LITHIUM ISOTOPIC CONSTRAINTS ON THE ORIGIN OF I- AND A-TYPE GRANITES FROM EAST JUNGGAR (NW CHINA) OF THE CENTRAL ASIAN OROGENIC BELT: IMPLICATIONS FOR LI ISOTOPIC FRACTIONATION DURING CRUSTAL ANATEXIS

Though Li isotope fractionation during mantle melting and differentiation of basaltic melts have been proved insignificant, Li isotopic systems during crustal processes remain unclear. To study this, we report combined petrological, Nd-Sr and Li isotopic data for the late Paleozoic coexisting I- and A-type granites in the East Junggar orogen of the Central Asian Orogenic Belt. The granites were formed responding to underplating of mafic magmas in the lower crust in a postcollisional, extensional regime, and intruded into the Paleozoic foldbelts that formed due to extensive oceanic subduction-accretion processes.Though Li isotope fractionation during mantle melting and differentiation of basaltic melts have been proved insignificant, Li isotopic systems during crustal processes remain unclear. To study this, we report combined petrological, Nd-Sr and Li isotopic data for the late Paleozoic coexisting I- and A-type granites in the East Junggar orogen of the Central Asian Orogenic Belt. The granites were formed responding to underplating of mafic magmas in the lower crust in a postcollisional, extensional regime, and intruded into the Paleozoic foldbelts that formed due to extensive oceanic subduction-accretion processes

Evaluating Feynman integrals by the hypergeometry

The hypergeometric function method naturally provides the analytic expressions of scalar integrals from concerned Feynman diagrams in some connected regions of independent kinematic variables, also presents the systems of homogeneous linear partial differential equations satisfied by the corresponding scalar integrals. Taking examples of the one-loop $B_{_0}$ and massless $C_{_0}$ functions, as well as the scalar integrals of two-loop vacuum and sunset diagrams, we verify our expressions coinciding with the well-known results of literatures. Based on the multiple hypergeometric functions of independent kinematic variables, the systems of homogeneous linear partial differential equations satisfied by the mentioned scalar integrals are established. Using the calculus of variations, one recognizes the system of linear partial differential equations as stationary conditions of a functional under some given restrictions, which is the cornerstone to perform the continuation of the scalar integrals to whole kinematic domains numerically with the finite element methods. In principle this method can be used to evaluate the scalar integrals of any Feynman diagrams.Comment: 39 pages, including 2 ps figure

Hadronic production of the doubly charmed baryon via the proton-nucleus and the nucleus-nucleus collisions at the RHIC and LHC

We present a detailed discussion on the doubly charmed baryon $\Xi_{cc}$ production at the RHIC and LHC via the proton-nucleus ($p$-N) and nucleus-nucleus (N-N) collision modes. The extrinsic charm mechanism via the subprocesses $g+c\to (cc)[n]+\bar{c}$ and $c+c\to (cc)[n]+g$ together with the gluon-gluon fusion mechanism via the subprocess $g+g\to(cc)[n]+\bar{c}+\bar{c}$ have been taken into consideration, where the intermediate diquark is in $[n]=[^1S_0]_{\bf 6}$-state or $[^3S_1]_{\bar{\bf 3}}$-state, respectively. Total and differential cross sections have been discussed under various collision energies. To compare with the $\Xi_{cc}$ production via proton-proton collision mode at the LHC, we observe that sizable $\Xi_{cc}$ events can also be generated via $p$-N and N-N collision modes at the RHIC and LHC. For examples, about $8.1\times10^7$ and $6.7\times10^7$ $\Xi_{cc}$ events can be accumulated in $p$-Pb and Pb-Pb collision modes at the LHC within one operation year.Comment: 10 pages, 6 figure

Eradication of unresectable liver metastasis through induction of tumour specific energy depletion.

