Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data

Here, we present Anchored-fusion, a highly sensitive fusion gene detection tool. It anchors a gene of interest, which often involves driver fusion events, and recovers non-unique matches of short-read sequences that are typically filtered out by conventional algorithms. In addition, Anchored-fusion contains a module based on a deep learning hierarchical structure that incorporates self-distillation learning (hierarchical view learning and distillation [HVLD]), which effectively filters out false positive chimeric fragments generated during sequencing while maintaining true fusion genes. Anchored-fusion enables highly sensitive detection of fusion genes, thus allowing for application in cases with low sequencing depths. We benchmark Anchored-fusion under various conditions and found it outperformed other tools in detecting fusion events in simulated data, bulk RNA sequencing (bRNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Our results demonstrate that Anchored-fusion can be a useful tool for fusion detection tasks in clinically relevant RNA-seq data and can be applied to investigate intratumor heterogeneity in scRNA-seq data.

Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning

Accurate detection of liver lesions from multi-phase contrast-enhanced CT (CECT) scans is a fundamental step for precise liver diagnosis and treatment. However, the analysis of multi-phase contexts is heavily challenged by the misalignment caused by respiration coupled with the movement of organs. Here, we proposed an AI system for multi-phase liver lesion segmentation (named MULLET) for precise and fully automatic segmentation of real-patient CECT images. MULLET enables effectively embedding the important ROIs of CECT images and exploring multi-phase contexts by introducing a transformer-based attention mechanism. Evaluated on 1,229 CECT scans from 1,197 patients, MULLET demonstrated significant performance gains in terms of Dice, Recall, and F2 score, which are 5.80%, 6.57%, and 5.87% higher than state of the arts, respectively. MULLET has been successfully deployed in real-world settings. The deployed AI web server provides a powerful system to boost clinical workflows of liver lesion diagnosis and could be straightforwardly extended to general CECT analyses.

Hierarchical Knowledge Propagation and Distillation for Few-Shot Learning

Recent research efforts on Few-Shot Learning (FSL) have achieved extensive progress. However, the existing efforts primarily focus on the transductive setting of FSL, which is heavily challenged by the limited quantity of the unlabeled query set. Although a few inductive-based FSL methods have been studied, most of them emphasize learning superb feature extraction networks. As a result, they may ignore the relations between sample-level and class-level representations, which are particularly crucial when labeled samples are scarce. This paper proposes an inductive FSL framework that leverages the Hierarchical Knowledge Propagation and Distillation, named HKPD. To learn more discriminative sample-level representations, HKPD first constructs a sample-level information propagation module that explores pairwise sample relations. Subsequently, a class-level information propagation module is designed to obtain and update the class-level information. Moreover, a self-distillation module is adopted to further improve the learned representations by propagating the obtained knowledge across this hierarchical architecture. Extensive experiments conducted on the commonly used few-shot benchmark datasets demonstrate the superiority of the proposed HKPD method, which outperforms the current state-of-the-art methods.

Direction-Aware User Recommendation Based on Asymmetric Network Embedding

User recommendation aims at recommending users with potential interests in the social network. Previous works have mainly focused on the undirected social networks with symmetric relationship such as friendship, whereas recent advances have been made on the asymmetric relationship such as the following and followed by relationship. Among the few existing direction-aware user recommendation methods, the random walk strategy has been widely adopted to extract the asymmetric proximity between users. However, according to our analysis on real-world directed social networks, we argue that the asymmetric proximity captured by existing random walk based methods are insufficient due to the inbalance in-degree and out-degree of nodes.

To tackle this challenge, we propose InfoWalk, a novel informative walk strategy to efficiently capture the asymmetric proximity solely based on random walks. By transferring the direction information into the weights of each step, InfoWalk is able to overcome the limitation of edges while simultaneously maintain both the direction and proximity. Based on the asymmetric proximity captured by InfoWalk, we further propose the qualitative (DNE-L) and quantitative (DNE-T) directed network embedding methods, capable of preserving the two properties in the embedding space. Extensive experiments conducted on six real-world benchmark datasets demonstrate the superiority of the proposed DNE model over several state-of-the-art approaches in various tasks.

