Reviewerly/exHarmony huggingface dataset:
Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization

Real-world large-scale datasets are heteroskedastic and imbalanced --- labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imbalance challenge deep learning algorithms due to the difficulty of distinguishing among mislabeled, ambiguous, and rare examples. Addressing heteroskedasticity and imbalance simultaneously is under-explored. We propose a data-dependent regularization technique for heteroskedastic datasets that regularizes different regions of the input space differently. Inspired by the theoretical derivation of the optimal regularization strength in a one-dimensional nonparametric classification setting, our approach adaptively regularizes the data points in higher-uncertainty, lower-density regions more heavily. We test our method on several benchmark tasks, including a real-world heteroskedastic and imbalanced dataset, WebVision. Our experiments corroborate our theory and demonstrate a significant improvement over other methods in noise-robust deep learning.
Hyperspectral microscopic imaging for head and neck squamous cell carcinoma detection in histologic images

The purpose of this study is to investigate hyperspectral microscopic imaging and deep learning methods for automatic detection of head and neck squamous cell carcinoma (SCC) on histologic slides. Hyperspectral imaging (HSI) cubes were acquired from pathologic slides of 18 patients with SCC of the larynx, hypopharynx, and buccal mucosa. An Inception-based two-dimensional convolutional neural network (CNN) was trained and validated for the HSI data. The automatic deep learning method was tested with independent data of human patients. This study demonstrated the feasibility of using hyperspectral microscopic imaging and deep learning classification to aid pathologists in detecting SCC on histologic slides.
Experiments of Image Classification Using Dissimilarity Spaces Built with Siamese Networks

Traditionally, classifiers are trained to predict patterns within a feature space. The image classification system presented here trains classifiers to predict patterns within a vector space by combining the dissimilarity spaces generated by a large set of Siamese Neural Networks (SNNs). A set of centroids from the patterns in the training data sets is calculated with supervised k-means clustering. The centroids are used to generate the dissimilarity space via the Siamese networks. The vector space descriptors are extracted by projecting patterns onto the similarity spaces, and SVMs classify an image by its dissimilarity vector. The versatility of the proposed approach in image classification is demonstrated by evaluating the system on different types of images across two domains: two medical data sets and two animal audio data sets with vocalizations represented as images (spectrograms). Results show that the proposed system's performance competes competitively against the best-performing methods in the literature, obtaining state-of-the-art performance on one of the medical data sets, and does so without ad-hoc optimization of the clustering methods on the tested data sets.
Deep Learning for Sentiment Analysis of Tunisian Dialect

Automatic sentiment analysis has become one of the fastest growing research areas in the Natural Language Processing (NLP) field. Despite its importance, this is the first work towards sentiment analysis at both aspect and sentence levels for the Tunisian Dialect in the field of Tunisian supermarkets. Therefore, we experimentally evaluate, in this paper, three deep learning methods, namely convolution neural networks (CNN), long short-term memory (LSTM), and bi-directional long-short-term-memory (Bi-LSTM). Both LSTM and Bi-LSTM constitute two major types of Recurrent Neural Networks (RNN). Towards this end, we gathered a corpus containing comments posted on the official Facebook pages of Tunisian supermarkets. To conduct our experiments, this corpus was annotated on the basis of five criteria (very positive/ positive/ neutral/ negative/ very negative) and other twenty categories of aspects. In this evaluation, we show that the gathered features can lead to very encouraging performances through the use of CNN and Bi-LSTM neural networks.
Coarse2Fine: a two-stage training method for fine-grained visual classification

Small inter-class and large intra-class variations are the key challenges in fine-grained visual classification. Objects from different classes share visually similar structures, and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discriminative local features (e.g., bird’s beak or car’s headlight) is crucial. Most of the recent successes on this problem are based upon the attention models which can localize and attend the local discriminative objects parts. In this work, we propose a training method for visual attention networks, Coarse2Fine, which creates a differentiable path from the attended feature maps to the input space. Coarse2Fine learns an inverse mapping function from the attended feature maps to the informative regions in the raw image, which will guide the attention maps to better attend the fine-grained features. Besides, we propose an initialization method for the attention weights. Our experiments show that Coarse2Fine reduces the classification error by up to 5.1% on common fine-grained datasets.
Experimental hierarchy and optimal robustness of quantum correlations of two-qubit states with controllable white noise

