nils-herrmann/clean_scirepeval huggingface dataset:
Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information

3D ultrasound imaging has been increasingly used in clinics for fetal examination. However, manually searching for the optimal view of the fetal face in 3D ultrasound volumes is cumbersome and time-consuming even for expert physicians and sonographers. In this paper we propose a learning-based approach which combines both 3D and 2D information for automatic and fast fetal face detection from 3D ultrasound volumes. Our approach applies a new technique - constrained marginal space learning - for 3D face mesh detection, and combines a boosting-based 2D profile detection to refine 3D face pose. To enhance the rendering of the fetal face, an automatic carving algorithm is proposed to remove all obstructions in front of the face based on the detected face mesh. Experiments are performed on a challenging 3D ultrasound data set containing 1010 fetal volumes. The results show that our system not only achieves excellent detection accuracy but also runs very fast - it can detect the fetal face from the 3D data in 1 second on a dual-core 2.0 GHz computer.
Image based regression using boosting method

We present a general algorithm of image based regression that is applicable to many vision problems. The proposed regressor that targets a multiple-output setting is learned using boosting method. We formulate a multiple-output regression problem in such a way that overfitting is decreased and an analytic solution is admitted. Because we represent the image via a set of highly redundant Haar-like features that can be evaluated very quickly and select relevant features through boosting to absorb the knowledge of the training data, during testing we require no storage of the training data and evaluate the regression function almost in no time. We also propose an efficient training algorithm that breaks the computational bottleneck in the greedy feature selection process. We validate the efficiency of the proposed regressor using three challenging tasks of age estimation, tumor detection, and endocardial wall localization and achieve the best performance with a dramatic speed, e.g., more than 1000 times faster than conventional data-driven techniques such as support vector regressor in the experiment of endocardial wall localization.
Bimodal system for interactive indexing and retrieval of pathology images

We demonstrate the prototype of an image understanding based system to support decision making in clinical pathology. The system employs all four major low level vision queues (shape, texture, color, metric measures) in content-based retrieval of visual information. The reliability of the central module of the system, the fast color segmenter, makes possible on-line analysis of the query image. The user interface is bimodal (speech and mouse input), allowing a natural communication with the system.
Conditional feature sensitivity: a unifying view on active recognition and feature selection

The objective of active recognition is to iteratively collect the next "best" measurements (e.g., camera angles or viewpoints), to maximally reduce ambiguities in recognition. However, existing work largely overlooked feature interaction issues. Feature selection, on the other hand, focuses on the selection of a subset of measurements for a given classification task, but is not context sensitive (i.e., the decision does not depend on the current input). This paper proposes a unified perspective through conditional feature sensitivity analysis, taking into account both current context and feature interactions. Based on different representations of the contextual uncertainties, we present three treatment models and exploit their joint power for dealing with complex feature interactions. Synthetic examples are used to systematically test the validity of the proposed models. A practical application in medical domain is illustrated using an echocardiography database with more than 2000 video segments with both subjective (from experts) and objective validations.
Quantum dot technology delivers wide color gamut in large Screen LCD TV

This work reports a wide color gamut solution using quantum dot (QD) technology. Quantum dots are nano-crystal semiconductor material with a much narrower full width at half-maximum (FWHM) emission spectrum than that of phosphors. The emission wavelength of QD light can be tuned by varying its particle size to create almost any color desired. In this work, an edge-lit LED backlight is developed by combining a blue LED chip with green and red QDs. The QD materials are hermetically sealed inside an optical glass tube. A mixing cup is used to couple a blue LED light bar with the QD tube, providing mechanical spacing control, light recycling and mixing. By applying typical light guide plate and color filters of LCDs, a wide color gamut of 105% NTSC is attained on a 55” TV.
Research on cross-linked structure of poly(styrene-co-divinyl benzene) and vacuum flashover characteristics

