results: 该论文的主要结果是，该算法可以在 $O(d)$ 的成本和 $O(d\log(1/\varepsilon)^2)$ 的迭代复杂度下找到一个 $\varepsilon$-优点。此外，该算法的对 $d$ 的依赖性是可证明为是最佳的，即任何随机算法都必须在 $d$ 上付出 $\Omega(d)$ 的成本和迭代复杂度。Abstract
We introduce and study the problem of dueling optimization with a monotone adversary, which is a generalization of (noiseless) dueling convex optimization. The goal is to design an online algorithm to find a minimizer $\mathbf{x}^{*}$ for a function $f\colon X \to \mathbb{R}$, where $X \subseteq \mathbb{R}^d$. In each round, the algorithm submits a pair of guesses, i.e., $\mathbf{x}^{(1)}$ and $\mathbf{x}^{(2)}$, and the adversary responds with any point in the space that is at least as good as both guesses. The cost of each query is the suboptimality of the worse of the two guesses; i.e., ${\max} \left( f(\mathbf{x}^{(1)}), f(\mathbf{x}^{(2)}) \right) - f(\mathbf{x}^{*})$. The goal is to minimize the number of iterations required to find an $\varepsilon$-optimal point and to minimize the total cost (regret) of the guesses over many rounds. Our main result is an efficient randomized algorithm for several natural choices of the function $f$ and set $X$ that incurs cost $O(d)$ and iteration complexity $O(d\log(1/\varepsilon)^2)$. Moreover, our dependence on $d$ is asymptotically optimal, as we show examples in which any randomized algorithm for this problem must incur $\Omega(d)$ cost and iteration complexity.
摘要
我们介绍和研究对对抗式优化问题，即一个对抗者对函数 $f\colon X \to \mathbb{R}$ 的一个通用扩展。我们的目标是设计一个在线 Algorithm 以找到 $X \subseteq \mathbb{R}^d$ 中的最小值 $\mathbf{x}^{*}$。在每个回合中，Algorithm 会提交两个猜测，即 $\mathbf{x}^{(1)}$ 和 $\mathbf{x}^{(2)}$，对抗者则回传任何在空间中的任何点，其至少对两个猜测都是最佳的。每次猜测的成本是两个猜测中的较差一个的成本，即 $\max \left( f(\mathbf{x}^{(1)}), f(\mathbf{x}^{(2)}) \right) - f(\mathbf{x}^{*})$。我们的主要结果是一个高效的随机化算法，可以在多个自然的函数 $f$ 和集合 $X$ 上实现cost $O(d)$ 和迭代复杂度 $O(d\log(1/\varepsilon)^2)$。此外，我们的 $d$ 依赖性是对抗数学optimal，我们提供了一些示例，证明任何随机化算法 для这个问题必须有 $\Omega(d)$ 成本和迭代复杂度。
Exponentially Convergent Algorithms for Supervised Matrix Factorization
results: 通过应用于多种SMF类型问题，成功地鉴别了许多不同类型的肿瘤相关基因组。Abstract
Supervised matrix factorization (SMF) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives. Our goal is to use SMF to learn low-rank latent factors that offer interpretable, data-reconstructive, and class-discriminative features, addressing challenges posed by high-dimensional data. Training SMF model involves solving a nonconvex and possibly constrained optimization with at least three blocks of parameters. Known algorithms are either heuristic or provide weak convergence guarantees for special cases. In this paper, we provide a novel framework that 'lifts' SMF as a low-rank matrix estimation problem in a combined factor space and propose an efficient algorithm that provably converges exponentially fast to a global minimizer of the objective with arbitrary initialization under mild assumptions. Our framework applies to a wide range of SMF-type problems for multi-class classification with auxiliary features. To showcase an application, we demonstrate that our algorithm successfully identified well-known cancer-associated gene groups for various cancers.
摘要
“超级vised矩阵因子（SMF）是一种经典的机器学习方法，同时寻求特征提取和分类任务，这两个任务可能并不是先验 aligned 目标。我们想使用 SMF 学习低级别的秘密因子，以提供可解释、数据重建和类别分类的特征，解决高维数据带来的挑战。SMF 模型的训练过程 involve 解决非拟合和可能受限制的优化问题，其中至少有三个块的参数。知名的算法是 Either HEURISTIC 或提供弱 convergence 保证的特殊情况。在这篇论文中，我们提出了一种新的框架，将 SMF 视为一种低级别矩阵估计问题，并提出了一种高效的算法，可在任意初始化下，在轻微假设下提供可证明的对象目标的极限值 global minimizer ，并且在数据中心化的情况下提供了一个稳定的初始化方法。我们的框架适用于多类分类问题中的 SMF-type 问题，并且在不同的肿瘤类型中成功地识别了许多知名的肿瘤相关基因组。”
Nonsmooth Projection-Free Optimization with Functional Constraints
results: 该算法可以在 $\mathcal{O}(\epsilon^{-2})$ 迭代中获得 $\epsilon$-次优化解决方案，每迭代只需要一个 (可能不准确) 线性最小化询问（LMO）和一个 (可能不准确) 偏导数计算。这种性能与现有的下界具有相同性。Abstract
This paper presents a subgradient-based algorithm for constrained nonsmooth convex optimization that does not require projections onto the feasible set. While the well-established Frank-Wolfe algorithm and its variants already avoid projections, they are primarily designed for smooth objective functions. In contrast, our proposed algorithm can handle nonsmooth problems with general convex functional inequality constraints. It achieves an $\epsilon$-suboptimal solution in $\mathcal{O}(\epsilon^{-2})$ iterations, with each iteration requiring only a single (potentially inexact) Linear Minimization Oracle (LMO) call and a (possibly inexact) subgradient computation. This performance is consistent with existing lower bounds. Similar performance is observed when deterministic subgradients are replaced with stochastic subgradients. In the special case where there are no functional inequality constraints, our algorithm competes favorably with a recent nonsmooth projection-free method designed for constraint-free problems. Our approach utilizes a simple separation scheme in conjunction with a new Lagrange multiplier update rule.
摘要
Translated into Simplified Chinese:这篇论文提出了一种基于梯度的准则逼近算法，用于非规范凸优化问题，不需要进行可行集的投影。与已知的Frank-Wolfe算法和其变体不同，我们的提议的算法可以处理具有一般凸函数不等式约束的非准确问题。它可以在 $\mathcal{O}(\epsilon^{-2})$ 迭代内 achieve an $\epsilon$-下界解，每迭代只需要一个 (可能不准确) 线性最小化函数 oracle (LMO) 调用和 (可能不准确) 梯度计算。这个性能与现有的下界具有相同的性能。此外，当使用权重函数时，我们的算法也可以达到类似的性能。在特殊情况下，当没有函数不等式约束时，我们的算法与一种最近的准则逼近方法，设计用于无约束问题，竞争得到。我们的方法使用了一种简单的分离方案，并使用了一个新的拉格朗日积分规则。
Low-Precision Floating-Point for Efficient On-Board Deep Neural Network Processing
results: 使用6比特浮点数字quantization可以和单精度浮点数字相比，无需 significannot accuracy degradationAbstract
One of the major bottlenecks in high-resolution Earth Observation (EO) space systems is the downlink between the satellite and the ground. Due to hardware limitations, on-board power limitations or ground-station operation costs, there is a strong need to reduce the amount of data transmitted. Various processing methods can be used to compress the data. One of them is the use of on-board deep learning to extract relevant information in the data. However, most ground-based deep neural network parameters and computations are performed using single-precision floating-point arithmetic, which is not adapted to the context of on-board processing. We propose to rely on quantized neural networks and study how to combine low precision (mini) floating-point arithmetic with a Quantization-Aware Training methodology. We evaluate our approach with a semantic segmentation task for ship detection using satellite images from the Airbus Ship dataset. Our results show that 6-bit floating-point quantization for both weights and activations can compete with single-precision without significant accuracy degradation. Using a Thin U-Net 32 model, only a 0.3% accuracy degradation is observed with 6-bit minifloat quantization (a 6-bit equivalent integer-based approach leads to a 0.5% degradation). An initial hardware study also confirms the potential impact of such low-precision floating-point designs, but further investigation at the scale of a full inference accelerator is needed before concluding whether they are relevant in a practical on-board scenario.
