Miracle-dz/UniFAR-FTUs huggingface dataset:
Context Attention Module for Human Hand Detection

Human hand detection is still a challenging problem in a real world setting because the hand appearance features are less and varied form. So it is very important to make good use of context and enhance hand feature through each other. In this paper, we propose Context Attention Feature Pyramid Networks CA-FPN for human hand detection, where Context Attention Module (CAM) is introduced as a new component of the pyramid architecture. The CAM aims to: (1)capture context information and reserve local edge feature, (2)figure out the relative context for hands. The proposed model achieves state-of-the-art performance on two challenging hand detection datasets, i.e. the Oxford hand dataset and the Vision for Intelligent and Applications (VIVA) Challenge dataset. What's more, processing time of CA-FPN is about 8.5FPS on one TITAN X GPU, which could be applied for real time application.
A lumped parameter model of the boom of a mobile concrete pump

The use of active vibration damping control can improve the handling and safety of mobile concrete pumps. In model-based control design, dynamic models of the system are needed. On the one hand, these models should capture the dominant dynamic effects of the system. On the other hand they need to be computational efficient to run on the control unit of the vehicle. In this paper a computationally efficient lumped parameter model of a concrete pump is derived. The multibody system takes into account the flexibility of the segments and the hydraulic actuators as well as the mechanical linkage of the cylinder to the joint. For validation, the model is compared with measurements of a real concrete pump.
Acting Together, Acting Stronger? Interference between Participants during Face-to-face Cooperative Interception Task

People generally coordinate their action to be more effective. However, in some cases, interference between them occur, resulting in an inefficient collaboration. The main goal of this study is to explore the way two persons regulate their actions when performing a cooperative task of ball interception, and how interference between them may occur. Starting face to face, twenty-four participants (twelve teams of two) had to physically intercept balls moving down from the roof to the floor in a virtual room. To this end, they controlled a virtual paddle attached to their hand moving along the anterior-posterior axis, and were not allowed to communicate. Results globally showed participants were often able to intercept balls without collision by dividing the interception space in two equivalent parts. However, an area of uncertainty (where many trials were not intercepted) appeared in the center of the scene highlighting the presence of interference between participants. The width of this area increased when situation became more complex and when less information was available. Moreover, participants often interpreted balls starting above them as balls they should intercept, even when these balls were in fine intercepted by their partner. Overall, results showed that team coordination emerges from between-participants interactions in this ball interception task and that interference between them depends on task complexity (uncertainty on partner's action and visual information available).
Handling Inherent Delays in Virtual IoT Gateways

Massive deployment of diverse ultra-low power wireless devices in different application areas has given rise to a plethora of heterogeneous architectures and communication protocols. It is challenging to provide convergent access to these miscellaneous collections of communicating devices. In this paper, we propose VGATE, an edge-based virtualized IoT gateway for bringing these devices together in a single framework using SDRs as technology agnostic radioheads. SDR platforms, however, suffer from large unpredictable delays. We design a GNU Radio-based IEEE 802.15. 4 experimental setup using LimeSDR, where the data path is time-stamped at various points of interest to get a comprehensive understanding of the characteristics of the delays. Our analysis shows that GNU Radio processing and LimeSDR buffering delays are the major delays. We decrease the LimeSDR buffering delay by decreasing the USB transfer size but show that this comes at the cost of increased processing overhead. We modify the USB transfer packet size to investigate which USB transfer size provides the best balance between buffering delay and processing overhead across two different host computers. Our experiments show that for the best measured configuration the mean and jitter of latency decreases by 37% and 40% respectively for the host computer with higher processing resources. We also show that the throughput is not affected by these modifications.
Performance Limits of Low Inertia Power Systems Based on Minimum Energy Control

It is currently recognized that integration of renewable sources is tightly linked to the behavior of low-inertia power systems. In this light, our aim in the current study is to find a lower bound on the energy storage required for reliable operation of a small network. A central idea in the proposed analysis is the ideal dq storage device-a conceptual device capable of unlimited control over its AC voltage. We use this so-called device to explore what is the minimal locally stored energy that allows the system to operate reliably, such that the voltage stays within an acceptable range during a transient. We show that this problem can be posed as an optimal control problem, and propose a suboptimal solution based on minimum energy control. The results provide a conservative lower bound on the locally stored energy.
Dual-Polarized Power-Combining Waveguide Antenna for LINC Transmitters

This paper addresses the design of a dual-polarized power-combining waveguide antenna for LINC transmitters. The proposed antenna has a port isolation of −13 dB, a fractional bandwidth of > 60% and produces a radiation pattern having a beamwidth of 60° at the design frequency of 2.45 GHz. Measurements of LINC performance for a 32 QAM modulated signal yield an EVM of 0.12% and an ACPR of 30 dBc in boresight direction.
Design of Bus Bar Structures in Power Converter Circuit Considering Both Parasitic Inductance and AC Resistance

