Reviewerly/exHarmony huggingface dataset:
High-Efficiency Arbitrary Quantum Operation on a High-Dimensional Quantum System

The ability to manipulate quantum systems lies at the heart of the development of quantum technology. The ultimate goal of quantum control is to realize arbitrary quantum operations (AQUOs) for all possible open quantum system dynamics. However, the demanding extra physical resources impose great obstacles. Here, we experimentally demonstrate a universal approach of AQUO on a photonic qudit with the minimum physical resource of a two-level ancilla and a ${\mathrm{log}}_{2}d$-scale circuit depth for a $d$-dimensional system. The AQUO is then applied in a quantum trajectory simulation for quantum subspace stabilization and quantum Zeno dynamics, as well as incoherent manipulation and generalized measurements of the qudit. Therefore, the demonstrated AQUO for complete quantum control would play an indispensable role in quantum information science.
Bound Entanglement in Thermalized States and Black Hole Radiation

We study the mixed-state entanglement structure of chaotic quantum many-body systems at late times using the recently developed $\textit{equilibrium approximation}$. A rich entanglement phase diagram emerges when we generalize this technique to evaluate the logarithmic negativity for various universality classes of macroscopically thermalized states. Unlike in the infinite temperature case, when we impose energy constraints at finite temperature, the phase diagrams for the logarithmic negativity and the mutual information become distinct. In particular, we identify a regime where the negativity is extensive but the mutual information is sub-extensive, indicating a large amount of $\textit{bound entanglement}$. When applied to evaporating black holes, these results imply that there is quantum entanglement within the Hawking radiation long before the Page time, although this entanglement may not be distillable into EPR pairs.
BERT-Based Sentiment Analysis: A Software Engineering Perspective

Sentiment analysis can provide a suitable lead for the tools used in software engineering along with the API recommendation systems and relevant libraries to be used. In this context, the existing tools like SentiCR, SentiStrength-SE, etc. exhibited low f1-scores that completely defeats the purpose of deployment of such strategies, thereby there is enough scope for performance improvement. Recent advancements show that transformer based pre-trained models (e.g., BERT, RoBERTa, ALBERT, etc.) have displayed better results in the text classification task. Following this context, the present research explores different BERT-based models to analyze the sentences in GitHub comments, Jira comments, and Stack Overflow posts. The paper presents three different strategies to analyse BERT based model for sentiment analysis, where in the first strategy the BERT based pre-trained models are fine-tuned; in the second strategy an ensemble model is developed from BERT variants, and in the third strategy a compressed model (Distil BERT) is used. The experimental results show that the BERT based ensemble approach and the compressed BERT model attain improvements by 6-12% over prevailing tools for the F1 measure on all three datasets.
Equivalence Checking of Sequential Quantum Circuits

We define a formal framework for equivalence checking of sequential quantum circuits. The model we adopt is a quantum state machine, which is a natural quantum generalisation of Mealy machines. A major difficulty in checking quantum circuits (but not present in checking classical circuits) is that the state spaces of quantum circuits are continuums. This difficulty is resolved by our main theorem showing that equivalence checking of two quantum Mealy machines can be done with input sequences that are taken from some chosen basis (which are finite) and have a length quadratic in the dimensions of the state Hilbert spaces of the machines. Based on this theoretical result, we develop an (and to the best of our knowledge, the first) algorithm for checking equivalence of sequential quantum circuits with running time $\mathcal{O}(2^{3m+5l}(2^{3m}+2^{3l}))$, where $m$ and $l$ denote the numbers of input and internal qubits, respectively. The complexity of our algorithm is comparable with that of the known algorithms for checking classical sequential circuits in the sense that both are exponential in the number of (qu)bits. Several case studies and experiments are presented.
Auto-SDA

