Survival Analysis (SA) is about modeling the time for an event of interest to occur, which has important applications in many fields, including medicine, defense, finance, and aerospace. Recent work has demonstrated the benefits of using Neural Networks (NNs) to capture complicated relationships in SA. However, the datasets used to train these models are often subject to uncertainty (e.g., noisy measurements, human error), which we show can substantially degrade the performance of existing techniques. To address this issue, this work leverages recent advances in NN verification to provide new algorithms for generating fully parametric survival models that are robust to such uncertainties. In particular, we introduce a robust loss function for training the models and use CROWN-IBP regularization to address the computational challenges with solving the resulting Min-Max problem. To evaluate the proposed approach, we apply relevant perturbations to publicly available datasets in the SurvSet repository and compare survival models against several baselines. We empirically show that Survival Analysis with Adversarial Regularization (SAWAR) method on average ranks best for dataset perturbations of varying magnitudes on metrics such as Negative Log Likelihood (NegLL), Integrated Brier Score (IBS), and Concordance Index (CI), concluding that adversarial regularization enhances performance in SA. Code: https://github.com/mlpotter/SAWAR

We introduce a suite of new particle-based algorithms for sampling in constrained domains which are entirely learning rate free. Our approach leverages coin betting ideas from convex optimisation, and the viewpoint of constrained sampling as a mirrored optimisation problem on the space of probability measures. Based on this viewpoint, we also introduce a unifying framework for several existing constrained sampling algorithms, including mirrored Langevin dynamics and mirrored Stein variational gradient descent. We demonstrate the performance of our algorithms on a range of numerical examples, including sampling from targets on the simplex, sampling with fairness constraints, and constrained sampling problems in post-selection inference. Our results indicate that our algorithms achieve competitive performance with existing constrained sampling methods, without the need to tune any hyperparameters.

With El Niño slated to drop a warm, wet winter on most of the US in the coming months, everybody's going to need something good to read while the weather outside is frightful. Engadget's well-read staff have some suggestions: our favorite books of 2023! We've got a phenomenal assortment of genres and titles for you this year, from horror and true crime to rom-coms and fantasy adventures, here to provide months of entertainment for even the most voracious reader. I love horror movies but horror novels are kind of hit and miss for me. I was immediately pulled into Final Girl Support Group, though, which does a lot of winking and nodding at classic slasher flicks while creating a completely unique story. Grady Hendrix's novel doesn't satirize the final girl, but imagines what life might be like for them after the end of their movie. Each of the main characters is (loosely) based on the final girl of a classic slasher, though their storylines don't feel contrived or predictable. It reads like a fast-paced thriller but, like so many of the best horror movies, it's also a poignant reflection on trauma.

Even if you believe, as some do, that the world has moved from Peak TV to Trough TV, there are still more shows released in any given year than any one person could consume (trust us, we tried). Between major networks, cable television channels, and streaming services, there's just too much to watch. You're bound to miss your new favorite binge-watch. Below are our picks for the best TV shows you might have missed in 2023. If you buy something using links in our stories, we may earn a commission. This helps support our journalism.

Kurt Knutsson talks about an innovative robot that can explore the depths of the ocean and capture stunning photos and videos. Drones are everywhere these days. They can fly, swim, and even transform into different shapes. They can deliver packages, be used to spy, pick fruits, and even explore the ocean depths. Some of them are downright creepy.

Although the Transformer is currently the best-performing architecture in the homogeneous configuration (self-attention only) in Neural Machine Translation, many State-of-the-Art models in Natural Language Processing are made of a combination of different Deep Learning approaches. However, these models often focus on combining a couple of techniques only and it is unclear why some methods are chosen over others. In this work, we investigate the effectiveness of integrating an increasing number of heterogeneous methods. Based on a simple combination strategy and performance-driven synergy criteria, we designed the Multi-Encoder Transformer, which consists of up to five diverse encoders. Results showcased that our approach can improve the quality of the translation across a variety of languages and dataset sizes and it is particularly effective in low-resource languages where we observed a maximum increase of 7.16 BLEU compared to the single-encoder model.

Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work studies the former. Specifically, the Perception and Decision-making Interleaving Transformer (PDiT) network is proposed, which cascades two Transformers in a very natural way: the perceiving one focuses on \emph{the environmental perception} by processing the observation at the patch level, whereas the deciding one pays attention to \emph{the decision-making} by conditioning on the history of the desired returns, the perceiver's outputs, and the actions. Such a network design is generally applicable to a lot of deep RL settings, e.g., both the online and offline RL algorithms under environments with either image observations, proprioception observations, or hybrid image-language observations. Extensive experiments show that PDiT can not only achieve superior performance than strong baselines in different settings but also extract explainable feature representations. Our code is available at \url{https://github.com/maohangyu/PDiT}.

Consider a directed network where each node is either red (using the red product), blue (using the blue product), or uncolored (undecided). Then in each round, an uncolored node chooses red (resp. blue) with some probability proportional to the number of its red (resp. blue) out-neighbors. What is the best strategy to maximize the expected final number of red nodes given the budget to select $k$ red seed nodes? After proving that this problem is computationally hard, we provide a polynomial time approximation algorithm with the best possible approximation guarantee, building on the monotonicity and submodularity of the objective function and exploiting the Monte Carlo method. Furthermore, our experiments on various real-world and synthetic networks demonstrate that our proposed algorithm outperforms other algorithms. Additionally, we investigate the convergence time of the aforementioned process both theoretically and experimentally. In particular, we prove several tight bounds on the convergence time in terms of different graph parameters, such as the number of nodes/edges, maximum out-degree and diameter, by developing novel proof techniques.

Different realisations of this generic methodology may appear in accordance with the level where the fusion is practised, including data fusion, feature fusion, soft decision fusion, or hard decision fusion, etc. Classifier fusion, and in particular, a soft combination of the output scores of multiple learners has been established as a standard approach to improve classification performance in various learning scenarios [1]. The motivating principle behind adopting a classifier fusion approach is to leverage the collective ability of multiple models, presumed to be as independent as possible, to mitigate the shortcomings of a single model, thus improving the overall performance. In general, classifier fusion approaches are expected to yield better results by - reducing the risk of selecting an inaccurate individual learner; - minimising the chances of settling for a suboptimal solution when individual learners may be stuck in local optima; - allowing for a better exploration of the potential solution space; - potentially providing a better capacity to deal with imbalanced training data; - being more capable of adapting to dynamic scenarios where the representations and labels may change over time, and - helping to mitigate the curse of dimensionality and reducing the chances of overfitting [2]. Despite its appealing properties and its widespread application in multiclass classification scenarios where significant performance improvements have been observed [1], the one-class classifier fusion paradigm has not been explored widely. In a one-class classification (OCC) setting, one is interested in classifying an observation as normal/positive/target or as abnormal/negative/anomaly by mainly training on positive samples [3]. The prevalent application of OCC is often witnessed in scenarios where the accumulation of counterexamples is either highly demanding or simply infeasible [4], challenging binary/multi-class classification approaches.

Purpose: Common dense stereo Simultaneous Localization and Mapping (SLAM) approaches in Minimally Invasive Surgery (MIS) require high-end parallel computational resources for real-time implementation. Yet, it is not always feasible since the computational resources should be allocated to other tasks like segmentation, detection, and tracking. To solve the problem of limited parallel computational power, this research aims at a lightweight dense stereo SLAM system that works on a single-core CPU and achieves real-time performance (more than 30 Hz in typical scenarios). Methods: A new dense stereo mapping module is integrated with the ORB-SLAM2 system and named BDIS-SLAM. Our new dense stereo mapping module includes stereo matching and 3D dense depth mosaic methods. Stereo matching is achieved with the recently proposed CPU-level real-time matching algorithm Bayesian Dense Inverse Searching (BDIS). A BDIS-based shape recovery and a depth mosaic strategy are integrated as a new thread and coupled with the backbone ORB-SLAM2 system for real-time stereo shape recovery. Results: Experiments on in-vivo data sets show that BDIS-SLAM runs at over 30 Hz speed on modern single-core CPU in typical endoscopy/colonoscopy scenarios. BDIS-SLAM only consumes around an additional 12% time compared with the backbone ORB-SLAM2. Although our lightweight BDIS-SLAM simplifies the process by ignoring deformation and fusion procedures, it can provide a usable dense mapping for modern MIS on computationally constrained devices. Conclusion: The proposed BDIS-SLAM is a lightweight stereo dense SLAM system for MIS. It achieves 30 Hz on a modern single-core CPU in typical endoscopy/colonoscopy scenarios (image size around 640*480). BDIS-SLAM provides a low-cost solution for dense mapping in MIS and has the potential to be applied in surgical robots and AR systems.