Reviewerly/exHarmony huggingface dataset:
Modeling Multi-hop Question Answering as Single Sequence Prediction

Fusion-in-decoder (FID) (Izacard and Grave, 2021) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA.However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach.In this work, we propose a simple generative approach (PATHFID) that extends the task beyond just answer generation by explicitly modeling the reasoning process to resolve the answer for multihop questions.By linearizing the hierarchical reasoning path of supporting passages, their key sentences, and finally the factoid answer, we cast the problem as a single sequence prediction task.To facilitate complex reasoning with multiple clues, we further extend the unified flat representation of multiple input documents by encoding cross-passage interactions.Our extensive experiments demonstrate that PATHFID leads to strong performance gains on two multihop QA datasets: HotpotQA and IIRC.Besides the performance gains, PATHFID is more interpretable, which in turn yields answers that are more faithfully grounded to the supporting passages and facts compared to the baseline FID model.
Big Data–Artificial Intelligence Fusion Technology in Education in the Context of the New Crown Epidemic

This article presents an in-depth study and analysis of the application of big data–artificial intelligence fusion technology to the field of education in the context of the New Crown epidemic. Since the outbreak of the New Crown epidemic, there is a need to understand the channels for solving the demands of productive life during the epidemic, and the main way to solve these problems is to apply the Internet, big data, and artificial intelligence. Therefore, exploring the application of big data–artificial intelligence fusion technology in education in the context of the New Crest pneumonia epidemic is a top priority for reform and development nowadays. This study uses the paradigm narrow shift analysis framework to verify whether Computer Aided Instructional design, multimedia instructional design, and informational instructional design produce migration. For the intelligent stage of instructional design, the inevitability of the change in basic assumptions of instructional design in the context of artificial intelligence (AI) is first explained in terms of the opportunities brought by AI to education and teaching, the problems of the original information-based instructional design itself, and the many challenges it faces. On this basis, we also use the change in basic assumptions analysis framework to explain the content of intelligent instructional design by using the four elements of beliefs, values, symbols, and paradigms promised by the members of the community, verify that it has shifted, and build a change in the basic assumptions model from multimedia instructional design to information-based instructional design to intelligent instructional design. The article gives three countermeasures to solve the problem, that is, raising awareness, improving the plan, and strengthening the drill. To ensure the smooth implementation of the emergency management of national covid control program (NCCP), higher education institutions should further strengthen the construction efforts of specialized psychological counseling teams, build an early warning mechanism for psychological problems of the NCCP epidemic in higher education institutions, and a multilevel supervision system.
Structure of working memory in children from 3 to 8 years old.

Several models of working memory (WM) have been proposed in the literature. Most of the research on the architecture of WM is based on adults or older children, but less is known about younger children. In this study, we tested various models of WM on a sample of 739 Italian children, ranging in age from 3 to 8 years, primarily of European heritage and from medium to medium-high socioeconomic background. Participants were assessed with 12 WM tasks, systematically varying the modality and level of executive control required (based on the number of activities to be performed at once: retention alone, ignoring distractors, and dealing with dual tasks). We examined younger children (n = 501, Mage = 56.8 months, SD = 6.4, 48% boys) and older children (n = 238, Mage = 80.0 months, SD = 9.0, 58% boys) separately using multigroup confirmatory factor analyses. A Bayesian analytical approach was adopted. Our results suggested that a four-factor model distinguishing between verbal, visual, spatial-simultaneous, and spatial-sequential components of WM achieved the best fit. Overall, the WM structure was very similar in the two groups. We further explored this result with an additional model with a central executive factor loaded on high-control tasks only and found evidence for the presence of an executive control component. The contribution of this factor in terms of explained variance was only modest, however. Our findings demonstrate that it is important to distinguish between WM components in young children. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure

Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of different methods have been based on empirical analyses in specific datasets. We used Monte Carlo simulations to determine when machine learning methods perform better than statistical learning methods in a specific setting. We evaluated six learning methods: stochastic gradient boosting machines using trees as the base learners, random forests, artificial neural networks, the lasso, ridge regression, and linear regression estimated using ordinary least squares (OLS). Our simulations were informed by empirical analyses in patients with acute myocardial infarction (AMI) and congestive heart failure (CHF) and used six data-generating processes, each based on one of the six learning methods, to simulate continuous outcomes in the derivation and validation samples. The outcome was systolic blood pressure at hospital discharge, a continuous outcome. We applied the six learning methods in each of the simulated derivation samples and evaluated performance in the simulated validation samples. The primary observation was that neural networks tended to result in estimates with worse predictive accuracy than the other five methods in both disease samples and across all six data-generating processes. Boosted trees and OLS regression tended to perform well across a range of scenarios.
NPBDREG: Uncertainty assessment in diffeomorphic brain MRI registration using a non-parametric Bayesian deep-learning based approach

