Miracle-dz/UniFAR-FTUs huggingface dataset:
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms

Recent years have witnessed the popularity of using rank minimization as a regularizer for various signal processing and machine learning problems. As rank minimization problems are often converted to nuclear norm minimization (NNM) problems, they have to be solved iteratively and each iteration requires computing a singular value decomposition (SVD). Therefore, their solution suffers from the high computation cost of multiple SVDs. To relieve this issue, we propose using the block Lanczos method to compute the partial SVDs, where the principal singular subspaces obtained in the previous iteration are used to start the block Lanczos procedure. To avoid the expensive reorthogonalization in the Lanczos procedure, the block Lanczos procedure is performed for only a few steps. Our block Lanczos with warm start (BLWS) technique can be adopted by different algorithms that solve NNM problems. We present numerical results on applying BLWS to Robust PCA and Matrix Completion problems. Experimental results show that our BLWS technique usually accelerates its host algorithm by at least two to three times.
POS-1 and GLD-1 repress glp-1 translation through a conserved binding-site cluster

POS-1 and GLD-1 are required to repress the Notch homologue glp-1 translation in Caenorhabditis elegans embryos. Both proteins bind to overlapping elements in the glp-1 3' UTR. Two POS-1 sites are present within this region, but only one is required for repression in vivo. It is proposed that POS-1 restricts access of other RNA-binding proteins to this region.
Social Costs of Iron Deficiency Anemia in 6–59-Month-Old Children in India

Introduction Inadequate nutrition has a severe impact on health in India. According to the WHO, iron deficiency is the single most important nutritional risk factor in India, accounting for more than 3% of all disability-adjusted life years (DALYs) lost. We estimate the social costs of iron deficiency anemia (IDA) in 6–59-month-old children in India in terms of intangible costs and production losses. Materials and Methods We build a health economic model estimating the life-time costs of a birth cohort suffering from IDA between the ages of 6 and 59 months. The model is stratified by 2 age groups (6–23 and 24–59-months), 2 geographical areas (urban and rural), 10 socio-economic strata and 3 degrees of severity of IDA (mild, moderate and severe). Prevalence of anemia is calculated with the last available National Family Health Survey. Information on the health consequences of IDA is extracted from the literature. Results IDA prevalence is 49.5% in 6–23-month-old and 39.9% in 24–58-month-old children. Children living in poor households in rural areas are particularly affected but prevalence is high even in wealthy urban households. The estimated yearly costs of IDA in 6–59-month-old children amount to intangible costs of 8.3 m DALYs and production losses of 24,001 m USD, equal to 1.3% of gross domestic product. Previous calculations have considerably underestimated the intangible costs of IDA as the improved WHO methodology leads to a threefold increase of DALYs due to IDA. Conclusion Despite years of iron supplementation programs and substantial economic growth, IDA remains a crucial public health issue in India and an obstacle to the economic advancement of the poor. Young children are especially vulnerable due to the irreversible effects of IDA on cognitive development. Our research may contribute to the design of new effective interventions aiming to reduce IDA in early childhood.
Facile Synthesis of Monodispersed SiO2@Fe3O4 Core–Shell Colloids for Printing and Three-Dimensional Coating with Noniridescent Structural Colors

