We present a detailed study on Variational Autoencoders (VAEs) for anomalous jet tagging at the Large Hadron Collider. By taking in low-level jet constituents' information, and training with background QCD jets in an unsupervised manner, the VAE is able to encode important information for reconstructing jets, while learning an expressive posterior distribution in the latent space. When using the VAE as an anomaly detector, we present different approaches to detect anomalies: directly comparing in the input space or, instead, working in the latent space. In order to facilitate general search approaches such as bump-hunt, mass-decorrelated VAEs based on distance correlation regularization are also studied. We find that the naive mass-decorrelated VAEs fail at maintaining proper detection performance, by assigning higher probabilities to some anomalous samples. To build a performant mass-decorrelated anomalous jet tagger, we propose the Outlier Exposed VAE (OE-VAE), for which some outlier samples are introduced in the training process to guide the learned information. OE-VAEs are employed to achieve two goals at the same time: increasing sensitivity of outlier detection and decorrelating jet mass from the anomaly score. We succeed in reaching excellent results from both aspects. Code implementation of this work can be found at https://github.com/taolicheng/VAE-Jet

Most camera lens systems are designed in isolation, separately from downstream computer vision methods. Recently, joint optimization approaches that design lenses alongside other components of the image acquisition and processing pipeline -- notably, downstream neural networks -- have achieved improved imaging quality or better performance on vision tasks. However, these existing methods optimize only a subset of lens parameters and cannot optimize glass materials given their categorical nature. In this work, we develop a differentiable spherical lens simulation model that accurately captures geometrical aberrations. We propose an optimization strategy to address the challenges of lens design -- notorious for non-convex loss function landscapes and many manufacturing constraints -- that are exacerbated in joint optimization tasks. Specifically, we introduce quantized continuous glass variables to facilitate the optimization and selection of glass materials in an end-to-end design context, and couple this with carefully designed constraints to support manufacturability. In automotive object detection, we show improved detection performance over existing designs even when simplifying designs to two- or three-element lenses, despite significantly degrading the image quality. Code and optical designs will be made publicly available.

People are not very good at detecting lies, which may explain why they refrain from accusing others of lying, given the social costs attached to false accusations - both for the accuser and the accused. Here we consider how this social balance might be disrupted by the availability of lie-detection algorithms powered by Artificial Intelligence. Will people elect to use lie detection algorithms that perform better than humans, and if so, will they show less restraint in their accusations? We built a machine learning classifier whose accuracy (67\%) was significantly better than human accuracy (50\%) in a lie-detection task and conducted an incentivized lie-detection experiment in which we measured participants' propensity to use the algorithm, as well as the impact of that use on accusation rates. We find that the few people (33\%) who elect to use the algorithm drastically increase their accusation rates (from 25\% in the baseline condition up to 86% when the algorithm flags a statement as a lie). They make more false accusations (18pp increase), but at the same time, the probability of a lie remaining undetected is much lower in this group (36pp decrease). We consider individual motivations for using lie detection algorithms and the social implications of these algorithms.

We consider a model where a signal (discrete or continuous) is observed with an additive Gaussian noise process. The signal is issued from a linear combination of a finite but increasing number of translated features. The features are continuously parameterized by their location and depend on some scale parameter. First, we extend previous prediction results for off-the-grid estimators by taking into account here that the scale parameter may vary. The prediction bounds are analogous, but we improve the minimal distance between two consecutive features locations in order to achieve these bounds. Next, we propose a goodness-of-fit test for the model and give non-asymptotic upper bounds of the testing risk and of the minimax separation rate between two distinguishable signals. In particular, our test encompasses the signal detection framework. We deduce upper bounds on the minimal energy, expressed as the 2-norm of the linear coefficients, to successfully detect a signal in presence of noise. The general model considered in this paper is a non-linear extension of the classical high-dimensional regression model. It turns out that, in this framework, our upper bound on the minimax separation rate matches (up to a logarithmic factor) the lower bound on the minimax separation rate for signal detection in the high dimensional linear model associated to a fixed dictionary of features. We also propose a procedure to test whether the features of the observed signal belong to a given finite collection under the assumption that the linear coefficients may vary, but do not change to opposite signs under the null hypothesis. A non-asymptotic upper bound on the testing risk is given. We illustrate our results on the spikes deconvolution model with Gaussian features on the real line and with the Dirichlet kernel, frequently used in the compressed sensing literature, on the torus.

Like fingerprints, cortical folding patterns are unique to each brain even though they follow a general species-specific organization. Some folding patterns have been linked with neurodevelopmental disorders. However, due to the high inter-individual variability, the identification of rare folding patterns that could become biomarkers remains a very complex task. This paper proposes a novel unsupervised deep learning approach to identify rare folding patterns and assess the degree of deviations that can be detected. To this end, we preprocess the brain MR images to focus the learning on the folding morphology and train a beta-VAE to model the inter-individual variability of the folding. We compare the detection power of the latent space and of the reconstruction errors, using synthetic benchmarks and one actual rare configuration related to the central sulcus. Finally, we assess the generalization of our method on a developmental anomaly located in another region. Our results suggest that this method enables encoding relevant folding characteristics that can be enlightened and better interpreted based on the generative power of the beta-VAE. The latent space and the reconstruction errors bring complementary information and enable the identification of rare patterns of different nature. This method generalizes well to a different region on another dataset. Code is available at https://github.com/neurospin-projects/2022_lguillon_rare_folding_detection.