Quantification of uncertainty in deep-neural-networks (DNN) based image registration algorithms plays a critical role in the deployment of image registration algorithms for clinical applications such as surgical planning, intraoperative guidance, and longitudinal monitoring of disease progression or treatment efficacy as well as in research-oriented processing pipelines. Currently available approaches for uncertainty estimation in DNN-based image registration algorithms may result in sub-optimal clinical decision making due to potentially inaccurate estimation of the uncertainty of the registration stems for the assumed parametric distribution of the registration latent space. We introduce NPBDREG, a fully non-parametric Bayesian framework for uncertainty estimation in DNN-based deformable image registration by combining an Adam optimizer with stochastic gradient Langevin dynamics (SGLD) to characterize the underlying posterior distribution through posterior sampling. Thus, it has the potential to provide uncertainty estimates that are highly correlated with the presence of out of distribution data. We demonstrated the added-value of NPBDREG, compared to the baseline probabilistic VoxelMorph model (PrVXM), on brain MRI image registration using 390 image pairs from four publicly available databases: MGH10, CMUC12, ISBR18 and LPBA40. The NPBDREG shows a better correlation of the predicted uncertainty with out-of-distribution data (r > 0.95 vs. r < 0.5) as well as a ~ 7.3 % improvement in the registration accuracy (Dice score, 0.74 vs. 0.69, p ≪ 0.01), and a ~ 18 % improvement in registration smoothness (percentage of folds in the deformation field, 0.014 vs. 0.017, p ≪ 0.01). Finally, NPBDREG demonstrated a better generalization capability for data corrupted by a mixed structure noise (Dice score of 0.73 vs. 0.69, p ≪ 0.01) compared to the baseline PrVXM approach.
Integrated Clinical Environment Security Analysis Using Reinforcement Learning

Many communication standards have been proposed recently and more are being developed as a vision for dynamically composable and interoperable medical equipment. However, few have security systems that are sufficiently extensive or flexible to meet current and future safety requirements. This paper aims to analyze the cybersecurity of the Integrated Clinical Environment (ICE) through the investigation of its attack graph and the application of artificial intelligence techniques that can efficiently demonstrate the subsystems' vulnerabilities. Attack graphs are widely used for assessing network security. On the other hand, they are typically too huge and sophisticated for security administrators to comprehend and evaluate. Therefore, this paper presents a Q-learning-based attack graph analysis approach in which an attack graph that is generated for the Integrated Clinical Environment system resembles the environment, and the agent is assumed to be the attacker. Q-learning can aid in determining the best route that the attacker can take in order to damage the system as much as possible with the least number of actions. Numeric values will be assigned to the attack graph to better determine the most vulnerable part of the system and suggest this analysis to be further utilized for bigger graphs.
CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions

Convolutional neural networks (CNNs) have achieved great success in image classification tasks. In the process of a convolutional operation, a larger input area can capture more context information. Stacking several convolutional layers can enlarge the receptive field, but this increases the parameters. Most CNN models use pooling layers to extract important features, but the pooling operations cause information loss. Transposed convolution can increase the spatial size of the feature maps to recover the lost low-resolution information. In this study, we used two branches with different dilated rates to obtain different size features. The dilated convolution can capture richer information, and the outputs from the two channels are concatenated together as input for the next block. The small size feature maps of the top blocks are transposed to increase the spatial size of the feature maps to recover low-resolution prediction maps. We evaluated the model on three image classification benchmark datasets (CIFAR-10, SVHN, and FMNIST) with four state-of-the-art models, namely, VGG16, VGG19, ResNeXt, and DenseNet. The experimental results show that CDTNet achieved lower loss, higher accuracy, and faster convergence speed in the training and test stages. The average test accuracy of CDTNet increased by 54.81% at most on SVHN with VGG19 and by 1.28% at least on FMNIST with VGG16, which proves that CDTNet has better performance and strong generalization abilities, as well as fewer parameters.
Guaranteeing Timed Opacity using Parametric Timed Model Checking

