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Reinforcement learning (RL) is a form of machine learning that can be harnessed to develop artificial intelligence by exposing the intelligence to multiple generations of data. The study demonstrates how reply buffer reward mechanics can inform the creation of new pruning methods to improve RL efficiency.

Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. Epidemiological models help propose pandemic control strategies based on non-pharmaceutical interventions such as social distancing, curfews, and lockdowns, reducing the economic impact of these restrictions. In this research, we utilized an epidemiological Susceptible, Exposed, Infected, Recovered, Deceased (SEIRD) model – a compartmental model for virtually simulating a pandemic day by day.

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.

The challenge of accurately identifying shark species is crucial for biodiversity monitoring but is often hindered by time-consuming and labor-intensive manual methods. To address this, SharkNet, a CNN model based on AlexNet, achieved 93% accuracy in classifying shark species using a limited dataset of 1,400 images across 14 species. SharkNet offers a more efficient and reliable solution for marine biologists and conservationists in species identification and environmental monitoring.

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.

Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.

The Tor network allows individuals to secure their online identities by encrypting their traffic, however it is vulnerable to fingerprinting attacks that threaten users' online privacy. In this paper, the authors develop a new video fingerprinting model to explore how well video streaming can be fingerprinted in Tor. They found that their model could distinguish which one of 50 videos a user was hypothetically watching on the Tor network with 85% accuracy, demonstrating that video fingerprinting is a serious threat to the privacy of Tor users.

This study focused on finding more sustainable growing methods that reduce chemical fertilizer or water usage and can be used at the household level for garden plants. Metrics for healthy plant growth were height at first bloom, growing time, and survival rate. The Deep Water Culture (DWC) treatment for garden cress plants significantly increased the height at first bloom compared to the control group. For rates of surviving plants, the treatments had little effect on garden cress, but the Eggshell Grounds, Wick System, and DWC system groups outperformed the control group for marigolds.

In the field of ecology, little is known about how plant communities originate. Through the process of characterizing dunes, mounds of sand formed by the wind, and their plant communities we can get to know the physiognomy and floristic composition of the territory. Based on the hypothesis that dune flora can emerge from seed islands: holes in the sand 6 cm deep containing a mixture of seeds, broken branches of shrubbery, and rabbit feces, during spring, the authors determined the composition of 20 seed islands in the sand dunes of Concon, Chile and measured how many seeds germinated in each one.

Machine translation remains a challenging area in artificial intelligence, with neural machine translation (NMT) making significant strides over the past decade but still facing hurdles, particularly in translation quality due to the reliance on expensive bilingual training data. This study explores whether large language models (LLMs), like GPT-4, can be effectively adapted for translation tasks and outperform traditional NMT systems.