Miracle-dz/UniFAR-FTUs huggingface dataset:
Age estimation from human body images

In this paper, we investigate the problem of estimating human ages from full body images. To our best knowledge, this problem has not been formally addressed before possibly due to the great challenges and lacking of such publicly available datasets. However, estimating human ages at a distance has a number of potential applications, especially for visual surveillance in such places as supermarkets, airports, building entrances, and shopping malls. In this paper, we propose a new human age estimation approach from full body images with frontal or back views. Our contributions are three-fold. First, we collect a human body image dataset containing 1500 public figures or celebrities searched from the internet, as well as the age label information of each image. Second, we explore several widely used human local appearance feature descriptors with a regression model to estimate human ages from these body images. Lastly, we apply a multiview canonical correlation analysis (MCCA) method by making use of multiple feature descriptors to exploit complementary information to further improve the age estimation performance. Experimental results have clearly demonstrated the feasibility of using fully body images to estimate human age and the efficacy of our proposed approach.
Array processing of SSR signals in the multilateration context, a decade survey

Secondary surveillance radar (SSR) was originated in the 40's as an Identification Friend or Foe (IFF) system and, starting from the psila50 and psila60, provided the air traffic control (ATC) with identity and altitude data for the aircraft under coverage, implementing a rough form of cooperative independent surveillance, complementing the non-cooperative surveillance of the primary radar and very often working in a joint manner with it (co-location, co-rotation of the antennae). In the psila70 and psila80 a deep system-level update of the SSR was adopted at international level, namely the selective mode, or Mode S, compatible with previous Modes A and C and providing a data link between the aircraft and the ground-station, i.e. the secondary radar. Following this update and its new signal formats, two main research directions were investigated from half psila90: distributed ground systems enabling multilateration, and array source separation enabling operation in high traffic on the downlink RF channel. This paper provides a survey of this last decade, as seen from the perspective of its authors.
Matched filter detection with dynamic threshold for cognitive radio networks

In cognitive radio networks, spectrum sensing aims to detect the unused spectrum channels in order to use the radio spectrum more efficiently. Various methods have been proposed in the past, such as energy, feature detection, and matched filter. These methods are characterized by a sensing threshold, which plays an important role in the sensing performance. Most of the existing techniques used a static threshold. However, the noise is random, and, thus the threshold should be dynamic. In this paper, we suggest an approach with an estimated and dynamic sensing threshold to increase the efficiency of the sensing detection. The matched filter method with dynamic threshold is simulated and its results are compared to those of other existing techniques.
New low-power 1.5-bit time-interleaved MDAC based on MOS capacitor amplification

In this paper a new time-interleaved 1.5-bit MDAC circuit is proposed. This circuit is well suited to be used in ultra low-power high-speed 4-to-8 bits pipeline ADCs. The required gain of two is implemented by switching a MOS capacitor from inversion into depletion within a clock-cycle. Low-power is achieved since no operational amplifiers are required but, instead, simple source-followers are used. Simulation results of a complete front-end stage of a 6-bit 2-channel pipeline ADC demonstrate the efficiency of the proposed technique.
Fast Kronecker Product Kernel Methods via Generalized Vec Trick

Kronecker product kernel provides the standard approach in the kernel methods’ literature for learning from graph data, where edges are labeled and both start and end vertices have their own feature representations. The methods allow generalization to such new edges, whose start and end vertices do not appear in the training data, a setting known as zero-shot or zero-data learning. Such a setting occurs in numerous applications, including drug-target interaction prediction, collaborative filtering, and information retrieval. Efficient training algorithms based on the so-called vec trick that makes use of the special structure of the Kronecker product are known for the case where the training data are a complete bipartite graph. In this paper, we generalize these results to noncomplete training graphs. This allows us to derive a general framework for training Kronecker product kernel methods, as specific examples we implement Kronecker ridge regression and support vector machine algorithms. Experimental results demonstrate that the proposed approach leads to accurate models, while allowing order of magnitude improvements in training and prediction time.
On the Dynamic Multicast Problem for Coded Networks

