Reviewerly/exHarmony huggingface dataset:
Simple criterion for local distinguishability of generalized Bell states in prime dimension

Local distinguishability of sets of generalized Bell states (GBSs) is investigated. We first clarify the conditions such that a set of GBSs can be locally transformed to a certain type of GBS set that is easily distinguishable within local operations and one-way classical communication. We then show that if the space dimension $d$ is a prime, these conditions are necessary and sufficient for sets of $d$ GBSs in ${\mathbb{C}}^{d}\ensuremath{\bigotimes}{\mathbb{C}}^{d}$ to be locally distinguishable. Thus we obtain a simple computable criterion for local distinguishability of sets of $d$ GBSs in prime dimension $d$.
Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples

We develop a simple Quantile Spacing (QS) method for accurate probabilistic estimation of one-dimensional entropy from equiprobable random samples, and compare it with the popular Bin-Counting (BC) and Kernel Density (KD) methods. In contrast to BC, which uses equal-width bins with varying probability mass, the QS method uses estimates of the quantiles that divide the support of the data generating probability density function (pdf) into equal-probability-mass intervals. And, whereas BC and KD each require optimal tuning of a hyper-parameter whose value varies with sample size and shape of the pdf, QS only requires specification of the number of quantiles to be used. Results indicate, for the class of distributions tested, that the optimal number of quantiles is a fixed fraction of the sample size (empirically determined to be ~0.25–0.35), and that this value is relatively insensitive to distributional form or sample size. This provides a clear advantage over BC and KD since hyper-parameter tuning is not required. Further, unlike KD, there is no need to select an appropriate kernel-type, and so QS is applicable to pdfs of arbitrary shape, including those with discontinuous slope and/or magnitude. Bootstrapping is used to approximate the sampling variability distribution of the resulting entropy estimate, and is shown to accurately reflect the true uncertainty. For the four distributional forms studied (Gaussian, Log-Normal, Exponential and Bimodal Gaussian Mixture), expected estimation bias is less than 1% and uncertainty is low even for samples of as few as 100 data points; in contrast, for KD the small sample bias can be as large as −10% and for BC as large as −50%. We speculate that estimating quantile locations, rather than bin-probabilities, results in more efficient use of the information in the data to approximate the underlying shape of an unknown data generating pdf.
Transfer of Learning in the Convolutional Neural Networks on Classifying Geometric Shapes Based on Local or Global Invariants

The convolutional neural networks (CNNs) are a powerful tool of image classification that has been widely adopted in applications of automated scene segmentation and identification. However, the mechanisms underlying CNN image classification remain to be elucidated. In this study, we developed a new approach to address this issue by investigating transfer of learning in representative CNNs (AlexNet, VGG, ResNet-101, and Inception-ResNet-v2) on classifying geometric shapes based on local/global features or invariants. While the local features are based on simple components, such as orientation of line segment or whether two lines are parallel, the global features are based on the whole object such as whether an object has a hole or whether an object is inside of another object. Six experiments were conducted to test two hypotheses on CNN shape classification. The first hypothesis is that transfer of learning based on local features is higher than transfer of learning based on global features. The second hypothesis is that the CNNs with more layers and advanced architectures have higher transfer of learning based global features. The first two experiments examined how the CNNs transferred learning of discriminating local features (square, rectangle, trapezoid, and parallelogram). The other four experiments examined how the CNNs transferred learning of discriminating global features (presence of a hole, connectivity, and inside/outside relationship). While the CNNs exhibited robust learning on classifying shapes, transfer of learning varied from task to task, and model to model. The results rejected both hypotheses. First, some CNNs exhibited lower transfer of learning based on local features than that based on global features. Second the advanced CNNs exhibited lower transfer of learning on global features than that of the earlier models. Among the tested geometric features, we found that learning of discriminating inside/outside relationship was the most difficult to be transferred, indicating an effective benchmark to develop future CNNs. In contrast to the “ImageNet” approach that employs natural images to train and analyze the CNNs, the results show proof of concept for the “ShapeNet” approach that employs well-defined geometric shapes to elucidate the strengths and limitations of the computation in CNN image classification. This “ShapeNet” approach will also provide insights into understanding visual information processing the primate visual systems.
A Proposal For COVID-19 Applications Enabling Extensive Epidemiological Studies

