A production modeling approach to the assessment of the horseshoe crab (Limulus polyphemus) population in Delaware Bay

Horseshoe crab (Limulus polyphemus) is harvested commercially, used by the biomedical industry, and provides food for migrating shorebirds, particularly in Delaware Bay. Recently, decreasing crab population trends in this region have raised concerns that the stock may be insufficient to fulfill the needs of these diverse user groups. To assess the Delaware Bay horseshoe crab population, we used surplus production models (programmed in ASPIC), which incorporated data from fishery-independent surveys, fishery-dependent catch-per-unit-of-effort data, and regional harvest. Results showed a depleted population (B2003/=0.03−0.71) BMSY and high relative fishing mortality /FMSY=0.9−9.5). Future harvest (F2002strategies for a 15-year period were evaluated by using population projections with ASPICP software. Under 2003 harvest levels (1356 t), population recovery to BMSY would take at least four years, and four of the seven models predicted that the population would not reach BMSY within the 15year period. Production models for horseshoe crab assessment provided management benchmarks for a species with limited data and no prior stock assessmen

Development of a coded 16-ary CPFSK coherent demodulator

Theory and hardware are described for a proof-of-concept 16-ary continuous phase frequency shift keying (16-CPFSK) digital modem. The 16 frequencies are spaced every 1/16th baud rate for 2 bits/sec/Hz operation. Overall rate 3/4 convolutional coding is incorporated. The demodulator differs significantly from typical quadrature phase detector approaches in that phase is coherently measured by processing the baseband output of a frequency discriminator. Baud rate phase samples from the baseband processor are decoded to yield the original data stream. The method of encoding onto the 16-ary phase nodes, together with convolutional coding gain, results in near quad PSK (QPSK) performance. The modulated signal is of constant envelope; thus the power amplifier can be saturated for peak performance. The spectrum is inherently bandlimited and requires no RF filter

On Ritsos\u27 Diary of Exile, Book One

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On Ritsos\u27 Diary of Exile, Book One

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Results of A Local Combination Therapy Antibiogram For \u3cem\u3ePseudomonas Aeruginosa\u3c/em\u3e Isolates: Is Double Worth The Trouble?

Purpose: To determine the frequency at which fluoroquinolones and aminoglycosides demonstrate in vitro activity against non-urinary, non-skin/skin structure Pseudomonas aeruginosa isolates exhibiting decreased susceptibilities to one or more β-lactam agents. Methods: β-lactam-non-susceptible P. aeruginosa isolates recovered from blood, bone, lower respiratory tract, pleural fluid, cerebrospinal fluid, or peritoneal fluid cultures between October 2010 and October 2014 were reviewed from four community hospitals within a single health-system. Only the first isolate per patient was included for analysis. The likelihood that each isolate was susceptible to a non-β-lactam antimicrobial was then determined and summarized within a combination antibiogram. Results: In total, 179 P. aeruginosa isolates with decreased susceptibilities to one or more β-lactam agents were assessed. Because no appreciable differences in antimicrobial susceptibility profile were observed between hospitals, the isolates were evaluated in aggregate. Susceptibility rates for β-lactam monotherapy ranged from 34% to 75%. Aminoglycosides possessed increased antibacterial activity compared to fluoroquinolones. Tobramycin was the non-β-lactam most likely to expand antimicrobial coverage against β-lactam-non-susceptible P. aeruginosa with activity against 64%, 66%, and 65% of cefepime-, piperacillin-tazobactam-, and meropenem-non-susceptible isolates, respectively (p \u3c 0.001 for all). Conclusions: The results of this study support the use of aminoglycosides over fluoroquinolones for achieving optimal, empiric antimicrobial combination therapy for P. aeruginosa when dual antimicrobial therapy is clinically necessary. Future efforts aimed at optimizing combination therapy for P. aeruginosa should focus on systemic interventions that limit the selection of fluoroquinolones in combination with β-lactams to expand coverage based on local susceptibility rates

Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images

Knowledge distillation (KD) has been a popular and effective method for model compression. One important assumption of KD is that the original training dataset is always available. However, this is not always the case due to privacy concerns and more. In recent years, "data-free" KD has emerged as a growing research topic which focuses on the scenario of performing KD when no data is provided. Many methods rely on a generator network to synthesize examples for distillation (which can be difficult to train) and can frequently produce images that are visually similar to the original dataset, which raises questions surrounding whether privacy is completely preserved. In this work, we propose a new approach to data-free KD that utilizes unnatural OpenGL images, combined with large amounts of data augmentation and adversarial attacks, to train a student network. We demonstrate that our approach achieves state-of-the-art results for a variety of datasets/networks and is more stable than existing generator-based data-free KD methods. Source code will be available in the future

Inducing Neural Collapse to a Fixed Hierarchy-Aware Frame for Reducing Mistake Severity

There is a recently discovered and intriguing phenomenon called Neural Collapse: at the terminal phase of training a deep neural network for classification, the within-class penultimate feature means and the associated classifier vectors of all flat classes collapse to the vertices of a simplex Equiangular Tight Frame (ETF). Recent work has tried to exploit this phenomenon by fixing the related classifier weights to a pre-computed ETF to induce neural collapse and maximize the separation of the learned features when training with imbalanced data. In this work, we propose to fix the linear classifier of a deep neural network to a Hierarchy-Aware Frame (HAFrame), instead of an ETF, and use a cosine similarity-based auxiliary loss to learn hierarchy-aware penultimate features that collapse to the HAFrame. We demonstrate that our approach reduces the mistake severity of the model's predictions while maintaining its top-1 accuracy on several datasets of varying scales with hierarchies of heights ranging from 3 to 12. We will release our code on GitHub in the near future

'Policing Rural Communities in North America': An International Society for the Study of Rural Crime Roundtable

Rural crime and criminal justice practices and responses face different challenges from those experienced in urban contexts. A practitioner-focused roundtable, convened by The International Society for the Study of Rural Crime (www.issrc.net), investigated challenges and innovations in international contexts on issues surrounding rural policing with a specific focus on rural policing in Canada and the United States. The roundtable was held online on 15 September 2021 and was moderated by Dr. Jessica Peterson, formerly of the University of Nebraska at Kearney (now an Assistant Professor at Southern Oregon University). Panellists were asked to respond to three key questions: What is the key element to successful community policing in your community? What is one initiative in which you have successfully engaged the community in crime-reduction efforts? What is the most significant challenge to successfully reducing crime in your community? The following are transcripts of the four presentations from the panelists on this Roundtable