A Measurement Study of TCP Performance for Chunk Delivery in DASH

Dynamic Adaptive Streaming over HTTP (DASH) has emerged as an increasingly popular paradigm for video streaming [13], in which a video is segmented into many chunks delivered to users by HTTP request/response over Transmission Control Protocol (TCP) con- nections. Therefore, it is intriguing to study the performance of strategies implemented in conventional TCPs, which are not dedicated for video streaming, e.g., whether chunks are efficiently delivered when users per- form interactions with the video players. In this paper, we conduct mea- surement studies on users chunk requesting traces in DASH from a rep- resentative video streaming provider, to investigate users behaviors in DASH, and TCP-connection-level traces from CDN servers, to investi- gate the performance of TCP for DASH. By studying how video chunks are delivered in both the slow start and congestion avoidance phases, our observations have revealed the performance characteristics of TCP for DASH as follows: (1) Request patterns in DASH have a great impact on the performance of TCP variations including cubic; (2) Strategies in conventional TCPs may cause user perceived quality degradation in DASH streaming; (3) Potential improvement to TCP strategies for better delivery in DASH can be further explored

Towards Network-Failure-Tolerant Content Delivery for Web Content

Popularly used to distribute a variety of multimedia content items in today Internet, HTTP-based web content delivery still suffers from various content delivery failures. Hindered by the expensive deployment cost, the conventional CDN can not deploy as many edge servers as possible to successfully deliver content items to all users under these delivery failures. In this paper, we propose a joint CDN and peer-assisted web content delivery framework to address the delivery failure problem. Different from conventional peer-assisted approaches for web content delivery, which mainly focus on alleviating the CDN servers bandwidth load, we study how to use a browser-based peer-assisted scheme, namely WebRTC, to resolve content delivery failures. To this end, we carry out large-scale measurement studies on how users access and view webpages. Our measurement results demonstrate the challenges (e.g., peers stay on a webpage extremely short) that can not be directly solved by conventional P2P strategies, and some important webpage viewing patterns. Due to these unique characteristics, WebRTC peers open up new possibilities for helping the web content delivery, coming with the problem of how to utilize the dynamic resources efficiently. We formulate the peer selection that is the critical strategy in our framework, as an optimization problem, and design a heuristic algorithm based on the measurement insights to solve it. Our simulation experiments driven by the traces from Tencent QZone demonstrate the effectiveness of our design: compared with non-peer-assisted strategy and random peer selection strategy, our design significantly improves the successful relay ratio of web content items under network failures, e.g., our design improves the content download ratio up to 60% even when users located in a particular region (e.g., city) where none can connect to the regional CDN server

Towards Wi-Fi AP-Assisted Content Prefetching for On-Demand TV Series: A Reinforcement Learning Approach

The emergence of smart Wi-Fi APs (Access Point), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (QoE) (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching, which predicts and accurately fetches contents ahead of users' requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users, and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a large-scale measurement study on users' AP connection and TV series watching patterns using real-traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov Decision Process (MDP). The objective is to strike a balance between the increased prefetching&storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first, we investigate the performance lower bound by using a random algorithm, and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm

Aggregation of BiTe Monolayer on Bi2_2Te3_3(111) Induced by Diffusion of Intercalated Atoms in van der Waals Gap

We report a post-growth aging mechanism of Bi$_2$Te$_3$(111) films with scanning tunneling microscopy in combination with density functional theory calculation. It is found that a monolayered structure with a squared lattice symmetry gradually aggregates from surface steps. Theoretical calculations indicate that the van der Waals (vdW) gap not only acts as a natural reservoir for self-intercalated Bi and Te atoms, but also provides them easy diffusion pathways. Once hopping out of the gap, these defective atoms prefer to develop into a two dimensional BiTe superstructure on the Bi$_2$Te$_3$(111) surface driven by positive energy gain. Considering the common nature of weakly bonding between vdW layers, we expect such unusual diffusion and aggregation of the intercalated atoms may be of general importance for most kinds of vdW layered materials

Influence of squirt flow on fundamental guided waves propagation in borehole embedded in saturated porous media

In this paper, the reservoir is modeled by homogeneous two-phase media based on BISQ model. We focus on the effects of the squirt flow on the fundamental guided waves propagation in borehole embedded in saturated porous media excited by monopole, dipole and quadrupole point sources. The full waveforms acoustic logging in a fluid-filled borehole are simulated. The curves of velocity dispersion, attenuation coefficients and excitation of the fundamental guided waves have shown that velocity dispersions are almost independent of the characteristic squirt flow length, attenuations of guided waves are enhanced due to the squirt flow, and excitations of guided waves are decreased due to the squirt flow. It is possible to estimate the characteristic squirt flow length by attenuation coefficients of the guided waves from acoustical logging data.Comment: all 18 pages 6 figure

Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models

The state-of-the-art pre-trained language representation models, such as Bidirectional Encoder Representations from Transformers (BERT), rarely incorporate commonsense knowledge or other knowledge explicitly. We propose a pre-training approach for incorporating commonsense knowledge into language representation models. We construct a commonsense-related multi-choice question answering dataset for pre-training a neural language representation model. The dataset is created automatically by our proposed "align, mask, and select" (AMS) method. We also investigate different pre-training tasks. Experimental results demonstrate that pre-training models using the proposed approach followed by fine-tuning achieve significant improvements over previous state-of-the-art models on two commonsense-related benchmarks, including CommonsenseQA and Winograd Schema Challenge. We also observe that fine-tuned models after the proposed pre-training approach maintain comparable performance on other NLP tasks, such as sentence classification and natural language inference tasks, compared to the original BERT models. These results verify that the proposed approach, while significantly improving commonsense-related NLP tasks, does not degrade the general language representation capabilities

Recovering the lost steerability of quantum states within non-Markovian environments by utilizing quantum partially collapsing measurements

In this Letter, we mainly investigate the dynamic behavior of quantum steering and how to effectively recover the lost steerability of quantum states within non-Markovian environments. We consider two different cases (one-subsystem or all-subsystem interacts with the dissipative environments), and obtain that the dynamical interaction between system initialized by a Werner state and the non-Markovian environments can induce the quasi-periodic quantum entanglement (concurrence) resurgence, however, quantum steering cannot retrieve in such a condition. And we can obtain that the resurgent quantum entanglement cannot be utilized to achieve quantum steering. Subsequently, we put forward a feasible physical scheme for recovering the steerability of quantum states within the non-Markovian noises by prior weak measurement on each subsystem before the interaction with dissipative environments followed by post weak measurement reversal. It is shown that the steerability of quantum states and the fidelity can be effectively restored. Furthermore, the results show that the larger the weak measurement strength is, the better the effectiveness of the scheme is. Consequently, our investigations might be beneficial to recover the lost steerability of quantum states within the non-Markovian regimes.Comment: Accepted for publication in Laser Physics Letters.17 pages, 8 figure

Landau-Zener-St\"uckelberg Interferometry for Majorana Qubit

Stimulated by a very recent experiment observing successfully two superconducting states with even- and odd-number of electrons in a nanowire topological superconductor as expected from the existence of two end Majorana quasiparticles (MQs) [Albrecht \textit{et al.}, Nature \textbf{531}, 206 (2016)], we propose a way to manipulate Majorana qubit exploiting quantum tunneling effects. The prototype setup consists of two one-dimensional (1D) topological superconductors coupled by a tunneling junction which can be controlled by gate voltage. We show that, upon current injection, the time evolution of superconducting phase difference at the junction induces an oscillation in energy levels of the Majorana parity states, whereas the level-crossing is avoided by a small coupling energy of MQs in the individual 1D superconductors. This results in a Landau-Zener-St\"{u}ckelberg (LZS) interference between the Majorana parity states. Adjusting the current pulse and gate voltage, one can build a LZS interferometry which provides an arbitrary manipulation of the Majorana qubit. The LZS rotation of Majorana qubit can be monitored by the microwave radiated from the junction

Manipulating the Majorana Qubit with the Landau-Zener-St\"{u}ckelberg Interference

Constructing a universal operation scheme for Majorana qubits remains a central issue for the topological quantum computation. We study the Landau-Zener-St\"{u}ckelberg interference in a Majorana qubit and show that this interference can be used to achieve controllable operations. The Majorana qubit consists of an rf SQUID with a topological nanowire Josephson junction which hosts Majorana bound states. In the SQUID, a magnetic flux pulse can drive the quantum evolution of the Majorana qubit. The qubit experiences two Landau-Zener transitions when the amplitude of the pulse is tuned around the superconducting flux quanta $2e/\hbar$. The Landau-Zener-St\"{u}ckelberg interference between the two transitions rotates the Majorana qubit, with the angle controlled by the time scale of the pulse. This rotation operation implements a high-speed single-qubit gate on the Majorana qubit, which is a necessary ingredient for the topological quantum computation

Proposal for a flux qubit in a dc SQUID with the 4π4\pi period Josephson effect

Constructing qubits which are suitable for quantum computation remains a notable challenge. Here, we propose a superconducting flux qubit in a dc SQUID structure, formed by a conventional insulator Josephson junction and a topological nanowire Josephson junction with Majorana bound states. The zero energy Majorana bound states transport $4\pi$ period Josephson currents in the nanowire junction. The interplay between this $4\pi$ period Josephson effect and the convectional $2\pi$ period Josephson effect in the insulator junction induces a double-well potential energy landscape in the SQUID. As a result, the two lowest energy levels of the SQUID are isolated from other levels. These two levels show contradicting circulating supercurrents, thus can be used as a flux qubit. We reveal that this flux qubit has the merits of stability to external noises, tolerance to the deviation of system parameters, and scalability to large numbers. Furthermore, we demonstrate how to couple this flux qubit with the Majorana qubit by tuning the junction parameters, and how to use this coupling to manipulate the Majorana qubit