Geometry-based spherical JND modeling for 360∘^\circ display

360$^\circ$ videos have received widespread attention due to its realistic and immersive experiences for users. To date, how to accurately model the user perceptions on 360$^\circ$ display is still a challenging issue. In this paper, we exploit the visual characteristics of 360$^\circ$ projection and display and extend the popular just noticeable difference (JND) model to spherical JND (SJND). First, we propose a quantitative 2D-JND model by jointly considering spatial contrast sensitivity, luminance adaptation and texture masking effect. In particular, our model introduces an entropy-based region classification and utilizes different parameters for different types of regions for better modeling performance. Second, we extend our 2D-JND model to SJND by jointly exploiting latitude projection and field of view during 360$^\circ$ display. With this operation, SJND reflects both the characteristics of human vision system and the 360$^\circ$ display. Third, our SJND model is more consistent with user perceptions during subjective test and also shows more tolerance in distortions with fewer bit rates during 360$^\circ$ video compression. To further examine the effectiveness of our SJND model, we embed it in Versatile Video Coding (VVC) compression. Compared with the state-of-the-arts, our SJND-VVC framework significantly reduced the bit rate with negligible loss in visual quality

MMPosE: Movie-induced multi-label positive emotion classification through EEG signals

Emotional information plays an important role in various multimedia applications. Movies, as a widely available form of multimedia content, can induce multiple positive emotions and stimulate people's pursuit of a better life. Different from negative emotions, positive emotions are highly correlated and difficult to distinguish in the emotional space. Since different positive emotions are often induced simultaneously by movies, traditional single-target or multi-class methods are not suitable for the classification of movie-induced positive emotions. In this paper, we propose TransEEG, a model for multi-label positive emotion classification from a viewer's brain activities when watching emotional movies. The key features of TransEEG include (1) explicitly modeling the spatial correlation and temporal dependencies of multi-channel EEG signals using the Transformer structure based model, which effectively addresses long-distance dependencies, (2) exploiting the label-label correlations to guide the discriminative EEG representation learning, for that we design an Inter-Emotion Mask for guiding the Multi-Head Attention to learn the inter-emotion correlations, and (3) constructing an attention score vector from the representation-label correlation matrix to refine emotion-relevant EEG features. To evaluate the ability of our model for multi-label positive emotion classification, we demonstrate our model on a state-of-the-art positive emotion database CPED. Extensive experimental results show that our proposed method achieves superior performance over the competitive approaches

A Class Incremental Extreme Learning Machine for Activity Recognition

Automatic activity recognition is an important problem in cognitive systems. Mobile phone-based activity recognition is an attractive research topic because it is unobtrusive. There are many activity recognition models that can infer a user's activity from sensor data. However, most of them lack class incremental learning abilities. That is, the trained models can only recognize activities that were included in the training phase, and new activities cannot be added in a follow-up phase. We propose a class incremental extreme learning machine (CIELM). It (1) builds an activity recognition model from labeled samples using an extreme learning machine algorithm without iterations; (2) adds new output nodes that correspond to new activities; and (3) only requires labeled samples of new activities and not previously used training data. We have tested the method using activity data. Our results demonstrated that the CIELM algorithm is stable and can achieve a similar recognition accuracy to the batch learning method.Automatic activity recognition is an important problem in cognitive systems. Mobile phone-based activity recognition is an attractive research topic because it is unobtrusive. There are many activity recognition models that can infer a user's activity from sensor data. However, most of them lack class incremental learning abilities. That is, the trained models can only recognize activities that were included in the training phase, and new activities cannot be added in a follow-up phase. We propose a class incremental extreme learning machine (CIELM). It (1) builds an activity recognition model from labeled samples using an extreme learning machine algorithm without iterations; (2) adds new output nodes that correspond to new activities; and (3) only requires labeled samples of new activities and not previously used training data. We have tested the method using activity data. Our results demonstrated that the CIELM algorithm is stable and can achieve a similar recognition accuracy to the batch learning method

