Reviewerly/exHarmony huggingface dataset:
Scalable, Axiomatic Explanations of Deep Alzheimer’s Diagnosis from Heterogeneous Data

Deep Neural Networks (DNNs) have an enormous potential to learn from complex biomedical data. In particular, DNNs have been used to seamlessly fuse heterogeneous information from neuroanatomy, genetics, biomarkers, and neuropsychological tests for highly accurate Alzheimer's disease diagnosis. On the other hand, their black-box nature is still a barrier for the adoption of such a system in the clinic, where interpretability is absolutely essential. We propose Shapley Value Explanation of Heterogeneous Neural Networks (SVEHNN) for explaining the Alzheimer's diagnosis made by a DNN from the 3D point cloud of the neuroanatomy and tabular biomarkers. Our explanations are based on the Shapley value, which is the unique method that satisfies all fundamental axioms for local explanations previously established in the literature. Thus, SVEHNN has many desirable characteristics that previous work on interpretability for medical decision making is lacking. To avoid the exponential time complexity of the Shapley value, we propose to transform a given DNN into a Lightweight Probabilistic Deep Network without re-training, thus achieving a complexity only quadratic in the number of features. In our experiments on synthetic and real data, we show that we can closely approximate the exact Shapley value with a dramatically reduced runtime and can reveal the hidden knowledge the network has learned from the data.
Fractal distribution of mineral species among the crystallographic point groups

Abstract Crystallographic data from 5289 IMA-approved mineral species in the RRUFF database were used to examine the distribution of species among the 32 crystallographic point groups. It is found that within each crystal system, minerals strongly prefer point groups with higher group orders. Within a crystal system, the abundance of minerals belonging to each point group approximately obeys a power law with respect to group order, the same mathematical formalism that describes objects with fractal geometry. In this framework, each crystal system has its own fractal dimension; crystal systems possessing threefold (or sixfold) symmetry elements (i.e., trigonal, hexagonal, isometric) have significantly lower fractal dimension (&amp;lt;2), while those with only one-, two-, or fourfold symmetry elements (triclinic, monoclinic, orthorhombic, tetragonal) have higher fractal dimension (&amp;gt;2). While higher symmetry is preferred within a crystal system, the opposite trend is observed when comparing between crystal systems, with more species preferring crystals systems with lower order symmetry elements than those with higher order symmetry elements at constant group order. The combination of these two competing trends leads to a complex distribution of minerals among the crystal systems, and to the monoclinic group 2/m, the orthorhombic group 2/m2/m2/m, and the triclinic group 1 being the three most popular point groups, respectively. The fractal behavior of symmetry distribution among minerals points toward universal scaling patterns not just in physical, geometric objects but also in the way that symmetry is incorporated into natural periodic structures.
slimTrain---A Stochastic Approximation Method for Training Separable Deep Neural Networks

Deep neural networks (DNNs) have shown their success as high-dimensional function approximators in many applications; however, training DNNs can be challenging in general. DNN training is commonly phrased as a stochastic optimization problem whose challenges include nonconvexity, nonsmoothness, insufficient regularization, and complicated data distributions. Hence, the performance of DNNs on a given task depends crucially on tuning hyperparameters, especially learning rates and regularization parameters. In the absence of theoretical guidelines or prior experience on similar tasks, this requires solving a series of repeated training problems which can be time-consuming and demanding on computational resources. This can limit the applicability of DNNs to problems with nonstandard, complex, and scarce datasets, e.g., those arising in many scientific applications. To remedy the challenges of DNN training, we propose \tt slimTrain, a stochastic optimization method for training DNNs with reduced sensitivity to the choice of hyperparameters and fast initial convergence. The central idea of slimTrain is to exploit the separability inherent in many DNN architectures; that is, we separate the DNN into a nonlinear feature extractor followed by a linear model. This separability allows us to leverage recent advances made for solving large-scale, linear, ill-posed inverse problems. Crucially, for the linear weights, \tt slimTrain does not require a learning rate and automatically adapts the regularization parameter. In our numerical experiments using function approximation tasks arising in surrogate modeling and dimensionality reduction, slimTrain outperforms existing DNN training methods with the recommended hyperparameter settings and reduces the sensitivity of DNN training to the remaining hyperparameters. Since our method operates on mini-batches, its computational overhead per iteration is modest and savings can be realized by reducing the number of iterations (due to quicker initial convergence) or the number of training problems that need to be solved to identify effective hyperparameters.
Integration Issues of Qualitative and Quantitative Methods in Psychological Research

