Trusted execution environments (TEE) such as Intels Software Guard Extension (SGX) have been widely studied to boost security and privacy protection for the computation of sensitive data such as human genomics. However, a performance hurdle is often
generated by SGX, especially from the small enclave memory. In this paper, we propose a new Hybrid Secured Flow framework (called HySec-Flow) for large-scale genomic data analysis using SGX platforms. Here, the data-intensive computing tasks can be partitioned into independent subtasks to be deployed into distinct secured and non-secured containers, therefore allowing for parallel execution while alleviating the limited size of Page Cache (EPC) memory in each enclave. We illustrate our contributions using a workflow supporting indexing, alignment, dispatching, and merging the execution of SGX- enabled containers. We provide details regarding the architecture of the trusted and untrusted components and the underlying Scorn and Graphene support as generic shielding execution frameworks to port legacy code. We thoroughly evaluate the performance of our privacy-preserving reads mapping algorithm using real human genome sequencing data. The results demonstrate that the performance is enhanced by partitioning the time-consuming genomic computation into subtasks compared to the conventional execution of the data-intensive reads mapping algorithm in an enclave. The proposed HySec-Flow framework is made available as an open-source and adapted to the data-parallel computation of other large-scale genomic tasks requiring security and scalable computational resources.
Spin-orbit coupling (SOC), the core of numerous condensed-matter phenomena such as nontrivial band gap, magnetocrystalline anisotropy, etc, is generally considered to be appreciable only in heavy elements, detrimental to the synthetization and applic
ation of functional materials. Therefore, amplifying the SOC effect in light elements is of great importance. Here, focusing on 3d and 4d systems, we demonstrate that the interplay between crystal symmetry and electron correlation can dramatically enhance the SOC effect in certain partially occupied orbital multiplets, through the self-consistently reinforced orbital polarization as a pivot. We then provide design principles and comprehensive databases, in which we list all the Wyckoff positions and site symmetries, in all two-dimensional (2D) and three-dimensional crystals that potentially have such enhanced SOC effect. As an important demonstration, we predict nine material candidates from our selected 2D material pool as high-temperature quantum anomalous Hall insulators with large nontrivial band gaps of hundreds of meV. Our work provides an efficient and straightforward way to predict promising SOC-active materials, releasing the burden of requiring heavy elements for next-generation spin-orbitronic materials and devices.
In this paper, we study the prescribed $Q$-curvature flow equation on a arbitrary even dimensional closed Riemannian manifold $(M,g)$, which was introduced by S. Brendle in cite{B2003}, where he proved the flow exists for long time and converges at i
nfinity if the GJMS operator is weakly positive with trivial kernel and $int_M Qdmu < (n-1)!Volleft( S^n right) $. In this paper we study the critical case that $int_M Qdmu = (n-1)!Volleft( S^n right)$, we will prove the convergence of the flow under some geometric hypothesis. In particular, this gives a new proof of Li-Li-Lius existence result in cite{LLL2012} in dimensiona 4 and extend the work of Li-Zhu cite{LZ2019} in dimension 2 to general even dimensions. In the proof, we give a explicit expression of the limit of the corresponding energy functional when the blow up occurs.
Two-dimensional (2D) half-metallic materials are of great interest for their promising applications in spintronics. Although numerous of 2D half-metals have been proposed theoretically, rarely of them can be synthesized experimentally. Here, exemplif
ied by monolayer FeCl2, we show three mechanisms in such quantum magnets that would cause the metal-insulator transition by using first-principles calculations. In particular, half-metallicity, especially that protected by symmetry-induced degeneracies, predicted by the previous theoretical simulations could be destroyed by electron correlation, spin-orbit coupling and further structural distortions to lower the total energy. Our work reveals the fragility of the symmetry-protected half-metals upon various competing energy-lowering mechanisms, which should be taken into account for theoretically predicting and designing quantum mateirals with exotic functionalities.
Symmetry formulated by group theory plays an essential role with respect to the laws of nature, from fundamental particles to condensed matter systems. Here, by combining symmetry analysis and tight-binding model calculations, we elucidate that the c
rystallographic symmetries of a vast number of magnetic materials with light elements, in which the neglect of relativistic spin-orbit coupling (SOC) is an appropriate approximation, are considerably larger than the conventional magnetic groups. Thus, a symmetry description that involves partially-decoupled spin and spatial rotations, dubbed as spin group, is required. Spin group permits more symmetry operations and thus more energy degeneracies that are disallowed by the magnetic groups. One consequence of the spin group is the new anti-unitary symmetries that protect SOC-free Z_2 topological phases with unprecedented surface node structures. Our work not only manifests the physical reality of materials with weak SOC, but also shed light on the understanding of all solids with and without SOC by a unified group theory.
