Reviewerly/exHarmony huggingface dataset:
The influence of children’s reading ability on initial letter position encoding during a reading-like task.

Previous studies exploring the cost of reading sentences with words that have two transposed letters in adults showed that initial letter transpositions caused the most disruption to reading, indicating the important role that initial letters play in lexical identification (e.g., Rayner et al., 2006). Regarding children, it is not clear whether differences in reading ability would affect how they encode letter position information as they attempt to identify misspelled words in a reading-like task. The aim of this experiment was to explore how initial-letter position information is encoded by children compared to adults when reading misspelled words, containing transpositions, during a reading-like task. Four different conditions were used: control (words were correctly spelled), TL12 (letters in first and second positions were transposed), TL13 (letters in first and third positions were transposed), and TL23 (letters in second and third positions were transposed). Results showed that TL13 condition caused the most disruption, whereas TL23 caused the least disruption to reading of misspelled words. Although disruption for the TL13 condition was quite rapid in adults, the immediacy of disruption was less so for the TL23 and TL12 conditions. For children, effects of transposition also occurred quite rapidly but were longer lasting. The time course was particularly extended for the less skilled relative to the more skilled child readers. This pattern of effects suggests that both adults and children with higher, relative to lower, reading ability encode internal letter position information more flexibly to identify misspelled words, with transposed letters, during a reading-like task. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Saliency is a Possible Red Herring When Diagnosing Poor Generalization

Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only in the training distribution instead of the true image features that denote a class. It is often thought that this can be diagnosed visually using attribution (aka saliency) maps. We study if this assumption is correct. In some prediction tasks, such as for medical images, one may have some images with masks drawn by a human expert, indicating a region of the image containing relevant information to make the prediction. We study multiple methods that take advantage of such auxiliary labels, by training networks to ignore distracting features which may be found outside of the region of interest. This mask information is only used during training and has an impact on generalization accuracy depending on the severity of the shift between the training and test distributions. Surprisingly, while these methods improve generalization performance in the presence of a covariate shift, there is no strong correspondence between the correction of attribution towards the features a human expert have labelled as important and generalization performance. These results suggest that the root cause of poor generalization may not always be spatially defined, and raise questions about the utility of masks as 'attribution priors' as well as saliency maps for explainable predictions.
User friendly, cloud based, whole slide image segmentation

Convolutional neural networks, the state of the art for image segmentation, have been successfully applied to histology images by many computational researchers. However, the translatability of this technology to clinicians and biological researchers is limited due to the complex and undeveloped user interface of the code, as well as the extensive computer setup required. We have developed a plugin for segmentation of whole slide images (WSIs) with an easy to use graphical user interface. This plugin runs a state-of-the-art convolutional neural network for segmentation of WSIs in the cloud. Our plugin is built on the open source tool HistomicsTK by Kitware Inc. (Clifton Park, NY), which provides remote data management and viewing abilities for WSI datasets. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Users can easily upload slides to a server where our plugin is installed and perform the segmentation analysis remotely. This plugin is open source and once trained, has the ability to be applied to the segmentation of any pathological structure. For a proof of concept, we have trained it to segment glomeruli from renal tissue images, demonstrating it on holdout tissue slides.
Forecasting Architectural Decay From Evolutionary History

As a software system evolves, its architecture tends to decay, leading to the occurrence of architectural elements that become resistant to maintenance or prone to defects. To address this problem, engineers can significantly benefit from determining which architectural elements will decay before that decay actually occurs. Forecasting decay allows engineers to take steps to prevent decay, such as focusing maintenance resources on the architectural elements most likely to decay. To that end, we construct novel models that predict the quality of an architectural element by utilizing multiple architectural views (both structural and semantic) and architectural metrics as features for prediction. We conduct an empirical study using our prediction models on 38 versions of five systems. Our findings show that we can predict low architectural quality, i.e., architectural decay, with high performance—even for cases of decay that suddenly occur in an architectural module. We further report the factors that best predict architectural quality.
Non-Markovianity as a resource for quantum correlation teleportation

