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GigaCollector: A Real-Time, Temporally Coherent Framework forWi-Fi Environment Control, Data Collection, and ML Inference
Get PDFWi-Fi is the primary wireless communication method for the majority of devices in both residential and commercial settings. The number of devices continues to increase, making latency, bandwidth, and security difficult to manage and balance to provide satisfactory performance for all users. Novel methods that rely on the collection of data from the networking stack have been developed in research settings to analyze and predict key parameters and network state to improve communication efficiency. However, crucial processes such as experimentation, data collection, and performance analysis have often been performed manually on offline data.
This thesis presents GigaCollector, a scalable end-to-end framework to enable temporally-coherent high-rate, realtime, and flexible environment control, experiment control, data collection, data collation, and model inference for the research, experimentation, and development of new machine learning-based edge systems such as Wi-Fi schedulers for improved latency and power consumption. It is also applicable to other fields such as sensor fusion for motion capture. The framework consists of five main components: (1) Active Environment Control (AEC), (2) the Experiment Controller (EC), (3) Data Sources (DSs), (4) the Collector & Collator (CC), and (5) Snapshot Consumers (SCs). Each component uses various techniques to enable the three core requirements: temporal coherence, high-rate and real-time operation, and flexibility. The AEC component uses closed-loop control algorithms incorporating system feedback to adaptively control experimental conditions. The EC automates AEC configuration, iterates through user-defined test cases with programmable start and stop conditions, and records all results. The DS, CC, and SC interfaces use ZeroMQ, a lightweight, low-latency, high-throughput, and open-source messaging system to enable language-agnostic interoperability between local and remote distributed components. The CC uses a Temporal Index-Matched List (TIML) data structure to allow large data history storage with O(log(n))-class fuzzy closest-timestamp data collation for temporally-coherent snapshots.
The results show that this framework is able to achieve its two core goals of (1) flexible, scalable, temporally-coherent, high-rate, and real-time data collection and snapshot collation, and (2) real-time closed-loop experimental environment control. For goal (1), on a 24-core dual-Xeon E5-2690 v3 workstation, GigaCollector achieves sub-millisecond end-to-end latency using a single processor core with an average of 500 microseconds (300 microseconds for collection, 70 microseconds to process, store, and collate data across 10 data sources each with 10 elements, and 150 microseconds to deliver the data to a snapshot consumer) with an average maximum message processing rate of 13,000 per second per CC instance under reasonable loads. Horizontal scalability with two parallel instances doubles throughput in certain circumstances with minimal impact on end-to-end latency. For goal (2), the system achieves independent control of uplink and downlink airtime utilization with roughly 10% deviation from the set point using an Atheros AR9462 Wi-Fi network interface connected to the same workstation as the server and a Raspberry Pi 5 as the client
Optimization of Sensor Placement for Modal Testing Using Machine Learning
Get PDFModal testing is a common step in aerostructure design, serving to validate the predicted natural frequencies and mode shapes obtained through computational methods. The strategic placement of sensors during testing is crucial for accurately measuring the intended natural frequencies. However, conventional methodologies for sensor placement are often time-consuming and involve iterative processes. This study explores the potential of machine learning techniques to enhance sensor selection methodologies. Three machine learning-based approaches are introduced and assessed, and their efficiencies are compared with established techniques. The evaluation of these methodologies is conducted using a numerical model of a beam to simulate real-world scenarios. The results offer insights into the efficacy of machine learning in optimizing sensor placement, presenting an innovative perspective on enhancing the efficiency and precision of modal testing procedures in aerostructure design
Hardware Software Co-Design of Zero-Knowledge Succinct Non-interactive Argument of Knowledge
Get PDFZero-Knowledge Succinct Non-interactive Argument of Knowledge (zk-SNARK) is an important security verification protocol in cryptography. However, zk-SNARK is a computationally expensive protocol in software, meaning that it takes a lot of time. In this paper, we focus on how we can increase the efficiency of the zk-SNARK protocol. The zk-SNARK protocol comprises of many algorithms put together. Our project focuses on optimizing one specific algorithm within the zk-SNARK protocol, the Number Theoretic Transform (NTT). We developed a hardware implementation of the NTT on a Field Programmable Gate Array (FPGA) board. To ensure the proper execution of the hardware, an external processor was used to ensure that data was being sent and received to and from the hardware at the correct time. This resulted in an hardware software co-design implementation of the NTT. Our experiment results showed that our hardware-accelerated NTT was 50 times more efficient than a software implementation of the NTT. Our optimization of the NTT through hardware means that the zk-SNARK protocol will take less time to execute leading to an improvement in efficiency. Consequently, improvements beyond our NTT hardware accelerator will make an impact in the zk-SNARK protocol. Developing multiple NTT hardware blocks in parallel or optimizing other algorithms within the zk-SNARK protocol will lead to further improvements in the efficiency of the zk-SNARK verification protocol
