Reviewerly/exHarmony huggingface dataset:
Quantum optical frequency up-conversion for polarisation entangled qubits: towards interconnected quantum information devices

Realising a global quantum network requires combining individual strengths of different quantum systems to perform universal tasks, notably using flying and stationary qubits.However, transferring coherently quantum information between different systems is challenging as they usually feature different properties, notably in terms of operation wavelength and wavepacket.To circumvent this problem for quantum photonics systems, we demonstrate a polarisation-preserving quantum frequency conversion device in which telecom wavelength photons are converted to the near infrared, at which a variety of quantum memories operate.Our device is essentially free of noise which we demonstrate through near perfect single photon state transfer tomography and observation of high-fidelity entanglement after conversion.In addition, our guided-wave setup is robust, compact, and easily adaptable to other wavelengths.This approach therefore represents a major building block towards advantageously connecting quantum information systems based on light and matter.
Anomaly and Novelty detection for robust semi-supervised learning

Three important issues are often encountered in Supervised and Semi-Supervised Classification: class memberships are unreliable for some training units (label noise), a proportion of observations might depart from the main structure of the data (outliers) and new groups in the test set may have not been encountered earlier in the learning phase (unobserved classes). The present work introduces a robust and adaptive Discriminant Analysis rule, capable of handling situations in which one or more of the aforementioned problems occur. Two EM-based classifiers are proposed: the first one that jointly exploits the training and test sets (transductive approach), and the second one that expands the parameter estimation using the test set, to complete the group structure learned from the training set (inductive approach). Experiments on synthetic and real data, artificially adulterated, are provided to underline the benefits of the proposed method.
Generalised Measures of Multivariate Information Content

The entropy of a pair of random variables is commonly depicted using a Venn diagram. This representation is potentially misleading, however, since the multivariate mutual information can be negative. This paper presents new measures of multivariate information content that can be accurately depicted using Venn diagrams for any number of random variables. These measures complement the existing measures of multivariate mutual information and are constructed by considering the algebraic structure of information sharing. It is shown that the distinct ways in which a set of marginal observers can share their information with a non-observing third party corresponds to the elements of a free distributive lattice. The redundancy lattice from partial information decomposition is then subsequently and independently derived by combining the algebraic structures of joint and shared information content.
Mapping the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mi>Y</mml:mi></mml:mrow></mml:math> Hamiltonian onto a network of coupled lasers

This article shows how dissipatively coupled laser networks evolve toward minimizing a Lyapunov function that turns out to be asymptotically equivalent with the XY Hamiltonian in the strong pump regime.
A Deep Multi-level Attentive Network for Multimodal Sentiment Analysis

Multimodal sentiment analysis has attracted increasing attention with broad application prospects. The existing methods focuses on single modality, which fails to capture the social media content for multiple modalities. Moreover, in multi-modal learning, most of the works have focused on simply combining the two modalities, without exploring the complicated correlations between them. This resulted in dissatisfying performance for multimodal sentiment classification. Motivated by the status quo, we propose a Deep Multi-Level Attentive network, which exploits the correlation between image and text modalities to improve multimodal learning. Specifically, we generate the bi-attentive visual map along the spatial and channel dimensions to magnify CNNs representation power. Then we model the correlation between the image regions and semantics of the word by extracting the textual features related to the bi-attentive visual features by applying semantic attention. Finally, self-attention is employed to automatically fetch the sentiment-rich multimodal features for the classification. We conduct extensive evaluations on four real-world datasets, namely, MVSA-Single, MVSA-Multiple, Flickr, and Getty Images, which verifies the superiority of our method.
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression

BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks. However, a major blocking issue of applying BERT to online services is that it is memory-intensive and leads to unsatisfactory latency of user requests, raising the necessity of model compression. Existing solutions leverage the knowledge distillation framework to learn a smaller model that imitates the behaviors of BERT. However, the training procedure of knowledge distillation is expensive itself as it requires sufficient training data to imitate the teacher model. In this paper, we address this issue by proposing a tailored solution named LadaBERT (Lightweight adaptation of BERT through hybrid model compression), which combines the advantages of different model compression methods, including weight pruning, matrix factorization and knowledge distillation. LadaBERT achieves state-of-the-art accuracy on various public datasets while the training overheads can be reduced by an order of magnitude.
State preparation and measurement in a quantum simulation of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> sigma model