During the next phase of COVID-19 outbreak, mobile applications could be the most used and proposed technical solution for monitoring and tracking, by acquiring data from subgroups of the population. A possible problem could be data fragmentation, which could lead to three harmful effects: i) data could not cover the minimum percentage of the people for monitoring efficacy, ii) it could be heavily biased due to different data collection policies, and iii) the app could not monitor subjects moving across different zones or countries. A common approach could solve these problems, defining requirements for the selection of observed data and technical specifications for the complete interoperability between different solutions. This work aims to integrate the international framework of requirements in order to mitigate the known issues and to suggest a method for clinical data collection that ensures to researchers and public health institution significant and reliable data. First, we propose to identify which data is relevant for COVID-19 monitoring through literature and guidelines review. Then we analysed how the currently available guidelines for COVID-19 monitoring applications drafted by European Union and World Health Organization face the issues listed before. Eventually we proposed the first draft of integration of current guidelines.
Ontology-based approach for cybersecurity recruitment

Human resource management is the strategic approach to effectively managing people in a business or organization so that their business contributes to gaining competitive advantage. Many human resource management tasks are based on finding and matching individuals to positions. In this paper an ontology-based approach is presented for effectively matching job seekers and job positions over multiple criteria by making use of different skills ontologies, for discovering a set of profiles that best fits the job description criteria and ranking applicants. The general architecture of the framework is outlined and ontologies operations are described.
On the cost of unsuccessful searches in search trees with two-way comparisons

Search trees are commonly used to implement access operations to a set of stored keys. If this set is static and the probabilities of membership queries are known in advance, then one can precompute an optimal search tree, namely one that minimizes the expected access cost. For a non-key query, a search tree can determine its approximate location by returning the inter-key interval containing the query. This is in contrast to other dictionary data structures, like hash tables, that only report a failed search. We address the question “what is the additional cost of determining approximate locations for non-key queries”? We prove that for two-way comparison trees this additional cost is at most 1. Our proof is based on a novel probabilistic argument that involves converting a search tree that does not identify non-key queries into a random tree that does.
Convergence theorems on multi-dimensional homogeneous quantum walks

Abstract We propose a general framework for quantum walks on d -dimensional spaces. We investigate asymptotic behavior of these walks. We prove that every homogeneous walks with finite degree of freedom have limit distribution. This theorem can also be applied to every crystal lattice. In this theorem, it is not necessary to assume that the support of the initial unit vector is finite. We also pay attention on 1-cocycles, which is related to Heisenberg representation of time evolution of observables. For homogeneous walks with finite degree of freedom, convergence of averages of 1-cocycles associated with the position observable is also proved.
Deep structure of the Salair fold-nappe terrane (NW CAOB) according to magnetotelluric sounding

The Salair fold-nappe terrane (a.k.a. Salair orogen, Salair) is the northwestern part of the Altai-Sayan folded area of the Central Asian Orogenic Belt. It is composed of Cambrian – Early Ordovician volcanic rocks and island-arc sedimentary deposits. In plan, Salair is a horseshoe-shaped structure with the northeast-facing convex side, which is formed by the outcrops of the Early Paleozoic folded basement. Its inner part is the Khmelev basin composed of Upper Devonian – Lower Carboniferous sandstones and siltstones. The Early Paleozoic volcanic rocks and sediments of Salair are overthrusted onto the Devonian-Permian sediments of the Kuznetsk basin. The Paleozoic thrusts, that were reactivated at the neotectonic stage, are observed in the modern relief as tectonic steps. Our study of the Salair deep structure was based on the data from two profiles of magnetotelluric sounding. These 175-km and 125-km long profiles go across the strike of the Salair structure and the western part of the Kuznetsk basin. Profile 1 detects a subhorizontal zone of increased conductivity (100–500 Ohm·m) at the depths of 8–15 km. At the eastern part of Profile 1, this zone gently continues upward, towards a shallow conducting zone that corresponds to the sediments of the Kuznetsk basin. Two high-resistance bodies (1000–7000 Ohm⋅m) are detected at the depths of 0–6 km in the middle of the section. They are separated by a subvertical conducting zone corresponding to the Kinterep thrust. The main features are the subhorizontal positions and the flattened forms of crustal conductivity anomalies. At the central part of Profile 2, there is a high-resistance block (above 150000 Ohm⋅m) over the entire depth range of the section, from the surface to the depths of about 20 km. In the eastern part of Profile 2, a shallow zone of increased conductivity corresponds to the sediments of the Kuznetsk basin. The subhorizontal mid-crust layer of increased conductivity, which is detected in the Salair crust, is typical of intracontinental orogens. The distribution pattern of electrical conductivity anomalies confirms the Salair thrust onto the Kuznetsk basin. The northern part of the Khmelev basin is characterized by high resistivity, which can be explained by abundant covered Late Permian granite massifs in that part of the Khmelev basin. The Kinterep thrust located in the northeastern part of the Khmelev basin is manifested in the deep geoelectric crust structure as a conducting zone, which can be considered as an evidence of the activity of this fault.
Babyface: Performance and Installation Art Exploring the Feminine Ideal in Gendered Machines

