On the Query Strategies for Efficient Online Active Distillation

Deep Learning (DL) requires lots of time and data, resulting in high computational demands. Recently, researchers employ Active Learning (AL) and online distillation to enhance training efficiency and real-time model adaptation. This paper evaluates a set of query strategies to achieve the best training results. It focuses on Human Pose Estimation (HPE) applications, assessing the impact of selected frames during training using two approaches: a classical offline method and a online evaluation through a continual learning approach employing knowledge distillation, on a popular state-of-the-art HPE dataset. The paper demonstrates the possibility of enabling training at the edge lightweight models, adapting them effectively to new contexts in real-time

A Cross-level Verification Methodology for Digital IPs Augmented with Embedded Timing Monitors

Smart systems are characterized by the integration in a single device of multi-domain subsystems of different technological domains, namely, analog, digital, discrete and power devices, MEMS, and power sources. Such challenges, emerging from the heterogeneous nature of the whole system, combined with the traditional challenges of digital design, directly impact on performance and on propagation delay of digital components. This article proposes a design approach to enhance the RTL model of a given digital component for the integration in smart systems with the automatic insertion of delay sensors, which can detect and correct timing failures. The article then proposes a methodology to verify such added features at system level. The augmented model is abstracted to SystemC TLM, which is automatically injected with mutants (i.e., code mutations) to emulate delays and timing failures. The resulting TLM model is finally simulated to identify timing failures and to verify the correctness of the inserted delay monitors. Experimental results demonstrate the applicability of the proposed design and verification methodology, thanks to an efficient sensor-aware abstraction methodology, by applying the flow to three complex case studies

Social Media and Large Carnivores : Sharing Biased News on Attacks on Humans

The Internet and social media have profoundly changed the way the public receives and transmits news. The ability of the web to quickly disperse information both geographically and temporally allows social media to reach a much wider audience compared to traditional mass media. A powerful role is played by sharing, as millions of people routinely share news on social media platforms, influencing each other by transmitting their mood and feelings to others through emotional contagion. Thus, social media has become crucial in driving public perception and opinion. Humans have an instinctive fear of large carnivores, but such a negative attitude may be amplified by news media presentations and their diffusion on social media. Here, we investigated how reports of predator attacks on humans published in online newspapers spread on social media. By means of multi-model inference, we explored the contribution of four factors in driving the number of total shares (NTS) of news reports on social media: the graphic/sensationalistic content, the presence of images, the species, as well as the newspaper coverage. According to our results, the information delivered by social media is highly biased toward a graphic/sensationalistic view of predators. Thus, such negative coverage might lead to an unjustified and amplified fear in the public with consequent lower tolerance toward predators and decrease in the support for conservation plans. However, because social media represents a powerful communication tool, its role might be reversed to positive if used appropriately. Thus, constant engagement of scientists on social media would be needed to both disseminate more accurate information on large carnivores and stem the tide of misinformation before its widespread diffusion, a crucial step for effective predator conservation.Peer reviewe

Products of polynomials in many variables

We study the product of two polynomials in many variables, in several norms, and show that under suitable assumptions this product can be bounded from below independently of the number of variables.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28840/1/0000675.pd

Hyperbolicity of varieties of log general type

These notes provide an overview of various notions of hyperbolicity for varieties of log general type from the viewpoint of both arithmetic and birational geometry. The main results are based on our paper entitled "Hyperbolicity and uniformity of varieties of log general type." They are expanded notes from a minicourse the authors gave as part of the Geometry and arithmetic of orbifolds workshop at UQ\'AM.Comment: Addressed some inaccuracies and typos pointed out by the referees and some readers. Slight change of title. To appear in CRM short courses (Arithmetic Geometry of Logarithmic Pairs and Hyperbolicity of Moduli Spaces: Hyperbolicity in Montr\'eal