Reviewerly/exHarmony huggingface dataset:
Interventionist Counterfactuals on Causal Teams

We introduce an extension of team semantics which provides a framework for the logic of manipulationist theories of causation based on structural equation models, such as Woodward's and Pearl's; our causal teams incorporate (partial or total) information about functional dependencies that are invariant under interventions. We give a unified treatment of observational and causal aspects of causal models by isolating two operators on causal teams which correspond, respectively, to conditioning and to interventionist counterfactual implication. We then introduce formal languages for deterministic and probabilistic causal discourse, and show how various notions of cause (e.g. direct and total causes) may be defined in them. Through the tuning of various constraints on structural equations (recursivity, existence and uniqueness of solutions, full or partial definition of the functions), our framework can capture different causal models. We give an overview of the inferential aspects of the recursive, fully defined case; and we dedicate some attention to the recursive, partially defined case, which involves a shift of attention towards nonclassical truth values.
Passive, deterministic photonic conditional-phase gate via two-level systems

We show that an array of identical two-level systems coupled losslessly to a one-dimensional waveguide is able to realize a high-fidelity conditional phase shift useful for quantum logic. We propose two arrangements of emitters (one that relies on direct interactions between the emitters, and one that does not), and describe possible physical realizations and limitations.
Novel multispectral face descriptor using orthogonal walsh codes

This paper proposes a novel high-performance multispectral local descriptor that uses orthogonal Walsh codes during the generation of the discriminative feature set. Rotational variance and noise are compelling factors that significantly affect the distinctive performance of a facial identifier. The descriptor proposed in this article handles these challenges favourably sacrificing any distinctive performance. Orthogonal Walsh codes are used during the generation of the local descriptor. A Walsh code is assigned to each neighbour of a reference pixel. Before the assignment of an orthogonal code to each neighbouring pixel, these pixels are sorted in ascending order that consolidates the robustness of the method against rotational variances. Almost all methods proposed so far focus on grayscale images. However, colour bands contain important information about the relationship between pixels. Therefore, authors’ method considers RGB colour bands of pixels to improve distinctive performance. The results of extensive simulations show the remarkable and competitive performance of the proposed method regarding recognition accuracy and robustness against rotational variances as well as noise effects.
Delta-Ramp Encoder for Amplitude Sampling and Its Interpretation as Time Encoding

The theoretical basis for conventional acquisition of bandlimited signals typically relies on uniform time sampling and assumes infinite-precision amplitude values. In this paper, we explore signal representation and recovery based on uniform amplitude sampling with assumed infinite precision timing information. The approach is based on the delta-ramp encoder which consists of applying a one-level level-crossing detector to the result of adding an appropriate sawtooth-like waveform to the input signal. The output samples are the time instants of these level crossings, thus representing a time-encoded version of the input signal. For theoretical purposes, this system can be equivalently analyzed by reversibly transforming through ramp addition a nonmonotonic input signal into a monotonic one, which is then uniformly sampled in amplitude. The monotonic function is then represented by the times at which the signal crosses a predefined and equally-spaced set of amplitude values. We refer to this technique as amplitude sampling. The time sequence generated can be interpreted alternatively as nonuniform time sampling of the original source signal. We derive duality and frequency-domain properties for the functions involved in the transformation. Iterative algorithms are proposed and implemented for recovery of the original source signal. As indicated in the simulations, the proposed iterative amplitude-sampling algorithm achieves a faster convergence rate than frame-based reconstruction for nonuniform sampling. The performance can also be improved by appropriate choice of the parameters while maintaining the same sampling density.
Automatic Subject Indexing of Text

Automatic subject indexing addresses problems of scale and sustainability and can be at the same time used to enrich existing metadata records, establish more connections across and between resources from various metadata and resource collections, and enhance consistency of the metadata. In this work, automatic subject indexing focuses on assigning index terms or classes from established knowledge organization systems (KOSs) for subject indexing like thesauri, subject headings systems and classification systems. The following major approaches are discussed, in terms of their similarities and differences, advantages and disadvantages for automatic assigned indexing from KOSs: “text categorization,” “document clustering,” and “document classification.” Text categorization is perhaps the most widespread, machine-learning approach with what seems generally good reported performance. Document clustering automatically both creates groups of related documents and extracts names of subjects depicting the group at hand. Document classification re-uses the intellectual effort invested into creating a KOS for subject indexing and even simple string-matching algorithms have been reported to achieve good results, because one concept can be described using a number of different terms, including equivalent, related, narrower and broader terms. Finally, applicability of automatic subject indexing to operative information systems and challenges of evaluation are outlined, suggesting the need for more research.
The Dynamic Role of Augmented Reality in Tourism

