Lifelong person Re-IDentification (LReID) aims to match the same person employing continuously collected individual data from different scenarios. To achieve continuous all-day person matching across day and night, Visible-Infrared Lifelong person Re-IDentification (VI-LReID) focuses on sequential training on data from visible and infrared modalities and pursues average performance over all data. To this end, existing methods typically exploit cross-modal knowledge distillation to alleviate the catastrophic forgetting of old knowledge. However, these methods ignore the mutual interference of modality-specific knowledge acquisition and modality-common knowledge anti-forgetting, where conflicting knowledge leads to collaborative forgetting. To address the above problems, this paper proposes a Cross-modality Knowledge Disentanglement and Alignment method, called CKDA, which explicitly separates and preserves modality-specific knowledge and modality-common knowledge in a balanced way. Specifically, a Modality-Common Prompting (MCP) module and a Modality-Specific Prompting (MSP) module are proposed to explicitly disentangle and purify discriminative information that coexists and is specific to different modalities, avoiding the mutual interference between both knowledge. In addition, a Cross-modal Knowledge Alignment (CKA) module is designed to further align the disentangled new knowledge with the old one in two mutually independent inter- and intra-modality feature spaces based on dual-modality prototypes in a balanced manner. Extensive experiments on four benchmark datasets verify the superiority of our CKDA against state-of-the-art methods.

Lifelong person Re-IDentification (L-ReID) exploits sequentially collected data to continuously train and update a ReID model, focusing on the overall performance of all data. Its main challenge is to avoid the catastrophic forgetting problem of old knowledge while training on new data. Existing L-ReID methods typically re-extract new features for all historical gallery images for inference after each update, known as "re-indexing". However, historical gallery data typically suffers from direct saving due to the data privacy issue and the high re-indexing costs for large-scale gallery images. As a result, it inevitably leads to incompatible retrieval between query features extracted by the updated model and gallery features extracted by those before the update, greatly impairing the re-identification performance. To tackle the above issue, this paper focuses on a new task called Re-index Free Lifelong person Re-IDentification (RFL-ReID), which requires performing lifelong person re-identification without re-indexing historical gallery images. Therefore, RFL-ReID is more challenging than L-ReID, requiring continuous learning and balancing new and old knowledge in diverse streaming data, and making the features output by the new and old models compatible with each other. To this end, we propose a Bidirectional Continuous Compatible Representation (Bi-C$^{2}$2R) framework to continuously update the gallery features extracted by the old model to perform efficient L-ReID in a compatible manner. Specifically, a bidirectional compatible transfer network is first designed to bridge the relationship between new and old knowledge and continuously update the old gallery features to the new feature space after the updating. Secondly, a bidirectional compatible distillation module and a bidirectional anti-forgetting distillation model are designed to balance the compatibility between the new and old knowledge in dual feature spaces. Finally, a feature-level exponential moving average strategy is designed to adaptively fill the diverse knowledge gaps between different data domains. Finally, we verify our proposed Bi-C$^{2}$2R method through theoretical analysis and extensive experiments on multiple benchmarks, which demonstrate that the proposed method can achieve leading performance on both the introduced RFL-ReID task and the traditional L-ReID task.

Pre-trained Vision Mamba~(Vim) models have demonstrated exceptional performance across various computer vision tasks in a computationally efficient manner, attributed to their unique design of selective state space models. To further extend their applicability to diverse downstream vision tasks, Vim models can be adapted using the efficient fine-tuning technique known as visual prompting. However, existing visual prompting methods are predominantly tailored for Vision Transformer (ViT)-based models that leverage global attention, neglecting the distinctive sequential token-wise compression and propagation characteristics of Vim. Specifically, existing prompt tokens prefixed to the sequence are insufficient to effectively activate the input and forget gates across the entire sequence, hindering the extraction and propagation of discriminative information. To address this limitation, we introduce a novel Selective Visual Prompting (SVP) method specifically for the efficient fine-tuning of Vim. To prevent the loss of discriminative information during state space propagation, SVP employs lightweight selective prompters for token-wise prompt generation, ensuring adaptive activation of the update and forget gates within Mamba blocks to promote discriminative information propagation. Moreover, considering that Vim propagates both shared cross-layer information and specific inner-layer information, we further refine SVP with a dual-path structure: Cross-Prompting and Inner-Prompting. Cross-Prompting utilizes shared parameters across layers, while Inner-Prompting employs distinct parameters, promoting the propagation of both shared and specific information, respectively. Extensive experimental results on various large-scale benchmarks demonstrate that our proposed SVP significantly outperforms state-of-the-art methods.

