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Zhi Wang

Publications and source records attributed to Zhi Wang.

At least 343 records · Page 19Linked to original sources
arXiv2018

Synchrotron X-ray tomography investigation of 3D morphology of intermetallic phases and pores and their effect on the mechanical properties of cast Al-Cu alloys

The influence of Fe content on the three-dimensional (3D) morphology of Fe-rich intermetallic phases (Fe phases), Al2Cu, and pores and mechanical properties of cast Al-5.0Cu-0.6Mn alloys with 0.5 and 1.0 wt. % Fe are characterized using synchrotron X-ray tomography and a tensile test. The results show that both Fe phases and Al2Cu exhibit a complex 3D network structure, and the pores are irregular with complex interconnected and near-globular shape. As the Fe content increases from 0.5 % to 1.0 %, the volume fraction and equivalent diameter of Fe phases decrease, whereas both their interconnectivities decrease. Skeletonization analysis shows that the Chinese-script-shaped Fe phase is compacted than the plate-like Fe phases. The equivalent diameter and sphericity of pores vary with Fe content, and their relationships follow exponential functions, Y = 7.14*X-1.29 and Y = 7.06*X-1.20, respectively. The addition of Fe results in a decrease in the ultimate tensile strength and elongation from 223.7 MPa to 199.8 MPa and from 5.51 % to 3.64 %, respectively, owing to increasing volume fraction of sharp-edged Fe phases and pores, resulting in stress concentration during tensile test.

cond-mat.mtrl-sci↗
arXiv2018

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

Estimating post-click conversion rate (CVR) accurately is crucial for ranking systems in industrial applications such as recommendation and advertising. Conventional CVR modeling applies popular deep learning methods and achieves state-of-the-art performance. However it encounters several task-specific problems in practice, making CVR modeling challenging. For example, conventional CVR models are trained with samples of clicked impressions while utilized to make inference on the entire space with samples of all impressions. This causes a sample selection bias problem. Besides, there exists an extreme data sparsity problem, making the model fitting rather difficult. In this paper, we model CVR in a brand-new perspective by making good use of sequential pattern of user actions, i.e., impression -> click -> conversion. The proposed Entire Space Multi-task Model (ESMM) can eliminate the two problems simultaneously by i) modeling CVR directly over the entire space, ii) employing a feature representation transfer learning strategy. Experiments on dataset gathered from Taobao's recommender system demonstrate that ESMM significantly outperforms competitive methods. We also release a sampling version of this dataset to enable future research. To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.

stat.ML↗
arXiv2018

Electric-field control of magnetism in few-layered van der Waals magnet

Manipulating quantum state via electrostatic gating has been intriguing for many model systems in nanoelectronics. When it comes to the question of controlling the electron spins, more specifically, the magnetism of a system, tuning with electric field has been proven to be elusive. Recently, magnetic layered semiconductors have attracted much attention due to their emerging new physical phenomena. However, challenges still remain in the demonstration of a gate controllable magnetism based on them. Here, we show that, via ionic gating, strong field effect can be observed in few-layered semiconducting Cr$_{2}$Ge$_{2}$Te$_{6}$ devices. At different gate doping, micro-area Kerr measurements in the studied devices demonstrate tunable magnetization loops below the Curie temperature, which is tentatively attributed to the moment re-balance in the spin-polarized band structure. Our findings of electric-field controlled magnetism in van der Waals magnets pave the way for potential applications in new generation magnetic memory storage, sensors, and spintronics.

cond-mat.mes-hall↗
arXiv2018

RDMAvisor: Toward Deploying Scalable and Simple RDMA as a Service in Datacenters

RDMA is increasingly adopted by cloud computing platforms to provide low CPU overhead, low latency, high throughput network services. On the other hand, however, it is still challenging for developers to realize fast deployment of RDMA-aware applications in the datacenter, since the performance is highly related to many lowlevel details of RDMA operations. To address this problem, we present a simple and scalable RDMA as Service (RaaS) to mitigate the impact of RDMA operational details. RaaS provides careful message buffer management to improve CPU/memory utilization and improve the scalability of RDMA operations. These optimized designs lead to simple and flexible programming model for common and knowledgeable users. We have implemented a prototype of RaaS, named RDMAvisor, and evaluated its performance on a cluster with a large number of connections. Our experiment results demonstrate that RDMAvisor achieves high throughput for thousand of connections and maintains low CPU and memory overhead through adaptive RDMA transport selection.

