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"content": "Identifying essential genes in comparison states (EGS) is vital to understanding cell differentiation, performing drug discovery, and identifying disease causes. Here, we present a machine learning method termed Prediction of Essential Genes in Comparison States (PreEGS). To capture the alteration of the network in comparison states, PreEGS extracts topological and gene expression features of each gene in a five-dimensional vector. PreEGS also recruits a positive sample expansion method to address the problem of unbalanced positive and negative samples, which is often encountered in practical applications. Different classifiers are applied to the simulated datasets, and the PreEGS based on the random forests model (PreEGSRF) was chosen for optimal performance. PreEGSRF was then compared with six other methods, including three machine learning methods, to predict EGS in a specific state. On real datasets with four gene regulatory networks, PreEGSRF predicted five essential genes related to leukemia and five enriched KEGG pathways. Four of the predicted essential genes and all predicted pathways were consistent with previous studies and highly correlated with leukemia. With high prediction accuracy and generalization ability, PreEGSRF is broadly applicable for the discovery of disease-causing genes, driver genes for cell fate decisions, and complex biomarkers of biological systems.",
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"normalizedAbstract": "Memorability of an image is a characteristic determined by the human observers' ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained independent of the observer. The current study aims to enhance our understanding and prediction of image memorability, improving upon existing approaches by incorporating the properties of cumulative human annotations. We propose a new concept called the Visual Memory Schema (VMS) referring to an organization of image components human observers share when encoding and recognizing images. The concept of VMS is operationalised by asking human observers to define memorable regions of images they were asked to remember during an episodic memory test. We then statistically assess the consistency of VMSs across observers for either correctly or incorrectly recognised images. The associations of the VMSs with eye fixations and saliency are analysed separately as well. Lastly, we adapt various deep learning architectures for the reconstruction and prediction of memorable regions in images and analyse the results when using transfer learning at the outputs of different convolutional network layers.",
"title": "Defining Image Memorability Using the Visual Memory Schema",
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"title": "Discriminability Tests for Visualization Effectiveness and Scalability",
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"abstract": "Data visualization design has a powerful effect on which patterns we see as salient and how quickly we see them. The visualization practitioner community prescribes two popular guidelines for creating clear and efficient visualizations: declutter and focus. The <italic>declutter</italic> guidelines suggest removing non-critical gridlines, excessive labeling of data values, and color variability to improve aesthetics and to maximize the emphasis on the data relative to the design itself. The <italic>focus</italic> guidelines for explanatory communication recommend including a clear headline that describes the relevant data pattern, highlighting a subset of relevant data values with a unique color, and connecting those values to written annotations that contextualize them in a broader argument. We evaluated how these recommendations impact recall of the depicted information across cluttered, decluttered, and decluttered+focused designs of six graph topics. Undergraduate students were asked to redraw previously seen visualizations, to recall their topics and main conclusions, and to rate the varied designs on aesthetics, clarity, professionalism, and trustworthiness. Decluttering designs led to higher ratings on professionalism, and adding focus to the design led to higher ratings on aesthetics and clarity. They also showed better memory for the highlighted pattern in the data, as reflected across redrawings of the original visualization and typed free-response conclusions, though we do not know whether these results would generalize beyond our memory-based tasks. The results largely empirically validate the intuitions of visualization designers and practitioners. The stimuli, data, analysis code, and Supplementary Materials are available at <uri>https://osf.io/wes9u/</uri>.",
