Reviewerly/exHarmony huggingface dataset:
Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays

Automatic medical image report generation has drawn growing attention due to its potential to alleviate radiologists' workload.Existing work on report generation often trains encoder-decoder networks to generate complete reports.However, such models are affected by data bias (e.g.label imbalance) and face common issues inherent in text generation models (e.g.repetition).In this work, we focus on reporting abnormal findings on radiology images; instead of training on complete radiology reports, we propose a method to identify abnormal findings from the reports in addition to grouping them with unsupervised clustering and minimal rules.We formulate the task as cross-modal retrieval and propose Conditional Visual-Semantic Embeddings to align images and fine-grained abnormal findings in a joint embedding space.We demonstrate that our method is able to retrieve abnormal findings and outperforms existing generation models on both clinical correctness and text generation metrics.
Fourier spectra for nonuniform phase-shifting algorithms based on principal component analysis

We develop an error-free nonuniform phase-stepping algorithm (nPSA) based on principal component analysis (PCA). PCA-based algorithms typically give phase-demodulation errors when applied to nonuniform phase-shifted interferograms. We present a straightforward way to correct those PCA phase-demodulation errors. We give mathematical formulas to fully analyze PCA-based nPSA (PCA-nPSA). These formulas give a) the PCA-nPSA frequency transfer function (FTF), b) its corrected Lissajous figure, c) the corrected PCA-nPSA formula, d) its harmonic robustness (RH), and e) its signal-to-noise-ratio (SNR). We show that the PCA-nPSA can be seen as a linear quadrature filter and, as consequence, one can find its FTF. Using the FTF, we show why plain PCA often fails to demodulate nonuniform phase-shifted fringes. Previous works on PCA-nPSA (without FTF), give specific numerical/experimental fringe data to "visually demonstrate" that their new nPSA works better than its competitors. This often leads to biased/favorable fringe pattern selections which "visually demonstrate" the superior performance of their new nPSA. This biasing is herein totally avoided because we provide figures-of-merit formulas based on linear systems and stochastic process theories. However, and for illustrative purposes only, we provide specific fringe data phase-demodulation, including comprehensive analysis and comparisons.
A Paul trap with sectored ring electrodes for experiments with two-dimensional ion crystals

We have developed a trapped ion system for producing two-dimensional (2D) ion crystals for applications in scalable quantum computing, quantum simulations, and 2D crystal phase transition and defect studies. The trap is a modification of a Paul trap with its ring electrode flattened and split into eight identical sectors and its two endcap electrodes shaped as truncated hollow cones for laser and imaging optics access. All ten trap electrodes can be independently DC-biased to create various aspect ratio trap geometries. We trap and Doppler cool 2D crystals of up to 30 Ba+ ions and demonstrate the tunability of the trapping potential both in the plane of the crystal and in the transverse direction.
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

One major factor impeding more widespread adoption of deep neural networks (DNNs) is their lack of robustness, which is essential for safety-critical applications such as autonomous driving. This has motivated much recent work on adversarial attacks for DNNs, which mostly focus on pixel-level perturbations void of semantic meaning. In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks. To do this, we re-frame the adversarial attack problem as learning a distribution of parameters that always fools the agent. In the semantic case, our proposed adversary (denoted as BBGAN) is trained to sample parameters that describe the environment with which the black-box agent interacts, such that the agent performs its dedicated task poorly in this environment. We apply BBGAN on three different tasks, primarily targeting aspects of autonomous navigation: object detection, self-driving, and autonomous UAV racing. On these tasks, BBGAN can generate failure cases that consistently fool a trained agent.
Geometric Estimation of Multivariate Dependency

This paper proposes a geometric estimator of dependency between a pair of multivariate random variables. The proposed estimator of dependency is based on a randomly permuted geometric graph (the minimal spanning tree) over the two multivariate samples. This estimator converges to a quantity that we call the geometric mutual information (GMI), which is equivalent to the Henze–Penrose divergence. between the joint distribution of the multivariate samples and the product of the marginals. The GMI has many of the same properties as standard MI but can be estimated from empirical data without density estimation; making it scalable to large datasets. The proposed empirical estimator of GMI is simple to implement, involving the construction of an minimal spanning tree (MST) spanning over both the original data and a randomly permuted version of this data. We establish asymptotic convergence of the estimator and convergence rates of the bias and variance for smooth multivariate density functions belonging to a Hölder class. We demonstrate the advantages of our proposed geometric dependency estimator in a series of experiments.
Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner

