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The invention discloses a smart home system based on deep learning. The system comprises a video recording subsystem, an information processing subsystem, an intelligent control subsystem, a behaviordata analysis subsystem and an early warning subsystem. The video recording subsystem is used for continuously recording the indoor real-time condition of a user for 24 hours by utilizing a high-definition camera and machine learning; the information processing subsystem is used for carrying out image data processing and feature value extraction on the recorded video data so as to further detect abnormal behavior data; a convolutional neural network (CNN) algorithm is adopted to detect abnormal behavior data, abnormal behaviors are divided according to abnormal levels, and different early warning measures are adopted according to a preset abnormal threshold value and the abnormal levels, so that the safety of a user is guaranteed to a great extent; and the system also has a behavior data analysis function, and daily behavior data of the user is analyzed by utilizing technologies such as a big data analysis technology, artificial intelligence and machine learning, so that the system canserve different users in a more humanized manner.

Inspired by Gmail Smart Compose, In this case study, I have tried to build a Deep -- Learning Model to give predictive suggestions for text input, which will reduce repetitive typing and assist users with rational predictions.

The existential threat of COVID-19 has highlighted an acute need to develop working therapeutics against emerging health threats. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds -- so long as we can keep up with the viral threat, and access the right data. As with all new medical maladies, oftentimes the data needs time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) to ask: how can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2? Typically, data scientists use deep learning to pick out drug combinations with large existing datasets for things like cancer and cardiovascular disease, but, understandably, they can't be used for new illnesses with limited data.

Different passenger demand rates in transit stations underscore the importance of adopting operational strategies to provide a demand-responsive service. Aiming at improving passengers' travel time, the present study introduces an advanced data-driven optimization approach to determine the optimal stop-skip pattern in urban rail transit lines. In detail, first, using the time-series smart card data for an entire month, we employ a Long Short-Term Memory (LSTM) deep learning model to predict the station-level demand rates for the peak hour. This prediction is based on four preceding hours and is especially important knowing that the true demand rates of the peak hour are posterior information that can be obtained only after the peak hour operation is finished. Moreover, utilizing a real-time prediction instead of assuming fixed demand rates, allows us to account for unexpected real-time changes which can be detrimental to the subsequent analyses. Then, we integrate the output of the LSTM model as an input to an optimization model with the objective of minimizing patrons' total travel time. Considering the exponential nature of the problem, we propose an Ant Colony Optimization technique to solve the problem in a desirable amount of time. Finally, the performance of the proposed models and the solution algorithm is assessed using real case data. The results suggest that the proposed approach can enhance the performance of the service by improving both passengers' in-vehicle time as well as passengers' waiting time.

Since the earliest days of robotics, researchers have sought to build embodied agents to perform a variety of jobs, such as assistive tasks in factories [Oliff et al., 2020] or wildfire surveillance [Julian and Kochenderfer, 2019]. Following tremendous advancements in deep learning and convolutional neural networks in the past decade, researchers have been able to develop reinforcement learning (RL)-based embodied agents that interact with the real world on the basis of sensory observations. Software platforms such as OpenAI Gym [Brockman et al., 2016], Unity ML-Agents Toolkit [Juliani et al., 2018], and AI Habitat [Savva et al., 2019] have emerged to address the community's need for training and evaluating RL-based embodied agents end-to-end. Our research group was particularly intrigued by the AI Habitat platform, which offers a high-performance, photorealistic simulator, access to a sizeable library of visually-rich scanned 3D environments, and a modular software design. However, even though these platforms allow roboticists to reuse existing RL algorithms and train agents in simulators with ease, there is a critical step to using them for embodied agents which is only partially addressed: Connecting the trained agent with a real robot. Ideally, after training an RL agent in simulation one would like to take advantage of the extensive set of tools and knowledge from the robotics community to make it easy to embody that agent. One particularly popular tool from the robotics community is ROS, a robotics-focused middleware platform with extensive support for classical robotic mapping, planning and control algorithms ([mov, dwa]) as well as drivers for a wide variety of compute, sensing and actuation hardware. But ROS support for directly training an RL agent is limited, and Gazebo-- the standard simulation environment used for ROS systems-- cannot match the level of photorealism or simulation speed of tools specifically designed to train large-scale RL agents [Liang et al., 2019].

Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes. Automatic rib segmentation methods can speed up the process through rib measurement and visualization. However, prior arts mostly use in-house labeled datasets that are publicly unavailable and work on dense 3D volumes that are computationally inefficient. To address these issues, we develop a labeled rib segmentation benchmark, named \emph{RibSeg}, including 490 CT scans (11,719 individual ribs) from a public dataset. For ground truth generation, we used existing morphology-based algorithms and manually refined its results. Then, considering the sparsity of ribs in 3D volumes, we thresholded and sampled sparse voxels from the input and designed a point cloud-based baseline method for rib segmentation. The proposed method achieves state-of-the-art segmentation performance (Dice~$\approx95\%$) with significant efficiency ($10\sim40\times$ faster than prior arts). The RibSeg dataset, code, and model in PyTorch are available at https://github.com/M3DV/RibSeg.

Real-time simulation of elastic structures is essential in many applications, from computer-guided surgical interventions to interactive design in mechanical engineering. The Finite Element Method is often used as the numerical method of reference for solving the partial differential equations associated with these problems. Yet, deep learning methods have recently shown that they could represent an alternative strategy to solve physics-based problems 1,2,3. In this paper, we propose a solution to simulate hyper-elastic materials using a data-driven approach, where a neural network is trained to learn the non-linear relationship between boundary conditions and the resulting displacement field. We also introduce a method to guarantee the validity of the solution. In total, we present three contributions: an optimized data set generation algorithm based on modal analysis, a physics-informed loss function, and a Hybrid Newton-Raphson algorithm. The method is applied to two benchmarks: a cantilever beam and a propeller. The results show that our network architecture trained with a limited amount of data can predict the displacement field in less than a millisecond. The predictions on various geometries, topologies, mesh resolutions, and boundary conditions are accurate to a few micrometers for non-linear deformations of several centimeters of amplitude.

Iterative Magnitude Pruning (IMP) is a network pruning method that repeats the process of removing weights with the least magnitudes and retraining the model. When visualizing the weight matrices of language models pruned by IMP, previous research has shown that a structured pattern emerges, wherein the resulting surviving weights tend to prominently cluster in a select few rows and columns of the matrix. Though the need for further research in utilizing these structured patterns for potential performance gains has previously been indicated, it has yet to be thoroughly studied. We propose SPUR (Structured Pattern pruning Using Regularization), a novel pruning mechanism that preemptively induces structured patterns in compression by adding a regularization term to the objective function in the IMP. Our results show that SPUR can significantly preserve model performance under high sparsity settings regardless of the language or the task. Our contributions are as follows: (i) We propose SPUR, a network pruning mechanism that improves upon IMP regardless of the language or the task. (ii) We are the first to empirically verify the efficacy of "structured patterns" observed previously in pruning research. (iii) SPUR is a resource-efficient mechanism in that it does not require significant additional computations.

Recommender system has been proven to be significantly crucial in many fields and is widely used by various domains. Most of the conventional recommender systems rely on the numeric rating given by a user to reflect his opinion about a consumed item; however, these ratings are not available in many domains. As a result, a new source of information represented by the user-generated reviews is incorporated in the recommendation process to compensate for the lack of these ratings. The reviews contain prosperous and numerous information related to the whole item or a specific feature that can be extracted using the sentiment analysis field. This paper gives a comprehensive overview to help researchers who aim to work with recommender system and sentiment analysis. It includes a background of the recommender system concept, including phases, approaches, and performance metrics used in recommender systems. Then, it discusses the sentiment analysis concept and highlights the main points in the sentiment analysis, including level, approaches, and focuses on aspect-based sentiment analysis.