nils-herrmann/clean_scirepeval huggingface dataset:
Loop scheduling with memory access reduction under register constraints for DSP applications

In embedded systems, high-performance DSP needs to be performed not only with high-data throughput but also with low-power consumption. In this paper, we propose an effective scheduling framework, MARLS (Memory Access Reduction Loop Scheduling), to reduce memory accesses for DSP applications with loops. In the framework, we generate register operations to replace redundant load operations, and schedule these operations while allocating available physical registers to their register operands. We implement our technique into the Trimaran compiler and conduct experiments using a set of benchmarks from DSPstone and MiBench on the cycle-accurate VLIW simulator of Trimaran. The experimental results show that our technique significantly reduces the number of memory accesses.
NOVEL CELL SEGMENTATION AND ONLINE LEARNING ALGORITHMS FOR CELL PHASE IDENTIFICATION IN AUTOMATED TIME-LAPSE MICROSCOPY

Automated identification of cell cycle phases captured via fluorescent microscopy technique is very important for cell cycle understanding and drug discovery. In this paper, we propose a novel cell detection method that utilizes both the intensity and shape information of cell to improve the segmentation quality. In contrast to conventional off-line learning algorithms for classification, our study necessitates the on-line adaptivity to accommodate the ever-changing experimental conditions. An online support vector classifier (OSVC) is thus proposed, which features the removal of support vectors from the old model and assigning the new training examples with different weights according to their importance. Experimental results show the proposed system is effective for cell imaging segmentation and cell phase identification in time-lapse microscopy
Reversed Spectral Hashing

Hashing is emerging as a powerful tool for building highly efficient indices in large-scale search systems. In this paper, we study spectral hashing (SH), which is a classical method of unsupervised hashing. In general, SH solves for the hash codes by minimizing an objective function that tries to preserve the similarity structure of the data given. Although computationally simple, very often SH performs unsatisfactorily and lags distinctly behind the state-of-the-art methods. We observe that the inferior performance of SH is mainly due to its imperfect formulation; that is, the optimization of the minimization problem in SH actually cannot ensure that the similarity structure of the high-dimensional data is really preserved in the low-dimensional hash code space. In this paper, we, therefore, introduce reversed SH (ReSH), which is SH with its input and output interchanged. Unlike SH, which estimates the similarity structure from the given high-dimensional data, our ReSH defines the similarities between data points according to the unknown low-dimensional hash codes. Equipped with such a reversal mechanism, ReSH can seamlessly overcome the drawback of SH. More precisely, the minimization problem in our ReSH can be optimized if and only if similar data points are mapped to adjacent hash codes, and mostly important, dissimilar data points are considerably separated from each other in the code space. Finally, we solve the minimization problem in ReSH by multilayer neural networks and obtain state-of-the-art retrieval results on three benchmark data sets.
Reflection Removal of Tongue Image via Total Variation-Based Image Inpainting

With the progress in image processing and pattern recognition, computerized tongue diagnosis has received considerable recent research interest. In computerized tongue diagnosis, reflection is unavoidable in the acquisition of tongue images. Correct detection of the highligt area and removal of reflection in tongue images is requisite and would greatly affect the performance of tongue texture analysis. In this paper, we first present a highlight detection method based on the color model, and then adopt a total variation (TV)-based image inpainting method for reflection removal. Fast gradient method is applied to enforce fast inpainting with guaranteed convergence performance. Experimental results on simulation and real data show that the proposed method is effective in highlight area detection and reflection removal.
Parallel design for ultrasound synthetic aperture imaging FPGA

Synthetic aperture (SA) imaging algorithm, which combines sub-aperture elements to form high resolution image, can improve image quality in vivo ultrasound image. However, large computation resource is generally required for the implementation of SA. In this paper, we propose a parallel structural design for SA imaging algorithm that can be realized in Field Programmable Gate Array (FPGA), so that light weight and miniaturized design can be achieved. The proposed design has been validated by MATLAB and was employed to construct a high resolution ultrasound imaging from the raw data of 128 array transducer elements. The processing time is about 2.6ms theoretically, which should be alright for usage.
A grey system model for simulation of economic development

Economic development is a continuous and dynamic process. It can be divided into three specific components according to different time scales: the long-term trend, the medium-term periodic variation and the short-term random fluctuation. In this paper we used the grey system model to simulate the long-term trend of economic development and obtained the periodic variation from analysis of residual model. Amalgamating the long-term trend and the periodic variation, we obtained periodic correction, from which we got the random fluctuation. Using the periodic variation and the random fluctuation, the whole economic development process has been simulated through developing a predictive model. The application of the model to Shanxi Province shows that the model performs well. This model provides a new analysis method for the development planning of regional economy,which can provide the main scientific basis for the adjustment of economic structure and the economic development of Shanxi Province in the future.
Urban Traffic Signal Learning Control Using SARSA Algorithm Based on Adaptive RBF Network

