Reviewerly/exHarmony huggingface dataset:
Classifier-agnostic saliency map extraction

Currently available methods for extracting saliency maps identify parts of the input which are the most important to a specific fixed classifier. We show that this strong dependence on a given classifier hinders their performance. To address this problem, we propose classifier-agnostic saliency map extraction, which finds all parts of the image that any classifier could use, not just one given in advance. We observe that the proposed approach extracts higher quality saliency maps than prior work while being conceptually simple and easy to implement. The method sets the new state of the art result for localization task on the ImageNet data, outperforming all existing weakly-supervised localization techniques, despite not using the ground truth labels at the inference time. The code reproducing the results is available at https://github.com/kondiz/casme.
High-fidelity quantum cloning of two nonorthogonal quantum states via weak measurements

We propose a scheme to enhance the fidelity of a symmetric quantum cloning machine via weak measurements. By adjusting the intensity of the weak measurement parameter $p$, we obtain copies of initial states with different values of fidelity. Choosing a proper value of $p$, we can obtain perfect copies. In this paper, we focus on $1\ensuremath{-}2$ quantum cloning for two nonorthogonal states. Sets containing more than two linear independent states are also briefly discussed. Due to the probabilistic nature of weak measurements, we obtain high-fidelity copies probabilistically. If the weak measurement is a success, we do subsequent operations to obtain high-fidelity copies; otherwise, the cloning process fails and we quit. From this perspective, the scheme we propose is economical for saving quantum resources and time. Our scheme may be very useful in quantum information processing.
Topic discovery in massive text corpora based on Min-Hashing

Topics have proved to be a valuable source of information for exploring, discovering, searching and representing the contents of text corpora. They have also been useful for different natural language processing tasks such as text classification, text summarization and machine translation. Most existing topic discovery approaches require the number of topics to be provided beforehand. However, an appropriate number of topics for a given corpus depends on its characteristics and is often difficult to estimate. In addition, in order to handle massive amounts of text documents, the vocabulary must be reduced considerably and large computer clusters and/or GPUs are typically required. This paper describes Sampled Min-Hashing (SMH), a scalable approach to topic discovery which does not require the number of topics to be specified in advance and can handle massive text corpora and large vocabularies using modest computer resources. The basic idea behind SMH is to generate multiple random partitions of the corpus vocabulary to find sets of highly co-occurring words, which are then clustered to produce the final topics. An extensive qualitative and quantitative evaluation on the 20 Newsgroups, Reuters, Spanish Wikipedia and English Wikipedia corpora shows that SMH is able to consistently discover meaningful and coherent topics at scale. Remarkably, the time required by SMH grows linearly with the size of the corpus and the number of words in the vocabulary; a non-parallel implementation of SMH was able to discover topics from the whole English version of Wikipedia (5M documents approximately) with a vocabulary of 1M words in less than 7 h. Our findings provide further evidence of the relevance and generality of beyond-pairwise co-occurrences for pattern discovery on large-scale discrete data, which opens the door for other applications and several interesting research directions.
Adiabatic quantum simulations with driven superconducting qubits

We propose a quantum simulator based on driven superconducting qubits where the interactions are generated parametrically by a polychromatic magnetic flux modulation of a tunable bus element. Using a time-dependent Schrieffer-Wolff transformation, we analytically derive a multi-qubit Hamiltonian which features independently tunable $XX$ and $YY$-type interactions as well as local bias fields over a large parameter range. We demonstrate the adiabatic simulation of the ground state of a hydrogen molecule using two superconducting qubits and one tunable bus element. The time required to reach chemical accuracy lies in the few microsecond range and therefore could be implemented on currently available superconducting circuits. Further applications of this technique may also be found in the simulation of interacting spin systems.
An Interactive Framework for Visualization of Weather Forecast Ensembles