We consider the problem of finding minimum-cost time-varying subgraphs that can deliver continuous service to dynamic multicast groups in coded networks (i.e. networks that use network coding). This problem is relevant for applications such as real-time media distribution. We formulate the problem within the framework of dynamic programming and apply dynamic programming theory to suggest how it may be solved.
Online Multiple Kernel Similarity Learning for Visual Search

Recent years have witnessed a number of studies on distance metric learning to improve visual similarity search in content-based image retrieval (CBIR). Despite their successes, most existing methods on distance metric learning are limited in two aspects. First, they usually assume the target proximity function follows the family of Mahalanobis distances, which limits their capacity of measuring similarity of complex patterns in real applications. Second, they often cannot effectively handle the similarity measure of multimodal data that may originate from multiple resources. To overcome these limitations, this paper investigates an online kernel similarity learning framework for learning kernel-based proximity functions which goes beyond the conventional linear distance metric learning approaches. Based on the framework, we propose a novel online multiple kernel similarity (OMKS) learning method which learns a flexible nonlinear proximity function with multiple kernels to improve visual similarity search in CBIR. We evaluate the proposed technique for CBIR on a variety of image data sets in which encouraging results show that OMKS outperforms the state-of-the-art techniques significantly.
Stratospheric impact of the Chisholm pyrocumulonimbus eruption: 1. Earth-viewing satellite perspective

[1] The pyrocumulonimbus storm near Chisholm, Alberta, on 28 May 2001 has been studied in depth. However, the impact of this eruption on the lower stratosphere has not been characterized. Here and in a companion paper we explore this topic. This paper focuses on the “young” Chisholm smoke plume, from the age of ∼3 h to 1 week, as observed by Earth-viewing satellite instruments. (The companion paper presents strictly profile data.) GOES visible and infrared image loops reveal the pyroconvective life cycle and initial transport of the smoke cloud. MISR stereographic heights are the first of their kind for a stratospheric cloud, showing smoke up to 5 km above the tropopause on 29 May. MODIS IR and visible images are analyzed to give constraints on plume height, thickness, and particle size. Infrared brightness temperature analyses reveal unique aspects of the “day-after” Chisholm plume. Particle sizes are 1/3 to 1/2 compared to normal cirrus crystals. The daytime 29 May plume is optically thick at tropopause temperatures yet smoky brown. A transition from deep anvil blow off to “dry” smoke is still occurring after ∼1.5 d. TOMS aerosol index is used as a proxy for areas of particularly high smoke plume altitude. The Chisholm smoke in the upper troposphere and lower stratosphere is traced with AI for 1 week as the plume blows across North America to western Europe. First estimates are made of stratospheric smoke mass in relation to emissions during pyroconvection. The 29 May stratospheric Chisholm pyroCb plume contains a mass between ∼1.39 × 104 and 1.09 × 105 t. This represents between ∼10% and 121% of total particle mass emitted from the fire on 28 May, calling into question some frequently assumed values for smoke single scatter albedo and/or emission estimates. Strictly in terms of mass, the stratospheric Chisholm plume amounted to ∼15% of background Northern Hemispheric stratospheric sulfate aerosol. Overall, the young pyroCb plume is seen to be a peculiar mixture of smoke aerosols and water-ice that confounds operational cloud/aerosol detection routines and exhibits extreme, and still mysterious, composition and life cycle features.
TrustMap: Towards Trust Recommendations for Maps

The web is a rich environment for exchanging spatial information. When spatial information is shared in the form of images, i.e., maps, these images almost never come with meta-information about how they were generated. This kind of meta-information is often called knowledge provenance. Access to knowledge provenance may facilitate users to make informed decisions about the quality of maps. In this paper, we propose TrustMap, a new approach for enhancing maps with trust recommendations. For a given map, TrustMap can generate recommendations from the knowledge provenance and a network of trust relations between sources of information used to derive the map. The paper describes a TrustMap implementation.
A vice peaks restraint algorithm with orthogonal subcarrier for AltBOC signal

