University of Kansas Center for Research Inc

NSF Awards · FY 2024 · 2024-08

Bees have great value as pollinators for crops, natural plant communities, and backyard gardens. Understanding the factors that affect bee diversity and function are therefore important subjects of basic ecology, conservation, and ensuring the continued provision of pollination services. The global bee decline highlights the need for effective conservation research. One essential yet challenging step in many bee studies is to identify bees so that species can be counted and population sizes measured. However, the identification process is time-consuming, expensive, and requires a high degree of expertise. But new technology in the field of artificial intelligence (AI) and computer vision is making automated identification from images a realistic goal. To develop the computer vision algorithms, we will need to generate a large dataset of bee images with known species IDs. Previous work has utilized public image repositories comprised mainly of bee images taken in natural settings. However, such images are often not amenable to identification by humans because the subtle characteristics that differentiate species are not visible and without a known ID, they cannot be used for training AI models. THe project will therefore use museum collections to generate a large image database from pinned bee specimens that have been expertly identified, representing at least 1,000 of the estimated 4,000 species in North America. This will allow researchers to develop a computer vision algorithm capable of identifying the most commonly seen species and all of the bee genera in North America. The resulting algorithm will be incorporated into a website (https://beemachine.ai), which will serve as a free identification resource and research hub. Users will be able to upload images of bees for identification and be able to discuss and validate machine-generated identifications. This research hub will therefore contribute to large-scale data collection and sharing among both scientists and citizen scientists. These large-scale data collection efforts are important for understanding bee trends that can span continental scales. Reliable identification of pollinators, such as bees, is critical for basic ecological research, conservation, and maintaining pollination services. However, species-level bee identification is difficult and requires specialized taxonomic knowledge because of their great diversity and often subtle morphological differences between species. The identification process results in a bottleneck that is expensive and time consuming, which slows the pace of research and adoption of new applications. Moreover, setbacks for pollinator research can result from errors based on misidentification if experts are unavailable or if funds are insufficient to employ them. However, cutting-edge technology in the fields of artificial intelligence (AI) and computer vision are enabling fast and reliable detection and classification of bees from images. Our project will cover three objectives aimed at greatly expanding the use of this technology for automated bee species identification and data sharing: (1) The project will develop a large and expertly labeled image dataset for model training that includes a minimum of 1,000 North American bee species. (2) The project will develop an AI-based classification model using convolutional neural networks to identify bee species in images. (3) The project will develop a website for novices to expert users that utilizes our computer vision algorithm to identify bees to the genus- and species-level. The website will be a substantial expansion of a previously released computer vision work, BeeMachine, and serve as a research hub for bee identification, data sharing, and communication among researchers. The current project will have bee identification capabilities far beyond superficially similar platforms, such as iNaturalist. Thus, automated identifications of user-contributed bee images will enable researchers to collect and share large-scale data more effectively and on a vastly greater diversity of bee species than is currently possible. Expert users will be able to verify AI-generated identifications so that contributed observations can be used to update and improve computer vision models. This project will provide a novel and transformative platform for research with high potential for transfer to other taxa beyond bees. Reducing the cost and time necessary to perform species-level identifications will allow researchers to expand the number of studies as well as the spatiotemporal scale of research so that the drivers of population and diversity change can be determined and so that effective conservation strategies can be enacted. The web-based research hub will increase access to expert-level identification capability, communication among researchers, and provide for a large-scale and ever-growing open dataset of bee distributions that can be used for research. The project website will be available at https://beemachine.ai This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Nontechnical Description: This project explores how light interacts with soft matter, such as liquids containing colloidal particles, to create new structures. By using light, these particles can be made to form unique configurations with special properties significantly changing how light interacts with them. One major goal is to develop "transient photonic devices," which can self-assemble and disassemble in response to light. These devices are stable and reconfigurable when illuminated and disappear when the light is off. This research is important for advancing optical science and has practical applications in various fields, such as new technologies for controlling light. The project also includes efforts to share findings with the public through online tutorials and demonstrations, making the science accessible and interesting to everyone. Additionally, the project emphasizes education by providing hands-on learning experiences for students and engaging underrepresented groups in science and engineering, ensuring a diverse and inclusive participation. By partnering with organizations such as Indigenous Hispanic African-American Women KU Engineering (IHAWKe), the project aims to inspire a broader audience and cultivate interest in the field of photonics, ultimately contributing to a more diverse STEM workforce. Technical Description: The research focuses on the fundamental science of light interacting with