Analysis of the Spatial Dynamics of the American Lobster (Homarus Americanus) Fishery along the Coast of Maine

The American lobster (Homarus americanus) supports the most valuable commercial fishery in the northeastern United States, thus the fishery is critical to Maine\u27s economy. No systematic study has been done to collect information about, identify, and quantify the spatial dynamics of the Maine lobster fishery. This project helps to provide a better understanding of Maine\u27s lobster fishery dynamics, and it will aid f\u27iture efforts to improve the stock assessment of Maine\u27s lobster fishery. The analysis consists of three distinct parts: (1) comparison of data collected by two separate fishery dependent sampling programs; (2) spatial analysis of electronic logbook data; and (3) harbor gang temtoriality evidenced by electronic logbook data. The Maine Department of Marine Resources has established two fishery- dependent sampling programs: sea sampling and port sampling. Using data from 1998 - 2000, we evaluated the consistency in size composition and catch per unit of effort (CPUE) between the sea and port sampling programs. The overall pattern that emerged was a stronger relationship between sea and port sampling data over time from 1998- 2000, implying the two sampling programs were consistent in describing temporal variations in CPUE. This study suggests that either program should be sufficient in monitoring temporal trends of the lobster fishery. The American lobster fishery exhibits strong seasonal variations in spatial distributions of traps. In this study, we developed and applied two spatial statistical models, a moving window model and the empirical distribution function (EDF) model, to explore and describe data from the lobster fishery in order to quantify the spatial and temporal dynamics of fishing effort. This study suggests that fishing effort data were clustered rather than randomly distributed for the entire fishing season in the Stonington area. Therefore, we can state the data are not random in space or in time, but rather trap locations are clustered. Plots of nearest trap locations from May to December indicated that the trap locations were also not random at the smaller time scale. The nearest location distances of trap locations varied by month, but a general trend of decreased distances from May to September was observed, followed by increased distances from October to December. Electronic logbook data were displayed using GIs software to analyze the various boundaries observed by lobstermen. Management zone boundaries affected Stonington, Vinalhaven, Tenants Harbor, Spruce Head, New Harbor, and Long Island fishing areas to varying degrees in most seasons. Unofficial or territorial boundaries were assumed to have affected all areas, but some more obviously than others. Among these most affected were Stonington, Tenants Harbor, Port Clyde, Metinic, Round Pond, New Harbor, Cousins Island, and Harpswell. Territoriality among harbor gangs was shown to have at least partially structured the fishing areas observed through Thistle Marine data. These analyses have provided the DMR with important information on their current sampling programs, methodologies for future analysis of the fishery, and information affecting future management decisions and stock assessments

Distribution of Isolated Volcanoes on the Flanks of the East Pacific Rise, 15.3°-20°S

Volcanic constructions, not associated with seamount (or volcano) chains, are abundant on the flanks of the East Pacific Rise (EPR) but are rare along the axial high. The distribution of isolated volcanoes, based on multibeam bathymetric maps, is approximately symmetric about the EPR axis. This symmetry contrasts with the asymmetries in the distribution of volcano chains (more abundant on the west flank), the seafloor subsidence rates (slower on the west flank), and the distribution of plate-motion-parallel gravity lineaments (more prominento nthe west flank). Most of the isolated volcanoes complete their growth within -14 km of the axis on crust younger than 0.2 Ma, while seamount chain volcanoes continue to be active on older crust. Volcanic edifices within 6 km of the ridge axis are primarily found adjacent to axial discontinuities, suggesting a more sporadic magma supply and stronger lithosphere able to support volcanic constructions near axial discontinuities. The volume of isolated near-axis volcanoes correlates with ridge axis cross-sectional area, suggesting a link between the magma budget of the ridge and the eruption of near-axis volcanoes. Within the study area, off-axis volcanic edifices cover at least 6% of the seafloor and contribute more than 0.2% to the volume of the crust. The inferred width of the zone where isolated volcanoes initially form increases with spreading rate for the Mid-Atlantic Ridge (\u3c4 km), northern EPR (\u3c20 km), and southern EPR(\u3c28 km), so that isolated volcanoes form primarily on lithosphere younger than 0.2 Ma (\u3c 4-6 km brittle thickness), independent of spreading rate. This suggests some form of lithospheric control on the eruption of isolated off-axis volcanoes due to brittle thickness, increased normal stresses across cracks impeding dike injection, or thermal stresses within the newly forming lithosphere

Three-Dimensional Basin and Fault Structure From a Detailed Seismic Velocity Model of Coachella Valley, Southern California

