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Towards Reducing Diagnostic Errors with Interpretable Risk Prediction 通过可解释的风险预测减少诊断错误
Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10109
Denis Jered McInerney, William Dickinson, Lucy Flynn, Andrea Young, Geoffrey Young, J.-W. van de Meent, Byron C. Wallace
Many diagnostic errors occur because clinicians cannot easily access relevant information in patient Electronic Health Records (EHRs). In this work we propose a method to use LLMs to identify pieces of evidence in patient EHR data that indicate increased or decreased risk of specific diagnoses; our ultimate aim is to increase access to evidence and reduce diagnostic errors. In particular, we propose a Neural Additive Model to make predictions backed by evidence with individualized risk estimates at time-points where clinicians are still uncertain, aiming to specifically mitigate delays in diagnosis and errors stemming from an incomplete differential. To train such a model, it is necessary to infer temporally fine-grained retrospective labels of eventual"true"diagnoses. We do so with LLMs, to ensure that the input text is from before a confident diagnosis can be made. We use an LLM to retrieve an initial pool of evidence, but then refine this set of evidence according to correlations learned by the model. We conduct an in-depth evaluation of the usefulness of our approach by simulating how it might be used by a clinician to decide between a pre-defined list of differential diagnoses.
由于临床医生无法轻松获取患者电子健康记录(EHR)中的相关信息,因此出现了许多诊断错误。在这项工作中,我们提出了一种方法,利用 LLMs 来识别病人电子健康记录数据中表明特定诊断风险增加或减少的证据片段;我们的最终目的是增加证据的获取途径,减少诊断错误。特别是,我们提出了一种神经相加模型,在临床医生仍不确定的时间点上,以证据为支持进行预测,并提供个性化的风险估计,目的是特别减少因不完全鉴别而导致的诊断延误和错误。要训练这样一个模型,就必须推断出最终 "真实 "诊断的时间细粒度回溯标签。我们使用 LLM 来完成这项工作,以确保在做出可靠诊断之前,输入的文本是真实的。我们使用 LLM 来检索初始证据库,然后根据模型学习到的相关性来完善这组证据。我们通过模拟临床医生如何使用我们的方法在预定义的鉴别诊断列表中做出决定,对我们的方法的实用性进行了深入评估。
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引用次数: 0
Classification Diffusion Models 分类扩散模型
Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10095
Shahar Yadin, Noam Elata, T. Michaeli
A prominent family of methods for learning data distributions relies on density ratio estimation (DRE), where a model is trained to $textit{classify}$ between data samples and samples from some reference distribution. These techniques are successful in simple low-dimensional settings but fail to achieve good results on complex high-dimensional data, like images. A different family of methods for learning distributions is that of denoising diffusion models (DDMs), in which a model is trained to $textit{denoise}$ data samples. These approaches achieve state-of-the-art results in image, video, and audio generation. In this work, we present $textit{Classification Diffusion Models}$ (CDMs), a generative technique that adopts the denoising-based formalism of DDMs while making use of a classifier that predicts the amount of noise added to a clean signal, similarly to DRE methods. Our approach is based on the observation that an MSE-optimal denoiser for white Gaussian noise can be expressed in terms of the gradient of a cross-entropy-optimal classifier for predicting the noise level. As we illustrate, CDM achieves better denoising results compared to DDM, and leads to at least comparable FID in image generation. CDM is also capable of highly efficient one-step exact likelihood estimation, achieving state-of-the-art results among methods that use a single step. Code is available on the project's webpage in https://shaharYadin.github.io/CDM/ .
