This paper considers multi-stage optimization problems under uncertainty that involve distinct offline and online phases. In particular it addresses the issue of integrating these phases to show how the two are often interrelated in real-world applications. Our methods are applicable under two (fairly general) conditions: 1) the uncertainty is exogenous; 2) it is possible to define a greedy heuristic for the online phase that can be modeled as a parametric convex optimization problem. We start with a baseline composed by a two-stage offline approach paired with the online greedy heuristic. We then propose multiple methods to tighten the offline/online integration, leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. Overall, our methods provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios. To test our methods, we ground our approaches on two real cases studies with both offline and online decisions: an energy management problem with uncertain renewable generation and demand, and a vehicle routing problem with uncertain travel times. The application domains feature respectively continuous and discrete decisions. An extensive analysis of the experimental results shows that indeed offline/online integration may lead to substantial benefits.

Slowing down climate change is an urgent matter. If we fail, our world will face a more extensive crisis than we experienced because of the global COVID-19 pandemic. When artificial intelligence (AI) technology helps solve a problem, problem-solving can be done quicker, and the solution is often one that would have taken longer for humans to discover. There's no time to waste: atmospheric CO2 levels are the highest ever (even with significant drops from the stay-at-home orders for COVID-19), average sea levels are rising (3 inches in the last 25 years alone), and 2019 was the hottest year on record for the world's oceans. Artificial intelligence isn't a silver bullet, but it can certainly help us reduce greenhouse gas (GHG) emissions in various ways.

In this paper, we present a novel general framework grounded in the factor graph theory to solve kinematic and dynamic problems for multi-body systems. Although the motion of multi-body systems is considered to be a well-studied problem and various methods have been proposed for its solution, a unified approach providing an intuitive interpretation is still pursued. We describe how to build factor graphs to model and simulate multibody systems using both, independent and dependent coordinates. Then, batch optimization or a fixed-lag-smoother can be applied to solve the underlying optimization problem that results in a highly-sparse nonlinear minimization problem. The proposed framework has been tested in extensive simulations and validated against a commercial multibody software. We release a reference implementation as an open-source C++ library, based on the GTSAM framework, a well-known estimation library. Simulations of forward and inverse dynamics are presented, showing comparable accuracy with classical approaches. The proposed factor graph-based framework has the potential to be integrated into applications related with motion estimation and parameter identification of complex mechanical systems, ranging from mechanisms to vehicles, or robot manipulators.

Systolic array-based deep neural network (DNN) accelerators have recently gained prominence for their low computational cost. However, their high energy consumption poses a bottleneck to their deployment in energy-constrained devices. To address this problem, approximate computing can be employed at the cost of some tolerable accuracy loss. However, such small accuracy variations may increase the sensitivity of DNNs towards undesired subtle disturbances, such as permanent faults. The impact of permanent faults in accurate DNNs has been thoroughly investigated in the literature. Conversely, the impact of permanent faults in approximate DNN accelerators (AxDNNs) is yet under-explored. The impact of such faults may vary with the fault bit positions, activation functions and approximation errors in AxDNN layers. Such dynamacity poses a considerable challenge to exploring the trade-off between their energy efficiency and fault resilience in AxDNNs. Towards this, we present an extensive layer-wise and bit-wise fault resilience and energy analysis of different AxDNNs, using the state-of-the-art Evoapprox8b signed multipliers. In particular, we vary the stuck-at-0, stuck-at-1 fault-bit positions, and activation functions to study their impact using the most widely used MNIST and Fashion-MNIST datasets. Our quantitative analysis shows that the permanent faults exacerbate the accuracy loss in AxDNNs when compared to the accurate DNN accelerators. For instance, a permanent fault in AxDNNs can lead up to 66\% accuracy loss, whereas the same faulty bit can lead to only 9\% accuracy loss in an accurate DNN accelerator. Our results demonstrate that the fault resilience in AxDNNs is orthogonal to the energy efficiency.

