A US physicist and a Canadian computer scientist have won this year's Turing Award for their invention of a form of seemingly unbreakable encryption. Charles H Bennett and Gilles Brassard's work, which dates back to 1984, is known as quantum cryptography and has redefined secure communication and computing, the award's body said. Scientists believe their work will be central to electronic communications in a world that depends heavily on data-sharing, but which for years has been trying to develop more powerful quantum computers. The Turing Award, named after the mathematician and code-breaker Alan Turing, is known as the Nobel Prize of computing. It comes with a $1m (£800,000) prize.

We develop a nonparametric and online learning algorithm that estimates the triggering functions of a multivariate Hawkes process (MHP). The approach we take approximates the triggering function $f_{i,j}(t)$ by functions in a reproducing kernel Hilbert space (RKHS), and maximizes a time-discretized version of the log-likelihood, with Tikhonov regularization. Theoretically, our algorithm achieves an $\calO(\log T)$ regret bound. Numerical results show that our algorithm offers a competing performance to that of the nonparametric batch learning algorithm, with a run time comparable to the parametric online learning algorithm.

We study the online learning problem of a bidder who participates in repeated auctions. With the goal of maximizing his T-period payoff, the bidder determines the optimal allocation of his budget among his bids for $K$ goods at each period. As a bidding strategy, we propose a polynomial-time algorithm, inspired by the dynamic programming approach to the knapsack problem. The proposed algorithm, referred to as dynamic programming on discrete set (DPDS), achieves a regret order of $O(\sqrt{T\log{T}})$. By showing that the regret is lower bounded by $\Omega(\sqrt{T})$ for any strategy, we conclude that DPDS is order optimal up to a $\sqrt{\log{T}}$ term. We evaluate the performance of DPDS empirically in the context of virtual trading in wholesale electricity markets by using historical data from the New York market. Empirical results show that DPDS consistently outperforms benchmark heuristic methods that are derived from machine learning and online learning approaches.

Inverse optimization is a powerful paradigm for learning preferences and restrictions that explain the behavior of a decision maker, based on a set of external signal and the corresponding decision pairs. However, most inverse optimization algorithms are designed specifically in batch setting, where all the data is available in advance. As a consequence, there has been rare use of these methods in an online setting suitable for real-time applications. In this paper, we propose a general framework for inverse optimization through online learning. Specifically, we develop an online learning algorithm that uses an implicit update rule which can handle noisy data. Moreover, under additional regularity assumptions in terms of the data and the model, we prove that our algorithm converges at a rate of $\mathcal{O}(1/\sqrt{T})$ and is statistically consistent. In our experiments, we show the online learning approach can learn the parameters with great accuracy and is very robust to noises, and achieves a dramatic improvement in computational efficacy over the batch learning approach.

Is Dubai's glossy image under threat? Stephanie Baker had been celebrating her birthday with friends at a bar on Palm Jumeirah - Dubai's iconic man-made palm-shaped island lined with luxury hotels and beach clubs. But as the group stepped outside to head to another nearby venue, something unusual streaked across the night sky. Moments later, debris from a drone struck the five-star Fairmont hotel - Baker and her friends were standing right across the street. We all were scared, she says.

Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Hollywood icon who starred in Psycho after Hitchcock dubbed her'my new Grace Kelly' looks incredible at 95 Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting Ben Affleck'scores $600m deal' with Netflix to sell his AI film start-up Long hair over 45 is ageing and try-hard. I've finally cut mine off. Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' Boys as young as 11 are being exposed to misogyny online, experts have warned, with three-quarters saying harmful content appears without actively searching for it. A study has found that teenage boys are receiving targeted content that promotes violence and derogatory views of women. It follows the widespread impact of Netflix's drama series Adolescence, which told the story of a 13-year-old boy who brutally murders his classmate.

Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Hollywood icon who starred in Psycho after Hitchcock dubbed her'my new Grace Kelly' looks incredible at 95 Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting Ben Affleck'scores $600m deal' with Netflix to sell his AI film start-up Long hair over 45 is ageing and try-hard. I've finally cut mine off. Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' A US patent for a potential breakthrough cancer treatment is drawing renewed attention after declassified CIA documents revealed how scientists may have been close to a cure 60 years ago. The patent, published by Johns Hopkins University in 2021 and titled'Mebendazole Polymorph for Treatment and Prevention of Tumors,' outlines how specific formulations of the drug mebendazole may be used to target cancer cells. Mebendazole has been used safely for more than four decades to treat parasitic worm infections in humans, but researchers have increasingly investigated whether the drug could also help fight certain cancers, including aggressive brain tumors.

Understanding minimal assumptions that enable learning and generalization is perhaps the central question of learning theory. Several celebrated results in statistical learning theory, such as the VC theorem and Littlestone's characterization of online learnability, establish conditions on the hypothesis class that allow for learning under independent data and adversarial data, respectively. Building upon recent work bridging these extremes, we study sequential decision making under distributional adversaries that can adaptively choose data-generating distributions from a fixed family $U$ and ask when such problems are learnable with sample complexity that behaves like the favorable independent case. We provide a near complete characterization of families $U$ that admit learnability in terms of a notion known as generalized smoothness i.e. a distribution family admits VC-dimension-dependent regret bounds for every finite-VC hypothesis class if and only if it is generalized smooth. Further, we give universal algorithms that achieve low regret under any generalized smooth adversary without explicit knowledge of $U$. Finally, when $U$ is known, we provide refined bounds in terms of a combinatorial parameter, the fragmentation number, that captures how many disjoint regions can carry nontrivial mass under $U$. These results provide a nearly complete understanding of learnability under distributional adversaries. In addition, building upon the surprising connection between online learning and differential privacy, we show that the generalized smoothness also characterizes private learnability under distributional constraints.

Microsoft has a new plan to prove what's real and what's AI online A new proposal calls on social media and AI companies to adopt strict verification, but the company hasn't committed to following its own recommendations. There are the high-profile cases you may easily spot, like when White House officials recently shared a manipulated image of a protester in Minnesota and then mocked those asking about it. Other times, it slips quietly into social media feeds and racks up views, like the videos that Russian influence campaigns are currently spreading to discourage Ukrainians from enlisting. It is into this mess that Microsoft has put forward a blueprint, shared with, for how to prove what's real online. An AI safety research team at the company recently evaluated how methods for documenting digital manipulation are faring against today's most worrying AI developments, like interactive deepfakes and widely accessible hyperrealistic models. It then recommended technical standards that can be adopted by AI companies and social media platforms.

In order to help clarify our contributions and or-2 ganize them for readers, we provide the following table to summarize the differences between regrets.3 REVIEWER 4 Thank you for your comments. Concept drift occurs when the optimal model attimetmay no longer bethe optimal model10 at timet+1. Consider an online learning problem with concept drift withT = 3 time periods and loss functions:11 f1(x) = (x 1)2,f2(x) = (x 2)2,f3(x) = (x 3)2. Figure 1: SGD online with momentum Theoretical motivation via Calibration: A more formal motivation of our regret23 can be related to the concept of calibration [1]. The comment on line 110 can be24 rewritten as: If the updates{x1,,xT} are well-calibrated, then perturbingxt by25 anyucannot substantially reduce the cumulative loss.Hence, itcan besaid that the26 sequence {x1,,xT} is asymptotically calibrated with respect to{f1,,fT} if:27 Weindeedranexperiments usingSGDwithmomentum forvariousdecayparameters andconcluded thatSGDwith36 momentum is not even as stable as SGD-online (standard SGD without momentum) as shown in Figure 1.