Miracle-dz/UniFAR-FTUs huggingface dataset:
Manifold-adaptive dimension estimation revisited

Data dimensionality informs us about data complexity and sets limit on the structure of successful signal processing pipelines. In this work we revisit and improve the manifold adaptive Farahmand-Szepesvári-Audibert (FSA) dimension estimator, making it one of the best nearest neighbor-based dimension estimators available. We compute the probability density function of local FSA estimates, if the local manifold density is uniform. Based on the probability density function, we propose to use the median of local estimates as a basic global measure of intrinsic dimensionality, and we demonstrate the advantages of this asymptotically unbiased estimator over the previously proposed statistics: the mode and the mean. Additionally, from the probability density function, we derive the maximum likelihood formula for global intrinsic dimensionality, if i.i. d. holds. We tackle edge and finite-sample effects with an exponential correction formula, calibrated on hypercube datasets. We compare the performance of the corrected median-FSA estimator with kNN estimators: maximum likelihood (Levina-Bickel), the 2NN and two implementations of DANCo (R and MATLAB). We show that corrected median-FSA estimator beats the maximum likelihood estimator and it is on equal footing with DANCo for standard synthetic benchmarks according to mean percentage error and error rate metrics. With the median-FSA algorithm, we reveal diverse changes in the neural dynamics while resting state and during epileptic seizures. We identify brain areas with lower-dimensional dynamics that are possible causal sources and candidates for being seizure onset zones.
Why are Developers Struggling to Put GDPR into Practice when Developing Privacy-Preserving Software Systems?

The use of software applications is inevitable as they provide different services to users. The software applications collect, store users' data, and sometimes share with the third party, even without the user consent. One can argue that software developers do not implement privacy into the software applications they develop or take GDPR (General Data Protection Law) law into account. Failing to do this, may lead to software applications that open up privacy breaches (e.g. data breach). The GDPR law provides a set of guidelines for developers and organizations on how to protect user data when they are interacting with software applications. Previous research has attempted to investigate what hinders developers from embedding privacy into software systems. However, there has been no detailed investigation on why they cannot develop privacy-preserving systems taking GDPR into consideration, which is imperative to develop software applications that preserve privacy. Therefore, this paper investigates the issues that hinder software developers from implementing software applications taking GDPR law on-board. Our study findings revealed that developers are not familiar with GDPR principles. Even some of them are, they lack knowledge of the GDPR principles and their techniques to use when developing privacy-preserving software systems
ASSESSMENT OF CIRCULATING TOTAL AND FREE IODOTHYRONINES’ PATTERNS IN ADULT OVINE AND CAPRINE SPECIMENS: INFLUENCES OF ENDEMIC GOITRE AREA

North-eastern Sicily is an area with iodine deficiency disorders occurring in both humans and animals. The aim of this study was to test the influences of an endemic goitre area on iodothyronine ranges and their pattern in adult ovine and caprine specimens stabled in different locations of Sicily, taking into account the different sexes. A total of 48 Comisana sheep and 51 Maltese goats was studied. The sheep included 10 females and 6 males stabled in a non-endemic goitre area (farm A: control group), and 16 females and 16 males stabled in an endemic goitre area (farm B: observational group). The goats included 6 females and 13 males stabled in a non-endemic goitre area (farm A: control group), and 16 females and 16 males stabled in an endemic goitre area (Farm B: observational group). The results showed lower T3 and higher fT4 (P < 0,0001), ki so bili nastanjeni na kmetiji B v primerjavi s kmetijo A. V primerjavi s kmetijo A so pri kozah, ki so bile nastanjene na kmetiji B, ugotovili visje ravni prostega T3 (p < 0,0001), pri samcih na kmetiji B nižje ravni T4 (p < 0,0001), pri samicah pa visjo raven prostega T4 (p < 0,0001). Ugotovljene so bile statisticno znacilne razlike med skupinami tako glede na spol kot glede na endemicnost podrocja na ravni vezanih in prostih jodotironinov pri ovcah in kozah. Pridobljeni podatki so pokazali, da obmocja z endemicno golsavostjo pri ljudeh nimajo nujno enakega ucinka pri ovcah in kozah, za katere se zdi, da so sposobne ustvariti prilagoditveno strategijo, zaradi katere se ne pokažejo klinicni znaki bolezni scitnice. Kljucne besede: scitnicni hormoni; ovce; koze; endemicna obmocja z golsavostjo
Sustainable behaviour at work and in private life: Preconditions, measures and tools for promoting a spillover

