Robust Semantic Communications with Masked VQ-VAE Enabled Codebook

Although semantic communications have exhibited satisfactory performance for a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise refers to the misleading between the intended semantic symbols and received ones, thus cause the failure of tasks. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. In particular, we analyze sample-dependent and sample-independent semantic noise. To combat the semantic noise, the adversarial training with weight perturbation is developed to incorporate the samples with semantic noise in the training dataset. Then, we propose to mask a portion of the input, where the semantic noise appears frequently, and design the masked vector quantized-variational autoencoder (VQ-VAE) with the noise-related masking strategy. We use a discrete codebook shared by the transmitter and the receiver for encoded feature representation. To further improve the system robustness, we develop a feature importance module (FIM) to suppress the noise-related and task-unrelated features. Thus, the transmitter simply needs to transmit the indices of these important task-related features in the codebook. Simulation results show that the proposed method can be applied in many downstream tasks and significantly improve the robustness against semantic noise with remarkable reduction on the transmission overhead.Comment: 16 pages, 11 figures. arXiv admin note: text overlap with arXiv:2202.0333

A Unified Multi-Task Semantic Communication System for Multimodal Data

Task-oriented semantic communication has achieved significant performance gains. However, the model has to be updated once the task is changed or multiple models need to be stored for serving different tasks. To address this issue, we develop a unified deep learning enabled semantic communication system (U-DeepSC), where a unified end-to-end framework can serve many different tasks with multiple modalities. As the difficulty varies from different tasks, different numbers of neural network layers are required for various tasks. We develop a multi-exit architecture in U-DeepSC to provide early-exit results for relatively simple tasks. To reduce the transmission overhead, we design a unified codebook for feature representation for serving multiple tasks, in which only the indices of these task-specific features in the codebook are transmitted. Moreover, we propose a dimension-wise dynamic scheme that can adjust the number of transmitted indices for different tasks as the number of required features varies from task to task. Furthermore, our dynamic scheme can adaptively adjust the numbers of transmitted features under different channel conditions to optimize the transmission efficiency. According to simulation results, the proposed U-DeepSC achieves comparable performance to the task-oriented semantic communication system designed for a specific task but with significant reduction in both transmission overhead and model size

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) communities. Such big models are not only strong performers in practice but also offer a promising way to break out of the task-specific modeling restrictions, thereby enabling task-agnostic and unified ML systems. However, such a popular paradigm is mainly unexplored by the recommender systems (RS) community. A critical issue is that standard recommendation models are primarily built on categorical identity features. That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms. To pursue the transferable recommendations, we propose studying pre-trained RS models in a novel scenario where a user's interaction feedback involves a mixture-of-modality (MoM) items, e.g., text and images. We then present TransRec, a very simple modification made on the popular ID-based RS framework. TransRec learns directly from the raw features of the MoM items in an end-to-end training manner and thus enables effective transfer learning under various scenarios without relying on overlapped users or items. We empirically study the transferring ability of TransRec across four different real-world recommendation settings. Besides, we look at its effects by scaling source and target data size. Our results suggest that learning neural recommendation models from MoM feedback provides a promising way to realize universal RS

FLT3L and Plerixafor Combination Increases Hematopoietic Stem Cell Mobilization and Leads to Improved Transplantation Outcome

AbstractHematopoietic stem cell (HSC) transplantation has curative potential for patients with hematological malignancies. Clinically, HSCs derived from mobilized peripheral blood are used more frequently than bone marrow. However, current standard mobilizing agents yield grafts that may not contain sufficient HSCs. Here, using murine models, we discovered that FLT3L synergized with plerixafor to mobilize phenotypically defined HSCs and their combination (FP) was superior to granulocyte colony-stimulating factor (G-CSF) alone or in combination with plerixafor (GP). Additionally, FP mobilized more regulatory T cells, natural killer cells, and plasmacytoid dendritic cells compared with G-CSF alone or GP. Both syngeneic and allogeneic grafts mobilized by FP led to long-term survival in transplanted mice. Collectively, FP represents a promising novel and potent mobilization regimen with potential clinical application in both the autologous and allogeneic transplantation settings

The More Similar, the Healthier: The Effect of Perceived Parent-Child Facial Resemblance on Parental Physical Health

Parent-child facial resemblance (PCFR) is one of the direct cues used to assess the genetic relationship between two individuals. Due to the inner fertilization of humans, fathers are liable to suffer from paternal uncertainty. When a father perceives low father-child facial resemblance, he would become anxious, which is detrimental to his immune system and physical health. For a mother, however, she can assure her genetic relationship to her children and does not need any external cues to verify her maternity. Thus, the mother-child facial resemblance does not influence the mothers’ physical health. To test these hypotheses, we examined the moderating effect of parental gender and the mediating effect of trait anxiety on the relationship between PCFR and physical health of parents. The results showed that fathers’ PCFR positively predicted their physical health, whereas the mothers’ PCFR failed to show any predicting effect on mothers’ physical health. Furthermore, trait anxiety mediated the relationship between fathers’ PCFR and their physical health. The implications for paternal uncertainty, gender difference, and public policy were discussed

