全球综合合作对于限制全球温度的升高至关重要，同时继续经济发展，例如减少严重的不平等或实现长期经济增长。与N战略代理进行缓解气候变化的长期合作提出了一个复杂的游戏理论问题。例如，代理商可以谈判并达成气候协议，但是没有中央权力可以执行遵守这些协议。因此，设计谈判和协议框架以促进合作，允许所有代理人达到其个人政策目标并激励长期遵守，这一点至关重要。这是一个跨学科的挑战，要求在机器学习，经济学，气候科学，法律，政策，道德和其他领域进行研究人员之间的合作。特别是，我们认为机器学习是解决该领域复杂性的关键工具。为了促进这项研究，在这里，我们介绍了一个多区域综合评估模型，模拟全球气候和经济，可用于设计和评估不同谈判和协议框架的战略成果。我们还描述了如何使用多代理增强学习来使用水稻N训练理性剂。该框架是全球气候合作的基础，这是一个工作组协作和气候谈判和协议设计的竞争。在这里，我们邀请科学界使用Rice-N，机器学习，经济直觉和其他领域知识来设计和评估其解决方案。更多信息可以在www.ai4climatecoop.org上找到。

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

Knowledge graph (KG) link prediction aims to infer new facts based on existing facts in the KG. Recent studies have shown that using the graph neighborhood of a node via graph neural networks (GNNs) provides more useful information compared to just using the query information. Conventional GNNs for KG link prediction follow the standard message-passing paradigm on the entire KG, which leads to over-smoothing of representations and also limits their scalability. On a large scale, it becomes computationally expensive to aggregate useful information from the entire KG for inference. To address the limitations of existing KG link prediction frameworks, we propose a novel retrieve-and-read framework, which first retrieves a relevant subgraph context for the query and then jointly reasons over the context and the query with a high-capacity reader. As part of our exemplar instantiation for the new framework, we propose a novel Transformer-based GNN as the reader, which incorporates graph-based attention structure and cross-attention between query and context for deep fusion. This design enables the model to focus on salient context information relevant to the query. Empirical results on two standard KG link prediction datasets demonstrate the competitive performance of the proposed method.

Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. By doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. As components of this system, we develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classification network to supervise a visuotactile edge grasp affordance network that can grasp edges with a 90% success rate. Once an edge is grasped, we demonstrate that the robot can slide along the cloth to the adjacent corner using tactile pose estimation/control in real time. See http://nehasunil.com/visuotactile/visuotactile.html for videos.

Unhealthy dietary habits are considered as the primary cause of multiple chronic diseases such as obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoF) of people with dietary related diseases through dietary assessment. In this work, we propose a novel contact-less radar-based food intake monitoring approach. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movement from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network (3D-TCN) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions. We create a public dataset that contains 48 meal sessions (3121 eating gestures and 608 drinking gestures) from 48 participants with a total duration of 783 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, 8-fold cross validation method is applied. Experimental results show that our proposed 3D-TCN outperforms the model that combines a convolutional neural network and a long-short-term-memory network (CNN-LSTM), and also the CNN-Bidirectional LSTM model (CNN-BiLSTM) in eating and drinking gesture detection. The 3D-TCN model achieves a segmental F1-score of 0.887 and 0.844 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.

Cement is the most used construction material. The performance of cement hydrate depends on the constituent phases, viz. alite, belite, aluminate, and ferrites present in the cement clinker, both qualitatively and quantitatively. Traditionally, clinker phases are analyzed from optical images relying on a domain expert and simple image processing techniques. However, the non-uniformity of the images, variations in the geometry and size of the phases, and variabilities in the experimental approaches and imaging methods make it challenging to obtain the phases. Here, we present a machine learning (ML) approach to detect clinker microstructure phases automatically. To this extent, we create the first annotated dataset of cement clinker by segmenting alite and belite particles. Further, we use supervised ML methods to train models for identifying alite and belite regions. Specifically, we finetune the image detection and segmentation model Detectron-2 on the cement microstructure to develop a model for detecting the cement phases, namely, Cementron. We demonstrate that Cementron, trained only on literature data, works remarkably well on new images obtained from our experiments, demonstrating its generalizability. We make Cementron available for public use.