scale adaptation of mean shift based on graph cuts theory

The classical Mean Shift can't change the scale of tracking window in real time while tracking target is changing in size. This paper adopts graph cuts theory to the problem of scale adaptation for Mean Shift tracking. According to the result of Mean Shift iteration in every frame, implementing graph cuts using skin color Gaussian mixture model(GMM) in a small area around it, and updating tracking window size through the largest skin lump among the result of graph cuts. Experimental results clearly demonstrate that the method can reflect the real scale change of tracking target, avoid the interference of other objects in background, and has good usability and robustness. Besides it enriches manipulation method of Human Computer Interaction by controlling entertainment games. © 2011 IEEE.China Computer FederationThe classical Mean Shift can't change the scale of tracking window in real time while tracking target is changing in size. This paper adopts graph cuts theory to the problem of scale adaptation for Mean Shift tracking. According to the result of Mean Shift iteration in every frame, implementing graph cuts using skin color Gaussian mixture model(GMM) in a small area around it, and updating tracking window size through the largest skin lump among the result of graph cuts. Experimental results clearly demonstrate that the method can reflect the real scale change of tracking target, avoid the interference of other objects in background, and has good usability and robustness. Besides it enriches manipulation method of Human Computer Interaction by controlling entertainment games. © 2011 IEEE

An Interactive SpiralTape Video Summarization

A majority of video summarization systems use linear representations, such as rectangular storyboards and timelines at linear scales. In this paper, we propose a novel nonlinear dynamic representation called SpiralTape that summarizes a video in a smooth spiral pattern. SpiralTape provides an unusual and fresh activity suitable for stimulating environments such as science and technology museums, in which children or young individuals can have enjoyable experiences that create meaningful learning outcomes. In addition, SpiralTape provides an uninterrupted overall structure of video content and takes design principles including compactness, continuity, efficient overview, and interactivity into consideration. A working SpiralTape system was developed and deployed in pilot applications and exhibitions. Elaborate user studies with evaluation benchmarks on multiple metrics were conducted to compare SpiralTape with two representative linear video summarization methods and a state-of-the-art radial video visualization. The evaluation results demonstrate the effectiveness and natural interaction performance of SpiralTape.</p

An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals

Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this article, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the functional relation between EEG signals at different electrodes. Our proposed deep model is called ATtention-based LSTM with Domain Discriminator (ATDD-LSTM), a model based on Long Short-Term Memory (LSTM) for emotion recognition that can characterize nonlinear relations among EEG signals of different electrodes. To achieve state-of-the-art emotion recognition performance, the architecture of ATDD-LSTM has two distinguishing characteristics: (1) By applying the attention mechanism to the feature vectors produced by LSTM, ATDD-LSTM automatically selects suitable EEG channels for emotion recognition, which makes the learned model concentrate on the emotion related channels in response to a given emotion; (2) To minimize the significant feature distribution shift between different sessions and/or subjects, ATDD-LSTM uses a domain discriminator to modify the data representation space and generate domain-invariant features. We evaluate the proposed ATDD-LSTM model on three public EEG emotional databases (DEAP, SEED and CMEED) for emotion recognition. The experimental results demonstrate that our ATDD-LSTM model achieves superior performance on subject-dependent (for the same subject), subject-independent (for different subjects) and cross-session (for the same subject) evaluation

Effect of Environmental Microorganisms on Fermentation Microbial Community of Sauce-Flavor <i>baijiu</i>

The compositions of the microbial community in fermented grains of Sauce-flavor baijiu produced in different regions have diverse characteristics; however, the reasons for this remain unclear. The present study investigated the contributions of environmental microorganisms to the microbial community as well as the volatile compounds in the fermented grains of Sauce-flavor baijiu produced in the Beijing region using high-throughput sequencing combined with sourcetracker analysis, and compared the differences of environmental microorganism and their roles in the production process of Sauce-flavor baijiu from different regions.The results showed that the environmental microorganisms in the tools were the main contributors of the bacterial and fungal communities in fermented grains during heap fermentation and at the beginning of pit fermentation. At the end of pit fermentation, pit mud was the main environmental source of bacterial community in fermented grains, while tools and Daqu were the main environmental sources of fungal community in fermented grains.Environmental microorganisms thrived on the functional microorganisms in the fermented grains of Sauce-flavor baijiu produced in the Beijing region and thus shaped the profiles of volatile compounds. Environmental microorganisms of Sauce-flavor baijiu in the Guizhou province and the Beijing region differed significantly, which is partially responsible for the distinctive characteristics in the microbial community structure of Sauce-flavor baijiu-fermented grains from different regions