Amorphous photonic structures (APSs) with short-range ordered arrangement have attracted great interest because of their wide view angles. However, the presented methods for the APSs color printing and 3D coating on different substrates and curvatures are lack of control. Here, APSs with angle-independent structural colors were fabricated by the self-assembly of SiO2@Fe3O4 core–shell nanostructures, which were prepared by the hydrolysis of Fe(acac)3 on the silica surfaces. The size of SiO2@Fe3O4 core–shell colloids can be controlled well through the tuning of SiO2 particle size, whereas the coverage of Fe3O4 on silica surfaces can be precisely tailored through altering the mass ratio between Fe3O4 precursor and SiO2. APSs with only short-range ordered structures, uniform noniridescent structural colors, and high color visibility can be obtained through the self-assembly of SiO2@Fe3O4 colloids of different particle sizes in a few minutes. They are mainly attributed to the weak electrostatic repulsion interactions between SiO2@Fe3O4 colloids because of the partial coverage of Fe3O4 on silica surfaces and absorption of the incoherent multiple scattering of visible light from Fe3O4. Moreover, SiO2@Fe3O4 colloids show good adhesion to various substrates, such as paper, glass, plastics, resins, ceramics, and wood, which facilitates the formation of uniform APSs on different substrates. Multicolor prints and 3D coating of APSs on substrates with different curves and roughness can be realized on the basis of the fast assembly of SiO2@Fe3O4 colloids.
Prediction of Fracture Failure of Steel Pipes With Sharp Corrosion Pits Using Time-Dependent Reliability Method With Lognormal Process

This paper presents a reliability-based methodology for assessing fracture failures of steel pipes with sharp corrosion pits. Based on newly developed models of elastic fracture toughness, the simple criterion of stress intensity factor (SIF) is used to establish the limit state functions for pipes with sharp corrosion pits in the longitudinal and circumferential directions. A stochastic model of load effect is developed and a time-dependent reliability method based on first passage probability for nonstationary lognormal processes is employed to quantify the probability of failure and predict the remaining service life. After applying the methodology to a case study, sensitivity analysis is carried out to identify the most influential variables on the probability of failure. It is found in the paper that the correlation coefficient has a considerable effect on probability of failure of steel pipes with sharp corrosion pits and that the larger the mode I fracture toughness is, the smaller the probability of pipe failure is.
Highly sensitive SPR based PCF for biological substance sensing: design and analysis

In this paper, a surface Plasmon resonance (SPR) based photonic crystal fiber has been proposed and numerically investigated by Finite Element Method (FEM). The proposed SPR-based PCF shows higher average wavelength interrogation sensitivity than the previous structures. Different plasmonic materials have been used to show the difference in results. Liquid filled cores with metallic surface can be exited with leaky-Gaussian core guided mode. Numerical investigation of optical properties for the proposed PCF has been established by changing the designing parameters like pitch, diameters etc. The proposed PCF is simple in nature and can be easily fabricated by existing methods. Biological substances, biochemical, organic chemical analysis, bimolecules can be detected by our proposed SPR based PCF.
Construction of high spectral resolution reflectance database

A planned satellite-mounted hyperspectral sensor will be able to observe 185 bands (wavelength resolution : approximately 10nm) of visible-shortwave near infrared, and is expected to be used in environmental and industrial applications. Hyperspectral data with high wavelength resolution has the potential to allow the type and contents of minerals to be identified from the absorption features of land surface spectral reflectance, and estimations of mineral composition may be possible by analyzing the change of absorption position. To analyze mineral compositions, a spectrum database from USGS, which was updated periodically, has been used. However, on the longer wavelength side of the spectrum, the database has lower wavelength resolution than that of the sensor to be mounted on the satellite. Because of this inconvenience, the long wavelength resolution of the hyperspectral data can be considered as being under-utilized. For this reason, we are now developing a mineral spectral reflectance database that has higher spectral resolution than the hyperspectral data over the 1,300nm wavelength region. To obtain the necessary spectral data, we have collected and researched around 150 mineral specimens.
Automated learning of muscle-actuated locomotion through control abstraction