The article discusses the integration of qualitative and quantitative methods in psychological research. It is shown that the idea of such integration is embedded in the B.F. Lomov’s systemic paradigm and is in the general trend of modern research related to the Mixed Methods approaches. Examples of the development of the systemic paradigm in scientific and practical research of cognition and communication are given, in particular, concerning the application of the strategy of triangulation and the principles of inductive analysis for the integration of qualitative and quantitative empirical data. The specific possibilities of multiple triangulation and inductive analysis of not only data obtained by qualitative methods, but also quantitative data obtained by instrumental methods are considered. Studies are presented where qualitative (verbal) data are converted into quantitative ones, to which standard statistical procedures are applicable to identify causal links.
An Overview of Recent Trends in OCR Systems for Manuscripts

Moudgil, Aditi Singh, Saravjeet Gautam, VinayOCR systems capable of recognizing characters have gained maximum concentration of researchers these days, especially when it comes to recognizing ancient documents. Digitization of these documents to make them readable and also searching from paper-based data becomes a great challenge. Literature shows that there are a lot of OCR systems that use different feature extraction and segmentation techniques to calculate the recognition accuracy. This paper focuses on a variety of classifiers and feature extraction techniques used to segment characters individually to make them readable by machines. The purpose of recognition of these documents is to preserve the heritage and valuable information contained in ancient manuscripts written in different languages.
Incremental Learning Implementations and Vision for Cyber Risk Detection in IoT

The Internet of Things (IoT) has emerged as an Internet-based extension and considerably changed our world. A tremendous amount of IoT applications has highly eased people's daily lives, and improved allocations and usage of a broad range of resources (power bank sharing, bike sharing, etc.). However, the ingrained openness of underlying wireless systems renders these IoT entities vulnerable to a broad range of cyber risks, such as vulnerabilities of spectrum that can be a source of adversarial inference. Furthermore, as our reliance on wireless/connected devices and radios has also been dramatically increasing, public safety, business operations, socializing, and navigation as well as critical national communication infrastructures have become more vulnerable to cyber threats. In addition, the stream of data from IoT devices are also prone to cyber risks, which is of major concern for end users, and could also mislead users and ML models with wrong information during the training and learning phases. Hence, policymakers and industries have recently begun to perceive that the expansion of connected devices and their cyber susceptibility results in a large malicious and inferential risk. As a result, there is a need to identify and comprehend these risks in order to elaborate efficient security solutions. Moreover, the economic impact of IoT devices and associated security vulnerabilities are further growing in accordance with artificial intelligence (AI) integration into human-computer interaction (HCI), including banking, insurance, and so on. Consequently, cyber risks are growing in terms of both frequency and acuteness. Therefore, this article discusses the evolution of common real-time machine learning (RTML) approaches, which are considered as enablers for ensemble learning-based use cases. We discuss a cyber risk detection framework that can be built effectively using Boosting- and Bagging-based ensemble learning approaches.
The readiness of nursing students for mobile learning: a cross-sectional study

Combining electronic learning with mobile informatics, mobile learning allows learning anytime and anywhere. Nursing education includes both theoretical and hands-on components. The use of mobile learning technologies in nursing education enables students to actively participate in the learning process, improve their cognitive and psychomotor skills, access information at any time, and meet lifelong learning needs.This study aimed to investigate the readiness of nursing students for mobile learning and related sociodemographic characteristics.This descriptive study included 678 nursing students studying at three different state universities. The data were collected using an identification form and the Mobile Learning Readiness Scale (MLRS).The study indicated that the students had a moderately high level of readiness for mobile learning. No significant relationship was found between gender and the MLRS scores (p>0.05). However, a significant relationship was found between age and the optimism sub-dimension of MLRS. Significant relationships were found between students' mobile learning readiness and their grade level and income (p<0.01 for both). Also, significant relationships were found between students' readiness and university they attended, except for the optimism sub-dimension (p>0.05).In conclusion, nursing students were found to have the level of awareness and readiness required for using mobile learning technologies in educational processes.Determining the level of readiness of student nurses for mobile learning technologies will help nurse educators in their educational processes.
Image detection method based on improved Canny algorithm

In the field of image edge detection, Canny algorithm has a better effect than other traditional edge detection algorithms in noise filtering and edge preserving. However, the traditional Canny algorithm has some defects in image preprocessing and adaptability. The Gaussian filter used in the traditional Canny algorithm is very poor in filtering out salt and pepper noise. The high and low threshold of the last step need to be set manually, which increases the time cost and lacks self-adaptability. This paper is proposed an improved Canny operator edge detection method, which uses adaptive median filter and guided filter instead of Gaussian filtering to smooth and denoise the image. Finally the improved Otsu algorithm is used to select the high and low thresholds to improve the overall adaptability. The simulation experiment results show that the improved Canny algorithm not only filters out most noises, especially salt and pepper noise, but also retains more image edge details, the identified image edges are smoother, and false edges are removed.
A method for image recognition of intersectant dual-pointer instrument