In power converter circuits, it is well known that a bus-bar structure affects both surge voltage and switching loss. The stray inductance and capacitance depending on bus-bar structure also can be one of the causes such as the oscillation waveform that induces EMI issues. This paper proposes a design procedure of an optimized bus bar structure taking AC resistance into consideration. Those parameters that have frequency characteristics can be analyzed by finite element method, and it is shown that the AC resistance of the bus bar has an ability to suppress the oscillation. The validation of simulation results are shown by comparing the simulation and experimental results using a buck chopper circuit with input voltage of 300 V and output current of 30 A. The surge voltage and oscillation current are evaluated by using the bus bar modeled as S-parameters. In addition, the gate resistor is inserted to suppress them so that this paper includes among the surge voltage, oscillation and the switching loss. It is shown that the optimal parameters of busbar structure and gate resistor can reduce the surge voltage and oscillation current.
Measuring International Uncertainty Using Global Vector Autoregressions with Drifting Parameters

This study investigates the time-varying effects of international uncertainty shocks. I use a global vector autoregressive model with drifting coefficients and factor stochastic volatility in the errors to model the G7 economies jointly. The measure of uncertainty is constructed by estimating a time-varying scalar driving the innovation variances of the latent factors, which is also included in the conditional mean of the process. To achieve regularization, I use Bayesian techniques for estimation, and rely on hierarchical global–local priors to shrink the high-dimensional multivariate system towards sparsity. I compare the obtained econometric measure of uncertainty to alternative indices and discuss commonalities and differences. Moreover, I find that international uncertainty may differ substantially compared to identically constructed domestic measures. Structural inference points towards pronounced real and financial effects of uncertainty shocks in all considered economies. These effects are subject to heterogeneities over time and the cross-section, providing empirical evidence in favor of using the flexible econometric framework introduced in this study.
WeGotYouCovered: The Winning Solver from the PACE 2019 Implementation Challenge, Vertex Cover Track

We present the winning solver of the PACE 2019 Implementation Challenge, Vertex Cover Track. The minimum vertex cover problem is one of a handful of problems for which kernelization---the repeated reducing of the input size via data reduction rules---is known to be highly effective in practice. Our algorithm uses a portfolio of techniques, including an aggressive kernelization strategy, local search, branch-and-reduce, and a state-of-the-art branch-and-bound solver. Of particular interest is that several of our techniques were not from the literature on the vertex over problem: they were originally published to solve the (complementary) maximum independent set and maximum clique problems. Aside from illustrating our solver's performance in the PACE 2019 Implementation Challenge, our experiments provide several key insights not yet seen before in the literature. First, kernelization can boost the performance of branch-and-bound clique solvers enough to outperform branch-and-reduce solvers. Second, local search can significantly boost the performance of branch-and-reduce solvers. And finally, somewhat surprisingly, kernelization can sometimes make branch-and-bound algorithms perform worse than running branch-and-bound alone.
Maximizing Spin-Orbit-Torque Efficiency of 
Pt/Ti
 Multilayers: Trade-Off Between Intrinsic Spin Hall Conductivity and Carrier Lifetime

We report a comprehensive study of the maximization of the spin Hall ratio ({\theta}SH) in Pt thin films by the insertion of sub-monolayer layers of Ti to decrease carrier lifetime while minimizing the concurrent reduction in the spin Hall conductivity. We establish that the intrinsic spin Hall conductivity of Pt, while robust against the strain and the moderate interruption of crystal order caused by these insertions, begins to decrease rapidly at high resistivity level because of the shortening carrier lifetime. The unavoidable trade-off between the intrinsic spin Hall conductivity and carrier lifetime sets a practical upper bound of {\theta}SH >=0.8 for heterogeneous materials where the crystalline Pt component is the source of the spin Hall effect and the resistivity is increased by shortening carrier lifetime. This work also establishes a very promising spin-Hall metal of [Pt 0.75 nm/Ti 0.2 nm]7/Pt 0.75 nm for energy-efficient, high-endurance spin-orbit torque technologies (e.g., memories, oscillators, and logic) due to its combination of a giant {\theta}SH of 0.8, or equivalently a dampinglike spin torque efficiency per unit current density of 0.35, with a relatively low resistivity (90 uOhm cm) and high suitability for practical technology integration.
Why So Down? The Role of Negative (and Positive) Pointwise Mutual Information in Distributional Semantics