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"abstract": "<italic>Heterogeneous information networks (HINs)</italic>, which are typed graphs with labeled nodes and edges, have attracted tremendous interest from academia and industry. Given two HIN nodes <inline-formula><tex-math notation=\"LaTeX\">Z_$s$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$t$_Z</tex-math></inline-formula>, and a natural number <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>, we study the discovery of the <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula> most important meta paths in real time, which can be used to support friend search, product recommendation, anomaly detection, and graph clustering. In this work, we argue that the shortest path between <inline-formula><tex-math notation=\"LaTeX\">Z_$s$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$t$_Z</tex-math></inline-formula> may not necessarily be the most important path. As such, we combine several ranking functions, which are based on <italic>frequency</italic> and <italic>rarity</italic>, to redefine the unified importance function of the meta paths between <inline-formula><tex-math notation=\"LaTeX\">Z_$s$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$t$_Z</tex-math></inline-formula>. Although this importance function can capture more information, it is very time-consuming to find top-<inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula> meta paths using this importance function. Therefore, we integrate this importance function into a multi-step framework, which can efficiently filter some impossible meta paths between <inline-formula><tex-math notation=\"LaTeX\">Z_$s$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$t$_Z</tex-math></inline-formula>. In addition, we combine bidirectional searching algorithm with this framework to further boost the efficiency performance. The experiment on different datasets shows that our proposed method outperforms state-of-the-art algorithms in terms of effectiveness with reasonable response time.",
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"content": "<italic>Heterogeneous information networks (HINs)</italic>, which are typed graphs with labeled nodes and edges, have attracted tremendous interest from academia and industry. Given two HIN nodes <inline-formula><tex-math notation=\"LaTeX\">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq1-3037218.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$t$</tex-math><alternatives><mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq2-3037218.gif\"/></alternatives></inline-formula>, and a natural number <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq3-3037218.gif\"/></alternatives></inline-formula>, we study the discovery of the <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq4-3037218.gif\"/></alternatives></inline-formula> most important meta paths in real time, which can be used to support friend search, product recommendation, anomaly detection, and graph clustering. In this work, we argue that the shortest path between <inline-formula><tex-math notation=\"LaTeX\">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq5-3037218.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$t$</tex-math><alternatives><mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq6-3037218.gif\"/></alternatives></inline-formula> may not necessarily be the most important path. As such, we combine several ranking functions, which are based on <italic>frequency</italic> and <italic>rarity</italic>, to redefine the unified importance function of the meta paths between <inline-formula><tex-math notation=\"LaTeX\">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq7-3037218.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$t$</tex-math><alternatives><mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq8-3037218.gif\"/></alternatives></inline-formula>. Although this importance function can capture more information, it is very time-consuming to find top-<inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq9-3037218.gif\"/></alternatives></inline-formula> meta paths using this importance function. Therefore, we integrate this importance function into a multi-step framework, which can efficiently filter some impossible meta paths between <inline-formula><tex-math notation=\"LaTeX\">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq10-3037218.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$t$</tex-math><alternatives><mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href=\"zhu-ieq11-3037218.gif\"/></alternatives></inline-formula>. In addition, we combine bidirectional searching algorithm with this framework to further boost the efficiency performance. The experiment on different datasets shows that our proposed method outperforms state-of-the-art algorithms in terms of effectiveness with reasonable response time.",
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"abstract": "Density Peaks (DP) Clustering organizes data into clusters by finding peaks in dense regions. This involves computing density (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\rho$_Z</tex-math></inline-formula>) and distance (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\delta$_Z</tex-math></inline-formula>) of every point. As such, though DP has been very effective in producing high quality clusters, their complexity is O(<inline-formula><tex-math notation=\"LaTeX\">Z_$N^2$_Z</tex-math></inline-formula>) where <inline-formula><tex-math notation=\"LaTeX\">Z_$N$_Z</tex-math></inline-formula> is the number of data points. In this paper, we propose a fast distributed density peaks clustering algorithm, FDDP, based on the z-value index. In FDDP, we first employ the z-value index to map multi-dimensional data points into one dimensional space, and then range-partition the data according to the z-value to balance the load across the processing nodes. We ensure minimal overlapping range to handle computations at the boundary points. We also propose FC, an efficient algorithm that employs a forward computing strategy to calculate <inline-formula><tex-math notation=\"LaTeX\">Z_$\\rho$_Z</tex-math></inline-formula> linearly. Additionally, we propose another algorithm, CB, which uses a caching and efficient searching strategy to compute <inline-formula><tex-math notation=\"LaTeX\">Z_$\\delta$_Z</tex-math></inline-formula>. Moreover, FDDP is able to reduce the time complexity from <inline-formula><tex-math notation=\"LaTeX\">Z_$O(N^2)$_Z</tex-math></inline-formula> to <inline-formula><tex-math notation=\"LaTeX\">Z_$O(N\\cdot log(N))$_Z</tex-math></inline-formula>. We provide a theoretical analysis of FDDP and evaluated FDDP empirically. Our experimental results show that FDDP outperforms the state-of-the-art algorithms significantly.",
