Miracle-dz/UniFAR-FTUs huggingface dataset:
Medium- and Short-Term Interventions with Ma-Pi 2 Macrobiotic Diet in Type 2 Diabetic Adults of Bauta, Havana

Background. In Cuba, the Ma-Pi 2 macrobiotic diet has shown positive results in 6-month assays with type 2 diabetic patients. The objective of this study was to assess the influence of this diet at short and medium terms. Methods. Sixty-five type 2 diabetic volunteers were included for dietary intervention, institutionally based for 21 days and followed later at home, until completing 3 months. 54 of them stayed until assay end. Before intervention, and after both assay periods, they were submitted to anthropometric records, body composition analyses and measurements of serum biochemical indicators, glycemic profile in capillary blood, blood pressure, and medication consumption; food intake was evaluated by the 3-day dietary recall. Results. During the intervention, the energy intake was 200 kcal higher at instance of more complex carbohydrates and dietary fiber and despite less fat and protein. Blood pressure and serum biochemical indicators decreased significantly in both periods; the safety nutritional indicators (hemoglobin, serum total proteins, and albumin) showed no variations. The global cardiovascular risk decreased and insulin consumption dropped by 46% and 64%, in both periods, respectively. Conclusions. The Ma-Pi 2 macrobiotic diet was a successful therapy at short term and after 3-month home-based intervention, for type 2 diabetics.
Improving scale invariant feature transform with local color contrastive descriptor for image classification

Image representation and classification are two fundamental tasks toward version understanding. Shape and texture provide two key features for visual representation and have been widely exploited in a number of successful local descriptors, e.g., scale invariant feature transform (SIFT), local binary pattern descriptor, and histogram of oriented gradient. Unlike these gradient-based descriptors, this paper presents a simple yet efficient local descriptor, named local color contrastive descriptor (LCCD), which captures the contrastive aspects among local regions or color channels for image representation. LCCD is partly inspired by the neural science facts that color contrast plays important roles in visual perception and there exist strong linkages between color and shape. We leverage f-divergence as a robust measure to estimate the contrastive features between different spatial locations and multiple channels. Our descriptor enriches local image representation with both color and contrast information. Due to that LCCD does not explore any gradient information, individual LCCD does not yield strong performance. But we verified experimentally that LCCD can compensate strongly SIFT. Extensive experimental results on image classification show that our descriptor improves the performance of SIFT substantially by combination on three challenging benchmarks, including MIT Indoor-67 database, SUN397, and PASCAL VOC 2007.
Automatic Piano Transcription Using Frequency and Time-Domain Information

The aim of this paper is to propose solutions to some problems that arise in automatic polyphonic transcription of recorded piano music. First, we propose a method that groups spectral information in the frequency-domain and uses a rule-based framework to deal with the known problems of polyphony and harmonicity. Then, we present a novel method for multipitch-estimation that uses both frequency and time-domain information. It assumes signal segments to be the linearly weighted sum of waveforms in a database of individual piano notes. We propose a solution to the problem of generating those waveforms, by using the frequency-domain approach. We show that accurate time-domain transcription can be achieved given an adequate estimation of the database. This suggests an alternative to common frequency-domain approaches that does not require any prior training on a separate database of isolated notes
CloudScope: Diagnosing and Managing Performance Interference in Multi-tenant Clouds

Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%.
Multi-channel and multi-scale mid-level image representation for scene classification

Abstract. Convolutional neural network (CNN)-based approaches have received state-of-the-art results in scene classification. Features from the output of fully connected (FC) layers express one-dimensional semantic information but lose the detailed information of objects and the spatial information of scene categories. On the contrary, deep convolutional features have been proved to be more suitable for describing an object itself and the spatial relations among objects in an image. In addition, the feature map from each layer is max-pooled within local neighborhoods, which weakens the invariance of global consistency and is unfavorable to scenes with highly complicated variation. To cope with the above issues, an orderless multi-channel mid-level image representation on pre-trained CNN features is proposed to improve the classification performance. The mid-level image representation of two channels from the FC layer and the deep convolutional layer are integrated at multi-scale levels. A sum pooling approach is also employed to aggregate multi-scale mid-level image representation to highlight the importance of the descriptors beneficial for scene classification. Extensive experiments on SUN397 and MIT 67 indoor datasets demonstrate that the proposed method achieves promising classification performance.
Efficient region-based approach for blotch detection in archived video using texture information