Protein Representation Learning (PRL) holds signif-icant value in various fields. However, existing methods primarily focus on static amino acid sequences or structures of proteins, paying less attention to their dynamic behaviors, which limits their ability to capture the intrinsic properties of proteins. In this paper, a novel multimodal protein representation learning method named IMPDI is proposed, which integrates amino acid sequences, structural information, and multiple dynamic information to construct a unified protein representation learning framework. To validate the effectiveness of the IMPDI model, we applied it to three typical protein-related downstream tasks: Protein-Protein Interaction (PPI) Prediction, Protein Secondary Structure Prediction, and Protein Thermostability Prediction. The results indicate that IMPDI outperforms baseline methods in various metrics, demonstrating strong generalizability and application potential. This study not only offers a new perspective on protein representation methods, but also provides robust technical support for related downstream tasks.

Existing breast cancer prognosis prediction models often rely on single-type data such as clinical or mRNA expression data, neglecting the prognostic value of gene mutations linked to tumor aggressiveness. Multi-omics integration remains difficult due to the sparsity of mutation data and the high dimensionality of expression data. To address these limitations, we propose ICMProg, an interpretable framework for breast cancer prognosis prediction by integrating clinical and multi-omics data. ICMProg uses a Frequency-aware Sparse Variational Autoencoder (FS-VAE) to extract informative features from sparse mutation data, and a Dynamic Pathway-Guided Variational Autoencoder (DPG-VAE) to capture biologically meaningful features from high-dimensional expression data. For model interpretability, we develop an interpretable method based on Grad-SHAP, systematically identifying critical biomarkers in clinical, mutational, gene, and pathway data. Experimental results on two breast cancer datasets show that ICM-Prog outperforms state-of-the-art methods, and it can significantly separate high-risk and low-risk patient groups. These results suggest that ICM-Prog can not only improve prognostic accuracy, but also support the discovery of potential therapeutic targets.

Lifelong person re-identification (LReID) aims to match the same person using sequentially collected data. However, due to the long-term nature of lifelong learning, the inevitable changes in human clothes prevent the model from relying on unified discriminative information (e.g., clothing style) to match the same person in the streaming data, demanding differentiated cloth-irrelevant information. Unfortunately, existing LReID methods typically fail to leverage such knowledge resulting in the exacerbation of catastrophic forgetting issues. Therefore, in this paper, we focus on a challenging practical task called Cloth-Hybrid Lifelong Person Re-identification (CH-LReID), which requires matching the same person wearing different clothes using sequentially collected data. A Differentiated Knowledge Consolidation (DKC) framework is designed to unify and balance distinct knowledge across streaming data. The core idea is to adaptively balance differentiated knowledge and compatibly consolidate cloth-relevant and cloth-irrelevant information. To this end, a Differentiated Knowledge Transfer (DKT) module and a Latent Knowledge Consolidation (LKC) module are designed to adaptively discover differentiated new knowledge, while eliminating the derived domain shift of old knowledge via reconstructing the old latent feature space, respectively. Then, to further alleviate the catastrophic conflict between differentiated new and old knowledge, we further propose a Dual-level Distribution Alignment (DDA) module to align the distribution of discriminative knowledge at both the instance level and the fine-grained level. Extensive experiments on multiple benchmarks demonstrate the superiority of our method against existing methods in both CH-LReID and traditional LReID tasks. The source code of this paper is available at https://github.com/PKU-ICST-MIPL/DKC-CVPR2025.