Comprehensive research is carried out on consumers with respect to their sustainable behaviour in private life. Other research focuses on employees and their sustainable behaviour in business life. This divide also characterises companies activities for promoting sustainable consumption and lifestyles. But how can both sides being brought together? Is it possible for companies to initiate a spillover in the sustainable routines of their employees from work to private life? In the transdisciplinary project Sustainable behaviour at work and in private life, empirical research was conducted on the interests and opportunities of Austrian companies for promoting a spillover in sustainable behaviour. The project is innovative in that it builds bridges in two ways. First of all, it links social and ecological fields of action within companies (health promotion including sustainable nutrition, physical exercises and work-life balance, on one hand, and sustainable mobility, on the other). Secondly, the project bridges the perspective of managerial staff and the everyday life perspective of employees. The paper presents first empirical results of this study. Insights concern the behavioural spillover, the success factors for companies sustainability activities and the perspectives of management and employees. Against this backdrop it is recommended to enrich workplace health approaches with environmental factors (and not conversely). Finally, a crucial insight into social learning in companies is presented, which could help to promote sustainable behaviour at work and in private life.
Antiphase Versus In-Phase Synchronization of Coupled Pendulum Clocks and Metronomes

In 1665, Huygens observed that two pendulum clocks hanging from the same beam became synchronized in antiphase after hundreds of swings. On the other hand, modern experiments with metronomes placed on a movable platform show that they tend to synchronize in phase, not antiphase. Here, using a simple model of coupled clocks and metronomes, we calculate the regimes where in-phase and antiphase synchronization are stable. Unusual features of our approach include its treatment of the escapement mechanism, a small-angle approximation up to cubic order, and a three-time scale asymptotic analysis.
Numerical investigation of transitional supersonic axisymmetric wakes

Transitional supersonic axisymmetric wakes are investigated by conducting various numerical experiments. The main objective is to identify hydrodynamic instability mechanisms in the flow at $M\,{=}\,2.46$ for several Reynolds numbers, and to relate these to coherent structures that are found from various visualization techniques. The premise for this approach is the assumption that flow instabilities lead to the formation of coherent structures. Three high-order accurate compressible codes were developed in cylindrical coordinates for this work: a spatial Navier–Stokes (N-S) code to conduct direct numerical simulations (DNS), a linearized N-S code for linear stability investigations using axisymmetric basic states, and a temporal N-S code for performing local stability analyses. The ability of numerical simulations to exclude physical effects deliberately is exploited. This includes intentionally eliminating certain azimuthal/helical modes by employing DNS for various circumferential domain sizes. With this approach, the impact of structures associated with certain modes on the global wake-behaviour can be scrutinized. Complementary spatial and temporal calculations are carried out to investigate whether instabilities are of local or global nature. Circumstantial evidence is presented that absolutely unstable global modes within the recirculation region co-exist with convectively unstable shear-layer modes. The flow is found to be absolutely unstable with respect to modes $k\,{>}\,0$ for $Re_D\,{>}\,5000$ and with respect to the axisymmetric mode $k\,{=}\,0$ for $Re_D\,{>}\,100\,000$. It is concluded that azimuthal modes $k\,{=}\,2$ and $k\,{=}\,4$ are the dominant modes in the trailing wake, producing a ‘four-lobe’ wake pattern. Two possible mechanisms responsible for the generation of longitudinal structures within the recirculation region are suggested.
Population Distribution in the Wake of a Sphere

The physics of heat and mass transfer from an object in its wake has significant importance in natural phenomena as well as across many engineering applications. Here, we report numerical results on the population density of the spatial distribution of fluid velocity, pressure, scalar concentration, and scalar fluxes of a wake flow past a sphere in the steady wake regime (Reynolds number 25 to 285). Our findings show that the spatial population distributions of the fluid and the transported scalar quantities in the wake follow a Cauchy-Lorentz or Lorentzian trend, indicating a variation in its sample number density inversely proportional to the squared of its magnitude. We observe this universal form of population distribution both in the symmetric wake regime and in the more complex three dimensional wake structure of the steady oblique regime with Reynolds number larger than 225. The population density distribution identifies the increase in dimensionless kinetic energy and scalar fluxes with the increase in Reynolds number, whereas the dimensionless scalar population density shows negligible variation with the Reynolds number. Descriptive statistics in the form of population density distribution of the spatial distribution of the fluid velocity and the transported scalar quantities is important for understanding the transport and local reaction processes in specific regions of the wake, which can be used e.g., for understanding the microphysics of cloud droplets and aerosol interactions, or in the technical flows where droplets interact physically or chemically with the environment.
How Android's UI Security is Undermined by Accessibility

