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Photonic circuits pave the way to ultrafast computing and real-time inference of applications with paramount importance, such as imaging flow cytometry (IFC). However, current implementations exhibit inherent restrictions that consequently diminish the neural networks (NN) complexity that can be supported. Thus, NN compression mechanisms are deemed critical for the efficient deployment of such demanding tasks.

We propose a novel approach to compress hyperspectral remote sensing images using convo- lutional neural networks, aimed at producing compression results competitive with common lossy compression standards such as JPEG 2000 and CCSDS 122.1-B-1 with a system far less complex than equivalent neural-network codecs used for natural images. Our method consists of a collection of smaller networks which compress the image band-by-band taking advantage of the very high similarity between bands on certain intervals.

Image compression is a key problem in this age of information explosion. With the help of machine learning, recent studies have shown that learning-based image compression methods tend to surpass traditional codecs. Image compression can be split into three steps: transform, quantization, and entropy estimation.

Bitvectors that support rank and select queries are the workhorses of succinct data structures, implementations of which are now widespread, for example, in bioinformatics software. To date, however, most bitvector implementations are static, thus forcing more complex data structures built from them to be static too. In this paper we explore dynamic bitvectors, which, in addition to rank and select queries, also support update operations, specifically: insert, remove, and modify. We first provide several practical optimizations to the recent B-tree based bitvectors of Prezza (Proc.

In the production plants in the era of industry 4.0, every unit in the production process will generate and transmit data. Transmitting this data in real-time is not unrestrictedly feasible due to the limited bandwidth of the Internet, and processing on the edge device of the unit is also not conceivable due to the limited computing capacity of the device.