Signature-based malware detectors have proven to be insufficient as even a small change in malignant executable code can bypass these signature-based detectors. Many machine learning-based models have been proposed to efficiently detect a wide variety of malware. Many of these models are found to be susceptible to adversarial attacks - attacks that work by generating intentionally designed inputs that can force these models to misclassify. Our work aims to explore vulnerabilities in the current state of the art malware detectors to adversarial attacks. We train a Transformers-based malware detector, carry out adversarial attacks resulting in a misclassification rate of 23.9% and propose defenses that reduce this misclassification rate to half. An implementation of our work can be found at https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers.

现代社会有兴趣由于复杂的相机的激增而捕获高分辨率和优质图像。但是，如果在计算机视觉任务中使用了此类图像，则图像中的噪声污染不仅较低，而且相反会影响随后的过程，例如遥感，对象跟踪等。高分辨率图像的时间处理受图像捕获仪器的硬件限制的限制。 Geodesic Gramian denoising（GGD）是一种基于多种噪声滤波方法，我们在过去的研究中介绍了该方法，它利用了Geodesics的Gramian Gramian矩阵的一些突出的奇异向量进行噪声滤波过程。 GDD遇到$ \ MATHCAL {O}（n^6）$时，GDD的适用性受到限制^2 $数据矩阵由单数值分解（SVD）实现。在这项研究中，我们通过用四种不同的单数矢量近似技术代替其SVD步骤来提高GGD框架的效率。在这里，我们比较集成到GGD中的四个技术之间的计算时间和噪声过滤性能。

人类在整个生命周期中不断学习，通过积累多样化的知识并为未来的任务进行微调。当出现类似目标时，神经网络会遭受灾难性忘记，在学习过程中跨顺序任务跨好任务的数据分布是否不固定。解决此类持续学习（CL）问题的有效方法是使用超网络为目标网络生成任务依赖权重。但是，现有基于超网的方法的持续学习性能受到整个层之间权重的独立性的假设，以维持参数效率。为了解决这一限制，我们提出了一种新颖的方法，该方法使用依赖关系保留超网络来为目标网络生成权重，同时还保持参数效率。我们建议使用基于复发的神经网络（RNN）的超网络，该网络可以有效地生成层权重，同时允许在它们的依赖关系中。此外，我们为基于RNN的超网络提出了新颖的正则化和网络增长技术，以进一步提高持续的学习绩效。为了证明所提出的方法的有效性，我们对几个图像分类持续学习任务和设置进行了实验。我们发现，基于RNN HyperNetworks的建议方法在所有这些CL设置和任务中都优于基准。

深度学习的成功归功于我们能够相对轻松地解决某些大规模的非凸优化问题。尽管非凸优化是NP硬化，但简单的算法（通常是随机梯度下降的变体）在拟合大型神经网络的实践中具有令人惊讶的有效性。我们认为，在考虑了所有可能的隐藏单元对称对称性之后，神经网络损失景观包含（几乎）一个盆地。我们介绍了三种算法以缩小一个模型的单元，以使它们与参考模型的单位保持一致。这种转换产生了一组功能等效的权重，该权重位于参考模型附近的大约凸盆地中。在实验上，我们证明了各种模型架构和数据集中的单个盆地现象，包括在CIFAR-10和CIFAR-100上独立训练的Resnet模型之间的第一个（据我们所知）的（据我们所知）的第一次演示。此外，我们确定了有趣的现象，将模型宽度和训练时间与各种模型和数据集的模式连接性有关。最后，我们讨论了单个盆地理论的缺点，包括对线性模式连接假设的反例。