The increasing ubiquity of the smart phone has undoubtedly paved the way for the utilizing of technological intermediation in tourism, notably in the case of augmented reality (AR). The current study seeks the opinions and perceptions of AR users about how such technologies impact their decisionmaking. The study seeks an enhanced understanding of contemporary tourist consumption practices, drawing upon the relationship between code and space using LAYAR AR applications. The research methods that have been deployed in this study involve the conduct of qualitative interviews with consumers and producers with an interest in AR applications using a snowball sampling approach. The results show that using AR produces the augmented construction of reality that is multidimensional and consists of the owner, site, and tourism agency. It has also been shown that AR is a secondary tool, where user awareness and sense of the physical environment decreases. It is concluded that integrating technologies into the tourist experience can enhance the enjoyment of the experience and improve expertise and skills in the context of tourist operations and attractions.
Enhancement of Extreme Learning Machine for Estimating Blocking Probability of OCS Networks With Fixed-Alternate Routing

In previous work, we proposed a neural network approach to estimate the blocking probability of optical networks with fixed routing. The neural network was implemented by the extreme learning machine (ELM) framework, in which the training inputs were the optical network parameters, and the output was the overall blocking probability. The numerical results showed that the neural-network-based estimation was accurate and thousands of times faster than a computer simulation. In this paper, we apply the neural network approach to optical circuit switching (OCS) networks with fixed-alternate routing and improve the training method by using an enhancement of ELM framework. Unlike the previous ELM framework, the enhancement of ELM framework provides a random-search-based selection phase for the hidden nodes during the training step. As a result, similar performance can be achieved using fewer hidden nodes than the previous ELM framework. The numerical results show that the new enhancement of ELM training algorithm provides more accurate blocking probability estimates while reducing the required number of hidden nodes by a third compared with the previous ELM training algorithm. Furthermore, for some light traffic loading situations, our new training algorithm is hundreds of times more accurate than the existing well-known analytical approximation method.
Why should you trust my interpretation? Understanding uncertainty in LIME predictions.

Methods for interpreting machine learning black-box models increase the outcomes' transparency and in turn generates insight into the reliability and fairness of the algorithms. However, the interpretations themselves could contain significant uncertainty that undermines the trust in the outcomes and raises concern about the model's reliability. Focusing on the method Local Interpretable Model-agnostic Explanations (LIME), we demonstrate the presence of two sources of uncertainty, namely the randomness in its sampling procedure and the variation of interpretation quality across different input data points. Such uncertainty is present even in models with high training and test accuracy. We apply LIME to synthetic data and two public data sets, text classification in 20 Newsgroup and recidivism risk-scoring in COMPAS, to support our argument.
Tutorial on Online User Engagement: Metrics and Optimization

User engagement plays a central role in companies operating online services, such as search engines, news portals, e-commerce sites, entertainment services, and social networks. A main challenge is to leverage collected knowledge about the daily online behavior of millions of users to understand what engage them short-term and more importantly long-term. Two critical steps of improving user engagement are metrics and their optimization. The most common way that engagement is measured is through various online metrics, acting as proxy measures of user engagement. This tutorial will review these metrics, their advantages and drawbacks, and their appropriateness to various types of online services. Once metrics are defined, how to optimize them will become the key issue. We will survey methodologies including machine learning models and experimental designs that are utilized to optimize these metrics via direct or indirect ways. As case studies, we will focus on four types of services, news, search, entertainment, and e-commerce. We will end with lessons learned and a discussion on the most promising research directions.
An Efficient Implementation of a Census-Based Stereo Matching and Its Applications in Medical Imaging

Compared to most conventional efficient stereo matching algorithms that based on NCC (Normalized Cross-Correlation) or SAD (Sum of Absolute Difference), stereo matching based on census transform is robust to radiometric distortion. Thus, in the paper we propose a census-based efficient implementation stereo algorithm for medical imaging. Firstly, census-based stereo matching is investigated, and its specific implementation process is analyzed in detail. Secondly, in order to simplify the calculation process and improve the efficiency, the moving window and memory organization optimized techniques are used. The program runs on standard PC hardware utilizing various SSE2 instructions. Finally, stereo matching of four standard image pairs on the Middlebury image datasets and a paired cervical images obtained from clinical colposcope are implemented in an efficient way. The experimental results on simulated and real medical images prove the effectiveness of the method for the computational cost.
Preserving measurements for optimal state discrimination over quantum channels