Recently, Singh and Chandrasekharan showed that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site. In a paper by the NuQS collaboration, the proposal is made to simulate such field theories on a quantum computer using the universal properties of a similar model. In this paper, following that direction, we demonstrate how to prepare the ground state of the model from and measure a dynamical quantity of interest, the O(3) Noether charge, on a quantum computer. In particular, we apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes and use shadow tomography to measure the dynamics of local observables. We then present and analyze a quantum algorithm based on non-unitary randomized simulation methods that may yield an approach suitable for intermediate-term noisy quantum devices.
Resilient Privacy Protection for Location-Based Services through Decentralization

Location-Based Services (LBSs) provide valuable services, with convenient features for mobile users. However, the location and other information disclosed through each query to the LBS erodes user privacy. This is a concern especially because LBS providers can be honest-but-curious , collecting queries and tracking users’ whereabouts and infer sensitive user data. This motivated both centralized and decentralized location privacy protection schemes for LBSs: anonymizing and obfuscating LBS queries to not disclose exact information, while still getting useful responses. Decentralized schemes overcome disadvantages of centralized schemes, eliminating anonymizers, and enhancing users’ control over sensitive information. However, an insecure decentralized system could create serious risks beyond private information leakage. More so, attacking an improperly designed decentralized LBS privacy protection scheme could be an effective and low-cost step to breach user privacy. We address exactly this problem, by proposing security enhancements for mobile data sharing systems. We protect user privacy while preserving accountability of user activities, leveraging pseudonymous authentication with mainstream cryptography. We show our scheme can be deployed with off-the-shelf devices based on an experimental evaluation of an implementation in a static automotive testbed.
On the efficiency of K-means clustering

This paper presents a thorough evaluation of the existing methods that accelerate Lloyd's algorithm for fast k-means clustering. To do so, we analyze the pruning mechanisms of existing methods, and summarize their common pipeline into a unified evaluation framework UniK. UniK embraces a class of well-known methods and enables a fine-grained performance breakdown. Within UniK, we thoroughly evaluate the pros and cons of existing methods using multiple performance metrics on a number of datasets. Furthermore, we derive an optimized algorithm over UniK, which effectively hybridizes multiple existing methods for more aggressive pruning. To take this further, we investigate whether the most efficient method for a given clustering task can be automatically selected by machine learning, to benefit practitioners and researchers.
Multithreaded two-pass connected components labelling and particle analysis in ImageJ

Sequential region labelling, also known as connected components labelling, is a standard image segmentation problem that joins contiguous foreground pixels into blobs. Despite its long development history and widespread use across diverse domains such as bone biology, materials science and geology, connected components labelling can still form a bottleneck in image processing pipelines. Here, I describe a multithreaded implementation of classical two-pass sequential region labelling and introduce an efficient collision resolution step, ‘ bucket fountain’ . Code was validated on test images and against commercial software (Avizo). It was performance tested on images from 2 MB (161 particles) to 6.5 GB (437 508 particles) to determine whether theoretical linear scaling ( O (n)) had been achieved, and on 1–40 CPU threads to measure speed improvements due to multithreading. The new implementation achieves linear scaling ( b = 0.905–1.052, time ∝ pixels b ; R 2 = 0.985–0.996), which improves with increasing thread number up to 8–16 threads, suggesting that it is memory bandwidth limited. This new implementation of sequential region labelling reduces the time required from hours to a few tens of seconds for images of several GB, and is limited only by hardware scale. It is available open source and free of charge in BoneJ.
Enabling Homomorphically Encrypted Inference for Large DNN Models