The Coachella Valley in the northern Salton Trough is known to produce destructive earthquakes, making it a high seismic hazard area. Knowledge of the seismic velocity structure and geometry of the sedimentary basins and fault zones is required to improve earthquake hazard estimates in this region. We simultaneously inverted first P wave travel times from the Southern California Seismic Network (39,998 local earthquakes) and explosions (251 land/sea shots) from the 2011 Salton Seismic Imaging Project to obtain a 3‐D seismic velocity model. Earthquakes with focal depths ≤10 km were selected to focus on the upper crustal structure. Strong lateral velocity contrasts in the top ~3 km correlate well with the surface geology, including the low‐velocity (<5 km/s) sedimentary basin and the high‐velocity crystalline basement rocks outside the valley. Sediment thickness is ~4 km in the southeastern valley near the Salton Sea and decreases to <2 km at the northwestern end of the valley. Eastward thickening of sediments toward the San Andreas fault within the valley defines Coachella Valley basin asymmetry. In the Peninsular Ranges, zones of relatively high seismic velocities (~6.4 km/s) between 2‐ and 4‐km depth may be related to Late Cretaceous mylonite rocks or older inherited basement structures. Other high‐velocity domains exist in the model down to 9‐km depth and help define crustal heterogeneity. We identify a potential fault zone in Lost Horse Valley unassociated with mapped faults in Southern California from the combined interpretation of surface geology, seismicity, and lateral velocity changes in the model

Invertible Zero-Shot Recognition Flows

© 2020, Springer Nature Switzerland AG. Deep generative models have been successfully applied to Zero-Shot Learning (ZSL) recently. However, the underlying drawbacks of GANs and VAEs (e.g., the hardness of training with ZSL-oriented regularizers and the limited generation quality) hinder the existing generative ZSL models from fully bypassing the seen-unseen bias. To tackle the above limitations, for the first time, this work incorporates a new family of generative models (i.e., flow-based models) into ZSL. The proposed Invertible Zero-shot Flow (IZF) learns factorized data embeddings (i.e., the semantic factors and the non-semantic ones) with the forward pass of an invertible flow network, while the reverse pass generates data samples. This procedure theoretically extends conventional generative flows to a factorized conditional scheme. To explicitly solve the bias problem, our model enlarges the seen-unseen distributional discrepancy based on a negative sample-based distance measurement. Notably, IZF works flexibly with either a naive Bayesian classifier or a held-out trainable one for zero-shot recognition. Experiments on widely-adopted ZSL benchmarks demonstrate the significant performance gain of IZF over existing methods, in both classic and generalized settings

Bayesian Zero-Shot Learning

Object classes that surround us have a natural tendency to emerge at varying levels of abstraction. We propose a Bayesian approach to zero-shot learning (ZSL) that introduces the notion of meta-classes and implements a Bayesian hierarchy around these classes to effectively blend data likelihood with local and global priors. Local priors driven by data from seen classes, i.e., classes available at training time, become instrumental in recovering unseen classes, i.e., classes that are missing at training time, in a generalized ZSL (GZSL) setting. Hyperparameters of the Bayesian model offer a convenient way to optimize the trade-off between seen and unseen class accuracy. We conduct experiments on seven benchmark datasets, including a large scale ImageNet and show that our model produces promising results in the challenging GZSL setting

Report on the BTAS 2016 Video Person Recognition Evaluation

© 2016 IEEE. This report presents results from the Video Person Recognition Evaluation held in conjunction with the 8th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS). Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1,401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. An additional experiment required algorithms to recognize people in videos from the Video Database of Moving Faces and People (VDMFP). There were 958 videos in this experiment of 297 subjects. Four groups from around the world participated in the evaluation. The top verification rate for PaSC from this evaluation is 0.98 at a false accept rate of 0.01 - a remarkable advancement in performance from the competition held at FG 2015

Subsurface Geometry of the San Andreas Fault in Southern California: Results from the Salton Seismic Imaging Project (SSIP) and Strong Ground Motion Expectations

The San Andreas fault (SAF) is one of the most studied strike‐slip faults in the world; yet its subsurface geometry is still uncertain in most locations. The Salton Seismic Imaging Project (SSIP) was undertaken to image the structure surrounding the SAF and also its subsurface geometry. We present SSIP studies at two locations in the Coachella Valley of the northern Salton trough. On our line 4, a fault‐crossing profile just north of the Salton Sea, sedimentary basin depth reaches 4 km southwest of the SAF. On our line 6, a fault‐crossing profile at the north end of the Coachella Valley, sedimentary basin depth is ∼2–3  km and centered on the central, most active trace of the SAF. Subsurface geometry of the SAF and nearby faults along these two lines is determined using a new method of seismic‐reflection imaging, combined with potential‐field studies and earthquakes. Below a 6–9 km depth range, the SAF dips ∼50°–60° NE, and above this depth range it dips more steeply. Nearby faults are also imaged in the upper 10 km, many of which dip steeply and project to mapped surface fault traces. These secondary faults may join the SAF at depths below about 10 km to form a flower‐like structure. In Appendix D, we show that rupture on a northeast‐dipping SAF, using a single plane that approximates the two dips seen in our study, produces shaking that differs from shaking calculated for the Great California ShakeOut, for which the southern SAF was modeled as vertical in most places: shorter‐period (T<1  s) shaking is increased locally by up to a factor of 2 on the hanging wall and is decreased locally by up to a factor of 2 on the footwall, compared to shaking calculated for a vertical fault