Information leakage can have dramatic consequences on systems security. Among harmful information leaks, the timing information leakage occurs whenever an attacker successfully deduces confidential internal information. In this work, we consider that the attacker has access (only) to the system execution time. We address the following timed opacity problem: given a timed system, a private location and a final location, synthesize the execution times from the initial location to the final location for which one cannot deduce whether the system went through the private location. We also consider the full timed opacity problem, asking whether the system is opaque for all execution times. We show that these problems are decidable for timed automata (TAs) but become undecidable when one adds parameters, yielding parametric timed automata (PTAs). We identify a subclass with some decidability results. We then devise an algorithm for synthesizing PTAs parameter valuations guaranteeing that the resulting TA is opaque. We finally show that our method can also apply to program analysis.
Data Acquisition and Preparation for Dual-Reference Deep Learning of Image Super-Resolution

The performance of deep learning based image super-resolution (SR) methods depend on how accurately the paired low and high resolution images for training characterize the sampling process of real cameras. Low and high resolution (LR~HR) image pairs synthesized by degradation models (e.g., bicubic downsampling) deviate from those in reality; thus the synthetically-trained DCNN SR models work disappointingly when being applied to real-world images. To address this issue, we propose a novel data acquisition process to shoot a large set of LR~HR image pairs using real cameras. The images are displayed on an ultra-high quality screen and captured at different resolutions. The resulting LR~HR image pairs can be aligned at very high sub-pixel precision by a novel spatial-frequency dual-domain registration method, and hence they provide more appropriate training data for the learning task of super-resolution. Moreover, the captured HR image and the original digital image offer dual references to strengthen supervised learning. Experimental results show that training a super-resolution DCNN by our LR HR dataset achieves higher image quality than training it by other datasets in the literature. Moreover, the proposed screen-capturing data collection process can be automated; it can be carried out for any target camera with ease and low cost, offering a practical way of tailoring the training of a DCNN SR model separately to each of the given cameras.
A Snapshot of Children’s Attitudes toward Machine Translation

Technologies offered and used on the Internet play a significant part in the lives of children; nevertheless, little research has been done on how children view and use machine translation (MT). According to recent literature, there are various benefits to using MT in teaching/learning foreign languages, such as more fluent writing, more effective communication, and fewer errors. Nevertheless, the use of MT in classroom settings is often viewed as problematic by language teachers. Despite the fact that a vast number of students have used MT for various purposes or have tried experimenting with MT for certain academic or entertainment purposes, they seem to have mixed feelings about it. The present qualitative study is based on semi-structured interviews and aims to capture a snapshot of Lithuanian children’s perceptions and awareness of MT technologies. The results of the interviews reveal that children mostly find out about MT as a result of their own efforts and employ MT tools for a variety of purposes; however, at school no systematic guidance and/or support in terms of MT use is provided and children tend to perceive that their teachers generally hold negative attitudes towards MT.
Neural language models for network configuration: Opportunities and reality check

Boosted by deep learning, natural language processing (NLP) techniques have recently seen spectacular progress, mainly fueled by breakthroughs both in representation learning with word embeddings (e.g. word2vec) as well as novel architectures (e.g. transformers). This success quickly invited researchers to explore the use of NLP techniques to other fields, such as computer programming languages, with the promise to automate tasks in software programming (bug detection, code synthesis, code repair, cross language translation etc.). By extension, NLP has potential for application to network configuration languages as well, for instance considering tasks such as network configuration verification, synthesis, and cross-vendor translation. In this paper, we survey recent advances in deep learning applied to programming languages, for the purpose of code verification, synthesis and translation: in particularly, we review their training requirements and expected performance, and qualitatively assess whether similar techniques can benefit corresponding use-cases in networking.
QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers

Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. QUILT is a framework for performing multi-class classification task designed to work effectively on current error-prone quantum computers. QUILT is evaluated with real quantum machines as well as with projected noise levels as quantum machines become more noise free. QUILT demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system.
Complementary Attention Gated Network for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is crucial in many practical applications due to the diversity of pedestrian movements, such as social interactions and individual motion behaviors. With similar observable trajectories and social environments, different pedestrians may make completely different future decisions. However, most existing methods only focus on the frequent modal of the trajectory and thus are difficult to generalize to the peculiar scenario, which leads to the decline of the multimodal fitting ability when facing similar scenarios. In this paper, we propose a complementary attention gated network (CAGN) for pedestrian trajectory prediction, in which a dual-path architecture including normal and inverse attention is proposed to capture both frequent and peculiar modals in spatial and temporal patterns, respectively. Specifically, a complementary block is proposed to guide normal and inverse attention, which are then be summed with learnable weights to get attention features by a gated network. Finally, multiple trajectory distributions are estimated based on the fused spatio-temporal attention features due to the multimodality of future trajectory. Experimental results on benchmark datasets, i.e., the ETH, and the UCY, demonstrate that our method outperforms state-of-the-art methods by 13.8% in Average Displacement Error (ADE) and 10.4% in Final Displacement Error (FDE). Code will be available at https://github.com/jinghaiD/CAGN
Wasserstein Adversarial Transformer for Cloud Workload Prediction