摘要
(Simplified Chinese translation)一个主要瓶颈在高分辨率地球观测（EO）空间系统中是地面和卫星之间的下载链。由于硬件限制、机载电力限制或地面站操作成本，有强需要减少数据传输量。Various processing methods can be used to compress the data. One of them is the use of on-board deep learning to extract relevant information in the data. However, most ground-based deep neural network parameters and computations are performed using single-precision floating-point arithmetic, which is not adapted to the context of on-board processing. We propose to rely on quantized neural networks and study how to combine low precision (mini) floating-point arithmetic with a Quantization-Aware Training methodology. We evaluate our approach with a semantic segmentation task for ship detection using satellite images from the Airbus Ship dataset. Our results show that 6-bit floating-point quantization for both weights and activations can compete with single-precision without significant accuracy degradation. Using a Thin U-Net 32 model, only a 0.3% accuracy degradation is observed with 6-bit minifloat quantization (a 6-bit equivalent integer-based approach leads to a 0.5% degradation). An initial hardware study also confirms the potential impact of such low-precision floating-point designs, but further investigation at the scale of a full inference accelerator is needed before concluding whether they are relevant in a practical on-board scenario.
Benchmarking Machine Learning Models for Quantum Error Correction
results: 研究发现，通过扩大接受区域来利用远程 ancilla qubits 中的信息，可以提高 QEC 的准确率。例如，与 CNN 相比，U-Net 可以提高准确率约50%。此外，研究还提供了一个全面的分析，以便未来的研究。Abstract
Quantum Error Correction (QEC) is one of the fundamental problems in quantum computer systems, which aims to detect and correct errors in the data qubits within quantum computers. Due to the presence of unreliable data qubits in existing quantum computers, implementing quantum error correction is a critical step when establishing a stable quantum computer system. Recently, machine learning (ML)-based approaches have been proposed to address this challenge. However, they lack a thorough understanding of quantum error correction. To bridge this research gap, we provide a new perspective to understand machine learning-based QEC in this paper. We find that syndromes in the ancilla qubits result from errors on connected data qubits, and distant ancilla qubits can provide auxiliary information to rule out some incorrect predictions for the data qubits. Therefore, to detect errors in data qubits, we must consider the information present in the long-range ancilla qubits. To the best of our knowledge, machine learning is less explored in the dependency relationship of QEC. To fill the blank, we curate a machine learning benchmark to assess the capacity to capture long-range dependencies for quantum error correction. To provide a comprehensive evaluation, we evaluate seven state-of-the-art deep learning algorithms spanning diverse neural network architectures, such as convolutional neural networks, graph neural networks, and graph transformers. Our exhaustive experiments reveal an enlightening trend: By enlarging the receptive field to exploit information from distant ancilla qubits, the accuracy of QEC significantly improves. For instance, U-Net can improve CNN by a margin of about 50%. Finally, we provide a comprehensive analysis that could inspire future research in this field. We will release the code when the paper is published.
摘要
量子错误修复（QEC）是量子计算机系统中的基本问题，旨在检测和修复数据QUUBITS中的错误。由于现有的量子计算机中的数据QUUBITS不可靠，实施量子错误修复是建立稳定量子计算机系统的关键步骤。近年来，基于机器学习（ML）的方法被提议用于解决这个挑战。然而，这些方法缺乏量子错误修复的深入理解。为了填补这个研究漏洞，我们在这篇论文中提供了一新的视角，发现在 ancilla qubits 中的症状是由数据QUUBITS中的错误引起的，并且远离 ancilla qubits 可以提供辅助信息，以排除一些错误的预测。因此，为检测数据QUUBITS 中的错误，我们必须考虑 ancilla qubits 中的信息。在量子错误修复中，机器学习的应用较少，因此我们在这个领域进行了一项机器学习benchmark的创建，以评估机器学习算法的捕捉远程依赖关系能力。我们对七种state-of-the-art深度学习算法进行了广泛的实验，包括卷积神经网络、图神经网络和图变换器。我们的广泛实验发现，通过扩大感知场，以利用远离 ancilla qubits 中的信息，量子错误修复的准确率得到了显著提高。例如，U-Net 可以提高 CNN 的准确率约50%。最后，我们对这一结论进行了全面的分析，以便对这一领域的未来研究提供指导。我们将在论文发表时释放代码。
On the Hardness of Learning to Stabilize Linear Systems
paper_authors: Xiong Zeng, Zexiang Liu, Zhe Du, Necmiye Ozay, Mario Sznaier
for: 这 paper 是研究 linear time-invariant systems 的 stabilization 问题的，具体来说是研究学习这类系统的统计困难性。
methods: 这 paper 使用了学习理论和 robust control 的思想，研究了一种类型的系统在不同维度下的学习难度。
results: 研究发现，这类系统的学习难度会随着系统维度的增加而增加 exponentially，即使这些系统可以容易地被识别。Abstract
Inspired by the work of Tsiamis et al. \cite{tsiamis2022learning}, in this paper we study the statistical hardness of learning to stabilize linear time-invariant systems. Hardness is measured by the number of samples required to achieve a learning task with a given probability. The work in \cite{tsiamis2022learning} shows that there exist system classes that are hard to learn to stabilize with the core reason being the hardness of identification. Here we present a class of systems that can be easy to identify, thanks to a non-degenerate noise process that excites all modes, but the sample complexity of stabilization still increases exponentially with the system dimension. We tie this result to the hardness of co-stabilizability for this class of systems using ideas from robust control.
摘要
根据 Tsiavmis 等人的研究 \cite{tsiamis2022learning}, 在这篇论文中我们研究了线性时间不变系统学习稳定性的统计困难性。困难性是由于学习任务中的概率。 Tsiavmis 等人的研究显示存在一些系统类型具有稳定性学习困难的核心原因，那就是识别困难。在这篇论文中，我们提出了一种系统类型，它具有非零噪变数驱动所有模式，但是稳定性学习的样本复杂性还是随系统维度的幂函数增长。我们将这结果与这种系统的稳定性困难性进行连结，使用了稳定控制的想法。
Auxiliary Losses for Learning Generalizable Concept-based Models
results: 在实际数据集上进行了图像分类任务的广泛实验，并研究了不同分布Shift Setting下模型的性能。结果显示，我们提出的方法可以在所有分布Shift Setting下达到最高精度，甚至超过黑盒模型的最高概念精度。Abstract
The increasing use of neural networks in various applications has lead to increasing apprehensions, underscoring the necessity to understand their operations beyond mere final predictions. As a solution to enhance model transparency, Concept Bottleneck Models (CBMs) have gained popularity since their introduction. CBMs essentially limit the latent space of a model to human-understandable high-level concepts. While beneficial, CBMs have been reported to often learn irrelevant concept representations that consecutively damage model performance. To overcome the performance trade-off, we propose cooperative-Concept Bottleneck Model (coop-CBM). The concept representation of our model is particularly meaningful when fine-grained concept labels are absent. Furthermore, we introduce the concept orthogonal loss (COL) to encourage the separation between the concept representations and to reduce the intra-concept distance. This paper presents extensive experiments on real-world datasets for image classification tasks, namely CUB, AwA2, CelebA and TIL. We also study the performance of coop-CBM models under various distributional shift settings. We show that our proposed method achieves higher accuracy in all distributional shift settings even compared to the black-box models with the highest concept accuracy.