学习数据分布的一系列著名方法都依赖于密度比估计(DRE),即训练模型在数据样本和来自某种参考分布的样本之间进行 $textit{classify}$。这些技术在简单的低维设置中取得了成功,但在复杂的高维数据(如图像)中却无法取得良好的效果。去噪扩散模型(Denoising diffusion models,DDMs)是学习分布的一个不同方法系列,其中一个模型被训练为 $textit{denoise}$ 数据样本。这些方法在图像、视频和音频生成方面取得了最先进的成果。在这项工作中,我们提出了$textit{分类扩散模型}$ (CDMs),这是一种生成技术,它采用了 DDMs 基于去噪的形式主义,同时利用分类器预测添加到干净信号中的噪声量,与 DRE 方法类似。我们的方法基于以下观察:白高斯噪声的 MSE 最佳去噪器可以用预测噪声水平的交叉熵最佳分类器的梯度来表示。正如我们所说明的,CDM 与 DDM 相比能获得更好的去噪效果,在生成图像时至少能达到相当的 FID。CDM 还能进行高效的单步精确似然估计,在使用单步估计的方法中取得了最先进的结果。代码可在该项目的网页 https://shaharYadin.github.io/CDM/ 上获取。
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引用次数: 0
RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models RS-DPO:用于大型语言模型对齐的混合拒绝采样和直接偏好优化方法
Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10038
Saeed Khaki, JinJin Li, Lan Ma, Liu Yang, Prathap Ramachandra
Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter finetuning, and computationally expensive to maximize the estimated reward during alignment. Recently, direct preference optimization (DPO) is proposed to address those challenges. However, DPO relies on contrastive responses generated from human annotator and alternative LLM, instead of the policy model, limiting the effectiveness of the RLHF. In this paper, we addresses both challenges by systematically combining rejection sampling (RS) and DPO. Our proposed method, RS-DPO, initiates with the development of a supervised fine-tuned policy model (SFT). A varied set of k responses per prompt are sampled directly from the SFT model. RS-DPO identifies pairs of contrastive samples based on their reward distribution. Finally, we apply DPO with the contrastive samples to align the model to human preference. Our experiments indicate that our proposed method effectively fine-tunes LLMs with limited resource environments, leading to improved alignment with user intent. Furthermore, it outperforms existing methods, including RS, PPO, and DPO.
来自人类反馈的强化学习(RLHF)已被广泛用于将大型语言模型与用户意图相匹配。然而,基于近端策略优化(PPO)的 RLHF 有时并不稳定,需要对超参数进行大量微调,而且在对齐过程中要使估计奖励最大化,计算成本很高。最近,有人提出了直接偏好优化(DPO)来应对这些挑战。然而,DPO 依赖于人类注释者和替代 LLM 生成的对比反应,而不是策略模型,从而限制了 RLHF 的有效性。在本文中,我们通过系统地结合拒绝采样(RS)和 DPO 来解决这两个难题。我们提出的 RS-DPO 方法首先要开发一个有监督的微调策略模型(SFT)。直接从 SFT 模型中抽取每个提示的 k 个不同响应集。RS-DPO 根据其奖励分布确定成对的对比样本。最后,我们对对比样本应用 DPO,使模型与人类偏好保持一致。我们的实验表明,我们提出的方法能在资源有限的环境下有效地微调 LLM,从而改善与用户意图的一致性。此外,它还优于 RS、PPO 和 DPO 等现有方法。
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引用次数: 0
Modeling the Impact of Timeline Algorithms on Opinion Dynamics Using Low-rank Updates 利用低等级更新模拟时间轴算法对舆论动态的影响
Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10053
Tianyi Zhou, Stefan Neumann, Kiran Garimella, A. Gionis
Timeline algorithms are key parts of online social networks, but during recent years they have been blamed for increasing polarization and disagreement in our society. Opinion-dynamics models have been used to study a variety of phenomena in online social networks, but an open question remains on how these models can be augmented to take into account the fine-grained impact of user-level timeline algorithms. We make progress on this question by providing a way to model the impact of timeline algorithms on opinion dynamics. Specifically, we show how the popular Friedkin--Johnsen opinion-formation model can be augmented based on aggregate information, extracted from timeline data. We use our model to study the problem of minimizing the polarization and disagreement; we assume that we are allowed to make small changes to the users' timeline compositions by strengthening some topics of discussion and penalizing some others. We present a gradient descent-based algorithm for this problem, and show that under realistic parameter settings, our algorithm computes a $(1+varepsilon)$-approximate solution in time $tilde{O}(msqrt{n} lg(1/varepsilon))$, where $m$ is the number of edges in the graph and $n$ is the number of vertices. We also present an algorithm that provably computes an $varepsilon$-approximation of our model in near-linear time. We evaluate our method on real-world data and show that it effectively reduces the polarization and disagreement in the network. Finally, we release an anonymized graph dataset with ground-truth opinions and more than 27,000 nodes (the previously largest publicly available dataset contains less than 550 nodes).
