The Vesicular Stomatitis virus (VSV) causes cases of livestock disease that occur every year in regions in Mexico. Every few years, VSV spreads northwards into the U.S. in large outbreak events affecting hundreds of livestock premises across multiple states, leading to significant economic losses due to quarantines, trade restrictions, and veterinary expenses. VSV cases are mainly driven by biting arthropod vectors from multiple genera with different ecologies, making outbreak control challenging. The sporadic nature of outbreaks and limited understanding of transmission dynamics further hinder containment efforts, reducing the effectiveness of preemptive measures. In this paper, we propose an interpretable model to elucidate the key rules governing the spread of VSV. This model employs a sparse symbolic regression model, SINDy (Sparse Identification of Nonlinear Dynamical Systems), to identify the most significant ecological variables in spread dynamics, considering both spatial and temporal factors. Since many counties did not have VSV cases during the study period, counties were clustered into 40 regions incorporating static environmental variables land cover, soil properties, livestock density, and climate data and using spatially constrained Agglomerative Clustering based on geographic adjacency, resulting in an average region size of approximately 90 counties. Ecological variables included dynamic and static variables such as temperature, humidity, wind, soil characteristics, and altitude associated with vectors and hosts (cattle, horses, and mules). The change in cases from month to month by region was modeled using two SINDy variants: a baseline model with only ecological features (Normal) and an extended model incorporating spatially derived graph features (Graph).Each alpha was chosen to minimize CV-MSE while retaining less than 11 terms. Graphical features greatly reduced model error, and the SINDy model with select graphical features had a slightly better CV-MSE score than when all graphical features were included. All models identified the infected species as important in capturing the dynamics of case differences between regions.

In this research, we propose an explanatory model that learns combinations of microbiome signatures that help predict reduced multidrug resistance (MDR) of disease-causing pathogens. A twin regression network in our model provides a means for estimating the MDR presence using microbiome samples. Further, we extend our model by using a backtracking approach that determines the effect of specific microbiome signatures on the prevalence of MDR, to identify potential beneficial combinations. Using high-throughput Illumina sequencing of the 16SrRNA gene data from 41 pastured poultry flocks in southeastern U.S. farms, the regression model identifies the factors influencing MDR prevalence in common foodborne pathogens (Salmonella, Campylobacter, and Listeria) at different stages of poultry growth.

Rapid advancements in Cyber-Physical System (CPS) capabilities have motivated farmers to deploy this ecosystem on their farms. However, there is a growing concern among users regarding the security risks associated with CPS. Especially with rising number of cyber-attacks on CPS, such as modifying sensor readings, interrupting operations, etc. Therefore, this paper describes a security surveillance framework to detect deviations in the ecosystem by incorporating a digital twin supported anomaly detection model. The reason for incorporating digital twins is that they add value by enabling real-time monitoring of connected smart farms. We pre-process the collected data from sensors deployed on the smart farm setup. The pre-processed data is fused with our smart farm ontology to populate a knowledge graph. The generated graph is further queried to extract the necessary sensor data. We utilize the extracted normal data to train the anomaly detection model. Further, we tested our model if it identifies abnormal values from sensors by simulating anomalous use case scenarios specific to our ecosystem.

We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a shared semantic/visual embedding that enables the learning of language about a wide range of real-world objects. We evaluate the efficacy of this learning by predicting the semantics of objects and comparing the performance with neural and non-neural inputs. We show that this generative approach exhibits promising results in language grounding without pre-specifying visual categories under low resource settings. Our experiments demonstrate that this approach is generalizable to multilingual, highly varied datasets.

Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language problems of varying complexity in order to analyze what methods are suitable for improving data efficiency in learning. We present a method for analyzing the complexity of data in this joint problem space, and report on how characteristics of the underlying task, along with design decisions such as feature selection and classification model, drive the results. We observe that representativeness, along with diversity, is crucial in selecting data samples.

Learning the meaning of grounded language - language that references a robot's physical environment and perceptual data - is an important and increasingly widely studied problem in robotics and human-robot interaction. However, with a few exceptions, research in robotics has focused on learning groundings for a single natural language pertaining to rich perceptual data. We present experiments on taking an existing natural language grounding system designed for English and applying it to a novel multilingual corpus of descriptions of objects paired with RGB-D perceptual data. We demonstrate that this specific approach transfers well to different languages, but also present possible design constraints to consider for grounded language learning systems intended for robots that will function in a variety of linguistic settings.

There has been substantial work in recent years on grounded language acquisition, in which language and sensor data are used to create a model relating linguistic constructs to the perceivable world. While powerful, this approach is frequently hindered by ambiguities, redundancies, and omissions found in natural language. We describe an unsupervised system that learns language by training visual classifiers, first selecting important terms from object descriptions, then automatically choosing negative examples from a paired corpus of perceptual and linguistic data. We evaluate the effectiveness of each stage as well as the system's performance on the overall learning task.