Synthesis of pyrroles by nickel-catalysed arylative cyclisations of alkynamides

A nickel-based catalytic system for the anti-carbometallative cyclisations of alkynamides to arylboronic acids is described. The reactions proceed using catalytic nickel and (rac)-Ph-PHOX, to provide alkenylnickel species which can undergo reversible E/Z-isomerisation, followed by cyclisation onto an N-tosylamide to give 2,3,4-trisubstituted pyrroles. Pyrroles are of widespread chemical significance, being present in numerous biologically active natural products. This methodology was used to generate a series of multisubstituted pyrroles and perform concise syntheses of BODIPY derivative and pyrrolyl propionic acid

Disentangling Boosted Higgs Boson Production Modes with Machine Learning

Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse momentum ($p_T$) are sensitive probes of physics beyond the Standard Model. However, high $p_T$ Higgs Boson production is contaminated by a diversity of production modes other than ggF: vector boson fusion, production of a Higgs boson in association with a vector boson, and production of a Higgs boson with a top-quark pair. Combining jet substructure and event information with modern machine learning, we demonstrate the ability to focus on particular production modes. These tools hold great discovery potential for boosted Higgs bosons produced via ggF and may also provide additional information about the Higgs Boson sector of the Standard Model in extreme phase space regions for other production modes as well.Comment: 17 pages, 9 figure

Exploring the Universality of Hadronic Jet Classification

The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applied to simulated Lorentz boosted Higgs jet tagging at the LHC.Comment: 25 pages, 7 figures, 7 table

The squeeze film effect on micro-electromechanical resonators

The air squeeze film damping effect on the dynamic responses of clamped micro- electromechanical resonators is investigated in this study. A dynamic model for a clamped micro-electromechanical resonator with the damping consideration is derived using Lagrange’s equation. The corresponding resonator eigen solutions are formulated and solved by employing the assumed-mode method. The effect of different parameters; i.e. the resonator size, ambient temperature and pressure on the squeeze film damping characteristics were simulated and investigated. The results indicate that the squeeze film damping effect may significantly affect the dynamic responses of micro-scale electromechanical resonator

Toward Joint Language Modeling for Speech Units and Text

Speech and text are two major forms of human language. The research community has been focusing on mapping speech to text or vice versa for many years. However, in the field of language modeling, very little effort has been made to model them jointly. In light of this, we explore joint language modeling for speech units and text. Specifically, we compare different speech tokenizers to transform continuous speech signals into discrete units and use different methods to construct mixed speech-text data. We introduce automatic metrics to evaluate how well the joint LM mixes speech and text. We also fine-tune the LM on downstream spoken language understanding (SLU) tasks with different modalities (speech or text) and test its performance to assess the model's learning of shared representations. Our results show that by mixing speech units and text with our proposed mixing techniques, the joint LM improves over a speech-only baseline on SLU tasks and shows zero-shot cross-modal transferability.Comment: EMNLP findings 202

Does Long-Term Use of Silver Nanoparticles Have Persistent Inhibitory Effect on H. pylori Based on Mongolian Gerbil’s Model?

It is urgent to find alternative agents due to increasing failure rate of Helicobacter pylori (H. pylori) eradication. The study surveyed the long-term effect of silver nanoparticles (AgNP) on H. pylori based on Mongolian gerbil's model