Pre-trained medical vision foundation models have exhibited strong generalization across diverse tasks. However, the diversity of imaging modalities and segmentation requirements often leads to heterogeneous model architectures, making it difficult to adapt pre-trained weights across different model architectures. To address these limitations, we propose DU-Adapter, a parameter-efficient fine-tuning framework designed for cross-architecture adaptation of heterogeneous pre-trained weights. It features a densely connected U-shaped adapter with learnable convolutional downsampling and upsampling modules to align representations across heterogeneous models. Additionally, a depth-adjustable dense skip connection module allows flexible control over trainable parameter capacity to suit varying downstream segmentation tasks. Extensive experiments on polyp segmentation datasets show that DUAdapter outperforms both fully fine-tuned and lightweight tuning baselines, achieving superior performance with significantly reduced training cost.

Patient data from real-world clinical practice often suffers from data scarcity and long-tail imbalances, leading to biased outcomes or algorithmic unfairness. This study addresses these challenges by generating lesion-containing image-segmentation pairs from lesion-free images. Previous efforts in medical imaging synthesis have struggled with separating lesion information from background, resulting in low-quality backgrounds and limited control over the synthetic output. Inspired by diffusion-based image inpainting, we propose LeFusion, a lesion-focused diffusion model. By redesigning the diffusion learning objectives to focus on lesion areas, we simplify the learning process and improve control over the output while preserving high-fidelity backgrounds by integrating forward-diffused background contexts into the reverse diffusion process. Additionally, we tackle two major challenges in lesion texture synthesis: 1) multi-peak and 2) multi-class lesions. We introduce two effective strategies: histogram-based texture control and multi-channel decomposition, enabling the controlled generation of high-quality lesions in difficult scenarios. Furthermore, we incorporate lesion mask diffusion, allowing control over lesion size, location, and boundary, thus increasing lesion diversity. Validated on 3D cardiac lesion MRI and lung nodule CT datasets, LeFusion-generated data significantly improves the performance of state-of-the-art segmentation models, including nnUNet and SwinUNETR.

Accurate disease grading (assessing lesion severity) and localization (pinpointing lesions) in CT images are critical for clinical diagnosis. While vision-language models typically require pixel-level annotations, medical CT annotation is timeconsuming, labor-intensive, and inconsistent due to differences in experience and annotation styles among experts. Consequently, most datasets provide only image-level category labels, posing significant challenges for current vision-language models under weak supervision, such as difficulty in accurately identifying ambiguous lesion features, susceptibility to confirmation bias, and the risk of error accumulation during training. To address these challenges, we propose CoWeT, a dual-branch collaborative weakly supervised text-guided visual-language model for disease grading and localization. Our model employs a dual-branch collaborative learning strategy, introducing mutual supervision between branches to refine features collaboratively and mitigate confirmation bias, alongside a novel Text Prompt Box (TPB) module that leverages pathology reports as textual prompts to intelligently filter pseudo-labels for robust lesion localization and grading under weak supervision. Evaluations on RTDP and LND datasets show that CoWeT outperforms state-of-the-art methods on accuracy and average precision.

Diabetic retinopathy (DR) is the prominent cause of permanent blindness, which brings the risk of visual damage to the all-age population. Accurate grading of DR severity aids doctors in diagnosing and treating this condition. In essence, DR grading is an implicit two-stage process. The first stage aims to detect suspicious lesion regions, and the second stage integrates lesion information to obtain DR grading results. However, the lack of pixel-level lesion annotations hinders the detection of suspicious lesions and impacts the performance of the automatic DR disease diagnosis system. Therefore, how to obtain reliable lesion features without pixel-level lesion annotations is essential for predicting the DR grading results. In this work, we introduce the prompt paradigm to DR grading and propose a hybrid CNN-Transformer framework named WSL-DR, which utilizes weakly-supervised lesion detection to obtain suspicious lesion prompt information for guiding DR grading. Furthermore, to mitigate the semantic gap, we design a prompt-based receptive channel field (PRCF) encoder to effectively fuse local lesion prompt information with global image information. Extensive experimental results have shown that our method outperforms state-of-the-art DR grading methods utilizing only image-level labels on the EyePACS and Messidor benchmark datasets.