Social distancing can reduce infection rates in respiratory pandemics such as COVID-19, especially in dense urban areas. To assess pedestrians' compliance with social distancing policies, we use the pilot site of the PAWR COSMOS wireless edge-cloud testbed in New York City to design and evaluate an Automated video-based Social Distancing Analyzer (Auto-SDA) pipeline. Auto-SDA derives pedestrians' trajectories and measures the duration of close proximity events. It relies on an object detector and a tracker, however, to achieve highly accurate social distancing analysis, we design and incorporate 3 modules into Auto-SDA: (i) a calibration module that converts 2D pixel distances to 3D on-ground distances with less than 10 cm error, (ii) a correction module that identifies pedestrians who were missed or assigned duplicate IDs by the object detection-tracker and rectifies their IDs, and (iii) a group detection module that identifies affiliated pedestrians (i.e., pedestrians who walk together as a social group) and excludes them from the social distancing violation analysis. We applied Auto-SDA to videos recorded at the COSMOS pilot site before the pandemic, soon after the lockdown, and after the vaccines became broadly available, and analyzed the impacts of the social distancing protocols on pedestrians' behaviors and their evolution. For example, the analysis shows that after the lockdown, less than 55% of the pedestrians violated the social distancing protocols, whereas this percentage increased to 65% after the vaccines became available. Moreover, after the lockdown, 0-20% of the pedestrians were affiliated with a social group, compared to 10-45% once the vaccines became available. Finally, following the lockdown, the density of the pedestrians at the intersection decreased by almost 50%.
Quantum fluctuations and correlations in open quantum Dicke models

In the vicinity of ground-state phase transitions quantum correlations can display nonanalytic behavior and critical scaling. This signature of emergent collective effects has been widely investigated within a broad range of equilibrium settings. However, under nonequilibrium conditions, as found in open quantum many-body systems, characterizing quantum correlations near phase transitions is challenging. Moreover, the impact of local and collective dissipative processes on quantum correlations is not broadly understood. Here we consider as a paradigmatic setting the superradiant phase transition of the open quantum Dicke model and characterize quantum and classical correlations across the phase diagram. We develop an approach to quantum fluctuations which allows us to show that local dissipation, which cannot be treated within the commonly employed Holstein-Primakoff approximation, rather unexpectedly leads to an enhancement of collective quantum correlations, and to the emergence of a nonequilibrium superradiant phase in which the bosonic and spin degrees of freedom of the Dicke model are entangled.
Using weak supervision to generate training datasets from social media data: a proof of concept to identify drug mentions

Twitter has been a remarkable resource for research in pharmacovigilance in the last decade. Traditionally, rule- or lexicon-based methods have been utilized for automatically extracting drug tweets for human annotation. The process of human annotation to create labeled sets for machine learning models is laborious, time consuming and not scalable. In this work, we demonstrate the feasibility of applying weak supervision (noisy labeling) to select drug data, and build machine learning models using large amounts of noisy labeled data instead of limited gold standard labelled sets. Our results demonstrate the models built with large amounts of noisy data achieve similar performance than models trained on limited gold standard datasets, hence demonstrating that weak supervision helps reduce the need to rely on manual annotation, allowing more data to be easily labeled and useful for downstream machine learning applications, in this case drug mention identification.
3D shape measurement method for high-reflection surface based on fringe projection

3D measurement methods based on fringe projection have attracted extensive research. However, it is a challenge to deal with overshooting on a high-reflection or specular surface. To eliminate the saturated pixels caused by overshooting, we propose a projection intensity adaptive adjustment method. First, we project three uniform gray-level images and estimate the projection intensity of the measured surface through the captured uniform gray-level images. Then we can obtain the optimal projection fringes in the camera coordinate system. Second, a set of horizontal and vertical gray-coded patterns are used to establish a coordinate matching relationship between the projected image and the captured image. To check the decoding result of the gray-coded patterns, a set of horizontal and vertical sinusoidal fringes are used to calculate the high-reflection mapping area (HRMA) in the projector coordinate system. Through the distribution of HRMA, we can check whether the decoding is reliable or not. Finally, we project the optimal intensity fringes and obtain the measurement results. We develop a measurement system to verify the validity of the proposed method. Experimental results show that the proposed method can effectively avoid overshooting and obtain measurement results with a minimum rms error.
CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing

