Reviewerly/exHarmony huggingface dataset:
Confusion2Vec: towards enriching vector space word representations with representational ambiguities

Word vector representations are a crucial part of Natural Language Processing (NLP) and Human Computer Interaction. In this paper, we propose a novel word vector representation, Confusion2Vec, motivated from the human speech production and perception that encodes representational ambiguity. Humans employ both acoustic similarity cues and contextual cues to decode information and we focus on a model that incorporates both sources of information. The representational ambiguity of acoustics, which manifests itself in word confusions, is often resolved by both humans and machines through contextual cues. A range of representational ambiguities can emerge in various domains further to acoustic perception, such as morphological transformations, paraphrasing for NLP tasks like machine translation etc. In this work, we present a case study in application to Automatic Speech Recognition (ASR), where the word confusions are related to acoustic similarity. We present several techniques to train an acoustic perceptual similarity representation ambiguity. We term this Confusion2Vec and learn on unsupervised-generated data from ASR confusion networks or lattice-like structures. Appropriate evaluations for the Confusion2Vec are formulated for gauging acoustic similarity in addition to semantic-syntactic and word similarity evaluations. The Confusion2Vec is able to model word confusions efficiently, without compromising on the semantic-syntactic word relations, thus effectively enriching the word vector space with extra task relevant ambiguity information. We provide an intuitive exploration of the 2-dimensional Confusion2Vec space using Principal Component Analysis of the embedding and relate to semantic, syntactic and acoustic relationships. The potential of Confusion2Vec in the utilization of uncertainty present in lattices is demonstrated through small examples relating to ASR error correction.
MAQUI: Interweaving Queries and Pattern Mining for Recursive Event Sequence Exploration

Exploring event sequences by defining queries alone or by using mining algorithms alone is often not sufficient to support analysis. Analysts often interweave querying and mining in a recursive manner during event sequence analysis: sequences extracted as query results are used for mining patterns, patterns generated are incorporated into a new query for segmenting the sequences, and the resulting segments are mined or queried again. To support flexible analysis, we propose a framework that describes the process of interwoven querying and mining. Based on this framework, we developed MAQUI, a Mining And Querying User Interface that enables recursive event sequence exploration. To understand the efficacy of MAQUI, we conducted two case studies with domain experts. The findings suggest that the capability of interweaving querying and mining helps the participants articulate their questions and gain novel insights from their data.
Universal MBQC with generalised parity-phase interactions and Pauli measurements

We introduce a new family of models for measurement-based quantum computation which are deterministic and approximately universal. The resource states which play the role of graph states are prepared via 2-qubit gates of the form <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>exp</mml:mi><mml:mo>⁡</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mo>−</mml:mo><mml:mi>i</mml:mi><mml:mfrac><mml:mi>π</mml:mi><mml:msup><mml:mn>2</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mfrac><mml:mi>Z</mml:mi><mml:mo>⊗</mml:mo><mml:mi>Z</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>. When <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn></mml:math>, these are equivalent, up to local Clifford unitaries, to graph states. However, when <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>2</mml:mn></mml:math>, their behaviour diverges in two important ways. First, multiple applications of the entangling gate to a single pair of qubits produces non-trivial entanglement, and hence multiple parallel edges between nodes play an important role in these generalised graph states. Second, such a state can be used to realise deterministic, approximately universal computation using only Pauli <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Z</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>X</mml:mi></mml:math> measurements and feed-forward. Even though, for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>2</mml:mn></mml:math>, the relevant resource states are no longer stabiliser states, they admit a straightforward, graphical representation using the ZX-calculus. Using this representation, we are able to provide a simple, graphical proof of universality. We furthermore show that for every <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>2</mml:mn></mml:math> this family is capable of producing all Clifford gates and all diagonal gates in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-th level of the Clifford hierarchy.
A Novel Automatic Modulation Classifier Using Graph-Based Constellation Analysis for &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;${M}$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-ary QAM

An innovative automatic modulation classification via graph-based constellation analysis for M-ary QAM signals is presented. In our framework, a unified mesh model for the constellation diagrams of the M-QAM signals within the modulation candidate set is first constructed and exploited to transform the received M-QAM signal into graph domain. The concise graph representation of the received M-QAM signal is established from its constellation according to the positions of the recovered symbols in the mesh model. Then, the modulation feature vector is built from the eigenvector(s) corresponding to the maximum eigenvalue of its adjacency matrix. The modulation type can be identified by measuring the angle between the feature vectors resulting from the training data and the test data. Monte Carlo simulation results and theoretical analysis demonstrate that the proposed method with lower computational complexity can provide superior performance to the existing subtractive clustering technique, and is robust to the residual phase and timing offsets.
Countering adaptive network covert communication with dynamic wardens

