Nonlinear independent component analysis (ICA) provides an appealing framework for unsupervised feature learning, but the models proposed so far are not identifiable. Here, we first propose a new intuitive principle of unsupervised deep learning from time series which uses the nonstationary structure of the data. Our learning principle, time-contrastive learning (TCL), finds a representation which allows optimal discrimination of time segments (windows). Surprisingly, we show how TCL can be related to a nonlinear ICA model, when ICA is redefined to include temporal nonstationarities. In particular, we show that TCL combined with linear ICA estimates the nonlinear ICA model up to point-wise transformations of the sources, and this solution is unique --- thus providing the first identifiability result for nonlinear ICA which is rigorous, constructive, as well as very general.

Joe Hogan, Align Technology's plastics-nerd CEO, says you shouldn't eat with your aligners and that you don't need to wear your retainers every night. Joe Hogan sees a lot of smiles. When people ask him where he works, he responds with "Align Technology," which inevitably prompts the follow up, "What's that?" After months, sometimes years, the discrete rival to braces promises to give people smiles they will want to show off. Hogan gets a look at them all. And he's eager to see more. Align is embarking on its biggest manufacturing overhaul since it was founded by two Stanford Graduate School of Business classmates 29 years ago. The company is preparing to begin directly 3D printing the aligners at the core of its business, ditching what Hogan describes as a longer, more wasteful process that involves making molds. A successful transition could lower costs and make treatment more affordable in the long run, bringing Invisalign to more customers and boosting Align's profits. It also, according to Hogan, would entrench Align as the world's biggest user of 3D printers .

This article was featured in the One Story to Read Today newsletter. T he smell was strange . The concrete wall was too close. One of my kids was standing on the sidewalk next to our car--not crying, just confused. The seat belt had held. The crumple zone had crumpled.

We investigate the statistical performance and computational efficiency of the alternating minimization procedure for nonparametric tensor learning. Tensor modeling has been widely used for capturing the higher order relations between multimodal data sources. In addition to a linear model, a nonlinear tensor model has been received much attention recently because of its high flexibility. We consider an alternating minimization procedure for a general nonlinear model where the true function consists of components in a reproducing kernel Hilbert space (RKHS). In this paper, we show that the alternating minimization method achieves linear convergence as an optimization algorithm and that the generalization error of the resultant estimator yields the minimax optimality. We apply our algorithm to some multitask learning problems and show that the method actually shows favorable performances.

The recent success of deep neural networks relies on massive amounts of labeled data. For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. In this paper, we propose a new approach to domain adaptation in deep networks that can jointly learn adaptive classifiers and transferable features from labeled data in the source domain and unlabeled data in the target domain. We relax a shared-classifier assumption made by previous methods and assume that the source classifier and target classifier differ by a residual function. We enable classifier adaptation by plugging several layers into deep network to explicitly learn the residual function with reference to the target classifier. We fuse features of multiple layers with tensor product and embed them into reproducing kernel Hilbert spaces to match distributions for feature adaptation. The adaptation can be achieved in most feed-forward models by extending them with new residual layers and loss functions, which can be trained efficiently via back-propagation. Empirical evidence shows that the new approach outperforms state of the art methods on standard domain adaptation benchmarks.

The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

Ties between the US tech giant Palantir and the United Kingdom government are coming under increased scrutiny following the arrest of former UK ambassador to the US Peter Mandelson over his links to the late convicted sex offender Jeffrey Epstein. Despite its public criticism of both Palantir and Mandelson, the UK government has entered into extensive contracts with the US tech giant, signing a defence contract worth 240 million pounds ($323m) in January. The contract was awarded to Palantir directly, while another, worth 330 million pounds ($444m) and involving the UK's Ministry of Health, was awarded in November 2023 following a bidding process. The latter contract's contents, campaigners say, remain heavily redacted . In addition to its role supporting US President Donald Trump's immigration crackdown, which has resulted in killings and unlawful deportations, Palantir has partnered extensively with the Israeli military and its operations in Gaza and the occupied West Bank.

Customer conversations with chatbots can include contact information and personal details that make it easier for scammers to launch phishing attacks and commit fraud.

Two Literal Crypto Bros Built a Real Estate Empire. In 2019, two Canadian brothers blew into Detroit with an irresistible pitch: For $50, almost anyone could become a property owner. When houses decayed and the city intervened, the blame games began. A fire broke out at 10410 Cadieux in March 2025, burning a hole in the roof. The smell hit me first: damp brick, stagnant water, mold, and bleach. I was partway down a flight of wooden stairs that led to the basement of a 1920s duplex in east Detroit, Michigan. Leading the way was Cornell Dorris, a tenant in the building for nearly a decade. Dorris is in his early forties, has two daughters who visit on weekends, and makes a living smoking meat and cooking for events. As my eyes adjusted, I made out rodent droppings and a black puddle that spread across the basement floor. "Anytime it rains, the water comes down," Dorris said. The air was unnaturally heavy, and I felt a nagging urge to leave. Dorris doesn't have a typical landlord. Almost four years ago, his building was acquired by a startup called RealToken, or RealT.

Should You Leave Your Phone Charging Overnight? It used to be common wisdom that leaving your phone charging overnight degrades the battery. But handset design has evolved to mitigate the harm caused by constant charging. You may have heard that leaving your smartphone charging overnight--either plugged in or atop a wireless charger --can damage your battery. But is it actually harmful or dangerous to do that?