Reviewerly/exHarmony huggingface dataset:
Measurement-based universal blind quantum computation with minor resources

Blind quantum computation (BQC) enables a client with less quantum computational ability to delegate her quantum computation to a server with strong quantum computational power while preserving the client’s privacy. Generally, many-qubit entangled states are often used to complete BQC tasks. But for a large-scale entangled state, it is difficult to be described since its Hilbert space dimension is increasing exponentially. Furthermore, the number of entangled qubits is limited in experiment of existing works. To tackle this problem, in this paper we propose a universal BQC protocol based on measurement with minor resources, where the trap technology is adopted to verify correctness of the server’s measurement outcomes during computation and testing process. In our model, there are two participants, a client who prepares initial single-qubit states and a server that performs universal quantum computation. The client is almost classical since she does not require any quantum computational power, quantum memory. To realize the client’s universal BQC, we construct an $$m\times n$$ latticed state composed of six-qubit cluster states and eight-qubit cluster states, which needs less qubits than the brickwork state. Finally, we analyze and prove the blindness, correctness, universality and verifiability of our proposed BQC protocol.
Symmetric versus bosonic extension for bipartite states

A bipartite state ${\ensuremath{\rho}}^{AB}$ has a $k$-symmetric extension if there exists a ($k+1$)-partite state ${\ensuremath{\rho}}^{A{B}_{1}{B}_{2}...{B}_{k}}$ with marginals ${\ensuremath{\rho}}^{A{B}_{i}}={\ensuremath{\rho}}^{AB},\ensuremath{\forall}i$. The $k$-symmetric extension is called bosonic if ${\ensuremath{\rho}}^{A{B}_{1}{B}_{2}...{B}_{k}}$ is supported on the symmetric subspace of ${B}_{1}{B}_{2}...{B}_{k}$. Understanding the structure of symmetric and bosonic extension has various applications in the theory of quantum entanglement, quantum key distribution, and the quantum marginal problem. In particular, bosonic extension gives a tighter bound for the quantum marginal problem based on separability. In general, it is known that a ${\ensuremath{\rho}}^{AB}$ admitting symmetric extension may not have bosonic extension. In this work, we show that, when the dimension of the subsystem $B$ is 2 (i.e., a qubit), ${\ensuremath{\rho}}^{AB}$ admits a $k$-symmetric extension if and only if it has a $k$-bosonic extension. Our result has an immediate application to the quantum marginal problem and indicates a special structure for qubit systems based on group representation theory.
Laplace transform approach for the dynamics of N qubits coupled to a resonator

An approach to use the method of Laplace transform for the perturbative solution of the Schrödinger equation at any order of the perturbation for a system of N qubits coupled to a cavity with n photons is suggested. We investigate the dynamics of a system of N superconducting qubits coupled to a common resonator with time-dependent coupling. To account for the contribution of the dynamical Lamb effect to the probability of excitation of the qubit, we consider counter-rotating terms in the qubit-photon interaction Hamiltonian. As an example, we illustrate the method for the case of two qubits coupled to a common cavity. The perturbative solutions for the probability of excitation of the qubit show excellent agreement with the numerical calculations.
Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction

Both a high spatial and a high temporal resolution of images and videos are desirable in many applications, such as entertainment systems, monitoring manufacturing processes, or video surveillance. Due to the limited throughput of pixels per second, however, there is always a tradeoff between acquiring sequences with a high spatial resolution at a low temporal resolution or vice versa. In this paper, a modified sensor concept is proposed which is able to acquire both a high spatial and a high temporal resolution. This is achieved by dynamically reading out only a subset of pixels in a non-regular order to obtain a high temporal resolution. A full high spatial resolution is then obtained by performing a subsequent 3D reconstruction of the partially acquired frames. The main benefit of the proposed dynamic readout is that for each frame, different sampling points are available, which is advantageous since this information can significantly enhance the reconstruction quality of the proposed reconstruction algorithm. Using the proposed dynamic readout strategy, gains in the peak-signal-to-noise ratio (PSNR) of up to 8.55 dB are achieved compared with a static readout strategy. Compared with the other state-of-the-art techniques, such as frame rate up-conversion or super-resolution, which are also able to reconstruct sequences with both a high spatial and a high temporal resolution, average gains in PSNR of up to 6.58 dB are possible.
Availability of private credit – does culture matter?

