catena-Poly[(chloridozinc)-μ-5-(1-methyl-1H-benzimidazol-2-yl-κN 3)-1,2,3-triazol-1-ido-κ2 N 1:N 3]

In the title complex, [Zn(C10H8N5)Cl]n, the ZnII ion is four-coordinated by one Cl atom and three N atoms from two in situ-generated deprotonated 5-(1-methyl-1H-benzimidazol-2-yl-κN 3)-1,2,3-triazol-1-ide ligands in a slightly distorted tetra­hedral geometry. The ZnII ions are bridged by the ligands, forming a helical chain along [001]. C—H⋯N and C—H⋯Cl hydrogen bonds and π–π inter­actions between the imidazole rings [centroid–centroid distance = 3.4244 (10) Å] assemble the chains into a three-dimensional supra­molecular network

1-(2,4-Dinitro­phen­yl)-3,3-dinitro­azetidine

In the title compound, C9H7N5O8, the dihedral angle between the mean plane of the azetidine ring and that of the benzene ring is 26.1 (1)°; the planes of the two nitro groups of the azetidine ring are aligned at 88.7 (1)°

Using γ\gamma-ray observations of dwarf spheroidal galaxies to test the possible common origin of the W-boson mass anomaly and the GeV γ\gamma-ray/antiproton excesses

A recent result from Fermilab suggests that the measured W-boson mass deviates from the prediction of the Standard Model (SM) with a significance of $>7\sigma$, and there may exist new physics beyond the SM. It is proposed that the inert two Higgs doublet model (i2HDM) can well explain the new W-boson mass. Meanwhile, the lightest neutral scalar $S$ in the i2HDM can be stable and play the role of dark matter with a preferred dark matter mass of $\sim 54-74$ GeV. It is also found that part of the parameter space of this model can explain both the Galactic center GeV gamma-ray excess detected by $Fermi$-LAT and the GeV antiproton excess detected by AMS-02 through a $SS\rightarrow WW^*$ annihilation. In this paper, we aim to test the possible common i2HDM origin of the three anomaly/excesses using the $Fermi$-LAT observations of Milky Way dwarf spheroidal (dSph) galaxies. We perform single and stacking analyses on 19 dSphs that have J-factor measurements. We find that our upper limits are below the favored parameters and seems to be able to exclude the possibility of a common origin of the three anomaly/excesses. However, because the J-factor measurements include relatively large uncertainties, which come from the measurements of stellar kinematics, whether this model could be reliably excluded needs to be further confirmed by future observations.Comment: 10 pages, 4 figures, updated to match the accepted versio

What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning

Visual instruction tuning is an essential approach to improving the zero-shot generalization capability of Multi-modal Large Language Models (MLLMs). A surge of visual instruction datasets with various focuses and characteristics have been proposed recently, enabling MLLMs to achieve surprising results on evaluation benchmarks. To develop more capable MLLMs, in this paper, we aim to investigate a more fundamental question: ``what makes for good visual instructions?''. By conducting a comprehensive empirical study, we find that instructions focused on complex visual reasoning tasks are particularly effective in improving the performance of MLLMs on evaluation benchmarks. Building upon this finding, we design a systematic approach to automatically creating high-quality complex visual reasoning instructions. Our approach employs a synthesis-complication-reformulation paradigm, leveraging multiple stages to gradually increase the complexity of the instructions while guaranteeing quality. Based on this approach, we create the synthetic visual reasoning instruction dataset consisting of 32K examples, namely ComVint, and fine-tune four MLLMs on it. Experimental results demonstrate that our dataset consistently enhances the performance of all the compared MLLMs, e.g., improving the performance of MiniGPT-4 and BLIP-2 on MME-Cognition by 32.6% and 28.8%, respectively. Our code and data are publicly available at the link: https://github.com/RUCAIBox/ComVint.Comment: Work in progres

Neonatal Oxytocin Treatment Ameliorates Autistic-Like Behaviors and Oxytocin Deficiency in Valproic Acid-Induced Rat Model of Autism

Autism spectrum disorder (ASD) is characterized by impaired social communication and repetitive/stereotyped behaviors. The neuropeptide oxytocin (OXT) plays a critical role in regulating social behaviors in the central nervous system, as indicated in both human and animal studies. We hypothesized that central OXT deficit is one of causes of etiology of ASD, which may be responsible for the social impairments. To test our hypothesis, central OXT system was examined in valproic acid (VPA)-induced rat model of autism (VPA rat). Our results showed that adolescent VPA rats exhibited a lower level of OXT mRNA and fewer OXT-ir cells in the hypothalamus than control rats. Additionally, OXT concentration in cerebrospinal fluid (CSF) was reduced. The number of OXT-ir cells in the supraoptic nucleus (SON) of neonatal VPA rats was also lower. Autistic-like behaviors were observed in these animals as well. We found that an acute intranasal administration of exogenous OXT restored the social preference of adolescent VPA rats. Additionally, early postnatal OXT treatment had long-term effects ameliorating the social impairments and repetitive behaviors of VPA rats until adolescence. This was accompanied by an increase in OXT-ir cells. Taken together, we demonstrated there was central OXT deficiency in the VPA-induced rat model of autism, and showed evidence that early postnatal OXT treatment had a long-term therapeutic effect on the autistic-like behaviors in VPA rats

1-Benzoyl-3,3-dinitro­azetidine

In the title gem-dinitro­azetidine derivative, C10H9N3O5, the azetidine ring is almost planar, the maximum value of the endocyclic torsion angle being 0.92 (14)°. The gem-dinitro groups are mutually perpendicular and the dihedral angle between the azetidine and benzene rings is 46.70 (10)

Towards artificial general intelligence via a multimodal foundation model

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI".Comment: Published by Nature Communications, see https://www.nature.com/articles/s41467-022-30761-

Novel effects of electromagnetic interaction on the correlation of nucleons in nuclear matter

The electromagnetic(EM) interactions between charged protons on the correlations of nucleons are discussed by introducing the Anderson-Higgs mechanism of broken U(1) EM symmetry into the relativistic nuclear theory with a parametric photon mass. The non-saturating Coulomb force contribution is emphasized on the equation of state of nuclear matter with charge symmetry breaking(CSB) at finite temperature and the breached $^1S_0$ pairing correlations of proton-proton and neutron-neutron. The universal properties given by an order parameter field with a non-zero vacuum expectation value (VEV) nearby phase transition are explored within the mean field theory(MFT) level. This mechanism can be extended to the charged or charge neutralized strongly coupling multi-components system for the discussion of binding or pairing issues.Comment: 6 pages, 3 figues, revised according to comments; improved with typos corrected and updated references added; final published version in Phys. Lett.

Trade-offs in carbon-degrading enzyme activities limit long-term soil carbon sequestration with biochar addition

We would like to thank all the authors whose data and work are included in this meta-analysis. This study was supported by the National Natural Science Foundation of China (32071595, 41830756 and 42177022). We also thank the Fundamental Research Funds for the Central Universities (Program no. 2662019QD055). We acknowledge Cunbin Gao, Qianqian Zhao and Qin Liu for their assistance in data collection. J.C. received funding from Aarhus Universitets Forskningsfond (AUFF-E-2019-7-1), EU H2020 Marie Skłodowska-Curie Actions (839806), Danish Independent Research Foundation (1127-00015B), and Nordic Committee of Agriculture and Food Research (https://nordicagriresearch.org/2020-5/). The authors declare no competing interests.Peer reviewedPublisher PD