With the continuous development of satellite navigation technology, on the basis of the original BOC signal that the AltBOC signal is proposed, which is applied in Galileo system and Compass system. Yet, inaccurate acquisition problem is also caused by the multi-peaks characteristic of AltBOC signal auto-correlation function. And the inter-modulation components and the plural form of subcarrier modulation mechanism are used in the AltBOC signal, which is extremely complex. Aiming at these problems, and considering the unsuitable problem of the traditional algorithms, the vice peaks restraint algorithm with orthogonal subcarrier is proposed. In this proposed algorithm, the square operation of the auto-correlation function and orthogonal correlation function for AltBOC signal is established to restrain the vice peaks. The simulation results show that this new algorithm is feasibility, and it has better anti-noise performance, which can provide theoretical basis for GNSS receiver.
The Phenomenology of Architecture

This piece focuses on the work of Juhani Pallasmaa who introduces phenomenological aspects of kinesthetic and multisensory perception of the human body into architecture theory. He argues that hand-drawing is a vital spatial and haptic exercise in facilitating architectural design. Through this process, architecture can emerge as the very ‘material’ existence of human embodied ‘immaterial’ emotion, feelings and wisdom. Hence, for Pallasmaa, architecture can be seen as an artistic practice, which entails multisensory and embodied thought in order to establish the sense of being in the world.
Coal-Packed Methane Biofilter for Mitigation of Green House Gas Emissions from Coal Mine Ventilation Air

Methane emitted by coal mine ventilation air (MVA) is a significant greenhouse gas. A mitigation strategy is the oxidation of methane to carbon dioxide, which is approximately twenty-one times less effective at global warming than methane on a mass-basis. The low non-combustible methane concentrations at high MVA flow rates call for a catalytic strategy of oxidation. A laboratory-scale coal-packed biofilter was designed and partially removed methane from humidified air at flow rates between 0.2 and 2.4 L min−1 at 30°C with nutrient solution added every three days. Methane oxidation was catalysed by a complex community of naturally-occurring microorganisms, with the most abundant member being identified by 16S rRNA gene sequence as belonging to the methanotrophic genus Methylocystis. Additional inoculation with a laboratory-grown culture of Methylosinus sporium, as investigated in a parallel run, only enhanced methane consumption during the initial 12 weeks. The greatest level of methane removal of 27.2±0.66 g methane m−3 empty bed h−1 was attained for the non-inoculated system, which was equivalent to removing 19.7±2.9% methane from an inlet concentration of 1% v/v at an inlet gas flow rate of 1.6 L min−1 (2.4 min empty bed residence time). These results show that low-cost coal packing holds promising potential as a suitable growth surface and contains methanotrophic microorganisms for the catalytic oxidative removal of methane.
Association Of Heterosis With Combining Ability And Genetic Divergence In Sesame (Sesamum Indicum L.)

Parental diversity is considered desirable to exploit heterosis in any breeding program. The present investigation was undertaken to search out whether any relationship existed between heterosis of cross combinations with phenetic divergence, combining ability and genetic divergence of parents in sesame. Seven sesame genotypes and their 21 cross combinations developed through half diallel mating were assessed for morphological markers, microsatellite markers and seed storage protein polymorphism to estimate different parameters. The clustering patterns of parents differed for morphological, protein and simple sequence repeats (SSRs), though some concordance was observed between phenetic and genetic divergence of parents. Heterosis, both mid-parent heterosis% (MPH%) and better parent heterosis% (BPH%), was positively and significantly correlated with specific combining ability and hybrids per se, but no specific trend transpired between morphological, protein and SSR marker data. However, SSR based genetic diversity (GD) value of above 0.5 between parents, predicted heterotic crosses more reliably.
Gender Expression, Body–Gender Identity Incongruence, Thin Ideal Internalization, and Lesbian Body Dissatisfaction

