Energy Efficient Cloud Networks

Cloud computing is expected to be a major factor that will dominate the future Internet service model. This paper summarizes our work on energy efficiency for cloud networks. We develop a framework for studying the energy efficiency of four cloud services in IP over WDM networks: cloud content delivery, storage as a service (StaaS), and virtual machines (VMS) placement for processing applications and infrastructure as a service (IaaS).Our approach is based on the co-optimization of both external network related factors such as whether to geographically centralize or distribute the clouds, the influence of users’ demand distribution, content popularity, access frequency and renewable energy availability and internal capability factors such as the number of servers, switches and routers as well as the amount of storage demanded in each cloud. Our investigation of the different energy efficient approaches is backed with Mixed Integer Linear Programming (MILP) models and real time heuristic

Crowd disagreement about medical images is informative

Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds. However, disagreement between annotators may be informative, and thus removing it may not be the best strategy. As a proof of concept, we predict whether a skin lesion from the ISIC 2017 dataset is a melanoma or not, based on crowd annotations of visual characteristics of that lesion. We compare using the mean annotations, illustrating consensus, to standard deviations and other distribution moments, illustrating disagreement. We show that the mean annotations perform best, but that the disagreement measures are still informative. We also make the crowd annotations used in this paper available at \url{https://figshare.com/s/5cbbce14647b66286544}.Comment: Accepted for publication at MICCAI LABELS 201

Synthetic induction of immunogenic cell death by genetic stimulation of endoplasmic reticulum stress.

Cis-diamminedichloridoplatinum(II) (CDDP), commonly referred to as cisplatin, is a chemotherapeutic drug used for the treatment of a wide range of solid cancers. CDDP is a relatively poor inducer of immunogenic cell death (ICD), a cell death modality that converts dying cells into a tumor vaccine, stimulating an immune response against residual cancer cells that permits long-lasting immunity and a corresponding reduction in tumor growth. The incapacity of CDDP to trigger ICD is at least partially due to its failure to stimulate the premortem endoplasmic reticulum (ER)-stress response required for the externalization of the "eat-me" signal calreticulin (CRT) on the surface of dying cancer cells. Here, we developed a murine cancer cell line genetically modified to express the ER resident protein reticulon-1c (Rtn-1c) by virtue of tetracycline induction and showed that enforced Rtn-1c expression combined with CDDP treatment promoted CRT externalization to the surface of cancer cells. In contrast to single agent treatments, the tetracycline-mediated Rtn-1c induction combined with CDDP chemotherapy stimulated ICD as measured by the capacity of dying tumor cells, inoculated into syngenic immunocompetent mice, to mount an immune response to tumor re-challenge 1 week later. More importantly, established tumors, forced to constitutively express Rtn-1c in vivo by continuous treatment with tetracycline, became responsive to CDDP and exhibited a corresponding reduction in the rate of tumor growth. The combined therapeutic effects of Rtn-1c induction with CDDP treatment was only detected in the context of an intact immune system and not in nu/nu mice lacking thymus-dependent T lymphocytes. Altogether, these results indicate that the artificial or "synthetic" induction of immunogenic cell death by genetic manipulation of the ER-stress response can improve the efficacy of chemotherapy with CDDP by stimulating anticancer immunity

Effect of 2-(3-carboxy-1-oxopropyl) amino-2-deoxy-D-glucose on human esophageal cancer cell line

Aim: To determine whether 2-(3-carboxy-1-oxopropy1) amino-2-deoxy-D-glucose (COPADG), a derivative of D-amino-glucose, inhibited the growth of human esophageal cancer cell line Eca-109. Methods: Effects of COPADG on Eca-109 cells cultured in RPMI 1640 medium were examined by a tetrazolium-based colorimetric assay (MTT assay). Results: COPADG inhibited the growth of Eca-109 cells in a dose- and time-dependent manner; the maximum inhibition rate was 83.75%. Conclusion: COPADG can directly inhibit the proliferation of Eca-109 cells, which may serve as the experimental evidence for development of new drugs for esophageal cancer therapy. Copyright © 2004 by The WJG.published_or_final_versio

