GreenDCN: a General Framework for Achieving Energy Efficiency in Data Center Networks

The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.Comment: 14 pages, accepted by IEEE JSA

Optimization of Protein-Protein Interaction Measurements for Drug Discovery Using AFM Force Spectroscopy

Increasingly targeted in drug discovery, protein-protein interactions challenge current high throughput screening technologies in the pharmaceutical industry. Developing an effective and efficient method for screening small molecules or compounds is critical to accelerate the discovery of ligands for enzymes, receptors and other pharmaceutical targets. Here, we report developments of methods to increase the signal-to-noise ratio (SNR) for screening protein-protein interactions using atomic force microscopy (AFM) force spectroscopy. We have demonstrated the effectiveness of these developments on detecting the binding process between focal adhesion kinases (FAK) with protein kinase B (Akt1), which is a target for potential cancer drugs. These developments include optimized probe and substrate functionalization processes and redesigned probe-substrate contact regimes. Furthermore, a statistical-based data processing method was developed to enhance the contrast of the experimental data. Collectively, these results demonstrate the potential of the AFM force spectroscopy in automating drug screening with high throughput

Incorporating Rate Adaptation into Green Networking for Future Data Centers

Despite some proposals for energy-efficient topologies, most of the studies for saving energy in data center networks are focused on traffic engineering, i.e., consolidating flows and switching off unnecessary network devices. The major weakness of this approach is network oscillation brought by the frequent change of network topology when traffic fluctuates very fast. In this paper, we propose to incorporate rate adaptation into green data center networks. With rate adaptive network devices, we aim at approaching network-wide energy proportionality by routing optimization. We formalize the problem with an integer program and propose an efficient approximation algorithm –TSRR, solving the problem quickly while guaranteeing a constant performance ratio. Extensive range of simulations confirm that more than 40% of the energy can be saved while introducing very slight stretch on network delay.TRUEpu

Energy-Efficient Network Routing with Discrete Cost Functions

Energy consumption is an important issue in the design and use of networks. In this paper, we explore energy savings in networks via a rate adaptation model. This model can be represented by a cost-minimization network routing problem with discrete cost functions. We formulate this problem as an integer program, which is proved to be NP-hard. Then a constant approximation algorithm is developed. In our proposed method, we first transform the program into a continuous-cost network routing problem, and then we approximate the optimal solution by a two-step rounding process. We show by analysis that, for uniform demands, our method provides a constant approximation for the uniform network routing problem with discrete costs. A bicriteria network routing problem is also developed so that a trade-off can be made between energy consumption and network delay. Analytical results for this latter model are also presented.TRUEpu

Superconductivity and vortex structure on Bi2_{2}Te3_{3}/FeTe0.55_{0.55}Se0.45_{0.45} heterostructures with different thickness of Bi2_{2}Te3_{3} films

Using scanning tunnel microscopy (STM), we investigate the superconductivity and vortex properties in topological insulator Bi$_{2}$Te$_{3}$ thin films grown on the iron-based superconductor FeTe$_{0.55}$Se$_{0.45}$. The proximity-induced superconductivity weakens in the Bi$_{2}$Te$_{3}$ film when the thickness of the film increases. Unlike the elongated shape of vortex cores observed in the Bi$_{2}$Te$_{3}$ film with 2-quintuple-layer (QL) thickness, the isolated vortex cores exhibit a star shape with six rays in the 1-QL film, and the rays are along the crystalline axes of the film. This is consistent with the sixfold rotational symmetry of the film lattice, and the proximity-induced superconductivity is still topologically trivial in the 1-QL film. At a high magnetic field, when the direction between the two nearest neighbored vortices deviates from that of any crystalline axes, two cores connect each other by a pair of adjacent rays, forming a new type of electronic structure of vortex cores. On the 3-QL film, the vortex cores elongate along one of the crystalline axes of the Bi$_{2}$Te$_{3}$ film, similar to the results obtained on 2-QL films. The elongated vortex cores indicate a twofold symmetry of the superconducting gap induced by topological superconductivity with odd parity. This observation confirms possible topological superconductivity in heterostructures with a thickness of more than 2 QLs. Our results provide rich information for the vortex cores and vortex-bound states on the heterostructures consisting of the topological insulator and the iron-based superconductor.Comment: 8 pages, 8 figure

Severe postpartum disruption of the pelvic ring: report of two cases and review of the literature

Pelvic dislocations are rare during labor, and the treatment is controversial. We report two cases of young women who sustained postpartum disruption of the pelvic ring: one case is an 8.8 cm wide separation of the pubic symphysis with sacroiliac joint disruption underwent surgical stabilization and the second case with 4.0 cm disruption being treated non-operatively. These cases illustrated of importance of accurate diagnosis, careful physical exam, fully informed consent and specific treatment for this condition

Shadow and Light: Digitally Reconstructed Radiographs for Disease Classification

In this paper, we introduce DRR-RATE, a large-scale synthetic chest X-ray dataset derived from the recently released CT-RATE dataset. DRR-RATE comprises of 50,188 frontal Digitally Reconstructed Radiographs (DRRs) from 21,304 unique patients. Each image is paired with a corresponding radiology text report and binary labels for 18 pathology classes. Given the controllable nature of DRR generation, it facilitates the inclusion of lateral view images and images from any desired viewing position. This opens up avenues for research into new and novel multimodal applications involving paired CT, X-ray images from various views, text, and binary labels. We demonstrate the applicability of DRR-RATE alongside existing large-scale chest X-ray resources, notably the CheXpert dataset and CheXnet model. Experiments demonstrate that CheXnet, when trained and tested on the DRR-RATE dataset, achieves sufficient to high AUC scores for the six common pathologies cited in common literature: Atelectasis, Cardiomegaly, Consolidation, Lung Lesion, Lung Opacity, and Pleural Effusion. Additionally, CheXnet trained on the CheXpert dataset can accurately identify several pathologies, even when operating out of distribution. This confirms that the generated DRR images effectively capture the essential pathology features from CT images. The dataset and labels are publicly accessible at https://huggingface.co/datasets/farrell236/DRR-RATE