Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. In this work, we propose to capture each of these aspects by modeling the surgical scene with a disentangled latent scene graph representation, which we can then process using a graph neural network. Unlike previous approaches using graph representations, we explicitly encode in our graphs semantic information such as object locations and shapes, class probabilities and visual features. We also incorporate an auxiliary image reconstruction objective to help train the latent graph representations. We demonstrate the value of these components through comprehensive ablation studies and achieve state-of-the-art results for critical view of safety prediction across multiple experimental settings.

折射率是最常见的眼睛障碍，是可更正视觉障碍的关键原因，造成了美国近80％的视觉障碍。可以使用多种方法诊断折射误差，包括主观折射，视网膜镜检查和自动磨蚀器。尽管主观折射是黄金标准，但它需要患者的合作，因此不适合婴儿，幼儿和发育迟缓的成年人。视网膜镜检查是一种客观折射方法，不需要患者的任何输入。但是，视网膜镜检查需要镜头套件和训练有素的检查员，这限制了其用于大规模筛查的使用。在这项工作中，我们通过将智能手机连接到视网膜镜和录制视网膜镜视频与患者戴着定制的纸框架来自动化自动化。我们开发了一个视频处理管道，该管道将视网膜视频视为输入，并根据我们提出的视网膜镜检查数学模型的扩展来估算净屈光度错误。我们的系统减轻了对镜头套件的需求，可以由未经培训的检查员进行。在一项185只眼睛的临床试验中，我们的灵敏度为91.0％，特异性为74.0％。此外，与主观折射测量相比，我们方法的平均绝对误差为0.75 $ \ pm $ 0.67D。我们的结果表明，我们的方法有可能用作现实世界中医疗设置中的基于视网膜镜检查的折射率筛选工具。

近年来，随着深度神经网络方法的普及，手术计算机视觉领域经历了相当大的突破。但是，用于培训的标准全面监督方法需要大量的带注释的数据，从而实现高昂的成本；特别是在临床领域。已经开始在一般计算机视觉社区中获得吸引力的自我监督学习（SSL）方法代表了对这些注释成本的潜在解决方案，从而使仅从未标记的数据中学习有用的表示形式。尽管如此，SSL方法在更复杂和有影响力的领域（例如医学和手术）中的有效性仍然有限且未开发。在这项工作中，我们通过在手术计算机视觉的背景下研究了四种最先进的SSL方法（Moco V2，Simclr，Dino，SWAV），以解决这一关键需求。我们对这些方法在cholec80数据集上的性能进行了广泛的分析，以在手术环境理解，相位识别和工具存在检测中为两个基本和流行的任务。我们检查了它们的参数化，然后在半监督设置中相对于训练数据数量的行为。如本工作所述和进行的那样，将这些方法的正确转移到手术中，可以使SSL的一般用途获得可观的性能 - 相位识别率高达7％，而在工具存在检测方面，则具有20％ - 半监督相位识别方法高达14％。该代码将在https://github.com/camma-public/selfsupsurg上提供。

The paper presents a cross-domain review analysis on four popular review datasets: Amazon, Yelp, Steam, IMDb. The analysis is performed using Hadoop and Spark, which allows for efficient and scalable processing of large datasets. By examining close to 12 million reviews from these four online forums, we hope to uncover interesting trends in sales and customer sentiment over the years. Our analysis will include a study of the number of reviews and their distribution over time, as well as an examination of the relationship between various review attributes such as upvotes, creation time, rating, and sentiment. By comparing the reviews across different domains, we hope to gain insight into the factors that drive customer satisfaction and engagement in different product categories.

Code generation models have achieved impressive performance. However, they tend to be brittle as slight edits to a prompt could lead to very different generations; these robustness properties, critical for user experience when deployed in real-life applications, are not well understood. Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation. In this paper, we propose ReCode, a comprehensive robustness evaluation benchmark for code generation models. We customize over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. They are carefully designed to be natural in real-life coding practice, preserve the original semantic meaning, and thus provide multifaceted assessments of a model's robustness performance. With human annotators, we verified that over 90% of the perturbed prompts do not alter the semantic meaning of the original prompt. In addition, we define robustness metrics for code generation models considering the worst-case behavior under each type of perturbation, taking advantage of the fact that executing the generated code can serve as objective evaluation. We demonstrate ReCode on SOTA models using HumanEval, MBPP, as well as function completion tasks derived from them. Interesting observations include: better robustness for CodeGen over InCoder and GPT-J; models are most sensitive to syntax perturbations; more challenging robustness evaluation on MBPP over HumanEval.