cs.DC↗
arXiv2017

Effect of Si on Fe-rich intermetallic formation and mechanical properties of heattreated Al-Cu-Mn-Fe alloys

The effect of Si on Fe-rich intermetallics formation and mechanical properties of heat-treated squeeze cast Al-5.0Cu-0.6Mn-0.7Fe alloy was investigated. Our results show that increasing Si content promotes the formation of Al15(FeMn)3(SiCu)2 ($α$-Fe), and varying the morphology of T (Al20Cu3Mn2) where the size decreases and the amount increases. The major reason is that Si promotes heterogeneous nucleation of the intermetallics leading to finer precipitates. Si addition significantly enhances ultimate tensile strength and yield strength of the alloys. The strengthening effect is mainly owing to the dispersoid strengthening by increasing volume fraction of T phase and less harmful $α$-Fe with a compact structure, which make the cracks more difficult to initiate and propagation during tensile test. The squeeze cast Al-5.0Cu-0.6Mn-0.7Fe alloy with 1.1% Si shows significantly improved mechanical properties than the alloy without Si addition, which has tensile strength of 386 MPa, yield strength of 280 MPa and elongation of 8.6%.

cond-mat.mtrl-sci↗
arXiv2017

Majorana phi0 Junction in a Disordered Spin-orbit Coupling Nanowire with Tilted Magnetic Field

Majorana Josephson junctions in nanowire systems exhibit a pseudo-$4π$ period current-phase relation in the clean limit. In this work, we study how this current-phase relation responds to a tilted magnetic field in a disordered Majorana Josephson junction within the Bogoliubov-de Gennes approach. We show that the tilted magnetic field induces a $ϕ_0$ phase shift to the current-phase relation. Most importantly, we find that this $ϕ_0$-junction behavior is robust even in the presence of disorders.

cond-mat.mes-hall↗
arXiv2017

Critical Current Anomaly at the Topological Quantum Phase Transition in a Majorana Josephson Junction

Majorana bound states in topological Josephson junctions induce a 4? period current-phase relation. Direct detection of the 4? periodicity is complicated by the quasiparticle poisoning. We reveal that Majorana bound states are also signaled by the anomalous enhancement on the critical current of the junction. We show the landscape of the critical current for a nanowire Josephson junction under a varying Zeeman field, and reveal a sharp step feature at the topological quantum phase transition point, which comes from the anomalous enhancement of the critical current at the topological regime. In multi-band wires, the anomalous enhancement disappears for an even number of bands, where the Majorana bound states fuse into Andreev bound states. This anomalous critical current enhancement directly signals the existence of the Majorana bound states, and also provides a valid signature for the topological quantum phase transition.

cond-mat.supr-con↗
arXiv2017

Downgrade Attack on TrustZone

Security-critical tasks require proper isolation from untrusted software. Chip manufacturers design and include trusted execution environments (TEEs) in their processors to secure these tasks. The integrity and security of the software in the trusted environment depend on the verification process of the system. We find a form of attack that can be performed on the current implementations of the widely deployed ARM TrustZone technology. The attack exploits the fact that the trustlet (TA) or TrustZone OS loading verification procedure may use the same verification key and may lack proper rollback prevention across versions. If an exploit works on an out-of-date version, but the vulnerability is patched on the latest version, an attacker can still use the same exploit to compromise the latest system by downgrading the software to an older and exploitable version. We did experiments on popular devices on the market including those from Google, Samsung and Huawei, and found that all of them have the risk of being attacked. Also, we show a real-world example to exploit Qualcomm's QSEE. In addition, in order to find out which device images share the same verification key, pattern matching schemes for different vendors are analyzed and summarized.

