nils-herrmann/clean_scirepeval huggingface dataset:
Femtosecond Laser Direct Ablating Micro/Nanostructures and Micropatterns on CH3NH3 PbI3 Single Crystal

Single crystal perovskite materials with the intriguing properties of wide optical absorption range, low trap density, photoluminescence, and superior charge-transfer have emerged as a new class of revolutionary photovoltaic semiconductors promising for various applications. A technique to realize microstructures or microdevices on the surface of single crystal can facilitate the incorporation of these materials into optoelectronic applications. Here, we first reported the fabrication of 1-D mciropores, microlines, and 2-D micropatterns such as word “USTC,” number “2016” and “Olympic rings” on the surface of CH3NH3 PbI3 single crystal by femtosecond laser ablating. The effect of parameters such as exposure time, laser peak intensity, and scanning speed was quantitatively investigated to determine the parameter intervals capable of ensuring the required resolution. By fabricating periodic grating microstructures, the perovskite surfaces showed bright iridescence from dark blue to red due to the grating diffraction effect. Moreover, it is found that the fluorescence peak strength of processed area obviously grows with laser peak intensity and scanning speed, which is attributed to the increasing surface roughness caused by higher laser peak intensity and slower scanning speed. These works are beneficial for researchers to better understand the intrinsic property of single crystal perovskite and accelerate perovskite-based optoelectronics applications.
On Scalability of Two NoSQL Data Stores for Processing Interactive Social Networking Actions

This paper quantifies the scalability of a document store named MongoDB and an extensible store named HBase for processing simple operations using a social networking benchmark named BG (www.bgbenchmark.org). We report on the vertical and the horizontal scalability of both data stores. We quantify speedup and scaleup characteristics of each data store's Social Action Rating (SoAR). SoAR is the highest observed throughput with a data store while satisfying a Service Level Agreement (SLA) such as 95% of requests observing a response time of 100 milliseconds or faster. While the speedup experiments maintain a fix sized social graph and vary the number of nodes, the scaleup experiments vary both the size of the social graph and the number of nodes proportionally. A system provides a super-linear SoAR speedup as a function of the number of nodes when it transitions from fully utilizing its disk bandwidth to fully utilizing either its CPU cores or network bandwidth. The speedup and scaleup of both data stores is limited with a resource such as the network interface card of one to three nodes becoming fully utilized and dictating the SoAR of a many (12) node deployment. One may configure MongoDB to use replicas of shards, enhancing its speedup characteristics and incurring a very small amount (
Quantitative /sup 131/I SPECT with triple energy window Compton scatter correction

In this work accuracy of quantitative /sup 131/I SPECT with triple energy window (TEW) scatter correction is evaluated by phantom measurements. The application is tumor imaging of B-cell lymphoma patients treated with /sup 131/I radioimmunotherapy. The TEW method is a pixel by pixel correction where the scatter fraction in the photopeak window is estimated by linear interpolation between two adjacent narrow sub-windows. For the phantom measurements performed in this work the TEW estimate of scattered counts was close to one half of the total photopeak window counts. Quantification procedure includes marker based X-ray CT-SPECT image fusion to determine object boundaries and to generate attenuation maps. TEW scatter correction significantly reduces the effect of background activity on reconstructed counts within an object, but it still exists due to the finite spatial resolution of the system. Therefore, a background dependent calibration factor had to be used to achieve good quantitative accuracy. Quantitative accuracy with TEW correction was 5% and 14% for a tumor and lung respectively of a physical phantom with non-uniform activity and non-uniform scattering medium. With no scatter subtraction but using a background dependent calibration the quantitative accuracy was 7% and 18% for the tumor and lung respectively.
Label-free detection of prostate-specific antigen with FBAR-based sensor with oriented antibody immobilization

This paper reports a real-time, label-free detection of prostate-specific-antigen (PSA) through mass sensing with film bulk acoustic resonator (FBAR) coated with an oriented antibody layer. The oriented antibody layer is produced with protein A forming a polarized bond between antibody, at the Fc (fragment crystallizable) region of an IgG (immunoglobulin G), and a gold layer. The first prototype sensor is capable of detecting PSA concentration in hundreds of nanogram per milliliter range, and can be regenerated with 0.1 M glycine pH 2.5 that strips off only the antibody-antigen complex. Also, preliminary testing shows the absence of cross-reaction with bovine serum albumin (BSA). An improved version of the sensor utilizing DTSSP (3,3'-Dithiobis[sulfosuccinimidylpropionate]) crosslinker shows a better response, and increases the sensitivity to a level where PSA detection down to nanograms per milliliter range is possible.
Research on Kiln Body Health Monitoring of Ceramic Roller Kiln with Oxygen-Enriched and Pure Oxygen Combustion

