The Ursinus Weekly, June 8, 1970

289 students graduate Ursinus today: Brooks Hays selected speaker at graduation • 1970 Honorary Degree recipients • Ursinus College class of 1970 • Focus: Bob Keehn • Departmental honorshttps://digitalcommons.ursinus.edu/weekly/1162/thumbnail.jp

Challenges, Opportunities, and Progress in Librarianship since the Great Recession: An Examination of Graduate ALA-Accredited Curricula, the Workforce, and Professional Development Trends

The article asserts that practical experience is a critical and too often neglected component in Master of Library and Information Science/Library and Information Studies (MLIS/LIS) graduate programs. It advocates for the necessity of enhancing professional development initiatives such as mandating internships as a condition of graduation, which will offer students a significantly greater chance of successfully transitioning from graduate study to professional employment. It investigates U.S. workforce trends from 2008-2020, which reveal the competitiveness of the field and the difficulties experienced by graduates attempting to obtain employment in the absence of concrete, hands-on, library-based experience

Towards Private Biometric Authentication and Identification

Handwriting and speech are important parts of our everyday lives. Handwriting recognition is the task that allows the recognizing of written text, whether it be letters, words or equations, from given data. When analyzing handwriting, we can analyze static images or the recording of written text through sensors. Handwriting recognition algorithms can be used in many applications, including signature verification, electronic document processing, as well as e-security and e-health related tasks. The OnHW datasets consists of a set of datasets which, through the use of various sensors, captures the writing of characters, words, symbols and equations, recorded in the form of multivariate time series. We begin by developing character recognition models, targeting letters (and later symbols), trained and tested using the OnHW-chars dataset (and later the split OnHW-equations dataset). Our models were able to improve upon the accuracy of the previous best results on both datasets explored. Using our machine learning (ML) models, we provide 11.3%-23.56% improvements over the previous best ML models. Using deep learning (DL), as well as ensemble techniques, we were able to improve on the best previous models by 3.08%-7.01%. In addition to the accuracy improvements, we aim to provide some level of explainability, using a specialized version of LIME for time series data. This explanation helps provide some rationale for why the models make sense for the data, as well as why ensemble methods may be useful to improve accuracy rates for this task. To verify the robustness of our models trained over the OnHW-chars dataset, we trained our DL models using the same model parameters over a more recently published OnHW-equations dataset. Our DL models with ensemble learning provide 0.05%-4.75% improvements over the previous best DL models. While the character recognition task has many applications, when using it to provide a service, it is important to consider user privacy since handwriting is biometric data and contains private information. Next, we design a framework that uses multiparty computation (MPC) to provide users with privacy over their handwritten data, when providing a service for character recognition. We then implement the framework using the models trained on public data to provide private inference on hidden user data. This framework is implemented in the CrypTen MPC framework. We obtain results on the accuracy difference of the models when making inference using MPC, as well as the costs associated with performing this inference. We found a 0.55%-1.42% accuracy difference between plaintext inference and inference with MPC. Next, we pivot to explore writer identification, which involves identifying the writer of some handwritten text. We use the OnHW-equations dataset for our analysis, which at the time of writing has not been used for this task before. We first analyze and reformat the data to fit the writer identification task, as well as remove bias. Using DL models, we obtain accuracy results of up to 91.57% in identifying the writer using their handwriting. As with private inference in the character recognition task, it is important to account for user privacy when training writer identification models and making inference. We design and implement a framework for private training and inference for the writer recognition task, using the CrypTen MPC framework. Since training these models is very costly, we use simpler CNN's for private writer recognition. The chosen CNN trained privately in MPC obtained an accuracy of 77.45%. Next, we analyze the costs associated with privately training the CNN and other CNN's with altered model architectures. Finally, we switch to explore voice as a biometric in the speaker verification task. As with handwriting, a person's voice contains unique characteristics which can be used to determine the speaker. Not only can voice be analyzed similarly with handwriting, in that we can explore the speech recognition and speaker identification tasks, it comes with similar privacy risks for users. We design and implement a unique framework for private speaker verification using the MP-SPDZ MPC framework. We analyze the costs associated with training the model and making inferences, with our main goal being to determine the time it takes to make private inference. We then used these times as part of a survey conducted to determine how much people value the privacy of their biometrics and how long they were willing to wait for the increased privacy. We found that people were willing to tolerate significant time delays in order to privately authenticate themselves, when primed with the benefits of using MPC for privacy

