Miracle-dz/UniFAR-FTUs huggingface dataset:
Flight control law clearance using optimization-based worst-case search

Abstract The flight control law clearance problem can be formulated as a robustness analysis problem, where a set of suitably defined clearance criteria must be checked to lie within certain limits for all admissible variations of aircraft parameters, pilot inputs and all flight conditions. The idea of optimisation based worst case clearance is to use available and efficient optimisation methods to find those parameters/inputs/flight conditions for which the criteria are violated or poorly satisfied. The potentials of this approach are its general usability for both frequency-domain and time-domain analysis, and for both linear and non-linear models and control laws including protections. Moreover, there is no limitation on the number of parametric uncertainties that can be investigated and the method does not itself add conservatism to the clearance problem as many other alternatives do, mainly those based on approximations or simplifications. The most challenging problem with the optimisation based approach is not to find hidden weaknesses if they exist, but to confidently assert that no weakness exists for all parameters/inputs/flight conditions. To assert this a global optimum solution has to be found usually resulting in a huge amount of computational work. For highly nonlinear clearance problems regarding manoeuvrability in the peripheral flight envelope of a civil transportation aircraft the suitability of the optimisation based approach is demonstrated. Different global optimisation algorithms and strategies are investigated with respect to its reliability in finding worst cases, computational efficiency and its potential for distributed computation in order to reduce time to clearance.
Modelling of local length‐scale dynamics and isotropizing deformations

The correlation length‐scale which characterizes the shape of the correlation function is often used to parametrize correlation models. This article describes how the length‐scale dynamics can be employed to estimate a spatial deformation (coordinate transformation). Of particular interest is the isotropizing deformation, which transforms anisotropic correlation functions into quasi‐isotropic ones. The evolution of the length‐scale field under a simple advection dynamics is described in terms of the local metric tensor. This description leads to a quadratic constraint satisfied by the isotropizing deformation and from which a system of Poisson‐like partial differential equations is deduced. The isotropizing deformation is obtained as the solution of a coupled system of Poisson‐like partial differential equations. This system is then solved with a pseudo‐diffusion scheme, where the isotropizing deformation is the steady‐state solution. The isotropization process is illustrated within a simulated 2D setting. The method is shown to provide an accurate estimation of the original deformation used to build the anisotropic correlations in this idealized framework.
Asymmetric Dependence Measurement and Testing

Measuring the (causal) direction and strength of dependence between two variables (events), Xi and Xj , is fundamental for all science. Our survey of decades-long literature on statistical dependence reveals that most assume symmetry in the sense that the strength of dependence of Xi on Xj exactly equals the strength of dependence of Xj on Xi. However, we show that such symmetry is often untrue in many real-world examples, being neither necessary nor sufficient. Vinod's (2014) asymmetric matrix R* in [-1, 1] of generalized correlation coefficients provides intuitively appealing, readily interpretable, and superior measures of dependence. This paper proposes statistical inference for R* using Taraldsen's (2021) exact sampling distribution of correlation coefficients and the bootstrap. When the direction is known, proposed asymmetric (one-tail) tests have greater power.
Monitoring Arithmetic Temporal Properties on Finite Traces

We study monitoring of linear-time arithmetic properties against finite traces generated by an unknown dynamic system. The monitoring state is determined by considering at once the trace prefix seen so far, and all its possible finite-length, future continuations. This makes monitoring at least as hard as satisfiability and validity. Traces consist of finite sequences of assignments of a fixed set of variables to numerical values. Properties are specified in a logic we call ALTLf, combining LTLf (LTL on finite traces) with linear arithmetic constraints that may carry lookahead, i.e., variables may be compared over multiple instants of the trace. While the monitoring problem for this setting is undecidable in general, we show decidability for (a) properties without lookahead, and (b) properties with lookahead that satisfy the abstract, semantic condition of finite summary, studied before in the context of model checking. We then single out concrete, practically relevant classes of constraints guaranteeing finite summary. Feasibility is witnessed by a prototype implementation.
DiProber: Using Dual Probing to Estimate Tor Relay Capacities in Underloaded Networks

—Tor is the most popular anonymous communication network. It has millions of daily users seeking privacy while browsing the internet. It has thousands of relays to route and anonymize the source and destinations of the users packets. To create a path, Tor authorities generate a probability distribution over relays based on the estimates of the capacities of the relays. An incoming user will then sample this probability distribution and choose three relays for their paths. The estimates are based on the bandwidths of observation probes the authority assigns to each relay in the network. Thus, in order to achieve better load balancing between users, accurate estimates are necessary. Unfortunately, the currently implemented estimation algorithm generate inaccurate estimates causing the network to be under utilized and its capacities unfairly distributed between the users paths. We propose DiProber , a new relay capacity estimation algorithm. The algorithm proposes a new measurement scheme in Tor consisting of two probes per relay and uses maximum likelihood to estimate their capacities. We show that the new technique works better in the case of under-utilized networks where users tend to have very low demand on the Tor network.
Scope ambiguities and conditionals

