Reviewerly/exHarmony huggingface dataset:
GAZED Gaze-guided Cinematic Editing of Wide-Angle Monocular Video Recordings

We present GAZED– eye GAZe-guided EDiting for videos captured by a solitary, static, wide-angle and high-resolution camera. Eye-gaze has been effectively employed in computational applications as a cue to capture interesting scene content; we employ gaze as a proxy to select shots for inclusion in the edited video. Given the original video, scene content and user eye-gaze tracks are combined to generate an edited video comprising cinematically valid actor shots and shot transitions to generate an aesthetic and vivid representation of the original narrative. We model cinematic video editing as an energy minimization problem over shot selection, whose constraints capture cinematographic editing conventions. Gazed scene locations primarily determine the shots constituting the edited video. Effectiveness of GAZED against multiple competing methods is demonstrated via a psychophysical study involving 12 users and twelve performance videos.
Full Database Reconstruction in Two Dimensions

In the past few years, we have seen multiple attacks on one-dimensional databases that support range queries. These attacks achieve full database reconstruction by exploiting access pattern leakage along with known query distribution or search pattern leakage. We are the first to go beyond one dimension, exploring this threat in two dimensions. We unveil an intrinsic limitation of reconstruction attacks by showing that there can be an exponential number of distinct databases that produce equivalent leakage. Next, we present a full database reconstruction attack. Our algorithm runs in polynomial time and returns a poly-size encoding of all databases consistent with the given leakage profile. We implement our algorithm and observe real-world databases that admit a large number of equivalent databases, which aligns with our theoretical results.
Uncertainty-Aware Machine Support for Paper Reviewing on the Interspeech 2019 Submission Corpus

The evaluation of scientific submissions through peer review is both the most fundamental component of the publication process, as well as the most frequently criticised and questioned.Academic journals and conferences request reviews from multiple reviewers per submission, which an editor, or area chair aggregates into the final acceptance decision.Reviewers are often in disagreement due to varying levels of domain expertise, confidence, levels of motivation, as well as due to the heavy workload and the different interpretations by the reviewers of the score scale.Herein, we explore the possibility of a computational decision support tool for the editor, based on Natural Language Processing, that offers an additional aggregated recommendation.We provide a comparative study of state-of-the-art text modelling methods on the newly crafted, largest review dataset of its kind based on Interspeech 2019, and we are the first to explore uncertainty-aware methods (soft labels, quantile regression) to address the subjectivity inherent in this problem.
Adaptive optics benefit for quantum key distribution uplink from ground to a satellite

Abstract For quantum communications, the use of Earth-orbiting satellites to extend distances has gained significant attention in recent years, exemplified in particular by the launch of the Micius satellite in 2016. The performance of applied protocols such as quantum key distribution (QKD) depends significantly on the transmission efficiency through the turbulent atmosphere, which is especially challenging for ground-to-satellite uplink scenarios. Adaptive optics (AO) techniques have been used in astronomical, communication, and other applications to reduce the detrimental effects of turbulence for many years, but their applicability to quantum protocols, and their requirements specifically in the uplink scenario, is not well established. Here, we model the effect of the atmosphere on link efficiency between an Earth station and a satellite using an optical uplink and how AO can help recover from loss due to turbulence. Examining both low Earth orbit and geostationary uplink scenarios, we find that a modest link transmissivity improvement of about 3 dB can be obtained in the case of a coaligned downward beacon, while the link can be dramatically improved, up to 7 dB, using an offset beacon, such as a laser guide star. AO coupled with a laser guide star would thus deliver a significant increase in the secret key generation rate of the QKD ground-to-space uplink system, especially as reductions of channel loss have a favourably nonlinear key-rate response within this high-loss regime.
Statistical learning affects the time courses of salience-driven and goal-driven selection.

The present study investigated how statistical regularities present in the display affected the time courses associated with salience-driven and goal-driven visual selection. In two experiments, participants were instructed to make a speeded saccade toward a prespecified target that was presented simultaneously with a distractor among multiple homogeneously oriented background lines. The distractor was presented more often at one location than at all other locations. We found that the statistical regularity regarding the distractor location affected visual selection very early, modulating the time courses associated with both salience-driven and goal-driven selection. These results suggest that statistical learning induces a continuous bias in visual selection, operating above and beyond salience-driven and goal-driven control. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Feature Extraction for Medical CT Images of Sports Tear Injury

