When a Fish is a Fish: The Economic Impacts of Escaped Farmed Fish

The escape of cultured fish from a marine aquaculture facility is a type of biological invasion that may lead to a variety of potential ecological and economic effects on native fish. This paper develops a general invasive species impact model to capture explicitly both the ecological and economic effects of invasive species, especially escaped farmed fish, on native stocks and harvests. First, the possible effects of escaped farmed fish on the growth and stock size of a native fish are examined. Next, a bioeconomic model to analyze changes in yield, benefit distribution, and overall profitability is constructed. Different harvesting scenarios, such as commercial, recreational, and joint commercial and recreational fishing, are explored. The model is illustrated by a case study of the interaction between native and farmed Atlantic salmon in Norway. The results suggest that both the harvest and profitability of a native fish stock may decline after an invasion, but the total profits from the harvest of both native and farmed stocks may increase or decrease, depending on the strength of the ecological and economic parameters.

Assessing the impact of environmental variability on harvest in a heterogeneous fishery: a case study of the Canadian lobster fishery

Global fisheries face significant challenges in the coming years due to climate change. Understanding and anticipating the impacts of climate change is a necessity for implementing appropriate fisheries management. This study uses a panel dataset of individual fishing vessels to examine how variation in ocean temperature affects fish harvest. Using the American lobster (Homarus americanus) fishery in the Maritimes region of Canada as a case study, this paper employs a generalised linear mixed model (GLMM) taking into account heterogeneity amongst fishers, gear, vessels, and fishing areas. The GLMM is found to have better performance and estimations when compared against alternative specifications. As expected, a significant and positive relationship was found, further contributing to the existing evidence of warming impacts on the lobster fishery. The implications of this study are twofold: first, it provides further evidence that environmental change does have a significant positive impact on harvest. This information should be considered by fishing industry and fisheries authorities when implementing appropriate adaptive management strategies and measures in their decision making. Second, it illustrates that allowing for mixed-effects using GLMMs is a valuable empirical tool when dealing with hierarchical data structures

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective

Support of modified Archimedes' law theory in granular media.

We study the resistance force of cylindrical objects penetrating quasi-statically into granular media experimentally and numerically. Simulations are validated against experiments. In contrast to previous studies, we find in both experiments and simulations that the force-depth relation consists of three regimes, rather than just two: transient and steady-state. The three regimes are driven by different dynamics: an initial matter compression, a developing stagnant zone, and an increase in steady-state force with a fully developed stagnant zone. By simulations, we explored the effects of a wide range of parameters on the penetration dynamics. We find that the initial packing fraction, the inter-granular sliding friction coefficient, and the grain shape (aspect ratio) have a significant effect on the gradient Kφ of the force-depth relation in the steady-state regime, while the rolling friction coefficient noticeably affects only the initial compression regime. Conversely, Kφ is not sensitive to the following grain properties: size, size distribution, shear modulus, density, and coefficient of restitution. From the stress fields observed in the simulations, we determine the internal friction angles φ, using the Mohr-Coulomb yield criterion, and use these results to test the recently-proposed modified Archimedes' law theory. We find excellent agreement, with the results of all the simulations falling very close to the predicted curve of φ vs. Kφ. We also examine the extreme case of frictionless spheres and find that, although no stagnant zone develops during penetration into such media, the value of their internal friction angle, φ = 9° ± 1°, also falls squarely on the theoretical curve. Finally, we use the modified Archimedes' law theory and an expression for the time-dependent growth of the stagnant zone to propose an explicit constitutive relation that fits excellently the force-depth curve throughout the entire penetration process

EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data

Gradient clipping is an important technique for deep neural networks with exploding gradients, such as recurrent neural networks. Recent studies have shown that the loss functions of these networks do not satisfy the conventional smoothness condition, but instead satisfy a relaxed smoothness condition, i.e., the Lipschitz constant of the gradient scales linearly in terms of the gradient norm. Due to this observation, several gradient clipping algorithms have been developed for nonconvex and relaxed-smooth functions. However, the existing algorithms only apply to the single-machine or multiple-machine setting with homogeneous data across machines. It remains unclear how to design provably efficient gradient clipping algorithms in the general Federated Learning (FL) setting with heterogeneous data and limited communication rounds. In this paper, we design EPISODE, the very first algorithm to solve FL problems with heterogeneous data in the nonconvex and relaxed smoothness setting. The key ingredients of the algorithm are two new techniques called \textit{episodic gradient clipping} and \textit{periodic resampled corrections}. At the beginning of each round, EPISODE resamples stochastic gradients from each client and obtains the global averaged gradient, which is used to (1) determine whether to apply gradient clipping for the entire round and (2) construct local gradient corrections for each client. Notably, our algorithm and analysis provide a unified framework for both homogeneous and heterogeneous data under any noise level of the stochastic gradient, and it achieves state-of-the-art complexity results. In particular, we prove that EPISODE can achieve linear speedup in the number of machines, and it requires significantly fewer communication rounds. Experiments on several heterogeneous datasets show the superior performance of EPISODE over several strong baselines in FL.Comment: Accepted by ICLR 2023. The code is available at https://github.com/MingruiLiu-ML-Lab/episod

Effect of Ultrasound Pressure on the Distribution of Bovine Serum Albumin Delivered by Focused Ultrasound-mediated Blood-Brain Barrier Opening in Cleared Mouse Brains

