ZS-VAT: Learning Unbiased Attribute Knowledge for Zero-Shot Recognition Through Visual Attribute Transformer.

In zero-shot learning (ZSL), attribute knowledge plays a vital role in transferring knowledge from seen classes to unseen classes. However, most existing ZSL methods learn biased attribute knowledge, which usually results in biased attribute prediction and a decline in zero-shot recognition performance. To solve this problem and learn unbiased attribute knowledge, we propose a visual attribute Transformer for zero-shot recognition (ZS-VAT), which is an effective and interpretable Transformer designed specifically for ZSL. In ZS-VAT, we design an attribute-head self-attention (AHSA) that is capable of learning unbiased attribute knowledge. Specifically, each attribute head in AHSA first transforms the local features into attribute-reinforced features and then accumulates the attribute knowledge from all corresponding reinforced features, reducing the mutual influence between attributes and avoiding information loss. AHSA finally preserves unbiased attribute knowledge through attribute embeddings. We also propose an attribute fusion model (AFM) that learns to recover the correct category knowledge from the attribute knowledge. In particular, AFM takes all features from AHSA as input and generates global embeddings. We carried out experiments to demonstrate that the attribute knowledge from AHSA and the category knowledge from AFM are able to assist each other. During the final semantic prediction, we combine the attribute embedding prediction (AEP) and global embedding prediction (GEP). We evaluated the proposed scheme on three benchmark datasets. ZS-VAT outperformed the state-of-the-art generalized ZSL (GZSL) methods on two datasets and achieved competitive results on the other dataset.

EGCN++: A New Fusion Strategy for Ensemble Learning in Skeleton-Based Rehabilitation Exercise Assessment.

Skeleton-based exercise assessment focuses on evaluating the correctness or quality of an exercise performed by a subject. Skeleton data provide two groups of features (i.e., position and orientation), which existing methods have not fully harnessed. We previously proposed an ensemble-based graph convolutional network (EGCN) that considers both position and orientation features to construct a model-based approach. Integrating these types of features achieved better performance than available methods. However, EGCN lacked a fusion strategy across the data, feature, decision, and model levels. In this paper, we present an advanced framework, EGCN++, for rehabilitation exercise assessment. Based on EGCN, a new fusion strategy called MLE-PO is proposed for EGCN++; this technique considers fusion at the data and model levels. We conduct extensive cross-validation experiments and investigate the consistency between machine and human evaluations on three datasets: UI-PRMD, KIMORE, and EHE. Results demonstrate that MLE-PO outperforms other EGCN ensemble strategies and representative baselines. Furthermore, the MLE-PO's model evaluation scores are more quantitatively consistent with clinical evaluations than other ensemble strategies.

Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images.

The task of instance segmentation in remote sensing images, aiming at performing per-pixel labeling of objects at the instance level, is of great importance for various civil applications. Despite previous successes, most existing instance segmentation methods designed for natural images encounter sharp performance degradations when they are directly applied to top-view remote sensing images. Through careful analysis, we observe that the challenges mainly come from the lack of discriminative object features due to severe scale variations, low contrasts, and clustered distributions. In order to address these problems, a novel context aggregation network (CATNet) is proposed to improve the feature extraction process. The proposed model exploits three lightweight plug-and-play modules, namely, dense feature pyramid network (DenseFPN), spatial context pyramid (SCP), and hierarchical region of interest extractor (HRoIE), to aggregate global visual context at feature, spatial, and instance domains, respectively. DenseFPN is a multi-scale feature propagation module that establishes more flexible information flows by adopting interlevel residual connections, cross-level dense connections, and feature reweighting strategy. Leveraging the attention mechanism, SCP further augments the features by aggregating global spatial context into local regions. For each instance, HRoIE adaptively generates RoI features for different downstream tasks. Extensive evaluations of the proposed scheme on iSAID, DIOR, NWPU VHR-10, and HRSID datasets demonstrate that the proposed approach outperforms state-of-the-arts under similar computational costs. Source code and pretrained models are available at https://github.com/yeliudev/CATNet.

Pendelluft in patients with acute respiratory distress syndrome during trigger and reverse triggering breaths.