Urban traffic control is very complicated, so to build a precise mathematical model for it is very difficult, In this paper, we use the SARSA reinforcement leaning algorithm to control the traffic signal, thus the decision can be made dynamically according to real-time traffic state information, and the change of environment can be adapted automatically; As the state space is too big to be stored and expressed directly, we applied radial basis function neural network (RBF) to approximate the state value function. By training self-adapted non-linear processing unit, and realizing online and adaptive constructing of state space, the approximation is improved and thus the control of traffic signal at single crossroad is solved. The simulation results show that the effectiveness of the new control algorithm is obviously better than traditional fixed time slot allocation method.
The analysis about factors influencing the supervised classification accuracy for vegetation hyperspectral remote sensing imagery

Classification of hyperspectral imagery has drawn much attention in recent years since the development of hyperspectral sensor. The hyperspectral sensor may offer hundreds of contiguous and very narrow spectral channels which may detect more detailed classes and improve the classification accuracy. However, with the increasing dimensionalities of classification data space, the parameters in the supervised classification models are also increasing quickly, which will need much more numbers of training samples to ensure the parameters estimation accuracy. Most recent researches focus on developing novel classification algorithms to improve the hyperspectral imagery processing performance. There are still lack of systematic researches about the factors (such as numbers and distributions of training samples, classification algorithms, dimensionalities of feature space) influencing classification accuracy for vegetation hyperspectral imagery so far. To analyze the factors, firstly, two methods for dimensionality reduction are proposed based on correlation coefficient and spectrum curve inflection points; On this basis, we use region of interest (ROI) constructed by different training numbers of pixels and different distributions to classify OMIS aerial hyperspectral image of ZaoYuan Town of Yanan City by several often used supervised classification algorithms; then analyze the relationships among classifiers and training samples and feature space dimensionality. This research shows that the classifiers based on Mahal distance and Maximum Likelihood (ML) that use secondary moment are superior to Euclid distance (ED), parallelepiped (PP) and spectral angle mapper (SAM); when the numbers of training samples are large enough, dimensionality reduction makes little influence on classification accuracy; when the numbers of training samples are limited, the influence is distinct on Mahal distance and ML but not distinct on Euclidean distance, PP and SAM.
Real-Time Loop Scheduling with Leakage Energy Minimization for Embedded VLIW DSP Processors

In this paper, we develop a novel real-time instruction-level loop scheduling technique to reduce leakage energy consumption for applications with loops on VLIW architecture. We first prove that the scheduling problem with the minimum leakage energy consumption within a timing constraint is NP-complete. Then, LEMLS (leakage energy minimization loop scheduling) algorithm is designed to repeatedly regroup a loop based on rotation scheduling (Chao et al., 1997), and decrease leakage energy integrating with leakage power reduction mechanism. We conduct experiments on a set of DSP benchmarks based on the power model of the VLIW processors in (Liao et al., 2002). The results show that our algorithm achieves significant leakage energy saving compared with list scheduling and the algorithm in (You et al., 2006).
Product Adoption Rate Prediction in a Competitive Market

As the worlds of commerce and the Internet technology become more inextricably linked, a large number of user consumption series become available for online market intelligence analysis. A critical demand along this line is to predict the future product adoption state of each user, which enables a wide range of applications such as targeted marketing. Nevertheless, previous works only aimed at predicting if a user would adopt a particular product or not with a binary buy-or-not representation. The problem of tracking and predicting users’ adoption rates, i.e., the frequency and regularity of using each product over time, is still under-explored. To this end, we present a comprehensive study of product adoption rate prediction in a competitive market. This task is nontrivial as there are three major challenges in modeling users’ complex adoption states: the heterogeneous data sources around users, the unique user preference and the competitive product selection. To deal with these challenges, we first introduce a flexible factor-based decision function to capture the change of users’ product adoption rate over time, where various factors that may influence users’ decisions from heterogeneous data sources can be leveraged. Using this factor-based decision function, we then provide two corresponding models to learn the parameters of the decision function with both generalized and personalized assumptions of users’ preferences. We further study how to leverage the competition among different products and simultaneously learn product competition and users’ preferences with both generalized and personalized assumptions. Finally, extensive experiments on two real-world datasets show the superiority of our proposed models.
Fault diagnosis method for hydraulic pump based on order tracking

