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Driven by a wide range of essential applications, significant achievements are made to explore WiFi-based Human Activity Recognition (HAR) techniques that utilize the information collected by commercial off-the-shelf WiFi infrastructures to infer human activities without the need for subjects to carry any devices. Although existing WiFi-based HAR systems achieve satisfactory performance in some instances, they are faced with a severe challenge that the impacts of ubiquitous Co-channel Interference (CCI) on WiFi signals are inevitable. This downgrades the performance of these HAR systems significantly. To address this challenge, we propose PhaseAnti, a novel WiFi-based HAR system to exploit the CCI-independent phase component, Nonlinear Phase Error Variation (NLPEV), of WiFi Channel State Information to cope with the negative effects of CCI. The stability of NLPEV data and the sensibility of this component to motions are rigorously analyzed. Furthermore, validated by extensive properly designed experiments, this phase component across subcarriers is invariant under various CCI scenarios while sufficiently distinct for different motions. Therefore, the NLPEV data can be used and processed effectively to perform HAR in CCI scenarios. Extensive experiments with various daily activities in different indoor rooms demonstrate the superior effectiveness and generalizability of the proposed PhaseAnti system under various CCI scenarios.

The scene graph is a symbolic data structure that comprehensively describes the objects and visual relations in a visual scene, while ignoring the inherent perceptual saliency of each visual relation (i.e., relation saliency). However, humans often quickly allocate attention to important/salient visual relations in a scene. To align with such human perception of a scene, we explicitly model the perceptual saliency of visual relation in scene graph by upgrading each graph edge (i.e., visual relation) with an attribute of relation saliency. We present a new design, named as Saliency-guided Message Passing (SMP), that boosts the generation of such scene graph structure with the guidance from the visual relation saliency. Technically, an object interaction encoder is first utilized to strengthen object relation representations by jointly exploiting the appearance, semantic, and spatial relations in between. A branch is further leveraged to estimate the relation saliency of each visual relation by ordinal regression. Next, conditioned on the object and relation features (coupled with the estimated relation saliency), our SMP enhances scene graph generation by performing message passing over the objects and the most salient relations. Extensive experiments on VG-KR and VG150 datasets demonstrate the superiority of SMP for the scene graph generation. Moreover, we empirically validate the compelling generalizability of the learned scene graphs via SMP on downstream tasks like cross-model retrieval and image captioning.

We present an analysis to extract kaon parton distribution functions (PDFs) for the first time using meson-induced Drell-Yan and quarkonium production data. Starting from the statistical model first developed for determining the partonic structure of spin-1/2 nucleon and later applied to the spin-0 pion, we have extended this approach to perform a global fit to existing kaon-induced Drell-Yan and $J/\psi$ production data. These data are well described by the statistical model, allowing a first extraction of the kaon PDFs. We find that both the Drell-Yan and the $J/\psi$ data favor a harder valence distribution for strange quark than for up quark in kaon. The kaon gluon distribution is further constrained by the $J/\psi$ production data. In particular, the momentum fraction carried by gluons is found to be similar for pion and kaon., Comment: 6 pages, 3 figures. arXiv admin note: text overlap with arXiv:2202.12547

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 GWAS (Genome-Wide Association Study) analysis based on structural variations (SVs) 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-chilling requirement 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 budbreak 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.

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.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.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 and0.001, 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.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.

Generative Adversarial Networks (GAN) has become more and more popular these years. However, it is difficult to train and suffers from the training instability problem. To tackle this difficulty, this paper proposes a novel approach. Our idea is intuitive but proven to be very useful. In essence, it combines saturating loss and non-saturating loss into the loss function. Thus it will exploit the complementary statistical properties from two kinds of loss functions to effectively improve the training stability. We term our method twin discriminator Generative Adversarial Networks (TWGAN) which, unlike GAN, has a generator and a twin discriminator. The twin discriminator consists of two discriminators with identical architecture and both of them aim to distinguish whether the samples are from real data or fake data. We develop theoretical analysis to show that, given the optimal discriminators, optimizing the generator of TWGAN reduces to minimizing the Kullback-Leibler (KL) divergence between the distribution of generated data (Pg) and the distribution of real data (Pdata), hence effectively addressing the training instability problem. Extensive experiments on MNIST, Fashion MNIST, CIFAR-10/100 and STL-10 datasets demonstrate that the competitive performance of our TWGAN in generating good quality and diverse samples over baselines. The obtained highest inception score (IS) and lowest Frechet Inception Distance (FID), compared with other state-of-the-art GANs, show the superiority of our TWGAN.

