Singapore

Reliable detection of methane plumes in vegetated regions remains challenging due to the spectral similarity between vegetation reflectance and methane absorption features, as well as the coarse spectral resolution of freely available multispectral satellites such as Sentinel-2. To address this problem, we propose VegiSR, a vegetation-aware spectral super-resolution framework that reconstructs EMIT-like hyperspectral representations from Sentinel-2 imagery. VegiSR leverages cross-sensor spectral learning using a large-scale Sentinel-2 and EMIT paired dataset we constructed, and incorporates a vegetation-aware spectral regularization and spectral attention mechanism to suppress vegetation-induced confusion while enhancing methane-sensitive wavelengths. The reconstructed hyperspectral data is then integrated with matched filter to detect methane plumes in vegetated area where direct Sentinel-2 detection fails. Experimental results demonstrate that VegiSR achieves superior spectral reconstruction fidelity and significantly improves methane detection accuracy over
vegetated surfaces compared to U-Net and direct Sentinel-2 methods. This work highlights the potential of cross-sensor spectral super resolution for global methane monitoring using free multispectral observations.

AAAI 2026

Learning Cross-Sensor Spectral Super-Resolution for Methane Plume Detection over Vegetated Areas Using Sentinel-2 and EMIT Data

multispectral

methane

hyperspectral

detection

Welcome to Delaware State University’s Annual Summer Research Symposium, and thank you for attending! Our symposium represents the culmination of students’ summer experiences in research at Delaware State University. This event showcases the work of college students working on faculty-led research projects, as well as middle and high school students experiencing research for the first time working in research groups. Whatever the case, this summer has been a unique experience for the students, and we hope a transformative one. Even for students who are not planning a career in scientific research, their summer experience has given them skills they will draw on for years to come. 
 Please join us on the mainstage and in Gather for the live event, and visit the recorded poster presentations any time.
 The student participants and organizers of the various summer programs at DSU would like to thank all of the research mentors who guided our summer program participants. There were many DSU faculty, graduate students, and other research and program staff who gave their time and energy to help our students have a fun and educational summer experience. Without their generosity and patience these summer programs would not be possible.
 For the research participants, we hope that you will maintain contact with your research mentors and colleagues. Science is a relatively small community made up of disciplinary “neighborhoods”. If you continue to work in a discipline, the other folks in that discipline will be like your neighbors that you run into regularly. Staying in contact with them will help you keep up with the news of the neighborhood, and open doors to educational and career opportunities. Whether you continue in science or take a different career path, for this summer you have been part of the world of research, and you will benefit from that perspective for the rest of your life.

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DSU Summer Research Symposium 2021

Welcome to Delaware State University’s Annual Summer Research Symposium, and thank you for attending!

workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Processing wildlife imagery for conservation and management poses significant challenges, especially when limited data hinders the ability of classification models to extract sufficient features from each wildlife class. In this study, we propose the use of a 3D free-viewpoint image-to-video generative model to augment the dataset by synthesizing new images for fine-tuning classification models. Our results demonstrate a notable improvement in the F1-score, increasing from a baseline of 0.338 to 0.525. While the quality of the synthesized images can be further enhanced, particularly in terms of incorporating wildlife-specific semantics, the study highlights the potential of generative AI not only for media creation but also for advancing environmental monitoring applications, despite challenges such as high computational cost.

Diffusion-based free-viewpoint synthesis for dataset expansion and wildlife classification

Recent progress in vision language models (VLMs) has enabled remarkable perception and reasoning capabilities, yet their potential for scientific regression in Earth Observation (EO) remains largely unexplored. Existing EO datasets mainly emphasize semantic understanding tasks such as captioning or classification, lacking benchmarks that align multimodal perception with measurable biophysical variables. To fill this gap, we present REO-Instruct, the first unified benchmark designed for both descriptive and regression tasks in EO. REO-Instruct establishes a cognitively interpretable logic chain in forest ecological scenario: human activity → land-cover classification → ecological patch counting → above-ground biomass (AGB) regression, bridging qualitative understanding and quantitative prediction. The dataset integrates co-registered Sentinel-2 and ALOS-2 imagery with structured textual annotations generated and validated through a hybrid human–AI pipeline. Comprehensive evaluation protocols and baseline results across generic VLMs reveal that current models struggle with numeric reasoning, highlighting an essential challenge for scientific VLMs. REO-Instruct offers a standardized foundation for developing and assessing next-generation geospatial models capable of both description and scientific inference. The dataset will be publicly available.