Network covert channels are hidden communication channels in computer networks. They influence several factors of the cybersecurity economy. For instance, by improving the stealthiness of botnet communications, they aid and preserve the value of darknet botnet sales. Covert channels can also be used to secretly exfiltrate confidential data out of organizations, potentially resulting in loss of market/research advantage. Considering the above, efforts are needed to develop effective countermeasures against such threats. Thus in this paper, based on the introduced novel warden taxonomy, we present and evaluate a new concept of a dynamic warden. Its main novelty lies in the modification of the warden’s behavior over time, making it difficult for the adaptive covert communication parties to infer its strategy and perform a successful hidden data exchange. Obtained experimental results indicate the effectiveness of the proposed approach.
Improving the performance of twin-field quantum key distribution

Among the various versions of the twin-field quantum key distribution (TF-QKD) protocol [M. Lucamarini, Z. Yuan, J. Dynes, and A. Shields, Nature (London) 557, 400 (2018)] that can overcome the rate-distance limit, the TF-QKD without phase postselection proposed by Cui et al. [Phys. Rev. Appl. 11, 034053 (2019)] is an elegant TF-QKD that can provide high key rates since the postselection of global phases has been removed. However, the achievable distance of this variant is shorter than that of the original phase-matching QKD [X. Ma, P. Zeng, and H. Zhou, Phys. Rev. X 8, 031043 (2018)]. In this paper, we propose a method for improving its performance by introducing an additional decoy mode. The upper bound of the information leakage can be more tightly estimated; hence, both the key rate and the achievable distance are significantly improved. Interestingly, the operation of the proposed additional decoy mode is the same as that of the code mode; hence, it does not introduce difficulties into the experimental system. In addition, the improvement is substantial with finite decoy states, which is meaningful in practice.
High-Speed and Adaptive FPGA-Based Privacy Amplification in Quantum Key Distribution

Privacy amplification (PA) is a vital procedure in quantum key distribution (QKD) to shrink the eavesdropper's information about the final key almost to zero. With the increase of repeat frequency of discrete variable QKD (DV-QKD) system, PA processing speed has become the bottleneck in many high-speed DV-QKD systems. In this paper, a high-speed adaptive field-programmable gate array (FPGA)-based PA scheme using a fast Fourier transform (FFT) is presented. To decrease the computation complexity, a modified 2-D FFT-based Toeplitz PA scheme is designed. To increase the processing speed of the scheme on the constraint of limited resources, a real-value oriented FFT acceleration method and a fast read/write balanced matrix transposition method are designed and implemented in our scheme. The experimental results on a Xilinx Virtex-6 FPGA demonstrate that the throughput is nearly double of the latest FPGA based Toeplitz PA scheme according to the literature. Besides, this scheme owns not only the good adaptivity to compression ratio but also the compression ratio independent resource consumption. Therefore, this scheme can fit many high-speed QKD applications.
A Simple Secret Key Generation by Using a Combination of Pre-Processing Method with a Multilevel Quantization

Limitations of the computational and energy capabilities of IoT devices provide new challenges in securing communication between devices. Physical layer security (PHYSEC) is one of the solutions that can be used to solve the communication security challenges. In this paper, we conducted an investigation on PHYSEC which utilizes channel reciprocity in generating a secret key, commonly known as secret key generation (SKG) schemes. Our research focused on the efforts to get a simple SKG scheme by eliminating the information reconciliation stage so as to reduce the high computational and communication cost. We exploited the pre-processing method by proposing a modified Kalman (MK) and performing a combination of the method with a multilevel quantization, i.e., combined multilevel quantization (CMQ). Our approach produces a simple SKG scheme for its significant increase in reciprocity so that an identical secret key between two legitimate users can be obtained without going through the information reconciliation stage.
Automatic look-up table based real-time phase unwrapping for phase measuring profilometry and optimal reference frequency selection

For temporal phase unwrapping in phase measuring profilometry, it has recently been reported that two phases with co-prime frequencies can be absolutely unwrapped using a look-up table; however, frequency selection and table construction has been performed manually without optimization. In this paper, a universal phase unwrapping method is proposed to unwrap phase flexibly and automatically by using geometric analysis, and thus we can programmatically build a one-dimensional or two-dimensional look-up table for arbitrary two co-prime frequencies to correctly unwrap phases in real time. Moreover, a phase error model related to the defocus effect is derived to figure out an optimal reference frequency co-prime to the principal frequency. Experimental results verify the correctness and computational efficiency of the proposed method.
Multi-Scale Residual Hierarchical Dense Networks for Single Image Super-Resolution

