Reviewerly/exHarmony huggingface dataset:
A Hierarchy of Anyon Models Realised by Twists in Stacked Surface Codes

Braiding defects in topological stabiliser codes can be used to fault-tolerantly implement logical operations. Twists are defects corresponding to the end-points of domain walls and are associated with symmetries of the anyon model of the code. We consider twists in multiple copies of the 2d surface code and identify necessary and sufficient conditions for considering these twists as anyons: namely that they must be self-inverse and that all charges which can be localised by the twist must be invariant under its associated symmetry. If both of these conditions are satisfied the twist and its set of localisable anyonic charges reproduce the behaviour of an anyonic model belonging to a hierarchy which generalises the Ising anyons. We show that the braiding of these twists results in either (tensor products of) the S gate or (tensor products of) the CZ gate. We also show that for any number of copies of the 2d surface code the application of H gates within a copy and CNOT gates between copies is sufficient to generate all possible twists.
Critical neuromorphic computing based on explosive synchronization

Synchronous oscillations in neuronal ensembles have been proposed to provide a neural basis for the information processes in the brain. In this work, we present a neuromorphic computing algorithm based on oscillator synchronization in a critical regime. The algorithm uses the high-dimensional transient dynamics perturbed by an input and translates it into proper output stream. One of the benefits of adopting coupled phase oscillators as neuromorphic elements is that the synchrony among oscillators can be finely tuned at a critical state. Especially near a critical state, the marginally synchronized oscillators operate with high efficiency and maintain better computing performances. We also show that explosive synchronization that is induced from specific neuronal connectivity produces more improved and stable outputs. This work provides a systematic way to encode computing in a large size coupled oscillator, which may be useful in designing neuromorphic devices.
Qubit movement-assisted entanglement swapping

We propose a scheme to generate entanglement between two distant qubits (two-level atom) which are separately trapped in their own (in general) non-Markovian dissipative cavities by utilizing entangling swapping, considering the case in which the qubits can move along their cavity axes rather than a static state of motion. We first examine the role of movement of the qubit by studying the entropy evolution for each subsystem. The average entropy over the initial states of the qubit is calculated. Then by performing a Bell state measurement on the fields leaving the cavities, we swap the entanglement between qubit-field in each cavity into qubit-qubit and field-field subsystems. The entangling power is used to measure the average amount of swapped entanglement over all possible pure initial states. Our results are presented in two weak and strong coupling regimes, illustrating the positive role of movement of the qubits on the swapped entanglement. It is revealed that by considering certain conditions for the initial state of qubits, it is possible to achieve a maximally long-leaving stationary entanglement (Bell state) which is entirely independent of the environmental variables as well as the velocity of qubits. This happens when the two qubits have the same velocities.
Unitary dilations of discrete-time quantum-dynamical semigroups

We show that the discrete-time evolution of an open quantum system generated by a single quantum channel T can be embedded in the discrete-time evolution of an enlarged closed quantum system, i.e., we construct a unitary dilation of the discrete-time quantum-dynamical semigroup (Tn)n∈N0. In the case of a cyclic channel T, the auxiliary space may be chosen (partially) finite-dimensional. We further investigate discrete-time quantum control systems generated by finitely many commuting quantum channels and prove a similar unitary dilation result as in the case of a single channel.
Using the Past Knowledge to Improve Sentiment Classification

This paper studies sentiment classification in the lifelong learning setting that incrementally learns a sequence of sentiment classification tasks.It proposes a new lifelong learning model (called L2PG) that can retain and selectively transfer the knowledge learned in the past to help learn the new task.A key innovation of this proposed model is a novel parameter-gate (p-gate) mechanism that regulates the flow or transfer of the previously learned knowledge to the new task.Specifically, it can selectively use the network parameters (which represent the retained knowledge gained from the previous tasks) to assist the learning of the new task t.Knowledge distillation is also employed in the process to preserve the past knowledge by approximating the network output at the state when task t -1 was learned.Experimental results show that L2PG outperforms strong baselines, including even multiple task learning.
Decoy-state round-robin differential-phase-shift quantum key distribution with source errors

As a promising quantum key distribution (QKD), most of the existing round-robin differential-phase-shift quantum key distribution (RRDPS-QKD) protocols have adopted the decoy-state method and have assumed the source states are exactly controlled. However, the precise manipulation of source states is impossible for any practical experiment, and the RRDPS-QKD with source errors has an unignorable impact on the performance of the protocol. In the paper, we study the four-intensity decoy-state RRDPS-QKD protocol with source errors, formulate the secure generation key rate of the proposed protocol, and do the numerical simulations to testify the deductions. The results show that our evaluation can estimate the influence of source errors.
Community-Aware Graph Signal Processing: Modularity Defines New Ways of Processing Graph Signals

