Low Latency Datacenter Networking: A Short Survey

Datacenters are the cornerstone of the big data infrastructure supporting numerous online services. The demand for interactivity, which significantly impacts user experience and provider revenue, is translated into stringent timing requirements for flows in datacenter networks. Thus low latency networking is becoming a major concern of both industry and academia. We provide a short survey of recent progress made by the networking community for low latency datacenter networks. We propose a taxonomy to categorize existing work based on four main techniques, reducing queue length, accelerating retransmissions, prioritizing mice flows, and exploiting multi-path. Then we review select papers, highlight the principal ideas, and discuss their pros and cons. We also present our perspectives of the research challenges and opportunities, hoping to aspire more future work in this space.Comment: 6 page

An Abstract Stabilization Method with Applications to Nonlinear Incompressible Elasticity

In this paper, we propose and analyze an abstract stabilized mixed finite element framework that can be applied to nonlinear incompressible elasticity problems. In the abstract stabilized framework, we prove that any mixed finite element method that satisfies the discrete inf-sup condition can be modified so that it is stable and optimal convergent as long as the mixed continuous problem is stable. Furthermore, we apply the abstract stabilized framework to nonlinear incompressible elasticity problems and present numerical experiments to verify the theoretical results

Ab initio calculation of the local magnetic moment in titanium doped zinc oxide with a corrected-band-gap scheme

The local magnetic moment of Ti:ZnO is calculated from first principles by using the corrected-band-gap scheme (CBGS). The results shows that the system is magnetic with the magnetization of 0.699 $\mu_B$ per dopant. The origin of the local magnetic moment is considered to be the impurity band partially occupied by the donor electrons in the conduction band. Further, the impacts of applying Hubbard U to Ti-d orbital on the magnetic moment have been investigated

Compare Contact Model-based Control and Contact Model-free Learning: A Survey of Robotic Peg-in-hole Assembly Strategies

In this paper, we present an overview of robotic peg-in-hole assembly and analyze two main strategies: contact model-based and contact model-free strategies. More specifically, we first introduce the contact model control approaches, including contact state recognition and compliant control two steps. Additionally, we focus on a comprehensive analysis of the whole robotic assembly system. Second, without the contact state recognition process, we decompose the contact model-free learning algorithms into two main subfields: learning from demonstrations and learning from environments (mainly based on reinforcement learning). For each subfield, we survey the landmark studies and ongoing research to compare the different categories. We hope to strengthen the relation between these two research communities by revealing the underlying links. Ultimately, the remaining challenges and open questions in the field of robotic peg-in-hole assembly community is discussed. The promising directions and potential future work are also considered

Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification

In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e.g. color, texture and shape). Currently available tools ignore either the label relationship or the view complementary. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multi-label structure in the output space, we introduce multi-view vector-valued manifold regularization (MV$\mathbf{^3}$MR) to integrate multiple features. MV$\mathbf{^3}$MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conducted extensive experiments on two challenging, but popular datasets, PASCAL VOC' 07 (VOC) and MIR Flickr (MIR), and validated the effectiveness of the proposed MV$\mathbf{^3}$MR for image classification

Numerical analysis of the production of D(∗)(3000)D^{(*)}(3000), DsJ(3040)D_{sJ}(3040) and their partners through the semileptonic decays of B(s)B_{(s)} mesons in terms of the light-front quark model

Inspired by the newly observed $D^{(*)}(3000)$, and $D_{sJ}(3040)$ states, in this work we study the production of $D^{(*)}(3000)$, $D_{sJ}(3040)$ and their partners through the semileptonic decays of $B_{(s)}$ mesons, where the light-front Quark model is applied to the whole calculation. Our numerical results indicate that the $B_{(s)}$ semileptonic decays into the $2P$ states of the charmed or charmed-strange meson family have considerable branching ratios, which shows that these semileptonic decays can be accessible at future experiments, especially LHCb and the forthcoming Belle II.Comment: 12 pages, 1 figure, and 6 tables. More discussions added. Accepted by Phys. Rev.

