Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks

This paper introduces a novel deep neural network architecture for solving the inverse scattering problem in frequency domain with wide-band data, by directly approximating the inverse map, thus avoiding the expensive optimization loop of classical methods. The architecture is motivated by the filtered back-projection formula in the full aperture regime and with homogeneous background, and it leverages the underlying equivariance of the problem and compressibility of the integral operator. This drastically reduces the number of training parameters, and therefore the computational and sample complexity of the method. In particular, we obtain an architecture whose number of parameters scale sub-linearly with respect to the dimension of the inputs, while its inference complexity scales super-linearly but with very small constants. We provide several numerical tests that show that the current approach results in better reconstruction than optimization-based techniques such as full-waveform inversion, but at a fraction of the cost while being competitive with state-of-the-art machine learning methods.Comment: 21 pages, 9 figures, and 4 table

Positive l

This paper studies the problem of positive l1 state-bounding observer design for a class of positive Markovian jump systems with interval parameter uncertainties by a linear programming approach. For the first, necessary and sufficient conditions are obtained for stochastic stability and l1 performance of positive Markovian jump systems by an “equivalent” deterministic positive linear system. Furthermore, based on the results obtained in this paper, sufficient conditions for the existence of the positive l1 state-bounding observer are derived. The conditions can be solved in terms of linear programming. Finally, a numerical example is used to illustrate the effectiveness of the results obtained

Safe DreamerV3: Safe Reinforcement Learning with World Models

The widespread application of Reinforcement Learning (RL) in real-world situations is yet to come to fruition, largely as a result of its failure to satisfy the essential safety demands of such systems. Existing safe reinforcement learning (SafeRL) methods, employing cost functions to enhance safety, fail to achieve zero-cost in complex scenarios, including vision-only tasks, even with comprehensive data sampling and training. To address this, we introduce Safe DreamerV3, a novel algorithm that integrates both Lagrangian-based and planning-based methods within a world model. Our methodology represents a significant advancement in SafeRL as the first algorithm to achieve nearly zero-cost in both low-dimensional and vision-only tasks within the Safety-Gymnasium benchmark. Our project website can be found in: https://sites.google.com/view/safedreamerv3

Intraspecific divergences and phylogeography of Panzerina lanata (Lamiaceae) in northwest China

Climatic fluctuations during the Quaternary significantly affect many species in their intraspecific divergence and population structure across northwest China. In order to investigate the impact of climate change on herbaceous plants, we studied Panzerina lanata (Lamiaceae), a widely distributed species. Sequences of two chloroplast DNA (cpDNA) intergenic spacers (trnH-psbA and rpoB-trnC) and a nuclear ribosomal region (nrDNA, ITS) were generated from 27 populations of Panzerina lanata and resulted in the identification of seven chloroplast haplotypes and thirty-two nuclear haplotypes. We applied AMOVA, neutrality test and mismatch distribution analysis to estimate genetic differentiation and demographic characteristics. The divergence times of the seven cpDNA haplotypes were estimated using BEAST. Our results revealed high levels of genetic diversity (cpDNA: Hcp = 0.6691, HT = 0.673; nrDNA: Hnr = 0.5668, HT = 0.577). High level of genetic differentiation (GST = 0.950) among populations was observed in the cpDNA sequences, while the genetic differentiation values (GST = 0.348) were low in nuclear sequences. AMOVA results revealed major genetic variation among the three groups: northern, central, and eastern group. However, the genetic differentiation in ITS data was not found. The species distribution modeling and demographic analysis indicated that P. lanata had not experienced recent range expansion. The occurrence of divergence between seven cpDNA haplotypes, probably during Pleistocene, coincides with aridification and expansion of the desert across northwest China that resulted in species diversification and habitat fragmentation. In addition, we discovered that the deserts and the Helan Mountains acted as effective geographic barriers that promoting the intraspecific diversity of P. lanata

