nils-herrmann/clean_scirepeval huggingface dataset:
APPLICABILITY OF SHAPE MEMORY ALLOY WIRE FOR AN ACTIVE , SOFT ORTHOTIC

Treatments for gait pathologies associated with neuromuscular disorders (such as dropfoot, spasticity, etc.) may employ a passive mechanical brace . Depending on the gait pathology, the brace may be applied to the hip, knee, ankle, or any combination thereof. While passive mechanical braces provide certain benefits, they may lead to additional medical problems. For example, an ankle-foot orthotic (AFO) is typically used to prevent the toe from dragging on the ground in the case of drop-foot. Rigid versions of the AFO constrain the ankle to a specific position. By limiting the range of motion, the toe can clear the ground, thus allowing gait to progress more naturally. However, the use of the AFO may result in a reduction in power generation at the ankle, as it limits active plantar flexion. Moreover, the AFO may lead to disuse atrophy of the muscles, such as the tibialis anterior muscle, potentially leading to long-term dependence . While previous researchers have examined actuating a rigid orthotic , we examine using NiTi shape memory alloy (SMA) wires to embed actuation within a soft material. In this manner, the orthotic can provide variable assistance depending on the gait cycle phase, activity level, and needs of the wearer. Thus, the subject can have individualized control, causing the muscles to be used more appropriately, possibly leading to a reeducation of the motor system and eventual independence from the orthotic system.
Demographic Factors Improve Classification Performance

Extra-linguistic factors influence language use, and are accounted for by speakers and listeners. Most natural language processing (NLP) tasks to date, however, treat language as uniform. This assumption can harm performance. We investigate the effect of including demographic information on performance in a variety of text-classification tasks. We find that by including age or gender information, we consistently and significantly improve performance over demographic-agnostic models. These results hold across three text-classification tasks in five languages.
Finding Structure in Reinforcement Learning

Anton Schwartz Dept. of Computer Science Stanford University Stanford, CA 94305 Email: schwartz@cs.stanford.edu Reinforcement learning addresses the problem of learning to select actions in order to maximize one's performance in unknown environments. To scale reinforcement learning to complex real-world tasks, such as typically studied in AI, one must ultimately be able to discover the structure in the world, in order to abstract away the myriad of details and to operate in more tractable problem spaces. This paper presents the SKILLS algorithm. SKILLS discovers skills, which are partially defined action policies that arise in the context of multiple, related tasks. Skills collapse whole action sequences into single operators. They are learned by minimizing the compactness of action policies, using a description length argument on their representation. Empirical results in simple grid navigation tasks illustrate the successful discovery of structure in reinforcement learning.
Being there : the experience of presence in mediated environments

Presence, the experience of ‘being there’ in a mediated environment, has become closely associated with VR and other advanced media. Different types of presence are discussed, including physical presence, social presence, and co-presence. Fidelity-based approaches to presence research emphasize the fact that as media become increasingly interactive, perceptually realistic, and immersive, the experience of presence becomes more convincing. In addition, the ecological-cultural approach is described, pointing out the importance of the possibility of action in mediated environments, as well as the role that a common cultural framework plays in engendering a sense of presence. In particular for multi-user or collaborative virtual environments (CVEs), processes of negotiation and community creation need to be supported by the CVE design to enable communication and the creation of a social context within the CVE.
Model Predictive Multi-Objective Vehicular Adaptive Cruise Control

This paper presents a novel vehicular adaptive cruise control (ACC) system that can comprehensively address issues of tracking capability, fuel economy and driver desired response. A hierarchical control architecture is utilized in which a lower controller compensates for nonlinear vehicle dynamics and enables tracking of desired acceleration. The upper controller is synthesized under the framework of model predictive control (MPC) theory. A quadratic cost function is developed that considers the contradictions between minimal tracking error, low fuel consumption and accordance with driver dynamic car-following characteristics while driver longitudinal ride comfort, driver permissible tracking range and rear-end safety are formulated as linear constraints. Employing a constraint softening method to avoid computing infeasibility, an optimal control law is numerically calculated using a quadratic programming algorithm. Detailed simulations with a heavy duty truck show that the developed ACC system provides significant benefits in terms of fuel economy and tracking capability while at the same time also satisfying driver desired car following characteristics.
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

