Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification

In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, shareable filters are jointly learned for classes which share the similar patterns. Discriminative power of the filters is achieved by enforcing the features from the same category to be close, while features from different categories to be far away from each other. Furthermore, we also propose two exemplar selection methods to iteratively select training data for more efficient and effective learning. Based on the experimental results, DDSFL can achieve very promising performance, and it also shows great complementary effect to the state-of-the-art Caffe features.Comment: Pattern Recognition, Elsevier, 201

Underdetermined Separation of Speech Mixture Based on Sparse Bayesian Learning

This paper describes a novel algorithm for underdetermined speech separation problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The proposed algorithm consists of two steps. The unknown mixing matrix is firstly estimated from the speech mixtures in the transform domain by using K-means clustering algorithm. In the second step, the speech sources are recovered based on an autocalibration sparse Bayesian learning algorithm for speech signal. Numerical experiments including the comparison with other sparse representation approaches are provided to show the achieved performance improvement

Probing Dark Energy with the Kunlun Dark Universe Survey Telescope

Dark energy is an important science driver of many upcoming large-scale surveys. With small, stable seeing and low thermal infrared background, Dome A, Antarctica, offers a unique opportunity for shedding light on fundamental questions about the universe. We show that a deep, high-resolution imaging survey of 10,000 square degrees in \emph{ugrizyJH} bands can provide competitive constraints on dark energy equation of state parameters using type Ia supernovae, baryon acoustic oscillations, and weak lensing techniques. Such a survey may be partially achieved with a coordinated effort of the Kunlun Dark Universe Survey Telescope (KDUST) in \emph{yJH} bands over 5000--10,000 deg$^2$ and the Large Synoptic Survey Telescope in \emph{ugrizy} bands over the same area. Moreover, the joint survey can take advantage of the high-resolution imaging at Dome A to further tighten the constraints on dark energy and to measure dark matter properties with strong lensing as well as galaxy--galaxy weak lensing.Comment: 9 pages, 6 figure

THE OXYGEN FEATURES IN TYPE Ia SUPERNOVAE AND IMPLICATIONS FOR THE NATURE OF THERMONUCLEAR EXPLOSIONS

The absorption feature O I 7773 is an important spectral indicator for type Ia supernovae (SNe~Ia) that can be used to trace the unburned material at outer layers of the exploding white dwarf. In this work, we use a large sample of SNe~Ia to examine this absorption at early phases (i.e., -13 days <t <-7 days), and make comparisons with the absorption features of Si~II 6355 and Ca~II near-infrared (NIR) triplet. We show that for a subgroup of spectroscopically normal SNe with normal photospheric velocities (i.e., v_si < 12,500 km s^{-1} at optical maximum), the line strength of high velocity feature (HVF) of O~I is inversely correlated with that of Si~II (or Ca~II), and this feature also shows a negative correlation with the luminosity of SNe Ia. This finding, together with other features we find for the O~I HVF, reveal that for this subgroup of SNe~Ia explosive oxygen burning occurs at the outermost layer of supernova and difference in burning there could lead to the observed diversity, which are in remarkable agreement with the popular delayed-detonation model of Chandrasekhar mass WD.Comment: 37 pages, 10 figures, accepted for publication in the Astrophysical Journa

JWST PEARLS. Prime Extragalactic Areas for Reionization and Lensing Science: Project Overview and First Results

We give an overview and describe the rationale, methods, and first results from NIRCam images of the JWST “Prime Extragalactic Areas for Reionization and Lensing Science” (PEARLS) project. PEARLS uses up to eight NIRCam filters to survey several prime extragalactic survey areas: two fields at the North Ecliptic Pole (NEP); seven gravitationally lensing clusters; two high redshift protoclusters; and the iconic backlit VV 191 galaxy system to map its dust attenuation. PEARLS also includes NIRISS spectra for one of the NEP fields and NIRSpec spectra of two high-redshift quasars. The main goal of PEARLS is to study the epoch of galaxy assembly, active galactic nucleus (AGN) growth, and First Light. Five fields—the JWST NEP Time-Domain Field (TDF), IRAC Dark Field, and three lensing clusters—will be observed in up to four epochs over a year. The cadence and sensitivity of the imaging data are ideally suited to find faint variable objects such as weak AGN, high-redshift supernovae, and cluster caustic transits. Both NEP fields have sightlines through our Galaxy, providing significant numbers of very faint brown dwarfs whose proper motions can be studied. Observations from the first spoke in the NEP TDF are public. This paper presents our first PEARLS observations, their NIRCam data reduction and analysis, our first object catalogs, the 0.9–4.5 μm galaxy counts and Integrated Galaxy Light. We assess the JWST sky brightness in 13 NIRCam filters, yielding our first constraints to diffuse light at 0.9–4.5 μm. PEARLS is designed to be of lasting benefit to the community

