Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Scheduling for Efficient Execution of Dynamic Stream Workflows

Stream workflow application such as online anomaly detection or online traffic monitoring, integrates multiple streaming big data applications into data analysis pipeline. This application can be highly dynamic in nature, where the data velocity may change at runtime and therefore the resources should be managed overtime. To manage these changes, the orchestration of this application requires a dynamic execution environment and dynamic scheduling technique. For the former requirement, Multicloud environment is a visible solution to cope with the dynamic aspects of this workflow application. While for the latter requirement, dynamic scheduling technique not only need to adhere to end user's requirements in terms of data processing and deadline for decision making, and data stream sources location constraints, but also adjust provisioning and scheduling plan at runtime to cope with dynamic variations of stream data rates. Therefore, we propose a two-phase adaptive scheduling technique to efficiently schedule dynamic workflow application in Multicloud environment that can respond to changes in the velocity of data at runtime. The experimental results showed that the proposed technique is close to the lower bound and effective for different experiment scenarios.
Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

With the development of Earth observation technology, a very-high-resolution (VHR) image has become an important data source of change detection (CD). These days, deep learning (DL) methods have achieved conspicuous performance in the CD of VHR images. Nonetheless, most of the existing CD models based on DL require annotated training samples. In this article, a novel unsupervised model, called kernel principal component analysis (KPCA) convolution, is proposed for extracting representative features from multitemporal VHR images. Based on the KPCA convolution, an unsupervised deep siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary and multiclass CD. In the KPCA-MNet, the high-level spatial–spectral feature maps are extracted by a deep siamese network consisting of weight-shared KPCA convolutional layers. Then, the change information in the feature difference map is mapped into a 2-D polar domain. Finally, the CD results are generated by threshold segmentation and clustering algorithms. All procedures of KPCA-MNet do not require labeled data. The theoretical analysis and experimental results in two binary CD datasets and one multiclass CD datasets demonstrate the validity, robustness, and potential of the proposed method.
Global well posedness of the two-dimensional stochastic nonlinear wave equation on an unbounded domain

We study the two-dimensional wave equation with cubic nonlinearity posed on $\mathbb R^2$, with space-time white noise forcing. After a suitable renormalisation of the nonlinearity, we prove global well-posedness for this equation for initial data in $\mathcal H^s$, $s>\frac45$.
Class Regularization: Improve Few-shot Image Classification by Reducing Meta Shift

Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class. Recent advances benefit from the meta-learning process where episodic tasks are formed to train a model that can adapt to class change. However, these task sare independent to each other and existing works mainly rely on limited samples of individual support set in a single meta task. This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set. To avoid this problem, we propose a concise Semantic RegularizationNetwork to learn a common semantic space under the framework of meta-learning. In this space, all class descriptors can be regularized by the learned semantic basis, which can effectively solve the meta shift problem. The key is to train a class encoder and decoder structure that can encode the sample embedding features into the semantic domain with trained semantic basis, and generate a more stable and general class descriptor from the decoder. We evaluate our work by extensive comparisons with previous methods on three benchmark datasets (MiniImageNet, TieredImageNet, and CUB). The results show that the semantic regularization module improves performance by 4%-7% over the baseline method, and achieves competitive results over the current state-of-the-art models.
Feedback Control of Dynamical Systems with Given Restrictions on Output Signal

A novel method for control of dynamical systems, proposed in the paper, ensures an output signal belonging to the given set at any time. The method is based on a special change of coordinates such that the initial problem with given restrictions on an output variable can be performed as the problem of the input-to-state stability analysis of a new extended system without restrictions. The new control laws for linear plants, systems with sector nonlinearity and systems with an arbitrary relative degree are proposed. Examples of change of coordinates are given, and they are utilized to design the control algorithms. The simulations confirm theoretical results and illustrate the effectiveness of the proposed method in the presence of parametric uncertainty and external disturbances.
Relational Mimic for Visual Adversarial Imitation Learning

In this work, we introduce a new method for imitation learning from video demonstrations. Our method, Relational Mimic (RM), improves on previous visual imitation learning methods by combining generative adversarial networks and relational learning. RM is flexible and can be used in conjunction with other recent advances in generative adversarial imitation learning to better address the need for more robust and sample-efficient approaches. In addition, we introduce a new neural network architecture that improves upon the previous state-of-the-art in reinforcement learning and illustrate how increasing the relational reasoning capabilities of the agent enables the latter to achieve increasingly higher performance in a challenging locomotion task with pixel inputs. Finally, we study the effects and contributions of relational learning in policy evaluation, policy improvement and reward learning through ablation studies.
Multi-criteria--based Dynamic User Behaviour--aware Resource Allocation in Fog Computing