The ceramic roller kiln with oxygen-enriched and pure oxygen combustion (CRKOC) is proposed by authors. According to the structural feature and thermal characteristics of CRKOC, a new concept is proposed in this paper, which is the kiln body real-time health monitoring of CRKOC. Three aspects are mainly researched. Firstly, the necessity of the health monitoring on kiln is analyzed. Secondly, the superiority of using fiber grating temperature sensors to measure the temperature of kiln body is discussed. The basic principle of kiln body real-time health monitoring system based on fiber grating temperature sensors is introduced. Meanwhile, an evaluation method about real-time health status of kiln body is put forward, which is the average value law. Thirdly, the whole framework of kiln body real-time health monitoring system based on Internet is constructed, which includes four parts. The function of every part is introduced in details. Furthermore, a hardware system based on four-channel fiber grating temperature sensors is designed, and its working flow is described in details. The result shows that this kiln body real-time health monitoring system presented in this paper has the merits of early detection and early warning. Therefore, the kiln real-time health monitoring system could greatly improve the safety of the kiln body.
Regional landslide susceptibility assessment based on Bivariate statistical analysis and application

The statistic analysis mothed is used to analyze the region landlside susceptibility in landslide hazard analysis. In the mothod, landslide invetntory and the data of enviromental factors which induce landslides are essential. The Cameron Highlands of Malaysia was selected as the experimental studying area. Based on GIS and the Bivariate statistical analysis mothod, the relationships among landslides and environmental factors were analyzed. Then, Probability Index Model and Distance Evaluation Model were developed. The comparison and assessment of two models show that they have higher prediction precision and universality. The effect of Distance Evaluation Model is better than that of Probability Index Model.
Probabilistic inference schemes for sparsity structures of Fortran 90 array intrinsics

In this paper, we address the issues of partitioning sparse arrays whose non-zero elements are distributed non-uniformly. We consider inference schemes for Fortran 90 array intrinsics so that the non-zero structure of the output array can be deduced from the non-zero structures of the input arrays. Experiments are conducted to measure the effectiveness of our method with the Harwell-Boeing sparse matrix collection. We also demonstrate that, given the sparsity structures of the source arrays and with the help of our inference schemes, one can predict the performance differences among a collection of equivalent Fortran 90 code for sample on-line analytical processing (OLAP). The experiments are performed on an IBM SP2 cluster with the library support of our sparse array intrinsics.
Assimilation of land surface temperature data from ATSR in an NWP environment-case studies with ATSR data in Spain and The Netherlands

Land surface parameterizations in numerical weather prediction describe the exchange of energy and water at the land-atmosphere interface. They are generally based on a coupled energy-water budget equation, and the results depend critically on (prescribed) land surface characteristics, notably albedo, soil water storage capacity and aerodynamic roughness. In a recent case-study bi-angular (nadir and forward) observations provided by the ATSR instrument were used to estimate the component soil and foliage temperature. For two images overlying the Netherlands on 14 May 1998 and South-East Spain on 9 September 1998 vegetation and bare ground temperatures were derived from ATSR data, and aggregated to a grid of 25/spl times/25 km, compatible with a NWP model. This NWP model was equipped with a land surface parameterization scheme in which vegetation and bare ground temperature were calculated separately. In a variational assimilation scheme, the NWP model was forced to match the observed component temperatures by changing both the soil moisture content and the aerodynamic roughness for the heat exchange of the bare ground component. It appeared that the optimal solution differed significantly for the two regions. For The Netherlands, changing the aerodynamic roughness did not affect the correspondence to observations, and soil moisture had to be changed to increase the overall model performance.
Research of Dynamic Spectrum Allocation Based on Service Demand

Based on the ideas of priority service, self-adaptation to loading, and conflict-free transmission with high-efficiency, this article proposes a new analysis model of MAC protocol of wireless communication network, which has made possible the polling service capable of differentiating the stations of different priority levels under mixed services policy and has improved the system’s flexibility and adaptability via adjustment of the switch-in service in time of load change input. The theoretical model of this protocol is established through Markov chain and probability generating function. Mathematical analysis is made on the mean queue length and the mean cyclic time of the common queue and the key station. It is found that the findings from theoretical analysis correspond well with those from simulated experiments.
An Online LC-KSVD Based Dictionary Learning for Multi-target Tracking

In this paper, we propose a novel framework for multi-objects tracking on solving two kind of challenges. One is how to discriminate different targets with similar appearance, the other is distinct the single target with serious variation over time. The proposed framework extracts discriminative appearance information of different objects from historical recordings of all tracked targets by a label consistent K-SVD (LC-KSVD) dictionary learning method. We validated our proposed framework on three publicly available video sequences with some state-of-the-art approaches. The experiment results showed that our proposed method achieves competitive results with 7.7% improvement in MOTP.
Research on relative error rate analysis method between the Earth surface safety monitoring devices of vehicle operation estate