时间轴算法是在线社交网络的关键部分,但近年来它们却被指责为加剧社会两极分化和分歧的罪魁祸首。舆论动力学模型已被用于研究在线社交网络中的各种现象,但如何增强这些模型以考虑到用户级时间轴算法的细粒度影响,仍是一个未决问题。我们提供了一种方法来模拟时间轴算法对舆论动态的影响,从而在这一问题上取得了进展。具体来说,我们展示了流行的弗里德金-约翰逊(Friedkin-Johnsen)舆论形成模型如何基于从时间轴数据中提取的综合信息进行扩展。我们使用我们的模型来研究最小化两极分化和分歧的问题;我们假设允许我们通过加强一些讨论话题和惩罚另一些讨论话题来对用户的时间轴构成进行微小的改变。我们针对这个问题提出了一种基于梯度下降的算法,并证明在现实的参数设置下,我们的算法可以在 $tilde{O}(msqrt{n} 的时间内计算出一个 $(1+varepsilon)$ 近似解。lg(1/varepsilon))$,其中 $m$ 是图中边的数量,$n$ 是顶点的数量。我们还提出了一种算法,可以证明它能在接近线性的时间内计算出我们模型的 $varepsilon$ 近似值。我们在真实世界的数据上评估了我们的方法,结果表明它能有效减少网络中的两极分化和分歧。最后,我们发布了一个匿名图数据集,其中包含地面实况意见和超过 27,000 个节点(之前最大的公开数据集包含不到 550 个节点)。
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引用次数: 0
VisIRNet: Deep Image Alignment for UAV-taken Visible and Infrared Image Pairs VisIRNet:无人机拍摄的可见光和红外图像对的深度图像配准
Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09635
Sedat Ozer, A. P. Ndigande
This paper proposes a deep learning based solution for multi-modal image alignment regarding UAV-taken images. Many recently proposed state-of-the-art alignment techniques rely on using Lucas-Kanade (LK) based solutions for a successful alignment. However, we show that we can achieve state of the art results without using LK-based methods. Our approach carefully utilizes a two-branch based convolutional neural network (CNN) based on feature embedding blocks. We propose two variants of our approach, where in the first variant (ModelA), we directly predict the new coordinates of only the four corners of the image to be aligned; and in the second one (ModelB), we predict the homography matrix directly. Applying alignment on the image corners forces algorithm to match only those four corners as opposed to computing and matching many (key)points, since the latter may cause many outliers, yielding less accurate alignment. We test our proposed approach on four aerial datasets and obtain state of the art results, when compared to the existing recent deep LK-based architectures.
本文针对无人机拍摄的图像,提出了一种基于深度学习的多模态图像配准解决方案。最近提出的许多最先进的配准技术都依赖于使用基于卢卡斯-卡纳德(LK)的解决方案来成功配准。然而,我们的研究表明,无需使用基于 LK 的方法,我们也能获得最先进的结果。我们的方法谨慎地利用了基于特征嵌入块的双分支卷积神经网络(CNN)。我们提出了两种方法的变体,在第一种变体(模型 A)中,我们只直接预测待对齐图像四个角的新坐标;而在第二种变体(模型 B)中,我们直接预测同构矩阵。与计算和匹配许多(关键)点相比,只对图像的四个角进行配准会迫使算法只匹配这四个角,因为后者可能会导致许多异常值,从而降低配准的准确性。我们在四个航空数据集上测试了我们提出的方法,并与现有的基于深度 LK 的架构进行了比较,得出了最先进的结果。
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引用次数: 0
Inversion of limited-aperture Fresnel experimental data using orthogonality sampling method with single and multiple sources 使用正交采样法对有限孔径菲涅尔实验数据进行单源和多源反演
Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09740
Won-Kwang Park
In this study, we consider the application of orthogonality sampling method (OSM) with single and multiple sources for a fast identification of small objects in limited-aperture inverse scattering problem. We first apply the OSM with single source and show that the indicator function with single source can be expressed by the Bessel function of order zero of the first kind, infinite series of Bessel function of nonzero integer order of the first kind, range of signal receiver, and the location of emitter. Based on this result, we explain that the objects can be identified through the OSM with single source but the identification is significantly influenced by the location of source and applied frequency. For a successful improvement, we then consider the OSM with multiple sources. Based on the identified structure of the OSM with single source, we design an indicator function of the OSM with multiple sources and show that it can be expressed by the square of the Bessel function of order zero of the first kind an infinite series of the square of Bessel function of nonzero integer order of the first kind. Based on the theoretical results, we explain that the objects can be identified uniquely through the designed OSM. Several numerical experiments with experimental data provided by the Institute Fresnel demonstrate the pros and cons of the OSM with single source and how the designed OSM with multiple sources behave.
在本研究中,我们考虑应用单源和多源的正交采样法(OSM)来快速识别有限孔径反向散射问题中的小物体。我们首先应用了单源的正交采样法,结果表明单源的指示函数可以用第一类零阶贝塞尔函数、第一类非零整数阶贝塞尔函数的无穷级数、信号接收器的范围和发射器的位置来表示。基于这一结果,我们解释说,通过单源 OSM 可以识别物体,但识别效果受到源位置和应用频率的显著影响。为了成功改进,我们随后考虑了多源 OSM。根据单源 OSM 的识别结构,我们设计了多源 OSM 的指示函数,并证明它可以用第一类零阶贝塞尔函数的平方和第一类非零整数阶贝塞尔函数平方的无穷级数来表示。基于理论结果,我们解释了通过设计的 OSM 可以唯一地识别物体。利用菲涅尔研究所提供的实验数据进行的几项数值实验证明了单光源 OSM 的优缺点,以及所设计的多光源 OSM 的性能。
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引用次数: 0
Reproducing, Extending, and Analyzing Naming Experiments 复制、扩展和分析命名实验
Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10022
Rachel Alpern, Ido Lazer, Issar Tzachor, Hanit Hakim, Sapir Weissbuch, D. Feitelson
Naming is very important in software development, as names are often the only vehicle of meaning about what the code is intended to do. A recent study on how developers choose names collected the names given by different developers for the same objects. This enabled a study of these names' diversity and structure, and the construction of a model of how names are created. We reproduce different parts of this study in three independent experiments. Importantly, we employ methodological variations rather than striving of an exact replication. When the same results are obtained this then boosts our confidence in their validity by demonstrating that they do not depend on the methodology. Our results indeed corroborate those of the original study in terms of the diversity of names, the low probability of two developers choosing the same name, and the finding that experienced developers tend to use slightly longer names than inexperienced students. We explain name diversity by performing a new analysis of the names, classifying the concepts represented in them as universal (agreed upon), alternative (reflecting divergent views on a topic), or optional (reflecting divergent opinions on whether to include this concept at all). This classification enables new research directions concerning the considerations involved in naming decisions. We also show that explicitly using the model proposed in the original study to guide naming leads to the creation of better names, whereas the simpler approach of just asking participants to use longer and more detailed names does not.