A Model-Data-Hybrid-Driven Diagnosis Method for Open-Switch Faults in Power Converters

To combine the advantages of both model-driven and data-driven methods, this paper proposes a model-data-hybrid-driven (MDHD) method to diagnose open-switch faults in power converters. This idea is based on the explicit analytical model of converters and the learning capability of artificial neural network (ANN). The process of the method is divided into two parts: offline model analysis and learning, and online fault diagnosis. For both parts, model-driven and data-driven are combined. With the model information and data-based learning capability, a fast diagnosis for various operating conditions can be achieved without high computation burden, tricky threshold selection and complex rulemaking. This can greatly contribute to the practical application. The open-switch fault diagnosis in a two-level three-phase converter is studied for method validation. For this converter, an ANN is trained with two input elements, seven output elements, and two neurons in the hidden layer. Experimental results are given to demonstrate good performance

Ligand and structure-based approaches for the exploration of structure–activity relationships of fusidic acid derivatives as antibacterial agents

Introduction: Fusidic acid (FA) has been widely applied in the clinical prevention and treatment of bacterial infections. Nonetheless, its clinical application has been limited due to its narrow antimicrobial spectrum and some side effects.Purpose: Therefore, it is necessary to explore the structure–activity relationships of FA derivatives as antibacterial agents to develop novel ones possessing a broad antimicrobial spectrum.Methods and result: First, a pharmacophore model was established on the nineteen FA derivatives with remarkable antibacterial activities reported in previous studies. The common structural characteristics of the pharmacophore emerging from the FA derivatives were determined as those of six hydrophobic centers, two atom centers of the hydrogen bond acceptor, and a negative electron center around the C-21 field. Then, seven FA derivatives have been designed according to the reported structure–activity relationships and the pharmacophore characteristics. The designed FA derivatives were mapped on the pharmacophore model, and the Qfit values of all FA derivatives were over 50 and FA-8 possessed the highest value of 82.66. The molecular docking studies of the partial target compounds were conducted with the elongation factor G (EF-G) of S. aureus. Furthermore, the designed FA derivatives have been prepared and their antibacterial activities were evaluated by the inhibition zone test and the minimum inhibitory concentration (MIC) test. The derivative FA-7 with a chlorine group as the substituent group at C-25 of FA displayed the best antibacterial property with an MIC of 3.125 µM. Subsequently, 3D-QSAR was carried on all the derivatives by using the CoMSIA mode of SYBYL-X 2.0.Conclusion: Hence, a computer-aided drug design model was developed for FA, which can be further used to optimize FA derivatives as highly potent antibacterial agents

Synthesis and Biological Evaluation of Novel Fusidic Acid Derivatives as Two-in-One Agent with Potent Antibacterial and Anti-Inflammatory Activity

Fusidic acid (FA), a narrow-spectrum antibiotics, is highly sensitive to various Gram-positive cocci associated with skin infections. It has outstanding antibacterial effects against certain Gram-positive bacteria whilst no cross-resistance with other antibiotics. Two series of FA derivatives were synthesized and their antibacterial activities were tested. A new aromatic side-chain analog, FA-15 exhibited good antibacterial activity with MIC values in the range of 0.781-1.563 µM against three strains of Staphylococcus spp. Furthermore, through the assessment by the kinetic assay, similar characteristics of bacteriostasis by FA and its aromatic derivatives were observed. In addition, anti-inflammatory activities of FA and its aromatic derivatives were evaluated by using a 12-O-tetradecanoylphorbol-13-acetate (TPA) induced mouse ear edema model. The results also indicated that FA and its aromatic derivatives effectively reduced TPA-induced ear edema in a dose-dependent manner. Following, multiform computerized simulation, including homology modeling, molecular docking, molecular dynamic simulation and QSAR was conducted to clarify the mechanism and regularity of activities. Overall, the present work gave vital clues about structural modifications and has profound significance in deeply scouting for bioactive potentials of FA and its derivatives

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan