Miracle-dz/UniFAR-FTUs huggingface dataset:
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks

Deep neural networks (DNNs) are vulnerable to the adversarial attack which is maliciously implemented by adding human-imperceptible perturbation to images and thus leads to incorrect prediction. Existing studies have proposed various methods to detect the new adversarial attacks. However, new attack methods keep evolving constantly and yield new adversarial examples to bypass the existing detectors. It needs to collect tens of thousands samples to train detectors, while the new attacks evolve much more frequently than the high-cost data collection. Thus, this situation leads the newly evolved attack samples to remain in small scales. To solve such few-shot problem with the evolving attacks, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples. Specifically, the learning consists of a double-network framework: a task-dedicated network and a master network which alternatively learn the detection capability for either seen attack or a new attack. To validate the effectiveness of our approach, we construct the benchmarks with few-shot-fashion protocols based on three conventional datasets, i.e. CIFAR-10, MNIST and Fashion-MNIST. Comprehensive experiments are conducted on them to verify the superiority of our approach with respect to the traditional adversarial attack detection methods. The implementation code is available online.
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint action space that grows exponentially with the number of agents. While the tractability of independent agent-wise exploration is appealing, this approach fails on tasks that require elaborate group strategies. We argue that coordinating the agents' policies can guide their exploration and we investigate techniques to promote such an inductive bias. We propose two policy regularization methods: TeamReg, which is based on inter-agent action predictability and CoachReg that relies on synchronized behavior selection. We evaluate each approach on four challenging continuous control tasks with sparse rewards that require varying levels of coordination. Our methodology allocates the same hyper-parameter search budget across our algorithms and baselines and we find that our approaches are more robust to hyper-parameter variations. Our experiments show that our methods significantly improve performance on cooperative multi-agent problems and scale well when the number of agents is increased. Finally, we quantitatively analyze the effects of our proposed methods on the policies that our agents learn and we show that our methods successfully enforce the qualities that we propose as proxies for coordinated behaviors.
Review of the AC loss computation for HTS using H formulation

This article presents a review of the finite-element method (FEM) model based on the H formulation of Maxwell’s equations used to calculate AC losses in high-temperature superconductor (HTS) tapes, cables and windings for different applications. This model, which uses the components of the magnetic field as state variables, has been gaining a great popularity and has been in use in tens of research groups around the world. This contribution first reviews the equations on which the model is based and their implementation in FEM programs for different cases, such as 2D longitudinal and axis-symmetric geometries and 3D geometries. Modeling strategies to tackle large number of HTS tapes, such as multi-scale and homogenization methods, are also introduced. Then, the second part of the article reviews the applications for which the H formulation has been used to calculate AC losses, ranging from individual tapes to complex cables and large magnet windings. Afterwards, a section is dedicated to the discussion of the H formulation in terms of computational efficiency and easiness of implementation. Its pros and cons are listed. Finally, the last section draws the main conclusions.
Comprehensive Fuzzy Turing Machines, An Evolution to the Concept of Finite State Machine Control

The Turing machine is an abstract concept of a computing device which introduced new models for computation. The idea of Fuzzy algorithms defined by Zadeh and Lee was followed by introducing Fuzzy Turing Machine (FTM) to create a platform for a new fuzzy computation model. Then, in his investigations on its computational power, Wiedermann showed that FTM is able to solve undecidable problems. His suggested FTM structure, which highly resembles the original definition was one of the most well-known classical definitions of FTM this http URL improve some of its weaknesses and vague points which will be discussed extensively in this paper, we will develop a more complete definition for fuzzy Turing machines. Our proposed definition of FTM, which encompasses the conventional definition, is motivated from the definition of General Fuzzy Automata (GFA) introduced by Doostfatemeh and Kremer. As it improved the conventional definition of fuzzy automata, especially the problem of membership assignment and multi-membership resolution, we also improved the same aspects of FTM through the definition of Comprehensive Fuzzy Turing Machine (CFTM). In addition, we address on some possible vaguenesses in FTM was not the subject of focus in fuzzy automata. As example, we investigate the issue of multi-path and multi-direction which are possible in case of nondeterminism. Finally, we show the simplicity, applicability and computational efficiency of the CFTM through an explanatory example.
High-Fidelity Measurement of Qubits Encoded in Multilevel Superconducting Circuits