Numerical Weather Prediction (NWP) ensembles are commonly used to assess the uncertainty and confidence in weather forecasts. Spaghetti plots are conventional tools for meteorologists to directly examine the uncertainty exhibited by ensembles, where they simultaneously visualize isocontours of all ensemble members. To avoid visual clutter in practical usages, one needs to select a small number of informative isovalues for visual analysis. Moreover, due to the complex topology and variation of ensemble isocontours, it is often a challenging task to interpret the spaghetti plot for even a single isovalue in large ensembles. In this paper, we propose an interactive framework for uncertainty visualization of weather forecast ensembles that significantly improves and expands the utility of spaghetti plots in ensemble analysis. Complementary to state-of-the-art methods, our approach provides a complete framework for visual exploration of ensemble isocontours, including isovalue selection, interactive isocontour variability exploration, and interactive sub-region selection and re-analysis. Our framework is built upon the high-density clustering paradigm, where the mode structure of the density function is represented as a hierarchy of nested subsets of the data. We generalize the high-density clustering for isocontours and propose a bandwidth selection method for estimating the density function of ensemble isocontours. We present novel visualizations based on high-density clustering results, called the mode plot and the simplified spaghetti plot. The proposed mode plot visually encodes the structure provided by the high-density clustering result and summarizes the distribution of ensemble isocontours. It also enables the selection of subsets of interesting isocontours, which are interactively highlighted in a linked spaghetti plot for providing spatial context. To provide an interpretable overview of the positional variability of isocontours, our system allows for selection of informative isovalues from the simplified spaghetti plot. Due to the spatial variability of ensemble isocontours, the system allows for interactive selection and focus on sub-regions for local uncertainty and clustering re-analysis. We examine a number of ensemble datasets to establish the utility of our approach and discuss its advantages over state-of-the-art visual analysis tools for ensemble data.
Leftover Hashing From Quantum Error Correction: Unifying the Two Approaches to the Security Proof of Quantum Key Distribution

We show that the Mayers-Shor-Preskill approach and Renner's approach to proving the security of quantum key distribution (QKD) are essentially the same. We begin our analysis by considering a special case of QKD called privacy amplification (PA). PA itself is an important building block of cryptography, both classical and quantum. The standard theoretical tool used for its security proof is called the leftover hashing lemma (LHL). We present a direct connection between the LHL and the coding theorem of a certain quantum error correction code. Then we apply this result to proving the equivalence between the two approaches to proving the security of QKD.
Experimental Reconstruction of Entanglement Quasiprobabilities

We report on the first experimental reconstruction of an entanglement quasiprobability. In contrast to related techniques, the negativities in our distributions are a necessary and sufficient identifier of separability and entanglement and enable a full characterization of the quantum state. A reconstruction algorithm is developed, a polarization Bell state is prepared, and its entanglement is certified based on the reconstructed entanglement quasiprobabilities, with a high significance and without correcting for imperfections.Received 18 October 2018DOI:https://doi.org/10.1103/PhysRevLett.122.053602© 2019 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement detectionPhoton pairs & parametric down-conversionQuantum entanglementQuantum opticsTechniquesPhase space methodsQuantum InformationAtomic, Molecular & Optical
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics

We study stochastic variance reduction-based Langevin dynamic algorithms, SVRG-LD and SAGA-LD \citep{dubey2016variance}, for sampling from non-log-concave distributions. Under certain assumptions on the log density function, we establish the convergence guarantees of SVRG-LD and SAGA-LD in $2$-Wasserstein distance. More specifically, we show that both SVRG-LD and SAGA-LD require $ \tilde O\big(n+n^{3/4}/\epsilon^2 + n^{1/2}/\epsilon^4\big)\cdot \exp\big(\tilde O(d+\gamma)\big)$ stochastic gradient evaluations to achieve $\epsilon$-accuracy in $2$-Wasserstein distance, which outperforms the $ \tilde O\big(n/\epsilon^4\big)\cdot \exp\big(\tilde O(d+\gamma)\big)$ gradient complexity achieved by Langevin Monte Carlo Method \citep{raginsky2017non}. Experiments on synthetic data and real data back up our theory.
Universal bound on sampling bosons in linear optics and its computational implications

ABSTRACT In linear optics, photons are scattered in a network through passive optical elements including beam splitters and phase shifters, leading to many intriguing applications in physics, such as Mach–Zehnder interferometry, the Hong–Ou–Mandel effect, and tests of fundamental quantum mechanics. Here we present the fundamental limit in the transition amplitudes of bosons, applicable to all physical linear optical networks. Apart from boson sampling, this transition bound results in many other interesting applications, including behaviors of Bose–Einstein condensates (BEC) in optical networks, counterparts of Hong–Ou–Mandel effects for multiple photons, and approximating permanents of matrices. In addition, this general bound implies the existence of a polynomial-time randomized algorithm for estimating the transition amplitudes of bosons, which represents a solution to an open problem raised by Aaronson and Hance (Quantum Inf Comput 2012; 14: 541–59). Consequently, this bound implies that computational decision problems encoded in linear optics, prepared and detected in the Fock basis, can be solved efficiently by classical computers within additive errors. Furthermore, our result also leads to a classical sampling algorithm that can be applied to calculate the many-body wave functions and the S-matrix of bosonic particles.
Bridging Social Critique and Design

