Liberalization First, Democratization Later: The Linkage Between Income Inequality, Economic Development, and Democratization

This dissertation proposes a model of two-stage process of democratization. In the first stage, income inequality is associated with political liberalization, but this association is conditional on economic development. In the second stage, political liberalization is associated with democratization. By looking at 125 authoritarian regimes from 1960 to 2010, I find that in rich countries, high income inequality is associated with low political liberalization, which may stabilize autocratic regimes; while low income inequality is associated with high political liberalization. In poor countries, high income inequality is associated with political liberalization, while low inequality has no effect. In the second stage, I find that political liberalization, such as electoral component, liberal component, and participatory component of democracy are associated with democratization. These components of democracy may help citizens to develop prodemocracy attitude. This research attempts to extend the understanding of the relationship between income inequality, economic development, and democratization by including political liberalization

MelHuBERT: A simplified HuBERT on Mel spectrograms

Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks. However, most self-supervised models require a large amount of compute and multiple GPUs to train, significantly hampering the development of self-supervised learning. In an attempt to reduce the computation of training, we revisit the training of HuBERT, a highly successful self-supervised model. We improve and simplify several key components, including the loss function, input representation, and training in multiple stages. Our model, MelHuBERT, is able to achieve favorable performance on phone recognition, speaker identification, and automatic speech recognition against HuBERT, while saving 31.2% of the pre-training time, or equivalently 33.5% MACs per one second speech. The code and pre-trained models are available in https://github.com/nervjack2/MelHuBERT.Comment: ASRU 202

Ischaemic stroke and influenza A H1N1 vaccination: a case report

We report a 75-year-old male patient who suffered posterior circulation ischaemia after influenza A/H1N1 vaccination. Vaccination provokes a variable magnitude of inflammatory and immunological response that modifies the risk for ischaemic stroke. Whereas a causal relation between vaccination and ischaemic stroke is still unsettled, an inflammatory/immunological response after vaccination may trigger thrombosis superimposing a pre-existing prothrombotic state. Careful monitoring is strongly suggested for individuals who received H1N1 vaccine, especially those with high ischaemic stroke risk

KINETIC PROPERTIES AND EMG ACTIVITY OF NORMAL AND OVER-SPEED PEDALING IN TRACK SPRINT CYCLISTS: A CASE STUDY

Track sprint cycling requires unique skills. We investigate the pedaling kinetics and muscle coordination of a male track sprinter (170cm, 65kg, peak power 1513W) to see if they differ from that of a non-sprinter, and if the subject’s own technique vary from normal riding to an under-load maximal cadence sprint. Two trials were collected using 3D motion capture technology. EMG signals of 8 leg muscles were recorded. Joint torque and power of each trial were calculated using a subject specific musculoskeletal model, with realistic pedal forces as input to our dynamic simulation. Flexion torque appears at the knee during its extension, different from the non-sprinters. Joint torque and power appears similar for both trials, but 6 of the 8 muscles showed differences in EMG patterns. These findings could potentially benefit the evolvement of training methods

On Scalable Service Function Chaining with O(1) Flowtable Entries

The emergence of Network Function Virtualization (NFV) enables flexible and agile service function chaining in a Software Defined Network (SDN). While this virtualization technology efficiently offers customization capability, it however comes with a cost of consuming precious TCAM resources. Due to this, the number of service chains that an SDN can support is limited by the flowtable size of a switch. To break this limitation, this paper presents CRT-Chain, a service chain forwarding protocol that requires only constant flowtable entries, regardless of the number of service chain requests. The core of CRT-Chain is an encoding mechanism that leverages Chinese Remainder Theorem (CRT) to compress the forwarding information into small labels. A switch does not need to insert forwarding rules for every service chain request, but only needs to conduct very simple modular arithmetic to extract the forwarding rules directly from CRT-Chain's labels attached in the header. We further incorporate prime reuse and path segmentation in CRT-Chain to reduce the header size and, hence, save bandwidth consumption. Our evaluation results show that, when a chain consists of no more than 5 functions, CRT-Chain actually generates a header smaller than the legacy 32-bit header defined in IETF. By enabling prime reuse and segmentation, CRT-Chain further reduces the total signaling overhead to a level lower than the conventional scheme, showing that CRT-Chain not only enables scalable flowtable-free chaining but also improves network efficiency

KINEMATICS ANALYSIS OF THE UPPER EXTREMITY DURING THE TWOHANDED BACKHAND DRIVE VOLLEY FOR FEMALE TENNIS PLAYERS

The purpose of this study was to discuss the motion characteristics of the arms in the two-handed backhand drive volley. Five elite female tennis players participated in this study, their two-handed backhand drive volley strokes were analysed, and all participants are right handed. Motion Analysis System with 10 Eagle Digital inferred high speed cameras at 200Hz were used for this study. The results show a similar elbow and wrist speed strategy in x-axis between two-handed ground stroke and drive volley, our study also found that the rear arm dominates the stroke and mainly provide the topspin that is required for the skill of the drive volley. In order to create better stroke efficiency, the right elbow reached peak velocity first, followed by the right wrist before racket impact with the ball

Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds

Point clouds provide intrinsic geometric information and surface context for scene understanding. Existing methods for point cloud segmentation require a large amount of fully labeled data. Using advanced depth sensors, collection of large scale 3D dataset is no longer a cumbersome process. However, manually producing point-level label on the large scale dataset is time and labor-intensive. In this paper, we propose a weakly supervised approach to predict point-level results using weak labels on 3D point clouds. We introduce our multi-path region mining module to generate pseudo point-level label from a classification network trained with weak labels. It mines the localization cues for each class from various aspects of the network feature using different attention modules. Then, we use the point-level pseudo labels to train a point cloud segmentation network in a fully supervised manner. To the best of our knowledge, this is the first method that uses cloud-level weak labels on raw 3D space to train a point cloud semantic segmentation network. In our setting, the 3D weak labels only indicate the classes that appeared in our input sample. We discuss both scene- and subcloud-level weakly labels on raw 3D point cloud data and perform in-depth experiments on them. On ScanNet dataset, our result trained with subcloud-level labels is compatible with some fully supervised methods.Comment: Accepted by CVPR202