UMBC Faculty Collection

Permanent URI for this collectionhttp://hdl.handle.net/11603/15

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Assessing Annotation Accuracy in Ice Sheets Using Quantitative Metrics
(IEEE, 2024-09-05) Tama, Bayu Adhi; Janeja, Vandana; Purushotham, Sanjay
The increasing threat of sea level rise due to climate change necessitates a deeper understanding of ice sheet structures. This study addresses the need for accurate ice sheet data interpretation by introducing a suite of quantitative metrics designed to validate ice sheet annotation techniques. Focusing on both manual and automated methods, including ARESELP and its modified version, MARESELP, we assess their accuracy against expert annotations. Our methodology incorporates several computer vision metrics, traditionally under-utilized in glaciological research, to evaluate the continuity and connectivity of ice layer annotations. The results demonstrate that while manual annotations provide invaluable expert insights, automated methods, particularly MARESELP, improve layer continuity and alignment with expert labels.
Creating Geospatial Trajectories from Human Trafficking Text Corpora
(2024-05-09) Karabatis, Saydeh N.; Janeja, Vandana
Human trafficking is a crime that affects the lives of millions of people across the globe. Traffickers exploit the victims through forced labor, involuntary sex, or organ harvesting. Migrant smuggling could also be seen as a form of human trafficking when the migrant fails to pay the smuggler and is forced into coerced activities. Several news agencies and anti-trafficking organizations have reported trafficking survivor stories that include the names of locations visited along the trafficking route. Identifying such routes can provide knowledge that is essential to preventing such heinous crimes. In this paper we propose a Narrative to Trajectory (N2T) information extraction system that analyzes reported narratives, extracts relevant information through the use of Natural Language Processing (NLP) techniques, and applies geospatial augmentation in order to automatically plot trajectories of human trafficking routes. We evaluate N2T on human trafficking text corpora and demonstrate that our approach of utilizing data preprocessing and augmenting database techniques with NLP libraries outperforms existing geolocation detection methods.
Harnessing Feature Clustering For Enhanced Anomaly Detection With Variational Autoencoder And Dynamic Threshold
(IEEE, 2024-09-05) Ale, Tolulope; Janeja, Vandana; Schlegel, Nicole-Jeanne
We introduce an anomaly detection method for multivariate time series data with the aim of identifying critical periods and features influencing extreme climate events like snowmelt in the Arctic. This method leverages Variational Autoencoder (VAE) integrated with dynamic thresholding and correlationbased feature clustering. This framework enhances the VAE’s ability to identify localized dependencies and learn the temporal relationships in climate data, thereby improving the detection of anomalies as demonstrated by its higher F1-score on benchmark datasets. The study’s main contributions include the development of a robust anomaly detection method, improving feature representation within VAEs through clustering, and creating a dynamic threshold algorithm for localized anomaly detection. This method offers explainability of climate anomalies across different regions.
Deep Learning for Antarctic Sea Ice Anomaly Detection and Prediction: A Two-Module Framework
(ACM, 2024-11-06) Devnath, Maloy Kumar; Chakraborty, Sudip; Janeja, Vandana
The Antarctic sea ice cover plays a crucial role in regulating global climate and sea level rise. The recent retreat of the Antarctic Sea Ice Extent and the accelerated melting of ice sheets (which causes sea level rise) raise concerns about the impact of climate change. Understanding the spatial patterns of anomalous melting events in sea ice is crucial for improving climate models and predicting future sea level rise, as sea ice serves as a protective barrier for ice sheets. This paper proposes a two-module framework based on Deep Learning that utilizes satellite imagery to identify and predict non-anomalous and anomalous melting regions in Antarctic sea ice. The first module focuses on identifying non-anomalous and anomalous melting regions in the current day by analyzing the difference between consecutive satellite images over time. The second module then leverages the current day's information and predicts the next day's non-anomalous and anomalous melting regions. This approach aims to improve our ability to monitor and predict critical changes in the Antarctic sea ice cover.
Detection asymmetry in solar energetic particle events
(2024-11-12) Dalla, S.; Hutchinson, Adam; Hyndman, R. A.; Kihara, K.; Nitta, N.; Rodriguez-Garcia, L.; Laitinen, T.; Waterfall, C. O. G.; Brown, D. S.
