Flux-Weakening Control for Permanent-Magnet Synchronous Motors Based on Z-Source Inverters

Permanent magnet synchronous machines (PMSMs) have high efficiency, high power density, high torque-to-inertia ratio, and fast dynamic response. These features make this kind of machines very attractive for electric vehicle (EV) applications. However, because of their nature, i.e., constant magnet flux provided by magnets, these machines have a narrow constant power speed range (CPSR). This limitation is a strong drawback for application of PMSMs in electric vehicles, where high speed is the top requirement. Two different approaches can extend the maximum speed under constant power: (1) Increasing a drive\u27s output voltage, and (2) implementing flux-weakening (FW) control methods. However, a conventional drive\u27s output voltage is limited by its dc bus. Furthermore, FW control methods are constrained by the maximum output voltage of a drive. In this work, a new approach is demonstrated to obtain a wider CPSR range by implementing a Z-source inverter as a motor-drive. Such a Z-source inverter can provide highly boosted voltage and is immune to dead time and shoot through issues. In addition, in this thesis, a constant power FW control algorithm is developed and simulated for this new approach

Social Barriers to Implementing Continuous Improvement Initiatives

Organizations report challenges in implementing continuous improvement or operational excellence initiatives as they strive for sustainability, yet few have considered the impact that social barriers have in creating resistance to implementation. Through a qualitative grounded theory method, this study highlights several contributions. First, social barriers are stronger than other challenges to implementing operational excellence. Second, these barriers include interpersonal (e.g., communication challenges, unwillingness to change, and workplace relationships) and organizational (e.g., employee treatment, cultural values, and formal organizational characteristics) issues. This article thus links sustainability to operational excellence and suggests that the greatest barriers to becoming more sustainable are likely social in nature. The study then concludes, in addition to these contributions, with a consideration of limitations and directions for future research

Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning

Currently, graph learning models are indispensable tools to help researchers explore graph-structured data. In academia, using sufficient training data to optimize a graph model on a single device is a typical approach for training a capable graph learning model. Due to privacy concerns, however, it is infeasible to do so in real-world scenarios. Federated learning provides a practical means of addressing this limitation by introducing various privacy-preserving mechanisms, such as differential privacy (DP) on the graph edges. However, although DP in federated graph learning can ensure the security of sensitive information represented in graphs, it usually causes the performance of graph learning models to degrade. In this paper, we investigate how DP can be implemented on graph edges and observe a performance decrease in our experiments. In addition, we note that DP on graph edges introduces noise that perturbs graph proximity, which is one of the graph augmentations in graph contrastive learning. Inspired by this, we propose leveraging graph contrastive learning to alleviate the performance drop resulting from DP. Extensive experiments conducted with four representative graph models on five widely used benchmark datasets show that contrastive learning indeed alleviates the models' DP-induced performance drops.Comment: Accepted by Information Science

Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization

Out-of-Distribution (OOD) Generalization aims to learn robust models that generalize well to various environments without fitting to distribution-specific features. Recent studies based on Lottery Ticket Hypothesis (LTH) address this problem by minimizing the learning target to find some of the parameters that are critical to the task. However, in OOD problems, such solutions are suboptimal as the learning task contains severe distribution noises, which can mislead the optimization process. Therefore, apart from finding the task-related parameters (i.e., invariant parameters), we propose Exploring Variant parameters for Invariant Learning (EVIL) which also leverages the distribution knowledge to find the parameters that are sensitive to distribution shift (i.e., variant parameters). Once the variant parameters are left out of invariant learning, a robust subnetwork that is resistant to distribution shift can be found. Additionally, the parameters that are relatively stable across distributions can be considered invariant ones to improve invariant learning. By fully exploring both variant and invariant parameters, our EVIL can effectively identify a robust subnetwork to improve OOD generalization. In extensive experiments on integrated testbed: DomainBed, EVIL can effectively and efficiently enhance many popular methods, such as ERM, IRM, SAM, etc.Comment: 27 pages, 9 figure