Fog computing is a promising computing paradigm in which IoT data can be processed near the edge to support time-sensitive applications. However, the availability of resources in computation devices is not stable, since they may not be exclusively dedicated to the Fog application processing in the Fog environment. This, combined with dynamic user behaviour, can affect the execution of applications. To address dynamic changes in user behaviour in resource-limited Fog devices, this article proposes a multi-criteria–based resource allocation policy with resource reservation to minimise overall delay, processing time, and SLA violations. This process considers Fog computing–related characteristics, such as device heterogeneity, resource constraints, and mobility, as well as dynamic changes in user requirements. We employ multiple objective functions to find appropriate resources for executing time-sensitive tasks in the Fog environment. Experimental results show that our proposed policy performs better than the existing one, reducing the total delay by 51%. The proposed algorithm also reduces processing time and SLA violations, which is beneficial for running time-sensitive applications in the Fog environment.
P-CapsNets: a General Form of Convolutional Neural Networks

We propose Pure CapsNets (P-CapsNets) which is a generation of normal CNNs structurally. Specifically, we make three modifications to current CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST\&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99\% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to CNNs.
How India Censors the Web

One of the primary ways in which India engages in online censorship is by ordering Internet Service Providers (ISPs) operating in its jurisdiction to block access to certain websites for its users. This paper reports the different techniques Indian ISPs are using to censor websites, and investigates whether website blocklists are consistent across ISPs. We propose a suite of tests that prove more robust than previous work in detecting DNS and HTTP based censorship. Our tests also discern the use of SNI inspection for blocking websites, which is previously undocumented in the Indian context. Using information from court orders, user reports and government orders, we compile the largest known list of potentially blocked websites in India. We pass this list to our tests and run them from connections of six different ISPs, which together serve more than 98% of Internet users in India. Our findings not only confirm that ISPs are using different techniques to block websites, but also demonstrate that different ISPs are not blocking the same websites.
Dissipation in Langevin Equation and Construction of Mobility Tensor from Dissipative Heat Flow

The rheological behavior of a material is strongly related to the energy dissipation, and the understanding and modeling of dissipation is important from the view point of rheology. To study rheological properties with some mesoscopic and macroscopic dynamics models, the modeling of dynamic equation which appropriately incorporates the dissipation is important. Although there are several methods to construct mesoscopic and macroscopic dynamic equations, such as the Onsager's method, their validity is not fully clear. In this work, we theoretically analyze the dissipation in a mesoscopic Langevin equation in detail, from the view point of stochastic energetics. We show that the dissipative heat flow from the heat bath to the system plays an important role in the mesoscopic dynamics. The dissipative heat flow is unchanged under the variable transform, and thus it is a covariant quantity. We show that we can construct the Langevin equation and also perform a coarse-graining based on the dissipative heat flow. We can derive the mobility tensor from the dissipative heat flow, and construct the Langevin equation by combining it and the free energy. Our method can be applied to various systems, such as the dumbbell model and the diffusion type equation for the density field, to give the coarse-grained dynamic equations for them.
Centralized Conflict-free Cooperation for Connected and Automated Vehicles at Intersections by Proximal Policy Optimization

Connected vehicles will change the modes of future transportation management and organization, especially at intersections. There are mainly two categories coordination methods at unsignalized intersection, i.e. centralized and distributed methods. Centralized coordination methods need huge computation resources since they own a centralized controller to optimize the trajectories for all approaching vehicles, while in distributed methods each approaching vehicles owns an individual controller to optimize the trajectory considering the motion information and the conflict relationship with its neighboring vehicles, which avoids huge computation but needs sophisticated manual design. In this paper, we propose a centralized conflict-free cooperation method for multiple connected vehicles at unsignalized intersection using reinforcement learning (RL) to address computation burden naturally by training offline. We firstly incorporate a prior model into proximal policy optimization (PPO) algorithm to accelerate learning process. Then we present the design of state, action and reward to formulate centralized cooperation as RL problem. Finally, we train a coordinate policy by our model-accelerated PPO (MA-PPO) in a simulation setting and analyze results. Results show that the method we propose improves the traffic efficiency of the intersection on the premise of ensuring no collision.
Calcaneus Radiograph Analysis System: Rotation-Invariant Landmark Detection, Calcaneal Angles Measurement and Fracture Identification

