Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Shot near Edge X-ray Fine Structure (NEXAFS) Spectroscopy Using a Laboratory Laser-Plasma Light Source

We present a proof of principle experiment on single-shot near edge soft X-ray fine structure (NEXAFS) spectroscopy with the use of a laboratory laser-plasma light source. The source is based on a plasma created as a result of the interaction of a nanosecond laser pulse with a double stream gas puff target. The laser-plasma source was optimized for efficient soft X-ray (SXR) emission from the krypton/helium target in the wavelength range from 2 nm to 5 nm. This emission was used to acquire simultaneously emission and absorption spectra of soft X-ray light from the source and from the investigated sample using a grazing incidence grating spectrometer. NEXAFS measurements in a transmission mode revealed the spectral features near the carbon K-α absorption edge of thin polyethylene terephthalate (PET) film and L-ascorbic acid in a single-shot. From these features, the composition of the PET sample was successfully obtained. The NEXAFS spectrum of the L-ascorbic acid obtained in a single-shot exposure was also compared to the spectrum obtained a multi-shot exposure and to numerical simulations showing good agreement. In the paper, the detailed information about the source, the spectroscopy system, the absorption spectra measurements and the results of the studies are presented and discussed.
The Top-K QoS-aware Paths Discovery for Source Routing in SDN

Source routing is the routing scheme that arranges the whole path from source to target at the origin node that may suit the requirements from the upper layer applications’ perspective. The centralized control in SDN (Software-Defined Networking) networks enables the awareness of the global topology at the controller. Therefore, augmented source routing schemes can be designed to achieve various purposes. This paper proposes a source routing scheme that conducts the top-K QoS-aware paths discovery in SDN. First, the novel non-invasive QoS over LLDP scheme is designed to collect QoS information based on LLDP in a piggyback fashion. Then, variations of the KSP (K Shortest Paths) algorithm are derived to find the unconstrained/constrained top-K ranked paths with regard to individual/overall path costs, reflecting the Quality of Service. The experiment results show that the proposed scheme can efficiently collect the QoS information and find the top-K paths. Also, the performance of our scheme is applicable in QoS-sensitive application scenarios compared with previous works.
NONLINEAR OBSERVER IN THE ORIGINAL COORDINATES WITH DIFFEOMORPHISM EXTENSION AND JACOBIAN COMPLETION

Typical difficulties we face in implementing observers in applications are: – because of physical constraints or observability singularities, the estimated state should remain in some restricted region of the system state manifold ; – observability may also impose the observer dynamics to live in an observer state manifold with higher dimension than the system state. In such a case, the observer implementation needs a left inverse of an injective immersion. In the approach we propose to round these difficulties, we do not modify the given converging dynamics of the observer, assumed to be globally defined. Instead we select carefully the coordinates to express them. This is done via appropriate diffeomorphisms: – one diffeomorphism sending the restricted region to the whole space. This is done by an image extension. – one function extending the injective immersion into a diffeomorphism and making the observer state coordinates equivalent to the system state coordinates complemented with extra ones introduced to fill the dimension gap. This is done via a Jacobian completion. Such a design makes possible the expression of the observer dynamics in the, maybe complemented, original coordinates. Several examples illustrate our results.
Pooled Resistance Analysis in Patients with Hepatitis C Virus Genotype 1 to 6 Infection Treated with Glecaprevir-Pibrentasvir in Phase 2 and 3 Clinical Trials