We demonstrate a hierarchy of various classes of quantum correlations on experimentally prepared two-qubit Werner-like states with controllable white noise. Werner states, which are white-noise-affected Bell states, are prototypal examples for studying such a hierarchy as a function of the amount of white noise. We experimentally generated Werner states and their generalizations, i.e., partially entangled pure states affected by white noise. These states enabled us to study the hierarchy of the following classes of correlations: separability, entanglement, steering in three- and two-measurement scenarios, and Bell nonlocality. We show that the generalized Werner states (GWSs) reveal fundamentally different aspects of the hierarchy compared to the Werner states. In particular, we find five different parameter regimes of the GWSs, including those steerable in a two-measurement scenario but not violating Bell inequalities. This regime cannot be observed for the usual Werner states. Furthermore, we find threshold curves separating different regimes of the quantum correlations and find the optimal states which allow for the largest amount of white noise, which does not destroy their specific quantum correlations (e.g., unsteerable entanglement). Thus, we could identify the optimal Bell-nondiagonal GWSs, which are, for this specific meaning, more robust against white noise compared to the Bell-diagonal GWSs (i.e., Werner states).
Sentiment Analysis of Moroccan Tweets Using Text Mining

This paper introduces a framework system that analyzes the data extracted from the social network Twitter. In our study, we deal with Moroccan tweets written in Standard Arabic (SA) and Moroccan Dialect (MD). This system allows collecting, processing and visualizing sentiments that could be extracted from Moroccan user’s communication. As a training set, 36114 tweets were collected but only 13,550 tweets were found to be valid. Firstly, the tweets are classified into 2 categories: SA and MD. The tweets belonging to each category are then classified into 4 classes: positive, negative, neutral or mixed. We have performed several experiments to evaluate all the possible combinations of the following weight schemes: N-grams and stemming terms techniques. The experimental results provide the best scenario for each classifier and indicate that LSTM classifier using word embedding without removing Stopwords outperforms CNN classifier as well as the CNN-LSTM classifier (with the same features), and it outperforms SVM with term frequency–inverse document frequency (TF-IDF) weighting scheme with stemming through Bigrams feature. Moreover, the obtained results outperformed other comparable related work results.
A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.
Investigating microwave loss of SiGe using superconducting transmon qubits

Silicon-Germanium (SiGe) is a material that possesses a multitude of applications ranging from transistors to eletro-optical modulators and quantum dots. The diverse properties of SiGe also make it attractive to implementations involving superconducting quantum computing. Here we demonstrate the fabrication of transmon quantum bits on SiGe layers and investigate the microwave loss properties of SiGe at cryogenic temperatures and single photon microwave powers. We find relaxation times of up to 100 $μ$s, corresponding to a quality factor Q above 4 M for large pad transmons. The high Q values obtained indicate that the SiGe/Si heterostructure is compatible with state of the art performance of superconducting quantum circuits.
Covid-19 Response From Global Makers: The Careables Cases of Global Design and Local Production

Makerspaces—informal shared spaces that offer access to technologies, resources and a community of peer learners for making—across the globe initiated a rapid response to the lack of medical hardware supplies during the global pandemic outbreak in early 2020 caused by the Corona virus (COVID-19). As our health systems faced unexperienced pressure, being close to collapsing in some countries, and global supply chains failing to react immediately, makers started to prototype, locally produce and globally share designs of Open Source healthcare products, such as face shields and other medical supplies. Local collaboration with hospitals and healthcare professionals were established. These bottom-up initiatives from maker networks across the globe are showing us how responsible innovation is happening outside the constraints of profit-driven large industries. In this qualitative study we present five cases from a global network of makers that contributed to the production of personal protective equipment (PPE) and healthcare-related products. We draw our cases from the experiences made in Careables , a mixed community of people and organizations committed to the co-design and making of open, personalized healthcare for everyone. With the presented cases we reflect on the potential implications for post-pandemic local production of healthcare products and analyze them from a social innovation perspective. These global experiences are valuable indications of transformative innovations that can reduce dependencies from international supply chains and mainstream mass production.
Manual and Automatic Image Analysis Segmentation Methods for Blood Flow Studies in Microchannels