1 Beijing Institute of Technology, Beijing 100081, China 2 The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121001, China 3 Zhejiang University of Science & Technology, Zhejiang 310032, China 4 Peking University People’s Hospital, Beijing 100044, China Abstract. Calcaneus is the largest tarsal bone to withstand the daily stresses of weight bearing. The calcaneal fracture is the most common type in the tarsal bone fractures. After a fracture is suspected, plain radiographs should be taken first. Bohler’s Angle (BA) and Critical Angle of Gissane (CAG), measured by four anatomic landmarks in lateral foot radiograph, can aid operative restoration of the fractured calcaneus and fracture diagnosis and assessment. The aim of this study is to develop a system to automatically locate four anatomic landmarks and measure BA and CAG for fracture assessment. To solved the problem of fickle rotation of calcaneus, we proposed a coarse-to-fine Rotation-Invariant Regression-Voting (RIRV) landmark detection method based on Supported Vector Regression (SVR) and Scale Invariant Feature Transform (SIFT) patch descriptor. By normalizing displacements into coordinates of oriented SIFT patches, our method is explicit rotation-invariance comparing to traditional regressive method. A multi-stream CNN structure with multi-region input is designed to screen calcaneus fracture. The input ROIs of multi-stream CNN are normalized by detected landmarks to uniform view, orientation and scale. Experiments show that our CNN can accurately identify the fractures with sensitivity of 96.81% and specificity of 90%.
Analysis of Propagation for Impulsive Reaction-Diffusion Models

We study a hybrid impulsive reaction-advection-diffusion model given by a reaction-advection-diffusion equation composed with a discrete-time map in space dimension $n\in\mathbb N$. The reaction-advection-diffusion equation takes the form \begin{equation*}\label{} u^{(m)}_t = \text{div}(A\nabla u^{(m)}-q u^{(m)}) + f(u^{(m)}) \quad \text{for} \ \ (x,t)\in\mathbb R^n \times (0,1] , \end{equation*} for some function $f$, a drift $q$ and a diffusion matrix $A$. When the discrete-time map is local in space we use $N_m(x)$ to denote the density of population at a point $x$ at the beginning of reproductive season in the $m$th year and when the map is nonlocal we use $u_m(x)$. The local discrete-time map is \begin{eqnarray*}\label{}\left\{ \begin{array}{lcl} u^{(m)}(x,0) = g(N_m(x)) \quad \text{for} \ \ x\in \mathbb R^n , \\ N_{m+1}(x):=u^{(m)}(x,1) \quad \text{for} \ \ x\in \mathbb R^n , \end{array}\right. \end{eqnarray*} for some function $g$. The nonlocal discrete time map is \begin{eqnarray*}\label{}\left\{ \begin{array}{lcl} u^{(m)}(x,0) = u_{m}(x) \quad \text{for} \ \ x\in \mathbb R^n , \\ \label{mainb2} u_{m+1}(x) := g\left(\int_{\mathbb R^n} K(x-y)u^{(m)}(y,1) dy\right) \quad \text{for} \ \ x\in \mathbb R^n, \end{array}\right. \end{eqnarray*} when $K$ is a nonnegative normalized kernel. SEE THE ARTICLE FOR COMPLETE ABSTRACT.
Binsec/Rel: Efficient Relational Symbolic Execution for Constant-Time at Binary-Level

The constant-time programming discipline (CT) is an efficient countermeasure against timing side-channel attacks, requiring the control flow and the memory accesses to be independent from the secrets. Yet, writing CT code is challenging as it demands to reason about pairs of execution traces (2-hypersafety property) and it is generally not preserved by the compiler, requiring binary-level analysis. Unfortunately, current verification tools for CT either reason at higher level (C or LLVM), or sacrifice bug-finding or bounded-verification, or do not scale. We tackle the problem of designing an efficient binary-level verification tool for CT providing both bug-finding and bounded-verification. The technique builds on relational symbolic execution enhanced with new optimizations dedicated to information flow and binary-level analysis, yielding a dramatic improvement over prior work based on symbolic execution. We implement a prototype, BINSEC/REL, and perform extensive experiments on a set of 338 cryptographic implementations, demonstrating the benefits of our approach in both bug-finding and bounded-verification. Using BINSEC/REL, we also automate a previous manual study of CT preservation by compilers. Interestingly, we discovered that gcc -O0 and backend passes of clang introduce violations of CT in implementations that were previously deemed secure by a state-of-the-art CT verification tool operating at LLVM level, showing the importance of reasoning at binary-level.
5G Positioning and Mapping With Diffuse Multipath

5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters (delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods break down or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach.
Gapped 
Z2
 spin liquid in the breathing kagome Heisenberg antiferromagnet

We investigate the spin-1/2 Heisenberg antiferromagnet on a kagome lattice with breathing anisotropy (i.e., with weak and strong triangular units), constructing an improved simplex resonating valence bond (RVB) ansatz by successive applications (up to three times) of local quantum gates, which implement a filtering operation on the bare nearest-neighbor RVB state. The resulting projected entangled pair state involves a small number of variational parameters (only one at each level of application) and preserves full lattice and spin-rotation symmetries. Despite its simple analytic form, the simplex RVB provides very good variational energies at strong and even intermediate breathing anisotropy. We show that it carries ${\mathbb{Z}}_{2}$ topological order which does not fade away under the first few applications of the quantum gates, suggesting that the RVB topological spin liquid becomes a competing ground state candidate for the kagome antiferromagnet at large breathing anisotropy.
Vector analysis of Contoura Vision for the correction of myopia and myopic astigmatism.