The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in studying graph properties and measuring graph signal smoothness. Here, instead, we propose the graph modularity matrix as the centerpiece of GSP to incorporate knowledge about graph community structure when processing signals on the graph but without the need for community detection. We study this approach in several generic settings, such as filtering, optimal sampling and reconstruction, surrogate data generation, and denoising. Feasibility is illustrated by a small-scale example and a transportation network data set as well as one application in human neuroimaging where community-aware GSP reveals relationships between behavior and brain features that are not shown by Laplacian-based GSP. This work demonstrates how concepts from network science can lead to new, meaningful operations on graph signals.
CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams

Mining association rules from data streams is a challenging task due to the (typically) limited resources available vs. the large size of the result. Frequent closed itemsets (FCI) enable an efficient first step, yet current FCI stream miners are not optimal on resource consumption, e.g. they store a large number of extra itemsets at an additional cost. In a search for a better storage-efficiency trade-off, we designed Ciclad, an intersection-based sliding-window FCI miner. Leveraging in-depth insights into FCI evolution, it combines minimal storage with quick access. Experimental results indicate Ciclad's memory imprint is much lower and its performances globally better than competitor methods.
A Comment on Privacy-Preserving Scalar Product Protocols as Proposed in “SPOC”

Privacy-preserving scalar product (PPSP) protocols are an important building block for secure computation tasks in various applications. Lu et al. (TPDS'13) introduced a PPSP protocol that does not rely on cryptographic assumptions and that is used in a wide range of publications to date. In this comment paper, we show that Lu et al.'s protocol is insecure and should not be used. We describe specific attacks against it and, using impossibility results of Impagliazzo and Rudich (STOC'89), show that it is inherently insecure and cannot be fixed without relying on at least some cryptographic assumptions.
Quantum Fisher information and coherence of an atom in a dissipative cavity

In this work, we investigate quantum Fisher information (QFI) and quantum coherence (QC) of an atom in a dissipative cavity. In a zero-temperature reservoir and with one excitation number, we obtain the analytical solutions of QFI and QC as well as their relationship for Ohmic and Lorentzian reservoirs, respectively. The results show that both the atom–cavity coupling and the cavity–reservoir coupling can effectively protect QFI and QC. In particular, QFI and QC will tend to their stable values when the atom–cavity coupling or cavity–reservoir coupling is larger than a certain value. QC can augment QFI and can effectively improve the quantum metrology. In addition, we give a physical explanation of the dynamic behavior of QFI and QC using the decoherence rate.
Identifying Users with Wearable Sensors based on Activity Patterns

We live in a world where ubiquitous systems surround us in the form of automated homes, smart appliances and wearable devices. These ubiquitous systems not only enhance productivity but can also provide assistance given a variety of different scenarios. However, these systems are vulnerable to the risk of unauthorized access, hence the ability to authenticate the end-user seamlessly and securely is important. This paper presents an approach for user identification given the physical activity patterns captured using on-body wearable sensors, such as accelerometer, gyroscope, and magnetometer. Three machine learning classifiers have been used to discover the activity patterns of users given the data captured from wearable sensors. The recognition results prove that the proposed scheme can effectively recognize a user’s identity based on his/her daily living physical activity patterns.
Automatic Generation of Topic Labels

Topic modelling is a popular unsupervised method for identifying the underlying themes in document collections that has many applications in information retrieval. A topic is usually represented by a list of terms ranked by their probability but, since these can be difficult to interpret, various approaches have been developed to assign descriptive labels to topics. Previous work on the automatic assignment of labels to topics has relied on a two-stage approach: (1) candidate labels are retrieved from a large pool (e.g. Wikipedia article titles); and then (2) re-ranked based on their semantic similarity to the topic terms. However, these extractive approaches can only assign candidate labels from a restricted set that may not include any suitable ones. This paper proposes using a sequence-to-sequence neural-based approach to generate labels that does not suffer from this limitation. The model is trained over a new large synthetic dataset created using distant supervision. The method is evaluated by comparing the labels it generates to ones rated by humans.
EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering.