Abstract. We propose a method for blotch detection in archived videos by modeling their spatiotemporal properties. We introduce an adaptive spatiotemporal segmentation to extract candidate regions that can be classified as blotches. Then, the similarity between the preselected regions and their corresponding motion-compensated regions in the adjacent frames is assessed by means of motion trajectory estimation and textural information analysis. Perceived ground truth based on just noticeable contrast is employed for the evaluation of our approach against the state-of-the-art, and the reported results show a better performance for our approach.
Object detection system based on multimodel saliency maps

Abstract. Detection of visually salient image regions is extensively applied in computer vision and computer graphics, such as object detection, adaptive compression, and object recognition, but any single model always has its limitations to various images, so in our work, we establish a method based on multimodel saliency maps to detect the object, which intelligently absorbs the merits of various individual saliency detection models to achieve promising results. The method can be roughly divided into three steps: in the first step, we propose a decision-making system to evaluate saliency maps obtained by seven competitive methods and merely select the three most valuable saliency maps; in the second step, we introduce heterogeneous PCNN algorithm to obtain three prime foregrounds; and then a self-designed nonlinear fusion method is proposed to merge these saliency maps; at last, the adaptive improved and simplified PCNN model is used to detect the object. Our proposed method can constitute an object detection system for different occasions, which requires no training, is simple, and highly efficient. The proposed saliency fusion technique shows better performance over a broad range of images and enriches the applicability range by fusing different individual saliency models, this proposed system is worthy enough to be called a strong model. Moreover, the proposed adaptive improved SPCNN model is stemmed from the Eckhorn’s neuron model, which is skilled in image segmentation because of its biological background, and in which all the parameters are adaptive to image information. We extensively appraise our algorithm on classical salient object detection database, and the experimental results demonstrate that the aggregation of saliency maps outperforms the best saliency model in all cases, yielding highest precision of 89.90%, better recall rates of 98.20%, greatest F-measure of 91.20%, and lowest mean absolute error value of 0.057, the value of proposed saliency evaluation EHA reaches to 215.287. We deem our method can be wielded to diverse applications in the future.
License plate detection in an open environment by density-based boundary clustering

Abstract. Due to the variation of background, illumination, and view point, license plate detection in an open environment is challenging. We propose a detection method by boundary clustering. To start with, a boundary map is obtained through Canny edge detector and removal of unwanted horizontal background edges. Second, boundaries are classified into different clusters by a density-based approach. In the approach, the density of each boundary is defined by the total gradient intensity of its neighboring and reachable boundaries. Also, the cluster centers and the number of them are determined automatically according to a minimum-distance principle. At last, a set of horizontal candidate regions with accurately located borders are extracted for classification. The classifier is trained on the histogram of oriented gradient feature by a linear support vector machine model. Experiments on three public datasets including images captured under different scenarios demonstrate that the proposed method outperforms several state-of-the-art methods in detection accuracy and its performance in efficiency is also comparable.
Three-dimensional object tracking based on perspective scale invariant feature transform correspondences

Abstract. Reconstructing three-dimensional (3-D) poses from matched feature correspondences is widely used in 3-D object tracking. The precision of correspondence matching plays a major role in the pose reconstruction. Without prior knowledge of the perspective camera model, state-of-the-art methods only deal with two-dimensional (2-D) planar affine transforms. An interest point’s detector and descriptor [perspective scale invariant feature transform (SIFT)] is proposed to overcome the side effects of viewpoint changing, i.e., our detector is invariant to viewpoint changing. Perspective SIFT is detected by the SIFT approach, where the sample region is determined by projecting the original sample region to the image plane based on the established camera model. An iterative algorithm then modifies the pose of the tracked object and it generally converges to a 3-D perspective invariant point. The pose of the tracked object is finally estimated by the combination of template warping and perspective SIFT correspondences. Thorough evaluations are performed on two public databases, the Biwi Head Pose dataset and the Boston University dataset. Comparisons illustrate that the proposed keypoint’s detector largely improves the tracking performance.
Hyperspectral image filtering with adaptive manifold for classification