AIM To evaluate the visual outcomes of Contoura Vision (CV) with automatic eye tracking system in eyes with myopia and myopic astigmatism. METHODS This prospective study included 160 eyes (80 patients) with moderate myopia and irregular astigmatism between January and August 2018. Subjects were randomly divided into CV group (80 eyes) that underwent CV femtosecond laser-assisted in situ keratomileusis (FS-LASIK) and a control group (80 eyes) that underwent wavefront-optimized FS-LASIK. Visual outcomes and astigmatic vector analysis were evaluated and compared between preoperatively and 3mo postoperatively. RESULTS Basic details were similar in both groups (P>0.05). At 3mo postoperatively, uncorrected distance visual acuity was 20/16, 20/20, and 20/25 in 24, 76, and 80 eyes of patients in CV group, respectively. The CV group was better in predictability of astigmatism correction at 3mo postoperatively. In CV group, 64 eyes had deviation of astigmatic axis within 15° and 28 eyes had deviation of astigmatic axis within 5°, both were better than those in the control group. The number of eyes with residual astigmatism within 0.5 D were less in CV group (48 eyes) than the control group (40 eyes). Compared with the preoperative, C7 significantly reduced to 0.056±0.030 in CV group at 3mo after the procedure (P<0.05), and were significantly lower than those in the control group (P<0.05). CONCLUSION CV with automatic eye tracking system is safe and effective for the correction of myopia and myopic astigmatism.
Identifying Fiscal Inflation

Fiscal theorists warn about the risk of future inflation as a consequence of current fiscal imbalances in the US. Because actual inflation remains historically low and data on inflation expectations do not corroborate such risks, warnings for fiscal inflation are often ignored in policy and academic circles. This paper shows that a canonical NK-DSGE model enables identifying an anticipated component of inflation expectations that is closely related to fiscal policy. Estimation results suggest that fiscal inflation concerns have induced a 1.63-points increase in long-run inflation since 2001. The model also rationalizes why data on inflation expectations do not reveal such concerns outright.
Beach safety: can drones provide a platform for sighting sharks?

Abstract Context. A series of unprovoked shark attacks on New South Wales (Australia) beaches between 2013 and 2015 triggered an investigation of new and emerging technologies for protecting bathers. Traditionally, bather protection has included several methods for shark capture, detection and/or deterrence but has often relied on environmentally damaging techniques. Heightened environmental awareness, including the important role of sharks in the marine ecosystem, demands new techniques for protection from shark attack. Recent advances in drone-related technologies have enabled the possibility of real-time shark detection and alerting. Aim. To determine the reliability of drones to detect shark analogues in the water across a range of environmental conditions experienced on New South Wales beaches. Methods. A standard multirotor drone (DJI Inspire 1) was used to detect shark analogues as a proxy during flights at 0900, 1200 and 1500 hours over a 3-week period. The 27 flights encompassed a range of environmental conditions, including wind speed (2–30.0 km h−1), turbidity (0.4–6.4 m), cloud cover (0–100%), glare (0–100%), seas (0.4–1.4 m), swells (1.4–2.5 m) and sea state (Beaufort Scale 1–5 Bf). Key results. Detection rates of the shark analogues over the 27 flights were significantly higher for the independent observer conducting post-flight video analysis (50%) than for the drone pilot (38%) (Wald P = 0.04). Water depth and turbidity significantly impaired detection of analogues (Wald P = 0.04). Specifically, at a set depth of 2 m below the water surface, very few analogues were seen by the observer or pilot when water turbidity reduced visibility to less than 1.5 m. Similarly, when water visibility was greater than 1.5 m, the detection rate was negatively related to water depth. Conclusions. The present study demonstrates that drones can fly under most environmental conditions and would be a cost-effective bather protection tool for a range of user groups. Implications. The most effective use of drones would occur during light winds and in shallow clear water. Although poor water visibility may restrict detection, sharks spend large amounts of time near the surface, therefore providing a practical tool for detection in most conditions.