soft matter on a mesoscopic scale, using principles from statistical physics and nonlinear optics. The main objective is to redefine optically guided assembly by understanding and manipulating the interaction between light and colloids. This will create a versatile framework for developing advanced optical materials with enhanced properties. The innovations from this research are expected to benefit the field of optical manipulation and biomedical optics, potentially leading to new diagnostic tools and improved imaging techniques. The project will explore controlling the extreme nonlinearity of colloidal nanosuspensions for applications like optical limiting, spatial light modulation, and real-time holography. It aims to demonstrate the practical value of creating soft-matter "transient photonic devices," where light controls the structure in real time. This research will also benefit other fields, such as chemistry and biology, by allowing precise optical control of particle concentrations. Educationally, the project will train students in optical measurement techniques and integrate research concepts into university courses, promoting interest and diversity in photonics through outreach activities and collaborations with organizations such as Indigenous Hispanic African-American Women KU Engineering (IHAWKe). Additionally, the project will produce and share simulation software on platforms such as nanohub.org, further extending its impact on both scientific and public communities. This project is jointly funded by the Electronic and Photonic Materials (EPM) program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Computing systems' ability to efficiently and timely process large amounts of data is a key enabler in the modern landscape of data-driven applications. To bridge the widening gap between memory technology and processors, computing systems continue to rely heavily on complex multi-level cache hierarchies. Caches can prevent costly accesses to downstream memory if the processed data items exhibit good spatiotemporal locality. Unfortunately, locality does not always emerge naturally in complex data processing pipelines. Platform-specific algorithmic optimizations are often necessary to rearrange the algorithm’s memory access pattern for better locality while striving to maintain the original semantics. When operating on high-dimensional objects (e.g., tensors), data locality unlocks crucial performance gains, but it becomes harder to achieve. This project proposes a novel class of architectural data transformation units to be interposed between memory and compute, for example Central Processing Units (CPUs) and Graphics Processing Units (GPUs). By relying on knowledge of the data access pattern followed by the algorithmic semantics, they decouple the in-memory geometry of data items from the access sequence required by the computational logic. As such, they make data items requested sequentially appear to the processing unit—and cache hierarchy—as if they were stored sequentially without data duplication through on-the-fly transformations. This enables spatiotemporal locality to be achieved effortlessly, i.e., without the need for heavy algorithmic re-engineering. The findings will be integrated into undergraduate and graduate courses at Boston University and the University of Kansas, enhancing topics such as data systems, system performance evaluation, embedded real-time systems, and operating systems. The project will support underrepresented populations across educational levels and foster strong industry connections. This project explores the theory and practice concerning the formulation, design, and implementation of architectural on-the-fly Data Transformation Units (DTUs). It does so by thrusting along three interconnected research avenues. First, the investigators focus on developing a foundational science of on-the-fly data transformation. A key stepping stone is formulating an access pattern specification language that is both expressive and efficiently interpretable in hardware. In the second thrust, two alternative architectural paradigms are explored, namely (1) the integration of DTUs as a component logically placed on the memory bus and (2) the integration of a DTU directly into the memory controller. Doing so places data transformation as close as possible to the memory cells to exploit their inherent parallelism while supporting unmodified commercial memory modules. The third thrust explores which programming models can best empower application designers to use DTUs via a combination of instruction-set architecture extensions, operating system-level support, and user-space libraries. Finally, the fourth thrust aims at identifying widely adopted data processing pipelines that can greatly benefit from using DTUs, specifically focusing on relational databases and machine learning. These will be used to concretely showcase the potential of the proposed on-the-fly data transformation approach. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award supports a theoretical study of the behavior of matter in super-strong magnetic fields. Observational discoveries of compact stars in our galaxy that are extraordinarily strong magnets, called neutron stars and magnetars, brings modern science into an uncharted territory of enormous magnetic fields. This is where quantum mechanics, rather than everyday classical physics, describes objects as massive as our Sun. Recent advances in laser science and engineering allow the most advanced national laser facilities to approach regimes relevant for studies of such extreme conditions. The neutron stars and magnetars can be viewed as astrophysical laboratories for studies of the interplay of quantum effects in large classical systems, at scale not yet achievable at laser laboratory facilities. Broader impacts of this project include training of graduate students and outreach to students from rural areas and underrepresented minority groups. Magnetars -- neutron stars with magnetic fields exceeding a critical (or Schwinger) field -- are the primary astrophysical sources of interest where quantum electrodynamic (QED) effects strongly affect the behavior, properties and dynamics of plasma. State-of-the-art high-intensity laser systems are beginning to approach regimes relevant for studies of plasma under such extreme, super-critical field conditions. The upcoming laser-plasma experiments and astrophysical observations will allow one to probe into extreme plasma and astrophysical phenomena that were previously inaccessible. This