Reviewerly/exHarmony huggingface dataset:
Bidirectional optimal quantum control: A use case of polarization in liquid crystals

We present optimal control of quantum devices using deep neural networks. Our approach avoids the ambiguity of classical control variables by unsupervised-like learning. The method is used for the accurate preparation of polarization quantum bits.
Towards quantum memory heralding using photon-photon non-linearities in a double cavity-QED system

Entanglement-on-demand networks hold tremendous promise, but need heralding and storing. We report progress towards building a quantum processor using a cold rubidium ensemble in double cavity-QED system which helps herald single-photons.
Quantum interference between two independent sources in telecommunication band

We report a raw quantum interference visibility of 90 ± 1% between telecom photons emitted by a weak coherent state source (WCSS) and an entangled photon pair source (EPPS).
A quantum-capable internet testbed connecting room temperature quantum memories

We present a first-generation long-distance quantum internet testbed that utilizes quantum memories to communicate quantum information across an intercity fiber infrastructure. We have observed Hong-Ou-Mandel interference between telecom photons produced in two independent room temperature quantum memories separated by a distance of 158 km.
Robust Linear Optical Quantum Computation with Non-Abelian Geometric Phases

A proposal for the construction of geometrically robust linear optical quantum circuits by means of nonadiabatic geometric phases is presented. Our proof-of-principle designs of holonomic single-qubit gates enable high-fidelity unitaries.
Developing a layered quantum-capable internet

We are currently developing a long-baseline quantum network in Long Island, New York. We have communicated quantum memories over a 158km network between Stony Brook and Brookhaven. Here we present the quantum enabling tools necessary to construct a quantum capable internet.
Quantum/classical hybrid compute and applications

Classical co-processors are an important component of any full quantum stack. We shall discuss how the Quantinuum System Model H1 machine interacts with a classical co-processor to implement crucial features of quantum error correction.
Generation of High-Dimensional Entanglement on a Silicon Photonic Chip

We present an on-chip source of path-entangled photonic qudits by parallel excitation of 8 microring pair sources. The device is foundry-fabricated and uses advanced packaging techniques, a significant advance in generating high-dimensional quantum light on-chip.
A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling

With the advent and popularity of big data mining and huge text analysis in modern times, automated text summarization became prominent for extracting and retrieving important information from documents. This research investigates aspects of automatic text summarization from the perspectives of single and multiple documents. Summarization is a task of condensing huge text articles into short, summarized versions. The text is reduced in size for summarization purpose but preserving key vital information and retaining the meaning of the original document. This study presents the Latent Dirichlet Allocation (LDA) approach used to perform topic modelling from summarised medical science journal articles with topics related to genes and diseases. In this study, PyLDAvis web-based interactive visualization tool was used to visualise the selected topics. The visualisation provides an overarching view of the main topics while allowing and attributing deep meaning to the prevalence individual topic. This study presents a novel approach to summarization of single and multiple documents. The results suggest the terms ranked purely by considering their probability of the topic prevalence within the processed document using extractive summarization technique. PyLDAvis visualization describes the flexibility of exploring the terms of the topics' association to the fitted LDA model. The topic modelling result shows prevalence within topics 1 and 2. This association reveals that there is similarity between the terms in topic 1 and 2 in this study. The efficacy of the LDA and the extractive summarization methods were measured using Latent Semantic Analysis (LSA) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics to evaluate the reliability and validity of the model.
La Qualité Institutionnelle Améliore-t-elle l'Inclusion Financiere en Afrique Subsaharienne?

This article studies the impact of institutional quality on financial inclusion in forty (40) countries in sub-Saharan Africa over the period 2000-2017. We use the Generalized Moments Method (GMM) in dynamic panel. Three measures of financial inclusion were highlighted : the number of ATMs per 100,000 adults, the number of commercial bank branches per 100,000 adults, and the number of depositors with commercial banks per 1,000 adults. The first two assess access to financial services and the third measures their use. The results obtained that the quality of regulation, control of corruption, and government efficiency positively impact access to financial services. In contrast, the rule of law as well as expression and democratic accountability exert the opposite effect. In addition, the use of financial services is positively influenced by the quality of regulation, the rule of law and the control of corruption. However, effective government as well as democratic expression and political stability have the opposite effect. In addition, the conclusions reached stress the importance of other control variables, such as GDP per capita, private credit-to-GDP ratio and gross secondary school enrollment rate.
A Novel Image Encryption Algorithm Based on Double Permutation and Random Diffusion

