Notes

The result involving Sea Thiosulfate in Cardio-arterial Calcification in Hemodialysis People.
Simulations and phantom experiments highlight the ability of SPANNER to improve contrast to background ratio by up to 20 dB compared to all other algorithms, as well as a 3-fold increase in axial resolution compared to DAS and UBP. Applying SPANNER on contrast-enhanced PA images acquired from prostate cancer patients yielded a statistically significant difference before and after contrast agent administration, while the other three image reconstruction methods did not, thus highlighting SPANNER's performance in differentiating intrinsic from extrinsic PA signals and its ability to quantify PA signals from the contrast agent more accurately.This paper presents a hybrid animation approach that combines example-based and neural animation methods to create a simple, yet powerful animation regime for human faces. Example-based methods usually employ a database of pre-recorded sequences that are concatenated or looped in order to synthesize novel animations. In contrast to this traditional example-based approach, we introduce a light-weight auto-regressive network to transform our animation-database into a parametric model. During training, our network learns the dynamics of facial expressions, which enables the replay of annotated sequences from our animation database as well as their seamless concatenation in new order. This representation is especially useful for the synthesis of visual speech, where co-articulation creates inter-dependencies between adjacent visemes, which affects their appearance. Instead of creating an exhaustive database that contains all viseme variants, we use our animation-network to predict the correct appearance. This allows realistic synthesis of novel facial animation sequences like visual-speech but also general facial expressions in an example-based manner.Virtual reality shows a wide variety of potentials for education. Also, 360-degree-videos can provide educational experiences within such dangerous or non-tangible settings. But what is the potential for the teaching of 360-degree-videos in virtual reality environments Regarding the use of real VR settings in the classroom, research is still scarce. Lenvatinib In the context of a systematic review, we would like to investigate use cases, advantages and limitations, interaction characteristics, and real VR scenarios. By analyzing 65 articles in-depth, our results suggest that 360-degree-videos can be used for a wide variety of topics. While only a few articles report technological benefits, there are indicators that 360-degree-videos can benefit learning processes regarding performance, motivation, and knowledge retention. Most papers report positive effects on other human factors such as presence, perception, engagement, emotions, and empathy. Also, an open research gap in use cases for real VR has been identified.Saliency detection by human refers to the ability to identify pertinent information using our perceptive and cognitive capabilities. While human perception is attracted by visual stimuli, our cognitive capability is derived from the inspiration of constructing concepts of reasoning. Saliency detection has gained intensive interest with the aim of resembling human perceptual system. However, saliency related to human cognition, particularly the analysis of complex salient regions (cogitating process), is yet to be fully exploited. We propose to resemble human cognition, coupled with human perception, to improve saliency detection. We recognize saliency in three phases (Seeing - Perceiving - Cogitating), mimicking human's perceptive and cognitive thinking of an image. In our method, Seeing phase is related to human perception, and we formulate the Perceiving and Cogitating phases related to the human cognition systems via deep neural networks (DNNs) to construct a new module (Cognitive Gate) that enhances the DNN features for saliency detection. To the best of our knowledge, this is the first work that established DNNs to resemble human cognition for saliency detection. In our experiments, our approach outperformed 17 benchmarking DNN methods on six well-recognized datasets, demonstrating that resembling human cognition improves saliency detection.This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. We design a progressive generator which comprises a sequence of transfer blocks. Each block performs an intermediate transfer step by modeling the relationship between the condition and the target poses with attention mechanism. Two types of blocks are introduced, namely Pose-Attentional Transfer Block (PATB) and Aligned Pose-Attentional Transfer Block (APATB). Compared with previous works, our model generates more photorealistic person images that retain better appearance consistency and shape consistency compared with input images. We verify the efficacy of the model on the Market-1501 and DeepFashion datasets, using quantitative and qualitative measures. Furthermore, we show that our method can be used for data augmentation for the person re-identification task, alleviating the issue of data insufficiency.Code and pretrained models are available at https//github.com/tengteng95/Pose-Transfer.git.It is important and challenging to infer stochastic latent semantics for natural language applications. The difficulty in stochastic sequential learning is caused by the posterior collapse in variational inference. The input sequence is disregarded in the estimated latent variables. This paper proposes three components to tackle this difficulty and build the variational sequence autoencoder (VSAE) where sufficient latent information is learned for sophisticated sequence representation. First, the complementary encoders based on a long short-term memory (LSTM) and a pyramid bidirectional LSTM