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Sparse bayesian methods and their applications

The theory of compressed sensing (CS) has been extensively investigated and successfully applied in various areas over the past several decades. The key ingredient in this technique is the proper exploitation of sparsity, which allows the recovery of high-dimensional signals from their low-dimensional projections. Although a large number of sparse signal recovery algorithms have been proposed in the literature to achieve this objective, there still exist various opportunities and challenges in developing new algorithms to obtain more accurate and robust signal recovery performances. Among these algorithms, sparse Bayesian methods are recently developed to achieve higher accuracy and more flexibility. This thesis focuses on various issues of sparse Bayesian methods, including developing new algorithms and applying them to practical applications, such as radar imaging and wireless communication. In CS problems, the measurement matrix is often assumed to be known a priori, which unfortunately is not true in practical scenarios. The first task of the thesis is to consider the CS problems with multiplicative perturbations. We formulate this problem into a probabilistic model and develop an auto-calibration sparse Bayesian learning algorithm based on this model. In this method, signals and perturbations are iteratively estimated to achieve sparsity. Results from numerical experiments have demonstrated that the proposed algorithm achieves improvements on the accuracy of signal reconstruction. In radar imaging applications, phase errors often exist in the pre-processed data, which can be considered as a multiplicative perturbation model. However, different from the formulation in the first task, the received radar data are complex valued and the phase errors exhibit redundancy across range cells. To properly solve this problem, a multi-task Bayesian model is utilized to probabilistically model the sparse target scene and phase errors. The superiority of this method is that the uncertainty information of the estimation can be properly incorporated to obtain enhanced estimation accuracy. Experimental results based on synthetic and practical data have demonstrated that our method has a desirable de-noising capability and can produce a relatively well-focused image of the target, particularly in low signal-to-noise ratio (SNR) and high under-sampling ratio scenarios. As an extension of the sparse Bayesian auto-focus in the second task, we are motivated to further improve the imaging performances by incorporating structural information apart from sparsity. In this work, the structured sparse prior is imposed on the target scene in a statistical manner. Based on this statistical framework, the proposed algorithm can simultaneously cope with structured sparse recovery and phase error correction in an integrated manner. Due to the utilization of structured sparse constraint, the proposed algorithm can desirably preserve the target region and alleviate over-shrinkage problem, compared with the previous sparsity-driven auto-focus approaches. Moreover, to accelerate the convergence rate of the algorithm, we propose to adaptively eliminate portion of the noise-only range cells in the phase error estimation stage. The simulated and real data experimental results demonstrate that the proposed algorithm can obtain more concentrated imagery result with a much smaller number of iterations, particularly in low SNR and highly under-sampling scenarios. Finally, we consider a problem of recovering time-varying sparse signals with a particular structure. More specifically, the problem of estimating multiple frequency hopping signals with unknown hopping pattern is considered. Inspired by the sparse Bayesian learning algorithm, the problem is formulated hierarchically to induce sparsity. In addition to the sparsity, the hopping pattern is exploited via temporal-aware clustering by exerting a dependent Dirichlet process prior over the latent parametric space. The estimation accuracy of the parameters can be greatly improved by this particular information-sharing scheme, and sharp boundary of the hopping time estimation is manifested. Moreover, the proposed algorithm is further extended to multi-channel cases, where task-relation is utilized to obtain robust clustering of the latent parameters for better estimation performance. Since the problem is formulated in a full Bayesian framework, labor-intensive parameter tuning process can be avoided. Another superiority of the approach is that high-resolution instantaneous frequency estimation can be directly obtained without further refinement of the time frequency representation. Results of numerical experiments show that the proposed algorithm can achieve superior performance particularly in low SNR scenarios compared with other recently reported ones.Doctor of Philosophy (EEE