The discovery of two-dimensional (2D) ferroelectrics with switchable out-of-plane polarization such as monolayer $alpha$-In$_2$Se$_3$ offers a new avenue for ultrathin high-density ferroelectric-based nanoelectronics such as ferroelectric field effec
t transistors and memristors. The functionality of ferroelectrics depends critically on the dynamics of polarization switching in response to an external electric/stress field. Unlike the switching dynamics in bulk ferroelectrics that have been extensively studied, the mechanisms and dynamics of polarization switching in 2D remain largely unexplored. Molecular dynamics (MD) using classical force fields is a reliable and efficient method for large-scale simulations of dynamical processes with atomic resolution. Here we developed a deep neural network-based force field of monolayer In$_2$Se$_3$ using a concurrent learning procedure that efficiently updates the first-principles-based training database. The model potential has accuracy comparable with density functional theory (DFT), capable of predicting a range of thermodynamic properties of In$_2$Se$_3$ polymorphs and lattice dynamics of ferroelectric In$_2$Se$_3$. Pertinent to the switching dynamics, the model potential also reproduces the DFT kinetic pathways of polarization reversal and 180$^circ$ domain wall motions. Moreover, isobaric-isothermal ensemble MD simulations predict a temperature-driven $alpha rightarrow beta$ phase transition at the single-layer limit, as revealed by both local atomic displacement and Steinhardts bond orientational order parameter $Q_4$. Our work paves the way for further research on the dynamics of ferroelectric $alpha$-In$_2$Se$_3$ and related systems.
In this paper, we prove the existence of nontrivial unbounded domains $Omegasubsetmathbb{R}^{n+1},ngeq1$, bifurcating from the straight cylinder $Btimesmathbb{R}$ (where $B$ is the unit ball of $mathbb{R}^n$), such that the overdetermined elliptic pr
oblem begin{equation*} begin{cases} Delta u +f(u)=0 &mbox{in $Omega$, } u=0 &mbox{on $partialOmega$, } partial_{ u} u=mbox{constant} &mbox{on $partialOmega$, } end{cases} end{equation*} has a positive bounded solution. We will prove such result for a very general class of functions $f: [0, +infty) to mathbb{R}$. Roughly speaking, we only ask that the Dirichlet problem in $B$ admits a nondegenerate solution. The proof uses a local bifurcation argument.
Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models. However, mainstream evaluation criteria experience limitations, even yielding discrepancies among results under different settings. By ex
amining various attack algorithms, including gradient-based and query-based attacks, we notice the lack of a consensus on a uniform standard for unbiased performance evaluation. Accordingly, we propose a Piece-wise Sampling Curving (PSC) toolkit to effectively address the aforementioned discrepancy, by generating a comprehensive comparison among adversaries in a given range. In addition, the PSC toolkit offers options for balancing the computational cost and evaluation effectiveness. Experimental results demonstrate our PSC toolkit presents comprehensive comparisons of attack algorithms, significantly reducing discrepancies in practice.
A single perturbation can pose the most natural images to be misclassified by classifiers. In black-box setting, current universal adversarial attack methods utilize substitute models to generate the perturbation, then apply the perturbation to the a
ttacked model. However, this transfer often produces inferior results. In this study, we directly work in the black-box setting to generate the universal adversarial perturbation. Besides, we aim to design an adversary generating a single perturbation having texture like stripes based on orthogonal matrix, as the top convolutional layers are sensitive to stripes. To this end, we propose an efficient Decision-based Universal Attack (DUAttack). With few data, the proposed adversary computes the perturbation based solely on the final inferred labels, but good transferability has been realized not only across models but also span different vision tasks. The effectiveness of DUAttack is validated through comparisons with other state-of-the-art attacks. The efficiency of DUAttack is also demonstrated on real world settings including the Microsoft Azure. In addition, several representative defense methods are struggling with DUAttack, indicating the practicability of the proposed method.
The powerlaw X-ray spectra of active galactic nuclei at moderate to high accretion rates normally appear softer when they brighten, for which the underlying mechanisms are yet unclear. Utilizing XMM-Newton observations and excluding photons $<$ 2 keV
to avoid contamination from the soft excess, in this work we scrutinize the powerlaw spectral variability of NCG 4051 from two new aspects. We first find that a best-fit softer-when-brighter relation is statistically insufficient to explain the observed spectral variabilities, and intervals deviated from the empirical relation are clearly visible in the light curve of 2 -- 4 keV/4 -- 10 keV count rate ratio. The deviations are seen not only between but also within individual XMM-Newton exposures, consistent with random variations of the corona geometry or inner structure (with timescales as short as $sim$ 1 ks), in addition to those behind the smooth softer-when-brighter trend. We further find the softer-when-brighter trend gradually weakens with the decreasing timescale (from $sim$ 100 ks down to 0.5 ks). These findings indicate that the powerlaw spectral slope is not solely determined by its brightness. We propose a two-tier geometry, including flares/nano-flares on top of the inner disc and an embedding extended corona (heated by the flares, in analogy to solar corona) to explain the observations together with other observational clues in literature. Rapid spectral variabilities could be due to individual flares/nano-flares, while slow ones are driven by the variations in the global activity of inner disc region (akin to the variation of solar activity, but not the accretion rate) accompanied with heating/cooling and inflation/contraction of the extended corona.
