Nanoscale investigation of potential distribution in operating Cu(In,Ga)Se2 thin-film solar cells

The distribution of the electrostatic potential in and between the materials in Cu(In,Ga)Se2 thin-film solar cells has a major impact on their superior performance. This thesis reported on the nanoscale imaging of the electrostatic potential on untreated cross sections of operating Cu(In,Ga)Se2 solar cells using Kelvin probe force microscopy

On (non-)monotonicity and phase diagram of finitary random interlacement

In this paper, we study the evolution of a Finitary Random Interlacement (FRI) with respect to the expected length of each fiber. In contrast to the previously proved phase transition between sufficiently large and small fiber length, we show that for $d=3,4$, FRI is NOT stochastically monotone as fiber length increasing. At the same time, numerical evidences still strongly support the existence of a unique and sharp phase transition on the existence of a unique infinite cluster, while the critical value for phase transition is estimated to be an inversely proportional function with respect to the system intensity

Cross-CBAM: A Lightweight network for Scene Segmentation

Scene parsing is a great challenge for real-time semantic segmentation. Although traditional semantic segmentation networks have made remarkable leap-forwards in semantic accuracy, the performance of inference speed is unsatisfactory. Meanwhile, this progress is achieved with fairly large networks and powerful computational resources. However, it is difficult to run extremely large models on edge computing devices with limited computing power, which poses a huge challenge to the real-time semantic segmentation tasks. In this paper, we present the Cross-CBAM network, a novel lightweight network for real-time semantic segmentation. Specifically, a Squeeze-and-Excitation Atrous Spatial Pyramid Pooling Module(SE-ASPP) is proposed to get variable field-of-view and multiscale information. And we propose a Cross Convolutional Block Attention Module(CCBAM), in which a cross-multiply operation is employed in the CCBAM module to make high-level semantic information guide low-level detail information. Different from previous work, these works use attention to focus on the desired information in the backbone. CCBAM uses cross-attention for feature fusion in the FPN structure. Extensive experiments on the Cityscapes dataset and Camvid dataset demonstrate the effectiveness of the proposed Cross-CBAM model by achieving a promising trade-off between segmentation accuracy and inference speed. On the Cityscapes test set, we achieve 73.4% mIoU with a speed of 240.9FPS and 77.2% mIoU with a speed of 88.6FPS on NVIDIA GTX 1080Ti

Refining Wi-Fi Based Indoor Localization with Li-Fi Assisted Model Calibration in Smart Buildings

In recent years, there has been an increasing number of information technologies utilized in buildings to advance the idea of "smart buildings". Among various potential techniques, the use of Wi-Fi based indoor positioning allows to locate and track smartphone users inside a building, therefore, location-aware intelligent solutions can be applied to control and of building operations. These location-aware indoor services (e.g., path finding, internet of things, location based advertising) demand real-time accurate indoor localization, which is a key issue to guarantee high quality of service in smart buildings. This paper presents a new Wi-Fi based indoor localization technique that achieves significantly improvement of indoor positioning accuracy with the help of Li-Fi assisted coefficient calibration. The proposed technique leverages indoor existing Li-Fi lighting and Wi-Fi infrastructure, and results in a cost-effective and user-convenient indoor accurate localization framework. In this work, experimental study and measurements are conducted to verify the performance of the proposed idea. The results substantiate the concept of refining Wi-Fi based indoor localization with Li-Fi assisted computation calibration.Comment: International Conference on Computing in Civil and Building Engineering (ICCCBE) 201

Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction

The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inference processes of most existing methods rely on Monte Carlo random sampling, which is insufficient to cover the realistic paths with finite samples, due to the long tail effect of the predicted distribution. To promote the sampling process of stochastic prediction, we propose a novel method, called BOsampler, to adaptively mine potential paths with Bayesian optimization in an unsupervised manner, as a sequential design strategy in which new prediction is dependent on the previously drawn samples. Specifically, we model the trajectory sampling as a Gaussian process and construct an acquisition function to measure the potential sampling value. This acquisition function applies the original distribution as prior and encourages exploring paths in the long-tail region. This sampling method can be integrated with existing stochastic predictive models without retraining. Experimental results on various baseline methods demonstrate the effectiveness of our method

Modeling Long-term Dependencies and Short-term Correlations in Patient Journey Data with Temporal Attention Networks for Health Prediction

Building models for health prediction based on Electronic Health Records (EHR) has become an active research area. EHR patient journey data consists of patient time-ordered clinical events/visits from patients. Most existing studies focus on modeling long-term dependencies between visits, without explicitly taking short-term correlations between consecutive visits into account, where irregular time intervals, incorporated as auxiliary information, are fed into health prediction models to capture latent progressive patterns of patient journeys. We present a novel deep neural network with four modules to take into account the contributions of various variables for health prediction: i) the Stacked Attention module strengthens the deep semantics in clinical events within each patient journey and generates visit embeddings, ii) the Short-Term Temporal Attention module models short-term correlations between consecutive visit embeddings while capturing the impact of time intervals within those visit embeddings, iii) the Long-Term Temporal Attention module models long-term dependencies between visit embeddings while capturing the impact of time intervals within those visit embeddings, iv) and finally, the Coupled Attention module adaptively aggregates the outputs of Short-Term Temporal Attention and Long-Term Temporal Attention modules to make health predictions. Experimental results on MIMIC-III demonstrate superior predictive accuracy of our model compared to existing state-of-the-art methods, as well as the interpretability and robustness of this approach. Furthermore, we found that modeling short-term correlations contributes to local priors generation, leading to improved predictive modeling of patient journeys.Comment: 10 pages, 4 figures, accepted at ACM BCB 202

