nils-herrmann/clean_scirepeval huggingface dataset:
ClusType: Effective Entity Recognition and Typing by Relation Phrase-Based Clustering

Entity recognition is an important but challenging research problem. In reality, many text collections are from specific, dynamic, or emerging domains, which poses significant new challenges for entity recognition with increase in name ambiguity and context sparsity, requiring entity detection without domain restriction. In this paper, we investigate entity recognition (ER) with distant-supervision and propose a novel relation phrase-based ER framework, called ClusType, that runs data-driven phrase mining to generate entity mention candidates and relation phrases, and enforces the principle that relation phrases should be softly clustered when propagating type information between their argument entities. Then we predict the type of each entity mention based on the type signatures of its co-occurring relation phrases and the type indicators of its surface name, as computed over the corpus. Specifically, we formulate a joint optimization problem for two tasks, type propagation with relation phrases and multi-view relation phrase clustering. Our experiments on multiple genres---news, Yelp reviews and tweets---demonstrate the effectiveness and robustness of ClusType, with an average of 37% improvement in F1 score over the best compared method.
Waveguide slot antennas for circularly polarized radiated field

We present a new waveguide slot configuration able to radiate a circular polarization with a very low axial ratio. This configuration is mainly intended as a circularly polarized radiating element to be used in an array. Both left-hand and right-hand circular polarization can be independently obtained and therefore this configuration could be used to realize a polarization agile antenna. The proposed configuration consists of two very closely spaced radiating slots, so a specialized method of moments code has been developed to analyze, in a very effective way, the near-field interaction between such slots. Our method of moments code has been validated against a commercial finite-element method code, and then used to design a number of different circularly polarized radiating elements.
A 3-kW Wireless Power Transfer System for Sightseeing Car Supercapacitor Charge

Wireless power transfer (WPT) is the preferred charging method for electric vehicles (EVs) powered by battery and supercapacitor. In this paper, a novel WPT system with constant current charging capability for sightseeing car with supercapacitor storage is designed. First, an optimized magnetic coupler using ferrite cores and magnetic shielding structure is proposed to ensure stable power transfer and high efficiency. Compared with the traditional planar shape ferrite core coupler, the proposed magnetic coupler requires lesser ferrite material without degrading the performance of the WPT system. Second, the model of supercapacitor is applied to the WPT system and the relationship between equivalent load resistances of supercapacitor and charging time is analyzed in detail. Then, a Buck converter with proportional integral (PI) controller is implemented on the secondary side to maintain constant charging current for the variable load. Finally, the proposed design is verified by experiments. Constant charging current of 31.5 A across transfer distance of 15 cm is achieved. The peak transfer power and system efficiency are 2.86 kW and 88.05%, respectively.
Fundamentals of computer aided geometric design

A solution to get the problem off, have you found it? Really? What kind of solution do you resolve the problem? From what sources? Well, there are so many questions that we utter every day. No matter how you will get the solution, it will mean better. You can take the reference from some books. And the fundamentals of computer aided geometric design is one book that we really recommend you to read, to get more solutions in solving this problem.
First-Person Activity Recognition: What Are They Doing to Me?

This paper discusses the problem of recognizing interaction-level human activities from a first-person viewpoint. The goal is to enable an observer (e.g., a robot or a wearable camera) to understand 'what activity others are performing to it' from continuous video inputs. These include friendly interactions such as 'a person hugging the observer' as well as hostile interactions like 'punching the observer' or 'throwing objects to the observer', whose videos involve a large amount of camera ego-motion caused by physical interactions. The paper investigates multi-channel kernels to integrate global and local motion information, and presents a new activity learning/recognition methodology that explicitly considers temporal structures displayed in first-person activity videos. In our experiments, we not only show classification results with segmented videos, but also confirm that our new approach is able to detect activities from continuous videos reliably.
PANFIS: A Novel Incremental Learning Machine

Most of the dynamics in real-world systems are compiled by shifts and drifts, which are uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in nonstationary environment entails a system owning high degree of flexibility capable of assembling its rule base autonomously according to the degree of nonlinearity contained in the system. In practice, the rule growing and pruning are carried out merely benefiting from a small snapshot of the complete training data to truncate the computational load and memory demand to the low level. An exposure of a novel algorithm, namely parsimonious network based on fuzzy inference system (PANFIS), is to this end presented herein. PANFIS can commence its learning process from scratch with an empty rule base. The fuzzy rules can be stitched up and expelled by virtue of statistical contributions of the fuzzy rules and injected datum afterward. Identical fuzzy sets may be alluded and blended to be one fuzzy set as a pursuit of a transparent rule base escalating human's interpretability. The learning and modeling performances of the proposed PANFIS are numerically validated using several benchmark problems from real-world or synthetic datasets. The validation includes comparisons with state-of-the-art evolving neuro-fuzzy methods and showcases that our new method can compete and in some cases even outperform these approaches in terms of predictive fidelity and model complexity.
Bayesian Boolean Matrix Factorisation

Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns. We introduce the OrMachine, a probabilistic generative model for Boolean matrix factorisation and derive a Metropolised Gibbs sampler that facilitates efficient parallel posterior inference. On real world and simulated data, our method outperforms all currently existing approaches for Boolean matrix factorisation and completion. This is the first method to provide full posterior inference for Boolean Matrix factorisation which is relevant in applications, e.g. for controlling false positive rates in collaborative filtering and, crucially, improves the interpretability of the inferred patterns. The proposed algorithm scales to large datasets as we demonstrate by analysing single cell gene expression data in 1.3 million mouse brain cells across 11 thousand genes on commodity hardware.
Bypassing Space Explosion in High-Speed Regular Expression Matching

Network intrusion detection and prevention systems commonly use regular expression (RE) signatures to represent individual security threats. While the corresponding deterministic finite state automata (DFA) for any one RE is typically small, the DFA that corresponds to the entire set of REs is usually too large to be constructed or deployed. To address this issue, a variety of alternative automata implementations that compress the size of the final automaton have been proposed such as extended finite automata (XFA) and delayed input DFA (D2 FA). The resulting final automata are typically much smaller than the corresponding DFA. However, the previously proposed automata construction algorithms do suffer from some drawbacks. First, most employ a "Union then Minimize" framework where the automata for each RE are first joined before minimization occurs. This leads to an expensive nondeterministic finite automata (NFA) to DFA subset construction on a relatively large NFA. Second, most construct the corresponding large DFA as an intermediate step. In some cases, this DFA is so large that the final automaton cannot be constructed even though the final automaton is small enough to be deployed. In this paper, we propose a "Minimize then Union" framework for constructing compact alternative automata focusing on the D2 FA. We show that we can construct an almost optimal final D2 FA with small intermediate parsers. The key to our approach is a space-and time-efficient routine for merging two compact D2 FA into a compact D2 FA. In our experiments, our algorithm runs on average 155 times faster and uses 1500 times less memory than previous algorithms. For example, we are able to construct a D2 FA with over 80 000 000 states using only 1 GB of main memory in only 77 min.
An In-depth Study of Mobile Browser Performance

Mobile page load times are an order of magnitude slower compared to non-mobile pages. It is not clear what causes the poor performance: the slower network, the slower computational speeds, or other reasons. Further, most Web optimizations are designed for non-mobile browsers and do not translate well to the mobile browser. Towards understanding mobile Web page load times, in this paper we: (1) perform an in-depth pairwise comparison of loading a page on a mobile versus a non-mobile browser, and (2) characterize the bottlenecks in the mobile browser vis-a-vis nonmobile browsers. To this end, we build a testbed that allows us to directly compare the low-level page load activities and bottlenecks when loading a page on a mobile versus a non-mobile browser. We find that computation is the main bottleneck when loading a page on mobile browsers. This is in contrast to non-mobile browsers where networking is the main bottleneck. We also find that the composition of the critical path during page load is different when loading pages on the mobile versus the non-mobile browser. A key takeaway of our work is that we need to fundamentally rethink optimizations for mobile browsers.
On the Mathematics of Flat Origamis∗

Origami is the art of folding pieces of paper into works of sculpture without the aid of scissors or glue. Modern advancements in the complexity of origami (e.g., the work of Montroll and Maekawa) reveal a rich geometric structure governing the possibilities of paperfolding. In this paper we initiate a mathematical study of this “origami geometry” and explore the possibilities of a graph theoretic model. In particular, we study the properties of origami models which fold flat (i.e., can be pressed in a book without crumpling). Necessary and sufficient conditions are given for an origami model to locally fold flat, and the problems encountered in trying to extend these results globally are discussed.
Worm-Like Robotic Locomotion with a Compliant Modular Mesh

