Analyzing and Optimizing the Energy Operations on Campus

The Energy Dashboard is a way to track the University of Mississippi\u27s energy operations and find ways to optimize them. Data from 200 meters on campus was used to create the dashboard and perform some research. The insights obtained from the data raised some important questions for the University management

Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales

The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user's musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.Comment: Author pre-print version, 20 pages, 6 figures, 4 table

Eight years’ experience in mobile teleophthalmology for diabetic retinopathy screening

Background: Screening for diabetic retinopathy in the community without compromising the routine workof ophthalmologists at hospitals is the essence of teleophthalmology. This study was aimed at investigating theefficacy of teleophthalmology practice for screening diabetic retinopathy from 2012 to 2020. It was also aimed at comparing the 2-year prevalence of camps organized by a district hospital in South India, as well as the footfall, reporting, follow-up, patient response, and diagnostic efficacy at these camps. Methods: All patients with diabetes and unexplained vision deterioration attending the mobile camp unitsunderwent non-dilated fundus photography. Patients underwent teleconsultation with the ophthalmologist atthe district hospital, and those requiring intervention were called to the district hospital. Trends were studiedfor the number of patients reporting to the hospital. Patient satisfaction was recorded based on a questionnaire. Results: A total of 682 camps were held over 8 years, and 30 230 patients were examined. Teleconsultationwas done for 12 157 (40.21%) patients. Patients requiring further investigations, intervention for diabeticretinopathy, or further management of other ocular pathologies were urgently referred to the district hospital(n= 3293 [10.89%] of 30 230 examined patients). The severity and presence of clinically significant macularedema increased significantly with an increased duration of diabetes mellitus (P < 0.001). The percentage ofteleconsultations showed an increasing trend over the years (P = 0.001). Similarly, considering trends of patientsreporting to the hospital, the attrition rate decreased over the years (P < 0.05). A total of 10 974 of 12 157(90.27%) patients who underwent teleophthalmic consultation were satisfied with the service. Conclusions: Teleconsultations over the years showed an increasing trend, and the attrition rate decreased overthe years. Teleophthalmology is achieving success in providing high-quality service, easy access to care, and inincreasing patient satisfaction. Future studies on the role of teleophthalmology for other leading preventablecauses of blindness seem possible and necessary

Learning Neuro-symbolic Programs for Language Guided Robot Manipulation

Given a natural language instruction and an input scene, our goal is to train a model to output a manipulation program that can be executed by the robot. Prior approaches for this task possess one of the following limitations: (i) rely on hand-coded symbols for concepts limiting generalization beyond those seen during training [1] (ii) infer action sequences from instructions but require dense sub-goal supervision [2] or (iii) lack semantics required for deeper object-centric reasoning inherent in interpreting complex instructions [3]. In contrast, our approach can handle linguistic as well as perceptual variations, end-to-end trainable and requires no intermediate supervision. The proposed model uses symbolic reasoning constructs that operate on a latent neural object-centric representation, allowing for deeper reasoning over the input scene. Central to our approach is a modular structure consisting of a hierarchical instruction parser and an action simulator to learn disentangled action representations. Our experiments on a simulated environment with a 7-DOF manipulator, consisting of instructions with varying number of steps and scenes with different number of objects, demonstrate that our model is robust to such variations and significantly outperforms baselines, particularly in the generalization settings. The code, dataset and experiment videos are available at https://nsrmp.github.ioComment: International Conference on Robotics and Automation (ICRA), 202

Unilateral canine extraction for correction of malocclusion in a hyperdivergent case – (A case report)

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Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration.

Genome-wide association studies and other discovery genetics methods provide a means to identify previously unknown biological mechanisms underlying behavioral disorders that may point to new therapeutic avenues, augment diagnostic tools, and yield a deeper understanding of the biology of psychiatric conditions. Recent advances in psychiatric genetics have been made possible through large-scale collaborative efforts. These studies have begun to unearth many novel genetic variants associated with psychiatric disorders and behavioral traits in human populations. Significant challenges remain in characterizing the resulting disease-associated genetic variants and prioritizing functional follow-up to make them useful for mechanistic understanding and development of therapeutics. Model organism research has generated extensive genomic data that can provide insight into the neurobiological mechanisms of variant action, but a cohesive effort must be made to establish which aspects of the biological modulation of behavioral traits are evolutionarily conserved across species. Scalable computing, new data integration strategies, and advanced analysis methods outlined in this review provide a framework to efficiently harness model organism data in support of clinically relevant psychiatric phenotypes

BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs

Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThing Explorer is distributed as a lightweight application that dynamically retrieves information at query time. More information can be found at https://explorer.biothings.io, and code is available at https://github.com/biothings/biothings_explorer