Abstract Analysis of medical CT images directly affects the accuracy of clinical case diagnosis. Therefore, feature extraction problem of medical CT images is extremely important. A feature extraction algorithm for medical CT images of sports tear injury is proposed. First, CT images are decomposed into a low frequency component and a series of high frequency components in different directions by wavelet fast decomposition method. The high- and low-frequency information of CT images is enhanced by wavelet layered multi-directional image enhancement algorithm, and the multi-scale enhancement for medical CT images of sports tear injury is completed. Then, edge of the enhanced CT images is extracted using an image edge extraction algorithm based on extended mathematical morphology. Finally, based on the extracted edge information of CT images, feature extraction for medical CT images of sports tear injury is completed by the NSCT-GLCM based CT image feature extraction algorithm. Research results show that the proposed algorithm effectively extracts CT image features of sports tear injury and provides auxiliary information for doctor diagnosis.
Geometric Pathway to Scalable Quantum Sensing

Entangled resources enable quantum sensing that achieves Heisenberg scaling, a quadratic improvement on the standard quantum limit, but preparing large $N$ spin entangled states is challenging in the presence of decoherence. We present a quantum control strategy using highly nonlinear geometric phase gates which can be used for generic state or unitary synthesis on the Dicke subspace with $O(N)$ or $O({N}^{2})$ gates, respectively. The method uses a dispersive coupling of the spins to a common bosonic mode and does not require addressability, special detunings, or interactions between the spins. By using amplitude amplification our control sequence for preparing states ideal for metrology can be significantly simplified to $O({N}^{5/4})$ geometric phase gates with action angles $O(1/N)$ that are more robust to mode decay. The geometrically closed path of the control operations ensures the gates are insensitive to the initial state of the mode and the sequence has built-in dynamical decoupling providing resilience to dephasing errors.
Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification Using CTC-Based Soft VAD and Global Query Attention

Keyword spotting (KWS) and speaker verification (SV) have been studied independently although it is known that acoustic and speaker domains are complementary. In this paper, we propose a multi-task network that performs KWS and SV simultaneously to fully utilize the interrelated domain information. The multi-task network tightly combines sub-networks aiming at performance improvement in challenging conditions such as noisy environments, open-vocabulary KWS, and short-duration SV, by introducing novel techniques of connectionist temporal classification (CTC)-based soft voice activity detection (VAD) and global query attention. Frame-level acoustic and speaker information is integrated with phonetically originated weights so that forms a word-level global representation. Then it is used for the aggregation of feature vectors to generate discriminative embeddings. Our proposed approach shows 4.06% and 26.71% relative improvements in equal error rate (EER) compared to the baselines for both tasks. We also present a visualization example and results of ablation experiments.
Advances in Optimisation Algorithms and Techniques for Deep Learning

optimisation Neural network optimisersIn the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others.Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN).These typical parameters are obtained by some optimisation techniques which have been studied extensively.These research have produced state-of-art(SOTA) results on speed and memory improvements for deep neural networks(NN) architectures.However, the SOTA optimisers have continued to be an active research area with no compilations of the existing optimisers reported in the literature.This paper provides an overview of the recent advances in optimisation algorithms and techniques used in DNN, highlighting the current SOTA optimisers, improvements made on these optimisation algorithms and techniques, alongside the trends in the development of optimisers used in training DL based models.The results of the search of the Scopus database for the optimisers in DL provides the articles reported as the summary of the DL optimisers.From what we can tell, there is no comprehensive compilation of the optimisation algorithms and techniques so far developed and used in DL research and applications, and this paper summarises these facts.
Efficient and automated initial value estimation in digital image correlation for large displacement, rotation, and scaling

The initial value estimation for seed point is the first step in digital image correlation calculation. Among the existing algorithms, the Fourier–Mellin transform-based cross correlation (FMT-CC) algorithm is one of the most efficient and robust owing to its rotation- and scale-invariance. However, when the displacement is large (more than a hundred pixels), the FMT-CC algorithm fails. In this paper, an automated and efficient initial value estimation method based on an FMT-CC algorithm is presented to deal with large displacement, large rotation, and large isotropic scaling. The relationship between subset size and the maximal displacement in the FMT-CC algorithm is studied, and a strategy of setting the subset size according to the estimated displacement is proposed to improve the robustness of the FMT-CC algorithm. In addition, in cases of large displacement, a multi-scale search method is proposed to improve efficiency. The experimental results show that the proposed methods can realize rapid and automated initial value estimation even under conditions of large displacement, large rotation, and large isotropic scaling. The computational efficiency of the multi-scale search method is about one order of magnitude higher than the traditional FMT-CC method.
Linear-response functions of molecules on a quantum computer: Charge and spin responses and optical absorption