In order to mimic and better understand the way an earthworm uses its many segments to navigate diverse terrain, this paper describes the design, performance, and sensing capabilities of a new modular soft robotic worm. The robot, Compliant Modular Mesh Worm (CMMWorm), utilizes a compliant mesh actuated at modular segments to create waveforms along its body. These waveforms can generate peristaltic motion of the body similar to that of an earthworm. The modular mesh is constructed from 3-D printed and commercially available parts allowing for the testing of a variety of components that can be easily interchanged. In addition to having independently controlled segments and interchangeable mesh properties, CMMWorm also has greater range of contraction (52% of maximum diameter) than our previous robot Softworm (73% of maximum diameter). The six-segment robot can traverse flat ground and pipes. We show that a segment is able to detect the wall of a pipe and return to its initial position using actuator-based load-sensing. A simple kinematic model predicts the outer diameter of the worm robot’s mesh as a function of encoder position.
ELIMINET: A MODEL FOR ELIMINATING OPTIONS

The task of Reading Comprehension with Multiple Choice Questions, requires a human (or machine) to read a given {passage, question} pair and select one of the n given options. The current state of the art model for this task first computes a query-aware representation for the passage and then selects the option which has the maximum similarity with this representation. However, when humans perform this task they do not just focus on option selection but use a combination of elimination and selection. Specifically, a human would first try to eliminate the most irrelevant option and then read the document again in the light of this new information (and perhaps ignore portions corresponding to the eliminated option). This process could be repeated multiple times till the reader is finally ready to select the correct option. We propose ElimiNet, a neural network based model which tries to mimic this process. Specifically, it has gates which decide whether an option can be eliminated given the {document, question} pair and if so it tries to make the document representation orthogonal to this eliminatedd option (akin to ignoring portions of the document corresponding to the eliminated option). The model makes multiple rounds of partial elimination to refine the document representation and finally uses a selection module to pick the best option. We evaluate our model on the recently released large scale RACE dataset and show that it outperforms the current state of the art model on 7 out of the 13 question types in this dataset. Further we show that taking an ensemble of our elimination-selection based method with a selection based method gives us an improvement of 7% (relative) over the best reported performance on this dataset.
Low voltage adjustable CMOS Schmitt trigger

This paper presents a low voltage adjustable CMOS Schmitt trigger using dynamic threshold MOS (DTMOS). Cross-coupled inverter with body control is employed to speed up the switching process, and control the intensity of the feedback. The proposed Schmitt trigger has been designed using 0.18 μm 0.4 V CMOS technology and analyzed using PSPICE with BSIM3V3 device models. The simulation results show rail-to-rail operation and independently adjustable switching voltages for both low-to-high (VT(LH)) and high-to-low (VT(HL)) as high as 15 % of the supply voltage. The power dissipation is 0.13 μW.
Fisheye lens distortion correction on multicore and hardware accelerator platforms

Wide-angle (fisheye) lenses are often used in virtual reality and computer vision applications to widen the field of view of conventional cameras. Those lenses, however, distort images. For most real-world applications the video stream needs to be transformed, at real-time (20 frames/sec or better), back to the natural-looking, central perspective space. This paper presents the implementation, optimization and characterization of a fisheye lens distortion correction application on three platforms: a conventional, homogeneous multicore processor by Intel, a heterogeneous multicore (Cell BE), and an FPGA implementing an automatically generated streaming accelerator. We evaluate the interaction of the application with those architectures using both high- and low-level performance metrics. In macroscopic terms, we find that todays mainstream conventional multicores are not effective in supporting real-time distortion correction, at least not with the currently commercially available core counts. Architectures, such as the Cell BE and FPGAs, offer the necessary computational power and scalability, at the expense of significantly higher development effort. Among these three platforms, only the FPGA and a fully optimized version of the code running on the Cell processor can provide realtime processing speed. In general, FPGAs meet the expectations of performance, flexibility, and low overhead. General purpose multicores are, on the other hand, much easier to program.
Modern Microwave Ferrites

Microwave ferrites are ubiquitous in systems that send, receive, and manipulate electromagnetic signals across very high frequency to quasi-optical frequency bands. In this paper, modern microwave ferrites are reviewed including spinel, garnet, and hexaferrite systems as thin and thick films, powders and compacts, and metamaterials. Their fundamental properties and utility are examined in the context of high frequency applications ranging from the VHF to millimeter-wave bands. Perspective and outlook of advances in theory, processing, and devices occurring in the science and engineering communities since the year 2000 are presented and discussed.
ParseNet: Looking Wider to See Better

We present a technique for adding global context to deep convolutional networks for semantic segmentation. The approach is simple, using the average feature for a layer to augment the features at each location. In addition, we study several idiosyncrasies of training, significantly increasing the performance of baseline networks (e.g. from FCN [19]). When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines. Our proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines, and near current state-of-theart performance on PASCAL VOC 2012 semantic segmentation with a simple approach. Code is available at https: //github.com/weiliu89/caffe/tree/fcn.
Semantic Depth of Field

