Texas A&M University
  • College Station, United States
Recent publications
In the tritone paradox, tones separated by a half octave are heard by some as descending and by others as ascending. Different accounts of this phenomenon have been put forth, highlighting psychoacoustic variables or pitch class templates shaped by early language experience. If early language experience is a critical factor affecting tritone perception, would the paradox be perceived differently depending on whether the language experience was monolingual or bilingual?
  • Ingo Richter
    Ingo Richter
  • Noel Keenlyside
    Noel Keenlyside
  • Tomoki Tozuka
    Tomoki Tozuka
  • [...]
  • Hiroki Tokinaga
    Hiroki Tokinaga
Jiang et al. (2023), https://doi.org/10.1029/2023gl103777 argue that the apparent impact of the equatorial Atlantic on El Niño‐Southern Oscillation (ENSO) is a statistical artifact, and that the 6‐month lead correlation reported in previous studies stems from early developing ENSO events driving the equatorial Atlantic zonal mode (AZM) in boreal summer and maturing in winter. Closer examination, however, reveals that most AZM events develop too early to be driven by developing ENSO, and that the influence of decaying ENSO events has to be considered too. Thus, while early developing ENSO events may play a role, they do not fully explain observed AZM behavior. Our aim is not to argue for or against an AZM influence on ENSO, but rather to show that Jiang et al.’s analysis is insufficient to resolve this issue. More analysis will be needed for a deeper understanding of Atlantic‐Pacific interaction.
Performance modeling is a key bottleneck for analog design automation. Although machine learning-based models have advanced the state-of-the-art, they have so far suffered from huge data preparation cost, very limited reusability, and inadequate accuracy for large circuits. We introduce ML-based macro-modeling techniques to mitigate these problems for linear analog ICs and ADC/DACs. The modeling techniques are based on macro-models, which can be assembled to evaluate circuit system performance, and more appealingly can be reused across different circuit topologies. On representative testcases, our method achieves more than 1700× speedup for data preparation and remarkably smaller model errors compared to recent ML approaches. It also attains 3600× acceleration over SPICE simulation with very small errors and reduces data preparation time for an ADC design from 40 days to 9.6 hours.
Analog photonic links that leverage silicon photonic components offer potential advantages for building future wireless communication systems. A wideband electrical front-end circuit and a high speed electro-optic modulator are essential parts of the photonic receiver links. This paper presents a co-integrated 16-32GHz front-end link with an electrical driver in a 22nm CMOS FD-SOI process and a silicon photonic travelling-wave Mach-Zehnder modulator (TW-MZM) in a 220nm SOI process. A Magnetically-coupled resonator technique is adopted to realize wideband impedance matching network and a stack-FET structure is utilized for higher supply voltage operation, providing 13dBm output power from the driver. In the TW-MZM, a slow-wave transmission line electrode is implemented to improve its bandwidth by impedance and velocity matching. The CMOS and silicon photonic chips are co-designed considering the bond-wire and input impedance of TW-MZM. The calculated spurious free dynamic range (SFDR) of the link with a grating coupler is 89 dBHz(2/3)dB*Hz^{(2/3)} and it is possible to achieve 105 dBHz(2/3)dB*Hz^{(2/3)} using a lower-loss edge coupler.
This paper considers the problem of sequentially detecting a change in the joint distribution of multiple data sources under a sampling constraint. Specifically, the channels or sources generate observations that are independent over time, but not necessarily across channels. The joint distribution of an unknown subset of sources changes at an unknown time instant. Moreover, there is a hard constraint that only a fixed number of sources can be sampled at each time instant, but the sources can be selected dynamically based on the already collected data. The goal is to sequentially observe the sources according to the constraint, and stop sampling as quickly as possible after the change while controlling the false alarm rate below a user-specified level. Thus, a policy for this problem consists of a joint sampling and change-detection rule. A non-randomized policy is studied, and an upper bound is established on its worst-case conditional expected detection delay with respect to both the change point and the observations from the affected sources before the change. In certain cases, this rule achieves first-order asymptotic optimality as the false alarm rate tends to zero, simultaneously under every possible post-change distribution and among all schemes that satisfy the same sampling and false alarm constraints. These general results are subsequently applied to the problems of (i) detecting a change in the marginal distributions of (not necessarily independent) information sources, and (ii) detecting a change in the covariance structure of Gaussian information sources.
Recent advances have improved autonomous navigation and mapping under payload constraints, but current multi-robot inspection algorithms are unsuitable for nano-drones, due to their need for heavy sensors and high computational resources. To address these challenges, we introduce ExploreBug , a novel hybrid frontier range-bug algorithm designed to handle limited sensing capabilities for a swarm of nano-drones. This system includes three primary components: a mapping subsystem, an exploration subsystem, and a navigation subsystem. Additionally, an intra-swarm collision avoidance system is integrated to prevent collisions between drones. We validate the efficacy of our approach through extensive simulations and real-world exploration experiments, involving up to seven drones in simulations and three in real-world settings, across various obstacle configurations and with a maximum navigation speed of 0.75 m/s. Our tests prove that the algorithm efficiently completes exploration tasks, even with minimal sensing, across different swarm sizes and obstacle densities. Furthermore, our frontier allocation heuristic ensures an equal distribution of explored areas and paths traveled by each drone in the swarm. We publicly release the source code of the proposed system to foster further developments in mapping and exploration using autonomous nano drones.
