Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear Ascending Metrological Algorithm

The ubiquitous presence of shot noise sets a fundamental limit to the measurement precision in classical metrology. Recent advances in quantum devices and novel quantum algorithms utilizing interference effects are opening new routes for overcoming the detrimental noise tyranny. However, further progress is limited by the restricted capability of existing algorithms to account for the decoherence pervading experimental implementations. Here, adopting a systematic approach to the evaluation of effectiveness of metrological procedures, we devise the Linear Ascending Metrological Algorithm (LAMA), which offers a remarkable increase in precision in the demanding situation where a decohering quantum system is used to measure a continuously distributed variable. We introduce our protocol in the context of magnetic field measurements, assuming superconducting transmon devices as sensors operated in a qudit mode. Our findings demonstrate a quantum-metrological procedure capable of mitigating detrimental dephasing and relaxation effects.
Minimization of magnetic forces on stellarator coils

Magnetic confinement devices for nuclear fusion can be large and expensive. Compact stellarators are promising candidates for cost-reduction, but introduce new difficulties: confinement in smaller volumes requires higher magnetic field, which calls for higher coil-currents and ultimately causes higher Laplace forces on the coils—if everything else remains the same. This motivates the inclusion of force reduction in stellarator coil optimization. In the present paper we consider a coil winding surface, we prove that there is a natural and rigorous way to define the Laplace force (despite the magnetic field discontinuity across the current-sheet), we provide examples of cost associated (peak force, surface-integral of the force squared) and discuss easy generalizations to parallel and normal force-components, as these will be subject to different engineering constraints. Such costs can then be easily added to the figure of merit in any multi-objective stellarator coil optimization code. We demonstrate this for a generalization of the REGCOIL code (Landreman 2017 Nucl. Fusion 57 046003), which we rewrote in python, and provide numerical examples for the NCSX (Zarnstorff et al 2001 Plasma Phys. Control. Fusion 43 A237–49) (now QUASAR) design. We present results for various definitions of the cost function, including peak force reductions by up to 40%, and outline future work for further reduction.
Hierarchical deep reinforcement learning controlled three-dimensional navigation of microrobots in blood vessels

Designing intelligent microrobots that can autonomously navigate and perform instructed routines in blood vessels, a complex and crowded environment with obstacles including dense cells, different flow patterns and diverse vascular geometries, can offer enormous possibilities in biomedical applications. Here we report a hierarchical control scheme that enables a microrobot to efficiently navigate and execute customizable routines in blood vessels. The control scheme consists of two highly decoupled components: a high-level controller setting short-ranged dynamic targets to guide the microrobot to follow a preset path and a low-level deep reinforcement learning (DRL) controller responsible for maneuvering microrobots towards these dynamic guiding targets. The proposed DRL controller utilizes three-dimensional (3D) convolutional neural networks and is capable of learning control policy directly from a coarse raw 3D sensory input. In blood vessels with rich configurations of red blood cells and vessel geometry, the control scheme enables efficient navigation and faithful execution of instructed routines. The control scheme is also robust to adversarial perturbations including blood flows. This study provides a proof-of-principle for designing data-driven control systems for autonomous navigation in vascular networks; it illustrates the great potential of artificial intelligence for broad biomedical applications such as target drug delivery, blood clots clear, precision surgery, disease diagnosis, and more.
Information Freshness Analysis of Slotted ALOHA in Gilbert-Elliot Channels

This letter analyzes a class of information freshness metrics for large IoT systems in which terminals employ slotted ALOHA to access a common channel. Considering a Gilbert-Elliot channel model, information freshness is evaluated through a penalty function that follows a power law of the time elapsed since the last received update, in contrast with the linear growth of age of information. By means of a signal flow graph analysis of Markov processes, we provide exact closed form expressions for the average penalty and for the peak penalty violation probability.
Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas

HDR reconstruction is an important task in computer vision with many industrial needs. The traditional approaches merge multiple exposure shots to generate HDRs that correspond to the physical quantity of illuminance of the scene. However, the tedious capturing process makes such multi-shot approaches inconvenient in practice. In contrast, recent single-shot methods predict a visually appealing HDR from a single LDR image through deep learning. But it is not clear whether the previously mentioned physical properties would still hold, without training the network to explicitly model them. In this paper, we introduce the physical illuminance constraints to our single-shot HDR reconstruction framework, with a focus on spherical panoramas. By the proposed physical regularization, our method can generate HDRs which are not only visually appealing but also physically plausible. For evaluation, we collect a large dataset of LDR and HDR images with ground truth illuminance measures. Extensive experiments show that our HDR images not only maintain high visual quality but also top all baseline methods in illuminance prediction accuracy.
A New Paradigm of Threats in Robotics Behaviors

