An Artificial Immune System Algorithm with Social Learning and Its Application in Industrial PID Controller Design

A novel artificial immune system algorithm with social learning mechanisms (AIS-SL) is proposed in this paper. In AIS-SL, candidate antibodies are marked with an elitist swarm (ES) or a common swarm (CS). Correspondingly, these antibodies are named ES antibodies or CS antibodies. In the mutation operator, ES antibodies experience self-learning, while CS antibodies execute two different social learning mechanisms, that is, stochastic social learning (SSL) and heuristic social learning (HSL), to accelerate the convergence process. Moreover, a dynamic searching radius update strategy is designed to improve the solution accuracy. In the numerical simulations, five benchmark functions and a practical industrial application of proportional-integral-differential (PID) controller tuning is selected to evaluate the performance of the proposed AIS-SL. The simulation results indicate that AIS-SL has better solution accuracy and convergence speed than the canonical opt-aiNet, IA-AIS, and AAIS-2S

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

Voice Therapy For Transgender People

Transrodne osobe su osobe koje osjećaju nesklad između roda s kojim se poistovjećuju i spola u kojem su se rodili. Transrodnost je širok pojam i obuhvaća čitav spektar rodno nenormativnih identiteta. Dva glavna obrasca tranzicije prema drugom spolu, odnosno rodu su: Male-to-Female (MtF) i Female-to-Male (FtM). Transrodne osobe navode da ih njihov glas često “izdaje”, odnosno otkriva njihov biološki rod te predstavlja posljednju prepreku u potpuno uživljavanje u novu rodnu ulogu. Bez obzira na to, često nisu svjesni da se zbog glasa mogu obratiti logopedu. Cilj ovog preglednog rada je dati uvid u poremećaje glasa kod transrodnih osoba, odnosno opisati logopedsku procjenu te terapijske postupke, a sve u svrhu boljeg razumijevanja potreba ovih osoba.Transgender individuals are people who feel an incongruity between their self-identified gender and their birth gender. Transgenderism is a broad term and includes a spectrum of gender-nonconforming identities. The two main patterns of gender transition are Male-to-Female (MtF) and Female-to-Male (FtM).Transgender people often think their voice “betrays” them, i.e. it reveals their biological gender and represents the last obstacle to the individual’s full enjoyment of his/her new gender role. Regardless of this, many transgender individuals are not aware of the fact that they can go to speech and language pathologists (SLP) for therapy. The aim of this review article is to provide insight into the voice disorders of transgender people, i.e. to describe the assessment process and therapy protocols in hope of gaining a better understanding of the needs of this population

Molecular mechanism of activation of human musk receptors OR5AN1 and OR1A1 by (R)-muscone and diverse other musk-smelling compounds

We acknowledge support from NSF (CHE-1265679) and NIH (5R01DC014423 subaward) (EB), NIH (5R01 DC014423) (HM), the European Reasearch Council (ERC) and the Engineering Science Research Council (EPSRC) (DO'H), FAPESP and CNPq (RAC), the Chinese Scholarship Council (CSC) for studentship support (MY), National Science Foundation (31070972) (HZ), Science and Technology Commission of Shanghai Municipality (16ZR1418300) (HZ), the Shanghai Eastern Scholar Program (J50201) (HZ). VSB thanks NIH grant 1R01GM106121-01A1 and computational time from NERSC.Understanding olfaction at the molecular level is challenging due to the lack of crystallographic models of odorant receptors (ORs). To better understand the molecular mechanism of OR activation, we focused on chiral (R)-muscone and other musk smelling odorants due to their great importance and widespread use in perfumery and traditional medicine, as well as environmental concerns associated with bioaccumulation of musks with estrogenic/antiestrogenic properties.  We experimentally and computationally examined the activation of human receptors OR5AN1 and OR1A1, recently identified as specifically responding to musk compounds.  OR5AN1 responds at nanomolar concentrations to musk ketone and robustly to macrocyclic sulfoxides and fluorine-substituted macrocyclic ketones; OR1A1 responds only to nitromusks. Structural models of OR5AN1 and OR1A1 based on quantum mechanics/molecular mechanics (QM/MM) hybrid methods were validated through direct comparisons with activation profiles from site-directed mutagenesis experiments and analysis of binding energies for 35 musk-related odorants.  The experimentally found chiral selectivity of OR5AN1 to (R)- over (S)-muscone was also computationally confirmed for muscone and fluorinated (R)-muscone analogs.  Structural models show that OR5AN1, highly responsive to nitromusks over macrocyclic musks, stabilizes odorants by hydrogen bonding to Tyr260 of transmembrane a-helix 6 and hydrophobic interactions with surrounding aromatic residues Phe105, Phe194 and, Phe207.  The binding of OR1A1 to nitromusks is stabilized by hydrogen bonding to Tyr258 along with hydrophobic interactions with surrounding aromatic residues Tyr251 and Phe206.  Hydrophobic/nonpolar and hydrogen bonding interactions contribute, respectively, 77% and 13% to the odorant binding affinities, as shown by an atom-based quantitative structure-activity relationship model.PostprintPeer reviewe