Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying coherent anomalies in multi-scale spatio-temporal data using Markov random fields

Many physical processes involve spatio-temporal observations, which can be studied at different spatial and temporal scales. For example, rainfall data measured daily by rain gauges can be considered at daily, monthly or annual temporal scales, and local, grid-wise, region-wise or country-wise spatial scales. In this work, we focus on detection of anomalies in such multi-scale spatio-temporal data. We consider an anomaly as an event where the measured values over a spatio-temporally extended region are significantly different from their long-term means. However we aim to avoid setting any thresholds on the measured values and spatio-temporal sizes, because not only are thresholds subjective but also the long-term mean values often vary spatially and temporally. For this purpose we use spatio-Temporal Markov Random Field, where latent states indicate anomaly type (positive anomaly, negative anomaly, no anomaly/normal). Spatio-temporal coherence is maintained through suitable edge potentials. The model is extended to multiple spatio-temporal scales to achieve our second goal: anomalies at any scale should be defined both on the data at that scale, and also on anomalies at other scales. This allows us to trace an anomaly at a coarse scale to finer scales. For example, whether rainfall in a particular year is anomalous over a region should depend not only on the total volume of rainfall over the entire region, but also on whether there were such anomalies at the grid-scale, and the monthly scale. We use this approach to study rainfall anomalies over India-extremely diverse with respect to rainfall-for the period 1901–2011, and show its benefits over existing approaches.
Energy Requirements for Maintenance and Growth of Male Saanen Goat Kids

The aim of study was to determine the energy requirements for maintenance and growth of forty-one Saanen, intact male kids with initial body weight (BW) of 5.12±0.19 kg. The baseline (BL) group consisted of eight kids averaging 5.46±0.18 kg BW. An intermediate group consisted of six kids, fed for ad libitum intake, that were slaughtered when they reached an average BW of 12.9±0.29 kg. The remaining kids (n = 27) were randomly allocated into nine slaughter groups (blocks) of three animals distributed among three amounts of dry matter intake (DMI; ad libitum and restricted to 70% or 40% of ad libitum intake). Animals in a group were slaughtered when the ad libitum-treatment kid in the group reached 20 kg BW. In a digestibility trial, 21 kids (same animals of the comparative slaughter) were housed in metabolic cages and used in a completely randomized design to evaluate the energetic value of the diet at different feed intake levels. The net energy for maintenance (NEm) was 417 kJ/kg0.75 of empty BW (EBW)/d, while the metabolizable energy for maintenance (MEm) was 657 kJ/kg0.75 of EBW/d. The efficiency of ME use for NE maintenance (km) was 0.64. Body fat content varied from 59.91 to 92.02 g/kg of EBW while body energy content varied from 6.37 to 7.76 MJ/kg of EBW, respectively, for 5 and 20 kg of EBW. The net energy for growth (NEg) ranged from 7.4 to 9.0 MJ/kg of empty weight gain by day at 5 and 20 kg BW, respectively. This study indicated that the energy requirements in goats were lower than previously published requirements for growing dairy goats.
Pairwise similarity of TopSig document signatures

This paper analyses the pairwise distances of signatures produced by the TopSig retrieval model on two document collections. The distribution of the distances are compared to purely random signatures. It explains why TopSig is only competitive with state of the art retrieval models at early precision. Only the local neighbourhood of the signatures is interpretable. We suggest this is a common property of vector space models.
Smart cheaters do prosper: defeating trust and reputation systems

Traders in electronic marketplaces may behave dishonestly, cheating other agents. A multitude of trust and reputation systems have been proposed to try to cope with the problem of cheating. These systems are often evaluated by measuring their performance against simple agents that cheat randomly. Unfortunately, these systems are not often evaluated from the perspective of security---can a motivated attacker defeat the protection? Previously, it was argued that existing systems may suffer from vulnerabilities that permit effective, profitable cheating despite the use of the system. In this work, we experimentally substantiate the presence of these vulnerabilities by successfully implementing and testing a number of such 'attacks', which consist only of sequences of sales (honest and dishonest) that can be executed in the system. This investigation also reveals two new, previously-unnoted cheating techniques. Our success in executing these attacks compellingly makes a key point: security must be a central design goal for developers of trust and reputation systems.
Electrophoretic Deposition of Cadmium Sulfide Nanoparticles: Electric Field and Particle Size Effects

