nils-herrmann/clean_scirepeval huggingface dataset:
Compression of multispectral images by spectral classification and transform coding

This paper presents a new technique for the compression of multispectral images, which relies on the segmentation of the image into regions of approximately homogeneous land cover. The rationale behind this approach is that, within regions of the same land cover, the pixels have stationary statistics and are characterized by mostly linear dependency, contrary to what usually happens for unsegmented images. Therefore, by applying conventional transform coding techniques to homogeneous groups of pixels, the proposed algorithm is able to effectively exploit the statistical redundancy of the image, thereby improving the rate distortion performance. The proposed coding strategy consists of three main steps. First, each pixel is classified by vector quantizing its spectral response vector, so that both a reliable classification and a minimum distortion encoding of each vector are obtained. Then, the classification map is entropy encoded and sent as side information, Finally, the residual vectors are grouped according to their classes and undergo Karhunen-Loeve transforming in the spectral domain and discrete cosine transforming in the spatial domain. Numerical experiments on a six-band thematic mapper image show that the proposed technique outperforms the conventional transform coding technique by 1 to 2 dB at all rates of interest.
Recursive-TFR Algorithm for Segmentation of Remotely Sensed Images

Segmentation of remote sensing images is challenging task not only for the intrinsic complexity of imaged scenes but also for their multiple-scale interpretation. Hierarchical techniques, which provide a sequence of nested segmentation maps for the scene at different scales are therefore very promising. The Texture fragmentation and reconstruction technique (TFR) carries out a hierarchical image segmentation based mainly on textural image properties. In this work we consider its improved version, Recursive-TFR, based on recursive binary segmentation, assess its performance experimentally on a suitable segmentation benchmark, prove its potential for remore-sensing imagery and point out promising developments.
SAR image despeckling through convolutional neural networks

In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques.
Application of denoising techniques to micro-tomographic images

Microcomputed tomography (microCT) is a particular version of computerized axial tomography commonly used by radiologists which reaches resolutions of the order of a few micrometers. In biology, this technique is especially useful for the study of hard tissues, such as calcified bone and dental matrices, because of their high linear attenuation coefficient. MicroCT images, though, are affected by a strong noise component, neither Gaussian nor white, caused by the characteristics of the acquisition system itself. In this paper we consider the problem of microCT image denoising, and compare the performance of two well-known denoising techniques and of BM3D, a recent technique based on the nonlocal approach. Although for the time being the performance analysis is mostly qualitative, results speak clearly in favor of BM3D.
Noise model discrimination for digital images based on variance-stabilizing transforms and on local statistics: Preliminary results

Most of the image restoration algorithms assumed the noise model and its parameters as an a priori information. Nevertheless this is not necessarily the case for real scenarios. Moreover, lack of knowledge about the noise parameters leads to heuristically approaches to choose the restoration algorithm's parameters. Given a non-texture observed image, which can be noise-free or corrupted with some kind of noise (we consider Gaussian, Poisson, Gamma and Rayleigh) we propose a simple yet effective method to discriminate the noise model (or lack of) that corrupts the observed image by first applying a set of variance-stabilizing transforms and then proceed to estimate the variance using a local statistics estimator; the estimated variance will be unitary only for the particular variance-stabilizing transform that matches the correct noise model.
Shear Wave Speed Estimation from Crawling Wave Sonoelastography: A comparison between AM-FM Dominant Component Analysis and Phase Derivation Methods

Crawling Wave Sonoelastography (CWS) offers quantitative estimation of shear wave speed (SWS) in soft tissues. Recently, two different methods to estimate the SWS from the crawling wave patterns have been proposed. The first method is based on the spatial phase derivation from the slow-time signal (CWS-PD) using the knowledge of the difference in vibration frequency (DVF) between external sources. The second method is based on the AM-FM Dominant Component Analysis (DCA) model, which allows estimating the spatial frequency without a-priori information of the DVF and without any noise model assumptions. In the present study, a comparison of the performance of CWS-PD and AM-FM-DCA for the estimation of SWS is presented through experiments with three gelatin phantoms with different concentrations (10%, 13% and 16%). Both CWS-PD and AM-FM-DCA provided similar SWS estimates with generally good agreement with the values obtained from mechanical measurements. Both methods presented larger errors for the 16% gelatin phantom and frequencies below 220 Hz due to the increased difficulty in tracking shear waves with longer wavelengths. These results suggest that further research may be needed for a more accurate evaluation of stiffer materials using CWS.
Topic Modeling and Visualization for Big Data in Social Sciences

