This study proposes a novel adaptive DBS control strategy for epilepsy treatment based on deep reinforcement learning. By establishing a random disturbance model of the cortical-thalamus loop, the neural modulation problem is successfully transformed into a Markov decision process. Deep Deterministic Policy Gradient (DDPG) algorithm is employed to achieve adaptive dynamic regulation of stimulation parameters, significantly reducing seizure frequency and duration in various epilepsy simulation scenarios. Experimental results demonstrate that the closed-loop control system can further reduce energy loss by [Formula: see text] ([Formula: see text]) compared to conventional open-loop system, while increase the proportion of non-epileptic states by [Formula: see text] ([Formula: see text]). Furthermore, we innovatively integrate Model-Agnostic Meta-Learning (MAML) with DDPG to develop a collaborative control strategy with transfer learning capabilities. This strategy demonstrates significant advantages across different epilepsy patient scenarios, which offers crucial technical support for the precise and adaptive development of epilepsy treatment.

Cellular automata (CA) have been prevalently used for the simulation of urban land change. However, how to effectively learn the spatial-temporal dynamics of urban development from time-series data remain an important challenge for CA-based models. To address this issue, we propose a new model for the simulation of urban development based on convolutional long short-term memory (ConvLSTM) neural networks. The core of the proposed model is a sequence of vanilla ConvLSTM cells integrated with the modules of channel attention and contextual embedding. Compared with conventional CA-based models, the proposed ConvLSTM model is more advanced in that it can better leverage the open access annual urban land maps to capture simultaneously the spatial structure and the temporal dependency of historical urban development, and further predict multiple maps of annual development for subsequent years (i.e., Maps-to-Maps). The performance of the ConvLSTM model is evaluated through the case studies in China’s three mega-urban regions, and ConvLSTM outperforms other state-of-the-art deep learning architectures at both the pixel level and the coarser grid level. The results also suggest the satisfactory transferability of ConvLSTM in that the model trained in one mega-urban region can be successfully re-used in others without fine tuning.

In this letter, a weighted strengthened local contrast measure (WSLCM) algorithm for infrared (IR) small target detection is proposed, it consists of two modules, the strengthened local contrast measure (SLCM), and the weighting function. In the SLCM calculation, the ideas of matched filter and background estimation are adopted to enhance true target and suppress complex background, then both ratio and difference operations are used to calculate the SLCM. In the weighting function definition, three components are considered: the characteristics of the target, the characteristics of the background, and the difference between them. Especially, an improved regional intensity level (IRIL) algorithm is proposed to evaluate the complexity of a cell, thus it can suppress random noises better. Experiments on some real IR images show that the proposed WSLCM can achieve a better detection performance under complex background.

Local contrast has been proved efficient for infrared (IR) small-target detection. However, current algorithms do not enhance true target purposefully before local contrast calculation and may easily be disturbed by noises. In this letter, a new detection framework named multiscale tri-layer local contrast measure (TLLCM) is proposed. First, a tri-layer filtering window is proposed, and it consists of a core layer, a reserve layer, and a surrounding layer. The idea of a matched filter is adopted, and a Gaussian filtering will be performed on the core layer to enhance true target purposefully according to the target shape. Then, the multiscale TLLCM of the central pixel of the window will be calculated between the enhanced core and the surrounding local background. Finally, the target can be extracted by an adaptive threshold. Experimental results show that the proposed method can achieve better detection performance than some existing algorithms.

Local contrast has been proven as an efficient method for infrared (IR) small target detection, but existing local contrast algorithms just directly choosing the neighboring area of a current position as the reference when calculating the local contrast of the current position, which may bring an inaccurate result. Meanwhile, existing algorithms are either ratio form or difference form, they cannot effectively enhance true target and suppress all the types of complex backgrounds simultaneously. In this letter, a new local contrast scheme that introduces the adaptive background estimation is proposed to provide a more accurate reference, and the multidirectional 2-D least mean square (MDTDLMS) algorithm that is more suitable for small target detection is presented. Then, a new ratio-difference joint local contrast measure (RDLCM) is proposed between raw IR image and the MDTDLMS result to enhance true small target and suppress all the types of complex backgrounds simultaneously. Experimental results show that the proposed MDTDLMS-RDLCM algorithm can achieve a good detection performance for different types of backgrounds and targets.

Simulation models based on cellular automata (CA) are widely used for understanding and simulating complex urban expansion process. Among these models, logistic CA (LCA) is commonly adopted. However, the performance of LCA models is often limited because the fixed coefficients obtained from binary logistic regression do not reflect the spatiotemporal heterogeneity of transition rules. Therefore, we propose a variable weights LCA (VW-LCA) model with dynamic transition rules. The regression coefficients in this VW-LCA model are based on VW by incorporating a genetic algorithm in a conventional LCA. The VW-LCA model and the conventional LCA model were both used to simulate urban expansion in Nanjing, China. The models were calibrated with data for the period 2000–2007 and validated for the period 2007–2013. The results showed that the VW-LCA model performed better than the LCA model in terms of both visual inspection and key indicators. For example, kappa, accuracy of urban land and figure of merit for the simulation results of 2013 increased by 3.26%, 2.96% and 4.44%, respectively. The VW-LCA model performs relatively better compared with other improved LCA models that are suggested in literature.

