This paper is related to the problem of finding the maximal quasi-bicliques in a bipartite graph (bigraph). A quasi-biclique in the bigraph is its "almost" complete subgraph. The relaxation of completeness can be understood variously; here, we assume that the subgraph is a $\gamma$-quasi-biclique if it lacks a certain number of edges to form a biclique such that its density is at least $\gamma \in (0,1]$. For a bigraph and fixed $\gamma$, the problem of searching for the maximal quasi-biclique consists of finding a subset of vertices of the bigraph such that the induced subgraph is a quasi-biclique and its size is maximal for a given graph. Several models based on Mixed Integer Programming (MIP) to search for a quasi-biclique are proposed and tested for working efficiency. An alternative model inspired by biclustering is formulated and tested; this model simultaneously maximizes both the size of the quasi-biclique and its density, using the least-square criterion similar to the one exploited by triclustering \textsc{TriBox}.

We consider a system to optimize duration of traffic signals using multi-agent deep reinforcement learning and Vehicle-to-Everything (V2X) communication. This system aims at analyzing independent and shared rewards for multi-agents to control duration of traffic lights. A learning agent traffic light gets information along its lanes within a circular V2X coverage. The duration cycles of traffic light are modeled as Markov decision Processes. We investigate four variations of reward functions. The first two are unshared-rewards: based on waiting number, and waiting time of vehicles between two cycles of traffic light. The third and fourth functions are: shared-rewards based on waiting cars, and waiting time for all agents. Each agent has a memory for optimization through target network and prioritized experience replay. We evaluate multi-agents through the Simulation of Urban MObility (SUMO) simulator. The results prove effectiveness of the proposed system to optimize traffic signals and reduce average waiting cars to 41.5 % as compared to the traditional periodic traffic control system.

The new goal is to seek high levels of human control AND high levels of automation, which is more likely to produce computer applications that are Reliable, Safe & Trustworthy (RST). Achieving this goal, especially for complex poorly understood problems, will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility. The traditional belief in computer autonomy is compelling for many artificial intelligence (AI) researchers, developers, journalists, and promoters. The goal of computer autonomy was central in Sheridan and Verplank's (1978) ten levels from human control to computer automation/autonomy (Table 1). Their widely cited one-dimensional list continues to guide much of the research and development, suggesting that increases in automation must come at the cost of lowering human control. Shifting to HCAI could liberate design thinking so as to produce computer applications that increase automation, while amplifying, augmenting, enhancing, and empowering people to innovatively apply systems and creatively refine them.

This paper studies competitive reinforcement learning (competitive RL), that is, reinforcement learning with two or more agents taking actions simultaneously, but each maximizing their own reward. Competitive RL is a major branch of the more general setting of multi-agent reinforcement learning (MARL), with the specification that the agents have conflicting rewards (so that they essentially compete with each other) yet can be trained in a centralized fashion (i.e. each agent has access to the other agents' policies) (Crandall and Goodrich, 2005). There are substantial recent progresses in competitive RL, in particular in solving hard multi-player games such as GO (Silver et al., 2017), Starcraft (Vinyals et al., 2019), and Dota 2 (OpenAI, 2018). A key highlight in their approaches is the successful use of self-play for achieving superhuman performance in absence of human knowledge or expert opponents. These self-play algorithms are able to learn a good policy for all players from scratch through repeatedly playing the current policies against each other and performing policy updates using these self-played game trajectories. The empirical success of self-play has challenged the conventional wisdom that expert opponents are necessary for achieving good performance, and calls for a better theoretical understanding. In this paper, we take initial steps towards understanding the effectiveness of self-play algorithms in competitive RL from a theoretical perspective.

The Messy, Secretive Reality Behind OpenAI's Bid to Save the World Karen Hao MIT Technology Review "There is a misalignment between what the company publicly espouses and how it operates behind closed doors. Over time, it has allowed a fierce competitiveness and mounting pressure for ever more funding to erode its founding ideals of transparency, openness, and collaboration." The Studs on This Punk Bracelet Are Actually Microphone-Jamming Ultrasonic Speakers Andrew Liszewski Gizmodo "You can prevent facial recognition cameras from identifying you by wearing face paint, masks, or sometimes just a pair of oversized sunglasses. Keeping conversations private from an ever-growing number of microphone-equipped devices isn't quite as easy, but researchers have created what could be the first wearable that actually helps increase your privacy." Iron Man Dreams Are Closer to Becoming a Reality Thanks to This New Jetman Dubai Video Julia Alexander The Verge "Tony Stark may have destroyed his Iron Man suits in Iron Man 3 (only to bring out a whole new line in Avengers: Age of Ultron), but Jetman Dubai's Iron Man-like dreams of autonomous human flight are realer than ever. A new video published by the company shows pilot Vince Reffet using a jet-powered, carbon-fiber suit to launch off the ground and fly 6,000 feet in the air."

A next generation AI-Analytics decisioning solution should be able to tell retailers precisely how their business is doing across various departments and metrics far better than traditional business intelligence solutions can do. More importantly, the AI platform can explain why something happened (causation), and what you should do about it. Traditional business intelligence can't do this! What are examples of what can be done better with predictive and prescriptive AI-analytics capabilities? AI Demand Forecasting can provide highly accurate day-of-week and time-of-day forecasting at an item/store level.

Autonomous emergency braking (AEB) is a continuously-on system which detects proximity with obstacles ahead. If the system detects an imminent crash, it warns the driver and primes the braking system. If the driver fails to respond, the car applies the brakes with as much force as necessary to prevent collision. Some AEB systems can also detect cyclists and pedestrians which may be hidden behind a blind spot until its too late. However, this isn't an assistance system -- you can't use an AEB-equipped car to take your foot off the brake in traffic.

The cellular industry's shift from long-distance radio signals to short-distance millimeter waves is one of the 5G era's biggest changes, expected to continue with submillimeter waves over the next decade. To more precisely direct millimeter wave and future terahertz-frequency signals toward user devices, Arizona State University researchers have developed ViWi-BT, a vision-wireless framework that improves beam tracking using computer vision and deep learning. Smartphones historically operated much like other long-distance radios, scanning the airwaves for omnidirectional tower signals and tuning into whatever was strongest and/or closest. But in the 5G and 6G eras, networks of small cells will use beamforming antennas to more specifically target their signals in a given direction toward discovered client devices, which may be contemplating connections from multiple base stations at once. ViWi-BT's goal is to use AI and a device's cameras or lidar capabilities to identify physical impediments and advantages for the beam targeting process, enabling "vision-aided wireless communications."

Robotic Process Automation (RPA), which allows for the automation of the tasks of workers, has been one of the hottest categories in tech. The reason is actually simple: the ROI (Return on Investment) has generally been fairly high. Yet there are some nagging issues. And perhaps the biggest is the scaling of the technology. But AI (Artificial Intelligence) is likely to help out.

As an activist, Nandini Jammi has become accustomed to getting harassed online, often by faceless social media accounts. But this time was different: a menacing tweet was sent her way from an account with a profile picture of a woman with blonde hair and a beaming smile. The woman went only by a first name, "Jessica," and her short Twitter biography read: "If you are a bully I will fight you." In her tweet sent to Jammi last July, she said: "why haven't you cleaned your info from Adult Friend Finder? It's only been three years."