Predictive VM (Virtual Machine) auto-scaling is a promising technique to optimize cloud applications’ operating costs and performance. Understanding the job arrival rate is crucial for accurately predicting future changes in cloud workloads and proactively provisioning and de-provisioning VMs for hosting the applications. However, developing a model that accurately predicts cloud workload changes is extremely challenging due to the dynamic nature of cloud workloads. Long- Short-Term-Memory (LSTM) models have been developed for cloud workload prediction. Unfortunately, the state-of-the-art LSTM model leverages recurrences to predict, which naturally adds complexity and increases the inference overhead as input sequences grow longer. To develop a cloud workload prediction model with high accuracy and low inference overhead, this work presents a novel time-series forecasting model called WGAN-gp Transformer, inspired by the Transformer network and improved Wasserstein-GANs. The proposed method adopts a Transformer network as a generator and a multi-layer perceptron as a critic. The extensive evaluations with real-world workload traces show WGAN- gp Transformer achieves 5× faster inference time with up to 5.1% higher prediction accuracy against the state-of-the-art. We also apply WGAN-gp Transformer to auto-scaling mechanisms on Google cloud platforms, and the WGAN-gp Transformer-based auto-scaling mechanism outperforms the LSTM-based mechanism by significantly reducing VM over-provisioning and under-provisioning rates.
Approaching Heisenberg-scalable thermometry with built-in robustness against noise

Abstract It is a major goal in quantum thermometry to reach a 1/ N scaling of thermometric precision known as Heisenberg scaling but is still in its infancy to date. The main obstacle is that the resources typically required are highly entangled states, which are very difficult to produce and extremely vulnerable to noises. Here, we propose an entanglement-free scheme of thermometry to approach Heisenberg scaling for a wide range of N , which has built-in robustness irrespective of the type of noise in question. Our scheme is amenable to a variety of experimental setups. Moreover, it can be used as a basic building block for promoting previous proposals of thermometry to reach Heisenberg scaling, and its applications are not limited to thermometry but can be straightforwardly extended to other metrological tasks.
An Accelerated Expectation-Maximization Algorithm for Multi-Reference Alignment

The multi-reference alignment (MRA) problem entails estimating an image from multiple noisy and rotated copies of itself. If the noise level is low, one can reconstruct the image by estimating the missing rotations, aligning the images, and averaging out the noise. While accurate rotation estimation is impossible if the noise level is high, the rotations can still be approximated, and thus can provide indispensable information. In particular, learning the approximation error can be harnessed for efficient image estimation. In this paper, we propose a new computational framework, called Synch-EM, that consists of angular synchronization followed by expectation-maximization (EM). The synchronization step results in a concentrated distribution of rotations; this distribution is learned and then incorporated into the EM as a Bayesian prior. The learned distribution also dramatically reduces the search space, and thus the computational load, of the EM iterations. We show by extensive numerical experiments that the proposed framework can significantly accelerate EM for MRA in high noise levels, occasionally by a few orders of magnitude, without degrading the reconstruction quality.
Self-Supervised Camera Self-Calibration from Video

Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration is a laborious procedure requiring specialized data collection and careful tuning. This process must be repeated whenever the parameters of the camera change, which can be a frequent occurrence for mobile robots and autonomous vehicles. In contrast, self-supervised depth and ego-motion estimation approaches can bypass explicit calibration by in-ferring per-frame projection models that optimize a view-synthesis objective. In this paper, we extend this approach to explicitly calibrate a wide range of cameras from raw videos in the wild. We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods. We validate our approach on a wide variety of camera geometries, including perspective, fisheye, and catadioptric. Finally, we show that our approach leads to improvements in the downstream task of depth estimation, achieving state-of-the-art results on the EuRoC dataset with greater computational efficiency than contemporary methods. The project page: https://sites.google.com/ttic.edu/self-sup-self-calib