Reviewerly/exHarmony huggingface dataset:
Non-Markovianity through quantum coherence in an all-optical setup

We propose an all-optical experiment to quantify non-Markovianity in an open quantum system through quantum coherence of a single quantum bit. We use an amplitude damping channel implemented by an optical setup with an intense laser beam simulating a single-photon polarization. The optimization over initial states required to quantify non-Markovianity is analytically evaluated. The experimental results are in very good agreement with the theoretical predictions.
On CCZ-equivalence, extended-affine equivalence, and function twisting

Two vectorial Boolean functions are "CCZ-equivalent" if there exists an affine permutation mapping the graph of one to the other. It preserves many of the cryptographic properties of a function such as its differential and Walsh spectra, which is why it could be used by Dillon et al. to find the first APN permutation on an even number of variables. However, the meaning of this form of equivalence remains unclear. In fact, to the best of our knowledge, it is not known how to partition a CCZ-equivalence class into its Extended-Affine (EA) equivalence classes; EA-equivalence being a simple particular case of CCZ-equivalence. In this paper, we characterize CCZ-equivalence as a property of the zeroes in the Walsh spectrum of a function F:F2n→F2m or, equivalently, of the zeroes in its Difference Distribution Table. We use this framework to show how to efficiently upper bound the number of distinct EA-equivalence classes in a given CCZ-equivalence class. More importantly, we prove that it is possible to go from a specific member of any EA-equivalence class to a specific member of another EA-equivalence class in the same CCZ-equivalence class using an operation called twisting; so that CCZ-equivalence can be reduced to the association of EA-equivalence and twisting. Twisting a function is a simple process and its possibility is equivalent to the existence of a particular decomposition of the function considered. Using this knowledge, we revisit several results from the literature on CCZ-equivalence and show how they can be interpreted in light of our new framework. Our results rely on a new concept, the "thickness" of a space (or linear permutation), which can be of independent interest.
Permutation-Invariant Constant-Excitation Quantum Codes for Amplitude Damping

The increasing interest in using quantum error correcting codes in practical devices has heightened the need for designing quantum error correcting codes that can correct against specialized errors, such as that of amplitude damping errors which model photon loss. Although considerable research has been devoted to quantum error correcting codes for amplitude damping, not so much attention has been paid to having these codes simultaneously lie within the decoherence free subspace of their underlying physical system. One common physical system comprises of quantum harmonic oscillators, and constant-excitation quantum codes can be naturally stabilized within them. The purpose of this paper is to give constant-excitation quantum codes that not only correct amplitude damping errors, but are also immune against permutations of their underlying modes. To construct such quantum codes, we use the nullspace of a specially constructed matrix based on integer partitions.
Dynamics of initially correlated open quantum systems: Theory and applications

We show that the dynamics of any open quantum system that is initially correlated with its environment can be described by a set of ${d}^{2}$ (or less) completely positive maps, where $d$ is the dimension of the system. Only one such map is required for the special case of no initial correlations. The same maps describe the dynamics of any system-environment state obtained from the initial state by a local operation on the system. The reduction of the system dynamics to a set of completely positive maps allows known numerical and analytic tools for uncorrelated initial states to be applied to the general case of initially correlated states, which we exemplify by solving the qubit dephasing model for such states, and provides a natural approach to quantum Markovianity for this case. We show that this set of completely positive maps can be experimentally characterized using only local operations on the system, via a generalization of noise spectroscopy protocols. As further applications, we first consider the problem of retrodicting the dynamics of an open quantum system which is in an arbitrary state when it becomes accessible to the experimenter and explore the conditions under which retrodiction is possible. We also introduce a related one-sided or limited-access tomography protocol for determining an arbitrary bipartite state, evolving under a sufficiently rich Hamiltonian, via local operations and measurements on just one component. We simulate this protocol for a physical model of particular relevance to nitrogen-vacancy centers and in particular show how to reconstruct the density matrix of a set of three qubits, interacting via dipolar coupling and in the presence of local magnetic fields, by measuring and controlling only one of them.
Outdoor Shadow Estimating Using Multiclass Geometric Decomposition Based on BLS

