如今，乳腺癌已成为近年来最突出的死亡原因之一。在所有恶性肿瘤中，这是全球妇女最常见和主要的死亡原因。手动诊断这种疾病需要大量的时间和专业知识。乳腺癌的检测是耗时的，并且可以通过开发基于机器的乳腺癌预测来减少疾病的传播。在机器学习中，系统可以从先前的实例中学习，并使用各种统计，概率和优化方法从嘈杂或复杂的数据集中找到难以检测的模式。这项工作比较了几种机器学习算法的分类准确性，精度，灵敏度和新近收集的数据集的特异性。在这种工作决策树，随机森林，逻辑回归，天真的贝叶斯和XGBoost中，已经实施了这五种机器学习方法，以在我们的数据集中获得最佳性能。这项研究的重点是找到最佳的算法，该算法可以预测乳腺癌，以最高的准确性。这项工作在效率和有效性方面评估了每种算法数据分类的质量。并与该领域的其他已发表工作相比。实施模型后，本研究达到了最佳模型准确性，在随机森林和XGBoost上达到94％。

Modern telecom systems are monitored with performance and system logs from multiple application layers and components. Detecting anomalous events from these logs is key to identify security breaches, resource over-utilization, critical/fatal errors, etc. Current supervised log anomaly detection frameworks tend to perform poorly on new types or signatures of anomalies with few or unseen samples in the training data. In this work, we propose a meta-learning-based log anomaly detection framework (LogAnMeta) for detecting anomalies from sequence of log events with few samples. LoganMeta train a hybrid few-shot classifier in an episodic manner. The experimental results demonstrate the efficacy of our proposed method

Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for several computer vision (CV) applications. However, most existing SNNs require multiple time steps for acceptable inference accuracy, hindering real-time deployment and increasing spiking activity and, consequently, energy consumption. Recent works proposed direct encoding that directly feeds the analog pixel values in the first layer of the SNN in order to significantly reduce the number of time steps. Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy. To mitigate this concern, we propose an in-sensor computing hardware-software co-design framework for SNNs targeting image recognition tasks. Our approach reduces the bandwidth between sensing and processing by 12-96x and the resulting total energy by 2.32x compared to traditional CV processing, with a 3.8% reduction in accuracy on ImageNet.

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.

Scene graphs provide a rich, structured representation of a scene by encoding the entities (objects) and their spatial relationships in a graphical format. This representation has proven useful in several tasks, such as question answering, captioning, and even object detection, to name a few. Current approaches take a generation-by-classification approach where the scene graph is generated through labeling of all possible edges between objects in a scene, which adds computational overhead to the approach. This work introduces a generative transformer-based approach to generating scene graphs beyond link prediction. Using two transformer-based components, we first sample a possible scene graph structure from detected objects and their visual features. We then perform predicate classification on the sampled edges to generate the final scene graph. This approach allows us to efficiently generate scene graphs from images with minimal inference overhead. Extensive experiments on the Visual Genome dataset demonstrate the efficiency of the proposed approach. Without bells and whistles, we obtain, on average, 20.7% mean recall (mR@100) across different settings for scene graph generation (SGG), outperforming state-of-the-art SGG approaches while offering competitive performance to unbiased SGG approaches.

Neuroimaging-based prediction methods for intelligence and cognitive abilities have seen a rapid development in literature. Among different neuroimaging modalities, prediction based on functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, but there are limited investigations on the merits of such analysis compared to prediction based on dynamic FC or region level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI data, we propose a deep neural network involving bi-directional long short-term memory (bi-LSTM) approach that also incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient GPU computation framework and applied to predict intelligence scores based on region level fMRI time series as well as dynamic FC. We compare the prediction performance for different intelligence measures based on static FC, dynamic FC, and region level time series acquired from the Adolescent Brain Cognitive Development (ABCD) study involving close to 7000 individuals. Our detailed analysis illustrates that static FC consistently has inferior prediction performance compared to region level time series or dynamic FC for unimodal rest and task fMRI experiments, and in almost all cases using a combination of task and rest features. In addition, the proposed bi-LSTM pipeline based on region level time series identifies several shared and differential important brain regions across task and rest fMRI experiments that drive intelligence prediction. A test-retest analysis of the selected features shows strong reliability across cross-validation folds. Given the large sample size from ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.

Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand. Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on how to turn it into one that can be productively studied empirically. We first present an experimental design centered on choosing tasks for which human specialists succeed but unaided humans and current general AI systems fail. We then present a proof-of-concept experiment following meant to demonstrate a key feature of this experimental design and show its viability with two question-answering tasks: MMLU and time-limited QuALITY. On these tasks, we find that human participants who interact with an unreliable large-language-model dialog assistant through chat -- a trivial baseline strategy for scalable oversight -- substantially outperform both the model alone and their own unaided performance. These results are an encouraging sign that scalable oversight will be tractable to study with present models and bolster recent findings that large language models can productively assist humans with difficult tasks.

有效的自定义合并技术可以积极地修剪特征图的尺寸，从而减少用于资源约束计算机视觉应用程序的推理计算和内存足迹，最近已获得了显着的牵引力。但是，先前的合并作品仅提取激活图的局部环境，从而限制了它们的有效性。相比之下，我们提出了一种新型的非本地自我煽动合并方法，该方法可用作标准合并层的液位替换，例如最大/平均池或跨性别卷积。所提出的自我发项模块使用斑块嵌入，多头自我注意力和空间通道恢复，然后进行乙状结肠激活和指数软效果。这种自我注意的机制有效地聚集了在下采样过程中非本地激活斑之间的依赖性。具有各种卷积神经网络（CNN）体系结构的标准对象分类和检测任务的广泛实验证明了我们所提出的机制优于最先进的（SOTA）合并技术。特别是，我们超过了在Imabilenet-V2上不同变体上的现有合并技术的测试准确性，平均平均为1.2％。随着初始层中激活图的激进下采样（可减少记忆消耗的22倍），与具有ISO-MEMORY足迹的SOTA技术相比，我们的方法的测试准确性提高了1.43％。这使我们的模型可以在内存受限的设备中部署，例如微型控制器（不会失去明显的精度），因为初始激活映射会消耗大量的芯片内存储器，用于复杂视觉任务所需的高分辨率图像。我们提出的合并方法还利用了通道修剪的想法，以进一步减少记忆足迹。

使用虚拟现实（VR）系统时，Cyber​​sickness的特征是恶心，眩晕，头痛，眼睛疲劳和其他不适。先前报道的机器学习（ML）和深度学习（DL）算法用于检测（分类）和预测（回归）VR Cyber​​sickness使用黑盒模型；因此，他们缺乏解释性。此外，VR传感器会产生大量数据，从而产生复杂的模型。因此，在Cyber​​sickness检测模型中具有固有的解释性可以显着提高该模型的可信度，并洞悉为什么ML/DL模型如何制定特定决定。为了解决此问题，我们提出了三个可解释的机器学习（XML）模型来检测和预测Cyber​​sickness：1）可解释的提升机（EBM），2）决策树（DT）和3）逻辑回归（LR）。我们通过公开可用的生理和游戏数据集评估了基于XML的模型。结果表明，EBM可以分别以99.75％和94.10％的精度检测Cyber​​sickness，分别为生理和游戏数据集检测到Cyber​​ness。另一方面，在预测Cyber​​sickness的同时，EBM导致生理数据集的均方根误差（RMSE）为0.071，游戏玩法数据集的根部误差（RMSE）为0.27。此外，基于EBM的全球解释揭示了曝光的长度，旋转和加速度作为在游戏玩法数据集中引起Cyber​​sickness的关键特征。相反，电流皮肤反应和心率在生理数据集中最为重要。我们的结果还表明，基于EBM的局部解释可以鉴定单个样本的引起网络核管的因素。我们认为，提出的基于XML的Cyber​​sickness检测方法可以帮助未来的研究人员理解，分析和设计更简单的Cyber​​sickness检测和还原模型。