我们在Nengo框架上介绍了基于纯净的神经网络（SNN）的基于稀疏分布式存储器（SDM）。我们基于Furber等人，2004年之前的工作，使用N-y-y of-of-modes实现SDM。作为SDM设计的组成部分，我们已经在Nengo上实现了使用SNN的相关矩阵存储器（CMM）。我们的SNN实施采用漏水集成和火（LIF）在Nengo上尖刺神经元模型。我们的目标是了解基于SNN的SDMS与传统SDMS相比如何进行。为此，我们在Nengo模拟了基于常规和基于SNN的SDM和CMM。我们观察到基于SNN的模型类似于传统的模型。为了评估不同SNN的性能，我们使用Adaptive-Lif，Spiking整流线性单元和Izhikevich模型重复实验并获得了类似的结果。我们得出结论，使用内存的神经元制定一些类型的关联存储器，其内存容量和其他功能类似于没有SNN的性能，确实可行。最后，我们已经实现了一个应用程序，其中使用N-M个代码编码的Mnist图像与其标签相关联并存储在基于SNN的SDM中。

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

There has been a concurrent significant improvement in the medical images used to facilitate diagnosis and the performance of machine learning techniques to perform tasks such as classification, detection, and segmentation in recent years. As a result, a rapid increase in the usage of such systems can be observed in the healthcare industry, for instance in the form of medical image classification systems, where these models have achieved diagnostic parity with human physicians. One such application where this can be observed is in computer vision tasks such as the classification of skin lesions in dermatoscopic images. However, as stakeholders in the healthcare industry, such as insurance companies, continue to invest extensively in machine learning infrastructure, it becomes increasingly important to understand the vulnerabilities in such systems. Due to the highly critical nature of the tasks being carried out by these machine learning models, it is necessary to analyze techniques that could be used to take advantage of these vulnerabilities and methods to defend against them. This paper explores common adversarial attack techniques. The Fast Sign Gradient Method and Projected Descent Gradient are used against a Convolutional Neural Network trained to classify dermatoscopic images of skin lesions. Following that, it also discusses one of the most popular adversarial defense techniques, adversarial training. The performance of the model that has been trained on adversarial examples is then tested against the previously mentioned attacks, and recommendations to improve neural networks robustness are thus provided based on the results of the experiment.

Non-convex AC optimal power flow (AC-OPF) is a fundamental optimization problem in power system analysis. The computational complexity of conventional solvers is typically high and not suitable for large-scale networks in real-time operation. Hence, deep learning based approaches have gained intensive attention to conduct the time-consuming training process offline. Supervised learning methods may yield a feasible AC-OPF solution with a small optimality gap. However, they often need conventional solvers to generate the training dataset. This paper proposes an end-to-end unsupervised learning based framework for AC-OPF. We develop a deep neural network to output a partial set of decision variables while the remaining variables are recovered by solving AC power flow equations. The fast decoupled power flow solver is adopted to further reduce the computational time. In addition, we propose using a modified augmented Lagrangian function as the training loss. The multipliers are adjusted dynamically based on the degree of constraint violation. Extensive numerical test results corroborate the advantages of our proposed approach over some existing methods.

Euclidean geometry is among the earliest forms of mathematical thinking. While the geometric primitives underlying its constructions, such as perfect lines and circles, do not often occur in the natural world, humans rarely struggle to perceive and reason with them. Will computer vision models trained on natural images show the same sensitivity to Euclidean geometry? Here we explore these questions by studying few-shot generalization in the universe of Euclidean geometry constructions. We introduce Geoclidean, a domain-specific language for Euclidean geometry, and use it to generate two datasets of geometric concept learning tasks for benchmarking generalization judgements of humans and machines. We find that humans are indeed sensitive to Euclidean geometry and generalize strongly from a few visual examples of a geometric concept. In contrast, low-level and high-level visual features from standard computer vision models pretrained on natural images do not support correct generalization. Thus Geoclidean represents a novel few-shot generalization benchmark for geometric concept learning, where the performance of humans and of AI models diverge. The Geoclidean framework and dataset are publicly available for download.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. COVID-19 literature retrieval task screens matching COVID-19 literature documents for textual user query, and COVID-19 question answering task predicts proper text fragments from text corpus as the answer of specific COVID-19 related questions. Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset, we display some examples to show the effectiveness of our proposed solutions.

太阳水箱是在太阳的Ca II K摄影观测中观察到的明亮的色球环特征。这些是高磁场浓度的区域，因此是太阳的磁性活性的示踪剂，并且是研究太阳长期可变性的最重要特征之一，因为记录了一个多世纪以来的Ca II K镜头。。但是，从一个世纪的数据库中检测到份额是一项非平凡的任务，需要大量的人力资源来手动进行。因此，在这项研究中，我们提出了一种图像处理算法，该算法可以从CA II K摄影观测中识别出太阳份量。拟议的研究已在Kodaikanal太阳能天文台的档案数据上实施。为了确保算法有效，无论噪声水平，亮度和其他图像属性如何，我们从数据存档中随机绘制图像样本以测试我们的算法。

仇恨语音检测的最先进方法通常在室外设置中表现出较差的性能。通常，这是由于分类器过度强调特定于源的信息，从而对其域的不变性产生负面影响。先前的工作试图使用功能归因方法从手动策划的列表中惩罚与仇恨语音有关的条款，该方法量化了分类器在做出预测时分配给输入术语的重要性。取而代之的是，我们提出了一种域适应方法，该方法会使用域分类器自动提取和惩罚特定于源的术语，该域分类器学会区分域和仇恨语音类别的功能 - 属性分数，从而在交叉域评估中始终如一地改进。

太阳能动力学天文台（SDO）是NASA多光谱十年的长达任务，每天都在日常产生来自Sun的观测数据的trabytes，以证明机器学习方法的潜力并铺路未来深空任务计划的方式。特别是，在最近的几项研究中提出了使用图像到图像翻译实际上产生极端超紫罗兰通道的想法，这是一种增强任务较少通道的提高任务的方法，并且由于低下链接而减轻了挑战。深空的速率。本文通过关注四个通道和基于编码器的建筑的排列来研究这种深度学习方法的潜力和局限性，并特别注意太阳表面的形态特征和亮度如何影响神经网络预测。在这项工作中，我们想回答以下问题：可以将通过图像到图像翻译产生的太阳电晕的合成图像用于太阳的科学研究吗？分析强调，神经网络在计数率（像素强度）上产生高质量的图像，通常可以在1％误差范围内跨通道跨通道重现协方差。但是，模型性能在极高的能量事件（如耀斑）的对应关系中大大减少，我们认为原因与此类事件的稀有性有关，这对模型训练构成了挑战。