Topic modeling is a widely used approach for analyzing large text collections. In particular, Latent Dirichlet Allocation (LDA) is one of the most popular topic modeling approaches to aggregate vocabulary from a document corpus to form latent "topics". However, learning meaningful topic models with massive document collections which contain millions of documents, billions of tokens is challenging, given the complexity of the data involved, the difficulty in distributing the computation across multiple computing nodes. In recent years some data processing frameworks, such as Spark, Mallet, others have been developed to address the issues associated with analyzing large volumes of unlabeled text pertaining to various domains in a scalable, efficient manner. In this paper, we will present a preliminary case study demonstrating the scholarship achieved in the study of political consumerism via XSEDE resources. The experimental study will showcase the use of digitized social sciences data, text analytics toolkits to generate topic models, visualize topics for empowering intersectional research engaging the relationship between consumption, race, class, gender in the area of sociology. Consequently, this comparative big data textual analysis involving use of JSTOR data, LDA modeling toolkit's, visualization techniques, computational components is of paramount importance, especially for researchers from academic domain dealing with social science applications involving big data.
A Systematic Literature Review for Development, Implementation and Deployment of MOOCs Focused on Older People

According to Administration of Aging (AoA), by the year 2060, the older people will represent among the 98 million of people of the total population. That is, much of this priority group will be involved with technology. Hence, the Massive Open Online Courses (MOOCs) are a good alternative for lifelong learning. Therefore, it should be taken into account the needs of older people according to their learning styles by using andragogical techniques, strategies and accessibility criteria. However, nowadays many courses are offered in different platforms, but they are not oriented to older people with special characteristics according to the age. Moreover, the methodologies reviewed do not take into account accessibility criteria and andragogical techniques and strategies for building MOOCs.
MOOCEP: A method for building massive open online courses for elderly people the analysis activity

By the year 2060, the elderly population will double its current size in comparison to the results presented in the last census, according to the Administration on Aging (AoA). By that time an important part of the elderly population will be involved in technology. Moreover, it should be taken into account that the technological needs of elderly people will differ from those of the rest of targets. However, elderly people may not be fully aware of their situation concerning their lifestyle (e.g., own well-being, rights, nutrition); therefore, e-Learning can be used to improve their knowledge and integration with peers. Hence, the Massive Open Online Courses (MOOCs) can work as online resources designed to help expanding the knowledge of elderly people by means of technological tools. Even though a complete method to build MOOCs for elderly people has been proposed previously, this paper details the analysis phase, which constitutes the first step in the method. This proposal will help MOOC developers during the elicitation of the requirements towards building effective MOOCs focused on elderly people. In order to demonstrate the feasibility of this proposal, a MOOC related to nutrition recommendations for elderly people will be presented.
An accelerated newton's method for projections onto the ℓ1-ball

We present a simple and computationally efficient algorithm, based on the accelerated Newton's method, to solve the root finding problem associated with the projection onto the ℓ1-ball problem. Considering an interpretation of the Michelot's algorithm as Newton method, our algorithm can be understood as an accelerated version of the Michelot's algorithm, that needs significantly less major iterations to converge to the solution. Although the worst-case performance of the propose algorithm is O(n2), it exhibits in practice an O(n) performance and it is empirically demonstrated that it is competitive or faster than existing methods.
A quasi-local method for instantaneous frequency estimation with application to structural magnetic resonance images

Spatially-varying signal content can be effectively modeled using amplitude modulation-frequency modulation (AM-FM) representations. The AM-FM representation allow us to extract instantaneous amplitude (IA) and instantaneous frequency (IF) components that can be used to measure non-stationary content in biomedical images and videos. This paper introduces a new method for estimating the IA and the IF based on a quasi-local method (QLM). We provide an extensive comparison of AM-FM demodulation approaches based on QLM and a quasi-eigenfunction approximation method using three different filter-banks: (i) a separable, equiripple design, (ii) a Gabor filter bank, and (iii) a directional filter bank approach based on the Contourlet transform. The results document that the use of the new QLM method with an equiripple filter bank design gave the best IF magnitude estimates for a synthetic image. The new QLM method is then applied to a multi-site schizophrenia dataset (N=307). The dataset included structure magnetic resonance images from healthy controls and patients diagnosed with schizophrenia. The IF magnitude is shown to be less sensitive to variations across sites as opposed to the standard use of SMRI images that suffered from significant dependency on the scanner configurations on different collection sites. Furthermore, the regions of interest identified through the use of the IF magnitude are in agreement with previous studies.
Alternating optimization low-rank expansion algorithm to estimate a linear combination of separable filters to approximate 2D filter banks

