Reviewerly/exHarmony huggingface dataset:
On the Discrepancy between Density Estimation and Sequence Generation

Many sequence-to-sequence generation tasks, including machine translation and text-tospeech, can be posed as estimating the density of the output y given the input x: p(y|x).Given this interpretation, it is natural to evaluate sequence-to-sequence models using conditional log-likelihood on a test set.However, the goal of sequence-to-sequence generation (or structured prediction) is to find the best output ŷ given an input x, and each task has its own downstream metric R that scores a model output by comparing against a set of references y * : R(ŷ, y * |x).While we hope that a model that excels in density estimation also performs well on the downstream metric, the exact correlation has not been studied for sequence generation tasks.In this paper, by comparing several density estimators on five machine translation tasks, we find that the correlation between rankings of models based on log-likelihood and BLEU varies significantly depending on the range of the model families being compared.First, log-likelihood is highly correlated with BLEU when we consider models within the same family (e.g.autoregressive models, or latent variable models with the same parameterization of the prior).However, we observe no correlation between rankings of models across different families:(1) among non-autoregressive latent variable models, a flexible prior distribution is better at density estimation but gives worse generation quality than a simple prior, and (2) autoregressive models offer the best translation performance overall, while latent variable models with a normalizing flow prior give the highest held-out log-likelihood across all datasets.
Qubit representation of qudit states: correlations and state reconstruction

A method to establish a qubit decomposition of a general qudit state is presented. This new representation allows a geometrical depiction of any qudit state in the Bloch sphere. Additionally, we show that the nonnegativity conditions of the qudit state imply the existence of quantum correlations between the qubits which compose it. These correlations are used to define new inequalities which the density matrices components must satisfy. The importance of such inequalities in the reconstruction of a qudit state is addressed. As an example of the general procedure the qubit decomposition of a qutrit system is shown, which allows a classification of the qutrit states by fixing their invariants ${\rm Tr}(\hat{\rho}^2)$, ${\rm Tr}(\hat{\rho}^3)$.
A general formulation of time-optimal quantum control and optimality of singular protocols

We present a general theoretical framework for finding the time-optimal unitary evolution of the quantum systems when the Hamiltonian is subject to arbitrary constraints. Quantum brachistochrone (QB) is such a framework based on the variational principle, whose drawback is that it deals with equality constraints only. While inequality constraints can be reduced to equality ones in some situations, there are situations where they cannot, especially when a drift field is present in the Hamiltonian. The drift which we cannot control appears in a wide range of systems. We first develop a framework based on Pontryagin's maximum principle (MP) in order to deal with inequality constraints as well. The new framework contains QB as a special case, and their detailed correspondence is given. Second, using this framework, we discuss general relations among the drift, the singular controls, and the inequality constraints. The singular controls are those that satisfy MP trivially so as to cause a trouble in determining the optimal protocol. Third, to overcome this issue, we derive an additional necessary condition for a singular protocol to be optimal by applying the generalized Legendre-Clebsch condition. This condition in particular reveals the physical meaning of singular controls. Finally, we demonstrate how our framework and results work in some examples.
Compounds of symmetric informationally complete measurements and their application in quantum key distribution

Symmetric informationally complete measurements (SICs) are elegant, celebrated, and broadly useful discrete structures in Hilbert space. We introduce a more sophisticated discrete structure compounded by several SICs. A SIC compound is defined to be a collection of d3 vectors in d-dimensional Hilbert space that can be partitioned in two different ways: into d SICs and into d2 orthonormal bases. While a priori their existence may appear unlikely when d>2, we surprisingly find an explicit construction for d=4. Remarkably this SIC compound admits a close relation to mutually unbiased bases, as is revealed through quantum state discrimination. Going beyond fundamental considerations, we leverage these exotic properties to construct a protocol for quantum key distribution and analyze its security under general eavesdropping attacks. We show that SIC compounds enable secure key generation in the presence of errors that are large enough to prevent the success of the generalization of the six-state protocol.Received 8 July 2020Accepted 14 August 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043122Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum cryptographyQuantum foundationsQuantum measurementsGeneral PhysicsQuantum Information
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Pre-trained language models (e.g., BERT (Devlin et al., 2018) and its variants) have achieved remarkable success in varieties of NLP tasks. However, these models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this work, we present a simple and effective approach to compress large Transformer (Vaswani et al., 2017) based pre-trained models, termed as deep self-attention distillation. The small model (student) is trained by deeply mimicking the self-attention module, which plays a vital role in Transformer networks, of the large model (teacher). Specifically, we propose distilling the self-attention module of the last Transformer layer of the teacher, which is effective and flexible for the student. Furthermore, we introduce the scaled dot-product between values in the self-attention module as the new deep self-attention knowledge, in addition to the attention distributions (i.e., the scaled dot-product of queries and keys) that have been used in existing works. Moreover, we show that introducing a teacher assistant (Mirzadeh et al., 2019) also helps the distillation of large pre-trained Transformer models. Experimental results demonstrate that our monolingual model outperforms state-of-the-art baselines in different parameter size of student models. In particular, it retains more than 99% accuracy on SQuAD 2.0 and several GLUE benchmark tasks using 50% of the Transformer parameters and computations of the teacher model. We also obtain competitive results in applying deep self-attention distillation to multilingual pre-trained models.
Machine Learning Applied to Quantum Synchronization‐Assisted Probing

