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Found 49 papers, 7 papers with code
SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis
Seven pre-trained models were evaluated in two tasks: high and low suicide risk, and fine-grained suicide risk classification on a level of 0 to 10.
AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts
Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of cognitive pathways to be successfully implemented in patient care.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Grid Monitoring and Protection with Continuous Point-on-Wave Measurements and Generative AI
Purpose This article presents a case for a next-generation grid monitoring and control system, leveraging recent advances in generative artificial intelligence (AI), machine learning, and statistical inference.
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution
Harnessing the potential of leveraging this a priori knowledge in the context of image super-resolution presents a compelling avenue.
Forecasting Electricity Market Signals via Generative AI
This paper presents a generative artificial intelligence approach to probabilistic forecasting of electricity market signals, such as real-time locational marginal prices and area control error signals.
A$^{3}$lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP
Specifically, our A$^{3}$lign-DFER method is designed with multiple modules that work together to obtain the most suitable expanded-dimensional embeddings for classification and to achieve alignment in three key aspects: affective, dynamic, and bidirectional.
Dynamic Facial Expression Recognition
Facial Expression Recognition
Generative Probabilistic Time Series Forecasting and Applications in Grid Operations
Generative probabilistic forecasting produces future time series samples according to the conditional probability distribution given past time series observations.
Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness
Agents can share information through a central server, with the objective of minimizing regret that is accumulating over time $T$ and aggregating over agents.
Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text Analysis
To address this, we have collected a huge dataset from Chinese social media platforms and enriched it with publicly available datasets to create a comprehensive database encompassing 3. 36 million text entries.
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification
Our focus was on understanding the impact of the freezing mechanism on performance.
Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects
The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems.
Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation
This innovative framework is specifically designed for weakly supervised lesion segmentation in early-stage breast ultrasound images.
Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach
Then, we develop a two-pathway data-driven model based on Bayesian Gaussian mixture models that predicts the solution to a typical problem by two different pre-fault and post-fault prediction pathways.
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization.
Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network
By training the network with the prior OTF of an arbitrary 128x128-pixel region and fine-tuning the network with other OTFs within rest regions of FOV, we realize both mask optimization and super-resolution imaging with up to 1020x1500 wide FOV.
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media
In response, we introduce two novel annotated datasets from Chinese social media, focused on cognitive distortions and suicidal risk classification.
Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals
In the contemporary landscape of social media, an alarming number of users express negative emotions, some of which manifest as strong suicidal intentions.
Non-parametric Probabilistic Time Series Forecasting via Innovations Representation
Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations.
On the Optimality of Procrastination Policy for EV charging under Net Energy Metering
When battery storage is part of the co-optimization, it is shown that the net consumption of the prosumer is a two-threshold piecewise linear function of the behind-the-meter renewable generation under the optimal policy, and the procrastination threshold policy remains optimal, although the thresholds cannot be computed easily.
Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach
In FL, selected clients train their local models and send a function of the models to the server, which consumes a random processing and transmission time.
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret
We develop a distributed online learning algorithm that achieves order-optimal cumulative regret with low communication cost measured in the total number of bits transmitted over the entire learning horizon.
Co-optimizing Consumption and EV Charging under Net Energy Metering
We consider the co-optimization of flexible household consumption, electric vehicle charging, and behind-the-meter distributed energy resources under the net energy metering tariff.
Distributed Linear Bandits under Communication Constraints
We consider distributed linear bandits where $M$ agents learn collaboratively to minimize the overall cumulative regret incurred by all agents.
Novelty Detection in Time Series via Weak Innovations Representation: A Deep Learning Approach
We consider novelty detection in time series with unknown and nonparametric probability structures.
Unresolved excess accumulation of myelin-derived cholesterol contributes to scar formation after spinal cord injury
Meanwhile, preventing RCT led to macrophage accumulation and fibrosis in injured peripheral nerves.
A detail-enhanced sampling strategy in Hadamard single-pixel imaging
Hadamard single-pixel imaging (HSI) is an appealing imaging technique due to its features of low hardware complexity and industrial cost.
