Convergence of the Ginzburg-Landau approximation for the Ericksen-Leslie system

We establish the local well-posedness of the general Ericksen-Leslie system in liquid crystals with the initial velocity and director field in $H^1 \times H_b^2$. In particular, we prove that the solutions of the Ginzburg-Landau approximation system converge smoothly to the solution of the Ericksen-Leslie system for any $t \in (0,T^\ast)$ with a maximal existence time $T^\ast$ of the Ericksen- Leslie system

Mixed-TD: Efficient Neural Network Accelerator with Layer-Specific Tensor Decomposition

Neural Network designs are quite diverse, from VGG-style to ResNet-style, and from Convolutional Neural Networks to Transformers. Towards the design of efficient accelerators, many works have adopted a dataflow-based, inter-layer pipelined architecture, with a customised hardware towards each layer, achieving ultra high throughput and low latency. The deployment of neural networks to such dataflow architecture accelerators is usually hindered by the available on-chip memory as it is desirable to preload the weights of neural networks on-chip to maximise the system performance. To address this, networks are usually compressed before the deployment through methods such as pruning, quantization and tensor decomposition. In this paper, a framework for mapping CNNs onto FPGAs based on a novel tensor decomposition method called Mixed-TD is proposed. The proposed method applies layer-specific Singular Value Decomposition (SVD) and Canonical Polyadic Decomposition (CPD) in a mixed manner, achieving 1.73x to 10.29x throughput per DSP to state-of-the-art CNNs. Our work is open-sourced: https://github.com/Yu-Zhewen/Mixed-TDComment: accepted by FPL202

Heavy quark fragmentation function in 't Hooft Model

We carry out a comprehensive study of the quark-to-meson fragmentation function in the 't Hooft model, i.e., the two-dimensional Quantum Chromodynamics (QCD) in $N_c\to \infty$ limit, following the operator definition pioneered by Collins and Soper. We apply the Hamiltonian approach as well as the diagrammatic approach to construct the functional form of the quark-to-meson fragmentation function in terms of the meson's light-cone wave function. For the sake of comparison, we also investigate the heavy quark fragmentation into quarkonium in two-dimensional QCD within the framework of the nonrelativistic QCD (NRQCD) factorization, at the lowest order in quark velocity. In the heavy quark limit, the quark fragmentation function obtained from the {\it ab initio} method agrees well, both analytically and numerically, with that obtained from the NRQCD approach. This agreement might be regarded as a nontrivial justification for the validity of both field-theoretical approaches to compute the heavy quark fragmentation function.Comment: 23 pages, 4 figures, 1 tabl

Photoproduction of C-even quarkonia at EIC and EicC

The $\eta_c$ photoproduction in $ep$ collision has long been proposed as an ideal process to probe the existence of odderon. In the current work, we systematically investigate the photoproduction of various $C$-even heavy quarkonia (exemplified by $\eta_{c(b)}$, and $\chi_{c(b)J}$ with $J=0,1,2$) via one-photon exchange channel, at the lowest order in $\alpha_s$ and heavy quark velocity in the context of NRQCD factorization. We find that the photoproduction rates of the $C$-even quarkonia through this mechanism are comparable in magnitude with that through the odderon-initiated mechanism, even in the Regge limit ($s\gg -t$), though the latter types of predictions suffers from considerable theoretical uncertainties. The future measurements of these types of quarkonium photoproduction processes in \texttt{EIC} and \texttt{EicC} are crucial to ascertain which mechanism plays the dominant role.Comment: 16 pages, 9 figure

SATAY: A Streaming Architecture Toolflow for Accelerating YOLO Models on FPGA Devices

AI has led to significant advancements in computer vision and image processing tasks, enabling a wide range of applications in real-life scenarios, from autonomous vehicles to medical imaging. Many of those applications require efficient object detection algorithms and complementary real-time, low latency hardware to perform inference of these algorithms. The YOLO family of models is considered the most efficient for object detection, having only a single model pass. Despite this, the complexity and size of YOLO models can be too computationally demanding for current edge-based platforms. To address this, we present SATAY: a Streaming Architecture Toolflow for Accelerating YOLO. This work tackles the challenges of deploying stateof-the-art object detection models onto FPGA devices for ultralow latency applications, enabling real-time, edge-based object detection. We employ a streaming architecture design for our YOLO accelerators, implementing the complete model on-chip in a deeply pipelined fashion. These accelerators are generated using an automated toolflow, and can target a range of suitable FPGA devices. We introduce novel hardware components to support the operations of YOLO models in a dataflow manner, and off-chip memory buffering to address the limited on-chip memory resources. Our toolflow is able to generate accelerator designs which demonstrate competitive performance and energy characteristics to GPU devices, and which outperform current state-of-the-art FPGA accelerators

Fast Prototyping Next-Generation Accelerators for New ML Models using MASE: ML Accelerator System Exploration

Machine learning (ML) accelerators have been studied and used extensively to compute ML models with high performance and low power. However, designing such accelerators normally takes a long time and requires significant effort. Unfortunately, the pace of development of ML software models is much faster than the accelerator design cycle, leading to frequent and drastic modifications in the model architecture, thus rendering many accelerators obsolete. Existing design tools and frameworks can provide quick accelerator prototyping, but only for a limited range of models that can fit into a single hardware device, such as an FPGA. Furthermore, with the emergence of large language models, such as GPT-3, there is an increased need for hardware prototyping of these large models within a many-accelerator system to ensure the hardware can scale with the ever-growing model sizes. In this paper, we propose an efficient and scalable approach for exploring accelerator systems to compute large ML models. We developed a tool named MASE that can directly map large ML models onto an efficient streaming accelerator system. Over a set of ML models, we show that MASE can achieve better energy efficiency to GPUs when computing inference for recent transformer models. Our tool will open-sourced upon publication

Cancer-associated fibroblast related gene signature in Helicobacter pylori-based subtypes of gastric carcinoma for prognosis and tumor microenvironment estimation in silico analysis

IntroductionGastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly.MethodsHP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model.Results and discussionIn this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies

PGAweb: A Web Server for Bacterial Pan-Genome Analysis

An astronomical increase in microbial genome data in recent years has led to strong demand for bioinformatic tools for pan-genome analysis within and across species. Here, we present PGAweb, a user-friendly, web-based tool for bacterial pan-genome analysis, which is composed of two main pan-genome analysis modules, PGAP and PGAP-X. PGAweb provides key interactive and customizable functions that include orthologous clustering, pan-genome profiling, sequence variation and evolution analysis, and functional classification. PGAweb presents features of genomic structural dynamics and sequence diversity with different visualization methods that are helpful for intuitively understanding the dynamics and evolution of bacterial genomes. PGAweb has an intuitive interface with one-click setting of parameters and is freely available at http://PGAweb.vlcc.cn/