Round number reference points and irregular patterns in reported gross margins
No full textWe find irregular patterns in the distribution of firms’ reported quarterly gross margin percentages. Specifically, there is significant bunching around percentage integers that are highly round (e.g., multiples of 10, such as 30%, 40%, etc.) or are neatly divisible (e.g., 25%, 75%), compared to what is predicted by counterfactual distributions. Further investigation reveals that highly round gross margin firms are smaller, exert higher effort, achieve higher productivity, have more difficult goals, and pay their CEOs with a higher portion of fixed income. We also find that highly round gross margins are associated with superior performance. Additionally, we do not find consistent evidence that highly round gross margin reference points are linked to external rewards. Collectively, our evidence is consistent with reference-dependent preferences for highly round gross margins likely being driven by intrinsic (rather than extrinsic) motivations
A psychological evaluation method incorporating noisy label correction mechanism
No full textUsing machine learning and deep learning methods to analyze text data from social media can effectively explore hidden emotional tendencies and evaluate the psychological state of social media account owners. However, the label noise caused by mislabeling may significantly influence the training and prediction results of traditional supervised models. To resolve this problem, this paper proposes a psychological evaluation method that incorporates a noisy label correction mechanism and designs an evaluation framework that consists of a primary classification model and a noisy label correction mechanism. Firstly, the social media text data are transformed into heterogeneous text graphs, and a classification model combining a pre-trained model with a graph neural network is constructed to extract semantic features and structural features, respectively. After that, the Gaussian mixture model is used to select the samples that are likely to be mislabeled. Then, soft labels are generated for them to enable noisy label correction without prior knowledge of the noise distribution information. Finally, the corrected and clean samples are composed into a new data set and re-input into the primary model for mental state classification. Results of experiments on three real data sets indicate that the proposed method outperforms current advanced models in classification accuracy and noise robustness under different noise ratio settings, and can efficiently explore the potential sentiment tendencies and users’ psychological states in social media text data
A clinical trial of the Examen and mindfulness within a secular substance use disorder treatment program
Get PDFThe Examen is a 500-year-old Jesuit introspective prayer and reflection. Recent research has indicated that it has utility in psychotherapy. This study implemented the Examen as a secular cognitive–behavioral tool in the first longitudinal clinical trial of the intervention with an addiction treatment population, comparing it directly to a treatment-as-usual mindfulness intervention. The study found that Examen and mindfulness are equivalent in outcomes on depression, anxiety, stress, and substance craving. Further research should continue to investigate the Examen as an alternative to mindfulness for religious and secular populations and the factors responsible for the success of these practices
African Women’s Liberating Philosophies, Theologies, and Ethics
No full textThis volume explores the ethical and philosophical paradigms presented by most of the influential Matriarchs of the Circle of African Women Theologians. It critically evaluates the effectiveness of their ethical and philosophical theories, models, and frameworks in pursuing justice and liberation for women in Africa and globally. The authors address critical questions: How have African women theologians reimagined existing ethical paradigms? What original ethical and philosophical ideas have they generated? How have their ethical frameworks influenced the theologies and interpretations they have developed? What purposes do their ethical and philosophical paradigms serve? How do these renderings intersect with various social categories, including gender, race, class, sexuality, capitalism, and colonialism? What liberating frameworks do they propose?
The volume further explores the dialogue between distinct African contexts and universal experiences and values. It explores how universal themes such as humanity, human dignity, rights, justice, motherhood, and more can coexist with communal African concepts and themes. It contemplates how embracing African approaches engages these themes more globally, bringing together particular African contexts of women and the universal ethical, philosophical, and theological theories, models, and frameworks to advance the cause of justice and liberation for African women and women worldwide into the future.https://scholarcommons.scu.edu/faculty_books/1640/thumbnail.jp