Coded Caching Scheme for Partially Connected Linear Networks Via Multi-antenna Placement Delivery Array

In this paper, we study the coded caching scheme for the $(K,L,M_{\text{T}},M_{\text{U}},N)$ partially connected linear network, where there are $N$ files each of which has an equal size, $K+L-1$ transmitters and $K$ users; each user and transmitter caches at most $M_{\text{U}}$ and $M_{\text{T}}$ files respectively; each user cyclically communicates with $L$ transmitters. The goal is to design caching and delivery schemes to reduce the transmission latency measured by the metric normalized delivery time (NDT). By delicately designing the data placement of the transmitters and users according to the topology, we show that a combinatorial structure called multiple-antenna placement delivery array (MAPDA), which was originally proposed for the multiple-input single-output broadcast channels, can be also used to design schemes for the partially connected linear network. Then, based on existing MAPDAs and our constructing approach, we propose new schemes that achieve the optimal NDT when ${M_\text{T}}+ {M_\text{U}}\geq N$ and smaller NDT than that of the existing schemes when (${M_\text{T}}+ {M_\text{U}}\leq N$, $\frac{M_\text{U}}{N}+\frac{M_\text{T}}{N} \frac{L}{K}\left\lceil \frac{K}{L} \right\rceil \geq 1$) or (${M_\text{U}}+ {M_\text{T}}< N, \frac{K}{L}\notin\mathbb{Z}^+$). Moreover, our schemes operate in one-shot linear delivery and significantly reduce the subpacketizations compared to the existing scheme, which implies that our schemes have a wider range of applications and lower complexity of implementation.Comment: 13 page

Study on the mixing performance of static mixers in selective catalytic reduction (SCR) systems

Selective catalytic reduction (SCR) is a promising technique for reducing nitrogen oxide (NOx) emissions from diesel engines. Static mixers are widely used in SCR systems before reactors to promote the mixing of ammonia and exhaust streams. This work aims to investigate the effects of the location of static mixers and the volume ratio of two species on mixing quality using the computational fluid dynamics (CFD) method. The simulation results show that a more homogenous ammonia distribution can be achieved at the exit of the pipe if static mixers are placed close to the ammonia injection point or if more ammonia is injected. Another phenomenon found in the study is that the mixing performance of an identical static mixer may behave discrepantly under different flow conditions if using B and C as the evaluating indexes for mixing homogenization

Nonlinear System Dynamic Reliability Analysis Using Equivalent Duffing System Method

Equivalent linearization method is the main approach for nonlinear structural system random response analysis. But it will generate big error that using the random response results of equivalent linearization method to analyze the structural dynamic reliability. In order to improve the analysis precision of dynamic reliability of nonlinear system, an equivalent nonlinear system method is presented in this paper. In this method general nonlinear systems are converted to equivalent Duffing nonlinear system according to minimum mean square error principle, whose exact analytic solution of steady state of random responses can be worked out by Fokker Planck Kolmogorov equation (FPK equation). Then the exact results of stochastic response processes are used for the analysis of structural dynamic reliability. So it is not only convenient for calculation but also with high degree of accuracy for the results that using the equivalent nonlinear system method to analyze structural dynamic reliability. In addition, the equivalent nonlinear system adopted in this work has a parameterεwhich controls the degree of nonlinear. Thus we can obtain conveniently the analysis results of converting the original system to equivalent nonlinear systems with different degree of nonlinear by changing the value of the parameterε. In particular, when the parameter ε is equal to zero we can obtain the analysis results of equivalent linearization method. It is shown from the example analysis that the analysis results of equivalent nonlinear system method presented in this paper is reliable and the calculation accuracy is higher than equivalent linear system method apparently

Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence

Open-source software (OSS) supply chain enlarges the attack surface, which makes package registries attractive targets for attacks. Recently, package registries NPM and PyPI have been flooded with malicious packages. The effectiveness of existing malicious NPM and PyPI package detection approaches is hindered by two challenges. The first challenge is how to leverage the knowledge of malicious packages from different ecosystems in a unified way such that multi-lingual malicious package detection can be feasible. The second challenge is how to model malicious behavior in a sequential way such that maliciousness can be precisely captured. To address the two challenges, we propose and implement Cerebro to detect malicious packages in NPM and PyPI. We curate a feature set based on a high-level abstraction of malicious behavior to enable multi-lingual knowledge fusing. We organize extracted features into a behavior sequence to model sequential malicious behavior. We fine-tune the BERT model to understand the semantics of malicious behavior. Extensive evaluation has demonstrated the effectiveness of Cerebro over the state-of-the-art as well as the practically acceptable efficiency. Cerebro has successfully detected 306 and 196 new malicious packages in PyPI and NPM, and received 385 thank letters from the official PyPI and NPM teams