Multi-Modality Deep Network for Extreme Learned Image Compression

Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur and severe semantics loss at extremely low bitrates. To address this issue, we propose a multimodal machine learning method for text-guided image compression, in which the semantic information of text is used as prior information to guide image compression for better compression performance. We fully study the role of text description in different components of the codec, and demonstrate its effectiveness. In addition, we adopt the image-text attention module and image-request complement module to better fuse image and text features, and propose an improved multimodal semantic-consistent loss to produce semantically complete reconstructions. Extensive experiments, including a user study, prove that our method can obtain visually pleasing results at extremely low bitrates, and achieves a comparable or even better performance than state-of-the-art methods, even though these methods are at 2x to 4x bitrates of ours.Comment: 13 pages, 14 figures, accepted by AAAI 202

An Olfactory Cilia Pattern in the Mammalian Nose Ensures High Sensitivity to Odors

SummaryIn many sensory organs, specialized receptors are strategically arranged to enhance detection sensitivity and acuity. It is unclear whether the olfactory system utilizes a similar organizational scheme to facilitate odor detection. Curiously, olfactory sensory neurons (OSNs) in the mouse nose are differentially stimulated depending on the cell location. We therefore asked whether OSNs in different locations evolve unique structural and/or functional features to optimize odor detection and discrimination. Using immunohistochemistry, computational fluid dynamics modeling, and patch clamp recording, we discovered that OSNs situated in highly stimulated regions have much longer cilia and are more sensitive to odorants than those in weakly stimulated regions. Surprisingly, reduction in neuronal excitability or ablation of the olfactory G protein in OSNs does not alter the cilia length pattern, indicating that neither spontaneous nor odor-evoked activity is required for its establishment. Furthermore, the pattern is evident at birth, maintained into adulthood, and restored following pharmacologically induced degeneration of the olfactory epithelium, suggesting that it is intrinsically programmed. Intriguingly, type III adenylyl cyclase (ACIII), a key protein in olfactory signal transduction and ubiquitous marker for primary cilia, exhibits location-dependent gene expression levels, and genetic ablation of ACIII dramatically alters the cilia pattern. These findings reveal an intrinsically programmed configuration in the nose to ensure high sensitivity to odors

Insight Derived from Molecular Docking and Molecular Dynamics Simulations into the Binding Interactions Between HIV-1 Protease Inhibitors and SARS-CoV-2 3CLpro

A novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) was identified from respiratory illness patients in Wuhan, Hubei Province, China, which has recently emerged as a serious threat to the world public health. Hower, no approved drugs have been found to effectively inhibit the virus. Since it has been reported that the HIV-1 protease inhibitors can be used as anti-SARS drugs by tegarting SARS-CoV 3CLpro, we choose six approved anti-HIV-1 drugs to investigate their binding interactions between 3CLpro, and to evaluate their potential to become clinical drugs for the new coronavirus pneumonia (COVID19) caused by SARS-CoV-2 infection. The molecular docking results indicate that, the 3CLpro of SARS-CoV-2 has a higher binding affinity for all the studied inhibitors than its SARS homologue. Two docking complexes (indinavir and darunavir) with high docking scores were futher subjected to MM-PBSA binding free energy calculations to detail the molecular interactions between these two proteinase inhibitors and the 3CLpro. Our results show that darunavir has the best binding affinity with SARS-CoV-2 and SARS-CoV 3CLpro among all inhibitors, indicating it has the potential to become an anti-COVID-19 clinical drug. The likely reason behind the increased binding affinity of HIV-1 protease inhibitors toward SARS-CoV2 3CLpro than that of SARS-CoV were investigated by MD simulations. Our study provides insight into the possible role of structural flexibility during interactions between 3CLpro and inhibitors, and sheds light on the structure-based design of anti-COVID-19 drugs targeting the SARS-CoV-2 3CLpro. </div

Multi-Modality Deep Network for Extreme Learned Image Compression

Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur and severe semantics loss at extremely low bitrates. To address this issue, we propose a multimodal machine learning method for text-guided image compression, in which the semantic information of text is used as prior information to guide image compression for better compression performance. We fully study the role of text description in different components of the codec, and demonstrate its effectiveness. In addition, we adopt the image-text attention module and image-request complement module to better fuse image and text features, and propose an improved multimodal semantic-consistent loss to produce semantically complete reconstructions. Extensive experiments, including a user study, prove that our method can obtain visually pleasing results at extremely low bitrates, and achieves a comparable or even better performance than state-of-the-art methods, even though these methods are at 2x to 4x bitrates of ours

A Finite Element Variational Multiscale Method Based on Two Local Gauss Integrations for Stationary Conduction-Convection Problems

A new finite element variational multiscale (VMS) method based on two local Gauss integrations is proposed and analyzed for the stationary conduction-convection problems. The valuable feature of our method is that the action of stabilization operators can be performed locally at the element level with minimal additional cost. The theory analysis shows that our method is stable and has a good precision. Finally, the numerical test agrees completely with the theoretical expectations and the “ exact solution,” which show that our method is highly efficient for the stationary conduction-convection problems

Evaluation of Physical Characteristics of Typical Maize Seeds in a Cold Area of North China Based on Principal Component Analysis

The physical properties of maize seeds are closely related to food processing and production. To study and evaluate the characteristics of maize seeds, typical maize seeds in a cold region of North China were used as test varieties. A variety of agricultural material test benches were built to measure the maize seeds’ physical parameters, such as thousand-grain weight, moisture content, triaxial arithmetic mean particle size, coefficient of static friction, coefficient of rolling friction, angle of natural repose, coefficient of restitution, and stiffness coefficient. Principal component and cluster comprehensive analyses were used to simplify the characteristic parameter index used to judge the comprehensive score of maize seeds. The results showed that there were significant differences in the main physical characteristics parameters of the typical maize varieties in this cold area, and there were different degrees of correlation among the physical characteristics. Principal component analysis was used to extract the first three principal component factors, whose cumulative contribution rate was over 80%, representing most of the information of the original eight physical characteristic parameters, and had good representativeness and objectivity. According to the test results, the classification standard of the evaluation of the physical characteristics of 15 kinds of maize seeds were determined, and appropriate evaluations were conducted. The 15 kinds of maize seeds were clustered into four groups by cluster analysis, and the physical characteristics of each groups were different. This study provides a new idea for the evaluation and analysis of the physical properties of agricultural materials, and provides a new method for the screening and classification of food processing raw materials

Design and Properties Prediction of <i>AM</i>CO₃F by First-Principles Calculations

Computer simulation accelerates the rate of identification and application of new materials. To search for new materials to meet the increasing demands of secondary batteries with higher energy density, the properties of some transition-metal fluorocarbonates ([CO₃F]^3–) were simulated in this work as cathode materials for Li- and Na-ion batteries based on first-principles calculations. These materials were designed by substituting the K⁺ ions in KCuCO₃F with Li⁺ or Na⁺ ions and the Cu²⁺ ions with transition-metal ions such as Fe²⁺, Co²⁺, Ni²⁺, and Mn²⁺ ions, respectively. The phase stability, electronic conductivity, ionic diffusion, and electrochemical potential of these materials were calculated by first-principles calculations. After taking comprehensive consideration of the kinetic and thermodynamic properties, LiCoCO₃F and LiFeCO₃F are believed to be promising novel cathode materials in all of the calculated <i>AM</i>CO₃F (<i>A</i> = Li and Na; <i>M</i> = Fe, Mn, Co, and Ni). These results will help the design and discovery of new materials for secondary batteries