Purpose This paper aims to examine the impact of national culture on private credit availability. The authors particularly focus on the masculinity dimension, as previous studies have not been able to reconcile this dimension in terms of results aligning with expectations. Design/methodology/approach Least-squares regression with country-cluster standard errors is used to estimate the impact of a nation’s cultural dimensions. Culture is assessed using Hofstede’s six cultural dimensions: masculinity, power distance, uncertainty avoidance, individualism, long-term orientation and indulgence. Estimation controls for country-level measures of economic growth and development, inflation, financial market development and the institutional, legal and bank environments. Data on more than 70 countries were collected from 2005 to 2014. Findings The authors find the masculinity dimension of culture has a significant negative impact on private credit access. Moreover, this result is driven by middle-income versus high-income countries. Interestingly, the authors also find the power distance dimension has a significant negative impact; however, this result is driven by high-income versus middle-income countries. Overall, these results are consistent with the authors’ argument that masculinity may be capturing traditionally defined gender roles, that masculinity (as the authors define it) is different from what power distance is capturing and that the impact of masculinity is influenced by a country’s economic stage. Originality/value The authors’ interpretation of masculinity, coupled with their results, presents researchers with an alternative perspective of a cultural dimension that previous studies have not been able to reconcile in terms of results aligning with expectations. Moreover, the authors show that the impact of the cultural dimensions on private credit differs for high- and middle-income countries, and thus has important implications.
Chinese Dialogue Intention Classification Based on Multi-Model Ensemble

In dialogue systems, understanding the user utterances is crucial for providing appropriate responses. A traditional dialogue act classification (DA) task is to classify each user reply into “ACCEPT, REJECT, PROPOSE, and others”. In contrast, in this paper, we define the DA task on multiple round conversations between humans. The re-defined task is to classify a full dialogue according to the intention of one participant. We term this task as intention classification (IC). We, then, propose a hybrid neural network-based ensemble model for solving this problem. Two novel ensemble schemes are introduced for combining the classification results or features from various classifiers. One is ensembling features from each individual classifier using stacking, and we term this scheme as SFE. The other is adding wrong examples' weight to loss functions of each individual classifier using the AdaBoost scheme, and we term this scheme as MN-Ada. We have empirically examined the performance of the proposed ensemble schemes by using three popular deep neural networks, as well as one newly modified networks for IC. Extensive experiments have been conducted on a Chinese dialogue corpus. Our model can achieve state-of-the-art accuracy on the experimental dialogue corpus.
Preserving quantum entanglement from parametric amplifications with a correlation modulation scheme

Amplification of an entangled state is a matter of great significance in quantum communication. However, any parametric amplifier (PA) will introduce added noise into the system which unavoidably degrades the entanglement or even makes it disappear. Recently, it has been experimentally demonstrated that amplification of one half of a two-mode squeezed state is possible while preserving entanglement [Phys. Rev. Lett. 103, 010501 (2009)]. However, such entanglement cannot be maintained when the strength of the amplification is large. To solve this problem, we propose a correlation modulation scheme (CMS), which fully exploits the quantum correlation between the two output states from the PA process, to suppress the added noise from the parametric amplifications. For amplifying an entangled state using a PA, we demonstrate that the CMS not only better maintains but also always preserves the entanglement whatever the strength of the PA. Such a CMS may pave the way to low-noise amplification of an entangled state.
Optically controlled entangling gates in randomly doped silicon

Donor qubits in bulk doped silicon have many competitive advantages for quantum computation in the solid state: not only do they offer a fast way to scalability, but they also show some of the longest coherence times found in any quantum computation proposal. We determine the densities of entangling gates in randomly doped silicon comprising two different dopant species. First, we define conditions and plot maps of the relative locations of the dopants necessary for them to form exchange interaction-mediated entangling gates. Second, using nearest neighbor Poisson point process theory, we calculate the doping densities necessary for maximal densities of single and dual-species gates. Third, using the moving-average cluster expansion technique, we make predictions for a proof of principle experiment demonstrating the control of the far-from-equilibrium magnetization dynamics of one species by the orbital excitation of another. We find agreement of our results with a Monte Carlo simulation that handles multiple donor structures and scales optimally with the number of dopants. The simulator can also extract donor structures not captured by our Poisson point process theory. The combined approaches to density optimization in random distributions presented here may be useful for other condensed matter systems as well as applications outside physics.
Sequential Deep Learning for Action Recognition with Synthetic Multi-view Data from Depth Maps