Abstract. Hyperspectral image (HSI) is a three-dimensional data cube containing two spatial information dimensions and one spectral information dimension. The spectral vectors of different classes may have similar tendency and value that may bring about negative influences on classification. It is, therefore, important to introduce signal preprocessing techniques in the spatial domain to improve classification accuracy of HSIs. Assuming that local pixels in HSI have some correlations with each other, this paper proposes a spatial filtering model based on adaptive manifold (AM) for HSI. The AM for spatial filtering emphasizes the similar neighboring pixels and is robust to resist the noisy points with fast speed. The rich information in the filtered data is effective for improving the performance of the subsequent classification. The filtered data are classified by an extreme learning machine (ELM). The experimental results indicate that the framework built based on AM and ELM provides competitive performance. Specifically, by classifying the filtered data, the average accuracy of ELM can be improved as high as 30.54%, while performing tens to hundreds times faster than those state-of-the-art classifiers.
Fuzzy logic method for partial fingerprint recognition

Minutia-based algorithms are the preferred methods for matching fingerprints, because of their temporal performances. Although the correlation techniques are considered to be more reliable, they are much slower due to the fact that a large number of alignments (rotations and displacements) need to be taken into account. On the other hand, minutiae-based methods perform poorly when applied to low quality images, and might be impossible to employ for partial fingerprint recognition. This paper proposes a novel fuzzy logic method for matching partial fingerprints. The inputs of the proposed fuzzy logic algorithm are the correlation degree and the relative surface of the input fingerprint.
Insulin, pioglitazone and Zingiber officinale administrations improve proliferating cell nuclear antigen immunostaining effects on diabetic and insulin resistant rat testis

This study accessed the effects of hypoglycaemic drugs on spermatogenesis in diabetic and insulin resistant rat testis following proliferating cell nuclear antigen (PCNA) immunostaining. Male adult Sprague-Dawley rats (120-140 g) were randomly divided into 5 groups. Group 1 served as control group; fed on normal rat pellets. Group 2 served as streptozotocin-insulin treated group; received a single dose IP Injection of streptozotocin 45 mg/kg BW in Na+ citrate buffer pH 4.5 and treated with insulin sub-cutaneously. Group 3 served as streptozotocin-ginger treated group; received a single dose IP injection of streptozotocin as in group 2 above and treated with 500 mg/Kg ginger extract orally. Group 4 served as Insulin resistant-pioglitazone treated group; fed ad libitum on a special diet containing 25% fructose mixed with 75% normal rat chow (w/w) and treated with Pioglitazone 15 mg/kg orally. Group 5 served as Insulin resistant-ginger treated group; fed ad libitum on a special diet as in group 4 above, and also treated with 500 mg/Kg ginger extract orally. Following hyperglycemia confirmation, animals were perfused with 4% Paraformaldehyde (PFA). Testes were isolated, and fixed in 4% PFA overnight, embedded in paraffin, 5μm thick sections were made and mounted on poly-L-lysine coated slides. Immunohistochemical staining was carried out on positively charged slides using PCNA as primary antibody. The results showed that insulin, pioglitazone and ginger administrations ameliorated the diabetic and insulin resistant effects on spermatogenesis in male rats. The precise interplay of hypothalamic, pituitary and steroid hormones is subject for further study in these group of animals. J. Exp. Clin. Med., 2013; 30:49-55
Desirability or Feasibility

Making decisions for the self and providing advice to others are common in daily life. The current research examines the differences in weight that people attach to desirability and feasibility when deciding for themselves versus others. Based on construal level theory, we propose that in a decision-making process, individuals who decide for others tend to focus more on desirability than on feasibility compared with those who decide for themselves. Across five experiments, the predicted self–other differences were observed in preference in the decision stage (Experiments 1a and 1b), information seeking in the predecision stage (Experiment 2), and information recall in the postdecision stage (Experiments 3a and 3b). These findings show that decision behaviors are determined by the decision target (i.e., for whom such decisions are made).
Physics of the Merging Clusters Cygnus A, A3667, and A2065

We present ASCA gas temperature maps of the nearby merging galaxy clusters C ygnus A, A3667, and A2065. Cygnus A appears to have a particularly simple merger geomet ry that allows an estimate of the subcluster collision velocity from the observed temperature variations. We esti mate it to be∼ 2000 km s−1. Interestingly, this is similar to the free-fall velocity that the two Cygnus A subcl usters should have achieved at the observed separation, suggesting that merger has been effective in dissipating th e kinetic energy of gas halos into thermal energy, without channeling its major fraction elsewhere (e.g., into turbul ence). In A3667, we may be observing a spatial lag between the shock front seen in the X-ray image and the corres ponding rise of the electron temperature. A lag of the order of hundreds of kiloparsecs is possible due to the combination of thermal conduction and a finite electron-ion equilibration time. Forthcoming better spat ial resolution data will allow a direct measurement of these phenomena in the cluster gas using such lags. A2065 has gas de nsity peaks coincident with two central galaxies. A merger with the collision velocity estimated from the temp erature map should have swept away such peaks if the subcluster total mass distributions had flat cores in t he centers. The fact that the peaks have survived (or quickly reemerged) suggests that the gravitational potent ial also is strongly peaked. Finally, the observed specific entropy variations in A3667 and Cygnus A indicate that energ y injection from a single major merger may be of the order of the full thermal energy of the gas. We hope that th ese order of magnitude estimates will encourage further work on hydrodynamic simulations, as well as more qu antitative representation of the simulation results, in anticipation of theChandra andXMM data. Subject headings: cooling flows — dark matter — galaxies: clusters: individual (Cygnus A, A2065, A3667) — intergalactic medium — X-rays: galaxies
Deformation-based atrophy computation by surface propagation and its application to Alzheimer’s disease

