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Found 42 papers, 8 papers with code
ValCAT: Variable-Length Contextualized Adversarial Transformations Using Encoder-Decoder Language Model
Adversarial texts help explore vulnerabilities in language models, improve model robustness, and explain their working mechanisms.
Probability Distribution Alignment and Low-Rank Weight Decomposition for Source-Free Domain Adaptive Brain Decoding
Brain decoding currently faces significant challenges in individual differences, modality alignment, and high-dimensional embeddings.
Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms
The attention maps from CNN-CBAM localized artifacts within the epoch with a sensitivity of 0. 71 and specificity of 0. 67.
mmHRR: Monitoring Heart Rate Recovery with Millimeter Wave Radar
Heart rate recovery (HRR) within the initial minute following exercise is a widely utilized metric for assessing cardiac autonomic function in individuals and predicting mortality risk in patients with cardiovascular disease.
D3: Diversity, Difficulty, and Dependability-Aware Data Selection for Sample-Efficient LLM Instruction Tuning
In this paper, we first establish data selection criteria based on three distinct aspects of data value: diversity, difficulty, and dependability, and then propose the D3 method comprising two key steps of scoring and selection.
X-GAN: A Generative AI-Powered Unsupervised Model for High-Precision Segmentation of Retinal Main Vessels toward Early Detection of Glaucoma
Structural changes in main retinal blood vessels serve as critical biomarkers for the onset and progression of glaucoma.
UniGenX: Unified Generation of Sequence and Structure with Autoregressive Diffusion
We validate the effectiveness of UniGenX on material and small molecule generation tasks, achieving a significant leap in state-of-the-art performance for material crystal structure prediction and establishing new state-of-the-art results for small molecule structure prediction, de novo design, and conditional generation.
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
In this study, we generate a substantially larger training set (PubChemQH) than used previously and use it to create a scalable model for DFT calculations with physical accuracy.
Empirical likelihood approach for high-dimensional moment restrictions with dependent data
Economic and financial models -- such as vector autoregressions, local projections, and multivariate volatility models -- feature complex dynamic interactions and spillovers across many time series.
NatureLM: Deciphering the Language of Nature for Scientific Discovery
Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages.
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties.
E2Former: A Linear-time Efficient and Equivariant Transformer for Scalable Molecular Modeling
Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science.
TAAD: Time-varying adversarial anomaly detection in dynamic graphs
The time-varying discriminator cooperates with the feature extractor to conduct adversarial training, which decreases the distributional differences in the feature representations of nodes between historical and newly emerged moments to learn transferable features.
Adapter-dependent Adapter Methylation Assay
Sensitive and reliable methylation assay is important for oncogentic studies and clinical applications.
GlaLSTM: A Concurrent LSTM Stream Framework for Glaucoma Detection via Biomarker Mining
Glaucoma is a complex group of eye diseases marked by optic nerve damage, commonly linked to elevated intraocular pressure and biomarkers like retinal nerve fiber layer thickness.
Temporal Graph Neural Network-Powered Paper Recommendation on Dynamic Citation Networks
Due to the rapid growth of scientific publications, identifying all related reference articles in the literature has become increasingly challenging yet highly demanding.
Superimposed Hi-C: A Solution Proposed for Identifying Single Cell's Chromosomal Interactions
Hi-C sequencing is widely used for analyzing chromosomal interactions.
Enabling Small Models for Zero-Shot Selection and Reuse through Model Label Learning
In this paper, we attempt to demonstrate that by constructing a model hub and aligning models with their functionalities using model labels, new tasks can be solved in a zero-shot manner by effectively selecting and reusing models in the hub.
ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data
Methods: To construct an integrated ontology of symptom phenotypes (ISPO), we manually annotated classical TCM textbooks and large-scale Chinese electronic medical records (EMRs) to collect symptom terms with support from a medical text annotation system.
Learning Service Selection Decision Making Behaviors During Scientific Workflow Development
In this paper, a novel context-aware approach is proposed to recommending next services in a workflow development process, through learning service representation and service selection decision making behaviors from workflow provenance.
Weakly Supervised Semantic Segmentation by Knowledge Graph Inference
Extensive experimentation on both the multi-label classification and segmentation network stages underscores the effectiveness of the proposed graph reasoning approach for advancing WSSS.
Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches
In this work, we introduce a novel end-to-end approach, Deep3DSketch+, which performs 3D modeling using only a single free-hand sketch without inputting multiple sketches or view information.
Achieving Covert Communication With A Probabilistic Jamming Strategy
In addition, we prove that the minimum jamming power should be the same as Alice's covert transmit power, depending on the covertness and average jamming power constraints.
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs).
Goal-Driven Context-Aware Next Service Recommendation for Mashup Composition
As service-oriented architecture becoming one of the most prevalent techniques to rapidly deliver functionalities to customers, increasingly more reusable software components have been published online in forms of web services.
Whole-Body Lesion Segmentation in 18F-FDG PET/CT
There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers.
LordNet: An Efficient Neural Network for Learning to Solve Parametric Partial Differential Equations without Simulated Data
We investigate the physical information in the MSR loss, which we called long-range entanglements, and identify the challenge that the neural network requires the capacity to model the long-range entanglements in the spatial domain of the PDE, whose patterns vary in different PDEs.
Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition
As increasingly more software services have been published onto the Internet, it remains a significant challenge to recommend suitable services to facilitate scientific workflow composition.
Learning Physics-Informed Neural Networks without Stacked Back-propagation
In this work, we develop a novel approach that can significantly accelerate the training of Physics-Informed Neural Networks.
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting
How to discover the useful implicit relation between entities and effectively utilize the relations for each entity under various circumstances is crucial.
AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation
As a specific semantic segmentation task, aerial imagery segmentation has been widely employed in high spatial resolution (HSR) remote sensing images understanding.
Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation
When tested on the wound images drawn from the supplemental data sets, the DS approach increased the mean MCC from 0. 17 to 0. 85.
Culling the herd of moments with penalized empirical likelihood
Models defined by moment conditions are at the center of structural econometric estimation, but economic theory is mostly agnostic about moment selection.
Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations
While adopting complex GNN models with more informative message passing and aggregation mechanisms can obviously benefit heterogeneous vertex representations and cooperative policy learning, it could, on the other hand, increase the training difficulty of MARL and demand more intense and direct reward signals compared to the original global reward.
COSEA: Convolutional Code Search with Layer-wise Attention
However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes.
A model-independent constraint on the Hubble constant with gravitational waves from the Einstein Telescope
In this paper, we investigate the expected constraints on the Hubble constant from the gravitational-wave standard sirens, in a cosmological-model-independent way.
Cosmology and Nongalactic Astrophysics
Light Multi-segment Activation for Model Compression
Inspired by the nature of the expressiveness ability in Neural Networks, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model.
TabNN: A Universal Neural Network Solution for Tabular Data
Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all.
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network
Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting.
Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks
With tens of thousands of electrocardiogram (ECG) records processed by mobile cardiac event recorders every day, heart rhythm classification algorithms are an important tool for the continuous monitoring of patients at risk.
Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising
In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers.
Randomized Mechanisms for Selling Reserved Instances in Cloud
We then extend our mechanism to the general case and achieve a competitive ratio $\frac{1}{42\log k\log T}$ for both social welfare and revenue, where $T$ is the ratio of the maximum request length to the minimum request length and $k$ is the ratio of the maximum request value density to the minimum request value density.
