Deep Learning Approaches for Chaotic Dynamics and High-Resolution Weather Simulations in the US Midwest

Weather prediction is indispensable across various sectors, from agriculture to disaster forecasting, deeply influencing daily life and work. Recent advancement of AI foundation models for weather and climate predictions makes it possible to perform a large number of predictions in reasonable time to support timesensitive policy- and decision-making. However, the uncertainty quantification, validation, and attribution of these models have not been well explored, and the lack of knowledge can eventually hinder the improvement of their prediction accuracy and precision. Our project is embarking on a two-fold approach leveraging deep learning techniques (LSTM and Transformer) architectures. Firstly, we model the Lorenz 63 and 96 systems, crucial for grasping chaotic dynamics. By harnessing these neural networks on local computers and the RCAC GPU cluster (Gilbreth), we aim for accurate multi-step forecasts, emphasizing hyperparameter influence on model performance. This research sets a foundation for advanced, transformer-based weather predictions. Secondly, noting the dearth of high-resolution weather data in the US Midwest, including cities like Chicago, we\u27re employing Nvidia\u27s FourCastNet model. Integrated with vision transformers and Adaptive Fourier Neural Operators (AFNOs), it simulates severe Midwest weather events. Using the RCAC\u27s Gilbreth cluster and tapping into the ECMWF Reanalysis (ERA5) dataset, FourCastNet forecasts up to a week ahead in under two seconds, outpacing existing systems. This efficient model promises enhanced weather predictions and extreme event risk assessments. Our goal: simulate the potent January 23, 2016, mid-Atlantic snowstorm and contrast results with traditional forecast models

DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets

Data imbalance is a well-known issue in the field of machine learning, attributable to the cost of data collection, the difficulty of labeling, and the geographical distribution of the data. In computer vision, bias in data distribution caused by image appearance remains highly unexplored. Compared to categorical distributions using class labels, image appearance reveals complex relationships between objects beyond what class labels provide. Clustering deep perceptual features extracted from raw pixels gives a richer representation of the data. This paper presents a novel method for addressing data imbalance in machine learning. The method computes sample likelihoods based on image appearance using deep perceptual embeddings and clustering. It then uses these likelihoods to weigh samples differently during training with a proposed $\textbf{Generalized Focal Loss}$ function. This loss can be easily integrated with deep learning algorithms. Experiments validate the method's effectiveness across autonomous driving vision datasets including KITTI and nuScenes. The loss function improves state-of-the-art 3D object detection methods, achieving over $200\%$ AP gains on under-represented classes (Cyclist) in the KITTI dataset. The results demonstrate the method is generalizable, complements existing techniques, and is particularly beneficial for smaller datasets and rare classes. Code is available at: https://github.com/towardsautonomy/DatasetEquityComment: ICCV 2023 Worksho

MicroRNAs: Role in hepatitis C virus pathogenesis

AbstractHepatitis C virus (HCV) is a global health burden with an estimated 170–200 million peoples chronically infected worldwide. HCV infection remains as an independent risk factor for chronic hepatitis, liver cirrhosis, hepatocellular carcinoma, and a major reason for liver transplantation. Discovery of direct acting antiviral (DAA) drugs have shown promising results with more than 90% success rate in clearing the HCV RNA in patients, although long-term consequences remain to be evaluated. microRNAs (miRNAs) are important players in establishment of HCV infection and target crucial host cellular factors needed for productive HCV replication and augmented cell growth. Altered expression of miRNAs is involved in the pathogenesis associated with HCV infection by controlling signaling pathways such as immune response, proliferation and apoptosis. miRNA is emerging as a means of communication between various cell types inside the liver. There is likely possibility of developing circulating miRNAs as biomarkers of disease progression and can also serve as diagnostic tool with potential of early therapeutic intervention in HCV associated end stage liver disease. This review focuses on recent studies highlighting the contribution of miRNAs in HCV life cycle and their coordinated regulation in HCV mediated liver disease progression

Propagating State Uncertainty Through Trajectory Forecasting

Uncertainty pervades through the modern robotic autonomy stack, with nearly every component (e.g., sensors, detection, classification, tracking, behavior prediction) producing continuous or discrete probabilistic distributions. Trajectory forecasting, in particular, is surrounded by uncertainty as its inputs are produced by (noisy) upstream perception and its outputs are predictions that are often probabilistic for use in downstream planning. However, most trajectory forecasting methods do not account for upstream uncertainty, instead taking only the most-likely values. As a result, perceptual uncertainties are not propagated through forecasting and predictions are frequently overconfident. To address this, we present a novel method for incorporating perceptual state uncertainty in trajectory forecasting, a key component of which is a new statistical distance-based loss function which encourages predicting uncertainties that better match upstream perception. We evaluate our approach both in illustrative simulations and on large-scale, real-world data, demonstrating its efficacy in propagating perceptual state uncertainty through prediction and producing more calibrated predictions.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2022 -- 8 pages, 6 figures, 4 table

