Building Bulletin 77 – Ergonomic content review

Building Bulletin 77 – Ergonomic content revie

See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media Data

Mental disorders such as depression and anxiety have been increasing at alarming rates in the worldwide population. Notably, the major depressive disorder has become a common problem among higher education students, aggravated, and maybe even occasioned, by the academic pressures they must face. While the reasons for this alarming situation remain unclear (although widely investigated), the student already facing this problem must receive treatment. To that, it is first necessary to screen the symptoms. The traditional way for that is relying on clinical consultations or answering questionnaires. However, nowadays, the data shared at social media is a ubiquitous source that can be used to detect the depression symptoms even when the student is not able to afford or search for professional care. Previous works have already relied on social media data to detect depression on the general population, usually focusing on either posted images or texts or relying on metadata. In this work, we focus on detecting the severity of the depression symptoms in higher education students, by comparing deep learning to feature engineering models induced from both the pictures and their captions posted on Instagram. The experimental results show that students presenting a BDI score higher or equal than 20 can be detected with 0.92 of recall and 0.69 of precision in the best case, reached by a fusion model. Our findings show the potential of large-scale depression screening, which could shed light upon students at-risk.Comment: This article was accepted (15 November 2019) and will appear in the proceedings of ICWSM 202

Portfolio Optimization and the Random Magnet Problem

Diversification of an investment into independently fluctuating assets reduces its risk. In reality, movement of assets are are mutually correlated and therefore knowledge of cross--correlations among asset price movements are of great importance. Our results support the possibility that the problem of finding an investment in stocks which exposes invested funds to a minimum level of risk is analogous to the problem of finding the magnetization of a random magnet. The interactions for this ``random magnet problem'' are given by the cross-correlation matrix {\bf \sf C} of stock returns. We find that random matrix theory allows us to make an estimate for {\bf \sf C} which outperforms the standard estimate in terms of constructing an investment which carries a minimum level of risk.Comment: 12 pages, 4 figures, revte

Traveler Gun Irrigation of Field Grown Nursery Stock

The objective of this study was to determine annual irrigation costs for field-grown plants in Ohio by species of plant and size of firm. This objective was accomplished by synthesizing two model field nurseries using an economic engineering approach. Once the nurseries were simulated, growing space was divided into five equal parts with each segment being assigned a plant group. In the 50-acre nursery each group was allocated 8 acres of field production plus corresponding space in the propagation house, overwintering facility, holding area, and field bed area. In the 200-acre nursery each plant group was allocated 35 acres, plus corresponding space in the central facility. In each plant group, one specific species was chosen as representative for the group. Total costs of installing irrigation systems were estimated at about $82,500 for a 50-acre field nursery and$167,800 for a 200-acre field nursery. Total annual costs for irrigating the 50-acre nursery were $15,095. Irrigation costs per salable plant (represents the total costs of irrigating the plant from the time it is placed in the field bed as a liner until sale) were$0.73 for slow growing evergreens (Taxus), $0.52 for fast growing evergreens (Juniperus),$0.49 for deciduous shrubs (Viburnum), $1.62 for shade trees (Acer rubrum),$1.11 for ornamental trees (Malus), and averaged $0.73 for all species, In the 50-acre nursery, costs of irrigation were approximately 3.3$35,355. Per salable plant costs were $0.39 for slow growing evergreens (Taxus),$0.28 for fast growing evergreens (Juniperus), $0.26 for deciduous shrubs (Viburnum),$0.86 for shade trees (Acer rubrum), $0.59 for ornamental trees (Malus), and averaged$0.39 for all species. Costs of irrigation were about 2.9% of total annual costs for the 200-acre nursery. Costs of irrigation averaged approximately 87% higher per salable plant in the 50-acre nursery than in the 200-acre. Large-size commercial field nurseries use equipment and labor more efficiently than small-sized nurseries. As a result, large nurseries have a lower cost of irrigation per salable plant

Multiscaled Cross-Correlation Dynamics in Financial Time-Series

The cross correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different timescales and then explore the eigenvalue spectrum over sliding time windows. The dynamics of the eigenvalue spectrum at different times and scales provides insight into the interactions between the numerous constituents involved. Eigenvalue dynamics are examined for both medium and high-frequency equity returns, with the associated correlation structure shown to be dependent on both time and scale. Additionally, the Epps effect is established using this multivariate method and analyzed at longer scales than previously studied. A partition of the eigenvalue time-series demonstrates, at very short scales, the emergence of negative returns when the largest eigenvalue is greatest. Finally, a portfolio optimization shows the importance of timescale information in the context of risk management