An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals

Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this paper, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the functional relation between EEG signals at different electrodes. Our proposed deep model is called ATtention-based LSTM with Domain Discriminator (ATDD-LSTM) that can characterize nonlinear relations among EEG signals of different electrodes. To achieve state-of-the-art emotion recognition performance, the architecture of ATDD-LSTM has two distinguishing characteristics: (1) By applying the attention mechanism to the feature vectors produced by LSTM, ATDD-LSTM automatically selects suitable EEG channels for emotion recognition, which makes the learned model concentrate on the emotion related channels in response to a given emotion; (2) To minimize the significant feature distribution shift between different sessions and/or subjects, ATDD-LSTM uses a domain discriminator to modify the data representation space and generate domain-invariant features. We evaluate the proposed ATDD-LSTM model on three public EEG emotional databases (DEAP, SEED and CMEED) for emotion recognition. The experimental results demonstrate that our ATDD-LSTM model achieves superior performance on subject-dependent (for the same subject), subject-independent (for different subjects) and cross-session (for the same subject) evaluation.</p

Scalable synthesis of a foam-like FeS2 nanostructure by a solution combustion-sulfurization process for high-capacity sodium-ion batteries

Pyrite-type FeS2 is regarded as a promising anode material for sodium ion batteries. The synthesis of FeS2 in large quantities accompanied by an improved cycling stability, as well as retaining high theoretical capacity, is highly desirable for its commercialization. Herein, we present a scalable and simple strategy to prepare a foam-like FeS2 (F-FeS2) nanostructure by combining solution combustion synthesis and solid-state sulfurization. The obtained F-FeS2 product is highly uniform and built from interconnected FeS2 nanoparticles (∼50 nm). The interconnected feature, small particle sizes and porous structure endow the product with high electrical conductivity, good ion diffusion kinetics, and high inhibition capacity of volume expansion. As a result, high capacity (823 mA h g−1 at 0.1 A g−1, very close to the theoretical capacity of FeS2, 894 mA h g−1), good rate capability (581 mA h g−1 at 5.0 A g−1) and cyclability (754 mA h g−1 at 0.2 A g−1 with 97% retention after 80 cycles) can be achieved. The sodium storage mechanism has been proved to be a combination of intercalation and conversion reactions based on in situ XRD. Furthermore, high pseudocapacitive contribution (i.e. ∼87.5% at 5.0 mV s−1) accounts for the outstanding electrochemical performance of F-FeS2 at high rates

MMPosE: Movie-induced Multi-label Positive Emotion Classification Through EEG Signals

Emotional&nbsp;information plays an important role in various multimedia applications. Movies, as a widely available form of multimedia content, can induce multiple&nbsp;positive&nbsp;emotions&nbsp;and stimulate people&#39;s pursuit of a better life. Different from negative&nbsp;emotions,&nbsp;positive&nbsp;emotions&nbsp;are highly correlated and difficult to distinguish in the&nbsp;emotional&nbsp;space. Since different&nbsp;positive&nbsp;emotions&nbsp;are often induced simultaneously by movies, traditional single-target or multi-class methods are not suitable for the&nbsp;classification&nbsp;of movie-induced&nbsp;positive&nbsp;emotions. In this paper, we propose TransEEG, a model for multi-label&nbsp;positive&nbsp;emotion&nbsp;classification&nbsp;from a viewer&#39;s brain activities when watching&nbsp;emotional&nbsp;movies. The key features of TransEEG include (1) explicitly modeling the spatial correlation and temporal dependencies of multi-channel&nbsp;EEG&nbsp;signals&nbsp;using the Transformer structure based model, which effectively addresses long-distance dependencies, (2) exploiting the label-label correlations to guide the discriminative&nbsp;EEG&nbsp;representation learning, for that we design an Inter-Emotion&nbsp;Mask for guiding the Multi-Head Attention to learn the inter-emotion&nbsp;correlations, and (3) constructing an attention score vector from the representation-label correlation matrix to refine&nbsp;emotion-relevant&nbsp;EEG&nbsp;features. To evaluate the ability of our model for multi-label&nbsp;positive&nbsp;emotion&nbsp;classification, we demonstrate our model on a state-of-the-art&nbsp;positive&nbsp;emotion&nbsp;database CPED. Extensive experimental results show that our proposed method achieves superior performance over the competitive approaches.</p