Improving Accuracy and Explainability of Online Handwriting Recognition

Handwriting recognition technology allows recognizing a written text from a given data. The recognition task can target letters, symbols, or words, and the input data can be a digital image or recorded by various sensors. A wide range of applications from signature verification to electronic document processing can be realized by implementing efficient and accurate handwriting recognition algorithms. Over the years, there has been an increasing interest in experimenting with different types of technology to collect handwriting data, create datasets, and develop algorithms to recognize characters and symbols. More recently, the OnHW-chars dataset has been published that contains multivariate time series data of the English alphabet collected using a ballpoint pen fitted with sensors. The authors of OnHW-chars also provided some baseline results through their machine learning (ML) and deep learning (DL) classifiers. In this paper, we develop handwriting recognition models on the OnHW-chars dataset and improve the accuracy of previous models. More specifically, our ML models provide $11.3\%$-$23.56\%$ improvements over the previous ML models, and our optimized DL models with ensemble learning provide $3.08\%$-$7.01\%$ improvements over the previous DL models. In addition to our accuracy improvements over the spectrum, we aim to provide some level of explainability for our models to provide more logic behind chosen methods and why the models make sense for the data type in the dataset. Our results are verifiable and reproducible via the provided public repository.Comment: 20 pages, 8 figures, 2 tables

Tuning the bandgap of Cs2AgBiBr6 through dilute tin alloying.

The promise of lead halide hybrid perovskites for optoelectronic applications makes finding less-toxic alternatives a priority. The double perovskite Cs2AgBiBr6 (1) represents one such alternative, offering long carrier lifetimes and greater stability under ambient conditions. However, the large and indirect 1.95 eV bandgap hinders its potential as a solar absorber. Here we report that alloying crystals of 1 with up to 1 atom% Sn results in a bandgap reduction of up to ca. 0.5 eV while maintaining low toxicity. Crystals can be alloyed with up to 1 atom% Sn and the predominant substitution pathway appears to be a ∼2 : 1 substitution of Sn2+ and Sn4+ for Ag+ and Bi3+, respectively, with Ag+ vacancies providing charge compensation. Spincoated films of 1 accommodate a higher Sn loading, up to 4 atom% Sn, where we see mostly Sn2+ substitution for both Ag+ and Bi3+. Density functional theory (DFT) calculations ascribe the bandgap redshift to the introduction of Sn impurity bands below the conduction band minimum of the host lattice. Using optical absorption spectroscopy, photothermal deflection spectroscopy, X-ray absorption spectroscopy, 119Sn NMR, redox titration, single-crystal and powder X-ray diffraction, multiple elemental analysis and imaging techniques, and DFT calculations, we provide a detailed analysis of the Sn content and oxidation state, dominant substitution sites, and charge-compensating defects in Sn-alloyed Cs2AgBiBr6 (1:Sn) crystals and films. An understanding of heterovalent alloying in halide double perovskites opens the door to a wider breadth of potential alloying agents for manipulating their band structures in a predictable manner

White Matter Microstructure Contributes to Age-Related Declines in Task-Induced Deactivation of the Default Mode Network

Task-induced deactivations within the brain’s default mode network (DMN) are thought to reflect suppression of endogenous thought processes to support exogenous goal-directed task processes. Older adults are known to show reductions in deactivation of the DMN compared to younger adults. However, little is understood about the mechanisms contributing to functional dysregulation of the DMN in aging. Here, we explored the relationships between functional modulation of the DMN and age, task performance and white matter (WM) microstructure. Participants were 117 adults ranging from 25 to 83 years old who completed an fMRI task switching paradigm, including easy (single) and difficult (mixed) conditions, and underwent diffusion tensor imaging (DTI). The fMRI results revealed an age by condition interaction (β = −0.13, t = −3.16, p = 0.002) such that increasing age affected deactivation magnitude during the mixed condition (β = −0.29, t = −3.24 p = 0.002) but not the single condition (p = 0.58). Additionally, there was a WM by condition interaction (β = 0.10, t = 2.33, p = 0.02) such that decreasing WM microstructure affected deactivation magnitude during the mixed condition (β = 0.30, t = 3.42 p = 0.001) but not the single condition (p = 0.17). Critically, mediation analyses indicated that age-related reductions in WM microstructure accounted for the relationship between age and DMN deactivation in the more difficult mixed condition. These findings suggest that age-related declines in anatomical connectivity between DMN regions contribute to functional dysregulation within the DMN in older adults

