Topological structures in the equities market network

We present a new method for articulating scale-dependent topological descriptions of the network structure inherent in many complex systems. The technique is based on "Partition Decoupled Null Models,'' a new class of null models that incorporate the interaction of clustered partitions into a random model and generalize the Gaussian ensemble. As an application we analyze a correlation matrix derived from four years of close prices of equities in the NYSE and NASDAQ. In this example we expose (1) a natural structure composed of two interacting partitions of the market that both agrees with and generalizes standard notions of scale (eg., sector and industry) and (2) structure in the first partition that is a topological manifestation of a well-known pattern of capital flow called "sector rotation.'' Our approach gives rise to a natural form of multiresolution analysis of the underlying time series that naturally decomposes the basic data in terms of the effects of the different scales at which it clusters. The equities market is a prototypical complex system and we expect that our approach will be of use in understanding a broad class of complex systems in which correlation structures are resident.Comment: 17 pages, 4 figures, 3 table

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

A Proximity-Aware Hierarchical Clustering of Faces

In this paper, we propose an unsupervised face clustering algorithm called "Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local structure of deep representations. In the proposed method, a similarity measure between deep features is computed by evaluating linear SVM margins. SVMs are trained using nearest neighbors of sample data, and thus do not require any external training data. Clusters are then formed by thresholding the similarity scores. We evaluate the clustering performance using three challenging unconstrained face datasets, including Celebrity in Frontal-Profile (CFP), IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3) datasets. Experimental results demonstrate that the proposed approach can achieve significant improvements over state-of-the-art methods. Moreover, we also show that the proposed clustering algorithm can be applied to curate a set of large-scale and noisy training dataset while maintaining sufficient amount of images and their variations due to nuisance factors. The face verification performance on JANUS CS3 improves significantly by finetuning a DCNN model with the curated MS-Celeb-1M dataset which contains over three million face images

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

zCOSMOS – 10k-bright spectroscopic sample : The bimodality in the galaxy stellar mass function: exploring its evolution with redshift

We present the galaxy stellar mass function (GSMF) to redshift z ≃ 1, based on the analysis of about 8500 galaxies with I < 22.5 (AB mag) over 1.4 deg^2, which are part of the zCOSMOS-bright 10k spectroscopic sample. We investigate the total GSMF, as well as the contributions of early- and late-type galaxies (ETGs and LTGs, respectively), defined by different criteria (broad-band spectral energy distribution, morphology, spectral properties, or star formation activities). We unveil a galaxy bimodality in the global GSMF, whose shape is more accurately represented by 2 Schechter functions, one linked to the ETG and the other to the LTG populations. For the global population, we confirm a mass-dependent evolution (“mass-assembly downsizing”), i.e., galaxy number density increases with cosmic time by a factor of two between z = 1 and z = 0 for intermediate-to-low mass (log(M/M_⊙) ~ 10.5) galaxies but less than 15% for log(M/M_⊙) > 11.We find that the GSMF evolution at intermediate-to- low values of M(log(M/M_⊙) < 10.6) is mostly explained by the growth in stellar mass driven by smoothly decreasing star formation activities, despite the redder colours predicted in particular at low redshift. The low residual evolution is consistent, on average, with ~0.16 merger per galaxy per Gyr (of which fewer than 0.1 are major), with a hint of a decrease with cosmic time but not a clear dependence on the mass. From the analysis of different galaxy types, we find that ETGs, regardless of the classification method, increase in number density with cosmic time more rapidly with decreasing M, i.e., follow a top-down building history, with a median “building redshift” increasing with mass (z > 1 for log(M/M_⊙) > 11), in contrast to hierarchical model predictions. For LTGs, we find that the number density of blue or spiral galaxies with log(M/M_⊙) > 10 remains almost constant with cosmic time from z ~ 1. Instead, the most extreme population of star-forming galaxies (with high specific star formation), at intermediate/high-mass, rapidly decreases in number density with cosmic time. Our data can be interpreted as a combination of different effects. Firstly, we suggest a transformation, driven mainly by SFH, from blue, active, spiral galaxies of intermediate mass to blue quiescent and subsequently (1−2 Gyr after) red, passive types of low specific star formation. We find an indication that the complete morphological transformation, probably driven by dynamical processes, into red spheroidal galaxies, occurred on longer timescales or followed after 1−2 Gyr. A continuous replacement of blue galaxies is expected to be accomplished by low-mass active spirals increasing their stellar mass. We estimate the growth rate in number and mass density of the red galaxies at different redshifts and masses. The corresponding fraction of blue galaxies that, at any given time, is transforming into red galaxies per Gyr, due to the quenching of their SFR, is on average ~25% for log(M/M_⊙) < 11. We conclude that the build-up of galaxies and in particular of ETGs follows the same downsizing trend with mass (i.e. occurs earlier for high-mass galaxies) as the formation of their stars and follows the converse of the trend predicted by current SAMs. In this scenario, we expect there to be a negligible evolution of the galaxy baryonic mass function (GBMF) for the global population at all masses and a decrease with cosmic time in the GBMF for the blue galaxy population at intermediate-high masses

Generating 3D faces using Convolutional Mesh Autoencoders

Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and non-linear expressions. To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-of-the-art face model with our autoencoder, achieves a lower reconstruction error. Our data, model and code are available at http://github.com/anuragranj/com