Reviewerly/exHarmony huggingface dataset:
Suggesting Descriptive Method Names: An Exploratory Study of Two Machine Learning Approaches

Programming is a form of communication between the person who is writing code and the one reading it. Nevertheless, very often developers neglect readability, and even well-written code becomes less understandable as software evolves. Together with the growing complexity of software systems, this creates an increasing need for automated tools for improving the readability of source code. In this work, we focus on method names and study how a descriptive name can be automatically generated from a method’s body. We experiment with two approaches from the field of text summarization: One based on TF-IDF and the other on deep recurrent neural network. We collect a dataset of methods from 50 real world projects. We evaluate our approaches by comparing the generated names to the actual ones and report the result using Precision and Recall metrics. For TF-IDF, we get results as good as 28% precision and 45% recall; and for deep neural network, 46% precision and 32% recall.
Upper bounds for relative entropy of entanglement based on active learning

Quantifying entanglement for multipartite quantum state is a crucial task in many aspects of quantum information theory. Among all the entanglement measures, relative entropy of entanglement $E_{R}$ is an outstanding quantity due to its clear geometric meaning, easy compatibility with different system sizes, and various applications in many other related quantity calculations. Lower bounds of $E_R$ were previously found based on distance to the set of positive partial transpose states. We propose a method to calculate upper bounds of $E_R$ based on active learning, a subfield in machine learning, to generate an approximation of the set of separable states. We apply our method to calculate $E_R$ for composite systems of various sizes, and compare with the previous known lower bounds, obtaining promising results. Our method adds a reliable tool for entanglement measure calculation and deepens our understanding for the structure of separable states.
Explaining Non-bisimilarity in a Coalgebraic Approach: Games and Distinguishing Formulas

Behavioural equivalences can be characterized via bisimulation, modal logics, and spoiler-duplicator games. In this paper we work in the general setting of coalgebra and focus on generic algorithms for computing the winning strategies of both players in a bisimulation game. The winning strategy of the spoiler (if it exists) is then transformed into a modal formula that distinguishes the given non-bisimilar states. The modalities required for the formula are also synthesized on-the-fly, and we present a recipe for re-coding the formula with different modalities, given by a separating set of predicate liftings. Both the game and the generation of the distinguishing formulas have been implemented in a tool called T-Beg.
Robust Bregman clustering

Using a trimming approach, we investigate a k-means type method based on Bregman divergences for clustering data possibly corrupted with clutter noise. The main interest of Bregman divergences is that the standard Lloyd algorithm adapts to these distortion measures, and they are well-suited for clustering data sampled according to mixture models from exponential families. We prove that there exists an optimal codebook, and that an empirically optimal codebook converges a.s. to an optimal codebook in the distortion sense. Moreover, we obtain the sub-Gaussian rate of convergence for k-means 1 $\sqrt$ n under mild tail assumptions. Also, we derive a Lloyd-type algorithm with a trimming parameter that can be selected from data according to some heuristic, and present some experimental results.
Temporal array with superconducting nanowire single-photon detectors for photon-number resolution

We present an experimental realization of a 16 element, temporal-array, photon-number-resolving (PNR) detector, which is a multiplexed single-photon detector that splits an input signal over multiple time bins, and the time bins are detected using two superconducting nanowire single-photon detectors (SNSPD). A theoretical investigation of the PNR capabilities of the detector is performed and it is concluded that, compared to a single-photon detector, our array detector can resolve one order of magnitude higher mean photon numbers, given the same number of input pulses to measure. This claim is experimentally verified and we show that the detector can accurately predict photon numbers between ${10}^{\ensuremath{-}3}$ and ${10}^{2}$. Our present detector is incapable of single-shot photon-number measurements with high precision since its effective quantum efficiency is $49%$. Using SNSPDs with a higher quantum efficiency the PNR performance will improve, but the photon-number resolution will still be limited by the array size.
Visual Perception System of EROS Humanoid Robot Soccer

In this paper, a humanoid robot soccer perception system, consisting of a ball, field detection, and localization, is developed in order to deal with the new rules in RoboCup. Color segmentation and image morphology are improved together with a more sophisticated machine learning algorithm to detect a soccer ball robustly. Those algorithms are still favorable due to its real-time running in most of the embedded platform. For localization, the field is divided into pre-deðned grids and employing k-NN (k-nearest neighbor) to determine the robot location in the grids. Pre-defined grids are used to reduce computation due to matching with a map. Experiment results show that the developed system relatively well for adapting to the new rules update.
Super Intelligence to Solve COVID-19 Problem