Glucose enhancement of memory is modulated by trait anxiety in healthy adolescent males

Glucose administration is associated with memory enhancement in healthy young individuals under conditions of divided attention at encoding. While the specific neurocognitive mechanisms underlying this ‘glucose memory facilitation effect’ are currently uncertain, it is thought that individual differences in glucoregulatory efficiency may alter an individual’s sensitivity to the glucose memory facilitation effect. In the present study, we sought to investigate whether basal hypothalamic–pituitary–adrenal axis function (itself a modulator of glucoregulatory efficiency), baseline self-reported stress and trait anxiety influence the glucose memory facilitation effect. Adolescent males (age range = 14–17 years) were administered glucose and placebo prior to completing a verbal episodic memory task on two separate testing days in a counter-balanced, within-subjects design. Glucose ingestion improved verbal episodic memory performance when memory recall was tested (i) within an hour of glucose ingestion and encoding, and (ii) one week subsequent to glucose ingestion and encoding. Basal hypothalamic–pituitary–adrenal axis function did not appear to influence the glucose memory facilitation effect; however, glucose ingestion only improved memory in participants reporting relatively higher trait anxiety. These findings suggest that the glucose memory facilitation effect may be mediated by biological mechanisms associated with trait anxiety

Motivational interviewing in paediatric residency

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135957/1/tct12503.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135957/2/tct12503_am.pd

The Ursinus Weekly, October 3, 1968

Fourteen join faculty; Dept. heads named for Philos. & Economics • 227 freshmen begin studies; 3 foreign students included • Professors recognized for teaching excellence • Stained glass exhibit on display at Ursinus • Joint effort concert features Intruders • Editorial: Student activism - radical or responsible? • Frosh at Shalom; Dinks on till 12th • The illegalities of Ursinus law • The great society? • Editorial: Everyone\u27s problem • Freeland\u27s days numbered; Library to rise on site • Graduate school aspirants urged to prepare early • Forums replace required chapel • Studio art to highlight new fine arts course • Annual Parents Day planned for Oct. 12 • Franklin & Marshall passing stops Bears in season opener • Ursinus hockey squad hopes for eighth undefeated year • Baker counts on frosh to bolster soccer team • Undefeated Harriers aim for MAC title • Greek gleaningshttps://digitalcommons.ursinus.edu/weekly/1163/thumbnail.jp

Randomized phase 3 evaluation of trifarotene 50 μg/g cream treatment of moderate facial and truncal acne.

BACKGROUND: Acne vulgaris often affects the face, shoulders, chest, and back, but treatment of nonfacial acne has not been rigorously studied. OBJECTIVES: Assess the safety and efficacy of trifarotene 50 μg/g cream, a novel topical retinoid, in moderate facial and truncal acne. METHODS: Two phase III double-blind, randomized, vehicle-controlled, 12-week studies of once-daily trifarotene cream versus vehicle in subjects aged 9 years or older. The primary end points were rate of success on the face, as determined by the Investigator\u27s Global Assessment (clear or almost clear and ≥2-grade improvement), and absolute change from baseline in inflammatory and noninflammatory counts from baseline to week 12. The secondary end points were rate of success on the trunk (clear or almost clear and ≥2-grade improvement) and absolute change in truncal inflammatory and noninflammatory counts from baseline to week 12. Safety was assessed through adverse events, local tolerability, vital signs, and routine laboratory testing results. RESULTS: In both studies, at week 12 the facial success rates according to the Investigator\u27s Global Assessment and truncal Physician\u27s Global Assessment and change in inflammatory and noninflammatory lesion counts (both absolute and percentage) were all highly significant (P \u3c .001) in favor of trifarotene when compared with the vehicle. LIMITATIONS: Adjunctive topical or systemic treatments were not studied. CONCLUSION: These studies demonstrate that trifarotene appears to be safe, effective, and well tolerated in treatment of both facial and truncal acne