Reviewerly/exHarmony huggingface dataset:
Adversarial Perturbation on MRI Modalities in Brain Tumor Segmentation

Convolutional neural networks (CNNs) have been widely used by biomedical image segmentation applications. U-net, as a semantic segmentation method, has become a mainstream approach to brain tumor segmentation. However, the intrinsic vulnerability of CNNs also brings potential risks to all CNN-based applications, including semantic segmentation applications. In this paper, we create a universal adversarial perturbation and apply it on every modality in order to investigate how the adversarial perturbation affects each Magnetic Resonance Imaging(MRI) modality and the MRI images overall. We evaluate the performance when all four modalities are attacked and when one modality is attacked. The results show the following: 1) The adversarial perturbation affects the accuracy performance greatly, regardless of the size of the perturbation; 2) When only one modality is attacked, the network structure and the other three modalities provide some resistance to the adversarial perturbation; and 3) There are performance differences in different modalities, which are strongly related to the intensity distribution. T2 is least affected by the adversarial perturbation, while T1 and T1ce are more affected by the adversarial perturbation.
Motor and Perception Constrained NMPC for Torque-Controlled Generic Aerial Vehicles

This letter presents a perception-aware and motor-level non-linear model predictive control scheme for multi-rotor aerial vehicles. Our formulation considers both real actuation limitations of the platform, and realistic perception objectives for the visibility coverage of an environmental feature while performing a reference task. It directly produces the rotor-level (torque) inputs of the platform motors at high frequency, hence it does not require an intermediate unconstrained controller to work. It is also meant to be generic, by covering standard coplanar quadrotors as well as tilted-propeller multi-rotors. We propose an open-source fully onboard implementation of the method, capable of running at 500 Hz under the intermittent and noisy measurements of one or more cameras. The implementation is extensively tested both in simulation and in real experiments with two substantially different multi-rotor platforms, an underactuated and a fully actuated one, both equipped with two cameras, clearly demonstrating the practicability and high performance of the method.
Predicting toxicity by quantum machine learning

Abstract In recent years, parameterized quantum circuits have been regarded as machine learning models within the framework of the hybrid quantum–classical approach. Quantum machine learning (QML) has been applied to binary classification problems and unsupervised learning. However, practical quantum application to nonlinear regression tasks has received considerably less attention. Here, we develop QML models designed for predicting the toxicity of 221 phenols on the basis of quantitative structure activity relationship. The results suggest that our data encoding enhanced by quantum entanglement provided more expressive power than the previous ones, implying that quantum correlation could be beneficial for the feature map representation of classical data. Our QML models performed significantly better than the multiple linear regression method. Furthermore, our simulations indicate that the QML models were comparable to those obtained using radial basis function networks, while improving the generalization performance. The present study implies that QML could be an alternative approach for nonlinear regression tasks such as cheminformatics.
Overcoming decoherence of Schrödinger cat states formed in a cavity using squeezed-state inputs

A cat-state is a superposition of two coherent states with amplitudes $\alpha_{0}$ and $-\alpha_{0}$. Recent experiments create cat states in a microwave cavity field using superconducting circuits. As with degenerate parametric oscillation (DPO) in an adiabatic and highly nonlinear limit, the states are formed in a signal cavity mode via a two-photon dissipative process induced by the down conversion of a pump field to generate pairs of signal photons. The damping of the signal and the presence of thermal fluctuations rapidly decoheres the state, and the effect on the dynamics is to either destroy the possibility of a cat state, or else to sharply reduce the lifetime and size of the cat-states that can be formed. In this paper, we study the effect on both the DPO and microwave systems of a squeezed reservoir coupled to the cavity. While the threshold nonlinearity is not altered, we show that the use of squeezed states significantly lengthens the lifetime of the cat states. This improves the feasibility of generating cat states of large amplitude and with a greater degree of quantum macroscopic coherence, which is necessary for many quantum technology applications. Using current experimental parameters for the microwave set-up, which requires a modified Hamiltonian, we further demonstrate how squeezed states enhance the quality of the cat states that could be formed in this regime. Squeezing also combats the significant decoherence due to thermal noise, which is relevant for microwave fields at finite temperature. By modeling a thermal squeezed reservoir, we show that the thermal decoherence of the dynamical cat states can be inhibited by a careful control of the squeezing of the reservoir. To signify the quality of the cat state, we consider different signatures including fringes and negativity, and the $C_{l_{1}}$ measure of quantum coherence.
TSF-DBSCAN: A Novel Fuzzy Density-Based Approach for Clustering Unbounded Data Streams

