From df27d2a720077f6455d9feb327eddd73fbbbbffd Mon Sep 17 00:00:00 2001
From: Qingyu Qu <52615090+ErikQQY@users.noreply.github.com>
Date: Fri, 18 Oct 2024 22:22:40 +0800
Subject: [PATCH 1/2] Some time should be PM

---
 2024.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/2024.md b/2024.md
index 3b81949..06388ae 100644
--- a/2024.md
+++ b/2024.md
@@ -41,7 +41,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 <td title="由于物理场在空间和时间上可能表现出显著的不均匀性，静态网格可能导致计算效率低下或较大的数值误差。自适应网格(AMR)根据解的演化特征重新分配计算资源，能够实现效率与精度之间的平衡。同时，基于树的笛卡尔网格能够灵活描述计算域的边界和间断解，在自动化、普适性方面具备优势。 KitAMR.jl专注于基于动理学方程的数值格式，期望给出近平衡流域流动的准确数值描述。由于非平衡流动通常伴随解在物理和速度空间的集中分布和剧烈变化，网格的自适应是十分必要的。基于P4est.jl和MPI.jl，我们开发了一个基于四叉树/八叉树的分布式求解器，用于求解跨流域的多尺度复杂流动。报告将简单介绍理论基础，通过全面的基准测试展示求解器的实用性，并在最后讨论使用Julia开发的优缺点和未来的展望。">KitAMR.jl: 分布式、自适应的非平衡流动求解器, <strong>葛龙庆</strong>，在读博士生，北京大学工学院力学与工程科学系，湍流与复杂系统国家重点实验室，应用物理与技术研究中心</td>
 </tr>
 <tr>
-<td>14:00AM-14:40AM </td>
+<td>14:00PM-14:40PM </td>
 <td title="Tensor network machine learning models are versatile in addressing complex data-driven tasks， ranging from quantum many-body problems to classical pattern recognitions. Despite their promising performance， understanding the underlying assumptions and limitations of these models remains largely unexplored. In this work， we focus on the exact formulation of no-free-lunch theories for tensor network-based machine learning models. Specifically， we rigorously analyze the generalization risks of learning target output functions from input data encoded in tensor network states. We first prove the no-free-lunch theory for the machine learning model based on the matrix product states， i.e.， the 1D tensor network states. Furthermore， to show the validity of our theoretical framework in various tensor network models， we prove the no-free-lunch theory for the case of 2D projected entangled-pair state， by introducing the combinatorial method associated to the “puzzle of polyominoes”. Our findings shed light on the intrinsic limitations of tensor network-based learning models in a rigorous fashion， and open up an avenue for further analytical research on exploring the advantages of quantum-inspired machine learning models.">在张量网络机器学习模型中制定无免费午餐理论, <strong>于立伟</strong>，特聘研究员，南开大学陈省身数学研究所</td>
 </tr>
 <tr>
@@ -49,7 +49,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 <td title="量子速度极限是量子力学中的一个基本概念，它描述了量子态实现指定演化目标的速度上限， 在量子计算、量子计量学和量子控制等领域具有广泛的应用潜力。 报告将尝试介绍量子速度极限的基本原理和数值计算。">Julia 与量子速度极限101, <strong>余怀明</strong>，在读学生，华中科技大学物理学院</td>
 </tr>
 <tr>
-<td>16:00AM-16:40AM </td>
+<td>16:00PM-16:40PM </td>
 <td title="TreeWidthSolver.jl 是一个用于搜索简单图树宽度的 Julia 包。树宽度是图论中的一个概念，用于描述图的结构复杂性。树宽度越小，图的结构越简单。在张量网络中，树宽度决定了张量网络收缩的顺序，从而影响计算的效率和精度。TreeWidthSolver.jl 提供了一种高效的算法来搜索树宽度，并将其应用于张量网络的收缩顺序优化。报告将介绍树宽度与张量网络收缩顺序的关系，并展示如何使用 TreeWidthSolver.jl 进行张量网络收缩顺序优化。">TreeWidthSolver.jl: 从树宽度到张量网络收缩顺序, <strong>高煊钊</strong>，在读博士生，香港科技大学（广州）功能枢纽，先进材料学域</td>
 </tr>
 </table>
@@ -67,7 +67,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 <td title="Quantum error-correcting codes (QECCs) are key techniques for overcoming noise in quantum computers. In this talk， I will introduce my work for QuantumClifford.jl: (1) The construction and evaluation of QECCs， including quantum low-density parity-check codes; (2) Decoders for these codes， including the BP-OSD decoder. The work is a project in Google Summer of Codes 2024.">QuantumClifford.jl 中的纠错码, <strong>鄢语轩</strong>，研究生，清华大学</td>
 </tr>
 <tr>
-<td>14:00AM-18:00AM </td>
+<td>14:00PM-18:00PM </td>
 <td>Hackathon</td>
 </tr>
 </table>

