A Comprehensive Study of the Enhanced Distributed Control Access (EDCA) Function

This technical report presents a comprehensive study of the Enhanced Distributed Control Access (EDCA) function defined in IEEE 802.11e. All the three factors are considered. They are: contention window size (CW), arbitration inter-frame space (AIFS), and transmission opportunity limit (TXOP). We first propose a discrete Markov chain model to describe the channel activities governed by EDCA. Then we evaluate the individual as well as joint effects of each factor on the throughput and QoS performance. We obtain several insightful observations showing that judiciously using the EDCA service differentiation mechanism is important to achieve maximum bandwidth utilization and user-specified QoS performance. Guided by our theoretical study, we devise a general QoS framework that provides QoS in an optimal way. The means of realizing the framework in a specific network is yet to be studied

Generic structure and APPRAISAL resources in the editorial article Free money

Editorial is one of news genres aiming for opinion making and persuading. These functions determine that there are abundant evaluative resources in this genre. Exploring evaluative resources in the editorial could be conducive to understanding editorial text better and providing sensible suggestions for English learners to produce effectively persuasive writings. In view of this, the present study sets out to analyze the generic structure of the editorial Free money, then examine usage patterns of APPRAISAL resources in this text, and finally explore variations of APPRAISAL resources at different stages of the genre of this text. All APPRAISAL resources were coded based on APPRAISAL system and analyzed from quantitative and qualitative perspectives. It shows that Free money employed discussion genre with exposition and challenge embedded in the Background stage. An investigation into the usage of APPRAISAL resources found that negative ATTITUDE resources were mainly used to form the prosody of the text; more negation and concession resources within ENGAGEMENT were deployed to contract the dialogue; far more force raising GRADUATION resources were applied to amplify the evaluation. The APPRAISAL resources used at different stages of the editorial demonstrate distinct features with the aim of serving specific function of each stage. For instance, attribution resources were used in Issue stage to expand the dialogue and engage the readers; invoked resources were primarily employed in Background stage to make the statement objective; far more negative impressions in Side stage indicated the author’s concern, and more inscribed resources in Resolution stage manifested author’s attitude and made the conclusion impressive

Multiple View Geometry Transformers for 3D Human Pose Estimation

In this work, we aim to improve the 3D reasoning ability of Transformers in multi-view 3D human pose estimation. Recent works have focused on end-to-end learning-based transformer designs, which struggle to resolve geometric information accurately, particularly during occlusion. Instead, we propose a novel hybrid model, MVGFormer, which has a series of geometric and appearance modules organized in an iterative manner. The geometry modules are learning-free and handle all viewpoint-dependent 3D tasks geometrically which notably improves the model's generalization ability. The appearance modules are learnable and are dedicated to estimating 2D poses from image signals end-to-end which enables them to achieve accurate estimates even when occlusion occurs, leading to a model that is both accurate and generalizable to new cameras and geometries. We evaluate our approach for both in-domain and out-of-domain settings, where our model consistently outperforms state-of-the-art methods, and especially does so by a significant margin in the out-of-domain setting. We will release the code and models: https://github.com/XunshanMan/MVGFormer.Comment: 14 pages, 8 figure

Noninvasive prenatal diagnosis of 21-Hydroxylase deficiency using target capture sequencing of maternal plasma DNA.

Here, we aimed to validate a noninvasive method using capture sequencing for prenatal diagnosis of congenital adrenal hyperplasia due to 21-Hydroxylase deficiency (21-OHD). Noninvasive prenatal diagnosis (NIPD) of 21-OHD was based on 14 plasma samples collected from 12 families, including four plasma sample collected during the first trimester. Targeted capture sequencing was performed using genomic DNA from the parents and child trios to determine the pathogenic and wild-type alleles associated with the haplotypes. Maternal plasma DNA was also sequenced to determine the fetal inheritance of the allele using hidden Markov model-based haplotype linkage analysis. The effect of fetal DNA fraction and sequencing depth on the accuracy of NIPD was investigated. The lower limit of fetal DNA fraction was 2% and the threshold mean sequence depth was 38, suggesting potential advantage if used in early gestation. The CYP21A2 genotype of the fetus was accurately determined in all the 14 plasma samples as early as day 1 and 8 weeks of gestation. Results suggest the accuracy and feasibility of NIPD of 21-OHD using a small target capture region with a low threshold for fetal DNA fraction and sequence depth. Our method is cost-effective and suggests diagnostic applications in clinical practice