Quantum teleportation of the quantum correlated states via noisy channels is investigated. The noisy channels are realized by a couple of two-level atoms (qubits) embedded in a zero-temperature bosonic bath. The entanglement of the channels is provided by the interqubit interaction and/or through the memory of the environment. Especially for the case of noninteracting qubits, the resource of the teleportation can be supplied by the entangled state of the channel, which is provided by information backflow in the non-Markovian regime of the evolution. More non-Markovianity of the dynamics generates a higher amount of induced entanglement and hence enhances the quality of the quantum correlation teleportation process. When the degree of non-Markovianity of dynamics is sufficiently high, quantum teleportation, which is superior to classical communication, is achievable.
Template-Free Try-On Image Synthesis via Semantic-Guided Optimization

The virtual try-on task is so attractive that it has drawn considerable attention in the field of computer vision. However, presenting the 3-D physical characteristic (e.g., pleat and shadow) based on a 2-D image is very challenging. Although there have been several previous studies on 2-D-based virtual try-on work, most: 1) required user-specified target poses that are not user-friendly and may not be the best for the target clothing and 2) failed to address some problematic cases, including facial details, clothing wrinkles, and body occlusions. To address these two challenges, in this article, we propose an innovative template-free try-on image synthesis (TF-TIS) network. The TF-TIS first synthesizes the target pose according to the user-specified in-shop clothing. Afterward, given an in-shop clothing image, a user image, and a synthesized pose, we propose a novel model for synthesizing a human try-on image with the target clothing in the best fitting pose. The qualitative and quantitative experiments both indicate that the proposed TF-TIS outperforms the state-of-the-art methods, especially for difficult cases.
A Two-Stream Continual Learning System With Variational Domain-Agnostic Feature Replay

Learning in nonstationary environments is one of the biggest challenges in machine learning. Nonstationarity can be caused by either task drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This article aims to tackle this challenge with a modularized two-stream continual learning (CL) system, where the model is required to learn new tasks from a support stream and adapted to new domains in the query stream while maintaining previously learned knowledge. To deal with both drifts within and across the two streams, we propose a variational domain-agnostic feature replay-based approach that decouples the system into three modules: an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inference module</i> that filters the input data from the two streams into domain-agnostic representations, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generative module</i> that facilitates the high-level knowledge transfer, and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">solver module</i> that applies the filtered and transferable knowledge to solve the queries. We demonstrate the effectiveness of our proposed approach in addressing the two fundamental scenarios and complex scenarios in two-stream CL.
Training (Overparametrized) Neural Networks in Near-Linear Time.

The slow convergence rate and pathological curvature issues of first-order gradient methods for training deep neural networks, initiated an ongoing effort for developing faster second-order optimization algorithms beyond SGD, without compromising the generalization error. Despite their remarkable convergence rate (independent of the training batch size n), second-order algorithms incur a daunting slowdown in the cost per iteration (inverting the Hessian matrix of the loss function), which renders them impractical. Very recently, this computational overhead was mitigated by the works of [Zhang et al., 2019; Cai et al., 2019], yielding an O(mn²)-time second-order algorithm for training two-layer overparametrized neural networks of polynomial width m. We show how to speed up the algorithm of [Cai et al., 2019], achieving an O(mn)-time backpropagation algorithm for training (mildly overparametrized) ReLU networks, which is near-linear in the dimension (mn) of the full gradient (Jacobian) matrix. The centerpiece of our algorithm is to reformulate the Gauss-Newton iteration as an 𝓁₂-regression problem, and then use a Fast-JL type dimension reduction to precondition the underlying Gram matrix in time independent of M, allowing to find a sufficiently good approximate solution via first-order conjugate gradient. Our result provides a proof-of-concept that advanced machinery from randomized linear algebra - which led to recent breakthroughs in convex optimization (ERM, LPs, Regression) - can be carried over to the realm of deep learning as well.
Resolvable Cluster Target Tracking Based on the DBSCAN Clustering Algorithm and Labeled RFS

