giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques, and its strong focus on data exploration and interpretability is aided by an intuitive plotting API. Source code, binaries, examples, and documentation can be found at https://github.com/giotto-ai/giotto-tda.Comment: 7 pages, 2 figure

The Comet Interceptor Mission

Here we describe the novel, multi-point Comet Interceptor mission. It is dedicated to the exploration of a little-processed long-period comet, possibly entering the inner Solar System for the first time, or to encounter an interstellar object originating at another star. The objectives of the mission are to address the following questions: What are the surface composition, shape, morphology, and structure of the target object? What is the composition of the gas and dust in the coma, its connection to the nucleus, and the nature of its interaction with the solar wind? The mission was proposed to the European Space Agency in 2018, and formally adopted by the agency in June 2022, for launch in 2029 together with the Ariel mission. Comet Interceptor will take advantage of the opportunity presented by ESA’s F-Class call for fast, flexible, low-cost missions to which it was proposed. The call required a launch to a halo orbit around the Sun-Earth L2 point. The mission can take advantage of this placement to wait for the discovery of a suitable comet reachable with its minimum ΔV capability of 600 ms−1. Comet Interceptor will be unique in encountering and studying, at a nominal closest approach distance of 1000 km, a comet that represents a near-pristine sample of material from the formation of the Solar System. It will also add a capability that no previous cometary mission has had, which is to deploy two sub-probes – B1, provided by the Japanese space agency, JAXA, and B2 – that will follow different trajectories through the coma. While the main probe passes at a nominal 1000 km distance, probes B1 and B2 will follow different chords through the coma at distances of 850 km and 400 km, respectively. The result will be unique, simultaneous, spatially resolved information of the 3-dimensional properties of the target comet and its interaction with the space environment. We present the mission’s science background leading to these objectives, as well as an overview of the scientific instruments, mission design, and schedule

Deep learning inference in GNU radio with ONNX

This paper introduces gr-dnn, an open source GNU Radio Out Of Tree (OOT) block capable of running deep learning inference inside GNU Radio flow graphs. This module integrates a deep learning inference engine from the Open Neural Network Exchange (ONNX) project. Thanks to the interoperability with most of the major deep learning frameworks, it does not impose any restriction on the tool used by the model designer. As an example, we demonstrate here its functionalities running a simple deep learning inference model on raw radio samples acquired with a PlutoSDR

Heterogeneous hardware from homogeneous software

Our society relies upon information processing at a scale never seen before in human history. We are indeed experiencing an exponential growth in processing demand, as more and more applications in the most disparate domains emerge. While continuous improvements in the manufacturing processes of microprocessors has been able so far to mitigate the ecological and economical costs this trend imposes, due to reaching physical limitations in the semiconductor industry - the end of Dennard scaling - we can likely no further rely on technological advancements to support the ongoing hunger for computing resources

HPA ::an opportunistic approach to embedded energy efficiency

Reducing energy consumption is a challenge that is faced on a daily basis by teams from the high-performance computing as well as the embedded domains. This issue is mostly approached from an hardware perspective by devising architectures that put energy efficiency as a primary target, often at the cost of processing power. Lately, computing platforms have become more and more heterogeneous, but the exploitation of these additional capabilities is so complex from the application developer's perspective that their optimization is often limited. In this paper we present a transparent, on-the-fly optimization scheme that allows a generic application to automatically and dynamically exploit the available computing units to partition its computational load. We have called our approach Heterogeneous Platform Accelerator (HPA). The idea is to use profiling to select a computing-intensive candidate for acceleration, and then distribute the computations to the different units by off-loading blocks of code to them. This is done automatically at run-time, thus requiring no effort from the developer and adapting to the current input data and load. Using an NVIDIA Jetson TK1 board, we validate our proposal on several benchmarks and on a real-world software package, the Unix text editor ed. The results we achieve substantiate our claim that not only HPA results in faster processing speed, but also in a considerable reduction in energy dissipation

Low complexity motion detection with background modeling

Motion detection is of utmost importance in several applications such as video surveillance, remote sensing and medical diagnosis, and a large number of algorithms have been proposed for implementation. In this paper we focus on low resources scenarios where standard solutions are inapplicable. We present a novel algorithm that exploits spacial correlation of neighbor pixels in an image to get the most accurate view of the moving scene without increasing computational burden. In conjunction with the proposed algorithm we investigate, to the best of our knowledge for the first time, several background models and update policies, comparing available approaches and exploring trade-offs between complexity and detection capability

Instructional quality of mathematics teaching and mathematics teacher education

The four articles in this issue have the instructional quality as a shared idea, although they differ on different levels of attention. These contributions display some facets of how the construct “instructional quality” is tackled in the mathematics education field, and particularly, in mathematics teacher education as a discipline. These facets are as follows: how instructional quality may be conceptualized and measured when classroom observation protocols are designed; the relationships between mathematical knowledge for teaching and the instructional quality; the relationship between credentials to enter a selective alternative route programs and the instructional quality of graduates; and how prospective mathematics teachers learn to identify and develop teaching strategies and practices for supporting the mathematical instruction of quality

FPGA partial reconfiguration in software Defined radio devices

Many SDR systems make effective use of FPGAs for data acquisition and heavy lifting DSP processing. This has resulted in several dedicated frameworks being developed, RFNoC being the most renowned. Even though FPGAs fabrics are, by their nature, reconfigurable, SDR systems often fail in exploiting this interesting opportunity at run-time. In this paper, we show how it is possible to make effective use of the Partial Reconfiguration capabilities of modern FPGA devices, extending the range of applications RFNoC can be applied to. In particular, it allows the live reconfiguration of signal processing chains, for instance to switch between wireless standards. This results in a better use of the limited FPGA resources by time-sharing them between processing blocks. Unfortunately, support for Partial Reconfiguration is not yet available in the software stack of commercially-available SDR devices. Our work thus aims at encouraging its integration