命名在软件开发中非常重要,因为名称往往是代码意图的唯一载体。最近一项关于开发人员如何选择名称的研究收集了不同开发人员为相同对象所起的名称。这使得我们能够对这些名称的多样性和结构进行研究,并构建名称创建模型。我们在三个独立实验中重现了这项研究的不同部分。重要的是,我们采用了不同的方法,而不是力求完全相同。当获得相同的结果时,我们就会增强对其有效性的信心,证明这些结果并不依赖于方法。我们的结果确实证实了原始研究的结果,包括名称的多样性、两个开发人员选择相同名称的概率较低,以及发现有经验的开发人员倾向于使用比没有经验的学生稍长的名称。我们通过对名称进行新的分析来解释名称的多样性,并将名称中代表的概念分为普遍概念(一致同意)、替代概念(反映了对某一主题的不同看法)或可选概念(反映了对是否包含这一概念的不同看法)。这种分类为命名决策中的考虑因素提供了新的研究方向。我们还表明,明确使用原始研究中提出的模型来指导命名会产生更好的名称,而仅仅要求参与者使用更长、更详细的名称这种简单的方法则不会。
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引用次数: 0
Characterizing Role Models in Software Practitioners' Career: An Interview Study 描述软件从业人员职业生涯中的榜样:访谈研究
Pub Date : 2024-02-15 DOI: 10.1145/3641822.3641883
Mary S'anchez-Gord'on, Ricardo Colomo Palacios, Alex Sanchez Gordon
A role model is a person who serves as an example for others to follow, especially in terms of values, behavior, achievements, and personal characteristics. In this paper, authors study how role models influence software practitioners careers, an aspect not studied in the literature before. By means of this study, authors aim to understand if there are any salient role model archetypes and what characteristics are valued by participants in their role models. To do so, authors use a thematic coding approach to analyze the data collected from interviewing ten Latin American software practitioners. Findings reveal that role models were perceived as sources of knowledge, yet the majority of participants, regardless of their career stage, displayed a stronger interest in the human side and the moral values that their role models embodied. This study also shows that any practitioner can be viewed as a role model.
榜样是指在价值观、行为、成就和个人特征等方面作为他人学习榜样的人。在本文中,作者研究了榜样如何影响软件从业人员的职业生涯,这是以前的文献中没有研究过的一个方面。通过这项研究,作者旨在了解是否存在任何突出的榜样原型,以及参与者重视榜样的哪些特征。为此,作者采用主题编码方法,对采访十位拉美软件从业人员收集到的数据进行了分析。研究结果表明,榜样被视为知识的源泉,但大多数参与者,无论其职业阶段如何,都对榜样所体现的人性一面和道德价值观表现出更浓厚的兴趣。这项研究还表明,任何从业人员都可以被视为榜样。
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引用次数: 0
Orthogonal Time Frequency Space for Integrated Sensing and Communication: A Survey 用于综合传感与通信的正交时频空间:调查
Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09637
Eyad Shtaiwi, Ahmed Abdelhadi, Husheng Li, Zhu Han, H. V. Poor
Sixth-generation (6G) wireless communication systems, as stated in the European 6G flagship project Hexa-X, are anticipated to feature the integration of intelligence, communication, sensing, positioning, and computation. An important aspect of this integration is integrated sensing and communication (ISAC), in which the same waveform is used for both systems both sensing and communication, to address the challenge of spectrum scarcity. Recently, the orthogonal time frequency space (OTFS) waveform has been proposed to address OFDM's limitations due to the high Doppler spread in some future wireless communication systems. In this paper, we review existing OTFS waveforms for ISAC systems and provide some insights into future research. Firstly, we introduce the basic principles and a system model of OTFS and provide a foundational understanding of this innovative technology's core concepts and architecture. Subsequently, we present an overview of OTFS-based ISAC system frameworks. We provide a comprehensive review of recent research developments and the current state of the art in the field of OTFS-assisted ISAC systems to gain a thorough understanding of the current landscape and advancements. Furthermore, we perform a thorough comparison between OTFS-enabled ISAC operations and traditional OFDM, highlighting the distinctive advantages of OTFS, especially in high Doppler spread scenarios. Subsequently, we address the primary challenges facing OTFS-based ISAC systems, identifying potential limitations and drawbacks. Then, finally, we suggest future research directions, aiming to inspire further innovation in the 6G wireless communication landscape.