are merged to characterize global and structural dependencies of an input sequence, respectively. Second, a stochastic self attention mechanism is incorporated in a recurrent decoder. The latent information is attended to encourage the interaction between inference and generation in an encoder-decoder training procedure. Third, an autoregressive Gaussian prior of latent variable is used to preserve the information bound. Different variants of VSAE are proposed to mitigate the posterior collapse in sequence modeling. A series of experiments are conducted to demonstrate that the proposed individual and hybrid sequence autoencoders substantially improve the performance for variational sequential learning in language modeling and semantic understanding for document classification and summarization.Stochastic gradient descent (SGD) has become the method of choice for training highly complex and nonconvex models since it can not only recover good solutions to minimize training errors but also generalize well. Computational and statistical properties are separately studied to understand the behavior of SGD in the literature. However, there is a lacking study to jointly consider the computational and statistical properties in a nonconvex learning setting. In this paper, we develop novel learning rates of SGD for nonconvex learning by presenting high-probability bounds for both computational and statistical errors. We show that the complexity of SGD iterates grows in a controllable manner with respect to the iteration number, which sheds insights on how an implicit regularization can be achieved by tuning the number of passes to balance the computational and statistical errors. As a byproduct, we also slightly refine the existing studies on the uniform convergence of gradients by showing its connection to Rademacher chaos complexities.Approximate Nearest Neighbor Search in high dimensional space is essential in DB and IR. Recently, NSG provides attractive theoretical analysis and achieves state-of-the-art performance. However, we find there are several limitations with NSG. In the theoretical aspect, NSG has no theoretical guarantee on searching for neighbors of not-in-database queries. In application, NSG is too sparse and thus has an inferior search performance. In addition, NSG's indexing complexity is also too high. To address above problems, we propose the Satellite System Graphs (inspired by the message transfer mechanism of the communication satellite system) and its approximation NSSG. Specifically, Satellite System Graphs define a new family of MSNETs in which the out-edges of each node are distributed evenly in all directions, and each node builds effective connections to its neighborhood omnidirectionally, whereupon we derive SSG's excellent theoretical properties for both in-database queries and not-in-database queries. We can adaptively adjust the sparsity of an SSG with a hyper-parameter to optimize the search performance. Further, NSSG is proposed to reduce the indexing complexity of the SSG for large-scale applications. Both theoretical and extensive experimental analysis are provided to demonstrate the strengths of the proposed approach over the state-of-the-art algorithms.We study network pruning which aims to remove redundant channels/kernels and accelerate the inference of deep networks. Existing pruning methods either train from scratch with sparsity constraints or minimize the reconstruction error between the feature maps of the pre-trained models and the compressed ones. Both strategies suffer from some limitations the former kind is computationally expensive and difficult to converge, while the latter kind optimizes the reconstruction error but ignores the discriminative power of channels. In this paper, we propose a discrimination-aware channel pruning (DCP) method to choose the channels that actually contribute to the discriminative power. Based on DCP, we further propose several techniques to improve the optimization efficiency. Note that the parameters of a channel (3D tensor) may contain redundant kernels (each with a 2D matrix). To solve this issue, we propose a discrimination-aware kernel pruning (DKP) method to select the kernels with promising discriminative power. Experiments on image classification and face recognition demonstrate the effectiveness of our methods. For example, on ILSVRC-12, the resultant ResNet-50 with 30% reduction of channels even outperforms the baseline model by 0.36% on Top-1 accuracy. The pruned MobileNetV1 and MobileNetV2 achieve 1.93x and 1.42x inference acceleration on a mobile device, respectively, with negligible performance degradation.
Changes in ultrasound backscatter energy (CBE) imaging can monitor thermal therapy. Catheter-based ultrasound (CBUS) can treat deep tumors with precise spatial control of energy deposition and ablation zones, of which CBE estimation can be limited by low contrast and robustness due to small or inconsistent changes in ultrasound data. This study develops a multi-spatiotemporal compounding CBE (MST-CBE) imaging approach for monitoring specific to CBUS thermal therapy.

Ex vivo thermal ablations were performed with stereotactic positioning of a 180 directional CBUS applicator, temperature monitoring probes, endorectal US probe, and subsequent lesion sectioning and measurement. Five frames of raw radiofrequency data were acquired throughout in 15s intervals. Using window-by-window estimation methods, absolute and positive components of MST-CBE images at each point were obtained by the compounding ratio of squared envelope data within an increasing spatial size in each short-time window.

Compared with conventional US, Nakagami, and CBE imaging, the detection contrast and robustness quantified by tissue-modification-ratio improved by 37.
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