Over 2,200 patients infected with hepatitis C virus (HCV) genotypes (GT) 1 to 6, with or without cirrhosis, who were treatment naive or experienced to interferon, ribavirin, and/or sofosbuvir were treated with glecaprevir/pibrentasvir for 8, 12, or 16 weeks in eight registrational phase 2 and 3 clinical studies. High rates of sustained virologic response at 12 weeks postdosing (SVR12) were achieved with a <1% virologic failure (VF) rate. ABSTRACT Over 2,200 patients infected with hepatitis C virus (HCV) genotypes (GT) 1 to 6, with or without cirrhosis, who were treatment naive or experienced to interferon, ribavirin, and/or sofosbuvir were treated with glecaprevir/pibrentasvir for 8, 12, or 16 weeks in eight registrational phase 2 and 3 clinical studies. High rates of sustained virologic response at 12 weeks postdosing (SVR12) were achieved with a <1% virologic failure (VF) rate. The prevalence of baseline polymorphisms (BPs) in NS3 at amino acid position 155 or 168 was low (<3%) in patients infected with GT1, GT2, GT3, GT4, and GT6, while 41.9% of the GT5-infected patients had NS3-D168E; BPs were not detected at position 156 in NS3. The prevalence of NS5A-BPs was high across genotypes, driven by common polymorphisms at amino acid position 30 or 31 in GT2, 58 in GT4, and 28 in GT6. The prevalence of NS5A T/Y93 polymorphisms was 5.5% in GT1, 4.9% in GT3, and 12.5% in GT6. Consistent with the activity of glecaprevir and pibrentasvir against most amino acid polymorphisms in vitro, BPs in NS3 and/or NS5A did not have an impact on treatment outcome for patients infected with GT1 to GT6, with the exception of treatment-experienced GT3-infected patients treated for 12 weeks, for whom a 16-week regimen of glecaprevir/pibrentasvir was required to achieve SVR12 rates of ≥95%. Among the 22 patients experiencing VF, treatment-emergent substitutions were detected in NS3 in 50% of patients and in NS5A in 82% of patients, frequently as a combination of substitutions that conferred resistance to glecaprevir and/or pibrentasvir. The glecaprevir/pibrentasvir regimen, when the recommended durations are used, allows for a pan-genotypic treatment option without the need for baseline resistance testing.
A Writer-Independent Approach for Offline Signature Verification using Deep Convolutional Neural Networks Features

The use of features extracted using a deep convolutional neural network (CNN) combined with a writer-dependent (WD) SVM classifier resulted in significant improvement in performance of handwritten signature verification (HSV) when compared to the previous state-of-the-art methods. In this work it is investigated whether the use of these CNN features provide good results in a writer-independent (WI) HSV context, based on the dichotomy transformation combined with the use of an SVM writer-independent classifier. The experiments performed in the Brazilian and GPDS datasets show that (i) the proposed approach outperformed other WI-HSV methods from the literature, (ii) in the global threshold scenario, the proposed approach was able to outperform the writer-dependent method with CNN features in the Brazilian dataset, (iii) in an user threshold scenario, the results are similar to those obtained by the writer-dependent method with CNN features.
Say Goodbye to Virtualization for a Safer Cloud

When it comes to isolation on the cloud, conventional wisdom holds that virtual machines (VMs) provide greater isolation than containers because of their low-level interface to the host. A lower-level interface reduces the amount of code and complexity needed in the kernel that must be relied upon for isolation. However, it is incorrectly assumed that virtualization mechanisms are required to achieve a low-level interface suitable for isolation. In this paper, we argue that the interface to the host can be lowered for any application by moving kernel components to userspace. We show that using a userspace network stack results in a 33% reduction in kernel code usage, which is 20% better than when resorting to virtualization mechanisms and using a VM.
Training Dialogue Systems With Human Advice

One major drawback of Reinforcement Learning (RL) Spoken Dialogue Systems is that they inherit from the general exploration requirements of RL which makes them hard to deploy from an industry perspective. On the other hand, industrial systems rely on human expertise and hand written rules so as to avoid irrelevant behavior to happen and maintain acceptable experience from the user point of view. In this paper, we attempt to bridge the gap between those two worlds by providing an easy way to incorporate all kinds of human expertise in the training phase of a Reinforcement Learning Dialogue System. Our approach, based on the TAMER framework, enables safe and efficient policy learning by combining the traditional Reinforcement Learning reward signal with an additional reward, encoding expert advice. Experimental results show that our method leads to substantial improvements over more traditional Reinforcement Learning methods.
Optimal Interconnection for Massive MIMO Self-Calibration

