing the articial intelligence eld. Currently, it serves as a good starting point for constructing intelligent autonomous systems which oer a beer knowledge of the visual world. It is possible to scale deep reinforcement learning with the use of deep learning and do amazing tasks such as use of pixels in playing video games. In this paper, key concepts of deep reinforcement learning including reward function, dierences between reinforcement learning and supervised learning and models for implementation of reinforcement are discussed. Key challenges related to the implementation of reinforcement learning in conversational AI domain are identied as well as discussed in detail. Various conversational models which are based on deep reinforcement learning (as well as deep learning) are also discussed. In summary, this paper discusses key aspects of deep reinforcement learning which are crucial for designing an ecient conversational AI.
Electrophysiology in the age of light

Electrophysiology, the 'gold standard' for investigating neuronal signalling, is being challenged by a new generation of optical probes. Together with new forms of microscopy, these probes allow us to measure and control neuronal signals with spatial resolution and genetic specificity that already greatly surpass those of electrophysiology. We predict that the photon will progressively replace the electron for probing neuronal function, particularly for targeted stimulation and silencing of neuronal populations. Although electrophysiological characterization of channels, cells and neural circuits will remain necessary, new combinations of electrophysiology and imaging should lead to transformational discoveries in neuroscience.
Realization by Biped Leg-wheeled Robot of Biped Walking and Wheel-driven Locomotion

Biped walking is easily adaptable to rough terrain such as stairs and stony paths, but the walk speed and energy efficiency on the flat ground is not so effective compared with wheeled locomotion. In this paper, we propose a biped robot to be able to walk or wheel according to the ground conditions. For wheeled locomotion, WS-2 (Waseda Shoes -No. 2) is developed which is composed of a DC motor, a spherical caster and two rubber pads on each foot. WS-2 is attached to the feet of WL-16 (Waseda Leg -No. 16) that is the world's first biped-walking robot capable of carrying a human. Also, a path planning for wheeled locomotion is presented. Through hardware experiments, the effectiveness of this foot module is confirmed.
Self-Adjusting Binary Search Trees

The splay tree, a self-adjusting form of binary search tree, is developed and analyzed. The binary search tree is a data structure for representing tables and lists so that accessing, inserting, and deleting items is easy. On an n-node splay tree, all the standard search tree operations have an amortized time bound of O(log n) per operation, where by “amortized time” is meant the time per operation averaged over a worst-case sequence of operations. Thus splay trees are as efficient as balanced trees when total running time is the measure of interest. In addition, for sufficiently long access sequences, splay trees are as efficient, to within a constant factor, as static optimum search trees. The efficiency of splay trees comes not from an explicit structural constraint, as with balanced trees, but from applying a simple restructuring heuristic, called splaying, whenever the tree is accessed. Extensions of splaying give simplified forms of two other data structures: lexicographic or multidimensional search trees and link/cut trees.
Socially-Driven Learning-Based Prefetching in Mobile Online Social Networks

Mobile online social networks OSNs are emerging as the popular mainstream platform for information and content sharing among people. In order to provide the quality of experience support for mobile OSN services, in this paper, we propose a socially-driven learning-based framework, namely Spice, for the media content prefetching to reduce the access delay and enhance mobile user’s satisfaction. Through a large-scale data-driven analysis over real-life mobile Twitter traces from over 17 000 users during a period of five months, we reveal that the social friendship has a great impact on user’s media content click behavior. To capture this effect, we conduct the social friendship clustering over the set of user’s friends, and then develop a cluster-based Latent Bias Model for socially-driven learning-based prefetching prediction. We then propose a usage-adaptive prefetching scheduling scheme by taking into account that different users may possess heterogeneous patterns in the mobile OSN app usage. We comprehensively evaluate the performance of Spice framework using trace-driven emulations on smartphones. Evaluation results corroborate that the Spice can achieve superior performance, with an average 80.6% access delay reduction at the low cost of cellular data and energy consumption. Furthermore, by enabling users to offload their machine learning procedures to a cloud server, our design can achieve up to a factor of 1000 speed-up over the local data training execution on smartphones.
Switchboard SWBD-DAMSL shallow-discourse-function annotation coders manual

