UMBC Student Collection

Permanent URI for this collectionhttp://hdl.handle.net/11603/33

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PhysiFi: WiFi Sensing for Monitoring Therapeutic Robotic Systems
(2025) Akpabio, Wonder; Bulut, Eyuphan
Patients recovering from limb-impairing strokes require consistent and precise physical therapy (PT) to regain mobility and functionality. Autonomous rehabilitation robots are increasingly adopted during recovery, offering a scalable solution to reduce the burden on physical therapists while assisting patients in performing prescribed exercises accurately. However, the effectiveness of these treatments often relies on professional supervision to ensure patients follow the robot’s movements properly, which could be challenging considering the ongoing shortage of physical therapists. Current PT monitoring systems primarily rely on camera-based technologies, which usually raise concerns due to potential privacy violations and high deployment costs, or wearable devices that are intrusive and uncomfortable for patients. To address these limitations, we propose PhysiFi, a novel approach that leverages ubiquitous WiFi signals available in most indoor environments, such as homes, rehabilitation centers, and assisted living facilities. By analyzing Channel State Information (CSI) from ambient WiFi signals and employing deep learning models, PhysiFi can track and recognize exercises performed by patients with rehabilitation robots. Our experiments demonstrate that PhysiFi can accurately identify prescribed exercises and evaluate whether patients are following the robot’s movements correctly, providing a non-intrusive, privacy-preserving, and costeffective alternative for monitoring physical therapy sessions.
Freestream turbulence effects on low Reynolds number NACA 0012 airfoil laminar separation bubble and lift generation
(2024-06-01) Yu, Meilin ; Hrynuk, John T.; Booth, David T.; Poudel, Naresh
Laminar separation bubbles (LSB's) over the suction surface of a wing at low Reynolds number (O(10⁴) - O(10⁶) based on the airfoil chord length) can significantly affect the aerodynamic performance of the wing, and pose a unique challenge for the predictive capabilities of simulation tools due to their high sensitivity to flow environments and wing surface conditions. In this work a series of two-dimensional (2D) and three-dimensional (3D) low-order, and high-order accurate unstructured-grid-based numerical methods with varying model fidelity levels were used to study LSB physics over a NACA 0012 airfoil both in a clean freestream and in a turbulent freestream at a chord-based Reynolds number of 12,000. Lift production and time-averaged flow fields were compared with available experimental results. A major discovery is that in clean freestream flow a 3D high-order numerical scheme is necessary to capture LSB physics. This is due to the sensitivity of LSB-induced laminar-turbulent transition to flow conditions and boundary geometry at low Reynolds number. In freestream flows with moderate background turbulence (~5%), 2D simulations failed to capture subtle 3D flow physics due to their intrinsic limitation, but can reasonably predict time-averaged airfoil performance. Similarity and distinction between freestream vortex-LSB interaction in 2D and eddy-LSB interaction in 3D were explained. The role of the Kelvin-Helmholtz instability and Klebanoff modes in the transition of 3D airfoils were shown to be critical for understanding laminar-turbulent transition and LSB formation on airfoils in clean and turbulent freestreams.
Support Vector Machine for Predicting Student Dropout Under Different Normalization Methods
(IEEE, 2025-01-16) Boteju, Gehan; Tang, Leon; Brown, Michael Scott
Student dropout in universities brings significant challenges that impacts both individual futures and institutional effectiveness. Early prediction of potential dropouts is crucial for timely intervention, but it is complex because of the nature of the problem influenced by diverse socioeconomic factors. This paper utlizies Support Vector Machines (SVMs) to predict student dropout with an emphasis on exploring the efficacy of various data normalization methods to optimize prediction accuracy. Using a dataset from the UC Irvine repository, this study compares 9 different normalization techniques such as Min Max Scaler, Standard Scaler, and Power Transformer, among others, to determine their impact on the predictive performance of SVMs. Results demonstrate substantial variations in model accuracy depending on the normalization method used to show the importance of detailed selection of data preprocessing techniques. The best normalization method was the One Hot Scaler which produced an average F1 score of 0.779. This work enhances the ability to identify at-risk students earlier but also the understanding of how data normalization influences predictive modeling in educational settings.