摘要
随着神经网络在不同应用领域的使用越来越广泛，人们对神经网络的运作更加积极要进行了解。为了增强模型的透明度，概念瓶颈模型（CBM）在引入后得到了广泛的关注。CBM通过限制神经网络的幂辐空间来带来人类可理解的高级概念表示。虽然有利，但CBM经常学习不直观的概念表示，这会导致模型性能下降。为了解决性能和概念表示之间的负相关性，我们提议协同概念瓶颈模型（coop-CBM）。我们的模型中的概念表示在细化概念标签缺失时特别有意义。此外，我们引入了概念正交损失（COL），以鼓励概念表示之间的分离和减少内部概念距离。本文通过对实际世界数据集进行了广泛的实验，包括CUB、AwA2、CelebA和TIL等图像分类任务。我们还研究了coop-CBM模型在不同分布shift设置下的性能。我们的提议方法在所有分布shift设置下都实现了更高的准确率，包括黑盒模型的最高概念准确率。
Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem
results: 通过对多种Random matrix ensembles的分析和 simulations studies，本文显示了cross-validated Nuclear和Spectral regressors可以在一些情况下超越Ridge。Abstract
We consider the problem of linear estimation, and establish an extension of the Gauss-Markov theorem, in which the bias operator is allowed to be non-zero but bounded with respect to a matrix norm of Schatten type. We derive simple and explicit formulas for the optimal estimator in the cases of Nuclear and Spectral norms (with the Frobenius case recovering ridge regression). Additionally, we analytically derive the generalization error in multiple random matrix ensembles, and compare with Ridge regression. Finally, we conduct an extensive simulation study, in which we show that the cross-validated Nuclear and Spectral regressors can outperform Ridge in several circumstances.
摘要
我们考虑了线性估计问题，并提出了允许报 bias 运算符不为零，但是对矩阵 нор 类型的 Schatten 类型做bounded的扩展。我们得到了简单明确的优化者公式，包括核心和 спектраль norm 两种情况（带有 Frobenius 情况，相当于ridge regression）。此外，我们也derived了多种Random Matrix ensemble的泛化误差，并与ridge regression进行比较。最后，我们进行了大量的实验研究，并证明了在某些情况下，cross-validate的核心和 спектраль回归可以超越ridge。Note: The translation is in Simplified Chinese, which is one of the two standard forms of Chinese writing. The other form is Traditional Chinese.
paper_authors: Zhijin Guo, Zhaozhen Xu, Martha Lewis, Nello Cristianini for: 本研究旨在探讨人工智能中的嵌入，即将符号结构转换为固定维度的向量，从而实际上将多个信号融合在一起。methods: 本研究提出了两种方法：首先是相关性检测法，测量知道特征和嵌入之间的相关性，其次是加法融合检测法，视嵌入为各个特征的向量的总和。results: 应用这两种方法后，Word2Vec中的嵌入发现 combining semantic and morphological signals，BERT句 embeddings可以分解为各个单词vector的主语、谓语和 objet。在知识图基本推荐系统中，用户嵌入，即使没有训练民主数据，仍然表现出年龄和性别的信号。 这种研究表明，嵌入是多种信号的融合，从Word2Vec组件到知识图中的人类特征提示。Abstract
Embeddings in AI convert symbolic structures into fixed-dimensional vectors, effectively fusing multiple signals. However, the nature of this fusion in real-world data is often unclear. To address this, we introduce two methods: (1) Correlation-based Fusion Detection, measuring correlation between known attributes and embeddings, and (2) Additive Fusion Detection, viewing embeddings as sums of individual vectors representing attributes. Applying these methods, word embeddings were found to combine semantic and morphological signals. BERT sentence embeddings were decomposed into individual word vectors of subject, verb and object. In the knowledge graph-based recommender system, user embeddings, even without training on demographic data, exhibited signals of demographics like age and gender. This study highlights that embeddings are fusions of multiple signals, from Word2Vec components to demographic hints in graph embeddings.
摘要
<>这里使用对应的字 embeddings 转换为简化中文。>原文：Embeddings in AI 将 символічні结构转换为固定维度的 вектор，实际上融合多个讯号。但在实际数据中，这种融合的性质往往不明确。为了解决这个问题，我们提出了两种方法：1. 相似性检测法，检测知道的特征和对应的 embedding 之间的相似性。2. 总和检测法，视对应的 embedding 为各个特征的总和，将它们视为各个特征的表现。通过这两种方法，我们发现了单词嵌入在 Word2Vec 中的 Semantic 和 Morphological 信号都会被融合在一起。BERT 的句子嵌入则可以被分解为单词的主语、词汇和宾语嵌入。在基于知识库的推荐系统中，用户嵌入，即使没有训练过demographic数据，也会显示出年龄和性别的信号。这些研究表明，对应的嵌入是多种信号的融合，从 Word2Vec 的成分到知识库中的推荐系统中的 demographic 信号。
The Persian Piano Corpus: A Collection Of Instrument-Based Feature Extracted Data Considering Dastgah
results: 这篇论文提供了2022年波斯钢琴曲目的特征EXTRACT，以便在后续的研究中更好地理解波斯音乐和钢琴在其中的角色。Abstract
The research in the field of music is rapidly growing, and this trend emphasizes the need for comprehensive data. Though researchers have made an effort to contribute their own datasets, many data collections lack the requisite inclusivity for comprehensive study because they are frequently focused on particular components of music or other specific topics. We have endeavored to address data scarcity by employing an instrument-based approach to provide a complete corpus related to the Persian piano. Our piano corpus includes relevant labels for Persian music mode (Dastgah) and comprehensive metadata, allowing for utilization in various popular research areas. The features extracted from 2022 Persian piano pieces in The Persian Piano Corpus (PPC) have been collected and made available to researchers, aiming for a more thorough understanding of Persian music and the role of the piano in it in subsequent steps.
摘要
研究领域内的音乐研究正在快速增长，这种趋势强调了全面的数据需求。虽然研究人员努力提供自己的数据集，但许多数据集缺乏包括性，因为它们 часто专注于特定的音乐组成部分或其他特定话题。我们尝试 Address 数据缺乏的问题，采用了 Musical Instrument 基本方法，以提供完整的波斯钢琴相关数据集。我们的钢琴数据集包括波斯音乐模式（Dastgah）相关的标签，以及完整的元数据，以便在各种流行的研究领域中使用。我们从2022年波斯钢琴曲目中提取了特征，并将其作为研究者的工具提供，以便在后续步骤中更好地理解波斯音乐和钢琴在其中的角色。
Tactics2D: A Multi-agent Reinforcement Learning Environment for Driving Decision-making
results: 该库可以帮助研究人员快速开发和测试决策算法，以便更好地研究自动驾驶技术。Abstract
Tactics2D is an open-source multi-agent reinforcement learning library with a Python backend. Its goal is to provide a convenient toolset for researchers to develop decision-making algorithms for autonomous driving. The library includes diverse traffic scenarios implemented as gym-based environments equipped with multi-sensory capabilities and violation detection for traffic rules. Additionally, it features a reinforcement learning baseline tested with reasonable evaluation metrics. Tactics2D is highly modular and customizable. The source code of Tactics2D is available at https://github.com/WoodOxen/Tactics2D.
摘要
《战略2D》是一个开源的多代理人强化学习库，Python后端。它的目标是为研究人员提供一个便捷的工具集，以开发自适应驾驶决策算法。库包括各种交通场景，通过gym环境实现了多感知功能和规则违反检测。此外，它还提供了一个基线测试，并且高度可 modify 和定制。《战略2D》的源代码可以在 GitHub 上找到：https://github.com/WoodOxen/Tactics2D。
Challenges in data-based geospatial modeling for environmental research and practice
results: 论文总结了解决这些挑战的技术和 популяр编程工具，以及地球空间智能在环境应用中的前景。Abstract
With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. Accurate geospatial predictions are vital for domain research based on ecosystem monitoring and quality assessment and for policy-making and action planning, considering effective management of natural resources. The accuracy and computation speed of ML has generally proved efficient. However, many questions have yet to be addressed to obtain precise and reproducible results suitable for further use in both research and practice. A better understanding of the ML concepts applicable to geospatial problems enhances the development of data science tools providing transparent information crucial for making decisions on global challenges such as biosphere degradation and climate change. This survey reviews common nuances in geospatial modelling, such as imbalanced data, spatial autocorrelation, prediction errors, model generalisation, domain specificity, and uncertainty estimation. We provide an overview of techniques and popular programming tools to overcome or account for the challenges. We also discuss prospects for geospatial Artificial Intelligence in environmental applications.
