The United States military has struck a number of cities in Yemen, including the capital, Sanaa, and the key port city of Hodeidah. Forces from the US Central Command (CENTCOM), the military command responsible for US forces in the Middle East, "conducted strikes on 15 Houthi targets in Iranian-backed Houthi-controlled areas of Yemen today", it said on X on Friday. Four strikes targeted Sanaa and seven hit Hodeidah, according to the Houthi-run Al Masirah TV network. Correspondents with the AFP news agency also reported hearing loud explosions in both cities. The Hodeidah strikes hit the airport and the Katheib area, which has a Houthi-controlled military base, Al Masirah said.

We consider the problem of estimating multiple related functions computed by weighted automata (WFA). We first present a natural notion of relatedness between WFAs by considering to which extent several WFAs can share a common underlying representation. We then introduce the novel model of vector-valued WFA which conveniently helps us formalize this notion of relatedness. Finally, we propose a spectral learning algorithm for vector-valued WFAs to tackle the multitask learning problem. By jointly learning multiple tasks in the form of a vector-valued WFA, our algorithm enforces the discovery of a representation space shared between tasks. The benefits of the proposed multitask approach are theoretically motivated and showcased through experiments on both synthetic and real world datasets.

Gen. Jack Keane joins'Fox Report' to discuss the escalating tensions in the Middle East amid fears of a wider war. While much of the world has eyes on Israel's battles with Hezbollah and Hamas, the U.S. Navy has its sights set on another of Iran's proxies, the Yemeni Houthi rebels. With a mission to keep international waterways at peace, the Navy now finds itself fending off attacks from the shadowy gang of pirates who have gone from arming themselves with assault rifles, pickup trucks and motorboats – to a seemingly unending supply of drones, missiles and other weaponry. The Houthis often attack unarmed Western ships carrying goods through the Red Sea and the Gulf of Aden – while the U.S. has responded in kind with drone attacks on Yemen. ISRAELI AIR FORCE STRIKES HOUTHI TARGETS IN YEMEN WITH'EXTENSIVE' OPERATION That's led to perilous waters along a trade route that typically sees some 1 trillion in goods pass through it, as well as shipments of aid to war-torn Sudan and the Yemeni people.

Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models.

The encoder-decoder framework has achieved promising progress for many sequence generation tasks, including machine translation, text summarization, dialog system, image captioning, etc. Such a framework adopts an one-pass forward process while decoding and generating a sequence, but lacks the deliberation process: A generated sequence is directly used as final output without further polishing. However, deliberation is a common behavior in human's daily life like reading news and writing papers/articles/books. In this work, we introduce the deliberation process into the encoder-decoder framework and propose deliberation networks for sequence generation. A deliberation network has two levels of decoders, where the first-pass decoder generates a raw sequence and the second-pass decoder polishes and refines the raw sentence with deliberation. Since the second-pass deliberation decoder has global information about what the sequence to be generated might be, it has the potential to generate a better sequence by looking into future words in the raw sentence. Experiments on neural machine translation and text summarization demonstrate the effectiveness of the proposed deliberation networks. On the WMT 2014 English-to-French translation task, our model establishes a new state-of-the-art BLEU score of 41.5.

We consider the above CP decomposition model of tensors, as well as the Tucker decomposition.

Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their theoretical appeal, the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification. In this paper we present a hierarchical approach to methods of moments to circumvent such limitations. Our method is based on replacing the tensor decomposition step used in previous algorithms with approximate joint diagonalization. Experiments on topic modeling show that our method outperforms previous tensor decomposition methods in terms of speed and model quality.

Neural Information Processing Systems http://nips.cc/

In recent years, stochastic gradient descent (SGD) based techniques has become the standard tools for training neural networks. However, formal theoretical understanding of why SGD can train neural networks in practice is largely missing. In this paper, we make progress on understanding this mystery by providing a convergence analysis for SGD on a rich subset of two-layer feedforward networks with ReLU activations. This subset is characterized by a special structure called "identity mapping". We prove that, if input follows from Gaussian distribution, with standard O(1/ d) initialization of the weights, SGD converges to the global minimum in polynomial number of steps.

Non-convex optimization with local search heuristics has been widely used in machine learning, achieving many state-of-art results. It becomes increasingly important to understand why they can work for these NP-hard problems on typical data. The landscape of many objective functions in learning has been conjectured to have the geometric property that "all local optima are (approximately) global optima", and thus they can be solved efficiently by local search algorithms. However, establishing such property can be very difficult. In this paper, we analyze the optimization landscape of the random over-complete tensor decomposition problem, which has many applications in unsupervised leaning, especially in learning latent variable models.