Given the gendered nature of body dissatisfaction and the especially varied experience of gender expression within lesbian subculture, we investigated how dimensions of lesbians’ gender expression and body–gender identity incongruence might account for heterogeneity in lesbian body dissatisfaction. In addition, we examined the potential mediating role of internalization of the thin appearance ideal in the dimensions of gender expression and body dissatisfaction links. Our study of 416 lesbians revealed that more masculine stereotypical traits were a unique negative predictor of body dissatisfaction. In addition, more masculine/butch (i.e., restrictive) emotional expression, higher feminine stereotypical traits, and more body–gender identity incongruence were unique positive predictors of body dissatisfaction. Finally, thin ideal internalization mediated the relations between masculine/butch appearance, masculine/butch emotional expression, and masculine stereotypical traits and body dissatisfaction. That is, higher levels of masculine/butch appearance and masculine stereotypical traits were related to less thin ideal internalization. In addition, higher levels of masculine/butch emotional expression (i.e., restricted emotionality) were related to more thin ideal internalization. Thin ideal internalization, in turn, was positively related to greater body dissatisfaction. These results highlight the importance of gender expression in identifying lesbians at greater and lesser risk for body image problems.
Evaluating youth character development programs using evolutionary evaluation and the systems evaluation protocol

ABSTRACT The importance of engaging in high quality program evaluation is a generally accepted principle underscored by external pressure from funders. High quality evaluation necessarily begins with good evaluation planning. This paper outlines Evolutionary Evaluation and specifically the Systems Evaluation Protocol, an approach that emphasizes practitioner-evaluator collaboration, results in tangible products for programs, and culminates in an evaluation plan appropriate for a specific program’s lifecycle stage. A case study of Inspire>Aspire, a program developed by Character Scotland and used widely in Scotland’s schools and elsewhere is presented and includes a discussion of creative breakthroughs, or ‘Aha! ’ Moments, that occurred.
A comparative study of Motion Descriptors and Zernike moments in color object recognition

Classification and object recognition is one of the most important tasks in image processing. Most appl ications deal with the classification of definite shapes, for example identifying a particular type aircraft. In these ap plications, compact visual descriptors are necessary to describe image content . Fourier descriptors are widely used in image proc essing to describe and classify object. Several techniques have proved useful momen t’s invariants. In this paper, we studied Motion de scriptors (MD) introduced recently by Gauthier et al.; combined with Zernike Moments (ZM). Experiments are conducted using three databases: COIL-100, which consists of 3D objects, A R faces and cellular phones database. Recognition is performed by a Supp ort Vector Machine as supervised classification method.
Discrete Shearlet Transform and Video Denoising

This paper introduces a numerical implementation of the 3D shearlet transform, a directional transform which is derived from the theory of shearlets. The shearlet approach belongs to a class of directional multiscale methods emerged during the last 10 years to overcome the limitations of traditional multiscale systems, which also include curvelets and contourlets. Unlike other methods, shearlets are derived from the theory of affine systems, which allows a very flexible mathematical structure and a natural transition from the continuous to the digital setting. Following the recent proof of the optimality of the 3D shearlet representation, in this paper we develop an algorithmic implementation of the 3D shearlet transform that follows closely the spatial-frequency pattern of the corresponding continuous transform. The performance of the algorithm is illustrated on problems of video denoising and successfully compared against other state-of-the-art multiscale techniques, including curvelets and surfacelets.
Fine-grained pose prediction, normalization, and recognition