The feature alignment methods currently employed in domain adaptation for enhancing model transfer ability tend to compromise the discriminative capability of the model towards target features from different categories, consequently leading to a degradation in detector performance. To address this issue, this paper proposes a domain adaptation method for SAR (Synthetic Aperture Radar) image object detection based on instance contrastive learning. By utilizing a dual-branch prediction network, foreground and background instances are more accurately selected. Through instance contrastive learning, the proposed method narrows the distance between target features of the same category across different domains while widening the distance between target features of different categories. This approach facilitates alignment of the distributions of target features of the same category while preserving the discriminative nature of target features across different categories. Experimental results demonstrate that the proposed model achieves respective improvements in the Average Precision (AP) metric on two domain adaptation object detection datasets from optical images to SAR remote sensing images compared to SOTA methods.

Nonvolatile memory (NVM) suffers from more serious nonuniform memory access (NUMA) effects than DRAM because of the lower bandwidth and higher latency. While numerous works have aimed at optimizing NVM indexes, only a few of them tried to address the NUMA impact. Existing approaches mainly rely on local NVM write buffers or DRAM-based read buffers to mitigate the cost of remote NVM access, which introduces memory overhead and causes performance degradation for lookup and scan operations. In this article, we present NOBtree, a new NUMA-optimized persistent tree index. The novelty of NOBtree is two-fold. First, NOBtree presents per-NUMA replication and an efficient node-migration mechanism to reduce remote NVM access. Second, NOBtree proposes a NUMA-aware NVM allocator to improve the insert performance and scalability. We conducted experiments on six workloads to evaluate the performance of NOBtree. The results show that NOBtree can effectively reduce the number of remote NVM accesses. Moreover, NOBtree outperforms existing persistent indexes, including TLBtree, Fast&Fair, ROART, and PACtree, by up to$3.23\times $in throughput and$4.07\times $in latency.

Colonoscopy has emerged as a crucial examination for early colorectal cancer (CRC) diagnosis. Early detection of polyps can significantly enhance the survival rate of colorectal cancer. Most recent weakly supervised methods for detecting polyps are based on multiple instance learning (MIL), which employs labeled training data at the video-level (bag-level) to identify polyps at the frame-level (instance-level). However, existing methods often use the same features without considering the differences of video and snippet. Video classification usually focuses more on global features, while snippet classification relies more on leveraging multi-granularity detail information. This paper proposes decoupling the MIL network into the feature encoder and instance decoder. Furthermore, we introduce a novel Snippet-wise Cross Fusion Attention (SCA) that captures rich temporal context semantic features for instance classification. Additionally, our approach incorporates a parameter-efficient finetuning architecture called convolutional adapters, which aims to enhance the training process stability and improve the model's performance. Experimental results demonstrate consistent improvements over state-of-the-art methods on a newly introduced large-scale colonoscopy video dataset by a considerable 7.9% AUC and 1.16% AP. Our code and dataset will be made publicly available at: https://github.com/kanydao/Decoupling-MIL.

Few-shot learning (FSL) is a machine learning method aimed at learning problem-solving models from a small number of samples. Few-shot classification is a task that utilizes few-shot learning for image classification. However, the existing models have poor classification performance when dealing with datasets with large differences in target scale distribution. In view of the large difference in the scale distribution of the sample target in the few-shot learning dataset, this paper proposes a novel few-shot classification model based on multi-scale Vision Transformer using semantic prompt. This model uses the Transformer block which has more generalization ability and long-range dependencies, and constructs a multi-scale feature fusion model on the large-scale cross-domain and cross-scale dataset Meta-Dataset which consists of 10 datasets, including natural image datasets, handwritten characters, and graffiti datasets. By using an explicit multi-scale feature fusion method, the model can obtain category features with cross scale information, thereby improving the robustness of the model to sample targets of different scales. In addition, the model improved its performance by learning additional semantic prompt, achieving a 2.43% improvement.