Pendelluft, the shift of air from non-dependent to dependent lung regions, is known to occur during active breathing in ventilated patients. However, information about pendelluft in ARDS patients under assisted mechanical ventilation is limited. In this prospectively collected and retrospectively analyzed study, we combined electrical impedance tomography and respiratory mechanics monitoring to quantitatively examine pendelluft in trigger and reverse triggering breaths in 20 mechanically ventilated patients with ARDS during the transition from controlled to active breaths under volume-cycled ventilation. Besides the 10 resting breaths in each patient, 20% of the counted active breaths were selected based on three levels of esophageal pressure swing (∆Pes): low (< 5 cm H2O, breaths = 471), moderate (≥ 5, < 10 cm H2O, breaths = 906), and high effort (≥ 10 cm H2O, breaths = 565). The pendelluft response to breathing efforts was significantly greater in trigger breaths than in reverse triggering breaths (p < 0.0001). Based on the pendelluft-∆Pes slope (ml/cmH2O), there were two distinct patterns of effort-related pendelluft (high vs. low pendelluft group). For trigger breaths, the high pendelluft group (n = 9, slope 0.7-2.4 ml/cmH2O) was significantly associated with lower peak airway/plateau pressure and lower respiratory system/lung elastance than the low pendelluft group (n = 11, slope - 0.1 to 0.3 ml/cmH2O). However, there was no difference in respiratory mechanics between high and low pendelluft groups for reverse triggering breathes. The use of ∆Pes to predict pendelluft was found to have a low positive predictive value.

SGCL: Spatial guided contrastive learning on whole-slide pathological images.

Self-supervised representation learning (SSL) has achieved remarkable success in its application to natural images while falling behind in performance when applied to whole-slide pathological images (WSIs). This is because the inherent characteristics of WSIs in terms of gigapixel resolution and multiple objects in training patches are fundamentally different from natural images. Directly transferring the state-of-the-art (SOTA) SSL methods designed for natural images to WSIs will inevitably compromise their performance. We present a novel scheme SGCL: Spatial Guided Contrastive Learning, to fully explore the inherent properties of WSIs, leveraging the spatial proximity and multi-object priors for stable self-supervision. Beyond the self-invariance of instance discrimination, we expand and propagate the spatial proximity for the intra-invariance from the same WSI and inter-invariance from different WSIs, as well as propose the spatial-guided multi-cropping for inner-invariance within patches. To adaptively explore such spatial information without supervision, we propose a new loss function and conduct a theoretical analysis to validate it. This novel scheme of SGCL is able to achieve additional improvements over the SOTA pre-training methods on diverse downstream tasks across multiple datasets. Extensive ablation studies have been carried out and visualizations of these results have been presented to aid understanding of the proposed SGCL scheme. As open science, all codes and pre-trained models are available at https://github.com/HHHedo/SGCL.

MADS-box protein PpDAM6 regulates chilling requirement-mediated dormancy and bud break in peach.

Bud dormancy is crucial for winter survival and is characterized by the inability of the bud meristem to respond to growth-promotive signals before the chilling requirement (CR) is met. However, our understanding of the genetic mechanism regulating CR and bud dormancy remains limited. This study identified PpDAM6 (DORMANCY-ASSOCIATED MADS-box) as a key gene for CR using a genome-wide association study analysis based on structural variations in 345 peach (Prunus persica (L.) Batsch) accessions. The function of PpDAM6 in CR regulation was demonstrated by transiently silencing the gene in peach buds and stably overexpressing the gene in transgenic apple (Malus × domestica) plants. The results showed an evolutionarily conserved function of PpDAM6 in regulating bud dormancy release, followed by vegetative growth and flowering, in peach and apple. The 30-bp deletion in the PpDAM6 promoter was substantially associated with reducing PpDAM6 expression in low-CR accessions. A PCR marker based on the 30-bp indel was developed to distinguish peach plants with non-low and low CR. Modification of the H3K27me3 marker at the PpDAM6 locus showed no apparent change across the dormancy process in low- and non-low- CR cultivars. Additionally, H3K27me3 modification occurred earlier in low-CR cultivars on a genome-wide scale. PpDAM6 could mediate cell-cell communication by inducing the expression of the downstream genes PpNCED1 (9-cis-epoxycarotenoid dioxygenase 1), encoding a key enzyme for ABA biosynthesis, and CALS (CALLOSE SYNTHASE), encoding callose synthase. We shed light on a gene regulatory network formed by PpDAM6-containing complexes that mediate CR underlying dormancy and bud break in peach. A better understanding of the genetic basis for natural variations of CR can help breeders develop cultivars with different CR for growing in different geographical regions.

Knowledge-Enriched Attention Network With Group-Wise Semantic for Visual Storytelling.