Pre-trained point cloud analysis models have shown promising advancements in various downstream tasks, yet their effectiveness is typically suffering from low-quality point cloud (i.e., noise and incompleteness), which is a common issue in real scenarios due to casual object occlusions and unsatisfactory data collected by 3D sensors. To this end, existing methods focus on enhancing point cloud quality by developing dedicated denoising and completion models. However, due to the isolation between the point cloud enhancement and downstream tasks, these methods fail to work in various real-world domains. In addition, the conflicting objectives between denoising and completing tasks further limit the ensemble paradigm to preserve critical geometric features. To tackle the above challenges, we propose a unified point-level prompting method that reformulates point cloud denoising and completion as a prompting mechanism, enabling robust analysis in a parameter-efficient manner. We start by introducing a Rectification Prompter to adapt to noisy points through the predicted rectification vector prompts, effectively filtering noise while preserving intricate geometric features essential for accurate analysis. Sequentially, we further incorporate a Completion Prompter to generate auxiliary point prompts based on the rectified point clouds, facilitating their robustness and adaptability. Finally, a Shape-Aware Unit module is exploited to efficiently unify and capture the filtered geometric features for the downstream point cloud analysis.Extensive experiments on four datasets demonstrate the superiority and robustness of our method when handling noisy and incomplete point cloud data against existing state-of-the-art methods. Our code is released at https://github.com/zhoujiahuan1991/ICCV2025-UPP.

Pre-trained 3D vision models have gained significant attention for their promising performance on point cloud data. However, fully fine-tuning these models for downstream tasks is computationally expensive and storage-intensive. Existing parameter-efficient fine-tuning (PEFT) approaches, which focus primarily on input token prompting, struggle to achieve competitive performance due to their limited ability to capture the geometric information inherent in point clouds. To address this challenge, we propose a novel Geometry-Aware Point Cloud Prompt (GAPrompt) that leverages geometric cues to enhance the adaptability of 3D vision models. First, we introduce a Point Prompt that serves as an auxiliary input alongside the original point cloud, explicitly guiding the model to capture fine-grained geometric details. Additionally, we present a Point Shift Prompter designed to extract global shape information from the point cloud, enabling instance-specific geometric adjustments at the input level. Moreover, our proposed Prompt Propagation mechanism incorporates the shape information into the model's feature extraction process, further strengthening its ability to capture essential geometric characteristics. Extensive experiments demonstrate that GAPrompt significantly outperforms state-of-the-art PEFT methods and achieves competitive results compared to full fine-tuning on various benchmarks, while utilizing only 2.19\% of trainable parameters.

Recent studies have delved into constructing generalist agents for open-world environments like Minecraft. Despite the encouraging results, existing efforts mainly focus on solving basic programmatic tasks, e.g., material collection and tool-crafting following the Minecraft tech-tree, treating the ObtainDiamond task as the ultimate goal. This limitation stems from the narrowly defined set of actions available to agents, requiring them to learn effective long-horizon strategies from scratch. Consequently, discovering diverse gameplay opportunities in the open world becomes challenging. In this work, we introduce Odyssey, a new framework that empowers Large Language Model (LLM)-based agents with open-world skills to explore the vast Minecraft world. Odyssey comprises three key parts: (1) An interactive agent with an open-world skill library that consists of 40 primitive skills and 183 compositional skills. (2) A fine-tuned LLaMA-3 model trained on a large question-answering dataset with 390k+ instruction entries derived from the Minecraft Wiki. (3) A new agent capability benchmark includes the long-term planning task, the dynamic-immediate planning task, and the autonomous exploration task. Extensive experiments demonstrate that the proposed Odyssey framework can effectively evaluate different capabilities of LLM-based agents. All datasets, model weights, and code are publicly available to motivate future research on more advanced autonomous agent solutions.

Recently, pre-trained state space models have shown great potential for video classification, which sequentially compresses visual tokens in videos with linear complexity, thereby improving the processing efficiency of video data while maintaining high performance. To apply powerful pre-trained models to downstream tasks, prompt learning is proposed to achieve efficient downstream task adaptation with only a small number of fine-tuned parameters. However, the sequentially compressed visual prompt tokens fail to capture the spatial and temporal contextual information in the video, thus limiting the effective propagation of spatial information within a video frame and temporal information between frames in the state compression model and the extraction of discriminative information. To tackle the above issue, we proposed a State Space Prompting (SSP) method for video understanding, which combines intra-frame and inter-frame prompts to aggregate and propagate key spatiotemporal information in the video. Specifically, an Intra-Frame Gathering (IFG) module is designed to aggregate spatial key information within each frame. Besides, an Inter-Frame Spreading (IFS) module is designed to spread discriminative spatio-temporal information across different frames. By adaptively balancing and compressing key spatio-temporal information within and between frames, our SSP effectively propagates discriminative information in videos in a complementary manner. Extensive experiments on four video benchmark datasets verify that our SSP significantly outperforms existing SOTA methods by 2.76\% on average while reducing the overhead of fine-tuning parameters.