Poly(styrene-co-divinyl benzene) bulk material is widely used in vacuum insulation system due to its excellent flashover hold-off capability. In this paper, the relationship between flashover voltages and cross-linking density was investigated. Poly(styrene-co-divinyl benzene) materials were synthesized by styrene and divinyl benzene (DVB) via free radical polymerization. Flashover voltages were measured under nanosecond pulse voltage. Cross-linking structures were characterized by Fourier transform infrared spectroscopy and Dynamic mechanical analysis. Relative permittivity (ε′) and decay curves of surface potential were tested for mechanism analyzation. Results show that the cross-linking density linearly increases with the content of DVB increases from 1.0 wt.% to 3.0 wt.%, while the flashover voltages improve firstly and then reduce with the peak voltage at 53.1 kV of sample P20. The relative permittivity (ε′) is found to be reduced with the increased cross-linking density, while the surface initial potential increases firstly and then decreases. Results indicate that, the decrease of ε′ can promote the improvement of the flashover voltages. However, when the value of ε′ decreases in a small range, the weakened capability of electrons capture caused by excessive cross-linking plays the leading role to reduce the flashover voltages.
A hybrid algorithm for earliness-tardiness scheduling problem with sequence dependent setup time

In this paper, we consider single machine earliness-tardiness scheduling problem with sequence dependent setup time under different due dates. The objective is to minimize the penalty function, which includes the penalty of completion time of the job is early or tardy for its due date and the penalty of total setup time between jobs. For this problem, we form a hybrid algorithm by combining the heuristic with a genetic algorithm after giving a heuristic. The results of experiment show this algorithm is efficient.
Bandwidth scheduling with multiple variable node-disjoint paths in high-performance networks

Many large-scale applications in science and business domains require the transfer of big data over high-performance networks for remote operations. Such big data transfer is increasingly supported by bandwidth reservation services that discover feasible and efficient routing options in dynamic network environments with time-varying resources. By exploring the flexility and capacity of variable paths, we formulate a generic problem of Bandwidth Scheduling with Two Variable Node-Disjoint Paths (BS-2VNDP), in which, we further consider two variable paths of fixed or variable bandwidth with negligible or non-negligible switching delay, referred to as 2VPFB/VB-0/1. We show the NP-completeness and propose a heuristic approach for each of them. We implement and test these proposed scheduling algorithms in both simulated and real-life networks. Extensive results show that they significantly outperform greedy scheduling methods in large-scale networks.
Sentiment Classification Based on Information Geometry and Deep Belief Networks

Sentiment classification for reviews has attracted increasingly more attention from the natural language processing community. By embedding prior knowledge into learning structures, classifiers often achieve a better performance than original methods. In this paper, we propose a sophisticated algorithm based on deep learning and information geometry in which the distribution of all training samples in the space is treated as prior knowledge and is encoded by deep belief networks (DBNs). From the view of information geometry, we construct the geodesic distance between the distributions over the features for classification. The study of the distributions contributes to the training of the DBN, since the distance is correlated to the error rate in the classification. Finally, we evaluate our proposal using empirical data sets that are dedicated for sentiment classification. The results show that our algorithm results in a significant improvement over existing methods.
Palmprint Recognition Based on Two-Dimensional Methods

Computer-aided personal recognition is becoming increasingly important in our information society. Human palmprint recognition has become an active area of research over the last decade. Principal component analysis (PCA) and linear discriminant analysis (LDA) are widely used in the field of palmprint recognition. However, the conventional PCA and LDA are both based on vectors. It means that the two-dimensional (2D) palmprint image matrices must be transformed into one-dimensional (ID) image vectors previously. The resulting image vectors of palmprint usually lead to a high dimensional image vector space. In this paper, two-dimensional PCA and LDA are used in palmprint recognition. Unlike conventional PCA and LDA that treat image as vectors, the 2D methods view an image as a matrix directly. The experimental results on our palmprint database show that two-dimensional PCA and LDA can obtain over 99% recognition rate in palmprint verification, while using less time and memory. They are more effective than conventional PCA and LDA in terms of accuracy and efficiency
Improved Characterization Methodology of Gate-Bulk Leakage and Capacitance for Ultrathin Oxide Partially Depleted SOI Floating-Body CMOS