Automated offensive language detection is essential in combating the spread of hate speech, particularly in social media. This paper describes our work on Offensive Language Identification in low resource Indic language Marathi. The problem is formulated as a text classification task to identify a tweet as offensive or non-offensive. We evaluate different mono-lingual and multi-lingual BERT models on this classification task, focusing on BERT models pre-trained with social media datasets. We compare the performance of MuRIL, MahaTweetBERT, MahaTweetBERT-Hateful, and MahaBERT on the HASOC 2022 test set. We also explore external data augmentation from other existing Marathi hate speech corpus HASOC 2021 and L3Cube-MahaHate. The MahaTweetBERT, a BERT model, pre-trained on Marathi tweets when fine-tuned on the combined dataset (HASOC 2021 + HASOC 2022 + MahaHate), outperforms all models with an F1 score of 98.43 on the HASOC 2022 test set. With this, we also provide a new state-of-the-art result on HASOC 2022 / MOLD v2 test set.

While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i.e., in-file context, but ignore the rich semantics in other files within the same project, i.e., cross-file context, a critical source of information that is especially useful in modern modular software development. Such overlooking constrains code language models' capacity in code completion, leading to unexpected behaviors such as generating hallucinated class member functions or function calls with unexpected arguments. In this work, we develop a cross-file context finder tool, CCFINDER, that effectively locates and retrieves the most relevant cross-file context. We propose CoCoMIC, a framework that incorporates cross-file context to learn the in-file and cross-file context jointly on top of pretrained code LMs. CoCoMIC successfully improves the existing code LM with a 19.30% relative increase in exact match and a 15.41% relative increase in identifier matching for code completion when the cross-file context is provided.

We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary order. Until now these requirements of continual release and user-level privacy were considered in isolation. But, in practice, both these requirements come together as the users often contribute data repeatedly and multiple queries are made. We provide an algorithm that outputs a mean estimate at every time instant $t$ such that the overall release is user-level $\varepsilon$-DP and has the following error guarantee: Denoting by $M_t$ the maximum number of samples contributed by a user, as long as $\tilde{\Omega}(1/\varepsilon)$ users have $M_t/2$ samples each, the error at time $t$ is $\tilde{O}(1/\sqrt{t}+\sqrt{M}_t/t\varepsilon)$. This is a universal error guarantee which is valid for all arrival patterns of the users. Furthermore, it (almost) matches the existing lower bounds for the single-release setting at all time instants when users have contributed equal number of samples.

Speech-centric machine learning systems have revolutionized many leading domains ranging from transportation and healthcare to education and defense, profoundly changing how people live, work, and interact with each other. However, recent studies have demonstrated that many speech-centric ML systems may need to be considered more trustworthy for broader deployment. Specifically, concerns over privacy breaches, discriminating performance, and vulnerability to adversarial attacks have all been discovered in ML research fields. In order to address the above challenges and risks, a significant number of efforts have been made to ensure these ML systems are trustworthy, especially private, safe, and fair. In this paper, we conduct the first comprehensive survey on speech-centric trustworthy ML topics related to privacy, safety, and fairness. In addition to serving as a summary report for the research community, we point out several promising future research directions to inspire the researchers who wish to explore further in this area.

The automated synthesis of correct-by-construction Boolean functions from logical specifications is known as the Boolean Functional Synthesis (BFS) problem. BFS has many application areas that range from software engineering to circuit design. In this paper, we introduce a tool BNSynth, that is the first to solve the BFS problem under a given bound on the solution space. Bounding the solution space induces the synthesis of smaller functions that benefit resource constrained areas such as circuit design. BNSynth uses a counter-example guided, neural approach to solve the bounded BFS problem. Initial results show promise in synthesizing smaller solutions; we observe at least \textbf{3.2X} (and up to \textbf{24X}) improvement in the reduction of solution size on average, as compared to state of the art tools on our benchmarks. BNSynth is available on GitHub under an open source license.