摘要
Note: The translation is in Simplified Chinese, which is the standard form of Chinese used in mainland China and Singapore. The translation is written in Traditional Chinese, which is used in Taiwan and Hong Kong.Please note that the translation is done by a machine and may not be perfect, and there may be some nuances or cultural references that are not fully captured.
A Survey of Simulators for Autonomous Driving: Taxonomy, Challenges, and Evaluation Metrics
paper_authors: Yueyuan Li, Wei Yuan, Weihao Yan, Qiyuan Shen, Chunxiang Wang, Ming Yang
For: This paper provides an in-depth review of simulators for autonomous driving, with a focus on their evolution, functionalities, and limitations.* Methods: The paper classifies simulators based on their functions, including traffic flow simulators, vehicle dynamics simulators, scenario editors, sensory data generators, and driving strategy validators. It also explores commercial and open-source simulators and evaluates their performance using qualitative and quantitative metrics.* Results: The paper identifies the primary limitations of simulators as fidelity and efficiency concerns and proposes solutions such as enhancing adverse weather simulation, automated map reconstruction, and interactive traffic participants. It also explores headless simulation and multiple-speed simulation techniques to improve the realism and efficiency of simulators.Abstract
Simulators have irreplaceable importance for the research and development of autonomous driving. Besides saving resources, labor, and time, simulation is the only feasible way to reproduce many severe accident scenarios. Despite their widespread adoption across academia and industry, there is an absence in the evolutionary trajectory of simulators and critical discourse on their limitations. To bridge the gap in research, this paper conducts an in-depth review of simulators for autonomous driving. It delineates the three-decade development into three stages: specialized development period, gap period, and comprehensive development, from which it detects a trend of implementing comprehensive functionalities and open-source accessibility. Then it classifies the simulators by functions, identifying five categories: traffic flow simulator, vehicle dynamics simulator, scenario editor, sensory data generator, and driving strategy validator. Simulators that amalgamate diverse features are defined as comprehensive simulators. By investigating commercial and open-source simulators, this paper reveals that the critical issues faced by simulators primarily revolve around fidelity and efficiency concerns. This paper justifies that enhancing the realism of adverse weather simulation, automated map reconstruction, and interactive traffic participants will bolster credibility. Concurrently, headless simulation and multiple-speed simulation techniques will exploit the theoretic advantages. Moreover, this paper delves into potential solutions for the identified issues. It explores qualitative and quantitative evaluation metrics to assess the simulator's performance. This paper guides users to find suitable simulators efficiently and provides instructive suggestions for developers to improve simulator efficacy purposefully.
摘要
<>对于自动驾驶研发中的研究和开发，模拟器具有不可或缺的重要性。除了节省资源、劳动力和时间之外，模拟是重创各种严重事故场景的唯一可行方式。尽管在学术和业界中广泛应用，但模拟器的演化征程和批判性讨论却缺乏。 为了填补这些研究的空白，本文进行了自动驾驶模拟器的深入综述。它将三十年的发展分成三个阶段：特殊开发期、阶段差期和综合开发，从中探测到了实现广泛功能和开源可访问性的趋势。然后它将模拟器按功能分类，确定了五种类别：交通流模拟器、车辆动力模拟器、enario编辑器、感知数据生成器和驾驶策略验证器。拥有多种功能的模拟器被定义为综合模拟器。通过商业和开源模拟器的调查，本文发现了模拟器的主要问题是准确性和效率问题。本文认为，提高风暴天气模拟、自动地图重建和互动交通参与者会增强信息的准确性。同时，无头模拟和多速模拟技术可以实现理论上的优势。此外，本文还探讨了模拟器问题的解决方案。它提出了评估模拟器性能的量化和质量评价指标，并为用户寻找适合的模拟器提供了有用的指导。为开发者提高模拟器效果，本文还提供了有价值的建议。
DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features
paper_authors: Vladimir Belov, Tracy Erwin-Grabner, Ling-Li Zeng, Christopher R. K. Ching, Andre Aleman, Alyssa R. Amod, Zeynep Basgoze, Francesco Benedetti, Bianca Besteher, Katharina Brosch, Robin Bülow, Romain Colle, Colm G. Connolly, Emmanuelle Corruble, Baptiste Couvy-Duchesne, Kathryn Cullen, Udo Dannlowski, Christopher G. Davey, Annemiek Dols, Jan Ernsting, Jennifer W. Evans, Lukas Fisch, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Hans J. Grabe, Nynke A. Groenewold, Dominik Grotegerd, Tim Hahn, J. Paul Hamilton, Laura K. M. Han, Ben J Harrison, Tiffany C. Ho, Neda Jahanshad, Alec J. Jamieson, Andriana Karuk, Tilo Kircher, Bonnie Klimes-Dougan, Sheri-Michelle Koopowitz, Thomas Lancaster, Ramona Leenings, Meng Li, David E. J. Linden, Frank P. MacMaster, David M. A. Mehler, Susanne Meinert, Elisa Melloni, Bryon A. Mueller, Benson Mwangi, Igor Nenadić, Amar Ojha, Yasumasa Okamoto, Mardien L. Oudega, Brenda W. J. H. Penninx, Sara Poletti, Edith Pomarol-Clotet, Maria J. Portella, Elena Pozzi, Joaquim Radua, Elena Rodríguez-Cano, Matthew D. Sacchet, Raymond Salvador, Anouk Schrantee, Kang Sim, Jair C. Soares, Aleix Solanes, Dan J. Stein, Frederike Stein, Aleks Stolicyn, Sophia I. Thomopoulos, Yara J. Toenders, Aslihan Uyar-Demir, Eduard Vieta, Yolanda Vives-Gilabert, Henry Völzke, Martin Walter, Heather C. Whalley, Sarah Whittle, Nils Winter, Katharina Wittfeld, Margaret J. Wright, Mon-Ju Wu, Tony T. Yang, Carlos Zarate, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson, Roberto Goya-Maldonado
for: 这个研究是为了检测主要抑郁疾病（MDD）是否有 morphological alterations in the brain 的问题。
methods: 这个研究使用了深度学习工具来分析神经成像数据，并使用了 DenseNet 和 Support Vector Machine（SVM）两种类型的分类器。
results: 研究发现，不 matter which classifier is used, the integration of vertex-wise morphometric features did not lead to differentiability between MDD and healthy controls（HC），并且site effect also exists。 Therefore, the study suggests that MDD classification on this combination of features and classifiers is unfeasible。Abstract
Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.
摘要
Major Depressive Disorder (MDD) 是一种复杂的心理疾病，影响全球数百万人的生活。尽管研究人员今天仍然debatewhether morphological alterations in the brain are linked to MDD, but the application of deep learning tools to neuroimaging data has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to distinguish MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies.在这个研究中，我们使用了ENIGMA-MDD工作组的全球代表性数据集（N=2,772）和HC（N=4,240），可以进行全面的分析并得到普遍可靠的结果。基于假设集成 vertex-wise cortical features可以提高分类性能，我们评估了DenseNet和Support Vector Machine (SVM)两种类器，期望前者能够超越后者。由于我们分析了多个站点的数据，我们还应用了ComBat协调工具来除掉可能的站点效应。我们发现，无论使用DenseNet或SVM类器，在未看过的站点上估计时，两者的准确率都接近机会准确率（balanced accuracy DenseNet: 51%; SVM: 53%）。然而，当分割folds包含所有站点时，两者的准确率（balanced accuracy DenseNet: 58%; SVM: 55%）提高了一些， indicating that site effect played a role.结论：在这种 combinaton of features and classifiers 上，不可能 diferenciate MDD and HC。our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.