IOT network: models, structure, communications, problems

A brief analysis of the concepts and applications of IoT networks is carried out. Four models of building these networks as variants of component interaction are given: terminal, gateway, cloud, application. Variants of IoT network architectures are presented. Seven variants of interaction in Yota networks are considered. The analysis of problems in these networks and the direction of their solution are carried out

Multidimensional features of sporadic Creutzfeldt-Jakob disease in the elderly: a case report and systematic review

BackgroundAs a rare neurodegenerative disease, sporadic Creutzfeldt-Jakob disease (sCJD) is poorly understood in the elderly populace. This study aims to enunciate the multidimensional features of sCJD in this group.MethodsA case of probable sCJD was reported in a 90-year-old Chinese man with initial dizziness. Then, available English literature of the elderly sCJD cases (aged 80 years and over) was reviewed and analyzed. Patients (15 cases) were subdivided and compared geographically.ResultsIn the elderly sCJD cohort, the onset age was 84.9 ± 4.5 years and the median disease duration was 6.8 months, with respiratory infection/failure as the commonest death cause. Various clinical symptoms were identified, with cognitive disorder (86.7%) as the commonest typical symptom and speech impairment (66.7%) as the most atypical one. Restricted hyperintensities were reported in 60.0% cases on DWI, periodic sharp wave complexes in 73.3% cases on electroencephalogram, and cerebral hypoperfusion/hypometabolism in 26.7% cases on molecular imaging. The sensitive cerebrospinal fluid biomarkers were total tau (83.3%), 14-3-3 protein (75.0%), and PrP RT-QuIC (75.0%). Neuropathological profiles in the cerebral cortex revealed vacuolar spongiosis, neuronal loss, gliosis, and aging-related markers, with synaptic deposit as the commonest PrP pattern (60.0%). The polymorphic PRNP analysis at codon 129 was M/M (90.9%), with MM1 and MM2C as the primary molecular phenotypes. Latency to first clinic visit, hyperintense signals on DWI, and disease duration were significantly different between the patient subgroups.ConclusionThe characteristics of sCJD are multidimensional in the elderly, deepening our understanding of the disease and facilitating an earlier recognition and better care for this group

GAIA: Zero-shot Talking Avatar Generation

Zero-shot talking avatar generation aims at synthesizing natural talking videos from speech and a single portrait image. Previous methods have relied on domain-specific heuristics such as warping-based motion representation and 3D Morphable Models, which limit the naturalness and diversity of the generated avatars. In this work, we introduce GAIA (Generative AI for Avatar), which eliminates the domain priors in talking avatar generation. In light of the observation that the speech only drives the motion of the avatar while the appearance of the avatar and the background typically remain the same throughout the entire video, we divide our approach into two stages: 1) disentangling each frame into motion and appearance representations; 2) generating motion sequences conditioned on the speech and reference portrait image. We collect a large-scale high-quality talking avatar dataset and train the model on it with different scales (up to 2B parameters). Experimental results verify the superiority, scalability, and flexibility of GAIA as 1) the resulting model beats previous baseline models in terms of naturalness, diversity, lip-sync quality, and visual quality; 2) the framework is scalable since larger models yield better results; 3) it is general and enables different applications like controllable talking avatar generation and text-instructed avatar generation.Comment: ICLR 2024. Project page: https://microsoft.github.io/GAIA