Recurrent neural network (RNN) has proven successful recently in action recognition. However, depth sequences are of high dimensionality and contain rich human dynamics, which makes traditional RNNs difficult to capture complex action information. This paper addresses the problem of human action recognition from sequences of depth maps using sequential deep learning. The proposed method first synthesizes multi-view depth sequences by rotating 3D point clouds from depth maps. Each depth sequence is then split into short-term temporal segments. For each segment, a multi-view depth motion template (MVDMT), which compresses the segment to a motion template, is constructed for short-term multi-view action representation. The MVDMT effectively characterizes the multi-view appearance and motion patterns within a short-term duration. Convolutional Neural Network (CNN) models are leveraged to extract features from MVDMT, and a CNN-RNN network is subsequently employed to learn an effective representation for sequential patterns of the multi-view depth sequence. The proposed multi-view sequential deep learning framework can simultaneously capture spatial-temporal appearance and motion features in the depth sequence. The proposed method has been evaluated on the MSR Action3D and MSR Action Pairs datasets, achieving promising results compared with the state-of-the-art methods based on depth data.
Tony's law

Seeking to promote regulations for reliable software for the long-term prosperity of the software industry.
A Novel Learning-to-Rank Method for Automated Camera Movement Control in E-Sports Spectating

The popularity of modern competitive gaming (or e-sports) has skyrocketed in the past decade. A key part of e-sports is the spectating experience where fans watch tournament games through a camera of the observer. Bigger tournaments hire professional human observers with high-end tools to monitor important events in the game map for broadcasting the game. This setup is prone to errors. It results in missing important events within the game and lowers the spectating experience overall. It is also not sustainable in long-term and not affordable for the small-scale tournaments. This paper proposes a novel method of automated camera movement control using the AdaRank learning-to-rank algorithm to find and predict important events so the camera can be focused on time. The Dota 2 game setup and its replay data are used in extensive experimental testing. The proposed method has shown to outperform the accuracy of both a past machine learning approach and a professional team of human observers.
Sealed Computation: Abstract Requirements for Mechanisms to Support Trustworthy Cloud Computing

In cloud computing, data processing is delegated to a remote party for efficiency and flexibility reasons. A practical user requirement usually is that the confidentiality and integrity of data processing needs to be protected. In the common scenarios of cloud computing today, this can only be achieved by assuming that the remote party does not in any form act maliciously. In this paper, we propose an approach that avoids having to trust a single entity. Our approach is based on two concepts: (1) the technical abstraction of sealed computation, i.e., a technical mechanism to confine the processing of data within a tamper-proof hardware container, and (2) the additional role of an auditing party that itself cannot add functionality to the system but is able to check whether the system (including the mechanism for sealed computation) works as expected. We discuss the abstract technical and procedural requirements of these concepts and explain how they can be applied in practice.
Implementation of a Feature-Adaptive Colour Image Copyright Protection Scheme

The increasing distribution of digital media over the Internet, and the simplicity of the digital systems have left the contents in the web vulnerable. With digital watermarking, we can impose ownership and copyright information inside digital media. In this paper, a phase congruency based digital colour image watermarking algorithm is proposed, which provides a higher degree of robustness, and excellent imperceptibility. Here, phase congruency has been used to detect the feature regions of the host image, and then the watermark has been infused into it using a novel technique called adaptive k-α blending. The coefficients of the k-α blending (k & α) are selected adaptively based on the phase congruency feature map of the host image. From rigorous testing, results indicate that our approach is robust against various geometric, non-geometric, and combined attacks.
Sensing electrons during an adiabatic coherent transport passage

We study the detection of electrons undergoing coherent transfer via adiabatic passage (CTAP) in a triple quantum-dot system with a quantum point-contact sensing the change of the middle dot. In the ideal scenario, the protocol amounts to perfect change transfer between the external dots with vanishing occupation of the central dot at all times, rendering the measurement and its backaction moot. Nevertheless, even with minor corrections to the protocol, a small population builds up in the central dot. We study the measurement backaction by a Bayesian formalism simulation of an instantaneous detection at the time of maximal occupancy of the dot. We show that the interplay between the measurement backaction and the non-adiabatic dynamics induce a change of the success probability of the protocol, which quantitatively agrees with a continuous detection treatment. We introduce a correlated measurement signal to certify the non-occupancy of the central dot for a successful CTAP protocol, which, in the weak measurement limit, confirms a vanishing occupation of the central dot. Our proposed correlated-signal purports that proper experimental method by which to confirm CTAP.
WSCC 2018: The World Speed Computer Chess Championship

WSCC 2018 was ICGA's continuation of its investigation of top chess engines playing at Blitz tempo with the consequential loss of accuracy. They demonstrated that, even so, the engines play at super-GM level. On this occasion, KOMODO demolished the field and so won all three ICGA titles in Stockholm.
UPM

