Reviewerly/exHarmony huggingface dataset:
Accelerated adiabatic quantum gates: Optimizing speed versus robustness

We develop protocols for high-fidelity single-qubit gates that exploit and extend theoretical ideas for accelerated adiabatic evolution. Our protocols are compatible with qubit architectures where direct transitions between logical states are either vanishingly small or nonexistent; in such systems traditional approaches cannot be implemented. Prime examples are superconducting fluxonium qubits, which have highly localized states, and AMO systems, where there are no dipole allowed transitions between the ground states encoding the logical states. By using an accelerated adiabatic protocol we can enforce the desired adiabatic evolution while having gate times that are comparable to the inverse adiabatic energy gap (a scale that is ultimately set by the amount of power used in the control pulses). By modeling the effects of decoherence, we explore the trade-off between speed and robustness that is inherent to shortcuts-to-adiabaticity approaches.
Robust image hashing via visual attention model and ring partition

Robustness is an important property of image hashing. Most of the existing hashing algorithms do not reach good robustness against large-angle rotation. Aiming at this problem, we jointly exploit visual attention model and ring partition to design a novel image hashing, which can make good rotation robustness. In the proposed image hashing, a visual attention model called PFT (Phase spectrum of Fourier Transform) model is used to detect saliency map of preprocessed image. The LL sub-band of saliency map is then divided into concentric circles invariant to rotation by ring partition, and the means and variances of DWT coefficients on concentric circles are taken as image features. Next, these features are encrypted by a chaotic map and the Euclidean distances between normalized encrypted features are finally exploited to generate hash. Similarity between hashes is measured by L 1 norm. Many experimental tests show that our image hashing is robust to digital operations including rotation and reaches good discrimination. Comparisons demonstrate that classification performance of our image hashing outperforms those of some well-known hashing algorithms in terms of receiver operating characteristics curves. Simulation of image copy detection is carried out on an open image database called UCID and the result validates effectiveness of our hashing.
Deep Language Statistics of Italian throughout Seven Centuries of Literature and Empirical Connections with Miller’s 7 &amp;amp;#8723; 2 Law and Short-Term Memory

Statistics of languages are usually calculated by counting characters, words, sentences, word rankings.Some of these random variables are also the main "ingredients" of classical readability formulae.Revisiting the readability formula of Italian, known as GULPEASE, shows that of the two terms that determine the readability index G-the semantic index C G , proportional to the number of characters per word, and the syntactic index G F , proportional to the reciprocal of the number of words per sentence-G F is dominant because G C is, in practice, constant for any author throughout seven centuries of Italian Literature.Each author can modulate the length of sentences more freely than he can do with the length of words, and in different ways from author to author.For any author, any couple of text variables can be modelled by a linear relationship y mx = , but with different slope m from author to author, except for the relationship between characters and words, which is unique for all.The most important relationship found in the paper is that between the short-term memory capacity, described by Miller's "7 ∓ 2 law" (i.e., the number of "chunks" that an average person can hold in the short-term memory ranges from 5 to 9), and the word interval, a new random variable defined as the average number of words between two successive punctuation marks.The word interval can be converted into a time interval through the average reading speed.The word interval spreads in the same range as Miller's law, and the time interval is spread in the same range of short-term memory response times.The connection between the word interval (and time interval) and short-term memory appears, at least empirically, justified and natural, however, to be further investigated.Technical and scientific writings (papers, essays, etc.) ask more to their
Graph Star Net for Generalized Multi-Task Learning

In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, graph classification and link prediction. GraphStar addresses many earlier challenges facing graph neural nets and achieves non-local representation without increasing the model depth or bearing heavy computational costs. We also propose a new method to tackle topic-specific sentiment analysis based on node classification and text classification as graph classification. Our work shows that 'star nodes' can learn effective graph-data representation and improve on current methods for the three tasks. Specifically, for graph classification and link prediction, GraphStar outperforms the current state-of-the-art models by 2-5% on several key benchmarks.
PDP-SAG: Personalized Privacy Protection in Moving Objects Databases by Combining Differential Privacy and Sensitive Attribute Generalization