Continual Test-Time Adaptation (CTTA) aims to quickly fine-tune the model during the test phase so that it can adapt to multiple unknown downstream domain distributions without pre-acquiring downstream domain data. To this end, existing advanced CTTA methods mainly reduce the catastrophic forgetting of historical knowledge caused by irregular switching of downstream domain data by restoring the initial model or reusing historical models. However, these methods are usually accompanied by serious insufficient learning of new knowledge and interference from potentially harmful historical knowledge, resulting in severe performance degradation. To this end, we propose a class-aware domain Knowledge Fusion and Fission method for continual test-time adaptation, called KFF, which adaptively expands and merges class-aware domain knowledge in old and new domains according to the test-time data from different domains, where discriminative historical knowledge can be dynamically accumulated. Specifically, considering the huge domain gap within streaming data, a domain Knowledge FIssion (KFI) module is designed to adaptively separate new domain knowledge from a paired class-aware domain prompt pool, alleviating the impact of negative knowledge brought by old domains that are distinct from the current domain. Besides, to avoid the cumulative computation and storage overheads from continuously fissioning new knowledge, a domain Knowledge FUsion (KFU) module is further designed to merge the fissioned new knowledge into the existing knowledge pool with minimal cost, where a greedy knowledge dynamic merging strategy is designed to improve the compatibility of new and old knowledge while keeping the computational efficiency.

The recent large-scale pre-trained models like CLIP have aroused great concern in vision-language tasks. However, when required to match image-text data collected in a streaming manner, namely Continual Vision-Language Retrieval (CVRL), their performances are still limited due to the catastrophic forgetting of the learned old knowledge. To handle this issue, advanced methods are proposed to distill the affinity knowledge between images and texts from the old model to the new one for anti-forgetting. Unfortunately, existing approaches neglect the impact of incorrect affinity, which prevents the balance between the anti-forgetting of old knowledge and the acquisition of new knowledge. Therefore, we propose a novel framework called Dynamic Knowledge Rectification (DKR) that simultaneously achieves incorrect knowledge filtering and rectification. Specifically, we first filter the incorrect affinity knowledge calculated by the old model on the new data. Then, a knowledge rectification method is designed to rectify the incorrect affinities while preserving the correct ones. In particular, for the new data that can only be correctly retrieved by the new model, we rectify them with the corresponding new affinity to protect them from negative transfer. Additionally, for those that can not be retrieved by either the old or the new model, we introduce paired ground-truth labels to promote the acquisition of both old and new knowledge. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our DKR and its superiority against state-of-the-art methods.

Lifelong Person Re-identification (L-ReID) aims to learn from sequentially collected data to match a person across different scenes. Once an L-ReID model is updated using new data, all historical images in the gallery are required to be re-calculated to obtain new features for testing, known as “re-indexing”. However, it is infeasible when raw images in the gallery are unavailable due to data privacy concerns, resulting in incompatible retrieval between the query and the gallery features calculated by different models, which causes significant performance degradation. In this pa-per, we focus on a new task called Re-indexing Free Life- long Person Re-identification (RFL-ReID), which requires achieving effective L-ReID without re-indexing raw images in the gallery. To this end, we propose a Continual Com-patible Representation (C2R) method, which facilitates the query feature calculated by the continuously updated model to effectively retrieve the gallery feature calculated by the old model in a compatible manner. Specifically, we design a Continual Compatible Transfer (CCT) network to con-tinuously transfer and consolidate the old gallery feature into the new feature space. Besides, a Balanced Compati-ble Distillation module is introduced to achieve compatibil-ity by aligning the transferred feature space with the new feature space. Finally, a Balanced Anti-forgetting Distillation module is proposed to eliminate the accumulated for- getting of old knowledge during the continual compatible transfer. Extensive experiments on several benchmark L- ReID datasets demonstrate the effectiveness of our method against state-of-the-art methods for both RFL-ReID and L- ReID tasks. The source code of this paper is available at https://github.com/PKU-ICST-MIPL/C2R_CVPR2024.