Single image super-resolution is known to be an ill-posed problem, which has been studied for decades. With the developments of deep convolutional neural networks, the CNN-based single image super-resolution methods have greatly improved the quality of the generated high-resolution images. However, it is difficult for image super-resolution to make full use of the relationship between pixels in low-resolution images. To address this issue, we propose a novel multi-scale residual hierarchical dense network, which tries to find the dependencies in multi-level and multi-scale features. Especially, we apply the atrous spatial pyramid pooling, which concatenates multiple atrous convolutions with different dilation rates, and design a residual hierarchical dense structure for single image super-resolution. The atrous-spatial-pyramid-pooling module is used for learning the relationship of features at multiple scales while the residual hierarchical dense structure, which consists of several hierarchical dense blocks with skip connections, aims to adaptively detect key information from multi-level features. Meanwhile, dense features from different groups are connected in a dense approach by hierarchical dense blocks, which can adequately extract local multi-level features. The extensive experiments on benchmark datasets illustrate the superiority of our proposed method compared with the state-of-the-art methods. The super-resolution results on benchmark datasets of our method can be downloaded from https://github.com/Rainyfish/MS-RHDN, and the source code will be released upon acceptance of the paper.
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition

Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge. Although it is inevitable for continual-learning systems to encounter such unseen concepts, the corresponding literature appears to nonetheless focus primarily on alleviating catastrophic interference with learned representations. In this work, we introduce a probabilistic approach that connects these perspectives based on variational inference in a single deep autoencoder model. Specifically, we propose to bound the approximate posterior by fitting regions of high density on the basis of correctly classified data points. These bounds are shown to serve a dual purpose: unseen unknown out-of-distribution data can be distinguished from already trained known tasks towards robust application. Simultaneously, to retain already acquired knowledge, a generative replay process can be narrowed to strictly in-distribution samples, in order to significantly alleviate catastrophic interference.
Witnessing incompatibility of quantum channels

We introduce the notion of incompatibility witness for quantum channels, defined as an affine functional that is non-negative on all pairs of compatible channels and strictly negative on some incompatible pair. This notion extends the recent definition of incompatibility witnesses for quantum measurements. We utilize the general framework of channels acting on arbitrary finite-dimensional von Neumann algebras, thus allowing us to investigate incompatibility witnesses on measurement-measurement, measurement-channel, and channel-channel pairs. We prove that any incompatibility witness can be implemented as a state discrimination task in which some intermediate classical information is obtained before completing the task. This implies that any incompatible pair of channels gives an advantage over compatible pairs in some such state discrimination task.
Regenerative random permutations of integers

Motivated by recent studies of large Mallows(q) permutations, we propose a class of random permutations of N + and of Z, called regenerative permutations.Many previous results of the limiting Mallows(q) permutations are recovered and extended.Three special examples: blocked permutations, p-shifted permutations and p-biased permutations are studied.
A Low-Latency and Low-Cost Montgomery Modular Multiplier Based on NLP Multiplication

In this brief, a non-least positive form (NLP) based modular multiplication method that combines Karatsuba and schoolbook multiplication is applied in Montgomery modular multiplication, which saves 2 base multiplications compared to Karatsuba-only designs and allows pipeline structure to make most use of the parallelism in large modular multiplications. Based on this method, 256-bit and 512-bit modular multipliers are constructed with 3-way and 4-way NLP multipliers on FPGA platform. Implemented on Virtex-6, the 256-bit design can perform a modular multiplication in 62.6 ns and only requires 3.5K LUTs and 24 DSPs, which exhibits low-latency and low-cost among previous works.
Accelerating Lattice Quantum Field Theory Calculations via Interpolator Optimization Using Noisy Intermediate-Scale Quantum Computing

The only known way to study quantum field theories in nonperturbative regimes is using numerical calculations regulated on discrete space-time lattices. Such computations, however, are often faced with exponential signal-to-noise challenges that render key physics studies untenable even with next generation classical computing. Here, a method is presented by which the output of small-scale quantum computations on noisy intermediate-scale quantum era hardware can be used to accelerate larger-scale classical field theory calculations through the construction of optimized interpolating operators. The method is implemented and studied in the context of the 1+1-dimensional Schwinger model, a simple field theory which shares key features with the standard model of nuclear and particle physics.
Online-Within-Online Meta-Learning