Learn 2D filter banks are currently being used in high-impact applications such convolutional neural networks, convolutional sparse representations, etc. However such filter banks usually have plentiful filters, each being non-separable, accounting for a large portion of the overall computational cost. In this paper we propose a novel and computationally appealing alternating optimization based algorithm to estimate a linear combination of separable (rank-1) filters to approximate 2D filter banks. Our computational results show that the proposed method can be faster than (state-of-the-art) tensor Canonical Polyadic decomposition (CPD) method to obtain an approximation of comparable accuracy.
Video background modeling under impulse noise

Video background modeling is an important task in many video processing applications. Most existing algorithms assume a Gaussian noise model, but digital videos are, in practice, prone to be degraded by impulse noise, due to transmission errors in wireless or high data-rate wired channels. Principal Component Pursuit (PCP), which also assumes a Gaussian noise model, is currently considered the state of the art for video background modeling. We propose a new PCP-based algorithm that fully integrates the impulse noise model and has computational performance comparable with that of current PCP implementations.
MIxed gaussian-impulse noise image restoration via total variation

Several Total Variation (TV) regularization methods have recently been proposed to address denoising under mixed Gaussian and impulse noise. While achieving high-quality denoising results, these new methods are based on complicated cost functionals that are difficult to optimize, which negatively affects their computational performance. In this paper we propose a simple cost functional consisting of a TV regularization term and ℓ2 and ℓ1 data fidelity terms, for Gaussian and impulse noise respectively, with local regularization parameters selected by an impulse noise detector. The computational performance of the proposed algorithm greatly exceeds that of the state of the art algorithms within the TV framework, and its reconstruction quality performance is competitive for high noise levels, for both grayscale and vector-valued images.
Transmission of high power 9 MHz signals near ionospheric cut-off from 'SURA' towards the Moon and reception of the echo with the radio telescope 'UTR2'

Two unique broad-band decametric arrays SURA and UTR2 are described in outline format. They are used for radio astronomy and radiophysical research (solar system investigations, ionospheric research, near-Earth plasma, etc). Both facilities were used last summer in 'bistatic' configuration, transmitting from SURA and receiving with UTR2. Moon and Sun were used as targets, NASA's WIND spacecraft was detecting with the RAD-2 receiver . Preliminary results are shown.
Ghosting suppression for incremental principal component pursuit algorithms

In video background modeling, ghosting occurs when an object that belongs to the background is assigned to the foreground. In the context of Principal Component Pursuit, this usually occurs when a moving object occludes a high contrast background object, a moving object suddenly stops, or a stationary object suddenly starts moving. Based on a previously developed incremental PCP method, we propose a novel algorithm that uses two simultaneous background estimates based on observations over the previous n1 and n2 (n1 C n2) frames in order to identify and diminish the ghosting effect. Our computational results show that the proposed method greatly improves both the subjective quality and accuracy as determined by the F-measure.
An Iteratively Reweighted Norm Algorithm for Minimization of Total Variation Functionals

Total variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. A number of authors have recently noted the advantages of replacing the standard lscr2data fidelity term with an lscr1 norm. We propose a simple but very flexible method for solving a generalized TV functional that includes both the lscr2 -TV and lscr1 -TV problems as special cases. This method offers competitive computational performance for lscr2 -TV and is comparable to or faster than any other lscr1 -TV algorithms of which we are aware.
Adaptive sampling and processing of ultrasound images

An AM-FM video image representation is developed and applied to M-mode ultrasound. The AM-FM representation describes the video in terms of an AM-FM series expansion. For estimation, the fundamental AM-FM harmonic is estimated using a collection of bandpass filters that are adaptively selected so that one with the maximum response is selected. For each bandpass filter, the output image is downsampled depending on the spectral support of the filter. Using results from multidimensional frequency modulation theory, it is shown that a fast separable implementation is possible. In turn, good estimates are obtained from a collection of only two separable, bandpass filters approximated with just a small number of 11 coefficients and the recently developed SIMD-FFT. The fundamental AM-FM harmonic is seen to capture the essential structure of the image and simple thresholding of the amplitude estimate yields very good segmentation results.
Jitter invariant incremental principal component pursuit for video background modeling on the TK1