Illumination is a significant component of an image, and illumination estimation of an outdoor scene from given images is still challenging yet it has wide applications. Most of the traditional illumination estimating methods require prior knowledge or fixed objects within the scene, which makes them often limited by the scene of a given image. We propose an optimization approach that integrates the multiclass cues of the image(s) [a main input image and optional auxiliary input image(s)]. First, Sun visibility is estimated by the efficient broad learning system. And then for the scene with visible Sun, we classify the information in the image by the proposed classification algorithm, which combines the geometric information and shadow information to make the most of the information. And we apply a respective algorithm for every class to estimate the illumination parameters. Finally, our approach integrates all of the estimating results by the Markov random field. We make full use of the cues in the given image instead of an extra requirement for the scene, and the qualitative results are presented and show that our approach outperformed other methods with similar conditions.
Adopting integrated application lifecycle management within a large-scale software company: An action research approach

Application Lifecycle Management (ALM) is a paradigm for integrating and managing the various activities related to the governance, development and maintenance of software products. In the last decade, several ALM tools have been proposed to support this process, and an increasing number of companies have started to adopt ALM. We aim to investigate the impact of adopting ALM in a real industrial context to understand and justify both the benefits and obstacles of applying integrated ALM. As a research methodology, we apply action research that we have carried out within HAVELSAN, a large-scale IT company. The research was carried out over a period of seven years starting in 2010 when the ALM initiative has been started in the company to increase productivity and decrease maintenance costs. The paper presents the results of the action research that includes the application of ALM practices. The transitions among the different steps are discussed in detail, together with the identified obstacles, benefits and lessons learned. Our seven-year study shows that the adoption of ALM processes is not trivial and its success is related to many factors. An important conclusion is that a piecemeal solution as provided by ALM 1.0 is not feasible for the complex process and tool integration problems of large enterprises. Hence the transition to ALM 2.0 was found necessary to cope with the organizational and business needs. Although ALM 2.0 appeared to be a more mature ALM approach, there are still obstacles that need attention from both researchers and practitioners.
MetaStyle: Three-Way Trade-off among Speed, Flexibility, and Quality in Neural Style Transfer

An unprecedented booming has been witnessed in the research area of artistic style transfer ever since Gatys et al. introduced the neural method. One of the remaining challenges is to balance a trade-off among three critical aspects—speed, flexibility, and quality: (i) the vanilla optimization-based algorithm produces impressive results for arbitrary styles, but is unsatisfyingly slow due to its iterative nature, (ii) the fast approximation methods based on feed-forward neural networks generate satisfactory artistic effects but bound to only a limited number of styles, and (iii) feature-matching methods like AdaIN achieve arbitrary style transfer in a real-time manner but at a cost of the compromised quality. We find it considerably difficult to balance the trade-off well merely using a single feed-forward step and ask, instead, whether there exists an algorithm that could adapt quickly to any style, while the adapted model maintains high efficiency and good image quality. Motivated by this idea, we propose a novel method, coined MetaStyle, which formulates the neural style transfer as a bilevel optimization problem and combines learning with only a few post-processing update steps to adapt to a fast approximation model with satisfying artistic effects, comparable to the optimization-based methods for an arbitrary style. The qualitative and quantitative analysis in the experiments demonstrates that the proposed approach achieves high-quality arbitrary artistic style transfer effectively, with a good trade-off among speed, flexibility, and quality.
Explicitly Imposing Constraints in Deep Networks via Conditional Gradients Gives Improved Generalization and Faster Convergence

A number of results have recently demonstrated the benefits of incorporating various constraints when training deep architectures in vision and machine learning. The advantages range from guarantees for statistical generalization to better accuracy to compression. But support for general constraints within widely used libraries remains scarce and their broader deployment within many applications that can benefit from them remains under-explored. Part of the reason is that Stochastic gradient descent (SGD), the workhorse for training deep neural networks, does not natively deal with constraints with global scope very well. In this paper, we revisit a classical first order scheme from numerical optimization, Conditional Gradients (CG), that has, thus far had limited applicability in training deep models. We show via rigorous analysis how various constraints can be naturally handled by modifications of this algorithm. We provide convergence guarantees and show a suite of immediate benefits that are possible — from training ResNets with fewer layers but better accuracy simply by substituting in our version of CG to faster training of GANs with 50% fewer epochs in image inpainting applications to provably better generalization guarantees using efficiently implementable forms of recently proposed regularizers.
Collaborative, Dynamic and Diversified User Profiling