Single-stage Human-Object Interaction (HOI) detection methods have attracted considerable attention due to their high efficiency. Existing methods tend to concentrate the detection of humans and objects in one decoder without considering the differences between them, which causes tremendous pressure on a single decoder and affects the detection effect. This paper aims to decouple the detection decoder of humans and objects. In particular, we advocate and propose a novel human-object decoupling network (HOD) that divides the decoder into three tasks: human detection, object detection, and action classification. The network uses the random erasure training strategy to improve the model’s generalization ability and introduces pose features to handle the long-tailed problem. In addition, we design a pose fusion branch to alleviate the semantic gap between pose and HOI datasets. The experimental results suggest that our method achieves consistent improvements over the state-of-theart across different datasets, utilizing only image information. Specifically for HICO-Det, our method outperforms existing methods by a large margin, with a significant relative mAP gain of 7.8%. Our source code will be publicly available upon acceptance.

Learned Indexes aim to use machine-learning models to predict the target addresses of requested data, which have been demonstrated efficient for in-memory data accesses. However, modern database systems mainly use flash-memory-based solid-state drives (SSDs) as storage devices, and current learned indexes fail to work on SSD-based databases. In this paper, we propose LIFM (Learned Index for Flash Memory), a new persistent and updatable learned index for flash-memory-based SSDs. Unlike existing learned indexes that aim to reduce memory access, LIFM is designed to reduce I/O costs. The novelty of LIFM lies in three aspects. First, LIFM proposes a hierarchical tree structure that combines the advantages of traditional B+-tree and in-memory learned indexes. Second, LIFM employs a model-based data placement scheme to reduce the page reads or writes. Third, LIFM uses a flash-memory-friendly storage layout to reduce additional access to flash memory. We conduct experiments on a real SSD and compare LIFM with B+-tree and two recently proposed learned indexes, ALEX and FITing-tree. The results suggest the efficiency of LIFM.

OLAP applications often perform aggregation queries, such as sum, avg, count, max, and min, to process a large dataset for getting a small result, and the key challenge for optimizing aggregation queries is to reduce the data volume needed to be read from disks. To address this problem, this paper presents an efficient caching component called FGCache (Fine-Grained Cache) for aggregation queries. FGCache is a query-driven caching policy, which aims to maintain the results of aggregation queries in the memory according to the predicates of user queries. The query results are divided into sets with fine-grained querying intervals, which are maintained via an in-memory tree index. We experimentally evaluate the performance of FGCache on various datasets and settings. The results suggest the efficiency and effectiveness of our proposal.

This paper proposes a novel learned index called DTtree that can optimize both read and write performance. DTtree adopts two novel designs. (1) To improve write performance, DTtree uses a dynamic filling rate and overflow buffers for each leaf node. The filling rate of a leaf node is dynamically determined according to the read/write tendency of the leaf node so as to improve space efficiency and reduce structure modification operations. In addition, DTtree uses an overflow buffer for a line in each leaf node. The overflow buffer is also dynamically allocated, and only write-intensive leaf nodes may have overflow buffers, reducing unnecessary space costs. (2) To improve read performance, we propose an in-node hot cache to cache hot records within a line of a leaf node, reducing additional memory access to the overflow buffer. As skewed access is common in real-world applications, using in-node hot caches can efficiently improve the read performance of DTtree. We experimentally evaluate DTtree on three datasets. The results on various workloads show that DTtree can reduce SMO operations effectively and achieve higher read/write performance than B+-tree and two representative learned indexes, PGM-Index and ALEX.

Object detectors trained with massive labeled data often suffer performance degradation in some particular scenarios with data distribution gap.Domain classifier is a commonly used method in the existing domain adaptation algorithms to alleviate the domain discrepancy.However, it has problems of instability in training, difficulty in obtaining optimal solutions and converging to an equilibrium point.To tackle this issue, we propose a novel balanced domain classifier (BDC), which not only eliminates domain discrepancy but also makes the domain classifier and the feature extractor maintain equilibrium during the adversarial learning.Furthermore, we propose an appropriate learning rate adjustment strategy, which makes the detection model converge to an equilibrium point more stably and more rapidly.Based on the domain-invariant region proposal network, we propose a cross-domain object detection model called Faster R-CNN with Balanced Domain Classifier (BDC-FR).The experimental results show that BDC-FR can effectively improve the performance of the cross-domain object detection model.