Context. Solar energetic particles (SEPs) are detected in interplanetary space in association with flares and coronal mass ejections (CMEs) at the Sun. The magnetic connection between the observing spacecraft and the solar active region (AR) source of the event is a key parameter in determining whether SEPs are observed and the properties of the particle event. Aims. We investigate whether an east-west asymmetry in the detection of SEP events is present in observations and discuss its possible link to corotation of magnetic flux tubes with the Sun. Methods. We used a published dataset of 239 CMEs recorded between 2006 and 2017 and having source regions both on the front side and far side of the Sun as seen from Earth. We produced distributions of occurrence of in-situ SEP intensity enhancements associated with the CME events, versus \Delta \phi, the separation in longitude between the source active region and the magnetic footpoint of the observing spacecraft based on the nominal Parker spiral. We focused on protons of energy >10 MeV measured by the STEREO A, STEREO B and GOES spacecraft at 1 au. We also considered the occurrence of 71-112 keV electron events detected by MESSENGER between 0.31 and 0.47 au. Results. We find an east-west asymmetry in the detection of >10 MeV proton events and of 71-112 keV electron events. For protons, observers for which the source AR is on the east side of the spacecraft footpoint and not well connected (-180 < \Delta \phi < -40) are 93% more likely to detect an SEP event compared to observers with +40 < \Delta \phi < +180. The asymmetry may be a signature of corotation of magnetic flux tubes with the Sun, given that for events with \Delta \phi < 0 corotation sweeps the particle-filled flux tubes towards the observing spacecraft, while for \Delta \phi > 0 it takes them away from it.
A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19
(2024-11-11) Khandelwal, Vedant; Gaur, Manas; Kursuncu, Ugur; Shalin, Valerie; Sheth, Amit
Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamically evolving environment. Human-curated symbolic knowledge sources, such as lexicons for standard language and slang terms, can potentially elevate social media signals in evolving language. We introduce a neurosymbolic method that integrates neural networks with symbolic knowledge sources, enhancing the detection and interpretation of mental health-related tweets relevant to COVID-19. Our method was evaluated using a corpus of large datasets (approximately 12 billion tweets, 2.5 million subreddit data, and 700k news articles) and multiple knowledge graphs. This method dynamically adapts to evolving language, outperforming purely data-driven models with an F1 score exceeding 92\%. This approach also showed faster adaptation to new data and lower computational demands than fine-tuning pre-trained large language models (LLMs). This study demonstrates the benefit of neurosymbolic methods in interpreting text in a dynamic environment for tasks such as health surveillance.
The effect of different feature selection methods on models created with XGBoost
(2024-11-08) Neyra, Jorge; Siramshetty, Vishal B.; Ashqar, Huthaifa
This study examines the effect that different feature selection methods have on models created with XGBoost, a popular machine learning algorithm with superb regularization methods. It shows that three different ways for reducing the dimensionality of features produces no statistically significant change in the prediction accuracy of the model. This suggests that the traditional idea of removing the noisy training data to make sure models do not overfit may not apply to XGBoost. But it may still be viable in order to reduce computational complexity.
In Context Learning and Reasoning for Symbolic Regression with Large Language Models
(2024-10-22) Sharlin, Samiha; Josephson, Tyler R.
Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not explicitly trained. Here, we explore the potential of LLMs to perform symbolic regression -- a machine-learning method for finding simple and accurate equations from datasets. We prompt GPT-4 to suggest expressions from data, which are then optimized and evaluated using external Python tools. These results are fed back to GPT-4, which proposes improved expressions while optimizing for complexity and loss. Using chain-of-thought prompting, we instruct GPT-4 to analyze the data, prior expressions, and the scientific context (expressed in natural language) for each problem before generating new expressions. We evaluated the workflow in rediscovery of five well-known scientific equations from experimental data, and on an additional dataset without a known equation. GPT-4 successfully rediscovered all five equations, and in general, performed better when prompted to use a scratchpad and consider scientific context. We also demonstrate how strategic prompting improves the model's performance and how the natural language interface simplifies integrating theory with data. Although this approach does not outperform established SR programs where target equations are more complex, LLMs can nonetheless iterate toward improved solutions while following instructions and incorporating scientific context in natural language.