—Robots applications in our daily life increase at an unprecedented pace. As robots will soon operate “out in the wild”, we must identify the safety and security vulnerabilities they will face. Robotics researchers and manufacturers focus their attention on new, cheaper, and more reliable applications. Still, they often disregard the operability in adversarial environments where a trusted or untrusted user can jeopardize or even alter the robot’s task. In this paper, we identify a new paradigm of security threats in the next generation of robots. These threats fall beyond the known hardware or network-based ones, and we must ﬁnd new solutions to address them. These new threats include malicious use of the robot’s privileged access, tampering with the robot sensors system, and tricking the robot’s deliberation into harmful behaviors. We provide a taxonomy of attacks that exploit these vulnerabilities with realistic examples, and we outline effective countermeasures to prevent better, detect, and mitigate them.
The Current Chinese Global Supply Chain Monopoly and the Covid-19 Pandemic

Because of the ongoing Covid-19 crisis, supply chain management performance seems to be struggling. The purpose of this paper is to examine a variety of critical factors related to the application of contingency theory to determine its feasibility in preventing future supply chain bottlenecks. The study reviewed current online news reports, previous research on contingency theory, as well as strategic and structural contingency theories. This paper also systematically reviewed several global supply chain management and strategic decision-making studies in an effort to promote a new strategy. The findings indicated that the need for mass production of products within the United States, as well as within trading partners, is necessary to prevent additional Covid-19 related supply chain gaps. The paper noted that in many instances, the United States has become dependent on foreign products, where the prevention of future supply chain gaps requires the United States restore its manufacturing prowess.
Exploiting the Unique Expression for Improved Sentiment Analysis in Software Engineering Text

Sentiment analysis on software engineering (SE) texts has been widely used in the SE research, such as evaluating app reviews or analyzing developers’ sentiments in commit messages. To better support the use of automated sentiment analysis for SE tasks, researchers built an SE-domain-specified sentiment dictionary to further improve the accuracy of the results. Unfortunately, recent work reported that current mainstream tools for sentiment analysis still cannot provide reliable results when analyzing the sentiments in SE texts. We suggest that the reason for this situation is because the way of expressing sentiments in SE texts is largely different from the way in social network or movie comments. In this paper, we propose to improve sentiment analysis in SE texts by using sentence structures, a different perspective from building a domain dictionary. Specifically, we use sentence structures to first identify whether the author is expressing her sentiment in a given clause of an SE text, and to further adjust the calculation of sentiments which are confirmed in the clause. An empirical evaluation based on four different datasets shows that our approach can outperform two dictionary-based baseline approaches, and is more generalizable compared to a learning-based baseline approach.
Hydrodynamic torque on a steadily rotating slender cylinder

Using fully-resolved simulations, we investigate the torque experienced by a ﬁnite-length circular cylinder rotating steadily perpendicularly to its symmetry axis. The aspect ratio χ , i.e. the ratio of the length of the cylinder to its diameter, is varied from 1 to 15. In the creeping-ﬂow regime, we employ the slender-body theory to derive the expression of the torque up to order 4 with respect to the small parameter 1 / ln(2 χ ) . Numerical results agree well with the corresponding predictions for χ & 3 . We introduce an ad hoc modiﬁcation in the theoretical prediction to ﬁt the numerical results obtained with shorter cylinders, and a second modiﬁcation to account for the increase of the torque resulting from ﬁnite inertial eﬀects. In strongly inertial regimes, a prominent wake pattern made of two pairs of counter-rotating vortices takes place. Nevertheless the ﬂow remains stationary and exhibits two distinct symmetries, one of which implies that the contributions to the torque arising from the two cylinder ends are identical. We build separate empirical formulas for the contributions of pressure and viscous stress to the torque provided by the lateral surface and the cylinder ends. We show that, in each contribution, the dominant scaling law may be inferred from simple physical arguments. This approach eventually results in an empirical formula for the rotation-induced torque valid throughout the range of inertial regimes and aspect ratios considered in the simulations.
Human Factors in Security Research: Lessons Learned from 2008-2018