Establishing an accurate and reliable network for the Earth surface safety monitoring devices of vehicle operation estate is the key factor to the railway intelligent traffic system in order to avoid incidents caused by super load of wagons. It is critical to seek an efficient analysis method about relative error rate analysis between devices so that designing technique of device can be checked. This paper presents a novel relative error rate analysis method DVM (distribution-oriented, verify and match). DVM includes two parts: distribution-model-oriented sample, verify and match of multi-system. Distribution-model-oriented sample can overcome defect in current sample methods. The maximum entropy theory is applied and distribution model of relative error rate is concluded. A verifiable match algorithm of multi-system realizes verify and match among the detective information, automatic identify system, and QB system. Application shows that distribution model of relative error rate can be obtained accurately according to DVM method.
Large-scale Satellite Image Browsing using Automatic Semantic Categorization

We approach the problem of large-scale satellite image browsing from a content-based retrieval and semantic categorization perspective. A two-stage method for query based automatic retrieval of satellite image patches is proposed. The semantic category of query patches are determined and patches from that category are ranked based on an image similarity measure. Semantic categorization is done by a learning approach involving the two-dimensional multi-resolution hidden Markov model (2-D MHMM). Patches that do not belong to any trained category are handled using a support vector machine (SVM) based classifier. Experiments yield promising results in modeling semantic categories within satellite images using 2-D MHMM, producing accurate and convenient browsing. We also show that prior semantic categorization improves retrieval performance.
Design and implementation of novel HF OFDM communication systems

Based on orthogonal frequency division multiplexing (OFDM) technology, a kind of high frequency (HF) communication systems supporting data transmission service is proposed. The presented HF OFDM communication systems occupy 3kHz bandwidth and employ the voice port of traditional radios. We provide the corresponding design scheme, system parameters and key technologies. In order to check up the performance of the proposed systems, we further implement the communication chain simulation of bit error rate (BER) properties with three different modulations of QPSK, 8PSK and 16QAM. The simulation results show that the designed HF OFDM communication system can obtain desirable BER performance even under poor shortwave channel.
Evaluation of soil erosion in Shangri-La County based on GIS and RS

soil erosion is one of the serious natural disasters in China. Soil erosion control is the main content of the comprehensive management of the river basin. Soil erosion in humid region is mainly caused by hydraulic erosion. In this paper, Shangri La County is taken as a study area; the 2014 Shangri La County soil erosion amount is calculated by modified soil loss equation (RUSLE). The classification of soil erosion intensity of Shangri La County is analyzed by various factors affecting soil erosion situation.
Asymptotic performance of vector quantizers with the perceptual distortion measure

This paper generalizes the asymptotic bounds for block quantizers to input weighted quadratic distortion, a class of distortion measure often used for perceptually meaningful distortion. The second problem considered in the paper is source mismatching. When the quantizer uses a probability density estimation mismatched to the source, the asymptotic performance in terms of distortion increase in dB is shown to be linear in the relative entropy of the real probability density and the estimated one.
Co-localization in Noisy Images through Minimizing a Ratio Function

Object co-localization is a recently proposed vision problem to jointly localize the target object in a collection of images. The main practical challenge for co-localization is the existence of noisy images, in which the target object may be absent. Previous study relied on a prior estimate of the number of noisy images given by human. However, this prior knowledge would be somewhat too strong in practice. To improve on this, we propose a novel formulation for object co-localization in a noisy image collection, which does not rely on any prior knowledge of the noisy images. This is done by incorporating the object co-localization and noisy image identification jointly into a ratio-form objective. We develop an efficient algorithm based on Newton's method for ratio optimization, which can converge to the global optimum in only a few iterations. Extensive experiments conducted on two public benchmarks show that our approach can achieve better or comparable performance compared with several state-of-the-arts, and is robust to different proportions of the noisy images.
New Type Power Line Carrier Relaying System with Directional Comparison for EHV Transmission Lines

This thesis suggests and studies a new type power line carrier protective relaying system with directional comparison for EHV transmission lines. It consists of the following three important improvements: (a) Adoption of the phase compensator type directional relay suggested and studied by the authors which can operate correctly in two phase operating conditions of the protected line;(b) with constant circulation of carrier frequency blocking current;(c) to take advantage of sudden decrease of received carrier signal level in case of internal three phase faults. Theoretical analysis, experimental data and field operating experience are given.
Reconstruction of 3D tubular structures from cone-beam projections

