Direction-of-Arrival Estimation Based on Joint Sparsity

We present a DOA estimation algorithm, called Joint-Sparse DOA to address the problem of Direction-of-Arrival (DOA) estimation using sensor arrays. Firstly, DOA estimation is cast as the joint-sparse recovery problem. Then, norm is approximated by an arctan function to represent joint sparsity and DOA estimation can be obtained by minimizing the approximate norm. Finally, the minimization problem is solved by a quasi-Newton method to estimate DOA. Simulation results show that our algorithm has some advantages over most existing methods: it needs a small number of snapshots to estimate DOA, while the number of sources need not be known a priori. Besides, it improves the resolution, and it can also handle the coherent sources well

From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

In the realm of Large Language Models, the balance between instruction data quality and quantity has become a focal point. Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from vast open-source datasets, effectively minimizing manual curation and potential cost for instruction tuning an LLM. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal tool to identify discrepancies between a model's expected responses and its autonomous generation prowess. Through the adept application of IFD, cherry samples are pinpointed, leading to a marked uptick in model training efficiency. Empirical validations on renowned datasets like Alpaca and WizardLM underpin our findings; with a mere 10% of conventional data input, our strategy showcases improved results. This synthesis of self-guided cherry-picking and the IFD metric signifies a transformative leap in the optimization of LLMs, promising both efficiency and resource-conscious advancements. Codes, data, and models are available: https://github.com/MingLiiii/Cherry_LL

A linear cellular automation technique for predicting dynamic failure mode of a single-layer shell

This paper presents a linear cellular automation (LCA) method for predicting the dynamic failure (DF) mode of both single-layer latticed shell and single-layer cylindrical latticed shell subjected to ground motions. The LCA model of the shell obtains the state values of cells/nodes including the nodal displacements state value and the nodal domain logarithmic strain energy density (NDLSED) state value through its finite element analysis (FEA). Meanwhile, the concepts of nodal domain and nodal domain similarity are derived based on the qualitative analysis of shells. Then, similar nodal domains between two shells are matched through the proposed criterion. Finally, the DF mode of an object shell is mapped using the criterion for projecting the formative values of a base shell to similar nodal domains in the object shell. Case studies show that the LCA method could be used for predicting the DF mode of an object shell. Consequently, the LCA method would explore an LCA application in analyzing shells, which costs much less time than the FEA method for calculating the DF shell mode

An H5N1 M2e-based multiple antigenic peptide vaccine confers heterosubtypic protection from lethal infection with pandemic 2009 H1N1 virus

Background. A 2009 global influenza pandemic caused by a novel swine-origin H1N1 influenza A virus has posted an increasing threat of a potential pandemic by the highly pathogenic avian influenza (HPAI) H5N1 virus, driving us to develop an influenza vaccine which confers cross-protection against both H5N1 and H1N1 viruses. Previously, we have shown that a tetra-branched multiple antigenic peptide (MAP) vaccine based on the extracellular domain of M2 protein (M2e) from H5N1 virus (H5N1-M2e-MAP) induced strong immune responses and cross-protection against different clades of HPAI H5N1 viruses. In this report, we investigated whether such M2e-MAP presenting the H5N1-M2e consensus sequence can afford heterosubtypic protection from lethal challenge with the pandemic 2009 H1N1 virus. Results. Our results demonstrated that H5N1-M2e-MAP plus Freund's or aluminum adjuvant induced strong cross-reactive IgG antibody responses against M2e of the pandemic H1N1 virus which contains one amino acid variation with M2e of H5N1 at position 13. These cross-reactive antibodies may maintain for 6 months and bounced back quickly to the previous high level after the 2nd boost administered 2 weeks before virus challenge. H5N1-M2e-MAP could afford heterosubtypic protection against lethal challenge with pandemic H1N1 virus, showing significant decrease of viral replications and obvious alleviation of histopathological damages in the challenged mouse lungs. 100% and 80% of the H5N1-M2e-MAP-vaccinated mice with Freund's and aluminum adjuvant, respectively, survived the lethal challenge with pandemic H1N1 virus. Conclusions. Our results suggest that H5N1-M2e-MAP has a great potential to prevent the threat from re-emergence of pandemic H1N1 influenza and possible novel influenza pandemic due to the reassortment of HPAI H5N1 virus with the 2009 swine-origin H1N1 influenza virus. © 2010 Zhao et al; licensee BioMed Central Ltd.published_or_final_versio