As a technically challenging topic, visual storytelling aims at generating an imaginary and coherent story with narrative multi-sentences from a group of relevant images. Existing methods often generate direct and rigid descriptions of apparent image-based contents, because they are not capable of exploring implicit information beyond images. Hence, these schemes could not capture consistent dependencies from holistic representation, impairing the generation of reasonable and fluent stories. To address these problems, a novel knowledge-enriched attention network with group-wise semantic model is proposed. Three main novel components are designed and supported by substantial experiments to reveal practical advantages. First, a knowledge-enriched attention network is designed to extract implicit concepts from external knowledge system, and these concepts are followed by a cascade cross-modal attention mechanism to characterize imaginative and concrete representations. Second, a group-wise semantic module with second-order pooling is developed to explore the globally consistent guidance. Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion. Substantial experiments on the visual storytelling datasets with both objective and subjective evaluation metrics demonstrate the superior performance of the proposed scheme as compared with other state-of-the-art methods. The source code of this work can be found in https://mic.tongji.edu.cn.

Spontaneous Breathing and Pendelluft in Patients with Acute Lung Injury: A Narrative Review.

Acute respiratory distress syndrome (ARDS) is characterized by acute-onset rapid-deteriorating inflammatory lung injury. Although the preservation of spontaneous breathing may have physiological benefits in oxygenation, increasing evidence shows that vigorous spontaneous breathing may aggravate lung injury (i.e., patient self-inflicted lung injury). Increased lung stress and pendelluft, which is defined as intrapulmonary gas redistribution without a significant change in tidal volume, are important mechanisms of patient self-inflicted lung injury. The presence of pendelluft may be considered a surrogate marker of vigorous inspiratory effort, which can cause the dependent lung to overstretch. In this review, we summarized three major methods for electrical impedance tomography-based pendelluft monitoring. Future studies are warranted to compare and validate the different methods of pendelluft estimation in patients with ARDS.

Beyond fine-tuning: Classifying high resolution mammograms using function-preserving transformations.

The task of classifying mammograms is very challenging because the lesion is usually small in the high resolution image. The current state-of-the-art approaches for medical image classification rely on using the de-facto method for convolutional neural networks-fine-tuning. However, there are fundamental differences between natural images and medical images, which based on existing evidence from the literature, limits the overall performance gain when designed with algorithmic approaches. In this paper, we propose to go beyond fine-tuning by introducing a novel framework called MorphHR, in which we highlight a new transfer learning scheme. The idea behind the proposed framework is to integrate function-preserving transformations, for any continuous non-linear activation neurons, to internally regularise the network for improving mammograms classification. The proposed solution offers two major advantages over the existing techniques. Firstly and unlike fine-tuning, the proposed approach allows for modifying not only the last few layers but also several of the first ones on a deep ConvNet. By doing this, we can design the network front to be suitable for learning domain specific features. Secondly, the proposed scheme is scalable to hardware. Therefore, one can fit high resolution images on standard GPU memory. We show that by using high resolution images, one prevents losing relevant information. We demonstrate, through numerical and visual experiments, that the proposed approach yields to a significant improvement in the classification performance over state-of-the-art techniques, and is indeed on a par with radiology experts. Moreover and for generalisation purposes, we show the effectiveness of the proposed learning scheme on another large dataset, the ChestX-ray14, surpassing current state-of-the-art techniques.

SABA prescriptions and asthma management practices in patients treated by specialists in Taiwan: Results from the SABINA III study.

PURPOSE: Limited data exist on asthma medication patterns in Taiwan. The objectives of the SABINA III cross-sectional study in Taiwan were thus, to describe patient demographics and clinical features and estimate short-acting ß2-agonist (SABA) and inhaled corticosteroids (ICS) prescriptions per patient. METHODS: Patients (≥18 years) with asthma were classified by investigator-defined asthma severity per the 2017 Global Initiative for Asthma (GINA) recommendations. Data on asthma symptom control (per GINA 2017 recommendations), severe exacerbation history, and prescribed treatments in the 12 months before study visit were collected using electronic case-report forms. Analyses were descriptive. RESULTS: Overall, all 294 analyzed patients (mean [SD] age, 57.9 [15.6] years; female, 69%) were enrolled by specialists and had fully reimbursed healthcare. Most patients were classified with moderate-to-severe asthma (93.2%; GINA steps 3-5), were obese (53.4%) and nonsmokers (79.6%), reported high school or university and/or postgraduate education (61.9%), and had ≤2 comorbidities (89.1%). Mean (SD) asthma duration was 8.3 (10.0) years, with 37.8% of patients experiencing ≥1 severe exacerbation 12 months before the study visit. Overall, 62.2%, 26.2%, and 11.6% of patients had well-controlled, partly controlled, and uncontrolled asthma, respectively. Crucially, 19.3% of patients were prescribed ≥3 SABA canisters in the preceding 12 months (overprescription). ICS, ICS + long-acting ß2-agonist fixed-dose combination, and oral corticosteroid bursts were prescribed to 6.5%, 97.3%, and 31.6% of patients, respectively. CONCLUSION: Despite treatment by specialists and fully reimbursed healthcare, findings indicate room for improvement in asthma control and SABA prescription practices in Taiwan, emphasizing the need to adhere to latest evidence-based guidelines.