The frequently changing user preferences and/or item profiles have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user/item records when splitting the already sparse data across time dimension, previous methods have to restrict the drifting purchasing patterns to pre-assumed distributions, and were hardly able to model them rather directly with, for example, time series analysis. Integrating content information helps to alleviate the problem in practical systems, but the domain-dependent content knowledge is expensive to obtain due to the large amount of manual efforts.

In this paper, we propose a real-time object tracking approach. It utilizes generalized part-based appearance model and structure-constrained motion model as auxiliary. The appearance of the target object is modeled by the proposed generalized part-based appearance model, which combines the appearance of different parts of the target object, adaptively updated by an efficient structure learning scheme based on the online Passive-Aggressive algorithm. By integrating the confidence scores of multiple parts, mutual compensation is realized, significantly enhances the robustness of our method against the structure deformation and partial occlusion during the tracking. In addition, we enhance the performance of our tracker by using a motion model. It employs a structure-constrained rule, that is, the change on the structure of the target object between consecutive frames is small. Experiments on public video sequences verify the superior performance of our algorithm.

We propose a novel method for the parsing of images and scanned point cloud in large-scale street environment. The proposed method significantly reduces the intensive labeling cost in previous works by automatically generating training data from the input data. The automatic generation of training data begins with the initialization of training data with weak priors in the street environment, followed by a filtering scheme to remove mislabeled training samples. We formulate the filtering as a binary labeling optimization problem over a conditional random filed that we call object graph, simultaneously integrating spatial smoothness preference and label consistency between 2D and 3D. Toward the final parsing, with the automatically generated training data, a CRF-based parsing method that integrates the coordination of image appearance and 3D geometry is adopted to perform the parsing of large-scale street scenes. The proposed approach is evaluated on city-scale Google Street View data, with an encouraging parsing performance demonstrated.

We propose an adaptive sub gradient descent method to efficiently learn the parameters of CRF models for image parsing. To balance the learning efficiency and performance of the learned CRF models, the parameter learning is iteratively carried out by solving a convex optimization problem in each iteration, which integrates a proximal term to preserve the previously learned information and the large margin preference to distinguish bad labeling and the ground truth labeling. A solution of sub gradient descent updating form is derived for the convex optimization problem, with an adaptively determined updating step-size. Besides, to deal with partially labeled training data, we propose a new objective constraint modeling both the labeled and unlabeled parts in the partially labeled training data for the parameter learning of CRF models. The superior learning efficiency of the proposed method is verified by the experiment results on two public datasets. We also demonstrate the powerfulness of our method for handling partially labeled training data.

In this paper, we propose an unwrappable representation for image-based façade modeling from multiple registered images. An unwrappable façade is represented by the mutually orthogonal baseline and profile. We first reconstruct semidense 3D points from images, then the baseline and profile are extracted from the point cloud to construct the base shape and compose the textures of the building from the images. Through our unwrapping process, the reconstructed 3D points and composed textures are further mapped to an unwrapped space that is parameterized by the baseline and profile. In doing so, the unwrapped space becomes equivalent to the planar space in which planar façade modeling techniques can be used to reconstruct the details of the buildings. Finally, the augmented details can be wrapped back to the original 3D space to generate the final model. This newly introduced unwrappable representation extends the state-of-the-art modeling for planar façades to a more general class of façades. We demonstrate the power of the unwrappable representation with a few examples in which the façade is not planar.

We present a novel method for translational symmetry detection, optimization, and symmetry object segmentation in façade images. Unlike most previous methods, our detection algorithm accumulates pixel-level correspondence in translation space. Thus it does not rely on feature point detection and handles patterns with low repetition counts. To improve the robustness with multiple interfering symmetries, we introduce an image-space global optimization, which resolves multiple per-pixel symmetry lattices. We then propose a learning-based method that generates refined segmentation of foreground symmetry objects of arbitrary shapes, with the aid of the per-pixel symmetry information. Our proposed method is accurate, robust and efficient as demonstrated by an extensive evaluation using a large façade image database.

In this paper we propose a novel nonparametric image parsing method for the image parsing problem in certain environment. A novel and efficient nearest neighbor matching scheme, the ANN bilateral matching scheme, is proposed. Based on the proposed matching scheme, we first retrieve some partially similar images for each given test image from the training image database. The test image can be well explained by these retrieved images, with similar regions existing in the retrieved images for each region in the test image. Then, we match the test image to the retrieved training images with the ANN bilateral matching scheme, and parse the test image by integrating multiple cues in a markov random field. Experiment on three datasets shows our method achieved promising parsing accuracy and outperformed two state-of-the-art nonparametric image parsing methods.

We propose an approach that parses registered images captured at ground level into architectural units for large-scale city modeling. Each parsed unit has a regularized shape, which can be used for further modeling purposes. In our approach, we first parse the environment into buildings, the ground, and the sky using a joint 2D-3D segmentation method. Then, we partition buildings into individual façades. The partition problem is formulated as a dynamic programming optimization for a sequence of natural vertical separating lines. Each façade is regularized by a floor line and a roof line. The floor line is the intersection line of the vertical plane of buildings and the horizontal plane of the ground. The roof line links edge points of roof region. The parsed results provide a first geometric approximation to the city environment, and can be further analyzed if necessary. The approach is demonstrated and validated on several large-scale city datasets.