Noiseless nonreciprocity in a parametric active device

Nonreciprocal devices such as circulators and isolators belong to an important class of microwave components employed in applications like the measurement of mesoscopic circuits at cryogenic temperatures. The measurement protocols usually involve an amplification chain which relies on circulators to separate input and output channels and to suppress backaction from different stages on the sample under test. In these devices the usual reciprocal symmetry of circuits is broken by the phenomenon of Faraday rotation based on magnetic materials and fields. However, magnets are averse to on-chip integration, and magnetic fields are deleterious to delicate superconducting devices. Here we present a new proposal combining two stages of parametric modulation emulating the action of a circulator. It is devoid of magnetic components and suitable for on-chip integration. As the design is free of any dissipative elements and based on reversible operation, the device operates noiselessly, giving it an important advantage over other nonreciprocal active devices for quantum information processing applications.Comment: 17 pages, 4 figures + 12 pages Supplementary Informatio

Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder.

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking

Gene network effects on brain microstructure and intellectual performance identified in 472 twins

A major challenge in neuroscience is finding which genes affect brain integrity, connectivity, and intellectual function. Discovering influential genes holds vast promise for neuroscience, but typical genome-wide searches assess approximately one million genetic variants one-by-one, leading to intractable false positive rates, even with vast samples of subjects. Even more intractable is the question of which genes interact and how they work together to affect brain connectivity. Here, we report a novel approach that discovers which genes contribute to brain wiring and fiber integrity at all pairs of points in a brain scan. We studied genetic correlations between thousands of points in human brain images from 472 twins and their nontwin siblings (mean age: 23.7 ± 2.1 SD years; 193 male/279 female). We combined clustering with genome-wide scanning to find brain systems with common genetic determination. We then filtered the image in a new way to boost power to find causal genes. Using network analysis, we found a network of genes that affect brain wiring in healthy young adults. Our new strategy makes it computationally more tractable to discover genes that affect brain integrity. The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic interactions and resilience to network disruption. Genetic variants at hubs of the network influence intellectual performance by modulating associations between performance intelligence quotient and the integrity of major white matter tracts, such as the callosal genu and splenium, cingulum, optic radiations, and the superior longitudinal fasciculus

Preferential Paths of Air-water Two-phase Flow in Porous Structures with Special Consideration of Channel Thickness Effects.

Accurate understanding and predicting the flow paths of immiscible two-phase flow in rocky porous structures are of critical importance for the evaluation of oil or gas recovery and prediction of rock slides caused by gas-liquid flow. A 2D phase field model was established for compressible air-water two-phase flow in heterogenous porous structures. The dynamic characteristics of air-water two-phase interface and preferential paths in porous structures were simulated. The factors affecting the path selection of two-phase flow in porous structures were analyzed. Transparent physical models of complex porous structures were prepared using 3D printing technology. Tracer dye was used to visually observe the flow characteristics and path selection in air-water two-phase displacement experiments. The experimental observations agree with the numerical results used to validate the accuracy of phase field model. The effects of channel thickness on the air-water two-phase flow behavior and paths in porous structures were also analyzed. The results indicate that thick channels can induce secondary air flow paths due to the increase in flow resistance; consequently, the flow distribution is different from that in narrow channels. This study provides a new reference for quantitatively analyzing multi-phase flow and predicting the preferential paths of immiscible fluids in porous structures

Energy Efficiency Measures for Future Core Networks

We summarize the various techniques developed by the GreenTouch consortium over the past 5 years to minimize core network power consumption. Adopting GreenTouch techniques can potentially improve the energy efficiency by 316x in a 2020 reference network compared to the state of the art in 2010