While Principal Component Pursuit (PCP) is currently considered to be the state of the art method for video background modeling, it suffers from a number of limitations, including high computational cost, batch operating mode, and sensitivity to camera jitter. In this work we present a real-time, CUDA-aware C / CUDA C implementation of a novel and fully incremental PCP algorithm for video background modeling that can also deal with rigid transformation jitter. Our implementation has a computational complexity that allows (TK1 platform) a processing frame rate throughput (jittered video) of 2.6 and 0.4 f.p.s. for color videos of 640 × 480 and 1920 × 1088 respectively.
A comparison of the computational performance of Iteratively Reweighted Least Squares and alternating minimization algorithms for ℓ1 inverse problems

Alternating minimization algorithms with a shrinkage step, derived within the Split Bregman (SB) or Alternating Direction Method of Multipliers (ADMM) frameworks, have become very popular for ℓ1-regularized problems, including Total Variation and Basis Pursuit Denoising. It appears to be generally assumed that they deliver much better computational performance than older methods such as Iteratively Reweighted Least Squares (IRLS). We show, however, that IRLS type methods are computationally competitive with SB/ADMM methods for a variety of problems, and in some cases outperform them.
A Matlab implementation of a fast incremental principal component pursuit algorithm for Video Background Modeling

In this proposal we present a Matlab-only implementation of a simple, novel, and fully incremental Principal Component Pursuit (PCP) algorithm for Video  Our implementation can process full HD color 1920 × 1088 videos at a rate of 0.61 seconds per frame running on a standard laptop (Intel i7-2670QM quad-core, 6GB RAM, 2.2 GHz). Unlike other incremental or online PCP-like algorithms, such as ReProCS, GRASTA or pROST, the initialization stage of our implementation is extremely fast, has modest memory requirements (6.5 seconds and less than 0.5 Gb for a full HD video), and is also able to quickly adapt to changes in the background. Moreover our implementation can also process live-feed videos, which in our proposed demonstration will be acquired via a wireless camera, resulting in an interactive demonstration where the the moving objects to be segmented are the audience.
2D instantaneous frequency-based method for motion estimation using total variation

We present a first approach to a new method to compute the motion estimation in digital videos using the two-dimensional instantaneous frequency information computed using amplitude-modulation frequency-modulation (AM-FM) methods. The optical flow vectors are computed using an iteratively reweighted norm for total variation (IRN-TV) algorithm. We compare the proposed method using synthetic videos versus a previous three-dimensional AM-FM based method and available motion estimation methods such as a phase-based, Horn-Schunck and the Lucas-Kanade methods. The results are promising producing a full density estimation with more accurate results than the other methods.
The Space of Feasible Execution Times for Asynchronous Periodic Task Systems Using Definitive Idle Times

Sensitivity analysis for real-time systems provides efficient methods to determine feasibility conditions in the case of changes in task parameters, such as different WCET values when porting a system to a different platform. The C-space defines the space of execution times for which a system is feasible, which can help system designers to more easily check if a task set is feasible on a set of candidate platforms. This paper extends previous works generalizing the description of the C-space for Earliest Deadline First scheduling to systems with initial task offsets. The feasibility gain offered by offsets is then expressed as the volume ratio between the C-space of an asynchronous system and its corresponding synchronous system when offsets are randomly chosen. The problem requires solving multiple similar instances of the Chinese Remainder Problem, for which an efficient algorithm in this context is provided. We show that even for random offsets, the gain can reach 15%.
Computational SIMD framework: split-radix SIMD-FFT algorithm, derivation, implementation and performance

A general framework to develop efficient single instruction multiple data (SIMD) compliant algorithms was previously proposed. In this paper a split-radix SIMD-FFT algorithm is derived under this framework and compared against the radix-2 SIMD-FFT algorithm, proven to have very efficient implementation. Regardless of the intrinsic irregular pattern present in the split-radix algorithm, it is shown that its performance improvement, when compared to the radix-2 algorithm, ranges from 2.5% up to 8.1%.
A 2D amplitude-modulation frequency-modulation representation for motion estimation

We present a new approach for motion estimation from digital videos based on the use of 2D amplitude-modulation frequency-modulation (AM-FM) models. The proposed approach uses an AM-FM representation to derive AM and FM based equations that can be applied to two consecutive frames to derive motion estimates. We test the proposed method using complex synthetic examples, with both amplitude-modulated and frequency-modulated components, for both sinusoidal, periodic motions and constant motion and compare against the popular Horn-Schunk and Lucas and Kanade method. Compared to the standard Horn-Schunk and Lucas and Kanade methods, the proposed approach leads to dramatic reductions in the mean-squared error for both constant, translational motion (from 19.73% to 95.46% reduction) and complex sinusoidal, periodic motions (up to 67.95% reduction).