In time-division duplexing (TDD) systems, massive multiple-input multiple-output (MIMO) relies on the channel reciprocity to obtain the downlink (DL) channel state information (CSI) with the acquired uplink (UL) CSI at the base station (BS). However, the mismatches in the radio frequency (RF) analog circuits among different antennas at the BS break the end-to-end UL and DL channel reciprocity. To avoid the severe performance degradation with massive MIMO, it is necessary to calibrate all the ℳ antennas at the BS to restore the end-to-end UL/DL channel reciprocity. In this paper, we examine the internal self-calibration scheme where different BS antennas are interconnected via hardware transmission lines. First, we study the resulting calibration performance for an arbitrary interconnection strategy. Next, we obtain closed-form Cramer-Rao lower bound (CRLB) expressions for each interconnection strategy at the BS with only $(M-1)$ transmission lines. Basing on the derived results, we further prove that the star interconnection strategy is optimal for internal self-calibration due to its lowest CRLB. Numerical simulation results corroborate our theoretical analyses and results.
Design and Simulation of MEMS Based Piezoresitive Pressure Sensor for Microfluidic Applications

This paper presents the design and simulation of MEMS based piezoresistive pressure sensor for microfluidic applications. Geometrical parameters are very much considerable when designing microstructure of the pressure sensor. Hence, an analysis is carried out by changing the dimensional parameters of three different diaphragm geometries namely square shaped diaphragm, circular shaped diaphragm and cross sectional beam shaped diaphragm respectively. This is performed in three dimensional mesh plots using Matlab. The Finite Element Method (FEM) analyses are performed in COMSOL and by comparing the results, the square type diaphragm is chosen as best diaphragm geometry for the microfluidic applications. In addition, modal analysis is carried out by using Ansys to identify the natural frequency of the best diaphragm geometry. Also Piezoresistive sensing elements are designed and simulated by performing coupled field analysis using COMSOL Multiphysics. Simulation results reveal that piezo resistive square type pressure sensors have high sensitivity in a wide range of pressures.
Eulerian-on-lagrangian cloth simulation

We resolve the longstanding problem of simulating the contact-mediated interaction of cloth and sharp geometric features by introducing an Eulerian-on-Lagrangian (EOL) approach to cloth simulation. Unlike traditional Lagrangian approaches to cloth simulation, our EOL approach permits bending exactly at and sliding over sharp edges, avoiding parasitic locking caused by over-constraining contact constraints. Wherever the cloth is in contact with sharp features, we insert EOL vertices into the cloth, while the rest of the cloth is simulated in the standard Lagrangian fashion. Our algorithm manifests as new equations of motion for EOL vertices, a contact-conforming remesher, and a set of simple constraint assignment rules, all of which can be incorporated into existing state-of-the-art cloth simulators to enable smooth, inequality-constrained contact between cloth and objects in the world.
Predicting Engagement Breakdown in HRI Using Thin-Slices of Facial Expressions

In many Human-Robot Interaction (HRI) scenarios, robots are expected to actively engage humans in interaction tasks for an extended period. We consider a successful robot to be alert to Engagement Breakdown (EB), a situation in which humans prematurely end the interaction before the robot had the chance to receive a complete feedback. In this paper, we present a method for early EB prediction using Echo State Networks (ESNs), a variant of Recurrent Neural Networks. The method is based on Action Units (AUs) of human facial expressions. We apply the proposed architecture to a real-world dataset and show that the architecture accurately predicts EB behavior using 30 seconds of facial expression features
Interpolation and Extrapolation of Multipath Fingerprints Using Virtual Transmitter Placement