Trans* & gender identity in the premodern Mediterranean
(Springer, 2024-11-14) McDonough, Susan; Armstrong-Partida, Michelle
This paper explores the intersection and imbrication of transness and Mediterraneaness in the premodern period. How did Mediterranean mobility, spaces, and creativity inform and make possible the ‘transing’ of gender? Re-examining previously considered sources with the benefit of recent scholarship on archival silences and trans history, we suggest that Mediterranean culture, migration, local community, and race shaped possibilities for transgender people. Prioritizing the agency of people who lived non-binary and trans lives, we make them legible to a contemporary audience while refraining from imposing our own labels upon them. The messy and contradictory lives of our subjects show the complexity of personhood and identity. We consider unrecorded suffering and reflect on how the physical bodies of those punished for transgressing gender norms were inscribed with meaning that resonated with hegemonic constructions of sexuality and identity. We center the bodies, identities, and experiences of trans people rather than the elite male discourses of a heteronormative society.
Search for Extended GeV Sources in the Inner Galactic Plane
(2024-11-11) Abdollahi, S.; Acero, F.; Acharyya, A.; Adelfio, A.; Ajello, M.; Baldini, L.; Ballet, J.; Bartolini, C.; Gonzalez, J. Becerra; Bellazzini, R.; Bissaldi, E.; Bonino, R.; Bruel, P.; Cameron, R. A.; Caraveo, P. A.; Castro, D.; Cavazzuti, E.; Cheung, C. C.; Cibrario, N.; Ciprini, S.; Cozzolongo, G.; Orestano, P. Cristarella; Cuoco, A.; Cutini, S.; D'Ammando, F.; Lalla, N. Di; Dinesh, A.; Venere, L. Di; Domínguez, A.; Fiori, A.; Funk, S.; Fusco, P.; Gargano, F.; Gasbarra, C.; Gasparrini, D.; Germani, S.; Giacchino, F.; Giglietto, N.; Giliberti, M.; Giordano, F.; Giroletti, M.; Green, D.; Grenier, I. A.; Guillemot, L.; Guiriec, S.; Gupta, R.; Hashizume, M.; Hays, E.; Hewitt, J. W.; Horan, D.; Hou, X.; Kayanoki, T.; Kuss, M.; Laviron, A.; Lemoine-Goumard, M.; Liguori, A.; Li, J.; Liodakis, I.; Loizzo, P.; Longo, F.; Loparco, F.; Lorusso, L.; Lovellette, M. N.; Lubrano, P.; Maldera, S.; Malyshev, D.; Martí-Devesa, G.; Martin, P.; Mazziotta, M. N.; Mereu, I.; Michelson, P. F.; Mirabal, Nestor; Mitthumsiri, W.; Mizuno, T.; Monti-Guarnieri, P.; Monzani, M. E.; Morselli, A.; Moskalenko, I. V.; Negro, M.; Omodei, N.; Orienti, M.; Orlando, E.; Paneque, D.; Panzarini, G.; Persic, M.; Pesce-Rollins, M.; Pillera, R.; Porter, T. A.; Rainò, S.; Rando, R.; Razzano, M.; Reimer, A.; Reimer, O.; Bernal, M. Rocamora; Sánchez-Conde, M.; Parkinson, P. M. Saz; Serini, D.; Sgrò, C.; Siskind, E. J.; Smith, D. A.; Spandre, G.; Spinelli, P.; Strong, A. W.; Suson, D. J.; Tajima, H.; Thayer, J. B.; Torres, D. F.; Valverde, Janeth; Wadiasingh, Z.; Wood, K.; Zaharijas, G.
The recent detection of extended γ-ray emission around middle-aged pulsars is interpreted as inverse-Compton scattering of ambient photons by electron-positron pairs escaping the pulsar wind nebula, which are confined near the system by unclear mechanisms. This emerging population of γ-ray sources was first discovered at TeV energies and remains underexplored in the GeV range. To address this, we conducted a systematic search for extended sources along the Galactic plane using 14 years of Fermi-LAT data above 10 GeV, aiming to identify a number of pulsar halo candidates and extend our view to lower energies. The search covered the inner Galactic plane (|l|≤ 100∘, |b|≤ 1∘) and the positions of known TeV sources and bright pulsars, yielding broader astrophysical interest. We found 40 such sources, forming the Second Fermi Galactic Extended Sources Catalog (2FGES), most with 68% containment radii smaller than 1.0∘ and relatively hard spectra with photon indices below 2.5. We assessed detection robustness using field-specific alternative interstellar emission models and by inspecting significance maps. Noting 13 sources previously known as extended in the 4FGL-DR3 catalog and five dubious sources from complex regions, we report 22 newly detected extended sources above 10 GeV. Of these, 13 coincide with H.E.S.S., HAWC, or LHAASO sources; six coincide with bright pulsars (including four also coincident with TeV sources); six are associated with 4FGL point sources only; and one has no association in the scanned catalogs. Notably, six to eight sources may be related to pulsars as classical pulsar wind nebulae or pulsar halos.
