In the era of the fourth industrial revolution, it is essential to automate fault detection and diagnosis of machineries so that a warning system can be developed that will help to take an appropriate action before any catastrophic damage. Some machines health monitoring systems are used globally but they are expensive and need trained personnel to operate and analyse. Predictive maintenance and occupational health and safety culture are not available due to inadequate infrastructure, lack of skilled manpower, financial crisis, and others in developing countries. Starting from developing a cost-effective DAS for collecting fault data in this study, the effect of limited data and resources has been investigated while automating the process. To solve this problem, A feature engineering and data reduction method has been developed combining the concepts from wavelets, differential calculus, and signal processing. Finally, for automating the whole process, all the necessary theoretical and practical considerations to develop a predictive model have been proposed. The DAS successfully collected the required data from the machine that is 89% accurate compared to the professional manual monitoring system. SVM and NN were proposed for the prediction purpose because of their high predicting accuracy greater than 95% during training and 100% during testing the new samples. In this study, the combination of the simple algorithm with a rule-based system instead of a data-intensive system turned out to be hybridization by validating with collected data. The outcome of this research can be instantly applied to small and medium-sized industries for finding other issues and developing accordingly. As one of the foundational studies in automatic FDD, the findings and procedure of this study can lead others to extend, generalize, or add other dimensions to FDD automation.

Optimal management of cross-asset infrastructure is a complex problem that requires adept inspection and maintenance policies addressing stochastic degradation impacts. According to the 2021 ASCE infrastructure report card [1], the US infrastructure is in fair to poor condition, earning a cumulative grade of C-, with components nearing the end of their useful lives and at high risk of failure. Pavements and bridges are indicative examples of inadequate infrastructure. One in every five miles of pavements is in poor condition, and 7.5% of bridges are structurally deficient. Economic analyses indicate that the US Department of Transportation fell 50% short of the funds required to sustain the national transportation system [1], which is also reflected in the available resources at individual State transportation agencies. The Virginia Department of Transportation, for example, reported that 50% of the State's bridges have exceeded their useful lives, and the required funds to replace them are five times greater than the estimated available funds over the next fifty years [2]. Inspection and Maintenance (I&M) policies are therefore indispensable towards efficiently distributing available economic and environmental resources for transportation systems. Making optimal decisions in complex and uncertain environments presents a variety of difficulties, including heterogeneity of asset classes, a high number of components resulting in vast state and action spaces, unreliable observations, limited availability of resources, and several related risks. Optimal solutions that define inspection and maintenance policies should thus incorporate concepts such as (i) online and offline data learning, (ii) imperfect information support, (iii) stochastic action outcomes considerations, and (iv) optimization of long-term goals under multiple constraints (e.g., safety targets or resource constraints).

In recent years, various computing-in-memory (CIM) processors have been presented, showing superior performance over traditional architectures. To unleash the potential of various CIM architectures, such as device precision, crossbar size, and crossbar number, it is necessary to develop compilation tools that are fully aware of the CIM architectural details and implementation diversity. However, due to the lack of architectural support in current popular open-source compiling stacks, existing CIM designs either manually deploy networks or build their own compilers, which is time-consuming and labor-intensive. Although some works expose the specific CIM device programming interfaces to compilers, they are often bound to a fixed CIM architecture, lacking the flexibility to support the CIM architectures with different computing granularity. On the other hand, existing compilation works usually consider the scheduling of limited operation types (such as crossbar-bound matrix-vector multiplication). Unlike conventional processors, CIM accelerators are featured by their diverse architecture, circuit, and device, which cannot be simply abstracted by a single level if we seek to fully explore the advantages brought by CIM. Therefore, we propose CIM-MLC, a universal multi-level compilation framework for general CIM architectures. We first establish a general hardware abstraction for CIM architectures and computing modes to represent various CIM accelerators. Based on the proposed abstraction, CIM-MLC can compile tasks onto a wide range of CIM accelerators having different devices, architectures, and programming interfaces. More importantly, compared with existing compilation work, CIM-MLC can explore the mapping and scheduling strategies across multiple architectural tiers, which form a tractable yet effective design space, to achieve better scheduling and instruction generation results.

Stochastic Gradient Descent (SGD) stands as a cornerstone optimization algorithm with proven real-world empirical successes but relatively limited theoretical understanding. Recent research has illuminated a key factor contributing to its practical efficacy: the implicit regularization it instigates. Several studies have investigated the linear stability property of SGD in the vicinity of a stationary point as a predictive proxy for sharpness and generalization error in overparameterized neural networks (Wu et al., 2022; Jastrzebski et al., 2019; Cohen et al., 2021). In this paper, we delve deeper into the relationship between linear stability and sharpness. More specifically, we meticulously delineate the necessary and sufficient conditions for linear stability, contingent on hyperparameters of SGD and the sharpness at the optimum. Towards this end, we introduce a novel coherence measure of the loss Hessian that encapsulates pertinent geometric properties of the loss function that are relevant to the linear stability of SGD. It enables us to provide a simplified sufficient condition for identifying linear instability at an optimum. Notably, compared to previous works, our analysis relies on significantly milder assumptions and is applicable for a broader class of loss functions than known before, encompassing not only mean-squared error but also cross-entropy loss.