The proliferation of machine learning services in the last few years has raised data privacy concerns.Homomorphic encryption (HE) enables inference using encrypted data but it incurs 100x-10,000x memory and runtime overheads.Secure deep neural network (DNN) inference using HE is currently limited by computing and memory resources, with frameworks requiring hundreds of gigabytes of DRAM to evaluate small models.To overcome these limitations, in this paper we explore the feasibility of leveraging hybrid memory systems comprised of DRAM and persistent memory.In particular, we explore the recently-released Intel R Optane TM PMem technology and the Intel R HE-Transformer nGraph R to run large neural networks such as MobileNetV2 (in its largest variant) and ResNet-50 for the first time in the literature.We present an in-depth analysis of the efficiency of the executions with different hardware and software configurations.Our results conclude that DNN inference using HE incurs on friendly access patterns for this memory configuration, yielding efficient executions.
Making Financial Sense from EaaS for MSE During Economic Uncertainty

Economic uncertainty and financial senses impacts have affected IT infrastructure implementation as Medium Size Enterprise goes bleeding for extravagant technology pursuing. Antecedents during stable market were not for optimization used during business uncertainty condition is been re-questioned for its relevancies. This mixed-mode with double data collection sequential strategy methodology lessens shallow perceptions or outlooks that simplify each phenomenal activities that merit the necessity for competitive innovation. The integrated framework applied to sampling users and tested using ordinal regression relationship that yields strong low relationship that provokes the null hypothesis. The embracing of BOINC shareware grid technology with consolidated computing configurations are applied to develop the system’s productivity by encouraging positive monetary prospect via synchronized interworks hence better team accomplishment and branding respect. The study proposed an elastic internal IT infrastructure alongside the market condition with the development of EaaS framework beyond the legacy TAM implementation.
Natural Instructions: Benchmarking Generalization to New Tasks from Natural Language Instructions

Can we enable NLP models to appropriately respond to instructional prompts and consequently generalize to new tasks? To study this question, we leverage the existing NLP datasets and the instructions that were used to crowdsource them to create NATURAL INSTRUCTIONS, a dataset of instructions and task-specific input/output data. This dataset consists of 61 distinct language instructions and about 600k task instances, and is used to evaluate existing state-of-the-art language-models (LMs) in addressing new tasks by few-shot prompting of GPT3 and fine-tuning BART. Our analysis indicates that: (a) the existing models indeed benefit from instructions and hence, show improved generalization to new tasks; (b) while models like GPT-3 generally benefit from instructions, the extent of their gains varies across different fields of instructions and also depends on the task being solved; (c) generalization to unseen tasks in NATURAL INSTRUCTIONS remains far from perfect for the state-of-the-art, indicating significant room for more progress in this direction.
Data-Seeking Behaviour in the Social Sciences

Abstract Purpose Publishing research data for reuse has become good practice in recent years. However, not much is known on how researchers actually find said data. In this exploratory study, we observe the information-seeking behaviour of social scientists searching for research data to reveal impediments and identify opportunities for data search infrastructure. Methods We asked 12 participants to search for research data and observed them in their natural environment. The sessions were recorded. Afterwards, we conducted semi-structured interviews to get a thorough understanding of their way of searching. From the recordings, we extracted the interaction behaviour of the participants and analysed the spoken words both during the search task and the interview by creating affinity diagrams. Results We found that literature search is more closely intertwined with dataset search than previous literature suggests. Both the search itself and the relevance assessment are very complex, and many different strategies are employed, including the creatively “misuse” of existing tools, since no appropriate tools exist or are unknown to the participants. Conclusion Many of the issues we found relate directly or indirectly to the application of the FAIR principles, but some, like a greater need for dataset search literacy, go beyond that. Both infrastructure and tools offered for dataset search could be tailored more tightly to the observed work processes, particularly by offering more interconnectivity between datasets, literature, and other relevant materials.
Continual Adaptation for Deep Stereo

Depth estimation from stereo images is carried out with unmatched results by convolutional neural networks trained end-to-end to regress dense disparities. Like for most tasks, this is possible if large amounts of labelled samples are available for training, possibly covering the whole data distribution encountered at deployment time. Being such an assumption systematically unmet in real applications, the capacity of adapting to any unseen setting becomes of paramount importance. Purposely, we propose a continual adaptation paradigm for deep stereo networks designed to deal with challenging and ever-changing environments. We design a lightweight and modular architecture, Modularly ADaptive Network (MADNet), and formulate Modular ADaptation algorithms (MAD, MAD++) which permit efficient optimization of independent sub-portions of the entire network. In our paradigm, the learning signals needed to continuously adapt models online can be sourced from self-supervision via right-to-left image warping or from traditional stereo algorithms. With both sources, no other data than the input images being gathered at deployment time are needed. Thus, our network architecture and adaptation algorithms realize the first real-time self-adaptive deep stereo system and pave the way for a new paradigm that can facilitate practical deployment of end-to-end architectures for dense disparity regression.
Image Difference Captioning With Instance-Level Fine-Grained Feature Representation