Abstract To improve the image transmission security, an image encryption algorithm based on double permutation with random diffusion is proposed in this paper. This algorithm consists of three stages: adaptive key generation, double scrambling and random diffusion. Firstly, in order to make the key highly correlated with the plaintext image, we use the SHA-256 value of the plaintext image to generate the key. Then, the double permutation is mainly composed of pixel-level permutation and bit-level permutation. The pixel-level permutation uses chaotic sequences generated by the chaotic system to generate the permutation sequences and uses these two sequences to rearrange the row and column coordinates of the image. Secondly, we implement bit-level permutation using the Fisher-Yates Shuffle algorithm as a group of every four neighboring pixels. The double permutation breaks the high correlation between image pixels. Thirdly, in the diffusion stage, we propose a random diffusion algorithm to address the problem that the diffusion order and direction of current diffusion methods are fixed and not random. The permuted image is divided into four blocks, and the order and direction of the diffusion operation for each pixel in each block is determined according to the chaotic sequence from the chaotic system. Finally, experimental results and security analysis show that the algorithm has good performance and sufficient security. It can resist a variety of attacks, such as statistical attacks, brute force attacks, and plaintext/selective plaintext attacks.
Improving Few-Shot Learning through Multi-task Representation Learning Theory

In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task. We start by reviewing recent advances in MTR theory and show that they can provide novel insights for popular meta-learning algorithms when analyzed within this framework. In particular, we highlight a fundamental difference between gradient-based and metric-based algorithms in practice and put forward a theoretical analysis to explain it. Finally, we use the derived insights to improve the performance of meta-learning methods via a new spectral-based regularization term and confirm its efficiency through experimental studies on few-shot classification benchmarks. To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of MTR theory into practice for the task of few-shot classification.
Generating Representative Video Teleconferencing Traffic

Video teleconferencing (VTC) is a dominant network application, yet there is a dearth of tools to generate such traffic for systematic and reproducible experimentation. We present a framework to create representative video teleconferencing traffic and discuss our methodology for behavioral control of multiple bots to create human-like dialog coordination, including interactive talking and silence patterns. Our framework can be coupled with proprietary commercial VTC applications as well as deployed completely within a testbed environment to benchmark emerging networking technology and evaluate the next generation of traffic classification, quality of service (QoS) algorithms, and traffic engineering systems. Our traffic generators are open source and freely available at https://mergetb.org/projects/searchlight/.
Echo State Network Optimization: A Systematic Literature Review

Abstract In the recent years, numerous studies have demonstrated the importance and efficiency of Reservoir Computing (RC) approaches. The choice of parameters and architecture in reservoir computing, on the other hand, frequently leads to an optimization task. This paper attempts to present an overview of the related work on Echo State Network (ESN) optimization and to collect research papers through a systematic Literature Review (SLR). This review covers 74 items published since 2004 and 2021 that are concerned with the issue of our focus. The collected papers are selected, analysed and reported. The results indicate that there are two techniques of parameters optimization (bio-inspired and non-bio-inspired methods) have been extensively used for various reasons. But the bio-inspired ones are the greatest. The potential use of Particle Swarm Optimization (PSO) has also been noted. A significant portion of the research done in this field focuses on the study of reservoirs and how they behave in relation to their unique qualities. In order to test reservoirs with varied parameters, topologies, or training techniques, NARMA, the Mackey glass, and Lorenz time-series prediction dataset are the most commonly employed in the literature. This research debate diverse point of view about ESN's hyper-parameter optimization, metrics, datasets, evaluation measures, and bio-inspired and non-bio-inspired techniques, this paper identifies and explores a number of research gaps.
ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries

Perception and prediction are two separate modules in the existing autonomous driving systems. They interact with each other via hand-picked features such as agent bounding boxes and trajectories. Due to this separation, prediction, as a downstream module, only receives limited information from the perception module. To make matters worse, errors from the perception modules can propagate and accumulate, adversely affecting the prediction results. In this work, we propose ViP3D, a query-based visual trajectory prediction pipeline that exploits rich information from raw videos to directly predict future trajectories of agents in a scene. ViP3D employs sparse agent queries to detect, track, and predict throughout the pipeline, making it the first fully differentiable vision-based trajectory prediction approach. Instead of using historical feature maps and trajectories, useful information from previous timestamps is encoded in agent queries, which makes ViP3D a concise streaming prediction method. Furthermore, extensive experimental results on the nuScenes dataset show the strong vision-based prediction performance of ViP3D over traditional pipelines and previous end-to-end models.
A comment on Guo et al. [arXiv:2206.11228]