When a sensor can resolve the members in a cluster, it is difficult to accurately track each target due to cooperative interaction among the targets. In this paper, we research the tracking problem of resolvable cluster targets with cooperative interaction. Firstly, we use the stochastic differential equation to model the cluster coordination rules, and the state equation of the single target in the cluster is derived. On this basis, a Bayes recursive filter tracking method based on the combination of the DBSCAN clustering algorithm and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -GLMB filter is proposed. In the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -GLMB filter prediction stage, the DBSCAN algorithm is used to determine the cluster where the target is located in real time. Then, the collaborative noise of the target is estimated, which will be used as the input to correct the prediction state of the target. The simulation and experiment results demonstrate the effectiveness of the proposed algorithm when the cluster is splitting, merging, and in reorganization.
Curation and Analysis of Global Sedimentary Geochemical Data to Inform Earth History

Large datasets increasingly provide critical insights into crustal and surface processes on Earth.These data come in the form of published and contributed observations, which often include associated metadata.Even in the best-case scenario of a carefully curated dataset, it may be nontrivial to extract meaningful analyses from such compilations, and choices made with respect to filtering, resampling, and averaging can affect the resulting trends and any interpretation(s) thereof.As a result, a thorough understanding of how to digest, process, and analyze large data compilations is required.Here, we present a generalizable workflow developed using the Sedimentary Geochemistry and Paleoenvironments Project database.We demonstrate the effects of filtering and weighted resampling on Al 2 O 3 and U contents, two representative geochemical components of interest in sedimentary geochemistry (one major and one trace element, respectively).Through our analyses, we highlight several methodological challenges in a "bigger data" approach to Earth science.We suggest that, with slight modifications to our workflow, researchers can confidently use large collections of observations to gain new insights into processes that have shaped Earth's crustal and surface environments.
On Entanglement Assisted Classical Optical Communications

Entanglement assisted communication is advocated by numerous authors as an alternative to classical communication offering significant improvement in channel capacity, in particular in noisy regime.In all those papers it is always assumed that entanglement can be distributed without any imperfections, except for attenuation.We demonstrate that under imperfect pre-shared entanglement distribution, assuming that entanglement distribution channel is modeled as a noisy and lossy Bosonic channel, the entanglement assisted communication can be inferior compared to the classical communication, depending on the parameters of the distribution channel.We identify the conditions under which entanglement assistance can still provide an advantage over the classical case.In particular, when both communication and entanglement distribution channels are not used for entanglement assisted communication but rather for classical transmission instead then the classical capacity is always higher than the entanglement assisted capacity.We also study the entanglement assisted communication under the strong atmospheric turbulence effects.
Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.
Construction and Exploration of an Intelligent Evaluation System for Educational APP through Artificial Intelligence Technology

To improve the evaluation accuracy of educational applications (APPs), the evaluation methods of educational APPs under artificial intelligence (AI) technology are explored. First, based on the principles of establishing evaluation indexes, the evaluation indexes for educational APPs are established. Second, an index evaluation system for educational APPs is constructed, and weights are assigned to the established evaluation indexes of educational APPs with the aid of analytic hierarchy process (AHP). Finally, the availability and effectiveness of the established evaluation system are investigated through empirical analysis. The results show: Five first-level indexes and 20 second-level indexes have been established through the existing index establishment principles, and a framework for intelligent evaluation of educational APPs has been successfully constructed; AHP is utilized to calculate the weight of each index; among the first-level indexes, the weight ratios of the educational and scientific indexes of the educational APPs are larger, whose proportion exceeds 60%; among the second-level indexes, the educational objective, educational principle, and knowledge systematization account for the highest proportion; therefore, the intelligent evaluation system of education APPs is obtained; finally, the empirical analysis has revealed that the score given by the intelligent evaluation system and the actual score of users have a high consistency, which proves that the proposed intelligent evaluation system is feasible and effective. The proposed intelligent evaluation system can be used as the basis for the design of educational APPs to improve the values of educational APPs.
3D Face Point Cloud Reconstruction and Recognition Using Depth Sensor

