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  1. Abstract In the 60 years since the invention of the laser, the scientific community has developed numerous fields of research based on these bright, coherent light sources, including the areas of imaging, spectroscopy, materials processing and communications. Ultrafast spectroscopy and imaging techniques are at the forefront of research into the light–matter interaction at the shortest times accessible to experiments, ranging from a few attoseconds to nanoseconds. Light pulses provide a crucial probe of the dynamical motion of charges, spins, and atoms on picosecond, femtosecond, and down to attosecond timescales, none of which are accessible even with the fastest electronic devices.more »Furthermore, strong light pulses can drive materials into unusual phases, with exotic properties. In this roadmap we describe the current state-of-the-art in experimental and theoretical studies of condensed matter using ultrafast probes. In each contribution, the authors also use their extensive knowledge to highlight challenges and predict future trends.« less
    Free, publicly-accessible full text available July 5, 2022
  2. Efficient storage systems come from the intelligent management of the data units, i.e., disk blocks in local file system level. Block correlations represent the semantic patterns in storage systems. These correlations can be exploited for data caching, pre-fetching, layout optimization, I/O scheduling, etc. to finally realize an efficient storage system. In this paper, we introduce Block2Vec, a deep learning based strategy to mine the block correlations in storage systems. The core idea of Block2Vec is twofold. First, it proposes a new way to abstract blocks, which are considered as multi-dimensional vectors instead of traditional block Ids. In this way, wemore »are able to capture similarity between blocks through the distances of their vectors. Second, based on vector representation of blocks, it further trains a deep neural network to learn the best vector assignment for each block. We leverage the recently advanced word embedding technique in natural language processing to efficiently train the neural network. To demonstrate the effectiveness of Block2Vec, we design a demonstrative block prediction algorithm based on mined correlations. Empirical comparison based on the simulation of real system traces shows that Block2Vec is capable of mining block-level correlations efficiently and accurately. This research and trial show that the deep learning strategy is a promising direction in optimizing storage system performance.« less
  3. In this paper, we present two commands to implement the testing and inference methods for conditional moment inequalities and equalities in Andrews and Shi (2013).