In a recent article, Guo et al. [arXiv:2206.11228] report that adversarially trained neural representations in deep networks may already be as robust as corresponding primate IT neural representations. While we find the paper's primary experiment illuminating, we have doubts about the interpretation and phrasing of the results presented in the paper.
A Novel Transformer Network with Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting

Earth Observatory is a growing research area that can capitalize on the powers of AI for short time forecasting, a Now-casting scenario. In this work, we tackle the challenge of weather forecasting using a video transformer network. Vision transformer architectures have been explored in various applications, with major constraints being the computational complexity of Attention and the data hungry training. To address these issues, we propose the use of Video Swin-Transformer, coupled with a dedicated augmentation scheme. Moreover, we employ gradual spatial reduction on the encoder side and cross-attention on the decoder. The proposed approach is tested on the Weather4Cast2021 weather forecasting challenge data, which requires the prediction of 8 hours ahead future frames (4 per hour) from an hourly weather product sequence. The dataset was normalized to 0-1 to facilitate using the evaluation metrics across different datasets. The model results in an MSE score of 0.4750 when provided with training data, and 0.4420 during transfer learning without using training data, respectively.
Multiview Regenerative Morphing with Dual Flows

This paper aims to address a new task of image morphing under a multiview setting, which takes two sets of multiview images as the input and generates intermediate renderings that not only exhibit smooth transitions between the two input sets but also ensure visual consistency across different views at any transition state. To achieve this goal, we propose a novel approach called Multiview Regenerative Morphing that formulates the morphing process as an optimization to solve for rigid transformation and optimal-transport interpolation. Given the multiview input images of the source and target scenes, we first learn a volumetric representation that models the geometry and appearance for each scene to enable the rendering of novel views. Then, the morphing between the two scenes is obtained by solving optimal transport between the two volumetric representations in Wasserstein metrics. Our approach does not rely on user-specified correspondences or 2D/3D input meshes, and we do not assume any predefined categories of the source and target scenes. The proposed view-consistent interpolation scheme directly works on multiview images to yield a novel and visually plausible effect of multiview free-form morphing.
SLA Management in Intent-Driven Service Management Systems: A Taxonomy and Future Directions

Traditionally, network and system administrators are responsible for designing, configuring, and resolving the Internet service requests. Human-driven system configuration and management are proving unsatisfactory due to the recent interest in time-sensitive applications with stringent quality of service (QoS). Aiming to transition from the traditional human-driven to zero-touch service management in the field of networks and computing, intent-driven service management (IDSM) has been proposed as a response to stringent quality of service requirements. In IDSM, users express their service requirements in a declarative manner as intents. IDSM, with the help of closed control-loop operations, perform configurations and deployments, autonomously to meet service request requirements. The result is a faster deployment of Internet services and reduction in configuration errors caused by manual operations, which in turn reduces the service-level agreement (SLA) violations. In the early stages of development, IDSM systems require attention from industry as well as academia. In an attempt to fill the gaps in current research, we conducted a systematic literature review of SLA management in IDSM systems. As an outcome, we have identified four IDSM intent management activities and proposed a taxonomy for each activity. Analysis of all studies and future research directions, are presented in the conclusions.
Unified Normalization for Accelerating and Stabilizing Transformers

Solid results from Transformers have made them prevailing architectures in various natural language and vision tasks. As a default component in Transformers, Layer Normalization (LN) normalizes activations within each token to boost the robustness. However, LN requires on-the-fly statistics calculation in inference as well as division and square root operations, leading to inefficiency on hardware. What is more, replacing LN with other hardware-efficient normalization schemes (e.g., Batch Normalization) results in inferior performance, even collapse in training. We find that this dilemma is caused by abnormal behaviors of activation statistics, including large fluctuations over iterations and extreme outliers across layers. To tackle these issues, we propose Unified Normalization (UN), which can speed up the inference by being fused with other linear operations and achieve comparable performance on par with LN. UN strives to boost performance by calibrating the activation and gradient statistics with a tailored fluctuation smoothing strategy. Meanwhile, an adaptive outlier filtration strategy is applied to avoid collapse in training whose effectiveness is theoretically proved and experimentally verified in this paper. We demonstrate that UN can be an efficient drop-in alternative to LN by conducting extensive experiments on language and vision tasks. Besides, we evaluate the efficiency of our method on GPU. Transformers equipped with UN enjoy about 31% inference speedup and nearly 18% memory reduction. Code will be released at https://github.com/hikvision-research/Unified-Normalization.
Bias Reduction for Sum Estimation