Moving objects databases have become an enabling technology for location-based applications.They mostly focus on the storing and processing of data about moving objects.Privacy protection is one of the most important concerns related to such databases.In recent years, some mechanisms have been proposed to answer statistical queries over moving objects databases, while satisfying differential privacy.However, none of them consider the case where a moving objects database contains non-spatiotemporal sensitive attributes other than spatiotemporal attributes.Besides, most of them do not support the personalized privacy protection requirements of different moving objects.In this paper, we address these problems by presenting PDP-SAG, a differentially private mechanism that combines the sensitive attribute generalization with personalized privacy in a unified manner.By this combination, we aim to provide different levels of differential privacy protection for moving objects that have non-spatiotemporal sensitive attributes as well.In this regard, we generalize the sensitive attribute values of trajectory data records based on their privacy descriptor and define a new personalized differentially private tree structure to keep different noisy frequencies for each trajectory according to the generalized sensitive attribute values of trajectory data records passing through that trajectory.We also propose intra-and inter-consistency constraints enforcements to make noisy frequencies consistent with each other.The extensive experiments on synthetic and real datasets verify that PDP-SAG significantly improves the utility of sensitive query answers and provides the required level of privacy protection for each moving object, in comparison to the case when no personalization and generalization are permitted.
Modeling and control of a reconfigurable photonic circuit using deep learning

The complexity of experimental quantum information processing devices is increasing rapidly, requiring new approaches to control them. In this paper, we address the problems of practically modeling and controlling an integrated optical waveguide array chip, a technology expected to have many applications in telecommunications and optical quantum information processing. This photonic circuit can be electrically reconfigured, but only the output optical signal can be monitored. As a result, the conventional control methods cannot be naively applied. Characterizing such a chip is challenging for three reasons. First, there are uncertainties associated with the Hamiltonian describing the chip. Second, we expect distortions of the control voltages caused by the chip's electrical response, which cannot be directly observed. Finally, there are imperfections in the measurements caused by losses from coupling the chip externally to optical fibers. We developed a deep neural network approach to solve these problems. The architecture is designed specifically to overcome the aforementioned challenges using a Gated Recurrent Unit (GRU)-based network as the central component. The Hamiltonian is estimated as a blackbox, while the rules of quantum mechanics such as state evolution is embedded in the structure as a whitebox. The resulting overall graybox model of the chip shows good performance both quantitatively in terms of the mean square error and qualitatively in terms of the predicted waveforms. We use this neural network to solve a classical and a quantum control problem. In the classical application we find a control sequence to approximately realize a time-dependent output power distribution. For the quantum application we obtain the control voltages to realize a target set of quantum gates. The proposed method is generic and can be applied to other systems that can only be probed indirectly.
A Coverless Plain Text Steganography Based on Character Features

The coverless text steganography has become a research hotspot in the field of information hiding because no modification to a text carrier will improve the concealment of steganography.However, compared with images, audios, videos, or other carriers, a plain text has less redundancy for modification, so the text steganographic embedding rate is usually lower, and semantic anomalies are more easily produced once it is modified or generated.This paper analyzes the parity of Chinese characters' stroke number and its statistical characteristic, and reach the conclusion that this parity feature exhibits a uniform distribution and it exists in every character.These indicate that the parity can be employed to express a binary digit and its combination can represent a binary digit string.In the light of the above analyses, we propose a new coverless plain text steganography based on the parity of Chinese characters' stroke number and extend it to further improve the embedding rate.By exploiting the space mapping concept, this method initializes a binary search tree based on the texts from the Internet and searches the corresponding texts according to the secret binary digit string generated from a secret.Thus, a secret will be mapped into a list of the corresponding uniform resource locators.That is, our proposed methods only employ the existing uniform resource locators to convey a secret.Compared with the existing text steganography techniques, our methods not only have high-embedding rate and good concealment but also can improve the embedding capacity without impact on the concealment, which eliminates the contradiction between the embedding capacity/rate and security, because no modification or additional information is generated.In addition, these methods have a prominent search success rate, and they can be extended to other language features as well as to other languages.
Ki-67, p53 and BCL-2 Expressions and their Association with Clinical Histopathology of Breast Cancer among Women in Tanzania

Abstract This study associated Ki-67, p53, and BCL-2 markers with clinical histopathological (CH) features using currently available limited data on these markers in Tanzania. Retrospective chart review study was conducted among females with confirmed breast cancer (BC) at Muhimbili National Hospital in Tanzania between 2016 and 2017. Inclusion criteria were met by 76 patients with a mean age of 51.32 ± 14.28 years. Of these, 86.4% were stage III and IV, whereas 83.5% cases had grade 2 and grade 3. Upon immunostaining, 85.5% and 57.9% were Ki-67 and BCL-2 positive respectively. Log-linear analysis showed no statistically significant association among biomarkers expression and CH features. However, multinomial linear regression showed higher possibility for association between high expression of Ki-67, low expression of p53 and high expression of BCL-2 with age, grade, stage and tumor (T) stage. BCL-2 was positively correlated with Ki-67 expression contrary to p53, which was negatively correlated with BCL-2. Conclusively, there is evidence of correlation between the studied markers with CH features. However, studies with larger sample sizes will likely reveal significant associations that will validate the role of these markers as tools for evaluating treatment response in individualized therapeutic schemes in Tanzania.
Anisotropic Super Resolution In Prostate Mri Using Super Resolution Generative Adversarial Networks