Facial recognition has attracted more and more attention since the rapid growth of artificial intelligence (AI) techniques in recent years. However, most of the related works about facial reconstruction and recognition are mainly based on big data collection and image deep learning related algorithms. The data driven based AI approaches inevitably increase the computational complexity of CPU and usually highly count on GPU capacity. One of the typical issues of RGB-based facial recognition is its applicability in low light or dark environments. To solve this problem, this paper presents an effective procedure for facial reconstruction as well as facial recognition via using a depth sensor. For each testing candidate, the depth camera acquires a multi-view of its 3D point clouds. The point cloud sets are stitched for 3D model reconstruction by using the iterative closest point (ICP). Then, a segmentation procedure is designed to separate the model set into a body part and head part. Based on the segmented 3D face point clouds, certain facial features are then extracted for recognition scoring. Taking a single shot from the depth sensor, the point cloud data is going to register with other 3D face models to determine which is the best candidate the data belongs to. By using the proposed feature-based 3D facial similarity score algorithm, which composes of normal, curvature, and registration similarities between different point clouds, the person can be labeled correctly even in a dark environment. The proposed method is suitable for smart devices such as smart phones and smart pads with tiny depth camera equipped. Experiments with real-world data show that the proposed method is able to reconstruct denser models and achieve point cloud-based 3D face recognition.
An Association Rule Mining Approach to Discover Demand and Supply Patterns Based on Thai Social Media Data

In the digital age, social media technology has an important role as a communication platform for interpersonal interactions in the online virtual world. In addition, social media has impacted product exchange behavior in both vendors and buyers, with a shift from the traditional sales model to communication between parties via social media. Social media marketing, an online means of buying, selling, and exchanging goods and services, is increasingly popular due to convenience, speed, and greater choices. This trend has grown rapidly and is set to expand, leading to increased interest in research which analyzes and processes social media marketing data to gain a new integrated body of knowledge to better serve online business transactions. This research covers a new field, which may cause research and development limitations requiring background knowledge in several areas, such as digital technology, data analytics, and business analysis. This research aims to develop a framework to search for association rule mining of demand and supply data on social media platforms. Data is collected from Twitter and underwent cleansing and labeling for separating into five groups. Hashtag data from tweets is then extracted and transformed to input attributes of the framework. Next, association rule mining is performed using the Apriori algorithm in order to determine frequent items and extract candidate association rules. The last stage is rule selection, which uses Twitter-specific statistical attributes, that is, number of retweets and likes, to select highly effective association rules. The findings are that it is possible to mine association rules relating to demand and supply on Twitter. Based on an analysis of the association rule results, the content of those rules reflects trending activities and events at different times. Such information can be analyzed in further research to design improvements in social media marketing.
Consistent Accelerated Inference via Confident Adaptive Transformers

We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now ubiquitous in natural language processing (NLP).Amortized or approximate computational methods increase efficiency, but can come with unpredictable performance costs.In this work, we present CATs-Confident Adaptive Transformers-in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence.Our method trains additional prediction heads on top of intermediate layers, and dynamically decides when to stop allocating computational effort to each input using a meta consistency classifier.To calibrate our early prediction stopping rule, we formulate a unique extension of conformal prediction.We demonstrate the effectiveness of this approach on four classification and regression tasks.
ChainCQG: Flow-Aware Conversational Question Generation

Conversational systems enable numerous valuable applications, and question-answering is an important component underlying many of these.However, conversational questionanswering remains challenging due to the lack of realistic, domain-specific training data.Inspired by this bottleneck, we focus on conversational question generation as a means to generate synthetic conversations for training and evaluation purposes.We present a number of novel strategies to improve conversational flow and accommodate varying question types and overall fluidity.Specifically, we design ChainCQG as a two-stage architecture that learns question-answer representations across multiple dialogue turns using a flow propagation training strategy.ChainCQG significantly outperforms both answer-aware and answer-unaware SOTA baselines (e.g., up to 48% BLEU-1 improvement).Additionally, our model is able to generate different types of questions, with improved fluidity and coreference alignment.