We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion models at inference-time via optimizing initial noise latents. Our method can be used to optimize through any differentiable feature matching loss to achieve a target (stylized) output and leverages gradient checkpointing for memory efficiency. We demonstrate a surprisingly wide-range of applications for music generation including inpainting, outpainting, and looping as well as intensity, melody, and musical structure control - all without ever fine-tuning the underlying model. When we compare our approach against related training, guidance, and optimization-based methods, we find DITTO achieves state-of-the-art performance on nearly all tasks, including outperforming comparable approaches on controllability, audio quality, and computational efficiency, thus opening the door for high-quality, flexible, training-free control of diffusion models. Sound examples can be found at https://DITTO-Music.github.io/web/.

The burgeoning volume of graph data poses significant challenges in storage, transmission, and particularly the training of graph neural networks (GNNs). To address these challenges, graph condensation (GC) has emerged as an innovative solution. GC focuses on synthesizing a compact yet highly representative graph, on which GNNs can achieve performance comparable to trained on the large original graph. The notable efficacy of GC and its broad prospects have garnered significant attention and spurred extensive research. This survey paper provides an up-to-date and systematic overview of GC, organizing existing research into four categories aligned with critical GC evaluation criteria: effectiveness, generalization, fairness, and efficiency. To facilitate an in-depth and comprehensive understanding of GC, we examine various methods under each category and thoroughly discuss two essential components within GC: optimization strategies and condensed graph generation. Additionally, we introduce the applications of GC in a variety of fields, and highlight the present challenges and novel insights in GC, promoting advancements in future research.

Variational regularization is commonly used to solve linear inverse problems, and involves augmenting a data fidelity by a regularizer. The regularizer is used to promote a priori information and is weighted by a regularization parameter. Selection of an appropriate regularization parameter is critical, with various choices leading to very different reconstructions. Classical strategies used to determine a suitable parameter value include the discrepancy principle and the L-curve criterion, and in recent years a supervised machine learning approach called bilevel learning has been employed. Bilevel learning is a powerful framework to determine optimal parameters and involves solving a nested optimization problem. While previous strategies enjoy various theoretical results, the well-posedness of bilevel learning in this setting is still an open question. In particular, a necessary property is positivity of the determined regularization parameter. In this work, we provide a new condition that better characterizes positivity of optimal regularization parameters than the existing theory. Numerical results verify and explore this new condition for both small and high-dimensional problems.

Controlling the interaction forces between a human and an exoskeleton is crucial for providing transparency or adjusting assistance or resistance levels. However, it is an open problem to control the interaction forces of lower-limb exoskeletons designed for unrestricted overground walking. For these types of exoskeletons, it is challenging to implement force/torque sensors at every contact between the user and the exoskeleton for direct force measurement. Moreover, it is important to compensate for the exoskeleton's whole-body gravitational and dynamical forces, especially for heavy lower-limb exoskeletons. Previous works either simplified the dynamic model by treating the legs as independent double pendulums, or they did not close the loop with interaction force feedback. The proposed whole-exoskeleton closed-loop compensation (WECC) method calculates the interaction torques during the complete gait cycle by using whole-body dynamics and joint torque measurements on a hip-knee exoskeleton. Furthermore, it uses a constrained optimization scheme to track desired interaction torques in a closed loop while considering physical and safety constraints. We evaluated the haptic transparency and dynamic interaction torque tracking of WECC control on three subjects. We also compared the performance of WECC with a controller based on a simplified dynamic model and a passive version of the exoskeleton. The WECC controller results in a consistently low absolute interaction torque error during the whole gait cycle for both zero and nonzero desired interaction torques. In contrast, the simplified controller yields poor performance in tracking desired interaction torques during the stance phase.

Optimal control (OC) algorithms such as Differential Dynamic Programming (DDP) take advantage of the derivatives of the dynamics to efficiently control physical systems. Yet, in the presence of nonsmooth dynamical systems, such class of algorithms are likely to fail due, for instance, to the presence of discontinuities in the dynamics derivatives or because of non-informative gradient. On the contrary, reinforcement learning (RL) algorithms have shown better empirical results in scenarios exhibiting non-smooth effects (contacts, frictions, etc). Our approach leverages recent works on randomized smoothing (RS) to tackle non-smoothness issues commonly encountered in optimal control, and provides key insights on the interplay between RL and OC through the prism of RS methods. This naturally leads us to introduce the randomized Differential Dynamic Programming (R-DDP) algorithm accounting for deterministic but non-smooth dynamics in a very sample-efficient way. The experiments demonstrate that our method is able to solve classic robotic problems with dry friction and frictional contacts, where classical OC algorithms are likely to fail and RL algorithms require in practice a prohibitive number of samples to find an optimal solution.

With the recent influx in demand for multi-robot systems throughout industry and academia, there is an increasing need for faster, robust, and generalizable path planning algorithms. Similarly, given the inherent connection between control algorithms and multi-robot path planners, there is in turn an increased demand for fast, efficient, and robust controllers. We propose a scalable joint path planning and control algorithm for multi-robot systems with constrained behaviours based on factor graph optimization. We demonstrate our algorithm on a series of hardware and simulated experiments. Our algorithm is consistently able to recover from disturbances and avoid obstacles while outperforming state-of-the-art methods in optimization time, path deviation, and inter-robot errors. See the code and supplementary video for experiments.