Ref-DVGO: Reflection-Aware Direct Voxel Grid Optimization for an Improved Quality-Efficiency Trade-Off in Reflective Scene Reconstruction

Neural Radiance Fields (NeRFs) have revolutionized the field of novel view synthesis, demonstrating remarkable performance. However, the modeling and rendering of reflective objects remain challenging problems. Recent methods have shown significant improvements over the baselines in handling reflective scenes, albeit at the expense of efficiency. In this work, we aim to strike a balance between efficiency and quality. To this end, we investigate an implicit-explicit approach based on conventional volume rendering to enhance the reconstruction quality and accelerate the training and rendering processes. We adopt an efficient density-based grid representation and reparameterize the reflected radiance in our pipeline. Our proposed reflection-aware approach achieves a competitive quality efficiency trade-off compared to competing methods. Based on our experimental results, we propose and discuss hypotheses regarding the factors influencing the results of density-based methods for reconstructing reflective objects. The source code is available at https://github.com/gkouros/ref-dvgo.Comment: 5 pages, 4 figures, 3 tables, ICCV TRICKY 2023 Worksho

Six senses while considering hydatid cyst as a differential for a swelling at nape of the neck: a case report

While cervical swellings usually are located in anterior midline like thyroglossal cyst, thyroid swellings, or in antero-lateral aspect of neck like cold abscess, branchial cyst, lymphangioma, cervical lymphadenopathy etc. Nape of the neck swelling is even less common with differentials including lipoma, sebaceous cyst, lymphangioma, etc. Hydatid cyst (HC) is often missed as a differential resulting in intraoperative surprises. This case report might change the mind of the readers to keep HC in back of their minds while approaching a case of swelling of the neck. Here we report a case of 15 years’ female who presented with swelling of nape of neck which on evaluation was inclining towards lipoma/epidermal cyst. With an intention for surgical exploration and excision, the patient was taken for operation, where we discovered it to be HC and the same was later confirmed by histopathology as well. Because of its rare presentation the primary diagnosis of HC is often missed out in spite of having sensitive cytology and imaging modalities. Hence, by reporting this case we intend to emphasize six facts a clinician, a radiologist and also a pathologist must consider while keeping primary HC at an unusual site as a differential diagnosis.

Genetic divergence of Chikungunya viruses in India (1963-2006) with special reference to the 2005-2006 explosive epidemic

Re-emergence of Chikungunya (CHIK), caused by CHIK virus, was recorded in India during 2005-2006 after a gap of 32 years, causing 1.3 million cases in 13 states. Several islands of the Indian Ocean reported similar outbreaks in the same period. These outbreaks were attributed to the African genotype of CHIK virus. To examine relatedness of the Indian isolates (IND-06) with Reunion Island isolates (RU), full-genome sequences of five CHIK virus isolates representative of different Indian states were determined. In addition, an isolate obtained from mosquitoes in the year 2000 (Yawat-2000), identified as being of the African genotype, and two older strains isolated in 1963 and 1973 (of the Asian genotype), were sequenced. The IND-06 isolates shared 99.9 % nucleotide identity with RU isolates, confirming involvement of the same strain in these outbreaks. The IND-06 isolates shared 98.2 % identity with the Yawat-2000 isolate. Of two crucial substitutions reported for RU isolates in the E1 region, M269V was noted in the Yawat-2000 and IND-06 isolates, whereas D284E was seen only in the IND-06 isolates. The A226V shift observed with the progression of the epidemic in Reunion Island, probably associated with adaptation to the mosquito vector, was absent in all of the Indian isolates. Three unique substitutions were noted in the IND-06 isolates: two (T128K and T376M) in the Nsp1 region and one (P23S) in the capsid protein. The two Asian strains showed 99.4 % nucleotide identity to each other, indicating relative stability of the virus. No evidence of recombination of the Asian and African genotypes, or of positive selection was observed. The results may help in understanding the association, if any, of the unique mutations with the explosive nature of the CHIK outbreak