The present work shows the electric field and particle size effects on the formation of nanostructured arrays by electrophoretic deposition of cadmium sulfide (CdS) nanoparticles. The CdS nanoparticles with mean diameter below 6 nm were prepared by a microwave assisted synthesis. These nanoparticles were aged for one and two weeks at room temperature in order to produce nanoparticle agglomeration. The CdS nanoparticles were deposited on aluminum plates, with 1 cm of distance between them, using a constant applied voltage of 600 and 900 mV for 1 min. The nanostructures formed using CdS nanoparticles freshly prepared under 900 mV show spherical morphology. Under a voltage of 600 mV, nanostructures with elongated morphology were obtained.
A New Approach to Dynamic Fuzzy Modeling of Genetic Regulatory Networks

In this paper, the dynamic fuzzy modeling approach is applied for modeling genetic regulatory networks from gene expression data. The parameters of the dynamic fuzzy model and the optimal number of fuzzy rules for the fuzzy gene network can be obtained via the proposed modeling approach from the measured gene expression data. One of the main features of the proposed approach is that the prior qualitative knowledge on the network structure can be easily incorporated in the proposed identification algorithm, so that the faster learning convergence of the algorithm can be achieved. Two sets of data, one the synthetic data, and the other the experimental SOS DNA repair network data with structural knowledge, have been used to validate the proposed modeling approach. It is shown that the proposed approach is effective in modeling genetic regulatory networks.
LOC 390443 (RNase 9) on Chromosome 14q11.2 Is Related to the RNase A Superfamily and Contains a Unique Amino-Terminal Preproteinlike Sequence

A new member of the human RNase A superfamily is reported. Identified in the human genome assembly as LOC 390443, this locus is located 128 kb telomeric to the established RNase A gene family cluster on chromosome 14q11.2. The amino acid sequence of this locus is sufficiently similar to the eight previously identified gene family members to warrant a designation as RNase 9. RNase 9 is expressed in a wide range of human tissues. In addition, a 30–amino acid sequence lying between a 26–amino acid putative signal peptide and the last 148 amino acids that align with the other RNases A is not seen in other members of the RNase A superfamily in any species. Nucleotide and amino acid sequences of RNase 9 in 13 nonhuman primate species were determined and indicate several conserved sites but, also, an excess of nonsynonymous substitutions, about one-third of which are radical substitutions. This suggests that RNase 9, similar to several other human RNases A, has been under diversifying selection in the primates. Data from the mouse and rat genomes indicate that RNase 9 is also present in rodents, thus making it older than most of the established members of the human RNase A superfamily. Many of the human RNases A have been shown to have antimicrobial, antiviral, or antiparasitic functions involved in host-defense mechanisms. The features of RNase 9 described here suggest that it, too, may be involved in host defense and that it, along with the rest of the superfamily, may prove to have played an important role in anthropoid evolution.
CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation

This paper proposes constraint propagation relaxation (CPR), a probabilistic approach to classical constraint propagation that provides another view on the whole parametric family of survey propagation algorithms SP(ρ). More importantly, the approach elucidates the implicit, but fundamental assumptions underlying SP(ρ), thus shedding some light on its effectiveness and leading to applications beyond k-SAT.
Block Based Video Watermarking Scheme Using Wavelet Transform and Principle Component Analysis

In this paper, a comprehensive approach for digital video watermarking is introduced, where a binary watermark image is embedded into the video frames. Each video frame is decomposed into sub-images using 2 level discrete wavelet transform then the Principle Component Analysis (PCA) transformation is applied for each block in the two bands LL and HH. The watermark is embedded into the maximum coefficient of the PCA block of the two bands. The proposed scheme is tested using a number of video sequences. Experimental results show high imperceptibility where there is no noticeable difference between the watermarked video frames and the original frames. The computed PSNR achieves high score which is 44.097 db. The proposed scheme shows high robustness against several attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, and contrast adjustment.
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank . In addition, existing approaches do not take into account uncertainty information of latent factors, as well as missing entries. To address these issues, we formulate CP factorization using a hierarchical probabilistic model and employ a fully Bayesian treatment by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters, resulting in automatic rank determination. To learn the model, we develop an efficient deterministic Bayesian inference algorithm, which scales linearly with data size. Our method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries. Extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground-truth of CP rank and prevent the overfitting problem, even when a large amount of entries are missing. Moreover, the results from real-world applications, including image inpainting and facial image synthesis, demonstrate that our method outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.
Biology, detection, and clinical implications of circulating tumor cells

