Energy-Efficient Flow Scheduling and Routing with Hard Deadlines in Data Center Networks

The power consumption of enormous network devices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing "deadline-constrained flows" where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combinatorial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows.Comment: 11 pages, accepted by ICDCS'1

Deep Recurrent Survival Analysis

Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential patterns within the feature space. In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at fine-grained level of the data, and survival analysis for tackling the censorship. By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i.e., the probability of the non-occurrence of the event, for the censored data. Meanwhile, without assuming any specific form of the event probability distribution, our model shows great advantages over the previous works on fitting various sophisticated data distributions. In the experiments on the three real-world tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code: https://github.com/rk2900/drs

Persistence Probabilities of the German DAX and Shanghai Index

We present a relatively detailed analysis of the persistence probability distributions in financial dynamics. Compared with the auto-correlation function, the persistence probability distributions describe dynamic correlations non-local in time. Universal and non-universal behaviors of the German DAX and Shanghai Index are analyzed, and numerical simulations of some microscopic models are also performed. Around the fixed point $z_0=0$, the interacting herding model produces the scaling behavior of the real markets

Spatial and Temporal Variation Patterns of Reference Evapotranspiration Across the Qinghai-Tibetan Plateau During 1971–2004

Reference evapotranspiration (RET), an indicator of atmospheric evaporating capability over a hypothetical reference surface, was calculated using the Penman-Monteith method for 75 stations across the Qinghai-Tibetan Plateau between 1971 and 2004. Generally, both annual and seasonal RET decreased for most part of the plateau during the study period. Multivariate linear models were used to determine the contributions of climate factors to RET change, including air temperature, air humidity, solar radiation, and wind speed. Spatial differences in the causes of RET change were detected by K-means clustering analysis. It indicates that wind speed predominated the changes of RET almost throughout the year, especially in the north of the study region, whereas radiation was the leading factor in the southeast, especially during the summertime. Although the recent warming trend over the plateau would have increased RET, the combined effect of the reduced wind speed and shortened sunshine duration negated the effect of rising temperature and caused RET to decrease in general. The significant decrease in surface wind speed corresponded to the decreasing trends of upper-air zonal wind and the decline of pressure gradient, possibly as a result of the recent warming

Wolbachia-Induced Cytoplasmic Incompatibility Is Associated with Decreased Hira Expression in Male Drosophila

BACKGROUND: Wolbachia are obligate endosymbiotic bacteria that infect numerous species of arthropods and nematodes. Wolbachia can induce several reproductive phenotypes in their insect hosts including feminization, male-killing, parthenogenesis and cytoplasmic incompatibility (CI). CI is the most common phenotype and occurs when Wolbachia-infected males mate with uninfected females resulting in no or very low numbers of viable offspring. However, matings between males and females infected with the same strain of Wolbachia result in viable progeny. Despite substantial scientific effort, the molecular mechanisms underlying CI are currently unknown. METHODOLOGY/PRINCIPAL FINDINGS: Gene expression studies were undertaken in Drosophila melanogaster and D. simulans which display differential levels of CI using quantitative RT-PCR. We show that Hira expression is correlated with the induction of CI and occurs in a sex-specific manner. Hira expression is significantly lower in males which induce strong CI when compared to males inducing no CI or Wolbachia-uninfected males. A reduction in Hira expression is also observed in 1-day-old males that induce stronger CI compared to 5-day-old males that induce weak or no CI. In addition, Hira mutated D. melanogaster males mated to uninfected females result in significantly decreased hatch rates comparing with uninfected crosses. Interestingly, wMel-infected females may rescue the hatch rates. An obvious CI phenotype with chromatin bridges are observed in the early embryo resulting from Hira mutant fertilization, which strongly mimics the defects associated with CI. CONCLUSIONS/SIGNIFICANCE: Our results suggest Wolbachia-induced CI in Drosophila occurs due to a reduction in Hira expression in Wolbachia-infected males leading to detrimental effects on sperm fertility resulting in embryo lethality. These results may help determine the underlying mechanism of CI and provide further insight in to the important role Hira plays in the interaction of Wolbachia and its insect host

Numerical model for geothermal energy utilization from double pipe heat exchanger in abandoned oil wells

