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Found 83 papers, 10 papers with code
CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images.
Dynamic Correlation of Market Connectivity, Risk Spillover and Abnormal Volatility in Stock Price
First, compared with tranquil periods, the SSE A-shares network experiences a more significant small-world and connective effect during the subprime mortgage crisis and the high leverage period in 2015.
sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks
Spiking Neural Networks (SNNs) are known to be biologically plausible and power-efficient.
Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder
To effectively trade-off communication, computation, and inference accuracy, we design a reward function and formulate the offloading problem of CNN inference as a maximization problem with the goal of maximizing the average inference accuracy and throughput over the long term.
Advanced Unstructured Data Processing for ESG Reports: A Methodology for Structured Transformation and Enhanced Analysis
In the evolving field of corporate sustainability, analyzing unstructured Environmental, Social, and Governance (ESG) reports is a complex challenge due to their varied formats and intricate content.
Empowering Working Memory for Large Language Model Agents
The limitations of traditional LLM memory designs are analyzed, including their isolation of distinct dialog episodes and lack of persistent memory links.
Minimum-Time Trajectory Optimization With Data-Based Models: A Linear Programming Approach
In this paper, we develop a computationally-efficient approach to minimum-time trajectory optimization using input-output data-based models, to produce an end-to-end data-to-control solution to time-optimal planning/control of dynamic systems and hence facilitate their autonomous operation.
OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection
We then pre-train a generative self-supervised graph autoencoder (GAE) to better learn the features of benign models in order to detect backdoor models without knowing the attack strategy.
Modeling User Viewing Flow Using Large Language Models for Article Recommendation
This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles.
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources
In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness.
Image Restoration with Point Spread Function Regularization and Active Learning
Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae.
A Comparison between Markov Chain and Koopman Operator Based Data-Driven Modeling of Dynamical Systems
The second part of this paper introduces these models and their corresponding control design methods.
LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models
Automated occupation extraction and standardization from free-text job postings and resumes are crucial for applications like job recommendation and labor market policy formation.
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation
Furthermore, to capture specific anatomical a priori information in medical images, we incorporate a shape a priori module.
MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention
MQENet treats the mesh evaluation task as a graph classification task for classifying the quality of the input structured mesh.
Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition
This paper studies the computational offloading of video action recognition in edge computing.
Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation
Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available.
SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model
We present SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles with an open-source Large Language Model (LLM) as backbone.
Scheduling Dosage of Proton Pump Inhibitors Using Constrained Optimization With Gastric Acid Secretion Model
Dosage schedule of the Proton Pump Inhibitors (PPIs) is critical for gastric acid disorder treatment.
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation
Moreover, we designed a backbone knowledge distillation structure to extract prior knowledge contained in normal samples.
Learning to Detect and Segment for Open Vocabulary Object Detection
With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction.
Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process
The IAISM, which is based on a Mask-Res-SE-U-Net architecture, is trained to predict the full thinning field of the as-formed component given an arbitrary blank shape.
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications
The proposed Edge Intelligence framework consists of the proposed effectiveness encoding and effectiveness decoding.
Attention-based Feature Compression for CNN Inference Offloading in Edge Computing
We design a feature compression module based on the channel attention method in CNN, to compress the intermediate data by selecting the most important features.
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
It is shown that the model selection with distributed inference HALP can significantly improve service reliability compared to the conventional stand-alone computation.
Safe Control and Learning Using Generalized Action Governor
This paper introduces the Generalized Action Governor, which is a supervisory scheme for augmenting a nominal closed-loop system with the capability of strictly handling constraints.
Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images.
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
To solve the maximization problem, we propose a graph reinforcement learning-based early-exit mechanism (GRLE), which outperforms the state-of-the-art work, deep reinforcement learning-based online offloading (DROO) and its enhanced method, DROO with early-exit mechanism (DROOE), under different dynamic scenarios.
Deep Spectro-temporal Artifacts for Detecting Synthesized Speech
The Audio Deep Synthesis Detection (ADD) Challenge has been held to detect generated human-like speech.
KG-MTT-BERT: Knowledge Graph Enhanced BERT for Multi-Type Medical Text Classification
Herein, we develop a new model called KG-MTT-BERT (Knowledge Graph Enhanced Multi-Type Text BERT) by extending the BERT model for long and multi-type text with the integration of the medical knowledge graph.
