Online Algorithms for Geographical Load Balancing

It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective

Glutathione-Mediated Neuroprotection Against Methylmercury Neurotoxicity in Cortical Culture is Dependent on MRP1

Methylmercury (MeHg) exposure at high concentrations poses significant neurotoxic threat to humans worldwide. The present study investigated the mechanisms of glutathione-mediated attenuation of MeHg neurotoxicity in primary cortical culture. MeHg (5 μM) caused depletion of mono- and disulfide glutathione in neuronal, glial and mixed cultures. Supplementation with exogenous glutathione, specifically glutathione monoethyl ester (GSHME) protected against the MeHg induced neuronal death. MeHg caused increased reactive oxygen species (ROS) formation measured by dichlorodihydrofluorescein (DCF) fluorescence with an early increase at 30 min and a late increase at 6 h. This oxidative stress was prevented by the presence of either GSHME or the free radical scavenger, trolox. While trolox was capable of quenching the ROS, it showed no neuroprotection. Exposure to MeHg at subtoxic concentrations (3 μM) caused an increase in system xc− mediated 14C-cystine uptake that was blocked by the protein synthesis inhibitor, cycloheximide (CHX). Interestingly, blockade of the early ROS burst prevented the functional upregulation of system xc−. Inhibition of multidrug resistance protein-1 (MRP1) potentiated MeHg neurotoxicity and increased cellular MeHg. Taken together, these data suggest glutathione offers neuroprotection against MeHg toxicity in a manner dependent on MRP1-mediated efflux

Differential regulation of the Drosophila sleep homeostat by circadian and arousal inputs.

One output arm of the sleep homeostat in Drosophila appears to be a group of neurons with projections to the dorsal fan-shaped body (dFB neurons) of the central complex in the brain. However, neurons that regulate the sleep homeostat remain poorly understood. Using neurogenetic approaches combined with Ca2+ imaging, we characterized synaptic connections between dFB neurons and distinct sets of upstream sleep-regulatory neurons. One group of the sleep-promoting upstream neurons is a set of circadian pacemaker neurons that activates dFB neurons via direct glutaminergic excitatory synaptic connections. Opposing this population, a group of arousal-promoting neurons downregulates dFB axonal output with dopamine. Co-activating these two inputs leads to frequent shifts between sleep and wake states. We also show that dFB neurons release the neurotransmitter GABA and inhibit octopaminergic arousal neurons. We propose that dFB neurons integrate synaptic inputs from distinct sets of upstream sleep-promoting circadian clock neurons, and arousal neurons

Heterogeneity of diabetes outcomes among asians and pacific islanders in the US: the diabetes study of northern california (DISTANCE).

ObjectiveEthnic minorities with diabetes typically have lower rates of cardiovascular outcomes and higher rates of end-stage renal disease (ESRD) compared with whites. Diabetes outcomes among Asian and Pacific Islander subgroups have not been disaggregated.Research design and methodsWe performed a prospective cohort study (1996-2006) of patients enrolled in the Kaiser Permanente Northern California Diabetes Registry. There were 64,211 diabetic patients, including whites (n = 40,286), blacks (n = 8,668), Latinos (n = 7,763), Filipinos (n = 3,572), Chinese (n = 1,823), Japanese (n = 951), Pacific Islanders (n = 593), and South Asians (n = 555), enrolled in the registry. We calculated incidence rates (means ± SD; 7.2 ± 3.3 years follow-up) and created Cox proportional hazards models adjusted for age, educational attainment, English proficiency, neighborhood deprivation, BMI, smoking, alcohol use, exercise, medication adherence, type and duration of diabetes, HbA(1c), hypertension, estimated glomerular filtration rate, albuminuria, and LDL cholesterol. Incidence of myocardial infarction (MI), congestive heart failure, stroke, ESRD, and lower-extremity amputation (LEA) were age and sex adjusted.ResultsPacific Islander women had the highest incidence of MI, whereas other ethnicities had significantly lower rates of MI than whites. Most nonwhite groups had higher rates of ESRD than whites. Asians had ~60% lower incidence of LEA compared with whites, African Americans, or Pacific Islanders. Incidence rates in Chinese, Japanese, and Filipinos were similar for most complications. For the three macrovascular complications, Pacific Islanders and South Asians had rates similar to whites.ConclusionsIncidence of complications varied dramatically among the Asian subgroups and highlights the value of a more nuanced ethnic stratification for public health surveillance and etiologic research

UAVM: Towards Unifying Audio and Visual Models

Conventional audio-visual models have independent audio and video branches. In this work, we unify the audio and visual branches by designing a Unified Audio-Visual Model (UAVM). The UAVM achieves a new state-of-the-art audio-visual event classification accuracy of 65.8% on VGGSound. More interestingly, we also find a few intriguing properties of UAVM that the modality-independent counterparts do not have.Comment: Published in Signal Processing Letters. Code at https://github.com/YuanGongND/uav

Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly accounting for heterogeneous measures poses important challenges related to the modeling of the variability and the interpretability of the results. These issues are here addressed by proposing a novel multi-channel stochastic generative model. We assume that a latent variable generates the data observed through different channels (e.g., clinical scores, imaging, ...) and describe an efficient way to estimate jointly the distribution of both latent variable and data generative process. Experiments on synthetic data show that the multi-channel formulation allows superior data reconstruction as opposed to the single channel one. Moreover, the derived lower bound of the model evidence represents a promising model selection criterion. Experiments on AD data show that the model parameters can be used for unsupervised patient stratification and for the joint interpretation of the heterogeneous observations. Because of its general and flexible formulation, we believe that the proposed method can find important applications as a general data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with MICCAI 2018, September 20, Granada, Spai