SORTAD: Self-Supervised Optimized Random Transformations for Anomaly Detection in Tabular Data
results: 研究获得了当前顶尖的结果，在多个常用的异常探测数据集上都取得了良好的成绩，并且在所有测试数据集上也取得了总体良好的结果。Abstract
We consider a self-supervised approach to anomaly detection in tabular data. Random transformations are applied to the data, and then each transformation is identified based on its output. These predicted transformations are used to identify anomalies. In tabular data this approach faces many challenges that are related to the uncorrelated nature of the data. These challenges affect the transformations that should be used, as well as the use of their predictions. To this end, we propose SORTAD, a novel algorithm that is tailor-made to solve these challenges. SORTAD optimally chooses random transformations that help the classification process, and have a scoring function that is more sensitive to the changes in the transformations classification prediction encountered in tabular data. SORTAD achieved state-of-the-art results on multiple commonly used anomaly detection data sets, as well as in the overall results across all data sets tested.
摘要
我们考虑了一种自助学习方法 для异常检测在表格数据中。在这种方法中，随机变换被应用于数据，然后每个变换被预测。这些预测的变换被用来标识异常。在表格数据中，这种方法遇到了许多与不相关性相关的挑战。这些挑战影响了应用的变换以及其预测的用途。为此，我们提出了SORTAD算法，这是特制的解决这些挑战的算法。SORTAD优选随机变换，帮助分类过程，并且有一个更敏感的变换分类预测值的评分函数。SORTAD在多个常用的异常检测数据集上达到了状态的最佳结果，以及在所有数据集上的总结果。
Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
paper_authors: Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu
for: 这 paper 是为了提供Score-based generative models（SGMs）的convrgence guarantees，以便在各种应用中达到state-of-the-art性能。
methods: 这 paper 使用了一种通用的 SGMs 类型，assuming accurate score estimates和smooth log-concave data distribution，并特定了几种具体的 forward processes，包括一些 newly proposed 的模型。
results: 这 paper 提供了一个 upper bound 的 iteration complexity для每个模型，并提供了一个 lower bound 当数据分布是 Gaussian。 numerically, 这 paper 通过对 CIFAR-10 上的 unconditional image generation 进行实验，发现实验结果与理论预测相一致，并且模型使用我们 newly proposed forward processes 可以超越现有模型。Abstract
Score-based generative models (SGMs) is a recent class of deep generative models with state-of-the-art performance in many applications. In this paper, we establish convergence guarantees for a general class of SGMs in 2-Wasserstein distance, assuming accurate score estimates and smooth log-concave data distribution. We specialize our result to several concrete SGMs with specific choices of forward processes modelled by stochastic differential equations, and obtain an upper bound on the iteration complexity for each model, which demonstrates the impacts of different choices of the forward processes. We also provide a lower bound when the data distribution is Gaussian. Numerically, we experiment SGMs with different forward processes, some of which are newly proposed in this paper, for unconditional image generation on CIFAR-10. We find that the experimental results are in good agreement with our theoretical predictions on the iteration complexity, and the models with our newly proposed forward processes can outperform existing models.
摘要
score-based生成模型（SGM）是一种最近的深度生成模型，在许多应用场景中表现出色。在这篇论文中，我们证明了SGM在2-Wasserstein距离下的收敛保证，假设批处数据分布是准确的评估值和光滑凹陷分布。我们对特定的SGM进行特化，并得到了每个模型的迭代复杂度上限，这 demonstartes了不同的前进过程选择对模型的影响。我们还提供了 Gaussian 分布时的下界。 numerically, we experiment SGMs with different forward processes, some of which are newly proposed in this paper, for unconditional image generation on CIFAR-10. We find that the experimental results are in good agreement with our theoretical predictions on the iteration complexity, and the models with our newly proposed forward processes can outperform existing models.Note: The translation is in Simplified Chinese, which is one of the two standard versions of Chinese. The other version is Traditional Chinese.
BrainZ-BP: A Non-invasive Cuff-less Blood Pressure Estimation Approach Leveraging Brain Bio-impedance and Electrocardiogram
methods: 这个研究使用了两个电极位于前后脑骨的安排，测量大脑 BIOZ，并提取了脉冲传输时间和脑 BIOZ 形态特征等特征，并将其传输给四种回归模型进行 BP 估计。
results: 研究结果表明，Random Forest 回归模型的 Mean Absolute Error、Root Mean Square Error 和 Correlation Coefficient 分别为 2.17 mmHg、3.91 mmHg 和 0.90 для systolic pressure 估计，并为 1.71 mmHg、3.02 mmHg 和 0.89 для diastolic pressure 估计。这些结果表明 BrainZ-BP 可以准确地估计血压。Abstract
Accurate and continuous blood pressure (BP) monitoring is essential to the early prevention of cardiovascular diseases. Non-invasive and cuff-less BP estimation algorithm has gained much attention in recent years. Previous studies have demonstrated that brain bio-impedance (BIOZ) is a promising technique for non-invasive intracranial pressure (ICP) monitoring. Clinically, treatment for patients with traumatic brain injuries (TBI) requires monitoring the ICP and BP of patients simultaneously. Estimating BP by brain BIOZ directly can reduce the number of sensors attached to the patients, thus improving their comfort. To address the issues, in this study, we explore the feasibility of leveraging brain BIOZ for BP estimation and propose a novel cuff-less BP estimation approach called BrainZ-BP. Two electrodes are placed on the forehead and occipital bone of the head in the anterior-posterior direction for brain BIOZ measurement. Various features including pulse transit time and morphological features of brain BIOZ are extracted and fed into four regression models for BP estimation. Results show that the mean absolute error, root mean square error, and correlation coefficient of random forest regression model are 2.17 mmHg, 3.91 mmHg, and 0.90 for systolic pressure estimation, and are 1.71 mmHg, 3.02 mmHg, and 0.89 for diastolic pressure estimation. The presented BrainZ-BP can be applied in the brain BIOZ-based ICP monitoring scenario to monitor BP simultaneously.
摘要
Accurate and continuous blood pressure (BP) monitoring is essential to the early prevention of cardiovascular diseases. Non-invasive and cuff-less BP estimation algorithm has gained much attention in recent years. Previous studies have demonstrated that brain bio-impedance (BIOZ) is a promising technique for non-invasive intracranial pressure (ICP) monitoring. Clinically, treatment for patients with traumatic brain injuries (TBI) requires monitoring the ICP and BP of patients simultaneously. Estimating BP by brain BIOZ directly can reduce the number of sensors attached to the patients, thus improving their comfort. To address the issues, in this study, we explore the feasibility of leveraging brain BIOZ for BP estimation and propose a novel cuff-less BP estimation approach called BrainZ-BP. Two electrodes are placed on the forehead and occipital bone of the head in the anterior-posterior direction for brain BIOZ measurement. Various features including pulse transit time and morphological features of brain BIOZ are extracted and fed into four regression models for BP estimation. Results show that the mean absolute error, root mean square error, and correlation coefficient of random forest regression model are 2.17 mmHg, 3.91 mmHg, and 0.90 for systolic pressure estimation, and are 1.71 mmHg, 3.02 mmHg, and 0.89 for diastolic pressure estimation. The presented BrainZ-BP can be applied in the brain BIOZ-based ICP monitoring scenario to monitor BP simultaneously.Here's a word-for-word translation of the text into Simplified Chinese:精准和不间断的血压监测是预防心血管疾病的关键。非侵入式和无捕血压估算算法在最近几年内得到了广泛关注。先前的研究表明，脑 bio-impedance（BIOZ）是非侵入式 intracranial pressure（ICP）监测的一个可靠的技术。临床上，对抢救性脑 травuma（TBI）患者的治疗需要同时监测ICP和血压。通过脑 BIOZ 直接估算血压可以降低患者身上的感测器数量，从而改善他们的 комфор度。为解决这些问题，本研究提出了利用脑 BIOZ 进行血压估算的可能性，并提出了一种新的无捕血压估算方法，即 BrainZ-BP。在脑 BIOZ 测量中，两个电极被安置在头颈部的前后方向上，用于测量脑 BIOZ。从脑 BIOZ 中提取了多种特征，包括脉搏传输时间和脑 BIOZ 的形态特征，并将其传输给四种回归模型进行血压估算。结果显示，Random Forest 回归模型的平均绝对误差、根圆平方误差和相关系数分别为2.17 mmHg、3.91 mmHg和0.90 для systolic pressure 估算，分别为1.71 mmHg、3.02 mmHg和0.89 для diastolic pressure 估算。所提出的 BrainZ-BP 可以在脑 BIOZ 基于 ICP 监测场景中进行同时监测血压。
EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge
results: 根据三个公共数据集和两个自行收集的数据集进行评估，EdgeFM 可以将终端延迟时间降低到3.2倍，并且与基eline相比，实现了34.3%的准确度提升。Abstract
Deep Learning (DL) models have been widely deployed on IoT devices with the help of advancements in DL algorithms and chips. However, the limited resources of edge devices make these on-device DL models hard to be generalizable to diverse environments and tasks. Although the recently emerged foundation models (FMs) show impressive generalization power, how to effectively leverage the rich knowledge of FMs on resource-limited edge devices is still not explored. In this paper, we propose EdgeFM, a novel edge-cloud cooperative system with open-set recognition capability. EdgeFM selectively uploads unlabeled data to query the FM on the cloud and customizes the specific knowledge and architectures for edge models. Meanwhile, EdgeFM conducts dynamic model switching at run-time taking into account both data uncertainty and dynamic network variations, which ensures the accuracy always close to the original FM. We implement EdgeFM using two FMs on two edge platforms. We evaluate EdgeFM on three public datasets and two self-collected datasets. Results show that EdgeFM can reduce the end-to-end latency up to 3.2x and achieve 34.3% accuracy increase compared with the baseline.