Instead of only considering technology, computer security research now strives to also take into account the human factor by studying regular users and, to a lesser extent, experts like operators and developers of systems. We focus our analysis on the research on the crucial population of experts, whose human errors can impact many systems at once, and compare it to research on regular users. To understand how far we advanced in the area of human factors, how the field can further mature, and to provide a point of reference for researchers new to this field, we analyzed the past decade of human factors research in security and privacy, identifying 557 relevant publications. Of these, we found 48 publications focused on expert users and analyzed all in depth. For additional insights, we compare them to a stratified sample of 48 end-user studies. In this paper we investigate: (i) The perspective on human factors, and how we can learn from safety science (ii) How and who are the participants recruited, and how this -- as we find -- creates a western-centric perspective (iii) Research objectives, and how to align these with the chosen research methods (iv) How theories can be used to increase rigor in the communities scientific work, including limitations to the use of Grounded Theory, which is often incompletely applied (v) How researchers handle ethical implications, and what we can do to account for them more consistently Although our literature review has limitations, new insights were revealed and avenues for further research identified.
Topic Modeling Genre: An Exploration of French Classical and Enlightenment Drama

The concept of literary genre is a highly complex one: not only are different genres frequently defined on several, but not necessarily the same levels of description, but consideration of genres as cognitive, social, or scholarly constructs with a rich history further complicate the matter. This contribution focuses on thematic aspects of genre with a quantitative approach, namely Topic Modeling. Topic Modeling has proven to be useful to discover thematic patterns and trends in large collections of texts, with a view to class or browse them on the basis of their dominant themes. It has rarely if ever, however, been applied to collections of dramatic texts. In this contribution, Topic Modeling is used to analyze a collection of French Drama of the Classical Age and the Enlightenment. The general aim of this contribution is to discover what semantic types of topics are found in this collection, whether different dramatic subgenres have distinctive dominant topics and plot-related topic patterns, and inversely, to what extent clustering methods based on topic scores per play produce groupings of texts which agree with more conventional genre distinctions. This contribution shows that interesting topic patterns can be detected which provide new insights into the thematic, subgenre-related structure of French drama as well as into the history of French drama of the Classical Age and the Enlightenment.
A Distributed Algorithm for Multi-scale Multi-stage Stochastic Programs with Application to Electricity Capacity Expansion

Multi-stage stochastic programs (MSPs) represent a framework for sequential decision making under uncertainties, where a sequence of uncertain data (ξ1, . . . , ξT ) is revealed gradually over stages. The decision variables xt, corresponding to each stage for t = 1, . , T , should be made adaptive to this process. We assume that the sequence (ξ1, . . . , ξT ) evolves according to a known stochastic process, and can be regarded as a sequence of random variables with a specified probability distribution. In each stage t, we use ξt to denote the random data vector and ξt to denote a specific realization; similarly, we use ξ[t] := (ξ1, . . . , ξt) to denote the history of the random sequence up to stage t and ξ[t] := (ξ1, . . . , ξt) to denote a specific realization. The decision dynamics is as follows: in each stage t, a realization ξt ∈ Rt is observed and followed by a decision variable xt, whose value, depending on ξt and xt−1 from previous stage, is determined to minimize the objective function value of the current stage and the expected value of future stages. A T -stage stochastic program problem can then be expressed into the following nested formulation:
Experimental Evidence for the Synthesis of Carbon Nanotubes Using Metal and Metal-Free Catalyst

Synthesis of carbon nanotubes use metals like iron, nickel, cobalt, noble metals like gold and silver, various ceramics and semiconductors and some metal/ metal oxide systems. The growth of carbon nanotubes can also be without any catalytic particle. Here the different growth systems of CNTs are briefly explained. Our work investigate, compare and report the experimental evidences for the formation mechanism of carbon nanotubes using two catalysts nickel and cobalt supported on MgO by CO disproportionation method and metallic catalyst free carbon nanotubes by chemical deposition method. There remain so many numbers of possible synthesis routes other than studied here and the fact that there is no single universal growth model. Thus the data collected from the experiments are being reported here.
From Social to Epidemic Criticality and Back