In recent years, weakly supervised object detection (WSOD) methods using only image-level labels have received increasing attention, Due to the difficulty of manually labeling large-scale remote sensing images. However, existing methods cannot generate high-quality proposals when applying proposal generation methods to RSIs. Meanwhile, these methods ignore the fact that there are a large number of objects of different scales in RSIs. To address these issues, we propose a unique end-to-end dynamic feature fusion network (DFFNet) for WSOD in RSIs. First, we propose an intersection-over-union selective search (IoU-SS) algorithm to generate high-quality proposals by preferentially merging regions with high IoU. Furthermore, we design a novel and flexible dynamic feature fusion (DFF) module to dynamically acquire features of objects at different scales based on the information of the input image. The performance of WSOD in RSIs is further improved by using high-quality proposals and dynamically fused features. Comprehensive experiments and comparisons with state-of-the-art methods on two datasets of RSIs, i.e., NWPU VHR-10.v2 and DIOR, demonstrate the superiority of our proposed method.

Cloud services such as multi-tenant content delivery networks (CDN) have become a trend because they can offer personalized business support for users. However, as the business modes of different users are usually different, it is not appropriate to use a single cache strategy on cloud servers. First, a single cache cannot adapt to various access patterns of tenants. Second, a single cache will also affect the isolation among different users. In this paper, we propose MyCache, a new framework to deliver personalized cache management for multi-tenant cloud services. We first discuss the impact of access patterns, which motivates MyCache. Then, we briefly introduce the architecture of MyCache, and finally, we present preliminary experimental results to show the feasibility and superiority of our proposal.

With the development of computer vision and the growth of image data volume, object detection is more and more widely used in people's life. Recently, object detection network base on transformer has good effect. However, it doesn't pay attention to objects' distinguishable features and the correlation of different distinguishable features, which makes its accuracy not satisfactory. In this paper, we propose a new transform correlation network (FTCN) based on rotational attention for object detection, which can be inserted into various modules of the object detection network theoretically. It can help the network to pay more attention to distinguishable features and their correlation, and increase the accuracy of the network. Compared to transformer, FTCN reduces the large computational cost and parameters that encoders and decoders require. Experimental results show that FTCN can increase the accuracy of the network to a certain extent in Microsoft COCO and Pascal VOC. The comparative results of GFLOPs and Param show that our approach has less computational cost and parameters compared with transformer methods.

Buffer management is an essential technology for database management systems.Traditional buffer management employs an empirical approach based on access recency or frequency which fails to adapt to access-pattern changes in various database applications.In this paper, we present a new access-pattern-aware buffer manager called PBM (Personalized Buffer Manager), which can detect the access patterns for each database file and use a specific buffering policy for each database file.In particular, we propose a workload classifier to detect the access pattern of a database file.Then, we partition the buffer into various zones, set different sizes for each zone, and select the most suitable buffering scheme for each zone.With such a mechanism, each zone is responsible for caching a specific database file, and we can realize a personalized buffer manager for different database files, which can improve the buffer efficiency and reduce the page I/Os of the buffer manager.We compare PBM with three existing buffering algorithms, including LRU, LFU, and LeCaR, on two workloads, namely a regular workload and a shifting workload, which are composed of different access patterns.The results show that PBM outperforms the three competitors in terms of hit ratio and page I/Os.As a consequence, PBM achieves 1.66x, 2.03x, and 1.39x hit-ratio improvements compared to LRU, LFU, and LeCaR, respectively, on the regular workload.While on the shifting workload, PBM achieves 1.90x, 1.55x, and 1.49x higher hit ratios than LRU, LFU, and LeCaR, respectively.

Non-Volatile Memory (NVM) has emerged as an alternative to next-generation main memories. Although many tree indices have been proposed for NVM, they generally use B+-tree-like structures. To further improve the performance of NVM-aware indices, we consider integrating learned indexes into NVM. The challenges of such an integration are two fold: (1) existing NVM indices rely on small nodes to accelerate insertions with crash consistency, but learned indices use huge nodes to obtain a flat structure. (2) the node structure of learned indices is not NVM friendly, meaning that accessing a learned node will cause multiple NVM block misses. Thus, in this paper, we propose a new persistent learned index called PLIN. The novelty of PLIN lies in four aspects: an NVM-aware data placement strategy, locally unordered and globally ordered leaf nodes, a model copy mechanism, and a hierarchical insertion strategy. In addition, PLIN is proposed for the NVM-only architecture, which can support instant recovery. We also present optimistic concurrency control and fine-grained locking mechanisms to make PLIN scalable to concurrent requests. We conduct experiments on real persistent memory with various workloads and compare PLIN with APEX, PACtree, ROART, TLBtree, and Fast&Fair. The results show that PLIN achieves 2.08x higher insertion performance and 4.42x higher query performance than its competitors on average. Meanwhile, PLIN only needs ~30 μs to recover from a system crash.

