Is there any way of fighting back? Anthony "Bingy" Arillotta waited years to become a made man in the Genovese crime family, and when at last the call came in August 2003, he followed directions to the letter. According to sworn testimony, Arillotta was summoned to a steak house in the Bronx, where he was made to hand over his cellphone, beeper, and jewelry before being driven to an apartment building. When he got there, he was taken to a small bathroom and strip-searched for electronic devices. For his big meeting with the boss, he was given a bathrobe to wear. Until recently, only spies and criminals had to worry this obsessively about their private statements being picked up by electronic equipment.

As some financial leaders fret publicly about the stock market falling to earth, Andrew Ross Sorkin's new book recounts the greatest crash of them all. As stocks plummeted on the morning of October 24th, 1929, a large crowd gathered on Wall Street outside of the New York Stock Exchange. Pat Bologna, a local shoeshiner whose life savings were invested in the market, dodged into a packed brokerage nearby. "Everybody is shouting," he later recalled. "They're all trying to reach the glass booth where the clerks are. Everybody wants to sell out. The boy at the quotation board is running scared. He can't keep up with the speed of the way stocks are dropping. The guy who runs it is Irish. I can't hear what he's saying. But a guy near me shouts, 'the sonofabitch has sold me out!' " The stock-market crash of 1929 occupies a dark but indelible place in the national imagination, and for good reason.

Including intricate topological information (e.g., cycles) provably enhances the expressivity of message-passing graph neural networks (GNNs) beyond the Weisfeiler-Leman (WL) hierarchy. Consequently, Persistent Homology (PH) methods are increasingly employed for graph representation learning. In this context, recent works have proposed decorating classical PH diagrams with vertex and edge features for improved expressivity. However, due to their dependence on features, these methods still fail to capture basic graph structural information. In this paper, we propose SpectRe -- a new topological descriptor for graphs that integrates spectral information into PH diagrams. Notably, SpectRe is strictly more expressive than existing descriptors on graphs. We also introduce notions of global and local stability to analyze existing descriptors and establish that SpectRe is locally stable. Finally, experiments on synthetic and real-world datasets demonstrate the effectiveness of SpectRe and its potential to enhance the capabilities of graph models in relevant learning tasks.

A spectre is haunting the United States--the spectre of drone warfare. Since the middle of November, unidentified unmanned aerial vehicles have lit up the skies above New Jersey, startling residents and baffling military and government officials. The US Army's Picatinny Arsenal research and manufacturing facility in the state's Morris County reported 11 confirmed instances of mysterious drones illegally entering its airspace since the middle of the month, while a dozen drones were spotted hovering over US Naval Weapons Station Earle in Monmouth County in early December. Similar sightings were reported in at least six other counties throughout the state; according to the Coast Guard, a group of drones even followed one of the service's vessels "in close pursuit" near a state park. The spate of drone sightings in the skies above New Jersey have caused alarm among state lawmakers, prompting one to call for a "limited state of emergency … until the public receives an explanation" regarding the source of the unidentified drones.

Throughout all of this, Microsoft's Surface lineup has remained pretty much unchanged for years. Shouldn't Microsoft be doing something about it? Microsoft launched the original Surface in 2012 to set new standards for the PC market. But lately it's looking more and more like other laptop manufacturers are blazing a trail, and Microsoft has let Surface devices lag behind. Its fourth-quarter earnings report detailed problems launching Surface devices, and executives said that falling device sales would actually accelerate into this quarter.

We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors. Spectral conditioning allows for direct modeling of the global and local graph structure and helps to overcome the expressivity and mode collapse issues of one-shot graph generators. Our novel GAN, called SPECTRE, enables the one-shot generation of much larger graphs than previously possible with one-shot models. SPECTRE outperforms state-of-the-art deep autoregressive generators in terms of modeling fidelity, while also avoiding expensive sequential generation and dependence on node ordering. A case in point, in sizable synthetic and real-world graphs SPECTRE achieves a 4-to-170 fold improvement over the best competitor that does not overfit and is 23-to-30 times faster than autoregressive generators.

Modern machine learning increasingly requires training on a large collection of data from multiple sources, not all of which can be trusted. A particularly concerning scenario is when a small fraction of poisoned data changes the behavior of the trained model when triggered by an attacker-specified watermark. Such a compromised model will be deployed unnoticed as the model is accurate otherwise. There have been promising attempts to use the intermediate representations of such a model to separate corrupted examples from clean ones. However, these defenses work only when a certain spectral signature of the poisoned examples is large enough for detection. There is a wide range of attacks that cannot be protected against by the existing defenses. We propose a novel defense algorithm using robust covariance estimation to amplify the spectral signature of corrupted data. This defense provides a clean model, completely removing the backdoor, even in regimes where previous methods have no hope of detecting the poisoned examples. Code and pre-trained models are available at https://github.com/SewoongLab/spectre-defense .

Measurement of the red damping wing of neutral hydrogen in quasar spectra provides a probe of the epoch of reionization in the early Universe. Such quantification requires precise and unbiased estimates of the intrinsic continua near Lyman-$\alpha$ (Ly$\alpha$), a challenging task given the highly variable Ly$\alpha$ emission profiles of quasars. Here, we introduce a fully probabilistic approach to intrinsic continua prediction. We frame the problem as a conditional density estimation task and explicitly model the distribution over plausible blue-side continua ($1190\ \unicode{xC5} \leq \lambda_{\text{rest}} < 1290\ \unicode{xC5}$) conditional on the red-side spectrum ($1290\ \unicode{xC5} \leq \lambda_{\text{rest}} < 2900\ \unicode{xC5}$) using normalizing flows. Our approach achieves state-of-the-art precision and accuracy, allows for sampling one thousand plausible continua in less than a tenth of a second, and can natively provide confidence intervals on the blue-side continua via Monte Carlo sampling. We measure the damping wing effect in two $z>7$ quasars and estimate the volume-averaged neutral fraction of hydrogen from each, finding $\bar{x}_\text{HI}=0.304 \pm 0.042$ for ULAS J1120+0641 ($z=7.09$) and $\bar{x}_\text{HI}=0.384 \pm 0.133$ for ULAS J1342+0928 ($z=7.54$).

Artificial intelligence (AI) is arguably the new spectre of digital cultures. By filtering information out of existing data, it determines the way we see the world and how the world sees us. Yet the vision algorithms have of our future is built on our past. What we teach these algorithms ultimately reflects back on us and it is therefore no surprise when artificial intelligence starts to classify on the basis of race, class and gender. This odd'hauntology'1Jacques Derrida, Spectres de Marx, Paris, Galilée, 1993. is at the core of what is currently discussed under the labels of algorithmic bias or pattern discrimination.2For

The CEO of Instagram has defended the company's decision not to take down a deepfaked video of Mark Zuckerberg two weeks after the doctored video was reported. Adam Mosseri told CBS' Gayle King - in his first US television interview since taking over the platform last year - that the company hasn't yet formulated an official policy on AI-altered video called'deepfakes', and until then taking action would be'inappropriate.' Mosseri said, 'I don't feel good about it,' but said there is no rush to remove the video, in part because'the damage is done.' Mosseri's comments about deepfakes come as a response to King's questioning about a faked video of Facebook CEO Mark Zuckerberg taken from an actual interview with CBSN in 2017. The doctored video features a fairly convincing Zuckerberg next to a superimposed CBSN logo talking about how Facebook wields power over its users.