Scope ambiguities in natural language have been much discussed by linguists and philosophers. There has been some psychological work on the scope ambiguities of negation, but we present the first experiments on modal scope ambiguities in conditionals, with special attention paid to the scope ambiguities of the probability operator. We also discuss the implications of our results for the assessment of the conditional probability hypothesis, according to which the probability of a conditional is the probability of its consequent given its antecedent.
Electrical characteristics of Zn in heavily doped InP grown by the liquid‐encapsulated Czochralski technique

The electrical activity of Zn in heavily doped InP crystals grown by the liquid‐encapsulated Czochralski technique has been studied through annealing experiments. When the wafers were annealed at 650 °C, the saturated hole concentration increased as the cooling rate after the heat treatment increased. Annealing at 400 °C led to a reduction in the saturated hole concentration, which was not dependent on the cooling rate after the heat treatment. Some defect reactions at 400 °C would account for this phenomenon. The experimental results of the saturation of the hole concentration may be explained in terms of a defect state whose nature depends on the Fermi level position, as has been suggested by Walukiewicz [Appl. Phys. Lett. 54, 2094 (1989)].
Mathematical modeling in neuroscience : collective behavior of neuronal networks & the role of local homeoproteins diffusion in morphogenesis

This work is devoted to the study of mathematical questions arising from the modeling of biological systems combining analytic and probabilistic tools. In the first part, we are interested in the derivation of the mean-field equations related to some neuronal networks, and in the study of the convergence to the equilibria of the solutions to the limit equations. In Chapter 2, we use the coupling method to prove the chaos propagation for a neuronal network with delays and random architecture. In Chapter 3, we consider a kinetic FitzHugh-Nagumo equation. We analyze the existence of solutions and prove the nonlinear exponential convergence in the weak connectivity regime. In the second part, we study the role of homeoproteins (HPs) on the robustness of boundaries of functional areas. In Chapter 4, we propose a general model for neuronal development. We prove that in the absence of diffusion, the HPs are expressed on irregular areas. But in presence of diffusion, even arbitrarily small, well defined boundaries emerge. In Chapter 5, we consider the general model in the one dimensional case and prove the existence of monotonic stationary solutions defining a unique intersection point for any arbitrarily small diffusion coefficient. Finally, in the third part, we study a subcritical Keller-Segel equation. We show the chaos propagation without any restriction on the force kernel. Eventually, we demonstrate that the propagation of chaos holds in the entropic sense.
From Fairall 9 to High-z Objects: Pattern of Change in Ionization Structure

A study of 79 IUE spectra of the Seyfert galaxy Fairall 9 shows a considerable change in the continuum shape which can be characterized by several sections. The flux at 3075 A changes in approximate proportion to the optical and X-ray flux, suggesting that they may be a part of the power law. The apparent fourfold change in the continuum slope between 1826 and 3075 A indicates a considerably different variations for the UV bump and the power-law continuum. The most variable part between 1338 and 1826 A keeps a constant slope. Significant differential variations are found in weaker emission lines, including N V λ1240, Si IV λ1400, He II λ1640, and C III] λ1909. Low-ionization lines appear to follow the UV bump, and high-ionization lines follow the power-law continuum. The results, in addition to the previous report on strong lines, suggest that the variation amplitude of lines is inversely correlated to the effective excitation level
Automatically Transforming Arrays to Columnar Storage at Run Time✱

Picking the right data structure for the right job is one of the key challenges for every developer. However, especially in the realm of object-oriented programming, the memory layout of data structures is often still suboptimal for certain data access patterns, due to objects being scattered across the heap. Therefore, this work presents an approach for the automated transformation of arrays of objects into a contiguous format (called columnar arrays). At run time, we identify suitable arrays, perform the transformation and use a dynamic compiler to gain performance improvements. In the evaluation, we show that our approach can improve the performance of certain queries over large, uniform arrays.
FIAT: frictionless authentication of IoT traffic