In this paper, we study the problem of dynamic user profiling in the context of streams of short texts. Previous work on user profiling works with long documents, do not consider collaborative information, and do not diversify the keywords for profiling users’ interests. In contrast, we address the problem by proposing a user profiling algorithm (UPA), which consists of two models: the proposed collaborative interest tracking topic model (CITM) and the proposed streaming keyword diversification model (SKDM). UPA first utilizes CITM to collaboratively track each user’s and his followees’ dynamic interest distributions in the context of streams of short texts, and then utilizes SKDM to obtain top-k relevant and diversified keywords to profile users’ interests at a specific point in time. Experiments were conducted on a Twitter dataset and we found that UPA outperforms state-of-the-art non-dynamic and dynamic user profiling algorithms.
Cross-Domain Visual Representations via Unsupervised Graph Alignment

In unsupervised domain adaptation, distributions of visual representations are mismatched across domains, which leads to the performance drop of a source model in the target domain. Therefore, distribution alignment methods have been proposed to explore cross-domain visual representations. However, most alignment methods have not considered the difference in distribution structures across domains, and the adaptation would subject to the insufficient aligned cross-domain representations. To avoid the misclassification/misidentification due to the difference in distribution structures, this paper proposes a novel unsupervised graph alignment method that aligns both data representations and distribution structures across the source and target domains. An adversarial network is developed for unsupervised graph alignment, which maps both source and target data to a feature space where data are distributed with unified structure criteria. Experimental results show that the graph-aligned visual representations achieve good performance on both crossdataset recognition and cross-modal re-identification.
Theoretical Analysis of Label Distribution Learning

As a novel learning paradigm, label distribution learning (LDL) explicitly models label ambiguity with the definition of label description degree. Although lots of work has been done to deal with real-world applications, theoretical results on LDL remain unexplored. In this paper, we rethink LDL from theoretical aspects, towards analyzing learnability of LDL. Firstly, risk bounds for three representative LDL algorithms (AA-kNN, AA-BP and SA-ME) are provided. For AA-kNN, Lipschitzness of the label distribution function is assumed to bound the risk, and for AA-BP and SA-ME, rademacher complexity is utilized to give data-dependent risk bounds. Secondly, a generalized plug-in decision theorem is proposed to understand the relation between LDL and classification, uncovering that approximation to the conditional probability distribution function in absolute loss guarantees approaching to the optimal classifier, and also data-dependent error probability bounds are presented for the corresponding LDL algorithms to perform classification. As far as we know, this is perhaps the first research on theory of LDL.
Segmentation of the Main Vessel of the Left Anterior Descending Artery Using Selective Feature Mapping in Coronary Angiography

X-ray angiography, used in the evaluation of coronary artery disease, presents difficulties in the performance of quantitative coronary angiography analysis, by identifying major vessels. These difficulties are due to problems such as non-uniform illumination, low contrast ratio, and the presence of other tissues. Therefore, segmentation of the desired vessels in images containing multiple blood vessels is clinically important. This paper proposes selective feature mapping as a method for segmenting the left anterior descending artery main vessel in coronary angiography images. The proposed method consists of two steps for generating a candidate area of an image and then segmenting it. To generate the candidate area, feature maps that overlap significantly with the area of the ground truth are selected and combined. Segmentation then is performed using a neural network that learns only the ground truth region of the input image. The proposed method consists of eight modules: pre-processing of the angiogram, resizing of ground truth, pre-processing for segmentation, post-processing for segmentation, network, and segmentation network. This method has a precision of about 0.066, recall of 0.091, and an F1 score of 0.094, values which are higher than those generated by the U-Net, one of the conventional techniques.
Transferable Interactive Memory Network for Domain Adaptation in Fine-Grained Opinion Extraction

In fine-grained opinion mining, aspect and opinion terms extraction has become a fundamental task that provides key information for user-generated texts. Despite its importance, a lack of annotated resources in many domains impede the ability to train a precise model. Very few attempts have applied unsupervised domain adaptation methods to transfer fine-grained knowledge (in the word level) from some labeled source domain(s) to any unlabeled target domain. Existing methods depend on the construction of “pivot” knowledge, e.g., common opinion terms or syntactic relations between aspect and opinion words. In this work, we propose an interactive memory network that consists of local and global memory units. The model could exploit both local and global memory interactions to capture intra-correlations among aspect words or opinion words themselves, as well as the interconnections between aspect and opinion words. The source space and the target space are aligned through these domaininvariant interactions by incorporating an auxiliary task and domain adversarial networks. The proposed model does not require any external resources and demonstrates promising results on 3 benchmark datasets.
Graph Based Translation Memory for Neural Machine Translation

