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Jing Ma

Publications and source records attributed to Jing Ma.

At least 181 records · Page 10Linked to original sources
arXiv2021

Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US

To mitigate the spread of COVID-19 pandemic, decision-makers and public authorities have announced various non-pharmaceutical policies. Analyzing the causal impact of these policies in reducing the spread of COVID-19 is important for future policy-making. The main challenge here is the existence of unobserved confounders (e.g., vigilance of residents). Besides, as the confounders may be time-varying during COVID-19 (e.g., vigilance of residents changes in the course of the pandemic), it is even more difficult to capture them. In this paper, we study the problem of assessing the causal effects of different COVID-19 related policies on the outbreak dynamics in different counties at any given time period. To this end, we integrate data about different COVID-19 related policies (treatment) and outbreak dynamics (outcome) for different United States counties over time and analyze them with respect to variables that can infer the confounders, including the covariates of different counties, their relational information and historical information. Based on these data, we develop a neural network based causal effect estimation framework which leverages above information in observational data and learns the representations of time-varying (unobserved) confounders. In this way, it enables us to quantify the causal impact of policies at different granularities, ranging from a category of policies with a certain goal to a specific policy type in this category. Besides, experimental results also indicate the effectiveness of our proposed framework in capturing the confounders for quantifying the causal impact of different policies. More specifically, compared with several baseline methods, our framework captures the outbreak dynamics more accurately, and our assessment of policies is more consistent with existing epidemiological studies of COVID-19.

cs.LG↗
arXiv2021

ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation

Social recommendation aims to fuse social links with user-item interactions to alleviate the cold-start problem for rating prediction. Recent developments of Graph Neural Networks (GNNs) motivate endeavors to design GNN-based social recommendation frameworks to aggregate both social and user-item interaction information simultaneously. However, most existing methods neglect the social inconsistency problem, which intuitively suggests that social links are not necessarily consistent with the rating prediction process. Social inconsistency can be observed from both context-level and relation-level. Therefore, we intend to empower the GNN model with the ability to tackle the social inconsistency problem. We propose to sample consistent neighbors by relating sampling probability with consistency scores between neighbors. Besides, we employ the relation attention mechanism to assign consistent relations with high importance factors for aggregation. Experiments on two real-world datasets verify the model effectiveness.

cs.SI↗
arXiv2021

Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption

With the advance of machine learning and the internet of things (IoT), security and privacy have become key concerns in mobile services and networks. Transferring data to a central unit violates privacy as well as protection of sensitive data while increasing bandwidth demands.Federated learning mitigates this need to transfer local data by sharing model updates only. However, data leakage still remains an issue. In this paper, we propose xMK-CKKS, a multi-key homomorphic encryption protocol to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, collaboration between all participating devices is required. This scheme prevents privacy leakage from publicly shared information in federated learning, and is robust to collusion between $k<N-1$ participating devices and the server. Our experimental evaluation demonstrates that the scheme preserves model accuracy against traditional federated learning as well as secure federated learning with homomorphic encryption (MK-CKKS, Paillier) and reduces computational cost compared to Paillier based federated learning. The average energy consumption is 2.4 Watts, so that it is suited to IoT scenarios.

cs.CR↗
arXiv2020

How the emotion's type and intensity affect rumor spreading

The implication and contagion effect of emotion cannot be ignored in rumor spreading. This paper sheds light on how DMs'emotional type and intensity affect rumor spreading. Based on the theory of RDEU and evolutionary game, we construct an evolutionary game model of rumor spreading by considering emotions, which takes netizens and the government as the core subjects. Through MATLAB to simulate and reveal the influencing mechanism of DMs'emotional type and intensity on rumor spreading. The results indicate that the DMs'choice of strategy is not only affected by their own emotional preference and intensity but also affected by that of the other player in rumor spreading. Pessimism has a more significant influence on the stability of the evolutionary game than optimism, the government's emotion types are more sensitive to the game results than netizens, and the emotional intensity is proportional to the evolution speed. More importantly, some significant emotional thresholds are found, which can be used to predict the behavior of netizens, helping the government gain critical time to deal with rumors and avoid the Tacitus Trap crisis. Furthermore, the simulation results are summarized as five types: risk, opportunity, ideal, security, and opposition. We hope that this work is beneficial to the government's public governance.