Body-Weight Gain in Women During Smoking Cessation Is a Sex-Specific Predictor of 6-Month Abstinence: A Retrospective Cohort Study.

Background: Smoking behavior differs between the sexes. Weight control is one of the main reasons leading to tobacco abuse in women but not in men. Studies on the predictive factors of cessation failure between sexes are scarce. This study is aim to investigate whether there are sex differences in the effect of weight gain on smoking cessation rate. Methods: Participants in the smoking-cessation program at a Medical Center in Taiwan between 2018 and 2019 were included. Details of age, sex, comorbidities, depression screening, nicotine dependence, body weight, and cessation medications of the participants were collected. The participants were classified based on their sex, and multivariable logistic regression analyses were conducted. Multivariable logistic regression analyses were performed for sensitivity analysis after stratifying the participants according to their weight loss (weight loss ≥ 1.5 kg and weight loss ≥ 3.0 kg). Results: A total of 1,475 participants were included. The body-weight gain in women was associated with failed abstinence (adjusted odds ratio (OR): 3.10, 95% CI: 1.10-9.04). In contrast, body-weight gain in men was associated with successful 6-month prolonged abstinence (adjusted OR: 0.77, 95% CI: 0.61-0.98). The adjusted ORs for any body-weight loss, body-weight loss ≥1.5 kg, and body-weight loss ≥3.0 kg were 0.28 (95% CI: 0.09-0.88), 0.14 (95% CI: 0.03-0.55), and 0.03 (95% CI: 0.01-0.42), respectively. Conclusion: Body-weight gain in women during a hospital-based smoking-cessation program is associated with abstinence failure. Further multicenter studies, including participants of different races and cultural backgrounds, are warranted.

Multiple in-hospital counseling increases six-month smoking abstinence among individuals participating in a hospital-initiated smoking cessation program.

BACKGROUND: A cessation program for hospitalized smokers is an effective strategy to achieve smoking abstinence. The effects of multiple in-hospital counseling sessions on 6-month smoking abstinence require further investigation. METHODS: We retrospectively analyzed the data of smokers who participated in hospital-initiated cessation programs at a medical center between 2017 and 2019. Data on age, sex, comorbidities, daily number of cigarettes, cessation motivation, nicotine dependence, cessation medications, discharge diagnosis, length of hospitalization, and intensive care unit admission were collected. We conducted multiple logistic regression analysis to investigate the effect of multiple in-hospital counseling sessions on 6-month sustained smoking abstinence. Sensitivity analyses were carried out excluding participants who underwent post-discharge cessation programs and assuming that the loss to follow-up participants had failure in 6-month smoking abstinence. RESULTS: A total of 1943 participants aged ≥ 20 years were analyzed. Compared with single in-hospital counseling session, the adjusted odds ratios (ORs) for 2 and ≥ 3 counseling sessions were 1.44 (95% confidence interval [CI] 1.05 to 1.98) and 2.02 (95% CI 1.27 to 3.22), respectively, with a significant trend for increasing the number of counseling sessions (P < 0.001). The results remained significant after excluding participants who underwent a post-discharge cessation program or when assuming that lost to follow-up participants had failure in smoking abstinence. CONCLUSION: Multiple in-hospital counseling sessions were associated with a higher 6-month sustained smoking abstinence rate. This strategy could be used to reduce the prevalence of smoking.

Clinical and Microbiological Characteristics of Culture-Positive, Influenza-Associated Pulmonary Aspergillosis: A Single-Center Study in Southern Taiwan, 2016-2019.