Android's accessibility API was designed to assist users with disabilities, or preoccupied users unable to interact with a device, e.g., while driving a car. Nowadays, many Android apps rely on the accessibility API for other purposes, including password managers but also malware. From a security perspective, the accessibility API is precarious as it undermines an otherwise strong principle of sandboxing in Android that separates apps. By means of an accessibility service, apps can interact with the UI elements of another app, including reading from its screen and writing to its text fields. As a consequence, design shortcomings in the accessibility API and other UI features such as overlays have grave security implications. We reveal flaws in the accessibility design of Android allowing information leakages and denial of service attacks against fully patched systems. With an enabled accessibility service, we are able to sniff sensitive data from apps, including the password of Android's own lock screen. To evaluate the effectiveness of our attacks against third-party apps, we examined the 1100 most downloaded apps from Google Play and found 99.25 % of them to be vulnerable. Although app-level protection measures against these attacks can be implemented, e.g., to prevent information leakage through password fields, the number of affected apps proves that these kind of vulnerabilities must be tackled by Google rather than app developers. From December 2017 to March 2018, we submitted seven bug reports to Google, from which three have been marked as won't fix while four are progressed but ranked with either low severity or no security bulletin class. We conclude our paper with a list of best practices for app-level protections for the time those bugs remain unfixed by Google.
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations

Using weight decay to penalize the L2 norms of weights in neural networks has been a standard training practice to regularize the complexity of networks. In this paper, we show that a family of regularizers, including weight decay, is ineffective at penalizing the intrinsic norms of weights for networks with positively homogeneous activation functions, such as linear, ReLU and max-pooling functions. As a result of homogeneity, functions specified by the networks are invariant to the shifting of weight scales between layers. The ineffective regularizers are sensitive to such shifting and thus poorly regularize the model capacity, leading to overfitting. To address this shortcoming, we propose an improved regularizer that is invariant to weight scale shifting and thus effectively constrains the intrinsic norm of a neural network. The derived regularizer is an upper bound for the input gradient of the network so minimizing the improved regularizer also benefits the adversarial robustness. Residual connections are also considered and we show that our regularizer also forms an upper bound to input gradients of such a residual network. We demonstrate the efficacy of our proposed regularizer on various datasets and neural network architectures at improving generalization and adversarial robustness.
Can Smartphone Co-locations Detect Friendship? It Depends How You Model It

We present a study to detect friendship, its strength, and its change from smartphone location data collectedamong members of a fraternity. We extract a rich set of co-location features and build classifiers that detectfriendships and close friendship at 30% above a random baseline. We design cross-validation schema to testour model performance in specific application settings, finding it robust to seeing new dyads and to temporalvariance.
Global Context Aware Convolutions for 3D Point Cloud Understanding

Recent advances in deep learning for 3D point clouds have shown great promises in scene understanding tasks thanks to the introduction of convolution operators to consume 3D point clouds directly in a neural network. Point cloud data, however, could have arbitrary rotations, especially those acquired from 3D scanning. Recent works show that it is possible to design point cloud convolutions with rotation invariance property, but such methods generally do not perform as well as translation-invariant only convolution. We found that a key reason is that compared to point coordinates, rotation-invariant features consumed by point cloud convolution are not as distinctive. To address this problem, we propose a novel convolution operator that enhances feature distinction by integrating global context information from the input point cloud to the convolution. To this end, a globally weighted local reference frame is constructed in each point neighborhood in which the local point set is decomposed into bins. Anchor points are generated in each bin to represent global shape features. A convolution can then be performed to transform the points and anchor features into final rotation-invariant features. We conduct several experiments on point cloud classification, part segmentation, shape retrieval, and normals estimation to evaluate our convolution, which achieves state-of-the-art accuracy under challenging rotations.
Viscoelastic Interfacial Modes in a Wetting Layer

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Sample-based calibration for cryogenic broadband microwave reflectometry measurements

The characteristic frequencies of a system provide important information on the phenomena that govern its physical properties. In this framework, there has recently been renewed interest in cryogenic microwave characterization for condensed matter systems since it allows to probe energy scales of the order of a few $\mu$eV. However, broadband measurements of the absolute value of a sample response in this frequency range are extremely sensitive to its environment and require a careful calibration. In this paper, we present an \textit{in situ} calibration method for cryogenic broadband microwave reflectometry experiments that is both simple to implement and through which the effect of the sample electromagnetic environment can be minimized. The calibration references are here provided by the sample itself, at three reference temperatures where its impedance is assumed or measured, and not by external standards as is usual. We compare the frequency-dependent complex impedance (0.1--2 GHz) of an a-Nb$_{15}$Si$_{85}$ superconducting thin film obtained through this Sample-Based Calibration (SBC) and through an Open-Short-Load Standard Calibration (SC) when working at very low temperature (0.02--4 K) and show that the SBC allows us to obtain the absolute response of the sample. This method brings the calibration planes as close as possible to the sample, so that the environment electrodynamic response does not affect the measurement, provided it is temperature independent. This results in a heightened sensitivity, for a given experimental set--up.
An Analogy between the Properties of “Dark Energy” and Physical Vacuum Consisting of Quantum Harmonic Oscillators Characterized by Zero-Point Energy

