nils-herrmann/clean_scirepeval huggingface dataset:
Computationally Efficient Adaptive Error-State Kalman Filter for Attitude Estimation

A geometrically intuitive quaternion-based complementary attitude and heading reference system (CAHRS) proposed in our previous work estimated the attitude of a magnetic and inertial measurement unit (MIMU). The method used two correction factors, <inline-formula> <tex-math notation="LaTeX">$\mu _{_{a}}$ </tex-math></inline-formula> that determined the rate at which the accelerometer corrected the inclination angle, and <inline-formula> <tex-math notation="LaTeX">$\mu _{_{m}}$ </tex-math></inline-formula> that governed the rate at which the magnetometer corrected the yaw angle. Improvements to the filter have been made by embedding each correction factor within an error-state Kalman filter (KF), enabling the correction rates to behave adaptively. The revised filter only estimates the error in two variables, thus remaining computationally efficient (65 addition, 88 subtraction, and 214 multiplication operations) compared with established algorithms in the literature for attitude estimation that utilize a KF or extended KF. The accuracy of the attitude estimated (i.e., the pitch, roll, and yaw angle errors <inline-formula> <tex-math notation="LaTeX">$\theta _{\text {RMSE}_{_{}}}^{\circ }$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\phi _{\text {RMSE}}^{\circ }$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\psi _{\text {RMSE}}^{\circ }$ </tex-math></inline-formula>) by the adaptive error-state KF was compared with the CAHRS algorithm and a cascaded KF that is representative of state-of-the-art methods. Each algorithm was assessed using a publicly available data set in which the attitude of a foot-worn magnetic and inertial measurement units was recorded by a motion capture system while participants walked and ran around a room for one or three minutes (<inline-formula> <tex-math notation="LaTeX">$\phi _{\text {RMSE}}^{\circ } = 2.08^\circ $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\theta _{\text {RMSE}}^{\circ } = 1.98^\circ $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\psi _{\text {RMSE}}^{\circ }= 5.25^\circ $ </tex-math></inline-formula>).
On the Costs of a Bank-Centered Financial System : Evidence from the Changing Main Bank Relations in Japan

We examine the effects of bank–firm relationships on firm performance in Japan. When access to capital markets is limited, close bank–firm ties increase the availability of capital to borrowing firms, but do not lead to higher profitability or growth. The cost of capital of firms with close bank ties is higher than that of their peers. This indicates that most of the benefits from these relationships are appropriated by the banks. Finally, the slow growth rates of bank clients suggest that banks discourage firms from investing in risky, profitable projects. However, liberalization of financial markets reduces the banks’ market power. THE CLOSE RELATIONSHIP BETWEEN manufacturing firms and financial institutions in Japan has recently been at the center of the debate on the appropriate financial system for the reforming economies of Eastern Europe. As opposed to the Anglo-Saxon separation of finance and industry, long-term ties between main banks and their client firms (involving bank loans, bank equity holding, and some bank-appointed personnel), which are common in Japan and Germany, have often been described as a growth-oriented financial system that could serve the needs of developing and reformed economies better than anonymous capital markets modeled after the American and British financial systems. The claim that close ties between investmentoriented banks and industrial firms could help overcome the difficulties of “relative backwardness” dates back to Gerschenkron (1962) who investigates European development. Rosovsky (1961) applies Gerschenkron’s framework to the analysis of Japanese growth. More recently, Aoki, Patrick, and Sheard (1994), Hoshi, Kashyap, and Loveman (1994), and Teranishi (1993), among others, portray the close ties between finance and industry in Japan as an important factor in the international competitiveness of modern Japanese * Weinstein is at the University of Michigan Business School. Yafeh is at the Economics Department, the Hebrew University. We thank Lee Branstetter, Richard Caves, Brian Hall, Takeo Hoshi, Steve Kaplan, Saul Lach, Henry Rosovsky, Mark Ramseyer, David Scharfstein, Paul Sheard, Adrian Tschoegl, David Weil, Jeffrey Williamson, Oved Yosha, and two anonymous referees for helpful comments and suggestions. Fumio Hayashi kindly provided some of the data on tape, and Emily McNeal provided research assistance. We also would like to thank Hiroyasu Sugihara and Kazuyuki Suzuki for help with the JDB data. David Weinstein would like to thank the Zengin Foundation for Studies on Economics and Finance for partial support for this project. Yishay Yafeh gratefully acknowledges financial support from the Harvard Academy for International and Area Studies and the Falk Institute for Economic Research in Israel.
As-rigid-as-possible surface modeling