We present a learning technique that automatically syn- thesizes realistic locomotion for the animation of physics-based models of animals. The method is especially suitable for animals with highly flexible, many-degree-of-freedom bodies and a consid- erable number of internal muscle actuators, such as snakes and fish. The multilevel learning process first performs repeated loco- motion trials in search of actuator control functions that produce efficient locomotion, presuming virtually nothing about the form of these functions. Applying a short-time Fourier analysis, the learn- ing process then abstracts control functions that produce effective locomotion into a compact representation which makes explicit the natural quasi-periodicities and coordination of the muscle actions. The artificial animals can finally put into practice the compact, efficient controllers that they have learned. Their locomotion learn- ing abilities enable them to accomplish higher-level tasks specified by the animator while guided by sensory perception of their vir- tual world; e.g., locomotion to a visible target. We demonstrate physics-based animation of learned locomotion in dynamic models of land snakes, fishes, and even marine mammals that have trained themselves to perform "SeaWorld" stunts.
Groupwork close up: a comparison of the group design process with and without a simple group editor

A simple collaborative tool, a shared text editor called ShrEdit, changed the way groups of designers performed their work, and changed it for the better. First, the designs produced by the 19 groups of three designers were of higher quality than those of the 19 groups who worked with conventional whiteboard, paper and pencil. The groups with the new tool reported liking their work process a little less, probably because they had to adapt their work style to a new tool. We expected, from the brainstorming literature and recent work on Group Support Systems, that the reason the designs were of better quality was that the supported groups generated more ideas. To our surprise, the groups working with ShrEdit generated fewer design ideas, but apparently better ones. It appears that the tool helped the supported groups keep more focused on the core issued in the emerging design, to waste less time on less important topics, and to capture what was said as they went. This suggests that small workgroups can capitalize on the free access they have to a shared workspace, without requiring a facilitator or a work process embedded in the software.
A robust reputation system for the grid

In the grid, the reliability of the providers who manage the resources is usually unknown to the consumers. Unreliable providers usually fail to complete consumers' task. Therefore, consumers take the risk that they lose their payment for the resource access. Establishing reputation systems is an alternative to manage such risk. However, existing attempts to establish reputation systems in the grid mainly focus on enabling reputation evaluation to measure the resource providers' reliability, but do not effectively address the inaccurate testimonies and malicious referrers, which generally deteriorate the performance of reputation systems. Against this background, this paper proposes a reputation system with the ability to mitigate the influence of the inaccurate testimonies and malicious referrers. Experimental studies show that the proposed system is efficient in mitigating the influence of these inaccurate testimonies and malicious referrers.
Kernel VA-Files for Nearest Neighbor Search in Large Image Databases

Many data partitioning index methods perform poorly in high dimensional space. The vector approximation file (VA-File) approach overcomes some of the difficulties of high dimensional vector spaces, but cannot be applied when using a kernel distance metric in the data measurement space. This paper introduces a novel KVA-File (kernel VA-File) that extends VAFile to kernel-based retrieval methods. A key observation is that kernel metrics may be non-linear in the input data space but is still linear in an induced feature space. It is this linear invariance in the induced feature space that enables KVA-File to work with kernel metrics. An efficient approach to approximating vectors in an induced feature space is presented with the corresponding upper and lower distance bounds. Thus an effective indexing method is provided for kernel-based image retrieval methods. Experimental results using large image data sets (approximately 100,000 images with 463 dimensions of measurement) validate the efficacy of our method.
Genetic Watermarking with Block-Based DCT Clustering

In this paper, we propose a new approach for digital image watermarking in DCT domain. This approach used a fuzzy C-mean algorithm (FCM) to classify 8times8 DCT blocks as texture or non texture region. The genetic algorithms (GAs) are then performed to find out the optimal parameters for watermark embedding, independently for each region type. The experimental results show that the proposed approach yields a watermark that is invisible to human eyes and robust to various image manipulations. The algorithm was also compared with the previous work, and our algorithm is superior for all test images.
Improving Minority Class Prediction Using Case-Specific Feature Weights