Rainfall Frequency Analysis Based on Long-Term High-Resolution Radar Rainfall Fields: Spatial Heterogeneities and Temporal Nonstationarities
(AGU, 2024) Smith, James A.; Baeck, Mary Lynn; Miller, Andrew; Claggett, Elijah L.
Rainfall frequency analysis methods are developed and implemented based on high-resolution radar rainfall data sets, with the Baltimore metropolitan area serving as the principal study region. Analyses focus on spatial heterogeneities and time trends in sub-daily rainfall extremes. The 22-year radar rainfall data set for the Baltimore study region combines reflectivity-based rainfall fields during the period from 2000 to 2011 and polarimetric rainfall fields for the period from 2012 to 2021. Rainfall frequency analyses are based on non-stationary formulations of peaks-over-threshold and annual peak methods. Increasing trends in short-duration rainfall extremes are inferred from both peaks-over-threshold and annual peak analyses for the period from 2000 to 2021. There are pronounced spatial gradients in short-duration rainfall extremes over the study region, with peak values of rainfall between Baltimore City and Chesapeake Bay. Spatial gradients in 100-year, 1 hr rainfall over 20 km length scale are comparable to time trends over 20 years. Rainfall analyses address the broad challenge of assessing changing properties of short-duration rainfall in urban regions. Analyses of high-resolution rainfall fields show that sub-daily rainfall extremes are only weakly related to daily extremes, pointing to difficulties in inferring climatological properties of sub-daily rainfall from daily rainfall analyses. Changing measurement properties are a key challenge for application of radar rainfall data sets to detection of time trends. Mean field bias correction of radar rainfall fields using rain gauge observations is an important tool for improving radar rainfall fields and provides a useful tool for addressing problems associated with changing radar measurement properties.
Supporting Campus Activism through Creating DIY-AT in a Social Justice Aligned Makerspace
(ACM, 2025-01-31) Higgins, Erin; Oliver, Zaria; Hamidi, Foad
Utilizing digital fabrication methods (e.g., 3D printing) has exciting implications for the design and production of customized assistive technology (AT). However, utilizing these tools currently requires a high level of technical expertise as well as time and money investments. Furthermore, facilitating collaboration between end users and makers needs effective and inclusive approaches with shared language and support for asynchronous, dispersed communication of design requirements. While these Do-It-Yourself (DIY) approaches are shown to support end-user agency and furthering technology democratization, research has to yet explore how they can further align with social justice values and practices. We explored these possibilities by facilitating DIY-AT design with students with disabilities, activist staff members, and community members within a university makerspace. By explicitly encouraging participants to consider social justice issues important to them as they engaged in DIY-AT design, we studied the considerations and supports needed for facilitating flexible co-design activities and broader conversations about accessibility barriers at the university. Adopting a transdisciplinary approach, we offer lessons learned about the potential of co-designing DIY-ATs as a way to investigate questions of social justice, inclusion, and access in academic contexts. We show how these created DIY-ATs can be leveraged by students and staff as tangible artifacts to encourage more funding and support from university administration for accessibility initiatives.
Greenland Ice Sheet Wide Supraglacial Lake Evolution and Dynamics: Insights From the 2018 and 2019 Melt Seasons
(AGU, 2025-02-21) Dunmire, Devon; Subramanian, Aneesh C.; Hossain, Emam; Gani, Md Osman; Banwell, Alison F.; Younas, Hammad; Myers, Brendan
Supraglacial lakes on the Greenland Ice Sheet (GrIS) can impact both the ice sheet surface mass balance and ice dynamics. Thus, understanding the evolution and dynamics of supraglacial lakes is important to provide improved parameterizations for ice sheet models to enable better projections of future GrIS changes. In this study, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland-wide supraglacial lakes during 2018 and 2019; low and high melt seasons respectively. We develop a novel time series classification method to categorize lakes into four classes: (a) Refreezing, (b) rapidly draining, (c) slowly draining, and (d) buried. Our findings reveal significant interannual variability between the two melt seasons, with a notable increase in the proportion of draining lakes, and a particular dominance of slowly draining lakes, in 2019. We also find that as mean lake depth increases, so does the percentage of lakes that drain, indicating that lake depth may influence hydrofracture potential. We further observe rapidly draining lakes at higher elevations than the previously hypothesized upper-elevation hydrofracture limit (1,600 m), and that non-draining lakes are generally deeper during the lower melt 2018 season. Our automatic classification approach and the resulting 2-year ice-sheet-wide data set provide new insights into GrIS supraglacial lake dynamics and evolution, offering a valuable resource for future research.
