We present a novel and systematic method, called Superfast Selection, for selecting the "optimal split" for decision tree and feature selection algorithms over tabular data. The method speeds up split selection on a single feature by lowering the time complexity, from O(MN) (using the standard selection methods) to O(M), where M represents the number of input examples and N the number of unique values. Additionally, the need for pre-encoding, such as one-hot or integer encoding, for feature value heterogeneity is eliminated. To demonstrate the efficiency of Superfast Selection, we empower the CART algorithm by integrating Superfast Selection into it, creating what we call Ultrafast Decision Tree (UDT). This enhancement enables UDT to complete the training process with a time complexity O(KM$^2$) (K is the number of features). Additionally, the Training Only Once Tuning enables UDT to avoid the repetitive training process required to find the optimal hyper-parameter. Experiments show that the UDT can finish a single training on KDD99-10% dataset (494K examples with 41 features) within 1 second and tuning with 214.8 sets of hyper-parameters within 0.25 second on a laptop.

Running deep neural networks (DNNs) on tiny Micro-controller Units (MCUs) is challenging due to their limitations in computing, memory, and storage capacity. Fortunately, recent advances in both MCU hardware and machine learning software frameworks make it possible to run fairly complex neural networks on modern MCUs, resulting in a new field of study widely known as TinyML. However, there have been few studies to show the potential for TinyML applications in cyber physical systems (CPS). In this paper, we present DeepPicarMicro, a small self-driving RC car testbed, which runs a convolutional neural network (CNN) on a Raspberry Pi Pico MCU. We apply a state-of-the-art DNN optimization to successfully fit the well-known PilotNet CNN architecture, which was used to drive NVIDIA's real self-driving car, on the MCU. We apply a state-of-art network architecture search (NAS) approach to find further optimized networks that can effectively control the car in real-time in an end-to-end manner. From an extensive systematic experimental evaluation study, we observe an interesting relationship between the accuracy, latency, and control performance of a system. From this, we propose a joint optimization strategy that takes both accuracy and latency of a model in the network architecture search process for AI enabled CPS.

Isolation (or Isola) is a turn-based strategy board game where two players try to confine their opponent on a 7x7 checker-like board. Eventually, they can no longer make a move (thus isolating them). Each player has one piece, which they can move around like a queen in chess -- up-down, left-right, and diagonal. In the above picture, you can see from the black squares that both players have placed their pieces on various parts of the board. But as the game progressed, it shows that the yellow player still has three possible moves (up and to the right, right one square, and right two squares).

Patients in intensive care units (ICU) are acutely ill and have the highest mortality rates for hospitalized patients. Predictive models and planning system could forecast and guide interventions to prevent the hazardous deterioration of patients’ physiologies, thereby giving the opportunity of employing machine learning and inference to assist with the care of ICU patients. We report on the construction of a prediction pipeline that estimates the probability of death by inferring rates of hazard over time, based on patients’ physiological measurements. The inferred model provided the contribution of each variable and information about the influence of sets of observations on the overall risks and expected trajectories of patients.