Aiming at the problem of extracting fault features from hydraulic pump under variable rotating speed, a fault diagnosis method based on order tracking was proposed. Firstly, the original signal was decomposed by ensemble empirical mode decomposition into a series of intrinsic mode function (IMF) components. Afterwards, the fault sensitive IMF was selected by combining energy spectrum and spectral kurtosis. Secondly, the selected IMF was processed by Hilbert demodulation and down-sampling. Moreover, an appropriate frequency band was chosen and the signal was further processed by wavelet cluster-based bandpass filtering. Then, the fault sensitive envelope signal was obtained. Thirdly, the instantaneous frequency was extracted from the envelop signal by wavelet ridge tracking, and the time instant of phase discrimination was further calculated. Moreover, the even angle spaced resample was performed to the envelope signal. Then, the time domain non-stationary signal was transformed to the angular domain stationary signal. Finally, the stationary signal was analyzed by order tracking, and the fault diagnosis was executed according to the order spectrum. Furthermore, an application was performed to the vibration signals of hydraulic pump under variable rotating speed. The signals were respectively acquired in four typical states, which were normal, loose slipper, slipper wear and swash plate wear. Results indicate that the proposed method can effectively extract the fault features of hydraulic pump and further realize fault diagnosis.
Transferring a generic pedestrian detector towards specific scenes

The performance of a generic pedestrian detector may drop significantly when it is applied to a specific scene due to mismatch between the source dataset used to train the detector and samples in the target scene. In this paper, we investigate how to automatically train a scene-specific pedestrian detector starting with a generic detector in video surveillance without further manually labeling any samples under a novel transfer learning framework. It tackles the problem from three aspects. (1) With a graphical representation and through exploring the indegrees from target samples to source samples, the source samples are properly re-weighted. The indegrees detect the boundary between the distributions of the source dataset and the target dataset. The re-weighted source dataset better matches the target scene. (2) It takes the context information from motions, scene structures and scene geometry as the confidence scores of samples from the target scene to guide transfer learning. (3) The confidence scores propagate among samples on a graph according to the underlying visual structures of samples. All these considerations are formulated under a single objective function called Confidence-Encoded SVM. At the test stage, only the appearance-based detector is used without the context cues. The effectiveness of the proposed framework is demonstrated through experiments on two video surveillance datasets. Compared with a generic pedestrian detector, it significantly improves the detection rate by 48% and 36% at one false positive per image on the two datasets respectively.
The Solution Path Algorithm for Identity-Aware Multi-object Tracking

We propose an identity-aware multi-object tracker based on the solution path algorithm. Our tracker not only produces identity-coherent trajectories based on cues such as face recognition, but also has the ability to pinpoint potential tracking errors. The tracker is formulated as a quadratic optimization problem with ℓ0 norm constraints, which we propose to solve with the solution path algorithm. The algorithm successively solves the same optimization problem but under different ℓp norm constraints, where p gradually decreases from 1 to 0. Inspired by the success of the solution path algorithm in various machine learning tasks, this strategy is expected to converge to a better local minimum than directly minimizing the hardly solvable ℓ0 norm or the roughly approximated ℓ1 norm constraints. Furthermore, the acquired solution path complies with the "decision making process" of the tracker, which provides more insight to locating potential tracking errors. Experiments show that not only is our proposed tracker effective, but also the solution path enables automatic pinpointing of potential tracking failures, which can be readily utilized in an active learning framework to improve identity-aware multi-object tracking.
Maximum power point tracking control method for proton exchange membrane fuel cell

The load and hydrogen pressure changes in a proton exchange membrane fuel cell (PEMFC) result in instability and reduce the efficiency of the output power. This study proposes an extension sliding mode controller (ESMC) for maximum power point tracking (MPPT) of the fuel cells to enhance system efficiency and to reach the steady state of the output power. The experimental platform is set at a 200-watt PEMFC in this study. Results show that the tracking speed of the ESMC is 0.95 s, and the booster conversion efficiency is 94.5%; both are superior to other conventional MPPT methods. This study demonstrates a number of key technologies for renewable energy systems.
Evaluation of plasma charging damage in ultrathin gate oxides

Monitoring of plasma charging damage in ultrathin oxides (e.g., <4 mm) is essential to understand its impact on device reliability. However, it is observed that the shift of several device parameters, including threshold voltage, transconductance, and subthreshold swing, are not sensitive to plasma charging and thus not suitable for this purpose. Consequently, some destructive methods, such as the charge-to-breakdown measurement, are necessary to evaluate plasma damage in the ultrathin oxides.
An adaptive testbed of energy harvesting active networked tags (EnHANTS) prototypes