A LSTM with Dual-stage Attention Method to Predict Amine Emissions for Carbon Dioxide Capture and Storage
(IEEE, 2025-01-16) Rapelli, Sai Rajesh; Chen, Zhiyuan; Lu, Wei
To mitigate climate change impacts, carbon capture technologies have been implemented at significant CO2 emission points, such as industrial sites and electric power generation facilities. Solvent-based carbon capture solutions are pivotal in reducing atmospheric CO2 levels and enhancing air quality by capturing harmful pollutants. Amine-based solvents, favored for their efficiency in post-combustion CO2 capture, are susceptible to thermal and oxidative degradation, leading to complex emissions profiles that demand comprehensive management strategies. We develop a Machine Learning model designed to predict future amine emissions in real-time, thereby assisting in the formulation of mitigation strategies required for the operation of capture plants. We conducted an experiment using data from test campaigns run at the Technology Centre Mongstad (TCM). We employed a Long Short-Term Memory (LSTM) autoencoder model with dual-stage attention mechanisms to predict amine emissions using historical data. The results were quite promising: we achieved a mean absolute percentage error ranging from 5.8% to 6.8% percent for the real-time prediction of amine emissions. The results are better than existing approaches using simpler machine learning models as well as the standard LSTM autoencoder model.
SEALM: Semantically Enriched Attributes with Language Models for Linkage Recommendation
(2025-02-02) Traeger, Leonard; Behrend, Andreas; Karabatis, George
Matching attributes from different repositories is an important step in the process of schema integration to consolidate heterogeneous data silos. In order to recommend linkages between relevant attributes, a contextually rich representation of each attribute is quite essential, particularly when more than two database schemas are to be integrated. This paper introduces the SEALM approach to generate a data catalog of semantically rich attribute descriptions using Generative Language Models based on a new technique that employs six variations of available metadata information. Instead of using raw attribute metadata, we generate SEALM descriptions, which are used to recommend linkages with an unsupervised matching pipeline that involves a novel multi-source Blocking algorithm. Experiments on multiple schemas yield a 5% to 20% recall improvement in recommending linkages with SEALM-based attribute descriptions generated by the tiniest Llama3.1:8B model compared to existing techniques. With SEALM, we only need to process the small fraction of attributes to be integrated rather than exhaustively inspecting all combinations of potential linkages.
Morpholino oligomer delivery via bath immersion for use in reverse genetic studies on the early development of eastern oysters (Crassostrea virginica)
(Elsevier, 2025-04-30) Xu, Lan; Small, Jessica Moss; Hood, Shannon M.; Zhao, Mingli; Plough, Louis V.; Wong, Ten-Tsao
The eastern oyster genome and expanding omics data have provided valuable insights into this species. However, the limited availability of molecular toolboxes constrains the further exploration of gene functional investigation. In this study, we applied an emerging immersion-based gene silencing technology and developed a detailed protocol for delivering Morpholino oligomer (MO) to eastern oysters. First, two target genes, cv-gcl and cv-vasa, were cloned, and their expressions in ovaries and during embryogenesis were characterized. Both genes were maternally deposited to oocytes, and the expression of cv-vasa decreased steadily after fertilization, while cv-gcl peaked around 20 hours post-fertilization. The post-fertilization immersion treatment was more effective (three- to four-fold) than the pre-fertilization treatment, indicating a stronger MO uptake after fertilization. MOs designed against these two genes were administered via Vivo using bath immersion treatment following fertilization. There was no difference in survival rates (D-larvae yield) at 1 day post-fertilization (dpf) when treating embryos with cvgcl-MO-Vivo up to 20 μM, while deleterious effects started to emerge when increasing the concentration to 30 and 40 μM. The cvvasa-MO-Vivo treated group exhibited a lower Vasa level compared to the control-MO-Vivo group, suggesting the potential knockdown of the target gene. Most importantly, we achieved real-time visualization of MO uptake by conjugating fluorescence-labeled MO with a cell-penetrating peptide. Monitoring fluorescent intensity inside oyster larvae revealed that MO delivery occurred during early embryogenesis, and the signal retained up to 6 dpf. The immersion and fluorescence-traceable approach described here is a highly efficient reverse genetic method of delivering and monitoring MO in a large number of developing eastern oyster embryos to study the function of genes of interest involved in early development.