In this paper, we present a novel scenario for directional modulation (DM) networks with a full-duplex (FD) malicious attacker (Mallory), where Mallory can eavesdrop the confidential message from Alice to Bob and simultaneously interfere Bob by sendi
ng a jamming signal. Considering that the jamming plus noise at Bob is colored, an enhanced receive beamforming (RBF), whitening-filter-based maximum ratio combining (MRC) (WFMRC), is proposed. Subsequently, two RBFs of maximizing the secrecy rate (Max-SR) and minimum mean square error (MMSE) are presented to show the same performance as WFMRC. To reduce the computational complexity of conventional MMSE, a low-complexity MMSE is also proposed. Eventually, to completely remove the jamming signal from Mallory and transform the residual interference plus noise to a white one, a new RBF, null-space projection (NSP) based maximizing WF receive power, called NSP-based Max-WFRP, is also proposed. From simulation results, we find that the proposed Max-SR, WFMRC, and low-complexity MMSE have the same SR performance as conventional MMSE, and achieve the best performance while the proposed NSP-based Max-WFRP performs better than MRC in the medium and high signal-to-noise ratio regions. Due to its low-complexity,the proposed low-complexity MMSE is very attractive. More important, the proposed methods are robust to the change in malicious jamming power compared to conventional MRC.
Forecasting is challenging since uncertainty resulted from exogenous factors exists. This work investigates the rank position forecasting problem in car racing, which predicts the rank positions at the future laps for cars. Among the many factors tha
t bring changes to the rank positions, pit stops are critical but irregular and rare. We found existing methods, including statistical models, machine learning regression models, and state-of-the-art deep forecasting model based on encoder-decoder architecture, all have limitations in the forecasting. By elaborative analysis of pit stops events, we propose a deep model, RankNet, with the cause effects decomposition that modeling the rank position sequence and pit stop events separately. It also incorporates probabilistic forecasting to model the uncertainty inside each sub-model. Through extensive experiments, RankNet demonstrates a strong performance improvement over the baselines, e.g., MAE improves more than 10% consistently, and is also more stable when adapting to unseen new data. Details of model optimization, performance profiling are presented. It is promising to provide useful forecasting tools for the car racing analysis and shine a light on solutions to similar challenging issues in general forecasting problems.
Subgraph counting aims to count occurrences of a template T in a given network G(V, E). It is a powerful graph analysis tool and has found real-world applications in diverse domains. Scaling subgraph counting problems is known to be memory bounded an
d computationally challenging with exponential complexity. Although scalable parallel algorithms are known for several graph problems such as Triangle Counting and PageRank, this is not common for counting complex subgraphs. Here we address this challenge and study connected acyclic graphs or trees. We propose a novel vectorized subgraph counting algorithm, named Subgraph2Vec, as well as both shared memory and distributed implementations: 1) reducing algorithmic complexity by minimizing neighbor traversal; 2) achieving a highly-vectorized implementation upon linear algebra kernels to significantly improve performance and hardware utilization. 3) Subgraph2Vec improves the overall performance over the state-of-the-art work by orders of magnitude and up to 660x on a single node. 4) Subgraph2Vec in distributed mode can scale up the template size to 20 and maintain good strong scalability. 5) enabling portability to both CPU and GPU.
The two-dimensional (2D) C3N has emerged as a material with promising applications in high performance device owing to its intrinsic bandgap and tunable electronic properties. Although there are several reports about the bandgap tuning of C3N via sta
cking or forming nanoribbon, bandgap modulation of bilayer C3N nanoribbons (C3NNRs) with various edge structures is still far from well understood. Here, based on extensive first-principles calculations, we demonstrated the effective bandgap engineering of C3N by cutting it into hydrogen passivated C3NNRs and stacking them into bilayer heterostructures. It was found that armchair (AC) C3NNRs with three types of edge structures are all semiconductors, while only zigzag (ZZ) C3NNRs with edges composed of both C and N atoms (ZZ-CN/CN) are semiconductors. The bandgaps of all semiconducting C3NNRs are larger than that of C3N nanosheet. More interestingly, AC-C3NNRs with CN/CN edges (AC-CN/CN) possess direct bandgap while ZZ-CN/CN have indirect bandgap. Compared with the monolayer C3NNR, the bandgaps of bilayer C3NNRs can be greatly modulated via different stacking orders and edge structures, varying from 0.43 eV for ZZ-CN/CN with AB-stacking to 0.04 eV for AC-CN/CN with AA-stacking. Particularly, transition from direct to indirect bandgap was observed in the bilayer AC-CN/CN heterostructure with AA-stacking, and the indirect-to-direct transition was found in the bilayer ZZ-CN/CN with AB-stacking. This work provides insights into the effective bandgap engineering of C3N and offers a new opportunity for its applications in nano-electronics and optoelectronic devices.
This paper considers a secure multigroup multicast multiple-input single-output (MISO) communication system aided by an intelligent reflecting surface (IRS). Specifically, we aim to minimize the transmit power at the Alice via jointly optimizing the
transmit beamformer, AN vector and phase shifts at the IRS subject to the secrecy rate constraints as well as the unit modulus constraints of IRS phase shifts. However, the optimization problem is non-convex and directly solving it is intractable. To tackle the optimization problem, we first transform it into a semidefinite relaxation (SDR) problem, and then alternately update the transmit beamformer and AN matrix as well as the phase shifts at the IRS. In order to reduce the high computational complexity, we further propose a low-complexity algorithm based on second-order cone programming (SOCP). We decouple the optimization problem into two sub-problems and optimize the transmit beamformer, AN vector and the phase shifts alternately by solving two corresponding SOCP sub-problem. Simulation results show that the proposed SDR and SOCP schemes require half or less transmit power than the scheme without IRS, which demonstrates the advantages of introducing IRS and the effectiveness of the proposed methods.