We present a new technique called Semantic Depth of Field (SDOF) as an alternative approach to focus-andcontext displays of information. We utilize a well-known method from photography and cinematography (depth-offield effect) for information visualization, which is to blur different parts of the depicted scene in dependence of their relevance. Independent of their spatial locations, objects of interest are depicted sharply in SDOF, whereas the context of the visualization is blurred. In this paper, we present a flexible model of SDOF which can be easily adopted to various types of applications. We discuss pros and cons of the new technique, give examples of application, and describe a fast prototype implementation of SDOF.
Adaptive configuration of lora networks for dense IoT deployments

Large-scale Internet of Things (IoT) deployments demand long-range wireless communications, especially in urban and metropolitan areas. LoRa is one of the most promising technologies in this context due to its simplicity and flexibility. Indeed, deploying LoRa networks in dense IoT scenarios must achieve two main goals: efficient communications among a large number of devices and resilience against dynamic channel conditions due to demanding environmental settings (e.g., the presence of many buildings). This work investigates adaptive mechanisms to configure the communication parameters of LoRa networks in dense IoT scenarios. To this end, we develop FLoRa, an open-source framework for end-to-end LoRa simulations in OMNeT++. We then implement and evaluate the Adaptive Data Rate (ADR) mechanism built into LoRa to dynamically manage link parameters for scalable and efficient network operations. Extensive simulations show that ADR is effective in increasing the network delivery ratio under stable channel conditions, while keeping the energy consumption low. Our results also show that the performance of ADR is severely affected by a highly-varying wireless channel. We thereby propose an improved version of the original ADR mechanism to cope with variable channel conditions. Our proposed solution significantly increases both the reliability and the energy efficiency of communications over a noisy channel, almost irrespective of the network size. Finally, we show that the delivery ratio of very dense networks can be further improved by using a network-aware approach, wherein the link parameters are configured based on the global knowledge of the network.
The Stanford Typed Dependencies Representation

This paper examines the Stanford typed dependencies representation, which was designed to provide a straightforward description of grammatical relations for any user who could benefit from automatic text understanding. For such purposes, we argue that dependency schemes must follow a simple design and provide semantically contentful information, as well as offer an automatic procedure to extract the relations. We consider the underlying design principles of the Stanford scheme from this perspective, and compare it to the GR and PARC representations. Finally, we address the question of the suitability of the Stanford scheme for parser evaluation.
An insight into assistive technology for the visually impaired and blind people: state-of-the-art and future trends

Assistive technology for the visually impaired and blind people is a research field that is gaining increasing prominence owing to an explosion of new interest in it from disparate disciplines. The field has a very relevant social impact on our ever-increasing aging and blind populations. While many excellent state-of-the-art accounts have been written till date, all of them are subjective in nature. We performed an objective statistical survey across the various sub-disciplines in the field and applied information analysis and network-theory techniques to answer several key questions relevant to the field. To analyze the field we compiled an extensive database of scientific research publications over the last two decades. We inferred interesting patterns and statistics concerning the main research areas and underlying themes, identified leading journals and conferences, captured growth patterns of the research field; identified active research communities and present our interpretation of trends in the field for the near future. Our results reveal that there has been a sustained growth in this field; from less than 50 publications per year in the mid 1990s to close to 400 scientific publications per year in 2014. Assistive Technology for persons with visually impairments is expected to grow at a swift pace and impact the lives of individuals and the elderly in ways not previously possible.
Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach

In this paper, we propose a machine-learning solution to problems consisting of many similar prediction tasks. Each of the individual tasks has a high risk of overrtting. We combine two types of knowledge transfer between tasks to reduce this risk: multi-task learning and hierarchical Bayesian modeling. Multi-task learning is based on the assumption that there exist features typical to the task at hand. To nd these features, we train a huge two-layered neural network. Each task has its own output, but shares the weights from the input to the hidden units with all other tasks. In this way a relatively large set of possible explanatory variables (the network inputs) is reduced to a smaller and easier to handle set of features (the hidden units). Given this set of features and after an appropriate scale transformation, we assume that the tasks are exchangeable. This assumption allows for a hierarchical Bayesian analysis in which the hyperparameters can be estimated from the data. EEectively, these hyperpa-rameters act as regularizers and prevent over-tting. We describe how to make the system robust against nonstationarities in the time series and give directions for further improvement. We illustrate our ideas on a database regarding the prediction of newspaper sales.