Efficiency of low versus high airline pressure in stunning cattle with a pneumatically powered penetrating captive bolt gun

The efficiency of stunning cattle was assessed in 443 animals (304 pure Zebu and 139 crossbred cattle), being mainly mature bulls and cows. Cattle were stunned using a Jarvis pneumatically powered penetrating captive bolt gun operating with low (160–175 psi, N = 82) and high (190 psi, N = 363) airline pressure, which was within the manufactures specifications. Signs of brain function and the position of the shots on the heads were recorded after stunning. Velocity of the captive bolt and its physical parameters were calculated. Cattle shot with low pressures showed more rhythmic respiration (27 vs. 8%, P < 0.001), less tongue protrusion (4 vs. 12%, P = 0.03) and less masseter relaxation (22 vs. 48%, P < 0.001). There was an increased frequency of shots in the ideal position when cattle were shot with the low compared to high airline pressures (15.3 vs. 3.1%). Bolt velocity and its physical parameters were significantly (P < 0.01) higher when using high pressure. Airline pressures below 190 psi are inappropriate when shooting adult Zebu beef cattle with pneumatically powered penetrating captive bolt guns

Enhancing The Quality Of CNN-Based Burned Area Detection In Satellite Imagery Through Data Augmentation

This study aims to enhance the quality of detecting burned areas in satellite imagery using deep learning by optimizing the training dataset volume through the application of various augmentation methods. The study analyzes the impact of image flipping, rotation, and noise addition on the overall accuracy for different classes of burned areas in a forest: fire, burned, smoke and background. Results demonstrate that while single augmentation techniques such as flipping and rotation alone did not result in significant improvements, a combined approach and the addition of noise resulted in an enhancement of the classification accuracy. Moreover, the study shows that augmenting the dataset through the use of multiple augmentation methods concurrently, resulting in a fivefold increase in input data, also enhanced the recognition accuracy. The study also highlights the need for further research in developing more efficient CNN models and in experimenting with additional augmentation methods to improve the accuracy of burned area detection, which would benefit environmental protection and emergency response services

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 187

This supplement to Aerospace Medicine and Biology lists 247 reports, articles and other documents announced during November 1978 in Scientific and Technical Aerospace Reports (STAR) or in International Aerospace Abstracts (IAA). In its subject coverage, Aerospace Medicine and Biology concentrates on the biological, physiological, psychological, and environmental effects to which man is subjected during and following simulated or actual flight in the earth's atmosphere or in interplanetary space. References describing similar effects of biological organisms of lower order are also included. Emphasis is placed on applied research, but reference to fundamental studies and theoretical principles related to experimental development also qualify for inclusion. Each entry in the bibliography consists of a bibliographic citation accompanied in most cases by an abstract

PAC Learnability under Explanation-Preserving Graph Perturbations

Graphical models capture relations between entities in a wide range of applications including social networks, biology, and natural language processing, among others. Graph neural networks (GNN) are neural models that operate over graphs, enabling the model to leverage the complex relationships and dependencies in graph-structured data. A graph explanation is a subgraph which is an `almost sufficient' statistic of the input graph with respect to its classification label. Consequently, the classification label is invariant, with high probability, to perturbations of graph edges not belonging to its explanation subgraph. This work considers two methods for leveraging such perturbation invariances in the design and training of GNNs. First, explanation-assisted learning rules are considered. It is shown that the sample complexity of explanation-assisted learning can be arbitrarily smaller than explanation-agnostic learning. Next, explanation-assisted data augmentation is considered, where the training set is enlarged by artificially producing new training samples via perturbation of the non-explanation edges in the original training set. It is shown that such data augmentation methods may improve performance if the augmented data is in-distribution, however, it may also lead to worse sample complexity compared to explanation-agnostic learning rules if the augmented data is out-of-distribution. Extensive empirical evaluations are provided to verify the theoretical analysis.Comment: 21 pages, 6 figures, 4 table

Chapter Sleep Spindles – As a Biomarker of Brain Function and Plasticity

Alternative & renewable energy sources & technolog

Sleep Spindles – As a Biomarker of Brain Function and Plasticity

Alternative & renewable energy sources & technolog

Perspectives on Incorporating Expert Feedback into Model Updates

Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficient consideration on how practitioners should translate domain expertise into ML updates. In this paper, we consider how to capture interactions between practitioners and experts systematically. We devise a taxonomy to match expert feedback types with practitioner updates. A practitioner may receive feedback from an expert at the observation- or domain-level, and convert this feedback into updates to the dataset, loss function, or parameter space. We review existing work from ML and human-computer interaction to describe this feedback-update taxonomy, and highlight the insufficient consideration given to incorporating feedback from non-technical experts. We end with a set of open questions that naturally arise from our proposed taxonomy and subsequent survey

Anatomic dissociation of selective and suppressive processes in visual attention

Visual spatial attention is associated with activation in parietal regions as well as with modulation of visual activity in ventral occipital cortex. Within the parietal lobe, localisation of activity has been hampered by variation in individual anatomy. Using fMRI within regions of interest derived from individual functional maps, we examined the response of superior parietal lobule, intraparietal sulcus, and ventral occipital cortex in 11 normal adults as attention was directed to the left and right visual hemifields during bilateral visual stimulation. Activation in ventral occipital cortex was augmented contralateral to the attended hemifield (p < 0.006), while intraparietal activation was augmented ipsilaterally (p < 0.009), and superior parietal lobule showed no modulation of activity as a function of attended hemifield. These findings suggest that spatial enhancement of relevant stimuli in ventral occipital cortex is complemented by an intraparietal response associated with suppression of, or preparation of a reflexive shift of attention towards, irrelevant stimuli. The spatial attention system in superior parietal cortex, in contrast, may be driven to equal degrees by currently attended stimuli and by stimuli that are potential targets of attention