Abstract A probing scheme is considered with an accessible and controllable qubit, used to probe an out‐of equilibrium system consisting of a second qubit interacting with an environment. Quantum spontaneous synchronization between the probe and the system emerges in this model and, by tuning the probe frequency, can occur both in‐phase and in anti‐phase. The capability of machine learning in this probing scheme is analyzed based on quantum synchronization. An artificial neural network is used to infer, from a probe observable, main dissipation features, such as the environment Ohmicity index. The efficiency of the algorithm in the presence of some noise in the dataset is also considered. It is shown that the performance in either classification and regression is significantly improved due to the in/anti‐phase synchronization transition. This opens the way to the characterization of environments with arbitrary spectral densities.
Fast, Asymptotically Efficient, Recursive Estimation in a Riemannian Manifold

Stochastic optimisation in Riemannian manifolds, especially the Riemannian stochastic gradient method, has attracted much recent attention. The present work applies stochastic optimisation to the task of recursive estimation of a statistical parameter which belongs to a Riemannian manifold. Roughly, this task amounts to stochastic minimisation of a statistical divergence function. The following problem is considered: how to obtain fast, asymptotically efficient, recursive estimates, using a Riemannian stochastic optimisation algorithm with decreasing step sizes. In solving this problem, several original results are introduced. First, without any convexity assumptions on the divergence function, we proved that, with an adequate choice of step sizes, the algorithm computes recursive estimates which achieve a fast non-asymptotic rate of convergence. Second, the asymptotic normality of these recursive estimates is proved by employing a novel linearisation technique. Third, it is proved that, when the Fisher information metric is used to guide the algorithm, these recursive estimates achieve an optimal asymptotic rate of convergence, in the sense that they become asymptotically efficient. These results, while relatively familiar in the Euclidean context, are here formulated and proved for the first time in the Riemannian context. In addition, they are illustrated with a numerical application to the recursive estimation of elliptically contoured distributions.
Spin selection in single-frequency two-photon excitation of alkali-metal atoms

We report on an experimental test of the spin selection rule for two-photon transitions in atoms. In particular, we demonstrate that the $5S_{1/2}\to 6S_{1/2}$ transition rate in a rubidium gas follows a quadratic dependency on the helicity parameter linked to the polarization of the excitation light. For excitation via a single Gaussian beam or two counterpropagating beams in a hot vapor cell, the transition rate scales as the squared degree of linear polarization. The rate reaches zero when the light is circularly polarized. In contrast, when the excitation is realized via an evanescent field near an optical nanofiber, the two-photon transition cannot be completely extinguished (theoretically, not lower than 13\% of the maximum rate, under our experimental conditions) by only varying the polarization of the fiber-guided light. Our findings lead to a deeper understanding of the physics of multiphoton processes in atoms in strongly nonparaxial light.
Enhancing Quantum Control by Improving Shaped-Pulse Generation

Most quantum processors requires pulse sequences for controlling quantum states. Here we present an alternative algorithm for computing an optimal pulse sequence so as to perform a specific task, being an implementation of a quantum gate or a quantum-state preparation. In our method, we reduce greatly the number of parameters to be fitted by using a limited number of functions as the modulations for the amplitude and phase of the radio-frequency pulses. Unlike other algorithms, ours uses some approximations to make it fast and scalable, and uses some efficient methods to obtain pulses that are robust to common types of experimental errors. We demonstrate the success of the proposed algorithm by performing several real experiments for 4-, 7-, and 12-quantum-bit systems using NMR. In addition, we show the efficiency of the algorithm, finding pulses for controlling with good fidelity the quantum states of spins in fictional square two-dimensional lattices containing 16, 36, and 100 qubits.
Bibliometric-Enhanced Information Retrieval 10th Anniversary Workshop Edition