A new method of reconstructing three-dimensional (3D) tubular structures from cone-beam projections is proposed in this paper. Minimal path method is applied to detect 2D tubular structures in the cone-beam projections. The extracted 2D information are fused to design a weight function over the 3D volume so that minimal path method can be applied again to extract centerlines of 3D tubular structures. The validity of the proposed weight function relies on the sparsity of 3D images containing tubular structures. The procedure of estimating the radius of 3D tube from 2D projections is also described. The proposed method requires neither cone-beam reconstruction, nor tree matching, one of which is usually necessary in the existing reconstruction methods. The method is robust to small motions between different projections.
Semantic object segmentation via detection in weakly labeled video

Semantic object segmentation in video is an important step for large-scale multimedia analysis. In many cases, however, semantic objects are only tagged at video-level, making them difficult to be located and segmented. To address this problem, this paper proposes an approach to segment semantic objects in weakly labeled video via object detection. In our approach, a novel video segmentation-by-detection framework is proposed, which first incorporates object and region detectors pre-trained on still images to generate a set of detection and segmentation proposals. Based on the noisy proposals, several object tracks are then initialized by solving a joint binary optimization problem with min-cost flow. As such tracks actually provide rough configurations of semantic objects, we thus refine the object segmentation while preserving the spatiotemporal consistency by inferring the shape likelihoods of pixels from the statistical information of tracks. Experimental results on Youtube-Objects dataset and SegTrack v2 dataset demonstrate that our method outperforms state-of-the-arts and shows impressive results.
Image classification based on a multiresolution two dimensional hidden Markov model

This paper presents an image classification algorithm based upon a two dimensional multiresolution hidden Markov model (MHMM). This model represents an image by feature vectors in several resolutions and considers the feature vectors statistically dependent through an underlying state process assumed to be a multiscale Markov mesh. To estimate the model by the maximum likelihood criterion, approximations are made successively based on the EM algorithm to reach feasible computation. To classify an image, the algorithm attempts to find the optimal set of states with the maximum a posteriori probability. The states are then mapped into classes. The multiresolution model enables multiscale context information to be incorporated into classification. Suboptimal algorithms based on the model provide progressive classification which greatly speeds up classification based on single resolution HMMs.
Multi-Pose Learning based Head-Shoulder Re-identification

The whole body of person is probably invisible in video surveillance because of occlusion and view angles (such as in crowded public places), on which occasion conventional person re-identification (i.e., whole-body based Re-ID) approaches may not work. To address this problem, we propose a novel deep pairwise model based on multi-pose learning (MPL) which aims at head-shoulder part instead of the whole body. The proposed method explicitly tackles pose variations by learning an ensemble verification conditional probability distribution about relationship among multiple poses. To facilitate the research on this problem, we contribute three head-shoulder datasets based on CUHK03, CUHK01 and VIPeR. Experiments on these datasets demonstrate that our proposed method achieves the state-of-the-art performance.
Research on dual-MIMU trajectory tracking based on support vector machine constraint

Using foot-mounted inertial sensor to track the trajectory is one of the main methods for indoor autonomous pedestrian navigation. However, due to its algorithm, inertial error accumulates over time. To solve this problem, the zero velocity update (ZUPT) takes the velocity error as the observation to amend other information of the carrier. Because zero velocity update can only observe the information of velocity and two horizontal angles, we cannot obtain the position information. In this paper, a dual-MIMU trajectory tracking based on support vector machine(SVM) constraint method is proposed. According to the motion of the human body, in the process of walking, the horizontal angular velocity is the greatest when the toes are off the ground. The theory of s SVM is used to collect the angular velocity of foot movement to classify the movements during the traveling process, Increase the number of observations, after observability analysis to improve positioning accuracy. According to the constraint of the largest step in walking, the inequality equation is constructed and a Kalman filter algorithm is designed. The average position error is 1.3% after 5min's walking. It verifies that this system has higher positioning accuracy.
A new approach to load measurement for industrial scale ball mill

Precise load measurement is important and difficult for an industrial scale ball mill in thermal power plant. This paper presents a new approach which combines a novel feature extraction method and a hierarchical model to achieve this purpose. By analyzing the sensitivity distributions of working condition transitions and removing the ineffective information, the characteristic power spectrums (CPS) of noise and vibration signals are obtained. Then, three kinds of features: the feature energy, the centroid frequency and the frequency domain variance are extracted from the CPS. Based on adaptive network-based fuzzy inference system (ANFIS), a hierarchical model which consists of three ANFIS model is designed. The rules and initial parameters of each ANFIS model are obtained by subtractive clustering method. Experimental results show that the potential advantages of the proposed method are low model complexity, high estimated accuracy and good generalization ability. Moreover, as multi-sensors' information are adopted, the measurement results may be more reliable.