Reviewerly/exHarmony huggingface dataset:
Learning facial action units with spatiotemporal cues and multi-label sampling

Facial action units (AUs) can be represented spatially, temporally, and in terms of their correlation. Previous research focuses on one or another of these aspects or addresses them disjointly. We propose a hybrid network architecture that jointly models spatial and temporal representations and their correlation. In particular, we use a Convolutional Neural Network (CNN) to learn spatial representations, and a Long Short-Term Memory (LSTM) to model temporal dependencies among them. The outputs of CNNs and LSTMs are aggregated into a fusion network to produce per-frame prediction of multiple AUs. The hybrid network was compared to previous state-of-the-art approaches in two large FACS-coded video databases, GFT and BP4D, with over 400,000 AU-coded frames of spontaneous facial behavior in varied social contexts. Relative to standard multi-label CNN and feature-based state-of-the-art approaches, the hybrid system reduced person-specific biases and obtained increased accuracy for AU detection. To address class imbalance within and between batches during network training, we introduce multi-labeling sampling strategies that further increase accuracy when AUs are relatively sparse. Finally, we provide visualization of the learned AU models, which, to the best of our best knowledge, reveal for the first time how machines see AUs.
Corrupted Reference Image Quality Assessment of Denoised Images

We propose corrupted reference image quality assessment (CRIQA), a novel foundation for reasoning about image quality and image denoising problems jointly. In order to assess the visual quality of a processed image relative to an ideal reference image (not provided), we predict the full-reference image quality assessment (FRIQA) scores of denoised images without having the direct access to the ideal reference image, but with the help of the observed corrupted image, instead. Our simulation studies verify that the CRIQA scores of denoised images indeed agree with the corresponding FRIQA scores, and human subject studies confirm that CRIQA scores are more consistent with the perceived image denoising quality than the NRIQA scores. We demonstrated the usefulness of CRIQA with an application in denoising parameter tuning.
Experimental demonstration of robust self-testing for bipartite entangled states

Abstract Quantum entanglement is the key resource for quantum information processing. Device-independent certification of entangled states is a long standing open question, which arouses the concept of self-testing. The central aim of self-testing is to certify the state and measurements of quantum systems without any knowledge of their inner workings, even when the used devices cannot be trusted. Specifically, utilizing Bell’s theorem, one can infer the appearance of certain entangled state when the maximum violation is observed, e.g., to self-test singlet state using CHSH inequality. In this work, by constructing a versatile entanglement source, we experimentally demonstrate a generalized self-testing proposal for various bipartite entangled states up to four dimensions. We show that the high-quality generated states can approach the maximum violations of the utilized Bell inequalities, and thus, their Schmidt coefficients can be precisely inferred by self-testing them into respective target states with near-unity fidelities. Our results indicate the superior completeness and robustness of this method and promote self-testing as a practical tool for developing quantum techniques.
On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative

Unobserved confounding is a central barrier to drawing causal inferences from observational data.Several authors have recently proposed that this barrier can be overcome in the case where one attempts to infer the effects of several variables simultaneously. In this paper, we present two simple, analytical counterexamples that challenge the general claims that are central to these approaches.We discuss some reasons for these failures and suggest directions for obtaining sufficient conditions for causal identifiaciton.Despite these negative results, we show that a simple modification to the multi-cause setting, incorporating a proxy or negative control variable, solves many of the problems highlighted by the examples, and suggest a way forward for causal inference with high-dimensional action spaces.
Optimized artificial potential field algorithm to multi-unmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment

Multi-unmanned aerial vehicle trajectory planning is one of the most complex global optimum problems in multi-unmanned aerial vehicle coordinated control. Results of recent research works on trajectory planning reveal persisting theoretical and practical problems. To mitigate them, this paper proposes a novel optimized artificial potential field algorithm for multi-unmanned aerial vehicle operations in a three-dimensional dynamic space. For all purposes, this study considers the unmanned aerial vehicles and obstacles as spheres and cylinders with negative electricity, respectively, while the targets are considered spheres with positive electricity. However, the conventional artificial potential field algorithm is restricted to a single unmanned aerial vehicle trajectory planning in two-dimensional space and usually fails to ensure collision avoidance. To deal with this challenge, we propose a method with a distance factor and jump strategy to resolve common problems such as unreachable targets and ensure that the unmanned aerial vehicle does not collide into the obstacles. The method takes companion unmanned aerial vehicles as the dynamic obstacles to realize collaborative trajectory planning. Besides, the method solves jitter problems using the dynamic step adjustment method and climb strategy. It is validated in quantitative test simulation models and reasonable results are generated for a three-dimensional simulated urban environment.
Encoding Syntactic Dependency and Topical Information for Social Emotion Classification