The Bibliometric-enhanced Information Retrieval workshop series (BIR) was launched at ECIR in 2014 \cite{MayrEtAl2014} and it was held at ECIR each year since then. This year we organize the 10th iteration of BIR. The workshop series at ECIR and JCDL/SIGIR tackles issues related to academic search, at the crossroads between Information Retrieval, Natural Language Processing and Bibliometrics. In this overview paper, we summarize the past workshops, present the workshop topics for 2020 and reflect on some future steps for this workshop series.
Experimental nonlocal measurement of a product observable

Nonlocal measurement, or instantaneous measurement of nonlocal observables, is a considerably difficult task even for a simple form of product observable since relativistic causality prohibits interaction between spacelike separate subsystems. Following a recent proposal for effectively creating the von Neumann measurement Hamiltonian of nonlocal observables [Brodutch and Cohen, Phys. Rev. Lett. 116, 070404 (2016)], here we report a proof-of-principle demonstration of nonlocally measuring a product observable using linear optics without the violation of relativistic causality. Our scheme provides a feasible approach to perform nonlocal measurements via quantum erasure with linear optics.
Enhancing a Near-Term Quantum Accelerator's Instruction Set Architecture for Materials Science Applications

Quantum computers with tens to hundreds of noisy qubits are being developed today. To be useful for real-world applications, we believe that these near-term systems cannot simply be scaled-down non-error-corrected versions of future fault-tolerant large-scale quantum computers. These near-term systems require specific architecture and design attributes to realize their full potential. To efficiently execute an algorithm, the quantum coprocessor must be designed to scale with respect to qubit number and to maximize useful computation within the qubits' decoherence bounds. In this work, we employ an application-system-qubit co-design methodology to architect a near-term quantum coprocessor. To support algorithms from the real-world application area of simulating the quantum dynamics of a material system, we design a (parameterized) arbitrary single-qubit rotation instruction and a two-qubit entangling controlled-Z instruction. We introduce dynamic gate set and paging mechanisms to implement the instructions. To evaluate the functionality and performance of these two instructions, we implement a two-qubit version of an algorithm to study a disorder-induced metal-insulator transition and run 60 random instances of it, each of which realizes one disorder configuration and contains 40 two-qubit instructions (or gates) and 104 single-qubit instructions. We observe the expected quantum dynamics of the time-evolution of this system.
Separability for mixed states with operator Schmidt rank two

The operator Schmidt rank is the minimum number of terms required to express a state as a sum of elementary tensor factors. Here we provide a new proof of the fact that any bipartite mixed state with operator Schmidt rank two is separable, and can be written as a sum of two positive semidefinite matrices per site. Our proof uses results from the theory of free spectrahedra and operator systems, and illustrates the use of a connection between decompositions of quantum states and decompositions of nonnegative matrices. In the multipartite case, we prove that any Hermitian Matrix Product Density Operator (MPDO) of bond dimension two is separable, and can be written as a sum of at most four positive semidefinite matrices per site. This implies that these states can only contain classical correlations, and very few of them, as measured by the entanglement of purification. In contrast, MPDOs of bond dimension three can contain an unbounded amount of classical correlations.
Cross-Scale Internal Graph Neural Network for Image Super-Resolution

Non-local self-similarity in natural images has been well studied as an effective prior in image restoration. However, for single image super-resolution (SISR), most existing deep non-local methods (e.g., non-local neural networks) only exploit similar patches within the same scale of the low-resolution (LR) input image. Consequently, the restoration is limited to using the same-scale information while neglecting potential high-resolution (HR) cues from other scales. In this paper, we explore the cross-scale patch recurrence property of a natural image, i.e., similar patches tend to recur many times across different scales. This is achieved using a novel cross-scale internal graph neural network (IGNN). Specifically, we dynamically construct a cross-scale graph by searching k-nearest neighboring patches in the downsampled LR image for each query patch in the LR image. We then obtain the corresponding k HR neighboring patches in the LR image and aggregate them adaptively in accordance to the edge label of the constructed graph. In this way, the HR information can be passed from k HR neighboring patches to the LR query patch to help it recover more detailed textures. Besides, these internal image-specific LR/HR exemplars are also significant complements to the external information learned from the training dataset. Extensive experiments demonstrate the effectiveness of IGNN against the state-of-the-art SISR methods including existing non-local networks on standard benchmarks.
Constructing Human Motion Manifold With Sequential Networks

