Florida Institute of Technology

A user's movement path can be precisely and concisely described as a concatenation of straight lines having the user's turns as their end points. Learning such a path description or representation from inertial measurement unit (IMU) sensors enables various mobile and IoT applications, as it allows efficient processing of the movement path data. It is, however, non-trivial to learn a succinct yet accurate path description from IMU sensor readings in the mobile device of a moving user on the fly due to the dynamically changing behaviors and the technical difficulty in detecting the user's turns. We propose PATHLIT, a novel online path description learning system based on IMU signals. PATHLIT learns position vectors of a user from IMU sensor readings by our custom-made self-attention network model. Once each position vector is learned, PATHLIT also decides whether or not to take it as a part of the resulting path description by our efficient online algorithm developed under the minimum description length principle, which essentially detects the user's turns along the path. We conduct extensive experiments on two large datasets. The experiment results show that PATHLIT achieves superior performance over state-of-the-art algorithms by up to 50% in absolute trajectory error using only 15% of trajectory data points.

Tailorability of a medium's optical properties, specifically refractive index and dispersion, is key to enabling compact optical designs. Chalcogenide glasses (ChGs) are widely used for infrared (IR) imaging applications, and the development of gradient refractive index (GRIN) optics. This work extends efforts to create and characterize 3D GRIN profiles in bulk multi‐component Ge‐As‐Pb‐Se (GAP‐Se) ChGs through spatially selective conversion of commercial glass to glass ceramic. This work extends prior efforts on bulk and film lab‐scale glass media, to that of a commercially produced material with improved optical homogeneity. Laser‐induced crystallization upon heat treatment results in the formation of high index Pb‐containing crystals that contribute to an increase in the nanocomposite's resulting effective refractive index, neff. The material's induced crystallinity imparted via laser exposure and heat treatment using metrology tools such as refractometry, X‐ray diffraction, FTIR, and TEM are studied. The resulting material response is quantified which is shown to be modulated via laser dose in both lateral and for the first time, axial directions enabling the first demonstration of a true, 3D GRIN profile. By comparing these outcomes to prior radial GRIN structures, the promise of these media as candidate materials for infrared systems.

Common hypotheses for the biomechanical cause underlying neonatal retinal hemorrhage include elevated intracranial pressure (ICP) inducing venous outflow obstruction and retinal deformation. A finite element computational model of the eye, optic nerve, and orbit was simulated with particular attention to the retinal vessels to analyze stress and strain on these structures during external head compression associated with normal vaginal delivery. Pressure from maternal contractions displaced the eye backward into the orbit, and the stiff optic nerve sheath provided localized resistance to this posterior displacement at its insertion point, resulting in tensile strain of 2.5% in the peripapillary (central) retina. Correspondingly, retinal vessels experienced tensile stress of up to 2.3 kPa near the optic nerve insertion point and opposing compressive stress of up to 3.2 kPa further away. The optic nerve was longitudinally compressed and experienced a mean radial tensile strain of 2.0%. Overall, forces associated with maternal labor resulted in a pattern of eye deformation that stretched the central retina in this simulation, mirroring the classical posterior localization of neonatal retinal hemorrhage. The optic nerve increased modestly in diameter despite rising ICP, suggesting retinal deformation is a more likely mechanism for retinal hemorrhage than occlusion of the central retinal vein.

Low cerebrospinal (CSF) arginine vasopressin (AVP) concentration is a biomarker of social impairment in low-social monkeys and children with autism, suggesting that AVP administration may improve primate social functioning. However, AVP administration also increases aggression, at least in “neurotypical” animals with intact AVP signaling. Here, we tested the effects of a voluntary drug administration method in low-social male rhesus monkeys with high autistic-like trait burden. Monkeys received nebulized AVP or placebo, using a within-subjects design. Study 1 (N = 8) investigated the effects of AVP administration on social cognition in two tests comparing responses to social versus nonsocial stimuli. Test 1: Placebo-administered monkeys lacked face recognition memory, whereas face recognition memory was “rescued” following AVP administration. In contrast, object recognition memory was intact and did not differ between administration conditions. Test 2: Placebo-administered monkeys did not respond to conspecific social communication cues, whereas following AVP administration, they reciprocated affiliative communication cues with species-typical affiliative responses. Importantly, AVP administration did not increase aggressive responses to conspecific aggressive or affiliative overtures. Study 2 (N = 4) evaluated the pharmacokinetics of this administration method. Following AVP nebulization, we observed a linear increase in cisternal CSF AVP levels, and a quadratic rise and fall in blood AVP levels. These findings indicate that nebulized AVP likely penetrates the central nervous system, selectively promotes species-typical responses to social information, and does not induce aggression in low-social individuals. Nebulized AVP therefore may hold promise for managing similar social symptoms in people with autism, particularly in very young or lower functioning individuals.