Cancer metastasis is the main cause of cancer‐related death, and dissemination of tumor cells through the blood circulation is an important intermediate step that also exemplifies the switch from localized to systemic disease. Early detection and characterization of circulating tumor cells (CTCs) is therefore important as a general strategy to monitor and prevent the development of overt metastatic disease. Furthermore, sequential analysis of CTCs can provide clinically relevant information on the effectiveness and progression of systemic therapies (e.g., chemo‐, hormonal, or targeted therapies with antibodies or small inhibitors). Although many advances have been made regarding the detection and molecular characterization of CTCs, several challenges still exist that limit the current use of this important diagnostic approach. In this review, we discuss the biology of tumor cell dissemination, technical advances, as well as the challenges and potential clinical implications of CTC detection and characterization.
Research on mixed model-based Chinese relation extraction

Relation Extraction is an important research field in Information Extraction. In this paper, we present a novel mixed model to extract relation between named entities in Chinese, which combines the merits of both feature based method and tree kernel based method. Feature based method captures the language information of the text, while, the tree kernel based method shows the structured information of the text. We evaluate the proposed model on the ACE(Automatic Content Extraction) 2005 corpus. The experiments show that our model can identify the majority of the non-relational instances and also has a good precision and recall rate on the identification of various relation types.
Hyperactive mTOR induces neuroendocrine differentiation in prostate cancer cell with concurrent up‐regulation of IRF1

Neuroendocrine‐differentiated prostate cancer (NEPCa) is refractory to androgen deprivation therapy and shows a poor prognosis. The underlying mechanisms responsible for neuroendocrine differentiation (NED) are yet to be clarified. In this study, we investigated the role of mammalian target of rapamycin (mTOR) in NEPCa.
Clinical performance of DIAGNOdent in the detection of secondary carious lesions

The diagnostic value of DIAGNOdent in detecting primary occlusal caries has been investigated in many studies, although its use in in vivo detection of secondary caries remains unclear. The aim of this study was to investigate the ability of DIAGNOdent in in vivo detection of secondary caries on teeth with amalgam restorations. The material comprised 51 posterior teeth restored with amalgam material. Bitewing radiographs were taken of all teeth, in accordance with the standard clinical protocol, and analysed by five observers with respect to secondary caries. The restoration margins of each tooth were carefully scanned with DIAGNOdent and the site of the highest reading and its value were registered in a digital picture. The color (stained/unstained) of the restoration margins was also documented. The restoration material was removed and all cavities were examined carefully by two observers together, both visually and by probe. The results showed that the sensitivity and specificity of DIAGNOdent and conventional radiography in detecting secondary caries were 0.60/0.81 and 0.56/0.92, respectively. For DIAGNOdent, 100% of the teeth in the false-positive fraction had stains. Regarding receiver operating characteristic analyses, the Az values were 0.78 and 0.69 for DIAGNOdent and radiography, respectively. We conclude that DIAGNOdent may be used only as an adjunct to conventional methods in detecting secondary caries on teeth with amalgam restorations.
Online AdaBoost ECOC for image classification

We present a novel online algorithm called online AdaBoost ECOC (error-correcting output codes) for image classification problems. In recent years, AdaBoost is very successful in many domains such as object detection in images and videos. It is a representative large margin classifier for binary classification problems and is efficient for on-line learning. However, image classification is a typical multi-class problem. It is difficult to use AdaBoost here, especially in an online version of image classification problem. In this paper, we combine online AdaBoost and ECOC algorithm to solve online multi-class image classification problems. We perform online AdaBoost ECOC on MNIST handwritten digit, ORL face and UCI image database. The results show our algorithm's accuracy and robustness.
Mosaic Evolution and Adaptive Brain Component Alteration under Domestication Seen on the Background of Evolutionary Theory

Brain sizes and brain component sizes of five domesticated pigeon breeds including homing (racing) pigeons are compared with rock doves (Columba livia) based on an allometric approach to test the influence of domestication on brain and brain component size. Net brain volume, the volumes of cerebellum and telencephalon as a whole are significantly smaller in almost all domestic pigeons. Inside the telencephalon, mesopallium, nidopallium (+ entopallium + arcopallium) and septum are smaller as well. The hippocampus is significantly larger, particularly in homing pigeons. This finding is in contrast to the predictions of the ‘regression hypothesis’ of brain alteration under domestication. Among the domestic pigeons homing pigeons have significantly larger olfactory bulbs. These data are interpreted as representing a functional adaptation to homing that is based on spatial cognition and sensory integration. We argue that domestication as seen in domestic pigeons is not principally different from evolution in the wild, but represents a heuristic model to understand the evolutionary process in terms of adaptation and optimization.
Racial Disparities in Breast Cancer Diagnosis and Treatment by Hormone Receptor and HER2 Status