Pressure-induced superconductivity in kagome single crystal Pd3P2S8

Kagome lattice offers unique opportunities for the exploration of unusual quantum states of correlated electrons. Here, we report on the observation of superconductivity in a kagome single crystal Pd3P2S8 when a semiconducting to metallic transition is driven by pressure. High-pressure resistance measurements show that the metallization and superconductivity are simultaneously observed at about 11 GPa. With increasing pressure, the superconducting critical temperature Tc is monotonously enhanced from 2.6 K to a maximum 7.7 K at ~52 GPa. Interestingly, superconductivity retains when the pressure is fully released. Synchrotron XRD and Raman experiments consistently evidence that the emergence of superconductivity is accompanied with an amorphization and the retainability of superconductivity upon decompression can be attributed to the irreversibility of the amorphization

A multi-wavelength mid-IR laser based on BaGa4Se7 optical parametric oscillators

A multi-wavelength mid-IR laser consisting of 3.05 μm, 4.25 μm, and 5.47 μm BaGa4Se7(BGSe)optical parametric oscillators (OPOs) switched by DKDP electro-optic switches with one 10 Hz/7.6 ns pumping wave is demonstrated. Maximum energies at 3.05 μm, 4.25 μm, and 5.47 μm are 1.35 mJ, 1.03 mJ, and 0.56 mJ, respectively, corresponding to optical-to-optical conversion efficiencies of 9.4%, 7.6%, and 4.2%. To the best of our knowledge, this study is the first of generation of three mid-IR wavelength lasers using electro-optic switches. Furthermore, this study provides a viable solution for a high-energy or high-power, compact, or even portable multi-wavelength mid-IR laser device that employs a single pumping wave

Nanoscale probing of electron-regulated structural transitions in silk proteins by near-field IR imaging and nano-spectroscopy

Silk protein fibres produced by silkworms and spiders are renowned for their unparalleled mechanical strength and extensibility arising from their high-β-sheet crystal contents as natural materials. Investigation of β-sheet-oriented conformational transitions in silk proteins at the nanoscale remains a challenge using conventional imaging techniques given their limitations in chemical sensitivity or limited spatial resolution. Here, we report on electron-regulated nanoscale polymorphic transitions in silk proteins revealed by near-field infrared imaging and nano-spectroscopy at resolutions approaching the molecular level. The ability to locally probe nanoscale protein structural transitions combined with nanometre-precision electron-beam lithography offers us the capability to finely control the structure of silk proteins in two and three dimensions. Our work paves the way for unlocking essential nanoscopic protein structures and critical conditions for electron-induced conformational transitions, offering new rules to design protein-based nanoarchitectures.National Science Foundation (U.S.) (1563422)National Science Foundation (U.S.) (1562915

Characterization and Evolution of microRNA Genes Derived from Repetitive Elements and Duplication Events in Plants

MicroRNAs (miRNAs) are a major class of small non-coding RNAs that act as negative regulators at the post-transcriptional level in animals and plants. In this study, all known miRNAs in four plant species (Arabidopsis thaliana, Populus trichocarpa, Oryza sativa and Sorghum bicolor) have been analyzed, using a combination of computational and comparative genomic approaches, to systematically identify and characterize the miRNAs that were derived from repetitive elements and duplication events. The study provides a complete mapping, at the genome scale, of all the miRNAs found on repetitive elements in the four test plant species. Significant differences between repetitive element-related miRNAs and non-repeat-derived miRNAs were observed for many characteristics, including their location in protein-coding and intergenic regions in genomes, their conservation in plant species, sequence length of their hairpin precursors, base composition of their hairpin precursors and the minimum free energy of their hairpin structures. Further analysis showed that a considerable number of miRNA families in the four test plant species arose from either tandem duplication events, segmental duplication events or a combination of the two. However, comparative analysis suggested that the contribution made by these two duplication events differed greatly between the perennial tree species tested and the other three annual species. The expansion of miRNA families in A. thaliana, O. sativa and S. bicolor are more likely to occur as a result of tandem duplication events than from segmental duplications. In contrast, genomic segmental duplications contributed significantly more to the expansion of miRNA families in P. trichocarpa than did tandem duplication events. Taken together, this study has successfully characterized miRNAs derived from repetitive elements and duplication events at the genome scale and provides comprehensive knowledge and deeper insight into the origins and evolution of miRNAs in plants