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.

图形神经网络已被证明可以为各种软件工程任务产生令人印象深刻的结果。但是，现有技术仍然有两个问题：（1）长期依赖性和（2）不同的代码组件在不应该的情况下被视为平等。为了解决这些问题，我们提出了一种表示代码为层次结构（代码层次结构）的方法，其中不同的代码组件在各个粒度级别分别表示。然后，为了处理每个表示级别的表示，我们设计了一个新颖的网络体系结构Echelon，它结合了异质图形变压器网络和基于树的卷积神经网络的优势，以学习具有代码依赖性信息丰富的抽象语法树。我们还提出了一个新颖的预处理目标，称为缺失子树预测以补充我们的代码层次结构。评估结果表明，我们的方法在三个任务中大大优于其他基准：任何代码完成，代码分类和代码克隆检测。

我们研究了两种可能不同质量的度量之间的不平衡最佳运输（UOT），其中最多是$ n $组件，其中标准最佳运输（OT）的边际约束是通过kullback-leibler差异与正则化因子$ \ tau $放松的。尽管仅在文献中分析了具有复杂性$ o \ big（\ tfrac {\ tau n^2 \ log（n）} {\ varepsilon} \ log \ big（\ tfrac {\ log（ n）} {{{\ varepsilon}} \ big）\ big）$）$用于实现错误$ \ varepsilon $，它们与某些深度学习模型和密集的输出运输计划不兼容，强烈阻碍了实用性。虽然被广泛用作计算现代深度学习应用中UOT的启发式方法，并且在稀疏的OT中表现出成功，但尚未正式研究用于UOT的梯度方法。为了填补这一空白，我们提出了一种基于梯度外推法（Gem-uot）的新颖算法，以找到$ \ varepsilon $ -Approximate解决方案，以解决$ o \ big中的UOT问题（\ kappa n^2 \ log \ log \ big（big） \ frac {\ tau n} {\ varepsilon} \ big）\ big）$，其中$ \ kappa $是条件号，具体取决于两个输入度量。我们的算法是通过优化平方$ \ ell_2 $ -norm UOT目标的新的双重配方设计的，从而填补了缺乏稀疏的UOT文献。最后，我们在运输计划和运输距离方面建立了UOT和OT之间近似误差的新颖表征。该结果阐明了一个新的主要瓶颈，该瓶颈被强大的OT文献忽略了：尽管OT放松了OT，因为UOT承认对离群值的稳健性，但计算出的UOT距离远离原始OT距离。我们通过基于Gem-uot从UOT中检索的原则方法来解决此类限制，并使用微调的$ \ tau $和后进程投影步骤来解决。关于合成和真实数据集的实验验证了我们的理论，并证明了我们的方法的良好性能。

客户端之间的非独立和相同分布（非IID）数据分布被视为降低联合学习（FL）性能的关键因素。处理非IID数据（如个性化FL和联邦多任务学习（FMTL）的几种方法对研究社区有很大兴趣。在这项工作中，首先，我们使用Laplacian正规化制定FMTL问题，明确地利用客户模型之间的关系进行多任务学习。然后，我们介绍了FMTL问题的新视图，首次表明配制的FMTL问题可用于传统的FL和个性化FL。我们还提出了两种算法FEDU和DFEDU，分别解决了通信集中和分散方案中的配制FMTL问题。从理论上讲，我们证明了两种算法的收敛速率实现了用于非凸起目标的强大凸起和载位加速的线性加速。实验，我们表明我们的算法优于FL设置的传统算法FedVG，在FMTL设置中的Mocha，以及个性化流程中的PFEDME和PER-FEDAVG。

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices. Principal Components Analysis (PCA) has been proposed to separate network traffics into two disjoint subspaces corresponding to normal and malicious behaviors for anomaly detection. However, the privacy concerns and limitations of devices' computing resources compromise the practical effectiveness of PCA. We propose a federated PCA-based Grassmannian optimization framework that coordinates IoT devices to aggregate a joint profile of normal network behaviors for anomaly detection. First, we introduce a privacy-preserving federated PCA framework to simultaneously capture the profile of various IoT devices' traffic. Then, we investigate the alternating direction method of multipliers gradient-based learning on the Grassmann manifold to guarantee fast training and the absence of detecting latency using limited computational resources. Empirical results on the NSL-KDD dataset demonstrate that our method outperforms baseline approaches. Finally, we show that the Grassmann manifold algorithm is highly adapted for IoT anomaly detection, which permits drastically reducing the analysis time of the system. To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Deep-learning-based technologies such as deepfakes ones have been attracting widespread attention in both society and academia, particularly ones used to synthesize forged face images. These automatic and professional-skill-free face manipulation technologies can be used to replace the face in an original image or video with any target object while maintaining the expression and demeanor. Since human faces are closely related to identity characteristics, maliciously disseminated identity manipulated videos could trigger a crisis of public trust in the media and could even have serious political, social, and legal implications. To effectively detect manipulated videos, we focus on the position offset in the face blending process, resulting from the forced affine transformation of the normalized forged face. We introduce a method for detecting manipulated videos that is based on the trajectory of the facial region displacement. Specifically, we develop a virtual-anchor-based method for extracting the facial trajectory, which can robustly represent displacement information. This information was used to construct a network for exposing multidimensional artifacts in the trajectory sequences of manipulated videos that is based on dual-stream spatial-temporal graph attention and a gated recurrent unit backbone. Testing of our method on various manipulation datasets demonstrated that its accuracy and generalization ability is competitive with that of the leading detection methods.

Protein structure prediction is a fundamental problem in computational molecular biology. Classical algorithms such as ab-initio or threading as well as many learning methods have been proposed to solve this challenging problem. However, most reinforcement learning methods tend to model the state-action pairs as discrete objects. In this paper, we develop a reinforcement learning (RL) framework in a continuous setting and based on a stochastic parametrized Hamiltonian version of the Pontryagin maximum principle (PMP) to solve the side-chain packing and protein-folding problem. For special cases our formulation can be reduced to previous work where the optimal folding trajectories are trained using an explicit use of Langevin dynamics. Optimal continuous stochastic Hamiltonian dynamics folding pathways can be derived with use of different models of molecular energetics and force fields. In our RL implementation we adopt a soft actor-critic methodology however we can replace this other RL training based on A2C, A3C or PPO.