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"abstract": "By employing time-varying proximal functions, adaptive subgradient methods (ADAGRAD) have improved the regret bound and been widely used in online learning and optimization. However, ADAGRAD with full matrix proximal functions (ADA-FULL) cannot handle large-scale problems due to the impractical <inline-formula><tex-math notation=\"LaTeX\">Z_$O(d^3)$_Z</tex-math></inline-formula> time and <inline-formula><tex-math notation=\"LaTeX\">Z_$O(d^2)$_Z</tex-math></inline-formula> space complexities, though it has better performance when gradients are correlated. In this paper, we propose two efficient variants of ADA-FULL via a matrix sketching technique called frequent directions (FD). The first variant named as ADA-FD directly utilizes FD to maintain and manipulate low-rank matrices, which reduces the space and time complexities to <inline-formula><tex-math notation=\"LaTeX\">Z_$O(\\tau d)$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$O(\\tau ^2d)$_Z</tex-math></inline-formula> respectively, where <inline-formula><tex-math notation=\"LaTeX\">Z_$d$_Z</tex-math></inline-formula> is the dimensionality and <inline-formula><tex-math notation=\"LaTeX\">Z_$\\tau \\ll d$_Z</tex-math></inline-formula> is the sketching size. The second variant named as ADA-FFD further adopts a doubling trick to accelerate FD used in ADA-FD, which reduces the average time complexity to <inline-formula><tex-math notation=\"LaTeX\">Z_$O(\\tau d)$_Z</tex-math></inline-formula> while only doubles the space complexity of ADA-FD. Theoretical analysis reveals that the regret of ADA-FD and ADA-FFD is close to that of ADA-FULL as long as the outer product matrix of gradients is approximately low-rank. Experimental results demonstrate the efficiency and effectiveness of our algorithms.",
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"content": "By employing time-varying proximal functions, adaptive subgradient methods (ADAGRAD) have improved the regret bound and been widely used in online learning and optimization. However, ADAGRAD with full matrix proximal functions (ADA-FULL) cannot handle large-scale problems due to the impractical <inline-formula><tex-math notation=\"LaTeX\">$O(d^3)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>d</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wan-ieq1-3096880.gif\"/></alternatives></inline-formula> time and <inline-formula><tex-math notation=\"LaTeX\">$O(d^2)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>d</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wan-ieq2-3096880.gif\"/></alternatives></inline-formula> space complexities, though it has better performance when gradients are correlated. In this paper, we propose two efficient variants of ADA-FULL via a matrix sketching technique called frequent directions (FD). The first variant named as ADA-FD directly utilizes FD to maintain and manipulate low-rank matrices, which reduces the space and time complexities to <inline-formula><tex-math notation=\"LaTeX\">$O(\\tau d)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>τ</mml:mi><mml:mi>d</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wan-ieq3-3096880.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$O(\\tau ^2d)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>τ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mi>d</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wan-ieq4-3096880.gif\"/></alternatives></inline-formula> respectively, where <inline-formula><tex-math notation=\"LaTeX\">$d$</tex-math><alternatives><mml:math><mml:mi>d</mml:mi></mml:math><inline-graphic xlink:href=\"wan-ieq5-3096880.gif\"/></alternatives></inline-formula> is the dimensionality and <inline-formula><tex-math notation=\"LaTeX\">$\\tau \\ll d$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>τ</mml:mi><mml:mo>≪</mml:mo><mml:mi>d</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"wan-ieq6-3096880.gif\"/></alternatives></inline-formula> is the sketching size. The second variant named as ADA-FFD further adopts a doubling trick to accelerate FD used in ADA-FD, which reduces the average time complexity to <inline-formula><tex-math notation=\"LaTeX\">$O(\\tau d)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>τ</mml:mi><mml:mi>d</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wan-ieq7-3096880.gif\"/></alternatives></inline-formula> while only doubles the space complexity of ADA-FD. Theoretical analysis reveals that the regret of ADA-FD and ADA-FFD is close to that of ADA-FULL as long as the outer product matrix of gradients is approximately low-rank. Experimental results demonstrate the efficiency and effectiveness of our algorithms.",
