Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing Error Distribution by Kernelized Stein Discrepancy in Multivariate Time Series Models

Abstract Knowing the error distribution is important in many multivariate time series applications. To alleviate the risk of error distribution mis-specification, testing methodologies are needed to detect whether the chosen error distribution is correct. However, the majority of existing tests only deal with the multivariate normal distribution for some special multivariate time series models, and thus cannot be used for testing the often observed heavy-tailed and skewed error distributions in applications. In this article, we construct a new consistent test for general multivariate time series models, based on the kernelized Stein discrepancy. To account for the estimation uncertainty and unobserved initial values, a bootstrap method is provided to calculate the critical values. Our new test is easy-to-implement for a large scope of multivariate error distributions, and its importance is illustrated by simulated and real data. As an extension, we also show how to test for the error distribution in copula time series models.
A Centralized Channel Allocation Method in Clustered Ad Hoc Networks

Cognitive radio networks (CRNs) is the next generation of wireless communication. This type of network requires efficent spectrum allocation methods. This paper presents a new meta-heuristic evolutionary method for solving the channel allocation problem in an ad hoc network context. The suggested method is based on a graph-theoretic model and seeks a solution for the spectrum allocation problem in a clustered ad hoc network topology.The method is referred to as imperialist competitive algorithm (ICA)and provides a scheme for allocating the available channels to cluster heads maximizing spectrum efficiency and minimizes co-channel interference. The suggested methods are tested for several scenarios; the performance of the ICA-based scheme is compared with the genetic algorithm based scheme.
Online Prediction with History‐Dependent Experts: The General Case

We study the problem of prediction of binary sequences with expert advice in the online setting, which is a classic example of online machine learning. We interpret the binary sequence as the price history of a stock, and view the predictor as an investor, which converts the problem into a stock prediction problem. In this framework, an investor, who predicts the daily movements of a stock, and an adversarial market, who controls the stock, play against each other over N turns. The investor combines the predictions of n≥2 experts in order to make a decision about how much to invest at each turn, and aims to minimize their regret with respect to the best‐performing expert at the end of the game. We consider the problem with history‐dependent experts, in which each expert uses the previous d days of history of the market in making their predictions. We prove that the value function for this game, rescaled appropriately, converges as N→∞ at a rate of ON−1/6 to the viscosity solution of a nonlinear degenerate elliptic PDE, which can be understood as the Hamilton‐Jacobi‐Issacs equation for the two‐person game. As a result, we are able to deduce asymptotically optimal strategies for the investor. Our results extend those established by the first author and R.V. Kohn [14] for n=2 experts and d≤4 days of history. © 2022 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.
Intelligent Management of Mobile Systems through Computational Self-Awareness

Runtime resource management for many-core systems is increasingly complex. The complexity can be due to diverse workload characteristics with conflicting demands, or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, while coordinating the high-level system goals at runtime in a scalable and robust manner. To address the complexity of dynamic resource management in many-core systems, state-of-the-art techniques that use heuristics have been proposed. These methods lack the formalism in providing robustness against unexpected runtime behavior. One of the common solutions for this problem is to deploy classical control approaches with bounds and formal guarantees. Traditional control theoretic methods lack the ability to adapt to (1) changing goals at runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled system (i.e., self-optimization). In this chapter, we explore adaptive resource management techniques that provide self-optimization and self-adaptivity by employing principles of computational self-awareness, specifically reflection. By supporting these self-awareness properties, the system can reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads.
Diabetic Retinopathy Diagnosis based on Convolutional Neural Network

Diabetic Retinopathy DR is a popular disease for many people as a result of age or the diabetic, as a result, it can cause blindness. therefore, diagnosis of this disease especially in the early time can prevent its effect for a lot of patients. To achieve this diagnosis, eye retina must be examined continuously. Therefore, computer-aided tools can be used in the field based on computer vision techniques. Different works have been performed using various machine learning techniques. Convolutional Neural Network is one of the promise methods, so it was for Diabetic Retinopathy detection in this paper. Also, the proposed work contains visual enhancement in the pre-processing phase, then the CNN model is trained to be able for recognition and classification phase, to diagnosis the healthy and unhealthy retina image. Three public dataset DiaretDB0, DiaretDBl and DrimDB were used in practical testing. The implementation of this work based on Matlab-R2019a, deep learning toolbox and deep network designer to design the architecture of the convolutional neural network and train it. The results were evaluated to different metrics; accuracy is one of them. The best accuracy that was achieved: for DiaretDB0 is 100%, DiaretDB1 is 99.495% and DrimDB is 97.55%.
An adaptive BDF2 implicit time-stepping method for the phase field crystal model