cs.CR↗
arXiv2017

Self-organized compound pattern and pulsation of dissipative solitons in a passively mode-locked fiber laser

We experimentally observe soliton self-organization and pulsation in a passively mode-locked fiber laser. The optomechanical interaction in the optical fiber is key to the formation of equidistant soliton bunches. These solitons simultaneously undergo a pulsation process with a period corresponding to tens of the cavity round trip time. Using the dispersive Fourier transformation technique, we find that the Kelly sidebands in the shot-to-shot spectra appear periodically, synchronizing with the pulsation.

physics.optics↗
arXiv2017

Why It Takes So Long to Connect to a WiFi Access Point

Today's WiFi networks deliver a large fraction of traffic. However, the performance and quality of WiFi networks are still far from satisfactory. Among many popular quality metrics (throughput, latency), the probability of successfully connecting to WiFi APs and the time cost of the WiFi connection set-up process are the two of the most critical metrics that affect WiFi users' experience. To understand the WiFi connection set-up process in real-world settings, we carry out measurement studies on $5$ million mobile users from $4$ representative cities associating with $7$ million APs in $0.4$ billion WiFi sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS App market. To the best of our knowledge, we are the first to do such large scale study on: how large the WiFi connection set-up time cost is, what factors affect the WiFi connection set-up process, and what can be done to reduce the WiFi connection set-up time cost. Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from $33\%$ to $3.6\%$ and reducing $80\%$ time costs of the connection set-up processes by more than $10$ times.

cs.NI↗
arXiv2017

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.

cs.MM↗
arXiv2017

Understanding Performance of Edge Content Caching for Mobile Video Streaming

Today's Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks--from regional CDN servers to peer CDN servers (e.g., smartrouters in users' homes)--to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world datasets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over 2 weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors. Using trace-driven experiments, we compare strategies for edge content caching including LRU and LFU, in terms of supporting mobile video requests. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance.

cs.MM↗
arXiv2017

Parameter Estimation of Complex Fractional Ornstein-Uhlenbeck Processes with Fractional Noise

We obtain strong consistency and asymptotic normality of a least squares estimator of the drift coefficient for complex-valued Ornstein-Uhlenbeck processes disturbed by fractional noise, extending the result of Y. Hu and D. Nualart, [Statist. Probab. Lett., 80 (2010), 1030-1038] to a special 2-dimensions. The strategy is to exploit the Garsia-Rodemich-Rumsey inequality and complex fourth moment theorems. The main ingredients of this paper are the sample path regularity of a multiple Wiener-Ito integral and two equivalent conditions of complex fourth moment theorems in terms of the contractions of integral kernels and complex Malliavin derivatives.

math.PR↗
arXiv2016

Stable Memory Allocation in the Hippocampus: Fundamental Limits and Neural Realization

It is believed that hippocampus functions as a memory allocator in brain, the mechanism of which remains unrevealed. In Valiant's neuroidal model, the hippocampus was described as a randomly connected graph, the computation on which maps input to a set of activated neuroids with stable size. Valiant proposed three requirements for the hippocampal circuit to become a stable memory allocator (SMA): stability, continuity and orthogonality. The functionality of SMA in hippocampus is essential in further computation within cortex, according to Valiant's model. In this paper, we put these requirements for memorization functions into rigorous mathematical formulation and introduce the concept of capacity, based on the probability of erroneous allocation. We prove fundamental limits for the capacity and error probability of SMA, in both data-independent and data-dependent settings. We also establish an example of stable memory allocator that can be implemented via neuroidal circuits. Both theoretical bounds and simulation results show that the neural SMA functions well.

cs.NE↗
arXiv2016

Landau-Zener-Stückelberg 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-Stückelberg (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.

cond-mat.supr-con↗
arXiv2016

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.

cs.MM↗
arXiv2016

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.

cs.MM↗