We propose the combination of forward shape derivatives and the use of an iterative inversion scheme for Bayesian optimization to find optimal designs of nanophotonic devices. This approach widens the range of applicability of Bayesian optmization to situations where a larger number of iterations is required and where derivative information is available. This was previously impractical because the computational efforts required to identify the next evaluation point in the parameter space became much larger than the actual evaluation of the objective function. We demonstrate an implementation of the method by optimizing a waveguide edge coupler.

Imagine life moving at 40,000 times the current speed. A flight from New York to Los Angeles would take a mere half a second, and a tomato would be ripe three minutes after its seed was planted. A research team at the Sandia National Laboratories (Sandia) in the U.S. has found a way to improve machine learning so that the design process of materials for new technologies could be 40,000 times faster. Their research was published in Computational Materials on Monday. The team at Sandia managed to use machine learning to complete materials science calculations at 40,000 times the regular speed.

New year means CES, which means new TVs. LG already announced it'll launch a line of QNED 4K and 8K screens with mini LED backlighting, and now Samsung revealed its Neo QLED TVs will feature its own Quantum Mini LEDs. TCL has already used the tech in its LCD sets to great effect, and it's good to see it rolling out in more displays. We don't quite expect it to overtake OLED as the display champ, but as usual, Samsung will keep pressing. Perhaps more interestingly, Samsung also revealed its new SolarCell remote will be powered by ambient light (or USB) instead of disposable batteries and the TVs can use an optional camera attachment to run home workout software.

Labs across the UK are to be upgraded to help tackle infectious diseases, cut greenhouse emissions and more -- thanks to a £213 million government investment. The support -- part of the British government's wider'Research & Development Roadmap' -- was announced yesterday by Science Minister Amanda Solloway. It will give British scientists access to facilities including super computers in Cardiff to track infectious diseases and a floating offshore wind testing lab in Plymouth. The government's roadmap aims to make the UK'the best place in the world for scientists, researchers and entrepreneurs to live and work.' The new investment will not only provide support for the sciences, however, but will also be used to promote research in the arts and humanities.

Instead, it was born with an "application-first philosophy," meaning that its exclusive purpose is to dedicate its computational excellence to tackling some of the world's biggest challenges, such as climate change, says Satoshi Matsuoka, 57, the mastermind behind the project. "Benchmark excellence is not our priority," he said in an interview conducted in fluent, near flawless English. Instead, he said, its success is assessed "based on how much we can accelerate the applications that are important in society." As the director of Riken's Center for Computational Science, Matsuoka and his team have set out nine application areas for Fugaku to work on that are of importance to society, such as medicine, pharmacology, disaster prediction and prevention, environmental sustainability and energy. Matsuoka began leading the team developing the next-generation supercomputer in around 2010, just before its predecessor K computer became the world's fastest supercomputer in the Top500 benchmark by conducting more than 10 quadrillion calculations per second.

In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model can perfectly reproduce users' mobility trajectories. In the reverse direction, once the predictive model can imitate users' mobility patterns, the learned user profiles are also optimal. Such intuition motivates us to propose an imitation-based mobile user profiling framework by exploiting reinforcement learning, in which the agent is trained to precisely imitate users' mobility patterns for optimal user profiles. Specifically, the proposed framework includes two modules: (1) representation module, which produces state combining user profiles and spatio-temporal context in real-time; (2) imitation module, where Deep Q-network (DQN) imitates the user behavior (action) based on the state that is produced by the representation module. However, there are two challenges in running the framework effectively. First, epsilon-greedy strategy in DQN makes use of the exploration-exploitation trade-off by randomly pick actions with the epsilon probability. Such randomness feeds back to the representation module, causing the learned user profiles unstable. To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module. Second, the representation module updates users' profiles in an incremental manner, requiring integrating the temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM), we introduce a gated mechanism to incorporate new and old user characteristics into the user profile.