1-Bromo-3,5-diphenyl­benzene

The title compound, C18H13Br, crystallizes with two crystallographically independent mol­ecules in the asymmetric unit. The C—Br bond lengths and the C—C bond lengths between the benzene rings are slightly different in the two mol­ecules. The dihedral angles between adjacent benzene rings are 26.85 (2) and 39.99 (2)° in one mol­ecule, and 29.90 (2) and 38.01 (2)° in the other. There are three types of inter­molecular C—H⋯π inter­actions in the crystal structure

2-Ferrocenyl-6-methyl­pyridin-3-ol

In the title compound, [Fe(C5H5)(C11H10NO)], the dihedral angle between the pyridyl and substituted cyclo­penta­dienyl rings is 20.4 (3)°. The H atoms of the methyl group are disordered over two positions; their site-occupation factors were fixed at 0.5. The crystal structure is stabilized by well defined inter­molecular O—H⋯N and C—H⋯O hydrogen bonds, leading to the formation of a two-dimensional network parallel to (101)

Tunable synchrotron-like radiation from centimeter scale plasma channels

Synchrotron radiation sources are immensely useful tools for scientific researches and many practical applications. Currently, the state-of-the-art synchrotrons rely on conventional accelerators, where electrons are accelerated in a straight line and radiate in bending magnets or other insertion devices. However, these facilities are usually large and costly. Here, we study a compact all-optical synchrotron like radiation source based on laser-plasma acceleration either in a straight or a curved plasma channel. With the laser pulse off-axially injected, its centroid oscillates transversely in the plasma channel. This results in a wiggler motion of the whole accelerating structure and the self-trapped electrons behind the laser pulse, leading to strong synchrotron-like radiations with tunable spectra. It is further shown that a palmtop ring-shaped synchrotron is possible with current high power laser technologies. With its potential of high flexibility and tunability, such light sources once realized would find applications in wide areas and make up the shortage of large synchrotron radiation facilities

Trade-Offs between the Metabolic Rate and Population Density of Plants

The energetic equivalence rule, which is based on a combination of metabolic theory and the self-thinning rule, is one of the fundamental laws of nature. However, there is a progressively increasing body of evidence that scaling relationships of metabolic rate vs. body mass and population density vs. body mass are variable and deviate from their respective theoretical values of 3/4 and −3/4 or −2/3. These findings questioned the previous hypotheses of energetic equivalence rule in plants. Here we examined the allometric relationships between photosynthetic mass (Mp) or leaf mass (ML) vs. body mass (β); population density vs. body mass (δ); and leaf mass vs. population density, for desert shrubs, trees, and herbaceous plants, respectively. As expected, the allometric relationships for both photosynthetic mass (i.e. metabolic rate) and population density varied with the environmental conditions. However, the ratio between the two exponents was −1 (i.e. β/δ = −1) and followed the trade-off principle when local resources were limited. Our results demonstrate for the first time that the energetic equivalence rule of plants is based on trade-offs between the variable metabolic rate and population density rather than their constant allometric exponents

First statistical measurement of the Hubble constant using unlocalized fast radio bursts

Fast radio bursts (FRBs) can be used to measure the Hubble constant by employing the Macquart relation. However, at present, only a small number of FRB events are localized to their host galaxies with known redshifts. In this paper, we develop a Bayesian method to statistically measure the Hubble constant using unlocalized FRBs and galaxy catalog data, which makes it possible to constrain cosmological parameters by using a large number of FRB data without known redshift information. Using the six FRB events observed by ASKAP combined with the big bang nucleosynthesis result, we obtain $H_0=71.7^{+8.8}_{-7.4}$ km s$^{-1}$ Mpc$^{-1}$ in the simulation-based case and $H_0=71.5^{+10.0}_{-8.1}$ km s$^{-1}$ Mpc$^{-1}$ in the observation-based case ($68\%$ highest-density interval), assuming different host galaxy population parameters. We also estimate that in the next few years, using thousands of FRBs could achieve a $3\%$ precision on the random error of the Hubble constant.Comment: 9 pages, 3 figure

Small LLMs Are Weak Tool Learners: A Multi-LLM Agent

Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete various tasks in a self-directed fashion. The challenge of tool use demands that LLMs not only understand user queries and generate answers accurately but also excel in task planning, tool invocation, and result summarization. While traditional works focus on training a single LLM with all these capabilities, performance limitations become apparent, particularly with smaller models. To overcome these challenges, we propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. Each component is implemented by a single LLM that focuses on a specific capability and collaborates with others to accomplish the task. This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability. To effectively train this framework, we introduce a two-stage training paradigm. First, we fine-tune a backbone LLM on the entire dataset without discriminating sub-tasks, providing the model with a comprehensive understanding of the task. Second, the fine-tuned LLM is used to instantiate the planner, caller, and summarizer respectively, which are continually fine-tuned on respective sub-tasks. Evaluation across various tool-use benchmarks illustrates that our proposed multi-LLM framework surpasses the traditional single-LLM approach, highlighting its efficacy and advantages in tool learning.Comment: On progress, github repo: https://github.com/X-PLUG/Multi-LLM-Agen