In quantum field theory, the physical vacuum, free from magnetic and electric fields (without regard to gravitational energy), is defined not as an empty space but as the ground state of the field consisting of quantum harmonic oscillators (QHOs) characterized by zero-point energy. The aim of this work is to show that such physical vacuum may possess the properties similar to the properties of dark energy: the positive density, the negative pressure, and the possibility of so-called accelerated expansion. In the model discussed, the mass of QHOs determines the positive density of dark energy. The observed electric polarization of physical vacuum in an electric field means the existence of electric dipole moment of QHO, which, in turn, suggests the existence inside the QHO of a repulsive force between unlike charges compensating the attractive Coulomb force between the charges. The existence of such repulsive force may be treated as the existence of omniradial tensions inside every QHO. In terms of hydrodynamics, it means that the vacuum with this property may be regarded as a medium with negative pressure. The electric dipole-dipole interaction of QHOs under some condition may result in the expansion of physical vacuum consisting of QHOs. It is shown also that the physical vacuum consisting of QHOs is a luminiferous medium, and based on this concept the conditions are discussed for the emergence of invisiblity of any objects (in particular, dark matter). The existence of luminiferous medium does not contradict the second postulate of special relativity (the principle of constancy of the velocity of light in inertial systems), if to take into account the interaction of photons with QHOs and with virtual photons (the virtual particles pairs) created by quantum entities that constitute the inertial systems.
Fitting Ideals in Two-variable Equivariant Iwasawa Theory and an Application to CM Elliptic Curves

We study equivariant Iwasawa theory for two-variable abelian extensions of an imaginary quadratic field. One of the main goals of this paper is to describe the Fitting ideals of Iwasawa modules using $p$-adic $L$-functions. We also provide an application to Selmer groups of elliptic curves with complex multiplication.
Towards Sample Efficient Agents through Algorithmic Alignment

In this work, we propose and explore Deep Graph Value Network (DeepGV) as a promising method to work around sample complexity in deep reinforcement-learning agents using a message-passing mechanism. The main idea is that the agent should be guided by structured non-neural-network algorithms like dynamic programming. According to recent advances in algorithmic alignment, neural networks with structured computation procedures can be trained efficiently. We demonstrate the potential of graph neural network in supporting sample efficient learning by showing that Deep Graph Value Network can outperform unstructured baselines by a large margin in solving Markov Decision Process (MDP). We believe this would open up a new avenue for structured agents design. See https://github.com/drmeerkat/Deep-Graph-Value-Network for the code.
Three Potentially Interrelated Aspects of Phosphine Toxicity

Fumigation with phosphine gas is by far the most widely used treatment for the protection of stored grain against insect pests. The development of high-level resistance in insects now threatens its continued use. As there is no suitable chemical to replace phosphine, it is essential to understand the mechanisms of phosphine toxicity to increase the effectiveness of resistance management. Because phosphine is such a simple molecule (PH3), the chemistry of phosphorus is central to its toxicity. The elements above and below phosphorus in the periodic table are nitrogen (N) and arsenic (As), which also produce toxic hydrides, namely, NH3 and AsH3. The three hydrides cause related symptoms and similar changes to cellular and organismal physiology, including disruption of the sympathetic nervous system, suppressed energy metabolism and toxic changes to the redox state of the cell. We propose that these three effects are interdependent contributors to phosphine toxicity.
IoT-Based 3D Visualisation Platform for an Efficient Management of the Smart City Ecosystem

Nowadays, most of the urban developments are based on ICT (Information and Communication Technology) solutions which can increase the socio-economic development the quality of life. Smart Cities have been pointed out as the most promising applications of Internet of Things (IoT). Despite all standardisation processes, building a Smart City IoT platform remains a challenge. The continuous changes in IoT environment require the system to adopt new technologies dynamically. To address this challenge, our proposal is the deployment of the CitiSim platform which can simulate 3D models for events processing and supports different Operating Systems (OS) enabling new business models. The main advantage of the CitiSim platform is that it enables Big Data analytics tools to monitor data coming from heterogeneous sources. The primary objective is the identification of the environmental, social, and economic indicators used in order to build smart urban models that contribute to the process of decision making for improving land use, transport, energy, and user needs, classified and evaluated with respect to the criteria of a specific district.