Reviewerly/exHarmony huggingface dataset:
The relative contribution of five key perceptual cues and their interaction to the sense of embodiment.

A range of perceptual cues drive the Sense of Embodiment (SoE) with an external object, such as a virtual arm that looks like one's own or can be controlled like one's own.Since most experiments test one or two cues at a time, it is difficult to establish their relative contribution and possible interaction.This work aims at investigating the importance of five key perceptual cues (field of view, visuo-proprioceptive synchronicity, tactile feedback, visual human likeness, and connectedness) and their potential interaction in a single, full-factorial experiment.Participants touched a target dot, which changed position after a hit, for 1 min in a virtual environment seen through a head-mounted display.Participants' arm and hand motions were mapped to a virtual arm and hand.All perceptual cues had two levels: SoE supportive and SoE suppressive.Twenty-eight participants completed the task in all possible combinations.We recorded Task Performance and self-ratings of Sense of Ownership and Sense of Agency.Results showed that visuo-proprioceptive synchronicity had the largest effect on all three measures.The relative importance of the remaining four cues differed for the three dependent variables.The cues did not have significant interactive effects.We conclude that when designing an interface for maximum supportive embodiment, visuo-proprioceptive synchronicity is the most important perceptual cue.The extent to which other supportive cues can improve the embodiment experience depends on the considered variables, but generally, the more other supportive cues can be added, the better.
Photon-pair blockade in a Josephson-photonics circuit with two nondegenerate microwave resonators

We propose to generate photon-pair blockade in a Josephson-photonics circuit that consists of a dc voltage-biased Josephson junction in series with a superconducting charge qubit and two nondegenerate microwave resonators. The two-level charge qubit is utilized to create anticorrelations of the charge transport; that is, the simultaneous Cooper pair tunneling events are inhibited. When the Josephson frequency matches the sum of resonance frequencies of the charge qubit and the two resonators, we demonstrate that the two resonators can release their energies in the form of antibunched pairs of two strongly correlated photons. The present work provides a practical way for producing a bright microwave source of antibunched photon pairs, with potential applications ranging from spectroscopy and metrology to quantum information processing.
Combating the effects of disorder in quantum state transfer

In this paper we examine disorder (i.e., static imperfections in manufacture) for the fixed-Hamiltonian evolution protocol of quantum state transfer. We improve the performance by optimizing the choice of Hamiltonian and by implementing an encoding and decoding procedure on small regions at either end of the chain. We find that encoding in only the single-excitation subspace is optimal and provides substantial enhancement to the operating regime of these systems.
Frequency, Informativity and Word Length: Insights from Typologically Diverse Corpora

Zipf's law of abbreviation, which posits a negative correlation between word frequency and length, is one of the most famous and robust cross-linguistic generalizations. At the same time, it has been shown that contextual informativity (average surprisal given previous context) is more strongly correlated with word length, although this tendency is not observed consistently, depending on several methodological choices. The present study examines a more diverse sample of languages than the previous studies (Arabic, Finnish, Hungarian, Indonesian, Russian, Spanish and Turkish). I use large web-based corpora from the Leipzig Corpora Collection to estimate word lengths in UTF-8 characters and in phonemes (for some of the languages), as well as word frequency, informativity given previous word and informativity given next word, applying different methods of bigrams processing. The results show different correlations between word length and the corpus-based measure for different languages. I argue that these differences can be explained by the properties of noun phrases in a language, most importantly, by the order of heads and modifiers and their relative morphological complexity, as well as by orthographic conventions.
Automatic recognition of Arabic alphabets sign language using deep learning

&lt;span&gt;Technological advancements are helping people with special needs overcome many communications’ obstacles. Deep learning and computer vision models are innovative leaps nowadays in facilitating unprecedented tasks in human interactions. The Arabic language is always a rich research area. In this paper, different deep learning models were applied to test the accuracy and efficiency obtained in automatic Arabic sign language recognition. In this paper, we provide a novel framework for the automatic detection of Arabic sign language, based on transfer learning applied on popular deep learning models for image processing. Specifically, by training AlexNet, VGGNet and GoogleNet/Inception models, along with testing the efficiency of shallow learning approaches based on support vector machine (SVM) and nearest neighbors algorithms as baselines. As a result, we propose a novel approach for the automatic recognition of Arabic alphabets in sign language based on VGGNet architecture which outperformed the other trained models. The proposed model is set to present promising results in recognizing Arabic sign language with an accuracy score of 97%. The suggested models are tested against a recent fully-labeled dataset of Arabic sign language images. The dataset contains 54,049 images, which is considered the first large and comprehensive real dataset of Arabic sign language to the furthest we know.&lt;/span&gt;
Named Entity Recognition of Medical Text Based on the Deep Neural Network

