Historical game is an important genre in the mass media either for educational or recreational purpose. It often requires an interdisciplinary professions, such as history research and education, game design, and artistic design, etc., to design a historical game. Instead of only relying on the intuitions and subjective preferences of members in the design teams, this study suggests a heuristic process to design historical games. In the emerging field of historioludicity, in which the researchers and practitioners look for interesting and meaningful ludic experiences for the public to engage a representation of the past. The proposed methodology is helpful to produce historical games systematically and effectively.

BACKGROUND: Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots. RESULTS: The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. CONCLUSION: With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.

Drug discovery is a costly process of searching for new candidate medications. Among its various stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose an automated lead optimization workflow that uses data mining methods in components such as execution of molecular simulations, feature extraction, and clustering with convolutional variational autoencoder. The end-to-end execution produces protein-ligand binding affinity of atoms in the lead molecule which serves as metrics for identifying modifiable atoms. In contrast to known methods, our method provides new hints for drug modification hotspots which can be used to improve drug efficacy. Our workflow can potentially reduce the lead optimization turnaround time from months/years to several days compared with the conventional labor-intensive process and thus will become a valuable tool for medical researchers.

Training large, deep neural network acoustic models for speech recognition on large datasets takes a long time on a single GPU, motivating research on parallel training algorithms. We present an approach for training a bidirectional LSTM acoustic model on the 2000-hour Switchboard corpus. The model we train achieves state-of-the-art word error rate, 7.5% on the Hub5-2000 Switchboard test set and 13.1% on the Callhome test set, and scales to an unprecedented 96 learners while employing only 12 global reductions per epoch of training. As our implementation incurs far fewer reductions than prior work, it does not require aggressively optimized communication primitives to reach state-of-the-art performance in a short amount of time. With 48 NVIDIA V100 GPUs training takes 5 hours; with 96 GPUs, training takes around 3 hours.

Data loading can dominate deep neural network training time on large-scale systems. We present a comprehensive study on accelerating data loading performance in large-scale distributed training. We first identify performance and scalability issues in current data loading implementations. We then propose optimizations that utilize CPU resources to the data loader design. We use an analytical model to characterize the impact of data loading on the overall training time and establish the performance trend as we scale up distributed training. Our model suggests that I/O rate limits the scalability of distributed training, which inspires us to design a locality-aware data loading method. By utilizing software caches, our method can drastically reduce the data loading communication volume in comparison with the original data loading implementation. Finally, we evaluate the proposed optimizations with various experiments. We achieved more than 30x speedup in data loading using 256 nodes with 1,024 learners.

Detecting actions in videos requires understanding the temporal relationships among frames. Typical action recognition approaches rely on optical flow estimation methods to convey temporal information to a CNN. Recent studies employ 3D convolutions in addition to optical flow to process the temporal information. While these models achieve slightly better results than two-stream 2D convolutional approaches, they are significantly more complex, requiring more data and time to be trained. We propose an efficient, adaptive batch size distributed training algorithm with customized optimizations for training the two 2D streams. We introduce a new 2D convolutional temporal stream that is trained end-to-end with a neural network. The flexibility to freeze some network layers from training in this temporal stream brings the possibility of ensemble learning with more than one temporal streams. Our architecture that combines three streams achieves the highest accuracies as we know of on UCF101 and HMDB51 by systems that do not pretrain on much larger datasets (e.g., Kinetics). We achieve these results while keeping our spatial and temporal streams 4.67x faster to train than the 3D convolution approaches.

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Distributed object-oriented environments have become important platforms for parallel and distributed service frameworks. Among distributed object-oriented software, .NET Remoting provides a language layer of abstractions for performing parallel and distributed computing in .NET environments. In this paper, we present our methodologies in supporting .NET Remoting over meta-clustered environments. We take the advantage of the programmability of network processors to develop the content-based switch for distributing workloads generated from remote invocations in .NET. Our scheduling mechanisms include stateful supports for .NET Remoting services. In addition, we also propose scheduling policy to incorporate workflow models as the models are now incorporated in many of tools of grid architectures. Experiments done at clusters with IXP 1200 network processors show that our scheme can significantly enhance the system throughput (up to 55%) compared to NLB method when the traffic is heavy. Our schemes are effective in supporting the switching of .NET Remoting computations over meta-cluster environments.