Listening to the World Improves Speech Command Recognition

We study transfer learning in convolutional network architectures applied to the task of recognizing audio, such as environmental sound events and speech commands. Our key finding is that not only is it possible to transfer representations from an unrelated task like environmental sound classification to a voice-focused task like speech command recognition, but also that doing so improves accuracies significantly. We also investigate the effect of increased model capacity for transfer learning audio, by first validating known results from the field of Computer Vision of achieving better accuracies with increasingly deeper networks on two audio datasets: UrbanSound8k and the newly released Google Speech Commands dataset. Then we propose a simple multiscale input representation using dilated convolutions and show that it is able to aggregate larger contexts and increase classification performance. Further, the models trained using a combination of transfer learning and multiscale input representations need only 40% of the training data to achieve similar accuracies as a freshly trained model with 100% of the training data. Finally, we demonstrate a positive interaction effect for the multiscale input and transfer learning, making a case for the joint application of the two techniques.Comment: 8 page

An Automatic Dialog System for Student Advising

Automatic dialog systems are an implementation of natural language processing theory with the goal of allowing the use of natural sentences to communicate with a computer system. The general purpose of this project was to design and implement an automatic dialog system for augmenting university student advising. Student advising is a relatively narrow domain of possible questions and responses. The automatic dialog system focused on prescriptive advising rather than developmental advising to further narrow the domain to scheduling and registration matters. A student advisor was interviewed and recorded during a mock advising session in order to model the interaction between students and their advisors. The phrases and advising information have been encoded using Artificial Intelligence Markup Language (AIML) and the dialog system has been implemented in the programming language Python. Future work includes expanding the database to include information directly from the Minnesota State University, Mankato student registration system as well as to implement a spoken language interface

Cross Amplification of Microsatellite loci Developed for \u3ci\u3eAtractosteus spatula\u3c/i\u3e in \u3ci\u3eAtractosteus tropicus\u3c/i\u3e

Due to recent population declines in tropical gar (Atractosteus tropicus), a greater understanding of its population structure is needed. A key step in gaining this understanding is the development of microsatellite loci for use in this species. For this purpose, 33 microsatellite loci from alligator gar (A. spatula) were screened in 52 individuals from a population in Zanjón del Chino, El Salvador. Twenty-five of these loci successfully amplified in this species, and 9 of those loci were polymorphic in this population. These loci should provide a useful tool for genotyping A. tropicus, both in studying existing wild populations and in monitoring genetic diversity inaquaculture

Genetic Diversity of Near Genome-Wide Hepatitis C Virus Sequences during Chronic Infection: Evidence for Protein Structural Conservation Over Time

Infection with hepatitis C virus (HCV) is one of the leading causes of chronic hepatitis, liver cirrhosis and end-stage liver disease worldwide. The genetics of HCV infection in humans and the disease course of chronic hepatitis C are both remarkably variable. Although the response to interferon treatment is largely dependent on HCV genotypes, whether or not a relationship exists between HCV genome variability and clinical course of hepatitis C disease still remains unknown. To more thoroughly understand HCV genome evolution over time in association with disease course, near genome-wide HCV genomes present in 9 chronically infected participants over 83 total study years were sequenced. Overall, within HCV genomes, the number of synonymous substitutions per synonymous site (dS) significantly exceeded the number of non-synonymous substitutions per site (dN). Although both dS and dN significantly increased with duration of chronic infection, there was a highly significant decrease in dN/dS ratio in HCV genomes over time. These results indicate that purifying selection acted to conserve viral protein structure despite persistence of high level of nucleotide mutagenesis inherent to HCV replication. Based on liver biopsy fibrosis scores, HCV genomes from participants with advanced fibrosis had significantly greater dS values and lower dN/dS ratios compared to participants with mild liver disease. Over time, viral genomes from participants with mild disease had significantly greater annual changes in dN, along with higher dN/dS ratios, compared to participants with advanced fibrosis. Yearly amino acid variations in the HCV p7, NS2, NS3 and NS5B genes were all significantly lower in participants with severe versus mild disease, suggesting possible pathogenic importance of protein structural conservation for these viral gene products