Large language models (LLMs) have demonstrated notable potential in conducting complex tasks and are increasingly utilized in various financial applications. However, high-quality sequential financial investment decision-making remains challenging. These tasks require multiple interactions with a volatile environment for every decision, demanding sufficient intelligence to maximize returns and manage risks. Although LLMs have been used to develop agent systems that surpass human teams and yield impressive investment returns, opportunities to enhance multi-source information synthesis and optimize decision-making outcomes through timely experience refinement remain unexplored. Here, we introduce FinCon, an LLM-based multi-agent framework tailored for diverse financial tasks. Inspired by effective real-world investment firm organizational structures, FinCon utilizes a manager-analyst communication hierarchy. This structure allows for synchronized cross-functional agent collaboration towards unified goals through natural language interactions and equips each agent with greater memory capacity than humans. Additionally, a risk-control component in FinCon enhances decision quality by episodically initiating a self-critiquing mechanism to update systematic investment beliefs. The conceptualized beliefs serve as verbal reinforcement for the future agent’s behavior and can be selectively propagated to the appropriate node that requires knowledge updates. This feature significantly improves performance while reducing unnecessary peer-to-peer communication costs. Moreover, FinCon demonstrates strong generalization capabilities in various financial tasks, including stock trading and portfolio management.

Visible-infrared person re-identification (VI-ReID) aims to identify the same person across visible and infrared images. Its main challenge is how to extract modality-irrelevant person identity information. To alleviate cross-modality discrepancies, existing methods typically follow two paradigms: 1) Transform visible images into gray-scale color space and map them into the infrared domain. 2) Stack infrared images into RGB color space and map them into the visible domain. However, limited by different optical properties of visible and infrared waves, such mapping commonly leads to information asymmetry. Although some efforts prevent such discrepancies by data-level alignment, they typically meanwhile introduce misleading information and bring extra divergence. Therefore, existing methods fail on effectively eliminating the modality discrepancies. In this paper, we first analyze the essential factors to the generation of modality discrepancies. Secondly, we propose a novel Dual Modality-aware Alignment (DMA) model for VI-ReID, which can preserve discriminative identity information and suppress the misleading information within a uniform scheme. Particularly, based on the intrinsic optical properties of both modalities, a Dual Modality Transfer (DMT) module is proposed to perform compensation for the information asymmetry in HSV color space, thereby effectively alleviating cross-modality discrepancies and better preserving discriminative identity features. Further, an Intra-local Alignment (IA) module is proposed to suppress the misleading information, where a fine-grained local consistency objective function is designed to achieve more compact intra-class representations. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our method and competitive performance with state-of-the-art methods. The source code of this paper is available athttps://github.com/PKU-ICST-MIPL/DMA_TIFS2023.

BACKGROUND: This study aimed to observe the potential impact of known cuproptosis-related genes (CRGs) on triple negative breast cancer (TNBC) development, as well as their associated molecular mechanisms, immune infiltration mechanisms and potential therapeutic agents. RESULTS: Based on the Cox Proportional Hazard Model, 11 CRGs may be especially important in TNBC development and progression (considered as the Key-TNBC-CRGs). The expression of several Key-TNBC-CRGs (e.g., ATP7A, PIK3CA, LIAS, and LIPT) are associated with common mutations. The SCNA variation of 11 Key-TNBC-CRGs are related to differences immune infiltration profiles. In particular, depletion of ATP7A, ATP7B, CLS, LIAS, and SCL31A1 and while high amplification of NLRP3 and LIPT2 are correlated with decreased immune infiltration. In our Cox proportional hazards regression model, there is a significant difference in the overall survival between high-risk and low-risk groups. The HR in the high-risk group is 3.891 versus the low-risk group. And this model has a satisfactory performance in Prediction of 5-15-year survival, in particular in the 10-year survival (AUC = 0.836). Finally, we discovered some potential drugs for TNBC treatment based on the strategy of targeting 11 Key-TNBC-CRGs, such as Dasatinib combined with ABT-737, Erastin or Methotrexate, and Docetaxel/Ispinesib combination. CONCLUSION: In conclusion, CRGs may play important roles in TNBC development, and they can impact tumor immune microenvironment and patient survival. The Key-TNBC-CRGs interact mutually and can be influenced by common BC-related mutations. Additionally, we established a 11-gene risk model with a robust performance in prediction of 5-15-year survival. As well, some new drugs are proposed potentially effective in TNBC based on the CRG strategy.

