4,561 research outputs found
Lighting : an atrium core to reconnect with the sun
At the Rhode Island School of Design, students work so much that they treat their studio as home; the majority stay in studio past midnight, which leads to lack of sleep. How to improve students’ physical and mental health is a question demanding immediate investigation, particularly as relates to rest. According to the scientists at the Lighting Research Center (LRC) in Troy, N.Y, engagement with daylight environments increase occupant productivity and comfort, and provide the mental and visual stimulation necessary to regulate circadian rhythms, encouraging more restful sleep. Students cannot function healthily because their busy schedules remove them from the world.
The Design Center of the Rhode Island School of Design has a complex program, hosting Apparel Design, Graphic Design, the RISD Store, Photography, Liberal Arts, dining and several campus service areas. There are some classrooms without windows in the Design Center, but the Photography Department has need for a darkroom which cannot have any windows; clearly it is necessary to rearrange those rooms and utilize the existing properties of the space. In this complex environment of competing departmental needs, it is necessary to create inspiring spaces to improve those departments’ student productivity, physical and mental health. As the original structure of the Design Center blocks vast amounts of potential natural light, this thesis proposes the intervention of several large atriums supported by a new structural system. The atriums not only allow natural light to penetrate deep into this [however many stories the design center is] storey building, they alter circulation throughout. The core of the Design Center is given a sense of the passage of time and the seasons, reconnecting students to the natural world that their busy schedules do not allow them to experience firsthand
Learning Fully Dense Neural Networks for Image Semantic Segmentation
Semantic segmentation is pixel-wise classification which retains critical
spatial information. The "feature map reuse" has been commonly adopted in CNN
based approaches to take advantage of feature maps in the early layers for the
later spatial reconstruction. Along this direction, we go a step further by
proposing a fully dense neural network with an encoder-decoder structure that
we abbreviate as FDNet. For each stage in the decoder module, feature maps of
all the previous blocks are adaptively aggregated to feed-forward as input. On
the one hand, it reconstructs the spatial boundaries accurately. On the other
hand, it learns more efficiently with the more efficient gradient
backpropagation. In addition, we propose the boundary-aware loss function to
focus more attention on the pixels near the boundary, which boosts the "hard
examples" labeling. We have demonstrated the best performance of the FDNet on
the two benchmark datasets: PASCAL VOC 2012, NYUDv2 over previous works when
not considering training on other datasets
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand
vehicle sharing services. However, predicting passenger demand over multiple
time horizons is generally challenging due to the nonlinear and dynamic
spatial-temporal dependencies. In this work, we propose to model multi-step
citywide passenger demand prediction based on a graph and use a hierarchical
graph convolutional structure to capture both spatial and temporal correlations
simultaneously. Our model consists of three parts: 1) a long-term encoder to
encode historical passenger demands; 2) a short-term encoder to derive the
next-step prediction for generating multi-step prediction; 3) an
attention-based output module to model the dynamic temporal and channel-wise
information. Experiments on three real-world datasets show that our model
consistently outperforms many baseline methods and state-of-the-art models.Comment: 7 page
Reducing the Tension Between the BICEP2 and the Planck Measurements: A Complete Exploration of the Parameter Space
A large inflationary tensor-to-scalar ratio is reported by the BICEP2 team based on their B-mode
polarization detection, which is outside of the confidence level of the
Planck best fit model. We explore several possible ways to reduce the tension
between the two by considering a model in which ,
, and the neutrino parameters and
are set as free parameters. Using the Markov Chain
Monte Carlo (MCMC) technique to survey the complete parameter space with and
without the BICEP2 data, we find that the resulting constraints on
are consistent with each other and the apparent tension
seems to be relaxed. Further detailed investigations on those fittings suggest
that probably plays the most important role in reducing the
tension. We also find that the results obtained from fitting without adopting
the consistency relation do not deviate much from the consistency relation.
With available Planck, WMAP, BICEP2 and BAO datasets all together, we obtain
, ,
, and
; if the consistency relation is
adopted, we get .Comment: 8 pages, 4 figures, submitted to PL
Diagnostic accuracy of cardiovascular magnetic resonance for patients with suspected cardiac amyloidosis: a systematic review and meta-analysis
Search strategy. (DOCX 142 kb
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