Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model

The outbreak of novel coronavirus pneumonia (COVID-19) caused thousands of deaths worldwide, and the number of total infections is still rising. However, the development of effective vaccine for this novel virus would take a few months. Thus it is urgent to identify some potentially effective old drugs that can be used immediately. Fortunately, some compounds that can inhibit coronavirus in vitro have been reported. In this study, the coronavirus-specific dataset was used to fine-tune our pre-trained multi-task deep model. Next we used the re-trained model to select available commercial drugs against targeted proteins of SARS-CoV-2. The results show that abacavir, a powerful nucleoside analog reverse transcriptase inhibitor used to treat HIV, is predicted to have high binding affinity with several proteins of SARS-CoV-2. Almitrine mesylate and roflumilast which are used for respiratory diseases such as chronic obstructive pulmonary disease are also predicted to have inhibitory effect. Overall, ten drugs are listed as potential inhibitors and the important sites for these binding by our model are exhibited. We hope these results would be useful in the fight against SARS-CoV-2

Computational and Data-driven Discovery of Novel Redox Materials for Ammonia Synthesis

With the rising awareness of climate change and sustainable energy, there is a pressing need to ameliorate the heavy greenhouse gas emissions and energy consumption in the ammonia industry. Chemical looping ammonia synthesis (CLAS) offers a flexible and feasible industrial solution, which drives the synthesis process from renewable resources. However, the performance and efficiency of such a novel method cannot compete with the traditional Haber-Bosch (HB) process, which impedes its further implementation in the industrial pipeline. While the traditional trial-and-error approach is costly and time-consuming, the data-centric material discovery could leverage the advances in first-principles calculations and data science to enable large-scale screening. In this thesis, we first study the reaction Gibbs free energies of 1699 bicationic inorganic redox pairs to investigate their potential as efficient redox materials in four different CLAS schemes. A machine learning strategy is further deployed to significantly widen the chemical space for discovering promising redox materials from more than half a million candidates. Furthermore, we utilise the strategy of random sampling to investigate the impact of the new chemical bonding states on the thermal stabilities of the redox materials and the reaction thermodynamics in CLAS. 2283 polymorphs generated from Co3W3N/CoWO4 are studied using first-principles calculations. Cr has been determined as a promising A-site mixing element, which has the tendency of forming the quaternary redox pair and enhancing ammonia formation. Mo is considered the most suitable B-site element with the capability of encouraging the reduction of the oxide, which is often the rate-determining step in CLAS practices. Lastly, we leverage the chemical reaction networks (CRNs) to investigate the reaction pathways of the state-of-the-art binary system facilitated by MnO/Mn2N. For the first time, it reveals the underlying mechanisms of the enhanced ammonia yield experimentally observed in the NaOH-assisted hydrolysis of Mn2N. Metal amides have been predicted as the key intermediates in the reaction pathways, which could significantly lower the energy barriers to promote ammonia generation. We have also expanded the chemical space to 329 binary oxide/nitride redox pairs using this pathway-searching strategy. Key insights have been extracted regarding how the reaction energetics are correlated with the costs in the construction of CRNs

Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications

The complexity of learning problems, such as Generative Adversarial Network (GAN) and its variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world vision applications, demands a deeper understanding of their underlying coupling mechanisms. Existing approaches often address these problems in isolation, lacking a unified perspective that can reveal commonalities and enable effective solutions. Therefore, in this work, we proposed a new framework, named Learning with Constraint Learning (LwCL), that can holistically examine challenges and provide a unified methodology to tackle all the above-mentioned complex learning and vision problems. Specifically, LwCL is designed as a general hierarchical optimization model that captures the essence of these diverse learning and vision problems. Furthermore, we develop a gradient-response based fast solution strategy to overcome optimization challenges of the LwCL framework. Our proposed framework efficiently addresses a wide range of applications in learning and vision, encompassing three categories and nine different problem types. Extensive experiments on synthetic tasks and real-world applications verify the effectiveness of our approach. The LwCL framework offers a comprehensive solution for tackling complex machine learning and computer vision problems, bridging the gap between theory and practice

