Research towards creating systems for automatic grading of student answers to quiz and exam questions in educational settings has been ongoing since 1966. Over the years, the problem was divided into many categories. Among them, grading text answers were divided into short answer grading, and essay grading. The goal of this work was to develop an ML-based short answer grading system. I hence built a system which uses finetuning on Roberta Large Model pretrained on STS benchmark dataset and have also created an interface to show the production readiness of the system. I evaluated the performance of the system on the Mohler extended dataset and SciEntsBank Dataset. The developed system achieved a Pearsons Correlation of 0.82 and RMSE of 0.7 on the Mohler Dataset which beats the SOTA performance on this dataset which is correlation of 0.805 and RMSE of 0.793. Additionally, Pearsons Correlation of 0.79 and RMSE of 0.56 was achieved on the SciEntsBank Dataset, which only reconfirms the robustness of the system. A few observations during achieving these results included usage of batch size of 1 produced better results than using batch size of 16 or 32 and using huber loss as loss function performed well on this regression task. The system was tried and tested on train and validation splits using various random seeds and still has been tweaked to achieve a minimum of 0.76 of correlation and a maximum 0.15 (out of 1) RMSE on any dataset.

Complication risk profiling is a key challenge in the healthcare domain due to the complex interaction between heterogeneous entities (e.g., visit, disease, medication) in clinical data. With the availability of real-world clinical data such as electronic health records and insurance claims, many deep learning methods are proposed for complication risk profiling. However, these existing methods face two open challenges. First, data heterogeneity relates to those methods leveraging clinical data from a single view only while the data can be considered from multiple views (e.g., sequence of clinical visits, set of clinical features). Second, generalized prediction relates to most of those methods focusing on single-task learning, whereas each complication onset is predicted independently, leading to suboptimal models. We propose a multi-view multi-task network (MuViTaNet) for predicting the onset of multiple complications to tackle these issues. In particular, MuViTaNet complements patient representation by using a multi-view encoder to effectively extract information by considering clinical data as both sequences of clinical visits and sets of clinical features. In addition, it leverages additional information from both related labeled and unlabeled datasets to generate more generalized representations by using a new multi-task learning scheme for making more accurate predictions. The experimental results show that MuViTaNet outperforms existing methods for profiling the development of cardiac complications in breast cancer survivors. Furthermore, thanks to its multi-view multi-task architecture, MuViTaNet also provides an effective mechanism for interpreting its predictions in multiple perspectives, thereby helping clinicians discover the underlying mechanism triggering the onset and for making better clinical treatments in real-world scenarios.

Convolutional neural networks (CNNs) have recently achieved remarkable success in automatically identifying organs or lesions on 3D medical images. Meanwhile, vision transformer networks have exhibited exceptional performance in 2D image classification tasks. Compared with CNNs, transformer networks have an obvious advantage of extracting long-range features due to their self-attention algorithm. Therefore, in this paper we present a CNN-Transformer combined model called BiTr-Unet for brain tumor segmentation on multi-modal MRI scans. The proposed BiTr-Unet achieves good performance on the BraTS 2021 validation dataset with mean Dice score 0.9076, 0.8392 and 0.8231, and mean Hausdorff distance 4.5322, 13.4592 and 14.9963 for the whole tumor, tumor core, and enhancing tumor, respectively.

You will establish a research line at the interface between photogrammetry and deep learning developing innovative solutions. You will explore how to embed deep learning algorithms in the image orientation and 3D reconstruction tasks and their possible fusion with semantic segmentation. Your goal will be to implement reliable and robust solutions to be applied in several applications, with specific regard to the use of very high-resolution data. Your background is in deep learning, photogrammetry and computer vision and must be confirmed by an excellent track of record. Besides your research activities, you may be occasionally involved in educational activities.

The first step to countering adversarial attacks is to understand the different types and the weak spots of the machine-learning pipeline. The threat of adversarial attacks has become one of the important concerns of machine-learning (ML) applications. Adversarial attacks are different from other types of security threats that programmers are used to dealing with. Machine learning needs new perspectives on security. Developers must learn to adjust their software development practices according to the emerging threats of deep learning as it becomes an increasingly important part of their applications.

Yellowstone Caldera Chronicles is a weekly column written by scientists and collaborators of the Yellowstone Volcano Observatory. This week's contribution is from Keith Koper, director of the University of Utah Seismograph Stations and professor at the University of Utah Department of Geology and Geophysics, and Alysha Armstrong, graduate student at the University of Utah Department of Geology and Geophysics. While the automated monitoring system currently in place for detecting and processing earthquakes in Yellowstone National Park works well most of the time, its solutions need to be reviewed and refined by a seismic analyst. This means that the larger earthquakes -- generally over M1-- get most of the attention, and smaller earthquakes, which are harder to locate, are not always processed. The current system can also struggle in situations like earthquake swarms, where there is a lot of seismicity close together in space and time.

When people first get into contact with artificial intelligence, they tend to focus on algorithms. How they recognize pictures of cats and dogs, learn to play chess, or compose music and write poetry amaze people because it feels like magic. There are many kinds of algorithms, but most newsworthy milestones are generated by just one type -- that the media loves so much -- neural networks. People care about what deep neural networks are capable of, but they forget these "black-box" models are nothing more than empty casings without the large datasets that train them into becoming powerful predictors and classifiers. Practice makes perfect and it's no different for deep neural nets.

SAN LUIS OBISPO –– A pair of Cal Poly professors and a team of students have used artificial intelligence to train a computer to quickly assess wildfire damage -- potentially improving response time for efforts to recover from major wildfires. Accurate and timely damage assessment has become critical for response and recovery as the threat of wildfires increases. Damage assessment reports inform first responders' strategies, affect residents' ability to file insurance claims, and guide state and federal authorities' plans for future disaster relief and financial aid. To date, most wildfire event inspectors must personally visit affected areas and manually document the severity of building damage, a process that often takes weeks. Social sciences Assistant Professor Andrew Fricker, computer science Assistant Professor Jonathan Ventura, visiting Cal Poly undergraduate student Gustave Rousselet, and a team of Stanford doctoral students sought to streamline this process with artificial intelligence (AI) deep learning.

Artificial Intelligence and Machine Learning are a lot of trends and stressed terms nowadays. Machine Learning (ML) is a subset of Artificial Intelligence. ML is a technology of designing and using algorithms that can probably analyze topics from past instances. ML can be accomplished to remedy hard issues like credit card fraud detection, permit self-driving cars, and face detection and recognition. ML uses complex algorithms that constantly iterate over massive data sets, reading the patterns in data and facilitating machines to answer to unique situations for which they've now no longer been explicitly programmed.

"PonderNet tries to find a sweet spot between training prediction accuracy, computational cost, and generalisation." As humans, we think many times before speaking our thoughts out loud. But, can we expect the same from machines? Last week, Deepmind introduced PonderNet, a new algorithm that allows artificial neural networks to learn to think for a while before answering. Halting to think is something very familiar to humans.