Convolutional Neural Networks have achieved state-ofthe-art performance on a wide range of tasks. Most benchmarks are led by ensembles of these powerful learners, but ensembling is typically treated as a post-hoc procedure implemented by averaging independently trained models with model variation induced by bagging or random initialization. In this paper, we rigorously treat ensembling as a firstclass problem to explicitly address the question: what are the best strategies to create an ensemble? We first compare a large number of ensembling strategies, and then propose and evaluate novel strategies, such as parameter sharing (through a new family of models we call TreeNets) as well as training under ensemble-aware and diversity-encouraging losses. We demonstrate that TreeNets can improve ensemble performance and that diverse ensembles can be trained endto-end under a unified loss, achieving significantly higher “oracle” accuracies than classical ensembles.
Salient pole shoe shapes of interior permanent magnet synchronous machines

This paper presents two pole shoe shapes used in interior permanent magnet (IPM) machines to produce a sinusoidal air-gap field. The first shape has an air-gap length which varies with the inverse cosine of the angle from the pole shoe middle. The second shape uses an arc with its centre offset from the origin. Although both designs are documented in the literature, no design rules exist regarding their optimum geometry for use in IPM machines. This paper corrects this by developing optimum ratios of the q-axis to d-axis air-gap lengths. The centre-offset arc design is improved by introducing flux barriers into its geometry through developing an optimum ratio of pole shoe width to permanent magnet (PM) width. Consequent pole rotors are also investigated and a third optimum ratio, the soft magnetic pole shoe angle to pole pitch, is developed. The three ratios will aid machine designers in their design work.
Concealed neuroanatomy in Michelangelo's Separation of Light From Darkness in the Sistine Chapel.

Michelangelo Buonarroti (1475-1564) was a master anatomist as well as an artistic genius. He dissected cadavers numerous times and developed a profound understanding of human anatomy. From 1508 to 1512, Michelangelo painted the ceiling of the Sistine Chapel in Rome. His Sistine Chapel frescoes are considered one of the monumental achievements of Renaissance art. In the winter of 1511, Michelangelo entered the final stages of the Sistine Chapel project and painted 4 frescoes along the longitudinal apex of the vault, which completed a series of 9 central panels depicting scenes from the Book of Genesis. It is reported that Michelangelo concealed an image of the brain in the first of these last 4 panels, namely, the Creation of Adam. Here we present evidence that he concealed another neuronanatomic structure in the final panel of this series, the Separation of Light From Darkness, specifically a ventral view of the brainstem. The Separation of Light From Darkness is an important panel in the Sistine Chapel iconography because it depicts the beginning of Creation and is located directly above the altar. We propose that Michelangelo, a deeply religious man and an accomplished anatomist, intended to enhance the meaning of this iconographically critical panel and possibly document his anatomic accomplishments by concealing this sophisticated neuroanatomic rendering within the image of God.
Speech Recognition Using Linear Predictive Coding and Artificial Neural Network for Controlling Movement of Mobile Robot

This paper describes about implementation of speech recognition system on a mobile robot for controlling movement of the robot. The methods used for speech recognition system are Linear Predictive Coding (LPC) and Artificial Neural Network (ANN). LPC method is used for extracting feature of a voice signal and ANN is used as the recognition method. Backpropagation method is used to train the ANN. Voice signals are sampled directly from the microphone and then they are processed using LPC method for extracting the features of voice signal. For each voice signal, LPC method produces 576 data. Then, these data become the input of the ANN. The ANN was trained by using 210 data training. This data training includes the pronunciation of the seven words used as the command, which are created from 30 different people. Experimental results show that the highest recognition rate that can be achieved by this system is 91.4%. This result is obtained by using 25 samples per word, 1 hidden layer, 5 neurons for each hidden layer, and learning rate 0.1.
Ownership structure , corporate governance , and fraud : Evidence from China

Our study examines whether ownership structure and boardroom characteristics have an effect on corporate financial fraud in China. The data come from the enforcement actions of the Chinese Securities Regulatory Commission (CSRC). The results from univariate analyses, where we compare fraud and nofraud firms, show that ownership and board characteristics are important in explaining fraud. However, using a bivariate probit model with partial observability we demonstrate that boardroom characteristics are important, while the type of owner is less relevant. In particular, the proportion of outside directors, the number of board meetings, and the tenure of the chairman are associated with the incidence of fraud. Our findings have implications for the design of appropriate corporate governance systems for listed firms. Moreover, our results provide information that can inform policy debates within the CSRC. D 2005 Elsevier B.V. All rights reserved.
A Comparative Study of Face Landmarking Techniques

Face landmarking, defined as the detection and localization of certain characteristic points on the face, is an important intermediary step for many subsequent face processing operations that range from biometric recognition to the understanding of mental states. Despite its conceptual simplicity, this computer vision problem has proven extremely challenging due to inherent face variability as well as the multitude of confounding factors such as pose, expression, illumination and occlusions. The purpose of this survey is to give an overview of landmarking algorithms and their progress over the last decade, categorize them and show comparative performance statistics of the state of the art. We discuss the main trends and indicate current shortcomings with the expectation that this survey will provide further impetus for the much needed high-performance, real-life face landmarking operating at video rates.
Dense and Low-Rank Gaussian CRFs Using Deep Embeddings

