We propose a new strategy for blackjack, BB-Player, which leverages Hidden Markov Models (HMMs) in online planning to sample a normalized predicted deck distribution for a partially-informed distance heuristic. Viterbi learning is applied to the most-likely sampled future sequence in each game state to generate transition and emission matrices for this upcoming sequence. These are then iteratively updated with each observed game on a given deck. Ultimately, this process informs a heuristic to estimate the true symbolic distance left, which allows BB-Player to determine the action with the highest likelihood of winning (by opponent bust or blackjack) and not going bust. We benchmark this strategy against six common card counting strategies from three separate levels of difficulty and a randomized action strategy. On average, BB-Player is observed to beat card-counting strategies in win optimality, attaining a 30.00% expected win percentage, though it falls short of beating state-of-the-art methods.

The HLA, Human Leukocyte Antigens, are encoded by a polymorphic set of genes where even a single base change can impact the function of the body’s immune response to foreign antigens [1]. Although many methods exist to type these alleles using whole-genome sequencing (WGS), few can use RNA sequencing (RNA-seq) to show the functional expression of the alleles with its inconsistency in coverage, and none of these allow for novel allele discovery. We present an approach using partially ordered graphs to project sequenced data onto the known alleles allowing for accurate and efficient typing of the HLA genes with flexibility for discovering new alleles and tolerance for poor sequence quality. This graph-guided approach to assembling and typing the HLA genes from RNA-seq has applications throughout precision medicine, facilitating the prevention and treatment of autoimmune diseases where allele expression can change. It is also a necessary step for determining donors for organ transplants with the least likelihood of rejection. This novel approach of combining database matching with partially ordered graphs for assembling genetic sequences of RNA-seq data could be applied towards typing other alleles.

Over the years, advances in research have continued to decrease the size of computers from the size of<br/>a room to a small device that could fit in one’s palm. However, if an application does not require extensive<br/>computation power nor accessories such as a screen, the corresponding machine could be microscopic,<br/>only a few nanometers big. Researchers at MIT have successfully created Syncells, which are micro-<br/>scale robots with limited computation power and memory that can communicate locally to achieve<br/>complex collective tasks. In order to control these Syncells for a desired outcome, they must each run a<br/>simple distributed algorithm. As they are only capable of local communication, Syncells cannot receive<br/>commands from a control center, so their algorithms cannot be centralized. In this work, we created a<br/>distributed algorithm that each Syncell can execute so that the system of Syncells is able to find and<br/>converge to a specific target within the environment. The most direct applications of this problem are in<br/>medicine. Such a system could be used as a safer alternative to invasive surgery or could be used to treat<br/>internal bleeding or tumors. We tested and analyzed our algorithm through simulation and visualization<br/>in Python. Overall, our algorithm successfully caused the system of particles to converge on a specific<br/>target present within the environment.