Organizing for More Just and Inclusive Futures: A Community Discussion
(ACM, 2024-11-13) Fernandes, Kim; Alharbi, Rahaf; Sum, Cella; Kameswaran, Vaishnav; Spektor, Franchesca; Thuppilikkat, Ashique Ali; Petterson, Adrian; Marathe, Megh; Hamidi, Foad; Chandra, Priyank
This Special Interest Group brings together researchers and practitioners to examine the critical questions, innovative methods and emerging possibilities that arise from an orientation toward disability justice within CSCW research particularly and HCI research more broadly. We will focus on how digital technologies influence the ways disabled people organize and advocate for their rights, and how disabled people influence and configure technologies as well. By attending to the intersections of technology, disability justice, and social movements, we aim to explore how HCI and CSCW research can support the organizing efforts of disabled communities. This SIG emphasizes the ways in which disabled people and communities have been organizing and are continuing to organize in response to various forms of oppression. The SIG will provide a platform for scholars and activists to engage in conversations around technologies, disability justice, and social movements. By centering disability justice as a framework, we hope to foster a deeper understanding of how HCI and CSCW research can support and amplify the efforts of disabled communities. Participants will share their insights, collaborate on research ideas, and contribute to a collective vision of a more inclusive and justice-oriented HCI and CSCW. Through these discussions, we aim to generate actionable strategies for future research and practice in supporting organizing efforts.
Toward Transdisciplinary Approaches to Audio Deepfake Discernment
(2024-11-08) Janeja, Vandana; Mallinson, Christine
This perspective calls for scholars across disciplines to address the challenge of audio deepfake detection and discernment through an interdisciplinary lens across Artificial Intelligence methods and linguistics. With an avalanche of tools for the generation of realistic-sounding fake speech on one side, the detection of deepfakes is lagging on the other. Particularly hindering audio deepfake detection is the fact that current AI models lack a full understanding of the inherent variability of language and the complexities and uniqueness of human speech. We see the promising potential in recent transdisciplinary work that incorporates linguistic knowledge into AI approaches to provide pathways for expert-in-the-loop and to move beyond expert agnostic AI-based methods for more robust and comprehensive deepfake detection.
A Machine-Learning Approach to Mitigate Ground Clutter Effects in the GPM Combined Radar-Radiometer Algorithm (CORRA) Precipitation Estimates
(AMS, 2024-11-13) Grecu, Mircea; Heymsfield, Gerald M.; Nicholls, Stephen; Lang, Stephen; Olson, William S.
In this study, a machine-learning based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Mission Combined Radar-Radiometer Algorithm. Ground clutter can corrupt and obscure precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflectivity observations above the clutter are included in a fixed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A Neural Network Model (NN) is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN’s limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.
The Dual Role of Student and Creator: Exploring the TikTok Experience
(ACM, 2024-11-13) Bulley, Bharadwaj Kuruba; Tirumala, Shravika; Mahamkali, Bhavani Shankar; Sakib, Md Nazmus; Ahmed, Saquib; Dey, Sanorita
TikTok is one of the most common content-creating social media platforms for youth in the USA. In recent years, its engaging content has significantly influenced people, shaping trends, behaviors, and communication styles among its predominantly young user base. This study evaluates TikTok's impact on college and university students as they invest a lot of time creating content and engaging on TikTok besides their studies. While existing research highlights TikTok's educational benefits and adverse societal and psychological effects, our mixed-method approach provides a focused analysis of student content creators. Survey data quantifies usage patterns and their correlation with academic and mental health indicators, while interviews offer qualitative insights into personal experiences. Findings reveal that TikTok affects students' time management, mental health, academic performance, and self-perception. Although TikTok facilitates creativity and social connections, it also induces stress and distraction. This study aims to fill research gaps and propose new directions, offering practical recommendations for balancing TikTok's benefits and drawbacks for student content creators.