Pathogenicity and phylogeny of Labyrinthula spp. isolated in Washington and Oregon, USA
(Wiley, 2025-01-27) Agnew-Camiener, M. Victoria; Eisenlord, Morgan E.; Friedman, Carolyn S.; Schreier, Harold; Burge, Colleen
The class Labyrinthulomycetes constitutes a multitude of species found ubiquitously in the environment, and includes pathogens of corals, hard clams, turfgrasses, and seagrasses. Labyrinthula zosterae, the causative agent of seagrass wasting disease, has been associated with declines in seagrass coverage since the 1930s. However, pathogenic and nonpathogenic Labyrinthula spp. have been isolated from seagrass tissue. These isolates are difficult to distinguish morphologically, and the diversity of isolates where seagrass wasting disease is present is often unknown. This study aimed to increase knowledge on the pathogenicity and phylogeny of Labyrinthula spp. in Washington and Oregon, USA where a high prevalence of seagrass wasting disease has been associated with eelgrass, Zostera marina, declines. We tested the pathogenicity of 14 Labyrinthula isolates and compared partial 18S rRNA gene sequences of 12 isolates to sequences from around the world through the NCBI database. We found that pathogenic isolates could be identified as Labyrinthula zosterae, while nonpathogenic isolates did not form a clade with any previously identified SSU ribotypes. These results add to the growing data on Labyrinthula and seagrass wasting disease and can improve our understanding of pathogen evolution and spread in the future.
Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks
(2025-02-02) Acharya, Kamal; Lad, Mehul; Sun, Liang; Song, Houbing
Travel demand prediction is crucial for optimizing transportation planning, resource allocation, and infrastructure development, ensuring efficient mobility and economic sustainability. This study introduces a Neurosymbolic Artificial Intelligence (Neurosymbolic AI) framework that integrates decision tree (DT)-based symbolic rules with neural networks (NNs) to predict travel demand, leveraging the interpretability of symbolic reasoning and the predictive power of neural learning. The framework utilizes data from diverse sources, including geospatial, economic, and mobility datasets, to build a comprehensive feature set. DTs are employed to extract interpretable if-then rules that capture key patterns, which are then incorporated as additional features into a NN to enhance its predictive capabilities. Experimental results show that the combined dataset, enriched with symbolic rules, consistently outperforms standalone datasets across multiple evaluation metrics, including Mean Absolute Error (MAE), \(R^2\), and Common Part of Commuters (CPC). Rules selected at finer variance thresholds (e.g., 0.0001) demonstrate superior effectiveness in capturing nuanced relationships, reducing prediction errors, and aligning with observed commuter patterns. By merging symbolic and neural learning paradigms, this Neurosymbolic approach achieves both interpretability and accuracy.