Image recognition of pointer instrument based on machine vision has been widely used. However, traditional algorithms are inadequate in the case of intersectant dual-pointer instrument. This paper provides a complete solution, including image pretreatment, extraction of the position of pointer and reading identification. In particular, the target line segment filter method is invented.
Local Interpretable Explanations for GBDT

It is a challenging problem for humans to understand the predictions made by sophisticated machine learning techniques. This paper proposes a method to interpret complex machine learning models on individual samples. We propose to first transfer the generalization abilities from the target model to be interpreted, such as deep neural networks, to GBDT (Gradient Boosting Decision Tree) through knowledge distillation. We then present a local explanation method for GBDT by faithfully ranking the relative importance of interpretable predicates for explaining an individual sample. To show the effectiveness of our method, we study and explain machine learning tasks on a diverse of domains including sentiment analysis, tabular classification, and image classification. Experiments show competitive results of explanations that would help users better understand the predictions made by black-box models in some cases, as compared to related state-of-the-art explanation methods.
FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos

Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures. If segmented accurately, haustral folds can allow for better estimation of missed surface and can also serve as valuable landmarks for registering pre-treatment virtual (CT) and optical colonoscopies, to guide navigation towards the anomalies found in pre-treatment scans. We present a novel generative adversarial network, FoldIt, for feature-consistent image translation of optical colonoscopy videos to virtual colonoscopy renderings with haustral fold overlays. A new transitive loss is introduced in order to leverage ground truth information between haustral fold annotations and virtual colonoscopy renderings. We demonstrate the effectiveness of our model on real challenging optical colonoscopy videos as well as on textured virtual colonoscopy videos with clinician-verified haustral fold annotations. All code and scripts to reproduce the experiments of this paper will be made available via our Computational Endoscopy Platform at https://github.com/nadeemlab/CEP.
DocReader: Bounding-Box Free Training of a Document Information Extraction Model

Information extraction from documents is a ubiquitous first step in many business applications. During this step, the entries of various fields must first be read from the images of scanned documents before being further processed and inserted into the corresponding databases. While many different methods have been developed over the past years in order to automate the above extraction step, they all share the requirement of bounding-box or text segment annotations of their training documents. In this work we present DocReader, an end-to-end neural-network-based information extraction solution which can be trained using solely the images and the target values that need to be read. The DocReader can thus leverage existing historical extraction data, completely eliminating the need for any additional annotations beyond what is naturally available in existing human-operated service centres. We demonstrate that the DocReader can reach and surpass other methods which require bounding-boxes for training, as well as provide a clear path for continual learning during its deployment in production.
Prudent Policy Gradient with Auxiliary Actor in Multi-degree-of-freedom Robotic Tasks

The overestimation bias caused by the function approximation error is a common problem of the value-based reinforcement learning algorithms. A clipped Double Q-learning method and delayed policy updates are adopted by Twin Delayed Deep Deterministic policy gradient(TD3) algorithm to reduce the impact of this problem. Although TD3 brings some feasibility, the problem still has not been solved ideally. Thus, based on TD3 an novel algorithm named as Prudent Policy Gradient(PPG) is proposed, where an auxiliary actor is used to prevent actor from selecting exceeding actions and makes the agent’s behavior more prudent. This allows the proposed PPG to find a more efficient and stable policy. The experimental results illustrate that the proposed PPG outperforms TD3 in robotic tasks of several MuJoCo benchmarks and path explorations.
Hierarchical Prosody Modeling and Control in Non-Autoregressive Parallel Neural TTS

Neural text-to-speech (TTS) synthesis can generate speech that is indistinguishable from natural speech. However, the synthetic speech often represents the average prosodic style of the database instead of having more versatile prosodic variation. Moreover, many models lack the ability to control the output prosody, which does not allow for different styles for the same text input. In this work, we train a non-autoregressive parallel neural TTS front-end model hierarchically conditioned on both coarse and fine-grained acoustic speech features to learn a latent prosody space with intuitive and meaningful dimensions. Experiments show that a non-autoregressive TTS model hierarchically conditioned on utterance-wise pitch, pitch range, duration, energy, and spectral tilt can effectively control each prosodic dimension, generate a wide variety of speaking styles, and provide word-wise emphasis control, while maintaining equal or better quality to the baseline model.
Marketing Professional Development Online Streaming Videos to Faculty via Email During COVID-19