cs.SI↗
arXiv2020

NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis

This paper describes our contribution to SemEval 2020 Task 8: Memotion Analysis. Our system learns multi-modal embeddings from text and images in order to classify Internet memes by sentiment. Our model learns text embeddings using BERT and extracts features from images with DenseNet, subsequently combining both features through concatenation. We also compare our results with those produced by DenseNet, ResNet, BERT, and BERT-ResNet. Our results show that image classification models have the potential to help classifying memes, with DenseNet outperforming ResNet. Adding text features is however not always helpful for Memotion Analysis.

cs.CL↗
arXiv2020

Few-Mode Fiber Coupling Efficiency for Free-Space Optical Communication

Few-mode fiber is a significant component of free-space optical communication at the receiver to obtain achievable high coupling efficiency. A theoretical coupling model from the free-space optical communication link to a few-mode fiber is proposed based on a scale-adapted set of Laguerre-Gaussian modes. It is found that the coupling efficiency of various modes behaves differently in the presence of atmospheric turbulence or random jitter. Based on this model, the optimal coupling geometry parameter is obtained to maximize the coupling efficiency of the selected mode of few-mode fiber. The communication performance with random jitter is investigated. It is shown that the few-mode fiber has better bit-error rate performance than single-mode fiber, especially in high signal-to-noise ratio regimes.

physics.optics↗
arXiv2020

Role of bridge nodes in epidemic spreading: Different regimes and crossovers

Power-law behaviors are common in many disciplines, especially in network science. Real-world networks, like disease spreading among people, are more likely to be interconnected communities, and show richer power-law behaviors than isolated networks. In this paper, we look at the system of two communities which are connected by bridge links between a fraction $r$ of bridge nodes, and study the effect of bridge nodes to the final state of the Susceptible-Infected-Recovered model, by mapping it to link percolation. By keeping a fixed average connectivity, but allowing different transmissibilities along internal and bridge links, we theoretically derive different power-law asymptotic behaviors of the total fraction of the recovered $R$ in the final state as $r$ goes to zero, for different combinations of internal and bridge link transmissibilities. We also find crossover points where $R$ follows different power-law behaviors with $r$ on both sides when the internal transmissibility is below but close to its critical value, for different bridge link transmissibilities. All of these power-law behaviors can be explained through different mechanisms of how finite clusters in each community are connected into the giant component of the whole system, and enable us to pick effective epidemic strategies and to better predict their impacts.

physics.soc-ph↗
arXiv2020

Spatio-Temporal Tensor Sketching via Adaptive Sampling

Mining massive spatio-temporal data can help a variety of real-world applications such as city capacity planning, event management, and social network analysis. The tensor representation can be used to capture the correlation between space and time and simultaneously exploit the latent structure of the spatial and temporal patterns in an unsupervised fashion. However, the increasing volume of spatio-temporal data has made it prohibitively expensive to store and analyze using tensor factorization. In this paper, we propose SkeTenSmooth, a novel tensor factorization framework that uses adaptive sampling to compress the tensor in a temporally streaming fashion and preserves the underlying global structure. SkeTenSmooth adaptively samples incoming tensor slices according to the detected data dynamics. Thus, the sketches are more representative and informative of the tensor dynamic patterns. In addition, we propose a robust tensor factorization method that can deal with the sketched tensor and recover the original patterns. Experiments on the New York City Yellow Taxi data show that SkeTenSmooth greatly reduces the memory cost and outperforms random sampling and fixed rate sampling method in terms of retaining the underlying patterns.

cs.LG↗
arXiv2020

Review-guided Helpful Answer Identification in E-commerce

Product-specific community question answering platforms can greatly help address the concerns of potential customers. However, the user-provided answers on such platforms often vary a lot in their qualities. Helpfulness votes from the community can indicate the overall quality of the answer, but they are often missing. Accurately predicting the helpfulness of an answer to a given question and thus identifying helpful answers is becoming a demanding need. Since the helpfulness of an answer depends on multiple perspectives instead of only topical relevance investigated in typical QA tasks, common answer selection algorithms are insufficient for tackling this task. In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds' opinions reflected in the reviews, which is another important factor to identify helpful answers. Moreover, we tackle the task of determining opinion coherence as a language inference problem and explore the utilization of pre-training strategy to transfer the textual inference knowledge obtained from a specifically designed trained network. Extensive experiments conducted on real-world data across seven product categories show that our proposed model achieves superior performance on the prediction task.

