Reviewerly/exHarmony huggingface dataset:
Fixing an ambiguity in two dimensional string theory using string field theory

A bstract In a recent paper, Balthazar, Rodriguez and Yin found some remarkable agreement between the results of c = 1 matrix model and D-instanton corrections in two dimensional string theory. Their analysis left undetermined two constants in the string theory computation which had to be fixed by comparing the results with the matrix model results. One of these constants is affected by possible renormalization of the D-instanton action that needs to be computed separately. In this paper we fix the other constant by reformulating the world-sheet analysis in the language of string field theory.
Entanglement-enhanced testing of multiple quantum hypotheses

Abstract Quantum hypothesis testing has been greatly advanced for the binary discrimination of two states, or two channels. In this setting, we already know that quantum entanglement can be used to enhance the discrimination of two bosonic channels. Here, we remove the restriction of binary hypotheses and show that entangled photons can remarkably boost the discrimination of multiple bosonic channels. More precisely, we formulate a general problem of channel-position finding where the goal is to determine the position of a target channel among many background channels. We prove that, using entangled photons at the input and a generalized form of conditional nulling receiver at the output, we may outperform any classical strategy. Our results can be applied to enhance a range of technological tasks, including the optical readout of sparse classical data, the spectroscopic analysis of a frequency spectrum, and the determination of the direction of a target at fixed range.
Developing a brain atlas through deep learning

Neuroscientists have devoted substantial effort to the creation of standard brain reference atlases for high-throughput registration of anatomical regions of interest. However, the variability in brain size and form across individuals poses a considerable challenge for such reference atlases. To overcome these limitations, we introduce a fully automated deep neural network-based method (named SeBRe) for segmenting brain regions of interest with minimal human supervision. We demonstrate the validity of our method on brain images from different developmental time points of mice, across a range of neuronal markers and imaging modalities. We further assess the performance of our method on images of magnetic resonance-scanned human brains. Our registration method can accelerate brain-wide exploration of region-specific changes in brain development and, by easily segmenting brain regions of interest for high-throughput brain-wide analysis, offer an alternative to existing complex brain registration techniques. High-throughput brain image registration methods that are independent of any pre-processing steps, and are robust under mild image transformations, could accelerate the study of region-specific changes in brain development. A deep learning-based method is therefore developed for automated registration through segmenting brain regions of interest with minimal human supervision.
A Question-Centric Model for Visual Question Answering in Medical Imaging

Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks. With their potential use in clinical routine, their lack of transparency has however been one of their few weak points, raising concerns regarding their behavior and failure modes. While most research to infer model behavior has focused on indirect strategies that estimate prediction uncertainties and visualize model support in the input image space, the ability to explicitly query a prediction model regarding its image content offers a more direct way to determine the behavior of trained models. To this end, we present a novel Visual Question Answering approach that allows an image to be queried by means of a written question. Experiments on a variety of medical and natural image datasets show that by fusing image and question features in a novel way, the proposed approach achieves an equal or higher accuracy compared to current methods.
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning

Captioning is a crucial and challenging task for video understanding.In videos that involve active agents such as humans, the agent's actions can bring about myriad changes in the scene.Observable changes such as movements, manipulations, and transformations of the objects in the scene, are reflected in conventional video captioning.Unlike images, actions in videos are also inherently linked to social aspects such as intentions (why the action is taking place), effects (what changes due to the action), and attributes that describe the agent.Thus for video understanding, such as when captioning videos or when answering questions about videos, one must have an understanding of these commonsense aspects.We present the first work on generating commonsense captions directly from videos, to describe latent aspects such as intentions, effects, and attributes.We present a new dataset "Video-to-Commonsense (V2C)" that contains ∼ 9k videos of human agents performing various actions, annotated with 3 types of commonsense descriptions.Additionally we explore the use of open-ended video-based commonsense question answering (V2C-QA) as a way to enrich our captions.Both the generation task and the QA task can be used to enrich video captions.Group of runners get prepared to run a race. Conventional Caption Commonsense-Enriched CaptionIn order to win a medal, a group of runners get prepared to run a race.As a result they are congratulated at the finish line.They are athletic. Commonsense Question AnsweringWhat happens next to the runners?{ Are congratulated at the finish line become tired. frame frame frame CNN
Robust entanglement preparation against noise by controlling spatial indistinguishability

Abstract Initialization of composite quantum systems into highly entangled states is usually a must to enable their use for quantum technologies. However, unavoidable noise in the preparation stage makes the system state mixed, hindering this goal. Here, we address this problem in the context of identical particle systems within the operational framework of spatially localized operations and classical communication (sLOCC). We define the entanglement of formation for an arbitrary state of two identical qubits. We then introduce an entropic measure of spatial indistinguishability as an information resource. Thanks to these tools we find that spatial indistinguishability, even partial, can be a property shielding nonlocal entanglement from preparation noise, independently of the exact shape of spatial wave functions. These results prove quantum indistinguishability is an inherent control for noise-free entanglement generation.
Manipulating deformable objects by interleaving prediction, planning, and control