We propose a scheme for the construction of charge and spin linear-response functions of an interacting electronic system via quantum phase estimation and statistical sampling on a quantum computer. By using the unitary decomposition of electronic operators for avoiding the difficulty due to their non-unitarity, we provide the circuits equipped with ancillae for probabilistic preparation of qubit states on which the necessary non-unitary operators have acted. We perform simulations of such construction of the response functions for C2 and N2 molecules by comparing with the accurate ones based on the full configuration interaction calculations. It is found that the accurate detection of subtle structures coming from the weak poles in the response functions requires a large number of measurements.
Learning to summarize from human feedback

As language models become more powerful, training and evaluation are increasingly bottlenecked by the data and metrics used for a particular task. For example, summarization models are often trained to predict human reference summaries and evaluated using ROUGE, but both of these metrics are rough proxies for what we really care about---summary quality. In this work, we show that it is possible to significantly improve summary quality by training a model to optimize for human preferences. We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning. We apply our method to a version of the TL;DR dataset of Reddit posts and find that our models significantly outperform both human reference summaries and much larger models fine-tuned with supervised learning alone. Our models also transfer to CNN/DM news articles, producing summaries nearly as good as the human reference without any news-specific fine-tuning. We conduct extensive analyses to understand our human feedback dataset and fine-tuned models We establish that our reward model generalizes to new datasets, and that optimizing our reward model results in better summaries than optimizing ROUGE according to humans. We hope the evidence from our paper motivates machine learning researchers to pay closer attention to how their training loss affects the model behavior they actually want.
Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases

Multi-hop relation reasoning over knowledge base is to generate effective and interpretable relation prediction through reasoning paths.The current methods usually require sufficient training data (fact triples) for each query relation, impairing their performances over fewshot relations (with limited triples) which are common in knowledge base.To this end, we propose FIRE, a novel few-shot multihop relation learning model.FIRE applies reinforcement learning to model the sequential steps of multi-hop reasoning, besides performs heterogeneous structure encoding and knowledge-aware search space pruning.The meta-learning technique is employed to optimize model parameters that could quickly adapt to few-shot relations.Empirical study on two datasets demonstrate that FIRE outperforms state-of-the-art methods.
Incorporating a Local Translation Mechanism into Non-autoregressive Translation

In this work, we introduce a novel local autoregressive translation (LAT) mechanism into non-autoregressive translation (NAT) models so as to capture local dependencies among target outputs.Specifically, for each target decoding position, instead of only one token, we predict a short sequence of tokens in an autoregressive way.We further design an efficient merging algorithm to align and merge the output pieces into one final output sequence.We integrate LAT into the conditional masked language model (CMLM;Ghazvininejad et al., 2019) and similarly adopt iterative decoding.Empirical results on five translation tasks show that compared with CMLM, our method achieves comparable or better performance with fewer decoding iterations, bringing a 2.5x speedup.Further analysis indicates that our method reduces repeated translations and performs better at longer sentences.The code for our model is available at https://github.
Evidence, proofs, and derivations

The traditional view of evidence in mathematics is that evidence is just proof and proof is just derivation. There are good reasons for thinking that this view should be rejected: it misrepresents both historical and current mathematical practice. Nonetheless, evidence, proof, and derivation are closely intertwined. This paper seeks to tease these concepts apart. It emphasizes the role of argumentation as a context shared by evidence, proofs, and derivations. The utility of argumentation theory, in general, and argumentation schemes, in particular, as a methodology for the study of mathematical practice is thereby demonstrated. Argumentation schemes represent an almost untapped resource for mathematics education. Notably, they provide a consistent treatment of rigorous and non-rigorous argumentation, thereby working to exhibit the continuity of reasoning in mathematics with reasoning in other areas. Moreover, since argumentation schemes are a comparatively mature methodology, there is a substantial body of existing work to draw upon, including some increasingly sophisticated software tools. Such tools have significant potential for the analysis and evaluation of mathematical argumentation. The first four sections of the paper address the relationships of evidence to proof, proof to derivation, argument to proof, and argument to evidence, respectively. The final section directly addresses some of the educational implications of an argumentation scheme account of mathematical reasoning.
Experimental Implementation of a Raman-Assisted Eight-Wave Mixing Process

