Chindia: Collaboration, Compromising, or Competition?

As two fast-growing economies with huge populations, China and India have become formidable forces in the world today. How do these two Asian giants see each other? Are they economic partners, political equals, and friendly neighbors? Or are they economic competitors, political rivals, and territorial enemies when it comes to border conflicts? This research attempts to answer some of the above questions

Chinese Perspectives on India and Indian People

As the two most populous countries with a growing economy, China and India have become formidable forces on the world stage. The relationship between the two countries are more than complex. How Chinese people see India and Indian people has become a significant topic to study. This poster is designed to study the Sino-Indian relations with a focus on Chinese public opinions on India and Indian people

CoLight: Learning Network-level Cooperation for Traffic Signal Control

Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.Comment: 10 pages. Proceedings of the 28th ACM International on Conference on Information and Knowledge Management. ACM, 201

Measurement and interpretation of electrocardiographic QT intervals in murine hearts.

Alterations in ECG QT intervals correlate with the risk of potentially fatal arrhythmias, for which transgenic murine hearts are becoming increasingly useful experimental models. However, QT intervals are poorly defined in murine ECGs. As a consequence, several different techniques have been used to measure murine QT intervals. The present work develops a consistent measure of the murine QT interval that correlates with changes in the duration of ventricular myocyte action potentials (APs). Volume-conducted ECGs were compared with simultaneously recorded APs, obtained using floating intracellular microelectrodes in Langendorff-perfused mouse hearts. QT intervals were measured from the onset of the QRS complex. The interval, Q-APR90, measured to the time at 90% AP recovery, was compared with two measures of the QT interval. QT1 was measured to the recovery of the ECG trace to the isoelectric baseline for entirely positive T-waves or to the trough of any negative T-wave undershoot. QT2-used extensively in previous studies-was measured to the return of any ECG trough to the isoelectric baseline. QT1, but not QT2, closely correlated with changes in Q-APR90. These findings were confirmed over a range of pacing rates, in low K(+) concentration solutions, and in Scn5a+/ΔKPQ hearts used to model human long QT syndrome. Application of this method in whole anesthetized mice similarly demonstrated a prolonged corrected QT (QTc) in Scn5a+/ΔKPQ hearts. We therefore describe a robust method for the determination of QT and QTc intervals that correlate with the duration of ventricular myocyte APs in murine hearts.This is the final published version. It has been published by the American Physiological Society in the American Journal of Physiology Heart and Circulatory Physiology here: http://ajpheart.physiology.org/content/306/11/H1553

Abnormal Ca2+ homeostasis, atrial arrhythmogenesis, and sinus node dysfunction in murine hearts modeling RyR2 modification

Ryanodine receptor type 2 (RyR2) mutations are implicated in catecholaminergic polymorphic ventricular tachycardia (CPVT) thought to result from altered myocyte Ca(2+) homeostasis reflecting inappropriate “leakiness” of RyR2-Ca(2+) release channels arising from increases in their basal activity, alterations in their phosphorylation, or defective interactions with other molecules or ions. The latter include calstabin, calsequestrin-2, Mg(2+), and extraluminal or intraluminal Ca(2+). Recent clinical studies additionally associate RyR2 abnormalities with atrial arrhythmias including atrial tachycardia (AT), fibrillation (AF), and standstill, and sinus node dysfunction (SND). Some RyR2 mutations associated with CPVT in mouse models also show such arrhythmias that similarly correlate with altered Ca(2+) homeostasis. Some examples show evidence for increased Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) phosphorylation of RyR2. A homozygotic RyR2-P2328S variant demonstrates potential arrhythmic substrate resulting from reduced conduction velocity (CV) in addition to delayed afterdepolarizations (DADs) and ectopic action potential (AP) firing. Finally, one model with an increased RyR2 activity in the sino-atrial node (SAN) shows decreased automaticity in the presence of Ca(2+)-dependent decreases in I(Ca, L) and diastolic sarcoplasmic reticular (SR) Ca(2+) depletion

Estimation of Crop Gross Primary Production (GPP)

Satellite remote sensing estimates of Gross Primary Production (GPP) have routinely been made using spectral Vegetation Indices (VIs) over the past two decades. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the green band Wide Dynamic Range Vegetation Index (WDRVIgreen), and the green band Chlorophyll Index (CIgreen) have been employed to estimate GPP under the assumption that GPP is proportional to the product of VI and photosynthetically active radiation (PAR) (where VI is one of four VIs: NDVI, EVI, WDRVIgreen, or CIgreen). However, the empirical regressions between VI*PAR and GPP measured locally at flux towers do not pass through the origin (i.e., the zero X-Y value for regressions). Therefore they are somewhat difficult to interpret and apply. This study investigates (1) what are the scaling factors and offsets (i.e., regression slopes and intercepts) between the fraction of PAR absorbed by chlorophyll of a canopy (fAPARchl) and the VIs, and (2) whether the scaled VIs developed in (1) can eliminate the deficiency and improve the accuracy of GPP estimates. Three AmeriFlux maize and soybean fields were selected for this study, two of which are irrigated and one is rainfed. The four VIs and fAPARchl of the fields were computed with the MODerate resolution Imaging Spectroradiometer (MODIS) satellite images. The GPP estimation performance for the scaled VIs was compared to results obtained with the original VIs and evaluated with standard statistics: the coefficient of determination (R2), the root mean square error (RMSE), and the coefficient of variation (CV). Overall, the scaled EVI obtained the best performance. The performance of the scaled NDVI, EVI and WDRVIgreen was improved across sites, crop types and soil/background wetness conditions. The scaled CIgreen did not improve results, compared to the original CIgreen. The scaled green band indices (WDRVIgreen, CIgreen) did not exhibit superior performance to either the scaled EVI or NDVI in estimating crop daily GPP at these agricultural fields. The scaled VIs are more physiologically meaningful than original un-scaled VIs, but scaling factors and offsets may vary across crop types and surface conditions

Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition

Self-supervised learning (SSL) based speech pre-training has attracted much attention for its capability of extracting rich representations learned from massive unlabeled data. On the other hand, the use of weakly-supervised data is less explored for speech pre-training. To fill this gap, we propose a weakly-supervised speech pre-training method based on speaker-aware speech data. It adopts a similar training procedure to the widely-used masked speech prediction based SSL framework, while incorporating additional target-speaker enrollment information as an auxiliary input. In this way, the learned representation is steered towards the target speaker even in the presence of highly overlapping interference, allowing potential applications to tasks such as target speech recognition. Our experiments on Libri2Mix and WSJ0-2mix datasets show that the proposed model achieves significantly better ASR performance compared to WavLM, the state-of-the-art SSL model with denoising capability.Comment: Accepted by Interspeech; 5 pages, 1 figure, 3 table

Application of Learning Strategies to Culture-Based Language Instruction

Learning strategy is one of the most important factors that determine the learning result. So, teaching learners to grasp certain kinds of strategies is a key factor which can promote the learning efficiency. This thesis discusses the learning strategies in the theoretical and pedagogical aspects, illustrates the significance of culture-based language instruction in second language teaching, and elaborates three ways to help students use appropriate strategies in their culture-based language learning

Daily MODIS 500 m Reflectance Anisotropy Direct Broadcast (DB) Products for Monitoring Vegetation Phenology Dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest