Reviewerly/exHarmony huggingface dataset:
Two-photon resonance fluorescence of a ladder-type atomic system

Multi-photon emitters are a sought-after resource in quantum photonics. Nonlinear interactions between a multi-level atomic system and a coherent drive can lead to resonant two-photon emission, but harvesting light from this process has remained a challenge due to the small oscillator strengths involved. Here we present a study of two-photon resonance fluorescence at microwave frequencies, using a superconducting, ladder-type artificial atom, a transmon, strongly coupled to a waveguide. We drive the two-photon transition between the ground and second-excited state at increasingly high powers and observe a resonance fluorescence peak whose intensity becomes comparable to single-photon emission until it splits into a Mollow-like triplet. We measure photon correlations of frequency-filtered spectral lines and find that while emission at the fundamental frequency stays antibunched, the resonance fluorescence peak at the two-photon transition is superbunched. Our results provide a route towards the realization of multi-photon sources in the microwave domain.
Study and Scope of Signcryption for Cloud Data Access Control

As cloud storage service provides convenience, high computation and a capable service from local to remote server through the internet; it attracts the user to outsource data from remoteDebnath,Somencloud server. Storing and outsourcing of sensitive data directly to multi-tenant cloud is unsafe and it also raises the privacy and security issues. Ciphertext Policy-Attribute Based Encryption (CP-ABE) with attribute based signature (ABS) provides confidentiality and anonymous authentication for such environment and called Attribute Based Syncryption(ABSC). This technique can acquireNunsanga,Morrel V.L. security and privacy together, which might be more suitable for cloud data outsourcing. This paper delivers a widespread survey on attribute-based signcryption schemes to finding out the suitability and requirements for access control of outsourced cloud data and identifyBhuyan,Bubu the research gap. Moreover, we compare the existing scheme’s functionality and characteristic to analyses efficiency and performance of recent attribute-based signcryption to fulfill outsourcing properties.
Interactively shaping robot behaviour with unlabeled human instructions

In this paper, we propose a framework that enables a human teacher to shape a robot behaviour by interactively providing it with unlabeled instructions. We ground the meaning of instruction signals in the task-learning process, and use them simultaneously for guiding the latter. We implement our framework as a modular architecture, named TICS (Task-Instruction-Contingency-Shaping) that combines different information sources: a predefined reward function, human evaluative feedback and unlabeled instructions. This approach provides a novel perspective for robotic task learning that lies between Reinforcement Learning and Supervised Learning paradigms. We evaluate our framework both in simulation and with a real robot. The experimental results demonstrate the effectiveness of our framework in accelerating the task-learning process and in reducing the number of required teaching signals.
Multidimensional data-driven classification of emission-line galaxies

We propose a new soft clustering scheme for classifying galaxies in different activity classes using simultaneously 4 emission-line ratios; log([NII ]/Ha), log([SII]/Ha), log([OI]/Ha) and log([OIII]/Hb). We fit 20 multivariate Gaussian distributions to the 4-dimensional distribution of these lines obtained from the Sloan Digital Sky Survey (SDSS) in order to capture local structures and subsequently group the multivariate Gaussian distributions to represent the complex multi-dimensional structure of the joint distribution of galaxy spectra in the 4 dimensional line ratio space. The main advantages of this method are the use of all four optical-line ratios simultaneously and the adoption of a clustering scheme. This maximises the available information, avoids contradicting classifications, and treats each class as a distribution resulting in soft classification boundaries and providing the probability for an object to belong to each class. We also introduce linear multi-dimensional decision surfaces using support vector machines based on the classification of our soft clustering scheme. This linear multi-dimensional hard clustering technique shows high classification accuracy with respect to our soft-clustering scheme.
Provable Data Integrity in the Pharmaceutical Industry based on Version Control Systems and the Blockchain

In the pharmaceutical industry, process validation tasks are based on the raw data and its derived analytical results generated from the process. Process validation failure affects both patient safety and the economic success of the manufacturing company. Hence, data integrity is highly critical in this area. Regulatory agencies, such as the Food and Drug Administration (FDA), reacted to past data integrity breaches by publishing new guidelines on data integrity for the correct handling of data in the pharmaceutical context. In this contribution, we want to show how data integrity can be improved on a technological level, removing the need for trusted third parties and centralized systems for this task. Therefore, we implemented an approach that uses existing tools, currently mostly used by software developers, and combined them with a new smart contract built on top of the Ethereum blockchain. In a case study, we test how data manipulation or backdating of results can be easily detected and how regulatory agencies can audit the complete data flow from the regulatory report back to the original raw data. The results of this contribution outline a possible road map for the development of production-ready tools, such as versioned database systems that natively interoperate with distributed ledgers. This will improve the trustworthiness of pharmaceutical manufacturing data by both protecting the intellectual property of the industrial company and improving the safety of the patients. <i>LAY ABSTRACT:</i> In the pharmaceutical industry, economically driven manufacturing companies are regulated and controlled by regulatory agencies. The pharmaceutical manufacturing companies need to produce large amounts of process and analytical data to show that their products are safe for patients. As the decisions of the regulatory agencies rely on this data, manufacturing companies need to prove how their generated data can be protected from technical breaches or data manipulation. As of today, the available technical solutions to provide data integrity are not working well enough. Regulatory agencies have published multiple documents highlighting the current data integrity issues. In this contribution, we show how blockchain, a technology that multiple cryptocurrencies like Bitcoin rely on, can help to improve the integrity of manufacturing data and data science analysis procedures. Therefore, we combined a smart contract on the Ethereum blockchain with tools currently mostly used by software developers. The presented workflow shows how data integrity can be guaranteed on a technological level without the need for trusted third parties.
Connecting velocity and entanglement in quantum walks

We investigate the relation between transport properties and entanglement between the internal (spin) and external (position) degrees of freedom in one-dimensional discrete time quantum walks. We obtain closed-form expressions for the long-time position variance and asymptotic entanglement of quantum walks whose time evolution is given by any balanced quantum coin, starting from any initial qubit and position states following $\ensuremath{\delta}$-like (local) and Gaussian distributions. We find out that the knowledge of the limit velocity of the walker together with the polar angle of the initial qubit provide the asymptotic entanglement for local states, while this velocity with the quantum coin phases give it for highly delocalized states.
A Binary Algebraic Differential Evolution for the MultiDimensional Two-Way Number Partitioning Problem

This paper introduces MADEB, a Memetic Algebraic Differential Evolution algorithm for the Binary search space. MADEB has been applied to the Multidimensional Two-Way Number Partitioning Problem (MDTWNPP) and its main components are the binary differential mutation operator and a variable neighborhood descent procedure. The binary differential mutation is a concrete application of the abstract algebraic framework for the binary search space. The variable neighborhood descent is a local search procedure specifically designed for MDTWNPP. Experiments have been held on a widely accepted benchmark suite and MADEB is experimentally compared with respect to the current state-of-the-art algorithms for MDTWNPP. The experimental results clearly show that MADEB is the new state-of-the-art algorithm in the problem here investigated.
Parameter Identification of Single-Phase Inverter Based on Improved Moth Flame Optimization Algorithm

By identifying the parameters of electronic circuit, parametric fault diagnosis of power electronic circuits can be realized. Many intelligent optimization algorithms are used to identify the parameters of electronic circuit, but most of them have the defects of slow convergence rate and easy to fall into local minimum. Moth flame optimization algorithm is a novel swarm intelligence bionic algorithm based on the intelligence behavior of moth positioning, which also has the above drawbacks. In order to improve the performance of algorithm, when updating the moth position, moth firstly moves in a straight line to the optimal position, then Levy flight is added. The improved algorithm improves the global optimization ability and accelerates the convergence speed. The improved moth flame optimization algorithm is applied for the parameter identification of single-phase inverter. The identification result is compared with the results of the other optimization techniques. The effectiveness and superiority of the improved algorithm are verified.
Cascaded asymmetric local pattern: a novel descriptor for unconstrained facial image recognition and retrieval

Feature description is one of the most frequently studied areas in the expert systems and machine learning. Effective encoding of the images is an essential requirement for accurate matching. These encoding schemes play a significant role in recognition and retrieval systems. Facial recognition systems should be effective enough to accurately recognize individuals under intrinsic and extrinsic variations of the system. The templates or descriptors used in these systems encode spatial relationships of the pixels in the local neighbourhood of an image. Features encoded using these hand crafted descriptors should be robust against variations such as; illumination, background, poses, and expressions. In this paper a novel hand crafted cascaded asymmetric local pattern (CALP) is proposed for retrieval and recognition facial image. The proposed descriptor uniquely encodes relationship amongst the neighbouring pixels in horizontal and vertical directions. The proposed encoding scheme has optimum feature length and shows significant improvement in accuracy under environmental and physiological changes in a facial image. State of the art hand crafted descriptors namely; LBP, LDGP, CSLBP, SLBP and CSLTP are compared with the proposed descriptor on most challenging datasets namely; Caltech-face, LFW, and CASIA-face-v5. Result analysis shows that, the proposed descriptor outperforms state of the art under uncontrolled variations in expressions, background, pose and illumination.
Cloud Pricing

Cloud computing providers must constantly hold many idle compute instances available (e.g., for maintenance, or for users with long-term contracts). A natural idea to increase the provider's profit is to sell these idle instances on a spot market where users can be preempted. However, this ignores the possible "market cannibalization'' that may occur in equilibrium. In particular, users who would generate more profit in the provider's existing fixed-price market might move to the spot market and generate less profit. In this paper, we model the provider's profit optimization problem using queuing theory and game theory and analyze the equilibria of the resulting queuing system. Our main result is an easy-to-check condition under which offering a market consisting of fixed-price instances as well as some spot instances (using idle resources) increases the provider's profit over offering only fixed-price instances. Furthermore, we show that under our condition, such a profit increase can always be combined with a Pareto improvement for the users. Finally, we illustrate our results numerically to demonstrate the effects the provider's costs and her strategy have on her profit. Full paper: https://ssrn.com/abstract=3383420
Conditions allowing error correction in driven qubits

We consider a qubit that is driven along its logical $z$ axis, with noise along the $z$ axis in the driving field $\Omega$ proportional to some function $f(\Omega)$, as well as noise along the logical $x$ axis. We establish that whether or not errors due to both types of noise can be canceled out, even approximately, depends on the explicit functional form of $f(\Omega)$ by considering a power-law form, $f(\Omega)\propto\Omega^k$. In particular, we show that such cancellation is impossible for $k=0$, $1$, or any even integer. However, any other odd integer value of $k$ besides $1$ does permit cancellation; in fact, we show that both types of errors can be corrected with a sequence of four square pulses of equal duration. We provide sets of parameters that correct for errors for various rotations and evaluate the error, measured by the infidelity, for the corrected rotations versus the na\"ive rotations, i.e., the operations that, in the complete absence of noise, would produce the desired rotations (in this case a single pulse of appropriate duration and magnitude). We also consider a train of four trapezoidal pulses, which take into account the fact that there will be, in real experimental systems, a finite rise time, again providing parameters for error-corrected rotations that employ such pulse sequences. Our dynamical decoupling error correction scheme works for any qubit platform as long as the errors are quasistatic.
Block Cipher Invariants as Eigenvectors of Correlation Matrices

A new approach to invariant subspaces and nonlinear invariants is developed. This results in both theoretical insights and practical attacks on block ciphers. It is shown that, with minor modifications to some of the round constants, Midori-64 has a nonlinear invariant with $$2^{96} + 2^{64}$$ corresponding weak keys. Furthermore, this invariant corresponds to a linear hull with maximal correlation. By combining the new invariant with integral cryptanalysis, a practical key-recovery attack on ten rounds of unmodified Midori-64 is obtained. The attack works for $$2^{96}$$ weak keys and irrespective of the choice of round constants. The data complexity is $$1.25 \cdot 2^{21}$$ chosen plaintexts, and the computational cost is dominated by $$2^{56}$$ block cipher calls. The validity of the attack is verified by means of experiments.
Classification of CT Scan Images of Lungs Using Deep Convolutional Neural Network with External Shape-Based Features

In this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape-based features, viz. solidity, circularity, discrete Fourier transform of radial length (RL) function, histogram of oriented gradient (HOG), moment, and histogram of active contour image, have also been identified and embedded into the proposed convolutional neural network. The performance is measured in terms of average recall and average precision values and compared with six similar methods for biomedical image classification. The average precision obtained for the proposed system is found to be 95.26% and the average recall value is found to be 69.56% in average for the two databases.
Framework for Developing a Mobile Augmented Reality for Learning Chemical Bonds

This paper presents the framework for developing a Mobile Augmented Reality (MAR) for learning Chemical Bonds, which is believed to reduce misconceptions among the students. Misconceptions always occur in classrooms, especially in science subject’s which consist of abstract concepts. It is very important that these misconceptions be reduced, because they may affect the student’s understandings of topics they have learned about. These may interfere with the student’s education, and may also affect the student’s performances afterwards. Visualization is one key approach that has been proven to help when seeking to improve the understanding of students, which has consequently led to a reduction in misconceptions. There are many technologies that have been integrated within education, and that show strong potential for producing visualizations. One of the recent popular technologies that have the potential for visualization is Mobile Augmented Reality. Mobile Augmented Reality has the ability to visualize abstract concepts through 3D images
Relationship between subjecting the qubit to dynamical decoupling and to a sequence of projective measurements

We consider a qubit coupled to another system (its environment), and discuss the relationship between the effects of subjecting the qubit to either a dynamical decoupling sequence of unitary operations, or a sequence of projective measurements. We give a formal statement concerning equivalence of a sequence of coherent operations on a qubit, precisely operations from a minimal set $\left\{\mathbf{I}_Q,\hat{\sigma}_x\right\}$, and a sequence of projective measurements of $\hat{\sigma}_x$ observable. Using it we show that when the qubit is subjected to $n$ such successive projective measurements at certain times, the expectation value of the {\it last} measurement can be expressed as a linear combination of expectation values of $\hat{\sigma}_x$ observed after subjecting the qubit to dynamical decoupling sequences of $\pi$ pulses, with $k\leq n$ of them applied at subsets of these times. Performing a sequence of measurements on the qubit gives then access to the same properties of the environment and qubit-environment coupling that are affecting the coherence observed in a dynamical decoupling experiment. Analysing the latter has been widely used to characterize the environmental dynamics (perform so-called noise spectroscopy), so our result shows how the results obtained with dynamical decoupling based protocols are related to those that can be obtained just by performing multiple measurements on the qubit. We also discuss in more detail the application of the general result to the case of the qubit undergoing pure dephasing, and outline possible extensions to higher-dimensional (a qudit or multiple qubits) systems.
Model-Free Tracker for Multiple Objects Using Joint Appearance and Motion Inference

Model-free tracking is a widely-accepted approach to track an arbitrary object in a video using a single frame annotation with no further prior knowledge about the object of interest. Extending this problem to track multiple objects is really challenging because: a) the tracker is not aware of the objects' type while trying to distinguish them from background (detection task), and b) The tracker needs to distinguish one object from other potentially similar objects (data association task) to generate stable trajectories. In order to track multiple arbitrary objects, most existing model-free tracking approaches rely on tracking each target individually by updating their appearance model independently. Therefore, in this scenario they often fail to perform well due to confusion between the appearance of similar objects, their sudden appearance changes and occlusion. To tackle this problem, we propose to use both appearance and motion models, and to learn them jointly using graphical models and deep neural networks features. We introduce an indicator variable to predict sudden appearance change and/or occlusion. When these happen, our model does not update the appearance model thus avoiding using the background and/or incorrect object to update the appearance of the object of interest mistakenly, and relies on our motion model to track. Moreover, we consider the correlation among all targets, and seek the joint optimal locations for all targets simultaneously as a graphical model inference problem. We learn the joint parameters for both appearance model and motion model in an online fashion under the framework of LaRank. Experiment results show that our method achieved superior performance compared to the competitive methods.
Robust website fingerprinting through the cache occupancy channel

Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user's computer and the Tor network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of running a small amount of unprivileged code on the target user's computer. Under this model, the attacker can mount cache side-channel attacks, which exploit the effects of contention on the CPU's cache, to identify the website being browsed. In an important special case of this attack model, a JavaScript attack is launched when the target user visits a website controlled by the attacker. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. In this work we show that cache website fingerprinting attacks in JavaScript are highly feasible, even when they are run from highly restrictive environments, such as the Tor Browser. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification accuracy in both the open-world and the closed-world models. We further show that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack.
Microscopic and phenomenological models of driven systems in structured reservoirs

We study the paradigmatic model of a qubit interacting with a structured environment and driven by an external field by means of a microscopic and a phenomenological model. The validity of the so-called fixed-dissipator (FD) assumption, where the dissipation is taken as the one of the undriven qubit, is discussed. In the limit of a flat spectrum, the FD model and the microscopic one remarkably practically coincide. For a structured reservoir, we show in the secular limit that steady states can be different from those determined from the FD model, opening the possibility for exploiting reservoir engineering. We explore it as a function of the control field parameters, of the characteristics of the spectral density, and of the environment temperature. The observed widening of the family of target states by reservoir engineering suggests new possibilities in quantum control protocols.