TigeCMN: On exploration of temporal interaction graph embedding via Coupled Memory Neural Networks

With the increasing demand of mining rich knowledge in graph structured data, graph embedding has become one of the most popular research topics in both academic and industrial communities due to its powerful capability in learning effective representations. The majority of existing work overwhelmingly learn node embeddings in the context of static, plain or attributed, homogeneous graphs. However, many real-world applications frequently involve bipartite graphs with temporal and attributed interaction edges, named temporal interaction graphs. The temporal interactions usually imply different facets of interest and might even evolve over the time, thus putting forward huge challenges in learning effective node representations. Furthermore, most existing graph embedding models try to embed all the information of each node into a single vector representation, which is insufficient to characterize the node's multifaceted properties. In this paper, we propose a novel framework named TigeCMN to learn node representations from a sequence of temporal interactions. Specifically, we devise two coupled memory networks to store and update node embeddings in the external matrices explicitly and dynamically, which forms deep matrix representations and thus could enhance the expressiveness of the node embeddings. Then, we generate node embedding from two parts: a static embedding that encodes its stationary properties and a dynamic embedding induced from memory matrix that models its temporal interaction patterns. We conduct extensive experiments on various real-world datasets covering the tasks of node classification, recommendation and visualization. The experimental results empirically demonstrate that TigeCMN can achieve significant gains compared with recent state-of-the-art baselines.

[Operative efficacy of 13 malignant uterine tumors after Cf-252 intracavitary irradiation]

Objective: To investigate the surgical therapeutic efficacy of uterine tumors patients underwent Cf-252 neutron intra-cavity and external radiotherapy, and evaluate the application value of Cf-252 neutron radiotherapy. Methods: Thirteen cases of uterine tumor with local suspicious lesions or poor prognostic factors after CF-252 neutron intracavity and external radiotherapy were treated with surgery. Among them, 12 cases underwent extrafascial hysterectomy, 1 case underwent extensive hysterectomy and lymphadenectomy. The postoperative pathology and follow-up results were used to evaluate the efficacy. Results: Nine cases showed severe response to radiotherapy in postoperative cervical pathological tissues without residual tumor, and survived for more than 3-14 years, the median survival time was 8 years. All of 4 cases with residual tumor died within 1 year. Delayed healing of vaginal wounds occurred in 3 of the 12 cases. Conclusions: Cf-252 is a good brachytherapy source. The cervical tissue shows severe response to radiotherapy and prolonged healing time of vaginal wound is observed in some cases after CF-252 radiotherapy. To those uterine tumor patients with local suspicious lesions or poor prognostic factors after CF-252 neutron intracavity and external radiotherapy, extrafascial hysterectomy is a safe and feasible treatment method.

Primary Adenoid Cystic Carcinoma of the Upper Anterior Mediastinum Mimicking a Thyroid Tumor: A Case Report and Review of Literature

Primary adenoid cystic carcinoma (ACC) of the upper anterior mediastinum mimicking a thyroid tumor has rarely been seen in clinical practice and lacks a standard of care therapy. Here, we report a 47-year old female patient with an ACC originated from the upper anterior mediastinum presenting as a thyroid gland tumor. The patient received gross surgical resection of the tumor and underwent post-surgical chemotherapy and radiotherapy. The patient was free from local recurrence 3-years following initial treatment, but developed multiple lung metastases. She remains under clinical observation without discomfort and is still followed as an outpatient. Here, we also summarized recent reports of similar cases with hope to provide some experience for future clinical practice.

[Identification of Methamphetamine Abuse and Selegiline Use： Chiral Analysis of Methamphetamine and Amphetamine in Urine]

OBJECTIVES

To study the content variation of selegiline and its metabolites in urine, and based on actual cases, to explore the feasibility for the identification of methamphetamine abuse and selegiline use by chiral analysis.

METHODS

The urine samples were tested by chiral separation and LC-MS/MS method using CHIROBIOTIC™ V2 chiral liquid chromatography column. The chiral analysis of methamphetamine and amphetamine were performed on the urine samples from volunteers of selegiline use and drug addicts whom suspected taking selegiline.

RESULTS

After 5 mg oral administration, the positive test time of selegiline in urine was less than 7 h. The mass concentrations of R（-）-methamphetamine and R（-）-amphetamine in urine peaked at 7 h which were 0.86 μg/mL and 0.18 μg/mL and couldn't be detected after 80 h and 168 h, respectively. The sources of methamphetamine and amphetamine in the urine from the drug addicts whom suspected taking selegiline were analysed successfully by present method.

CONCLUSIONS

The chiral analysis of methamphetamine and amphetamine, and the determination of selegiline's metabolites can be used to distinguish methamphetamine abuse from selegiline use.