This paper addresses the problem of handling skewed class distributions within the case-based learning (CBL) framework. We rst present as a baseline an informationgain-weighted CBL algorithm and apply it to three data sets from natural language processing (NLP) with skewed class distributions. Although overall performance of the baseline CBL algorithm is good, we show that the algorithm exhibits poor performance on minority class instances. We then present two CBL algorithms designed to improve the performance of minority class predictions. Each variation creates test-case-speci c feature weights by rst observing the path taken by the test case in a decision tree created for the learning task, and then using pathspeci c information gain values to create an appropriate weight vector for use during case retrieval. When applied to the NLP data sets, the algorithms are shown to signi cantly increase the accuracy of minority class predictions while maintaining or improving overall classi cation accuracy.
UCCAApp: Web-application for Syntactic and Semantic Phrase-based Annotation

We present UCCAApp, an open-source, ﬂexible web-application for syntactic and semantic phrase-based annotation in general, and for UCCA annotation in particular. UCCAApp supports a variety of formal properties that have proven useful for syntactic and semantic representation, such as discontiguous phrases, multiple parents and empty elements, making it useful to a variety of other annotation schemes with similar formal properties. UCCAApp’s user interface is intuitive and user friendly, so as to support annotation by users with no background in linguistics or formal representation. In-deed, a pilot version of the application has been successfully used in the compilation of the UCCA Wikipedia treebank by anno-tators with no previous linguistic training. The application and all accompanying resources are released as open source under the GNU public license, and are available online along with a live demo. 1
Hormonal Signaling Systems of the Brain in Diabetes Mellitus

Diabetes mellitus (DM) is nowadays a major global health problem affecting more than 200 million people worldwide. It is one of the most severe metabolic disorders in humans characterized by hyperglycemia due to a relative or an absolute lack of insulin or the action of insulin on its target tissue or both. Many neurodegenerative disorders, such as diabetic encephalopathy and Alzheimer's disease (AD), are associated with the type 1, insulindependent, and the type 2, non-insulin-dependent, diabetes mellitus (DM1 and DM2). Manifestations of these disorders in diabetic patients include alterations in neurotransmission, electrophysiological abnormalities, structural changes and cognitive deficit (Biessels et al., 2001). In the recent time attention to the neurological consequences of DM in the CNS has increased considerably. Many approaches and tools have been used to study etiology and pathogenesis of DM and DM-associated neurodegenerative disorders, and their diagnostics and treatment. The most perspective approaches are based on a combined use of the methods of biochemistry, molecular biology and physiology, they include clinical investigations of diabetic patients and the experimental models of DM and their complications, such as the model of DM1 induced by streptozotocin (STZ) treatment of young or adult rodents, the neonatal model of DM2 induced by the STZ treatment of newborn rats, and also the models of spontaneous DM and nutritional background causing DM2, as well as the models produced by transgenic manipulations or gene knockout techniques are all successfully used to study the molecular, cellular and morphological changes in diabetic brain (Shafrir, 2010). A severe hyperglycemia in DM1, mild hyperglycemia typical of DM2, and recurrent hypoglycemia induced by inadequate insulin therapy are the major factors responsible for the development of CNS complications in DM. The brain is mainly a glucose-dependent organ, which can be damaged by hyperas well as by hypoglycemia (Scheen, 2010). Being a major problem in clinical practice, hypoglycemia unawareness is associated with an increased risk of coma. Note that low blood glucose level induces negative mood states, primarily self-reported “nervousness” (Boyle & Zrebiec, 2007). Moreover, patients with a history of severe hypoglycemia show a much higher level of anxiety compared to other DM patients (Wredling, 1992). The prolonged influence of mild hypoglycemia on the brain leads to deregulation of many processes in CNS, which underlines the importance of scrupulously avoiding even mild hypoglycemic episodes in patients with DM. Hypoglycemia induces
Rapid flow cytometric method for measuring senescence associated β‐galactosidase activity in human fibroblasts