Representations of gender in new technologies like the Siri, Pepper, and Sophia robotic assistants, as well as the commodification of features associated with gender on platforms like Instagram, inspire questions about how and whether robotic tools can have gender and what it means to people if they do. One possible response to this is through artistic creation of dance performance. This paper reports on one such project where, along the route to this inquiry, creation of machine augmentation – of both the performer and audience member – was necessary to communicate the artistic ideas grappled with therein. Thus, this article describes the presentation of Babyface , a machine-augmented, participatory contemporary dance performance. This work is a reaction to feminized tropes in popular media and modern technology, and establishes a parallel between the ways that women and machines are talked about, treated, and – in the case of machines – designed to look and behave. This paper extends prior reports on the creation of this piece and its accompanying devices to describe extensions with audience member participation, and reflect on the responses of these audience members. These fabricated elements alongside the actions of the performer and a soundscape that quotes statements made by real “female” robots create an otherwordly, sad cyborg character that causes viewers to question their assumptions about and pressures on the feminine ideal.
Pre-Training on Mixed Data for Low-Resource Neural Machine Translation

The pre-training fine-tuning mode has been shown to be effective for low resource neural machine translation. In this mode, pre-training models trained on monolingual data are used to initiate translation models to transfer knowledge from monolingual data into translation models. In recent years, pre-training models usually take sentences with randomly masked words as input, and are trained by predicting these masked words based on unmasked words. In this paper, we propose a new pre-training method that still predicts masked words, but randomly replaces some of the unmasked words in the input with their translation words in another language. The translation words are from bilingual data, so that the data for pre-training contains both monolingual data and bilingual data. We conduct experiments on Uyghur-Chinese corpus to evaluate our method. The experimental results show that our method can make the pre-training model have a better generalization ability and help the translation model to achieve better performance. Through a word translation task, we also demonstrate that our method enables the embedding of the translation model to acquire more alignment knowledge.
The optimal approximation of qubit states with limited quantum states

Measuring the closest distance between two states is an alternative and significant approach in the resource quantification, which is the core task in the resource theory. Quite limited progress has been made for this approach even in simple systems due to the various potential complexities. Here we analytically solve the optimal scheme to find out the closest distance between the objective qubit state and all the possible states convexly mixed by some limited states, namely, to optimally construct the objective qubit state using the quantum states within any given state set. In particular, we find the least number of (not more than four) states within a given set to optimally construct the objective state and also find that any state can be optimally established by at most four quantum states of the set. The examples in various cases are presented to verify our analytic solutions further.
Specfuscator: Evaluating Branch Removal as a Spectre Mitigation

Attacks exploiting speculative execution, known as Spectre attacks, have gained substantial attention in the scientific community and in industry with a broad range of defense techniques proposed. In particular, in-software defenses for commodity systems attempt to leave the program structure as is, but defuse every potential Spectre gadget by, e.g., stopping the speculation, or limiting value ranges. While these mitigations disrupt the program flow on every conditional branch, they still contain every single conditional branch instruction. In this paper, we show that one dimension of Spectre mitigations has been overlooked entirely. We explore a novel principled Spectre mitigation that sits at the other end of the scale: the absence of conditional and indirect branches. Our mitigation is based on automatically linearizing the program flow through a special compiler pass, eliminating all conditional and indirect branches. We show that our Spectre mitigation has very clear security guarantees. We explore the feasibility of this unorthodox approach and evaluate its performance in comparison to the more conservative approaches presented so far. We observe that the performance overhead can be low, e.g., $$5\,\%$$ , for certain use cases, being on-par with state-of-the-art mitigations, but very high for other use cases, e.g., and overhead factor of 1000. Our results demonstrate the feasibility of Spectre defenses that eliminate branches and indicate good performance-security trade-offs for Spectre defenses can be achieved by sticking to neither of the extremes.
Study of Various Cloud Service Providers: A Comparative Analysis