Cases of stallion subfertility due to acrosome dysfunction have been recognized since the 1990s. While some of these were observed in stallions with reduced sperm motility and morphology, a more severe form has been reported in stallions with normal-to-excellent sperm quality parameters, which is also uniquely observed in individuals of the Thoroughbred registry. These stallions carry a susceptibility genotype (A/A-A A in the gene FKBP6, exon 5) for Impaired Acrosomal Exocytosis (IAE). Current clinical observations from our group have identified a few highly subfertile stallions from other breed registries that also display a lower ability to undergo acrosomal exocytosis (AE) but do not carry the A/A-A/A genotype. This document provides a concise review of the role of acrosome dysfunction as a cause of stallion subfertility, including methods to estimate acrosome function and clinical descriptions of IAE in TB and non-TB stallions.
Digital Twins (DTs) are increasingly recognized for their potential to improve efficiency and decision-making in various domains of the built environment. Despite their promise, challenges like cost, complexity, interoperability, and data integration remain. This paper introduces a novel interactive visual analytics system that tackles these issues, using a case study of simulating class distribution and campus building capacity at a large public university. The system leverages enrollment data, converting it into a spatial-temporal format for interactive exploration and analysis of class distribution and resource utilization. Through case studies, we demonstrate the system's effectiveness, adaptability, and real-world applicability, highlighting its role in practical DT implementation for built environments.
As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.
Data were collected from four plant materials courses over the span of 20 years. Two classes were at the undergraduate level, Trees and Shrubs for Sustainable Built Environments (HORT 306) and Plants for Sustainable Landscapes (HORT 308), and two classes were at the graduate level, Plants for Landscape Design (HORT 608) and Plants for Landscape Design II (HORT 609). Data from these courses were analyzed to determine trends in student performance and benchmarks that might be associated with student success. Data included student enrollment, midterm course grades, final course grades, number of unexcused absences, student-reported study times for various activities, student major, student experience (class rank), whether the courses were required, and perceived difficulty levels of the courses. Trends in grade distributions were fairly stable until the last 3 to 4 years before COVID-19, when mean final grades and the frequencies of A or B grades increased. Midterm grades were strongly positively correlated with final course grades across majors in all four courses before COVID-19 ( R ² = 0.90, 0.74, 0.72, and 0.54 for HORT 306, HORT 308, HORT 608, and HORT 609, respectively; P ≤ 0.001) and continued to be positively correlated after COVID-19. The number of unexcused absences was negatively significantly correlated with final course grades across majors ( R ² = −0.69, −0.63, −0.21, and −0.45 for HORT 306, HORT 308, HORT 608, and HORT 609, respectively) before COVID-19 ( P ≤ 0.001) and continued to be similarly correlated after COVID-19. Fewer reductions from midterm to final grades were observed for fall courses than for spring courses, particularly for seniors. Self-reported time spent studying all aspects of the courses was either not significantly correlated ( P ≤ 0.05) or surprisingly slightly negatively correlated with the final course grades for all four courses. Perceptions of courses as moderately difficult (range, 7.0–8.0 out of 10.0) were remarkably stable overall but varied considerably by major (range, 5.0–8.3) and experience (range, 7.5 for seniors to 8.1 for freshman). More than 96% of enrollment in the graduate courses both before and after COVID-19 comprised Horticulture and Landscape Architecture majors, whereas undergraduate enrollment included a wide diversity of majors; however, the majority of those students were horticulture or landscape architecture majors. Biological science students or students who were architectural design majors were the top-performing students in both undergraduate courses, whereas undeclared majors, social science majors, and those in probationary major categories were among the lower-performing students in both undergraduate courses.
Background Oncocytoma is a primary benign epithelial neoplasm comprising less than 2% of salivary tumors with a low recurrence rate. Methods A systematic review of documented case reports and case series of oncocytomas is presented. Searches from different databases were performed to identify articles from 1956 to 2024. The variables included were gender, age, symptoms, duration time before diagnosis, type of gland, histological features, special or immunohistochemical evaluation, treatment, follow-up, recurrence, and relation with a medical condition or syndrome. Results Of the 147 cases reported, 53.1% affected females, and 46.9% were in males. The average age was 58.7 years, and the mean size was 2.3 cm. The most common clinical presentation was swelling (92.6%) and 66.7% were asymptomatic. The parotid was the most commonly affected gland with 66% of cases, the submandibular gland with 23.3%, and the minor salivary glands with Phosphotungstic acid-hematoxylin (PTAH) was the most common special stain used in 36.7%, followed by a combination with Periodic acid-Schiff (PAS) with and without diastase in 26.6%. Excisional biopsy was the most common treatment in 38.1% followed by superficial parotidectomy in 32.7%. Follow-up was 34.7 months on average. Bilateral oncocytomas were found in 4.8% with a 6 to 1 female-male proportion. Recurrence was found in 2.7% and association with Birt-Hogg-Dube (BHD) syndrome was 8.2%. Conclusion Salivary oncocytoma is a rare epithelial neoplasm with nonspecific clinical presentations. Diagnosis can be suspected on cytology and confirmed by histologic examination. The lesion has an indolent clinical course and most of the reported cases did not recur. There seems to be an association between bilateral oncocytomas and females and a low but interesting association with BHD. Overall, this review serves to better highlight the features of this rare benign neoplasm.