Co-Optimizing Distributed Energy Resources in Linear Complexity under Net Energy Metering
The co-optimization of behind-the-meter distributed energy resources is considered for prosumers under the net energy metering tariff.
Collaborative Learning in Kernel-based Bandits for Distributed Users
We study collaborative learning among distributed clients facilitated by a central server.
Provably and Practically Efficient Neural Contextual Bandits
The non-asymptotic error bounds may be of broader interest as a tool to establish the relation between the smoothness of the activation functions in neural contextual bandits and the smoothness of the kernels in kernel bandits.
Learning Optimal Topology for Ad-hoc Robot Networks
This model is an stacked ensemble whose output is the topology prediction for a particular robot.
As Easy as ABC: Adaptive Binning Coincidence Test for Uniformity Testing
The alternative hypothesis is a composite set of Lipschitz continuous distributions that are at least $\varepsilon$ away in $\ell_1$ distance from the uniform distribution.
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning
The past decade witnesses a rapid development in the measurement and monitoring technologies for food science.
Mid-wave infrared super-resolution imaging based on compressive calibration and sampling
By the use of CS, we measure and calculate the calibration matrix of optical system efficiently and precisely, which improves the imaging contrast and signal-to-noise ratio(SNR) compared with previous work.
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance
We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space.
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
A framework based on iterative coordinate minimization (CM) is developed for stochastic convex optimization.
DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation
The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects.
Memory-Constrained No-Regret Learning in Adversarial Bandits
An adversarial bandit problem with memory constraints is studied where only the statistics of a subset of arms can be stored.
Distributed No-Regret Learning in Multi-Agent Systems
In this tutorial article, we give an overview of new challenges and representative results on distributed no-regret learning in multi-agent systems modeled as repeated unknown games.
Stereo-based Multi-motion Visual Odometry for Mobile Robots
In this letter, a stereo-based multi-motion visual odometry method is proposed to acquire the poses of the robot and other moving objects.
Disagreement-based Active Learning in Online Settings
We show that the proposed algorithm has a label complexity of $O(dT^{\frac{2-2\alpha}{2-\alpha}}\log^2 T)$ under a constraint of bounded regret in terms of classification errors, where $d$ is the VC dimension of the hypothesis space and $\alpha$ is the Tsybakov noise parameter.
Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization
Online minimization of an unknown convex function over the interval $[0, 1]$ is considered under first-order stochastic bandit feedback, which returns a random realization of the gradient of the function at each query point.
Decision Variance in Online Learning
In this paper, a risk-averse online learning problem under the performance measure of the mean-variance of the rewards is studied.
Multi-Armed Bandits on Partially Revealed Unit Interval Graphs
Two settings of complete and partial side information based on whether the UIG is fully revealed are studied and a general two-step learning structure consisting of an offline reduction of the action space and online aggregation of reward observations from similar arms is proposed to fully exploit the topological structure of the side information.
Algorithmic Bidding for Virtual Trading in Electricity Markets
It is shown that the proposed algorithm converges, with an almost optimal convergence rate, to the global optimal corresponding to the case when the underlying price distribution is known.
Anomaly Detection in Hierarchical Data Streams under Unknown Models
We consider the problem of detecting a few targets among a large number of hierarchical data streams.
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions
By showing that the regret is lower bounded by $\Omega(\sqrt{T})$ for any strategy, we conclude that DPDS is order optimal up to a $\sqrt{\log{T}}$ term.
Efficient phase retrieval based on dark fringe recognition with an ability of bypassing invalid fringes
This paper discusses the noisy phase retrieval problem: recovering a complex image signal with independent noise from quadratic measurements.
Risk-Averse Multi-Armed Bandit Problems under Mean-Variance Measure
We show that the model-specific regret and the model-independent regret in terms of the mean-variance of the reward process are lower bounded by $\Omega(\log T)$ and $\Omega(T^{2/3})$, respectively.
