Self-limited self-assembly of chiral filaments

The assembly of filamentous bundles with controlled diameters is common in biological systems and desirable for the development of nanomaterials. We discuss dynamical simulations and free energy calculations on patchy spheres with chiral pair interactions that spontaneously assemble into filamentous bundles. The chirality frustrates long-range crystal order by introducing twist between interacting subunits. For some ranges of system parameters this constraint leads to bundles with a finite diameter as the equilibrium state, and in other cases frustration is relieved by the formation of defects. While some self-limited structures can be modeled as twisted filaments arranged with local hexagonal symmetry, other structures are surprising in their complexity.Comment: 5 pages, 5 figure

A Fire Sale without Fire: An Explanation of Labor-Intensive FDI in China

Using a large firm-level panel dataset from the Chinese National Bureau of Statistics, we examine the effect of financial distortions on FDI inflows in China's labor-intensive industries. Following Whited and Wu (2006), we estimate the investment Euler equation and construct a financing constraint index for each firm. We find that among domestic firms, the financing constraint index is highest for private firms and lowest for state-owned firms. This finding is consistent with the political pecking order hypothesis that states that there is a severe lending bias in China's financial system against private firms in favor of state-owned enterprises. Then we estimate a probit model of joint-venture decisions by private firms. We show that firms with greater financing constraints are more likely to be acquired and controlled by foreign firms. We interpret this evidence to be consistent with the fire-sale hypothesis that states that private firms relinquish their equity and control to foreign investors in order to raise financing for growth. We find that those firms in the top 25 percent of the most financing constraints could have avoided losing 31.5 percent of the equity share to foreigners had they faced the same favorable financing constraints as a typical firm in Zhejiang Province

Extinction Pattern of Reef Ecosystems in Latest Permian

Studies of two Permian-Triassic boundary (PTB) sections on top of a Changhsingian reef in Ziyun, Guizhou Province, southwestern China indicate that the end-Permian mass extinction of reef ecosystems occurred in two steps. The first step is the extinction of all stenotropic organisms such as calcisponges and fusulinids in the latest Permian (in the Clarkina yini conodont zone). The biota after the first extinction is simple, comprising eurytropic organisms including microgastropods, ostracods, and some small burrowing organisms, or only algal mats. At the beginning of the Early Triassic (i.e. the beginning of the Hindeodus parvus zone), the environments became anoxic, and the microgastropod dominated biota or algal mats disappeared, which constituted the second episode of the mass extinction. The biota after the second extinction comprises small spherical microproblematica, some kinds of specialized organisms tolerant of anoxic or oxygen-poor conditions. As the environments became oxygenated, the specialized biota was replaced by a microgastropod-dominated simple biota. When the environmental conditions improved further, the simple biota was replaced by a diverse biota with normal-sized ammonoids, bivalves, and gastropods, representing restoration of normal oceanic conditions. Comparison with PTB sections in Dolomites, Italy and Meishan, Zhejiang Province shows that non-reef ecosystems had a similar first episode of mass extinction in the latest Permian. In the case that oceanic anoxia happened, non-reef ecosystems had a second extinction episode similar to that of reef ecosystems

ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation

While language-guided image manipulation has made remarkable progress, the challenge of how to instruct the manipulation process faithfully reflecting human intentions persists. An accurate and comprehensive description of a manipulation task using natural language is laborious and sometimes even impossible, primarily due to the inherent uncertainty and ambiguity present in linguistic expressions. Is it feasible to accomplish image manipulation without resorting to external cross-modal language information? If this possibility exists, the inherent modality gap would be effortlessly eliminated. In this paper, we propose a novel manipulation methodology, dubbed ImageBrush, that learns visual instructions for more accurate image editing. Our key idea is to employ a pair of transformation images as visual instructions, which not only precisely captures human intention but also facilitates accessibility in real-world scenarios. Capturing visual instructions is particularly challenging because it involves extracting the underlying intentions solely from visual demonstrations and then applying this operation to a new image. To address this challenge, we formulate visual instruction learning as a diffusion-based inpainting problem, where the contextual information is fully exploited through an iterative process of generation. A visual prompting encoder is carefully devised to enhance the model's capacity in uncovering human intent behind the visual instructions. Extensive experiments show that our method generates engaging manipulation results conforming to the transformations entailed in demonstrations. Moreover, our model exhibits robust generalization capabilities on various downstream tasks such as pose transfer, image translation and video inpainting

Dynamics in the Metabasin Space of a Lennard-Jones Glass Former: Connectivity and Transition Rates

Using simulations, we construct the effective dynamics in metabasin space for a Lennard-Jones glass-former. Metabasins are identified via a scheme that measures transition rates between inherent structures, and generates clusters of inherent structures by drawing in branches that have the largest transition rates. The effective dynamics is shown to be Markovian but differs significantly from the simplest trap models. We specifically show that retaining information about the connectivity in metabasin space is crucial for reproducing the slow dynamics observed in this system.Comment: 8 pages, 10 figures. References add. A typo correcte

Improved analysis of inorganic coal properties based on near-infrared reflectance spectroscopy

A novel method is proposed to improve the analysis accuracy of inorganic properties by adding organic information.</p

Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue

Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue systems either focus on a single knowledge source or overlook the dependency between multiple sources of knowledge, which may result in generating inconsistent or even paradoxical responses. To incorporate multiple knowledge sources and dependencies between them, we propose SAFARI, a novel framework that leverages the exceptional capabilities of large language models (LLMs) in planning, understanding, and incorporating under both supervised and unsupervised settings. Specifically, SAFARI decouples the knowledge grounding into multiple sources and response generation, which allows easy extension to various knowledge sources including the possibility of not using any sources. To study the problem, we construct a personalized knowledge-grounded dialogue dataset \textit{\textbf{K}nowledge \textbf{B}ehind \textbf{P}ersona}~(\textbf{KBP}), which is the first to consider the dependency between persona and implicit knowledge. Experimental results on the KBP dataset demonstrate that the SAFARI framework can effectively produce persona-consistent and knowledge-enhanced responses

SPHERE: SPherical Harmonic Elastic REgistration of HARDI data

In contrast to the more common Diffusion Tensor Imaging (DTI), High Angular Resolution Diffusion Imaging (HARDI) allows superior delineation of angular microstructures of brain white matter, and makes possible multiple-fiber modeling of each voxel for better characterization of brain connectivity. However, the complex orientation information afforded by HARDI makes registration of HARDI images more complicated than scalar images. In particular, the question of how much orientation information is needed for satisfactory alignment has not been sufficiently addressed. Low order orientation representation is generally more robust than high order representation, although the latter provides more information for correct alignment of fiber pathways. However, high order representation, when naïvely utilized, might not necessarily be conducive to improving registration accuracy since similar structures with significant orientation differences prior to proper alignment might be mistakenly taken as non-matching structures. We present in this paper a HARDI registration algorithm, called SPherical Harmonic Elastic REgistration (SPHERE), which in a principled means hierarchically extracts orientation information from HARDI data for structural alignment. The image volumes are first registered using robust, relatively direction invariant features derived from the Orientation Distribution Function (ODF), and the alignment is then further refined using spherical harmonic (SH) representation with gradually increasing orders. This progression from non-directional, single-directional to multi-directional representation provides a systematic means of extracting directional information given by diffusion-weighted imaging. Coupled with a template-subject-consistent soft-correspondence-matching scheme, this approach allows robust and accurate alignment of HARDI data. Experimental results show marked increase in accuracy over a state-of-the-art DTI registration algorithm