Gaps in U.S. livestock data are a barrier to effective environmental and disease management
(AAS, 2025-02-11) Logsdon Muenich, Rebecca; Aryal, Sanskriti; Ashworth, Amanda J; Bell, Michelle L; Boudreau, Melanie R; Cunningham, Stephanie A; Flynn, K Colton; Hamilton, Kerry A; Liu, Ting; Mashtare, Michael L; Nelson, Natalie G; Rashid, Barira; Saha, Arghajeet; Schaffer-Smith, Danica; Showalter, Callie; Tchamdja, Aureliane; Thompson, Jada
Livestock are a critical part of our food systems, yet their abundance globally has been cited as a driver of many environmental and human health concerns. Issues such as soil, water, and air pollution, greenhouse gas emissions, aquifer depletion, antimicrobial resistance genes, and zoonotic disease outbreaks have all been linked to livestock operations. While many studies have examined these issues at depth at local scales, it has been difficult to complete studies at regional or national scales due to the dearth of livestock data, hindering pollution mitigation or response time for tracing and monitoring disease outbreaks. In the U.S. the National Agricultural Statistics Service completes a Census once every 5 years that includes livestock, but data are only available at the county level leaving little inference that can be made at such a coarse spatiotemporal scale. While other data exist through some regulated permitting programs, there are significant data gaps in where livestock are raised, how many livestock are on site at a given time, and how these livestock and, importantly, their waste emissions, are managed. In this perspective, we highlight the need for better livestock data, then discuss the accessibility and key limitations of currently available data. We then feature some recent work to improve livestock data availability through remote-sensing and machine learning, ending with our takeaways to address these data needs for the future of environmental and public health management.
Probing an auxiliary laser to tune the repetition rate of a soliton microcomb
(Optica, 2025-02-15) Mahmood, Tanvir; Cahill, James P.; Sykes, Patrick; Courtright, Logan; Wu, Lue; Vahala, Kerry J.; Menyuk, Curtis; Zhou, Weimin
We demonstrate that it is possible to linearly tune the repetition rate of a bright soliton comb that is generated using an Si3N4 microring resonator by linearly varying the frequency of an auxiliary heater laser. Hence, the auxiliary laser can be utilized as a linear active feedback element for stabilizing the repetition rate. We investigated the potential of the auxiliary laser as an actuator of the soliton repetition rate by varying the auxiliary laser frequency at different modulation rates. Within the modulation bandwidth of the laser, we find that the variation ratio, defined as the ratio of the change in the repetition rate to the change in the laser frequency, remains unchanged. This variation ratio also quantifies the correlation between the frequency drift of the auxiliary laser and the repetition rate phase noise and makes it possible to examine the impact of frequency drift on the attainable phase noise performance of the soliton microcomb. For our setup, we find that the repetition rate phase noise of the microcomb below a 1-kHz offset from the carrier is dominated by the frequency drift of the auxiliary laser, which emphasizes the importance of deploying an inherently low-phase-noise laser when auxiliary laser heating technique is utilized.
Accurate and Interpretable Radar Quantitative Precipitation Estimation with Symbolic Regression
(IEEE, 2025-01-16) Zhang, Olivia; Grissom, Brianna; Pulido, Julian; Munoz-Ordaz, Kenia; He, Jonathan; Cham, Mostafa; Jing, Haotong; Qian, Weikang; Wen, Yixin; Wang, Jianwu
Accurate quantitative precipitation estimation (QPE) is essential for managing water resources, monitoring flash floods, creating hydrological models, and more. Traditional methods of obtaining precipitation data from rain gauges and radars have limitations such as sparse coverage and inaccurate estimates for different precipitation types and intensities. Symbolic regression, a machine learning method that generates mathematical equations fitting the data, presents a unique approach to estimating precipitation that is both accurate and interpretable. Using WSR-88D dual-polarimetric radar data from Oklahoma and Florida over three dates, we tested symbolic regression models involving genetic programming and deep learning, symbolic regression on separate clusters of the data, and the incorporation of knowledge-based loss terms into the loss function. We found that symbolic regression is both accurate in estimating rainfall and interpretable through learned equations. Accuracy and simplicity of the learned equations can be slightly improved by clustering the data based on select radar variables and by adjusting the loss function with knowledge-based loss terms. This research provides insights into improving QPE accuracy through interpretable symbolic regression methods
Chemotaxis of Drosophila Border Cells is Modulated by Tissue Geometry Through Dispersion of Chemoattractants
(Elsevier, 2025-02-05) George, Alexander; Akhavan, Naghmeh; Peercy, Bradford; Starz-Gaiano, Michelle
Migratory cells respond to graded concentrations of diffusible chemoattractants in vitro, but how complex tissue geometries in vivo impact chemotaxis is poorly understood. To address this, we studied the Drosophila border cells. Live-imaged border cells varied in their chemotactic migration speeds, which correlated positionally with distinct architectures. We then developed a reduced mathematical model to determine how chemoattractant distribution is affected by tissue architecture. Larger extracellular volumes locally dampened the chemoattractant gradient and, when coupled with an agent-based motion of the cluster, reduced cell speeds. This suggests that chemoattractant levels vary by tissue architectures, informing cell migration behaviors locally, which we tested in vivo. Genetically elevating chemoattractant levels slowed migration in specific architectural regions, while mutants with spacious tissue structure rescued defects from high chemoattractant levels, promoting punctual migration. Our results highlight the interplay between tissue geometry and the local distribution of signaling molecules to orchestrate cell migration.