Most common diagnosis and therapeutic methods have low effectiveness when used on brain diseases. The key obstacle is that the blood-brain barrier (BBB) prevents most drugs from entering the brain. Some strategies have been developed to improve the efficiency of drug delivery crossing BBB. Among all these strategies, focused ultrasound-mediated BBB opening (FUS-BBB Opening) stands out since it is noninvasive and can be located to the target area. Detailed studies are required on the distribution of drugs delivered by FUS-BBB opening and the effects of FUS parameters on the distribution. This thesis proposes a pipeline involving tissue clearing and lightsheet microscopy to study the distribution of BSA relative to vessels in mouse brains treated with FUS and the effect of ultrasound pressure on the delivery pattern. As mentioned before, slices (1 mm thick) from mouse brains treated with FUS were cleared until their transparency meets the requirement of large-volume three-dimensional (3D) imaging. Blood vessels and BSA clusters inthe 3D images obtained from lightsheet microscopy were segmented and the distance of every cluster from the nearest vessel was collected in the distance map. Comparing the distance maps of different pressures, it is indicated that FUS with the pressure of 0.4 MPa significantly increases the amount of BSAclusters in brains, especially those distributed closer to the outer surface of vessels. BSA delivered by 0.2 MPa FUS and 0.4 MPa FUS has different distribution patterns relative to vessels. At the same time, this thesis discussed the feasibility of this pipeline to study FUS-BBB opening-induced drug delivery

Social-Cultural Ecosystem Services of Sea Trout Recreational Fishing in Norway

This paper explores the ecosystem services provided by anadromous brown trout (often termed sea trout) populations in Norway. Sea trout is an important species in both freshwater and marine ecosystems and provides important demand-driven ecological provisioning and socio-cultural services. While the sea trout once provided an important provisioning service through a professional fishery and subsistence fishing, fishing for sea trout in the near shore coastal areas and in rivers is today a very popular and accessible recreational activity and generates primarily socio-cultural services. The recreational fishery contributes to local cultural heritage, its folkways and lore, to the development and transfer of local ecological knowledge and fishing experience to the young and to human well-being. As a salmonid species, the sea trout is sensitive to negative environmental conditions in both freshwater and marine coastal areas and is in general decline. A recent decision to expand production of farmed salmon may increase pressure on stocks. Good management of recreational fishing is accordingly important for the species to thrive, but knowledge of what fishers value with respect to fishing sea trout and what management measures they will accept is limited. Researchers sought to capture information about non-extractive direct use value (non-monetary) of the sea trout recreational fishery using questionnaire surveys targeting Norwegian anglers around the country. Results indicate that the most important ecosystem services delivered by recreational sea trout fisheries are social-cultural ecosystem services at the level of individual fishers; fishing sea trout most likely also has important social functions. Fishers are prepared to accept stricter management measures that reduce catches and allow fishing to continue but they oppose paying higher fees

The Economics of Controlling Diseases on Fish Farms

Disease is a primary threat to the continued growth in salmon aquaculture due to its extensive effects on the sector. Aquaculture farms suffer the most direct and immediate economic losses through reduction in growth, low feed efficiency and market prices, increasing mortality rates, and expenditures on prevention and treatment measures. Prevention and control strategies and management practices are at the core in eliminating or minimizing the disease, while cost-effective disease control strategies at the level of the fish farm are needed to enhance productivity and profitability. This paper aims to develop a bioeconomic model to determine the optimal set of disease control strategies for sea lice at a farm level. The optimal strategies that minimize total disease costs including direct production losses and control costs and maximize overall profit depend on the integration between economics and epidemiology of disease. A production function will be first constructed to incorporate the effects on production at a farm level, followed by the development of a dynamic profit optimization model to take into account several prevention and treatment strategies. The model will be applied to case studies in Norway

Fast Composite Optimization and Statistical Recovery in Federated Learning

As a prevalent distributed learning paradigm, Federated Learning (FL) trains a global model on a massive amount of devices with infrequent communication. This paper investigates a class of composite optimization and statistical recovery problems in the FL setting, whose loss function consists of a data-dependent smooth loss and a non-smooth regularizer. Examples include sparse linear regression using Lasso, low-rank matrix recovery using nuclear norm regularization, etc. In the existing literature, federated composite optimization algorithms are designed only from an optimization perspective without any statistical guarantees. In addition, they do not consider commonly used (restricted) strong convexity in statistical recovery problems. We advance the frontiers of this problem from both optimization and statistical perspectives. From optimization upfront, we propose a new algorithm named \textit{Fast Federated Dual Averaging} for strongly convex and smooth loss and establish state-of-the-art iteration and communication complexity in the composite setting. In particular, we prove that it enjoys a fast rate, linear speedup, and reduced communication rounds. From statistical upfront, for restricted strongly convex and smooth loss, we design another algorithm, namely \textit{Multi-stage Federated Dual Averaging}, and prove a high probability complexity bound with linear speedup up to optimal statistical precision. Experiments in both synthetic and real data demonstrate that our methods perform better than other baselines. To the best of our knowledge, this is the first work providing fast optimization algorithms and statistical recovery guarantees for composite problems in FL.Comment: This is a revised version to fix the imprecise statements about linear speedup from the ICML proceedings. We use another averaging scheme for the returned solutions in Theorem 2.1 and 3.1 to guarantee linear speedup when the number of iterations is larg