Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly detection and assessment of PM10 functional data at several locations in the Klang Valley, Malaysia

Abstract In environmental data sets, the occurrence of a high concentration of an unusual pollutant, more formally known as an anomaly, may indicate air quality problems. Thus, a critical understanding of the behavior of anomalies is increasingly becoming very important for air pollution investigations. This study was conducted to detect anomalies in daily PM 10 functional data, to investigate the patterns of behavior as well as to identify possible factors that determine PM 10 anomalies at three selected air quality monitoring stations (Klang, Kuala Selangor and Petaling Jaya) in the Klang Valley, Malaysia. The statistical method employed to detect these anomalies consisted of a combination of the robust projection pursuit and the robust Mahalanobis distance methods using air quality data recorded from 2005 to 2010. Analysis of obtained anomalous PM 10 profiles showed that data recorded during El Nino years (2005, 2006 and 2009) contained the highest frequency of anomalies. More frequent anomalies appeared during the southwest (SW) monsoon which occurs in the months of July and August as well as during the northeast (NE) monsoon in February. A lesser number of anomalies were also observed during weekends compared to weekdays. The weekend and monsoonal effect phenomena were shown to be significantly existent at all stations while wind speed was positively associated with extreme PM 10 anomalies at the Klang and Petaling Jaya stations. In conclusion, anomalies detection was found useful for air pollution investigation in this study. The findings of this study imply that the location and background of a station, as well as wind speed, seasonal (monsoon) and weekdays-weekend variations play important role in influencing PM 10 anomalies.
ActiveGuard: An Active DNN IP Protection Technique via Adversarial Examples

The training of Deep Neural Networks (DNN) is costly, thus DNN can be considered as the intellectual properties (IP) of model owners. To date, most of the existing protection works focus on verifying the ownership after the DNN model is stolen, which cannot resist piracy in advance. To this end, we propose an active DNN IP protection method based on adversarial examples against DNN piracy, named ActiveGuard. ActiveGuard aims to achieve authorization control and users' fingerprints management through adversarial examples, and can provide ownership verification. Specifically, ActiveGuard exploits the elaborate adversarial examples as users' fingerprints to distinguish authorized users from unauthorized users. Legitimate users can enter fingerprints into DNN for identity authentication and authorized usage, while unauthorized users will obtain poor model performance due to an additional control layer. In addition, ActiveGuard enables the model owner to embed a watermark into the weights of DNN. When the DNN is illegally pirated, the model owner can extract the embedded watermark and perform ownership verification. Experimental results show that, for authorized users, the test accuracy of LeNet-5 and Wide Residual Network (WRN) models are 99.15% and 91.46%, respectively, while for unauthorized users, the test accuracy of the two DNNs are only 8.92% (LeNet-5) and 10% (WRN), respectively. Besides, each authorized user can pass the fingerprint authentication with a high success rate (up to 100%). For ownership verification, the embedded watermark can be successfully extracted, while the normal performance of the DNN model will not be affected. Further, ActiveGuard is demonstrated to be robust against fingerprint forgery attack, model fine-tuning attack and pruning attack.
The scalar, vector, and tensor modes in gravitational wave turbulence simulations

We study the gravitational wave (GW) signal sourced by primordial turbulence that is assumed to be present at cosmological phase transitions like the electroweak and quantum chromodynamics phase transitions. We consider various models of primordial turbulence, such as those with and without helicity, purely hydrodynamical turbulence induced by fluid motions, and magnetohydrodynamic turbulence whose energy can be dominated either by kinetic or magnetic energy, depending on the nature of the turbulence. We also study circularly polarized GWs generated by parity violating sources such as helical turbulence. Our ultimate goal is to determine the efficiency of GW production through different classes of turbulence. We find that the GW energy and strain tend to be large for acoustic or irrotational turbulence, even though its tensor mode amplitude is relatively small at most wave numbers. Only at very small wave numbers is the spectral tensor mode significant, which might explain the efficient GW production in that case.
Optimized Ring Tensile Test Method and Hydrogen Effect on Mechanical Properties of Zircaloy Cladding in Hoop Direction

Data pertaining to the mechanical properties of the fuel cladding in the hoop direction are required for the analysis of high burn-up fuel behavior under reactivity-initiated accident (RIA) conditions. In the present study, by minimizing undesirable effects of friction and bending, the ring tensile test method was optimized to obtain precise data pertaining to the mechanical properties of the fuel cladding in the hoop direction. The optimized specimen has a single gauge section and is stretched using the tooling consisting of two half-mandrels. The gauge section is set on top of a half-mandrel; this arrangement is unique in comparison with other methods. Using the optimized test method, the mechanical properties of the prehydrided Zircaloy-4 cladding in the hoop direction were evaluated as functions of hydrogen concentration and test temperature. When the hydrogen concentration is below 500 ppm, the decrease in ductility due to hydriding is relatively small at all test temperatures. When the hydrogen concentration is above 600 ppm, the ductility in the tests at 300K remarkably decreases due to hydriding, while the hydrogen effect decreases in magnitude in the tests above 473 K.
Coordination Among Neural Modules Through a Shared Global Workspace