Modeling Study of the Air Quality Impact of Record-Breaking Southern California Wildfires in December 2017

We investigate the air quality impact of record‐breaking wildfires in Southern California during 5–18 December 2017 using the Weather Research and Forecasting model with Chemistry in combination with satellite and surface observations. This wildfire event was driven by dry and strong offshore Santa Ana winds, which played a critical role in fire formation and air pollutant transport. By utilizing fire emissions derived from the high‐resolution (375 × 375 m²) Visible Infrared Imaging Radiometer Suite active fire detections, the simulated magnitude and temporal evolution of fine particulate matter (PM_(2.5)) concentrations agree reasonably well with surface observations (normalized mean bias = 4.0%). Meanwhile, the model could generally capture the spatial pattern of aerosol optical depth from satellite observations. Sensitivity tests reveal that using a high spatial resolution for fire emissions and a reasonable treatment of plume rise (a fair split between emissions injected at surface and those lifted to upper levels) is important for achieving decent PM_(2.5) simulation results. Biases in PM_(2.5) simulation are relatively large (about 50%) during the period with the strongest Santa Ana wind, due to a possible underestimation of burning area and uncertainty in wind field variation. The 2017 December fire event increases the 14‐day averaged PM_(2.5) concentrations by up to 231.2 μg/m³ over the downwind regions, which substantially exceeds the U.S. air quality standards, potentially leading to adverse health impacts. The human exposure to fire‐induced PM_(2.5) accounts for 14–42% of the annual total PM_(2.5) exposure in areas impacted by the fire plumes

Biocompatible Nanocomplexes for Molecular Targeted MRI Contrast Agent

Accurate diagnosis in early stage is vital for the treatment of Hepatocellular carcinoma. The aim of this study was to investigate the potential of poly lactic acid–polyethylene glycol/gadolinium–diethylenetriamine-pentaacetic acid (PLA–PEG/Gd–DTPA) nanocomplexes using as biocompatible molecular magnetic resonance imaging (MRI) contrast agent. The PLA–PEG/Gd–DTPA nanocomplexes were obtained using self-assembly nanotechnology by incubation of PLA–PEG nanoparticles and the commercial contrast agent, Gd–DTPA. The physicochemical properties of nanocomplexes were measured by atomic force microscopy and photon correlation spectroscopy. The T1-weighted MR images of the nanocomplexes were obtained in a 3.0 T clinical MR imager. The stability study was carried out in human plasma and the distribution in vivo was investigated in rats. The mean size of the PLA–PEG/Gd–DTPA nanocomplexes was 187.9 ± 2.30 nm, and the polydispersity index was 0.108, and the zeta potential was −12.36 ± 3.58 mV. The results of MRI test confirmed that the PLA–PEG/Gd–DTPA nanocomplexes possessed the ability of MRI, and the direct correlation between the MRI imaging intensities and the nano-complex concentrations was observed (r = 0.987). The signal intensity was still stable within 2 h after incubation of the nanocomplexes in human plasma. The nanocomplexes gave much better image contrast effects and longer stagnation time than that of commercial contrast agent in rat liver. A dose of 0.04 mmol of gadolinium per kilogram of body weight was sufficient to increase the MRI imaging intensities in rat livers by five-fold compared with the commercial Gd–DTPA. PLA–PEG/Gd–DTPA nanocomplexes could be prepared easily with small particle sizes. The nanocomplexes had high plasma stability, better image contrast effect, and liver targeting property. These results indicated that the PLA–PEG/Gd–DTPA nanocomplexes might be potential as molecular targeted imaging contrast agent

Expression profiles of microRNAs in skeletal muscle of sheep by deep sequencing

Objective MicroRNAs are a class of endogenous small regulatory RNAs that regulate cell proliferation, differentiation and apoptosis. Recent studies on miRNAs are mainly focused on mice, human and pig. However, the studies on miRNAs in skeletal muscle of sheep are not comprehensive. Methods RNA-seq technology was used to perform genomic analysis of miRNAs in prenatal and postnatal skeletal muscle of sheep. Targeted genes were predicted using miRanda software and miRNA-mRNA interactions were verified by quantitative real-time polymerase chain reaction. To further investigate the function of miRNAs, candidate targeted genes were enriched for analysis using gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment. Results The results showed total of 1,086 known miRNAs and 40 new candidate miRNAs were detected in prenatal and postnatal skeletal muscle of sheep. In addition, 345 miRNAs (151 up-regulated, 94 down-regulated) were differentially expressed. Moreover, miRanda software was performed to predict targeted genes of miRNAs, resulting in a total of 2,833 predicted targets, especially miR-381 which targeted multiple muscle-related mRNAs. Furthermore, GO and KEGG pathway analysis confirmed that targeted genes of miRNAs were involved in development of skeletal muscles. Conclusion This study supplements the miRNA database of sheep, which provides valuable information for further study of the biological function of miRNAs in sheep skeletal muscle