Learn from the Past: A Proxy based Adversarial Defense Framework to Boost Robustness

In light of the vulnerability of deep learning models to adversarial samples and the ensuing security issues, a range of methods, including Adversarial Training (AT) as a prominent representative, aimed at enhancing model robustness against various adversarial attacks, have seen rapid development. However, existing methods essentially assist the current state of target model to defend against parameter-oriented adversarial attacks with explicit or implicit computation burdens, which also suffers from unstable convergence behavior due to inconsistency of optimization trajectories. Diverging from previous work, this paper reconsiders the update rule of target model and corresponding deficiency to defend based on its current state. By introducing the historical state of the target model as a proxy, which is endowed with much prior information for defense, we formulate a two-stage update rule, resulting in a general adversarial defense framework, which we refer to as `LAST' ({\bf L}earn from the P{\bf ast}). Besides, we devise a Self Distillation (SD) based defense objective to constrain the update process of the proxy model without the introduction of larger teacher models. Experimentally, we demonstrate consistent and significant performance enhancements by refining a series of single-step and multi-step AT methods (e.g., up to $\bf 9.2\%$ and $\bf 20.5\%$ improvement of Robust Accuracy (RA) on CIFAR10 and CIFAR100 datasets, respectively) across various datasets, backbones and attack modalities, and validate its ability to enhance training stability and ameliorate catastrophic overfitting issues meanwhile.Comment: 16 Page

Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments

Super-resolution tasks oriented to images captured in ultra-dark environments is a practical yet challenging problem that has received little attention. Due to uneven illumination and low signal-to-noise ratio in dark environments, a multitude of problems such as lack of detail and color distortion may be magnified in the super-resolution process compared to normal-lighting environments. Consequently, conventional low-light enhancement or super-resolution methods, whether applied individually or in a cascaded manner for such problem, often encounter limitations in recovering luminance, color fidelity, and intricate details. To conquer these issues, this paper proposes a specialized dual-modulated learning framework that, for the first time, attempts to deeply dissect the nature of the low-light super-resolution task. Leveraging natural image color characteristics, we introduce a self-regularized luminance constraint as a prior for addressing uneven lighting. Expanding on this, we develop Illuminance-Semantic Dual Modulation (ISDM) components to enhance feature-level preservation of illumination and color details. Besides, instead of deploying naive up-sampling strategies, we design the Resolution-Sensitive Merging Up-sampler (RSMU) module that brings together different sampling modalities as substrates, effectively mitigating the presence of artifacts and halos. Comprehensive experiments showcases the applicability and generalizability of our approach to diverse and challenging ultra-low-light conditions, outperforming state-of-the-art methods with a notable improvement (i.e., $\uparrow$5\% in PSNR, and $\uparrow$43\% in LPIPS). Especially noteworthy is the 19-fold increase in the RMSE score, underscoring our method's exceptional generalization across different darkness levels. The code will be available online upon publication of the paper.Comment: 9 page

Sub-THz Ray Tracing Simulation and Experimental Validation for Indoor Scenarios

Sub-terahertz (THz) communication is envisioned as one of the key components for 6G because of the abundantly available spectrum resource. Accurate and efficient channel models are prerequisites for developing sub-THz communicationsystems. Due to the sparsity and more ray optics propagation characteristics of the sub-THz channel, deterministic Ray-Tracing (RT) has attracted much attention for sub-THz channel modeling, which shows the potential of reducing the simulation complexity yet maintaining the accuracy. This paper presents an implementation of RT for sub-THz channel modeling and demonstrates its performance based on sub-THz channel measurements. A virtual massive multiple-inputmultiple-output (MIMO) channel operating at 100 GHz anda double-directional 300 GHz channel are considered in the RT implementation, where the RT achieves a high similarity compared to the channel measurements in terms of channel impulse response and power angular spectrum. Besides, thenear-field and spatial non-stationary properties of the sub-THz massive MIMO channel and the dominant multipaths of the 300 GHz channel are accurately reconstructed in the RT simulation. This work can provide insights into deterministic sub-THz channel modeling research from the implementation,evaluation, and challenges perspectives