Adding Subtraction: Wasting Time in Space

Architecture is designed to increase our productivity – think of features like uniform workspaces, straight pathways, or purely functional rooms arranged to optimize tasks. When forced into constant productivity, we gain efficiency, but we end up exhausted and disconnected from one another. We need to design subtraction spaces in our workspaces and everyday life, spaces that accommodate the feelings and dreams of the occupant: spaces where we can wander, wonder, feel, connect, relax, restore, and reset. By challenging the perception that time just moves on and cannot be controlled, people can shift time: they can start, reverse, break, accumulate, prolong, and rerun time. Subtraction spaces invite people to choose to actively shift time. These spaces alter time depending on the condition of the host building. Time becomes space through transformation into architectural elements and sensory experiences. Different programs, such as schools, offices, factories, and hospitals require various strategies for subtraction spaces. The CIT and Fletcher buildings at RISD are used here as the host structure to demonstrate the addition of subtraction spaces. While addressing the social problem of excessive productivity with an architectural solution, I seek to improve mental health and create spaces that encourage connection between people. Subtracting programmed areas while simultaneously adding undefined spaces into existing buildings displays the ability of architecture to foster moments of freedom in overly efficient lives and reconfigure life around what matters

Vlog as a Branding Tool : How to Build a Brand with a Video Blog in Social Media

The purpose of the final year project was to examine how a video blog can help to build a brand and impact consumer engagement in social media. The goal of the project is to determine the possibility of a video blog channel as a branding tool with real-time online user engagement involvement. The project began with research on branding and social media presence including case studies. The project process was executed with the author building her own personal brand and creating a video blog channel with a social media strategy. The thesis illustrates how the branding process, content planning, audiovisual production, rebranding and content development, the implementation of a social media strategy and community management were executed in the project. The data was collected using YouTube analytics and the Facebook insights tool to analyse consumers’ reactions and their engagement numerically. The results show that there was an increase in consumer awareness of the brand and that changes occurred in consumers’ behaviour and preferences on the brand’s vlog and in social media. Evaluating the project demonstrates that in spite of the success of vlog utilisation, a diverse range of content was missed, which resulted in the brand having incorrect reputation. Overall, the project demonstrated the effect of vlog utilisation for branding and the impact of consumers’ influential engagement with brands in social media

Resolving Class Imbalance for LiDAR-based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling

An autonomous driving system requires a 3D object detector, which must perceive all present road agents reliably to navigate an environment safely. However, real-world driving datasets often suffer from the problem of data imbalance, which causes difficulties in training a model that works well across all classes, resulting in an undesired imbalanced sub-optimal performance. In this work, we propose a method to address this data imbalance problem. Our method consists of two main components: (i) a LiDAR-based 3D object detector with per-class multiple detection heads where losses from each head are modified by dynamic weight average to be balanced. (ii) Contextual ground truth (GT) sampling, where we improve conventional GT sampling techniques by leveraging semantic information to augment point cloud with sampled ground truth GT objects. Our experiment with KITTI and nuScenes datasets confirms our proposed method's effectiveness in dealing with the data imbalance problem, producing better detection accuracy compared to existing approaches.Comment: 10 page

Land Surface Models Evaluation for Two Different Land-Cover Types: Cropland and Forest

Land Surface Model (LSM) is an important tool used to understand the complicated hydro-meteorological flux interaction systems between the land surface and atmosphere in hydrological cycles. Over the past few decades, LSMs have further developed to more accurately estimate weather and climate hydrological processes. Common Land Model (CLM) and Noah Land Surface Model (Noah LSM) are used in this paper to estimate the hydro-meteorological fluxes for model applicability assessment at two different flux tower sites in Korea during the summer monsoon season. The estimated fluxes such as net radiation (RN), sensible heat flux (H), latent heat flux (LE), ground heat flux (G), and soil temperature (Ts) were compared with the observed data from flux towers. The simulated RN from both models corresponded well with the in situ data. The root-mean-square error (RMSE) values were 39 - 44 W m-2 for the CLM and 45 - 50 W m-2 for the Noah LSM while the H and LE showed relatively larger discrepancies with each observation. The estimated Ts from the CLM corresponded comparatively well with the observed soil temperature. The CLM estimations generally showed better statistical results than those from the Noah LSM, even though the estimated hydro-meteorological fluxes from both models corresponded reasonably with the observations. A sensitivity test indicated that differences according to different locations between the estimations from models and observations were caused by field conditions including the land-cover type and soil texture. In addition the estimated RN, H, LE, and G were more sensitive than the estimated Ts in both models