We propose EXAMS -- a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. We collected more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. EXAMS offers a fine-grained evaluation framework across multiple languages and subjects, which allows precise analysis and comparison of various models. We perform various experiments with existing top-performing multilingual pre-trained models and we show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains. We hope that EXAMS will enable researchers to explore challenging reasoning and knowledge transfer methods and pre-trained models for school question answering in various languages which was not possible before. The data, code, pre-trained models, and evaluation are available at this https URL.
Exploration of End-to-End ASR for OpenSTT – Russian Open Speech-to-Text Dataset

This paper presents an exploration of end-to-end automatic speech recognition systems (ASR) for the largest open-source Russian language data set – OpenSTT. We evaluate different existing end-to-end approaches such as joint CTC/Attention, RNN-Transducer, and Transformer. All of them are compared with the strong hybrid ASR system based on LF-MMI TDNN-F acoustic model.For the three available validation sets (phone calls, YouTube, and books), our best end-to-end model achieves word error rate (WER) of 34.8%, 19.1%, and 18.1%, respectively. Under the same conditions, the hybrid ASR system demonstrates 33.5%, 20.9%, and 18.6% WER.
Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets

Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19. In this paper, we describe our system entry for WNUT 2020 Shared Task-3. The task was aimed at automating the extraction of a variety of COVID-19 related events from Twitter, such as individuals who recently contracted the virus, someone with symptoms who were denied testing and believed remedies against the infection. The system consists of separate multi-task models for slot-filling subtasks and sentence-classification subtasks while leveraging the useful sentence-level information for the corresponding event. The system uses COVID-Twitter-Bert with attention-weighted pooling of candidate slot-chunk features to capture the useful information chunks. The system ranks 1st at the leader-board with F1 of 0.6598, without using any ensembles or additional datasets. The code and trained models are available at this https URL.
Fast and scalable earth texture synthesis using spatially assembled generative adversarial neural networks

The earth texture with complex morphological geometry and compositions such as shale and carbonate rocks, is typically characterized with sparse field samples because of an expensive and time-consuming characterization process. Accordingly, generating arbitrary large size of the geological texture with similar topological structures at a low computation cost has become one of the key tasks for realistic geomaterial reconstruction and subsequent hydro-mechanical evaluation for science and engineering applications. Recently, generative adversarial neural networks (GANs) have demonstrated a potential of synthesizing input textural images and creating equiprobable geomaterial images for stochastic analysis of hydrogeological properties, for example, the feasibility of CO2 storage sites and exploration of unconventional resources. However, the texture synthesis with the GANs framework is often limited by the computational cost and scalability of the output texture size. In this study, we proposed a spatially assembled GANs (SAGANs) that can generate output images of an arbitrary large size regardless of the size of training images with computational efficiency. The performance of the SAGANs was evaluated with two and three dimensional (2D and 3D) rock image samples widely used in geostatistical reconstruction of the earth texture and Lattice-Boltzmann (LB) simulations were performed to compare pore-scale flow patterns and upscaled permeabilities of training and generated geomaterial images. We demonstrate SAGANs can generate the arbitrary large size of statistical realizations with connectivity and structural properties and flow characteristics similar to training images, and also can generate a variety of realizations even on a single training image. In addition, the computational time was significantly improved compared to standard GANs frameworks.
Multi-outcome homodyne detection in a coherent-state light interferometer

The Cram\'{e}r-Rao bound plays a central role in both classical and quantum parameter estimation, but finding the observable and the resulting inversion estimator that saturates this bound remains an open issue for general multi-outcome measurements. Here we consider multi-outcome homodyne detection in a coherent-light Mach-Zehnder interferometer and construct a family of inversion estimators that almost saturate the Cram\'{e}r-Rao bound over the whole range of phase interval. This provides a clue on constructing optimal inversion estimators for phase estimation and other parameter estimation in any multi-outcome measurement.
Comparative evaluation of pattern mining techniques: an empirical study

Abstract Pattern mining has emerged as a compelling field of data mining over the years. Literature has bestowed ample endeavors in this field of research ranging from frequent pattern mining to rare pattern mining. A precise and impartial analysis of the existing pattern mining techniques has therefore become essential to widen the scope of data analysis using the notion of pattern mining. This paper is therefore an attempt to provide a comparative scrutiny of the fundamental algorithms in the field of pattern mining through performance analysis based on several decisive parameters. The paper provides a structural classification of the widely referenced techniques in four pattern mining categories: frequent, maximal frequent, closed frequent and rare. It provides an analytical comparison of these techniques based on computational time and memory consumption using benchmark real and synthetic data sets. The results illustrate that tree based approaches perform exceptionally well over level wise approaches in case of dense data sets for all the categories. However, for sparse data sets, level wise approaches performed better than the former ones. This study has been carried out with an aim to analyze the pros and cons of the well known pattern mining techniques under different categories. Through this empirical study, an endeavor has been made to enable the researchers identify some fruitful and promising research directions in one of the most remarkable area of research, pattern mining.
Proof–Based Synthesis of Sorting Algorithms Using Multisets in Theorema