This study delineated the characteristics of 24 (11.2%) culture-positive, influenza-associated pulmonary aspergillosis (IAPA) patients out of 215 patients with severe influenza during 2016-2019 in a medical center in southern Taiwan. Twenty (83.3%) patients did not have EORTC/MSG-defined host factors. The mean time from influenza diagnosis to Aspergillus growth was 4.4 days, and 20 (83.3%) developed IAPA within seven days after influenza diagnosis. All patients were treated in intensive care units and all but one (95.8%) received mechanical ventilation. Aspergillus tracheobronchitis was evident in 6 (31.6%) of 19 patients undergoing bronchoscopy. Positive galactomannan testing of either serum or bronchoalveolar lavage was noted in all patients. On computed tomography imaging, IAPA was characterized by peribronchial infiltrates, multiple nodules, and cavities superimposed on ground-glass opacities. Pure Aspergillus growth without bacterial co-isolation in culture was found in 17 (70.8%) patients. A. fumigatus (15, 62.5%), A. flavus (6, 25.0%), and A. terreus (4, 16.7%) were the major causative species. Three patients had mixed Aspergillus infections due to two species, and two had mixed azole-susceptible and azole-resistant A. fumigatus infection. All patients received voriconazole with an all-cause mortality of 41.6%. Of 14 survivors, the mean duration of antifungal use was 40.5 days. In conclusion, IAPA is an early and rapidly deteriorating complication following influenza that necessitates clinical vigilance and prompt diagnostic workup.

Invasive pulmonary aspergillosis is associated with cytomegalovirus viremia in critically ill patients - A retrospective cohort study.

BACKGROUND/PURPOSE: Cytomegalovirus (CMV) viremia is associated with a higher mortality rate and prolonged intensive care unit (ICU) stay for critically ill patients. CMV infection causes transient but substantial immunosuppression for transplant recipients, increasing risk of fungal infection. The association between CMV viremia and invasive pulmonary aspergillosis (IPA) for critically ill patients is still unknown. METHODS: We retrospectively analyzed patients received bronchoalveolar lavage (BAL), galactomannan test, influenza survey and blood CMV viral load test in ICUs of a university hospital between April 2017 and May 2020. Independent risks for IPA were analyzed by multivariable logistic regression. RESULTS: A total of 136 patients were included. Twenty-one patients had IPA, 48 patients had CMV viremia and 22 patients had influenza. In a multivariable logistic regression model, patients with CMV viremia or influenza had higher IPA risk (adjusted odds ratio, 3.98 and 8.72; 95% CI, 1.26-12.60 and 2.64-28.82; p value = 0.019 and <0.001, respectively.). Patients with detectable CMV in BAL fluid did not have higher IPA risk (crude odds ratio, 0.95; 95% CI, 0.33-2.79; p value = 0.933). After stratifying patients by CMV viral load, the IPA risk is higher for patients with higher viral loads. There is an additive synergistic effect on IPA risk between CMV viremia and influenza infection. CONCLUSION: For critically ill patients, CMV viremia is an independent risk factor of IPA. Patients with higher blood CMV viral loads have a higher risk of IPA. CMV viremia and influenza have an additive synergistic effect for IPA risk in critically ill patients.

The occurrence of and risk factors for developing acute critical illness during quarantine as a response to the COVID-19 pandemic.

BACKGROUND/PURPOSE: Early detection and timely quarantine measures are necessary to control disease spread and prevent nosocomial outbreaks of Coronavirus disease 2019 (COVID-19). In this study, we aimed to investigate the impact of a quarantine strategy on patient safety and quality of care. METHODS: This retrospective cohort study enrolled patients admitted to the quarantine ward in a tertiary hospital in southern Taiwan. The incidence and causes of acute critical illness, including clinical deterioration and unexpected complications during the quarantine period, were reviewed. Further investigation was performed to identify risk factors for acute critical illness during quarantine. RESULTS: Of 320 patients admitted to the quarantine ward, more than two-thirds were elderly, and 37.8% were bedridden. During the quarantine period, 68 (21.2%) developed acute critical illness, which more commonly occurred among patients older than 80 years and with a bedridden status, nasogastric tube feeding, or dyspnea symptoms. Bedridden status was an independent predictor of acute critical illness. Through optimization of sampling for COVID-19 and laboratory schedules, both the duration of quarantine and the proportion of acute critical illness among bedridden patients during quarantine exhibited a decreasing trend. There was no COVID-19 nosocomial transmission during the study period. CONCLUSION: The quarantine ward is a key measure to prevent nosocomial transmission of COVID-19 but may carry a potential negative impact on patient care and safety. For patients with multiple comorbidities and a bedridden status, healthcare workers should remain alert to rapid deterioration and unexpected adverse events during quarantine.