project aims to lay the theoretical foundations for studying collective plasma phenomena in a strong field QED plasma. The primary goal of this study is the development of theoretical understanding of plasmas under extreme astrophysical conditions and the creation of a comprehensive knowledge base of extreme plasmas with full QED. This project is jointly funded by the Division of Physics and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Mid-career researchers generally face a variety of distinct challenges: increased teaching loads, greater expectations for service and advising, a more competitive landscape for funding, and a lack of the variety of targeted programs that supported them as early-career scholars. As a result, many mid-career researchers do not make the impact they set out to achieve, or simply leave academia. Without a structured support system and training, these faculty do not have the skills they need to endure -- let alone flourish -- at this critical point in their careers. Even more importantly, a lack of diversity in senior academic positions limits the community’s ability to build a workforce of future educators and researchers necessary to address local to global challenges of climate change. The absence of structured training leaves professionals in these fields ill-equipped to navigate difficult conversations, power dynamics, and the overwhelming demands of their multifaceted roles. Research suggests that leadership training programs improve leadership effectiveness, project outcomes, research engagement, emotional intelligence and confidence, while also reducing workplace conflict. Even though mid-career faculty comprise the largest segment of academia and that the benefits of mid-career leadership training are resounding, leadership programs are rarely available for them. ClimPraxis will develop a framework for climate and environmental scholars at the mid-career stage to build community and gain critical leadership skills. The goal of this project is to provide structure and opportunities for mid-career cryosphere scholars to interact and support each other. Through intentional leadership training and goal setting, this project will support new leaders that we believe will administer to a more diverse community and will enable scholars to work together on bigger, multidisciplinary problems that the traditionally siloed structure of academia discourages. This pilot program will include a cohort of climate scientists working in the cryosphere; focusing this effort on a small community with contiguous career goals and substantive, field-specific, leadership challenges will facilitate cross-pollination both within disciplines and between institutions, and generate targeted support and training. This pilot program will focus on career reflection and assessment, leadership skill building, career planning, and career action. By documenting the process and collecting feedback from participants, this pilot will also investigate the individual needs of mid-career researchers and effective ways to meet those needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Clear or hyperarticulated speech is a strategy that speakers use to overcome potential communication problems that arise in situations like communicating with someone who does not share the same first language. However, not all clear speech is the same nor does it always accomplish its goal of aiding understanding, ultimately improving intelligibility. This doctoral dissertation examines the acoustic properties of clearly spoken language use produced by native and non-native speakers. Additionally, it examines if and how much the clear speech improves intelligibility for native and non-native listeners. Previous research has shown that the acoustic properties speakers enhance in clear speech tend to be the primary cues to the category. However, primary cues can differ from language to language. There is reason to believe that non-native clear speech, which is likely influenced by native cues to categories, will differ from native clear speech in acoustic modifications and therefore may not be as effective at improving intelligibility. Understanding exactly how non-native clear speech is produced and to what extent it enhances intelligibility can inform linguistic theories of second-language speech perception by shedding light on how phonetic categories are organized and interact within the mind of a second-language learner. Additionally, knowing specifically what speech modifications are beneficial (and which are not) can lead to focused instruction and ultimate removal of communication barriers. The doctoral dissertation aims to understand the characteristics of second-language (L2) clear speech, probe the potential differences between contrast-specific clear-speech productions (Did you say X?) and general clear-speech productions (What did you say?), and determine the intelligibility benefit received by both native and non-native speakers as a direct consequence of clear-speech enhancements. The Revised Speech Learning Model (SLM-r) posits that L2 phonetic categories are position-sensitive input distributions situated in an n-dimensional phonetic space shared with the native language (L1). Crucially, this model proposes that the more similar an L2 phone is to an L1 category, the more difficult it will be to unlink the L2 phone (and its primary cue) from the L1 category. Taken together with the claim that clear-speech modifications tend to be made along the primary cue dimension, these claims predict differences in clear-speech strategies for phones that differ in their L2-to-L1 mapping. The present dissertation utilizes a simulated interactive computer program methodology to elicit clear speech for three types of mappings: similar phones (oral stops), new phones (front rounded vowels), and position-specific allophones (vowel nasalization). Contrast-specific clear-speech productions are clear speech productions in response to a specific competitor. This distinction from general clear speech is important, as it determines whether the presence of a contrast influences the cues used in clear speech. Lastly, the present dissertation determines the intelligibility benefit received by the clear speech productions using identification- and discrimination-in-noise tasks. In sum, the doctoral dissertation seeks to understand how native and non-native speakers’ phonetic and phonological knowledge is utilized to maintain contrasts and increase intelligibility. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.