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Found 38 papers, 11 papers with code
Import competition and domestic vertical integration: Theory and Evidence from Chinese firms
Our findings indicate that a decrease in downstream tariffs lead to an increase in vertical integration.
An Asynchronous Multi-core Accelerator for SNN inference
In this paper, we propose an asynchronous architecture for Spiking Neural Networks (SNNs) that eliminates the need for inter-core synchronization, thus enhancing speed and energy efficiency.
Bottleneck-Minimal Indexing for Generative Document Retrieval
GDR can be considered to involve information transmission from documents $X$ to queries $Q$, with the requirement to transmit more bits via the indexes $T$.
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals
The model leverages causal inference to extract the intrinsic causal feature in data, enhancing the agent's utilization of prior knowledge and improving its generalization capability.
Correlation Dimension of Natural Language in a Statistical Manifold
The correlation dimension of natural language is measured by applying the Grassberger-Procaccia algorithm to high-dimensional sequences produced by a large-scale language model.
Prompt When the Animal is: Temporal Animal Behavior Grounding with Positional Recovery Training
Temporal grounding is crucial in multimodal learning, but it poses challenges when applied to animal behavior data due to the sparsity and uniform distribution of moments.
LAECIPS: Large Vision Model Assisted Adaptive Edge-Cloud Collaboration for IoT-based Perception System
We propose to update the edge model and its collaboration strategy with the cloud under the supervision of the large vision model, so as to adapt to the dynamic IoT data streams.
A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems
Representations of the agents and the system are learned by preserving the intrinsic spatio-temporal consistency in a self-supervised manner.
Digital Twin Brain: a simulation and assimilation platform for whole human brain
In comparison to most brain simulations with a homogeneous global structure, we highlight that the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of the brain has an essential impact on the efficiency of brain simulation, which is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access intensive computing systems rather than computation-intensive.
Frequency-dependent Switching Control for Disturbance Attenuation of Linear Systems
We show that our FDSC scheme is capable to approximate the solid in-band performance while maintaining acceptable out-of-band performance with regard to global time horizons as well as localized time horizons.
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications
We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare.
Cross-Modal Retrieval for Motion and Text via DopTriple Loss
On the latest HumanML3D dataset, we achieve a recall of 62. 9% for motion retrieval and 71. 5% for text retrieval (both based on R@10).
Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration
To this end, we define a threat model for backdoor attacks in VFL and introduce a universal adversarial backdoor (UAB) attack to poison the predictions of VFL.
Frequency-limited H$_2$ Model Order Reduction Based on Relative Error
Relative error, which represents the percentage error, becomes particularly relevant when reducing a plant model for the purpose of designing a reduced-order controller.
Relative Error-based Time-limited H2 Model Order Reduction via Oblique Projection
Inspired by these conditions, an oblique projection algorithm is proposed that ensures small H2-norm relative error within the desired time interval.
ClassPruning: Speed Up Image Restoration Networks by Dynamic N:M Pruning
Image restoration tasks have achieved tremendous performance improvements with the rapid advancement of deep neural networks.
Modular Degradation Simulation and Restoration for Under-Display Camera
Together with a pixel-wise discriminator and supervised loss, we can train the generator to simulate the UDC imaging degradation process.
Rethinking Performance Gains in Image Dehazing Networks
Image dehazing is an active topic in low-level vision, and many image dehazing networks have been proposed with the rapid development of deep learning.
Ranked #2 on
Image Dehazing
on RS-Haze
Risk-Driven Design of Perception Systems
We formulate a risk function to quantify the effect of a given perceptual error on overall safety, and show how we can use it to design safer perception systems by including a risk-dependent term in the loss function and generating training data in risk-sensitive regions.
NLOS Error Mitigation Using Weighted Least Squares and Kalman Filter in UWB Positioning
In this letter, we propose the Weighted-Least-Squares Robust Kalman Filter (WLS-RKF) for NLOS identification and mitigation.
Vision Transformers for Single Image Dehazing
Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images.
Ranked #1 on
Image Dehazing
on RS-Haze
Multi-Curve Translator for High-Resolution Photorealistic Image Translation
The dominant image-to-image translation methods are based on fully convolutional networks, which extract and translate an image's features and then reconstruct the image.
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
Robustness evaluations like our checklist will be crucial in future safety evaluations of visual perception modules, and be useful for a wide range of stakeholders including designers, deployers, and regulators involved in the certification of these systems.
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework
We develop a general Dynamic-weight Particle-based Variational Inference (DPVI) framework according to a novel continuous composite flow, which evolves the positions and weights of particles simultaneously.
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Specifically, we propose to leverage causal knowledge by regarding the distributional shifts in subpopulations and deployment environments as the results of interventions on the underlying system.
StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement
To make the method more practical, we propose a well-designed enhancer that can process a 4K-resolution image over 200 FPS but surpasses the contemporaneous single style image enhancement methods in terms of PSNR, SSIM, and LPIPS.
On frequency- and time-limited H2-optimal model order reduction
Moreover, stationary point iteration algorithms that satisfy two out of three necessary conditions for the local minimizer are also proposed.
Frequency-weighted H2-optimal model order reduction via oblique projection
In this paper, a projection-based model order reduction algorithm is proposed that constructs reduced-order models that nearly satisfy the first-order optimality conditions for the frequency-weighted H2-optimal model order reduction problem.
Iterative Rational Krylov Algorithms for model reduction of a class of constrained structural dynamic system with Engineering applications
This paper discusses model order reduction of large sparse second-order index-3 differential algebraic equations (DAEs) by applying Iterative Rational Krylov Algorithm (IRKA).
Optimization and Control Computational Engineering, Finance, and Science Dynamical Systems
Stock Embeddings Acquired from News Articles and Price History, and an Application to Portfolio Optimization
The stock embedding is acquired with a deep learning framework using both news articles and price history.
A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage
The information fusion component adopts a group of encoders and decoders to fuse heterogeneous information and generate discriminative node embeddings for preliminary matching.
Causal Discovery from Incomplete Data: A Deep Learning Approach
As systems are getting more autonomous with the development of artificial intelligence, it is important to discover the causal knowledge from observational sensory inputs.
Adaptive Frequency-limited H2-Model Order Reduction
In this paper, we present an adaptive framework for constructing a pseudo-optimal reduced model for the frequency-limited H2-optimal model order reduction problem.
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, where there exists confounding bias; on the other hand, we have to deal with the identification of CATE when the distribution of covariates in treatment and control groups are imbalanced.
Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention
The results demonstrate cluster analysis using outcome-driven multi-task neural network as promising for patient classification and subtyping.
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding
Almost all previous methods represent a node into a point in space and focus on local structural information, i. e., neighborhood information.
Semi-supervised structured output prediction by local linear regression and sub-gradient descent
The existing semi-supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction.
Semi-supervised learning of local structured output predictors
In this paper, we study the problem of semi-supervised structured output prediction, which aims to learn predictors for structured outputs, such as sequences, tree nodes, vectors, etc., from a set of data points of both input-output pairs and single inputs without outputs.