In this work, we consider optimal state discrimination for a quantum system that interacts with an environment, i.e., states evolve under a quantum channel. We show the conditions on a quantum channel and an ensemble of states such that a measurement for optimal state discrimination is preserved. In particular, we show that when an ensemble of states with equal {\it a priori} probabilities is given, an optimal measurement can be preserved over any quantum channel by applying local operations and classical communication, that is, by manipulating the quantum states before and after the channel application. Examples are provided for illustration. Our results can be readily applied to quantum communication protocols over various types of noise.
Quantum-inspired canonical correlation analysis for exponentially large dimensional data

Canonical correlation analysis (CCA) is a technique to find statistical dependencies between a pair of multivariate data. However, its application to high dimensional data is limited due to the resulting time complexity. While the conventional CCA algorithm requires polynomial time, we have developed an algorithm that approximates CCA with computational time proportional to the logarithm of the input dimensionality using quantum-inspired computation. The computational efficiency and approximation performance of the proposed quantum-inspired CCA (qiCCA) algorithm are experimentally demonstrated. Furthermore, the fast computation of qiCCA allows us to directly apply CCA even after nonlinearly mapping raw input data into very high dimensional spaces. Experiments performed using a benchmark dataset demonstrated that, by mapping the raw input data into the high dimensional spaces with second-order monomials, the proposed qiCCA extracted more correlations than linear CCA and was comparable to deep CCA and kernel CCA. These results suggest that qiCCA is considerably useful and quantum-inspired computation has the potential to unlock a new field in which exponentially large dimensional data can be analyzed.
Shortening time scale to reduce thermal effects in quantum transistors

In this article, we present a quantum transistor model based on a network of coupled quantum oscillators destined to quantum information processing tasks in linear optics. To this end, we show in an analytical way how a set of $N$ quantum oscillators (data-bus) can be used as an optical quantum switch, in which the energy gap of the data bus oscillators plays the role of an adjustable "potential barrier". This enables us to "block or allow" the quantum information to flow from the source to the drain. In addition, we discuss how this device can be useful for implementing single qubit phase-shift quantum gates with high fidelity, so that it can be used as a useful tool. To conclude, during the study of the performance of our device when considering the interaction of this with a thermal reservoir, we highlight the important role played by the set of oscillators which constitute the data-bus in reducing the unwanted effects of the thermal reservoir. This is achieved by reducing the information exchange time (shortening time scale) between the desired oscillators. In particular, we have identified a non-trivial criterion in which the ideal size of the data-bus can be obtained so that it presents the best possible performance. We believe that our study can be perfectly adapted to a large number of thermal reservoir models.
Sparsely Activated Networks

Previous literature on unsupervised learning focused on designing structural priors with the aim of learning meaningful features.However, this was done without considering the description length of the learned representations which is a direct and unbiased measure of the model complexity.In this paper, first we introduce the φ metric that evaluates unsupervised models based on their reconstruction accuracy and the degree of compression of their internal representations.We then present and define two activation functions (Identity, ReLU) as base of reference and three sparse activation functions (top-k absolutes, Extrema-Pool indices, Extrema) as candidate structures that minimize the previously defined φ.We lastly present Sparsely Activated Networks (SANs) that consist of kernels with shared weights that, during encoding, are convolved with the input and then passed through a sparse activation function.During decoding, the same weights are convolved with the sparse activation map and subsequently the partial reconstructions from each weight are summed to reconstruct the input.We compare SANs using the five previously defined activation functions on a variety of datasets (Physionet, UCI-epilepsy, MNIST, FMNIST) and show that models that are selected using φ have small description representation length and consist of interpretable kernels.
Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors

In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain. The chaotic system is extremely robust and consists of a strong chaotic generator and a 2-D Cat map. Its main role is to secure the content of a message in case a message is detected. Secondly, three blind steganalysis methods, based on multi-resolution wavelet decomposition, are used to detect whether an embedded message is hidden in the tested image (stego image) or not (cover image). The steganalysis approach is based on the hypothesis that message-embedding schemes leave statistical evidence or structure in images that can be exploited for detection. The simulation results show that the Support Vector Machine (SVM) classifier and the Fisher Linear Discriminant (FLD) cannot distinguish between cover and stego images if the message size is smaller than 20% in the EEALSBMR steganographic method and if the message size is smaller than 15% in the EDCT steganographic method. However, SVM and FLD can distinguish between cover and stego images with reasonable accuracy in the EDWT steganographic method, irrespective of the message size.
Analysing billion-objects catalogue interactively: Apache Spark for physicists