Fingerprinting-based localization approaches generally show the highest localization precision in complex indoor non-line-of-site environments. However, they require the construction of a dense fingerprint map, which can lead to high system deployment and maintenance costs. In this paper, we focus on extending a measured fingerprint map though interpolation and extrapolation. In this way, the accuracy of an indoor localization system (ILS) can be improved without increasing its deployment cost. We focus on channel state information (CSI) based fingerprinting approaches as they have shown superior localization accuracy and robustness. This paper presents a novel algorithm for interpolating and extrapolating CSI and multipath fingerprints by calculating the positions of the so called virtual transmitters (VTs). The performance of the interpolation scheme is validated through simulation. The localization error distribution of an ILS using an interpolated and extrapolated fingerprint map is evaluated and compared to the case when the original map is used. The obtained results demonstrate a 20$\%$ decrease in the average localization error through the use of the proposed fingerprint interpolation scheme. Furthermore, the presented results show that when an extrapolated map is used, the probability that a user is precisely localized is less dependent on his position in the indoor environment.
Mitigating Traffic Analysis Attack in Smartphones with Edge Network Assistance

With the growth of smartphone sales and app usage, fingerprinting and identification of smartphone apps have become a considerable threat to user security and privacy. Traffic analysis is one of the most common methods for identifying apps. Traditional countermeasures towards traffic analysis includes traffic morphing and multipath routing. The basic idea of multipath routing is to increase the difficulty for adversary to eavesdrop all traffic by splitting traffic into several subflows and transmitting them through different routes. Previous works in multipath routing mainly focus on Wireless Sensor Networks (WSNs) or Mobile Ad Hoc Networks (MANETs). In this paper, we propose a multipath routing scheme for smartphones with edge network assistance to mitigate traffic analysis attack. We consider an adversary with limited capability, that is, he can only intercept the traffic of one node following certain attack probability, and try to minimize the traffic an adversary can intercept. We formulate our design as a flow routing optimization problem. Then a heuristic algorithm is proposed to solve the problem. Finally, we present the simulation results for our scheme and justify that our scheme can effectively protect smartphones from traffic analysis attack.
Robust optimization for topological surface reconstruction

Surface reconstruction is one of the central problems in computer graphics. Existing research on this problem has primarily focused on improving the geometric aspects of the reconstruction (e.g., smoothness, features, element quality, etc.), and little attention has been paid to ensure it also has desired topological properties (e.g., connectedness and genus). In this paper, we propose a novel and general optimization method for surface reconstruction under topological constraints. The input to our method is a prescribed genus for the reconstructed surface, a partition of the ambient volume into cells, and a set of possible surface candidates and their associated energy within each cell. Our method computes one candidate per cell so that their union is a connected surface with the prescribed genus that minimizes the total energy. We formulate the task as an integer program, and propose a novel solution that combines convex relaxations within a branch and bound framework. As our method is oblivious of the type of input cells, surface candidates, and energy, it can be applied to a variety of reconstruction scenarios, and we explore two of them in the paper: reconstruction from cross-section slices and iso-surfacing an intensity volume. In the first scenario, our method outperforms an existing topology-aware method particularly for complex inputs and higher genus constraints. In the second scenario, we demonstrate the benefit of topology control over classical topology-oblivious methods such as Marching Cubes.
A Robust Sparse Representation Based Pattern Recognition Approach for Myoelectric Control