From a89bd00caf781e32e4bdfb94315b031c50adcd44 Mon Sep 17 00:00:00 2001
From: Qingyu Qu <2283984853@qq.com>
Date: Fri, 18 Oct 2024 22:27:21 +0800
Subject: [PATCH 2/2] Some time should be PM

Signed-off-by: Qingyu Qu <2283984853@qq.com>
---
 2024.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/2024.md b/2024.md
index 06388ae..5bce100 100644
--- a/2024.md
+++ b/2024.md
@@ -41,7 +41,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 <td title="由于物理场在空间和时间上可能表现出显著的不均匀性，静态网格可能导致计算效率低下或较大的数值误差。自适应网格(AMR)根据解的演化特征重新分配计算资源，能够实现效率与精度之间的平衡。同时，基于树的笛卡尔网格能够灵活描述计算域的边界和间断解，在自动化、普适性方面具备优势。 KitAMR.jl专注于基于动理学方程的数值格式，期望给出近平衡流域流动的准确数值描述。由于非平衡流动通常伴随解在物理和速度空间的集中分布和剧烈变化，网格的自适应是十分必要的。基于P4est.jl和MPI.jl，我们开发了一个基于四叉树/八叉树的分布式求解器，用于求解跨流域的多尺度复杂流动。报告将简单介绍理论基础，通过全面的基准测试展示求解器的实用性，并在最后讨论使用Julia开发的优缺点和未来的展望。">KitAMR.jl: 分布式、自适应的非平衡流动求解器, <strong>葛龙庆</strong>，在读博士生，北京大学工学院力学与工程科学系，湍流与复杂系统国家重点实验室，应用物理与技术研究中心</td>
 </tr>
 <tr>
-<td>14:00PM-14:40PM </td>
+<td>2:00PM-2:40PM </td>
 <td title="Tensor network machine learning models are versatile in addressing complex data-driven tasks， ranging from quantum many-body problems to classical pattern recognitions. Despite their promising performance， understanding the underlying assumptions and limitations of these models remains largely unexplored. In this work， we focus on the exact formulation of no-free-lunch theories for tensor network-based machine learning models. Specifically， we rigorously analyze the generalization risks of learning target output functions from input data encoded in tensor network states. We first prove the no-free-lunch theory for the machine learning model based on the matrix product states， i.e.， the 1D tensor network states. Furthermore， to show the validity of our theoretical framework in various tensor network models， we prove the no-free-lunch theory for the case of 2D projected entangled-pair state， by introducing the combinatorial method associated to the “puzzle of polyominoes”. Our findings shed light on the intrinsic limitations of tensor network-based learning models in a rigorous fashion， and open up an avenue for further analytical research on exploring the advantages of quantum-inspired machine learning models.">在张量网络机器学习模型中制定无免费午餐理论, <strong>于立伟</strong>，特聘研究员，南开大学陈省身数学研究所</td>
 </tr>
 <tr>
@@ -49,7 +49,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 <td title="量子速度极限是量子力学中的一个基本概念，它描述了量子态实现指定演化目标的速度上限， 在量子计算、量子计量学和量子控制等领域具有广泛的应用潜力。 报告将尝试介绍量子速度极限的基本原理和数值计算。">Julia 与量子速度极限101, <strong>余怀明</strong>，在读学生，华中科技大学物理学院</td>
 </tr>
 <tr>
-<td>16:00PM-16:40PM </td>
+<td>4:00PM-4:40PM </td>
 <td title="TreeWidthSolver.jl 是一个用于搜索简单图树宽度的 Julia 包。树宽度是图论中的一个概念，用于描述图的结构复杂性。树宽度越小，图的结构越简单。在张量网络中，树宽度决定了张量网络收缩的顺序，从而影响计算的效率和精度。TreeWidthSolver.jl 提供了一种高效的算法来搜索树宽度，并将其应用于张量网络的收缩顺序优化。报告将介绍树宽度与张量网络收缩顺序的关系，并展示如何使用 TreeWidthSolver.jl 进行张量网络收缩顺序优化。">TreeWidthSolver.jl: 从树宽度到张量网络收缩顺序, <strong>高煊钊</strong>，在读博士生，香港科技大学（广州）功能枢纽，先进材料学域</td>
 </tr>
 </table>
@@ -67,7 +67,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 <td title="Quantum error-correcting codes (QECCs) are key techniques for overcoming noise in quantum computers. In this talk， I will introduce my work for QuantumClifford.jl: (1) The construction and evaluation of QECCs， including quantum low-density parity-check codes; (2) Decoders for these codes， including the BP-OSD decoder. The work is a project in Google Summer of Codes 2024.">QuantumClifford.jl 中的纠错码, <strong>鄢语轩</strong>，研究生，清华大学</td>
 </tr>
 <tr>
-<td>14:00PM-18:00PM </td>
+<td>2:00PM-6:00PM </td>
 <td>Hackathon</td>
 </tr>
 </table>