Medical text data records detailed clinical data; named entity recognition is the basis of text information processing and an important part of mining valuable information in medical texts. The named entity recognition technology can accurately identify the information needed in medical texts and help medical staff make clinical decision-making, evidence-based medicine, and epidemic disease monitoring. This paper proposes a hybrid neural network medical text named entity recognition model. First, a coding method based on a fully self-attentive mechanism is proposed. The vector representation of each word is related to the entire sentence through the attention mechanism. It determines the weight distribution by scoring the characters or words in all positions and obtains the position information in the sentence that needs the most attention. The encoding vector at each position is integrated with the context information of full sentence, which solves the ambiguity problem. Second, a multivariate convolutional decoding method is proposed. This method can effectively pay attention to the characteristics of medical text named entity recognition in the decoding process. It uses two-dimensional convolutional decoding to associate the current position word with surrounding words to improve decoding efficiency while extracting features from the logic of the preceding and following words. Using the same number of convolution kernels as the entity category, it can effectively extract effective features from the label dimension. Besides, according to the characteristics of the named entity recognition task, a special mixed loss is designed. The experimental results verify that the proposed method is effective, and it is improved compared with some existing medical text named entity recognition methods.
A Contribution-Based Device Selection Scheme in Federated Learning

In a Federated Learning (FL) setup, a number of devices contribute to the training of a common model. We present a method for selecting the devices that provide updates in order to achieve improved generalization, fast convergence, and better device-level performance. We formulate a min-max optimization problem and decompose it into a primal-dual setup, where the duality gap is used to quantify the device-level performance. Our strategy combines \emph{exploration} of data freshness through a random device selection with \emph{exploitation} through simplified estimates of device contributions. This improves the performance of the trained model both in terms of generalization and personalization. A modified Truncated Monte-Carlo (TMC) method is applied during the exploitation phase to estimate the device's contribution and lower the communication overhead. The experimental results show that the proposed approach has a competitive performance, with lower communication overhead and competitive personalization performance against the baseline schemes.
DDPG-Driven Deep-Unfolding With Adaptive Depth for Channel Estimation With Sparse Bayesian Learning

Deep-unfolding neural networks (NNs) have received great attention since they achieve satisfactory performance with relatively low complexity. Typically, these deep-unfolding NNs are restricted to a fixed-depth for all inputs. However, the optimal number of layers required for convergence changes with different inputs. In this paper, we first develop a framework of deep deterministic policy gradient (DDPG)-driven deep-unfolding with adaptive depth for different inputs, where the trainable parameters of deep-unfolding NN are learned by DDPG, rather than updated by the stochastic gradient descent algorithm directly. Specifically, the optimization variables, trainable parameters, and architecture of deep-unfolding NN are designed as the state, action, and state transition of DDPG, respectively. Then, this framework is employed to deal with the channel estimation problem in massive multiple-input multiple-output systems. Specifically, first of all we formulate the channel estimation problem with an off-grid basis and develop a sparse Bayesian learning (SBL)-based algorithm to solve it. Secondly, the SBL-based algorithm is unfolded into a layer-wise structure with a set of introduced trainable parameters. Thirdly, the proposed DDPG-driven deep-unfolding framework is employed to solve this channel estimation problem based on the unfolded structure of the SBL-based algorithm. To realize adaptive depth, we design the halting score to indicate when to stop, which is a function of the channel reconstruction error. Furthermore, the proposed framework is extended to realize the adaptive depth of the general deep neural networks (DNNs). Simulation results show that the proposed algorithm outperforms the conventional optimization algorithms and DNNs with fixed depth with much reduced number of layers.
Extracting perfect GHZ states from imperfect weighted graph states via entanglement concentration

Photonic GHZ states serve as the central resource for a number of important applications in quantum information science, including secret sharing, sensing, and fusion-based quantum computing. The use of photon-emitter entangling gates is a promising approach to creating these states that sidesteps many of the difficulties associated with intrinsically probabilistic methods based on linear optics. However, the efficient creation of high-fidelity GHZ states of many photons remains an outstanding challenge due to both coherent and incoherent errors during the generation process. Here, we propose an entanglement concentration protocol that is capable of generating perfect GHZ states using only local gates and measurements on imperfect weighted graph states. We show that our protocol is both efficient and robust to incoherent noise errors.
HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing

Developing clinical natural language systems based on machine learning and deep learning is dependent on the availability of large-scale annotated clinical text datasets, most of which are time-consuming to create and not publicly available. The lack of such annotated datasets is the biggest bottleneck for the development of clinical NLP systems. Zero-Shot Learning (ZSL) refers to the use of deep learning models to classify instances from new classes of which no training data have been seen before. Prompt-based learning is an emerging ZSL technique in NLP where we define task-based templates for different tasks. In this study, we developed a novel prompt-based clinical NLP framework called HealthPrompt and applied the paradigm of prompt-based learning on clinical texts. In this technique, rather than fine-tuning a Pre-trained Language Model (PLM), the task definitions are tuned by defining a prompt template. We performed an in-depth analysis of HealthPrompt on six different PLMs in a no-training-data setting. Our experiments show that HealthPrompt could effectively capture the context of clinical texts and perform well for clinical NLP tasks without any training data.
Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks

Story ending generation is an interesting and challenging task, which aims to generate a coherent and reasonable ending given a story context. The key challenges of the task lie in how to comprehend the story context sufficiently and handle the implicit knowledge behind story clues effectively, which are still under-explored by previous work. In this paper, we propose a Story Heterogeneous Graph Network (SHGN) to explicitly model both the information of story context at different granularity levels and the multi-grained interactive relations among them. In detail, we consider commonsense knowledge, words and sentences as three types of nodes. To aggregate non-local information, a global node is also introduced. Given this heterogeneous graph network, the node representations are updated through graph propagation, which adequately utilizes commonsense knowledge to facilitate story comprehension. Moreover, we design two auxiliary tasks to implicitly capture the sentiment trend and key events lie in the context. The auxiliary tasks are jointly optimized with the primary story ending generation task in a multi-task learning strategy. Extensive experiments on the ROCStories Corpus show that the developed model achieves new state-of-the-art performances. Human study further demonstrates that our model generates more reasonable story endings.
Matching and maximum likelihood decoding of a multi-round subsystem quantum error correction experiment

Quantum error correction offers a promising path for performing quantum computations with low errors. Although a fully fault-tolerant execution of a quantum algorithm remains unrealized, recent experimental developments, along with improvements in control electronics, are enabling increasingly advanced demonstrations of the necessary operations for applying quantum error correction. Here, we perform quantum error correction on superconducting qubits connected in a heavy-hexagon lattice. The full processor can encode a logical qubit with distance three and perform several rounds of fault-tolerant syndrome measurements that allow the correction of any single fault in the circuitry. Furthermore, by using dynamic circuits and classical computation as part of our syndrome extraction protocols, we can exploit real-time feedback to reduce the impact of energy relaxation error in the syndrome and flag qubits. We show that the logical error varies depending on the use of a perfect matching decoder compared to a maximum likelihood decoder. We observe a logical error per syndrome measurement round as low as $\sim0.04$ for the matching decoder and as low as $\sim0.03$ for the maximum likelihood decoder. Our results suggest that more significant improvements to decoders are likely on the horizon as quantum hardware has reached a new stage of development towards fully fault-tolerant operations.
Multitasking Framework for Unsupervised Simple Definition Generation

The definition generation task can help language learners by providing explanations for unfamiliar words.This task has attracted much attention in recent years.We propose a novel task of Simple Definition Generation (SDG) to help language learners and low literacy readers.A significant challenge of this task is the lack of learner's dictionaries in many languages, and therefore the lack of data for supervised training.We explore this task and propose a multitasking framework SimpDefiner that only requires a standard dictionary with complex definitions and a corpus containing arbitrary simple texts.We disentangle the complexity factors from the text by carefully designing a parameter sharing scheme between two decoders.By jointly training these components, the framework can generate both complex and simple definitions simultaneously.We demonstrate that the framework can generate relevant, simple definitions for the target words through automatic and manual evaluations on English and Chinese datasets.Our method outperforms the baseline model by a 1.77 SARI score on the English dataset, and raises the proportion of the low level (HSK level 1-3) words in Chinese definitions by 3.87% 1 .
Optimization and implementation of a surface-electrode ion trap junction

Abstract We describe the design of a surface-electrode ion trap junction, which is a key element for large-scale ion trap arrays. A bi-objective optimization method is used for designing the electrodes, which maintains the total pseudo-potential curvature while minimizing the axial pseudo-potential gradient along the ion transport path. To facilitate the laser beam delivery for parallel operations in multiple trap zones, we implemented integrated optics on each arm of this X-junction trap. The layout of the trap chip for commercial foundry fabrication is presented. This work suggests routes to improving ion trap junction performance in scalable implementations. Together with integrated optical addressing, this contributes to modular trapped-ion quantum computing in interconnected two-dimensional arrays.
Orders of magnitude reduction in the computational overhead for quantum many-body problems on quantum computers via an exact transcorrelated method