Acquiring High Resolution (HR) Magnetic Resonance (MR) images requires the patient to remain still for long periods of time, which causes patient discomfort and increases the probability of motion induced image artifacts. A possible solution is to acquire low resolution (LR) images and to process them with the Super Resolution Generative Adversarial Network (SRGAN) to create a super-resolved version. This work applies SRGAN to MR images of the prostate and performs three experiments. The first experiment explores improving the in-plane MR image resolution by factors of 4 and 8, and shows that, while the PSNR and SSIM (Structural SIMilarity) metrics are lower than the isotropic bicubic interpolation baseline, the SRGAN is able to create images that have high edge fidelity. The second experiment explores anisotropic super-resolution via synthetic images, in that the input images to the network are anisotropically downsampled versions of HR images. This experiment demonstrates the ability of the modified SRGAN to perform anisotropic super-resolution, with quantitative image metrics that are comparable to those of the anisotropic bicubic interpolation baseline. Finally, the third experiment applies a modified version of the SRGAN to super-resolve anisotropic images obtained from the through-plane slices of the volumetric MR data. The output super-resolved images contain a significant amount of high frequency information that make them visually close to their HR counterparts. Overall, the promising results from each experiment show that super-resolution for MR images is a successful technique and that producing isotropic MR image volumes from anisotropic slices is an achievable goal.
An empirical research on sentiment analysis using machine learning approaches

Nowadays users of social networks are very much interested in expressing their opinions about different sorts of products or services in social media which leads to the growth of user-generated web contents. Their reviews on social media have a significant impact on customers for making effective and optimal decisions for buying products or using services. In sentiment analysis, most of the used approaches are based on machine learning techniques. In this paper, the well-known methods of machine learning are reviewed and compared against each other. Then the comparative studies on the performance of these techniques on online user reviews that come from multiple industry domains are performed. The experiments involve many different data sets from various domains including Amazon, Yelp and IMDb. Well-known methods such as Support Vector Machine, Decision Tree, Bagging, Boosting, Random Forest and Maximum Entropy are implemented in the experiments. Based on the experimental results it is found that users can extract applicable information from review data sets for business intelligence and better product sales production, and that Boosting and Maximum Entropy outperform the other examined machine learning algorithms for detecting sentiments in online user reviews.
Random Shuffling Beats SGD after Finite Epochs

A long-standing problem in the theory of stochastic gradient descent (SGD) is to prove that its without-replacement version RandomShuffle converges faster than the usual with-replacement version. We present the first (to our knowledge) non-asymptotic solution to this problem, which shows that after a number of epochs RandomShuffle indeed converges faster than SGD. Specifically, we prove that under strong convexity and second-order smoothness, the sequence generated by RandomShuffle converges to the optimal solution at the rate O(1/T^2 + n^3/T^3), where n is the number of components in the objective, and T is the total number of iterations. This result shows that after a reasonable number of epochs RandomShuffle is strictly better than SGD (which converges as O(1/T)). The key step toward showing this better dependence on T is the introduction of n into the bound; and as our analysis will show, in general a dependence on n is unavoidable without further changes to the algorithm. We show that for sparse data RandomShuffle has the rate O(1/T^2), again strictly better than SGD. Furthermore, we discuss extensions to nonconvex gradient dominated functions, as well as non-strongly convex settings.
CourtTime: Generating Actionable Insights into Tennis Matches Using Visual Analytics

Tennis players and coaches of all proficiency levels seek to understand and improve their play. Summary statistics alone are inadequate to provide the insights players need to improve their games. Spatio-temporal data capturing player and ball movements is likely to provide the actionable insights needed to identify player strengths, weaknesses, and strategies. To fully utilize this spatio-temporal data, we need to integrate it with domain-relevant context meta-data. In this paper, we propose CourtTime, a novel approach to perform data-driven visual analysis of individual tennis matches. Our visual approach introduces a novel visual metaphor, namely 1-D Space-Time Charts that enable the analysis of single points at a glance based on small multiples. We also employ user-driven sorting and clustering techniques and a layout technique that aligns the last few shots in a point to facilitate shot pattern discovery. We discuss the usefulness of CourtTime via an extensive case study and report on feedback from an amateur tennis player and three tennis coaches.
On tensor products of CSS codes