An adaptive BDF2 implicit time-stepping method is analyzed for the phase field crystal model. The suggested method is proved to preserve a modified energy dissipation law at the discrete levels when the time-step ratios satisfy $r_k:=\tau _k/\tau _{k-1}<3.561$, which is the zero-stability restriction of the variable-step BDF2 scheme for ordinary differential equations. With the help of discrete orthogonal convolution kernels and corresponding convolution inequalities, an optimal $L^2$ norm error estimate is established under the weak step-ratio restriction $0<r_k<3.561$ to ensure energy stability. As far as we know, this is the first time that such an error estimate is theoretically proved for a nonlinear parabolic equation. Based on tests on random temporal meshes an effective adaptive time-stepping strategy is suggested to efficiently capture the multi-scale behavior and accelerate the numerical simulations.
Bayesian Optimization for Selecting Efficient Machine Learning Models

The performance of many machine learning models depends on their hyper-parameter settings. Bayesian Optimization has become a successful tool for hyper-parameter optimization of machine learning algorithms, which aims to identify optimal hyper-parameters during an iterative sequential process. However, most of the Bayesian Optimization algorithms are designed to select models for effectiveness only and ignore the important issue of model training efficiency. Given that both model effectiveness and training time are important for real-world applications, models selected for effectiveness may not meet the strict training time requirements necessary to deploy in a production environment. In this work, we present a unified Bayesian Optimization framework for jointly optimizing models for both prediction effectiveness and training efficiency. We propose an objective that captures the tradeoff between these two metrics and demonstrate how we can jointly optimize them in a principled Bayesian Optimization framework. Experiments on model selection for recommendation tasks indicate models selected this way significantly improves model training efficiency while maintaining strong effectiveness as compared to state-of-the-art Bayesian Optimization algorithms.
audioLIME: Listenable Explanations Using Source Separation

Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks but their predictions are usually not interpretable. We propose audioLIME, a method based on Local Interpretable Model-agnostic Explanations (LIME) extended by a musical definition of locality. The perturbations used in LIME are created by switching on/off components extracted by source separation which makes our explanations listenable. We validate audioLIME on two different music tagging systems and show that it produces sensible explanations in situations where a competing method cannot.
Absolute frequency of 87Sr at 1.8 × 10−16 uncertainty by reference to remote primary frequency standards

The optical lattice clock NICT-Sr1 regularly reports calibration measurements of the international timescale TAI. By comparing measurement results to the reports of eight primary frequency standards, we find the absolute frequency of the 87Sr clock transition to be fSr=429228004229873.08276Hz , with a fractional uncertainty of less than 1.8 × 10−16 approaching the systematic limits of the best realization of the SI second. Our result is consistent with other recent measurements and further supported by the loop closure over the absolute frequencies of 87Sr, 171Yb and direct optical measurements of their ratio.
Multitask learning for instrument activation aware music source separation

Music source separation is a core task in music information retrieval which has seen a dramatic improvement in the past years. Nevertheless, most of the existing systems focus exclusively on the problem of source separation itself and ignore the utilization of other~---possibly related---~MIR tasks which could lead to additional quality gains. In this work, we propose a novel multitask structure to investigate using instrument activation information to improve source separation performance. Furthermore, we investigate our system on six independent instruments, a more realistic scenario than the three instruments included in the widely-used MUSDB dataset, by leveraging a combination of the MedleyDB and Mixing Secrets datasets. The results show that our proposed multitask model outperforms the baseline Open-Unmix model on the mixture of Mixing Secrets and MedleyDB dataset while maintaining comparable performance on the MUSDB dataset.
Low temperature homoepitaxy of (010) β-Ga2O3 by metalorganic vapor phase epitaxy: Expanding the growth window