In classical statistics and distribution testing, it is often assumed that elements can be sampled from some distribution $P$, and that when an element $x$ is sampled, the probability $P$ of sampling $x$ is also known. Recent work in distribution testing has shown that many algorithms are robust in the sense that they still produce correct output if the elements are drawn from any distribution $Q$ that is sufficiently close to $P$. This phenomenon raises interesting questions: under what conditions is a "noisy" distribution $Q$ sufficient, and what is the algorithmic cost of coping with this noise? We investigate these questions for the problem of estimating the sum of a multiset of $N$ real values $x_1, \ldots, x_N$. This problem is well-studied in the statistical literature in the case $P = Q$, where the Hansen-Hurwitz estimator is frequently used. We assume that for some known distribution $P$, values are sampled from a distribution $Q$ that is pointwise close to $P$. For every positive integer $k$ we define an estimator $\zeta_k$ for $\mu = \sum_i x_i$ whose bias is proportional to $\gamma^k$ (where our $\zeta_1$ reduces to the classical Hansen-Hurwitz estimator). As a special case, we show that if $Q$ is pointwise $\gamma$-close to uniform and all $x_i \in \{0, 1\}$, for any $\epsilon > 0$, we can estimate $\mu$ to within additive error $\epsilon N$ using $m = \Theta({N^{1-\frac{1}{k}} / \epsilon^{2/k}})$ samples, where $k = \left\lceil (\log \epsilon)/(\log \gamma)\right\rceil$. We show that this sample complexity is essentially optimal. Our bounds show that the sample complexity need not vary uniformly with the desired error parameter $\epsilon$: for some values of $\epsilon$, perturbations in its value have no asymptotic effect on the sample complexity, while for other values, any decrease in its value results in an asymptotically larger sample complexity.
Multi-Module G2P Converter for Persian Focusing on Relations between Words

In this paper, we investigate the application of end-to-end and multi-module frameworks for G2P conversion for the Persian language. The results demonstrate that our proposed multi-module G2P system outperforms our end-to-end systems in terms of accuracy and speed. The system consists of a pronunciation dictionary as our look-up table, along with separate models to handle homographs, OOVs and ezafe in Persian created using GRU and Transformer architectures. The system is sequence-level rather than word-level, which allows it to effectively capture the unwritten relations between words (cross-word information) necessary for homograph disambiguation and ezafe recognition without the need for any pre-processing. After evaluation, our system achieved a 94.48% word-level accuracy, outperforming the previous G2P systems for Persian.
BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection

PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media.Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recognized, making the performance of common text-classification approaches disappointed. Targeting the PCL detection problem in SemEval-2022 Task 4, in this paper, we give an introduction to our team's solution, which exploits the power of prompt-based learning on paragraph classification. We reformulate the task as an appropriate cloze prompt and use pre-trained Masked Language Models to fill the cloze slot. For the two subtasks, binary classification and multi-label classification, DeBERTa model is adopted and fine-tuned to predict masked label words of task-specific prompts. On the evaluation dataset, for binary classification, our approach achieves an F1-score of 0.6406; for multi-label classification, our approach achieves an macro-F1-score of 0.4689 and ranks first in the leaderboard.
Multidimensional Psychological Model Construction of Public English Teaching Based on Deep Learning from Multimodal Perspective

Public English instruction focuses on language skill development while also enhancing students’ business professional knowledge and all-around ability under the direction of public English teaching objectives. From a knowledge standpoint, public English instruction contains a lot of tacit knowledge, making it appropriate for case teaching. This paper analyses the knowledge-locking factors in traditional case teaching based on deep learning from the multimodality perspective and builds a mental model to compensate for the shortcomings of the traditional case teaching. In this paper, a multidimensional psychological model is built that overcomes the limitations of conventional approaches. It is based on the multimodal feature extraction and modal selection algorithm of deep learning and multidimensional psychology in public English teaching. The method suggested in this paper outperforms the benchmark model created for the three evaluation indices and also demonstrates the benefits of multimodality. Create a fully integrated multidimensional mental model of the teaching and activity situations so that students can learn with a passion, become more engaged in practising their English, and ultimately develop the language skills required for employment.