MODEL-BASED DESIGN AND IMPLEMENTATION OF DEEP WAVEFORM ANALYSIS SYSTEMS

Analysis of signals of relatively long duration, an area that is referred to as deep waveform analysis, is of increasing importance in instrumentation systems for wireless communications. For example, jitter measurement of deep waveforms must be performed during design and manufacturing tests for complex communications circuitry or equipment. As requirements for bit error rate performance become more stringent and data volumes increase, it becomes increasingly important and interesting to perform deep waveform analysis computations in long, or even temporally unbounded, waveforms. Real-time response and limited hardware resources challenge the design methods of deep waveform analysis systems. Previous methods for deep waveform analysis required storage and computation across all samples of the waveform at once. However, as the amount of data in the waveform grows, and especially if the waveform is unbounded, storage of the waveform in its entirety becomes impractical. The need to satisfy stringent real-time constraints, handle large volumes of data at high sample rates, and operate on resource-constrained platforms result in challenging problems in the development of advanced systems for deep waveform analysis. In this thesis, we have developed new design methodologies and design optimization methods to address these problems. The contributions of the thesis are geared toward handling large, possibly unbounded, signal data sets, and providing novel trade-offs among measurement accuracy, memory constraints, and real-time performance. Motivated by performance bottlenecks that we observed in our experimentation with deep waveform analysis, we have also developed a new model of computation for representing signal processing applications in a way that improves the efficiency of data communication between computational modules. The main contributions of this thesis are summarized in the following. (1). Design methodology for deep waveform analysis systems. We have developed a new design methodology for deep waveform analysis under limited resources. The methodology builds on the formalisms of dataflow-based design and implementation of signal processing systems. Our proposed methodology is shown to help significantly advance the prior state of the art in jitter measurement system design, and it forms an important foundation for later contributions that are presented in the thesis. Our approach is demonstrated through extensive experiments using actual measured data. Through its incorporation of high-level dataflow principles, the approach is suitable for efficient mapping to a variety of platforms, including multicore processors and graphics processing unit (GPU) devices for high performance signal processing. (2). Design optimization for gapless deep waveform analysis. We have developed novel models and design optimization methods for addressing the real-time processing challenges of gapless deep waveform applications. A gapless signal processing application is characterized by one or more continuous streams of input data, where the data must be processed reliably without dropping any of the input samples. The strict real-time processing requirements for gapless deep waveform applications can be very challenging when input data rates are high, processing requirements are intensive, or the target platform is significantly resource constrained. The methods developed in this part of the thesis focus on optimizing the throughput of deep waveform analysis subject to the on-board memory constraints of a given data acquisition system interface, processor memory constraints, and the constraint of gapless processing. (3). Passive-active flow graphs for dataflow-based implementation. We introduce a new model of computation called passive-active flow graphs (PAFGs), which complement conventional dataflow-based application representations. We have developed PAFGs to address important bottlenecks in dataflow graph implementation associated with communication between computational modules (dataflow graph vertices). We demonstrate the use of PAFGs as an intermediate representation for refining dataflow graphs into efficient implementations. We develop formal underpinnings of the PAFG model of computation, and introduce systematic transformation techniques for deriving and optimizing PAFG representations

CommitBART: A Large Pre-trained Model for GitHub Commits

GitHub commits, which record the code changes with natural language messages for description, play a critical role for software developers to comprehend the software evolution. To promote the development of the open-source software community, we collect a commit benchmark including over 7.99 million commits across 7 programming languages. Based on this benchmark, we present CommitBART, a large pre-trained encoder-decoder Transformer model for GitHub commits. The model is pre-trained by three categories (i.e., denoising objectives, cross-modal generation and contrastive learning) for six pre-training tasks to learn commit fragment representations. Furthermore, we unify a ``commit intelligence'' framework with one understanding task and three generation tasks for commits. The comprehensive experiments on these tasks demonstrate that CommitBARTsignificantly outperforms previous pre-trained works for code. Further analysis also reveals each pre-training task enhances the model performance

An Empirical Study on the Effectiveness of Noisy Label Learning for Program Understanding