In year 2020, the world faced an epidemic of the new coronavirus SARS-CoV-2, that lead to a global crisis. COVID-19 crisis is the complex and challenging problem of the real world so to solve such a crisis is a good task for super intelligent system because the devil is in the details so the agent has to model the world with some really good degree of competency. We propose a conceptual scheme to solve this task by building a modeling (including using an agent-based approach) and analyzing system involving intelligent systems (artificial or hybrid). Conceptually this system has to be able to discover the most accurate model of the problem and to offer the best actions to solve it. In this article we propose the approach to solve COVID-19 problem and to build super intelligent system for this. We analyze the world experience in applying information technology to solving problems of system analysis of such a degree of complexity. A series of simple experiments to illustrate the idea proposed in the article is shown.
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments

Evolution strategies (ESs), as a family of black-box optimization algorithms, recently emerge as a scalable alternative to reinforcement learning (RL) approaches such as Q-learning or policy gradient and are much faster when many central processing units (CPUs) are available due to better parallelization. In this article, we propose a systematic incremental learning method for ES in dynamic environments. The goal is to adjust previously learned policy to a new one incrementally whenever the environment changes. We incorporate an instance weighting mechanism with ES to facilitate its learning adaptation while retaining scalability of ES. During parameter updating, higher weights are assigned to instances that contain more new knowledge, thus encouraging the search distribution to move toward new promising areas of parameter space. We propose two easy-to-implement metrics to calculate the weights: instance novelty and instance quality. Instance novelty measures an instance's difference from the previous optimum in the original environment, while instance quality corresponds to how well an instance performs in the new environment. The resulting algorithm, instance weighted incremental evolution strategies (IW-IESs), is verified to achieve significantly improved performance on challenging RL tasks ranging from robot navigation to locomotion. This article thus introduces a family of scalable ES algorithms for RL domains that enables rapid learning adaptation to dynamic environments.
Quantum simulation dynamics and circuit synthesis of FMO complex on an NMR quantum computer

Recently, the dynamics simulation of light-harvesting complexes as an open quantum system, in the weak and strong coupling regimes, has received much attention. In this paper, we investigate a digital quantum simulation approach of the Fenna-Matthews-Olson (FMO) photosynthetic pigment-protein complex surrounded with a Markovian bath, i.e., memoryless, based on a nuclear magnetic resonance (NMR) quantum computer. For this purpose, we apply the decoupling(recoupling) method, which is turn off(on) the couplings and also Solovay-Kitaev techniques to decompose Hamiltonian and Lindbladians into efficient elementary gates on an NMR simulator. Finally, we design the quantum circuits for the unitary and non-unitary part due to the system-environment interactions of the open system dynamics.
GTOPX space mission benchmarks

This contribution introduces the GTOPX space mission benchmark collection, which is an extension of the GTOP database published by the European Space Agency (ESA). The term GTOPX stands for Global Trajectory Optimization Problems with eXtension. GTOPX consists of ten individual benchmark instances representing real-world interplanetary space trajectory design problems. In regard to the original GTOP collection, GTOPX includes three new problem instances featuring mixed-integer and multi-objective properties. GTOPX enables a simplified user-handling, unified benchmark function call and some minor bug corrections to the original GTOP implementation. Furthermore, GTOPX is linked from original C++ source code to Python and Matlab based on dynamic link libraries, assuring computationally fast and accurate reproduction of the benchmark results in all programming languages. We performed a comprehensive landscape analysis to characterize the properties of the fitness landscape of GTOPX benchmarks. Space mission trajectory design problems as those represented in GTOPX are known to be highly non-linear and difficult to solve. The GTOPX collection therefore aims in particular at researchers wishing to put advanced (meta)heuristic and hybrid optimization algorithms to the test.
Photon-mediated entanglement scheme between a ZnO semiconductor defect and a trapped Yb ion

We propose an optical scheme to generate an entangled state between a trapped ion and a solid state donor qubit through which-path erasure of identical photons emitted from the two systems. The proposed scheme leverages the similar transition frequencies between In donor bound excitons in ZnO and the P21/2 to S21/2 transition in Yb+. The lifetime of the relevant ionic state is longer than that of the ZnO system by a factor of 6, leading to a mismatch in the temporal profiles of emitted photons. A detuned cavity-assisted Raman scheme weakly excites the donor with a shaped laser pulse to generate photons with a 0.99 temporal overlap to the Yb+ emission and partially shift the emission of the defect toward the Yb+ transition. The remaining photon shift is accomplished via the dc Stark effect. We show that an entanglement rate of 2.1×104 s−1 and an entanglement fidelity of 94% can be attained using a weak excitation scheme with reasonable parameters.
Generation of minimum-energy entangled states