ARF-BP1/Mule Is a Critical Mediator of the ARF Tumor Suppressor

SummaryAlthough the importance of the ARF tumor suppressor in p53 regulation is well established, numerous studies indicate that ARF also suppresses cell growth in a p53/Mdm2-independent manner. To understand the mechanism of ARF-mediated tumor suppression, we identified a ubiquitin ligase, ARF-BP1, as a key factor associated with ARF in vivo. ARF-BP1 harbors a signature HECT motif, and its ubiquitin ligase activity is inhibited by ARF. Notably, inactivation of ARF-BP1, but not Mdm2, suppresses the growth of p53 null cells in a manner reminiscent of ARF induction. Surprisingly, in p53 wild-type cells, ARF-BP1 directly binds and ubiquitinates p53, and inactivation of endogenous ARF-BP1 is crucial for ARF-mediated p53 stabilization. Thus, our study modifies the current view of ARF-mediated p53 activation and reveals that ARF-BP1 is a critical mediator of both the p53-independent and p53-dependent tumor suppressor functions of ARF. As such, ARF-BP1 may serve as a potential target for therapeutic intervention in tumors regardless of p53 status

InstanT: Semi-supervised Learning with Instance-dependent Thresholds

Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for decades. The primary family of SSL algorithms, known as pseudo-labeling, involves assigning pseudo-labels to confident unlabeled instances and incorporating them into the training set. Therefore, the selection criteria of confident instances are crucial to the success of SSL. Recently, there has been growing interest in the development of SSL methods that use dynamic or adaptive thresholds. Yet, these methods typically apply the same threshold to all samples, or use class-dependent thresholds for instances belonging to a certain class, while neglecting instance-level information. In this paper, we propose the study of instance-dependent thresholds, which has the highest degree of freedom compared with existing methods. Specifically, we devise a novel instance-dependent threshold function for all unlabeled instances by utilizing their instance-level ambiguity and the instance-dependent error rates of pseudo-labels, so instances that are more likely to have incorrect pseudo-labels will have higher thresholds. Furthermore, we demonstrate that our instance-dependent threshold function provides a bounded probabilistic guarantee for the correctness of the pseudo-labels it assigns.Comment: Accepted as poster for NeurIPS 202

Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of computation, especially for minor editing operations. In this work, we present Spatially Sparse Inference (SSI), a general-purpose technique that selectively performs computation for edited regions and accelerates various generative models, including both conditional GANs and diffusion models. Our key observation is that users prone to gradually edit the input image. This motivates us to cache and reuse the feature maps of the original image. Given an edited image, we sparsely apply the convolutional filters to the edited regions while reusing the cached features for the unedited areas. Based on our algorithm, we further propose Sparse Incremental Generative Engine (SIGE) to convert the computation reduction to latency reduction on off-the-shelf hardware. With about $1\%$-area edits, SIGE accelerates DDPM by $3.0\times$ on NVIDIA RTX 3090 and $4.6\times$ on Apple M1 Pro GPU, Stable Diffusion by $7.2\times$ on 3090, and GauGAN by $5.6\times$ on 3090 and $5.2\times$ on M1 Pro GPU. Compared to our conference version, we extend SIGE to accommodate attention layers and apply it to Stable Diffusion. Additionally, we offer support for Apple M1 Pro GPU and include more results with large and sequential edits.Comment: NeurIPS 2022 T-PAMI 2023 Website: https://www.cs.cmu.edu/~sige/ Code: https://github.com/lmxyy/sig

AutoMLP: Automated MLP for Sequential Recommendations

Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience, which is either highly inefficient or yields subpar results. The recent advanced transformer-based models can achieve state-of-the-art performances despite the aforementioned issue, but they have a quadratic computational complexity to the length of the input sequence. To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users' long/short-term interests from their historical interactions. In addition, we design an automated and adaptive search algorithm for preferable short-term interest length via end-to-end optimization. Through extensive experiments, we show that AutoMLP has competitive performance against state-of-the-art methods, while maintaining linear computational complexity.Comment: Accepted by WWW'2