When to Commute During the COVID-19 Pandemic and Beyond: Analysis of Traffic Crashes in Washington, D.C
(2024-11-08) Choi, Joanne; Clark, Sam; Jaiswal, Ranjan; Kirk, Peter; Jayaraman, Sachin; Ashqar, Huthaifa
Many workers in cities across the world, who have been teleworking because of the COVID-19 pandemic, are expected to be back to their commutes. As this process is believed to be gradual and telecommuting is likely to remain an option for many workers, hybrid model and flexible schedules might become the norm in the future. This variable work schedules allows employees to commute outside of traditional rush hours. Moreover, many studies showed that commuters might be skeptical of using trains, buses, and carpools and could turn to personal vehicles to get to work, which might increase congestion and crashes in the roads. This study attempts to provide information on the safest time to commute to Washington, DC area analyzing historical traffic crash data before the COVID-19 pandemic. It also aims to advance our understanding of traffic crashes and other relating factors such as weather in the Washington, DC area. We created a model to predict crashes by time of the day, using a negative binomial regression after rejecting a Poisson regression, and additionally explored the validity of a Random Forest regression. Our main consideration for an eventual application of this study is to reduce crashes in Washington DC, using this tool that provides people with better options on when to commute and when to telework, if available. The study also provides policymakers and researchers with real-world insights that decrease the number of traffic crashes to help achieve the goals of The Vision Zero Initiative adopted by the district.
Post-Roe Public Discourse: A Temporal Analysis of Discussion on US Abortion Law Changes
(ACM, 2024-11-13) Venkata, Harisahan Nookala; Palakurthi, Varshitha; Devalam, Sree Sai Bindu; Sakib, Md Nazmus; Ahmed, Saquib; Dey, Sanorita
The "Post-Roe" refers to the period following the June 2022 Supreme Court decision to overrule Roe v. Wade, the 1973 abortion right law. Since this overturn of this law, substantial public discourse was noticed across the US and this controversial issue has trended on all news media agencies and social media platforms. Several studies have analyzed public opinion and the impact of this legal change on healthcare, economic challenges, and society. However, little work has been done to identify the shift in discussion over time. Our research analyzes YouTube and Reddit comments to perceive insights into the evolving spectrum of public opinion on abortion legislation by utilizing NLP techniques and opinion mining. By systematically categorizing comments to extract themes and employing a temporal analysis approach, we identify shifts in public sentiment across different phases of time, such as immediate reactions, peak debate, and long-term responses. Our preliminary findings show that different themes prevail at different phases and primary concerns shift over time. This study might help policymakers, activists, and social commentators understand these shifts to effectively address the evolving concerns of the public and take measures accordingly.
A Framework for Empirical Fourier Decomposition based Gesture Classification for Stroke Rehabilitation
(IEEE, 2024-11-11) Chen, Ke; Wang, Honggang; Catlin, Andrew; Satyanarayana, Ashwin; Vinjamuri, Ramana; Kadiyala, Sai Praveen
The demand for surface electromyography (sEMG) based exoskeletons is rapidly increasing due to their non-invasive nature and ease of use. With increase in use of Internet-of-Things (IoT) based devices in daily life, there is a greater acceptance of exoskeleton based rehab. As a result, there is a need for highly accurate and generalizable gesture classification mechanisms based on sEMG data. In this work, we present a framework which pre-processes raw sEMG signals with Empirical Fourier Decomposition (EFD) based approach followed by dimension reduction. This resulted in improved performance of the hand gesture classification. EFD decomposition’s efficacy of handling mode mixing problem on non-stationary signals, resulted in less number of decomposed components. In the next step, a thorough analysis of decomposed components as well as inter-channel analysis is performed to identify the key components and channels that contribute towards the improved gesture classification accuracy. As a third step, we conducted ablation studies on time-domain features to observe the variations in accuracy on different models. Finally, we present a case study of comparison of automated feature extraction based gesture classification vs. manual feature extraction based methods. Experimental results show that manual feature based gesture classification method thoroughly outperformed automated feature extraction based methods, thus emphasizing a need for rigorous fine tuning of automated models.
Morphology and Luminescence Properties of Transition Metal Doped Zinc Selenide Crystals
(Springer Nature, 2024-11-11) Bowman, Eric; Scheurer, Leslie; Arnold, Bradley; Su, Ching Hua; Choa, Fow-Sen; Cullum, Brian; Singh, Narsingh
Zinc selenide is an excellent matrix material to dope with rare-earth and transition metal to achieve mid-infrared luminescence to develop high power lasers. The luminescence, morphology and refractive index is significantly affected by the doping and defects generated due to size and valency of dopants, concentration, growth process and convection during the growth. The aim of the study is to investigate effect of point and line defects generated due to low doping of iron and chromium on the emission and morphology of the zinc selenide. Luminescence and morphological properties of large iron and chromium doped zinc selenide single crystals were studied to evaluate the effect of extremely low residual impurities and defects associated with the doping process. The emission properties following both short wavelength (i.e., ultraviolet; 350–370 nm) excitation and longer wavelength (i.e., near infrared; 850–870 nm) excitation were characterized. Luminescence emission bands were identified in both doped crystals. In addition to the primary emission bands, satellite peaks and intra-center transitions were also observed. Due to local population defects associated with the residual impurities (ppm to ppb) in the Fe-ZnSe and Cr-ZnSe crystals, peak emission wavelengths were observed to shift. The emission bands were found to decrease in intensity due to recombination of residual impurity co-dopants and complex defects generated during growth and fabrication. Cryogenic temperature analyses revealed a very clean emission band due to freezing of some of the point and line defects. An emission band observed at 980 nm for both crystals at room temperature as well as cryogenic temperatures indicates a vibronic peak in ZnSe. The scanning electron microscopy (SEM) images of the local morphology support the conclusion that small crystallites in doped crystals are also present.