Qubit measurements are central to quantum information processing. In the field of superconducting qubits, standard readout techniques are not only limited by the signal-to-noise ratio, but also by state relaxation during the measurement. In this work, we demonstrate that the limitation due to relaxation can be suppressed by using the many-level Hilbert space of superconducting circuits: in a multilevel encoding, the measurement is only corrupted when multiple errors occur. Employing this technique, we show that we can directly resolve transmon gate errors at the level of one part in $10^3. $ Extending this idea, we apply the same principles to the measurement of a logical qubit encoded in a bosonic mode and detected with a transmon ancilla, implementing a proposal by Hann et al. [Phys. Rev. A \textbf{98} 022305 (2018)]. Qubit state assignments are made based on a sequence of repeated readouts, further reducing the overall infidelity. This approach is quite general and several encodings are studied; the codewords are more distinguishable when the distance between them is increased with respect to photon loss. The tradeoff between multiple readouts and state relaxation is explored and shown to be consistent with the photon-loss model. We report a logical assignment infidelity of $5.8\times 10^{-5}$ for a Fock-based encoding and $4.2\times 10^{-3}$ for a QEC code (the $S=2,N=1$ binomial code). Our results will not only improve the fidelity of quantum information applications, but also enable more precise characterization of process or gate errors.
Parallelization of Kmeans++ using CUDA

K-means++ is an algorithm which is invented to improve the process of finding initial seeds in K-means algorithm. In this algorithm, initial seeds are chosen consecutively by a probability which is proportional to the distance to the nearest center. The most crucial problem of this algorithm is that when running in serial mode, it decreases the speed of clustering. In this paper, we aim to parallelize the most time consuming steps of the k-means++ algorithm. Our purpose is to reduce the running time while maintaining the quality of the serial algorithm.
On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond

The DANE algorithm is an approximate Newton method popularly used for communication-efficient distributed machine learning. Reasons for the interest in DANE include scalability and versatility. Convergence of DANE, however, can be tricky; its appealing convergence rate is only rigorous for quadratic objective, and for more general convex functions the known results are no stronger than those of the classic first-order methods. To remedy these drawbacks, we propose in this paper some new alternatives of DANE which are more suitable for analysis. We first introduce a simple variant of DANE equipped with backtracking line search, for which global asymptotic convergence and sharper local non-asymptotic convergence rate guarantees can be proved for both quadratic and non-quadratic strongly convex functions. Then we propose a heavy-ball method to accelerate the convergence of DANE, showing that nearly tight local rate of convergence can be established for strongly convex functions, and with proper modification of algorithm the same result applies globally to linear prediction models. Numerical evidence is provided to confirm the theoretical and practical advantages of our methods.
Invariant mass distribution of top quark pairs near threshold

We investigate the invariant mass distribution of top quark pairs near the $2m_t$ threshold, where current theoretical predictions are incompatible with experimental measurements at the LHC. We show that higher-order non-relativistic Coulomb corrections lead to large corrections, which cannot be incorporated at fixed orders due to the divergent behavior. We derive a factorization formula to resum such corrections to all orders in the strong coupling, and calculate necessary ingredients to perform the resummation at next-to-leading power. We combine the resummation with fixed-order results and present phenomenologically relevant numeric results. We find that the resummation effect significantly enhances the differential cross section in the threshold region, and makes the theoretical prediction more compatible with experimental data.
Blind Sparse Estimation of Intermittent Sources Over Unknown Fading Channels