We are living in the age of technology where every field is blended with technology either its healthcare or education or entertainment and immeasurably more. For these, IT organizations play a vital role. IT organizations are going up so high either they are small or big. Small IT organizations are running very smoothly and efficiently. This is becoming possible because of Cloud-Computing and its splendid services. Different cloud-service-providers are available all over the globe. So, it is becoming necessary for a client to make the right choice in accordance with their requirement. So, this paper differentiate cloud-service-providers are compared and on the basis of their virtual machine types, their storage as a service, and their availability zones.
Psychological influence of Chinese film and television works in teaching Chinese as a foreign language

The purpose of this article is to study the psychological impact of Chinese film and television works in the teaching of Chinese as a foreign language at intermediate and advanced levels, analyze the role of Chinese film and television works in teaching Chinese as a foreign language at intermediate and advanced levels, and to understand the basic situation and influence of Chinese film and television works in teaching Chinese as a foreign language at intermediate and advanced levels, as well as their use this method contrasts before and after the study of the influence of Chinese film and television works in teaching Chinese as a foreign language at intermediate and advanced levels. Using the method of this article, through the analysis of experimental data, I understand the role of Chinese film and television works in teaching Chinese as a foreign language at intermediate and advanced levels, and find that Chinese film and television works play an important role in teaching Chinese as a foreign language. Based on the combination of theory and experimental data, it is analyzed that the effect of Chinese film and television works on teaching Chinese as a foreign language is very good. The research results show that Chinese film and television works have an important role in teaching Chinese as a foreign language at intermediate and advanced levels. Contemporary Chinese film and television not only impresses foreigners with vivid stories and vivid characters.
DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution

Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. Therefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-the-art deep learning methods of natural images to MRI super resolution. Compared with the traditional image super-resolution method, the deep learning super-resolution method has stronger feature extraction and characterization ability, can learn prior knowledge from a large number of sample data, and has a more stable and excellent image reconstruction effect. We propose a unified framework of deep learning -based MRI super resolution, which has five current deep learning methods with the best super-resolution effect. In addition, a high-low resolution MR image dataset with the scales of ×2, ×3, and ×4 was constructed, covering 4 parts of the skull, knee, breast, and head and neck. Experimental results show that the proposed unified framework of deep learning super resolution has a better reconstruction effect on the data than traditional methods and provides a standard dataset and experimental benchmark for the application of deep learning super resolution in MR images.
Strategic Planning of Information System in PT Satya Mitra Sejahtera Using Ward and Peppard

Increasing demand in every company for information systems strategic planning in order to increase efficiency, effectiveness and increase competitive advantage programmatically with a priority scale, given the information system resources and other supporting devices are still limited, so the existence of an information system strategic plan will help the company. One of them is PT Satya Mitra Sejahtera, which is engaged in business, which is currently not fully using information systems to its full potential. The quality of information systems becomes the most important component because it requires an analysis of the strategic planning of information systems to determine whether the information system that was developed can be used properly. In this study, the author will conduct an information system strategic planning using the Ward and Peppard method. This method was chosen because it can evaluate business conditions and information systems that occur at this time, by looking at the business environment and information system environment externally and internally. The Ward and Peppard method have several stages, the first stage of analyzing the internal and external business environment using the PEST and value chain. Analysis of the internal and external environment of information systems using SWOT, while the application portfolio mapping uses Mc Farlan Strategic Grid. The results of strategic design research using Ward and Peppard methods can produce maps of internal and external conditions of the organization, so that can find out the strengths, weaknesses, opportunities, and threats owned by PT. Satya Mitra Sejahtera.