Abstract. Obtaining regional volume changes from a deformation field is more precise when using simplex counting (SC) compared with Jacobian integration (JI) due to the numerics involved in the latter. Although SC has been proposed before, numerical properties underpinning the method and a thorough evaluation of the method against JI is missing in the literature. The contributions of this paper are: (a) we propose surface propagation (SP)—a simplification to SC that significantly reduces its computational complexity; (b) we will derive the orders of approximation of SP which can also be extended to SC. In the experiments, we will begin by empirically showing that SP is indeed nearly identical to SC, and that both methods are more stable than JI in presence of moderate to large deformation noise. Since SC and SP are identical, we consider SP as a representative of both the methods for a practical evaluation against JI. In a real application on Alzheimer’s disease neuroimaging initiative data, we show the following: (a) SP produces whole brain and medial temporal lobe atrophy numbers that are significantly better than JI at separating between normal controls and Alzheimer’s disease patients; (b) SP produces disease group atrophy differences comparable to or better than those obtained using FreeSurfer, demonstrating the validity of the obtained clinical results. Finally, in a reproducibility study, we show that the voxel-wise application of SP yields significantly lower variance when compared to JI.
Understanding Multiple Features with Hypercube for Distinguishing Uncertain Objects in Mobile Crowdsensing

Uncertain data are inherent in mobile crowd sensing applications, and the objects that they correspond to are usually vaguely specified. In order to improve performance, we often increase the number of features. However, the more features are used, the more redundancy and cost are involved correspondingly. Therefore, the number of features we selected for a specified application is a tradeoffs between the accuracy and the cost. In this paper, we model such tradeoffs between accuracy and cost as an optimization problem. Moreover, for investigating this problem, we propose to model the sensing with multiple features under a hypercube structure. In our scheme, each feature of uncertain objects is represented as a component of the vertex's coordinate in hypercube. At the same time, we prefer to define the edges between vertices with relative entropy rather than Euclidean distance. Because the former one could accurately measures the difference between two probability distributions of data. We evaluate our proposed schemes with real data of a crowd sensing recognition case, which are collected by smartphones with sensors.
Hails From the Crypt

Interfacing the terror management health model with the meaning transfer model, we offer novel hypotheses concerning the effectiveness of celebrity and medical endorsements for consumer products and health behavior decisions. Studies 1 and 2 revealed that, compared with control topic primes, death thoughts in focal attention increased the effectiveness of health-oriented (doctor) endorsers but not culture-oriented (celebrity) endorsers, whereas death thoughts outside of focal attention increased the effectiveness of culture-oriented endorsers but not health-oriented endorsers. Studies 3 and 4 then focus more specifically on the valence and specificity of culture-oriented endorsements, revealing that death thoughts outside focal attention increase the effectiveness of culture-oriented endorsers only on the behaviors specifically endorsed and only when the endorser is characterized as possessing cultural value. Discussion focuses on everyday management of existential concerns and implications for persuasive communications in the health domain.
Video Games Do Affect Social Outcomes

Whether video game play affects social behavior is a topic of debate. Many argue that aggression and helping are affected by video game play, whereas this stance is disputed by others. The present research provides a meta-analytical test of the idea that depending on their content, video games do affect social outcomes. Data from 98 independent studies with 36,965 participants revealed that for both violent video games and prosocial video games, there was a significant association with social outcomes. Whereas violent video games increase aggression and aggression-related variables and decrease prosocial outcomes, prosocial video games have the opposite effects. These effects were reliable across experimental, correlational, and longitudinal studies, indicating that video game exposure causally affects social outcomes and that there are both short- and long-term effects.