World-Wide Body Size Patterns in Freshwater Fish by Geography, Size Class, Trophic Level, and Taxonomy

The unifying themes of my thesis are patterns in world-wide freshwater fish body sizes and their underlying mechanisms. First, I explored Bergmann\u27s rule in unprecedented detail, which states that body size is negatively correlated to temperature. Categorizing species by body size into quantiles and by trophic levels, I regressed the classes against latitude, temperature, seasonality, minimum temperature and habitable space. I found that Bergmann\u27s rule applies to freshwater fish in general but the strength varies by size class and trophic levels. I concluded that Bergmann\u27s rule in fish is driven by the exclusion of small fish from cold climate due to limits in energy storage and behavioral thermoregulation. Second, I investigated the relationships between extreme body size and species richness. Stressful environments promote an ecological similarity among species, reducing body size ranges and species richness. Thus, there may be a strong relationship between extreme body size and species richness. However, I found that only the size of the smallest species were strongly related to species richness. The observed strong relationship may be due to physiological constraints on the smallest species in stressful environments. The lack of relationship between the size of the largest species and species richness may be due to the high dispersal ability of the largest species homogenizing body size across space and their relative insensitivity to harsh environments. Third, I examined body size and trophic level conservatism and similarity across species within a genus. Body size and trophic level are evolutionary conserved traits; thus I expected high body size conservatism, but due to constraints imposed on extreme body sizes and trophic levels, body size conservatism may vary for all body size classes and trophic levels. I found conservatism of these traits to be substantially lower in freshwater fish than mammals. Divergence in body size among closely related species that are very small or large may allow those species to reduce constraints due to extreme size and to coexist with other species, leading to lower body size conservatism than in the medium size class. Relatively low body size conservatism and high similarity in body size among families and genera, suggest that freshwater fish body sizes are, in contrast to mammals, highly plastic and responsive to environmental variations. Trophic levels were more conserved than body size. Trophic adaptation connects species by their function and physiology. In addition, it also demonstrates the direct interaction between species and their community and ecosystem processes

Inferring Causal Effects Under Heterogeneous Peer Influence

Causal inference in networks should account for interference, which occurs when a unit's outcome is influenced by treatments or outcomes of peers. There can be heterogeneous peer influence between units when a unit's outcome is subjected to variable influence from different peers based on their attributes and relationships, or when each unit has a different susceptibility to peer influence. Existing solutions to causal inference under interference consider either homogeneous influence from peers or specific heterogeneous influence mechanisms (e.g., based on local neighborhood structure). This paper presents a methodology for estimating individual causal effects in the presence of heterogeneous peer influence due to arbitrary mechanisms. We propose a structural causal model for networks that can capture arbitrary assumptions about network structure, interference conditions, and causal dependence. We identify potential heterogeneous contexts using the causal model and propose a novel graph neural network-based estimator to estimate individual causal effects. We show that existing state-of-the-art methods for individual causal effect estimation produce biased results in the presence of heterogeneous peer influence, and that our proposed estimator is robust

Machine learning in and out of equilibrium

The algorithms used to train neural networks, like stochastic gradient descent (SGD), have close parallels to natural processes that navigate a high-dimensional parameter space -- for example protein folding or evolution. Our study uses a Fokker-Planck approach, adapted from statistical physics, to explore these parallels in a single, unified framework. We focus in particular on the stationary state of the system in the long-time limit, which in conventional SGD is out of equilibrium, exhibiting persistent currents in the space of network parameters. As in its physical analogues, the current is associated with an entropy production rate for any given training trajectory. The stationary distribution of these rates obeys the integral and detailed fluctuation theorems -- nonequilibrium generalizations of the second law of thermodynamics. We validate these relations in two numerical examples, a nonlinear regression network and MNIST digit classification. While the fluctuation theorems are universal, there are other aspects of the stationary state that are highly sensitive to the training details. Surprisingly, the effective loss landscape and diffusion matrix that determine the shape of the stationary distribution vary depending on the simple choice of minibatching done with or without replacement. We can take advantage of this nonequilibrium sensitivity to engineer an equilibrium stationary state for a particular application: sampling from a posterior distribution of network weights in Bayesian machine learning. We propose a new variation of stochastic gradient Langevin dynamics (SGLD) that harnesses without replacement minibatching. In an example system where the posterior is exactly known, this SGWORLD algorithm outperforms SGLD, converging to the posterior orders of magnitude faster as a function of the learning rate.Comment: 24 pages, 6 figure

Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions

Cannabis legalization has been welcomed by many U.S. states but its role in escalation from tobacco e-cigarette use to cannabis vaping is unclear. Meanwhile, cannabis vaping has been associated with new lung diseases and rising adolescent use. To understand the impact of cannabis legalization on escalation, we design an observational study to estimate the causal effect of recreational cannabis legalization on the development of pro-cannabis attitude for e-cigarette users. We collect and analyze Twitter data which contains opinions about cannabis and JUUL, a very popular e-cigarette brand. We use weakly supervised learning for personal tweet filtering and classification for stance detection. We discover that recreational cannabis legalization policy has an effect on increased development of pro-cannabis attitudes for users already in favor of e-cigarettes.Comment: Published at ICWSM 202