In this work, we report on the growth of high-mobility $\beta$-Ga$_2$O$_3$ homoepitaxial thin films grown at a temperature much lower than the conventional growth temperature window for metalorganic vapor phase epitaxy. Low-temperature $\beta$-Ga$_2$O$_3$ thin films grown at 600$^{\circ}$C on Fe-doped (010) bulk substrates exhibits remarkable crystalline quality which is evident from the measured room temperature Hall mobility of 186 cm$^2$/Vs for the unintentionally doped films. N-type doping is achieved by using Si as a dopant and controllable doping in the range of 2$\times$10$^{16}$ - 2$\times$10$^{19}$ cm$^{-3}$ is studied. Si incorporation and activation is studied by comparing silicon concentration from secondary ion mass spectroscopy (SIMS) and electron concentration from temperature-dependent Hall measurements. The films exhibit high purity (low C and H concentrations) with a very low concentration of compensating acceptors (2$\times$10$^{15}$ cm$^{-3}$) even at this growth temperature. Additionally, abrupt doping profile with forward decay of $\sim$ 5nm/dec (10 times improvement compared to what is observed for thin films grown at 810$^{\circ}$C) is demonstrated by growing at a lower temperature.
PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature Embedding

We propose a simple, fast, and flexible framework to generate simultaneously semantic and instance masks for panoptic segmentation. Our method, called PanoNet, incorporates a clean and natural structure design that tackles the problem purely as a segmentation task without the time-consuming detection process. We also introduce position-sensitive embedding for instance grouping by accounting for both object's appearance and its spatial location. Overall, PanoNet yields high panoptic quality results of high-resolution Cityscapes images in real-time, significantly faster than all other methods with comparable performance. Our approach well satisfies the practical speed and memory requirement for many applications like autonomous driving and augmented reality.
A spatial multinomial logit model for analysing urban expansion

ABSTRACT The paper proposes a Bayesian multinomial logit model to analyse spatial patterns of urban expansion. The specification assumes that the log-odds of each class follow a spatial autoregressive process. Using recent advances in Bayesian computing, our model allows for a computationally efficient treatment of the spatial multinomial logit model. This allows us to assess spillovers between regions and across land-use classes. In a series of Monte Carlo studies, we benchmark our model against other competing specifications. The paper also showcases the performance of the proposed specification using European regional data. Our results indicate that spatial dependence plays a key role in the land-sealing process of cropland and grassland. Moreover, we uncover land-sealing spillovers across multiple classes of arable land.
Adaptive Compressive Sampling for Mid-Infrared Spectroscopic Imaging

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data collection speed and spatial resolution. Recent technological breakthroughs, such as photothermal MIRSI, provide an order of magnitude improvement in spatial resolution. However, this comes at the cost of acquisition speed, which is impractical for clinical tissue samples. This paper introduces an adaptive compressive sampling technique to reduce hyperspectral data acquisition time by an order of magnitude by leveraging spectral and spatial sparsity. This method identifies the most informative spatial and spectral features, integrates a fast tensor completion algorithm to reconstruct megapixel-scale images, and demonstrates speed advantages over FTIR imaging while providing spatial resolutions comparable to new photothermal approaches.
What Can We Learn about SARS-CoV-2 Prevalence from Testing and Hospital Data?

Measuring the prevalence of active SARS-CoV-2 infections in the general population is difficult because tests are conducted on a small and non-random segment of the population. However, hospitalized patients are tested at very high rates, even those admitted for non-COVID reasons. We show how to use information on testing of non-COVID hospitalized patients to obtain tight bounds on population prevalence, under conditions weaker than those usually used. We apply our approach to the population of test and hospitalization data for Indiana, and validate our approach. Our bounds could be constructed at relatively low cost, and for other heavily tested populations.
Wind–MRI interactions in local models of protoplanetary discs – I. Ohmic resistivity