Towards Unified Vision Language Models for Forest Ecological Analysis in Earth Observation

Typical domestic Immersion water heater systems are always turned on during the winter, it heats quickly rather than efficiently and ignores predictable demand windows and ambient losses. We study deadline-aware control, where the aim is to reach a target temperature at a specified time while minimising energy. We introduce an efficient Gymnasium environment that models an immersion hot-water heater with first-order thermal losses and discrete on and off actions ${0, 6000}$ W applied every 120 s. Methods include a time-optimal bang-bang baseline, a zero-shot Monte Carlo Tree Search planner, and a Proximal Policy Optimization policy. We report total energy (Wh) under identical physics. Across sweeps of initial temperature (10–30 °C), deadline (30–90 steps), and target temperature (40–80 °C), PPO achieves the most energy-efficient performance at a 60-step horizon (2 h) it uses 3.23 kWh, versus bang-bang’s 4.37–10.45 kWh and MCTS’s 4.18–6.46 kWh, yielding savings of 26\% at 30 steps and 69\% at 90 steps. In a representative trajectory (50 kg, 20 °C ambient, 60 °C target), PPO consumes 54\% less energy than bang-bang and 33\% less than MCTS. These results show that learned, deadline-aware control reduces energy under identical physics where planners provide partial savings without training, while policies offer near-zero-cost inference once trained.

Deadline-Aware, Energy-Efficient Control of Domestic Immersion Hot Water Heater

Infrared and visible object detection (IVOD) is essential for numerous around-the-clock applications. Despite notable advancements, current IVOD models exhibit notable performance declines when confronted with incomplete modality data, particularly if the dominant modality is missing. In this paper, we take a thorough investigation on modality incomplete IVOD problem from an architecture compatibility perspective. Specifically, we propose a plug-and-play Scarf Neck module for DETR variants, which introduces a modalityagnostic deformable attention mechanism to enable the IVOD detector to flexibly adapt to any single or double modalities during training and inference. When training Scarf-DETR, we design a pseudo modality dropout strategy to fully utilize the multi-modality information, making the detector compatible and robust to both working modes of single and double modalities. Moreover, we introduce a comprehensive benchmark for the modality-incomplete IVOD task aimed at thoroughly assessing situations where the absent modality is either dominant or secondary. Our proposed Scarf-DETR not only performs excellently in missing modality scenarios but also achieves superior performances on the standard IVOD modality complete benchmarks. We will release our code.

On Modality Incomplete Infrared-Visible Object Detection: An Architecture Compatibility Perspective

Semi-supervised learning (SSL) has emerged as a critical paradigm for medical image segmentation, mitigating the immense cost of dense annotations. However, prevailing SSL frameworks are fundamentally “inward-looking”, recycling information and biases solely from within the target dataset. This design triggers a vicious cycle of confirmation bias under class imbalance, leading to the catastrophic failure to recognize minority classes. To dismantle this systemic issue, we propose a paradigm shift to a multi-level “outward-looking” framework. Our primary innovation is Foundational Knowledge Distillation (FKD), which looks outward beyond the confines of medical imaging by introducing a pre-trained visual foundation model, DINOv3, as an unbiased external semantic teacher. Instead of trusting the student’s biased high confidence, our method distills knowledge from DINOv3’s robust understanding of high semantic uniqueness, providing a stable, cross-domain supervisory signal that anchors the learning of minority classes. To complement this core strategy, we further look outward within the data by proposing Progressive Imbalance-aware CutMix (PIC), which creates a dynamic curriculum that adaptively forces the model to focus on minority classes in both labeled and unlabeled subsets. This layered strategy forms our framework, DINO-Mix, which breaks the vicious cycle of bias and achieves remarkable performance on challenging semi-supervised class-imbalanced medical image segmentation benchmarks Synapse and AMOS.

DINO-Mix: Distilling Foundational Knowledge with Cross-Domain CutMix for Semi-supervised Class-imbalanced Medical Image Segmentation

Membership inference attacks (MIAs) test whether a data point was part of a model's training set, posing serious privacy risks. Existing methods often depend on shadow models or heavy query access, which limits their practicality. We propose GP-MIA, an efficient and interpretable approach based on Gaussian process (GP) meta-modeling. Using post-hoc metrics such as accuracy, entropy, dataset statistics, and optional sensitivity features (e.g. gradients, NTK measures) from a single trained model, GP-MIA trains a GP classifier to distinguish members from non-members while providing calibrated uncertainty estimates. Experiments on synthetic data, real-world fraud detection data, CIFAR-10, and WikiText-2 show that GP-MIA achieves high accuracy and generalizability, offering a practical alternative to existing MIAs.

