Reviewerly/exHarmony huggingface dataset:
Multicopy Adaptive Local Discrimination: Strongest Possible Two-Qubit Nonlocal Bases

Ensembles of composite quantum states can exhibit nonlocal behavior in the sense that their optimal discrimination may require global operations. Such an ensemble containing $N$ pairwise orthogonal pure states, however, can always be perfectly distinguished under an adaptive local scheme if ($N\ensuremath{-}1$) copies of the state are available. In this Letter, we provide examples of orthonormal bases in two-qubit Hilbert space whose adaptive discrimination require three copies of the state. For this composite system, we analyze multicopy adaptive local distinguishability of orthogonal ensembles in full generality which, in turn, assigns varying nonlocal strength to different such ensembles. We also come up with ensembles whose discrimination under an adaptive separable scheme require less numbers of copies than adaptive local schemes. Our construction finds important application in multipartite secret sharing tasks and indicates toward an intriguing superadditivity phenomenon for locally accessible information.
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks

Word sense disambiguation is a well-known source of translation errors in NMT.We posit that some of the incorrect disambiguation choices are due to models' over-reliance on dataset artifacts found in training data, specifically superficial word co-occurrences, rather than a deeper understanding of the source text.We introduce a method for the prediction of disambiguation errors based on statistical data properties, demonstrating its effectiveness across several domains and model types.Moreover, we develop a simple adversarial attack strategy that minimally perturbs sentences in order to elicit disambiguation errors to further probe the robustness of translation models.Our findings indicate that disambiguation robustness varies substantially between domains and that different models trained on the same data are vulnerable to different attacks. 1
Effect of initial system–environment correlations with spin environments

Understanding the dynamics of open quantum systems is a highly important task for the implementation of emerging quantum technologies. To make the problem tractable theoretically, it is common to neglect initial system-environment correlations. However, this assumption is questionable in situations where the system is interacting strongly with the environment. In particular, the system state preparation can then influence the dynamics of the system via the system-environment correlations. To gain insight into the effect of these correlations, we solve an exactly solvable model of a quantum spin interacting with a spin environment both with and without initial correlations for arbitrary system-environment coupling strengths. We show that the effect of the system state preparation may or may not be significant in the strong system-environment coupling regime at low temperatures. We also study the dynamics of the entanglement between two spins interacting with a common spin environment with and without initial system-environment correlations to demonstrate that the correlations can play a significant role in the dynamics of two-qubit systems as well.
The Pitfalls of Simplicity Bias in Neural Networks

Several works have proposed Simplicity Bias (SB)---the tendency of standard training procedures such as Stochastic Gradient Descent (SGD) to find simple models---to justify why neural networks generalize well [Arpit et al. 2017, Nakkiran et al. 2019, Soudry et al. 2018]. However, the precise notion of simplicity remains vague. Furthermore, previous settings that use SB to theoretically justify why neural networks generalize well do not simultaneously capture the non-robustness of neural networks---a widely observed phenomenon in practice [Goodfellow et al. 2014, Jo and Bengio 2017]. We attempt to reconcile SB and the superior standard generalization of neural networks with the non-robustness observed in practice by designing datasets that (a) incorporate a precise notion of simplicity, (b) comprise multiple predictive features with varying levels of simplicity, and (c) capture the non-robustness of neural networks trained on real data. Through theory and empirics on these datasets, we make four observations: (i) SB of SGD and variants can be extreme: neural networks can exclusively rely on the simplest feature and remain invariant to all predictive complex features. (ii) The extreme aspect of SB could explain why seemingly benign distribution shifts and small adversarial perturbations significantly degrade model performance. (iii) Contrary to conventional wisdom, SB can also hurt generalization on the same data distribution, as SB persists even when the simplest feature has less predictive power than the more complex features. (iv) Common approaches to improve generalization and robustness---ensembles and adversarial training---can fail in mitigating SB and its pitfalls. Given the role of SB in training neural networks, we hope that the proposed datasets and methods serve as an effective testbed to evaluate novel algorithmic approaches aimed at avoiding the pitfalls of SB.
Multiparameter estimation, lower bound on quantum Fisher information, and non-Markovianity witnesses of noisy two-qubit systems

