Florida Polytechnic University

About: Florida Polytechnic University is a education organization based out in Lakeland, Florida, United States. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 302 authors who have published 538 publications receiving 6549 citations. The organization is also known as: Florida Poly.

Mitigating medical alarm fatigue with cognitive heuristics

Abstract: Automated patient monitoring systems suffer from several design problems. Among them, alarm fatigue is one of the most critical issues, as evidenced by the Sentinel Event Alert that The Joint Commission - the U.S. hospital-accrediting body - recently issued. In this study, we explore fast-and-frugal heuristics that may be used to prioritize patient alarms, while continuing to monitor patient physiological state. By using a combination of human factors methodologies and the theory of Distributed Cognition (DCog), we studied alarm fatigue and its relationship to the underlying hospital systems. We identified three specific factors that we envision to be helpful for clinical personnel: ventilator presence, number of intravenous drips, and number of medications. We discuss their application in daily hospital operation.

Photo-spin-voltaic effect in PtMn/Y3Fe5O12 thin films.

Abstract: The photo-spin-voltaic effect is revealed by the presence of a spin voltage generated by photons when a non-magnetic metal (e.g., Pt) is in close proximity to a ferrimagnetic insulator (e.g., Y3Fe5O12 (YIG)). This is attributed to the excited electrons and holes diffusing from the proximized layer near the interface to the metallic surface. By using a dual-ion-beam sputtering deposition technique, a metallic PtMn layer was deposited on YIG /Gd3Ga5O12 (GGG) (111) substrates. We report on the photo-induced-spin voltaic effect in a PtMn/YIG/GGG heterostructure. The sign of the photo-generated voltage was found to switch with magnetic field polarity and its intensity to decrease with increasing PtMn thickness. This indicates that spin-polarized electrons are confined near the interface in the metal. Photo-excitation of these carriers, together with spin-orbit coupling with Pt atoms, is at the origin of the measured transverse voltage. The design may find applications in antiferromagnetic spintronics.

LSHvec: a vector representation of DNA sequences using locality sensitive hashing and FastText word embeddings

Abstract: Drawing from the analogy between natural language and "genomic sequence language", we explored the applicability of word embeddings in natural language processing (NLP) to represent DNA reads in Metagenomics studies. Here, k-mer is the equivalent concept of word in NLP and it has been widely used in analyzing sequence data. However, directly replacing word embedding with k-mer embedding is problematic due to two reasons: First, the number of distinct k-mers is far more than the number of different words in our vocabulary, making the model too huge to be stored in memory. Second, sequencing errors create lots of novel k-mers (noise), which significantly degrade model performance. In this work, we introduce LSHvec, a model that leverages Locality Sensitive Hashing (LSH) for k-mer encoding to overcome these challenges. After k-mers are LSH encoded, we adopt the skip-gram with negative sampling to learn k-mer embeddings. Experiments on metagenomic datasets with labels demonstrate that k-mer encoding using LSH can not only accelerate training time and reduce the memory requirements to store the model, but also achieve higher accuracy than using alternative encoding methods. We validate that LSHvec is robust on reads with high sequencing error rates and works well with any sequencing technologies. In addition, the trained low-dimensional k-mer embeddings can be potentially used for accurate metagenomic read clustering and taxonomic classification. Finally, We demonstrate the unprecedented capability of LSHvec by participating in the second round of CAMI challenges and show that LSHvec is able to handle metagenome datasets that exceed Terabytes in size through distributed training across multiple nodes.

Phychoacoustic Masking of Delta and Time -Difference Cepstrum Features for Deception Detection

Abstract: This paper presents the test results of analyzing mel frequency cepstrum coefficient (MFCC), delta and difference cepstrum features to detect and distinguish the truthful and deceptive speech. The features are extracted based on the psychoacoustic masking property of human speech and how it is perceived. Truthful and deceptive speeches are preset based off a guilty male speaker in police custody. Delta cepstrum and time-difference cepstrum features at triangular critical bands filter and a neural network show the distinctions that determine whether an utterance is truthful or deceptive. In this paper, we analyze the extracted MFCC, delta cepstrum and time-difference cepstrum features to see how stress in speech accurately conveys human speech emotion and deception. Finally, we feed the data into an artificial neural network model to test out the results.