Deep learning has seen a movement away from representing examples with a monolithic hidden state towards a richly structured state. For example, Transformers segment by position, and object-centric architectures decompose images into entities. In all these architectures, interactions between different elements are modeled via pairwise interactions: Transformers make use of self-attention to incorporate information from other positions; object-centric architectures make use of graph neural networks to model interactions among entities. However, pairwise interactions may not achieve global coordination or a coherent, integrated representation that can be used for downstream tasks. In cognitive science, a global workspace architecture has been proposed in which functionally specialized components share information through a common, bandwidth-limited communication channel. We explore the use of such a communication channel in the context of deep learning for modeling the structure of complex environments. The proposed method includes a shared workspace through which communication among different specialist modules takes place but due to limits on the communication bandwidth, specialist modules must compete for access. We show that capacity limitations have a rational basis in that (1) they encourage specialization and compositionality and (2) they facilitate the synchronization of otherwise independent specialists.
Joint Location and Communication Study for Intelligent Reflecting Surface Aided Wireless Communication System

Intelligent reflecting surface (IRS) is a novel burgeoning concept, which possesses advantages in enhancing wireless communication and user localization, while maintaining low hardware cost and energy consumption. Herein, we establish an IRS-aided mmWave-MIMO based joint localization and communication system (IMM-JLCS), and probe into its performance evaluation and optimization design. Specifically, first, we provide the signal, channel and estimation error models, and contrive the working process of the IMM-JLCS in detail. Then, by configuring appropriate IRS phase shifts, we derive the closed-form expressions of the Cramer-Rao Lower Bound (CRLB) of the position/orientation estimation errors and the effective achievable data rate (EADR), with respect to the time allocation ratio of the beam alignment and localization stage (BALS). Subsequently, we investigate the trade-off between the two performance metrics, for which we propose a joint optimization algorithm. Finally, we carry out simulations and comparisons to view the trade-off and validate the effectiveness of the proposed algorithm, in the presence of distinct levels of estimation uncertainty and user mobility. Our results demonstrate that the proposed algorithm can find the joint optimal solution for the position/orientation estimation accuracy and EADR, with its optimization performance being robust to slight localization or channel estimation errors and user mobility.
Respiratory aerosols and droplets in the transmission of infectious diseases

Knowing the physicochemical properties of exhaled droplets and aerosol particles is a prerequisite for a detailed mechanistic understanding and effective prevention of the airborne transmission of infectious human diseases. This article provides a critical review and synthesis of scientific knowledge on the number concentrations, size distributions, composition, mixing state, and related properties of respiratory particles emitted upon breathing, speaking, singing, coughing, and sneezing. We derive and present a parameterization of respiratory particle size distributions based on five lognormal modes related to different origins in the respiratory tract, which can be used to trace and localize the sources of infectious particles. This approach may support the medical treatment as well as the risk assessment for aerosol and droplet transmission of infectious diseases. It was applied to analyze which respiratory activities may drive the spread of specific pathogens, such as Mycobacterium tuberculosis, influenza viruses, and SARS-CoV-2 viruses. The results confirm the high relevance of vocalization for the transmission of SARS-CoV-2 as well as the usefulness of physical distancing, face masks, room ventilation, and air filtration as preventive measures against COVID-19 and other airborne infectious diseases.
Instabilities in liquid crystal elastomers

