Link and sign prediction in complex networks bring great help to decision-making and recommender systems, such as in predicting potential relationships or relative status levels. Many previous studies focused on designing the special algorithms to pe
rform either link prediction or sign prediction. In this work, we propose an effective model integration algorithm consisting of network embedding, network feature engineering, and an integrated classifier, which can perform the link and sign prediction in the same framework. Network embedding can accurately represent the characteristics of topological structures and cooperate with the powerful network feature engineering and integrated classifier can achieve better prediction. Experiments on several datasets show that the proposed model can achieve state-of-the-art or competitive performance for both link and sign prediction in spite of its generality. Interestingly, we find that using only very low network embedding dimension can generate high prediction performance, which can significantly reduce the computational overhead during training and prediction. This study offers a powerful methodology for multi-task prediction in complex networks.
In this paper, we study the problem of consensus-based distributed optimization where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents cost functions collaboratively. In existing distributed optimization app
roaches (Push-Pull/AB) for directed graphs, all agents exchange their states with neighbors to achieve the optimal solution with a constant stepsize, which may lead to the disclosure of sensitive and private information. For privacy preservation, we propose a novel state-decomposition based gradient tracking approach (SD-Push-Pull) for distributed optimzation over directed networks that preserves differential privacy, which is a strong notion that protects agents privacy against an adversary with arbitrary auxiliary information. The main idea of the proposed approach is to decompose the gradient state of each agent into two sub-states. Only one substate is exchanged by the agent with its neighbours over time, and the other one is kept private. That is to say, only one substate is visible to an adversary, protecting the privacy from being leaked. It is proved that under certain decomposition principles, a bound for the sub-optimality of the proposed algorithm can be derived and the differential privacy is achieved simultaneously. Moreover, the trade-off between differential privacy and the optimization accuracy is also characterized. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach.
We consider growing random networks ${mathcal{G}_n}_{n ge 1}$ where, at each time, a new vertex attaches itself to a collection of existing vertices via a fixed number $m ge 1$ of edges, with probability proportional to a function $f$ of their degree
s. It was shown in cite{BBpersistence} that such network models exhibit two distinct regimes: (i) the persistent regime, corresponding to $sum_{i=1}^{infty}f(i)^{-2} < infty$, where the top $K$ maximal degree vertices fixate over time for any given $K$, and (ii) the non-persistent regime with $sum_{i=1}^{infty}f(i)^{-2} = infty$ where the identities of these vertices keep changing infinitely often over time. In this article, we develop root finding algorithms based on the empirical degree structure of a snapshot of such a network at some large time. In the persistent regime, the algorithm is purely based on degree centrality. In this case, the size of the confidence set of the root is shown to be stable in network size, and is explicitly characterized in terms of the attachment function $f$. In the non-persistent regime, analogous algorithms are developed based on centrality measures where one assigns to each vertex $v$ the maximal degree among vertices in a neighborhood of $v$ of radius $r_n$, where $r_n$ is much smaller than the diameter of the network. A bound on the size of the associated confidence set is also obtained, and it is shown that, when $f(k) = k^{alpha}, k ge 1,$ for any $alpha in (0,1/2]$, this size grows at a smaller rate than any positive power of the network size.
We conduct a combined experimental and theoretical study of the quantum-confined Stark effect in GaAs/AlGaAs quantum dots obtained with the local droplet etching method. In the experiment, we probe the permanent electric dipole and polarizability of
neutral and positively charged excitons weakly confined in GaAs quantum dots by measuring their light emission under the influence of a variable electric field applied along the growth direction. Calculations based on the configuration-interaction method show excellent quantitative agreement with the experiment and allow us to elucidate the role of Coulomb interactions among the confined particles and -- even more importantly -- of electronic correlation effects on the Stark shifts. Moreover, we show how the electric field alters properties such as built-in dipole, binding energy, and heavy-light hole mixing of multiparticle complexes in weakly confining systems, underlining the deficiencies of commonly used models for the quantum-confined Stark effect.
Digital contact tracing is being used by many countries to help contain COVID-19s spread in a post-lockdown world. Among the various available techniques, decentralized contact tracing that uses Bluetooth received signal strength indication (RSSI) to
detect proximity is considered less of a privacy risk than approaches that rely on collecting absolute locations via GPS, cellular-tower history, or QR-code scanning. As of October 2020, there have been millions of downloads of such Bluetooth-based contract-tracing apps, as more and more countries officially adopt them. However, the effectiveness of these apps in the real world remains unclear due to a lack of empirical research that includes realistic crowd sizes and densities. This study aims to fill that gap, by empirically investigating the effectiveness of Bluetooth-based contact tracing in crowd environments with a total of 80 participants, emulating classrooms, moving lines, and other types of real-world gatherings. The results confirm that Bluetooth RSSI is unreliable for detecting proximity, and that this inaccuracy worsens in environments that are especially crowded. In other words, this technique may be least useful when it is most in need, and that it is fragile when confronted by low-cost jamming. Moreover, technical problems such as high energy consumption and phone overheating caused by the contact-tracing app were found to negatively influence users willingness to adopt it. On the bright side, however, Bluetooth RSSI may still be useful for detecting coarse-grained contact events, for example, proximity of up to 20m lasting for an hour. Based on our findings, we recommend that existing contact-tracing apps can be re-purposed to focus on coarse-grained proximity detection, and that future ones calibrate distance estimates and adjust broadcast frequencies based on auxiliary information.