A challenge-based survey of e-recruitment recommendation systems
The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers' and the recruiters' preferences.
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues
Specifically, we firstly illuminate EDL from machine learning and EC and regard EDL as an optimization problem.
Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing
To reduce the computation time and communication overhead, we propose a novel collaborative edge computing using fused-layer parallelization to partition a CNN model into multiple blocks of convolutional layers.
Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing
This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing.
Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning
The action governor is an add-on scheme to a nominal control loop that monitors and adjusts the control actions to enforce safety specifications expressed as pointwise-in-time state and control constraints.
Identifying outliers in astronomical images with unsupervised machine learning
For comparison, we construct three methods, which are built upon the k-nearest neighbors (KNN), Convolutional Auto-Encoder (CAE)+ KNN, and CAE + KNN + Attention Mechanism (attCAE KNN) separately.
Neural Program Synthesis with Query
In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space.
Illumination-Invariant Active Camera Relocalization for Fine-Grained Change Detection in the Wild
Active camera relocalization (ACR) is a new problem in computer vision that significantly reduces the false alarm caused by image distortions due to camera pose misalignment in fine-grained change detection (FGCD).
Natural Image Stitching Using Depth Maps
Then, we draw a triangulation of the target image and estimate multiple local homographies, one per triangle, based on the locations of their vertices, the rectified depth values and the epipolar geometry.
Development of a deep learning platform for optimising sheet stamping geometries subject to manufacturing constraints
Manufacturing constraints are imposed based on these distributions and are used to formulate a novel objective function for optimisation.
Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios
Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into.
Eco-Coasting Strategies Using Road Grade Preview: Evaluation and Online Implementation Based on Mixed Integer Model Predictive Control
However, the comprehensive comparison between different coasting strategies and online performance of the eco-coasting strategy using road grade preview are still unclear because of the oversimplification and the integer variable in the optimal control problems.
On the feasibility of small-data learning in simulation-driven engineering tasks with known mechanisms and effective data representations
The application of machine learning (ML) in scientific tasks is increasing, especially ML in simulation-driven engineering tasks.
Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search
In the deployment of deep neural models, how to effectively and automatically find feasible deep models under diverse design objectives is fundamental.
Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models
A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs.
Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks
In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback.
A Novel Framework Integrating AI Model and Enzymological Experiments Promotes Identification of SARS-CoV-2 3CL Protease Inhibitors and Activity-based Probe
The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery.
Damage accumulation during high temperature fatigue of Ti/SiC$_f$ metal matrix composites under different stress amplitudes
The three-dimensional morphology of the crack and fibre fractures has been mapped by CT. During stable growth, matrix cracking dominates with the crack deflecting (by 50-100 $\mu$m in height) when bypassing bridging fibres.
Computed Tomography (CT)
X-Ray Diffraction (XRD)
Materials Science
Applied Physics
Safe Reinforcement Learning Using Robust Action Governor
Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process.
Some results on transcendental entire solutions of certain nonlinear differential-difference equations
In this paper, we study the transcendental entire solutions for the nonlinear differential-difference equations of the forms: $f^{2}(z)+\widetilde{\omega} f(z)f'(z)+q(z)e^{Q(z)}f(z+c)=u(z)e^{v(z)}$, and $f^{n}(z)+\omega f^{n-1}(z)f'(z)+q(z)e^{Q(z)}f(z+c)=p_{1}e^{\lambda_{1} z}+p_{2}e^{\lambda_{2} z}, \quad n\geq 3,$ where $\omega$ is a constant, $\widetilde{\omega}, c, \lambda_{1}, \lambda_{2}, p_{1}, p_{2}$ are non-zero constants, $q, Q, u, v$ are polynomials such that $Q, v$ are not constants and $q, u\not\equiv0$.
Complex Variables Primary 30D35, Secondary 39B32
Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance
This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable, and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed.
A study on using image based machine learning methods to develop the surrogate models of stamp forming simulations
It is demonstrated that the IBMLM model is advantageous over the MLP SBMLM model in accuracy, generalizability, robustness, and informativeness.