The durability of long-lasting insecticidal nets distributed to the households between 2009 and 2013 in Nepal

Background: Understanding and improving the durability of long-lasting insecticidal nets (LLINs) in the field are critical for planning future implementation strategies including behavioral change for care and maintenance. LLIN distribution at high coverage is considered to be one of the adjunctive transmission reduction strategies in Nepal’s Malaria Strategic Plan 2014–2025. The main objective of this study was to assess the durability through assessment of community usage, physical integrity, residual bio-efficacy, and chemical retention in LLINs: Interceptor®, Yorkool®, and PermaNet ®2.0 which were used in Nepal during 2009 through 2013. Methods: Assessments were conducted on random samples (n = 440) of LLINs from the eleven districts representing four ecological zones: Terai plain region (Kailali and Kanchanpur districts), outer Terai fluvial ecosystem (Surkhet, Dang, and Rupandhei districts), inner Terai forest ecosystem (Mahhothari, Dhanusa, and Illam districts), and Hills and river valley (Kavrepalanchock and Sindhupalchok districts). For each LLIN, fabric integrity in terms of proportionate hole index (pHI) and residual bio-efficacy were assessed. However, for chemical retention, a representative sample of 44 nets (15 Yorkool®, 10 Permanet®2.0, and 19 Interceptor®) was evaluated. Data were analyzed using descriptive statistics stratified by LLINs brand, districts, and duration of exposure. Results: On average, duration of use of LLINs was shortest for the Yorkool® samples, followed by PermaNet® 2.0 and Interceptor® with median ages of 8.9 (IQR = 0.4), 23.8 (IQR = 3.2), and 50.1 (IQR = 3.2) months, respectively. Over 80% of field distributed Yorkool® and PermaNet® 2.0 nets were in good condition (pHI< 25) compared to Interceptor® (66%). Bio-efficacy analysis showed that average mortality rates of Interceptor and Yorkool were below World Health Organization (WHO) optimal effectiveness of ≥ 80% compared to 2-year-old PermaNet 2.0 which attained 80%. Chemical retention analysis was consistent with bio-efficacy results. Conclusion: This study shows that distribution of LLINs is effective for malaria control; however, serviceable life of LLINs should be considered in terms of waning residual bio-efficacy that warrants replacement. As an adjunctive malaria control tool, National Malaria Control Program of Nepal can benefit by renewing the distribution of LLINs in an appropriate time frame in addition to utilizing durable and effective LLINs

Determining causality in travel mode choice

This article presents one of the pioneering studies on causal modeling in travel mode choice decision-making using causal discovery algorithms. These models are a major advancement from conventional correlation-based techniques. We propose a novel methodology that combines causal discovery with structural equation modeling (SEM). This modeling approach overcomes some of the limitations of SEM by combining the strengths of both causal discovery and SEM. Causal discovery algorithms determine causal graphs from observational data and domain knowledge, and SEMs estimate direct causal effects and test the performance of causal discovery algorithms. In this study, we test four causal discovery algorithms: Peter-Clark (PC), Fast Causal Inference (FCI), Fast Greedy Equivalence Search (FGES), and Direct Linear Non-Gaussian Acyclic Models (DirectLiNGAM). The results show that DirectLiNGAM based SEM model best captures causality in mode choice behavior. It passes several goodness-of-fit tests, including Root Mean Square Error of Approximation (RMSEA) and Goodness-of-Fit Index (GFI), and it achieves the lowest Bayesian Information Criterion (BIC) value. The analyses are conducted on data collected from the 2017 National Household Travel Survey in the New York Metropolitan area

Effect of packaging materials and storage temperature on the retention of physicochemical properties of vacuum packed pink guava powder

Storage shelf life of fruit powder is an important concern in fruit powder industry. The objective of this study was to explore the effect of storage conditions on the retention of physicochemical properties of guava powder. The spray-dried guava powder was packed by LDPE, PET laminated and OPP laminated film and stored at 5 °C and 25 °C for 10 weeks. The shelf life prediction was measured from the linear regression kinetic equation of water activity. Packaging film, storage temperature and time had significant effect on powder properties. PET laminated film showed the most significant effect in retention of moisture, water activity and lycopene. LDPE packed powder was the least effective in moisture control, which led to increase of glass transition temperature (Tg) and degree of caking (CD) and loss of color and lycopene. Higher storage temperature (25 °C) considerably increased the moisture gain, water activity, Tg and CD. The suitable storage condition for guava powder was PET laminated film at 5 °C that showed the maximum predicted shelf life (34.95 weeks) with the highest lycopene retention (74.56%) and low moisture content of < 3%