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"abstract": "Creating metrically accurate avatars is important for many applications such as virtual clothing try-on, ergonomics, medicine, immersive social media, telepresence, and gaming. Creating avatars that precisely represent a particular individual is challenging however, due to the need for expensive 3D scanners, privacy issues with photographs or videos, and difficulty in making accurate tailoring measurements. We overcome these challenges by creating “The Virtual Caliper”, which uses VR game controllers to make simple measurements. First, we establish what body measurements users can reliably make on their own body. We find several distance measurements to be good candidates and then verify that these are linearly related to 3D body shape as represented by the SMPL body model. The Virtual Caliper enables novice users to accurately measure themselves and create an avatar with their own body shape. We evaluate the metric accuracy relative to ground truth 3D body scan data, compare the method quantitatively to other avatar creation tools, and perform extensive perceptual studies. We also provide a software application to the community that enables novices to rapidly create avatars in fewer than five minutes. Not only is our approach more rapid than existing methods, it exports a metrically accurate 3D avatar model that is rigged and skinned.",
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"content": "Creating metrically accurate avatars is important for many applications such as virtual clothing try-on, ergonomics, medicine, immersive social media, telepresence, and gaming. Creating avatars that precisely represent a particular individual is challenging however, due to the need for expensive 3D scanners, privacy issues with photographs or videos, and difficulty in making accurate tailoring measurements. We overcome these challenges by creating “The Virtual Caliper”, which uses VR game controllers to make simple measurements. First, we establish what body measurements users can reliably make on their own body. We find several distance measurements to be good candidates and then verify that these are linearly related to 3D body shape as represented by the SMPL body model. The Virtual Caliper enables novice users to accurately measure themselves and create an avatar with their own body shape. We evaluate the metric accuracy relative to ground truth 3D body scan data, compare the method quantitatively to other avatar creation tools, and perform extensive perceptual studies. We also provide a software application to the community that enables novices to rapidly create avatars in fewer than five minutes. Not only is our approach more rapid than existing methods, it exports a metrically accurate 3D avatar model that is rigged and skinned.",
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"title": "The Virtual Caliper: Rapid Creation of Metrically Accurate Avatars from 3D Measurements",
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"givenName": "Sergi",
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"affiliation": "Amazon",
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"fullName": "Anne Thaler",
"affiliation": "Max Planck Institute for Biological Cybernetics",
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"abstract": "With the popularity of Virtual Reality (VR) on the rise, creators from a variety of fields are building increasingly complex experiences that allow users to express themselves more naturally. Self-avatars and object interaction in virtual worlds are at the heart of these experiences. However, these give rise to several perception based challenges that have been the focus of research in recent years. One area that garners most interest is understanding the effects of self-avatars and object interaction on action capabilities or affordances in VR. Affordances have been shown to be influenced by the anthropometric and anthropomorphic properties of the self-avatar embodied. However, self-avatars cannot fully represent real world interaction and fail to provide information about the dynamic properties of surfaces in the environment. For example, pressing against a board to feel its rigidity. This lack of accurate dynamic information can be further amplified when interacting with virtual handheld objects as the weight and inertial feedback associated with them is often mismatched. To investigate this phenomenon, we looked at how the absence of dynamic surface properties affect lateral passability judgments when carrying virtual handheld objects in the presence or absence of gender matched body-scaled self-avatars. Results suggest that participants can calibrate to the missing dynamic information in the presence of self-avatars to make lateral passability judgments, but rely on their internal body schema of a compressed physical body depth in the absence of self-avatars.",
"abstracts": [
