Quantized generalized minimum error entropy for kernel recursive least squares adaptive filtering

The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic learning criteria, such as minimum error entropy (MEE) and generalized MEE (GMEE), which also improves the computational complexity of the KRLS-type algorithms to a certain extent. To reduce the computational load of the KRLS-type algorithms, the quantized GMEE (QGMEE) criterion, in this paper, is combined with the KRLS algorithm, and as a result two kinds of KRLS-type algorithms, called quantized kernel recursive MEE (QKRMEE) and quantized kernel recursive GMEE (QKRGMEE), are designed. As well, the mean error behavior, mean square error behavior, and computational complexity of the proposed algorithms are investigated. In addition, simulation and real experimental data are utilized to verify the feasibility of the proposed algorithms

Towards Making the Most of ChatGPT for Machine Translation

ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g, low-resource and distant-language-pairs translation. However, they usually adopt simple prompts which can not fully elicit the capability of ChatGPT. In this report, we aim to further mine ChatGPT's translation ability by revisiting several aspects: temperature, task information, and domain information, and correspondingly propose two (simple but effective) prompts: Task-Specific Prompts (TSP) and Domain-Specific Prompts (DSP). We show that: 1) The performance of ChatGPT depends largely on temperature, and a lower temperature usually can achieve better performance; 2) Emphasizing the task information further improves ChatGPT's performance, particularly in complex MT tasks; 3) Introducing domain information can elicit ChatGPT's generalization ability and improve its performance in the specific domain; 4) ChatGPT tends to generate hallucinations for non-English-centric MT tasks, which can be partially addressed by our proposed prompts but still need to be highlighted for the MT/NLP community. We also explore the effects of advanced in-context learning strategies and find a (negative but interesting) observation: the powerful chain-of-thought prompt leads to word-by-word translation behavior, thus bringing significant translation degradation.Comment: Work in progress, 9 page

A predator-prey interaction between a marine Pseudoalteromonas sp. and Gram-positive bacteria

Predator-prey interactions play important roles in the cycling of marine organic matter. Here we show that a Gram-negative bacterium isolated from marine sediments (Pseudoalteromonas sp. strain CF6-2) can kill Gram-positive bacteria of diverse peptidoglycan (PG) chemotypes by secreting the metalloprotease pseudoalterin. Secretion of the enzyme requires a Type II secretion system. Pseudoalterin binds to the glycan strands of Gram positive bacterial PG and degrades the PG peptide chains, leading to cell death. The released nutrients, including PG-derived D-amino acids, can then be utilized by strain CF6-2 for growth. Pseudoalterin synthesis is induced by PG degradation products such as glycine and glycine-rich oligopeptides. Genes encoding putative pseudoalterin-like proteins are found in many other marine bacteria. This study reveals a new microbial interaction in the ocean

Intramolecular π Stacking in Cationic Iridium(III) Complexes with Phenyl-Functionalized Cyclometalated Ligands: Synthesis, Structure, Photophysical Properties, and Theoretical Studies

The syntheses of two new heteroleptic cationic iridium complexes containing 2,6-diphenylpyridine (Hdppy) and 2,4,6-triphenylpyridine (Htppy) as the cyclometalated ligands, namely, [Ir(dppy)2phen]PF6 (1, phen = 1,10-phenanthroline) and [Ir(tppy)2phen]PF6 (2), are described. The X-ray crystal structure of 2 reveals a distorted octahedral geometry around the Ir center and close intramolecular face-to-face π–π stacking interactions between the pendant phenyl rings at the 2-position of the cyclometalated ligands and the NN ancillary ligand. This represents a new π–π stacking mode in charged Ir complexes. Complexes 1 and 2 are green photoemitters: their photophysical and electrochemical properties are interpreted with the assistance of density functional theory (DFT) calculations. These calculations also establish that the observed intramolecular interactions cannot effectively prevent the lengthening of the Ir–N bonds of the complexes in their metal-centered (3MC) states. Complexes 1 and 2 do not emit light in light-emitting electrochemical cells (LECs) under conditions in which the model compound [Ir(ppy)2phen]PF6 (3) emits strongly. This is explained by degradation reactions of the 3MC state of 1 and 2 under the applied bias during LEC operation facilitated by the enhanced distortions in the geometry of the complexes. These observations have important implications for the future design of complexes for LEC applications

3′,4′-Dimeth­oxy­biphenyl-4-carbonitrile

The title compound, C15H13NO2, was prepared through a palladium-catalysed Suzuki–Miyaura coupling reaction. The dihedral angle between the biphenyl rings is 40.96 (6)°. The meth­oxy groups are twisted slightly out of the plane of the benzene ring [C—C—C—C torsion angles = −3.61 (18) and 12.6 (2)°]. The packing of the molecules is stabilized by van der Waals inter­actions