In this work we introduce a structured prediction model that endows the Deep Gaussian Conditional Random Field (G-CRF) with a densely connected graph structure. We keep memory and computational complexity under control by expressing the pairwise interactions as inner products of low-dimensional, learnable embeddings. The G-CRF system matrix is therefore low-rank, allowing us to solve the resulting system in a few milliseconds on the GPU by using conjugate gradient. As in G-CRF, inference is exact, the unary and pairwise terms are jointly trained end-to-end by using analytic expressions for the gradients, while we also develop even faster, Potts-type variants of our embeddings. We show that the learned embeddings capture pixel-to-pixel affinities in a task-specific manner, while our approach achieves state of the art results on three challenging benchmarks, namely semantic segmentation, human part segmentation, and saliency estimation. Our implementation is fully GPU based, built on top of the Caffe library, and is available at https://github.com/siddharthachandra/gcrf-v2.0.
Quintic G2-splines for the iterative steering ofvision-based autonomous vehicles

This paper presents a new motion planning primitive to be used for the iterative steering of vision-based autonomous vehicles. This primitive is a parameterized quintic spline, denoted as -spline, that allows interpolating an arbitrary sequence of points with overall second-order geometric ( -) continuity. Issues such as completeness, minimality, regularity, symmetry, and flexibility of these -splines are addressed in the exposition. The development of the new primitive is tightly connected to the inversion control of nonholonomic car-like vehicles. The paper also exposes a supervisory strategy for iterative steering that integrates feedback vision data processing with the feedforward inversion control.
WorldKit: rapid and easy creation of ad-hoc interactive applications on everyday surfaces

Instant access to computing, when and where we need it, has long been one of the aims of research areas such as ubiquitous computing. In this paper, we describe the WorldKit system, which makes use of a paired depth camera and projector to make ordinary surfaces instantly interactive. Using this system, touch-based interactivity can, without prior calibration, be placed on nearly any unmodified surface literally with a wave of the hand, as can other new forms of sensed interaction. From a user perspective, such interfaces are easy enough to instantiate that they could, if desired, be recreated or modified "each time we sat down" by "painting" them next to us. From the programmer's perspective, our system encapsulates these capabilities in a simple set of abstractions that make the creation of interfaces quick and easy. Further, it is extensible to new, custom interactors in a way that closely mimics conventional 2D graphical user interfaces, hiding much of the complexity of working in this new domain. We detail the hardware and software implementation of our system, and several example applications built using the library.
Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic

Inspired by the recent evolution of deep neural networks (DNNs) in machine learning, we explore their application to PL-related topics. This paper is the first step towards this goal; we propose a proofsynthesis method for the negation-free propositional logic in which we use a DNN to obtain a guide of proof search. The idea is to view the proof-synthesis problem as a translation from a proposition to its proof. We train seq2seq, which is a popular network in neural machine translation, so that it generates a proof encoded as a λ-term of a given proposition. We implement the whole framework and empirically observe that a generated proof term is close to a correct proof in terms of the tree edit distance of AST. This observation justifies using the output from a trained seq2seq model as a guide for proof search.
Burs of free C-space: A novel structure for path planning

This paper presents a new approach to C-space exploration and path planning for robotic manipulators using the structure named bur of free C-space. This structure builds upon the so-called bubble, which is a local volume of free C-space, easily computed using the distance information in the workspace. We show how the same distance information can be used to compute the bur that can reach substantially beyond the boundary of the bubble. It is shown how burs can be used to form a rapidly exploring bur tree (RBT): a space-filling tree that resembles RRT. Such a structure can easily be used within a suitably tailored path planning algorithm. Simulation study shows how the RBT-based algorithm outperforms the classical RRT-based method.
Intelligence Testing for Autonomous Vehicles: A New Approach

In this paper, we study how to test the intelligence of an autonomous vehicle. Comprehensive testing is crucial to both vehicle manufactories and customers. Existing testing approaches can be categorized into two kinds: scenario-based testing and functionality-based testing. We first discuss the shortcomings of these two kinds of approaches, and then propose a new testing framework to combine the benefits of them. Based on the new semantic diagram definition for the intelligence of autonomous vehicles, we explain how to design a task for autonomous vehicle testing and how to evaluate test results. Experiments show that this new approach provides a quantitative way to test the intelligence of an autonomous vehicle.
The Optimal Noise-Adding Mechanism in Differential Privacy