This project aims to develop a voice training application for transgender people. Voice training is typically conducted by a speech therapist, and consists of personalized sessions that support individuals in changing their voices (such as modifying pitch, resonance, or speech patterns). The reasons why people may pursue voice training are varied, but often includes discomfort with voice being misaligned with gender identity. Training with a speech therapist may be inaccessible due to health disparities; thus, a technological solution, as I propose in my research, is necessary. This project will address existing constraints to design a novel voice training application in partnership with community members, using a participatory research methodology and combining the fields of speech science, feminist and queer theory, and HCI.
Speed-up and multi-view extensions to subclass discriminant analysis

In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based on graph embedding and spectral regression approaches that involve eigendecomposition of the corresponding Laplacian matrix and regression to its eigenvectors. We show that by exploiting the structure of the between-class Laplacian matrix, the eigendecomposition step can be substituted with a much faster process. Furthermore, we formulate a novel criterion for multi-view subclass discriminant analysis and show that an efficient solution to it can be obtained in a similar manner to the single-view case. We evaluate the proposed methods on nine single-view and nine multi-view datasets and compare them with related existing approaches. Experimental results show that the proposed solutions achieve competitive performance, often outperforming the existing methods. At the same time, they significantly decrease the training time.
Two-Stage Estimation for Quantum Detector Tomography: Error Analysis, Numerical and Experimental Results

Quantum detector tomography is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, a novel quantum detector tomography method is proposed. First, a series of different probe states are used to generate measurement data. Then, using constrained linear regression estimation, a stage-1 estimation of the detector is obtained. Finally, the positive semidefinite requirement is added to guarantee a physical stage-2 estimation. This Two-stage Estimation (TSE) method has computational complexity $O(nd^2M)$, where $n$ is the number of $d$-dimensional detector matrices and $M$ is the number of different probe states. An error upper bound is established, and optimization on the coherent probe states is investigated. We perform simulation and a quantum optical experiment to testify the effectiveness of the TSE method.
Supervised methods to extract clinical events from cardiology reports in Italian

Clinical narratives are a valuable source of information for both patient care and biomedical research. Given the unstructured nature of medical reports, specific automatic techniques are required to extract relevant entities from such texts. In the natural language processing (NLP) community, this task is often addressed by using supervised methods. To develop such methods, both reliably-annotated corpora and elaborately designed features are needed. Despite the recent advances on corpora collection and annotation, research on multiple domains and languages is still limited. In addition, to compute the features required for supervised classification, suitable language- and domain-specific tools are needed. In this work, we propose a novel application of recurrent neural networks (RNNs) for event extraction from medical reports written in Italian. To train and evaluate the proposed approach, we annotated a corpus of 75 cardiology reports for a total of 4365 mentions of relevant events and their attributes (e.g., the polarity). For the annotation task, we developed specific annotation guidelines, which are provided together with this paper. The RNN-based classifier was trained on a training set including 3335 events (60 documents). The resulting model was integrated into an NLP pipeline that uses a dictionary lookup approach to search for relevant concepts inside the text. A test set of 1030 events (15 documents) was used to evaluate and compare different pipeline configurations. As a main result, using the RNN-based classifier instead of the dictionary lookup approach allowed increasing recall from 52.4% to 88.9%, and precision from 81.1% to 88.2%. Further, using the two methods in combination, we obtained final recall, precision, and F1 score of 91.7%, 88.6%, and 90.1%, respectively. These experiments indicate that integrating a well-performing RNN-based classifier with a standard knowledge-based approach can be a good strategy to extract information from clinical text in non-English languages.
Predicting Image Aesthetics for Intelligent Tourism Information Systems

Image perception can vary considerably between subjects, yet some sights are regarded as aesthetically pleasant more often than others due to their specific visual content, this being particularly true in tourism-related applications. We introduce the ESITUR project, oriented towards the development of ’smart tourism’ solutions aimed at improving the touristic experience. The idea is to convert conventional tourist showcases into fully interactive information points accessible from any smartphone, enriched with automatically-extracted contents from the analysis of public photos uploaded to social networks by other visitors. Our baseline, knowledge-driven system reaches a classification accuracy of 64.84 ± 4.22% telling suitable images from unsuitable ones for a tourism guide application. As an alternative we adopt a data-driven Mixture of Experts (MEX) approach, in which multiple learners specialize in partitions of the problem space. In our case, a location tag is attached to every picture providing a criterion to segment the data by, and the MEX model accordingly defined achieves an accuracy of 85.08 ± 2.23%. We conclude ours is a successful approach in environments in which some kind of data segmentation can be applied, such as touristic photographs.
Analyses of Classifier’s Performance Measures Used in Software Fault Prediction Studies