By using the quantum Fisher information (QFI), we address the process of single-parameter estimation in the presence of bosonic as well as fermionic environments and protection of information against the noise. In particular, the quantum interferometric power (IP) of the evolved state of the system is uncovered as an important lower bound for the QFIs of initially encoded parameters. Moreover, we unveil new witnesses of non-Markovianity that can be used to detect efficiently the memory effects and backflow of information from the environment to the system. On the other hand, we also investigate the multiparameter estimation of initial parameters encoded into the quantum state of a two-qubit system and obtain analytical formula of the corresponding QFI matrix. In particular, the corresponding quantum Cramer–Rao bounds in both single and multiparameter estimations are analyzed. In addition, we illustrate that the quantum coherence and purity of the evolved state of the probes are two key elements in realizing optimum multiparameter estimation.
Local quantum uncertainty for multipartite quantum systems

Local quantum uncertainty captures purely quantum correlations excluding their classical counterpart. This measure is quantum discord type, however with the advantage that there is no need to carry out the complicated optimization procedure over measurements. This measure is initially defined for bipartite quantum systems and a closed formula exists only for 2⊗d systems. We extend the idea of local quantum uncertainty to multi-qubit systems and provide the similar closed formula to compute this measure. We explicitly calculate local quantum uncertainty for various quantum states of three and four qubits, like GHZ state, W state, Dicke state, Cluster state, Singlet state, and Chi state all mixed with white noise. We compute this measure for some other well known three qubit quantum states as well. We show that for all such symmetric states, it is sufficient to apply measurements on any single qubit to compute this measure, whereas in general one has to apply measurements on all parties as local quantum uncertainties for each bipartition can be different for an arbitrary quantum state.
Investigating Annotator Bias with a Graph-Based Approach

A challenge that many online platforms face is hate speech or any other form of online abuse.To cope with this, hate speech detection systems are developed based on machine learning to reduce manual work for monitoring these platforms.Unfortunately, machine learning is vulnerable to unintended bias in training data, which could have severe consequences, such as a decrease in classification performance or unfair behavior (e.g., discriminating minorities).In the scope of this study, we want to investigate annotator bias -a form of bias that annotators cause due to different knowledge in regards to the task and their subjective perception.Our goal is to identify annotation bias based on similarities in the annotation behavior from annotators.To do so, we build a graph based on the annotations from the different annotators, apply a community detection algorithm to group the annotators, and train for each group classifiers whose performances we compare.By doing so, we are able to identify annotator bias within a data set.The proposed method and collected insights can contribute to developing fairer and more reliable hate speech classification models.
Directional emission of down-converted photons from a dielectric nanoresonator

Creation of entangled photon pairs is one of the key topics in contemporary quantum optics. In the present article, we theoretically describe the generation of photon pairs in the process of spontaneous parametric down-conversion of light by a resonant spherical nanoparticle made of a dielectric material with bulk ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\ensuremath{\chi}}}^{(2)}$ nonlinearity. We consider the nanoparticle size that satisfies the condition of excitation of resonant eigenmodes described by Mie theory. We demonstrate that a highly directional photon pair generation can be observed utilizing the nonlinear Kerker-type effect, and that this regime provides useful polarization correlations between the emitted photons.
Critic Regularized Regression

Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction. It addresses challenges with regard to the cost of data collection and safety, both of which are particularly pertinent to real-world applications of RL. Unfortunately, most off-policy algorithms perform poorly when learning from a fixed dataset. In this paper, we propose a novel offline RL algorithm to learn policies from data using a form of critic-regularized regression (CRR). We find that CRR performs surprisingly well and scales to tasks with high-dimensional state and action spaces -- outperforming several state-of-the-art offline RL algorithms by a significant margin on a wide range of benchmark tasks.
Multitask Learning of Negation and Speculation using Transformers