Interferences in the form of white Gaussian noise (WGN) are inevitable during long-term electromyogram (EMG) recordings. Even with the aid of advanced signal denoising techniques, such an intermittent interference is hardly detected and attenuated in the practical use of EMG-driven control systems. Hence, a robust pattern recognition scheme that is invariant to noise contamination would potentially aid the realization of an efficient EMG-based pattern recognition (EMG-PR) control system. To this end, an EMG-PR scheme driven by sparse representation-based classification (SRC) algorithm and root mean square (rms) descriptor (RMS-SRC) is proposed in this paper. The accuracy and the robustness of the proposed scheme were investigated using the high-density surface EMG recordings from 12 traumatic brain-injured patients and 5 post-stroke survivors. For benchmark comparison, another three different feature sets and four pattern recognition algorithms were considered. The optimal pattern recognition schemes with respect to each feature-classifier combination were first selected in the absence of WGN contamination. Then, six levels of WGN with a signal-to-noise ratio (SNR) ranging from 5 to 30 dB were added into the EMG recordings, respectively, to mimic the different WGN interferences. Our result showed that the proposed RMS-SRC scheme could achieve a similar accuracy with the benchmark schemes in the presence of limited noise contamination (0–15 dB), and significantly outperformed the other schemes when the SNR of WGN increased (20–30 dB). More notably, the RMS-SRC scheme significantly outperformed the other pattern recognition schemes when the WGN existed in either training set or testing set only. The findings proved the comparative advantage of the proposed RMS-SRC pattern recognition scheme over the other currently used schemes in the myoelectric control. Thus, the proposed scheme would potentially facilitate the development of EMG-driven rehabilitation robots for accurate and dexterous assistive training for patients with neurological disorders.
The Malware Text Collection and Mining Project

We have released a malware collection in TREC style. It contains scripts, html documents and text files extracted from binary files of about 650K malwares. The objective of the project is to index, extract significant features and classify them into malware families. At this aim we will also release a TREC style set of queries for classification tasks. In this abstract, we briefly describe the test collection, the project aims and the problems underlying the use of text mining and information retrieval techniques to malware classification.
Towards Data-Driven On-Demand Transport

On-demand transport has been disrupted by Uber and other providers, which are challenging the traditional approach adopted by taxi services. Instead of using fixed passenger pricing and driver payments, there is now the possibility of adaptation to changes in demand and supply. Properly designed, this new approach can lead to desirable tradeoffs between passenger prices, individual driver profits and provider revenue. However, pricing and allocations—known as mechanisms—are challenging problems falling in the intersection of economics and computer science. In this paper, we develop a general framework to classify mechanisms in on-demand transport. Moreover, we show that data is key to optimizing each mechanism and analyze a dataset provided by a real-world on-demand transport provider. This analysis provides valuable new insights into efficient pricing and allocation in on-demand transport.
Towards dynamic alternating tripod trotting of a pony-sized hexapod robot for disaster rescuing based on multi-modal impedance control

SUMMARY Hexapod robots are well suited for disaster rescuing tasks due to their stability and load capability. However, most current hexapod robots still rely on static gaits that largely limit their locomotion speed. This paper introduces a hierarchical control strategy to realize a dynamic alternating tripod trotting gait for a hexapod robot based on multi-modal impedance control. At the low level, a position-based impedance controller is developed to realize an adjustable compliant behavior for each leg. At the high level, a new gait controller is developed to generate a stable alternating tripod trotting gait, in which a gait state machine, a leg compliance modulation strategy, and a close-looped body attitude stabilizer are imposed. As a result, the alternating tripod trotting of the hexapod robot can be synchronized as the running of a bipedal robot with stable body attitude. Moreover, this control strategy was verified by experiments on a newly designed pony-sized disaster rescuing robot, HexbotIV, which successfully achieved a dynamic trotting gait with ability to resist the disturbances of mildly uneven terrains. Our control strategy as well as the experimental study can be a valuable reference for other hexapod robots and thus paves a way to the practical deployment of disaster rescuing robots.
Reproducing kernel method in Hilbert spaces for solving the linear and nonlinear four-point boundary value problems

ABSTRACT An analysis was performed in this paper to study the problem of magnetohydrodynamic squeezing flow of nanofluid between parallel disks. This paper also proposed a reproducing kernel method for solving fourth-order four-point boundary value problems. In order to eliminate the singularity of the equation, a transform was used. Convergence analysis and error estimation for the present method in -space were also discussed. The exact solution was represented in the form of series in the reproducing kernel Hilbert space which proved higher accuracy in numerical computation. The algorithm derived from this approach can be easily implemented. The ideas and techniques presented in this paper will be useful for solving many other problems.