Panning for gold with the Neil Gehrels Swift Observatory: an optimal strategy for finding the counterparts to gravitational wave events
(2024-11-07) Eyles-Ferris, R. A. J.; Evans, P. A.; Breeveld, A. A.; Cenko, S. B.; Dichiara, S.; Kennea, J. A.; Klingler, Noel; Kuin, N. P. M.; Marshall, F. E.; Oates, S. R.; Page, M. J.; Ronchini, S.; Siegel, M. H.; Tohuvavohu, A.; Campana, S.; D'Elia, V.; Osborne, J. P.; Page, K. L.; Pasquale, M. De; Troja, E.
The LIGO, Virgo and KAGRA gravitational wave observatories are currently undertaking their O4 observing run offering the opportunity to discover new electromagnetic counterparts to gravitational wave events. We examine the capability of the Neil Gehrels Swift Observatory (Swift) to respond to these triggers, primarily binary neutron star mergers, with both the UV/Optical Telescope (UVOT) and the X-ray Telescope (XRT). We simulate Swift's response to a trigger under different strategies using model skymaps, convolving these with the 2MPZ catalogue to produce an ordered list of observing fields, deriving the time taken for Swift to reach the correct field and simulating the instrumental responses to modelled kilonovae and short gamma-ray burst afterglows. We find that UVOT using the u filter with an exposure time of order 120 s is optimal for most follow-up observations and that we are likely to detect counterparts in ∼6% of all binary neutron star triggers. We find that the gravitational wave 90% error area and measured distance to the trigger allow us to select optimal triggers to follow-up. Focussing on sources less than 300 Mpc away or 500 Mpc if the error area is less than a few hundred square degrees, distances greater than previously assumed, offer the best opportunity for discovery by Swift with ∼5−30% of triggers having detection probabilities ≥0.5. At even greater distances, we can further optimise our follow-up by adopting a longer 250 s or 500 s exposure time.
Unsupervised Domain Adaptation for Action Recognition via Self-Ensembling and Conditional Embedding Alignment
(2024-10-23) Ghosh, Indrajeet; Chugh, Garvit; Faridee, Abu Zaher Md; Roy, Nirmalya
Recent advancements in deep learning-based wearable human action recognition (wHAR) have improved the capture and classification of complex motions, but adoption remains limited due to the lack of expert annotations and domain discrepancies from user variations. Limited annotations hinder the model's ability to generalize to out-of-distribution samples. While data augmentation can improve generalizability, unsupervised augmentation techniques must be applied carefully to avoid introducing noise. Unsupervised domain adaptation (UDA) addresses domain discrepancies by aligning conditional distributions with labeled target samples, but vanilla pseudo-labeling can lead to error propagation. To address these challenges, we propose μDAR, a novel joint optimization architecture comprised of three functions: (i) consistency regularizer between augmented samples to improve model classification generalizability, (ii) temporal ensemble for robust pseudo-label generation and (iii) conditional distribution alignment to improve domain generalizability. The temporal ensemble works by aggregating predictions from past epochs to smooth out noisy pseudo-label predictions, which are then used in the conditional distribution alignment module to minimize kernel-based class-wise conditional maximum mean discrepancy (kCMMD) between the source and target feature space to learn a domain invariant embedding. The consistency-regularized augmentations ensure that multiple augmentations of the same sample share the same labels; this results in (a) strong generalization with limited source domain samples and (b) consistent pseudo-label generation in target samples. The novel integration of these three modules in μDAR results in a range of ≈4-12% average macro-F1 score improvement over six state-of-the-art UDA methods in four benchmark wHAR datasets

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