A micro-pressure sensor with an isosceles trapezoidal beam-membrane (ITBM) is proposed in this paper, consisting of a square silicon membrane, four isosceles trapezoidal beams and four piezoresistors.To investigate how the elastic silicon membrane af
fects pressure sensitive characteristics, a simulation models based on ANSYS 15.0 software were used to analyze the effect of structural dimension on characteristics of pressure sensor. According to that, the chips of micro-pressure sensors were fabricated by micro-electro-mechanical system (MEMS) technology on a silicon wafer with <100> orientation.The experimental results show that the proposed sensor achieves a better sensitivity of 9.64 mV/kPa and an excellent linearity of 0.09%F.S. in the range of 0~3.0 kPa at room temperature and a supply voltage of 5.0 V,with a super temperature coefficient of sensitivity(TCS) about - 684 ppm/K from 235.15 K to 360.15 K and low pressure measurement less than 3.0 kPa.
Point process models have been used to analyze interaction event times on a social network, in the hope to provides valuable insights for social science research. However, the diagnostics and visualization of the modeling results from such an analysi
s have received limited discussion in the literature. In this paper, we develop a systematic set of diagnostic tools and visualizations for point process models fitted to data from a network setting. We analyze the residual process and Pearson residual on the network by inspecting their structure and clustering structure. Equipped with these tools, we can validate whether a model adequately captures the temporal and/or network structures in the observed data. The utility of our approach is demonstrated using simulation studies and point process models applied to a study of animal social interactions.
Grid computing typically provides most of the data processing resources for large High Energy Physics experiments. However typical grid sites are not fully utilized by regular workloads. In order to increase the CPU utilization of these grid sites, t
he ATLAS@Home volunteer computing framework can be used as a backfilling mechanism. Results show an extra 15% to 42% of CPU cycles can be exploited by backfilling grid sites running regular workloads while the overall CPU utilization can remain over 90%. Backfilling has no impact on the failure rate of the grid jobs, and the impact on the CPU efficiency of grid jobs varies from 1% to 11% depending on the configuration of the site. In addition the throughput of backfill jobs in terms of CPU time per simulated event is the same as for resources dedicated to ATLAS@Home. This approach is sufficiently generic that it can easily be extended to other clusters.
In this paper we focus on the problem of human activity recognition without identification of the individuals in a scene. We consider using Wi-Fi signals to detect certain human mobility behaviors such as stationary, walking, or running. The main obj
ective is to successfully detect these behaviors for the individuals and based on that enable detection of the crowds overall mobility behavior. We propose a method which infers mobility behaviors in two stages: from Wi-Fi signals to trajectories and from trajectories to the mobility behaviors. We evaluate the applicability of the proposed approach using the StudentLife dataset which contains Wi-Fi, GPS, and accelerometer measurements collected from smartphones of 49 students within a three-month period. The experimental results indicate that there is high correlation between stability of Wi-Fi signals and mobility activity. This unique characteristic provides sufficient evidences to extend the proposed idea to mobility analytics of groups of people in the future.
Crowd behaviour analytics focuses on behavioural characteristics of groups of people instead of individuals activities. This work considers human queuing behaviour which is a specific crowd behavior of groups. We design a plug-and-play system solutio
n to the queue detection problem based on Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs) captured by multiple signal sniffers. The goal of this work is to determine if a device is in the queue based on only RSSIs. The key idea is to extract features not only from individual devices data but also mobility similarity between data from multiple devices and mobility correlation observed by multiple sniffers. Thus, we propose single-device feature extraction, cross-device feature extraction, and cross-sniffer feature extraction for model training and classification. We systematically conduct experiments with simulated queue movements to study the detection accuracy. Finally, we compare our signal-based approach against camera-based face detection approach in a real-world social event with a real human queue. The experimental results indicate that our approach can reach minimum accuracy of 77% and it significantly outperforms the camera-based face detection because people block each others visibility whereas wireless signals can be detected without blocking.