Radio frequency sources are observed at a fusion center via sensor measurements made over slow flat-fading channels. The number of sources may be larger than the number of sensors, but their activity is sparse and intermittent with bursty transmission patterns. To account for this, sources are modeled as hidden Markov models with known or unknown parameters. The problem of blind source estimation in the absence of channel state information is tackled via a novel algorithm, consisting of a dictionary learning (DL) stage and a per-source stochastic filtering (PSF) stage. The two stages work in tandem, with the latter operating on the output produced by the former. Both stages are designed so as to account for the sparsity and memory of the sources. To this end, smooth LASSO is integrated with DL, while the forward-backward algorithm and Expectation Maximization (EM) algorithm are leveraged for PSF. It is shown that the proposed algorithm can enhance the detection and the estimation performance of the sources, and that it is robust to the sparsity level.
Omni SCADA Intrusion Detection Using Deep Learning Algorithms

In this article, we investigate deep-learning-based omni intrusion detection system (IDS) for supervisory control and data acquisition (SCADA) networks that are capable of detecting both temporally uncorrelated and correlated attacks. Regarding the IDSs developed in this article, a feedforward neural network (FNN) can detect temporally uncorrelated attacks at an F1 of 99.967±0.005% but correlated attacks as low as 58±2%. In contrast, long short-term memory (LSTM) detects correlated attacks at 99.56±0.01% while uncorrelated attacks at 99.3±0.1%. Combining LSTM and FNN through an ensemble approach further improves the IDS performance with F1 of 99.68±0.04% regardless the temporal correlations among the data packets.
A Stroke Detection System Based on Cincinnati Prehospital Stroke Scale

Stroke, also known as cerebrovascular events, is mainly caused by the obstruction of blood flow in the brain, which leads to an inability to supply oxygen to the brain. By 2013, stroke had become the second most common cause of death in Taiwan (accounting for about 12% of all deaths). This study used image processing technology and speech recognition, following the Cincinnati Prehospital Stroke Scale, to determine whether or not the user had a stroke. The Cincinnati Prehospital Stroke Scale has three indicators, including facial droop, arm drift, and speech. Patients with 1 of these 3 findings have a 72% probability of having had an ischemic stroke. If all satisfies, the probability of the stroke is more than 85%. In addition, we developed a mobile APP based on this method to detect whether or not the user had a stroke, and hope to reduce stroke hazards.
Diagnosis of Methylmalonic Acidemia using Machine Learning Methods

Methylmalonic acidemia (MMA) is an autosomal recessive metabolic disorder. Traditional diagnosis needs physicians' personal level of professional medical knowledge and clinical experience. In this paper, we employ machine learning methods to diagnose MMA based on patients' laboratory blood tests and laboratory urine tests, in order to make a timely diagnosis and reduce dependence on physicians' personal level of professional medical knowledge and clinical experience. By comparing different machine learning algorithms for diagnosing MMA, we obtain the following conclusions: (a) machine learning methods can perform well for diagnosing MMA (all established predictive models obtain high accuracies and AUC values which are greater than 0.85 over all data sets, and some of these results are even more than 0.98); (b) random forest algorithm performs best among the compared algorithms; and (c) diagnosis based on the data combining both urine tests and blood tests is better than diagnosis based on single test alone in general. The conclusions show that applying machine learning algorithms to the diagnosis of MMA can achieve good performance. Thus, it is credible to build machine learning models to give an initial diagnosis without professional medical knowledge.
Prediction of Cryptocurrency Price Dynamics with Multiple Machine Learning Techniques