A translation memory (TM) is proved to be helpful to improve neural machine translation (NMT). Existing approaches either pursue the decoding efficiency by merely accessing local information in a TM or encode the global information in a TM yet sacrificing efficiency due to redundancy. We propose an efficient approach to making use of the global information in a TM. The key idea is to pack a redundant TM into a compact graph and perform additional attention mechanisms over the packed graph for integrating the TM representation into the decoding network. We implement the model by extending the state-of-the-art NMT, Transformer. Extensive experiments on three language pairs show that the proposed approach is efficient in terms of running time and space occupation, and particularly it outperforms multiple strong baselines in terms of BLEU scores.
Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations

We propose a simple yet effective method for unsupervised domain adaptation. When training and test distributions are different, standard supervised learning methods perform poorly. Semi-supervised domain adaptation methods have been developed for the case where labeled data in the target domain are available. However, the target data are often unlabeled in practice. Therefore, unsupervised domain adaptation, which does not require labels for target data, is receiving a lot of attention. The proposed method minimizes the discrepancy between the source and target distributions of input features by transforming the feature space of the source domain. Since such unilateral transformations transfer knowledge in the source domain to the target one without reducing dimensionality, the proposed method can effectively perform domain adaptation without losing information to be transfered. With the proposed method, it is assumed that the transformed features and the original features differ by a small residual to preserve the relationship between features and labels. This transformation is learned by aligning the higher-order moments of the source and target feature distributions based on the maximum mean discrepancy, which enables to compare two distributions without density estimation. Once the transformation is found, we learn supervised models by using the transformed source data and their labels. We use two real-world datasets to demonstrate experimentally that the proposed method achieves better classification performance than existing methods for unsupervised domain adaptation.
Price clustering in Bitcoin market—An extension

This paper extends current literature on price clustering in Bitcoin market. We analyze intraday data of various time frames and document evidence of clustering for open, high and low prices. We discover and explain different patterns of clustering and their relation with the time frame. We further examine the effect of price level on these findings.
Effectiveness Evaluation Model of Moving Target Defense Based on System Attack Surface

Evaluation of moving target defense (MTD) effectiveness has become one of the fundamental problems in current studies.In this paper, an evaluation model of MTD effectiveness based on system attack surface (SAS) is proposed to extend this model covering enterprise-class topology and multi-layered moving target (MT) techniques.The model is focused on the problem of incorrect performance assessment caused by inaccurately characterizing the process of attacking and defending.Existing evaluation models often fail to describe MTD dynamically in a process.To deal with this static view, offensive and defensive process based on a player's move is presented.Besides, it converts all the attack and defense actions into the process, and interactivities are evaluated by system view extended attack surface model.Previously, the proposed attack surface models are not concerned about the links between nodes and vulnerabilities affected by topologies.After comprehensively analyzing the impact of interactions in the system, a SAS model is proposed to demonstrate how resources of the system are affected by the actions of attackers and defenders, thus ensuring the correctness of parameters for SAS in measuring MT technology.Moreover, by generating a sequence of those shifting parameters, a nonhomogeneous hierarchical hidden Markov model is used to find the possible sequence of attacking states by introducing the partial Viterbi algorithm.Also, a sequence of attacking states is defined to illustrate how adversaries are handled by MT technologies and how much additional consumption costs are increased by the system resource reconfiguration.Finally, the simulation of the proposed approach is given in a case study to demonstrate the feasibility and validity of the proposed effectiveness evaluation model in a systematic and dynamic view.
Shared neurocognitive mechanisms of attenuating self-touch and illusory self-touch

Despite the fact that any successful achievement of willed actions necessarily entails the sense of body ownership (the feeling of owning the moving body parts), it is still unclear how this happens. To address this issue at both behavioral and neural levels, we capitalized on sensory attenuation (SA) phenomenon (a self-generated stimulus is perceived as less intense than an identical externally generated stimulus). We compared the intensity of somatosensory stimuli produced by one's own intended movements and by movements of an embodied fake hand. Then, we investigated if in these two conditions SA was equally affected by interfering with the activity of the supplementary motor area (SMA; known to be related to motor intention and SA) using single-pulse transcranial magnetic stimulation. We showed that ownership of the fake hand triggered attenuation of somatosensory stimuli generated by its movements that were comparable to the attenuation of self-generated stimuli. Furthermore, disrupting the SMA eliminated the SA effect regardless of whether it was triggered by actual participant's movements or by illusory ownership. Our findings suggest that SA triggered by body ownership relies, at least in part, on the activation of the same brain structures as SA triggered by motor-related signals.