Stability is an important and fruitful avenue of research for liquid crystal elastomers. At constant temperature, upon stretching, the homogeneous state of a nematic body becomes unstable, and alternating shear stripes develop at very low stress. Moreover, these materials can experience classical mechanical effects, such as necking, void nucleation and cavitation, and inflation instability, which are inherited from their polymeric network. We investigate the following two problems: First, how do instabilities in nematic bodies change from those found in purely elastic solids? Second, how are these phenomena modified if the material constants fluctuate? To answer these questions, we present a systematic study of instabilities occurring in nematic liquid crystal elastomers, and examine the contribution of the nematic component and of fluctuating model parameters that follow probability laws. This combined analysis may lead to more realistic estimations of subsequent mechanical damage in nematic solid materials. Because of their complex material responses in the presence of external stimuli, liquid crystal elastomers have many potential applications in science, manufacturing, and medical research. The modeling of these materials requires a multiphysics approach, linking traditional continuum mechanics with liquid crystal theory, and has led to the discovery of intriguing mechanical effects. An important problem for both applications and our fundamental understanding of nematic elastomers is their instability under large strains, as this can be harnessed for actuation, sensing, or patterning. The goal is then to identify parameter values at which a bifurcation emerges, and how these values change with external stimuli, such as temperature or loads. However, constitutive parameters of real manufactured materials have an inherent variation that should also be taken into account, thus the need to quantify uncertainties in physical responses, which can be done by combining the classical field theories with stochastic methods that enable the propagation of uncertainties from input data to output quantities of interest. The present study demonstrates how to characterize instabilities found in nematic liquid crystal elastomers with probabilistic material parameters at the macroscopic scale, and paves the way for a systematic theoretical and experimental study of these fascinating materials.
Effects of sub-chronic exposure to moderate intensity static magnetic field (SMF) on oxidative stress parameters of male rats

Increasing exposure to electromagnetic fields has become inevitable for people living in industrialized environments. Magnetic fields can increase the production of free radicals; reports also suggest that extremely low frequency magnetic fields (ELF-MF) can increase free radical life-span. This study evaluated the effects of moderate intensity static magnetic field (SMF) (56 Hz, 41 mT, 6 h/day) on the oxidative status of male rats. Plasma and tissue (liver, heart, kidney, brain and testes) superoxide dismutase (SOD) and catalase (CAT) activities, as well as malondialdehyde (MDA) concentrations were measured after 35 consecutive days of exposure to the SMF. SMF exposure caused significant reductions in the body weight of rats but did not alter relative organ weight. Plasma and tissue MDA concentration were statistically insignificantly reduced, tissue SOD activities were slightly increased, while plasma and liver CAT activities were significantly (p<0.05) increased in SMF exposed rats. The conditions of our study show that while an oxidative challenge was experienced by the rats exposed to SMF, oxidative stress did not occur. Higher intensity SMF and/or longer duration of exposure may however lead to oxidative stress.
Validation of cluster analysis results on validation data: A systematic framework

Cluster analysis refers to a wide range of data analytic techniques for class discovery and is popular in many application fields. To assess the quality of a clustering result, different cluster validation procedures have been proposed in the literature. While there is extensive work on classical validation techniques, such as internal and external validation, less attention has been given to validating and replicating a clustering result using a validation dataset. Such a dataset may be part of the original dataset, which is separated before analysis begins, or it could be an independently collected dataset. We present a systematic, structured review of the existing literature about this topic. For this purpose, we outline a formal framework that covers most existing approaches for validating clustering results on validation data. In particular, we review classical validation techniques such as internal and external validation, stability analysis, and visual validation, and show how they can be interpreted in terms of our framework. We define and formalize different types of validation of clustering results on a validation dataset, and give examples of how clustering studies from the applied literature that used a validation dataset can be seen as instances of our framework.
Run-time Performance Monitoring of Heterogenous Hw/Sw Platforms Using PAPI

In the era of Cyber Physical Systems, designers need to offer support for run-time adaptivity considering different constraints, including the internal status of the system. This work presents a run-time monitoring approach, based on the Performance Application Programming Interface, that offers a unified interface to transparently access both the standard Performance Monitoring Counters (PMCs) in the CPUs and the custom ones integrated into hardware accelerators. Automatic tools offer to Sw programmers the support to design and implement Coarse-Grain Virtual Reconfigurable Circuits, instrumented with custom PMCs. This approach has been validated on a heterogeneous application for image/video processing with an overhead of 6% of the execution time.
A Spectral Enabled GAN for Time Series Data Generation

Time dependent data is a main source of information in today's data driven world. Generating this type of data though has shown its challenges and made it an interesting research area in the field of generative machine learning. One such approach was that by Smith et al. who developed Time Series Generative Adversarial Network (TSGAN) which showed promising performance in generating time dependent data and the ability of few shot generation though being flawed in certain aspects of training and learning. This paper looks to improve on the results from TSGAN and address those flaws by unifying the training of the independent networks in TSGAN and creating a dependency both in training and learning. This improvement, called unified TSGAN (uTSGAN) was tested and comapred both quantitatively and qualitatively to its predecessor on 70 benchmark time series data sets used in the community. uTSGAN showed to outperform TSGAN in 80\% of the data sets by the same number of training epochs and 60\% of the data sets in 3/4th the amount of training time or less while maintaining the few shot generation ability with better FID scores across those data sets.
Influence of the boundaries in the impedance of porous film electrodes

