Newton Loebens

In this paper we study the exponential decay parameter for continuous-time open quantum walks. This statistic is obtained by a composed function that associates the natural logarithm to the transition probability of the walk, being inspired by the classical decay parameter of continuous-time Markov chains. The existence of this statistic is proven for finite graphs where each vertex has a finite degree of freedom, and it is intimately connected to the spectrum of the Lindblad generator describing the dynamics. We show that faithful states yield the slowest decay, whereas non-faithful states can accelerate decay and modify the communication structure of the graph, a phenomenon with no classical analogue. Extensions to infinite graphs are discussed as open problems and conjectures. An equivalence theorem relating different notions of recurrence is established under the hypothesis that the decay parameter exists.

An animal nose identification system could allow efficient monitoring of pets, and it can be used in applications such as identifying animal breeds or possible diseases and/or injuries. For that, a bulldog image dataset was build from French bulldogs. To carry out the validation through cross-validation, 10 folds (K-folds) were created. Afterward, five convolutional neural networks (CNN) were trained with our dataset to identify the nose: Faster R-CNN (Region-based CNN), SABL (Side-Aware Boundary Localization), RetinaNet (ResNet50+FPN), VFNet (VarifocalNet), and ATSS (Adaptive Training Sample Selection). Faster R-CNN, SABL, RetinaNet, VFNet and ATSS were used for training in the first phase, while ATSS, Faster R-CNN, ATSS and SABL in the second. The results showed that the ATSS network obtained the highest values of mAP and Accuracy in the first phase. Moreover, SABL network achieved the highest values of mAP50, mAP75, Recall, F-Score and Accuracy at the end of the second phase.

The recognition of behavioral relationships related to aggression in pigs is an important task that is visually observed in the livestock industry. However, this task is laborious and susceptible to errors, which can be reduced through automation by visually classifying videos captured in a controlled environment. Video classification can be automated using techniques from computer vision and artificial intelligence, employing neural network methods. The primary techniques utilized in this study are variants of transformers: STAM, TimeSformer, and ViViT, as well as techniques using convolutions, such as ResNet3D2, ResNet(2+1)D, and CnnLstm. These techniques were compared to analyze their individual contributions. The performance was evaluated using metrics such as accuracy, precision, and recall. The TimeSformer technique demonstrated the most promising results in video classification, achieving a median accuracy of 0.729.

This study explores the use of a Siamese neural network architecture to enhance classification performance in few-shot learning scenarios, with a focus on bovine facial recognition. Traditional methodologies often require large datasets, which can significantly stress animals during data collection. In contrast, the proposed method aims to reduce the number of images needed, thereby minimizing animal stress. Systematic experiments conducted on datasets representing both full and few-shot learning scenarios revealed that the Siamese network consistently outperforms traditional models, such as ResNet101. It achieved notable improvements, with mean values increasing by over 6.5% and standard deviations decreasing by at least 0.010 compared to the ResNet101 baseline. These results highlight the Siamese network’s robustness and consistency, even in resource-constrained environments, and suggest that it offers a promising solution for enhancing model performance with fewer data and reduced animal stress, despite its slower training speed.

The aim of this paper is to evaluate the use of D-CNN (Deep Convolutional Neural Networks) algorithms to classify pig body conditions in normal or not normal conditions, with a focus on characteristics that are observed in sanitary monitoring, and were used six different algorithms to do this task. The study focused on five pig characteristics, being these caudophagy, ear hematoma, scratches on the body, redness, and natural stains (brown or black). The results of the study showed that D-CNN was effective in classifying deviations in pig body morphologies related to skin characteristics. The evaluation was conducted by analyzing the performance metrics Precision, Recall, and F-score, as well as the statistical analyses ANOVA and the Scott-Knott test. The contribution of this article is characterized by the proposal of using D-CNN networks for morphological classification in pigs, with a focus on characteristics identified in sanitary monitoring. Among the best results, the average Precision metric of 80.6\% to classify caudophagy was achieved for the InceptionResNetV2 network, indicating the potential use of this technology for the proposed task. Additionally, a new image database was created, containing various pig's distinct body characteristics, which can serve as data for future research.

The development of techniques that can be used to analyze and detect animal behavior is a crucial activity for the livestock sector, as it is possible to monitor the stress and animal welfare and contributes to decision making in the farm. Thus, the development of applications can assist breeders in making decisions to improve production performance and reduce costs, once the animal behavior is analyzed by humans and this can lead to susceptible errors and time consumption. Aggressiveness in pigs is an example of behavior that is studied to reduce its impact through animal classification and identification. However, this process is laborious and susceptible to errors, which can be reduced through automation by visually classifying videos captured in controlled environment. The captured videos can be used for training and, as a result, for classification through computer vision and artificial intelligence, employing neural network techniques. The main techniques utilized in this study are variants of transformers: STAM, TimeSformer, and ViViT, as well as techniques using convolutions, such as ResNet3D2, Resnet(2+1)D, and CnnLstm. These techniques were employed for pig video classification with the objective of identifying aggressive and non-aggressive behaviors. In this work, various techniques were compared to analyze the contribution of using transformers, in addition to the effectiveness of the convolution technique in video classification. The performance was evaluated using accuracy, precision, and recall. The TimerSformer technique showed the best results in video classification, with median accuracy of 0.729.