Detecting negation and speculation in language has been a task of considerable interest to the biomedical community, as it is a key component of Information Extraction systems from Biomedical documents.Prior work has individually addressed Negation Detection and Speculation Detection, and both have been addressed in the same way, using a 2 stage pipelined approach: Cue Detection followed by Scope Resolution.In this paper, we propose Multitask learning approaches over 2 sets of tasks: Negation Cue Detection & Speculation Cue Detection, and Negation Scope Resolution & Speculation Scope Resolution.We utilise transformer-based architectures like BERT, XLNet and RoBERTa as our core model architecture, and finetune these using the Multitask learning approaches.We show that this Multitask Learning approach outperforms the single task learning approach, and report new state-of-the-art results on Negation and Speculation Scope Resolution on the BioScope Corpus and the SFU Review Corpus.
An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Computing the distinct features from input data, before the classification, is a part of complexity to the methods of automatic modulation classification (AMC) which deals with modulation classification and is a pattern recognition problem. However, the algorithms that focus on multilevel quadrature amplitude modulation (M-QAM) which underneath different channel scenarios is well detailed. A search of the literature revealed that few studies were performed on the classification of high-order M-QAM modulation schemes such as 128-QAM, 256-QAM, 512-QAM, and 1024-QAM. This work focuses on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting higher order cumulant’s (HOC) features from input data received raw. The HOC signals were extracted under the additive white Gaussian noise (AWGN) channel with four effective parameters which were defined to distinguish the types of modulation from the set: 4-QAM∼1024-QAM. This approach makes the classifier more intelligent and improves the success rate of classification. The simulation results manifest that a very good classification rate is achieved at a low SNR of 5 dB, which was performed under conditions of statistical noisy channel models. This shows the potential of the logarithmic classifier model for the application of M-QAM signal classification. furthermore, most results were promising and showed that the logarithmic classifier works well under both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero). It can be considered as an integrated automatic modulation classification (AMC) system in order to identify the higher order of M-QAM signals that has a unique logarithmic classifier to represent higher versatility. Hence, it has a superior performance in all previous works in automatic modulation identification systems.
Lackadaisical quantum walk for spatial search

Lackadaisical quantum walk(LQW) has been an efficient technique in searching a target state from a database which is distributed on a two-dimensional lattice. We numerically study the quantum search algorithm based on the lackadaisical quantum walk on one- and two-dimensions. It is observed that specific values of the self-loop weight at each vertex of the graph is responsible for such speedup of the algorithm. Searching for a target state on one-dimensional lattice with periodic boundary conditions is possible using lackadaisical quantum walk, which can find a target state with $\mathcal{O}(1)$ success probability after $\mathcal{O} \left( N \right)$ time steps. In two-dimensions, our numerical simulation upto $M=6$ suggests that lackadaisical quantum walk can search one of the $M$ target states in $\mathcal{O}\left(\sqrt{\frac{N}{M}\log \frac{N}{M}}\right)$ time steps.
Adaptive Quantum Simulated Annealing for Bayesian Inference and Estimating Partition Functions

Markov chain Monte Carlo algorithms have important applications in counting problems and in machine learning problems, settings that involve estimating quantities that are difficult to compute exactly.How much can quantum computers speed up classical Markov chain algorithms?In this work we consider the problem of speeding up simulated annealing algorithms, where the stationary distributions of the Markov chains are Gibbs distributions at temperatures specified according to an annealing schedule.We construct a quantum algorithm that both adaptively constructs an annealing schedule and quantum samples at each temperature.Our adaptive annealing schedule roughly matches the length of the best classical adaptive annealing schedules and improves on nonadaptive temperature schedules by roughly a quadratic factor.Our dependence on the Markov chain gap matches other quantum algorithms and is quadratically better than what classical Markov chains achieve.Our algorithm is the first to combine both of these quadratic improvements.Like other quantum walk algorithms, it also improves on classical algorithms by producing "qsamples" instead of classical samples.This means preparing quantum states whose amplitudes are the square roots of the target probability distribution.In constructing the annealing schedule we make use of amplitude estimation, and we introduce a method for making amplitude estimation nondestructive at almost no additional cost, a result that may have independent interest.Finally we demonstrate how this quantum simulated annealing algorithm can be applied to the problems of estimating partition functions and Bayesian inference.
Simultaneous Machine Translation with Visual Context

Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible.The translation thus has to start with an incomplete source text, which is read progressively, creating the need for anticipation.In this paper, we seek to understand whether the addition of visual information can compensate for the missing source context.To this end, we analyse the impact of different multimodal approaches and visual features on state-of-the-art SiMT frameworks.Our results show that visual context is helpful and that visually-grounded models based on explicit object region information are much better than commonly used global features, reaching up to 3 BLEU points improvement under low latency scenarios.Our qualitative analysis illustrates cases where only the multimodal systems are able to translate correctly from English into gender-marked languages, as well as deal with differences in word order, such as adjective-noun placement between English and French.
Simple implementation of high fidelity controlled- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>i</mml:mi></mml:math> swap gates and quantum circuit exponentiation of non-Hermitian gates

