Using RL to Predict Crypto and the effect of COVID-19

The stock market has always been subject to speculation. Oftentimes it is volatile but it is still an important mode of investment for many people. It carries great weight and is reflective of the performance of an economy. In macroeconomic courses in college, students are taught that in times of crises, CASH is KING, not assets in the market. Naturally, one would assume the COVID-19 pandemic would cause people to flock to holding monetary assets in cash. However, recently, as a result of the pandemic there has been a rise in the demand for cryptocurrencies. This might be because people expected the government to inject money into the economy in order to stimulate demand, and therefore people expected a rise in inflation and a fall in the value of money. So they took to a form of currency that doesn’t fall under governmental control. Be it Bitcoin or Ethereum or even the rise in ”meme coins” such as DOGE. There has been a shift in how economics predicts how people behave

A NLP Approach to Automating the Generation of Surveys for Market Research

Market Research is vital but includes activities that are often laborious and time consuming. Survey questionnaires are one possible output of the process and market researchers spend a lot of time manually developing questions for focus groups. The proposed research aims to develop a software prototype that utilizes Natural Language Processing (NLP) to automate the process of generating survey questions for market research. The software uses a pre-trained Open AI language model to generate multiple choice survey questions based on a given product prompt, send it to a targeted email list, and also provides a real-time analysis of the responses stored in a SQL database. The idea is for market researchers to provide minimal information and have the system propose to them options for potential questions for the focus group. The project involves creating a web-based software prototype that features user-friendly interfaces for seamless interaction between companies and users. The expected outcome of the project is to provide a more efficient and effective method for companies to generate meaningful survey questions for market research

A Statistical View of Column Subset Selection

We consider the problem of selecting a small subset of representative variables from a large dataset. In the computer science literature, this dimensionality reduction problem is typically formalized as Column Subset Selection (CSS). Meanwhile, the typical statistical formalization is to find an information-maximizing set of Principal Variables. This paper shows that these two approaches are equivalent, and moreover, both can be viewed as maximum likelihood estimation within a certain semi-parametric model. Using these connections, we show how to efficiently (1) perform CSS using only summary statistics from the original dataset; (2) perform CSS in the presence of missing and/or censored data; and (3) select the subset size for CSS in a hypothesis testing framework

HEROES: Unreal Engine-based Human and Emergency Robot Operation Education System

Training and preparing first responders and humanitarian robots for Mass Casualty Incidents (MCIs) often poses a challenge owing to the lack of realistic and easily accessible test facilities. While such facilities can offer realistic scenarios post an MCI that can serve training and educational purposes for first responders and humanitarian robots, they are often hard to access owing to logistical constraints. To overcome this challenge, we present HEROES- a versatile Unreal Engine simulator for designing novel training simulations for humans and emergency robots for such urban search and rescue operations. The proposed HEROES simulator is capable of generating synthetic datasets for machine learning pipelines that are used for training robot navigation. This work addresses the necessity for a comprehensive training platform in the robotics community, ensuring pragmatic and efficient preparation for real-world emergency scenarios. The strengths of our simulator lie in its adaptability, scalability, and ability to facilitate collaboration between robot developers and first responders, fostering synergy in developing effective strategies for search and rescue operations in MCIs. We conducted a preliminary user study with an 81% positive response supporting the ability of HEROES to generate sufficiently varied environments, and a 78% positive response affirming the usefulness of the simulation environment of HEROES

#1 - Alu-Derived Orthologous Chromosome Classification for the Primate Order

Orthologous chromosomes between any family of related species have been difficult thus far to obtain, often requiring substantial biochemical testing and computationally-intensive genomic analysis. By employing computational strategies on repeated non-coding DNA, numerous advantages to accurately determining orthologous chromosomes between species can be ascertained. Throughout the primate genome, the Alu repeated element covers 10% of the genome among higher order primates, spanning across each chromosome. These non-protein-coding sequences replicate themselves repeatedly, with each iteration allowed to mutate more than their protein-coding counterparts. Therefore, upon examining the genetic sequences of such “junk” DNA, increasingly specific distinctions can be made between any two compared primate genomes. We propose a novel strategy of matching known Alu repeats by subfamily between two species, thereby ascertaining the not only the frequency of specific Alu elements conserved, but also which where each matched pair is located on the species’ chromosomes. By collecting Alu-identified primate genomes the University of California Santa Cruz Table Browser, this methodology was applied to 12 species-specific genomes. After comparing the Alu elements between each of the primates and subsequent frequency analysis, we were able to accurately highlight what chromosomes were conserved across members of the Order Primate. In addition, we were able to use our alignment with currently accepted literature to produce orthologous chromosomes for numerous species previously not compared against one another. In conclusion, we propose a far less computationally and resource intensive solution to determining conserved chromosomal relationships among primates

#11 - Sequence Analysis of Alu Repeated Elements for Primate Phylogenetic Tree Construction

Phylogenetic tree construction can be a particularly challenging and time-intensive process. This study employs a novel computational approach to phylogenetic tree construction, using the Alu repeating element, a SINE. Repetitive elements including Short and Long Interspersed Nuclear Elements (SINEs/LINEs) have successfully been applied as accurate tools for phylogenetic analysis, as they are predominately unidirectional and homoplasy-free. However, previous analysis of phylogenetic relationships using these repeating elements has been limited to a small number of isolated repeats among relatively few organisms. As a highly repetitive sequence, the Alu element and its associated subfamilies can provide detailed analysis on evolutionary divergence among species in the Order Primates. This study identified shared sequences as Alu repeating elements that were conserved in both location and base-pair sequence between the primate genomes of interest. These shared sequences, derived from the Genome Library at the University of California San Diego, were analyzed to construct individual phylogenetic trees for each of the 49 Alu subfamilies. As this method solely requires the sequence analysis of available primate genomes, this serves as a cheaper and more time-efficient approach to phylogenetic tree construction for the Order Primates relative to biochemical and anatomical analysis