Exemplar-Based Image and Video Stylization Using Fully Convolutional Semantic Features

Color and tone stylization in images and videos strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares, such as Adobe Lightroom and Instagram, provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent. On the other hand, stylistic enhancement needs to apply distinct adjustments to various semantic regions. Such an ability enables a broader range of visual styles. In this paper, we first propose a novel deep learning architecture for exemplar-based image stylization, which learns local enhancement styles from image pairs. Our deep learning architecture consists of fully convolutional networks for automatic semantics-aware feature extraction and fully connected neural layers for adjustment prediction. Image stylization can be efficiently accomplished with a single forward pass through our deep network. To extend our deep network from image stylization to video stylization, we exploit temporal superpixels to facilitate the transfer of artistic styles from image exemplars to videos. Experiments on a number of data sets for image stylization as well as a diverse set of video clips demonstrate the effectiveness of our deep learning architecture.

Where2Stand

People often take photographs at tourist sites and these pictures usually have two main elements: a person in the foreground and scenery in the background. This type of “souvenir photo” is one of the most common photos clicked by tourists. Although algorithms that aid a user-photographer in taking a well-composed picture of a scene exist [Ni et al. 2013], few studies have addressed the issue of properly positioning human subjects in photographs. In photography, the common guidelines of composing portrait images exist. However, these rules usually do not consider the background scene. Therefore, in this article, we investigate human-scenery positional relationships and construct a photographic assistance system to optimize the position of human subjects in a given background scene, thereby assisting the user in capturing high-quality souvenir photos. We collect thousands of well-composed portrait photographs to learn human-scenery aesthetic composition rules. In addition, we define a set of negative rules to exclude undesirable compositions. Recommendation results are achieved by combining the first learned positive rule with our proposed negative rules. We implement the proposed system on an Android platform in a smartphone. The system demonstrates its efficacy by producing well-composed souvenir photos.

Random geometric prior forest for multiclass object segmentation

Recent advances in object detection have led to the development of segmentation by detection approaches that integrate top-down geometric priors for multiclass object segmentation. A key yet under-addressed issue in utilizing top-down cues for the problem of multiclass object segmentation by detection is efficiently generating robust and accurate geometric priors. In this paper, we propose a random geometric prior forest scheme to obtain object-adaptive geometric priors efficiently and robustly. In the scheme, a testing object first searches for training neighbors with similar geometries using the random geometric prior forest, and then the geometry of the testing object is reconstructed by linearly combining the geometries of its neighbors. Our scheme enjoys several favorable properties when compared with conventional methods. First, it is robust and very fast because its inference does not suffer from bad initializations, poor local minimums or complex optimization. Second, the figure/ground geometries of training samples are utilized in a multitask manner. Third, our scheme is object-adaptive but does not require the labeling of parts or poselets, and thus, it is quite easy to implement. To demonstrate the effectiveness of the proposed scheme, we integrate the obtained top-down geometric priors with conventional bottom-up color cues in the frame of graph cut. The proposed random geometric prior forest achieves the best segmentation results of all of the methods tested on VOC2010/2012 and is 90 times faster than the current state-of-the-art method.

Learning to track multiple targets

Monocular multiple-object tracking is a fundamental yet under-addressed computer vision problem. In this paper, we propose a novel learning framework for tracking multiple objects by detection. First, instead of heuristically defining a tracking algorithm, we learn that a discriminative structure prediction model from labeled video data captures the interdependence of multiple influence factors. Given the joint targets state from the last time step and the observation at the current frame, the joint targets state at the current time step can then be inferred by maximizing the joint probability score. Second, our detection results benefit from tracking cues. The traditional detection algorithms need a nonmaximal suppression postprocessing to select a subset from the total detection responses as the final output and a large number of selection mistakes are induced, especially under a congested circumstance. Our method integrates both detection and tracking cues. This integration helps to decrease the postprocessing mistake risk and to improve performance in tracking. Finally, we formulate the entire model training into a convex optimization problem and estimate its parameters using the cutting plane optimization. Experiments show that our method performs effectively in a large variety of scenarios, including pedestrian tracking in crowd scenes and vehicle tracking in congested traffic.

Random forest construction with robust semisupervised node splitting

Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets.