Electrical Impedance Tomography Analysis Between Two Similar Respiratory System Compliance During Decremetal PEEP Titration in ARDS Patients.

Purpose: The positive end-expiratory pressure (PEEP) level with best respiratory system compliance (Crs) is frequently used for PEEP selection in acute respiratory distress syndrome (ARDS) patients. On occasion, two similar best Crs (where the difference between the Crs of two PEEP levels is < 1 ml/cm H2O) may be identified during decremental PEEP titration. Selecting PEEP under such conditions is challenging. The aim of this study was to provide supplementary rationale for PEEP selection by assessing the global and regional ventilation distributions between two PEEP levels in this situation. Methods: Eight ARDS cases with similar best Crs at two different PEEP levels were analyzed using examination-specific electrical impedance tomography (EIT) measures and airway stress index (SIaw). Five Crs were measured at PEEP values of 25 cm H2O (PEEP25), 20 cm H2O (PEEP20), 15 cm H2O (PEEPH), 11 cm H2O (PEEPI), and 7 cm H2O (PEEPL). The higher PEEP value of the two PEEPs with similar best Crs was designated as PEEPupper, while the lower designated as PEEPlower. Results: PEEPH and PEEPI shared the best Crs in two cases, while similar Crs was found at PEEPI and PEEPL in the remaining six cases. SIaw was higher with PEEPupper as compared to PEEPlower (1.06 ± 0.10 versus 0.99 ± 0.09, p = 0.05). Proportion of lung hyperdistension was significantly higher with PEEPupper than PEEPlower (7.0 ± 5.1% versus 0.3 ± 0.5%, p = 0.0002). In contrast, proportion of recruitable lung collapse was higher with PEEPlower than PEEPupper (18.6 ± 4.4% versus 5.9 ± 3.7%, p < 0.0001). Cyclic alveolar collapse and reopening during tidal breathing was higher at PEEPlower than PEEPupper (34.4 ± 19.3% versus 16.0 ± 9.1%, p = 0.046). The intratidal gas distribution (ITV) index was also significantly higher at PEEPlower than PEEPupper (2.6 ± 1.3 versus 1.8 ± 0.7, p = 0.042). Conclusions: PEEPupper is a rational selection in ARDS cases with two similar best Crs. EIT provides additional information for the selection of PEEP in such circumstances. Supplementary Information: The online version contains supplementary material available at 10.1007/s40846-021-00668-2.

Dry Wearable Textile Electrodes for Portable Electrical Impedance Tomography.

Electrical impedance tomography (EIT), a noninvasive and radiation-free medical imaging technique, has been used for continuous real-time regional lung aeration. However, adhesive electrodes could cause discomfort and increase the risk of skin injury during prolonged measurement. Additionally, the conductive gel between the electrodes and skin could evaporate in long-term usage and deteriorate the signal quality. To address these issues, in this work, textile electrodes integrated with a clothing belt are proposed to achieve EIT lung imaging along with a custom portable EIT system. The simulation and experimental results have verified the validity of the proposed portable EIT system. Furthermore, the imaging results of using the proposed textile electrodes were compared with commercial electrocardiogram electrodes to evaluate their performance.

Joint Learning of Latent Similarity and Local Embedding for Multi-View Clustering.

Spectral clustering has been an attractive topic in the field of computer vision due to the extensive growth of applications, such as image segmentation, clustering and representation. In this problem, the construction of the similarity matrix is a vital element affecting clustering performance. In this paper, we propose a multi-view joint learning (MVJL) framework to achieve both a reliable similarity matrix and a latent low-dimensional embedding. Specifically, the similarity matrix to be learned is represented as a convex hull of similarity matrices from different views, where the nuclear norm is imposed to capture the principal information of multiple views and improve robustness against noise/outliers. Moreover, an effective low-dimensional representation is obtained by applying local embedding on the similarity matrix, which preserves the local intrinsic structure of data through dimensionality reduction. With these techniques, we formulate the MVJL as a joint optimization problem and derive its mathematical solution with the alternating direction method of multipliers strategy and the proximal gradient descent method. The solution, which consists of a similarity matrix and a low-dimensional representation, is ultimately integrated with spectral clustering or K-means for multi-view clustering. Extensive experimental results on real-world datasets demonstrate that MVJL achieves superior clustering performance over other state-of-the-art methods.