Algorithm 1 demonstrates the step-by-step operations of our beam search algorithm (see Sec. 4.3). We consider recovering sentences in the current work. We leave recovering longer paragraphs as future work. We keep 2000 examples of each dataset as the evaluation set, and use the left for training. "End-to-End optimization", "Reg" means the inclusion of a regularization term, "DR" refers to a discrete token Our approach is unique as it does not rely on end-to-end optimization, is demonstrated on large batch sizes (i.e.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Man's parents helped him attack his ex and pry their grandson out of her arms, officials say The 1-year-old boy who allegedly was taken from his mother at knifepoint in City of Industry on Sunday was found in Arizona. This is read by an automated voice. Please report any issues or inconsistencies here . A 20-year-old man and his parents allegedly attacked his ex-partner outside a Target store, forcibly taking their baby from her arms.

Algorithm 1 demonstrates the step-by-step operations of our beam search algorithm (see Sec. 4.3). We consider recovering sentences in the current work. We leave recovering longer paragraphs as future work. We keep 2000 examples of each dataset as the evaluation set, and use the left for training. "End-to-End optimization", "Reg" means the inclusion of a regularization term, "DR" refers to a discrete token Our approach is unique as it does not rely on end-to-end optimization, is demonstrated on large batch sizes (i.e.

For the first time, we show the feasibility of recovering text from large batch sizes of up to 128 sentences.

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Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically defining these navigational patterns is nontrivial. This paper presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in real-world environments from partial observations only. We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enables our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data. We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field. Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset. We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception. Code is available at https://github.com/robin-karlsson0/dslp.

Federated learning allows distributed users to collaboratively train a model while keeping each user's data private. Recently, a growing body of work has demonstrated that an eavesdropping attacker can effectively recover image data from gradients transmitted during federated learning. However, little progress has been made in recovering text data. In this paper, we present a novel attack method FILM for federated learning of language models (LMs). For the first time, we show the feasibility of recovering text from large batch sizes of up to 128 sentences. Unlike image-recovery methods that are optimized to match gradients, we take a distinct approach that first identifies a set of words from gradients and then directly reconstructs sentences based on beam search and a prior-based reordering strategy. We conduct the FILM attack on several large-scale datasets and show that it can successfully reconstruct single sentences with high fidelity for large batch sizes and even multiple sentences if applied iteratively. We evaluate three defense methods: gradient pruning, DPSGD, and a simple approach to freeze word embeddings that we propose. We show that both gradient pruning and DPSGD lead to a significant drop in utility. However, if we fine-tune a public pre-trained LM on private text without updating word embeddings, it can effectively defend the attack with minimal data utility loss. Together, we hope that our results can encourage the community to rethink the privacy concerns of LM training and its standard practices in the future.

Landslide run-out modeling involves various uncertainties originating from model input data. It is therefore desirable to assess the model's sensitivity. A global sensitivity analysis that is capable of exploring the entire input space and accounts for all interactions, often remains limited due to computational challenges resulting from a large number of necessary model runs. We address this research gap by integrating Gaussian process emulation into landslide run-out modeling and apply it to the open-source simulation tool r.avaflow. The feasibility and efficiency of our approach is illustrated based on the 2017 Bondo landslide event. The sensitivity of aggregated model outputs, such as the apparent friction angle, impact area, as well as spatially resolved maximum flow height and velocity, to the dry-Coulomb friction coefficient, turbulent friction coefficient and the release volume are studied. The results of first-order effects are consistent with previous results of common one-at-a-time sensitivity analyses. In addition to that, our approach allows to rigorously investigate interactions. Strong interactions are detected on the margins of the flow path where the expectation and variation of maximum flow height and velocity are small. The interactions generally become weak with increasing variation of maximum flow height and velocity. Besides, there are stronger interactions between the two friction coefficients than between the release volume and each friction coefficient. In the future, it is promising to extend the approach for other computationally expensive tasks like uncertainty quantification, model calibration, and smart early warning.

Archaeologists have applied the principals of AI to distinguish between ancient human and dog poo, cleaning up a storm of scientific confusion over the matter. Looking deep into the future, in October 2019 Elon Musk posted a tweet using Starlink, a satellite constellation of thousands of orbiting mirrors constructed by his American company SpaceX, to provide satellite Internet access everywhere in the world. What's more, Amazon continue to apply advanced AI programs to predict what you and I might buy next; but looking backwards in time, archaeologists have now use artificial intelligence to distinguish whether a sample of ancient poo has human or canine origins. In a New Scientist article, Maxime Borry of the Max Planck Institute for the Science of Human History in Germany discusses his new paper, which says ancient poo, or " coprolites", provides a valuable source of information about the identity, diet, and health of people who lived thousands of years ago. But the researcher explained that dogs lived alongside ancient hunters and canine feces are also commonly found at archaeological sites, "It is challenging to tell them apart," said Dr. Borry.