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"content": "With the popularity of Virtual Reality (VR) on the rise, creators from a variety of fields are building increasingly complex experiences that allow users to express themselves more naturally. Self-avatars and object interaction in virtual worlds are at the heart of these experiences. However, these give rise to several perception based challenges that have been the focus of research in recent years. One area that garners most interest is understanding the effects of self-avatars and object interaction on action capabilities or affordances in VR. Affordances have been shown to be influenced by the anthropometric and anthropomorphic properties of the self-avatar embodied. However, self-avatars cannot fully represent real world interaction and fail to provide information about the dynamic properties of surfaces in the environment. For example, pressing against a board to feel its rigidity. This lack of accurate dynamic information can be further amplified when interacting with virtual handheld objects as the weight and inertial feedback associated with them is often mismatched. To investigate this phenomenon, we looked at how the absence of dynamic surface properties affect lateral passability judgments when carrying virtual handheld objects in the presence or absence of gender matched body-scaled self-avatars. Results suggest that participants can calibrate to the missing dynamic information in the presence of self-avatars to make lateral passability judgments, but rely on their internal body schema of a compressed physical body depth in the absence of self-avatars.",
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"abstract": "Most previous work on outfit recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proven to be effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural outfit recommendation (NOR), that simultaneously provides outfit recommendations and generates abstractive comments. Neural outfit recommendation (NOR) consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NOR achieves significant improvements over state-of-the-art baselines for outfit recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research.",
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"content": "Most previous work on outfit recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proven to be effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural outfit recommendation (NOR), that simultaneously provides outfit recommendations and generates abstractive comments. Neural outfit recommendation (NOR) consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NOR achieves significant improvements over state-of-the-art baselines for outfit recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research.",
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"normalizedAbstract": "Most previous work on outfit recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proven to be effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural outfit recommendation (NOR), that simultaneously provides outfit recommendations and generates abstractive comments. Neural outfit recommendation (NOR) consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NOR achieves significant improvements over state-of-the-art baselines for outfit recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research.",
"title": "Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation",
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"title": "CAS(ME)<sup>3</sup>: A Third Generation Facial Spontaneous Micro-Expression Database With Depth Information and High Ecological Validity",
"normalizedTitle": "CAS(ME)3: A Third Generation Facial Spontaneous Micro-Expression Database With Depth Information and High Ecological Validity",
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"abstract": "<inline-formula><tex-math notation=\"LaTeX\">Z_${}^{1}$_Z</tex-math></inline-formula>H High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) is a reliable technology used for detecting metabolites in solid tissues. Fast response time enables guiding surgeons in real time, for detecting tumor cells that are left over in the excision cavity. However, severe overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC) NMR is applied which can distinguish metabolites by generating 2D spectra (<inline-formula><tex-math notation=\"LaTeX\">Z_${}^{1}$_Z</tex-math></inline-formula>H-<inline-formula><tex-math notation=\"LaTeX\">Z_${}^{13}$_Z</tex-math></inline-formula>C). Unfortunately, this analysis requires much longer time and prohibits real time analysis. Thus, obtaining 2D spectrum fast has major implications in medicine. In this study, we show that using multiple multivariate regression and statistical total correlation spectroscopy, we can learn the relation between the <inline-formula><tex-math notation=\"LaTeX\">Z_${}^{1}$_Z</tex-math></inline-formula>H and <inline-formula><tex-math notation=\"LaTeX\">Z_${}^{13}$_Z</tex-math></inline-formula>C dimensions. Learning is possible with small sample sizes and without the need for performing the HSQC analysis, we can predict the <inline-formula><tex-math notation=\"LaTeX\">Z_${}^{13}$_Z</tex-math></inline-formula>C dimension by just performing <inline-formula><tex-math notation=\"LaTeX\">Z_${}^{1}$_Z</tex-math></inline-formula>H HRMAS NMR experiment. We show on a rat model of central nervous system tissues (80 samples, 5 tissues) that our methods achieve 0.971 and 0.957 mean <inline-formula><tex-math notation=\"LaTeX\">Z_$R^2$_Z</tex-math></inline-formula> values, respectively. Our tests on 15 human brain tumor samples show that we can predict 104 groups of 39 metabolites with 97 percent accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in <inline-formula><tex-math notation=\"LaTeX\">Z_${}^{1}$_Z</tex-math></inline-formula>H dimension. In practice, this information can provide valuable feedback to the surgeon to further resect the cavity to avoid potential recurrence.",