Time-domain global similarity method for automatic data cleaning for multi-channel measurement systems in magnetic confinement fusion devices

To guarantee the availability and reliability of data source in Magnetic Confinement Fusion (MCF) devices, incorrect diagnostic data, which cannot reflect real physical properties of measured objects, should be sorted out before further analysis and study. Traditional data sorting cannot meet the growing demand of MCF research because of the low-efficiency, time-delay, and lack of objective criteria. In this paper, a Time-Domain Global Similarity (TDGS) method based on machine learning technologies is proposed for the automatic data cleaning of MCF devices. Traditional data sorting aims to the classification of original diagnostic data sequences, which are different in both length and evolution properties under various discharge parameters. Hence the classification criteria are affected by many discharge parameters and vary shot by shot. The focus of TDGS method is turned to the physical similarity between data sequences from different channels, which are more essential and independent of discharge parameters. The complexity arisen from real discharge parameters during data cleaning is avoided in the TDGS method by transforming the general data sorting problem into a binary classification problem about the physical similarity between data sequences. As a demonstration of its application to multi-channel measurement systems, the TDGS method is applied to the EAST POlarimeter-INterferomeTer (POINT) system. The optimized performance of the method has reached 0.9871

Substation One-Line Diagram Automatic Generation and Visualization

In Energy Management System (EMS) applications and many other off-line planning and study tools, one-line diagram (OLND) of the whole system and stations is a straightforward view for planners and operators to design, monitor, analyze, and control the power system. Large-scale power system OLND is usually manually developed and maintained. The work is tedious, time-consuming and ease to make mistake. Meanwhile, the manually created diagrams are hard to be shared among the on-line and off-line systems. To save the time and efforts to draw and maintain OLNDs, and provide the capability to share the OLNDs, a tool to automatically develop substation based upon Common Information Model (CIM) standard is needed. Currently, there is no standard rule to draw the substation OLND. Besides, the substation layouts can be altered from the typical formats in textbooks based on factors of economy, efficiency, engineering practice, etc. This paper presents a tool on substation OLND automatic generation and visualization. This tool takes the substation CIM/E model as input, then automatically computes the coordinates of all components and generates the substation OLND based on its components attributes and connectivity relations. Evaluation of the proposed approach is presented using a real provincial power system. Over 95\% of substation OLNDs are decently presented and the rest are corner cases, needing extra effort to do specific reconfiguration.Comment: 6 pages, 6 figures, 1 table, accepted by 2019 IEEE PES ISGT ASI

Ab initio study of magnetic anisotropy in cobalt doped zinc oxide with electron-filling

Based on first-principles calculation, it has been predicted that the magnetic anisotropy energy (MAE) in Co-doped ZnO (Co:ZnO) depends on electron-filling. Results show that the charge neutral Co:ZnO presents a "easy plane" magnetic state. While modifying the total number of electrons, the easy axis rotates from in-plane to out-of-plane. The alternation of the MAE is considered to be the change of the ground state of Co ion, resulting from the relocating of electrons on Co d-orbitals with electron-filling.Comment: 3 pages, 4 figure

RepNet: Cutting Tail Latency in Data Center Networks with Flow Replication

Data center networks need to provide low latency, especially at the tail, as demanded by many interactive applications. To improve tail latency, existing approaches require modifications to switch hardware and/or end-host operating systems, making them difficult to be deployed. We present the design, implementation, and evaluation of RepNet, an application layer transport that can be deployed today. RepNet exploits the fact that only a few paths among many are congested at any moment in the network, and applies simple flow replication to mice flows to opportunistically use the less congested path. RepNet has two designs for flow replication: (1) RepSYN, which only replicates SYN packets and uses the first connection that finishes TCP handshaking for data transmission, and (2) RepFlow which replicates the entire mice flow. We implement RepNet on {\tt node.js}, one of the most commonly used platforms for networked interactive applications. {\tt node}'s single threaded event-loop and non-blocking I/O make flow replication highly efficient. Performance evaluation on a real network testbed and in Mininet reveals that RepNet is able to reduce the tail latency of mice flows, as well as application completion times, by more than 50\%