如欧洲 6G 旗舰项目 Hexa-X 所述,第六代(6G)无线通信系统预计将实现智能、通信、传感、定位和计算的集成。这种集成的一个重要方面是综合传感和通信(ISAC),即传感和通信两个系统使用相同的波形,以应对频谱稀缺的挑战。最近,有人提出了正交时频空间(OTFS)波形,以解决 OFDM 因未来某些无线通信系统中的高多普勒频差而受到的限制。在本文中,我们回顾了用于 ISAC 系统的现有 OTFS 波形,并对未来的研究提出了一些见解。首先,我们介绍了 OTFS 的基本原理和系统模型,并对这一创新技术的核心概念和架构提供了基础性的理解。随后,我们概述了基于 OTFS 的 ISAC 系统框架。我们全面回顾了 OTFS 辅助 ISAC 系统领域的最新研究进展和技术现状,以全面了解当前的格局和进展。此外,我们还对支持 OTFS 的 ISAC 操作和传统 OFDM 进行了全面比较,突出强调了 OTFS 的独特优势,尤其是在高多普勒传播情况下。随后,我们讨论了基于 OTFS 的 ISAC 系统面临的主要挑战,指出了潜在的局限性和缺点。最后,我们提出了未来的研究方向,旨在激发 6G 无线通信领域的进一步创新。
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引用次数: 0
Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fields Reg-NF:神经场内隐含曲面的高效注册
Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09722
Stephen Hausler, David Hall, Sutharsan Mahendren, Peyman Moghadam
Neural fields, coordinate-based neural networks, have recently gained popularity for implicitly representing a scene. In contrast to classical methods that are based on explicit representations such as point clouds, neural fields provide a continuous scene representation able to represent 3D geometry and appearance in a way which is compact and ideal for robotics applications. However, limited prior methods have investigated registering multiple neural fields by directly utilising these continuous implicit representations. In this paper, we present Reg-NF, a neural fields-based registration that optimises for the relative 6-DoF transformation between two arbitrary neural fields, even if those two fields have different scale factors. Key components of Reg-NF include a bidirectional registration loss, multi-view surface sampling, and utilisation of volumetric signed distance functions (SDFs). We showcase our approach on a new neural field dataset for evaluating registration problems. We provide an exhaustive set of experiments and ablation studies to identify the performance of our approach, while also discussing limitations to provide future direction to the research community on open challenges in utilizing neural fields in unconstrained environments.
神经场是一种基于坐标的神经网络,最近在隐式表示场景方面大受欢迎。与基于显式表示(如点云)的传统方法相比,神经场提供了一种连续的场景表示,能够以一种紧凑的方式表示三维几何和外观,是机器人应用的理想选择。然而,此前通过直接利用这些连续的隐式表示来研究多个神经场注册的方法非常有限。在本文中,我们介绍了 Reg-NF,这是一种基于神经场的配准方法,可优化两个任意神经场之间的相对 6-DoF 变换,即使这两个神经场具有不同的比例因子。Reg-NF 的关键组成部分包括双向配准损失、多视角表面采样和利用体积符号距离函数 (SDF)。我们在一个用于评估配准问题的新神经场数据集上展示了我们的方法。我们提供了一套详尽的实验和消融研究,以确定我们方法的性能,同时还讨论了局限性,为研究界在无约束环境中利用神经场的公开挑战提供了未来方向。
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引用次数: 0
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