Transcorrelated methods provide an efficient way of partially transferring the description of electronic correlations from the ground state wavefunction directly into the underlying Hamiltonian. In particular, Dobrautz et al. [Phys. Rev. B, 99(7), 075119, (2019)] have demonstrated that the use of momentum-space representation, combined with a non-unitary similarity transformation, results in a Hubbard Hamiltonian that possesses a significantly more compact ground state wavefunction, dominated by a single Slater determinant. This compactness/single-reference character greatly facilitates electronic structure calculations. As a consequence, however, the Hamiltonian becomes non-Hermitian, posing problems for quantum algorithms based on the variational principle. We overcome these limitations with the ansatz-based quantum imaginary time evolution algorithm and apply the transcorrelated method in the context of digital quantum computing. We demonstrate that this approach enables up to 4 orders of magnitude more accurate and compact solutions in various instances of the Hubbard model at intermediate interaction strength ($U/t=4$), enabling the use of shallower quantum circuits for wavefunction ansatzes. In addition, we propose a more efficient implementation of the quantum imaginary time evolution algorithm in quantum circuits that is tailored to non-Hermitian problems. To validate our approach, we perform hardware experiments on the ibmq_lima quantum computer. Our work paves the way for the use of exact transcorrelated methods for the simulations of ab initio systems on quantum computers.
SA-SASV: An End-to-End Spoof-Aggregated Spoofing-Aware Speaker Verification System

Research in the past several years has boosted the performance of automatic speaker verification systems and countermeasure systems to deliver low Equal Error Rates (EERs) on each system. However, research on joint optimization of both systems is still limited. The Spoofing-Aware Speaker Verification (SASV) 2022 challenge was proposed to encourage the development of integrated SASV systems with new metrics to evaluate joint model performance. This paper proposes an ensemble-free end-to-end solution, known as Spoof-Aggregated-SASV (SA-SASV) to build a SASV system with multi-task classifiers, which are optimized by multiple losses and has more flexible requirements in training set. The proposed system is trained on the ASVSpoof 2019 LA dataset, a spoof verification dataset with small number of bonafide speakers. Results of SASV-EER indicate that the model performance can be further improved by training in complete automatic speaker verification and countermeasure datasets.
Contextual Representation Learning beyond Masked Language Modeling

How do masked language models (MLMs) such as BERT learn contextual representations?In this work, we analyze the learning dynamics of MLMs.We find that MLMs adopt sampled embeddings as anchors to estimate and inject contextual semantics to representations, which limits the efficiency and effectiveness of MLMs.To address these issues, we propose TACO, a simple yet effective representation learning approach to directly model global semantics.TACO extracts and aligns contextual semantics hidden in contextualized representations to encourage models to attend global semantics when generating contextualized representations.Experiments on the GLUE benchmark show that TACO achieves up to 5x speedup and up to 1.2 points average improvement over existing MLMs.The code is available at https:// github.com/FUZHIYI/TACO.
Geochemical and multivariate assessment of groundwater resources of churachandpur sub-division of Manipur, India

Abstract Hydrogeochemical and multivariate assessment of groundwater samples of the Churachandpur sub-division of the state of Manipur was carried out to study its suitability for potable and irrigational uses. The study extensively discusses the underlying hydro-geochemistry using multivariate geostatistical tools for physicochemical parameters of 35 spatially distributed groundwater samples during pre- and post-monsoon seasons. In situ parameters (pH, TDS, DO, EC, salinity, ORP) were assessed in field conditions, and it was found that few samples exceed concentration than the prescribed standards. Both positive and negative ORP values and low DO levels of the samples indicate oxidizing and reducing aquifers due to recent unstable geologic formations. Although the concentration of Cl − , SO 4 2− , and NO 3 − is well within desirable levels, the concentration of F − and As 3+ of a few samples exceeds the prescribed standard. The water quality index of the groundwater samples signifies that more than 50% of samples are unsuitable for potable uses for both seasons. The geochemical analysis indicates that most of the water is Ca–Mg–Cl–HCO 3 type followed by Mg–Ca–Cl–HCO 3 types in both the seasons and dominated by the rock-weathering geochemical process. Multivariate analysis and geostatistical mapping highlight the spatial variability of water quality parameters and possibilities of the occurrence of geogenic contaminants in the aquifers of the study area.