Reviewerly/exHarmony huggingface dataset:
Twin-Field Quantum Key Distribution without Phase Postselection

Twin-field quantum key distribution (TF-QKD) protocol and its variants, e.g. phase-matching (PM) QKD and TF-QKD based on sending or not sending, are highly attractive since they are able to overcome the well-known rate-loss limit for QKD protocols without repeater: $R=O(\eta)$ with $\eta$ standing for the channel transmittance. However, all these protocols require active phase randomization and post-selection that play an essential role together in their security proof. Counterintuitively, we find that in TF-QKD, beating the rate-loss limit is still possible even if phase randomization and post-selection in the coding mode are both removed, which means our final secure key rate $R=O(\sqrt{\eta})$. Furthermore, our protocol is more feasible in practice and more promising according to its higher final key rate in the valid distance. Our security proof counters collective attack and can also counter coherent attack in asymptotical case
Learning Modality-Specific Representations for Visible-Infrared Person Re-Identification

Traditional person re-identification (re-id) methods perform poorly under changing illuminations. This situation can be addressed by using dual-cameras that capture visible images in a bright environment and infrared images in a dark environment. Yet, this scheme needs to solve the visible-infrared matching issue, which is largely under-studied. Matching pedestrians across heterogeneous modalities is extremely challenging because of different visual characteristics. In this paper, we propose a novel framework that employ modality-specific networks to tackle with the heterogeneous matching problem. The proposed framework utilizes the modality-related information and extracts modality-specific representations (MSR) by constructing an individual network for each modality. In addition, a cross-modality Euclidean constraint is introduced to narrow the gap between different networks. We also integrate the modality-shared layers into modality-specific networks to extract shareable information and use a modality-shared identity loss to facilitate the extraction of modality-invariant features. Then a modality-specific discriminant metric is learned for each domain to strengthen the discriminative power of MSR. Eventually, we use a view classifier to learn view information. The experiments demonstrate that the MSR effectively improves the performance of deep networks on VI-REID and remarkably outperforms the state-of-the-art methods.
Chip-to-chip quantum teleportation and multi-photon entanglement in silicon

Integrated optics provides a versatile platform for quantum information processing and transceiving with photons1–8. The implementation of quantum protocols requires the capability to generate multiple high-quality single photons and process photons with multiple high-fidelity operators9–11. However, previous experimental demonstrations were faced by major challenges in realizing sufficiently high-quality multi-photon sources and multi-qubit operators in a single integrated system4–8, and fully chip-based implementations of multi-qubit quantum tasks remain a significant challenge1–3. Here, we report the demonstration of chip-to-chip quantum teleportation and genuine multipartite entanglement, the core functionalities in quantum technologies, on silicon-photonic circuitry. Four single photons with high purity and indistinguishablity are produced in an array of microresonator sources, without requiring any spectral filtering. Up to four qubits are processed in a reprogrammable linear-optic quantum circuit that facilitates Bell projection and fusion operation. The generation, processing, transceiving and measurement of multi-photon multi-qubit states are all achieved in micrometre-scale silicon chips, fabricated by the complementary metal–oxide–semiconductor process. Our work lays the groundwork for large-scale integrated photonic quantum technologies for communications and computations. Four single-photon states are generated and entangled on a single micrometre-scale silicon chip, and provide the basis for the demonstration of chip-to-chip quantum teleportation.
Event Extraction as Machine Reading Comprehension

Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts.Previous methods for EE typically model it as a classification task, which are data-hungry and suffer from the data scarcity problem.In this paper, we propose a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC).Our approach includes an unsupervised question generation process, which can transfer event schema into a set of natural questions, followed by a BERTbased question-answering process to retrieve answers as EE results.This learning paradigm enables us to strengthen the reasoning process of EE, by introducing sophisticated models in MRC, and relieve the data scarcity problem, by introducing the large-scale datasets in MRC.The empirical results show that: i) our approach attains state-of-the-art performance by considerable margins over previous methods.ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49.8% in F1 for event argument extraction with only 1% data, compared with 2.2% of the previous method.iii) Our model also fits with zero-shot scenarios, achieving 37.0% and 16% in F1 on two datasets without using any EE training data.
Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning

Abstract The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The worldwide shortage of pathologists offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate the workload and increase diagnostic accuracy. Here, we report a clinically applicable system developed at the Chinese PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-level annotated H&amp;E-stained whole slide images. The model achieves a sensitivity near 100% and an average specificity of 80.6% on a real-world test dataset with 3,212 whole slide images digitalized by three scanners. We show that the system could aid pathologists in improving diagnostic accuracy and preventing misdiagnoses. Moreover, we demonstrate that our system performs robustly with 1,582 whole slide images from two other medical centres. Our study suggests the feasibility and benefits of using histopathological artificial intelligence assistance systems in routine practice scenarios.
Peripersonal space (PPS) as a multisensory interface between the individual and the environment, defining the space of the self

Our brain has developed a specific system to represent the space closely surrounding the body, termed peripersonal space (PPS). This space has a key functional role as it is where all physical interactions with objects in the environment occur. Here I describe how multisensory neurons in a specific fronto-parietal network map the PPS by integrating tactile stimuli on the body with visual or auditory information related to external objects specifically when they are close to the body. I show how PPS representation is not only multisensory, but actually multisensory-motor, as the PPS system interacts with motor areas to trigger appropriate responses. The extent of PPS is not fixed, but it is shaped by experience, as PPS may encompass farther portions of space, once the individual has interacted with them, (e.g., with tools), or it contracts, if interactions are limited because of external constraints, body, or brain injury. Interactions between the individual and the environment are not only physical but may also be “abstract”. Recent data show that PPS adapts as a consequence of technology-mediated or social interactions. Finally, I propose that besides low-level sensory-motor representations of the space around the different parts of the body, mediating body-objects interactions, the multisensory PPS system also underlies a general representation of the self as distinct from the environment and the others. PPS thus supports self-location, contributes to bodily self-consciousness and mediates higher-level cognitive functions.
Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach

In April 2021, the European Commission proposed a Regulation on Artificial Intelligence, known as the AI Act. We present an overview of the Act and analyse its implications, drawing on scholarship ranging from the study of contemporary AI practices to the structure of EU product safety regimes over the last four decades. Aspects of the AI Act, such as different rules for different risk-levels of AI, make sense. But we also find that some provisions of the Draft AI Act have surprising legal implications, whilst others may be largely ineffective at achieving their stated goals. Several overarching aspects, including the enforcement regime and the risks of maximum harmonisation pre-empting legitimate national AI policy, engender significant concern. These issues should be addressed as a priority in the legislative process.
Large-Scale Study of Perceptual Video Quality

The great variations of videographic skills, camera designs, compression and processing protocols, and displays lead to an enormous variety of video impairments. Current no-reference (NR) video quality models are unable to handle this diversity of distortions. This is true in part because available video quality assessment databases contain very limited content, fixed resolutions, were captured using a small number of camera devices by a few videographers and have been subjected to a modest number of distortions. As such, these databases fail to adequately represent real world videos, which contain very different kinds of content obtained under highly diverse imaging conditions and are subject to authentic, often commingled distortions that are impossible to simulate. As a result, NR video quality predictors tested on real-world video data often perform poorly. Towards advancing NR video quality prediction, we constructed a large-scale video quality assessment database containing 585 videos of unique content, captured by a large number of users, with wide ranges of levels of complex, authentic distortions. We collected a large number of subjective video quality scores via crowdsourcing. A total of 4776 unique participants took part in the study, yielding more than 205000 opinion scores, resulting in an average of 240 recorded human opinions per video. We demonstrate the value of the new resource, which we call the LIVE Video Quality Challenge Database (LIVE-VQC), by conducting a comparison of leading NR video quality predictors on it. This study is the largest video quality assessment study ever conducted along several key dimensions: number of unique contents, capture devices, distortion types and combinations of distortions, study participants, and recorded subjective scores. The database is available for download on this link: http://live.ece.utexas.edu/research/LIVEVQC/index.html .
An on-chip photonic deep neural network for image classification

Deep neural networks with applications from computer vision to medical diagnosis1-5 are commonly implemented using clock-based processors6-14, in which computation speed is mainly limited by the clock frequency and the memory access time. In the optical domain, despite advances in photonic computation15-17, the lack of scalable on-chip optical non-linearity and the loss of photonic devices limit the scalability of optical deep networks. Here we report an integrated end-to-end photonic deep neural network (PDNN) that performs sub-nanosecond image classification through direct processing of the optical waves impinging on the on-chip pixel array as they propagate through layers of neurons. In each neuron, linear computation is performed optically and the non-linear activation function is realized opto-electronically, allowing a classification time of under 570 ps, which is comparable with a single clock cycle of state-of-the-art digital platforms. A uniformly distributed supply light provides the same per-neuron optical output range, allowing scalability to large-scale PDNNs. Two-class and four-class classification of handwritten letters with accuracies higher than 93.8% and 89.8%, respectively, is demonstrated. Direct, clock-less processing of optical data eliminates analogue-to-digital conversion and the requirement for a large memory module, allowing faster and more energy efficient neural networks for the next generations of deep learning systems.
Seq2seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models

Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and "what if"-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization

Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Quadruplet Network With One-Shot Learning for Fast Visual Object Tracking

In the same vein of discriminative one-shot learning, Siamese networks allow recognizing an object from a single exemplar with the same class label. However, they do not take advantage of the underlying structure of the data and the relationship among the multitude of samples as they only rely on pairs of instances for training. In this paper, we propose a new quadruplet deep network to examine the potential connections among the training instances, aiming to achieve a more powerful representation. We design four shared networks that receive multi-tuple of instances as inputs and are connected by a novel loss function consisting of pair-loss and triplet-loss. According to the similarity metric, we select the most similar and the most dissimilar instances as the positive and negative inputs of triplet loss from each multi-tuple. We show that this scheme improves the training performance. Furthermore, we introduce a new weight layer to automatically select suitable combination weights, which will avoid the conflict between triplet and pair loss leading to worse performance. We evaluate our quadruplet framework by model-free tracking-by-detection of objects from a single initial exemplar in several Visual Object Tracking benchmarks. Our extensive experimental analysis demonstrates that our tracker achieves superior performance with a real-time processing speed of 78 frames-per-second (fps).
Covering aspects of the Niemeier lattices

The Niemeier lattices are the 23 unimodular even lattices of norm 2 in dimension 24. We determine computationally their covering radius for 16 of those lattices and give lower bounds for the remaining that we conjecture to be exact. This is achieved by computing the list of Delaunay polytopes of those lattices.
MMDAE: Dialog scenario editor for MMDAgent on the web browser

We have developed MMDAgent (a fully open-source toolkit for voice interaction systems), which runs on a variety of platforms such as personal computers and smartphones. From this, the editing environment of the dialog scenario also needs to be operated on various platforms. So, we develop a scenario editor that is implemented on a Web browser. The purpose of this paper also includes making it easy to edit the scenario. Experiments were conducted for subjects using the proposed scenario editor. It was found that our proposed system provides better readability of a scenario and allows easier editing.
Subject Guides: Scholarly Publishing: Home

This guide provides information about common issues in scholarly communications, including author rights, publishing open access, and depositing your work in a repository.
A scheme for preparing cluster state with nitrogen-vacancy centers and microtoroidal resonator coupled systems

Quantum entanglement is a key resource in quantum information processing. In this paper, we propose a scheme for preparing a cluster entangled state by means of multiple nitrogen vacancy centers and microtoroidal resonator coupled systems. The scheme employs an input–output process and single-photon interference, without any other auxiliary entanglement particles or classical communication. Considering the current experimental technology, the scheme will be implemented and has an important role in the preparation of cluster entangled state by means of nitrogen vacancy centers and microtoroidal resonator coupled systems.
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Online course: Cybersecurity introduction

This course provides information and advice, along with some easy steps to help minimise risk in today’s often confusing cyber landscape.
Efficient Algorithms for Finding the Closest l-mers in Biological Data

