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

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 $r_\mathrm{0.002} = 0.20^{+0.07}_{-0.05}$ is reported by the BICEP2 team based on their B-mode polarization detection, which is outside of the $95\%$ 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 $\alpha_\mathrm{s}$, $n_\mathrm{t}$, $n_\mathrm{s}$ and the neutrino parameters $N_\mathrm{eff}$ and $\Sigma m_\mathrm{\nu}$ 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 $r_\mathrm{0.002}$ are consistent with each other and the apparent tension seems to be relaxed. Further detailed investigations on those fittings suggest that $N_\mathrm{eff}$ 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 $r_{0.002} = 0.14_{-0.11}^{+0.05}$, $n_\mathrm{t} = 0.35_{-0.47}^{+0.28}$, $n_\mathrm{s}=0.98_{-0.02}^{+0.02}$, and $\alpha_\mathrm{s}=-0.0086_{-0.0189}^{+0.0148}$; if the consistency relation is adopted, we get $r_{0.002} = 0.22_{-0.06}^{+0.05}$.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