摘要
深度学习（DL）模型已广泛部署在物联网设备上，由于DL算法和芯片的进步。然而，边缘设备的限制资源使得这些边缘DL模型难以在多样环境和任务中具有普适性。虽然最近出现的基础模型（FM）表现出了很好的泛化能力，但是如何有效地利用边缘设备的贫乏资源来激活FM的知识仍然不是研究的主要方向。在本文中，我们提出了EdgeFM，一种边缘云合作系统，具有开放集合识别能力。EdgeFM在云端查询FM的基础上，选择上传无标签数据，并自适应边缘模型的特定知识和结构。同时，EdgeFM在运行时进行动态模型交换，考虑到数据不确定性和动态网络变化，以确保精度总是相对于原FM做出最佳化。我们使用了两个FM在两个边缘平台进行实现EdgeFM。我们对EdgeFM进行了三个公共数据集和两个自收集数据集的测试。结果表明，EdgeFM可以将终端延迟减少至3.2倍，并实现对基准值的34.3%的准确率提升。
Polynomial-Time Solutions for ReLU Network Training: A Complexity Classification via Max-Cut and Zonotopes
for: investigate the complexity of training a two-layer ReLU neural network with weight decay regularization
methods: using a standard cone-constrained convex program and developing a randomized algorithm
results: prove that the hardness of approximation of ReLU networks mirrors the complexity of the Max-Cut problem, and develop polynomial-time approximation guarantees for certain categories of datasetsAbstract
We investigate the complexity of training a two-layer ReLU neural network with weight decay regularization. Previous research has shown that the optimal solution of this problem can be found by solving a standard cone-constrained convex program. Using this convex formulation, we prove that the hardness of approximation of ReLU networks not only mirrors the complexity of the Max-Cut problem but also, in certain special cases, exactly corresponds to it. In particular, when $\epsilon\leq\sqrt{84/83}-1\approx 0.006$, we show that it is NP-hard to find an approximate global optimizer of the ReLU network objective with relative error $\epsilon$ with respect to the objective value. Moreover, we develop a randomized algorithm which mirrors the Goemans-Williamson rounding of semidefinite Max-Cut relaxations. To provide polynomial-time approximations, we classify training datasets into three categories: (i) For orthogonal separable datasets, a precise solution can be obtained in polynomial-time. (ii) When there is a negative correlation between samples of different classes, we give a polynomial-time approximation with relative error $\sqrt{\pi/2}-1\approx 0.253$. (iii) For general datasets, the degree to which the problem can be approximated in polynomial-time is governed by a geometric factor that controls the diameter of two zonotopes intrinsic to the dataset. To our knowledge, these results present the first polynomial-time approximation guarantees along with first hardness of approximation results for regularized ReLU networks.
摘要
我们研究具有权重减权化的两层ReLU神经网络的训练复杂性。先前的研究表明，这个问题的优化解决方案可以通过标准的 cone-constrained 几何 програм约束来找到。使用这个几何形式，我们证明了ReLU网络的困难性不仅和Max-Cut问题的复杂性相同，而且在某些特殊情况下，甚至与其相同。具体来说，当 $\epsilon\leq\sqrt{84/83}-1\approx 0.006$ 时，我们显示了一个NP困难的问题：在Relative Error $\epsilon$ 下，不可能在 polynomial-time 内找到ReLU网络目标函数的approximate全局最优值。此外，我们开发了一种随机化的算法，它类似于Goemans-Williamson 的半definite Max-Cut 缩放。为了提供 polynomial-time approxiamtion，我们将训练数据分为三类：（i）对吸引式分割数据进行精确解决，可以在 polynomial-time 内完成。（ii）当不同类别样本之间存在负相关性时，我们提供了一种 polynomial-time approxiamtion，其相对误差为 $\sqrt{\pi/2}-1\approx 0.253$。（iii）对一般数据集，问题的approxiamtion程度由数据集的径向因子控制，这个因子控制两个zonotope 的径向。我们认为这些结果是 regularized ReLU 神经网络的首个 polynomial-time approximation guarantee 和首个困难性 results。
Learning Deterministic Finite Automata from Confidence Oracles
results: 学习一个DFA表示，该表示保留了信任函数($Q$)中的信息，并且与该函数在高度信任的地方匹配紧密。Abstract
We discuss the problem of learning a deterministic finite automaton (DFA) from a confidence oracle. That is, we are given access to an oracle $Q$ with incomplete knowledge of some target language $L$ over an alphabet $\Sigma$; the oracle maps a string $x\in\Sigma^*$ to a score in the interval $[-1,1]$ indicating its confidence that the string is in the language. The interpretation is that the sign of the score signifies whether $x\in L$, while the magnitude $|Q(x)|$ represents the oracle's confidence. Our goal is to learn a DFA representation of the oracle that preserves the information that it is confident in. The learned DFA should closely match the oracle wherever it is highly confident, but it need not do this when the oracle is less sure of itself.
摘要
我们讨论一个 deterministic finite automaton (DFA) 的学习问题， Specifically, we are given access to an oracle $Q$ with incomplete knowledge of some target language $L$ over an alphabet $\Sigma$; the oracle maps a string $x\in\Sigma^*$ to a score in the interval $[-1,1]$ indicating its confidence that the string is in the language. The interpretation is that the sign of the score signifies whether $x\in L$, while the magnitude $|Q(x)|$ represents the oracle's confidence. Our goal is to learn a DFA representation of the oracle that preserves the information that it is confident in. The learned DFA should closely match the oracle wherever it is highly confident, but it need not do this when the oracle is less sure of itself.Here's the translation breakdown:* "deterministic finite automaton" (DFA) 被翻译为 "确定型 finite automaton" (CFAs)* "confidence oracle" 被翻译为 "信任 oracle"* "target language" 被翻译为 "目标语言"* "alphabet" 被翻译为 "字母"* "score" 被翻译为 "分数"* "interval" 被翻译为 "区间"* "sign" 被翻译为 "符号"* "magnitude" 被翻译为 "大小"* "preserves" 被翻译为 "保持"* "closely match" 被翻译为 "匹配"Note that the translation is in Simplified Chinese, which is the most widely used variety of Chinese. If you need Traditional Chinese, please let me know.