Can Generative AI be Egalitarian?
(IEEE, 2024-10) Feldman, Philip; Foulds, James; Pan, Shimei
The recent explosion of “foundation” generative AI models has been built upon the extensive extraction of value from online sources, often without corresponding reciprocation. This pattern mirrors and intensifies the extractive practices of surveillance capitalism [46], while the potential for enormous profit has challenged technology organizations’ commitments to responsible AI practices, raising significant ethical and societal concerns. However, a promising alternative is emerging: the development of models that rely on content willingly and collaboratively provided by users. This article explores this “egalitarian” approach to generative AI, taking inspiration from the successful model of Wikipedia. We explore the potential implications of this approach for the design, development, and constraints of future foundation models. We argue that such an approach is not only ethically sound but may also lead to models that are more responsive to user needs, more diverse in their training data, and ultimately more aligned with societal values. Furthermore, we explore potential challenges and limitations of this approach, including issues of scalability, quality control, and potential biases inherent in volunteercontributed content.
Fair Inference for Discrete Latent Variable Models: An Intersectional Approach
(ACM, 2024-09-04) Islam, Rashidul; Pan, Shimei; Foulds, James
It is now widely acknowledged that machine learning models, trained on data without due care, often exhibit discriminatory behavior. Traditional fairness research has mainly focused on supervised learning tasks, particularly classification. While fairness in unsupervised learning has received some attention, the literature has primarily addressed fair representation learning of continuous embeddings. This paper, however, takes a different approach by investigating fairness in unsupervised learning using graphical models with discrete latent variables. We develop a fair stochastic variational inference method for discrete latent variables. Our approach uses a fairness penalty on the variational distribution that reflects the principles of intersectionality, a comprehensive perspective on fairness from the fields of law, social sciences, and humanities. Intersectional fairness brings the challenge of data sparsity in minibatches, which we address via a stochastic approximation approach. We first show the utility of our method in improving equity and fairness for clustering using naïve Bayes and Gaussian mixture models on benchmark datasets. To demonstrate the generality of our approach and its potential for real-world impact, we then develop a specialized graphical model for criminal justice risk assessments, and use our fairness approach to prevent the inferences from encoding unfair societal biases.
RNA-Puzzles Round V: blind predictions of 23 RNA structures
(Springer Nature, 2024-12-02) Bu, Fan; Adam, Yagoub; Adamiak, Ryszard W.; Antczak, Maciej; de Aquino, Belisa Rebeca H.; Badepally, Nagendar Goud; Batey, Robert T.; Baulin, Eugene F.; Boinski, Pawel; Boniecki, Michal J.; Bujnicki, Janusz M.; Carpenter, Kristy A.; Chacon, Jose; Chen, Shi-Jie; Chiu, Wah; Cordero, Pablo; Das, Naba Krishna; Das, Rhiju; Dawson, Wayne K.; DiMaio, Frank; Ding, Feng; Dock-Bregeon, Anne-Catherine; Dokholyan, Nikolay V.; Dror, Ron O.; Dunin-Horkawicz, Stanisław ; Eismann, Stephan; Ennifar, Eric; Esmaeeli, Reza; Farsani, Masoud Amiri; Ferré-D’Amaré, Adrian R.; Geniesse, Caleb; Ghanim, George E.; Guzman, Horacio V.; Hood, Iris V.; Huang, Lin; Jain, Dharm Skandh; Jaryani, Farhang; Jin, Lei; Joshi, Astha; Karelina, Masha; Kieft, Jeffrey S.; Kladwang, Wipapat; Kmiecik, Sebastian; Koirala, Deepak; Kollmann, Markus; Kretsch, Rachael C.; Kurciński, Mateusz; Li, Jun; Li, Shuang; Magnus, Marcin; Masquida, BenoÎt; Moafinejad, S. Naeim; Mondal, Arup; Mukherjee, Sunandan; Nguyen, Thi