Training data membership inference via Gaussian process meta-modeling: a post-hoc analysis approach

Recursive Self-Improvement (RSI) is an effective approach to alleviating data scarcity. This paper explores the application of RSI in text-to-image diffusion models, addressing the challenge of training collapse caused by biased synthetic data. We identify two key factors contributing to this collapse: the lack of perceptual alignment and the accumulation of generative hallucinations. To mitigate these issues, we propose three strategies: (1) a prompt construction and filtering pipeline designed to facilitate the generation of perceptual aligned data, (2) a preference sampling method to identify human-preferred samples and filter out generative hallucinations, and (3) a distribution-based weighting scheme to penalize selected samples with hallucinatory errors. Our extensive experiments validate the effectiveness of these approaches.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models

Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. However, EHRs are often sparse and contain missing data due to various challenges and limitations in data collection and sharing between healthcare providers. Existing techniques for imputing missing values predominantly focus on known unknowns, such as missing or unavailable values of lab test results; most do not explicitly address situations where it is difficult to distinguish what is missing. For instance, a missing diagnosis code in an EHR could signify either that the patient has not been diagnosed with the condition or that a diagnosis was made, but not shared by a provider. Such situations fall into the paradigm of unknown unknowns. To address this challenge, we develop a general purpose algorithm for denoising data to recover unknown missing values in binary EHRs. We design a transformer-based denoising neural network where the output is thresholded adaptively to recover values in cases where we predict data are missing. Our method demonstrates improved accuracy in denoising medical codes within a real EHR dataset compared to existing imputation approaches and leads to increased performance on downstream tasks using the denoised data. In particular, when applying our method to a real world application, predicting hospital readmission from EHRs, our method achieves statistically significant improvement over all existing baselines.

Imputation of Unknown Missingness in Sparse Electronic Health Records

Sequential recommendation (SR) systems leverage users’ historical interaction sequences and are widely adopted on online platforms to deliver personalized suggestions effectively. Recent advancements in self-attention and contrastive learning-based models have significantly improved SR performance. However, these methods often utilize user behavior on a fixed, single time scale, making them insensitive to the temporal evolution of user interests. While these models effectively capture long-term preferences, they are biased toward persistent behavioral patterns and fail to reflect transient, short-term dynamics, as indicated by their minimal performance degradation on shuffled interaction sequences. To address these challenges, we propose Hierarchical INTerests-aware Sequential Recommendation (HINT). Instead of relying on contrastive learning and self-attention methods, HINT implements a hierarchical user interests modeling approach. Specifically, it first forms multiple sequences by applying variable-kernel average pooling to the original sequence, a technique designed to capture user interests across multiple time scales. It then employs a Fourier transform-based filtering to adaptively denoise each view by processing its low- and high-frequency components. Subsequently, HINT uses an attention-based adaptive aggregation mechanism to integrate the multi-scale views. This allows the model to focus on salient information within the hierarchical interests representations, resulting in a more accurate and comprehensive user profile. Extensive experiments conducted on four real-world datasets demonstrate that HINT outperforms other state-of-the-art baselines.

Hierarchical Interests Modeling on Multiple Time Scales for Sequential Recommendation

In data-scarce settings, learning accurate and interpretable
models for tabular classification remains a fundamental chal-
lenge. We propose a novel pipeline for few-shot tabular clas-
sification that distills the predictive power of large founda-
tion models into globally interpretable student models. Given
a tabular dataset, we generate synthetic data using CTGAN
to approximate the underlying distribution. We then train
high-capacity teacher models (TabLLM, TabPFN, TabM) on
a small number of labeled examples and use them to pseu-
dolabel the synthetic data. Finally, we train student explainer
models (i.e., XGBoost, logistic regression, decision trees,
Generalized Linear Rule Model (GLRM), and TTnet) on
this pseudolabeled synthetic dataset. These student models
are globally and exactly interpretable, yielding logical de-
cision rules (e.g., disjunctive normal form) that fully repro-
duce their predictions. Across 15 tabular binary classification
benchmarks, our distilled models generally outperform base-
lines trained directly on the few-shot data, with improved
ROC AUC scores in the low-shot (4-16 samples) to mid-
and higher-shot settings (up to 256 samples). In particular,
TabPFN-based distillation achieves strong and reliable im-
provements across the entire shot spectrum, not only in heavy
data scarcity but also in broader low-data regimes. This work
demonstrates that foundation models can be effectively lever-
aged as teachers to produce small, transparent, and high-
performing classifiers. Our approach advances the goal of re-
liable and interpretable machine learning in real-world set-
tings where labeled data is limited.

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Diffusion-based free-viewpoint synthesis for dataset expansion and wildlife classification

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