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective
To maximize cumulative user engagement (e. g. cumulative clicks) in sequential recommendation, it is often needed to tradeoff two potentially conflicting objectives, that is, pursuing higher immediate user engagement (e. g., click-through rate) and encouraging user browsing (i. e., more items exposured).
A Neural Architecture Search based Framework for Liquid State Machine Design
By exploring the design space in network architectures and parameters, recent works have demonstrated great potential for improving the accuracy of LSM model with low complexity.
ReADS: A Rectified Attentional Double Supervised Network for Scene Text Recognition
In this paper, we elaborately design a Rectified Attentional Double Supervised Network (ReADS) for general scene text recognition.
ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard
21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4.
Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder
Our method successfully picks up $\sim$63\ percent of lensing images from all lenses in the training set.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling
Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems.
Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles.
Robotics Systems and Control Systems and Control
Beating humans in a penny-matching game by leveraging cognitive hierarchy theory and Bayesian learning
It is a long-standing goal of artificial intelligence (AI) to be superior to human beings in decision making.
Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning
Different cost functions of traversability analysis are learned and tested at various scenes of capability in guiding the trajectory planning of different behaviors.
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization
To seamlessly combine deep learning and geometric vision, it is vital to perform learning and geometric optimization end-to-end.
Ranked #1 on
6D Pose Estimation using RGB
on LineMOD
(Accuracy metric)
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control
In this paper, we describe an integrated framework for autonomous decision making in a dynamic and interactive environment.
Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts
In this paper, we propose a decision making algorithm for autonomous vehicle control at a roundabout intersection.
An Attention-Based Approach for Single Image Super Resolution
We propose an attention-based approach to give a discrimination between texture areas and smooth areas.
Multinomial Logit Bandit with Linear Utility Functions
In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution property among items.
Advancing Tabu and Restart in Local Search for Maximum Weight Cliques
Our new restart strategy is based on the re-occurrence of a local search scenario instead of that of a candidate solution.
Graph-based Hypothesis Generation for Parallax-tolerant Image Stitching
The seam-driven approach has been proven fairly effective for parallax-tolerant image stitching, whose strategy is to search for an invisible seam from finite representative hypotheses of local alignment.
Single-Perspective Warps in Natural Image Stitching
Results of image stitching can be perceptually divided into single-perspective and multiple-perspective.
tau-FPL: Tolerance-Constrained Learning in Linear Time
Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications.
Mapping the world population one building at a time
Obtaining high accuracy in estimation of population distribution in rural areas remains a very challenging task due to the simultaneous requirements of sufficient sensitivity and resolution to detect very sparse populations through remote sensing as well as reliable performance at a global scale.
Open-Category Classification by Adversarial Sample Generation
ASG generates positive and negative samples of seen categories in the unsupervised manner via an adversarial learning strategy.
CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
We find on our simulated data set that for a rejection rate of non-lenses of 99%, a completeness of 90% can be achieved for lenses with Einstein radii larger than 1. 4" and S/N larger than 20 on individual $g$-band LSST exposures.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
Quasi-homography warps in image stitching
The naturalness of warps is gaining extensive attentions in image stitching.
Perception-based energy functions in seam-cutting
In this paper, we propose a novel perception-based energy function in the seam-cutting framework, which considers the nonlinearity and the nonuniformity of human perception in energy minimization.
Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems
Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help decrease the time and effort necessary for the development of the autonomous driving control algorithms by providing a venue where acceptable initial control calibrations can be achieved quickly and safely before actual road tests.
Transductive Optimization of Top k Precision
This paper introduces a new approach, Transductive Top K (TTK), that seeks to minimize the hinge loss over all training instances under the constraint that exactly $k$ test instances are predicted as positive.
Top Rank Optimization in Linear Time
Recent efforts of bipartite ranking are focused on optimizing ranking accuracy at the top of the ranked list.
Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners
This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs).
Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions
In this work, we explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms.
Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects
Therefore, it is necessary to develop efficient assembly-independent methods for accurate estimation of these genomic characteristics.
Clustering Aggregation as Maximum-Weight Independent Set
Since the clusters of each input clustering form a partition of the dataset, the vertices corresponding to each clustering form a maximal independent set (MIS) in the attributed graph.