In distributional semantics, the pointwise mutual information ($\mathit{PMI}$) weighting of the cooccurrence matrix performs far better than raw counts. There is, however, an issue with unobserved pair cooccurrences as $\mathit{PMI}$ goes to negative infinity. This problem is aggravated by unreliable statistics from finite corpora which lead to a large number of such pairs. A common practice is to clip negative $\mathit{PMI}$ ($\mathit{\texttt{-} PMI}$) at $0$, also known as Positive $\mathit{PMI}$ ($\mathit{PPMI}$). In this paper, we investigate alternative ways of dealing with $\mathit{\texttt{-} PMI}$ and, more importantly, study the role that negative information plays in the performance of a low-rank, weighted factorization of different $\mathit{PMI}$ matrices. Using various semantic and syntactic tasks as probes into models which use either negative or positive $\mathit{PMI}$ (or both), we find that most of the encoded semantics and syntax come from positive $\mathit{PMI}$, in contrast to $\mathit{\texttt{-} PMI}$ which contributes almost exclusively syntactic information. Our findings deepen our understanding of distributional semantics, while also introducing novel $PMI$ variants and grounding the popular $PPMI$ measure.
Fully Automated Image De-fencing using Conditional Generative Adversarial Networks

Image de-fencing is one of the important aspects of recreational photography in which the objective is to remove the fence texture present in an image and generate an aesthetically pleasing version of the same image without the fence texture. In this paper, we aim to develop an automated and effective technique for fence removal and image reconstruction using conditional Generative Adversarial Networks (cGANs). These networks have been successfully applied in several domains of Computer Vision focusing on image generation and rendering. Our initial approach is based on a two-stage architecture involving two cGANs that generate the fence mask and the inpainted image, respectively. Training of these networks is carried out independently and, during evaluation, the input image is passed through the two generators in succession to obtain the de-fenced image. The results obtained from this approach are satisfactory, but the response time is long since the image has to pass through two sets of convolution layers. To reduce the response time, we propose a second approach involving only a single cGAN architecture that is trained using the ground-truth of fenced de-fenced image pairs along with the edge map of the fenced image produced by the Canny Filter. Incorporation of the edge map helps the network to precisely detect the edges present in the input image, and also imparts it an ability to carry out high quality de-fencing in an efficient manner, even in the presence of a fewer number of layers as compared to the two-stage network. Qualitative and quantitative experimental results reported in the manuscript reveal that the de-fenced images generated by the single-stage de-fencing network have similar visual quality to those produced by the two-stage network. Comparative performance analysis also emphasizes the effectiveness of our approach over state-of-the-art image de-fencing techniques.
The beamed jet and quasar core of the distant blazar 4C 71.07

The object 4C 71.07 is a high-redshift blazar whose spectral energy distribution shows a prominent big blue bump and a strong Compton dominance. We present the results of a 2-yr multiwavelength campaign led by the Whole Earth Blazar Telescope (WEBT) to study both the quasar core and the beamed jet of this source. The WEBT data are complemented by ultraviolet and X-ray data from Swift, and by γ-ray data by Fermi. The big blue bump is modelled by using optical and near-infrared mean spectra obtained during the campaign, together with optical and ultraviolet quasar templates. We give prescriptions to correct the source photometry in the various bands for the thermal contribution, in order to derive the non-thermal jet flux. The role of the intergalactic medium absorption is analysed in both the ultraviolet and X-ray bands. We provide opacity values to deabsorb ultraviolet data, and derive a best-guess value for the hydrogen column density of $N_{\rm H}^{\rm best}=6.3 \times 10^{20} \rm \, cm^{-2}$ through the analysis of X-ray spectra. We estimate the disc and jet bolometric luminosities, accretion rate, and black hole mass. Light curves do not show persistent correlations among flux changes at different frequencies. We study the polarimetric behaviour and find no correlation between polarization degree and flux, even when correcting for the dilution effect of the big blue bump. Similarly, wide rotations of the electric vector polarization angle do not seem to be connected with the source activity.
Average-Case Lower Bounds for Learning Sparse Mixtures, Robust Estimation and Semirandom Adversaries

This paper develops several average-case reduction techniques to show new hardness results for three central high-dimensional statistics problems, implying a statistical-computational gap induced by robustness, a detection-recovery gap and a universality principle for these gaps. A main feature of our approach is to map to these problems via a common intermediate problem that we introduce, which we call Imbalanced Sparse Gaussian Mixtures. We assume the planted clique conjecture for a version of the planted clique problem where the position of the planted clique is mildly constrained, and from this obtain the following computational lower bounds: (1) a $k$-to-$k^2$ statistical-computational gap for robust sparse mean estimation, providing the first average-case evidence for a conjecture of Li (2017) and Balakrishnan et al. (2017); (2) a tight lower bound for semirandom planted dense subgraph, which shows that a semirandom adversary shifts the detection threshold in planted dense subgraph to the conjectured recovery threshold; and (3) a universality principle for $k$-to-$k^2$ gaps in a broad class of sparse mixture problems that includes many natural formulations such as the spiked covariance model. Our main approach is to introduce several average-case techniques to produce structured and Gaussianized versions of an input graph problem, and then to rotate these high-dimensional Gaussians by matrices carefully constructed from hyperplanes in $\mathbb{F}_r^t$. For our universality result, we introduce a new method to perform an algorithmic change of measure tailored to sparse mixtures. We also provide evidence that the mild promise in our variant of planted clique does not change the complexity of the problem.
Automated email Generation for Targeted Attacks using Natural Language