Senescence associated‐β‐galactosidase (SA‐β‐gal) activity is a widely used marker for cellular senenescence. SA‐β‐gal activity is routinely detected cytochemically, manually discriminating negative from positive cells. This method is time‐consuming, subjective and therefore prone to operator‐error. We aimed to optimize a flow cytometric method described by other workers using endothelial cells to better differentiate between populations of fibroblasts in degrees of SA‐β‐gal activity. Skin fibroblasts were isolated from young (mean age ± SD: 25.5 ± 1.8) and very old (age 90.2 ± 0.3) subjects. Different pH modulators were tested for toxicity. To induce stress‐induced senescence, fibroblasts were exposed to rotenone. Senescence was assessed measuring SA‐β‐gal activity by cytochemistry (X‐gal) and by flow cytometry (C12FDG). The pH modulator Bafilomycin A1 (Baf A1) was found to be least toxic for fibroblasts and to differentiate best between nonstressed and stressed fibroblast populations. Under nonstressed conditions, fibroblasts from very old subjects showed higher SA‐β‐gal activity than fibroblasts from young subjects. This difference was found for both the flow cytometric and cytochemical methods (P = 0.013 and P = 0.056 respectively). Under stress‐induced conditions the flow cytometric method but not the cytochemical method revealed significant higher SA‐β‐gal activity in fibroblasts from very old compared to young subjects (P = 0.004 and P = 0.635 respectively). We found the modified flow cytometric method measuring SA‐β‐gal activity superior in discriminating between degrees of senescence in different populations of fibroblasts. © 2009 International Society for Advancement of Cytometry
Flexible tool for small signal stability analysis

This paper presents the development of a flexible tool for the calculation of the state matrix A of the power system. This program is called SIAM (Sistema Informático para Análisis Modal - Computational System for Modal Analysis). The state matrix is obtained from the load flow solution data and from the dynamic model parameters employed. The detailed models of synchronous machines, automatic voltage regulators and power system stabilizers are linearized for the calculation. To evaluate the method proposed, there is a comparison of results between SIAM and a commercial program for a reference case published.
Discrete Laplace estimator with a variable moment order for the modified image reconstruction

In the paper, the discrete Laplace estimator with a variable moment order is considered. The application of this model in the image and video compression based on the discrete cosine transform (DCT) and quantization such as MPEG and JPEG standards is presented. Modeling the distribution based on the discrete variables collected at the decoder side can be used for the modified image reconstruction. The selection of the optimal moment order m for the discrete Laplace estimator with a variable moment order can outperform other discrete Laplace estimators in terms of PSNR criteria for different quality compression ratios and for the Y, U and V components.
A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques

Localization of a wireless device using the time-of-arrivals (TOAs) from different base stations has been studied extensively in the literature. Numerous localization algorithms with different accuracies, computational complexities, a-priori knowledge requirements, and different levels of robustness against non-line-of-sight (NLOS) bias effects also have been reported. However, to our best knowledge, a detailed unified survey of different localization and NLOS mitigation algorithms is not available in the literature. This paper aims to give a comprehensive review of these different TOA-based localization algorithms and their technical challenges, and to point out possible future research directions. Firstly, fundamental lower bounds and some practical estimators that achieve close to these bounds are summarized for line-of-sight (LOS) scenarios. Then, after giving the fundamental lower bounds for NLOS systems, different NLOS mitigation techniques are classified and summarized. Simulation results are also provided in order to compare the performance of various techniques. Finally, a table that summarizes the key characteristics of the investigated techniques is provided to conclude the paper.
Recurrent Additive Networks

We introduce recurrent additive networks (RANs), a new gated RNN which is distinguished by the use of purely additive latent state updates. At every time step, the new state is computed as a gated component-wise sum of the input and the previous state, without any of the non-linearities commonly used in RNN transition dynamics. We formally show that RAN states are weighted sums of the input vectors, and that the gates only contribute to computing the weights of these sums. Despite this relatively simple functional form, experiments demonstrate that RANs perform on par with LSTMs on benchmark language modeling problems. This result shows that many of the non-linear computations in LSTMs and related networks are not essential, at least for the problems we consider, and suggests that the gates are doing more of the computational work than previously understood.