Amazon has released a new trailer for Secret Level, the upcoming Prime Video show that tells stories set in the worlds of beloved, popular or even upcoming games (and also Concord). Given that it's an anthology series, there's unlikely to be an overarching plot, so there's not much to grok here from a narrative perspective. And the sooner there's a mortarium on trailers being soundtracked to that overused M83 song, the better. However, the visuals sure do look pretty. The quality of the animation is genuinely impressive.

Earlier this week, after warping across the galaxy for 90 hours in a sentient spacecraft, Twitch streamer John Wissmiller realized that Concord was the best first-person shooter he'd played in a decade. "The gunplay was crunchy, the movement was smooth, and the progression felt rewarding," he says. "I was even more enthralled by the world the developers had created when I looked into the lore." "One of the biggest perks about the game was the absence of toxicity within the player community," says Kelle Dees, a content creator at KDeesGamez. "Everything about the game was positive and inclusive."

It is fair to say that the video game industry is undergoing a period of alarming disarray. Studios are closing, development budgets are exploding and profitable genres are becoming saturated by mega-budget pick-me candidates that feel utterly interchangeable. Into this troubling market comes Concord, Sony's new five-v-five "hero" shooter, the subgenre of the multiplayer online blaster where players control characters with elaborate special powers rather than identikit spec-ops soldiers or space marines. Set in a warring galaxy controlled by an autocratic regime known as The Guild, the game gives us control of various Freegunners – mercenaries who plough the space lanes looking for jobs while also slinging one-liners at each other in the game's highly polished cutscenes. In the game, however, what they do is fight. All the standard characters of the hero shooter are present – vanilla soldier, floaty witch, teleporting weirdo, sassy tank – yet none have the immediate appeal of Overwatch denizens such as D.Va or Mei.

Deep learning is widely used to uncover hidden patterns in large code corpora. To achieve this, constructing a format that captures the relevant characteristics and features of source code is essential. Graph-based representations have gained attention for their ability to model structural and semantic information. However, existing tools lack flexibility in constructing graphs across different programming languages, limiting their use. Additionally, the output of these tools often lacks interoperability and results in excessively large graphs, making graph-based neural networks training slower and less scalable. We introduce CONCORD, a domain-specific language to build customizable graph representations. It implements reduction heuristics to reduce graphs' size complexity. We demonstrate its effectiveness in code smell detection as an illustrative use case and show that: first, CONCORD can produce code representations automatically per the specified configuration, and second, our heuristics can achieve comparable performance with significantly reduced size. CONCORD will help researchers a) create and experiment with customizable graph-based code representations for different software engineering tasks involving DL, b) reduce the engineering work to generate graph representations, c) address the issue of scalability in GNN models, and d) enhance the reproducibility of experiments in research through a standardized approach to code representation and analysis.

While large pre-trained language models are powerful, their predictions often lack logical consistency across test inputs. For example, a state-of-the-art Macaw question-answering (QA) model answers 'Yes' to 'Is a sparrow a bird?' and 'Does a bird have feet?' but answers 'No' to 'Does a sparrow have feet?'. To address this failure mode, we propose a framework, Consistency Correction through Relation Detection, or ConCoRD, for boosting the consistency and accuracy of pre-trained NLP models using pre-trained natural language inference (NLI) models without fine-tuning or re-training. Given a batch of test inputs, ConCoRD samples several candidate outputs for each input and instantiates a factor graph that accounts for both the model's belief about the likelihood of each answer choice in isolation and the NLI model's beliefs about pair-wise answer choice compatibility. We show that a weighted MaxSAT solver can efficiently compute high-quality answer choices under this factor graph, improving over the raw model's predictions. Our experiments demonstrate that ConCoRD consistently boosts accuracy and consistency of off-the-shelf closed-book QA and VQA models using off-the-shelf NLI models, notably increasing accuracy of LXMERT on ConVQA by 5% absolute. See https://ericmitchell.ai/emnlp-2022-concord/ for code and data.

A Northern California teen was arrested Wednesday for hacking a school district's computer system and changing the grades of up to 15 students. Authorities said they arrested David Rotaro, a sophomore at Ygnacio Valley High School in Concord, California, for infiltrating the school district's computer system. Rotaro, 16, said it was like "stealing candy from a baby," according to KGO-TV, an ABC affiliate in San Francisco. It took him five minutes to design a "phishing email," that he sent out to swipe login information from school faculty. Authorities didn't release Rotaro's name, however, he confessed to having committed the crime during an interview with KGO-TV.

We consider a graphical model where a multivariate normal vector is associated with each node of the underlying graph and estimate the graphical structure. We minimize a loss function obtained by regressing the vector at each node on those at the remaining ones under a group penalty. We show that the proposed estimator can be computed by a fast convex optimization algorithm. We show that as the sample size increases, the estimated regression coefficients and the correct graphical structure are correctly estimated with probability tending to one. By extensive simulations, we show the superiority of the proposed method over comparable procedures. We apply the technique on two real datasets. The first one is to identify gene and protein networks showing up in cancer cell lines, and the second one is to reveal the connections among different industries in the US. 1 2 Introduction

A report of drone sightings from the Federal Aviation Administration (FAA) shows that despite a new registration scheme, near misses between unmanned and piloted aircraft in American are on the rise. Sightings by pilots and airport officials have steadily increased from less than one a day in 2014, to over 3.5 between August 2015 and January this year, many of them from commercial passenger aircraft. In the most serious incident, the pilot of an American Airlines jet last September had to swerve to avoid a drone. On September 13, flight 475 took off from Atlanta, Georgia en route to Charlotte, North Carolina. It was climbing to 3,500 ft when the pilot of the Airbus had to take evasive action to avoid a collision with an unidentified unmanned aerial system (UAS) or drone.

Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l1-penalties to either (1) parametric likelihoods, or, (2) regularized regression/pseudo-likelihoods, with the latter having the distinct advantage that they do not explicitly assume Gaussianity. As none of the popular methods proposed for solving pseudo-likelihood based objective functions have provable convergence guarantees, it is not clear if corresponding estimators exist or are even computable, or if they actually yield correct partial correlation graphs. This paper proposes a new pseudo-likelihood based graphical model selection method that aims to overcome some of the shortcomings of current methods, but at the same time retain all their respective strengths. In particular, we introduce a novel framework that leads to a convex formulation of the partial covariance regression graph problem, resulting in an objective function comprised of quadratic forms. The objective is then optimized via a coordinate-wise approach. The specific functional form of the objective function facilitates rigorous convergence analysis leading to convergence guarantees; an important property that cannot be established using standard results, when the dimension is larger than the sample size, as is often the case in high dimensional applications. These convergence guarantees ensure that estimators are well-defined under very general conditions, and are always computable. In addition, the approach yields estimators that have good large sample properties and also respect symmetry. Furthermore, application to simulated/real data, timing comparisons and numerical convergence is demonstrated. We also present a novel unifying framework that places all graphical pseudo-likelihood methods as special cases of a more general formulation, leading to important insights.