nils-herrmann/clean_scirepeval huggingface dataset:
Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task

This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09%), XPOS tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment score (76.30%), and content word labeled attachment score (72.57%).
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet  on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13Ã— actual speedup over AlexNet while maintaining comparable accuracy.
Why are depictives different from resultatives

We analyse all of serial verbs, quasi-serials, depictives and resultatives as complex predicate structures semantically headed by the two-place asymmetric conjunction operator $. This uniformity necessitates an account of the fact that not all these $P behave in the same way. Our analysis exploits (1) EVENTS, where some $P encode single-event structures, and some multiple event structures, and (2) parametric differences associated with the $ head. In addition we exploit (3) the notion of ‘nil roles’ and their case properties, and (4) iconicity at LF. Our evidence comes from English, Dutch, Nupe and Korean.
Authoring Intelligent Tutoring Systems: An analysis of the state of the art

This paper consists of an in-depth summary and analysis of the research and development state of the art for intelligent tutoring system (ITS) authoring systems. A sevenpart categorization of two dozen authoring systems is given, followed by a characterization of the authoring tools and the types of ITSs that are built for each category. An overview of the knowledge acquisition and authoring techniques used in these systems is given. A characterization of the design tradeoffs involved in building an ITS authoring system is given. Next the pragmatic questions of real use, productivity findings, and evaluation are discussed. Finally, I summarize the major unknowns and bottlenecks to having widespread use of ITS authoring tools.
PD control with on-line gravity compensation for robots with elastic joints: Theory and experiments

A proportional-derivative (PD) control with on-line gravity compensation is proposed for regulation tasks of robot manipulators with elastic joints. The work extends a previous PD control with constant gravity compensation at the desired configuration. The control law requires measuring only position and velocity on the motor side of the elastic joints, while the on-line gravity compensation torque uses a biased measure of the motor position. It is proved via a Lyapunov argument that the control law globally asymptotically stabilizes the desired robot configuration. A simulation study on a two-joint arm reveals the better performance that can be obtained with the new scheme as compared to the case of constant gravity compensation. Moreover, the proposed controller is experimentally tested on an eightjoint cable-driven robot manipulator, in combination with a point-to-point interpolating trajectory, showing the practical advantages of the on-line compensation.
A Novel Reconfigurable Feeding Network for Quad-Polarization-Agile Antenna Design

In this communication, a reconfigurable feeding network is proposed for the design of a quad-polarization-agile antenna. By switching the PIN diodes, the four transmission modes with selected phase differences can be electrically tuned at two output ports. For each mode, the measured insertion loss of the feeding network is less than 0.8 dB at the operating frequency. Because of its compact size, the proposed feeding network can be further integrated with a dual-polarized aperture-coupled antenna, which can exhibit two orthogonal linear polarizations and two orthogonal circular polarizations. Measured results of the polarization-agile antenna are shown in detail, which verify its performance.
Automating metadata generation: the simple indexing interface

In this paper, we focus on the development of a framework for automatic metadata generation. The first step towards this framework is the definition of an Application Programmer Interface (API), which we call the Simple Indexing Interface (SII). The second step is the definition of a framework for implementation of the SII. Both steps are presented in some detail in this paper. We also report on empirical evaluation of the metadata that the SII and supporting framework generated in a real-life context.
Monitoring breathing activity and sleep patterns using multimodal non-invasive technologies

The monitoring of sleeping behavioral patterns is of major importance for various reasons such as the detection and treatment of sleep disorders, the assessment of the effect of different medical conditions or medications on the sleep quality, and the assessment of mortality risks associated with sleeping patterns in adults and children. Sleep monitoring by itself is a difficult problem due to both privacy and technical considerations.
 The proposed system uses a combination of non-invasive sensors to assess and report sleep patterns and breathing activity: a contact-based pressure mattress and a non-contact 2D image acquisition device. To evaluate our system, we used real data collected in Heracleia Lab's assistive living apartment. Our system uses Machine Learning and Computer Vision techniques to automatically analyze the collected data, recognize sleep patterns and track the breathing behavior. It is non-invasive, as it does not disrupt the user's usual sleeping behavior and it can be used both at the clinic and at home with minimal cost. Going one step beyond, we developed a mobile application for visualizing the analyzed data and monitor the patient's sleep status remotely.
Towards 3D Face Recognition in the Real: A Registration-Free Approach Using Fine-Grained Matching of 3D Keypoint Descriptors