CSS codes are in one-to-one correspondance with length 3 chain complexes. The latter are naturally endowed with a tensor product $\otimes$ which induces a similar operation on the former. We investigate this operation, and in particular its behavior with regard to minimum distances. Given a CSS code $\mathcal C$, we give a criterion which provides a lower bound on the minimum distance of $\mathcal C \otimes \mathcal D$ for every CSS code $\mathcal D$. From this criterion arises a generic bound for the minimum distance which is twice larger than the single bound previously known in the literature. We apply these results to study the behaviour of iterated tensor powers of codes. Such sequences of codes are logarithmically LDPC and we prove in particular that their minimum distances tend generically to infinity. More precisely, their minimum distance increases as $O(n^\alpha)$ for some $\alpha > 0$, where $n$ is the code length, while the row weight of their parity–check matrices grows as $O(\log(n))$. This entails a rather surprizing fact: even if a CSS code does not have quantum degeneracy, for a large enough $\ell$, its $\ell$-th iterated tensor power does. Different known results are also reinterpretated in terms of tensor products and three new families of LDPC CSS codes are studied.
ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

Big Data is rapidly gaining impetus and is attracting a community of researchers and organization from varying sectors due to its tremendous potential. Big Data is considered as a prospective raw material to acquire domain specific knowledge to gain insights related to management, planning, forecasting and security etc. Due to its inherent characteristics like capacity, swiftness, genuineness and diversity Big Data hampers the efficiency and effectiveness of search and leads to optimization problems. In this paper we explore the complexity imposed by big search spaces leading to optimization issues. In order to overcome the above mentioned issues we propose a hybrid algorithm for Big Data preprocessing ACO-clustering algorithm approach. The proposed algorithm can help to increase search speed by optimizing the process. As the proposed method using ant colony optimization with clustering algorithm it will also contribute to reducing pre-processing time and increasing analytical accuracy and efficiency.
Smart contract model for complex reality transaction

Purpose The application of smart contract can greatly reduce transaction costs and improve transaction efficiency. The existing smart contract are expensive, single application scenario and inefficient. This paper aims to propose a new smart contract model to solve these problems. Design/methodology/approach By investigating the research history, models and platforms, this paper summarizes the shortcomings of existing smart contracts. Based on the content and architecture of traditional contract, a smart contract model with wider application scope is designed. Findings In this paper, several models are used to describe the operation mechanism of smart contracts. To facilitate computer execution, a decomposition method is proposed, which divides smart contracts into several sub-contracts. Then, the advantages and deployment methods of smart contract are discussed. On this basis, a specific example is given to illustrate how the application of smart contract will change our life. Originality/value Smart contract is gradually applied to more fields. In this paper, the structure and operation mechanism of smart contract system in reality are given, which will be beneficial to the application of smart contract to more complex systems.
A comparative study between a computer-based and a mobile-based assessment

Purpose The purpose of this study is to compare the overall usability and user experience of desktop computers and mobile-devices when used in a summative assessment in the context of a higher education course.
A structural property of the wavelet packet transform method to localise incoherency of a signal

This paper proves a general structural property of the wavelet tree for a given seminorm in the context of the wavelet packet transform method. This structural property can be used in denoising algorithms of different applications to guarantee the optimality of novel search strategies. The property holds for any input signals using any orthogonal analysing wavelet families. The property holds for any norms, which results to be a convex function through the wavelet tree. Using a defined norms, seminorms or pseudonorms, this property can be used to detect incoherent parts of an input signal by using the minimal depth of the tree. In this sense the proposed denoising procedure works without thresholds for the localisation of different kinds of noise, as well as for a stop criterium for an optimal representation of the incoherency. The proof of this property is performed by mathematical induction and the demonstration is based on orthonormality with the help of the multiresolution framework aspects of the wavelets packet tree. The Theorem is independent of the definition of the adopted norm and of the incoherent part of an input signal. In this sense, the discovered property is a general one, which is related to any norm and any nature of the signal incoherence. It can be used in different applications, in which a minimum of a norm is required to be calculated through the wavelet tree.