Video tonal stabilization via color states smoothing

We address the problem of removing video color tone jitter that is common in amateur videos recorded with hand-held devices. To achieve this, we introduce color state to represent the exposure and white balance state of a frame. The color state of each frame can be computed by accumulating the color transformations of neighboring frame pairs. Then, the tonal changes of the video can be represented by a time-varying trajectory in color state space. To remove the tone jitter, we smooth the original color state trajectory by solving an L1 optimization problem with PCA dimensionality reduction. In addition, we propose a novel selective strategy to remove small tone jitter while retaining extreme exposure and white balance changes to avoid serious artifacts. Quantitative evaluation and visual comparison with previous work demonstrate the effectiveness of our tonal stabilization method. This system can also be used as a preprocessing tool for other video editing methods.

Secure and lightweight network admission and transmission protocol for body sensor networks

A body sensor network (BSN) is a wireless network of biosensors and a local processing unit, which is commonly referred to as the personal wireless hub (PWH). Personal health information (PHI) is collected by biosensors and delivered to the PWH before it is forwarded to the remote healthcare center for further processing. In a BSN, it is critical to only admit eligible biosensors and PWH into the network. Also, securing the transmission from each biosensor to PWH is essential not only for ensuring safety of PHI delivery, but also for preserving the privacy of PHI. In this paper, we present the design, implementation, and evaluation of a secure network admission and transmission subsystem based on a polynomial-based authentication scheme. The procedures in this subsystem to establish keys for each biosensor are communication efficient and energy efficient. Moreover, based on the observation that an adversary eavesdropping in a BSN faces inevitable channel errors, we propose to exploit the adversary's uncertainty regarding the PHI transmission to update the individual key dynamically and improve key secrecy. In addition to the theoretical analysis that demonstrates the security properties of our system, this paper also reports the experimental results of the proposed protocol on resource-limited sensor platforms, which show the efficiency of our system in practice.

Relational multimanifold coclustering

Coclustering targets on grouping the samples (e.g.,documents and users) and the features (e.g., words and ratings) simultaneously. It employs the dual relation and the bilateral information between the samples and features. In many real-world applications, data usually reside on a submanifold of the ambient Euclidean space, but it is nontrivial to estimate the intrinsic manifold of the data space in a principled way. In this paper, we focus on improving the coclustering performance via manifold ensemble learning, which is able to maximally approximate the intrinsic manifolds of both the sample and feature spaces. To achieve this, we develop a novel coclustering algorithm called relational multimanifold coclustering based on symmetric nonnegative matrix trifactorization, which decomposes the relational data matrix into three submatrices. This method considers the intertype relationship revealed by the relational data matrix and also the intratype information reflected by the affinity matrices encoded on the sample and feature data distributions. Specifically, we assume that the intrinsic manifold of the sample or feature space lies in a convex hull of some predefined candidate manifolds. We want to learn a convex combination of them to maximally approach the desired intrinsic manifold. To optimize the objective function, the multiplicative rules are utilized to update the submatrices alternatively. In addition, both the entropic mirror descent algorithm and the coordinate descent algorithm are exploited to learn the manifold coefficient vector. Extensive experiments on documents, images, and gene expression data sets have demonstrated the superiority of the proposed algorithm compared with other well-established methods.

Joint sparse learning for 3-D facial expression generation

3-D facial expression generation, including synthesis and retargeting, has received intensive attentions in recent years, because it is important to produce realistic 3-D faces with specific expressions in modern film production and computer games. In this paper, we present joint sparse learning (JSL) to learn mapping functions and their respective inverses to model the relationship between the high-dimensional 3-D faces (of different expressions and identities) and their corresponding low-dimensional representations. Based on JSL, we can effectively and efficiently generate various expressions of a 3-D face by either synthesizing or retargeting. Furthermore, JSL is able to restore 3-D faces with holes by learning a mapping function between incomplete and intact data. Experimental results on a wide range of 3-D faces demonstrate the effectiveness of the proposed approach by comparing with representative ones in terms of quality, time cost, and robustness.

Probabilistic graphlet transfer for photo cropping

As one of the most basic photo manipulation processes, photo cropping is widely used in the printing, graphic design, and photography industries. In this paper, we introduce graphlets (i.e., small connected subgraphs) to represent a photo's aesthetic features, and propose a probabilistic model to transfer aesthetic features from the training photo onto the cropped photo. In particular, by segmenting each photo into a set of regions, we construct a region adjacency graph (RAG) to represent the global aesthetic feature of each photo. Graphlets are then extracted from the RAGs, and these graphlets capture the local aesthetic features of the photos. Finally, we cast photo cropping as a candidate-searching procedure on the basis of a probabilistic model, and infer the parameters of the cropped photos using Gibbs sampling. The proposed method is fully automatic. Subjective evaluations have shown that it is preferred over a number of existing approaches.

