FTX's collapse is the latest blow for a company that has taken several hits over the past year. Earlier this year, more than a dozen current and former employees at TSM and its sister software firm, Blitz, told The Post that Dinh fostered a "culture of fear" at both companies. On July 13, Riot Games, the developer and publisher of "League" and "Valorant," games in which TSM has a presence, fined Dinh $75,000 and placed him on a two-year probation following an investigation that concluded that "there was a pattern and practice of disparaging and bullying behavior exhibited by Andy Dinh toward TSM players and staff members."

We consider an approach for community detection in time-varying networks. At its core, this approach maintains a small sketch graph to capture the essential community structure found in each snapshot of the full network. We demonstrate how the sketch can be used to explicitly identify six key community events which typically occur during network evolution: growth, shrinkage, merging, splitting, birth and death. Based on these detection techniques, we formulate a community detection algorithm which can process a network concurrently exhibiting all processes. One advantage afforded by the sketch-based algorithm is the efficient handling of large networks. Whereas detecting events in the full graph may be computationally expensive, the small size of the sketch allows changes to be quickly assessed. A second advantage occurs in networks containing clusters of disproportionate size. The sketch is constructed such that there is equal representation of each cluster, thus reducing the possibility that the small clusters are lost in the estimate. We present a new standardized benchmark based on the stochastic block model which models the addition and deletion of nodes, as well as the birth and death of communities. When coupled with existing benchmarks, this new benchmark provides a comprehensive suite of tests encompassing all six community events. We provide a set of numerical results demonstrating the advantages of our approach both in run time and in the handling of small clusters.

A screen, center, shows a professional video game competition between teams sitting on either side. Unionization of players is a big topic in the industry after several years of fast growth. A screen, center, shows a professional video game competition between teams sitting on either side. Unionization of players is a big topic in the industry after several years of fast growth. With threats of strikes and retaliatory layoffs, and no shortage of sniping on social media, a battle last winter had the stamps of a modern-day union war.

"It was really about doing something that I loved, doing something that I had a lot of fun with," Andy Dinh said of starting his eSport organization, Team SoloMid in 2009. Dinh intended to drop out of high school to attend college early, but in looking for work to pay for school, he found a lucrative business in offering guides to the then fledgling game, League of Legends. Looking to compete in the early tournaments for the game but failing to persuade an existing eSport organization to pick up his team, Dinh formed TSM with the revenue from his website. Seven years later Dinh's Team SoloMid is North America's top team in LoL, which now attracts over 100 million players monthly and sells out arenas like Madison Square Garden for its live competitions. Early on, the tireless Dinh both captained and managed the organization before stepping down from the former in 2013 to focus on the latter, bringing in sponsors like Geico, Axe and HTC and building a massive social following for its in-house content.

We study the behavior of the A* search algorithm when coupled with a heuristic h satisfying (1-epsilon1)h* <= h <=(1+epsilon2)h*, where 0 <= epsilon1, epsilon2 < 1 are small constants and h* denotes the optimal cost to a solution. We prove a rigorous, general upper bound on the time complexity of A* search on trees that depends on both the accuracy of the heuristic and the distribution of solutions. Our upper bound is essentially tight in the worst case; in fact, we show nearly matching lower bounds that are attained even by non-adversarially chosen solution sets induced by a simple stochastic model. A consequence of our rigorous results is that the effective branching factor of the search will be reduced as long as epsilon1+epsilon2 < 1 and the number of near-optimal solutions in the search tree is not too large. We go on to provide an upper bound for A* search on graphs and in this context establish a bound on running time determined by the spectrum of the graph. We then experimentally explore to what extent our rigorous upper bounds predict the behavior of A* in some natural, combinatorially-rich search spaces. We begin by applying A* to solve the knapsack problem with near-accurate admissible heuristics constructed from an efficient approximation algorithm for this problem. We additionally apply our analysis of A* search for the partial Latin square problem, where we can provide quite exact analytic bounds on the number of near-optimal solutions. These results demonstrate a dramatic reduction in effective branching factor of A* when coupled with near-accurate heuristics in search spaces with suitably sparse solution sets.