A magnetic disc wind is an important mechanism that may be responsible for driving accretion and structure formation in protoplanetary discs. Recent numerical simulations have shown that these winds can take either the traditional `hourglass' symmetry about the mid-plane, or a `slanted' symmetry dominated by a mid-plane toroidal field of a single sign. The formation of this slanted symmetry state has not previously been explained. We use radially local 1D vertical shearing box simulations to assess the importance of large-scale MRI channel modes in influencing the formation and morphologies of these wind solutions. We consider only Ohmic resistivity and explore the effect of different magnetisations, with the mid-plane $\beta$ parameter ranging from $10^5$ to $10^2$. We find that our magnetic winds go through three stages of development: cyclic, transitive and steady, with the steady wind taking a slanted symmetry profile similar to those observed in local and global simulations. We show that the cycles are driven by periodic excitation of the $n=2$ or $3$ MRI channel mode coupled with advective eviction, and that the transition to the steady wind is caused by a much more slowly growing $n=1$ mode altering the wind structure. Saturation is achieved through a combination of advective damping from the strong wind, and suppression of the instability due to a strong toroidal field. A higher disc magnetisation leads to a greater tendency towards, and more rapid settling into the slanted symmetry steady wind, which may have important implications for mass and flux transport processes in protoplanetary discs.
Data-driven modeling of public risk perception and emotion on Twitter during the Covid-19 pandemic

Successful navigation of the Covid-19 pandemic is predicated on public cooperation with safety measures and appropriate perception of risk, in which emotion and attention play important roles. Signatures of public emotion and attention are present in social media data, thus natural language analysis of this text enables near-to-real-time monitoring of indicators of public risk perception. We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from approx. 20 million unique Covid-19-related tweets from 12 countries posted between 10th March and 14th June 2020. We find evidence of psychophysical numbing: Twitter users increasingly fixate on mortality, but in a decreasingly emotional and increasingly analytic tone. We find that the national attention on Covid-19 mortality is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective. Our parameter estimates for these models are consistent with estimates from psychological experiments, and indicate that users in this dataset exhibit differential sensitivity by country to the national Covid-19 death rates. Our work illustrates the potential utility of social media for monitoring public risk perception and guiding public communication during crisis scenarios.
Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology: Using Natural Language Processing and Deep Learning for Decision Aid

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DMuABSA model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual expert evaluations are aggregated via a criteria weighting through the attention of the experts. We evaluate the methodology in a restaurant decision problem, hence we build the TripR-2020 dataset of restaurant reviews, which we manually annotate and release. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector.
Outcome of External Genital Reconstruction after Female Genital Mutilation (FGM): A Pilot in Selected Patients in the Netherlands.

Background: The demand for reconstruction of external genitalia after Female Genital Mutilation (FGM) is increasing as a result of the empowerment of migrated and naturalized women from Africa and the Middle East. We present our procedures and the outcome of our selection and surgery. Patients and Methods: From 2010 to 2015, 41 women presented with FGM. All underwent wellspecified patient selection before operation. Clitoroplasty according to Foldes was performed in selected patients. These patients were followed-up for 13.4 months and post-operatively assessed. Results: Sixteen of the 41 women (39%) were not operated because they had unrealistic expectation about the outcome of surgery (n=11) or had comorbidities that had to be dealt first (n=5). One woman chooses to have only her Bartholin cyst corrected after pre-opeartive consultation. Clitoroplasty was performed in the remaining 24 women. Post-operatively, we observed no major and only 3 minor post-operative complications. Nineteen of these 24 women were satisfied with the surgery and had a better self-esteem after surgery. Three of the remaining five women were lost for follow-up and two were disappointed with the surgical outcome. Conclusion: External genital reconstruction after well-specified preoperative selection leads too predictable results with minor surgical complications. A better self-esteem achieved by minor surgery should not be a contra-indication for reconstructing these women.
Cloud based enterprise global ontology for information enterprise: A proposed framework

Big Data in organizations have transformed the way organizations across industries implement new approach to handle huge amount of data. Organizations rely to this data to achieve specific business priorities. The challenge is how to capture this data to be considered relevant for the specific organization activities because determining relevant data is a key to deliver information from massive amounts of data. The aim of this paper is to integrate external data using an ontology to capture relevant information for efficient decision-making. In order to achieve this aim, we tackle the literature to incorporate cloud and ontology to retrieve external data such as social media and internal data such as organizational data. The results can lead to some new evaluation methods in big data era from different perspectives. The outcome will offer an enormous opportunity to advance the science of data analytics so that future researchers will have a new understanding on what is needed to improve their data analysis process. The research benefit nation, economy and society. The conduct of this survey will ensure the projects agility in responding to unfolding events, and substantially enhance its ability to engage in and impact on organizations and societies.