Person re-identification (Re-ID) plays an important role in many areas such as robotics, multimedia and forensics. However, it becomes difficult when considering long-term scenarios, due to changing clothes irregularly for people. Therefore, cloth-changing person re-identification (CC-ReID) has attracted more attention recently. CC-ReID aims to identify the same person but with different clothes. Its main challenge is how to disentangle clothes-irrelevant features, such as face, shape, body, etc. Most existing methods force the model to learn clothes-irrelevant features by changing the colour of clothes or reconstructing people dressed in different colours. However, due to the lack of the ground truth for supervision, these methods inevitably introduce noises which spoil the discriminativeness of features and lead to uncontrollable disentanglement. In this paper, we propose a novel disentanglement framework, called Deep Component Reconstruction Re-ID (DCR-ReID), which can disentangle the clothes-irrelevant features and the clothes-relevant features in a controllable manner. Specifically, we propose a Component Reconstruction Disentanglement (CRD) module to disentangle the clothes-irrelevant features and the clothes-relevant features based on the reconstruction of human component regions. In addition, we propose a Deep Assembled Disentanglement (DAD) module, which further improves the discriminativeness of these disentangled features. Extensive experiments on three real-world benchmark CC-ReID datasets, LTCC, PRCC, and CCVID, are conducted to demonstrate the effectiveness of the proposed DCR-ReID. Empirical studies show that our DCR-ReID achieves the state-of-the-art performance against the other CC-ReID methods. The source code of this paper is available athttps://github.com/PKU-ICST-MIPL/DCR-ReID_TCSVT2023.

Fill-in-the-blank question generation has become enormously popular and attracted lots of attention recently. However, most of the existing question generation datasets are developed for machine reading comprehension, which are not specifically designed for examination. To fill in the gap, in this paper, we propose a Chinese sentence fill-in-the-blank question generation dataset for examination (named CSFQGD), which will be released to the public11Resources are available at The dataset is composed of 20.5K questions from many real examinations in Chinese that cover a wide spectrum of learning subjects. Based on the proposed dataset, we test several well-known methods for fill-in-the-blank question generation and compare their performance. Our baseline study on this dataset shows that CSFQGD is a challenging test bed for further research.

Detecting rotated ships is difficult in optical remote sensing images due to the challenges of complex scenes. Existing advanced rotated ship detectors are typically anchor-based algorithms that require plenty of predefined anchors. However, the use of anchors brings three critical problems: 1) a large number of anchors bring a huge amount of calculation; 2) the attributes (e.g., size and aspect ratios) of anchors are designed viaad hocheuristics; and 3) only a tiny fraction of anchors that overlap with ground-truth bounding boxes of ships tightly can be considered as positive samples, which causes an extreme imbalance between positive and negative samples. As a result, the detection accuracy will be influenced seriously when the design of anchors is not suitable. To address the above problems, this article proposes a novel anchor-free rotated ship detection framework, called SKNet, which detects rotated ships as keypoints in optical remote sensing images. In SKNet, a ship target is modeled as its center keypoint and morphological sizes, including the width, height, and rotation angle. Accordingly, we design two customized modules: orthogonal pooling and soft-rotate-nonmaximum suppression (NMS), where the former is to improve the prediction accuracy of the center keypoint and the morphological size, and the latter is to effectively remove redundant rotated ship detection results. Extensive experiments are conducted to demonstrate the effectiveness of SKNet on three optical remote sensing image data sets: HRSC2016, DOTA-ship, and HPDM-OSOD, which is collected by ourselves and published in this article. Empirical studies show that SKNet achieves state-of-the-art detection performance while being time-efficient. Overall, SKNet achieves the best speed–accuracy tradeoff.