When we speak about discourse or conversational knowledge, we can talk about a number of different levels. At the level of plans and intentions, we can describe a conversation in terms of the high-level goals and plans of the participants. At the level of focus, we can describe a conversation in terms of center of attentional focus. We might call these intentional or attentional models deep discourse structure. At the level of speech acts, we can model the speech act type of each utterance. Or we can model sociolinguistic facts about conversation structure such how participants might expect one type of conversational units to be responsed to by another (adjacency pairs). We refer to these latter two types of discourse structure as shallow discourse structure.
3.38 Mhz operation of 1.2kV SiC MOSFET with integrated ultra-fast gate drive

With the commercialization of wide bandgap power devices such as SiC MOSFETs and JBS diodes, power electronics converters used in the harsh environments such as hybrid electric vehicles and aerospace attract more and more attentions. The low loss, high temperature and fast switching capabilities are utilized in the converters to improve the power density and efficiency. However, the EMI problem caused by the fast switching is a major constrain for improving switching frequency. For this reason, an integrated SiC module with 1.2kV MOSFET and ultra-fast gate drive circuits is proposed and developed. Two 1.2kV SiC MOSFETs bare dies and two high current gate driver chips are integrated in a compact integrated module package to reduce the parasitic inductance. 0Ω gate resistor therefore can be used in this module to improve the device at maximum speed. Noise free operation of the tested module is verified even under extremely high dV/dt and dI/dt conditions. The ultra-low turn-off loss of the module is being demonstrated. Finally, the integrated module is demonstrated in two megahertz converters: an 800W 1.5MHz synchronous boost converter and a 3.38MHz half bridge inverter. The era for high voltage-megahertz switching has arrived.
IEEE 802.11ad: Defining the Next Generation Multi-Gbps Wi-Fi

IEEE 802.11ad will take advantage of the large swath of available spectrum in the 60 GHz band to develop a protocol to enable throughput intensive applications such as wireless display and high speed sync-and-go file transfer. Functional requirements, evaluation methodology, and channel models are currently being developed by the task group in preparation for a call for proposals. Likely enhancements to 802.11 beyond a new 60 GHz PHY include Personal Basic Service Set, MAC modifications for directional antennas, fast session transfer between PHYs, beamforming, and spatial reuse. Therefore in this paper we give an overview of IEEE 802.11ad, which is poised to define the next generation multi-Gbps Wi-Fi.
EEVi - framework for evaluating the effectiveness of visualization in cyber-security

Cyber-security visualization is an up-and-coming area which aims to reduce security analysts' workload by presenting information as visual analytics rather than a string of text and characters. But the adoption of the resultant visualizations has not increased. The literature indicates a research gap of a lack of guidelines and standardized evaluation techniques for effective visualization in cyber-security, as a reason for it. Therefore, this research addresses the research gap by developing a framework called EEVi for effective cyber-security visualizations for the performed task. The term ‘effective visualization’ can be defined as the features of visualization that are crucial to perform a certain task successfully. EEVi has been developed by analyzing qualitative data that leads to the formation of cognitive relationships (called links) between data that act as guidelines for effective cyber-security visualization in terms of the performed task. The methodology to develop this framework can be applied to other fields to understand cognitive relationships between data. Additionally, the analysis presents a glimpse into the usage of EEVi in cyber-security visualization.
Evidence and solution of over-RESET problem for HfOX based resistive memory with sub-ns switching speed and high endurance

The memory performances of the HfOX based bipolar resistive memory, including switching speed and memory reliability, are greatly improved in this work. Record high switching speed down to 300 ps is achieved. The cycling test shed a clear light on the wearing behavior of resistance states, and the correlation between over-RESET phenomenon and the worn low resistance state in the devices is discussed. The modified bottom electrode is proposed for the memory device to maintain the memory window and to endure resistive switching up to 1010 cycles.
Rfam 12.0: updates to the RNA families database

The Rfam database (available at http://rfam.xfam.org) is a collection of non-coding RNA families represented by manually curated sequence alignments, consensus secondary structures and annotation gathered from corresponding Wikipedia, taxonomy and ontology resources. In this article, we detail updates and improvements to the Rfam data and website for the Rfam 12.0 release. We describe the upgrade of our search pipeline to use Infernal 1.1 and demonstrate its improved homology detection ability by comparison with the previous version. The new pipeline is easier for users to apply to their own data sets, and we illustrate its ability to annotate RNAs in genomic and metagenomic data sets of various sizes. Rfam has been expanded to include 260 new families, including the well-studied large subunit ribosomal RNA family, and for the first time includes information on short sequence- and structure-based RNA motifs present within families.