Join selectivity estimation is a fundamental problem in query optimization, which aims to estimate the cardinality returned by a join query. While join selectivity estimation has been extensively studied in relational databases, there are few studies toward join selectivity estimation for cross-model joins in multi-model databases, such as relation-tree joins between a relational table and a tree-structured document like a BSON file in MongoDB. So far, due to the popularity of MongoDB, many applications use both MongoDB and MySQL to organize heterogeneous data. Thus, it is necessary to devise efficient approaches for processing relation-tree joins running on relational and tree models. In this paper, we present an effective and efficient approach to estimate the join selectivity for relation-tree joins, which consists of a value join estimation and a structural join estimation. In particular, we propose a two-level sampling method that samples the relational tuples and tree nodes at two levels. Then, we apply the discrete learning algorithm to the tree node samples to estimate the join value distribution of the tree nodes. With this mechanism, we can capture the correlation between relational tuples and tree nodes and improve the estimation accuracy. We conduct experiments on the DBLP dataset and compare our approach to existing solutions, and the results suggest the effectiveness and efficiency of our proposal.

Existing approaches based on the SGR (single granularity recognition) improve the recognition accuracy by expanding the dataset, learning more robust feature representation or locating and amplifying significant parts. These approaches often suffer from heavy computational costs, high model complexity or additional non-classsification losses. In this paper, we propose a feature fusion method based on multi-classification losses, which can classify different granularity features and make the network focus on more useful information. The complexity and computational costs of our model have only increased slightly, which can be ignored. The results of comparative experiments show the effectiveness of our proposal on Stanford Cars dataset.

This paper addresses a domain adaptive person re-identification network to learn representations across domains for a challenging task: exploiting more robust features from a labeled source domain dataset and an unlabeled target domain dataset. Although convolutional networks have achieved high accuracy in single-domain re-identification task, the performance of trained models slumped in a new dataset. Current methods focus on global features, but part-based features can offer more details of pedestrians, which was been proved in recent works. In this paper, we consider local features as part invariance to reduce the gap between domains and learn discriminative features for cross-domain person re-identification. A part invariance network is proposed which implements a memory to store features extracted in the target domain to reduce camera and neighborhood invariance to improve the adaptability of the model. Experiments conducted show that the invariance proposed in this paper outperforms the existing methods by a large margin.

Non-volatile memory (NVM) as a new type of storage technology has many advantages such as non-volatility, byte addressability, high storage-density, and low energy consumption.Meanwhile, NVM has some limitations, e.g., asymmetric read and write latency, limited write endurance, and high price.Therefore, at present, it is not realistic to completely replace DRAM with NVM in computer systems.A more feasible scheme is to adopt the hybrid memory architecture composed of NVM and DRAM.Following the assumption of hybrid memory architecture, this paper proposes an NVM-friendly sorting algorithm called NVMSorting.Particularly, we introduce a new concept called natural runs to improve the existing MONTRES algorithm and present the cost analysis of the algorithm in the hybrid memory architecture.In order to verify the performance of our proposal, we implement six existing sorting algorithms as baselines, including the MONTRES algorithm, and conduct comparative experiments on an unsorted dataset and a partially sorted dataset.The experimental results suggest the efficiency of NVMsorting in terms of execution time and NVM writes.Especially, on the partially sorted dataset, NVMSorting has 6.2% improvement on time performance and 5.7% reduction on NVM writes compared to MONTRES, and 13.0% performance improvement and 27.1% NVM-write reduction compared to the traditional merge sorting algorithm.