We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or simulation. The key question we address is when should we use planning and when should we use control to achieve the task? Planners are designed to find paths through complex configuration spaces, but for highly underactuated systems, such as deformable objects, achieving a specific configuration is very difficult even with high-fidelity models. Conversely, controllers can be designed to achieve specific configurations, but they can be trapped in undesirable local minima owing to obstacles. Our approach consists of three components: (1) a global motion planner to generate gross motion of the deformable object; (2) a local controller for refinement of the configuration of the deformable object; and (3) a novel deadlock prediction algorithm to determine when to use planning versus control. By separating planning from control we are able to use different representations of the deformable object, reducing overall complexity and enabling efficient computation of motion. We provide a detailed proof of probabilistic completeness for our planner, which is valid despite the fact that our system is underactuated and we do not have a steering function. We then demonstrate that our framework is able to successfully perform several manipulation tasks with rope and cloth in simulation, which cannot be performed using either our controller or planner alone. These experiments suggest that our planner can generate paths efficiently, taking under a second on average to find a feasible path in three out of four scenarios. We also show that our framework is effective on a 16-degree-of-freedom physical robot, where reachability and dual-arm constraints make the planning more difficult.
Model-free readout-error mitigation for quantum expectation values

Measurements on current quantum processors are subject to hardware imperfections that lead to readout errors. These errors manifest themselves as a bias in quantum expectation values. Here, we consider a very simple method that forces the bias in the expectation value to appear as a multiplicative factor that can be measured directly and removed at the cost of an increase in the sampling complexity for the observable. This method was previously discussed by Karalekas et al. [Quantum Sci. Technol. 5, 024003 (2020)] for the product of single-qubit readout-error models. Here, we formally analyze the method and show that it can be applied to general noise models. In particular, we show the method does not need to assume any specific form of the noise model and requires only that the noise is ``weak'' to avoid excessive sampling overhead. We prove bounds relating the error in the expectation value to the sample complexity.
Exploring Controllable Text Generation Techniques

Neural controllable text generation is an important area gaining attention due to its plethora of applications.Although there is a large body of prior work in controllable text generation, there is no unifying theme.In this work, we provide a new schema of the pipeline of the generation process by classifying it into five modules.The control of attributes in the generation process requires modification of these modules.We present an overview of different techniques used to perform the modulation of these modules.We also provide an analysis on the advantages and disadvantages of these techniques.We further pave ways to develop new architectures based on the combination of the modules described in this paper.
Combination of Convolutional Neural Network and Gated Recurrent Unit for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to identify views and sentiment polarities towards a given aspect in reviews.Compared with general sentiment analysis, ABSA can provide more detailed and complete information.Recently, ABSA has become an important task for natural language understanding and has attracted considerable attention from both academic and industry fields.The sentiment polarity of a sentence is not only decided by its content but also has a relatively significant correlation with the targeted aspect.For this reason, we propose a model for aspect-based sentiment analysis which is a combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), utilizing the local features generated by CNN and the long-term dependency learned by GRU.Extensive experiments have been conducted on datasets of hotels and cars, and results show that the proposed model achieves excellent performance in terms of aspect extraction and sentiment classification.Experiments also demonstrate the great domain expansion capability of the model.
A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays

In this article, two different architectures of Artificial Neural Networks (ANN) are proposed as an efficient tool for predicting and estimating software effort. Artificial Neural Networks, as a branch of machine learning, are used in estimation because they tend towards fast learning and giving better and more accurate results. The search/optimization embraced here is motivated by the Taguchi method based on Orthogonal Arrays (an extraordinary set of Latin Squares), which demonstrated to be an effective apparatus in a robust design. This article aims to minimize the magnitude relative error (MRE) in effort estimation by using Taguchi's Orthogonal Arrays, as well as to find the simplest possible architecture of an artificial Neural Network for optimized learning. A descending gradient (GA) criterion has also been introduced to know when to stop performing iterations. Given the importance of estimating software projects, our work aims to cover as many different values of actual efficiency of a wide range of projects as possible by division into clusters and a certain coding method, in addition to the mentioned tools. In this way, the risk of error estimation can be reduced, to increase the rate of completed software projects.
Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers

Combinatorial optimization on near-term quantum devices is a promising path to demonstrating quantum advantage. However, the capabilities of these devices are constrained by high noise or error rates. In this article, inspired by the variational quantum eigensolver (VQE), we propose an iterative layer VQE (L-VQE) approach. We present a large-scale numerical study, simulating circuits with up to 40 qubits and 352 parameters, that demonstrates the potential of the proposed approach. We evaluate quantum optimization heuristics on the problem of detecting multiple communities in networks, for which we introduce a novel qubit-frugal formulation. We numerically compare L-VQE with the quantum approximate optimization algorithm (QAOA) and demonstrate that QAOA achieves lower approximation ratios while requiring significantly deeper circuits. We show that L-VQE is more robust to finite sampling errors and has a higher chance of finding the solution as compared with standard VQE approaches. Our simulation results show that L-VQE performs well under realistic hardware noise.
Implementing digital passports for SARS-CoV-2 immunization in Canada

