133 research outputs found
Normal families of meromorphic functions with multiple zeros
AbstractLet F be a family of meromorphic functions defined in a domain D such that for each f∈F, all zeros of f(z) are of multiplicity at least 3, and all zeros of f′(z) are of multiplicity at least 2 in D. If for each f∈F, f′(z)−1 has at most 1 zero in D, ignoring multiplicity, then F is normal in D
A New Single-Phase Single-Stage AC-DC Stacked Flyback Converter With Active Clamp ZVS
Single-stage AC-DC converters integrate an AC-DC front-end converter with a DC-DC back-end converter. Compared with conventional two-stage AC-DC converters, single-stage AC-DC converters use less components and only one controller, which is used to regulate the output voltage. As a result, the cost, size and complexity of AC-DC converters can be reduced, but single-stage converters do not perform as well as two-stage converters, and most have drawbacks that are related to the fact that the DC bus voltage is not controlled an can become excessive.
A new single-phase single-stage AC-DC converter that uses stacked flyback converters is proposed in this thesis. The proposed converter consists of two low power flyback converters stacked on top of each other and an active clamp that helps the main switches operate with ZVS. The stacked structure helps reduce the voltage stresses typical fund in many single-stage converters. In the thesis, the operation of the converter is explained, the steady-state characteristics of the converter are determined and its design is discussed. The feasibility of the new converter is confirmed with experimental results obtained from a 100VAC~220VAC worldwide input, 48V output, 100kHz switching frequency and 200 W output power prototype converter
Normal Families of Zero-Free Meromorphic Functions
Let (≠0),∈ℂ, and and be two positive integers such that ≥2. Let
ℱ be a family of zero-free meromorphic functions defined in a domain such
that for each ∈ℱ, +(())− has at most zeros, ignoring multiplicity.
Then ℱ is normal in
The Hausdorff dimension of directional edge escaping points set
In this paper, we define the directional edge escaping points set of function iteration under a given plane partition and then prove that the upper bound of Hausdorff dimension of the directional edge escaping points set of (S(z)=a e^{z}+b e^{-z}), where (a, bin mathbb{C}) and (|a|^{2}+|b|^{2}neq 0), is no more than 1
Differentially Private Numerical Vector Analyses in the Local and Shuffle Model
Numerical vector aggregation plays a crucial role in privacy-sensitive
applications, such as distributed gradient estimation in federated learning and
statistical analysis of key-value data. In the context of local differential
privacy, this study provides a tight minimax error bound of
, where represents the dimension of the
numerical vector and denotes the number of non-zero entries. By converting
the conditional/unconditional numerical mean estimation problem into a
frequency estimation problem, we develop an optimal and efficient mechanism
called Collision. In contrast, existing methods exhibit sub-optimal error rates
of or . Specifically,
for unconditional mean estimation, we leverage the negative correlation between
two frequencies in each dimension and propose the CoCo mechanism, which further
reduces estimation errors for mean values compared to Collision. Moreover, to
surpass the error barrier in local privacy, we examine privacy amplification in
the shuffle model for the proposed mechanisms and derive precisely tight
amplification bounds. Our experiments validate and compare our mechanisms with
existing approaches, demonstrating significant error reductions for frequency
estimation and mean estimation on numerical vectors.Comment: Full version of "Hiding Numerical Vectors in Local Private and
Shuffled Messages" (IJCAI 2021
Fine-grained Private Knowledge Distillation
Knowledge distillation has emerged as a scalable and effective way for
privacy-preserving machine learning. One remaining drawback is that it consumes
privacy in a model-level (i.e., client-level) manner, every distillation query
incurs privacy loss of one client's all records. In order to attain
fine-grained privacy accountant and improve utility, this work proposes a
model-free reverse -NN labeling method towards record-level private
knowledge distillation, where each record is employed for labeling at most
queries. Theoretically, we provide bounds of labeling error rate under the
centralized/local/shuffle model of differential privacy (w.r.t. the number of
records per query, privacy budgets). Experimentally, we demonstrate that it
achieves new state-of-the-art accuracy with one order of magnitude lower of
privacy loss. Specifically, on the CIFAR- dataset, it reaches test
accuracy with centralized privacy budget ; on the MNIST/SVHN dataset, it
reaches / accuracy respectively with budget . It is the
first time deep learning with differential privacy achieve comparable accuracy
with reasonable data privacy protection (i.e., ). Our
code is available at https://github.com/liyuntong9/rknn
Epidemiological investigation, determination of related factors, and spatial-temporal cluster analysis of wild type pseudorabies virus seroprevalence in China during 2022
IntroductionPseudorabies virus (PRV) is a linear DNA virus with a double-stranded structure, capable of infecting a diverse array of animal species, including humans. This study sought to ascertain the seroprevalence of Pseudorabies Virus (PRV) in China by conducting a comprehensive collection of blood samples from 16 provinces over the course of 2022.MethodsThe presence of PRV gE antibodies was detected through the utilization of an enzyme-linked immunosorbent assay (ELISA) technique. Logistic regression analysis was conducted to identify potential related factors associated with the serologic status of PRV gE at the animal level. Additionally, the SaTScan 10.1 software was used to analyze the spatial and temporal clusters of PRV gE seroprevalence.ResultsA comprehensive collection of 161,880 samples was conducted, encompassing 556 swine farms throughout the country. The analysis revealed that the seroprevalence of PRV gE antibodies was 12.36% (95% confidence interval [CI], 12.20% to 12.52%) at the individual animal level. However, at the swine farm level, the seroprevalence was considerably higher, reaching 46.22% (95% CI, 42.08% to 50.37%). Related factors for PRV infection at the farm level included the geographic distribution of farms and seasonal variables. Moreover, five distinct high seroprevalence clusters of PRV gE were identified across China, with the peak prevalence observed during the months of April through June 2022.ConclusionOur findings serve as a valuable addition to existing research on the seroprevalence, related factors, and temporal clustering of PRV gE in China. Furthermore, our study provides a reference point for the development of effective strategies for the prevention and control of pseudorabies and wild virus outbreaks
Characterization of garlic oil/β-cyclodextrin inclusion complexes and application
Garlic oil is a liquid extracted from garlic that has various natural antibacterial and anti-inflammatory properties and is believed to be used to prevent and treat many diseases. However, the main functional components of garlic oil are unstable. Therefore, in this study, encapsulating garlic oil with cyclodextrin using the saturated co-precipitation method can effectively improve its chemical stability and water solubility and reduce its characteristic odor and taste. After preparation, the microcapsules of garlic oil cyclodextrin were characterized, which proved that the encapsulation was successful. Finally, the results showed that the encapsulated garlic oil still had antioxidant ability and slow-release properties. The final addition to plant-based meat gives them a delicious flavor and adds texture and mouthfeel. Provided a new reference for the flavor application of garlic cyclodextrin micro-capsules in plant-based meat patties
Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare
This paper examines the application of WiFi signals for real-world monitoring
of daily activities in home healthcare scenarios. While the state-of-the-art of
WiFi-based activity recognition is promising in lab environments, challenges
arise in real-world settings due to environmental, subject, and system
configuration variables, affecting accuracy and adaptability. The research
involved deploying systems in various settings and analyzing data shifts. It
aims to guide realistic development of robust, context-aware WiFi sensing
systems for elderly care. The findings suggest a shift in WiFi-based activity
sensing, bridging the gap between academic research and practical applications,
enhancing life quality through technology
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