Quantum technologies exploiting bipartite entanglement could be made more efficient by using states having the minimum amount of energy for a given entanglement degree. Here, we study how to generate these states in the case of a bipartite system of arbitrary finite dimension either by applying a unitary transformation to its ground state or through a zero-temperature thermalization protocol based on turning on and off a suitable interaction term between the subsystems. In particular, we explicitly identify three possible unitary operators and five possible interaction terms. On one hand, two of the three unitary transformations turn out to be easily decomposable in terms of local elementary operations and a single nonlocal one, making their implementation easier. On the other hand, since the thermalization procedures can be easily adapted to generate many different states, we numerically show that, for each degree of entanglement, generating minimum-energy entangled states costs, in general, less than generating the vast majority of the other states.
A High-Quality VR Calibration and Real-Time Stitching Framework Using Preprocessed Features

Virtual Reality (VR) contents include 360°×180° seamless panoramic videos, stitched from multiple overlapping video streams. Many commercial VR devices use a two-camera rig to capture VR content. These devices have increased radial distortion effects along the seams of the stitching boundary. Moreover, a fixed number of cameras in the camera rig makes the VR system non-scalable. Since the VR experience is directly related to the quality of VR content, it is desirable to create a VR framework that is scalable in terms of the number of cameras attached to the camera-rig and has better geometric and photometric quality. In this paper, we propose an end-to-end VR system for stitching full spherical content. The VR system is composed of camera rig calibration and stitching modules. The calibration module performs a geometric alignment of the camera rig. The stitching module transforms texture from the camera or video stream into the VR stream using lookup tables (LUTs) and blend masks (BMs). In this work, our main contribution is the improvement of stitching quality. First, we propose a feature preprocessing method that filters out inconsistent, error-prone features. Secondly, we propose a geometric alignment method that outperforms state-of-the-art VR stitching solutions. We tested our system on diverse image sets and obtained state-of-the-art geometric alignment. Moreover, we achieved real-time stitching of camera and video streams up to 120 fps at 4K resolution. After stitching, we encode VR content for IP multicasting.
Remote Estimation of Target Height from Unmanned Aerial Vehicle (UAV) Images

This paper focuses on how the height of a target can be swiftly estimated using images acquired by a digital camera installed into moving platforms, such as unmanned aerial vehicles (UAVs). A pinhole camera model after distortion compensation was considered for this purpose since it does not need extensive processing nor vanishing lines. The pinhole model has been extensively employed for similar purposes in past studies but mainly focusing on fixed camera installations. This study analyzes how to tailor the pinhole model for gimballed cameras mounted into UAVs, considering camera parameters and flight parameters. Moreover, it indicates a solution that foresees correcting only a few needed pixels to limit the processing overload. Finally, an extensive analysis was conducted to define the uncertainty associated with the height estimation. The results of this analysis highlighted interesting relationships between UAV-to-target relative distance, camera pose, and height uncertainty that allow practical exploitations of the proposed approach. The model was tested with real data in both controlled and uncontrolled environments, the results confirmed the suitability of the proposed method and outcomes of the uncertainty analysis. Finally, this research can open consumer UAVs to innovative applications for urban surveillance.
On the equivalence between quantum and random walks on finite graphs

Quantum walks on graphs are ubiquitous in quantum computing finding a myriad of applications. Likewise, random walks on graphs are a fundamental building block for a large number of algorithms with diverse applications. While the relationship between quantum and random walks has been recently discussed in specific scenarios, this work establishes a formal equivalence between the processes on arbitrary finite graphs and general conditions for shift and coin operators. It requires empowering random walks with time heterogeneity, where the transition probability of the walker is non-uniform and time dependent. The equivalence is obtained by equating the probability of measuring the quantum walk on a given node of the graph and the probability that the random walk is at that same node, for all nodes and time steps. The result is given by the construction procedure of a matrix sequence for the random walk that yields the exact same vertex probability distribution sequence of any given quantum walk, including the scenario with multiple interfering walkers. Interestingly, these matrices allows for a different simulation approach for quantum walks where node samples respect neighbor locality and convergence is guaranteed by the law of large numbers, enabling efficient (polynomial) sampling of quantum graph trajectories (paths). Furthermore, the complexity of constructing this sequence of matrices is discussed in the general case.
Preventing Overfitting by Training Derivatives