We present a method for estimating lighting from a single perspective image of an indoor scene. Previous methods for predicting indoor illumination usually focus on either simple, parametric lighting that lack realism, or on richer representations that are difficult or even impossible to understand or modify after prediction. We propose a pipeline that estimates a parametric light that is easy to edit and allows renderings with strong shadows, alongside with a non-parametric texture with high-frequency information necessary for realistic rendering of specular objects. Once estimated, the predictions obtained with our model are interpretable and can easily be modified by an artist/user with a few mouse clicks. Quantitative and qualitative results show that our approach makes indoor lighting estimation easier to handle by a casual user, while still producing competitive results.

We propose a framework for learning a fragment of probabilistic computation tree logic (pCTL) formulae from a set of states that are labeled as safe or unsafe. We work in a relational setting and combine ideas from relational Markov Decision Processes with pCTL model-checking. More specifically, we assume that there is an unknown relational pCTL target formula that is satisfied by only safe states, and has a horizon of maximum $k$ steps and a threshold probability $\alpha$. The task then consists of learning this unknown formula from states that are labeled as safe or unsafe by a domain expert. We apply principles of relational learning to induce a pCTL formula that is satisfied by all safe states and none of the unsafe ones. This formula can then be used as a safety specification for this domain, so that the system can avoid getting into dangerous situations in future. Following relational learning principles, we introduce a candidate formula generation process, as well as a method for deciding which candidate formula is a satisfactory specification for the given labeled states. The cases where the expert knows and does not know the system policy are treated, however, much of the learning process is the same for both cases. We evaluate our approach on a synthetic relational domain.

当前的能源转变促进了电力和天然气系统之间的运行融合。在这个方向上，改善协调能力和气体调度内的非凸天然气体流动动力学的建模至关重要。在这项工作中，我们提出了一种神经网络受限的优化方法，其中包括基于监督机器学习的韦茅斯方程的回归模型。 Weymouth方程将气体流动与每个管道的入口和出口压力通过二次平等，该二次相等性，该平等被神经网络捕获。后者是通过可处理的混合插入线性程序编码为约束集的。此外，我们提出的框架能够考虑双向性，而无需求助于复杂且可能不准确的凸化方法。我们通过引入激活函数的重新制定来进一步增强我们的模型，从而提高计算效率。一项基于现实生活中的比利时力量和气体系统的广泛数值研究表明，所提出的方法在准确性和障碍方面产生了有希望的结果。

非结构化的文本数据是卫生系统的核心：医生之间的联络信，操作报告，根据ICD-10标准编码的程序等。这些文件中包含的详细信息使得更好地了解患者，更好地管理他或她，以更好地研究病理，以准确地偿还相关的医学行为\ ldots，这似乎（至少在部分）被人工智能技术触及了。但是，出于明显的隐私保护原因，这些AIS的设计师只要包含识别数据，就没有合法权利访问这些文件。取消识别这些文档，即检测和删除它们中存在的所有识别信息，是在两个互补世界之间共享此数据的法律必要步骤。在过去的十年中，已经提出了一些建议，主要是用英语来识别文件。虽然检测分数通常很高，但替代方法通常不是很健壮。在法语中，很少有基于任意检测和/或替代规则的方法。在本文中，我们提出了一种专门针对法语医学文件的新的综合识别方法。识别要素（基于深度学习）的检测方法及其替代（基于差异隐私）的方法都是基于最有效的现有方法。结果是一种方法，可以有效保护患者的隐私，这是这些医疗文件的核心。整个方法已经在法国公立医院的法语医学数据集上进行了评估，结果非常令人鼓舞。

从数字艺术到AR和VR体验，图像编辑和合成已经变得无处不在。为了生产精美的复合材料，需要对相机进行几何校准，这可能很乏味，需要进行物理校准目标。代替传统的多图像校准过程，我们建议使用深层卷积神经网络直接从单个图像中直接从单个图像中推断摄像机校准参数，例如音高，滚动，视场和镜头失真。我们使用大规模全景数据集中自动生成样品训练该网络，从而在标准L2误差方面产生了竞争精度。但是，我们认为将这种标准误差指标最小化可能不是许多应用程序的最佳选择。在这项工作中，我们研究了人类对几何相机校准中不准确性的敏感性。为此，我们进行了一项大规模的人类感知研究，我们要求参与者以正确和有偏见的摄像机校准参数判断3D对象的现实主义。基于这项研究，我们为摄像机校准开发了一种新的感知度量，并证明我们的深校准网络在标准指标以及这一新型感知度量方面都优于先前基于单像的校准方法。最后，我们演示了将校准网络用于多种应用程序，包括虚拟对象插入，图像检索和合成。可以在https://lvsn.github.io/deepcalib上获得我们方法的演示。