Pose variation and subtle differences in appearance are key challenges to fine-grained classification. While deep networks have markedly improved general recognition, many approaches to fine-grained recognition rely on anchoring networks to parts for better accuracy. Identifying parts to find correspondence discounts pose variation so that features can be tuned to appearance. To this end previous methods have examined how to find parts and extract pose-normalized features. These methods have generally separated fine-grained recognition into stages which first localize parts using hand-engineered and coarsely-localized proposal features, and then separately learn deep descriptors centered on inferred part positions. We unify these steps in an end-to-end trainable network supervised by keypoint locations and class labels that localizes parts by a fully convolutional network to focus the learning of feature representations for the fine-grained classification task. Experiments on the popular CUB200 dataset show that our method is state-of-the-art and suggest a continuing role for strong supervision.
Streaming 3D geometry data over lossy communication channels

In this paper we propose a progressive 3D geometry transmission technique that is robust with respect to data loss. In a preprocessing step we decompose a given polygon mesh model into a set of overlapping ellipsoids, representing the coarse shape of the model, and a stream of sample points, representing its fine detail. On the client-side, we derive a coarse approximation of the model from the ellipsoid decomposition and then re-insert the sample points to reconstruct the fine detail. The overlapping ellipsoids as well as the sample points represent independent pieces of geometric information, hence partial data loss can be tolerated by our reconstruction algorithm and will only lead to a gradual degradation of the reconstruction quality. We present a transmission scheme that is especially well-suited for geometry broadcasting where we exploit the fact that the order of the sample points can be arbitrarily permuted.
NODE ADDRESSING SCHEMES FOR SCALABLE ROUTING IN HIERARCHICAL WSN

Wireless Sensor Network has a capacity of communication computation with node in a network. The sensor performs many significant tasks in a selective area. In this paper proposing the new idea to improve issues on node address Methodology that combine the structure of hierarchical tree with the flexibility of AODV algorithm. The AODV Routing algorithm was proposed for mesh network with extremely mobile node and is not suitable for hierarchical sensor network. Previously this two algorithm ZigBee and Cskip implement’s a hierarchical routing but not support fault tolerance and has limitation on network depth. The basic plan is to use hierarchical address structure, to develop the proposed scheme, and assigning address to new node in a network. At last simulation results shows the new scheme importantly and decreases the failure of network formation
Electronic properties of graphene antidot lattices

Graphene antidot lattices constitute a novel class of nano-engineered graphene devices with controllable electronic and optical properties. An antidot lattice consists of a periodic array of holes that causes a band gap to open up around the Fermi level, turning graphene from a semimetal into a semiconductor. We calculate the electronic band structure of graphene antidot lattices using three numerical approaches with different levels of computational complexity, efficiency and accuracy. Fast finite-element solutions of the Dirac equation capture qualitative features of the band structure, while full tight-binding calculations and density functional theory (DFT) are necessary for more reliable predictions of the band structure. We compare the three computational approaches and investigate the role of hydrogen passivation within our DFT scheme.
Segmentation and Feature Extraction of Panoramic Dental X-Ray Images

Automating the process of analysis of Panoramic X-Ray images is important to help dentist procedures and diagnosis. Tooth segmentation from the radiographic images and feature extraction are essential steps. The authors propose a segmentation approach based on mathematical morphology, quadtree decomposition for mask generation, thresholding, and snake models. The feature extraction stage is steered by a shape model based on Principal Component Analysis (PCA). First, the authors take the quadtree decomposition of a low-pass version of the original image and select the smallest blocks to generate a mask. Then, the original image is processed by Otsuâ€™s thresholding. The result is improved by morphological operators and the quadtree mask is applied to address overlapping, a common problem in X-ray images. The obtained regions are searched and the larger ones are selected to find tooth candidates. The boundary of the obtained regions are extracted and aligned with the shape model in order to recognize the target tooth (molar). The selected curve is used in a search method to initialize a snake technique. Finally, morphometric data extraction is performed to obtain tooth measurements for dentist diagnosis. Experiments show the advantages of the proposed method to extract teeth from X-Ray images and discuss its drawbacks.