Deep learning based methods hold state-of-the-art results in low-level image processing tasks, but remain difficult to interpret due to their black-box construction. Unrolled optimization networks present an interpretable alternative to constructing deep neural networks by deriving their architecture from classical iterative optimization methods without use of tricks from the standard deep learning tool-box. So far, such methods have demonstrated performance close to that of state-of-the-art models while using their interpretable construction to achieve a comparably low learned parameter count. In this work, we propose an unrolled convolutional dictionary learning network (CDLNet) and demonstrate its competitive denoising and joint denoising and demosaicing (JDD) performance both in low and high parameter count regimes. Specifically, we show that the proposed model outperforms state-of-the-art fully convolutional denoising and JDD models when scaled to a similar parameter count. In addition, we leverage the model's interpretable construction to propose a noise-adaptive parameterization of thresholds in the network that enables state-of-the-art blind denoising performance, and near-perfect generalization on noise-levels unseen during training. Furthermore, we show that such performance extends to the JDD task and unsupervised learning.
Quantum dynamics of disordered arrays of interacting superconducting qubits: Signatures of quantum collective states

We study theoretically the collective quantum dynamics occurring in various interacting superconducting qubits arrays (SQAs) in the presence of a spread of individual qubit frequencies. The interaction is provided by mutual inductive coupling between adjacent qubits (short-range Ising interaction) or inductive coupling to a low-dissipative resonator (long-range exchange interaction). In the absence of interaction the Fourier transform of temporal correlation function of the total polarization ($z$-projection of the total spin), i.e. the dynamic susceptibility $C(\omega)$, demonstrates a set of sharp small magnitude resonances corresponding to the transitions of individual superconducting qubits. We show that even a weak interaction between qubits can overcome the disorder with a simultaneous formation of the collective excited states. This collective behavior manifests itself by a single large resonance in $C(\omega)$. In the presence of a weak non-resonant microwave photon field in the low-dissipative resonator, the positions of dominant resonances depend on the number of photons, i.e. the collective ac Stark effect. Coupling of an SQA to the transmission line allows a straightforward experimental access of the collective states in microwave transmission experiments and, at the same time, to employ SQAs as sensitive single-photon detectors.
Quantum computational phase transition in combinatorial problems

Abstract Quantum Approximate Optimization algorithm (QAOA) aims to search for approximate solutions to discrete optimization problems with near-term quantum computers. As there are no algorithmic guarantee possible for QAOA to outperform classical computers, without a proof that bounded-error quantum polynomial time (BQP) ≠ nondeterministic polynomial time (NP), it is necessary to investigate the empirical advantages of QAOA. We identify a computational phase transition of QAOA when solving hard problems such as SAT—random instances are most difficult to train at a critical problem density. We connect the transition to the controllability and the complexity of QAOA circuits. Moreover, we find that the critical problem density in general deviates from the SAT-UNSAT phase transition, where the hardest instances for classical algorithms lies. Then, we show that the high problem density region, which limits QAOA’s performance in hard optimization problems (reachability deficits), is actually a good place to utilize QAOA: its approximation ratio has a much slower decay with the problem density, compared to classical approximate algorithms. Indeed, it is exactly in this region that quantum advantages of QAOA over classical approximate algorithms can be identified.
Extending Coggia–Couvreur attack on Loidreau’s rank-metric cryptosystem

A recent paper by Coggia and Couvreur presents a polynomial time key-recovery attack on Loidreau's encryption scheme, based on rank-metric codes, for some parameters. The secret matrix component of Loidreau's scheme is chosen over a defining subspace of the field associated with the rank-metric code. We partially extend the Coggia-Couvreur attack to deal with the case when the defining subspace has dimension greater than 2.
Collaborative Learning of Label Semantics and Deep Label-Specific Features for Multi-Label Classification

