After defying multiple mass extinctions on Earth, the hardy plant passes an intergalactic test. Breakthroughs, discoveries, and DIY tips sent every weekday. While it may appear humble, Earth's moss is built darn tough. It thrives in extreme environments -from the bitter cold, low-oxygen air of the Himalayas, down to the parched sands of Death Valley. Some species even make their home among the lava fields of active volcanoes .

ABSTRACT In recent years, end-to-end approaches have made notable progress in addressing the challenge of speaker diarization, which involves segmenting and identifying speakers in multi-talker recordings. One such approach, Encoder-Decoder Attractors (EDA), has been proposed to handle variable speaker counts as well as better guide the network during training. In this study, we extend the attractor paradigm by moving beyond direct speaker modeling and instead focus on representing more detailed'speaker attributes' through a multistage process of intermediate representations. Additionally, we enhance the architecture by replacing transformers with conformers, a convolution-augmented transformer, to model local dependencies. Experiments demonstrate improved di-arization performance on the CALLHOME dataset.

For the third time in less than two years, I have COVID-19. Whenever an illness has forced me to stay in bed, my comfort food has been gaming. In 2009 I played through all of Assassin's Creed II in a feverish, swine flu-induced haze. When I was sick with COVID for the first time, I jumped into Red Dead Redemption 2 blind, and found a story about sickness and human mortality. Now, during one of the most stacked years in recent gaming history, I find myself under the covers not with Starfield, Spider-Man 2 or any of the other big fall releases.

We have developed a method for detecting real money traders (RMTers) to support the operators of massively multiplayer online role-playing games (MMORPGs). RMTers, who earn currency in the real world by selling properties in the virtual world, tend to form alliances and frequently exchange a huge volume of virtual currency within such a community. The proposed method exploits (1) the trading network, to identify the communities of characters, and (2) the volume of trades, to estimate the likelihood of communities and characters becoming engaged in real money trading. The results of an experiment using actual log data from a commercial MMORPG showed that using the trading network is more effective in detecting RMTers than conventional machine learning methods that assess individual character without referring to the trading network.

Sony Corp., which is emerging from five years of brutal restructuring that gutted its workforce and product lineup, wants to show off a few new things. There's the Aromastic, a digital smell dispenser, AeroSense self-flying drones and a collection of tech-infused accessories called "wena." These gadgets are being dreamed up by the Seed Accelerator Program (SAP), started by Chief Executive Officer Kazuo Hirai in 2014 to encourage invention and risk-taking. With Sony back on solid financial footing, shown by its estimate-topping results in the latest quarter, the company has more breathing room to experiment. Instead of focusing on raw, technical innovation, the devices coming out of the lab hark back to an era when Sony was able to take existing technology and combine it with slick marketing to create must-have gadgets such as the Walkman and Handycam. Another hit product could help Hirai cement his legacy as the one who not only turned Sony around, but got it inventing again.

Numerical optimization is arguably the most prominent computational framework in machine learning and AI. It can be seen as an assembly language for hard combinatorial problems ranging from classification and regression in learning, to computing optimal policies and equilibria in decision theory, to entropy minimization in information sciences. Unfortunately, specifying such problems in complex domains involving relations, objects and other logical dependencies is cumbersome at best, requiring considerable expert knowledge, and solvers require models to be painstakingly reduced to standard forms. To overcome this, we introduce a rich modeling framework for optimization problems that allows convenient codification of symbolic structure. Rather than reducing this symbolic structure to a sparse or dense matrix, we represent and exploit it directly using algebraic decision diagrams (ADDs). Combining efficient ADD-based matrix-vector algebra with a matrix-free interior-point method, we develop an engine that can fully leverage the structure of symbolic representations to solve convex linear and quadratic optimization problems. We demonstrate the flexibility of the resulting symbolic-numeric optimizer on decision making and compressed sensing tasks with millions of non-zero entries.

Sony has provided a robot platform for research and development in physical agents, namely, fully autonomous legged robots. In this article, we describe our work using Sony's legged robots to participate at the RoboCup-98 legged robot demonstration and competition. Robotic soccer represents a challenging environment for research in systems with multiple robots that need to achieve concrete objectives, particularly in the presence of an adversary. Furthermore, RoboCup offers an excellent opportunity for robot entertainment. We introduce the RoboCup context and briefly present Sony's legged robot. We developed a vision-based navigation and a Bayesian localization algorithm. Team strategy is achieved through predefined behaviors and learning by instruction.