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"abstract": "Compared with the widely studied negative emotions in which different classes are easy to distinguish, nowadays less attention is paid to the recognition of positive emotions that are not fully independent. In this article, we propose to recognize multiple continuous positive emotions that exhibit statistical dependencies using multi-target regression — by analyzing brain activities when an individual watches emotional film clips — and explore the neural representation of different positive emotions. Thirty-seven participants volunteered to participate in our study, in which their brain activities were recorded when watching five selected film clips (corresponding to five positive emotions: amusement, happiness, romance, tenderness and warmth). First, 150 well-known power features extracted from Electroencephalography (EEG) signals and 105 multimedia content analysis features were collected as the pool of candidate features. Second, based on the collected features, we propose to use a linear model (linear regression) and a nonlinear model (long short-term memory network, LSTM) to predict the percentage of five positive emotions. Then, percentage values were converted to ranking numbers and Kendall rank correlation coefficients were calculated. Our results showed that (1) ensemble of regressor chains (ERC) using LSTM as unit regressor obtained both the best regression results (with lowest RMSE = 8.325 and highest <inline-formula><tex-math notation=\"LaTeX\">Z_$\\text{R }^{2} = 0.346$_Z</tex-math></inline-formula>) and the best Kendall rank correlation coefficient (0.165) on EEG features merely, and (2) selective features from alpha frequency bands of EEG signals could represent different positive emotions. These results demonstrate the effectiveness of selective EEG features on recognizing different positive emotions.",
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"content": "Compared with the widely studied negative emotions in which different classes are easy to distinguish, nowadays less attention is paid to the recognition of positive emotions that are not fully independent. In this article, we propose to recognize multiple continuous positive emotions that exhibit statistical dependencies using multi-target regression — by analyzing brain activities when an individual watches emotional film clips — and explore the neural representation of different positive emotions. Thirty-seven participants volunteered to participate in our study, in which their brain activities were recorded when watching five selected film clips (corresponding to five positive emotions: amusement, happiness, romance, tenderness and warmth). First, 150 well-known power features extracted from Electroencephalography (EEG) signals and 105 multimedia content analysis features were collected as the pool of candidate features. Second, based on the collected features, we propose to use a linear model (linear regression) and a nonlinear model (long short-term memory network, LSTM) to predict the percentage of five positive emotions. Then, percentage values were converted to ranking numbers and Kendall rank correlation coefficients were calculated. Our results showed that (1) ensemble of regressor chains (ERC) using LSTM as unit regressor obtained both the best regression results (with lowest RMSE = 8.325 and highest <inline-formula><tex-math notation=\"LaTeX\">$\\text{R }^{2} = 0.346$</tex-math><alternatives><mml:math><mml:mrow><mml:msup><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>346</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"liu-ieq1-3043135.gif\"/></alternatives></inline-formula>) and the best Kendall rank correlation coefficient (0.165) on EEG features merely, and (2) selective features from alpha frequency bands of EEG signals could represent different positive emotions. These results demonstrate the effectiveness of selective EEG features on recognizing different positive emotions.",
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"title": "Multi-Target Positive Emotion Recognition From EEG Signals",
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"abstract": "Spectral clustering (SC) has become one of the most widely-adopted clustering algorithms, and been successfully applied into various applications. We in this work explore the problem of spectral clustering in a lifelong learning framework termed as <underline>G</underline>eneralized <underline>L</underline>ife<underline>l</underline>ong <underline>S</underline>pectral <underline>C</underline>lustering (GL<inline-formula><tex-math notation=\"LaTeX\">Z_$^2$_Z</tex-math></inline-formula>SC). Different from most current studies, which concentrate on a fixed spectral clustering task set and cannot efficiently incorporate a new clustering task, the goal of our work is to establish a generalized model for new spectral clustering tasks by “What” and “How” to lifelong learn from past tasks. In respect of “what to lifelong learn”, our GL<inline-formula><tex-math notation=\"LaTeX\">Z_$^2$_Z</tex-math></inline-formula>SC framework contains a dual memory mechanism with a deep orthogonal factorization manner: an orthogonal basis memory stores hidden and hierarchical clustering centers among learned tasks, and a feature