Differential privacy is a framework to quantify to what extent individual privacy in a statistical database is preserved while releasing useful aggregate information about the database. In this paper, within the classes of mechanisms oblivious of the database and the queriesqueries beyond the global sensitivity, we characterize the fundamental tradeoff between privacy and utility in differential privacy, and derive the optimal ϵ-differentially private mechanism for a single realvalued query function under a very general utility-maximization (or cost-minimization) framework. The class of noise probability distributions in the optimal mechanism has staircase-shaped probability density functions which are symmetric (around the origin), monotonically decreasing and geometrically decaying. The staircase mechanism can be viewed as a geometric mixture of uniform probability distributions, providing a simple algorithmic description for the mechanism. Furthermore, the staircase mechanism naturally generalizes to discrete query output settings as well as more abstract settings. We explicitly derive the parameter of the optimal staircase mechanism for ℓ<sup>1</sup> and ℓ<sup>2</sup> cost functions. Comparing the optimal performances with those of the usual Laplacian mechanism, we show that in the high privacy regime (ϵ is small), the Laplacian mechanism is asymptotically optimal as ϵ → 0; in the low privacy regime (ϵ is large), the minimum magnitude and second moment of noise are Θ(Δe<sup>(-ϵ/2)</sup>) and Θ(Δ<sup>2</sup>e<sup>(-2ϵ/3)</sup>) as ϵ → +∞, respectively, while the corresponding figures when using the Laplacian mechanism are Δ/ϵ and 2Δ<sup>2</sup>/ϵ<sup>2</sup>, where Δ is the sensitivity of the query function. We conclude that the gains of the staircase mechanism are more pronounced in the moderate-low privacy regime.
2.10 A 60GHz 28nm UTBB FD-SOI CMOS reconfigurable power amplifier with 21% PAE, 18.2dBm P1dB and 74mW PDC

The widespread deployment of high-data-rate wireless connectivity was enabled by the adoption of the WiGig (802.11ad) standard, consequently placing a challenge on integrated Power Amplifiers (PAs). To comply with system requirements, the PA must cover bands from 57 to 66GHz and deliver up to 10dBm RF modulated power, while OFDM modulations up to 16 or 64QAM are supported, implying a large Peak-to-Average Power Ratio (PAPR).
Unsupervised, Efficient and Semantic Expertise Retrieval

We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.
A Variational Approach for Pan-Sharpening

Pan-sharpening is a process of acquiring a high resolution multispectral (MS) image by combining a low resolution MS image with a corresponding high resolution panchromatic (PAN) image. In this paper, we propose a new variational pan-sharpening method based on three basic assumptions: 1) the gradient of PAN image could be a linear combination of those of the pan-sharpened image bands; 2) the upsampled low resolution MS image could be a degraded form of the pan-sharpened image; and 3) the gradient in the spectrum direction of pan-sharpened image should be approximated to those of the upsampled low resolution MS image. An energy functional, whose minimizer is related to the best pan-sharpened result, is built based on these assumptions. We discuss the existence of minimizer of our energy and describe the numerical procedure based on the split Bregman algorithm. To verify the effectiveness of our method, we qualitatively and quantitatively compare it with some state-of-the-art schemes using QuickBird and IKONOS data. Particularly, we classify the existing quantitative measures into four categories and choose two representatives in each category for more reasonable quantitative evaluation. The results demonstrate the effectiveness and stability of our method in terms of the related evaluation benchmarks. Besides, the computation efficiency comparison with other variational methods also shows that our method is remarkable.
A Lightweight Method for Building Reliable Operating Systems Despite Unreliable Device Drivers Technical Report IRCS-018 , January 2006

It has been well established that most operating system crashes are due to bugs in device drivers. Because drivers are normally linked into the kernel address space, a buggy driver can wipe out kernel tables and bring the system crashing to a halt. We have greatly mitigated this problem by reducing the kernel to an absolute minimum and running each driver as a separate, unprivileged process in user space. In addition, we implemented a POSIX-conformant operating system as multiple user-mode processes. In this design, all that is left in kernel mode is a tiny kernel of under 3800 lines of executable code for catching interrupts, starting and stopping processes, and doing IPC. By moving nearly the entire operating system to multiple, protected user-mode processes we reduce the consequences of faults, since a driver failure no longer is fatal and does not require rebooting the computer. In fact, our system incorporates a reincarnation server that is designed to deal with such errors and often allows for full recovery, transparent to the application and without loss of data. To achieve maximum reliability, our design was guided by simplicity, modularity, least authorization, and fault tolerance. This paper discusses our lightweight approach and reports on its performance and reliability. It also compares our design to other proposals for protecting drivers using kernel wrapping and virtual machines.