Treatment of liver metastasis experiences slow progress owing to the severe side effects. In this study, we demonstrate a strategy capable of eliminating metastatic cancer cells in a selective manner. Nucleus-targeting W18O49 nanoparticles (WONPs) are conjugated to mitochondria-selective mesoporous silica nanoparticles (MSNs) containing photosensitizer (Ce6) through a Cathepsin B-cleavable peptide. In hepatocytes, upon the laser irradiation, the generated singlet oxygen species are consumed by WONPs, in turn leading to the loss of their photothermally heating capacity, thereby sparing hepatocyte from thermal damage induced by the laser illumination. By contrast, in cancer cells, the cleaved peptide linker allows WONPs and MSNs to respectively target nucleus and mitochondria, where the therapeutic powers could be unleashed, both photodynamically and photothermally. This ensures the energy production of cancer cells can be abolished. We further assess the underlying molecular mechanism at both gene and protein levels to better understand the therapeutic outcome

Memory augment is All You Need for image restoration

Image restoration is a low-level vision task, most CNN methods are designed as a black box, lacking transparency and internal aesthetics. Although some methods combining traditional optimization algorithms with DNNs have been proposed, they all have some limitations. In this paper, we propose a three-granularity memory layer and contrast learning named MemoryNet, specifically, dividing the samples into positive, negative, and actual three samples for contrastive learning, where the memory layer is able to preserve the deep features of the image and the contrastive learning converges the learned features to balance. Experiments on Derain/Deshadow/Deblur task demonstrate that these methods are effective in improving restoration performance. In addition, this paper's model obtains significant PSNR, SSIM gain on three datasets with different degradation types, which is a strong proof that the recovered images are perceptually realistic. The source code of MemoryNet can be obtained from https://github.com/zhangbaijin/MemoryNe

Is ChatGPT a Good Multi-Party Conversation Solver?

Large Language Models (LLMs) have emerged as influential instruments within the realm of natural language processing; nevertheless, their capacity to handle multi-party conversations (MPCs) -- a scenario marked by the presence of multiple interlocutors involved in intricate information exchanges -- remains uncharted. In this paper, we delve into the potential of generative LLMs such as ChatGPT and GPT-4 within the context of MPCs. An empirical analysis is conducted to assess the zero-shot learning capabilities of ChatGPT and GPT-4 by subjecting them to evaluation across three MPC datasets that encompass five representative tasks. The findings reveal that ChatGPT's performance on a number of evaluated MPC tasks leaves much to be desired, whilst GPT-4's results portend a promising future. Additionally, we endeavor to bolster performance through the incorporation of MPC structures, encompassing both speaker and addressee architecture. This study provides an exhaustive evaluation and analysis of applying generative LLMs to MPCs, casting a light upon the conception and creation of increasingly effective and robust MPC agents. Concurrently, this work underscores the challenges implicit in the utilization of LLMs for MPCs, such as deciphering graphical information flows and generating stylistically consistent responses.Comment: Accepted by Findings of EMNLP 202

DiffuSIA: A Spiral Interaction Architecture for Encoder-Decoder Text Diffusion

Diffusion models have emerged as the new state-of-the-art family of deep generative models, and their promising potentials for text generation have recently attracted increasing attention. Existing studies mostly adopt a single encoder architecture with partially noising processes for conditional text generation, but its degree of flexibility for conditional modeling is limited. In fact, the encoder-decoder architecture is naturally more flexible for its detachable encoder and decoder modules, which is extensible to multilingual and multimodal generation tasks for conditions and target texts. However, the encoding process of conditional texts lacks the understanding of target texts. To this end, a spiral interaction architecture for encoder-decoder text diffusion (DiffuSIA) is proposed. Concretely, the conditional information from encoder is designed to be captured by the diffusion decoder, while the target information from decoder is designed to be captured by the conditional encoder. These two types of information flow run through multilayer interaction spirally for deep fusion and understanding. DiffuSIA is evaluated on four text generation tasks, including paraphrase, text simplification, question generation, and open-domain dialogue generation. Experimental results show that DiffuSIA achieves competitive performance among previous methods on all four tasks, demonstrating the effectiveness and generalization ability of the proposed method.Comment: Work in Progres