The southern regional conference on technology assessment: Summary

The proceedings of a conference on technology assessment are presented. A survey of recent Federal activity in technology assessment was discussed initially. Emphasis was placed on state and local activities with respect to technology assessment to include the following subjects: (1) the technology assessment desired by states, (2) organization of technology assessment activities, (3) how to perform technology assessments for less than $5,000, and (4) the preparation of environmental impact statements. Specific application of technology assessment to solid waste management in Connecticut is reported

Scaling Deep Learning on GPU and Knights Landing clusters

The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learning systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation

Technology assessment of space stations

The social impacts, both beneficial and detrimental, which can be expected from a system of space stations operating over relatively long periods of time in Earth orbit, are examined. The survey is an exercise in technology assessment. It is futuristic in nature. It anticipates technological applications which are still in the planning stage, and many of the conclusions are highly speculative and for this reason controversial

Videoconferencing via satellite. Opening Congress to the people: Technical report

The feasibility of using satellite videoconferencing as a mechanism for informed dialogue between Congressmen and constituents to strengthen the legislative process was evaluated. Satellite videoconferencing was defined as a two-way interactive television with the TV signals transmitted by satellite. With videoconferencing, one or more Congressmen in Washington, D. C. can see, hear and talk with groups of citizens at distant locations around the country. Simultaneously, the citizens can see, hear and talk with the Congressmen

Data information literacy instruction in Business and Public Health: Comparative case studies

Employers need a workforce capable of using data to create actionable information. This requires students to develop data information literacy competencies that enable them to navigate and create meaning in an increasingly complex information world. This article examines why data information literacy should be integrated into program curricula, specifically in the instances of business and public health, and offers strategies for how it can be accomplished. We approach this as a comparative case study within undergraduate business and master of public health programs at Indiana University-Purdue University Indianapolis. These case studies reveal several implications for practice that apply across social and health sciences programs

Curriculum mapping: Creating options for integrating DIL into a degree program

Students in undergraduate and graduate programs need to develop data information literacy (DIL) in order to be successful in their personal and professional lives. However, finding space for new content in curricula that are already full presents a challenge. Curriculum mapping can reveal where DIL naturally complements existing learning objectives and assist in identifying potential gaps. The process of mapping DIL competencies to a curriculum provides librarians with a deeper understanding of a discipline through detailed analysis of how existing course assignments may be adapted to incorporate elements of DIL. A curriculum map can also facilitate better communication between librarians and faculty as they determine the best strategy for integrating instruction. The panelists will discuss how they have used curriculum mapping within an undergraduate business program and a master of public health program to develop integration strategies, foster communication with faculty, and devise relevant disciplinary examples that resonate with students’ personal and professional lives. Presentation presented as part of the Curricular Challenges and Data Information Literacy panel at RDAP17

SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, \textit{Liveness Analysis}, \textit{Unified Tensor Pool}, and \textit{Cost-Aware Recomputation}, all together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in those memory saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has $10^4$ basic network layers on a 12GB K40c.Comment: PPoPP '2018: 23nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programmin

A novel erm(44) gene variant from a human Staphylococcus saprophyticus confers resistance to macrolides, lincosamides but not streptogramins.

A novel erm (44) gene variant, erm (44)v, has been identified by whole genome sequencing in a Staphylococcus saprophyticus isolated from the skin of a healthy person. It has the particularity to confer resistance to macrolides and lincosamides, but not to streptogramins B when expressed in S. aureus The erm (44)v gene resides on a 19,400-bp genomic island which contains phage-associated proteins and is integrated into the chromosome of S. saprophyticus