Background: African American and Hispanic women are more likely to be diagnosed with aggressive forms of breast cancer. Disparities within each subtype of breast cancer have not been well documented. Methods: Using data from 18 SEER cancer registries, we identified 102,064 women aged 20 years or older, diagnosed with invasive breast cancer in 2010–2011, and with known stage, hormone receptor (HR), and HER2 status. Associations between race/ethnicity and cancer stage and receipt of guideline-concordant treatment were evaluated according to HR/HER2 status. Results: Overall, African American and Hispanic women were 30% to 60% more likely to be diagnosed with stage II–IV breast cancer compared with non-Hispanic whites. African American women had 40% to 70% higher risks of stage IV breast cancer across all four subtypes. American Indian/Alaska Native women had a 3.9-fold higher risk of stage IV triple-negative breast cancer. African American and Hispanic whites were 30% to 40% more likely to receive non–guideline-concordant treatment for breast cancer overall and across subtypes. Conclusions: Women in several racial/ethnic groups are more likely to be diagnosed with more advanced stage breast cancer. African American and American Indian/Alaska Native women in particular had the highest risk of being diagnosed with stage IV triple-negative breast cancer. African American and Hispanic women were also consistently at higher risk of not receiving guideline-concordant treatment across subtypes. Impact: These findings provide important characterization of which subtypes of breast cancer racial/ethnic disparities in stage and treatment persist. Cancer Epidemiol Biomarkers Prev; 24(11); 1666–72. ©2015 AACR.
Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis.
From Scattered Samples to Smooth Surfaces

The problem of reconstructing smooth surfaces from discrete scattered sample points arises in many fields of science and engineering and the data sources include measured values (laser range scanning, meteorology, geology) as well as experimental results (engineering, physics, chemistry) and computational values (evaluation of functions, finite element solutions, numerical simulations). We describe a processing pipeline that can be understood as gradually adding order to a given unstructured point cloud until it is completely organized in terms of a smooth surface. The individual steps of this pipeline are triangulation, remeshing, and surface fitting and have in common that they are much simpler to perform in two dimensions than in three. We therefore propose to use a parameterization in each of these steps so as to decrease the dimensionality of the problem and to reduce the computational complexity. CR Descriptors: I.3. 5 [Computer Graphics]: Computational Geometry and Object Modeling; J.6 [Computer-Aided Engineering]: Computer-Aided Design (CAD); G.1. 2 [Approximation]: Approximation of surfaces and contours
Thin dye layers – vacuum deposition and structure

Vacuum-deposited layers of zinc phthalocyanine and perylene-3,4,9,10-tetracarboxylic dianhydride were obtained with regard to the development of converters of solar radiation into electricity, namely, organic solar cells. It was shown that there were no differences in the composition of the initial dyes and the deposited ones, which is a proof of their thermal stability and a prerequisite for the reproducibility of the composition of the single and mixed layers under study. The results obtained are interpreted in view of the possible applicability of the layers studied for the fabrication of organic solar cells in which the processes of creation, the lifetime and the diffusion paths of the current carriers depend significantly on the film composition and the defects formed at the time of deposition of the active dye layers.
Performance evaluation of single-tier and two-tier cloudlet assisted applications

As the usage of the mobile devices increases, the user expectations for the performance of the smart applications grow as well. One of the basic criteria of the performance is the seamless user experience. Edge Computing approaches are becoming popular for supporting real time applications for which the traditional cloud based solutions do not suffice. For time-sensitive applications such as the real time speech-to-text conversion, face recognition and simultaneous translation, users require fast responsivity while communicating with the remote servers. High latency during this communication will yield to poor user experience. To overcome this major bottleneck, the cloudlet which is a specific form of Edge Computing bring opportunities. The cloudlets provide better performance by faster service compared to the conventional remote cloud connections. The cloudlets are used for different purposes on different architectures to solve domain specific problems such as caching, reducing delay, taking real-time decisions and bringing the cloud resources to closer. In this paper, the performance of three possible cloudlet-assisted Edge Computing architectures are evaluated by considering a generic delay-intolerant application.