Device scaling of partially depleted (PD) silicon-on-insulator (SOI) has the potential to increase speed. However, the increased gate tunneling and capacitance will complicate device behaviors and increase the difficulty in characterization for modeling purpose. For the first time, gate-bulk leakage current <i>I</i><sub>gb</sub> and gate capacitance <i>C</i><sub>gg</sub> characterization methodology for PD SOI floating-body (FB) CMOS with high accuracy is proposed and verified in 40-nm SOI devices. These devices are with ultrathin equivalent oxide thickness of 12Å, and radio frequency-capacitance voltage (RF-CV) technique is used for <i>C</i><sub>gg</sub> measurement to overcome the impact of leaky gate current. This methodology can eliminate properly the parasitic elements due to the coexistence of opposite poly gate type in the SOI T-shape body-tied device and accurately characterize and model <i>I</i><sub>gb</sub> and <i>C</i><sub>gg</sub> behaviors for the PD SOI FB devices. Test patterns are designed with RF ground-signal-ground configuration and same test patterns can be used for both <i>I</i><sub>gb</sub> and <i>C</i><sub>gg</sub> characterization. Impact of <i>I</i><sub>gb</sub> and <i>C</i><sub>gg</sub> changes on the history effect, and speed and body potential is analyzed by BSIMSOI4.0 models. Simulation accuracy of history effect will have at least 3% improvement with this proposed methodology.
Multiwavelength Thulium-Doped Fiber Laser Using a Micro Fiber-Optic Fabry–Perot Interferometer

A compact, all-fiberized multiwavelength thulium-doped fiber laser based on a micro fiber-optic Fabry–Perot interferometer (FPI) and a nonlinear optical loop mirror (NOLM) has been demonstrated. The FPI with a free spectral range of ∼26 nm was used as an effective wavelength selective filter, which was constructed by splicing a ∼71.4-μm long air-filled hollow-core fiber with single-mode fibers at both ends. An NOLM, served as an amplitude equalizer, was used for stabilizing multiwavelength laser operation. Besides switchable single-wavelength operations (1948.00 and 1972.24 nm), stable dual-wavelength operations (1948.02/1974.25 and 1974.16/1975.30 nm) and triwavelength operation (1947.94/1973.03/1974.14 nm) have been obtained. The optical signal-to-noise ratio of each emission laser line is larger than ∼47 dB.
Joint Learning of Labels and Distance Metric

Machine learning algorithms frequently suffer from the insufficiency of training data and the usage of inappropriate distance metric. In this paper, we propose a joint learning of labels and distance metric (JLLDM) approach, which is able to simultaneously address the two difficulties. In comparison with the existing semi-supervised learning and distance metric learning methods that focus only on label prediction or distance metric construction, the JLLDM algorithm optimizes the labels of unlabeled samples and a Mahalanobis distance metric in a unified scheme. The advantage of JLLDM is multifold: 1) the problem of training data insufficiency can be tackled; 2) a good distance metric can be constructed with only very few training samples; and 3) no radius parameter is needed since the algorithm automatically determines the scale of the metric. Extensive experiments are conducted to compare the JLLDM approach with different semi-supervised learning and distance metric learning methods, and empirical results demonstrate its effectiveness.
Prediction and analysis of rotor tip-clearance flows using large-eddy simulation