We report on experimental implementation of a machine-learned quantum gate driven by a classical control. The gate learns optimal phase-covariant cloning in a reinforcement learning scenario having fidelity of the clones as reward. In our experiment, the gate learns to achieve nearly optimal cloning fidelity allowed for this particular class of states. This makes it a proof of present-day feasibility and practical applicability of the hybrid machine learning approach combining quantum information processing with classical control. Moreover, our experiment can be directly generalized to larger interferometers where the computational cost of classical computer is much lower than the cost of boson sampling.
What time is it? Temporal Analysis of Novels

Recognizing the flow of time in a story is a crucial aspect of understanding it.Prior work related to time has primarily focused on identifying temporal expressions or relative sequencing of events, but here we propose computationally annotating each line of a book with wall clock times, even in the absence of explicit time-descriptive phrases.To do so, we construct a data set of hourly time phrases from 52,183 fictional books.We then construct a time-of-day classification model that achieves an average error of 2.27 hours.Furthermore, we show that by analyzing a book in whole using dynamic programming of breakpoints, we can roughly partition a book into segments that each correspond to a particular time-of-day.This approach improves upon baselines by over two hour.Finally, we apply our model to a corpus of literature categorized by different periods in history, to show interesting trends of hourly activity throughout the past.Among several observations we find that the fraction of events taking place past 10 P.M jumps past 1880 -coincident with the advent of the electric light bulb and city lights.
A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization

Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing.In this paper, we focus on Chinese medical procedure entity normalization.However, nonstandard Chinese expressions and combined procedures present challenges in our problem.The existing strategies relying on the discriminative model are poorly to cope with normalizing combined procedure mentions.We propose a sequence generative framework to directly generate all the corresponding medical procedure entities.we adopt two strategies: category-based constraint decoding and category-based model refining to avoid unrealistic results.The method is capable of linking entities when a mention contains multiple procedure concepts and our comprehensive experiments demonstrate that the proposed model can achieve remarkable improvements over existing baselines, particularly significant in the case of multi-implication Chinese medical procedures.
What Do We Really Need? A Systematic Literature Review of the Requirements for Blockchain-Based E-government Services

Information Systems research acknowledges the importance of identifying requirements to ensure the artifact’s relevance. However, many research articles addressing blockchain technology for e-government capture the requirements that need to be fulfilled only implicitly by defining system objectives or evaluation criteria. Furthermore, focusing on specific use-cases encompasses the risk of overlooking those requirements, which are not as obvious but equally important. This procedure causes uncertainty regarding the requirements a blockchain-based e-government service needs to fulfill. Therefore, we conducted a systematic literature review on blockchain-based government-to-citizen (G2C) e-government services. On this basis, we categorized the requirements as we find that they address either the data of the system, the user, or the system itself. Our categorization provides a structured overview supporting researchers in conducting research on blockchain technology in the public sector and giving practitioners input to develop, test, and evaluate new blockchain-based G2C e-government services.
How to Compute Using Quantum Walks

Quantum walks are widely and successfully used to model diverse physical processes.This leads to computation of the models, to explore their properties.Quantum walks have also been shown to be universal for quantum computing.This is a more subtle result than is often appreciated, since it applies to computations run on qubit-based quantum computers in the single walker case, and physical quantum walks in the multi-walker case (quantum cellular automata).Nonetheless, quantum walks are powerful tools for quantum computing when correctly applied.In this paper, I explain the relationship between quantum walks as models and quantum walks as computational tools, and give some examples of their application in both contexts.
Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays

This paper proposes to adapt self-attention to discourse level for modeling discourse elements in argumentative student essays.Specifically, we focus on two issues.First, we propose structural sentence positional encodings to explicitly represent sentence positions.Second, we propose to use inter-sentence attentions to capture sentence interactions and enhance sentence representation.We conduct experiments on two datasets: a Chinese dataset and an English dataset.We find that (i) sentence positional encodings can lead to a large improvement for identifying discourse elements; (ii) a structural relative positional encoding of sentences shows to be most effective; (iii) inter-sentence attention vectors are useful as a kind of sentence representation for identifying discourse elements.
Efficient Entanglement Measure for Graph States