BVRI Light Curves for 29 Type Ia Supernovae

BVRI light curves are presented for 27 Type Ia supernovae discovered during the course of the Calan/Tololo Survey and for two other SNe Ia observed during the same period. Estimates of the maximum light magnitudes in the B, V, and I bands and the initial decline rate parameter m15(B) are also given.Comment: 17 pages, figures and tables are not included (contact first author if needed), to appear in the Astronomical Journa

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

The first 20 months of the COVID-19 pandemic: Mortality, intubation and ICU rates among 104,590 patients hospitalized at 21 United States health systems

Main objective There is limited information on how patient outcomes have changed during the COVID-19 pandemic. This study characterizes changes in mortality, intubation, and ICU admission rates during the first 20 months of the pandemic. Study design and methods University of Wisconsin researchers collected and harmonized electronic health record data from 1.1 million COVID-19 patients across 21 United States health systems from February 2020 through September 2021. The analysis comprised data from 104,590 adult hospitalized COVID-19 patients. Inclusion criteria for the analysis were: (1) age 18 years or older; (2) COVID-19 ICD-10 diagnosis during hospitalization and/or a positive COVID-19 PCR test in a 14-day window (+/- 7 days of hospital admission); and (3) health system contact prior to COVID-19 hospitalization. Outcomes assessed were: (1) mortality (primary), (2) endotracheal intubation, and (3) ICU admission. Results and significance The 104,590 hospitalized participants had a mean age of 61.7 years and were 50.4% female, 24% Black, and 56.8% White. Overall risk-standardized mortality (adjusted for age, sex, race, ethnicity, body mass index, insurance status and medical comorbidities) declined from 16% of hospitalized COVID-19 patients (95% CI: 16% to 17%) early in the pandemic (February-April 2020) to 9% (CI: 9% to 10%) later (July-September 2021). Among subpopulations, males (vs. females), those on Medicare (vs. those on commercial insurance), the severely obese (vs. normal weight), and those aged 60 and older (vs. younger individuals) had especially high mortality rates both early and late in the pandemic. ICU admission and intubation rates also declined across these 20 months. Conclusions Mortality, intubation, and ICU admission rates improved markedly over the first 20 months of the pandemic among adult hospitalized COVID-19 patients although gains varied by subpopulation. These data provide important information on the course of COVID-19 and identify hospitalized patient groups at heightened risk for negative outcomes. Trial registration ClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528)

An Automatic Dialog System for Student Advising

Automatic dialog systems are an implementation of natural language processing theory with the goal of allowing the use of natural sentences to communicate with a computer system. The general purpose of this project was to design and implement an automatic dialog system for augmenting university student advising. Student advising is a relatively narrow domain of possible questions and responses. The automatic dialog system focused on prescriptive advising rather than developmental advising to further narrow the domain to scheduling and registration matters. Student advisors were interviewed and recorded during mock advising sessions in order to best model the interaction between students and their advisors. The phrases and advising information have been encoded using AIML (Artificial Intelligence Markup Language) and the dialog system has been implemented in the programming language Python. To test it for quality, the system was used by students and advisors. Future work includes expanding the database to include information directly from the Minnesota State University, Mankato student registration system as well as to implement a spoken language interface

A Dialog System to Supplement Student Advising

Dialog systems are an implementation of natural language processing theory which allows use of natural sentences for communication with a computer system. The purpose of this project was to design and implement a dialog system to augment university student advising, which is a relatively narrow domain of possible questions and responses. To further narrow the domain the dialog system focused on prescriptive advising rather than developmental advising. To better understand the domain, a professional advisor was recorded during a mock advising session to model student-advisor interaction. The natural phrases from the mock advising session were transcribed. The session was analyzed to develop the conversation sub-goals to gain data for organizing dialog. Phrases were encoded using Artificial Intelligence Markup Language (AIML). The system included a state manger to facilitate the conversation and AIML interaction. The purpose of the state manager is to control the flow of the conversation and ensure that the sub-goals are satisfied. A rule-based expert system was implemented to process the conversation results and make advising decisions, generating sentences and a visual representation of a proposed schedule to return to the student. The system was evaluated by an advisor for correctness and tested on several students for feedback on ease of use and likelihood of using the system. Future work will include expanding the conversational ability and the knowledge domain so that the system can be useful at other universities