Disaggregation
Disaggregation is the breakdown of observations, usually within a common branch of a hierarchy, to a more detailed level to that at which detailed observations are taken. …

Deeply Supervised Object Detector (DSOD)
We propose Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch. Recent advances in object detection heavily depend on the off-the-shelf models pre-trained on large-scale classification datasets like ImageNet and OpenImage. However, one problem is that adopting pre-trained models from classification to detection task may incur learning bias due to the different objective function and diverse distributions of object categories. Techniques like fine-tuning on detection task could alleviate this issue to some extent but are still not fundamental. Furthermore, transferring these pre-trained models across discrepant domains will be more difficult (e.g., from RGB to depth images). Thus, a better solution to handle these critical problems is to train object detectors from scratch, which motivates our proposed method. Previous efforts on this direction mainly failed by reasons of the limited training data and naive backbone network structures for object detection. In DSOD, we contribute a set of design principles for learning object detectors from scratch. One of the key principles is the deep supervision, enabled by layer-wise dense connections in both backbone networks and prediction layers, plays a critical role in learning good detectors from scratch. After involving several other principles, we build our DSOD based on the single-shot detection framework (SSD). We evaluate our method on PASCAL VOC 2007, 2012 and COCO datasets. DSOD achieves consistently better results than the state-of-the-art methods with much more compact models. Specifically, DSOD outperforms baseline method SSD on all three benchmarks, while requiring only 1/2 parameters. We also observe that DSOD can achieve comparable/slightly better results than Mask RCNN + FPN (under similar input size) with only 1/3 parameters, using no extra data or pre-trained models. …

PAI Data
The Project PAI Data Protocol (‘PAI Data’) is a specification that extends the Project PAI Blockchain Protocol to include a method of securing and provisioning access to arbitrary data. In the context of PAI Coin Development Proposal (PDP) 2, this paper defines two important transaction types that PAI Data supports: Storage Transactions, which facilitate storage of data and proof of ownership, and Sharing Transactions, designed to enable granting and revocation of data access to designated recipients. A comparative analysis of PAI Data against similar blockchain-based file storage systems is also presented. …

Average Shifted Histogram (ASH)
A simple device has been proposed for eliminating the bin edge problem of the frequency polygon while retaining many of the computational advantages of a density estimate based on bin counts. Scott (1983, 1985b) considered the problem of choosing among the collection of multivariate frequency polygons, each with the same smoothing parameter but di ering bin origins. Rather than choosing the \smoothest’ such curve or surface, he proposed averaging several of the shifted frequency polygons. As the average of piecewise linear curves is also piecewise linear, the resulting curve appears to be a frequency polygon as well. If the weights are nonnegative and sum to 1, the resulting \averaged shifted frequency polygon’ (ASFP) is nonnegative and integrates to 1. A nearly equivalent device is to average several shifted histograms, which is just as general but simpler to describe and analyze. The result is the \averaged shifted histogram’ (ASH). Since the average of piecewise constant functions such as the histogram is also piecewise constant, the ASH appears to be a histogram as well. In practice, the ASH is made continuous using either of the linear interpolation schemes described for the frequency polygon in Chapter 4 and will be referred to as the frequency polygon of the averaged shifted histogram (FP-ASH). The ASH is the practical choice for computationally and statistically e cient density estimation. …