We study the problem of learning a series of tasks in a fully online Meta-Learning setting. The goal is to exploit similarities among the tasks to incrementally adapt an inner online algorithm in order to incur a low averaged cumulative error over the tasks. We focus on a family of inner algorithms based on a parametrized variant of online Mirror Descent. The inner algorithm is incrementally adapted by an online Mirror Descent meta-algorithm using the corresponding within-task minimum regularized empirical risk as the meta-loss. In order to keep the process fully online, we approximate the meta-subgradients by the online inner algorithm. An upper bound on the approximation error allows us to derive a cumulative error bound for the proposed method. Our analysis can also be converted to the statistical setting by online-to-batch arguments. We instantiate two examples of the framework in which the meta-parameter is either a common bias vector or feature map. Finally, preliminary numerical experiments confirm our theoretical findings.
Radiology-Pathology Correlation to Facilitate Peer Learning: An Overview Including Recent Artificial Intelligence Methods

Correlation of pathology reports with radiology examinations has long been of interest to radiologists and helps to facilitate peer learning. Such correlation also helps meet regulatory requirements, ensures quality, and supports multidisciplinary conferences and patient care. Additional offshoots of such correlation include evaluating for and ensuring concordance of pathology results with radiology interpretation and procedures as well as ensuring specimen adequacy after biopsy. For much of the history of radiology, this correlation has been done manually, which is time consuming and cumbersome and provides coverage of only a fraction of radiology examinations performed. Electronic storage and indexing of radiology and pathology information laid the foundation for easier access and for the development of automated artificial intelligence methods to match pathology information with radiology reports. More recent techniques have resulted in near comprehensive coverage of radiology examinations with methods to present results and solicit feedback from end users. Newer deep learning language modeling techniques will advance these methods by providing more robust automated and comprehensive radiology-pathology correlation with the ability to rapidly, flexibly, and iteratively tune models to site and user preference.
Multilingual multimodal machine translation for Dravidian languages utilizing phonetic transcription

This work is supported by a research grant fromScience Foundation Ireland, co-funded by the European Regional Development Fund, for the Insight Centre under Grant Number SFI/12/RC/2289and the European Union’s Horizon 2020 researchand innovation programme under grant agreementNo 731015, ELEXIS - European Lexical Infrastructure and grant agreement No 825182, Pret- ˆ a-`LLOD.
Sparse long‐time coherent integration‐based detection method for radar low‐observable manoeuvring target

Low-observable manoeuvring target detection is a challenging problem for radar signal processing, due to the complex environment, target manoeuvrability, limited observation time etc. The study proposed a novel long-time coherent integration (LTCI) method for radar manoeuvring target, especially for low-observable unmanned aerial vehicle targets. The fast non-parametric searching LTCI via a non-uniform (NU) resampling and scale processing (SP) technique is proposed, where the high-order signal phase is reduced to linear term using NU resampling. After that, the SP method is applied to realise the range migration compensation. The sparse Fourier transform (FT) and inverse FT are performed for the final integration. The manoeuvring target would appear as peaks in the non-uniformly resampling and SP-sparse LTCI domain. Experiments using two sets of real radar data indicate that the proposed method can achieve a good balance between computational cost and integration gain. In addition, detailed detection performances are compared with the traditional methods, e.g., moving target detection, fractional FT, fractional ambiguity function, Radon-based LTCIs, and sparse fractional FT.
Eliminating Leakage Errors in Hyperfine Qubits

Population leakage outside the qubit subspace presents a particularly harmful source of error that cannot be handled by standard error correction methods. Using a trapped $^{171}$Yb$+$ ion, we demonstrate an optical pumping scheme to suppress leakage errors in atomic hyperfine qubits. The selection rules and narrow linewidth of a quadrupole transition are used to selectively pump population out of leakage states and back into the qubit subspace. Each pumping cycle reduces the leakage population by a factor of $\sim3$, allowing for an exponential suppression in the number of cycles. We use interleaved randomized benchmarking on the qubit subspace to show that this pumping procedure has negligible side-effects on un-leaked qubits, bounding the induced qubit memory error by $\leq2.0(8)\times10^{-5}$ per cycle, and qubit population decay to $\leq1.4(3)\times10^{-7}$ per cycle. These results clear a major obstacle for implementations of quantum error correction and error mitigation protocols.