The task of image difference captioning aims at locating changed objects in similar image pairs and describing the difference with natural language. The key challenges of this task are to comprehend the context of image pairs sufficiently and locate the changed objects accurately in the presence of viewpoint change. Previous studies focus on pixel-level image features, neglecting rich explicit features of objects in an image pair which are beneficial to generate a fine-grained difference caption. Additionally, existing generative models suffer from accurately locate the differences in the interference of viewpoint change. To address these issues, we propose an Instance-Level Fine-Grained Difference Captioning (IFDC) model, which consists of a fine-grained feature extraction module, a multi-round feature fusion module, a similarity-based difference finding module, and a difference captioning module. To describe the changed objects comprehensively, we extract the fine-grained features, i.e., visual features, semantic features, and positional features at instance-level, as the objects’ representation. To enhance the model’s immunity to viewpoint change, we design a similarity-based difference finding module to locate the changed objects accurately. Extensive experiments show that our IFDC model achieves comparable performance with the state-of-the-art models on the datasets of CLEVR-Change and Spot-the-Diff, thus verifying the effectiveness of our proposed model. Our source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/VISLANG-Lab/IFDC</uri> .
An Intelligent Transaction Migration Scheme for RAFT-Based Private Blockchain in Internet of Things Applications

The integration of multi-access edge computing (MEC) and RAFT consensus makes it feasible to deploy blockchain on trustful base stations and gateways to provide efficient and tamper-proof edge data services for Internet of Things (IoT) applications. However, reducing the latency of storing data on blockchain remains a challenge, especially when an anomalytriggered data flow in a certain area exceeds the block generation speed. This letter proposes an intelligent transaction migration scheme for RAFT-based private blockchain in IoT applications to migrate transactions in busy areas to idle regions intelligently. Simulation results show that the proposed scheme can apparently reduce the latency in high data flow circumstances.
Quantum confined Rydberg excitons in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Cu</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math> nanoparticles

The quantum confinement of Rydberg excitons is an important step towards exploiting their large nonlinearities for quantum applications. We observe Rydberg excitons in natural nanoparticles of Cu$_2$O. We resolve up to the principal quantum number $n=12$ in a bulk Cu$_2$O crystal and up to $n=6$ in nanoparticles extracted from the same crystal. The exciton transitions in nanoparticles are broadened and their oscillator strengths decrease as $\propto n^{-4}$ compared to those in the bulk (decreasing as $\propto n^{-3}$). We explain our results by including the effect of quantum confinement of exciton states in the nanoparticles. Our results provide an understanding of the physics of Cu$_2$O Rydberg excitons in confined dimensions.
Ghost-UNet: An Asymmetric Encoder-Decoder Architecture for Semantic Segmentation From Scratch

One of the most important key points in the intelligent transportation systems is scene understanding of the known and unknown surrounding environment to achieve a safe driving for smart mobile robots and cars. Semantic segmentation can address most of the perception needs of mobile robots and Intelligent Vehicles (IV). There are several deep learning approaches based on Convolutional Neural Network (CNN) for semantic segmentation. Most of these techniques have been designed on a pretrained network base and loading a specific weight file is necessary for them. In this paper, we propose a deep architecture for semantic segmentation from scratch based on an asymmetry encoder- decoder architecture using Ghost-Net and U-Net which we have called it Ghost-UNet. This model can be used for precise segmentation using a combination of low-level spatial information and high-level feature maps. We focus our work on outdoor datasets to evaluate the proposed model which is tested on the Cityscapes dataset. The proposed model has good pixel accuracy and mean Intersection over Union (mIoU) compared with other valid literature.