Assessing the quality of the software is both important and difficult. For this purpose, software fault prediction (SFP) models have been extensively used. However, selecting the right model and declaring the best out of multiple models are dependent on the performance measures. We analyze 14 frequently used, non-graphic classifier's performance measures used in SFP studies. These analyses would help machine learning practitioners and researchers in SFP to select the most appropriate performance measure for the models' evaluation. We analyze the performance measures for resilience against producing invalid values through our proposed plausibility criterion. After that, consistency and discriminancy analyses are performed to find the best out of the 14 performance measures. Finally, we draw the order of the selected performance measures from better to worse in both balance and imbalance datasets. Our analyses conclude that the F-measure and the G-mean1 are equally the best candidates to evaluate the SFP models with careful analysis of the result, as there is a risk of invalid values in certain scenarios.
SmartAnvil

Smart contracts are new computational units with special properties: they act as classes with aspectual concerns; their memory structure is more complex than mere objects; they are obscure in the sense that once deployed it is difficult to access their internal state; they reside in an append-only chain. There is a need to support the building of new generation tools to help developers. Such support should tackle several important aspects: (1) the static structure of the contract, (2) the object nature of published contracts, and (3) the overall data chain composed of blocks and transactions. In this chapter, we present SmartAnvil an open platform to build software analysis tools around smart contracts. We illustrate the general components and we focus on three important aspects: support for static analysis of Solidity smart contracts, deployed smart contract binary analysis through inspection, and blockchain navigation and querying. SmartAnvil is open-source and supports a bridge to the Moose data and software analysis platform.
Research on Data Acquisition Algorithms Based on Image Processing and Artificial Intelligence

At present, image recognition processing technology has been playing a decisive role in the field of pattern recognition, of which automatic recognition of bank notes is an important research topic. Due to the limitation of the size of bill layout and printing method, many invoice layouts are not clear, skewed or distorted, and even there are irregular handwritten signature contents, which lead to the problem of recognition of digital characters on bill surface. In this regard, this paper proposes a data acquisition and recognition algorithm based on improved BP neural network for ticket number identification, which is based on the theory of image processing and recognition, combined with improved bill information recognition technology. First, in the pre-processing stage of bill image, denoising and graying of bill image are processed. After binarization of bill image, the tilt detection method based on Bresenham integer algorithm is used to correct the tilted bill image. Secondly, character localization and feature extraction are carried out for par characters, and the target background is separated from the interference background in order to extract the desired target characters. Finally, the improved BP neural network-based bill digit data acquisition and recognition algorithm is used to realize the classification and recognition of bill characters. The experimental results show that the improved method has better classification and recognition effect than other data acquisition and recognition algorithms.
Speckle pattern shifting deflectometry based on digital image correlation

A method based on digital image correlation (DIC) for the surface shape measurement of specular surface by shifting a speckle pattern, which is displayed on an LCD screen, is proposed in this paper. With this method, the deformed information of test surface is encoded within the displacement distribution between the two recorded speckle images before and after the speckle pattern shifted. The displacement distribution is calculated by the DIC algorithm, then the slope data and the surface shape are obtained. The principle and algorithm of speckle pattern shifting deflectometry (SPSD) are described in detail. The correctness and feasibility of the proposed method are verified by simulation, and the source of error is analyzed as well. Finally, the shape of an acrylic plastic plate and a silicon wafer are measured. The experimental result of the proposed method is compared with that of PMD, and the figure error is around 1μm RMS with a measured diameter of about 100mm. This method has the advantages of fast measurement, simple device, low cost and needlessness of reference element. It provides a new approach to measure the shape of specular surface.
Reconstructing probability distributions with Gaussian processes

ABSTRACT Modern cosmological analyses constrain physical parameters using Markov Chain Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques are computationally expensive to run and require up to thousands of CPU hours to complete. Here we present a method for reconstructing the log-probability distributions of completed experiments from an existing chain (or any set of posterior samples). The reconstruction is performed using Gaussian process regression for interpolating the log-probability. This allows for easy resampling, importance sampling, marginalization, testing different samplers, investigating chain convergence, and other operations. As an example use case, we reconstruct the posterior distribution of the most recent Planck 2018 analysis. We then resample the posterior, and generate a new chain with 40 times as many points in only 30 min. Our likelihood reconstruction tool is made publicly available online.