cs.CL↗
arXiv2019

Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis

Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meaningful clinical concepts. While distributing the tensor factorization tasks to local sites can avoid direct data sharing, it still requires the exchange of intermediary results which could reveal sensitive patient information. Therefore, the challenge is how to jointly decompose the tensor under rigorous and principled privacy constraints, while still support the model's interpretability. We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR. It embeds advanced privacy-preserving mechanisms with collaborative learning. Hospitals can keep their EHR database private but also collaboratively learn meaningful clinical concepts by sharing differentially private intermediary results. Moreover, DPFact solves the heterogeneous patient population using a structured sparsity term. In our framework, each hospital decomposes its local tensors, and sends the updated intermediary results with output perturbation every several iterations to a semi-trusted server which generates the phenotypes. The evaluation on both real-world and synthetic datasets demonstrated that under strict privacy constraints, our method is more accurate and communication-efficient than state-of-the-art baseline methods.

cs.LG↗
arXiv2019

Waring-Goldbach problem for unlike powers

In this paper, we investigate exceptional sets in the Waring-Goldbach problem for unlike powers. For example, estimates are obtained for sufficiently large integers below a parameter subject to the necessary local conditions that do not have a representation as the sum of a square of prime, a cube of prime and a sixth power of prime and a $k$-th power of prime. These results improve the recent result due to Brüdern in the order of magnitude. Furthermore, the method can be also applied to the similar estimates for the exceptional sets for Waring-Goldbach problem for unlike powers.

math.NT↗
arXiv2019

Gate-tunable flat bands in van der Waals patterned dielectric superlattices

Superlattice engineering provides the means to reshape the fabric felt by quasiparticles moving in a material. Here we argue that bandstructure engineering with superlattices can be pushed to the extreme limit by stacking gapped van der Waals (vdW) materials on patterned dielectric substrates. Specifically, we find that high quality vdW patterned dielectric superlattices (PDS) realize a series of robust flat bands that can be directly switched on and off by gate voltage in situ. In contrast to existing superlattice platforms, these flat bands are realized without the need for fine tuning. Instead, the bands become flat as the gate voltage increases in magnitude. The characteristics of PDS flatbands are highly tunable: the type of flatband (single non-degenerate or dirac-cone-like), localization length, and interaction energy are sensitive to the applied gate voltage. As a result, electron-electron interactions in the PDS flatbands can become stronger than both the bandwidth and disorder broadening, providing a setting for correlated behavior such as flatband ferromagnetism. We expect PDS flatbands can be experimentally realized in a range of readily available gapped vdW materials such as monolayer transition metal dichalcogenides, e.g. WSe2.

cond-mat.mes-hall↗
arXiv2018

A protonated brownmillerite electrolyte for superior low-temperature proton conductivity

Design novel solid oxide electrolyte with enhanced ionic conductivity forms one of the Holy Grails in the field of materials science due to its great potential for wide range of energy applications. Conventional solid oxide electrolyte typically requires elevated temperature to activate the ionic transportation, while it has been increasing research interests to reduce the operating temperature due to the associated scientific and technological importance. Here, we report a conceptually new solid oxide electrolyte, HSrCoO2.5, which shows an exceptional enhanced proton conductivity at low temperature region (from room temperature to 140 oC). Combining both the experimental results and corresponding first-principles calculations, we attribute these intriguing properties to the extremely-high proton concentration as well as the well-ordered oxygen vacancy channels inherited from the novel crystalline structure of HSrCoO2.5. This result provides a new strategy to design novel solid oxide electrolyte with excellent proton conductivity for wide ranges of energy-related applications.

cond-mat.mtrl-sci↗
arXiv2018

Ionic Modulation of Interfacial Magnetism through Electrostatic Doping in Pt/YIG bilayer heterostructure