Registration algorithms performed on point clouds or range images of face scans have been successfully used for automatic 3D face recognition under expression variations, but have rarely been investigated to solve pose changes and occlusions mainly since that the basic landmarks to initialize coarse alignment are not always available. Recently, local feature-based SIFT-like matching proves competent to handle all such variations without registration. In this paper, towards 3D face recognition for real-life biometric applications, we significantly extend the SIFT-like matching framework to mesh data and propose a novel approach using fine-grained matching of 3D keypoint descriptors. First, two principal curvature-based 3D keypoint detectors are provided, which can repeatedly identify complementary locations on a face scan where local curvatures are high. Then, a robust 3D local coordinate system is built at each keypoint, which allows extraction of pose-invariant features. Three keypoint descriptors, corresponding to three surface differential quantities, are designed, and their feature-level fusion is employed to comprehensively describe local shapes of detected keypoints. Finally, we propose a multi-task sparse representation based fine-grained matching algorithm, which accounts for the average reconstruction error of probe face descriptors sparsely represented by a large dictionary of gallery descriptors in identification. Our approach is evaluated on the Bosphorus database and achieves rank-one recognition rates of 96.56, 98.82, 91.14, and 99.21 % on the entire database, and the expression, pose, and occlusion subsets, respectively. To the best of our knowledge, these are the best results reported so far on this database. Additionally, good generalization ability is also exhibited by the experiments on the FRGC v2.0 database.
Fbufs: A High-Bandwidth Cross-Domain Transfer Facility

We have designed and implemented a new operating system facility for I/O buffer management and data transferacross protection domain boundaries on shared memory machines. This facility, called fast buffers (fbufs), combines virtual page remapping with shared virtual memory, and exploits locality in I/O traffic to achieve high throughput without compromising protection, security, or modularity. goal is to help deliver the high bandwidth afforded by emerging high-speed networks to user-level processes, both in monolithic and microkernel-based operating systems.This paper outlines the requirements for a cross-domain transfer facility, describes the design of the fbuf mechanism that meets these requirements, and experimentally quantifies the impact of fbufs on network performance.
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution.
Genetic programming - on the programming of computers by means of natural selection

Page ii Complex Adaptive Systems John H. Holland, Christopher Langton, and Stewart W. Wilson, advisors Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press edition John H. Holland Toward a Practice of Autonomous Systems:  On the Programming of Computers by Means of Natural Selection John R. Koza
Bandwidth Broadening of Dual-Slot Antenna Using Substrate Integrated Waveguide (SIW)

This letter presents a dual-slot antenna etched on a substrate integrated waveguide. This antenna is designed, fabricated, and measured to operate at X-band. It uses two unequal slots in order to increase the bandwidth. The measured results show a bandwidth of 8.5%, which is twice that of the standard dual-slot antennas. Attractive features including low cost, compact size, and planar form make this antenna easy to integrate within microwave planar circuits.
Multi-criteria decision making for choosing socially responsible investment within a behavioral portfolio theory framework: a new way of investing into a crisis environment

The current economic crisis fuels the financial social responsibility after an epoch of many excesses with damaging effects. This work tackles two emerging streams in the financial literature: the behavioral portfolio theory with mental accounting and the socially responsible investment (SRI). Promoting SRI is regarded by a lot of financial experts, policymakers and researchers from the field of economic and social sciences, as one of the potential solutions in order to avoid future crises. Therefore, new models for this investment approach are necessary. We try to support the class of investors that select their investments under a mental accounting framework and also they want to achieve a certain level of SR quality in their portfolios. In order to reconcile the two choice frames, avoiding unnecessary sacrifices in financial performance, we have designed a model based on goal programming that integrates the two cornerstones of the investor. Furthermore, we propose a fuzzy inference system to determine the amount of money allocated to each mental account as well as the confidence level assigned to each mental account. This tool is based on expert knowledge modeled by fuzzy if–then rules.
HANDSET ANTENNA DESIGN: PRACTICE AND THEORY