Using multisets, we develop novel techniques for mechanizing the proofs of the synthesis conjectures for list-sorting algorithms, and we demonstrate them in the Theorema system. We use the classical principle of extracting the algorithm as a set of rewrite rules based on the witnesses found in the proof of the synthesis conjecture produced from the specification of the desired function (input and output conditions). The proofs are in natural style, using standard rules, but most importantly domain specific inference rules and strategies. In particular the use of multisets allows us to develop powerful strategies for the synthesis of arbitrarily structured recursive algorithms by general Noetherian induction, as well as for the automatic generation of the specifications of all necessary auxiliary functions (insert, merge, split), whose synthesis is performed using the same method.
Adversarial Self-Supervised Data-Free Distillation for Text Classification

Large pre-trained transformer-based language models have achieved impressive results on a wide range of NLP tasks.In the past few years, Knowledge Distillation(KD) has become a popular paradigm to compress a computationally expensive model to a resource-efficient lightweight model.However, most KD algorithms, especially in NLP, rely on the accessibility of the original training dataset, which may be unavailable due to privacy issues.To tackle this problem, we propose a novel twostage data-free distillation method, named Adversarial self-Supervised Data-Free Distillation (AS-DFD), which is designed for compressing large-scale transformer-based models (e.g., BERT).To avoid text generation in discrete space, we introduce a Plug & Play Embedding Guessing method to craft pseudo embeddings from the teacher's hidden knowledge.Meanwhile, with a self-supervised module to quantify the student's ability, we adapt the difficulty of pseudo embeddings in an adversarial training manner.To the best of our knowledge, our framework is the first data-free distillation framework designed for NLP tasks.We verify the effectiveness of our method on several text classification datasets.
The effect of strategic human resources management (SHRM) on organizational excellence

Human resources at different levels are the executives of organizational tasks toward excellence. Missing the track of human resources will leave the organization behind. The objective of this research is to investigate the effect of SHRM components on organizational excellence. The study was applied to industrial organizations in Industrial City in Northern Jordan. A quantitative approach was used to accomplish the objectives. A questionnaire was used to collect data. The questionnaire was composed of three parts: the first part designed to collect demographic data, the second part designed to collect information about SHRM (recruitment, training, development, and career development), the last part designed to collect information about organizational excellence. A simple random sample of 120 organizations’ managers was studied. The results showed that the data collection tool was reliable. The results showed that training was of high concern by managers to reach excellence, followed by organizational development, then career development, and the least evaluation was for recruitment. The SHRM components affect the components of organizational excellence (customer satisfaction, technology deployment, product quality, and competitiveness) (p &amp;amp;lt; 0.05). Recruitment was the highest contributor to organizational excellence related to technology deployment, product quality, and competitiveness, but customer satisfaction was affected by development and career development. The study recommended that the organization connect the SHRM strategy with the other organization activities that lead to excellence with the recruitment process’s concentration as it affects the products of the organizations.
Realizability in the Unitary Sphere

In this paper we present a semantics for a linear algebraic lambda-calculus based on realizability. This semantics characterizes a notion of unitarity in the system, answering a long standing issue. We derive from the semantics a set of typing rules for a simply-typed linear algebraic lambda-calculus, and show how it extends both to classical and quantum lambda-calculi.
End-to-End Speech Recognition and Disfluency Removal

Disfluency detection is usually an intermediate step between an automatic speech recognition (ASR) system and a downstream task.By contrast, this paper aims to investigate the task of end-to-end speech recognition and disfluency removal.We specifically explore whether it is possible to train an ASR model to directly map disfluent speech into fluent transcripts, without relying on a separate disfluency detection model.We show that end-to-end models do learn to directly generate fluent transcripts; however, their performance is slightly worse than a baseline pipeline approach consisting of an ASR system and a specialized disfluency detection model.We also propose two new metrics for evaluating integrated ASR and disfluency removal models.The findings of this paper can serve as a benchmark for further research on the task of end-to-end speech recognition and disfluency removal in the future.