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 story. 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 popular Visual Storytelling dataset with both objective and subjective evaluation metrics demonstrate the superior performance of the proposed scheme as compared with other state-of-the-art methods.

In this paper, we present LensCast, a novel cross-layer video transmission framework for wireless networks, which seamlessly integrates millimeter wave (mmWave) lens multiple-input multiple-output (MIMO) with robust video transmission. LensCast is designed to exploit the video content diversity at the application layer, together with the spatial path diversity of lens antenna array at the physical layer, to achieve graceful video transmission performance under varying channel conditions. In LensCast, a transmission distortion minimization problem is formulated with the consideration of video chunk scheduling, path matching and power allocation, which is an intractable mixed integer non-linear programming (MINLP) problem. The solution of this MINLP problem is converted into resource allocation (i.e., joint path matching and power allocation) plus chunk scheduling. First, resource allocation is investigated with given chunk scheduling results. By analyzing the optimality of the resource allocation problem, a winner-takes-all assignment is obtained to guide resource allocation. After that, a greedy water-filling algorithm is proposed as a near-optimal solution. Second, we propose a low-complexity chunk scheduling algorithm to schedule chunks for each transmission. Simulation results demonstrate that the proposed LensCast achieves an improved performance in terms of both peak signal-to-noise ratio and visual quality comparing with reference schemes.

Background Pendelluft occurs in patients with acute respiratory distress syndrome (ARDS) during spontaneous breaths. Quantifying pendelluft in ARDS patients has not been performed before. We combined electrical impedance tomography (EIT) and respiratory mechanics monitoring to quantitatively examine pendelluft in trigger and reverse triggering breaths in mechanically ventilated patients with ARDS. Methods EIT and respiratory mechanics measurements were analyzed in 20 mechanically ventilated patients with ARDS during transitioning from controlled to spontaneous breaths following discontinuance of myorelaxant under volume-cycled ventilation. Breath selection was based on 4 levels of esophageal pressure swing (∆Pes). These were rest (0 cm H2O, breaths = 200), low (2O, breaths = 471), moderate (≥ 5, < 10cm H2O, breaths = 906), and high effort (≥ 10cm H2O, breaths = 565). A total of 2,142 breaths were analyzed. Results The pendelluft response to breathing efforts varied and was significantly greater in trigger versus reverse triggering breaths (3.9 ± 6.8 vs 1.9 ± 2.8 ml, respectively, p es slope (ml/cmH2O). For trigger breaths, high pendelluft group (n = 9, slope 0.7 to 2.4 ml/cmH2O) was significantly associated with lower peak airway/plateau pressure and lower respiratory system/lung elastance than low pendelluft group (n = 11, slope − 0.1 to 0.3 ml/cmH2O). For reverse triggering breaths, there was no difference in respiratory mechanics between high (n = 4, 0.49 to 2.53 ml/cmH2O) and low pendelluft (n = 12, -0.22 to 0.25 ml/cmH2O) groups. The prediction of pendelluft by ∆Pes was characterized by a low positive predictive value. ARDS cases with high effort-related pendelluft were uncommon. The prediction of pendelluft was not significant in reverse triggering breaths. Conclusions Effort-related pendelluft should be individualized. Pendelluft is higher in trigger breaths than reverse triggering breaths. For trigger breaths, high pendelluft is likely to occur in ARDS patients with lower lung elastance and lower peak airway/plateau pressure. However, ARDS cases with high effort-related pendelluft were uncommon in our study population.