In this paper, we study the multipartite entanglement properties of graph states up to seven qubits. Our analysis shows that the generalized concurrence measure (GCM) is more efficient than geometric entanglement measure (GEM) for measuring entanglement quantity in the multi-qubit graph states.
Differentially Private Language Models Benefit from Public Pre-training

Language modeling is a keystone task in natural language processing.When training a language model on sensitive information, differential privacy (DP) allows us to quantify the degree to which our private data is protected.However, training algorithms which enforce differential privacy often lead to degradation in model quality.We study the feasibility of learning a language model which is simultaneously high-quality and privacy preserving by tuning a public base model on a private corpus.We find that DP fine-tuning boosts the performance of language models in the private domain, making the training of such models possible.
Robust and efficient transport of two-qubit entanglement via disordered spin chains

We investigate how robust is the modified XX spin-1/2 chain of Vieira and Rigolin (Phys Lett A 382:2586, 2018) in transmitting entanglement when several types of disorder and noise are present. First, we consider how deviations about the optimal settings that lead to almost perfect transmission of a maximally entangled two-qubit state affect the entanglement reaching the other side of the chain. Those deviations are modeled by static, dynamic, and fluctuating disorder. We then study how spurious or undesired interactions and external magnetic fields diminish the entanglement transmitted through the chain. For chains of the order of hundreds of qubits, we show for all types of disorder and noise here studied that the system is not appreciably affected when we have weak disorder (deviations of less than 1% about the optimal settings) and that for moderate disorder it still beats the standard and ordered XX model when deployed to accomplish the same task.
Local Alignment Deep Network for Infrared-Visible Cross-Modal Person Reidentification in 6G-Enabled Internet of Things

In this article, we propose a novel deep framework termed local alignment deep network (LADN) for infrared-visible cross-modal person reidentification (IVCM ReID) in 6G-enabled IoT, which could meet the demands of all-day and real-time surveillance. The proposed LADN is designed as a two-stream structure, and it learns shallow interested feature maps and common subspace feature maps to reduce the gap between IR and RGB images. To overcome the challenge of pose and viewpoint variations of pedestrians, we learn the local features in the deep layers. We also propose the local alignment triplet loss (LAT) to align local features, which could capture the consistent local information via comparing noncorresponding local features in a certain range. Furthermore, we learn the global features to provide the global field of vision for the representation. The proposed LADN is optimized in an end-to-end way by combining different cross-modality losses. We evaluate the proposed method on two standard benchmark data sets, i.e., SYSU-MM01 and RegDB, and the results demonstrate the effectiveness of LADN.
Exploring the Boundaries of Low-Resource BERT Distillation

In recent years, large pre-trained models have demonstrated state-of-the-art performance in many NLP tasks.However, the deployment of these models on devices with limited resources is challenging due to the models' large computational consumption and memory requirements.Moreover, the need for a considerable amount of labeled training data also hinders real-world deployment scenarios.Model distillation has shown promising results for reducing model size, computational load and data efficiency.In this paper we test the boundaries of BERT model distillation in terms of model compression, inference efficiency and data scarcity.We show that classification tasks that require the capturing of general lexical semantics can be successfully distilled by very simple and efficient models and require relatively small amount of labeled training data.We also show that the distillation of large pretrained models is more effective in real-life scenarios where limited amounts of labeled training are available.
Cubic bent functions outside the completed Maiorana-McFarland class

Abstract In this paper we prove that in opposite to the cases of 6 and 8 variables, the Maiorana-McFarland construction does not describe the whole class of cubic bent functions in n variables for all $$n\ge 10$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>10</mml:mn> </mml:mrow> </mml:math> . Moreover, we show that for almost all values of n , these functions can simultaneously be homogeneous and have no affine derivatives.
A polar decomposition for quantum channels (with applications to bounding error propagation in quantum circuits)

Inevitably, assessing the overall performance of a quantum computer must rely on characterizing some of its elementary constituents and, from this information, formulate a broader statement concerning more complex constructions thereof. However, given the vastitude of possible quantum errors as well as their coherent nature, accurately inferring the quality of composite operations is generally difficult. To navigate through this jumble, we introduce a non-physical simplification of quantum maps that we refer to as the leading Kraus (LK) approximation. The uncluttered parameterization of LK approximated maps naturally suggests the introduction of a unitary-decoherent polar factorization for quantum channels in any dimension. We then leverage this structural dichotomy to bound the evolution -- as circuits grow in depth -- of two of the most experimentally relevant figures of merit, namely the average process fidelity and the unitarity. We demonstrate that the leeway in the behavior of the process fidelity is essentially taken into account by physical unitary operations.
Personalizing Activity Recognition With a Clustering Based Semi-Population Approach