In recent years, several clustering algorithms have been proposed with the aim of mining knowledge from streams of data generated at a high speed by a variety of hardware platforms and software applications. Among these algorithms, density-based approaches have proved to be particularly attractive, thanks to their capability of handling outliers and capturing clusters with arbitrary shapes. The streaming setting poses additional challenges that need to be addressed as well: data streams are potentially unbounded and affected by concept drift, i.e., a modification over time in the underlying data generation process. In this article, we propose temporal streaming fuzzy density-based spatial clustering of applications with noise (TSF-DBSCAN), a novel fuzzy clustering algorithm for streaming data. TSF-DBSCAN is an extension of the well-known DBSCAN algorithm, one of the most popular density-based clustering approaches. Fuzziness is introduced in TSF-DBSCAN to model the uncertainty about the distance threshold that defines the neighborhood of an object. As a consequence, TSF-DBSCAN identifies clusters with fuzzy overlapping borders. A fading model, which makes objects less relevant as they become more remote in time, endows TSF-DBSCAN with the capability of adapting to evolving data streams. The integration of the model in a two-stage approach ensures computational and memory efficiency: during the online stage, continuously arriving objects are organized in proper data structures that are later exploited in the offline stage to determine a fine-grained partition. An extensive experimental analysis on synthetic and real-world datasets shows that TSF-DBSCAN yields competitive performance when compared to other clustering algorithms recently proposed for streaming data.
Maximum Entropy Subspace Clustering Network

Deep subspace clustering networks have attracted much attention in subspace clustering, in which an auto-encoder non-linearly maps the input data into a latent space, and a fully connected layer named self-expressiveness module is introduced to learn the affinity matrix via a typical regularization term (e.g., sparse or low-rank).However, the adopted regularization terms ignore the connectivity within each subspace, limiting their clustering performance.In addition, the adopted framework suffers from the coupling issue between the auto-encoder module and the self-expressiveness module, making the network training non-trivial.To tackle these two issues, we propose a novel deep subspace clustering method named Maximum Entropy Subspace Clustering Network (MESC-Net).Specifically, MESC-Net maximizes the entropy of the affinity matrix to promote the connectivity within each subspace, in which its elements corresponding to the same subspace are uniformly and densely distributed.Meanwhile, we design a novel framework to explicitly decouple the auto-encoder module and the self-expressiveness module.Besides, we also theoretically prove that the learned affinity matrix satisfies the block-diagonal property under the assumption of independent subspaces.Extensive quantitative and qualitative results on commonly used benchmark datasets validate MESC-Net significantly outperforms state-of-the-art methods.The code is publicly available at https://github.com/ZhihaoPENG-CityU/MESC.
Creating Digital Musical Instruments with libmosaic-sound and Mosaicode

Music has been influenced by digital technology over the last few decades. With the computer and the Digital Musical Instruments, the musical composition could trespass the use of acoustic instruments demanding to musicians and composers a sort of computer programming skills for the development of musical applications. In order to simplify the development of musical applications several tools and musical programming languages arose bringing some facilities to lay-musicians on computer programming to use the computer to make music. This work presents the development of a Visual Programming Language (VPL) to develop DMI applications in the Mosaicode programming environment, simplifying sound design and making the creation of digital instruments more accessible to digital artists. It is also presented the implementation of libmosaic-sound library, which supported the VPL development, for the specific domain of Music Computing and DMI creation.
OFCOD: On the Fly Clustering Based Outlier Detection Framework

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.
No quantum speedup with Grover-Rudolph state preparation for quantum Monte Carlo integration

We prove that there is no quantum speed-up when using quantum Monte-Carlo to estimate the mean (and other moments) of analytically-defined log-concave probability distributions prepared as quantum states using the Grover-Rudolph method.
A probabilistic separation logic

Probabilistic independence is a useful concept for describing the result of random sampling—a basic operation in all probabilistic languages—and for reasoning about groups of random variables. Nevertheless, existing verification methods handle independence poorly, if at all. We propose a probabili stic separation logic PSL, where separation models probabilistic independence. We first give a new, probabilistic model of the logic of bunched implications (BI). We then build a program logic based on these assertions, and prove soundness of the proof system. We demonstrate our logic by verifying information-theoretic security of cryptographic constructions for several well-known tasks, including private information retrieval, oblivious transfer, secure multi-party addition, and simple oblivious RAM. Our proofs reason purely in terms of high-level properties, like independence and uniformity.
Nonlinear optimization generating the Tomb Mural Blocks by GANS