Chemotherapy uptake and wait times in early-stage non-small-cell lung cancer

BACKGROUND

Treatment uptake and elapsed times along the care path have emerged as potential quality indicators for cancer care delivery. This retrospective study examined changes in adjuvant chemotherapy uptake and elapsed times along the care path for patients in 2005 and in 2007 who had early-stage non-small-cell lung cancer (nsclc) and who underwent curative-intent surgery in Nova Scotia, Canada.

METHODS

All patients who underwent curative-intent surgery for stages i-iii nsclc in the two years of interest were included. Logistic regression and general linear models were used to examine factors associated with chemotherapy uptake patterns and, at various resolutions (low, intermediate, high), elapsed times between all care events in the care path.

RESULTS

In the 223 patients who underwent curative-intent surgery (108 in 2005, 115 in 2007), several factors were associated with uptake patterns and elapsed times. Cohort year (2007 vs. 2005) was not associated with referral to medical oncology [odds ratio (or): 1.05; 95% confidence interval (ci): 0.51 to 2.15; p = 0.905], but it was associated with less treatment after referral (or: 0.34; 95% ci: 0.11 to 1.00; p = 0.057) and less overall uptake (or: 0.35; 95% ci: 0.13 to 0.95; p = 0.040). Patients were referred sooner to medical oncology in 2007 than in 2005 (21 days vs. 35 days, p = 0.008), but experienced longer waits between consultation and chemotherapy delivery (18 days vs. 7 days, p = 0.001).

CONCLUSIONS

Significant differences were observed in care patterns over time. Frequent monitoring of care patterns at high resolution may optimize insights into emerging trends within cancer care systems.

A distributed trust evaluation model and its application scenarios for medical sensor networks

The development of medical sensor networks (MSNs) is imperative for e-healthcare, but security remains a formidable challenge yet to be resolved. Traditional cryptographic mechanisms do not suffice given the unique characteristics of MSNs, and the fact that MSNs are susceptible to a variety of node misbehaviors. In such situations, the security and performance of MSNs depend on the cooperative and trust nature of the distributed nodes, and it is important for each node to evaluate the trustworthiness of other nodes. In this paper, we identify the unique features of MSNs and introduce relevant node behaviors, such as transmission rate and leaving time, into trust evaluation to detect malicious nodes. We then propose an applicationindependent and distributed trust evaluation model for MSNs. The trust management is carried out through the use of simple cryptographic techniques. Simulation results demonstrate that the proposed model can be used to effectively identify malicious behaviors and thereby exclude malicious nodes. This paper also reports the experimental results of the Collection Tree Protocol with the addition of our proposed model in a network of TelosB motes, which show that the network performance can be significantly improved in practice. Further, some suggestions are given on how to employ such a trust evaluation model in some application scenarios.

A unified feature and instance selection framework using optimum experimental design

The goal of feature selection is to identify the most informative features for compact representation, whereas the goal of active learning is to select the most informative instances for prediction. Previous studies separately address these two problems, despite of the fact that selecting features and instances are dual operations over a data matrix. In this paper, we consider the novel problem of simultaneously selecting the most informative features and instances and develop a solution from the perspective of optimum experimental design. That is, by using the selected features as the new representation and the selected instances as training data, the variance of the parameter estimate of a learning function can be minimized. Specifically, we propose a novel approach, which is called Unified criterion for Feature and Instance selection (UFI), to simultaneously identify the most informative features and instances that minimize the trace of the parameter covariance matrix. A greedy algorithm is introduced to efficiently solve the optimization problem. Experimental results on two benchmark data sets demonstrate the effectiveness of our proposed method.

Three-dimensional face reconstruction from a single image by a coupled RBF network

Reconstruction of a 3-D face model from a single 2-D face image is fundamentally important for face recognition and animation because the 3-D face model is invariant to changes of viewpoint, illumination, background clutter, and occlusions. Given a coupled training set that contains pairs of 2-D faces and the corresponding 3-D faces, we train a novel coupled radial basis function network (C-RBF) to recover the 3-D face model from a single 2-D face image. The C-RBF network explores: 1) the intrinsic representations of 3-D face models and those of 2-D face images; 2) mappings between a 3-D face model and its intrinsic representation; and 3) mappings between a 2-D face image and its intrinsic representation. Since a particular face can be reconstructed by its nearest neighbors, we can assume that the linear combination coefficients for a particular 2-D face image reconstruction are identical to those for the corresponding 3-D face model reconstruction. Therefore, we can reconstruct a 3-D face model by using a single 2-D face image based on the C-RBF network. Extensive experimental results on the BU3D database indicate the effectiveness of the proposed C-RBF network for recovering the 3-D face model from a single 2-D face image.