EnHANTs will be small, flexible, and self-powered devices that will be attached to objects that are traditionally not networked (books, furniture, clothing), thereby enabling various tracking applications (more details are available at enhants.ee.columbia.edu). We demonstrate a small testbed of En- HANTs prototypes. The prototypes communicate using in-house-developed ultra-wide-band impulse radio (UWB-IR) transceivers. They create a multihop network and adapt the topology and communication patterns to the light energy harvested by the solar cells. The testbed includes a unique software-based light control system which enables controlled experiments. This demonstration accompanies .
A Study on the Inter-Influence of Science and Technology Progress and Chinese Textile Industry Economic Growth

With the development of science and technology, the basic characters of corporations competition are changing, a new hyper competition time is coming. Therefore, the function on the economic growth of the science and technology progress is more and more remarkable, and it has already become the source of economic growth and forever motive force. The science and technology progress influences not only the speed of the economic growth but also the quality and the way of the economic growth directly. This article takes Chinese textile industry as the example, analysis the influence of science and technology progress to textile industry economic growth, The main research of the article is to find out the question between science and technology progress and textile industry economic growth, put forward the countermeasure and suggestion to promote national and regional scientific and technology progress and influence each other in economy growth greatly, and promote the style of Chinese textile economic growth change from extensive style to intensive style. Chinese textile industry; science and technology progress; international competence
Label Consistent Fisher Vectors for Supervised Feature Aggregation

In this paper, we present a simple and efficient way to add supervised information into Fisher vectors, which has become a popular image representation method for image classification and retrieval purposes in recent years. The basic idea of our approach is to improve the Fisher kernel in the training process by adding a discriminative label comparison matrix to it. The resulting new representations, which we call Label Consistent Fisher Vectors (LCFV), can be solved for both over determined and underdetermined cases. We show that LCFV has better classification performance than traditional Fisher vectors on three public datasets.
Performance of SBS Modified Asphalt and Influence of Aging

In order to study the performance of SBS modified asphalt and the influence of aging, two types of SBS and three kinds of base asphalts are selected according to SHRP index, and performances of combination of modified asphalt at different aging phases are evaluated. The results indicate that the high temperature performance of modified asphalt aged increases, but low temperature property is dependent on its combination. The study also shows that linear type SBS and star type SBS have the similar modification effect, while base asphalt plays an important role in modification effect.
Scene-Specific Pedestrian Detection for Static Video Surveillance

The performance of a generic pedestrian detector may drop significantly when it is applied to a specific scene due to the mismatch between the source training set and samples from the target scene. We propose a new approach of automatically transferring a generic pedestrian detector to a scene-specific detector in static video surveillance without manually labeling samples from the target scene. The proposed transfer learning framework consists of four steps. 1) Through exploring the indegrees from target samples to source samples on a visual affinity graph, the source samples are weighted to match the distribution of target samples. 2) It explores a set of context cues to automatically select samples from the target scene, predicts their labels, and computes confidence scores to guide transfer learning. 3) The confidence scores propagate among target samples according to their underlying visual structures. 4) Target samples with higher confidence scores have larger influence on training scene-specific detectors. All these considerations are formulated under a single objective function called confidence-encoded SVM, which avoids hard thresholding on confidence scores. During test, only the appearance-based detector is used without context cues. The effectiveness is demonstrated through experiments on two video surveillance data sets. Compared with a generic detector, it improves the detection rates by 48 and 36 percent at one false positive per image (FPPI) on the two data sets, respectively. The training process converges after one or two iterations on the data sets in experiments.
Delay-Power Tradeoff of Fixed-Rate Wireless Transmission With Arbitrarily Bursty Traffics

Wireless communication system is expected to provide service with low latency and high energy efficiency. To improve the energy efficiency, the transceiver prefers sending packets, when the channel states are good. However, such opportunistic transmission may induce undesirably large latency. Therefore, a fundamental tradeoff exists between the average transmission power and average queuing delay, and is studied in this paper via cross-layer probabilistic scheduling. In particular, we consider the delay-power tradeoff when the packet arrivals have arbitrary probabilistic distributions. A Markov reward model is adopted to model the queue of the backlogged packets. Based on that, we formulate a nonlinear optimization problem and convert it into a linear programming (LP) problem by using variable substitution. The optimal solution to the LP problem allows us to derive the optimal scheduling parameters. Based on the optimal solution, we can derive the optimal scheduling policy, which turns out to be threshold-based. Besides, we consider the source scheduling with the specific packet arrival distribution being unknown. Adaptive algorithms are proposed to achieve the corresponding delay-power tradeoff.