We present a new convolution layer for deep learning architectures which we call QuadConv -- an approximation to continuous convolution via quadrature. Our operator is developed explicitly for use on unstructured data, and accomplishes this by learning a continuous kernel that can be sampled at arbitrary locations. In the setting of neural compression, we show that a QuadConv-based autoencoder, resulting in a Quadrature Convolutional Neural Network (QCNN), can match the performance of standard discrete convolutions on structured uniform data, as in CNNs, and maintain this accuracy on unstructured data.

建模大规模复杂物理系统的最新进展已将研究的重点转移到数据驱动的技术上。但是，通过模拟复杂系统来生成数据集可能需要大量的计算资源。同样，获取实验数据集也可能很难。对于这些系统，通常在计算上便宜，但通常不准确，可用的模型可用。在本文中，我们为复杂的物理系统提出了一种双性模型建模方法，在这种情况下，我们使用深层操作员网络从True System的响应中建立了True System的响应与低保真响应之间的差异（DeepOnet），一种适用于近似非线性操作员的神经网络体系结构。我们将方法应用于具有参数不确定性并且部分未知的模型系统。三个数值示例用于显示所提出的方法对不确定且部分未知的复杂物理系统进行建模的功效。

The main objective of Prognostics and Health Management is to estimate the Remaining Useful Lifetime (RUL), namely, the time that a system or a piece of equipment is still in working order before starting to function incorrectly. In recent years, numerous machine learning algorithms have been proposed for RUL estimation, mainly focusing on providing more accurate RUL predictions. However, there are many sources of uncertainty in the problem, such as inherent randomness of systems failure, lack of knowledge regarding their future states, and inaccuracy of the underlying predictive models, making it infeasible to predict the RULs precisely. Hence, it is of utmost importance to quantify the uncertainty alongside the RUL predictions. In this work, we investigate the conformal prediction (CP) framework that represents uncertainty by predicting sets of possible values for the target variable (intervals in the case of RUL) instead of making point predictions. Under very mild technical assumptions, CP formally guarantees that the actual value (true RUL) is covered by the predicted set with a degree of certainty that can be prespecified. We study three CP algorithms to conformalize any single-point RUL predictor and turn it into a valid interval predictor. Finally, we conformalize two single-point RUL predictors, deep convolutional neural networks and gradient boosting, and illustrate their performance on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data sets.

With the advent of deep learning application on edge devices, researchers actively try to optimize their deployments on low-power and restricted memory devices. There are established compression method such as quantization, pruning, and architecture search that leverage commodity hardware. Apart from conventional compression algorithms, one may redesign the operations of deep learning models that lead to more efficient implementation. To this end, we propose EuclidNet, a compression method, designed to be implemented on hardware which replaces multiplication, $xw$, with Euclidean distance $(x-w)^2$. We show that EuclidNet is aligned with matrix multiplication and it can be used as a measure of similarity in case of convolutional layers. Furthermore, we show that under various transformations and noise scenarios, EuclidNet exhibits the same performance compared to the deep learning models designed with multiplication operations.

Performance metrics-driven context caching has a profound impact on throughput and response time in distributed context management systems for real-time context queries. This paper proposes a reinforcement learning based approach to adaptively cache context with the objective of minimizing the cost incurred by context management systems in responding to context queries. Our novel algorithms enable context queries and sub-queries to reuse and repurpose cached context in an efficient manner. This approach is distinctive to traditional data caching approaches by three main features. First, we make selective context cache admissions using no prior knowledge of the context, or the context query load. Secondly, we develop and incorporate innovative heuristic models to calculate expected performance of caching an item when making the decisions. Thirdly, our strategy defines a time-aware continuous cache action space. We present two reinforcement learning agents, a value function estimating actor-critic agent and a policy search agent using deep deterministic policy gradient method. The paper also proposes adaptive policies such as eviction and cache memory scaling to complement our objective. Our method is evaluated using a synthetically generated load of context sub-queries and a synthetic data set inspired from real world data and query samples. We further investigate optimal adaptive caching configurations under different settings. This paper presents, compares, and discusses our findings that the proposed selective caching methods reach short- and long-term cost- and performance-efficiency. The paper demonstrates that the proposed methods outperform other modes of context management such as redirector mode, and database mode, and cache all policy by up to 60% in cost efficiency.

We propose a framework in which multiple entities collaborate to build a machine learning model while preserving privacy of their data. The approach utilizes feature embeddings from shared/per-entity feature extractors transforming data into a feature space for cooperation between entities. We propose two specific methods and compare them with a baseline method. In Shared Feature Extractor (SFE) Learning, the entities use a shared feature extractor to compute feature embeddings of samples. In Locally Trained Feature Extractor (LTFE) Learning, each entity uses a separate feature extractor and models are trained using concatenated features from all entities. As a baseline, in Cooperatively Trained Feature Extractor (CTFE) Learning, the entities train models by sharing raw data. Secure multi-party algorithms are utilized to train models without revealing data or features in plain text. We investigate the trade-offs among SFE, LTFE, and CTFE in regard to performance, privacy leakage (using an off-the-shelf membership inference attack), and computational cost. LTFE provides the most privacy, followed by SFE, and then CTFE. Computational cost is lowest for SFE and the relative speed of CTFE and LTFE depends on network architecture. CTFE and LTFE provide the best accuracy. We use MNIST, a synthetic dataset, and a credit card fraud detection dataset for evaluations.

This work presents an actuation framework for a bioinspired flapping drone called Aerobat. This drone, capable of producing dynamically versatile wing conformations, possesses 14 body joints and is tail-less. Therefore, in our robot, unlike mainstream flapping wing designs that are open-loop stable and have no pronounced morphing characteristics, the actuation, and closed-loop feedback design can pose significant challenges. We propose a framework based on integrating mechanical intelligence and control. In this design framework, small adjustments led by several tiny low-power actuators called primers can yield significant flight control roles owing to the robot's computational structures. Since they are incredibly lightweight, the system can host the primers in large numbers. In this work, we aim to show the feasibility of joint's motion regulation in Aerobat's untethered flights.

Flying animals, such as bats, fly through their fluidic environment as they create air jets and form wake structures downstream of their flight path. Bats, in particular, dynamically morph their highly flexible and dexterous armwing to manipulate their fluidic environment which is key to their agility and flight efficiency. This paper presents the theoretical and numerical analysis of the wake-structure-based gait design inspired by bat flight for flapping robots using the notion of reduced-order models and unsteady aerodynamic model incorporating Wagner function. The objective of this paper is to introduce the notion of gait design for flapping robots by systematically searching the design space in the context of optimization. The solution found using our gait design framework was used to design and test a flapping robot.

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning.

In recent decades, several assistive technologies for visually impaired and blind (VIB) people have been developed to improve their ability to navigate independently and safely. At the same time, simultaneous localization and mapping (SLAM) techniques have become sufficiently robust and efficient to be adopted in the development of assistive technologies. In this paper, we first report the results of an anonymous survey conducted with VIB people to understand their experience and needs; we focus on digital assistive technologies that help them with indoor and outdoor navigation. Then, we present a literature review of assistive technologies based on SLAM. We discuss proposed approaches and indicate their pros and cons. We conclude by presenting future opportunities and challenges in this domain.