In blood flow studies, image analysis plays an extremely important role to examine raw data obtained by high-speed video microscopy systems. This work shows different ways to process the images which contain various blood phenomena happening in microfluidic devices and in microcirculation. For this purpose, the current methods used for tracking red blood cells (RBCs) flowing through a glass capillary and techniques to measure the cell-free layer thickness in different kinds of microchannels will be presented. Most of the past blood flow experimental data have been collected and analyzed by means of manual methods, that can be extremely reliable, but they are highly time-consuming, user-intensive, repetitive, and the results can be subjective to user-induced errors. For this reason, it is crucial to develop image analysis methods able to obtain the data automatically. Concerning automatic image analysis methods for individual RBCs tracking and to measure the well known microfluidic phenomena cell-free layer, two developed methods are presented and discussed in order to demonstrate their feasibility to obtain accurate data acquisition in such studies. Additionally, a comparison analysis between manual and automatic methods was performed.
Time Your Hedge With Deep Reinforcement Learning

Can an asset manager plan the optimal timing for her/his hedging strategies given market conditions? The standard approach based on Markowitz or other more or less sophisticated financial rules aims to find the best portfolio allocation thanks to forecasted expected returns and risk but fails to fully relate market conditions to hedging strategies decision. In contrast, Deep Reinforcement Learning (DRL) can tackle this challenge by creating a dynamic dependency between market information and hedging strategies allocation decisions. In this paper, we present a realistic and augmented DRL framework that: (i) uses additional contextual information to decide an action, (ii) has a one period lag between observations and actions to account for one day lag turnover of common asset managers to rebalance their hedge, (iii) is fully tested in terms of stability and robustness thanks to a repetitive train test method called anchored walk forward training, similar in spirit to k fold cross validation for time series and (iv) allows managing leverage of our hedging strategy. Our experiment for an augmented asset manager interested in sizing and timing his hedges shows that our approach achieves superior returns and lower risk.
An Automated Multiple-Choice Question Generation using Natural Language Processing Techniques

Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.
MPTP: Motion-planning-aware task planning for navigation in belief space

We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environments. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In this paper, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated in simulation, in an office environment and its scalability is tested in the larger Willow Garage world. A reasonable comparison with a work that is closest to our approach is also provided. We also demonstrate the adaptability of our approach by considering a building floor navigation domain. Finally, we also discuss the limitations of our approach and put forward suggestions for improvements and future work.
Attention-Enhanced Graph Convolutional Networks for Aspect-Based Sentiment Classification with Multi-Head Attention

The purpose of aspect-based sentiment classification is to identify the sentiment polarity of each aspect in a sentence. Recently, due to the introduction of Graph Convolutional Networks (GCN), more and more studies have used sentence structure information to establish the connection between aspects and opinion words. However, the accuracy of these methods is limited by noise information and dependency tree parsing performance. To solve this problem, we proposed an attention-enhanced graph convolutional network (AEGCN) for aspect-based sentiment classification with multi-head attention (MHA). Our proposed method can better combine semantic and syntactic information by introducing MHA and GCN. We also added an attention mechanism to GCN to enhance its performance. In order to verify the effectiveness of our proposed method, we conducted a lot of experiments on five benchmark datasets. The experimental results show that our proposed method can make more reasonable use of semantic and syntactic information, and further improve the performance of GCN.
On reaction network implementations of neural networks

This paper is concerned with the utilization of deterministically modelled chemical reaction networks for the implementation of (feed-forward) neural networks. We develop a general mathematical framework and prove that the ordinary differential equations (ODEs) associated with certain reaction network implementations of neural networks have desirable properties including (i) existence of unique positive fixed points that are smooth in the parameters of the model (necessary for gradient descent) and (ii) fast convergence to the fixed point regardless of initial condition (necessary for efficient implementation). We do so by first making a connection between neural networks and fixed points for systems of ODEs, and then by constructing reaction networks with the correct associated set of ODEs. We demonstrate the theory by constructing a reaction network that implements a neural network with a smoothed ReLU activation function, though we also demonstrate how to generalize the construction to allow for other activation functions (each with the desirable properties listed previously). As there are multiple types of 'networks' used in this paper, we also give a careful introduction to both reaction networks and neural networks, in order to disambiguate the overlapping vocabulary in the two settings and to clearly highlight the role of each network's properties.
Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.