Average consensus is extensively used in distributed networks for computation and control, where all the agents constantly communicate with each other and update their states in order to reach an agreement. Under a general average consensus algorithm
, information exchanged through wireless or wired communication networks could lead to the disclosure of sensitive and private information. In this paper, we propose a privacy-preserving push-sum approach for directed networks that can protect the privacy of all agents while achieving average consensus simultaneously. Each node decomposes its initial state arbitrarily into two substates, and their average equals to the initial state, guaranteeing that the agents state will converge to the accurate average consensus. Only one substate is exchanged by the node with its neighbours over time, and the other one is reserved. That is to say, only the exchanged substate would be visible to an adversary, preventing the initial state information from leakage. Different from the existing state-decomposition approach which only applies to undirected graphs, our proposed approach is applicable to strongly connected digraphs. In addition, in direct contrast to offset-adding based privacy-preserving push-sum algorithm, which is vulnerable to an external eavesdropper, our proposed approach can ensure privacy against both an honest-but-curious node and an external eavesdropper. A numerical simulation is provided to illustrate the effectiveness of the proposed approach.
In the system we study, 1s and 0s represent occupied and vacant sites in the contact process with births at rate $lambda$ and deaths at rate 1. $-1$s are sterile individuals that do not reproduce but appear spontaneously on vacant sites at rate $alph
a$ and die at rate $thetaalpha$. We show that the system (which is attractive but has no dual) dies out at the critical value and has a nontrivial stationary distribution when it is supercritical. Our most interesting results concern the asymptotics when $alphato 0$. In this regime the process resembles the contact process in a random environment.
The 3D volumetric shape of the hearts left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation. Many cardiac image segmentation methods have relied on detection of reg
ion-of-interest as a pre-requisite for shape segmentation and modeling. With segmentation results, a 3D surface mesh and a corresponding point cloud of the segmented cardiac volume can be reconstructed for further analyses. Although state-of-the-art methods (e.g., U-Net) have achieved decent performance on cardiac image segmentation in terms of accuracy, these segmentation results can still suffer from imaging artifacts and noise, which will lead to inaccurate shape modeling results. In this paper, we propose a PC-U net that jointly reconstructs the point cloud of the LV MYO wall directly from volumes of 2D CT slices and generates its segmentation masks from the predicted 3D point cloud. Extensive experimental results show that by incorporating a shape prior from the point cloud, the segmentation masks are more accurate than the state-of-the-art U-Net results in terms of Dices coefficient and Hausdorff distance.The proposed joint learning framework of our PC-U net is beneficial for automatic cardiac image analysis tasks because it can obtain simultaneously the 3D shape and segmentation of the LV MYO walls.
Despite widespread use of smartphones, there is no measurement standard targeted at smartphone security behaviors. In this paper we translate a well-known cybersecurity behavioral scale into the smartphone domain and show that we can improve on this
translation by following an established psychometrics approach surveying 1011 participants. We design a new 14-item Smartphone Security Behavioral Scale (SSBS) exhibiting high reliability and good fit to a two-component behavioural model based on technical versus social protection strategies. We then demonstrate how SSBS can be applied to measure the influence of mental health issues on smartphone security behavior intentions. We found significant correlations that predict SSBS profiles from three types of MHIs. Conversely, we are able to predict presence of MHIs using SSBS profiles.We obtain prediction AUCs of 72.1% for Internet addiction,75.8% for depression and 66.2% for insomnia.
Researches using margin based comparison loss demonstrate the effectiveness of penalizing the distance between face feature and their corresponding class centers. Despite their popularity and excellent performance, they do not explicitly encourage th
e generic embedding learning for an open set recognition problem. In this paper, we analyse margin based softmax loss in probability view. With this perspective, we propose two general principles: 1) monotonic decreasing and 2) margin probability penalty, for designing new margin loss functions. Unlike methods optimized with single comparison metric, we provide a new perspective to treat open set face recognition as a problem of information transmission. And the generalization capability for face embedding is gained with more clean information. An auto-encoder architecture called Linear-Auto-TS-Encoder(LATSE) is proposed to corroborate this finding. Extensive experiments on several benchmarks demonstrate that LATSE help face embedding to gain more generalization capability and it boosted the single model performance with open training dataset to more than $99%$ on MegaFace test.
