Japan's arm-based Fugaku supercomputing system has been acknowledged as the world's most powerful supercomputer. In June 2020, the system earned the top spot in the Top500 ranking of the 500 most powerful commercially available computer systems on the planet, for its performance on a longstanding metric for massive scientific computation. Although modern supercomputing tasks often emphasize somewhat different capabilities, Fugaku also outperforms by other measures as well. This architecture just wins big time," said Torsten Hoefler of the Swiss Federal Institute of Technology (ETH) Zurich. "It is a super-large step." Hoefler shared the 2019 ACM Gordon Bell Prize with an ETH Zurich team for simulations of heat and quantum electronic flow in nanoscale transistors performed in part on the previous Top500 leader, the Summit System at the U.S. Department of Energy's Oak Ridge National Laboratory (ORNL) in Tennessee.

Dubai/Washington – An American nuclear-powered guided-missile submarine traversed the strategically vital waterway between Iran and the Arabian Peninsula on Monday, the U.S. Navy said, in a rare announcement that comes amid rising tensions with Iran. The Navy's 5th Fleet, based in Bahrain, said the Ohio-class guided-missile submarine USS Georgia, accompanied by two other warships, passed through the Strait of Hormuz, a narrow passageway through which a fifth of the world's oil supplies travel. The unusual transit in the Persian Gulf's shallow waters, aimed at underscoring American military might in the region, follows the killing last month of Mohsen Fakhrizadeh, an Iranian scientist named by the West as the leader of the Islamic Republic's disbanded military nuclear program. It also comes some two weeks before the anniversary of the American drone strike near Baghdad airport in Iraq that killed top Iranian military commander Gen. Qassem Soleimani on Jan. 3. Iran has promised to seek revenge for both killings. The Ohio-class ballistic-missile submarine's presence in Mideast waterways signals the U.S. Navy's "commitment to regional partners and maritime security with a full spectrum of capabilities," the Navy said, demonstrating its readiness "to defend against any threat at any time."

There is an increasing interest in the entwining of the field of antitrust with the field of Artificial Intelligence (AI), frequently referred to jointly as Antitrust and AI (AAI) in the research literature. This study focuses on the synergies entangling antitrust and AI, doing so to extend the literature by proffering the primary ways that these two fields intersect, consisting of: (1) the application of antitrust to AI, and (2) the application of AI to antitrust. To date, most of the existing research on this intermixing has concentrated on the former, namely the application of antitrust to AI, entailing how the marketplace will be altered by the advent of AI and the potential for adverse antitrust behaviors arising accordingly. Opting to explore more deeply the other side of this coin, this research closely examines the application of AI to antitrust and establishes an antitrust vigilance lifecycle to which AI is predicted to be substantively infused for purposes of enabling and bolstering antitrust detection, enforcement, and post-enforcement monitoring. Furthermore, a gradual and incremental injection of AI into antitrust vigilance is anticipated to occur as significant advances emerge amidst the Levels of Autonomy (LoA) for AI Legal Reasoning (AILR).

The prevalence of state-sponsored propaganda on the Internet has become a cause for concern in the recent years. While much effort has been made to identify state-sponsored Internet propaganda, the problem remains far from being solved because the ambiguous definition of propaganda leads to unreliable data labelling, and the huge amount of potential predictive features causes the models to be inexplicable. This paper is the first attempt to build a balanced dataset for this task. The dataset is comprised of propaganda by three different organizations across two time periods. A multi-model framework for detecting propaganda messages solely based on the visual and textual content is proposed which achieves a promising performance on detecting propaganda by the three organizations both for the same time period (training and testing on data from the same time period) (F1=0.869) and for different time periods (training on past, testing on future) (F1=0.697). To reduce the influence of false positive predictions, we change the threshold to test the relationship between the false positive and true positive rates and provide explanations for the predictions made by our models with visualization tools to enhance the interpretability of our framework. Our new dataset and general framework provide a strong benchmark for the task of identifying state-sponsored Internet propaganda and point out a potential path for future work on this task.

We dare to make use of a possible analogy between neurons in a brain and people in society, asking ourselves whether individual intelligence is necessary in order to collective wisdom to emerge and, most importantly, what sort of individual intelligence is conducive of greater collective wisdom. We review insights and findings from connectionism, agent-based modeling, group psychology, economics and physics, casting them in terms of changing structure of the system's Lyapunov function. Finally, we apply these insights to the sort and degrees of intelligence of preys and predators in the Lotka-Volterra model, explaining why certain individual understandings lead to co-existence of the two species whereas other usages of their individual intelligence cause global extinction.

