Neutrinos and Ultra-High-Energy Cosmic-Ray Nuclei from Blazars

We discuss the production of ultra-high-energy cosmic ray (UHECR) nuclei and neutrinos from blazars. We compute the nuclear cascade in the jet for both BL Lac objects and flat-spectrum radio quasars (FSRQs), and in the ambient radiation zones for FSRQs as well. By modeling representative spectral energy distributions along the blazar sequence, two distinct regimes are identified, which we call "nuclear survival" -- typically found in low-luminosity BL Lacs, and "nuclear cascade" -- typically found in high-luminosity FSRQs. We quantify how the neutrino and cosmic-ray (CR) emission efficiencies evolve over the blazar sequence, and demonstrate that neutrinos and CRs come from very different object classes. For example, high-frequency peaked BL Lacs (HBLs) tend to produce CRs, and HL-FSRQs are the more efficient neutrino emitters. This conclusion does not depend on the CR escape mechanism, for which we discuss two alternatives (diffusive and advective escape). Finally, the neutrino spectrum from blazars is shown to significantly depend on the injection composition into the jet, especially in the nuclear cascade case: Injection compositions heavier than protons lead to reduced neutrino production at the peak, which moves at the same time to lower energies. Thus, these sources will exhibit better compatibility with the observed IceCube and UHECR data.Comment: 23 pages, 20 figure

Cosmic-Ray and Neutrino Emission from Gamma-Ray Bursts with a Nuclear Cascade

We discuss neutrino and cosmic-ray emission from Gamma-Ray Bursts (GRBs) with the injection of nuclei, where we take into account that a nuclear cascade from photo-disintegration can fully develop in the source. One of our main objectives is to test if recent results from the IceCube and the Pierre Auger Observatory can be accommodated with the paradigm that GRBs are the sources of Ultra-High Energy Cosmic Rays (UHECRs). While our key results are obtained using an internal shock model, we discuss how the secondary emission from a GRB shell can be interpreted in terms of other astrophysical models. It is demonstrated that the expected neutrino flux from GRBs weakly depends on the injection composition, which implies that prompt neutrinos from GRBs can efficiently test the GRB-UHECR paradigm even if the UHECRs are nuclei. We show that the UHECR spectrum and composition, as measured by the Pierre Auger Observatory, can be self-consistently reproduced in a combined source-propagation model. In an attempt to describe the energy range including the ankle, we find tension with the IceCube bounds from the GRB stacking analyses. In an alternative scenario, where only the UHECRs beyond the ankle originate from GRBs, the requirement for a joint description of cosmic-ray and neutrino observations favors lower luminosities, which does not correspond to the typical expectation from {\gamma}-ray observations.Comment: 36 pages, 25 figure

Physical Representation-based Predicate Optimization for a Visual Analytics Database

Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy. Unfortunately, these methods are slow: processing a single image can take about 10 milliseconds on modern GPU-based hardware. As massive video libraries become ubiquitous, running a content-based query over millions of video frames is prohibitive. One promising approach to reduce the runtime cost of queries of visual content is to use a hierarchical model, such as a cascade, where simple cases are handled by an inexpensive classifier. Prior work has sought to design cascades that optimize the computational cost of inference by, for example, using smaller CNNs. However, we observe that there are critical factors besides the inference time that dramatically impact the overall query time. Notably, by treating the physical representation of the input image as part of our query optimization---that is, by including image transforms, such as resolution scaling or color-depth reduction, within the cascade---we can optimize data handling costs and enable drastically more efficient classifier cascades. In this paper, we propose Tahoma, which generates and evaluates many potential classifier cascades that jointly optimize the CNN architecture and input data representation. Our experiments on a subset of ImageNet show that Tahoma's input transformations speed up cascades by up to 35 times. We also find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy, and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Unwinding Inflation

Higher-form flux that extends in all 3+1 dimensions of spacetime is a source of positive vacuum energy that can drive meta-stable eternal inflation. If the flux also threads compact extra dimensions, the spontaneous nucleation of a bubble of brane charged under the flux can trigger a classical cascade that steadily unwinds many units of flux, gradually decreasing the vacuum energy while inflating the bubble, until the cascade ends in the self-annihilation of the brane into radiation. With an initial number of flux quanta Q_{0} \simgeq N, this can result in N efolds of inflationary expansion while producing a scale-invariant spectrum of adiabatic density perturbations with amplitude and tilt consistent with observation. The power spectrum has an oscillatory component that does not decay away during inflation, relatively large tensor power, and interesting non-Gaussianities. Unwinding inflation fits naturally into the string landscape, and our preliminary conclusion is that consistency with observation can be attained without fine-tuning the string parameters. The initial conditions necessary for the unwinding phase are produced automatically by bubble formation, so long as the critical radius of the bubble is smaller than at least one of the compact dimensions threaded by flux.Comment: 29+15 pages, 10 figures, published versio