Home IoT (Internet of Things) deployments are vulnerable to local adversaries, compromising a LAN, and remote adversaries, compromising either the accounts associated with IoT devices or third-party devices like mobile phones used to control the IoT. There is, however, a fundamental difference between an attacker and a legitimate IoT user: the physical interaction with the device (e.g., via a mobile app) used to operate the IoT. Such physical interactions can be used to build frictionless authentications. However, their integration with IoT requires each vendor to independently adopt them, which is both complex and expensive. We instead design and build FIAT, the first third-party mechanism to automatically authorize IoT traffic by learning recurring traffic and validating human actions behind unpredictable traffic. FIAT does not require modification of the IoT devices or apps, as it operates passively on network traffic. Our evaluation shows that FIAT achieves high accuracy with minimal impact on the user experience.
Globally Injective Flattening via a Reduced Harmonic Subspace

We present a highly efficient-and-robust method for free-boundary flattening of disk-like triangle meshes in a globally injective manner. We show that by restricting the solution to a low-dimensional subspace of harmonic maps, we can dramatically accelerate the process while obtaining a low-distortion result. The algorithm consists of two main steps. A linear subspace construction, and a nonlinear nonconvex optimization for finding a low-distortion globally injective map within that subspace. The complexity of the first step dominates the algorithm's runtime and is merely that of solving a linear system. We combine recent results for computing locally-and-globally injective maps with that of harmonic maps into a conceptually simple algorithm that guarantees global injectivity. We demonstrate the great efficiency of our method over a dataset of 100 large scale models with more than 2M triangles each. Our algorithm is 10 times faster on average compared to the state-of-the-art Efficient Bijective Parameterizations (EBP) method [Su et al. 2020], on these high-resolution meshes, and more than 20 times faster on challenging examples (Figures 1,11). The speedup over [Jiang et al. 2017; Smith and Schaefer 2015] is even more dramatic.
A research on illegitimate tasks, job autonomy, organizational justice and job anxiety——Inspired by standardization management

Objective: Inspired by the autonomy and legitimacy perspective of standardization management, this research tries explore the impact of illegitimate tasks on job anxiety, the mediating role of organizational justice, and the moderating role of job autonomy. Methods: Using SPSS 26.0 and Process plug-in to analyze 293 questionnaires and test the hypothesis. Results: The research can summarize three findings. First, illegitimate tasks are positively related to job anxiety and negatively related to organizational justice. Second, organizational justice plays a mediating role in the relationship between illegitimate tasks and job anxiety. Third, low job autonomy will moderate the effect of illegitimate tasks on organizational justice. Conclusion: Illegitimate tasks will affect job anxiety. Job autonomy moderates the mediating role of organizational justice. Therefore, it is still necessary to strengthen an atmosphere of autonomy and justice in the work environment.
Pattern-Based Cloth Registration and Sparse-View Animation

We propose a novel multi-view camera pipeline for the reconstruction and registration of dynamic clothing. Our proposed method relies on a specifically designed pattern that allows for precise video tracking in each camera view. We triangulate the tracked points and register the cloth surface in a fine-grained geometric resolution and low localization error. Compared to state-of-the-art methods, our registration exhibits stable correspondence, tracking the same points on the deforming cloth surface along the temporal sequence. As an application, we demonstrate how the use of our registration pipeline greatly improves state-of-the-art pose-based drivable cloth models. Furthermore, we propose a novel model, Garment Avatar, for driving cloth from a dense tracking signal which is obtained from two opposing camera views. The method produces realistic reconstructions which are faithful to the actual geometry of the deforming cloth. In this setting, the user wears a garment with our custom pattern which enables our driving model to reconstruct the geometry. Our code and data are available at https://github.com/HalimiOshri/Pattern-Based-Cloth-Registration-and-Sparse-View-Animation. The released data includes our pattern and registered mesh sequences containing four different subjects and 15k frames in total.
Efficient Differentiation of Pixel Reconstruction Filters for Path-Space Differentiable Rendering

Pixel reconstruction filters play an important role in physics-based rendering and have been thoroughly studied. In physics-based differentiable rendering, however, the proper treatment of pixel filters remains largely under-explored. We present a new technique to efficiently differentiate pixel reconstruction filters based on the path-space formulation. Specifically, we formulate the pixel boundary integral that models discontinuities in pixel filters and introduce new antithetic sampling methods that support differentiable path sampling methods, such as adjoint particle tracing and bidirectional path tracing. We demonstrate both the need and efficacy of antithetic sampling when estimating this integral, and we evaluate its effectiveness across several differentiable- and inverse-rendering settings.
Enabling emerging edge applications through a 5G control plane intervention