Voltage modulation of yttrium iron garnet (YIG) with compactness, high speed response, energy efficiency and both practical/theoretical siginificances can be widely applied to various YIG based spintronics such as spin Hall, spin pumping, spin Seeback effects. Here we initial an ionic modulation of interfacial magnetism process on YIG/Pt bilayer heterostructures, where the Pt capping would influence the ferromagnetic (FMR) field position significantly, and realize a significant magnetism enhancement in bilayer system. A large voltage induced FMR field shifts of 690 Oe has been achieved in YIG (13 nm)/Pt (3 nm) multilayer heterostructures under a small voltage bias of 4.5 V. The remarkable ME tunability comes from voltage induced extra FM ordering in Pt metal layer near the Pt/YIG interface. The first-principle theoretical simulation reveal that the electrostatic doping induced Pt5+ ions have strong magnetic ordering due to uncompensated d orbit electrons. The large voltage control of FMR change pave a foundation towards novel voltage tunable YIG based spintronics.

cond-mat.mtrl-sci↗
arXiv2017

Geometric theory of the natural optical activity in noncentrosymmetric metals

This is a PhD dissertation. Ignited by the chiral anomaly of recently discovered Weyl (semi-)metals, we study the chiral magnetic effect and the natural optical activity of noncentrosymmetric metals. Both phenomena are related to the linear-in-$\mathbf{q}$ spatial dispersion of the optical conductivity tensor, and can be calculated within the formalism of the semiclassical kinetic equation. Therefore, we calculate the dispersion of optical conductivity up to the linear order of the wave vector, in the low frequency regime, with both the semiclassical Boltzmann equation and the Kubo formula. The two different methods of calculation provide us the same result. In this result, the static and dynamic chiral magnetic effects are revealed to have different origin: one comes from topology, related to Berry monopoles, and the other has a geometric origin, which is determined by the orbital magnetic moment. The Faraday rotation of the polarization of light transmitted through a slab of the sample provides us the most direct way to measure the magnitude of dynamic chiral magnetic effect. We develop an effective medium theory for electromagnetic wave propagating through gapless nonuniform systems, to calculate macroscopic sample inhomogeneities induced corrections to the chiral magnetic conductivity. We show that, in metals with low carrier density, the way in which macroscopic fluctuations of the local conductivity affect the frequency dependent of the measured optical polarization rotation angle: by creating a sharp feature near the plasma edge, which can be detected by experiments. Then we narrow down our research a bit further to the current induced magnetization.

cond-mat.mes-hall↗
arXiv2016

Dynamic chiral magnetic effect and Faraday rotation in macroscopically disordered helical metals

We develop an effective medium theory for electromagnetic wave propagation through gapless non-uniform systems with dynamic chiral magnetic effect. The theory allows to calculate macroscopic-disorder-induced corrections to the values of optical, as well as chiral magnetic conductivities. In particular, we show that spatial fluctuations of optical conductivity induce corrections to the effective value of the chiral magnetic conductivity. Experimentally, these corrections can be observed as sharp features in the Faraday rotation angle near frequencies that correspond to the bulk plasmon resonances of a material. Such features are not expected to be present in single crystal samples.

cond-mat.mes-hall↗
arXiv2016

Network-Based Pathway Enrichment Analysis with Incomplete Network Information

Pathway enrichment analysis has become a key tool for biomedical researchers to gain insight into the underlying biology of differentially expressed genes, proteins and metabolites. It reduces complexity and provides a system-level view of changes in cellular activity in response to treatments and/or in disease states. Methods that use existing pathway network information have been shown to outperform simpler methods that only take into account pathway membership. However, despite significant progress in understanding the association amongst members of biological pathways, and expansion of data bases containing information about interactions of biomolecules, the existing network information may be incomplete or inaccurate, and is not cell-type or disease condition-specific. We propose a constrained network estimation framework that combines network estimation based on cell- and condition-specific high-dimensional Omics data with interaction information from existing data bases. The resulting pathway topology information is subsequently used to provide a framework for simultaneous testing of differences in expression levels of pathway members, as well as their interactions. We study the asymptotic properties of the proposed network estimator and the test for pathway enrichment, and investigate its small sample performance in simulated and real data settings.

stat.ME↗