The purpose of the influence maximization is to find the top k influential seeds which can maximize the influence spread. Recently, some researchers address this problem through community structure. However, most of these community-based studies only consider the static social network, which ignore s that social networks change frequently. In order to deal with the above problem, we present a community-based algorithm on the dynamic social network, which is divided into three phases: (i) community detection, (ii) candidate seed set on the dynamic network, and (iii) final seed set. In the first phase, we use the Louvain algorithm to obtain the community structure. In each community, we analyze the node location to judge the importance of the node. In the second phase, considering the dynamic social network, when a node is added to the network or removed from the network, we update the structure of the social network. Then, the candidate nodes are those nodes with a large influence in each community. And in the third phase, we select k influential seeds from the candidate seeds by CELF algorithm. Extensive experimental results show that our algorithm obtains a better influence spread than many baseline algorithms as well as an acceptable running time while considering the dynamic social networks.

An estimated 24 million people worldwide have dementia, the majority of whom are thought to have Alzheimer's disease(AD). Nowadays, Alzheimer's disease represents a significant public health concern and has been identified as a research priority. Most unfortunately, there is little chance of a cure for Alzheimer's disease, and the disease is difficult to detect before the dominant characteristics such as memory loss are manifested. Therefore, the diagnosis of Alzheimer's disease has become an urgent problem today. Studies have shown that mild cognitive impairment(MCI) is a state between Alzheimer's disease and normal, and the chance of it turning into Alzheimer's disease is high. Therefore, if machines can automatically learn the characteristics of three kinds of human brain magnetic resonance(MR) images through deep learning, and help doctors to diagnose patients with mild cognitive impairment or Alzheimer's disease accurately, it will be beneficial for the early diagnosis of Alzheimer's disease. In this paper, we improve the Inception(V3) neural network and further test the effectiveness of the enhanced network based on the international Alzheimer's disease data set, which consists of brain magnetic resonance images. The results show that the average accuracy of our approach can reach 85.7%.

Neural Networks have achieved great success in many computer vision tasks, especially in image recognition. However, as neural networks grow deeper and deeper, to some extend, we've found them becoming difficult to train, and requiring samples in large scale dramatically, even with the help of Dropout and Dropconnect methods, which do improve the accuracy a bit but burdens the training process as a sacrifice. To overcome this, we propose a novel neuron connection model to generate dynamic graphs of computation. As synapses have two kinds: excitatory and inhibitory ones, our model also has two kinds of connections for neurons. In addition, we propose a training algorithm that deals with non-differentiable because the equations of the connections and activation function of neurons in our model are not really differentiable. To evaluate the effectiveness the proposed method, we apply it to the image recognition task, and the results show that our proposed model achieves state-of-the-art performance on three public datasets: MNIST, CIFAR-10, and CIFAR-100.

The recognition of cloud maps plays an important role in the field of meteorological prediction. The mainstream recognition of cloud maps mainly focuses on satellite cloud maps, while research on ground-to-air cloud maps is less. In this paper, we propose an analysis framework - YUN - to quickly locate and identify clouds in these maps. A Quick Cloud region Proposal(QCP) was designed to segment cloud maps to the greatest extent. Then we use the convolutional neural network to extract features of the cloud maps and use fully connected network to predict the type of cloud. In addition, a voting mechanism with a variety of methods has achieved good prediction results.

Nowadays, it is common that both mobile applications and websites log users' visits and keep record of users browser path, even every time user click and every word user type. Each tracking information will be respectively sent to the third-party server via HTTP request. Previous studies have shown that combining multiple small HTTP requests can significantly decrease energy consumption, but not have considered a typical scenario where massive tracking records can be efficiently compressed and transferred with a delay. In this paper, we propose a comprehensive approach to reduce the energy consumption of online tracker by bundling multiple HTTP requests. Our approach first intercepts all tracking-related HTTP requests, logging and compressing the requests for bundling, and then uses a proxy-based technique to combine these requests at runtime. Through a set of real-world experiment, the evaluation demonstrates that our approach can achieve an average energy reduction of 10% for mobile devices and make apparent performance and security improvement.