Smartphone-based approaches for Human Activity Recognition have become prevalent in recent years.Despite the amount of research undertaken in the field, issues such as cross-subject variability are still posing an obstacle to the deployment of solutions in large scale, free-living settings.Personalized methods (i.e.aiming to adapt a generic classifier to a specific target user) attempt to solve this problem.The lack of labeled data for training purposes, however, represents a major barrier.This is especially the case when taking into consideration that personalization generally requires labeled data to be user-specific.This paper presents a novel personalization method combining a semi-population based approach with user adaptation.Personalization is achieved through the following.Firstly, the proposed method identifies a subset of users from the available population as best candidates for initializing the classifier to the target user.Subsequently, a semi-population Neural Network classifier is trained using data from this subset of users.The classifier's network weights are then updated using a small amount of labeled data from the target user subsequently implementing personalization.This approach was validated on a large publicly available dataset collected in a free-living scenario.The personalized approach using the proposed method has shown to improve the overall F-score to 74.4% compared to 70.9% when using a generic non-personalized approach.Results obtained, with statistical significance being confirmed on a set of 57 users, indicate that model initialization using the semi-population approach can reduce the amount of labeled data required for personalization.As such, the proposed method for model initialization could facilitate the real-world deployment of systems implementing personalization by reducing the amount of data needed for personalization.
Smoothing for signals with discontinuities using higher order Mumford–Shah models

Minimizing the Mumford–Shah functional is frequently used for smoothing signals or time series with discontinuities. A significant limitation of the standard Mumford–Shah model is that linear trends—and in general polynomial trends—in the data are not well preserved. This can be improved by building on splines of higher order which leads to higher order Mumford–Shah models. In this work, we study these models in the univariate situation: we discuss important differences to the first order Mumford–Shah model, and we obtain uniqueness results for their solutions. As a main contribution, we derive fast minimization algorithms for Mumford–Shah models of arbitrary orders. We show that the worst case complexity of all proposed schemes is quadratic in the length of the signal. Remarkably, they thus achieve the worst case complexity of the fastest solver for the piecewise constant Mumford–Shah model (which is the simplest model of the class). Further, we obtain stability results for the proposed algorithms. We complement these results with a numerical study. Our reference implementation processes signals with more than 10,000 elements in less than 1 s.
Within-Between Lexical Relation Classification

We propose the novel Within-Between Relation model for recognizing lexical-semantic relations between words.Our model integrates relational and distributional signals, forming an effective sub-space representation for each relation.We show that the proposed model is competitive and outperforms other baselines, across various benchmarks.
Cross-Thought for Sentence Encoder Pre-training

In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering.Instead of using the original signals of full sentences, we train a Transformer-based sequence encoder over a large set of short sequences, which allows the model to automatically select the most useful information for predicting masked words.Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders trained with continuous sentence signals as well as traditional masked language modeling baselines.Our proposed approach also achieves new state of the art on HotpotQA (full-wiki setting) by improving intermediate information retrieval performance. 1
Personalised novel and explainable matrix factorisation

Recommendation systems personalise suggestions to individuals to help them in their decision making and exploration tasks. In the ideal case, these recommendations, besides of being accurate, should also be novel and explainable. However, up to now most platforms fail to provide both, novel recommendations that advance users' exploration along with explanations to make their reasoning more transparent to them. For instance, a well-known recommendation algorithm, such as matrix factorisation (MF), optimises only the accuracy criterion, while disregarding other quality criteria such as the explainability or the novelty, of recommended items. In this paper, to the best of our knowledge, we propose a new model, denoted as NEMF, that allows to trade-off the MF performance with respect to the criteria of novelty and explainability, while only minimally compromising on accuracy. In addition, we recommend a new explainability metric based on nDCG, which distinguishes a more explainable item from a less explainable item. An initial user study indicates how users perceive the different attributes of these "user" style explanations and our extensive experimental results demonstrate that we attain high accuracy by recommending also novel and explainable items.