In this paper, we study the spreading of epidemic in a network of individuals who may either contract a disease through contact with infected nearest neighbors or be vaccinated under the influence of neighbors who are already vaccinated. We show that both interaction between susceptible S and infected individuals I and the imitation of vaccination, a form of sociological interaction between susceptible S and vaccinated V individuals, may lead to a phase transition. If the spreading of epidemic is in the supercritical condition, corresponding to an unlimited growth of infection, the interaction between S and V must reach the supercritical condition to generate control of the spreading of infection, and bring the system to criticality. By adopting a theoretical perspective like that of multilayer complex networks, we study the case where the epidemiological network is under the influence of a sociological debate on whether to be vaccinated. We show that at criticality this debate generates clusters of individuals in favor of vaccination and clusters of individuals opposing it. 3 , 4 We study the influence of this debate on the spread of infection. We show that because of this debate in the epidemic network, a pattern emerges mirroring the structures of the sociological network. Finally, we introduce feedback of the epidemic network on the sociological network, and we prove that because of this feedback the sociological system undergoes a process of self-organization maintaining it at criticality. This system exhibits temporal complexity and critical slowing down. 1 We hope that these results may have an important effect of giving interesting suggestions to behavioral psychologists and information scientists actively involved in the analysis of the social debate on the moral issues connected to sexual activities.
Photoionisation modelling of the X-ray emission line regions within the Seyfert 2 AGN NGC 1068

Aims. We investigate the photoionised X-ray emission line regions (ELRs) within the Seyfert 2 galaxy NGC 1068 to determine if there are any characteristic changes between observations taken 14 years apart. Methods. We compared XMM-Newton observations collected in 2000 and 2014, simultaneously fitting the reflection grating spectrometer and EPIC-pn spectra of each epoch, for the first time, with the photoionisation model, PION, in SPEX. Results. We find that four PION components are required to fit the majority of the emission lines in the spectra of NGC 1068, with log ξ = 1−4, log NH > 26 m−2, and vout = −100 to −600 km s−1 for both epochs. Comparing the ionisation state of the components shows almost no difference between the two epochs, while there is an increase in the total equivalent column density. To estimate the locations of these plasma regions from the central black hole, we compare distance methods, excluding the variability arguments as there is no spectral change between observations. Although the methods are unable to constrain the distances for each plasma component, the locations are consistent with the narrow line region, with the possibility of the higher ionised component being part of the broad line region; we cannot conclude this for certain, but the photoionisation modelling does suggest this is possible. In addition, we find evidence for emission from collisionally ionised plasma, while previous analysis had suggested that collisional plasma emission was unlikely. However, although PION is unable to account for the Fe XVII emission lines at 15 and 17 Å, we do not rule out that photoexcitation is a valid processes to produce these lines as well. Conclusions. NGC 1068 has not changed, both in terms of the observed spectra or from our modelling, within the 14 year time period between observations. This suggests that the ELRs are fairly static relative to the 14 year time frame between observations, or there is no dramatic change in the spectral energy distribution, resulting from a lack of black hole variability.
Outage Probability Expressions for an IRS-Assisted System With and Without Source-Destination Link for the Case of Quantized Phase Shifts in κ – μ Fading

In this work, we study the outage probability (OP) at the destination of an intelligent reflecting surface (IRS) assisted communication system in a $\kappa -\mu $ fading environment. A practical system model that takes into account the presence of phase error due to quantization at the IRS when a) source-destination (SD) link is present and b) SD link is absent is considered. First, an exact expression is derived, and then we derive three simple approximations for the OP using the following approaches: (i) uni-variate dimension reduction, (ii) moment matching and, (iii) Kullback–Leibler (KL) divergence minimization. The resulting expressions for OP are simple to evaluate and quite tight even in the tail region. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We also study the impact of the number of bits available for quantization, the position of IRS with respect to the source and destination and the number of IRS elements on the OP for systems with and without an SD link. We also demonstrate how the method of moment matching and KL divergence minimization can be used to analyze systems experiencing spatial correlation between the IRS elements.
A massively parallel time-domain coupled electrodynamics–micromagnetics solver

We present a high-performance coupled electrodynamics–micromagnetics solver for full physical modeling of signals in microelectronic circuitry. The overall strategy couples a finite-difference time-domain approach for Maxwell’s equations to a magnetization model described by the Landau–Lifshitz–Gilbert equation. The algorithm is implemented in the Exascale Computing Project software framework, AMReX, which provides effective scalability on manycore and GPU-based supercomputing architectures. Furthermore, the code leverages ongoing developments of the Exascale Application Code, WarpX, which is primarily being developed for plasma wakefield accelerator modeling. Our temporal coupling scheme provides second-order accuracy in space and time by combining the integration steps for the magnetic field and magnetization into an iterative sub-step that includes a trapezoidal temporal discretization for the magnetization. The performance of the algorithm is demonstrated by the excellent scaling results on NERSC multicore and GPU systems, with a significant (59×) speedup on the GPU using a node-by-node comparison. We demonstrate the utility of our code by performing simulations of an electromagnetic waveguide and a magnetically tunable filter.