The current success of deep learning depends on large-scale labeled datasets. In practice, high-quality annotations are expensive to collect, but noisy annotations are more affordable. Previous works report mixed empirical results when training with noisy labels: neural networks can easily memorize random labels, but they can also generalize from noisy labels. To explain this puzzle, we study how architecture affects learning with noisy labels. We observe that if an architecture "suits" the task, training with noisy labels can induce useful hidden representations, even when the model generalizes poorly; i.e., the last few layers of the model are more negatively affected by noisy labels. This finding leads to a simple method to improve models trained on noisy labels: replacing the final dense layers with a linear model, whose weights are learned from a small set of clean data. We empirically validate our findings across three architectures (Convolutional Neural Networks, Graph Neural Networks, and Multi-Layer Perceptrons) and two domains (graph algorithmic tasks and image classification). Furthermore, we achieve state-of-the-art results on image classification benchmarks by combining our method with existing approaches on noisy label training.

We extract data-driven, intrinsic spatial coordinates from observations of the dynamics of large systems of coupled heterogeneous agents. These coordinates then serve as an emergent space in which to learn predictive models in the form of partial differential equations (PDEs) for the collective description of the coupled-agent system. They play the role of the independent spatial variables in this PDE (as opposed to the dependent, possibly also data-driven, state variables). This leads to an alternative description of the dynamics, local in these emergent coordinates, thus facilitating an alternative modeling path for complex coupled-agent systems. We illustrate this approach on a system where each agent is a limit cycle oscillator (a so-called Stuart-Landau oscillator); the agents are heterogeneous (they each have a different intrinsic frequency $\omega$) and are coupled through the ensemble average of their respective variables. After fast initial transients, we show that the collective dynamics on a slow manifold can be approximated through a learned model based on local "spatial" partial derivatives in the emergent coordinates. The model is then used for prediction in time, as well as to capture collective bifurcations when system parameters vary. The proposed approach thus integrates the automatic, data-driven extraction of emergent space coordinates parametrizing the agent dynamics, with machine-learning assisted identification of an "emergent PDE" description of the dynamics in this parametrization.

On November 30, Chinese foreign ministry spokesman Lijian Zhao pinned an image to his Twitter profile. In it, a soldier stands on an Australian flag and grins maniacally as he holds a bloodied knife to a boy's throat. The boy, whose face is covered by a semi-transparent veil, carries a lamb. Alongside the image, Zhao tweeted, "Shocked by murder of Afghan civilians & prisoners by Australian soldiers. We strongly condemn such acts, & call [sic] for holding them accountable."

U.S. Department of Defense and United Airlines conduct study and find the risk of exposure to coronavirus on commercial airlines is'virtually nonexistent'; Fox News correspondent Bryan Llenas reports. We're not sure this pilot should be flying with such an incurable case of Pac-Man fever. A pilot flying over the Lincolnshire, England, paid homage to one of their favorite arcade games on Sunday by drawing Pac-Man -- complete with one of Pac-Man's nemesis ghosts -- with their flight path. The flight, which took off from Retford Gamston Airport (EGNE) in Nottingham at 11:35 a.m., lasted about an hour and a half. The flight, which took off from Retford Gamston Airport (EGNE) in Nottingham at 11:35 a.m., lasted about an hour and a half, according to FlightRadar24.

Gov. Baker asked the Massachusetts Legislature to amend an expansive police reform bill last week, citing the bill's ban on facial recognition technology. Despite his refusal to sign the bill in its current form, Gov. Baker agrees with legislators on many other measures, such as new training standards. The Bay State's efforts to restore public trust in police are commendable, but state officials left out a crucial component: rural police departments. While the debate over facial recognition technology rages in Boston, towns in Western Massachusetts are struggling to pay for new brakes in police cruisers. Higher training standards are a step in the right direction, but small departments often don't have enough officers to cover shifts while other officers are in class. And at the core of this disconnect, urban activists and journalists characterize police departments as structurally broken and officers as occupying militants, whereas small town residents tend to see police officers as friends and neighbors.