In order to analyze the dynamics of rotor tip-clearance flow and determine the underlying mechanism for the tip-leakage cavitation, a newly developed large-eddy simulation (LES) solver which combines an immersed-boundary method with a generalized curvilinear structured grid has been employed. An analysis of the LES results has been performed to understand the mean flow field, turbulence characteristics, vortex dynamics, and pressure fluctuations in the turbomachinery cascade with tip gap. Based on thorough analysis of the flow field, a guideline for reducing viscous losses in the cascade is provided. Analyses of the energy spectra and space-time correlations of the velocity fluctuations suggest that the tip-leakage vortex is subject to pitchwise wandering motion. The largest pressure drop and most intense pressure fluctuations due to the formation of the tip-leakage vortex are found in the region where the tip-leakage vortex is strongest. The effects of tip-gap size and an end-wall groove on the tip-clearance vortical structures and on the velocity and pressure fields have been investigated to explore ways for minimizing the detrimental effects of cavitation. Attempts to investigate the vortex-rotor interaction and to enhance the LES capability for realistic rotors are also discussed.
A novel cell tracking algorithm and continuous hidden Markov model for cell phase identification

Time-lapse microscopy cell imaging is attracting more and more attentions due to its potential in achieving new and high throughput ways to conduct drug discovery and quantitative cellular studies. However, the lacking of effective automatic systems for studying a large population of cell nuclei is limiting the application of it. In this paper, we propose a novel hybrid merging algorithm for cell nuclei segmentation and propose a novel favorite matching plus local tree matching algorithm to track dynamic behaviors of a large population of cell nuclei in time-lapse microscopy. And then we propose to identify the phases of cell nuclei using context information of tracks by continuous hidden Markov model. Experimental results show the whole proposed system is very effective for time-lapse microscopy cell imaging segmentation, tracking and cell phase identification
Image Classification by Selective Regularized Subspace Learning

Feature learning is an intensively studied research topic in image classification. Although existing methods like sparse coding, locality-constrained linear coding, fisher vector encoding, etc., have shown their effectiveness in image representation, most of them overlook a phenomenon called thesmall sample size problem, where the number of training samples is relatively smaller than the dimensionality of the features, which may limit the predictive power of the classifier. Subspace learning is a strategy to mitigate this problem by reducing the dimensionality of the features. However, most conventional subspace learning methods attempt to learn a global subspace to discriminate all the classes, which proves to be difficult and ineffective in multi-class classification task. To this end, we propose to learn a local subspace for each sample instead of learning a global subspace for all samples. Our key observation is that, in multi-class image classification, the label of each testing sample is only confused by a few classes which have very similar visual appearance to it. Thus, in this work, we propose a coarse-to-fine strategy, which first picks out such classes, and then conducts a local subspace learning to discriminate them. As the subspace learning method is regularized and conducted within some selected classes, we term it selective regularized subspace learning (SRSL), and we term our classification pipeline selective regularized subspace learning based multi-class image classification (SRSL_MIC). Experimental results on four representative datasets (Caltech-101, Indoor-67, ORL Faces and AR Faces) demonstrate the effectiveness of the proposed method.
Fog computing based content-aware taxonomy for caching optimization in information-centric networks

Traditional internet architecture is challenged and it cannot satisfy the growing demand of todays networks. Information-centric Networks (ICN) is regarding as a promising replacement to meet this trend. The main characteristic in ICN is Content Store (CS), which is used to enable users to retrieve data from nearby nodes instead of remote server. However, with the limited storage capacity of routers, we cannot make the most use of the concept of CS. In this paper, we proposed a novel framework using fog computing as a middle level to communicate both with underlying network and ICN global network, where data is preprocessed and classed in fog node before transferring to ICN. In this way, we can reduce the total number of caching content in network by labeling the dynamic data and user-shareable data. We proved that with limited storage capacity of content store, we cannot profit from in-network caching. This result suggests the necessity of proposed framework.
Likelihood of cyber data injection attacks to power systems