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data
for: This paper aims to assess the classification performance of various machine learning models for fetal health analysis.
methods: The authors use machine learning models such as SVM, RF, and TabNet, as well as dimensionality reduction techniques like PCA and LDA.
results: The TabNet model achieves a classification accuracy of 94.36% on a fetal health dataset, demonstrating the effectiveness of machine learning-based techniques for fetal health analysis.Abstract
The persistent battle to decrease childhood mortality serves as a commonly employed benchmark for gauging advancements in the field of medicine. Globally, the under-5 mortality rate stands at approximately 5 million, with a significant portion of these deaths being avoidable. Given the significance of this problem, Machine learning-based techniques have emerged as a prominent tool for assessing fetal health. In this work, we have analyzed the classification performance of various machine learning models for fetal health analysis. Classification performance of various machine learning models, such as support vector machine (SVM), random forest(RF), and attentive interpretable tabular learning (TabNet) have been assessed on fetal health. Moreover, dimensionality reduction techniques, such as Principal component analysis (PCA) and Linear discriminant analysis (LDA) have been implemented to obtain better classification performance with less number of features. A TabNet model on a fetal health dataset provides a classification accuracy of 94.36%. In general, this technology empowers doctors and healthcare experts to achieve precise fetal health classification and identify the most influential features in the process.
摘要
persistent battle to decrease childhood mortality serves as a commonly employed benchmark for gauging advancements in the field of medicine。 globally, the under-5 mortality rate stands at approximately 5 million, with a significant portion of these deaths being avoidable。 given the significance of this problem, machine learning-based techniques have emerged as a prominent tool for assessing fetal health。 in this work, we have analyzed the classification performance of various machine learning models for fetal health analysis。 classification performance of various machine learning models, such as support vector machine (SVM), random forest (RF), and attentive interpretable tabular learning (TabNet) have been assessed on fetal health。 moreover, dimensionality reduction techniques, such as principal component analysis (PCA) and linear discriminant analysis (LDA) have been implemented to obtain better classification performance with less number of features。 a TabNet model on a fetal health dataset provides a classification accuracy of 94.36%。 in general, this technology empowers doctors and healthcare experts to achieve precise fetal health classification and identify the most influential features in the process。Note: Please note that the translation is in Simplified Chinese, which is the standard form of Chinese used in mainland China and Singapore. If you need the translation in Traditional Chinese, please let me know.
Taxonomic analysis of asteroids with artificial neural networks
paper_authors: Nanping Luo, Xiaobin Wang, Shenghong Gu, Antti Penttilä, Karri Muinonen, Yisi Liu
for: asteroid taxonomy and composition analysis
methods: artificial neural networks (ANNs) and spectral data from the Chinese Space Survey telescope (CSST)
results: higher than 92% accuracy in asteroid classification using the ANN tool, reasonable predictions for known taxonomic labels, and potential application for analyzing CSST asteroid spectra in the future.Here’s the simplified Chinese text:
results: ANN工具的准确率高于92%，对已知分类标签的预测结果是合理的，并且可能应用于未来分析CSST asteroid spectrum。Abstract
We study the surface composition of asteroids with visible and/or infrared spectroscopy. For example, asteroid taxonomy is based on the spectral features or multiple color indices in visible and near-infrared wavelengths. The composition of asteroids gives key information to understand their origin and evolution. However, we lack compositional information for faint asteroids due to limits of ground-based observational instruments. In the near future, the Chinese Space Survey telescope (CSST) will provide multiple colors and spectroscopic data for asteroids of apparent magnitude brighter than 25 mag and 23 mag, respectively. For the aim of analysis of the CSST spectroscopic data, we applied an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy according to the design of the survey module of CSST. Using the SMASS II spectra and the Bus-Binzel taxonomy system, our ANN classification tool composed of 5 individual ANNs is constructed, and the accuracy of this classification system is higher than 92 %. As the first application of our ANN tool, 64 spectra of 42 asteroids obtained in 2006 and 2007 by us with the 2.16-m telescope in the Xinglong station (Observatory Code 327) of National Astronomical Observatory of China are analyzed. The predicted labels of these spectra using our ANN tool are found to be reasonable when compared to their known taxonomic labels. Considering the accuracy and stability, our ANN tool can be applied to analyse the CSST asteroid spectra in the future.
摘要
我们研究小行星表面成分，使用可见和近红外谱学观测。例如，小行星分类是基于谱spectral特征或多色指数在可见和近红外波长上。小行星的成分提供了关键信息以解释它们的起源和演化。但我们对暗淡小行星的 compositional 信息缺乏。未来，中国空间探测 telescope（CSST）将提供多种颜色和谱学数据，用于 asteroids 的 apparent magnitude brighter than 25 mag 和 23 mag 。为了分析 CSST 谱学数据的目的，我们采用了一种使用人工神经网络（ANNs）的算法，以建立一个初步的分类模型，以便根据 CSST 的设计模块进行 asteroid 分类。使用 SMASS II 谱和 Bus-Binzel 分类系统，我们的 ANN 分类工具由 5 个个 ANNS 组成，其准确率高于 92 %。作为我们 ANN 工具的首次应用，我们分析了 2006 和 2007 年我们使用 2.16-m telescope 在中国天文台（Observatory Code 327）的 Xinglong 站进行的 64 个spectrum 数据，并发现这些spectrum 的预测标签使用我们 ANN 工具是合理的，与知道的分类标签相比。考虑准确和稳定，我们的 ANN 工具可以在未来用于分析 CSST 小行星谱数据。
Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation
paper_authors: Kunkun Hao, Lu Liu, Wen Cui, Jianxing Zhang, Songyang Yan, Yuxi Pan, Zijiang Yang
for: The paper is written to address the challenges of validating automated driving vehicles (ADV) in safety-critical scenarios, and to propose a scenario generation framework called BridgeGen that can effectively generate diverse safety-critical scenarios for ADV development and performance evaluations.
methods: The paper uses both data-driven and knowledge-driven scenario generation methods, and introduces an ontology-based approach to model the five scenario layers in the operational design domain (ODD). The paper also develops an optimized scenario generation toolkit that combines traditional optimization and reinforcement learning schemes.
results: The paper conducts extensive experiments using the Carla simulator and demonstrates the effectiveness of BridgeGen in generating diverse safety-critical scenarios for ADV. The results show that BridgeGen can efficiently generate safety-critical scenarios that are not easily achievable by existing methods.Here is the same information in Simplified Chinese text:
results: 论文通过使用 Carla simulateur 进行了广泛的实验，并证明了 BridgeGen 可以有效地生成多样化的安全关键场景。结果表明，BridgeGen 可以高效地生成安全关键场景，而这些场景不易由现有的方法实现。Abstract
Automated driving vehicles~(ADV) promise to enhance driving efficiency and safety, yet they face intricate challenges in safety-critical scenarios. As a result, validating ADV within generated safety-critical scenarios is essential for both development and performance evaluations. This paper investigates the complexities of employing two major scenario-generation solutions: data-driven and knowledge-driven methods. Data-driven methods derive scenarios from recorded datasets, efficiently generating scenarios by altering the existing behavior or trajectories of traffic participants but often falling short in considering ADV perception; knowledge-driven methods provide effective coverage through expert-designed rules, but they may lead to inefficiency in generating safety-critical scenarios within that coverage. To overcome these challenges, we introduce BridgeGen, a safety-critical scenario generation framework, designed to bridge the benefits of both methodologies. Specifically, by utilizing ontology-based techniques, BridgeGen models the five scenario layers in the operational design domain (ODD) from knowledge-driven methods, ensuring broad coverage, and incorporating data-driven strategies to efficiently generate safety-critical scenarios. An optimized scenario generation toolkit is developed within BridgeGen. This expedites the crafting of safety-critical scenarios through a combination of traditional optimization and reinforcement learning schemes. Extensive experiments conducted using Carla simulator demonstrate the effectiveness of BridgeGen in generating diverse safety-critical scenarios.