  The number of abandonded wells are increasing in the late period of oilfield development. The utilization of these abandonded oil wells is promising and environment-friendly for geothermal development. In this study, a numerical model for geothermal heating is derived from a double pipe heat exchanger in abandoned oil wells. The main influencing factors of injection rate, injection time, and the types of filler in casing annulus on temperature profiles and outlet temperature have been considered in this model. The influences of injection rate on heat-mining rate are then discussed. Results show that the double pipe heat exchanger can gain higher temperature at the outlet when the casing annulus is filled by liquid other than dry cement under the given parameter combination. The outlet temperature decreases with the increase in injection rate and injection time. The temperature rapidly decreases in the first 40 days during the injection process. The balance between heat mining rate and outlet temperature is important for evaluating a double pipe heat exchanger in abandoned oil wells. This work may provide a useful tool for a field engineer to estimate the temperature of liquid in wellhead and evaluate the heat transfer efficiency for double pipe heat exchanger in abandoned oil wells.Cited as: Lin, Z., Liu, K., Liu, J., Geng, D., Ren, K., Zheng, Z. Numerical model for geothermal energy utilization from double pipe heat exchanger in abandoned oil wells. Advances in Geo-Energy Research, 2021, 5(2): 212-221, doi: 10.46690/ager.2021.02.1

Co-administration of a DNA vaccine encoding the prostate specific membrane antigen and CpG oligodeoxynucleotides suppresses tumor growth

BACKGROUND: Prostate-specific membrane antigen (PSMA) is a well characterized prostate-specific tumor associated antigen. Its expression is elevated in prostate carcinoma, particularly in metastatic and recurrent lesions. These observations suggest that PSMA can be used as immune target to induce tumor cell-specific recognition by the host and, consequently tumor rejection. We utilized a DNA-based vaccine to specifically enhance PSMA expression. An immune modulator, such as CpG oligodeoxynucleotides which promote Th1-type immune responses was combined to increase the efficacy of tumor recognition and elimination. METHODS: A eukaryotic expression plasmid pCDNA3.1-PSMA encoding full-length PSMA was constructed. C57BL/6 mice were immunized with endotoxin-free pCDNA3.1-PSMA alone or in combination with CpG oligodeoxynucleotides by intramuscular injection. After 4 immunizations, PSMA specific antibodies and cytotoxic T lymphocyte reactivity were measured. Immunized C57BL/6 mice were also challenged subcutaneously with B16 cells transfected with PSMA to evaluate suppression of tumor growth. RESULTS: Vaccine-specific cytotoxic T lymphocytes reactive with B16 cells expressing PSMA could be induced with this treatment schedule. Immune protection was observed in vaccinated mice as indicated by increased tumor growth in the control group (100%) compared with the groups vaccinated with DNA alone (66.7%) or DNA plus CpG oligodeoxynucleotides (50%) respectively. Average tumor volume was smaller in vaccinated groups and tumor-free survival time was prolonged by the vaccination. CONCLUSION: The current findings suggest that specific anti-tumor immune response can be induced by DNA vaccines expressing PSMA. In addition, the suppression of in vivo growth of tumor cells expressing PSMA was augmented by CpG oligodeoxynucleotides. This strategy may provide a new venue for the treatment of carcinoma of prostate after failure of standard therapy

Multiple superconducting gap and anisotropic spin fluctuations in iron arsenides: Comparison with nickel analog

We present extensive 75As NMR and NQR data on the superconducting arsenides PrFeAs0.89F0.11 (Tc=45 K), LaFeAsO0.92F0.08 (Tc=27 K), LiFeAs (Tc = 17 K) and Ba0.72K0.28Fe2As2 (Tc = 31.5 K) single crystal, and compare with the nickel analog LaNiAsO0.9F0.1 (Tc=4.0 K) . In contrast to LaNiAsO0.9F0.1 where the superconducting gap is shown to be isotropic, the spin lattice relaxation rate 1/T1 in the Fe-arsenides decreases below Tc with no coherence peak and shows a step-wise variation at low temperatures. The Knight shift decreases below Tc and shows a step-wise T variation as well. These results indicate spinsinglet superconductivity with multiple gaps in the Fe-arsenides. The Fe antiferromagnetic spin fluctuations are anisotropic and weaker compared to underdoped copper-oxides or cobalt-oxide superconductors, while there is no significant electron correlations in LaNiAsO0.9F0.1. We will discuss the implications of these results and highlight the importance of the Fermi surface topology.Comment: 6 pages, 11 figure