5G networks are considered potential enablers for many emerging edge applications, such as those related to autonomous vehicles, virtual and augmented reality, and online gaming. However, recent works have shown the cellular control plane is a potential bottleneck in enabling such applications --- control plane operations are slow, frequent, and can directly impact the delay experienced by end-user applications. Moreover, failures in the cellular control plane can significantly degrade application performance. In this paper, we consider the problem of enabling latency-sensitive and safety-critical edge applications on 5G networks. We identify fundamental control plane design challenges and posit enabling these applications requires re-thinking the cellular control plane. We propose a new edge-based cellular control plane, CellClone, which provides fast and fault-tolerant control plane processing. CellClone employs multiple active control plane clones at the network edge to mask control plane faults and speedup control processing. Central to its design is a custom quorum-based consistency protocol that provides state consistency with low latency. Testbed evaluations using real cellular traces show a median improvement of more than 3.8× in speeding up control plane operations with outright node failures and stragglers. These improvements translate into better application performance; with CellClone, autonomous cars and VR applications reduce missed application deadlines by more than 90%.
Progressive Simulation for Cloth Quasistatics

The trade-off between speed and fidelity in cloth simulation is a fundamental computational problem in computer graphics and computational design. Coarse cloth models provide the interactive performance required by designers, but they can not be simulated at higher resolutions ("up-resed") without introducing simulation artifacts and/or unpredicted outcomes, such as different folds, wrinkles and drapes. But how can a coarse simulation predict the result of an unconstrained, high-resolution simulation that has not yet been run? We propose Progressive Cloth Simulation (PCS), a new forward simulation method for efficient preview of cloth quasistatics on exceedingly coarse triangle meshes with consistent and progressive improvement over a hierarchy of increasingly higher-resolution models. PCS provides an efficient coarse previewing simulation method that predicts the coarse-scale folds and wrinkles that will be generated by a corresponding converged, high-fidelity C-IPC simulation of the cloth drape's equilibrium. For each preview PCS can generate an increasing-resolution sequence of consistent models that progress towards this converged solution. This successive improvement can then be interrupted at any point, for example, whenever design parameters are updated. PCS then ensures feasibility at all resolutions, so that predicted solutions remain intersection-free and capture the complex folding and buckling behaviors of frictionally contacting cloth.
Perception of Emotional Relationships by Observing Body Expressions between Multiple Robots

Emotional expressions are essential in augmenting a robot’s expression. Many robots with limited facial expression freedom, such as the Nao robot, can effectively express emotions using body movement. Previous studies have used a single robot and evaluated its expression. Multiple robots expressing emotion in their interactions may have a greater impact than robots expressing emotion only when interacting with humans. The relationships between these robots should allow them to develop more diverse modes of expression. However, it is unclear how people perceive relationships by observing robots’ emotional expressions. In this study, we applied every combination of four characteristic body emotion expressions (Sadness, Fear, Pride, and Happiness) based on Russell’s circumplex model to robots. Furthermore, we investigated how the relationships were evaluated in an online video-based experiment. The results show that the relationships between the two robots are influenced by each robot’s body emotional movement and can be interpreted using the valence-arousal model.
“I Have No Idea What a Social Bot Is”: On Users’ Perceptions of Social Bots and Ability to Detect Them

Social bots—software agents controlling accounts on online social networks (OSNs)—have been employed for various malicious purposes, including spreading disinformation and scams. Understanding user perceptions of bots and ability to distinguish them from other accounts can inform mitigations. To this end, we conducted an online study with 297 users of seven OSNs to explore their mental models of bots and evaluate their ability to classify bots and non-bots correctly. We found that while some participants were aware of bots’ primary characteristics, others provided abstract descriptions or confused bots with other phenomena. Participants also struggled to classify accounts correctly (e.g., misclassifying > 50% of accounts) and were more likely to misclassify bots than non-bots. Furthermore, we observed that perceptions of bots had a significant effect on participants’ classification accuracy. For example, participants with abstract perceptions of bots were more likely to misclassify. Informed by our findings, we discuss directions for developing user-centered interventions against bots.
Batching Adaptive Variance Reduction

Adaptive Monte Carlo variance reduction is an effective framework for running a Monte Carlo simulation along with a parameter search algorithm for variance reduction, whereas an initialization step is required for preparing problem parameters in some instances. In spite of the effectiveness of adaptive variance reduction in various fields of application, the length of the preliminary phase has often been left unspecified for the user to determine on a case-by-case basis, much like in typical sequential frameworks. This uncertain element may possibly be even fatal in realistic finite-budget situations, since the pilot run may take most of the budget, or possibly use up all of it. To unnecessitate such an ad hoc initialization step, we develop a batching procedure in adaptive variance reduction, and provide an implementable formula of the learning rate in the parameter search which minimizes an upper bound of the theoretical variance of the empirical batch mean. We analyze decay rates of the minimized upper bound towards the minimal estimator variance with respect to the predetermined computing budget, and provide convergence results as the computing budget increases progressively when the batch size is fixed. Numerical examples are provided to support theoretical findings and illustrate the effectiveness of the proposed batching procedure.