This paper presents an enhanced model for the impedance of porous film electrodes. The impedance of a transmission line with two transport channels, a crosswise element and arbitrary terminal loads is solved analytically. The local impedances at the boundaries represent a frequency-dependent response of the blocking of ionic and electronic charge carriers at the two faces of the electrode region. A general expression is found that contains, as particular cases, a number of models of impedance for porous electrodes used in the literature. Some examples of the generalised transmission line illustrate the use of the model in the interpretation of experimental data. First, a polarisable electrode showing low-frequency dispersion of the constant phase element (CPE) type is analysed, and diagnosis criteria are derived to recognise whether the dispersion is caused by the boundary or the inner surface. Secondly, the manifestation in the impedance of the failure of a porous electrode due to direct charge transfer between the substrate and a redox couple in solution is investigated.
Object Allocation Over a Network of Objects: Mobile Agents with Strict Preferences

In recent work, Gourv\`es, Lesca, and Wilczynski propose a variant of the classic housing markets model where the matching between agents and objects evolves through Pareto-improving swaps between pairs of adjacent agents in a social network. To explore the swap dynamics of their model, they pose several basic questions concerning the set of reachable matchings. In their work and other follow-up works, these questions have been studied for various classes of graphs: stars, paths, generalized stars (i.e., trees where at most one vertex has degree greater than two), trees, and cliques. For generalized stars and trees, it remains open whether a Pareto-efficient reachable matching can be found in polynomial time. In this paper, we pursue the same set of questions under a natural variant of their model. In our model, the social network is replaced by a network of objects, and a swap is allowed to take place between two agents if it is Pareto-improving and the associated objects are adjacent in the network. In those cases where the question of polynomial-time solvability versus NP-hardness has been resolved for the social network model, we are able to show that the same result holds for the network-of-objects model. In addition, for our model, we present a polynomial-time algorithm for computing a Pareto-efficient reachable matching in generalized star networks. Moreover, the object reachability algorithm that we present for path networks is significantly faster than the known polynomial-time algorithms for the same question in the social network model.
Observation of Hydrogen-Induced Dzyaloshinskii-Moriya Interaction and Reversible Switching of Magnetic Chirality

The Dzyaloshinskii-Moriya interaction (DMI) has drawn great attention as it stabilizes magnetic chirality, with important implications in fundamental and applied research. This antisymmetric exchange interaction is induced by the broken inversion symmetry at interfaces or in non-centrosymmetric lattices. Significant interfacial DMI was found often at magnetic / heavy-metal interfaces with large spin-orbit coupling. Recent studies have shown promise of induced DMI at interfaces involving light elements such as carbon (graphene) or oxygen. Here we report direct observation of induced DMI by chemisorption of the lightest element, hydrogen, on a ferromagnetic layer at room temperature, which is supported by density functional theory calculations. We further demonstrate a reversible chirality transition of the magnetic domain walls due to the induced DMI via hydrogen chemisorption/desorption. These results shed new light on the understanding of DMI in low atomic number materials and design of novel chiral spintronics and magneto-ionic devices.
Label-Imbalanced and Group-Sensitive Classification under Overparameterization

The goal in label-imbalanced and group-sensitive classification is to optimize relevant metrics such as balanced error and equal opportunity. Classical methods, such as weighted cross-entropy, fail when training deep nets to the terminal phase of training (TPT), that is training beyond zero training error. This observation has motivated recent flurry of activity in developing heuristic alternatives following the intuitive mechanism of promoting larger margin for minorities. In contrast to previous heuristics, we follow a principled analysis explaining how different loss adjustments affect margins. First, we prove that for all linear classifiers trained in TPT, it is necessary to introduce multiplicative, rather than additive, logit adjustments so that the interclass margins change appropriately. To show this, we discover a connection of the multiplicative CE modification to the cost-sensitive support-vector machines. Perhaps counterintuitively, we also find that, at the start of training, the same multiplicative weights can actually harm the minority classes. Thus, while additive adjustments are ineffective in the TPT, we show that they can speed up convergence by countering the initial negative effect of the multiplicative weights. Motivated by these findings, we formulate the vector-scaling (VS) loss, that captures existing techniques as special cases. Moreover, we introduce a natural extension of the VS-loss to group-sensitive classification, thus treating the two common types of imbalances (label/group) in a unifying way. Importantly, our experiments on state-of-the-art datasets are fully consistent with our theoretical insights and confirm the superior performance of our algorithms. Finally, for imbalanced Gaussian-mixtures data, we perform a generalization analysis, revealing tradeoffs between balanced / standard error and equal opportunity.