This paper presents a novel recurrent neural network-based method to construct a latent motion manifold that can represent a wide range of human motions in a long sequence. We introduce several new components to increase the spatial and temporal coverage in motion space while retaining the details of motion capture data. These include new regularization terms for the motion manifold, combination of two complementary decoders for predicting joint rotations and joint velocities, and the addition of the forward kinematics layer to consider both joint rotation and position errors. In addition, we propose a set of loss terms that improve the overall quality of the motion manifold from various aspects, such as the capability of reconstructing not only the motion but also the latent manifold vector, and the naturalness of the motion through adversarial loss. These components contribute to creating compact and versatile motion manifold that allows for creating new motions by performing random sampling and algebraic operations, such as interpolation and analogy, in the latent motion manifold.
A homotopy training algorithm for fully connected neural networks

In this paper, we present a homotopy training algorithm (HTA) to solve optimization problems arising from fully connected neural networks with complicated structures. The HTA dynamically builds the neural network starting from a simplified version and ending with the fully connected network via adding layers and nodes adaptively. Therefore, the corresponding optimization problem is easy to solve at the beginning and connects to the original model via a continuous path guided by the HTA, which provides a high probability of obtaining a global minimum. By gradually increasing the complexity of the model along the continuous path, the HTA provides a rather good solution to the original loss function. This is confirmed by various numerical results including VGG models on CIFAR-10. For example, on the VGG13 model with batch normalization, HTA reduces the error rate by 11.86% on the test dataset compared with the traditional method. Moreover, the HTA also allows us to find the optimal structure for a fully connected neural network by building the neutral network adaptively.
Towards Real-Time Head Pose Estimation: Exploring Parameter-Reduced Residual Networks on In-the-wild Datasets

Head poses are a key component of human bodily communication and thus a decisive element of human-computer interaction. Real-time head pose estimation is crucial in the context of human-robot interaction or driver assistance systems. The most promising approaches for head pose estimation are based on Convolutional Neural Networks (CNNs). However, CNN models are often too complex to achieve real-time performance. To face this challenge, we explore a popular subgroup of CNNs, the Residual Networks (ResNets) and modify them in order to reduce their number of parameters. The ResNets are modified for different image sizes including low-resolution images and combined with a varying number of layers. They are trained on in-the-wild datasets to ensure real-world applicability. As a result, we demonstrate that the performance of the ResNets can be maintained while reducing the number of parameters. The modified ResNets achieve state-of-the-art accuracy and provide fast inference for real-time applicability.
Augmenting the fine beam tube: From hybrid measurements to magnetic field visualization

Since the emergence of augmented reality (AR), it has been a constant subject of educational research, as it can improve conceptual understanding and generally promote learning. In addition, a motivational effect and improved interaction and collaboration through AR were observed. Recently, AR technologies have taken a major leap forward in development, such that head-mounted devices or smartglasses in particular are now finding their first applications in STEM education, especially in experiments. In line with these developments, we here present an AR experiment in electrodynamics for undergraduate laboratory courses in physics using real-time physical data from and virtual tools on mobile devices to both analyze and visualize physical phenomena.
Photon-antibunching in the fluorescence of statistical ensembles of emitters at an optical nanofiber-tip

This proposal investigates the photon-statistics of light emitted by a statistical ensemble of cold atoms excited by the near-field of an optical nanofiber tip. Dipole–dipole interactions of atoms at such short distance from each other suppress the simultaneous emission of more than one photon and lead to antibunching of photons. We consider a mean atom number on the order of one and deal with a Poissonian mixture of one and two atoms including dipole–dipole interactions and collective decay. Time tracks of the atomic states are simulated in quantum Monte Carlo simulations from which the g(2)-photon autocorrelation function is derived. The general results can be applied to any statistical ensemble of emitters that are interacting by dipole–dipole interactions.
Subspaces intersecting in at most a point

We improve on the lower bound of the maximum number of planes in $${\text {PG}}(8,q)\cong \mathbb {F}_q^{9}$$ pairwise intersecting in at most a point. In terms of constant dimension codes this leads to $$A_q(9,4;3)\ge q^{12}+ 2q^8+2q^7+q^6+2q^5+2q^4-2q^2-2q+1$$. This result is obtained via a more general construction strategy, which also yields other improvements.
Computing abelian varieties over finite fields isogenous to a power

In this paper we give a module-theoretic description of the isomorphism classes of abelian varieties $A$ isogenous to $B^r$, where the characteristic polynomial $g$ of Frobenius of $B$ is an ordinary square-free $q$-Weil polynomial, for a power $q$ of a prime $p$, or a square-free $p$-Weil polynomial with no real roots. Under some extra assumptions on the polynomial $g$ we give an explicit description of all the isomorphism classes which can be computed in terms of fractional ideals of an order in a finite product of number fields. In the ordinary case, we also give a module-theoretic description of the polarizations of $A$.