ReTrust: attack-resistant and lightweight trust management for medical sensor networks

Wireless medical sensor networks (MSNs) enable ubiquitous health monitoring of users during their everyday lives, at health sites, without restricting their freedom. Establishing trust among distributed network entities has been recognized as a powerful tool to improve the security and performance of distributed networks such as mobile ad hoc networks and sensor networks. However, most existing trust systems are not well suited for MSNs due to the unique operational and security requirements of MSNs. Moreover, similar to most security schemes, trust management methods themselves can be vulnerable to attacks. Unfortunately, this issue is often ignored in existing trust systems. In this paper, we identify the security and performance challenges facing a sensor network for wireless medical monitoring and suggest it should follow a two-tier architecture. Based on such an architecture, we develop an attack-resistant and lightweight trust management scheme named ReTrust. This paper also reports the experimental results of the Collection Tree Protocol using our proposed system in a network of TelosB motes, which show that ReTrust not only can efficiently detect malicious/faulty behaviors, but can also significantly improve the network performance in practice.

Probabilistic exposure fusion

The luminance of a natural scene is often of high dynamic range (HDR). In this paper, we propose a new scheme to handle HDR scenes by integrating locally adaptive scene detail capture and suppressing gradient reversals introduced by the local adaptation. The proposed scheme is novel for capturing an HDR scene by using a standard dynamic range (SDR) device and synthesizing an image suitable for SDR displays. In particular, we use an SDR capture device to record scene details (i.e., the visible contrasts and the scene gradients) in a series of SDR images with different exposure levels. Each SDR image responds to a fraction of the HDR and partially records scene details. With the captured SDR image series, we first calculate the image luminance levels, which maximize the visible contrasts, and then the scene gradients embedded in these images. Next, we synthesize an SDR image by using a probabilistic model that preserves the calculated image luminance levels and suppresses reversals in the image luminance gradients. The synthesized SDR image contains much more scene details than any of the captured SDR image. Moreover, the proposed scheme also functions as the tone mapping of an HDR image to the SDR image, and it is superior to both global and local tone mapping operators. This is because global operators fail to preserve visual details when the contrast ratio of a scene is large, whereas local operators often produce halos in the synthesized SDR image. The proposed scheme does not require any human interaction or parameter tuning for different scenes. Subjective evaluations have shown that it is preferred over a number of existing approaches.

Active Learning Based on Locally Linear Reconstruction

We consider the active learning problem, which aims to select the most representative points. Out of many existing active learning techniques, optimum experimental design (OED) has received considerable attention recently. The typical OED criteria minimize the variance of the parameter estimates or predicted value. However, these methods see only global euclidean structure, while the local manifold structure is ignored. For example, I-optimal design selects those data points such that other data points can be best approximated by linear combinations of all the selected points. In this paper, we propose a novel active learning algorithm which takes into account the local structure of the data space. That is, each data point should be approximated by the linear combination of only its neighbors. Given the local reconstruction coefficients for every data point and the coordinates of the selected points, a transductive learning algorithm called Locally Linear Reconstruction (LLR) is proposed to reconstruct every other point. The most representative points are thus defined as those whose coordinates can be used to best reconstruct the whole data set. The sequential and convex optimization schemes are also introduced to solve the optimization problem. The experimental results have demonstrated the effectiveness of our proposed method.

Graph regularized sparse coding for image representation

Sparse coding has received an increasing amount of interest in recent years. It is an unsupervised learning algorithm, which finds a basis set capturing high-level semantics in the data and learns sparse coordinates in terms of the basis set. Originally applied to modeling the human visual cortex, sparse coding has been shown useful for many applications. However, most of the existing approaches to sparse coding fail to consider the geometrical structure of the data space. In many real applications, the data is more likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. It has been shown that the geometrical information of the data is important for discrimination. In this paper, we propose a graph based algorithm, called graph regularized sparse coding, to learn the sparse representations that explicitly take into account the local manifold structure of the data. By using graph Laplacian as a smooth operator, the obtained sparse representations vary smoothly along the geodesics of the data manifold. The extensive experimental results on image classification and clustering have demonstrated the effectiveness of our proposed algorithm.