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In order to simplify the muon detection system, this paper proposes a high-precision time measurement method based on phase-fitting by using the digitized muon pulses which are often used to extract the energy information of muons. Thus, the time and energy measurement can share a set of ADC modules, eliminating the need for separate hardware for time measurement. The relative time of flight (TOF) of muon pulses can be obtained with high precision through cross-correlation and phase fitting techniques. In this paper, the theory of time measurement based on this digital phase-fitting method was analyzed and an electronics prototype was designed to validate the feasibility of the method by utilizing a 40 MSPS ADC for muon pulse digitization and an FPGA for phase fitting implementation. The test results indicate that a time precision better than 50 ps was achieved when the signal-to-noise ratio (SNR) of the input pulse exceeds 64 dB. Furthermore, in cosmic ray tests, the prototype exhibited favorable timing performance. These results indicate that this proposed method has the potential to be a high-precision TOF measurement for muon and other nuclear pulse signals.

This paper studies the minimum observability of probabilistic Boolean networks (PBNs), the main objective of which is to add the fewest measurements to make an unobservable PBN become observable. First of all, the algebraic form of a PBN is established with the help of semi-tensor product (STP) of matrices. By combining the algebraic forms of two identical PBNs into a parallel system, a method to search the states that need to be H-distinguishable is proposed based on the robust set reachability technique. Secondly, a necessary and sufficient condition is given to find the minimum measurements such that a given set can be H-distinguishable. Moreover, by comparing the numbers of measurements for all the feasible H-distinguishable state sets, the least measurements that make the system observable are gained. Finally, an example is given to verify the validity of the obtained results.

California voters decided to collect a 50 cent-per-pack tax on cigarette and other tobacco products and use the money to fund early childhood services under Proposition 10, California Children and Families First Act of 1998. In compliance to the legislative requirement of Result-Based Accountability (RBA) on the revenue spending, this report delineates evaluation findings from 39 programs that received over $8.8 million of the annual state investment in Kern County, the third largest county in California by land area. Following the RBA model, this report incorporates qualitative and quantitative analyses to present in-depth success stories and statistical results from extensive assessments of program effectiveness and service integration. A five-chapter structure is adopted to support dissemination of the multilevel findings in Fiscal Year 2021-2022 -- Built on description of the Commission leadership in Chapter 1, Chapter 2 contains assessment findings to address the results-based accountability across programs in Child Health, Family Functioning, and Child Development. Chapter 3 clarifies service partnership building in Systems of Care. Improvement of child wellbeing and family functioning is summarized in Chapter 4 to document the turning the curve process on important indicators of child wellbeing and parent support. The report concludes in Chapter 5 with a review of past recommendations and an introduction to new recommendations for next year. Netdraw, Quanteda in R, SAS and SPSS packages are employed to support data visualization, text analytics, and statistical computing. The report includes two appendices, 33 figures, 77 tables, and 151 reference items. [For "First 5 Kern Annual Report 2020-2021," see ED618879.]

Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rank approximation, which suffers from high computational costs when dealing with large-scale tensor data. Moreover, most of them are only applicable to third-order tensors. Against these issues, in this article, two efficient low-rank tensor approximation approaches fusing randomized techniques are first devised under the order-d (d >= 3) T-SVD framework. On this basis, we then further investigate the robust high-order tensor completion (RHTC) problem, in which a double nonconvex model along with its corresponding fast optimization algorithms with convergence guarantees are developed. To the best of our knowledge, this is the first study to incorporate the randomized low-rank approximation into the RHTC problem. Empirical studies on large-scale synthetic and real tensor data illustrate that the proposed method outperforms other state-of-the-art approaches in terms of both computational efficiency and estimated precision.

This report is developed with dual foci: (1) justifying attainment of the grant milestones in Year 2; and (2) supporting ongoing improvement of the program performance, to evaluate the second year operation of a five-year grant, "Promoting Excellence in Graduate Education and Increasing Hispanic STEM Related Degree Completion". Well-rounded findings from quantitative and qualitative approaches are gathered in four sections: Fulfillment of Year 2 benchmark is addressed in Section I to match the original milestones in the grant proposal. Based on conceptualization of a "Rate of Progress" indicator in Year 1 report, Section II is devoted to description of student tracking across program pipelines toward degree completion. Impact stories are gathered in Section III to provide in-depth faculty assessment of profound outcomes in "teaching," "research," and "mentorship" domains. The qualitative inquiry is further expanded in Section IV to support social network analyses of faculty collaboration across academic disciplines. Triangulation of the evaluation findings consistently indicates satisfactory performance of the PPOHA grant in Academic Year 2020-2021. The report concludes with three recommendations for future improvement. [For "PPOHA Project Evaluation: Promoting Program Quality and Accessibility for STEM Graduate Degree Completion (Evaluator Report on Year 1 Operation)," see ED611787.]

For more than two decades, California has been charging a 50 cent-per-pack tax on cigarette and other tobacco products and using the money to fund early childhood services under Proposition 10, California Children and Families First Act of 1998. In response to the legislative requirement of Result-Based Accountability (RBA), this report delineates evaluation findings from 39 programs that received $9.2 million of the annual state investment in Kern County, the third largest county in California by land area. Following the RBA model, this report incorporates qualitative and quantitative analyses to present in-depth success stories and statistical results from extensive assessments of program effectiveness and service integration. A five-chapter structure is adopted to support dissemination of the multilevel findings in Fiscal Year 2020-2021. More specifically, built on description of the Commission leadership in Chapter 1, Chapter 2 contains assessment findings to address the results-based accountability across programs in Child Health, Family Functioning, and Child Development. Chapter 3 clarifies service partnership building in Systems of Care. Improvement of child wellbeing and family functioning is summarized in Chapter 4 to document the turning the curve process on important indicators of child wellbeing and parent support. Despite the extensive impact of COVID-19 on young children and their families, positive results are summarized in Chapter 5 to conclude the report with a review of past recommendations and an introduction to new recommendations for next year. Netdraw, Quanteda in R, SAS and SPSS packages are employed to support data visualization, text analytics, and statistical computing. The report includes two appendices, 35 figures, 73 tables, and 141 reference items. [For "First 5 Kern Annual Report 2019-2020," see ED610410.]

The tensor data recovery task has thus attracted much research attention in recent years. Solving such an ill-posed problem generally requires to explore intrinsic prior structures underlying tensor data, and formulate them as certain forms of regularization terms for guiding a sound estimate of the restored tensor. Recent research have made significant progress by adopting two insightful tensor priors, i.e., global low-rankness (L) and local smoothness (S) across different tensor modes, which are always encoded as a sum of two separate regularization terms into the recovery models. However, unlike the primary theoretical developments on low-rank tensor recovery, these joint L+S models have no theoretical exact-recovery guarantees yet, making the methods lack reliability in real practice. To this crucial issue, in this work, we build a unique regularization term, which essentially encodes both L and S priors of a tensor simultaneously. Especially, by equipping this single regularizer into the recovery models, we can rigorously prove the exact recovery guarantees for two typical tensor recovery tasks, i.e., tensor completion (TC) and tensor robust principal component analysis (TRPCA). To the best of our knowledge, this should be the first exact-recovery results among all related L+S methods for tensor recovery. Significant recovery accuracy improvements over many other SOTA methods in several TC and TRPCA tasks with various kinds of visual tensor data are observed in extensive experiments. Typically, our method achieves a workable performance when the missing rate is extremely large, e.g., 99.5%, for the color image inpainting task, while all its peers totally fail in such challenging case.