Bloom filter is an efficient technique to improve query performance in LSM-tree-based databases, such as RocksDB, HBase, and Cassandra.However, the original Bloom filter uses a fixed false positive rate (FPR), which makes it inefficient for mixed queries that involve both point and range queries.To solve this problem, in this paper, we present an improved Bloom filter called RoBF (Range-Query-Oriented Bloom Filter), which uses a mixture of Bloom filters and can process mixed queries on LSM-tree efficiently.We design an efficient algorithm for generating the solution based on the query distribution.We compare our proposal with the trie-based filter and find out that each has its own advantages for various scenarios.Therefore, we propose to use different filters with varied sizes for different levels on LSM-tree.Following this idea, we present an algorithm to generate specific filters with a specific size for different levels on LSM-tree to optimize the performance of mixed queries under limited memory space.We conduct comparative experiments and compare the proposed RoBF with various competitors, and the results show that RoBF can improve the performance of evaluating mixed queries by up to 6x to 30x, compared to the original Bloom filter in RocksDB.

Data-intensive systems are typical applications in the big data era. Most data-intensive systems employ LSM (Log-Structured Merge)-tree-based key-value stores, such as RocksDB and LevelDB. The LSM-tree has a multi-level data structure and can transform random writes into sequential ones by a special operation called compaction. However, compaction operations are costly and will increase the processing latency and lower the throughput of the LSM-tree. In this paper, we propose an adaptive lazy compaction method called ALC for the LSM-tree. The main idea of ALC is to delay compactions operations when the current workload is high, and to perform compactions when the system is low-loaded. With this mechanism, we can lower the impact of compactions on the system's performance and achieve high stability and low latency. Our preliminary experiments show that ALC outperforms RocksDB and the state-of-the-art method SILK in latency and throughput-stability.

In this paper, to improve the read performance of LSM-tree, we propose an enhanced LSM-tree called HotKey-LSM. The key idea of HotKey-LSM is to put hot keys and cold keys in two separated column families. Thus, when a hot key is not in the block cache, LSM-tree only needs to access a small hot-key LSM-tree to read the key. With this mechanism, most hot-key requests will be answered with low latency: if the hot key is in the block cache, we can return the memory address of the key; if it is not in the cache, we search the small hot-key LSM-tree. This differs from the traditional LSM-tree in that a hot-key request may traverse a large LSM-tree, which causes a high read latency. We implement HotKey-LSM on RocksDB and compare HotKey-LSM with the original RocksDB. The result in terms of QPS suggests the efficiency of our proposal.

Classification for big image data is an essential field of big data technology. Big image data classification has a wide range of applications, such as face recognition, object detection, and human posture recognition. Traditional methods for big image data classification usually ignore the close connection between different levels of features in neural networks. In this paper, we propose a new way for big image data classification that improves the previous method through the Multi-Level Feature Connection Network (MFC-Net). MFC-Net presents a multi-level feature connection module to mine more useful features for classification. Experiments show that MFC-Net significantly improves its baseline SSD (Single Shot Multibox Detector) and other advanced classification neural networks in the classic datasets Pascal VOC and Microsoft COCO.

Non-Volatile Memory (NVM) has emerged as an alternative to next-generation memories. Compared to the traditional DRAM, NVM offers data persistency and higher density. However, so far, NVM has higher accessing latency than DRAM. Therefore, to ensure the high performance of data accessing, we still need to consider using DRAM in memory architecture. This leads to the hybrid memory architecture involving DRAM and NVM. Some previous benchmark works have shown that such hybrid memory architecture is more efficient than NVM-only architecture. Due to NVM's unique properties, the traditional memory B+-tree becomes unsuitable for NVM because of its high cost of maintaining node orderliness and high space-filling feature. In this paper, we propose to optimize the Adaptive Radix Tree (ART) for the hybrid memory architecture and offer a new index called HART (Hybrid Adaptive Radix Tree). HART takes advantage of ART's deterministic structure to get good query performance. Meanwhile, we only selectively persist linked list to reduce NVM access cost. In particular, we exploit the compression path to improve the leaf node's space utilization, making the subtree shorter. We run a preliminary experiment on a server with Intel Optane DC Persistent Memory and compare HART with several NVM-aware indexes. The results suggest the efficiency of our proposal.