Modeling tasks, such as surface deformation and editing, can be analyzed by observing the local behavior of the surface. We argue that defining a modeling operation by asking for rigidity of the local transformations is useful in various settings. Such formulation leads to a non-linear, yet conceptually simple energy formulation, which is to be minimized by the deformed surface under particular modeling constraints. We devise a simple iterative mesh editing scheme based on this principle, that leads to detail-preserving and intuitive deformations. Our algorithm is effective and notably easy to implement, making it attractive for practical modeling applications.
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments

We present a method for localizing and separating sound sources in stereo recordings that is robust to reverberation and does not make any assumptions about the source statistics. The method consists of a probabilistic model of binaural multisource recordings and an expectation maximization algorithm for finding the maximum likelihood parameters of that model. These parameters include distributions over delays and assignments of time-frequency regions to sources. We evaluate this method against two comparable algorithms on simulations of simultaneous speech from two or three sources. Our method outperforms the others in anechoic conditions and performs as well as the better of the two in the presence of reverberation.
Prospective validation of the palliative prognostic index in patients with cancer.

The Palliative Prognostic Index (PPI) was devised and validated in patients with cancer in a hospice inpatient unit in Japan. The aim of this study was to test its accuracy in a different population, in a range of care settings and in those receiving palliative chemotherapy and radiotherapy. The information required to calculate the PPI was recorded for patients referred to a hospital-based consultancy palliative care service, a hospice home care service, and a hospice inpatient unit. One hundred ninety-four patients were included in the study, 43% of whom were receiving chemotherapy /or radiotherapy or both. Use of the PPI split patients into three subgroups based on PPI score. Group 1 corresponded to patients with PPI<or=4, median survival 68 days (95% confidence interval [CI] 52, 115 days). Group 2 corresponded to those with PPI>4 and <or=6, median survival 21 days (95% CI 13, 33), and Group 3 corresponded to patients with PPI>6, median survival five days (95% CI 3, 11). Using the PPI, survival of less than three weeks was predicted with a positive predictive value of 86% and negative predictive value of 76%. Survival of less than six weeks was predicted with a positive predictive value of 91% and negative predictive value of 64%. The PPI is quick and easy to use, and can be applied to patients with cancer, in hospital, in hospice, and at home. It may be used by general physicians to achieve prognostic accuracy comparable, if not superior, to that of physicians experienced in oncology and palliative care, and by oncology and palliative care specialists, to improve the accuracy of their survival predictions.
C OMPARATIVE S TUDY O F H AND G ESTURE R ECOGNITION SYSTEM

Human imitation for his surrounding environment makes him interfere in every details of this great environment, hear impaired people are gesturing with each other for delivering a specific message, this method of communication also attracts human imitation attention to cast it on human-computer interaction. The faculty of vision based gesture recognition to be a natural, powerful, and friendly tool for supporting efficient interaction between human and machine. In this paper a review of recent hand gesture recognition systems is presented with description of hand gestures modelling, analysis and recognition. A comparative study included in this paper with focusing on different segmentation, features extraction and recognition tools, research advantages and drawbacks are provided as well.
Frappé: Functional Reactive Programming in Java

Functional Reactive Programming (FRP) is a declarative programming model for constructing interactive applications based on a continuous model of time. FRP programs are described in terms of behaviors (continuous, timevarying, reactive values), and events (conditions that occur at discrete points in time). This paper presents Frappé, an implementation of FRP in the Java progamming language. The primary contribution of Frappé is its integration of the FRP event/behavior model with the Java Beans event/property model. At the interface level, any Java Beans component may be used as a source or sink for the FRP event and behavior combinators. This provides a mechanism for extending Frappé with new kinds of I/O connections and allows FRP to be used as a high-level declarative model for composing applications from Java Beans components. At the implementation level, the Java Beans event model is used internally by Frappé to propagate FRP events and changes to FRP behaviors. This allows Frappé applications to be packaged as Java Beans components for use in other applications, and yields an implementation of FRP well-suited to the requirements of event-driven applications (such as graphical user interfaces).
Real-time object detection and localization with SIFT-based clustering