Abstract Tomb murals are the special kind of murals that are buried underground. Due to the narrow exit of the tomb passage, the tomb murals were excavated by dividing the whole mural into blocks, which made lots of information missing between the blocks. The digital restoration technology Image inpainting uses the edge information around the missing parts to spread the information inside of the defect area and fills the information from the outside to the inside. But it is not suitable for filling the missing parts between the tomb mural blocks. Because these parts are large for exemplar-based inpainting which may make texture dislocation and for PDE which may make cartoon blur. It is a need to generate the information outwards to complete the information. The generative adversarial network uses deep learning training by the murals remains to generate the information from inside to outside, but the typical GAN doesn‘t have a good nonlinear feature. This paper provided a generating technology based on the deep convolution generative adversarial network to rebuild the missing information between the tomb mural blocks. It built the training data set of the simulation platform with Keras and designed a whole mural generation scheme based on DCGAN. In order to get better generated results to avoid the bad artifacts; it adds the nonlinear layers by choosing 13 layers convolution and 2 deconvolution layers of the generator and contained 5 layers convolution discriminator; it designed a new phased nonlinear loss function by using Pycharm pretreatment for Numpy array file data sets; finally, it completed the generate tomb mural information to obtain the good simulation effect.
Digitalization of the Educational Environment: Risk Assessment of Distance Education by Russian and Vietnamese Students

The digitalization of higher education is a long-term trend that gained a new impetus for further development because of the forced transition to distance learning during the COVID-19 pandemic. The aim of the article is to analyze the impact of digital transformation on the educational process in universities and to describe the risks through the students’ eyes. The analysis is based on the results of a survey conducted by the authors in 2020 among students of two universities - Moscow State Institute of International Relations (MGIMO) and the Institute of the Trade Union Movement of the General Confederation of Labor of Vietnam (IPLV). The article describes the methodology of the online survey. The attitude towards distance learning is interpreted as a three-level attitude with emotional, cognitive and behavioral aspects. It is suggested that students’ readiness to switch to distance learning is determined by a set of macro and micro factors. Among the macro factors are the national specifics of the educational system, traditions in the field of higher education, infrastructure, the national educational policy, and the mobilization potential of the population. Among the micro factors are the cognitive and other psychological characteristics of the students, the socio-psychological openness to innovation. According to the survey results, there is certain similarity in the way Russian and Vietnamese students assess their experience of distance learning. At the same time, significant differences in the perception of the outcome of the digital transformation of education have been revealed. For MGIMO students, major risks are associated with dehumanization, the severing of social ties, and the possible loss of student status. For Vietnamese students the most significant risks are mainly associated with the fears of the decreasing quality of education. It is therefore concluded that distance learning is both the field of opportunities and possible source of individual and institutional risks.
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning

The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely on domain-specific data augmentations, which are not easy to generate for all data modalities. Pseudo-labeling (PL) is a general SSL approach that does not have this constraint but performs relatively poorly in its original formulation. We argue that PL underperforms due to the erroneous high confidence predictions from poorly calibrated models; these predictions generate many incorrect pseudo-labels, leading to noisy training. We propose an uncertainty-aware pseudo-label selection (UPS) framework which improves pseudo labeling accuracy by drastically reducing the amount of noise encountered in the training process. Furthermore, UPS generalizes the pseudo-labeling process, allowing for the creation of negative pseudo-labels; these negative pseudo-labels can be used for multi-label classification as well as negative learning to improve the single-label classification. We achieve strong performance when compared to recent SSL methods on the CIFAR-10 and CIFAR-100 datasets. Also, we demonstrate the versatility of our method on the video dataset UCF-101 and the multi-label dataset Pascal VOC.
PyAutoFit: A Classy Probabilistic Programming Language for Model Composition and Fitting