In 1997 Amazon started as a small online bookseller. It is now the largest bookseller in the US and one of the largest companies in the world, due, in part, to its implementation of algorithms and access to user data. This Element explains how these algorithms work, and specifically how they recommend books and make them visible to readers. It argues that framing algorithms as felicitous or infelicitous allows us to reconsider the imagined authority of an algorithm's recommendation as a culturally situated performance. It also explores the material effects of bookselling algorithms on the forms of labor of the bookstore. The Element ends by considering future directions for research, arguing that the bookselling industry would benefit from an investment in algorithmic literacy.

Sensory-based interventions are commonly utilized by pediatric occupational therapy practitioners when working with children with disabilities. The purpose of this study was to examine the effects of fixed-time breaks with access to sensory stimuli on behavior that interfered with participation in pediatric occupational therapy sessions. Two boys who were diagnosed with autism spectrum disorder and who were receiving applied behavior analysis services participated in this study. These children had previously been discharged from occupational therapy at another community-based clinic due to their problem behaviors that interfered with their participation in occupational therapy sessions. Three occupational therapy practitioners collaborated with a doctoral-level behavior analyst to design the conditions of the study. At first, the occupational therapy practitioners implemented breaks from task with access to sensory activities contingent upon problem behavior. Then, in a second condition, the practitioners presented breaks from task with access to sensory activities under a yoked fixed-time (FT) schedule. Each condition was repeated. Results showed that for both participants, problem behavior occurred more often when breaks were presented contingent on problem behavior than when breaks were presented under the FT schedule. The results suggest that when breaks with access to sensory activities are used therapeutically, the timing of the breaks could be an important factor. Moreover, these findings were the result of the collaborative efforts between practitioners of occupational therapy and behavior analysis. In this context, we discuss the nature of the collaboration and the respective roles. A framework for future collaborations is also presented.

The Maritime Continent (MC) exhibits a pronounced diurnal cycle in precipitation, with many high‐resolution models overestimating the diurnal peak and predicting earlier precipitation over the islands than observed. We hypothesize that part of this model bias comes from ignoring precipitation‐induced surface sensible heat flux (QP). To test this conjecture, we performed simulations with and without QP for April 2009 and June 2006. The inclusion of QP reduced the bias in diurnal peak precipitation amplitude by 83% in April 2009 and 23% in June 2006. Similarly, the bias in precipitation peak timing decreased by 26% and 15%, respectively. This bias reduction was even more prominent during periods of heavier rainfall. This improvement in both the amplitude and phase of diurnal precipitation also led to a reduction in bias for total precipitation by ∼10%. These findings suggest that QP cannot be neglected over the MC, particularly during heavy precipitation.

Behavior analysis is an emerging field of practice across the world. However, a lack of global standardization has led to disparities in the quality and scope of practice in different countries. In Latin America, the field of behavior analysis is still relatively new, and the issue of regulation has been a significant challenge for professionals seeking to establish and expand their practice. This paper provides an overview of the current situation in the regulation of behavior analysis in 15 Latin American countries, examining each country’s regulations, laws, and coverage, and identifying the challenges and opportunities for implementing and enforcing behavior analysis practices. By identifying these challenges and opportunities, this paper seeks to contribute to the ongoing efforts of behavior analysts in Latin America to establish a robust and sustainable framework for the regulation of behavior analysis.

The surge in remote and hybrid work has provided many benefits in terms of flexibility and autonomy, but it also presents challenges when it comes to team collaboration and meetings. Using a mixed-methods field study, we compare hybrid and remote meeting configurations to better understand how to improve the inclusiveness and effectiveness of these different meeting modalities. Our findings indicate that overall, fully-remote meetings are the most inclusive and effective. Fully-remote meetings are perceived as more inclusive of remote attendees despite their lower chat usage compared to hybrid meetings. We also find that people's preferred format varies depending on the purpose or type of meeting and the skill set of the meeting moderator. We provide recommendations for improving meeting inclusion and effectiveness in both hybrid and remote meetings, such as training moderators and integrating chats with the main meeting.

Here, we developed and applied models to quantitatively reconstruct forest cover and biomass changes at three lakes in northwestern Amazonia over the past > 1500 yr. We used remotely sensed data and a modern dataset of 50 Amazonian lakes to develop generalized linear models that predict aboveground biomass, using phytolith morphotypes and forest cover as predictor variables. Also, we applied a published beta regression model to predict forest cover within 200 m of each lake, using Poaceae phytoliths. Charcoal and maize phytoliths were analysed to identify past land use. Results showed forest cover and biomass changes at our study sites ranged between 48–84% and 142–438 Mg ha⁻¹, respectively. Human occupation was discontinuous, with major changes in forest cover and biomass coinciding with periods of land use. Forest cover and biomass decreased notably after fire (at all sites) or cultivation events (Lakes Zancudococha, Kumpaka). The timing and ecological impact of past land use were spatially and temporally variable. Our results suggest past human impact was small‐scaled and heterogenous in northwestern Amazonia, with a significant impact of fire on forest cover and biomass changes.

This paper explores how mechanical and aerospace engineering (MAE) students understand and improve their data proficiency throughout their engineering curriculum. Data is essential for engineering students to be proficient in handling, as it is involved in every aspect of engineering. With the growing ubiquity of data and data analysis in all engineering fields, engineering students need to learn and master data skills to be competitive in the current and future job market. However, there is a lack of research on how non-computer science or software engineering majors perceive data proficiency and how they seek opportunities to develop data skills, especially as it relates to specific subdomains. In this paper, we investigate how students perceive data proficiency and how they develop using interview data from N = 27 MAE students at a research institution in the southeastern United States. Using the How People Learn framework, we analyzed the data through thematic analysis methods with a postpositivist approach, considering the bounded context of this study. The results show that MAE students value data proficiency as a crucial skill for their future careers and recognize its importance in making evidence-based engineering decisions. The study also reveals that, even though data proficiency is often a “hidden competency,” MAE students intuitively find various ways to enhance their data skills. These findings may help engineering educators to tailor their instruction to their students’ needs, address misconceptions about data and data proficiency, and prepare a data-literate future engineering workforce.

In Roul and Warbhe (2016) J. Comp. Appl. Math. 296: 661–676, Roul and Warbhe proposed a computational technique for solving a class of doubly singular boundary value problems (DSBVP). This method approximates the solution of DSBVP in the form of a series but requires a large number of components in the series to achieve a reasonably good accuracy. In this paper, a fast and computationally efficient approach is introduced to approximate the solution to the same DSBVP. Additionally, convergence of the suggested scheme is rigorously proven. Two test problems are considered to demonstrate the efficiency and accuracy of the method. Comparison is performed between the proposed method and the method in Roul and Warbhe (2016) J. Comp. Appl. Math. 296: 661–676. The execution time of the present method is provided.

Intolerance of uncertainty (IU) is commonly defined as the tendency for one to interpret uncertainty as negative or threatening. Most general or non-specific measures of IU show a strong relationship with worry and generalized anxiety disorder symptoms; however, a specialized measure of intolerance of uncertainty in social situations could provide insight into the role of IU in social anxiety. The purpose of this study was the development and preliminary validation of the Intolerance of Uncertainty in Social Interactions Scale (IU-SIS), a comprehensive measure designed to assess intolerance of uncertainty in social situations. Participants consisted of a non-referred sample. Based on an exploratory factor analysis, a two-factor solution was retained, with factors labelled Social Ambiguity and Need to Reduce . Both subscales were found to have good reliability and validity. Both subscales of the IU-SIS predicted up variance on measures of social anxiety after controlling for variance explained by a well-established general/non-specific measure of IU. Overall, the IU-SIS shows promise as a tool to elucidate the association between intolerance of uncertainty and social anxiety.

Hollow-core anti-resonant fibers (HC-ARFs) have proven to be an indispensable platform for various emerging applications due to their unique and extraordinary optical properties. However, accurately estimating the propagation loss of nested HC-ARFs remains a challenging task due to their complex structure and the lack of precise analytical and theoretical models. To address this challenge, we propose a supervised machine-learning framework that presents an effective solution to accurately predict the propagation loss of a 5-tube nested HC-ARF. Multiple supervised learning models, including random forest, logistic regression, quadratic discriminant analysis, tree-based methods, extreme gradient boosting, and K-nearest neighbors are implemented and compared using a simulated dataset. Among these methods, the random forest algorithm is identified as the most effective, delivering accurate predictions. Notably, this study considers the impact of random structural perturbations on fiber geometry, encompassing random variations in tube wall thicknesses and tube gap separations. In particular, these perturbations involve randomly varying outer and nested tube wall thicknesses, tube angle offsets, and randomly distributed non-circular, anisotropic shapes within the cladding structure. It is worth noting that these specific perturbations have not been previously investigated. Each tube exhibits its unique set of random values, leading to longer simulation times for combinations of these values compared to regular random variables in HC-ARFs with similar tube characteristics. The comprehensive consideration of these factors allows for precise predictions, significantly contributing to the advancement of HC-ARFs for many emerging applications.