Non-volatile memory (NVM) as a new kind of future memory has several special properties such as non-volatility, read/write asymmetry, and byte address-ability. This makes it difficult to directly replace DRAM with NVM in the current memory hierarchy. Thus, a practical way is to construct a hybrid memory composed of both NVM and DRAM. Such hybrid memory architecture introduces many new challenges for existing algorithms. In this paper, we focus on the data placement issue in NVM-based hybrid memory systems, i.e., how to place the data on DRAM and NVM for a data-centric algorithm so that it can achieve high performance on hybrid memory. Particularly, we propose an optimal data placement model (ODP) to properly store data structures on DRAM and NVM during the execution of an algorithm. We present the theoretical proof to ODP to ensure the correctness of the model. To demonstrate the efficiency of ODP, we apply the ODP to two kinds of data-centric algorithms, namely sorting and database join. For sorting algorithms, we implement four ODP-based sorting algorithms, including Insertion Sort, Selection Sort, Heapsort, and Merge Sort. For join algorithms, we implement four ODP-based join strategies, including Nested Loops Join, Sort Join, Hash Join, and Virtual Partitioning Join. We conduct comparative experiments to evaluate the performance of the sorting/join algorithms. The results show that the ODP-based sorting/join strategies are much faster than the classical sorting/join algorithms that are not NVM-aware. In addition, the ODP-based implementation can reduce more NVM writes, showing that it is more NVM-friendly.

The performances of object detectors are highly impacted by the discrepancy between existing data sets and application scenarios, leading to the so-called domain shift problem. Previous works, based on Faster R-CNN, focus on aligning the image-level features and the region-level features. However, the Region Proposal Network (RPN), as a key module between the image-level and the region-level modules, still has the problem of domain shift that leads to inaccurate or even false detected results. To tackle this issue, we propose a new design, Domain-Invariant RPN (DIR), which adopts adversarial learning to eliminate the domain shift in RPN, and thereby, significantly improving the accuracy and robustness of bounding box proposals. Furthermore, we propose a Double-Consistency Regularization (DCR) to improve the overall feature alignment. Extensive experiments show that our approach outperforms state-of-the-art methods.

With the development of networked radars and wireless communication technologies, electromagnetic data is becoming a new type of big data.Compared with other kinds of big data, such as Internet big data, financial big data, and healthcare big data, electromagnetic big data has some special properties.For example, they usually contain rich and varying labels that describe the features of electromagnetic space.However, existing big-data benchmark tools cannot support the generation and test of electromagnetic big data.In this paper, we aim at providing a simulation-based testbed for electromagnetic big data.The testbed, named STEM, can simulate real-world electromagnetic big data.It supports generating real-time electromagnetic data streams with varying labels.In addition, it is reusable, reconfigurable, and flexible for users to generate workloads for different scenarios.After a brief introduction on the architecture of STEM, we present the implemental details of STEM.Then, we present a case study as well as performance evaluation to demonstrate the usability and flexibility of STEM.

Compressive sensing (CS)-based in-network data processing is a promising approach to reduce packet transmission in wireless sensor networks. Existing CS-based data gathering methods require a large number of sensors involved in each CS measurement gathering, leading to the relatively high data transmission cost. In this paper, we propose a sparsest random scheduling for compressive data gathering scheme, which decreases each measurement transmission cost from O(N) to O(log(N)) without increasing the number of CS measurements as well. In our scheme, we present a sparsest measurement matrix, where each row has only one nonzero entry. To satisfy the restricted isometric property, we propose a design method for representation basis, which is properly generated according to the sparsest measurement matrix and sensory data. With extensive experiments over real sensory data of CitySee, we demonstrate that our scheme can recover the real sensory data accurately. Surprisingly, our scheme outperforms the dense measurement matrix with a discrete cosine transformation basis over 5 dB on data recovery quality. Simulation results also show that our scheme reduces almost 10 × energy consumption compared with the dense measurement matrix for CS-based data gathering.

Indoor moving objects management has been a research focus in recent years. In order to get the trajectory data of indoor moving objects, people have to deploy a lot of positioning equipment, such as RFID readers and tags, which takes lots of money, time, and other costs. In addition, it is a very complex and costly process to construct different environment settings for various indoor applications. Aiming to provide experimental trajectory data for various indoor operations and mining algorithms, in this paper we present a flexible tool to generate trajectories for indoor moving objects, which is named IndoorSTG (Indoor Spatiotemporal Trajectory Generator). IndoorSTG can simulate different indoor environments using various elements including rooms, doors, corridors, stairs, elevators, and virtual positioning devices such as RFID or Bluetooth readers. Meanwhile, it can generate semantic-based trajectories for indoor moving objects in a specific indoor space. After an overview of the general features of IndoorSTG, we discuss the architecture and implementation of IndoorSTG. And finally, a case study of IndoorSTG's demonstration is presented.