Nowadays, encrypted digital currency offers a new way of secure trading and exchanging and has become increasingly important in our financial system. However, the temporal dynamics of cryptocurrencies is highly complex, and predictions are still challenging. In this study, we establish two prevailing machine learning models, fully-connected Artificial Neural Network (ANN) and the Long-Short-Term-Memory (LSTM), to predictively model the price of several popular cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Stellar Lumens (XLM), Litecoin (LTC), and Monero (XMR). We evaluate model performance and conduct sensitivity analysis to further understand our model behaviors. We find that although LSTM seems more appropriate for time sequence prediction task, ANN, in general, outrivals LSTM in our experiments. Using price information from other different cryptocurrencies for joint training and prediction could largely facilitate the prediction of BTC. Finally, the model predictive error is highly sensitive to the time scale of interest.
Generating traffic data for any city using SMARTS simulator

We have recently developed a flexible traffic simulator called Scalable Microscopic Adaptive Road Traffic Simulator (SMARTS) [13]. Among many important features of SMARTS in this article, we focus on traffic generation, which is key in analyzing and understanding traffic. SMARTS is a fully distributed simulator that can run on a computer cluster, enabling it to perform large-scale simulations faster than real time. SMARTS has a number of features that empower users to build realistic simulations. For example, users can download road map data for an arbitrarily chosen area for traffic simulation.
Revision 2 1 On the relative timing of listwaenite formation and chromian spinel 2 equilibration in serpentinites 3

10 Ultramafic rocks exposed at the Earth’s surface generally record multiple stages of 11 evolution that may include melt extraction, serpentinization, carbonatization, and metamorphism. 12 When quantitative thermometry based on mineral chemistry is applied to such rocks, it is often 13 unclear what stage of their evolution is being observed. Here, in peridotites with extensive 14 replacement of silicate minerals by carbonates (listwaenites), we present a case study that 15 addresses the timing of carbonate formation relative to closure of exchange reactions among relict 16 primary minerals. 17 Massive and schistose serpentinized peridotites of Neoproterozoic age outcrop at Gabal 18 Sirsir, South Eastern Desert, Egypt (northwestern corner of the Arabian-Nubian Shield, ANS). 19 Petrography, bulk composition, and mineral chemistry are all consistent with a strongly depleted 20 mantle harzburgite protolith for the serpentinites. Bulk compositions are low in Al2O3 and CaO 21 and high in Mg# [molar Mg/(Mg+Fe) = 0.89–0.93]. Relict spinel has high Cr# [molar 22 Cr/(Cr+Al)] and low Ti, while relict olivine has high Mg# and NiO contents. Based on 23 compositions of coexisting relict olivine and chromian spinel, the protolith experienced 19 to 24
BACKGROUND PAPER FOR ARE SEMINAR, OCT 28 2011 RATIONAL EXAGGERATION IN INFORMATION AGGREGATION GAMES

This paper studies a class of information aggregation mode ls which we call “aggregation games. ” It departs from the related literature in two ma in respects: information is aggregated by averaging rather than majority rule, and each player select s from a continuum of reports rather than making a binary choice. The game models in a stylized way the o peration of a class of aggregating institutions that play an increasing role in modern economi es. A leading example is the process by which LIBOR rates are determined. Each of a finite collection of players receives a private signal, then submits a report to the center, who then makes a decision based on the average of these reports. The essence of an aggregation game is that heterogeneous pla yers engage in a “tug-of-war,” as they attempt to manipulate the center’s decision process by misreporting their private information. When players have distinct biases, almost all of them rationally exaggerate the extent of these biases. The paper focuses primarily on games with a small number of playe rs. We identify a class of “anchored” games with quadratic payoffs for which sharp comparative st atics results can be obtained. These results relate to the impacts of changing the number of playe rs, the degree of player heterogeneity and the space of admissible announcements. We also show that as the number of players increases without bound, the relationship between players’ signals a nd the outcome becomes more and more tenuous, precisely as the relationship between these signa l a d the true state becomes more and more clearcut.
M E ] 2 0 D ec 2 01 7 Approximate Bayesian Forecasting ∗

Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably those characterized by an intractable likelihood function. In this paper, we focus on the use of ABC not as a tool for parametric inference, but as a means of generating probabilistic forecasts; or for conducting what we refer to as ‘approximate Bayesian forecasting’. The four key issues explored are: i) the link between the theoretical behavior of the ABC posterior and that of the ABC-based predictive; ii) the use of proper scoring rules to measure the (potential) loss of forecast accuracy when using an approximate rather than an exact predictive; iii) the performance of approximate Bayesian forecasting in state space models; and iv) the use of forecasting criteria to inform the selection of ABC summaries in empirical settings. The primary finding of the paper is that ABC can provide a computationally efficient means of generating probabilistic forecasts that are nearly identical to those produced by the exact predictive, and in a fraction of the time required to produce predictions via an exact method.
SPATIAL PATTERNS ESTIMATION OF URBAN HEAT ISLAND OF ZARAGOZA ( SPAIN ) USING GIS

29 daily sample maps of urban spatial distribution of temperatures, obtained by means of urban thermal transects, were used to generate mean and temporal variability maps of urban heat island in Zaragoza (Northeast of Spain). Both maps were obtained using GIS. Each daily map was generated from 217 records of temperature. Three cartransects were used to obtain this thermal data. Time duration of transects was about 1 hour, beginning 3 hours after nightfall. Negative trends are identified along duration time of transects because of the surface night cooling. Trends were eliminated by means of linear adjustments and residual analysis. Each daily-corrected data was standardized in order to obtain homogeneous spatial series. The standardized daily values were mapped in a GIS using semivariogram adjustment and ordinary kriging. The result was a set of 29 daily grid standardized maps of spatial thermal distribution in the city, at 30 m of spatial resolution. Using GIS analysis and daily grid temperature maps, we generate two thermal maps: the first one represents the average thermal characteristics; the second one indicates the temporal variability of temperature distribution within the city. The grid values of these digital maps can be incorporated in a GIS, and allow us to integrate thermal information with other spatial variables for monitoring the urban environment.
Recurrent Source Separation Structures as Iterative Methods for Solving Nonlinear Equation Systems

In this work, we present a new interpretation of recurrent separation strategies in terms of local inversion based on iterative methods for solving nonlinear equation systems. From this interpretation, we firstly obtain a fresh perspective on the important methods proposed by Hérault and Jutten and by Hosseini and Deville for solving, respectively, linear and nonlinear blind source separation problems. Afterwards, a new separation structure for dealing with linear-quadratic models is proposed, leading to improvements from the convergence standpoint with respect to the basic network proposed by Hosseini and Deville. Finally, elements of a dynamical analysis of the proposed network are provided, indicating, aside from the well-known risk of divergence towards infinity, the possibility of convergence to limit cycles and strange attractors. Keywords— Source Separation, Nonlinear Mixtures, Iterative Methods for Equation Solving, Recurrent Networks.
Fault Diagnosis in Large-Scale Computing Environments

• Introduction or Motivations As the scale of high performance computing (HPC) grows, the new generation of HPC systems involves tens-of-thousands to hundreds-of-thousands of components. For systems of this scale, reliability becomes a major concern as the system wide Mean-Time-Before-Failure (MTBF) decreases dramatically with the increasing count of components. Therefore, fault diagnosis is a critical point for long-running parallel distributed applications executing in Massive Cluster of Workstations (MCOW).
IMPACT OF ORGANIZATIONAL CULTURE & CLIMATE ON MANAGERIAL EFFECTIVENESS AN EMPIRICAL STUDY

URPOSE THIS study aims to draw attention towards the importance of managerial effectiveness and its dependency on variables like organizational climate and culture in the Indian context. In order to achieve the organizational objectives, managers play a vital role as the bridge between the top management and subordinates. Now, in order to achieve the desired objectives of the organization managerial effectiveness is very important. That is why in this study, it is being hypothesized that there is relationship between the managerial effectiveness and organizational culture and climate.