In multi-label classification, the strategy of label-specific features has been shown to be effective to learn from multi-label examples by accounting for the distinct discriminative properties of each class label. However, most existing approaches exploit the semantic relations among labels as immutable prior knowledge, which may not be appropriate to constrain the learning process of label-specific features. In this paper, we propose to learn label semantics and label-specific features in a collaborative way. Accordingly, a deep neural network (DNN) based approach named CLIF, i.e. Collaborative Learning of label semantIcs and deep label-specific Features for multi-label classification, is proposed. By integrating a graph autoencoder for encoding semantic relations in the label space and a tailored feature-disentangling module for extracting label-specific features, CLIF is able to employ the learned label semantics to guide mining label-specific features and propagate label-specific discriminative properties to the learning process of the label semantics. In such a way, the learning of label semantics and label-specific features interact and facilitate with each other so that label semantics can provide more accurate guidance to label-specific feature learning. Comprehensive experiments on 14 benchmark data sets show that our approach outperforms other well-established multi-label classification algorithms.
Opinion mining for national security: techniques, domain applications, challenges and research opportunities

Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people's thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people's sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people's sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap.In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS.This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people's sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted.Various applications of opinion mining techniques in mining people's sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people's sentiments based on text in cyberspace. Kansei approach can measure people's impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.
Sampling-Based MPC for Constrained Vision Based Control

In this paper, we open up new avenues for visual servoing systems built upon the Path Integral (PI) optimal control theory, in which the non-linear partial differential equation (PDE) can be transformed into an expectation over all possible trajectories using the Feynman-Kac (FK) lemma. More precisely, we propose an MPPI-VS control strategy, a real-time and inversion-free control strategy on the basis of sampling-based model predictive control (namely, Model Predictive Path Integral (MPPI) control) algorithm, for both image-based, 3D point, and position-based visual servoing techniques, taking into account the system constraints (such as visibility, 3D, and control constraints) and parametric uncertainties associated with the robot and camera models as well as measurement noise. Contrary to classical visual servoing control schemes, our control strategy directly utilizes the approximation of the interaction matrix, without the need for estimating the interaction matrix inversion or performing the pseudo-inversion. We validate the MPPI-VS control strategy as well as the classical control schemes on a 6-DoF Cartesian robot with an eye-in-hand camera based on the utilization of four points in the image plane as visual features. To better assess and demonstrate the robustness and potential advantages of our proposed control strategy compared to classical schemes, intensive simulations under various operating conditions are carried out and then discussed. The obtained results demonstrate the effectiveness and capability of the proposed scheme in coping easily with the system constraints, as well as its robustness in the presence of large errors in camera parameters and measurements.
The Diffusion of Disruptive Technologies

We identify novel technologies using textual analysis of patents, job postings, and earnings calls. Our approach enables us to identify and document the diffusion of 29 disruptive technologies across firms and labor markets in the U.S. Five stylized facts emerge from our data. First, the locations where technologies are developed that later disrupt businesses are geographically highly concentrated, even more so than overall patenting. Second, as the technologies mature and the number of new jobs related to them grows, they gradually spread geographically. While initial hiring is concentrated in high-skilled jobs, over time the mean skill level in new positions associated with the technologies declines, broadening the types of jobs that adopt a given technology. At the same time, the geographic diffusion of low-skilled positions is significantly faster than higher-skilled ones, so that the locations where initial discoveries were made retain their leading positions among high-paying positions for decades. Finally, these pioneer locations are more likely to arise in areas with universities and high skilled labor pools.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Towards Understanding Human Functional Brain Development With Explainable Artificial Intelligence: Challenges and Perspectives

The last decades have seen significant advancements in non-invasive neuroimaging technologies that have been increasingly adopted to examine human brain development.However, these improvements have not necessarily been followed by more sophisticated data analysis measures that are able to explain the mechanisms underlying functional brain development.For example, the shift from univariate (single area in the brain) to multivariate (multiple areas in brain) analysis paradigms is of significance as it allows investigations into the interactions between different brain regions.However, despite the potential of multivariate analysis to shed light on the interactions between developing brain regions, artificial intelligence (AI) techniques applied render the analysis non-explainable.The purpose of this paper is to understand the extent to which current state-ofthe-art AI techniques can inform functional brain development.In addition, a review of which AI techniques are more likely to explain their learning based on the processes of brain development as defined by developmental cognitive neuroscience (DCN) frameworks is also undertaken.This work also proposes that eXplainable AI (XAI) may provide viable methods to investigate functional brain development as hypothesised by DCN frameworks.