This paper presents a implementation for a controlled-iSWAP gate working by applying a single lux pulse. The gate can be implemented using superconducting circuits or other systems which natively features an XY-interaction. The paper also discuss a quantum circuit for probabilistic exponentiating the iSWAP gate and other non-Hermitian gates.
CHARM: Inferring Personal Attributes from Conversations

Personal knowledge about users' professions, hobbies, favorite food, and travel preferences, among others, is a valuable asset for individualized AI, such as recommenders or chatbots.Conversations in social media, such as Reddit, are a rich source of data for inferring personal facts.Prior work developed supervised methods to extract this knowledge, but these approaches can not generalize beyond attribute values with ample labeled training samples.This paper overcomes this limitation by devising CHARM: a zero-shot learning method that creatively leverages keyword extraction and document retrieval in order to predict attribute values that were never seen during training.Experiments with large datasets from Reddit show the viability of CHARM for open-ended attributes, such as professions and hobbies.
AttnIO: Knowledge Graph Exploration with In-and-Out Attention Flow for Knowledge-Grounded Dialogue

Retrieving the proper knowledge relevant to conversational context is an important challenge in dialogue systems, to engage users with more informative response.Several recent works propose to formulate this knowledge selection problem as a path traversal over an external knowledge graph (KG), but show only a limited utilization of KG structure, leaving rooms of improvement in performance.To this effect, we present AttnIO, a new dialog-conditioned path traversal model that makes a full use of rich structural information in KG based on two directions of attention flows.Through the attention flows, At-tnIO is not only capable of exploring a broad range of multi-hop knowledge paths, but also learns to flexibly adjust the varying range of plausible nodes and edges to attend depending on the dialog context.Empirical evaluations present a marked performance improvement of AttnIO compared to all baselines in OpenDi-alKG dataset.Also, we find that our model can be trained to generate an adequate knowledge path even when the paths are not available and only the destination nodes are given as label, making it more applicable to real-world dialogue systems.
Effectively pretraining a speech translation decoder with Machine Translation data

Directly translating from speech to text using an end-to-end approach is still challenging for many language pairs due to insufficient data.Although pretraining the encoder parameters using the Automatic Speech Recognition (ASR) task improves the results in low resource settings, attempting to use pretrained parameters from the Neural Machine Translation (NMT) task has been largely unsuccessful in previous works.In this paper, we will show that by using an adversarial regularizer, we can bring the encoder representations of the ASR and NMT tasks closer even though they are in different modalities, and how this helps us effectively use a pretrained NMT decoder for speech translation.
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples

We study the behavior of several black-box search algorithms used for generating adversarial examples for natural language processing (NLP) tasks.We perform a fine-grained analysis of three elements relevant to search: search algorithm, search space, and search budget.When new search algorithms are proposed in past work, the attack search space is often modified alongside the search algorithm.Without ablation studies benchmarking the search algorithm change with the search space held constant, one cannot tell if an increase in attack success rate is a result of an improved search algorithm or a less restrictive search space.Additionally, many previous studies fail to properly consider the search algorithms' run-time cost, which is essential for downstream tasks like adversarial training.Our experiments provide a reproducible benchmark of search algorithms across a variety of search spaces and query budgets to guide future research in adversarial NLP.Based on our experiments, we recommend greedy attacks with word importance ranking when under a time constraint or attacking long inputs, and either beam search or particle swarm optimization otherwise.⇤ * Equal contribution.Code implementation shared via https://github.com/QData/TextAttack 1 In this work, we use "adversarial example generation methods" and "adversarial attacks" interchangeably.
WER we are and WER we think we are

Natural language processing of conversational speech requires the availability of high-quality transcripts.In this paper, we express our skepticism towards the recent reports of very low Word Error Rates (WERs) achieved by modern Automatic Speech Recognition (ASR) systems on benchmark datasets.We outline several problems with popular benchmarks and compare three state-of-the-art commercial ASR systems on an internal dataset of real-life spontaneous human conversations and HUB'05 public benchmark.We show that WERs are significantly higher than the best reported results.We formulate a set of guidelines which may aid in the creation of real-life, multi-domain datasets with high quality annotations for training and testing of robust ASR systems.