Apache Spark is a Big Data framework for working on large distributed datasets. Although widely used in the industry, it remains rather limited in the academic community or often restricted to software engineers. The goal of this paper is to show with practical uses-cases that the technology is mature enough to be used without excessive programming skills by astronomers or cosmologists in order to perform standard analyses over large datasets, as those originating from future galaxy surveys. To demonstrate it, we start from a realistic simulation corresponding to 10 years of LSST data taking (6 billions of galaxies). Then, we design, optimize and benchmark a set of Spark python algorithms in order to perform standard operations as adding photometric redshift errors, measuring the selection function or computing power spectra over tomographic bins. Most of the commands execute on the full 110 GB dataset within tens of seconds and can therefore be performed interactively in order to design full-scale cosmological analyses. A jupyter notebook summarizing the analysis is available at https://github.com/astrolabsoftware/1807.03078.
Violation of Leggett-Garg-type inequalities in a driven two-level atom interacting with a squeezed thermal reservoir

The violation of Leggett-Garg-type inequalities (LGtIs) is studied on a two-level atom, driven by an external field in the presence of a squeezed thermal reservoir. The violations are observed in the underdamped regime where the spontaneous transition rate is much smaller compared to the Rabi frequency. An increase in thermal effects is found to decrease the extent of violation as well as the time over which the violation lasts. With the increase in the value of the squeezing parameter the extent of violation of LGtIs is seen to reduce. The violation of LGtIs is favored by an increase in the driving frequency. Further, the interplay of the degree of violation and strength of the measurements is studied. It is found that the maximum violation occurs for ideal projective measurements.
Certifying the purity of quantum states with temporal correlations

Correlations obtained from sequences of measurements have been employed to distinguish among different physical theories or to witness the dimension of a system. In this work we show that they can also be used to establish semi-device independent lower bounds on the purity of the initial quantum state or even on one of the post-measurement states. For single systems this provides information on the quality of the preparation procedures of pure states or the implementation of measurements with anticipated pure post-measurement states. For joint systems one can combine our bound with results from entanglement theory to infer an upper bound on the concurrence based on the temporal correlations observed on a subsystem.
Quantum signature scheme based on Hadamard and H<sub>π/4</sub> operators

Based on the Hadamard and Hπ/4 operators, a new quantum signature scheme is proposed. In our scheme, the signer's private key is generated by a trusted private key generator (PKG), while the identity information of the signer is used as the corresponding public key. Both of the private key and public key of the signer are classical bit strings, which can be easily stored and reused. Given a quantum signature, anyone can verify the validity of the quantum signature with the signer's identity. Therefore, our scheme is a public-key quantum signature without a digital certificate, which has the merits of an identity-based cryptosystem and can simplify the key management of the quantum signature system. On the other hand, our scheme need not use any quantum swap test during the signature verification phase. Furthermore, by the signature proof, our scheme can arbitrate the potential disputation of losing quantum signature, which cannot be arbitrated in most of the quantum signature schemes. So our scheme has the property of strong non-repudiation. It also has the security properties of information-theoretic security, unforgeability, etc. Our scheme can achieve a high efficiency of 70%. Therefore, our quantum signature scheme is more secure, practicable, and efficient than the similar schemes.
50 FPS Object-Level Saliency Detection via Maximally Stable Region

The human visual system tends to consider saliency of an object as a whole. Some object-level saliency detection methods have been proposed by leveraging object proposals in bounding boxes, and regarding the entire bounding box as one candidate salient region. However, the bounding boxes can not provide exact object position and a lot of pixels in bounding boxes belong to the background. Consequently, background pixels in bounding box also show high saliency. Besides, acquiring object proposals needs high time cost. In order to compute object-level saliency, we consider region growing from some seed superpixels, to find one surrounding region which probably represents the whole object. The desired surrounding region has similar appearance inside and obvious difference with the outside, which is proposed as maximally stable region (MSR) in this paper. In addition, one effective seed superpixel selection strategy is presented to improve speed. MSR based saliency detection is more robust than pixel or superpixel level methods and object proposal based methods. The proposed method significantly outperforms the state-of-the-art unsupervised methods at 50 FPS. Compared with deep learning based methods, we show worse performance, but with about 1200-1600 times faster, which means better trade-off between performance and speed.