Social emotion classification is to estimate the distribution of readers' emotion evoked by an article. In this paper, we design a new neural network model by encoding sentence syntactic dependency and document topical information into the document representation. We first use a dependency embedded recursive neural network to learn syntactic features for each sentence, and then use a gated recurrent unit to transform the sentences' vectors into a document vector. We also use a multi-layer perceptron to encode the topical information of a document into a topic vector. Finally, a gate layer is used to compose the document representation from the gated summation of the document vector and the topic vector. Experiment results on two public datasets indicate that our proposed model outperforms the state-of-the-art methods in terms of better average Pearson correlation coefficient and MicroF1 performance.
Practical Semantic Parsing for Spoken Language Understanding

Marco Damonte, Rahul Goel, Tagyoung Chung. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation

Xing Niu, Weijia Xu, Marine Carpuat. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Characterization of Arbitrary-Order Correlations in Quantum Baths by Weak Measurement

Correlations of fluctuations are the driving forces behind the dynamics and thermodynamics in quantum many-body systems. For qubits embedded in a quantum bath, the correlations in the bath are the key to understanding and combating decoherence -a critical issue in quantum information technology. However, there is no systematic method for characterizing the many-body correlations in quantum baths beyond the second order or the Gaussian approximation. Here we present a scheme to characterize the correlations in a quantum bath to arbitrary order. The scheme employs weak measurement of the bath via projective measurement of a central system. The bath correlations, including both the "classical" and the "quantum" parts, can be reconstructed from the correlations of the measurement outputs. The possibility of full characterization of many-body correlations in a quantum bath forms the basis for optimizing quantum control against decoherence in realistic environments, for studying the quantum characteristics of baths, and for quantum sensing of correlated clusters in quantum baths.
Graph Agreement Models for Semi-Supervised Learning

Graph-based algorithms are among the most successful paradigms for solving semi-supervised learning tasks. Recent work on graph convolutional networks and neural graph learning methods has successfully combined the expressiveness of neural networks with graph structures. We propose a technique that, when applied to these methods, achieves state-of-the-art results on semi-supervised learning datasets. Traditional graph-based algorithms, such as label propagation, were designed with the underlying assumption that the label of a node can be imputed from that of the neighboring nodes. However, real-world graphs are either noisy or have edges that do not correspond to label agreement. To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features. The agreement model is used when training a node classification model by encouraging agreement only for the pairs of nodes it deems likely to have the same label, thus guiding its parameters to better local optima. The classification and agreement models are trained jointly in a co-training fashion. Moreover, GAM can also be applied to any semi-supervised classification problem, by inducing a graph whenever one is not provided. We demonstrate that our method achieves a relative improvement of up to 72% for various node classification models, and obtains state-of-the-art results on multiple established datasets.
Multi-Robot Routing for Persistent Monitoring with Latency Constraints

In this paper we study a multi-robot path planning problem for persistent monitoring of an environment. We represent the areas to be monitored as the vertices of a weighted graph. For each vertex, there is a constraint on the maximum time spent by the robots between visits to that vertex, called the latency, and the objective is to find the minimum number of robots that can satisfy these latency constraints. The decision version of this problem is known to be PSPACE-complete. We present a O(log p) approximation algorithm for the problem where p is the ratio of the maximum and the minimum latency constraints. We also present an orienteering based heuristic to solve the problem and show through simulations that in most of the cases the heuristic algorithm gives better solutions than the approximation algorithm. We evaluate our algorithms on large problem instances in a patrolling scenario and in a persistent scene reconstruction application. We also compare the algorithms with an existing solver on benchmark instances.
ConfusionFlow: A Model-Agnostic Visualization for Temporal Analysis of Classifier Confusion