Resistance to overfitting is observed for neural networks trained with extended backpropagation algorithm. In addition to target values, its cost function uses derivatives of those up to the $4^{\mathrm{th}}$ order. For common applications of neural networks, high order derivatives are not readily available, so simpler cases are considered: training network to approximate analytical function inside 2D and 5D domains and solving Poisson equation inside a 2D circle. For function approximation, the cost is a sum of squared differences between output and target as well as their derivatives with respect to the input. Differential equations are usually solved by putting a multilayer perceptron in place of unknown function and training its weights, so that equation holds within some margin of error. Commonly used cost is the equation's residual squared. Added terms are squared derivatives of said residual with respect to the independent variables. To investigate overfitting, the cost is minimized for points of regular grids with various spacing, and its root mean is compared with its value on much denser test set. Fully connected perceptrons with six hidden layers and $2\cdot10^{4}$, $1\cdot10^{6}$ and $5\cdot10^{6}$ weights in total are trained with Rprop until cost changes by less than 10% for last 1000 epochs, or when the $10000^{\mathrm{th}}$ epoch is reached. Training the network with $5\cdot10^{6}$ weights to represent simple 2D function using 10 points with 8 extra derivatives in each produces cost test to train ratio of $1.5$, whereas for classical backpropagation in comparable conditions this ratio is $2\cdot10^{4}$.
Continuous Variables Graph States Shaped as Complex Networks: Optimization and Manipulation

Complex networks structures have been extensively used for describing complex natural and technological systems, like the Internet or social networks. More recently, complex network theory has been applied to quantum systems, where complex network topologies may emerge in multiparty quantum states and quantum algorithms have been studied in complex graph structures. In this work, we study multimode Continuous Variables entangled states, named cluster states, where the entanglement structure is arranged in typical real-world complex networks shapes. Cluster states are a resource for measurement-based quantum information protocols, where the quality of a cluster is assessed in terms of the minimal amount of noise it introduces in the computation. We study optimal graph states that can be obtained with experimentally realistic quantum resources, when optimized via analytical procedure. We show that denser and regular graphs allow for better optimization. In the spirit of quantum routing, we also show the reshaping of entanglement connections in small networks via linear optics operations based on numerical optimization.
Lower bounds on concurrence and negativity from a trace inequality

For bipartite quantum states we obtain lower bounds on two important entanglement measures, concurrence and negativity, studying the inequalities for the expectation value of a projector on some subspace of the Hilbert space. Several applications, including analysis of stability of entanglement under various perturbations of a state, are discussed.
Entanglement and complexity of interacting qubits subject to asymmetric noise

This paper shows that a chain of coupled qubits, interacting strongly with extremely noisy resonators, can still display all the hallmarks of significant quantum complexity and pose an intractable simulation problem for supercomputers.
A Categorical Construction for the Computational Definition of Vector Spaces

Lambda- $${\mathcal {S}}$$ is an extension to first-order lambda calculus unifying two approaches of non-cloning in quantum lambda-calculi. One is to forbid duplication of variables, while the other is to consider all lambda-terms as algebraic linear functions. The type system of Lambda- $${\mathcal {S}}$$ has a constructor S such that a type A is considered as the base of a vector space while S(A) is its span. Lambda- $${\mathcal {S}}$$ can also be seen as a language for the computational manipulation of vector spaces: The vector spaces axioms are given as a rewrite system, describing the computational steps to be performed. In this paper we give an abstract categorical semantics of Lambda- $${\mathcal {S}}^{*}$$ (a fragment of Lambda- $${\mathcal {S}}$$ ), showing that S can be interpreted as the composition of two functors in an adjunction relation between a Cartesian category and an additive symmetric monoidal category. The right adjoint is a forgetful functor U, which is hidden in the language, and plays a central role in the computational reasoning.
The Importance of Context When Recommending TV Content: Dataset and Algorithms

Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development and evaluation of context-aware recommender systems are scarce. In this paper we present a dataset of self-reported TV consumption enriched with contextual information of viewing situations. We show how choice of genre associates with, among others, the number of present users and users' attention levels. Furthermore, we evaluate the performance of predicting chosen genres given different configurations of contextual information, and compare the results to contextless predictions. The results suggest that including contextual features in the prediction cause notable improvements, and both temporal and social context show significant contributions.