KEY POINTS The emergence and deployment of vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are safe and effective offers hope for the alleviation of the enormous social, economic and health tolls of the coronavirus disease 2019 (COVID-19) pandemic. Public authorities
Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets

Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes: normal, benign, in situ carcinoma, and invasive carcinoma. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine-tuning on EfficientNets can achieve satisfactory results.
Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction

A lot of work has been done towards reconstructing the 3D facial structure from single images by capitalizing on the power of deep convolutional neural networks (DCNNs). In the recent works, the texture features either correspond to components of a linear texture space or are learned by auto-encoders directly from in-the-wild images. In all cases, the quality of the facial texture reconstruction is still not capable of modeling facial texture with high-frequency details. In this paper, we take a radically different approach and harness the power of generative adversarial networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images. That is, we utilize GANs to train a very powerful facial texture prior from a large-scale 3D texture dataset. Then, we revisit the original 3D Morphable Models (3DMMs) fitting making use of non-linear optimization to find the optimal latent parameters that best reconstruct the test image but under a new perspective. In order to be robust towards initialisation and expedite the fitting process, we propose a novel self-supervised regression based approach. We demonstrate excellent results in photorealistic and identity preserving 3D face reconstructions and achieve for the first time, to the best of our knowledge, facial texture reconstruction with high-frequency details.
A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.
Hyper-Heuristics to customise metaheuristics for continuous optimisation

Literature is prolific with metaheuristics for solving continuous optimisation problems. But, in practice, it is difficult to choose one appropriately for several reasons. First and foremost, ‘new’ metaheuristics are being proposed at an alarmingly fast rate, rendering impossible to know them all. Moreover, it is necessary to determine a good enough set of parameters for the selected approach. Hence, this work proposes a strategy based on a hyper-heuristic model powered by Simulated Annealing for customising population-based metaheuristics. Our approach considers search operators from 10 well-known techniques as building blocks for new ones. We test this strategy on 107 continuous benchmark functions and in up to 50 dimensions. Besides, we analyse the performance of our approach under different experimental conditions. The resulting data reveal that it is possible to obtain good-performing metaheuristics with diverse configurations for each case of study and in an automatic fashion. In this way, we validate the potential of the proposed framework for devising metaheuristics that solve continuous optimisation problems with different characteristics, similar to those from practical engineering scenarios.
A Simple Recipe for Multilingual Grammatical Error Correction

Sascha Rothe, Jonathan Mallinson, Eric Malmi, Sebastian Krause, Aliaksei Severyn. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2021.
Cross-View Cross-Scene Multi-View Crowd Counting

Multi-view crowd counting has been previously proposed to utilize multi-cameras to extend the field-of-view of a single camera, capturing more people in the scene, and improve counting performance for occluded people or those in low resolution. However, the current multi-view paradigm trains and tests on the same single scene and camera-views, which limits its practical application. In this paper, we propose a cross-view cross-scene (CVCS) multi-view crowd counting paradigm, where the training and testing occur on different scenes with arbitrary camera layouts. To dynamically handle the challenge of optimal view fusion under scene and camera layout change and non-correspondence noise due to camera calibration errors or erroneous features, we propose a CVCS model that attentively selects and fuses multiple views together using camera layout geometry, and a noise view regularization method to train the model to handle non-correspondence errors. We also generate a large synthetic multi-camera crowd counting dataset with a large number of scenes and camera views to capture many possible variations, which avoids the difficulty of collecting and annotating such a large real dataset. We then test our trained CVCS model on real multi-view counting datasets, by using unsupervised domain transfer. The proposed CVCS model trained on synthetic data outperforms the same model trained only on real data, and achieves promising performance compared to fully supervised methods that train and test on the same single scene.
Lyapunov-stable neural-network control

Deep learning has had a far reaching impact in robotics.Specifically, deep reinforcement learning algorithms have been highly effective in synthesizing neural-network controllers for a wide range of tasks.However, despite this empirical success, these controllers still lack theoretical guarantees on their performance, such as Lyapunov stability (i.e., all trajectories of the closed-loop system are guaranteed to converge to a goal state under the control policy).This is in stark contrast to traditional model-based controller design, where principled approaches (like LQR) can synthesize stable controllers with provable guarantees.To address this gap, we propose a generic method to synthesize a Lyapunov-stable neural-network controller, together with a neural-network Lyapunov function to simultaneously certify its stability.Our approach formulates the Lyapunov condition verification as a mixed-integer linear program (MIP).Our MIP verifier either certifies the Lyapunov condition, or generates counter examples that can help improve the candidate controller and the Lyapunov function.We also present an optimization program to compute an inner approximation of the region of attraction for the closed-loop system.We apply our approach to robots including an inverted pendulum, a 2D and a 3D quadrotor, and showcase that our neural-network controller outperforms a baseline LQR controller.The code is open sourced at https://github.com/StanfordASL/neural-network-lyapunov.