Classifiers are among the most widely used supervised machine learning algorithms. Many classification models exist, and choosing the right one for a given task is difficult. During model selection and debugging, data scientists need to assess classifiers' performances, evaluate their learning behavior over time, and compare different models. Typically, this analysis is based on single-number performance measures such as accuracy. A more detailed evaluation of classifiers is possible by inspecting class errors. The confusion matrix is an established way for visualizing these class errors, but it was not designed with temporal or comparative analysis in mind. More generally, established performance analysis systems do not allow a combined temporal and comparative analysis of class-level information. To address this issue, we propose ConfusionFlow, an interactive, comparative visualization tool that combines the benefits of class confusion matrices with the visualization of performance characteristics over time. ConfusionFlow is model-agnostic and can be used to compare performances for different model types, model architectures, and/or training and test datasets. We demonstrate the usefulness of ConfusionFlow in a case study on instance selection strategies in active learning. We further assess the scalability of ConfusionFlow and present a use case in the context of neural network pruning.
Teachers’ burnout: A SEM analysis in an Asian context

Researchers in educational psychology have researched Teacher Self-Concept (TSC) and Teacher Efficacy (TE) as two main predictors predicting burnout. Guided by a model developed by Zhu, Liu, Fu, Yang, Zhang & Shi (2018), the researchers aimed at building a model involving TSC, TE, and three components of burnout; Emotional Exhaustion (EE), Depersonalization (DP), and Reduced Personal Accomplishment (RPA) through Structural Equation Modeling (SEM). The researchers investigated predicting factors of burnout by reporting TSC and TE that might directly affect the components and examine the probability of TE to become a mediator of the correlation between TSC and burnout. This research also examined whether the difference emerges constantly among demographic information (gender and teaching experience) regarding all involved variables. A sample of 876 teachers across three Indonesian provinces completed a printed form of questionnaires. Some statistical procedures namely Content Validity Index (CVI), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Covariance-Based Structural Equation Modeling (CB-SEM), and t-test were conducted. Findings informed that the model is valid and reliable. TSC could directly affect EE, DE, and RPA, as well as indirectly influence them mediated by TE. Besides, TE is also reported to have significant relationships with EE, DE, and RPA. No significant differences in terms of age and teaching experiences emerge, except for EE.
Modeling noisy quantum circuits using experimental characterization

Noisy intermediate-scale quantum (NISQ) devices offer unique platforms to test and evaluate the behavior of quantum computing. However, validating circuits on NISQ devices is difficult due to fluctuations in the underlying noise sources and other nonreproducible behaviors that generate computational errors. Here we present a test-driven approach that decomposes a noisy, application-specific circuit into a series of bootstrapped experiments on a NISQ device. By characterizing individual subcircuits, we generate a composite noise model for the original quantum circuit. We demonstrate this approach to model applications of Greenberger-Horne-Zeilinger(GHZ)-state preparation and the Bernstein-Vazirani algorithm on a family of superconducting transmon devices. We measure the model accuracy using the total variation distance between predicted and experimental results, and we demonstrate that the composite model works well across multiple circuit instances. Our approach is shown to be computationally efficient and offers a trade-off in model complexity that can be tailored to the desired predictive accuracy.
An AutoML application to forecasting bank failures

We investigate the performance of an automated machine learning (AutoML) methodology in forecasting bank failures, called Just Add Data (JAD). We include all failed U.S. banks for 2007–2013 and twice as many healthy ones. An automated feature selection procedure in JAD identifies the most significant forecasters and a bootstrapping methodology provides conservative estimates of performance generalization and confidence intervals. The best performing model yields an AUC 0.985. The current work provides evidence that JAD, and AutoML tools in general, could increase the productivity of financial data analysts, shield against methodological statistical errors, and provide models at par with state-of-the-art manual analysis.
An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies

Background: Tree-based Long Short Term Memory (LSTM) network has become state-of-the-art for modeling the meaning of language texts as they can effectively exploit the grammatical syntax and thereby non-linear dependencies among words of the sentence. However, most of these models cannot recognize the difference in meaning caused by a change in semantic roles of words or phrases because they do not acknowledge the type of grammatical relations, also known as typed dependencies, in sentence structure. Methods: This paper proposes an enhanced LSTM architecture, called relation gated LSTM, which can model the relationship between two inputs of a sequence using a control input. We also introduce a Tree-LSTM model called Typed Dependency Tree-LSTM that uses the sentence dependency parse structure as well as the dependency type to embed sentence meaning into a dense vector. Results: The proposed model outperformed its type-unaware counterpart in two typical Natural Language Processing (NLP) tasks – Semantic Relatedness Scoring and Sentiment Analysis. The results were comparable or competitive with other state-of-the-art models. Qualitative analysis showed that changes in the voice of sentences had little effect on the model’s predicted scores, while changes in nominal (noun) words had a more significant impact. The model recognized subtle semantic relationships in sentence pairs. The magnitudes of learned typed dependency embeddings were also in agreement with human intuitions. Conclusion: The research findings imply the significance of grammatical relations in sentence modeling. The proposed models would serve as a base for future researches in this direction.
Lenslet Light Field Image Coding: Classifying, Reviewing and Evaluating

In recent years, visual sensors have been quickly improving, notably targeting richer acquisitions of the light present in a visual scene. In this context, the so-called lenslet light field (LLF) cameras are able to go beyond the conventional 2D visual acquisition models, by enriching the visual representation with directional light measures for each pixel position. LLF imaging is associated to large amounts of data, thus critically demanding efficient coding solutions in order applications involving transmission and storage may be deployed. For this reason, considerable research efforts have been invested in recent years in developing increasingly efficient LLF imaging coding (LLFIC) solutions. In this context, the main objective of this paper is to review and evaluate some of the most relevant LLFIC solutions in the literature, guided by a novel classification taxonomy, which allows better organizing this field. In this way, more solid conclusions can be drawn about the current LLFIC status quo, thus allowing to better drive future research and standardization developments in this technical area.
Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method

With the rapidly growing number of security-sensitive systems that use voice as the primary input, it becomes increasingly important to address these systems' potential vulnerability to replay attacks. Previous efforts to address this concern have focused primarily on single-channel audio. In this paper, we introduce a novel neural network-based replay attack detection model that further leverages spatial information of multi-channel audio and is able to significantly improve the replay attack detection performance.
3DFCNN: real-time action recognition using 3D deep neural networks with raw depth information

Abstract This work describes an end-to-end approach for real-time human action recognition from raw depth image-sequences. The proposal is based on a 3D fully convolutional neural network, named 3DFCNN, which automatically encodes spatio-temporal patterns from raw depth sequences. The described 3D-CNN allows actions classification from the spatial and temporal encoded information of depth sequences. The use of depth data ensures that action recognition is carried out protecting people’s privacy, since their identities can not be recognized from these data. The proposed 3DFCNN has been optimized to reach a good performance in terms of accuracy while working in real-time. Then, it has been evaluated and compared with other state-of-the-art systems in three widely used public datasets with different characteristics, demonstrating that 3DFCNN outperforms all the non-DNN-based state-of-the-art methods with a maximum accuracy of 83.6% and obtains results that are comparable to the DNN-based approaches, while maintaining a much lower computational cost of 1.09 seconds, what significantly increases its applicability in real-world environments.
Practical lessons for phone-based assessments of learning

School closures affecting more than 1.5 billion children are designed to prevent the spread of current public health risks from the COVID-19 pandemic, but they simultaneously introduce new short-term and long-term health risks through lost education. Measuring these effects in real time is critical to inform effective public health responses, and remote phone-based approaches are one of the only viable options with extreme social distancing in place. However, both the health and education literature are sparse on guidance for phone-based assessments. In this article, we draw on our pilot testing of phone-based assessments in Botswana, along with the existing literature on oral testing of reading and mathematics, to propose a series of preliminary practical lessons to guide researchers and service providers as they try phone-based learning assessments. We provide preliminary evidence that phone-based assessments can accurately capture basic numeracy skills. We provide guidance to help teams (1) ensure that children are not put at risk, (2) test the reliability and validity of phone-based measures, (3) use simple instructions and practice items to ensure the assessment is focused on the target skill, not general language and test-taking skills, (4) adapt the items from oral assessments that will be most effective in phone-based assessments, (5) keep assessments brief while still gathering meaningful learning data, (6) use effective strategies to encourage respondents to pick up the phone, (7) build rapport with adult caregivers and youth respondents, (8) choose the most cost-effective medium and (9) account for potential bias in samples.