embedding memory captures deep manifold representation common across multiple related tasks. When learning a new clustering task, the intuition here for “how to lifelong learn” is that GL<inline-formula><tex-math notation=\"LaTeX\">Z_$^2$_Z</tex-math></inline-formula>SC can transfer intrinsic knowledge from dual memory mechanism to obtain task-specific encoding matrix. Then the encoding matrix can redefine the dual memory over time to provide maximal benefits when learning future tasks, and reversely maximize performance for past tasks. To achieve this, we propose an alternative optimization formulation with convergence guarantee for solving our GL<inline-formula><tex-math notation=\"LaTeX\">Z_$^2$_Z</tex-math></inline-formula>SC model. To the end, empirical comparisons on several benchmark datasets show the effectiveness of our GL<inline-formula><tex-math notation=\"LaTeX\">Z_$^2$_Z</tex-math></inline-formula>SC, in comparison with several state-of-the-art clustering models.",
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"content": "Spectral clustering (SC) has become one of the most widely-adopted clustering algorithms, and been successfully applied into various applications. We in this work explore the problem of spectral clustering in a lifelong learning framework termed as <underline>G</underline>eneralized <underline>L</underline>ife<underline>l</underline>ong <underline>S</underline>pectral <underline>C</underline>lustering (GL<inline-formula><tex-math notation=\"LaTeX\">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href=\"sun-ieq1-3058852.gif\"/></alternatives></inline-formula>SC). Different from most current studies, which concentrate on a fixed spectral clustering task set and cannot efficiently incorporate a new clustering task, the goal of our work is to establish a generalized model for new spectral clustering tasks by “What” and “How” to lifelong learn from past tasks. In respect of “what to lifelong learn”, our GL<inline-formula><tex-math notation=\"LaTeX\">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href=\"sun-ieq2-3058852.gif\"/></alternatives></inline-formula>SC framework contains a dual memory mechanism with a deep orthogonal factorization manner: an orthogonal basis memory stores hidden and hierarchical clustering centers among learned tasks, and a feature embedding memory captures deep manifold representation common across multiple related tasks. When learning a new clustering task, the intuition here for “how to lifelong learn” is that GL<inline-formula><tex-math notation=\"LaTeX\">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href=\"sun-ieq3-3058852.gif\"/></alternatives></inline-formula>SC can transfer intrinsic knowledge from dual memory mechanism to obtain task-specific encoding matrix. Then the encoding matrix can redefine the dual memory over time to provide maximal benefits when learning future tasks, and reversely maximize performance for past tasks. To achieve this, we propose an alternative optimization formulation with convergence guarantee for solving our GL<inline-formula><tex-math notation=\"LaTeX\">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href=\"sun-ieq4-3058852.gif\"/></alternatives></inline-formula>SC model. To the end, empirical comparisons on several benchmark datasets show the effectiveness of our GL<inline-formula><tex-math notation=\"LaTeX\">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href=\"sun-ieq5-3058852.gif\"/></alternatives></inline-formula>SC, in comparison with several state-of-the-art clustering models.",
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"abstract": "Human perceptual and affective responses change dynamically when stimuli are experienced. In this study, we developed a method for modeling the causal structures of affective dynamics using time-series data. Using the temporal dominance of sensations method, perceptual and affective data were collected from individuals eating strawberries, and the resulting time-series data were mathematically represented using a vector auto-regression model. Multihierarchical and multidimensional causality structures that explain the temporal evolution of perceptual and affective responses were then established based on Granger causality and the information criterion. The established model suggests how affective and preferential responses are triggered following exposure to stimuli. We also assessed the quantitative and semantic validity of the model.",
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"content": "Human perceptual and affective responses change dynamically when stimuli are experienced. In this study, we developed a method for modeling the causal structures of affective dynamics using time-series data. Using the temporal dominance of sensations method, perceptual and affective data were collected from individuals eating strawberries, and the resulting time-series data were mathematically represented using a vector auto-regression model. Multihierarchical and multidimensional causality structures that explain the temporal evolution of perceptual and affective responses were then established based on Granger causality and the information criterion. The established model suggests how affective and preferential responses are triggered following exposure to stimuli. We also assessed the quantitative and semantic validity of the model.",
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