With an increasing number of malicious attacks, the number of people and organizations falling prey to social engineering attacks is proliferating. Despite considerable research in mitigation systems, attackers continually improve their modus operandi by using sophisticated machine learning, natural language processing techniques with an intent to launch successful targeted attacks aimed at deceiving detection mechanisms as well as the victims. We propose a system for advanced email masquerading attacks using Natural Language Generation (NLG) techniques. Using legitimate as well as an influx of varying malicious content, the proposed deep learning system generates \textit{fake} emails with malicious content, customized depending on the attacker's intent. The system leverages Recurrent Neural Networks (RNNs) for automated text generation. We also focus on the performance of the generated emails in defeating statistical detectors, and compare and analyze the emails using a proposed baseline.
Erosion of an exoplanetary atmosphere caused by stellar winds

Aims.We present a formalism for a first-order estimation of the magnetosphere radius of exoplanets orbiting stars in the range from 0.08 to 1.3M⊙. With this radius, we estimate the atmospheric surface that is not protected from stellar winds. We have analyzed this unprotected surface for the most extreme environment for exoplanets: GKM-type and very low-mass stars at the two limits of the habitable zone. The estimated unprotected surface makes it possible to define a likelihood for an exoplanet to retain its atmosphere. This function can be incorporated into the new habitability index SEPHI.Methods. Using different formulations in the literature in addition to stellar and exoplanet physical characteristics, we estimated the stellar magnetic induction, the main characteristics of the stellar wind, and the different star-planet interaction regions (sub- and super-Alfvénic, sub- and supersonic). With this information, we can estimate the radius of the exoplanet magnetopause and thus the exoplanet unprotected surface.Results. We have conducted a study of the auroral aperture angles for Earth-like exoplanets orbiting the habitable zone of its star, and found different behaviors depending on whether the star is in rotational saturated or unsaturated regimes, with angles of aperture of the auroral ring above or below 36°, respectively, and with different slopes for the linear relation between the auroral aperture angle at the inner edge of the habitable zone versus the difference between auroral aperture angles at the two boundaries of the habitable zone. When the planet is tidally locked, the unprotected angle increases dramatically to values higher than 40° with a low likelihood of keeping its atmosphere. When the impact of stellar wind is produced in the sub-Alfvénic regime, the likelihood of keeping the atmosphere is almost zero for exoplanets orbiting very close to their star, regardless of whether they are saturated or not.
HumanMeshNet: Polygonal Mesh Recovery of Humans

3D Human Body Reconstruction from a monocular image is an important problem in computer vision with applications in virtual and augmented reality platforms, animation industry, en-commerce domain, etc. While several of the existing works formulate it as a volumetric or parametric learning with complex and indirect reliance on re-projections of the mesh, we would like to focus on implicitly learning the mesh representation. To that end, we propose a novel model, HumanMeshNet, that regresses a template mesh's vertices, as well as receives a regularization by the 3D skeletal locations in a multi-branch, multi-task setup. The image to mesh vertex regression is further regularized by the neighborhood constraint imposed by mesh topology ensuring smooth surface reconstruction. The proposed paradigm can theoretically learn local surface deformations induced by body shape variations and can therefore learn high-resolution meshes going ahead. We show comparable performance with SoA (in terms of surface and joint error) with far lesser computational complexity, modeling cost and therefore real-time reconstructions on three publicly available datasets. We also show the generalizability of the proposed paradigm for a similar task of predicting hand mesh models. Given these initial results, we would like to exploit the mesh topology in an explicit manner going ahead.
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds

Most e-commerce product feeds provide blended results of advertised products and recommended products to consumers. The underlying advertising and recommendation platforms share similar if not exactly the same set of candidate products. Consumers' behaviors on the advertised results constitute part of the recommendation model's training data and therefore can influence the recommended results. We refer to this process as Leverage. Considering this mechanism, we propose a novel perspective that advertisers can strategically bid through the advertising platform to optimize their recommended organic traffic. By analyzing the real-world data, we first explain the principles of Leverage mechanism, i.e., the dynamic models of Leverage. Then we introduce a novel Leverage optimization problem and formulate it with a Markov Decision Process. To deal with the sample complexity challenge in model-free reinforcement learning, we propose a novel Hybrid Training Leverage Bidding (HTLB) algorithm which combines the real-world samples and the emulator-generated samples to boost the learning speed and stability. Our offline experiments as well as the results from the online deployment demonstrate the superior performance of our approach.