a r t i c l e i n f o Keywords: Pick-and-place applications Machine vision for industrial applications SIFT This paper presents an innovative approach for detecting and localizing duplicate objects in pick-and-place applications under extreme conditions of occlusion, where standard appearance-based approaches are likely to be ineffective. The approach exploits SIFT keypoint extraction and mean shift clustering to partition the correspondences between the object model and the image onto different potential object instances with real-time performance. Then, the hypotheses of the object shape are validated by a projection with a fast Euclidean transform of some delimiting points onto the current image. Moreover, in order to improve the detection in the case of reflective or transparent objects, multiple object models (of both the same and different faces of the object) are used and fused together. Many measures of efficacy and efficiency are provided on random disposals of heavily-occluded objects, with a specific focus on real-time processing. Experimental results on different and challenging kinds of objects are reported. Information technologies have become in the last decades a fundamental aid for helping the automation of everyday people life and industrial processes. Among the many different disciplines contributing to this process, machine vision and pattern recognition have been widely used for industrial applications and especially for robot vision. A typical need is to automate the pick-and-place process of picking up objects, possibly performing some tasks, and then placing them down on a different location. Most of the pick-and-place systems are basically composed of robotic systems and sensors. The sensors are in charge of driving the robot arms to the right 3D location and possibly orientation of the next object to be picked up, according to the robot's degrees of freedom. Object picking can be very complicated if the scene is not well structured and constrained. The automation of object picking by using cameras, however, requires to detect and localize objects in the scene; they are crucial tasks for several other computer vision applications, such as image/ video retrieval , or automatic robot navigation . This paper describes a new complete approach for pick-and-place processes with the following challenging requirements: 1. Different types of objects: the approach should work with every type of object of different dimension and complexity, with reflective surfaces or semi-transparent parts, such as in the case of pharmaceutical and cosmetic objects, often reflective or included in transparent flowpacks; 2. …
Vision for road inspection

Road surface inspection in cities is for the most part, a task performed manually. Being a subjective and labor intensive process, it is an ideal candidate for automation. We propose a solution based on computer vision and data-driven methods to detect distress on the road surface. Our method works on images collected from a camera mounted on the windshield of a vehicle. We use an automatic procedure to select images suitable for inspection based on lighting and weather conditions. From the selected data we segment the ground plane and use texture, color and location information to detect the presence of pavement distress. We describe an over-segmentation algorithm that identifies coherent image regions not just in terms of color, but also texture. We also discuss the problem of learning from unreliable human-annotations and propose using a weakly supervised learning algorithm (Multiple Instance Learning) to train a classifier. We present results from experiments comparing the performance of this approach against multiple individual human labelers, with the ground-truth labels obtained from an ensemble of other human labelers. Finally, we show results of pavement distress scores computed using our method over a subset of a citywide road network.
Structure preserving embedding

Structure Preserving Embedding (SPE) is an algorithm for embedding graphs in Euclidean space such that the embedding is low-dimensional and preserves the global topological properties of the input graph. Topology is preserved if a connectivity algorithm, such as k-nearest neighbors, can easily recover the edges of the input graph from only the coordinates of the nodes after embedding. SPE is formulated as a semidefinite program that learns a low-rank kernel matrix constrained by a set of linear inequalities which captures the connectivity structure of the input graph. Traditional graph embedding algorithms do not preserve structure according to our definition, and thus the resulting visualizations can be misleading or less informative. SPE provides significant improvements in terms of visualization and lossless compression of graphs, outperforming popular methods such as spectral embedding and Laplacian eigen-maps. We find that many classical graphs and networks can be properly embedded using only a few dimensions. Furthermore, introducing structure preserving constraints into dimensionality reduction algorithms produces more accurate representations of high-dimensional data.
DepSys: Dependency aware integration of cyber-physical systems for smart homes

As sensor and actuator networks mature, they become a core utility of smart homes like electricity and water and enable the running of many CPS applications. Like other Cyber-Physical Systems (CPSs), when a number of applications share physical world entities, it raises many systems of systems interdependency problems. Such problems arise in the cyber part mainly because each application has assumptions on the physical world entities without knowing how other applications work. In this work, we propose DepSys, a utility sensing and actuation infrastructure for smart homes that provides comprehensive strategies to specify, detect, and resolve conflicts in a home setting. Based on real home data, we demonstrate the severity of conflicts when multiple CPSs are integrated and the significant ability of detecting and resolving such conflicts using DepSys.
"I Always Wanted to See the Night Sky": Blind User Preferences for Sensory Substitution Devices

Sensory Substitution Devices (SSDs) convert visual information into another sensory channel (e.g. sound) to improve the everyday functioning of blind and visually impaired persons (BVIP). However, the range of possible functions and options for translating vision into sound is largely open-ended. To provide constraints on the design of this technology, we interviewed ten BVIPs who were briefly trained in the use of three novel devices that, collectively, showcase a large range of design permutations. The SSDs include the 'Depth-vOICe,' 'Synaestheatre' and 'Creole' that offer high spatial, temporal, and colour resolutions respectively via a variety of sound outputs (electronic tones, instruments, vocals). The participants identified a range of practical concerns in relation to the devices (e.g. curb detection, recognition, mental effort) but also highlighted experiential aspects. This included both curiosity about the visual world (e.g. understanding shades of colour, the shape of cars, seeing the night sky) and the desire for the substituting sound to be responsive to movement of the device and aesthetically engaging.
Visibility in bad weather from a single image

