We develop fast algorithms and robust software for convex optimization of two-layer neural networks with ReLU activation functions. Our work leverages a convex reformulation of the standard weight-decay penalized training problem as a set of group-$\ell_1$-regularized data-local models, where locality is enforced by polyhedral cone constraints. In the special case of zero-regularization, we show that this problem is exactly equivalent to unconstrained optimization of a convex "gated ReLU" network. For problems with non-zero regularization, we show that convex gated ReLU models obtain data-dependent approximation bounds for the ReLU training problem. To optimize the convex reformulations, we develop an accelerated proximal gradient method and a practical augmented Lagrangian solver. We show that these approaches are faster than standard training heuristics for the non-convex problem, such as SGD, and outperform commercial interior-point solvers. Experimentally, we verify our theoretical results, explore the group-$\ell_1$ regularization path, and scale convex optimization for neural networks to image classification on MNIST and CIFAR-10.

Demonstration learning aims to guide the prompt prediction via providing answered demonstrations in the few shot settings. Despite achieving promising results, existing work only concatenates the answered examples as demonstrations to the prompt template (including the raw context) without any additional operation, neglecting the prompt-demonstration dependencies. Besides, prior research found that randomly replacing the labels of demonstrations marginally hurts performance, illustrating that the model could not properly learn the knowledge brought by the demonstrations. Inspired by the human learning process, in this paper, we introduce Imitation DEMOnstration Learning (Imitation-Demo) to strengthen demonstration learning via explicitly imitating human review behaviour, which includes: (1) contrastive learning mechanism to concentrate on the similar demonstrations. (2) demonstration-label re-prediction method to consolidate known knowledge. Experiment results show that our proposed method achieves state-of-the-art performance on 11 out of 14 classification corpora. Further studies also prove that Imitation-Demo strengthen the association between prompt and demonstrations, which could provide the basis for exploring how demonstration learning works.

One of the main shortcomings of event data in football, which has been extensively used for analytics in the recent years, is that it still requires manual collection, thus limiting its availability to a reduced number of tournaments. In this work, we propose a deterministic decision tree-based algorithm to automatically extract football events using tracking data, which consists of two steps: (1) a possession step that evaluates which player was in possession of the ball at each frame in the tracking data, as well as the distinct player configurations during the time intervals where the ball is not in play to inform set piece detection; (2) an event detection step that combines the changes in ball possession computed in the first step with the laws of football to determine in-game events and set pieces. The automatically generated events are benchmarked against manually annotated events and we show that in most event categories the proposed methodology achieves $+90\%$ detection rate across different tournaments and tracking data providers. Finally, we demonstrate how the contextual information offered by tracking data can be leveraged to increase the granularity of auto-detected events, and exhibit how the proposed framework may be used to conduct a myriad of data analyses in football.

Iran's Islamic Revolutionary Guard Corps (IRGC) has seized an American sea drone in the Gulf and tried to tow it away, only releasing the unmanned vessel when a US Navy warship and helicopter approached, US officials have said. The incident on Tuesday marks the first time the Navy's Middle East-based 5th Fleet's new drone task force has been targeted by Iran. While the interception ended without incident, it comes amid growing tensions between the United States and Iran as negotiations over the tattered Iranian nuclear deal hang in the balance. The U.S. Navy prevented a support ship from Iran's Islamic Revolutionary Guard Corps Navy (IRGCN) from capturing an unmanned surface vessel operated by the U.S. 5th Fleet in the Arabian Gulf, Aug. 29-30. The IRGC's Shahid Baziar warship attached a line to the Saildrone Explorer in the central part of the Gulf in international waters late Monday night, said Commander Timothy Hawkins, a 5th Fleet spokesman.

An Iranian ship seized an American military unmanned research vessel in the Gulf but released it after a US Navy patrol boat and helicopter were deployed to the location, the Pentagon said Tuesday. The US Central Command's 5th Fleet said a support ship from Iran's Islamic Revolutionary Guard Corps Navy, the Shahid Baziar, was spotted towing the seven-meter (23-foot) Saildrone Explorer unmanned surface vessel (USV) late Monday. The US naval drone, equipped with an array of sensors, radars and cameras, was in international waters collecting navigation and other unspecified data, the 5th Fleet said in a statement. When the Iranian vessel was seen towing the unmanned boat, US forces sent the USS Thunderbolt coastal patrol ship, which was operating nearby, to the scene. In addition, an MH-60S Sea Hawk helicopter based in Bahrain flew to the location.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. Navy stopped an Iranian ship from taking an American sea drone in the Arabian Gulf Monday night. The Iranian Revolutionary Guard Corps Navy was in the process of towing the drone, which belongs to the U.S. Navy's 5th Fleet at 11 p.m. local time when the American Navy immediately sent out the nearby Navy coastal ship USS Thunderbolt. The 5th Fleet also repeatedly called Iranian officials, who then let the drone go.

The Mohajer-6 has the capability to carry out surveillance and reconnaissance missions, and the Shahed series is considered among the most capable of Iran's military drones, according to comments made by the Iranian military to local news media. Iran is a pioneer in drone technology, with at least four decades of design and manufacturing experience, and it has been providing combat drones to military groups and proxy militia in Yemen, Iraq, Syria, Lebanon and Gaza. Officials in Israel, the United States and some Sunni Arab countries like Saudi Arabia have said they are increasingly concerned that Iran's advancing drone technology could destabilize the region and empower militias backed by Iran. In the shadow war between Iran and Israel, Iranian drones have been involved in attacks on ships and have targeted U.S. military bases in Iraq and Syria. Israel has also attacked a secret facility in western Iran where hundreds of drones were believed to have been stored.

The graph represents a network of 3,162 Twitter users whose tweets in the requested range contained "datamining", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 29 August 2022 at 06:51 UTC. The requested start date was Monday, 29 August 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 22-hour, 56-minute period from Monday, 15 August 2022 at 01:03 UTC to Sunday, 28 August 2022 at 23:59 UTC.

Robotic process automation (RPA) has been well received and is making a significant difference to business processes across organisations. At its next level, RPA is being enhanced by artificial intelligence (AI) to transform business smartly. This is according to speakers at a roundtable hosted by UiPath in Cape Town, where executives discussed AI, automation the future of work. Michael Law, country manager at UiPath, told delegates: "RPA alone was last year. It has transformed areas such as finance and HR. UiPath is now bringing AI and automation together across the organisation."

Soon the turn of the year has arrived, bringing us the most unique technological solutions to rule over the outdated ones. One sector which is sure to see new techniques is that of software testing! New approaches to testing are being introduced in the IT industry due to the emergence of development technologies like DevOps and Agile. Therefore, the need to keep up and transform your own testing techniques according to the new ones is very important. For this reason, we have created a list of the important software testing techniques that you have to learn. The'Internet of Things is a technology that has brought with it a radical change in the way communication between multiple devices took place traditionally.