In content-based image retrieval (CBIR), color is the most intuitive image features and it is widely used. But The current color feature can describe the semantics of the whole image effectively, but does not reflect characteristics of the color salience objects in an image. For the purpose of giving prominence to the color characteristics of salience objects, this paper proposes a new color feature descriptor based on color visual perception model. By analyzing the human color visual perception process, the color perception model is proposed at first. This model integrates the intensity, the color contrast and self saliency, sparsity and centricity saliency to describe human color visual perception of the image. Then, the new color feature descriptor is calculated by weighting the significant bit-plane histograms with color perception map. Finally, similarity measure is presented for the new color feature. Experiment results show that the proposed color feature is more accurate and efficient in retrieving images with user-interested color objects. Compared with the other three retrieval methods, the proposed technique improves the retrieval accuracy effectively.

One-dimensional threshold segmentation algorithm has poor adaptability and poor noise immunity, while two-dimensional threshold segmentation has high computational complexity. To solve these problems, we propose a novel threshold segmentation algorithm for image segmentation. The whole method contains three steps: constructing one-dimensional histogram by using each pixel's gray value and its surrounding area's mean gray value, by integrating three classic segmentation algorithms: minimum error, maximum entropy and Otsu, we construct a new threshold selection algorithm, making use of the threshold to instruct segmentation. The experimental results show that our algorithm has strong adaptability, stable and robust noise immunity when comparing to one-dimensional threshold segmentation algorithm and a wider range of noise adaptability, lower computation complexity when comparing to two-dimensional Otsu algorithm.

Ship detection is one of the most important applications of target recognition based on optical remote sensing images. In this paper, we propose an uncertain ship target extraction algorithm based on dynamic fusion model of multi-feature and variance feature of optical remote sensing image. We choose several geometrical features, such as length, wide, rectangular ratio, tightness ratio and so on, using SVM to train and predict the uncertain ship targets extracted by our algorithm automatically. Experiments show that our algorithm is very robust, and the recognition rate of our algorithm can reach or even better than 95%, with the false alarm rate is kept at 3%.

In recent years, many research works indicate that human's visual attention is very helpful in some research areas that are related to computer vision, such as object recognition, scene understanding and object-based image/video retrieval or annotation. This paper presents a visual attention model for natural scenes based on a dynamic feature combination strategy. The model can be divided into three parts, which are feature extraction, dynamic feature combination and salient objects detection. First, the saliency features of color, information entropy and salient boundary are extracted from an original colored image. After that, two different evaluation measurements are proposed for two different categories of feature maps defined in this dynamic combination strategy, which measures the contribution of each feature map to saliency and carries out a dynamic weighting of individual feature maps. Finally, salient objects are located from an integrated saliency map and a computational method is given to simulate the location shift of the real human visual attention. Experimental results show that this model is effective and robust for saliency detection in natural scenes, also similar to the real human visual attention mechanism.

Salient objects detection in time sequenced images has a very important role in many applications such as surveillance systems, tracking and recognition systems, scene analysis and so on. This paper presents a novel approach for salient objects detection in time sequenced images. The approach in this paper is based on a visual saliency model which is proposed for analysis in time sequenced images. The model in this paper is based on a bottom-up visual saliency model which is presented by Itti in 1998. Multi different features are introduced to describe salient objects globally in time sequenced images. And they are combined into a single saliency map. Salient objects in time sequenced images can be detected by the final saliency map. The detection algorithm is unsupervised and fast. The results of the experiments indicate that our approach is effective and very robust to noise, blur, contrast level and brightness level.

In this paper, we present a CASE tool supporting conceptual modeling for moving objects database applications, which is called STXER (spatio-temporal extended entity-relational model). The main purpose of STXER is to support the database design for moving objects applications on typical object-relational DBMS. The STXER tool enhances the traditional ER model with moving characteristics. It can express rich spatiotemporal semantics for moving objects applications. After an overview of the general features of STXER, we discuss the architecture of STXER. And finally, a case study of STXER's demonstration is presented.