This paper is an official position statement of the American Geriatrics Society (AGS) and updates the 2017 AGS position statement, Making Medical Treatment Decisions for Unbefriended Older Adults. In this updated position statement, the term “unbefriended” is replaced by “unrepresented” as a term that is more value‐neutral, more accurately describes the circumstance in which a person without medical decision‐making capacity does not have recognized surrogate representation, and better aligns with increasingly preferred terminology as reflected in recent medical literature. We define unrepresented older adults as those who (1) lack decisional capacity to provide informed consent for a particular medical treatment, (2) have not executed an advance directive that addresses the medical treatment at hand and lack capacity to do so, and (3) lack representation from a surrogate decision‐maker (i.e., family, friend, or legally authorized surrogate). Making medical decisions on behalf of unrepresented older adults is exceptionally challenging and, given demographic trends, is likely to become increasingly common in the years ahead. The process of arriving at treatment decisions for this population should follow standards of procedural fairness and include capacity assessment, search for potential surrogates, team‐based efforts to determine the patient's values and preferences, and steps to guard against bias. Proactive measures are needed to identify older adults at risk for becoming unrepresented. This position statement also calls for national efforts to reduce state‐to‐state variability in legal approaches for unrepresented patients.
This paper presents a combined numerical and experimental investigation into the vibroacoustic behavior of a traditional oud. An experimental modal analysis was conducted using impact hammer testing to determine the oud’s soundboard’s dynamic characteristics and frequency response function for up to 400 Hz. Finite element analysis was used to model the oud, incorporating its precise geometry, the wood’s orthotropic material properties, and its interaction with the surrounding air. Validation was performed by matching the numerical and experimental mode shapes and the natural frequencies. Harmonic acoustic analysis examined the oud’s sound pressure level radiation and cavity resonance. Structural–acoustic optimizations were conducted systematically, varying the soundhole’s size, the soundboard’s thickness, and the dimensions of the internal bracing to maximize the acoustics properties while minimizing the structural stress. The effects of these geometric factors on the instrument’s tonal characteristics were quantified. The results provide physical insights into the relationship between the oud’s construction and sound production. The methodology demonstrates a rigorous approach combining simulations and experimentation to comprehensively evaluate and optimize the vibroacoustic behavior of a musical instrument. This fundamental understanding could guide future improvements in the design of ouds.
It is vital to have precise specifications and verification of UML class diagrams to ensure the correctness of complex software systems. However, current specification and verification methods often face a challenge known as the frame problem. This problem occurs due to incomplete operation specifications that can lead to unintended system behavior. To tackle this issue, we have developed an automated solution to autonomously identify and define frame conditions, effectively minimizing the frame problem’s impact on class diagram verification. Frame conditions are explicit contracts that meticulously outline the permissible effects of operations within the system. Our approach carefully analyzes the behavioral blueprint of a class diagram and extracts crucial information to create these conditions. Through rigorous evaluations encompassing diverse UML diagrams and simulated execution scenarios, we have demonstrated the effectiveness of our approach in preventing unintended system behavior caused by the frame problem. We have integrated the approach into the Temporal Property Validator tool, empowering practitioners to leverage its benefits for practical class diagram specification and verification.
Background Obesity is a chronic, relapsing, progressive disease of excess adiposity that increases the risk of dying from at least 16 types of cancer. The prevalence of obesity has increased more rapidly in cancer survivors compared with the general population. Tailored weight management strategies are needed to improve prognosis and health outcomes in the growing population of cancer survivors. However, certain cancer survivor population subgroups require unique consideration when developing weight management strategies. Methods In a symposium convened by The Obesity Society during ObesityWeek 2023 titled “From Surviving to Thriving: Key Considerations for Weight Control Across Diverse Cancer Survivorship Populations,” experts presented the current state of the science and highlighted existing research gaps. Results Topics included key considerations for weight management in adolescent and young adult cancer survivors, older adult cancer survivors, and understudied cancer survivor subgroups at high risk for poor health outcomes and innovative interventions that can be tested to improve cancer survivorship. Conclusions This report reviews the symposium and offers perspectives from the expert panel about unique opportunities for future research on tailored weight management strategies to equitably improve prognosis and health outcomes in the diverse and growing population of cancer survivors.
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23,958 members
Paul G Harms
  • Department of Animal Science
Haili Zhang
  • Department of Veterinary Pathobiology
Kevin Y Njabo
  • Texas A&M AgriLife Research
Shahnaz Majid Qadri
  • School of Pharmacy
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