In this paper, an attempt is made to present a theory for the design of handset antennas, which results from the long experience that the authors have in the field of handset antenna design. The proposed theory is based on the well-known skin effect and constructs the antenna using a thin wire model that represent the backbone of the final antenna. The analytical solution for the thin wire model is first obtained, and the main properties (such as the return loss and the radiation properties) of the antenna can then be studied using this analytical solution. Once the antenna backbone is constructed, other elements, such as stubs, patches, etc., can be added to optimize the match at the desired frequency bands. A number of numerical and analytical examples are provided throughout the paper to validate the theory. Different antenna types, such as wire antennas and planar antennas, are tested and designed using the thin wire model. The correspondence between the analytical results and those from the numerical simulations using full-wave solvers agree very well in all examples. The authors also present in this paper a novel design of three small antennas for handset applications, which are based on the simple wire monopole, but in a three-dimensional form. The proposed three-dimensional monopole antennas have multi-band and broadband properties that cover most frequency bands being used for the handset device. The antennas feature remarkable properties while occupying a significantly small space, which makes them strong candidates for handset applications and for the future multi-antenna applications too.
An LLCL Power Filter for Single-Phase Grid-Tied Inverter

This paper presents a new topology of higher order power filter for grid-tied voltage-source inverters, named the LLCL filter, which inserts a small inductor in the branch loop of the capacitor in the traditional LCL filter to compose a series resonant circuit at the switching frequency. Particularly, it can attenuate the switching-frequency current ripple components much better than an LCL filter, leading to a decrease in the total inductance and volume. Furthermore, by decreasing the inductance of a grid-side inductor, it raises the characteristic resonance frequency, which is beneficial to the inverter system control. The parameter design criteria of the proposed LLCL filter is also introduced. The comparative analysis and discussions regarding the traditional LCL filter and the proposed LLCL filter have been presented and evaluated through experiment on a 1.8-kW-single-phase grid-tied inverter prototype.
OSPRay - A CPU Ray Tracing Framework for Scientific Visualization

Scientific data is continually increasing in complexity, variety and size, making efficient visualization and specifically rendering an ongoing challenge. Traditional rasterization-based visualization approaches encounter performance and quality limitations, particularly in HPC environments without dedicated rendering hardware. In this paper, we present OSPRay, a turn-key CPU ray tracing framework oriented towards production-use scientific visualization which can utilize varying SIMD widths and multiple device backends found across diverse HPC resources. This framework provides a high-quality, efficient CPU-based solution for typical visualization workloads, which has already been integrated into several prevalent visualization packages. We show that this system delivers the performance, high-level API simplicity, and modular device support needed to provide a compelling new rendering framework for implementing efficient scientific visualization workflows.
HyperSentry: enabling stealthy in-context measurement of hypervisor integrity

. Unlike existing solutions for protecting privileged software, HyperSentry does not introduce a higher privileged software layer below the integrity measurement target, which could start another race with malicious attackers in obtaining the highest privilege in the system. Instead, HyperSentry introduces a software component that is properly isolated from the hypervisor to enable stealthy and in-context measurement of the runtime integrity of the hypervisor. While stealthiness is necessary to ensure that a compromised hypervisor does not have a chance to hide the attack traces upon detecting an up-coming measurement, in-context measurement is necessary to retrieve all the needed inputs for a successful integrity measurement.
 HyperSentry uses an out-of-band channel (e.g., Intelligent Platform Management Interface (IPMI), which is commonly available on server platforms) to trigger the stealthy measurement, and adopts the System Management Mode (SMM) to protect its base code and critical data. A key contribution of HyperSentry is the set of novel techniques that overcome SMM's limitation, providing an integrity measurement agent with (1) the same contextual information available to the hypervisor, (2) completely protected execution, and (3) attestation to its output. To evaluate HyperSentry, we implement a prototype of the framework along with an integrity measurement agent for the Xen hypervisor. Our experimental evaluation shows that HyperSentry is a low-overhead practical solution for real world systems.
One Model To Learn Them All

Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all.
User-defined Interface Gestures: Dataset and Analysis

We present a video-based gesture dataset and a methodology for annotating video-based gesture datasets. Our dataset consists of user-defined gestures generated by 18 participants from a previous investigation of gesture memorability. We design and use a crowd-sourced classification task to annotate the videos. The results are made available through a web-based visualization that allows researchers and designers to explore the dataset. Finally, we perform an additional descriptive analysis and quantitative modeling exercise that provide additional insights into the results of the original study. To facilitate the use of the presented methodology by other researchers we share the data, the source of the human intelligence tasks for crowdsourcing, a new taxonomy that integrates previous work, and the source code of the visualization tool.
ON MILLIMETER-WAVE IMAGING OF CONCEALED OBJECTS : APPLICATION USING BACK-PROJECTION ALGORITHM

Millimeter-wave (MMW) imaging is a powerful tool for the detection of objects concealed under clothing. Several factors including different kinds of objects, variety of covering materials and their thickness, accurate imaging of near-field scattered data affect the success of detection. To practice with such considerations, this paper presents the two-dimensional (2D) images of different targets hidden under various fabric sheets. The W-band inverse synthetic aperture radar (ISAR) data of various target-covering situations are acquired and imaged by applying both the focusing operator based inversion algorithm and the spherical back-projection algorithm. Results of these algorithms are demonstrated and compared to each other to assess the performance of the MMW imaging in detecting the concealed objects of both metallic and dielectric types.
Real-time hand posture recognition using range data

A hand posture recognition system using 3D data is described. The system relies on a novel 3D sensor that generates a dense range image of the scene. The main advantage of the proposed system, compared to other gesture recognition techniques, is the capability for robust unconstrained recognition of complex hand postures such as those encountered in sign language alphabets. This is achieved by explicitly utilizing 3D hand geometry. Moreover, the proposed approach does not rely on color information, and guarantees robust segmentation of the hand under varying illumination conditions, and scene content. Several novel 3D image analysis algorithms are presented, covering the complete processing chain: 3D image acquisition, arm segmentation, hand-forearm segmentation, hand pose estimation, 3D feature extraction, and gesture classification. The proposed system is extensively evaluated.
GraphZ: Improving the Performance of Large-Scale Graph Analytics on Small-Scale Machines

Recent programming frameworks enable small computing systems to achieve high performance on large-scale graph analytics by supporting out-of-core graph analytics (i.e., processing graphs that exceed main memory capacity). These frameworks make out-of-core programming easy by automating the tedious process of scheduling data transfer between memory and disk. This paper presents two innovations that improve the performance of software frameworks for out-of-core graph analytics. The first is degree-ordered storage, a new storage format that dramatically lowers book-keeping overhead when graphs are larger than memory. The second innovation replaces existing static messages with novel ordered dynamic messages which update their destination immediately, reducing both the memory required for intermediate storage and IO pressure. We implement these innovations in a framework called GraphZ—which we release as open source—and we compare its performance to two state-of-the-art out-of-core graph frameworks. For graphs that exceed memory size, GraphZ's harmonic mean performance improvements are 1.8–8.3× over existing solutions.
Very low-voltage fully differential amplifier for switched-capacitor applications

A fully differential opamp suitable for very-low voltage switched-capacitor circuits in standard CMOS technologies is introduced. The proposed two stage opamp needs a simple low voltage CMFB switched-capacitor circuit only for the second stage. Due to the reduced supply voltage, the CMFB circuit is implemented using boot-strapped switches. Minor modifications allow to use chop-per stabilization for flicker noise reduction. Two different compensation schemes are discussed and compared using an example for 1V operation of the amplifier.