Recently, deep learning models have been widely applied in program understanding tasks, and these models achieve state-of-the-art results on many benchmark datasets. A major challenge of deep learning for program understanding is that the effectiveness of these approaches depends on the quality of their datasets, and these datasets often contain noisy data samples. A typical kind of noise in program understanding datasets is label noises, which means that the target outputs for some inputs are mislabeled. Label noises may have a negative impact on the performance of deep learning models, so researchers have proposed various approaches to alleviate the impact of noisy labels, and formed a new research topic: noisy label learning (NLL). In this paper, we conduct an empirical study on the effectiveness of noisy label learning on deep learning for program understanding datasets. We evaluate various noisy label learning approaches and deep learning models on two tasks: program classification and code summarization. From the evaluation results, we find that the impact of label noise and NLL approaches on small deep learning models and large pre-trained models are different: small models are prone to label noises in program classification and NLL approaches can improve their robustness, while large pre-trained models are robust against label noises and NLL does not significantly improve their performances. On the other hand, NLL approaches have shown satisfying results in identifying noisy labeled samples for both tasks, indicating that these techniques can benefit researchers in building high-quality program understanding datasets

Models of Architecture: Reproducible Efficiency Evaluation for Signal Processing Systems

International audienceThe current trend in high performance and embedded signal processing consists of designing increasingly complex heterogeneous hardware architectures with non-uniform communication resources. In order to take hardware and software design decisions, early evaluations of the system non-functional properties are needed. These evaluations of system efficiency require high-level information on both the algorithms and the architecture. In this paper, we define the notion of Model of Architecture (MoA) and study the combination of a Model of Computation (MoC) and an MoA to provide a design space exploration environment for the study of the algorithmic and architectural choices. A cost is computed from the mapping of an application, represented by a model conforming a MoC onto an architecture represented by a model conforming an MoA. The cost is composed of a processing-related part and a communication-related part. It is an abstract scalar value to be minimized and can represent any non-functional requirement of a system such as memory, energy, throughput or latency

Models of Architecture: Reproducible Efficiency Evaluation for Signal Processing Systems

International audienceThe current trend in high performance and embedded signal processing consists of designing increasingly complex heterogeneous hardware architectures with non-uniform communication resources. In order to take hardware and software design decisions, early evaluations of the system non-functional properties are needed. These evaluations of system efficiency require high-level information on both the algorithms and the architecture. In this paper, we define the notion of Model of Architecture (MoA) and study the combination of a Model of Computation (MoC) and an MoA to provide a design space exploration environment for the study of the algorithmic and architectural choices. A cost is computed from the mapping of an application, represented by a model conforming a MoC onto an architecture represented by a model conforming an MoA. The cost is composed of a processing-related part and a communication-related part. It is an abstract scalar value to be minimized and can represent any non-functional requirement of a system such as memory, energy, throughput or latency

Enumeration and representation of spin space groups

Those fundamental properties, such as phase transitions, Weyl fermions and spin excitation, in all magnetic ordered materials was ultimately believed to rely on the symmetry theory of magnetic space groups. Recently, it has come to light that a more comprehensive group, known as the spin space group (SSG), which combines separate spin and spatial operations, is necessary to fully characterize the geometry and physical properties of magnetic ordered materials such as altermagnets. However, the basic theory of SSG has been seldomly developed. In this work, we present a systematic study of the enumeration and the representation theory of SSG. Starting from the 230 crystallographic space groups and finite translational groups with a maximum order of 8, we establish an extensive collection of over 80,000 SSGs under a four-segment nomenclature. We then identify inequivalent SSGs specifically applicable to collinear, coplanar, and noncoplanar magnetic configurations. Moreover, we derive the irreducible co-representations of the little group in momentum space within the SSG framework. Finally, we illustrate the SSGs and band degeneracies resulting from SSG symmetries through several representative material examples, including a well-known altermagnet RuO2, and a spiral magnet CeAuAl3. Our work advances the field of group theory in describing magnetic ordered materials, opening up avenues for deeper comprehension and further exploration of emergent phenomena in magnetic materials.Comment: 29 pages, 1 table, 5 figures and a Supplementary table with 1508 page

The Use of Solution-Focused Brief Therapy in Chinese Schools: A Qualitative Analysis of Practitioner Perceptions

Solution-focused brief therapy (SFBT) is a strengthens-based, future-oriented approach that has received promising results over the past decade. Literature on SFBT has demonstrated the approach’s ability to meet the unique needs of various client populations while adapting to a variety of service delivery settings. Schools are a specific setting in which SFBT has been successfully utilized in the United States. With the growing popularity of SFBT, countries outside to the United States are beginning to implement SFBT in their schools. This article explored perceptions of the use of SFBT in schools amongst Chinese mental health practitioners. A survey was conducted by the Chinese government and included 134 participants. The qualitative results showed the Chinese practitioners have a strong interest in the strengths-based approach and feel that SFBT is culturally-adaptive to the Chinese student population. However, the practitioners are not confidently able to utilize SFBT techniques. The Chinese practitioners related the lack of confidence to a lack of SFBT focused training and professional develop opportunities. As SFBT research and practice continues to grow in China, the need for affordable, accessible SFBT trainings and supervision grows as well