Identifying enrollment patterns associated with course success can help educators design better degree plans, and students make informed decisions about future enrollments. While discriminating pattern mining techniques can be used to address this problem, course enrollment patterns include sequence and quantity (grades) information. None of the existing methods were designed to account for both factors. In this work we present UPM, a Universal discriminating Pattern Mining framework that simultaneously mines various types of enrollment patterns while accounting for sequence and quantity using an expansion-specific approach. Unlike the existing methods, UPM expands a given pattern with an item by finding a minimum-entropy split over the item's quantities. We then use UPM to extract discriminating enrollment patterns from the high and the low performing student groups. These patterns can be utilized by educators for degree planning. To evaluate the quality of the extracted patterns, we adopt a supervised classification approach where we apply various classification techniques to label students according tho their performance based on the extracted patterns. Our evaluation shows that the classification accuracies obtained using the UPM extracted patterns are higher than the accuracies obtained using patterns extracted by other techniques. Accuracy improves significantly for students with larger numbers of patterns. Moreover, expansion-specific quantitative mining leads to more accurate classifications than the methods that do not account for quantities (grades).
Periodic revival of frustrated two-photon creation via interference

It has been known that suitably placed external mirrors can enhance and suppress emission of entangled photon pairs in spontaneous parametric down-conversion (SPDC), known as frustrated two-photon creation via interference. In this work, we report periodic revival of frustrated two-photon creation via interference with SPDC pumped by a continuous-wave (cw) multi-mode laser. As the mirrors are translated relative to the position of the SPDC source, the effect of frustrated two-photon creation via interference gradually dies off. However, as the mirrors are translated even further, the effect of frustrated two-photon creation via interference re-appears periodically. Our theoretical and numerical analyses show that this revival phenomenon is due to the nature of cw multi-mode pump laser. This work clearly demonstrates how the properties of the pump laser, in addition to suitably placed external mirrors, can be used to modify the process of spontaneous two-photon emission.
Modified linear block code with code rate 1/2 and less than 1/2

This work proposes a new code which can be considered as the modification of linear block code with code rate 1/2 and less than 1/2. The theme of the work is derived from the controllability of discrete time system and accordingly the generator matrix is chosen. A procedure is proposed in this paper which can transform the lower dimensional space to higher dimensional space. A Code is subspace of a space, so, with the help of addition of an equivalent identity matrix, between I (Identity matrix) and P (Parity Check) matrix of the established generator matrix, the lower dimensional space is transformed to higher dimensional space. Multiple numbers of errors could be corrected with the help of this designed code. It is also shown that decoding of this code can be established using the methods like syndrome detection and nearest neighbor decoding. The proposed code satisfies all the bounds. The minimum distance d, which could be achieved here, is at least equal to the total no. of information bits which enhances its error correction capacity.
Quantifying Dynamic Leakage - Complexity Analysis and Model Counting-based Calculation -

A program is non-interferent if it leaks no secret information to an observable output. However, non-interference is too strict in many practical cases and quantitative information flow (QIF) has been proposed and studied in depth. Originally, QIF is defined as the average of leakage amount of secret information over all executions of a program. However, a vulnerable program that has executions leaking the whole secret but has the small average leakage could be considered as secure. This counter-intuition raises a need for a new definition of information leakage of a particular run, i.e., dynamic leakage. As discussed in [5], entropy-based definitions do not work well for quantifying information leakage dynamically; Belief-based definition on the other hand is appropriate for deterministic programs, however, it is not appropriate for probabilistic ones.
Structured Knowledge Distillation for Semantic Segmentation

In this work, we consider transferring the structure information from large networks to compact ones for dense prediction tasks in computer vision. Previous knowledge distillation strategies used for dense prediction tasks often directly borrow the distillation scheme for image classification and perform knowledge distillation for each pixel separately, leading to sub-optimal performance. Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem. Specifically, we study two structured distillation schemes: i) pair-wise distillation that distills the pair-wise similarities by building a static graph; and ii) holistic distillation that uses adversarial training to distill holistic knowledge. The effectiveness of our knowledge distillation approaches is demonstrated by experiments on three dense prediction tasks: semantic segmentation, depth estimation and object detection. Code is available at: this https URL
Mathematical Model to Estimate Loss by Cyber Incident in Japan

There is a great demand from the viewpoint of security insurance to calculate the value of damage due to leakage of personal information. The Japan Network Security Association(JNSA) proposed a model to calculate the damage compensation amount. However, the coefficient was determined by experts’ subjective evaluations for which there is no basis. We propose a new mathematical model by applying multiple regression using cyber incident records and information such as enterprise size as explanatory variables and the value of extraordinary losses to a company as a target variable. We apply the damage model to 15,000 cyber incidents, compare the two models’ loss amounts, and consider the relationship between them.