Media
The Rise Of The Avatar and the Deep Fake
As a veteran of way too many zoom conferences, It's perhaps not all that surprising that I would have discovered one of the cooler features of the app - the ability to create an algorithmic green screen that lets you superimpose both static background and videos behind you. The algorithm is not perfect - every so often a shoulder will disappear or hair will suddenly do really weird things, but given that it's basically isolating your outline, ascertaining what's the background and what's you, and then superimposing (or, in the jargon of the SFX industry compositing) the new background over what's left, the effect is pretty damned impressive. There is now a thriving industry of companies that supply specialty green screens that you can set up behind you for a surprisingly inexpensive amount, letting you quite literally put yourself in the middle of the action. For remote roleplaying, especially as the dungeon master, it's hard to beat, but it's also an indication of just how rapidly we are reaching the point where what had once been studio-level graphical effects are now making their way to our desktops. In a previous article, I made the observation that there is typically a gap of about six years between the time that your favorite special effects first appear in Hollywood (or more properly, in the area around Skywalker ranch south of San Jose that's become the mecca of the digital effects industry) and the time that the same special effects make their way to high-end gaming systems.
Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning
Ong, Kevin Shen Hoong, Zhang, Yang, Niyato, Dusit
Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT), requiring increased decentralization and autonomous operation. To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. However, in complex and large-scale networks, the state and action spaces are usually large, and existing Tabular Reinforcement Learning technique is unable to find the optimal state-action policy quickly. In this paper, deep reinforcement learning is proposed to overcome the mentioned shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput. When benchmarked against advanced DQN techniques, our proposed DQN configuration offers performance speedup of up to 1.8x with good overall performance.
Hierarchical Qualitative Clustering: clustering mixed datasets with critical qualitative information
Seca, Diogo, Mendes-Moreira, João, Mendes-Neves, Tiago, Sousa, Ricardo
Clustering can be used to extract insights from data or to verify some of the assumptions held by the domain experts, namely data segmentation. In the literature, few methods can be applied in clustering qualitative values using the context associated with other variables present in the data, without losing interpretability. Moreover, the metrics for calculating dissimilarity between qualitative values often scale poorly for high dimensional mixed datasets. In this study, we propose a novel method for clustering qualitative values, based on Hierarchical Clustering (HQC), and using Maximum Mean Discrepancy. HQC maintains the original interpretability of the qualitative information present in the dataset. We apply HQC to two datasets. Using a mixed dataset provided by Spotify, we showcase how our method can be used for clustering music artists based on the quantitative features of thousands of songs. In addition, using financial features of companies, we cluster company industries, and discuss the implications in investment portfolios diversification.