Coverage Analysis of Dynamic Coordinated Beamforming for LEO Satellite Downlink Networks

In this paper, we investigate the coverage performance of downlink satellite networks employing dynamic coordinated beamforming. Our approach involves modeling the spatial arrangement of satellites and users using Poisson point processes situated on concentric spheres. We derive analytical expressions for the coverage probability, which take into account the in-cluster geometry of the coordinated satellite set. These expressions are formulated in terms of various parameters, including the number of antennas per satellite, satellite density, fading characteristics, and path-loss exponent. To offer a more intuitive understanding, we also develop an approximation for the coverage probability. Furthermore, by considering the distribution of normalized distances, we derive the spatially averaged coverage probability, thereby validating the advantages of coordinated beamforming from a spatial average perspective. Our primary finding is that dynamic coordinated beamforming significantly improves coverage compared to the absence of satellite coordination, in direct proportion to the number of antennas on each satellite. Moreover, we observe that the optimal cluster size, which maximizes the ergodic spectral efficiency, increases with higher satellite density, provided that the number of antennas on the satellites is sufficiently large. Our findings are corroborated by simulation results, confirming the accuracy of the derived expressions

Intercomparison of Downscaling Techniques for Satellite Soil Moisture Products

During recent decades, various downscaling methods of satellite soil moisture (SM) products, which incorporate geophysical variables such as land surface temperature and vegetation, have been studied for improving their spatial resolution. Most of these studies have used least squares regression models built from those variables and have demonstrated partial improvement in the downscaled SM. This study introduces a new downscaling method based on support vector regression (SVR) that includes the geophysical variables with locational weighting. Regarding the in situ SM, the SVR downscaling method exhibited a smaller root mean square error, from 0.09 to 0.07m(3).m(-3), and a larger average correlation coefficient increased, from 0.62 to 0.68, compared to the conventional method. In addition, the SM downscaled using the SVR method had a greater statistical resemblance to that of the original advanced scatterometer SM. A residual magnitude analysis for each model with two independent variables was performed, which indicated that only the residuals from the SVR model were not well correlated, suggesting a more effective performance than regression models with a significant contribution of independent variables to residual magnitude. The spatial variations of the downscaled SM products were affected by the seasonal patterns in temperature-vegetation relationships, and the SVR downscaling method showed more consistent performance in terms of seasonal effect. Based on these results, the suggested SVR downscaling method is an effective approach to improve the spatial resolution of satellite SM measurement

Emotional labor among team members: do employees follow emotional display norms for teams, not for customers?

Emotional labor is typically conceptualized as a process in which individuals regulate their emotions in response to display rules. Most research on emotional labor has focused on the influence of display rules at individual-level perceptions but is rarely examined at the team level. We examine the influence of the shared display rules in teams as emotional display norms. This study considers emotional dissonance as the difference between the positive emotional display norm at the team level and positive emotion at the individual level. To examine the purpose of this study, data were collected from leader-follower pairs within teams and based on a three-wave design. Thus, this study conducted a multi-level polynomial regression analysis and used the response surface methodology to interpret the incongruence effect. The results show that the incongruence effect of emotional dissonance is positively related to surface acting. In addition, the moderating effect of regulatory focus significantly strengthens the positive relationship between emotional dissonance and emotion regulation strategies. The results also show that surface acting strategy is negatively related to selfless Organizational citizenship behaviors (OCB). These findings highlight that emotional display norms play an important role as the standard for emotional experience in teams, and especially with the moderating effect of self-regulatory focus, emotion regulation strategies affect the selfless OCB rating of observers

Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning

Bipolar disorder (BD) is closely associated with an increased risk of suicide. However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefore, this study proposes a multi-task learning model for predicting the future suicidality of BD patients by jointly learning current symptoms. We build a novel BD dataset clinically validated by psychiatrists, including 14 years of posts on bipolar-related subreddits written by 818 BD patients, along with the annotations of future suicidality and BD symptoms. We also suggest a temporal symptom-aware attention mechanism to determine which symptoms are the most influential for predicting future suicidality over time through a sequence of BD posts. Our experiments demonstrate that the proposed model outperforms the state-of-the-art models in both BD symptom identification and future suicidality prediction tasks. In addition, the proposed temporal symptom-aware attention provides interpretable attention weights, helping clinicians to apprehend BD patients more comprehensively and to provide timely intervention by tracking mental state progression.Comment: KDD 2023 accepte