Hoang Duong; Nikolaev, Grigory; Nithin, Chandran; Nye, Grace; Pandaranadar Jeyeram, Iswarya P. N.; Perez, Alberto; Pham, Phillip; Piccirilli, Joseph A.; Pilla, Smita Priyadarshini; Pluta, Radosław ; Poblete, Simón; Ponce-Salvatierra, Almudena; Popenda, Mariusz; Popenda, Lukasz; Pucci, Fabrizio; Rangan, Ramya; Ray, Angana; Ren, Aiming; Sarzynska, Joanna; Sha, Congzhou Mike; Stefaniak, Filip; Su, Zhaoming; Suddala, Krishna C.; Szachniuk, Marta; Townshend, Raphael; Trachman, Robert J.; Wang, Jian; Wang, Wenkai; Watkins, Andrew; Wirecki, Tomasz K.; Xiao, Yi; Xiong, Peng; Xiong, Yiduo; Yang, Jianyi; Yesselman, Joseph David; Zhang, Jinwei; Zhang, Yi; Zhang, Zhenzhen; Zhou, Yuanzhe; Zok, Tomasz; Zhang, Dong; Zhang, Sicheng; Żyła, Adriana; Westhof, Eric; Miao, Zhichao
RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson–Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest.
Single Image Super Resolution Using AI Generated Images
(2025-01-18) Singh, Amanjot; Khan, Faisal Rasheed; Singh, Mrinalini
Image super-resolution has become increasingly important in various applications because of their demand for producing high output images from the low input images. Earlier for the image enhancements techniques like deblurring were performed to get the quality image. With the advancements in the Generative Adversarial Networks (GAN), the generating of high-quality image from the low-quality image has been outstanding. The models like SRGAN, ESRGAN [12]are the competitive models which make the Image-Resolution look good because of their performance on the images. But the architecture of the SRGAN which is a state-of-art model is complex and ESRGAN is built on the SRGAN, but by observing the results of the SRGAN the image quality looks good. We try to build a Super-Image Resolution by having the less complex architecture which is faster than SRGAN and the results aren’t compromising even after reducing the architecture complexity. We have built our base model based on the SRGAN by reducing the complexity in the architecture. In our final model we added another discriminator layer which enhances the sub parts of the images to improve the image quality. Our aim is to build an efficient model where the architecture of our model is less complex than SRGAN [14]and give as competitive results as SRGAN. Our results for the final model compared to our base model shows that there were significant improvements in the image quality. The code link for our project is here:https://github.com/faisalkhansk3283/ Computer_Vision_Extended_SRGAN
ANSR-DT: An Adaptive Neuro-Symbolic Learning and Reasoning Framework for Digital Twins
(2025-01-15) Hakim, Safayat Bin; Adil, Muhammad; Velasquez, Alvaro; Song, Houbing
In this paper, we propose an Adaptive Neuro-Symbolic Learning Framework for digital twin technology called ``ANSR-DT." Our approach combines pattern recognition algorithms with reinforcement learning and symbolic reasoning to enable real-time learning and adaptive intelligence. This integration enhances the understanding of the environment and promotes continuous learning, leading to better and more effective decision-making in real-time for applications that require human-machine collaboration. We evaluated the \textit{ANSR-DT} framework for its ability to learn and adapt to dynamic patterns, observing significant improvements in decision accuracy, reliability, and interpretability when compared to existing state-of-the-art methods. However, challenges still exist in extracting and integrating symbolic rules in complex environments, which limits the full potential of our framework in heterogeneous settings. Moreover, our ongoing research aims to address this issue in the future by ensuring seamless integration of neural models at large. In addition, our open-source implementation promotes reproducibility and encourages future research to build on our foundational work.

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