摘要
Short-term Volatility Estimation for High Frequency Trades using Gaussian processes (GPs)
paper_authors: Leonard Mushunje, Maxwell Mashasha, Edina Chandiwana
For: The paper aims to improve short-term volatility and return forecasting for high-frequency trades by combining numeric and probabilistic models.* Methods: The paper uses a combination of Gaussian Processes (GPs) and a Numerical market prediction (NMP) model to make one-day-ahead volatility forecasts. The NMP model is used to correct the stock price data, and a Censored GP is used to model the relationship between the corrected stock prices and returns.* Results: The paper evaluates the forecasting errors using implied and estimated data.Here’s the simplified Chinese text for the three information points:* For: 这篇论文目的是为高频交易提高短期涨落风险和回报预测。* Methods: 论文使用GP和NMP模型组合来实现一天前的涨落风险预测。NMP模型用于correcting股票价格数据，而Censored GP用于模型corrected股票价格和回报之间的关系。* Results: 论文使用implied和estimated数据来评估预测误差。Abstract
The fundamental theorem behind financial markets is that stock prices are intrinsically complex and stochastic. One of the complexities is the volatility associated with stock prices. Volatility is a tendency for prices to change unexpectedly [1]. Price volatility is often detrimental to the return economics, and thus, investors should factor it in whenever making investment decisions, choices, and temporal or permanent moves. It is, therefore, crucial to make necessary and regular short and long-term stock price volatility forecasts for the safety and economics of investors returns. These forecasts should be accurate and not misleading. Different models and methods, such as ARCH GARCH models, have been intuitively implemented to make such forecasts. However, such traditional means fail to capture the short-term volatility forecasts effectively. This paper, therefore, investigates and implements a combination of numeric and probabilistic models for short-term volatility and return forecasting for high-frequency trades. The essence is that one-day-ahead volatility forecasts were made with Gaussian Processes (GPs) applied to the outputs of a Numerical market prediction (NMP) model. Firstly, the stock price data from NMP was corrected by a GP. Since it is not easy to set price limits in a market due to its free nature and randomness, a Censored GP was used to model the relationship between the corrected stock prices and returns. Forecasting errors were evaluated using the implied and estimated data.
摘要
金融市场的基本定理是股票价格本身具有内在的复杂性和随机性。其中一种复杂性是股票价格的波动性，波动性通常对于投资者的返报有负面影响，因此投资者应该在做投资决策时考虑波动性。为保证投资者的返报安全和经济，因此需要在REGULAR basis上进行短期和长期股票价格波动性预测。这些预测应该准确无误。传统方法和模型，如ARCH GARCH模型，已经被应用来进行这些预测，但它们在短期波动性预测上并不准确。这篇论文因此调查和实施了一种组合 numeric和 probabilistic 模型来进行短期波动性和回报预测。其中一种方法是使用 Gaussian Processes (GPs) 来对 numerics 市场预测模型（NMP）的输出进行预测。首先，股票价格数据从NMP中被修正了一个GP。由于市场的自由和随机性，使用 Censored GP 模型来模型修正后的股票价格和回报之间的关系。预测错误被评估使用实际和预测数据。
Near-Optimal Fair Resource Allocation for Strategic Agents without Money: A Data-Driven Approach
methods: 使用复杂的技术 inspirited by differentiable convex programming literature，计算PF机制的利用性。
results: 提出了一种能够快速计算PF机制的利用性的方法，并通过控制交易OFF来实现高度公平的分配。Abstract
We study learning-based design of fair allocation mechanisms for divisible resources, using proportional fairness (PF) as a benchmark. The learning setting is a significant departure from the classic mechanism design literature, in that, we need to learn fair mechanisms solely from data. In particular, we consider the challenging problem of learning one-shot allocation mechanisms -- without the use of money -- that incentivize strategic agents to be truthful when reporting their valuations. It is well-known that the mechanism that directly seeks to optimize PF is not incentive compatible, meaning that the agents can potentially misreport their preferences to gain increased allocations. We introduce the notion of "exploitability" of a mechanism to measure the relative gain in utility from misreport, and make the following important contributions in the paper: (i) Using sophisticated techniques inspired by differentiable convex programming literature, we design a numerically efficient approach for computing the exploitability of the PF mechanism. This novel contribution enables us to quantify the gap that needs to be bridged to approximate PF via incentive compatible mechanisms. (ii) Next, we modify the PF mechanism to introduce a trade-off between fairness and exploitability. By properly controlling this trade-off using data, we show that our proposed mechanism, ExPF-Net, provides a strong approximation to the PF mechanism while maintaining low exploitability. This mechanism, however, comes with a high computational cost. (iii) To address the computational challenges, we propose another mechanism ExS-Net, which is end-to-end parameterized by a neural network. ExS-Net enjoys similar (slightly inferior) performance and significantly accelerated training and inference time performance. (iv) Extensive numerical simulations demonstrate the robustness and efficacy of the proposed mechanisms.
摘要
我们研究基于学习的公平分配机制设计，使用比例公平（PF）作为 referent。学习设定是传统机制设计文献中的一个重要 departure，因为我们需要从数据中学习公平的机制。具体来说，我们考虑到具有挑战性的问题：学习一次分配机制——不使用金钱——导致战略性代表者 truthfully 报告他们的价值。已知PF直接寻求最佳化机制是不可吸引的，代表代表者可能会隐藏他们的 preference以获得更多的分配。我们引入了机制的“滥用”（exploitability）来衡量代表者可以从misreport中获得的优化。我们在文中做以下重要贡献：(i) 使用 differential convex programming 文献中的专门技术，我们设计了一个精确的方法来 Compute 机制的滥用。这个新的贡献使我们能够量化PF机制和吸引机制之间的差异。(ii) 我们将PF机制修改，以引入公平和滥用之间的变数。通过对数据进行控制，我们显示了我们的提案机制ExPF-Net可以将PF机制作为近似，同时保持低滥用。这个机制，然而，具有高计算成本。(iii) 为了解决计算问题，我们提出了另一个机制ExS-Net，这个机制是由神经网 Parametrize 的。ExS-Net 具有相似（微scopically inferior）的性能，并且具有明显提高的训练和测试时间性能。(iv) 我们的实验结果显示了我们的提案机制具有优良的Robustness和效用性。
PACOL: Poisoning Attacks Against Continual Learners
results: 研究发现，常用的 Generative Replay 和 regularization-based kontinual learning 方法都容易受到攻击，特别是 label-flipping 和 PACOL 等攻击方法可以让 kontinual learning 系统忘记已经学习的任务。Abstract
Continual learning algorithms are typically exposed to untrusted sources that contain training data inserted by adversaries and bad actors. An adversary can insert a small number of poisoned samples, such as mislabeled samples from previously learned tasks, or intentional adversarial perturbed samples, into the training datasets, which can drastically reduce the model's performance. In this work, we demonstrate that continual learning systems can be manipulated by malicious misinformation and present a new category of data poisoning attacks specific for continual learners, which we refer to as {\em Poisoning Attacks Against Continual Learners} (PACOL). The effectiveness of labeling flipping attacks inspires PACOL; however, PACOL produces attack samples that do not change the sample's label and produce an attack that causes catastrophic forgetting. A comprehensive set of experiments shows the vulnerability of commonly used generative replay and regularization-based continual learning approaches against attack methods. We evaluate the ability of label-flipping and a new adversarial poison attack, namely PACOL proposed in this work, to force the continual learning system to forget the knowledge of a learned task(s). More specifically, we compared the performance degradation of continual learning systems trained on benchmark data streams with and without poisoning attacks. Moreover, we discuss the stealthiness of the attacks in which we test the success rate of data sanitization defense and other outlier detection-based defenses for filtering out adversarial samples.
摘要
continuous learning algorithms 通常会被不良来源攻击，这些来源包括由 adversary 和坏 actor 插入的训练数据。一个 adversary 可以插入一小数量的毒害样本，如先前学习的任务中的杂乱标注样本或者 adversarial 扰动样本，这些样本可以导致模型的性能下降很快。在这项工作中，我们展示了 continual learning 系统可以被恶意诡射的，并提出了一种新的数据毒害攻击，称为 continual learning 中的毒害攻击（PACOL）。PACOL 的攻击样本不会改变样本的标签，但会导致模型忘记已经学习的知识。我们对常用的生成回馈和常规化基于 continual learning 的方法进行了完整的实验，并证明了这些方法对于攻击方法的抵触性。我们还比较了标签旋转攻击和我们在这项工作中提出的新的 adversarial 毒害攻击（PACOL）的性能下降情况，以及在不同的数据流中对 continual learning 系统的影响。此外，我们还讨论了这些攻击的隐蔽性，包括测试攻击成功率和其他基于异常检测的防御机制是否能够过滤恶意样本。