A major trend in academia and data science is the rapid adoption of Bayesian statistics for data analysis and modeling, leading to the development of probabilistic programming languages (PPL). A PPL provides a framework that allows users to easily specify a probabilistic model and perform inference automatically. PyAutoFit is a Python-based PPL which interfaces with all aspects of the modeling (e.g., the model, data, fitting procedure, visualization, results) and therefore provides complete management of every aspect of modeling. This includes composing high-dimensionality models from individual model components, customizing the fitting procedure and performing data augmentation before a model-fit. Advanced features include database tools for analysing large suites of modeling results and exploiting domain-specific knowledge of a problem via non-linear search chaining. Accompanying PyAutoFit is the autofit workspace (see https://github.com/Jammy2211/autofit_workspace), which includes example scripts and the HowToFit lecture series which introduces non-experts to model-fitting and provides a guide on how to begin a project using PyAutoFit. Readers can try PyAutoFit right now by going to the introduction Jupyter notebook on Binder (see https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/HEAD) or checkout our readthedocs(see https://pyautofit.readthedocs.io/en/latest/) for a complete overview of PyAutoFit's features.
High-Fidelity Measurement of a Superconducting Qubit Using an On-Chip Microwave Photon Counter

We describe an approach to the high-fidelity measurement of a superconducting qubit using an on-chip microwave photon counter. The protocol relies on the transient response of a dispersively coupled measurement resonator to map the state of the qubit to "bright" and "dark" cavity pointer states that are characterized by a large differential photon occupation. Following this mapping, we photodetect the resonator using the Josephson Photomultipler (JPM), which transitions between classically distinguishable flux states when cavity photon occupation exceeds a certain threshold. Our technique provides access to the binary outcome of projective quantum measurement at the millikelvin stage without the need for quantum-limited preamplification and thresholding at room temperature. We achieve raw single-shot measurement fidelity in excess of 98% across multiple samples using this approach in total measurement times under 500 ns. In addition, we show that the backaction and crosstalk associated with our measurement protocol can be mitigated by exploiting the intrinsic damping of the JPM itself.
Research on Collaborative Governance of Smart Government Based on Blockchain Technology: An Evolutionary Approach

Smart government is an important means of optimizing government management, improving the government decision-making capacity, and pushing forward the public service. When the smart government process applies, the dire straits of collaborative governance among the different participants could not be ignored usually caused by maximizing their profits. Based on the current research, this paper introduces the blockchain technology into the smart government system and establishes a smart government platform architecture. Meanwhile, to analyse the evolutionary and stable strategies of the three parties under the blockchain technology, the evolutionary game model including functional departments, local governments, and end users as the main players is established on account of the bounded rationality. By examining the “blockchain + government service” in Beijing with the systemic dynamics theory, this paper changes the influencing factors simulated by changing the parameter assignment, to determine the evolutionary stable equilibrium under different external conditions. The results show that local government supervision plays a leading role in the process of collaborative governance of smart government based on blockchain technology; meanwhile, effective cost control is a key factor affecting the evolutionary stability strategy (ESS). Besides, the “decentralized” structure, “distrust” architecture, and “precision” mechanism of the blockchain are verified for the effect of the evolution process. Among them, precision service and flat management improve the possibility of collaborative governance, but the impact of the trust mechanism is not obvious. Therefore, the collaborative governance model of smart government based on blockchain technology is loaded with far-reaching significance for promoting the modernization of China’s governance capacity and governance system.
Face mask detection for covid_19 pandemic using pytorch in deep learning

Abstract The World Health Organization (WHO) has stated that there are two ways in which the spread of COVID 19 virus takes place that are respiratory droplets and physical contact. So, avoiding the spread of this virus need some precautionary steps to be taken that are social distancing and the wearing of masks. Among these two precautions the mask wearing is considered as the important factor for the spread of COVID 19 virus because these droplets can land on any surface. So, to keep track of the people that are wearing mask or not is more important. Here we have presented a mask detection system that is able to detect any type of mask and masks of different shapes from the video streams for following the rules that are applied by the government. Deep learning algorithm is used here and the PyTorch library of python is used for mask detection from the images/video streams. The proposed system is able to detect the mask wearing people and those one who are not wearing the masks.
An Open Source AutoML Benchmark

In recent years, an active field of research has developed around automated machine learning (AutoML). Unfortunately, comparing different AutoML systems is hard and often done incorrectly. We introduce an open, ongoing, and extensible benchmark framework which follows best practices and avoids common mistakes. The framework is open-source, uses public datasets and has a website with up-to-date results. We use the framework to conduct a thorough comparison of 4 AutoML systems across 39 datasets and analyze the results.