Cyber data attacks are the worst-case interacting bad data to power system state estimation and cannot be detected by existing bad data detectors. In this paper, we for the first time analyze the likelihood of cyber data attacks by characterizing the actions of a malicious intruder. We propose to use Markov decision process to model an intruder's strategy, where the objective is to maximize the cumulative reward across time. Linear programming method is employed to find the optimal attack policy from the intruder's perspective. Numerical experiments are conducted to study the intruder's attack strategy in test power systems.
Compact dual-band circularly polarised antenna with omnidirectional and unidirectional properties

A novel dual-band circularly polarised (CP) antenna is presented in this study. The proposed antenna is composed of a U-slot patch and four sequentially rotated shorted monopoles. Different from conventional omnidirectional CP antennas with shorted monopolar patches, a novel capacitively coupled technique is proposed to achieve good impedance matching, which makes it possible to generate unidirectional CP patterns simultaneously. This antenna can generate omnidirectional CP radiation patterns in the global positioning system L1 band (1575 ± 5 MHz) for navigation application and unidirectional CP radiation patterns in the 2.4-GHz industrial, scientific, and medical band (2.40-2.48 GHz) for municipal wireless networks. The dual-band performance can be tuned separately. Experimental results confirm the good performances of a compact antenna with the size of 0.26 λ
 0
 × 0.26 λ
 0
 × 0.026 λ
 0
. The obtained 10-dB impedance bandwidths can cover the two required bands. The 3-dB axial ratio (AR) bandwidths cover the lower band and the upper band from 2.425 to 2.46 GHz. In addition, a wide 3-dB AR beamwidth of 200° is obtained at 2.45 GHz. The simple structure and good performances make this antenna a good candidate for handheld devices and vehicular applications.
Influence of starting notch geometry parameters for engine crankshaft bearing block on fracture splitting force

Starting notch geometry parameters are key factors affecting fracture splitting quality. As usually used materials in engine crankshaft bearing block, both gray Cast Iron HT150 and ductile iron QT500-7 are selected in this paper. The influence of starting notch geometry parameters on separation force are analyzed by using ABAQUS software. To reduce the separation force in fracture splitting process and improve splitting quality, the optimum parameters including open angle α, notch depth h and curvature radius r have been identified. This paper provides theoretical basis for application of fracture splitting technology for engine crankshaft bearing block.
A study on STATCOM for voltage stability of wind power system

The change of the output of generating unit mass wind farms can cause access the network voltage fluctuations, serious when can make voltage collapse. Based on reactive power compensator (STATCOM) synchronization of wind power system, and the grid STATCOM can exchange the perceptual reactive and capacitive reactive, restrain system voltage fluctuation, maintain the stability of the system voltage. Wind power system discussed in the basic working principle and STATCOM control strategy and establish the relevant model, and the simulation of the actual system. The simulation results show that STAT-COM to maintaining the system, improve the wind power system voltage stability has a good result.
Learning Dynamic Guidance for Depth Image Enhancement

The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate with high resolution RGB camera for exploiting their statistical correlation. However, most existing methods are intuitive and limited in characterizing the complex and dynamic dependency between intensity and depth images. To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance. First, we generalize the analysis representation model by including a guided weight function for dependency modeling. And the task-driven learning formulation is introduced to obtain the optimized guidance tailored to specific enhancement task. Second, the depth image is gradually enhanced along with the iterations, and thus the guidance should also be dynamically adjusted to account for the updating of depth image. To this end, stage-wise parameters are learned for dynamic guidance. Experiments on guided depth image upsampling and noisy depth image restoration validate the effectiveness of our method.
Optical fiber fabry-perot pressure sensor machined by Nd:YAG laser

Through laser ablation process, a diaphragm with mesa structure is fabricated successfully in silicon wafer with Nd:YAG laser machining system. Then the silicon diaphragm is used to make optical fiber Fabry-Perot pressure sensor. The pressure sensing experiment showed the sensor had a good linear response with sensitivity 10.6 nm/kPa at the range 40-240 kPa.