Bad weather, such as fog and haze, can significantly degrade the visibility of a scene. Optically, this is due to the substantial presence of particles in the atmosphere that absorb and scatter light. In computer vision, the absorption and scattering processes are commonly modeled by a linear combination of the direct attenuation and the airlight. Based on this model, a few methods have been proposed, and most of them require multiple input images of a scene, which have either different degrees of polarization or different atmospheric conditions. This requirement is the main drawback of these methods, since in many situations, it is difficult to be fulfilled. To resolve the problem, we introduce an automated method that only requires a single input image. This method is based on two basic observations: first, images with enhanced visibility (or clear-day images) have more contrast than images plagued by bad weather; second, airlight whose variation mainly depends on the distance of objects to the viewer, tends to be smooth. Relying on these two observations, we develop a cost function in the framework of Markov random fields, which can be efficiently optimized by various techniques, such as graph-cuts or belief propagation. The method does not require the geometrical information of the input image, and is applicable for both color and gray images.
Adding Gradient Noise Improves Learning for Very Deep Networks

{ Adding Gradient Noise Improves Learning for Very Deep Networks. Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens. International Conference on Learning Representations Workshop (ICLR WS), 2016 { Generating Sentences from a Continuous Space. Samuel Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, Samy Bengio. International Conference on Learning Representations Workshop (ICLR WS), 2016 { Bethe Projections for Non-Local Inference. Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum. Uncertainty in Artificial Intelligence (UAI), 2015 { Learning Dynamic Feature Selection for Fast Sequential Prediction. Emma Strubell, Luke Vilnis, Kate Silverstein, Andrew McCallum. Annual Meeting of the Association for Computational Linguistics (ACL), 2015 { Word Representations via Gaussian Embedding. Luke Vilnis, Andrew McCallum. International Conference on Learning Representations (ICLR), 2015 { Generalized Eigenvectors for Large Multiclass Problems. Luke Vilnis, Nikos Karampatziakis, Paul Mineiro. Neural Information Processing Systems Workshop on Representation and Learning Methods for Complex Outputs (NIPS WS), 2014 { Training for Fast Sequential Prediction Using Dynamic Feature Selection. Emma Strubell, Luke Vilnis, Andrew McCallum. Neural Information Processing Systems Workshop on Modern Machine Learning and Natural Language Processing (NIPS WS), 2014
Compressed sensing radar

A stylized compressed sensing radar is proposed in which the time- frequency plane is discretized into an N times N grid. Assuming the number of targets K is small (i.e., K Lt N2), then we can transmit a sufficiently "incoherent" pulse and employ the techniques of compressed sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel compressed sensing approach offers great potential for better resolution over classical radar.
Controlling a Car Through OBD Injection

Internet of vehicles(IOV) is an application of Internet of things in Intelligent Transport System, and has attracted high attention of researchers. IOV brings network connectivity to traditional vehicles, while also introduces security risks. This paper presents experimental analysis on the security of vehicles with Internet connections and propose an approach to Controlling a Car Through OBD Injection. In the experiments, we successfully penetrated several types of cars in a wireless way. We also put out a multi-level safety model of cars, which divides cars into different groups and gives analysis and explanations of each group. All of these things are done for indicating a point of view that traditional cars are not safe enough on information security. It is surely risky to put a car without the ability to resist the attack of informational ways into the Internet of vehicles.
Performance Analysis of the Alpha 21364-BAsed HP GS1280 Multiprocessor

This paper evaluates performance characteristics of the HP GS1280 shared memory multiprocessor system. The GS1280 system contains up to 64 Alpha 21364 CPUs connected together via a torus-based interconnect. We describe architectural features of the GS1280 system. We compare and contrast the GS1280 to the previousgeneration Alpha systems: AlphaServer GS320 and ES45/SC45. We further quantitatively show the performance effects of these features using application results and profiling data based on the built-in performance counters. We find that the HP GS1280 often provides 2 to 3 times the performance of the AlphaServer GS320 at similar clock frequencies. We find the key reasons for such performance gains are advances in memory, inter-processor, and I/O subsystem designs.
A cluster validity index for fuzzy clustering

Cluster validity indexes have been used to evaluate the fitness of partitions produced by clustering algorithms. This paper presents a new validity index for fuzzy clustering called a partition coefficient and exponential separation (PCAES) index. It uses the factors from a normalized partition coefficient and an exponential separation measure for each cluster and then pools these two factors to create the PCAES validity index. Considerations involving the compactness and separation measures for each cluster provide different cluster validity merits. In this paper, we also discuss the problem that the validity indexes face in a noisy environment. The efficiency of the proposed PCAES index is compared with several popular validity indexes. More information about these indexes is acquired in series of numerical comparisons and also three real data sets of Iris, Glass and Vowel. The results of comparative study show that the proposed PCAES index has high ability in producing a good cluster number estimate and in addition, it provides a new point of view for cluster validity in a noisy environment.