Branchings and Time Evolution of Reaction Networks

In this thesis I analyze flows in reaction networks in terms of branchings in a digraph. If the coupled differential equations governing the rate of change of probabilities X of a state or species are finite-differenced in time, a matrix equation (I + Adt)X(t+dt) = X(t) results, where X(t) is a vector giving the probabilities at time t and X(t+dt) is a vector giving the probabilities at time t + dt. I demonstrate that the matrix (I + Adt) may be written as the product of an incidence matrix and a weight matrix for a directed graph (digraph) representing the network. From this I demonstrate that individual diagonal elements of the inverse matrix (I + Adt)-1 may be written as a sum of the exponential weight of all branchings rooted at the vertex corresponding to the root vertex in the digraph divided by the sum of the exponential weight of all branchings. I also demonstrate that the individual element of the inverse matrix at row i, column j is the sum of exponential weights of all branchings rooted at vertex i but with a path from vertex i to vertex j in the digraph divided by the sum of exponential weights of all branchings. From this I demonstrate how to compute X(t + dt) from X(t) in terms of sums of branchings and how to compute effective transition rates. I then consider long-term solutions and demonstrate how to condense linear networks that obey detailed balance. This provides a useful connection to equilibrium analysis of the network. I then consider some implications of the branching analysis for the statistical mechanics of reaction networks, and I extend the analysis to non-linear networks. Finally I provide some example applications. I conclude that branchings in network digraphs hold promise for analyzing complicated reaction flows, and I list some future directions of possible research

The role of cancer-associated fibroblasts in breast cancer metastasis

Breast cancer deaths are primarily caused by metastasis. There are several treatment options that can be used to treat breast cancer. There are, however, a limited number of treatments that can either prevent or inhibit the spread of breast tumor metastases. Thus, novel therapeutic strategies are needed. Studies have increasingly focused on the importance of the tumor microenvironment (TME) in metastasis of breast cancer. As the most abundant cells in the TME, cancer-associated fibroblasts (CAFs) play important roles in cancer pathogenesis. They can remodel the structure of the extracellular matrix (ECM) and engage in crosstalk with cancer cells or other stroma cells by secreting growth factors, cytokines, and chemokines, as well as components of the ECM, which assist the tumor cells to invade through the TME and cause distant metastasis. Clinically, CAFs not only foster the initiation, growth, angiogenesis, invasion, and metastasis of breast cancer but also serve as biomarkers for diagnosis, therapy, and prediction of prognosis. In this review, we summarize the biological characteristics and subtypes of CAFs and their functions in breast cancer metastasis, focusing on their important roles in the diagnosis, prognosis, and treatment of breast cancer. Recent studies suggest that CAFs are vital partners of breast cancer cells that assist metastasis and may represent ideal targets for prevention and treatment of breast cancer metastasis

A New Species of Genus Microhyla (Amphibia: Anura: Microhylidae) from Zhejiang Province, China

We described a new species, Microhyla beilunensis sp. nov., from Zhejiang Province of China. Phylogenetic analyses based on the mitochondrial 12S, 16S and CO1 gene sequences suggested that the new taxon was distinctly separated from its congeners and closed to M. mixtura and M. okinavensis. Morphologically, the new species could be identified from its congeners except M. mixtura by several characters: (1) rudimentary webs on toe base; (2) absence of disks and dorsal median longitudinal grooves on finger tips; (3) presence of disks and dorsal median longitudinal grooves on toe tips. As well, the new species could be identified from topotype M. mixtura by the combination of characters: (1) apart from the stripes, bar-shaped and oval-shaped patterns, the rounded spots present on the dorsum of body and legs; (2) the outer metacarpal tubercles prominently larger than the inner one; (3) of males, the ratios of HW, IND, UEW and LAW to SVL of the new species were significantly larger than those of M. mixtura (P < 0.01), and the ratios of SL, IOD, LAHL, HLL, TL, TFL and FL to SVL of the new species were significantly less than those of M. mixtura (P < 0.05)

Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis

Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a text-image pair with political content. We first collect five new large-scale datasets with English documents and images along with their ideological leanings, covering news articles from a wide range of US mainstream media and social media posts from Reddit and Twitter. We conduct in-depth analyses of news articles and reveal differences in image content and usage across the political spectrum. Furthermore, we perform extensive experiments and ablation studies, demonstrating the effectiveness of targeted pretraining objectives on different model components. Our best-performing model, a late-fusion architecture pretrained with a triplet objective over multimodal content, outperforms the state-of-the-art text-only model by almost 4% and a strong multimodal baseline with no pretraining by over 3%.Comment: EMNLP 202

Towards Accurate Data-free Quantization for Diffusion Models

In this paper, we propose an accurate data-free post-training quantization framework of diffusion models (ADP-DM) for efficient image generation. Conventional data-free quantization methods learn shared quantization functions for tensor discretization regardless of the generation timesteps, while the activation distribution differs significantly across various timesteps. The calibration images are acquired in random timesteps which fail to provide sufficient information for generalizable quantization function learning. Both issues cause sizable quantization errors with obvious image generation performance degradation. On the contrary, we design group-wise quantization functions for activation discretization in different timesteps and sample the optimal timestep for informative calibration image generation, so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead. Specifically, we partition the timesteps according to the importance weights of quantization functions in different groups, which are optimized by differentiable search algorithms. We also select the optimal timestep for calibration image generation by structural risk minimizing principle in order to enhance the generalization ability in the deployment of quantized diffusion model. Extensive experimental results show that our method outperforms the state-of-the-art post-training quantization of diffusion model by a sizable margin with similar computational cost

Timing Recovery for Point-to-Multi-Point Coherent Passive Optical Networks

We propose a timing recovery for point-to-multi-point coherent passive optical networks. The results show that the proposed algorithm has low complexity and better robustness against the residual chromatic dispersion.Comment: The artical have been submitted to SPPCom conferenc

Capacity Limitation and Optimization Strategy for Flexible Point-to-Multi-Point Optical Networks

Point-to-multi-point (PtMP) optical networks become the main solutions for network-edge applications such as passive optical networks and radio access networks. Entropy-loading digital subcarrier multiplexing (DSCM) is the core technology to achieve low latency and approach high capacity for flexible PtMP optical networks. However, the high peak-to-average power ratio of the entropy-loading DSCM signal limits the power budget and restricts the capacity, which can be reduced effectively by clipping operation. In this paper, we derive the theoretical capacity limitation of the flexible PtMP optical networks based on the entropy-loading DSCM signal. Meanwhile, an optimal clipping ratio for the clipping operation is acquired to approach the highest capacity limitation. Based on an accurate clipping-noise model under the optimal clipping ratio, we establish a three-dimensional look-up table for bit-error ratio, spectral efficiency, and link loss. Based on the three-dimensional look-up table, an optimization strategy is proposed to acquire optimal spectral efficiencies for achieving a higher capacity of the flexible PtMP optical networks.Comment: The paper has been submitted to the IEEE Transactions on Communication

Electrolysis of Converter Matte in Molten CaCl2-NaCl

The electrolytic production of nickel-copper alloy by electrochemical reduction of converter matte in molten salt has been investigated. The sintered solid porous pellets of Ni3S2, Cu2S and converter matte were electrolyzed at a voltage of 3.0 V in molten CaCl2-NaCl under the protection of argon gas at 700?, respectively. The electro-reduction processes were investigated and the products were characterized. The results show that the molten salt electro-reduction process can be used to produce nickel, copper and nickel-copper alloy directly from Ni3S2, Cu2S and converter matte precursors in molten CaCl2-NaCl, respectively. CaS would be formed as the intermediate compound during the electro-reduction process, and then the formed CaS can be gradually decomposed and removed with the increase of the electrolysis time. The experimental results show that the molten salt electro-reduction process has the potential to be used for the reduction of sulfide minerals in molten CaCl2-NaClpublishersversionPeer reviewe