With the advances in the next generation sequencing technology, huge amounts of data have been and get generated in biology. A bottleneck in dealing with such datasets lies in developing effective algorithms for extracting useful information from them. Algorithms for finding patterns in biological data pave the way for extracting crucial information from the voluminous datasets. In this paper we focus on a fundamental pattern, namely, the closest l-mers. Given a set of m biological strings S1,S2,…,Sm and an integer l, the problem of interest is that of finding an l-mer from each string such that the distance among them is the least. I.e., we want to find m l-mers X1,X2,…,Xm such that Xi is an l-mer in Si (for 1 ≤ i ≤ m) and the Hamming distance among these m l-mers is the least (from among all such possible l-mers). This problem has many applications including motif search. Algorithms for finding the closest l-mers have been used in solving the (l,d)-motif search problem (see e.g., \cite{PeSz00,DBR07}). In this paper novel algorithms are proposed for this problem for the case of . A comprehensive experimental evaluation is performed for m=3, along with a further empirical study of m=4 and 5.
Perencanaan Video Production dan Talent Agency "Eblo Production"

The development of technology such as mobile phones currently affect people's behavior. Almost everything can be accessed via mobile phones whenever and wherever. It’s also affects the function of other technologies, such as TV. Usefulness TV as entertainment is now rarely used, because online sites more accessible, one of which is Youtube. This research aims to design video production along with talent agencies to Youtube. These businesses create entertainment and commercial online. The design process also covers the phase of data collection such as interviews, literature studies, articles, and questionnaires.
Discrete Regular Polygons for Digital Shape Rigid Motion via Polygonization

Recently, a sufficient condition, namely quasi-regularity, has been proposed for preserving the connectivity during the process of digitization of a continuous object whose boundary is not necessarily differentiable. Under this condition, a rigid motion scheme for digital objects of $$\mathbb {Z}^2$$ is proposed to guarantee that a well-composed object will remain well-composed, and its global geometry will be approximately preserved. In this paper, we are interested in polygons generated from digital objects and their rigid motions in $$\mathbb {Z}^2$$ . For this, we introduce a notion of discrete regularity which is a restriction of quasi-regularity for polygons. This notion provides a simple geometric verification, based on the measure of lengths and angles, of quasi-regularity which is originally defined with morphological operators. Furthermore, we present a method for geometry-preserving rigid motions based on convex decomposition of polygons. This paper focuses on, the implementation and on the reproduction of the method linking to an online demonstration. The way of using the C++ source code in other contexts is shown as well.
Simulation on and HPC Simulation of Quantum Computers and Quantum Annealers

Results of benchmarking tests on gate-based quantum computers and quantum annealers are compared to results obtained with simulators of these new computing technologies. Simulating the behavior of (physical models) of quantum computing devices on supercomputers does not only shed light on the physical processes that are involved in the computation but is also useful for testing some applications.
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The Effects of Amplification of Fluctuation Energy Scale by Quantum Measurement Choice on Quantum Chaotic Systems: Semiclassical Analysis

Measurement choices in weakly-measured open quantum systems can affect quantum trajectory chaos. We consider this scenario semi-classically and show that measurement acts as nonlinear generalized fluctuation and dissipation forces. These can alter effective dissipation in the quantum spread variables and hence change the dynamics, such that measurement choices can enhance quantum effects and make the dynamics chaotic, for example. This analysis explains the measurement dependence of quantum chaos at a variety of parameter settings, and in particular we demonstrate that the choice of monitoring scheme can be more relevant than system scale $$\beta $$ in determining the ‘quantumness’ of the system.
Data Reduction Using Lossy Compression for Cosmology and Astrophysics Workflows

Abstract This paper concerns the use of compression methods applied to large scientific data. Specifically the paper addresses the effect of lossy compression on approximation error. Computer simulations, experiments and imaging technologies generate terabyte-scale datasets making necessary new approaches for compression coupled with data analysis. Lossless compression techniques compress data with no loss of information, but they generally do not produce a large-enough reduction when compared to lossy compression methods. Lossy multi-resolution compression techniques make it possible to compress large datasets significantly with small numerical error, preserving coherent features and statistical properties needed for analysis. Lossy data compression reduces I/O data transfer cost and makes it possible to store more data at higher temporal resolution. We present results obtained with lossy multi-resolution compression, with a focus on astrophysics datasets. Our results confirm that lossy data compression is capable of preserving data characteristics very well, even at extremely high degrees of compression.