Engineering higher-order nonlinear interactions is vital in autonomous protection of quantum systems against errors. Such interactions are often not directly available, though, or are slow compared to error rates of the system. The authors present a nonlinear eight-wave mixing process that exchanges four photons of a harmonic oscillator with two excitations of a transmon-qubit mode and two pump photons, by combining more accessible lower-order interactions via a sort of Raman transition. Surprisingly, this technique produces a stronger interaction than a six-wave mixing process in the same system. This eight-wave mixing process is expected to become a key component of autonomous continuous-variable quantum error correction.
plasp 3: Towards Effective ASP Planning

We describe the new version of the PDDL-to-ASP translator plasp. First, it widens the range of accepted PDDL features. Second, it contains novel planning encodings, some inspired by SAT planning and others exploiting ASP features such as well-foundedness. All of them are designed for handling multivalued fluents in order to capture both PDDL as well as SAS planning formats. Third, enabled by multishot ASP solving, it offers advanced planning algorithms also borrowed from SAT planning. As a result, plasp provides us with an ASP-based framework for studying a variety of planning techniques in a uniform setting. Finally, we demonstrate in an empirical analysis that these techniques have a significant impact on the performance of ASP planning.
On the insecurity of quantum Bitcoin mining

Grover’s algorithm confers on quantum computers a quadratic advantage over classical computers for searching in an arbitrary data set, a scenario that describes Bitcoin mining. It has previously been argued that the only side effect of quantum mining would be an increased difficulty. In this work, we argue that a crucial argument in the analysis of Bitcoin security breaks down when quantum mining is performed. Classically, a Bitcoin fork occurs rarely, i.e., when two miners find a block almost simultaneously, due to propagation time effects. The situation differs dramatically when quantum miners use Grover’s algorithm, which repeatedly applies a procedure called a Grover iteration. The chances of finding a block grow quadratically with the number of Grover iterations applied. Crucially, a miner does not have to choose how many iterations to apply in advance. Suppose Alice receives Bob’s new block. To maximize her revenue, she should stop and measure her state immediately in the hopes that her block (rather than Bob’s) will become part of the longest chain. The strong correlation between the miners’ actions and the fact that they all measure their states at the same time may lead to more forks—which is known to be a security risk for Bitcoin. We propose a mechanism that, we conjecture, will prevent this form of quantum mining, thereby circumventing the high rate of forks.
A Cooperative Multiagent Probabilistic Framework for Search and Track Missions

In this work a robust and scalable cooperative multi-agent searching and tracking framework is proposed. Specifically, we study the problem of cooperative searching and tracking of multiple moving targets by a group of autonomous mobile agents with limited sensing capabilities. We assume that the actual number of targets present is not known a priori and that target births/deaths can occur anywhere inside the surveillance region thus efficient search strategies are required to detect and track as many targets as possible. To address the aforementioned challenges we recursively compute and propagate in time the searching-and-tracking (SAT) density. Using the SAT-density, we then develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area.
On-demand generation and characterization of a microwave time-bin qubit

Superconducting circuits offer a scalable platform for the construction of large-scale quantum networks where information can be encoded in multiple temporal modes of propagating microwaves. Characterization of such microwave signals with a method extendable to an arbitrary number of temporal modes with a single detector and demonstration of their phase-robust nature are of great interest. Here we show the on-demand generation and Wigner tomography of a microwave time-bin qubit with superconducting circuit quantum electrodynamics architecture. We perform the tomography with a single heterodyne detector by dynamically changing the measurement quadrature with a phase-sensitive amplifier independently for the two temporal modes. By generating and measuring the qubits with hardware lacking a shared phase reference, we demonstrate conservation of phase information in each time-bin qubit generated.
Photonic integrated quantum key distribution receiver for multiple users

Integrated photonics has the advantages of miniaturization, low cost, and CMOS compatibility, and it provides a stable, highly integrated, and practical platform for quantum key distribution (QKD). While photonic integration of optical components has greatly reduced the overall cost of QKD systems, single-photon detectors (SPDs) have become the most expensive part of a practical QKD system. In order to circumvent this obstacle and make full use of SPDs, we have designed and fabricated a QKD receiver chip for multiple users. Our chip is based on a time-division multiplexing technique and makes use of a single set of SPDs to support up to four users' QKD. Our proof-of-principle chip-based QKD system is capable of producing an average secret key rate of 13.68 kbps for four users with a quantum bit error rate (QBER) as low as 0.51% over a simulated distance of 20 km in fiber. Our result clearly demonstrates the feasibility of multiplexing SPDs for setting QKD channels with different users using photonic integrated chip and may find applications in the commercialization of quantum communication technology.