Before the start of the COVID-19 pandemic, the College's Hirons Library & Learning Center (HLLC) and the Office of Institutional Research & Training (OIRT) marketing efforts included word of mouth, information literacy instruction session, physical and electronic displays, and announcements on the library webpage to users in person. In the Spring 2021 Semester, due to the COVID-19 pandemic, the HLLC partnered with OIRT to design a marketing campaign to promote professional development streaming videos to faculty using ConvertKit–an email marketing tool. We used Panopto to store the videos, and we put the handouts in FlipGrid. In this edition of the column, we describe how we are marketing professional development streaming videos to faculty, including which strategies we employed and the extent to which we recognized success with these new efforts.
Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Multi-scale Patch Learning with Mammography

Pathological complete response (pCR) indicates the absence of residual tumor in the breast and axillary nodes after neoadjuvant chemotherapy (NAC), which reduces cancerous tumor and improves the prognosis of breast-conserving surgery. To avoid eventual toxicities by NAC and improve the long-term survival outcome, the prediction of pCR using routine breast imaging is an important step to decide the patient treatment. In this paper, we propose a multi-scale patch learning method to predict pCR from pre-NAC mammography, which is widely used for early detection of breast cancer. We use two images (CC and MLO view) of mammography to integrate the texture and shape information of breast tumors in a form of image pyramid with multiple scales. We first extract fixed-sized patches from each pyramid level and concatenate them along the channel dimension to learn multi-scale features of the breast tumor and its surrounding regions. The proposed model achieved better prediction performance (0.803 AUC, 0.75 accuracy, 0.733 sensitivity, and 0.767 specificity) in pCR prediction task than other comparative methods which have been introduced for breast cancer characterization using mammography.
On the Synchronization Power of Token Smart Contracts

Modern blockchains support a variety of distributed applications beyond cryptocurrencies, including smart contracts, which let users execute arbitrary code in a distributed and decentralized fashion. Regardless of their intended application, blockchain platforms implicitly assume consensus for the correct execution of a smart contract, thus requiring that all transactions are totally ordered. It was only recently recognized that consensus is not necessary to prevent double-spending in a cryptocurrency, contrary to common belief. This result suggests that current implementations may be sacrificing efficiency and scalability because they synchronize transactions much more tightly than actually needed. In this work, we study the synchronization requirements of Ethereum's ERC20 token contract, one of the most widely adopted smart contacts. Namely, we model a smart-contract token as a concurrent object and analyze its consensus number as a measure of synchronization power. We show that the richer set of methods supported by ERC20 tokens, compared to standard cryptocurrencies, results in strictly stronger synchronization requirements. More surprisingly, the synchronization power of ERC20 tokens depends on the object's state and can thus be modified by method invocations. To prove this result, we develop a dedicated framework to express how the object's state affects the needed synchronization level. Our findings indicate that ERC20 tokens, as well as other token standards, are more powerful and versatile than plain cryptocurrencies, and are subject to dynamic requirements. Developing specific synchronization protocols that exploit these dynamic requirements will pave the way towards more robust and scalable blockchain platforms.
Improving Zero-shot Neural Machine Translation on Language-specific Encoders- Decoders

Recently, universal neural machine translation (NMT) with shared encoder-decoder gained good performance on zero-shot translation. Unlike universal NMT, jointly trained language-specific encoders-decoders aim to achieve universal representation across non-shared modules, each of which is for a language or language family. The non-shared architecture has the advantage of mitigating internal language competition, especially when the shared vocabulary and model parameters are restricted in their size. However, the performance of using multiple encoders and decoders on zero-shot translation still lags behind universal NMT. In this work, we study zero-shot translation using language-specific encoders-decoders. We propose to generalize the non-shared architecture and universal NMT by differentiating the Transformer layers between language-specific and interlingua. By selectively sharing parameters and applying cross-attentions, we explore maximizing the representation universality and realizing the best alignment of language-agnostic information. We also introduce a denoising auto-encoding (DAE) objective to jointly train the model with the translation task in a multi-task manner. Experiments on two public multilingual parallel datasets show that our proposed model achieves competitive or better results than universal NMT and the strong pivot baseline. Moreover, we experiment incrementally adding new language to the trained model by only updating the new model parameters. With this little effort, the zero-shot translation between this newly added language and existing languages achieves a comparable result with the model trained jointly from scratch on all languages.