An Unsupervised and Robust Line and Word Segmentation Method for Handwritten and Degraded Printed Document

Segmentation of text lines and words in an unconstrained handwritten or a machine-printed degraded document is a challenging document analysis problem due to the heterogeneity in the document structure. Often there is un-even skew between the lines and also broken words in a document. In this article, the contribution lies in segmentation of a document page image into lines and words. We have proposed an unsupervised, robust, and simple statistical method to segment a document image that is either handwritten or machine-printed (degraded or otherwise). In our proposed method, the segmentation is treated as a two-class classification problem. The classification is done by considering the distribution of gap size (between lines and between words) in a binary page image. Our method is very simple and easy to implement. Other than the binarization of the input image, no pre-processing is necessary. There is no need of high computational resources. The proposed method is unsupervised in the sense that no annotated document page images are necessary. Thus, the issue of a training database does not arise. In fact, given a document page image, the parameters that are needed for segmentation of text lines and words are learned in an unsupervised manner. We have applied our proposed method on several popular publicly available handwritten and machine-printed datasets (ISIDDI, IAM-Hist, IAM, PBOK) of different Indian and other languages containing different fonts. Several experimental results are presented to show the effectiveness and robustness of our method. We have experimented on ICDAR-2013 handwriting segmentation contest dataset and our method outperforms the winning method. In addition to this, we have suggested a quantitative measure to compute the level of degradation of a document page image.

Developing a Cross-lingual Semantic Word Similarity Corpus for English–Urdu Language Pair

Semantic word similarity is a quantitative measure of how much two words are contextually similar. Evaluation of semantic word similarity models requires a benchmark corpus. However, despite the millions of speakers and the large digital text of the Urdu language on the Internet, there is a lack of benchmark corpus for the Cross-lingual Semantic Word Similarity task for the Urdu language. This article reports our efforts in developing such a corpus. The newly developed corpus is based on the SemEval-2017 task 2 English dataset, and it contains 1,945 cross-lingual English–Urdu word pairs. For each of these pairs of words, semantic similarity scores were assigned by 11 native Urdu speakers. In addition to corpus generation, this article also reports the evaluation results of a baseline approach, namely “Translation Plus Monolingual Analysis” for automated identification of semantic similarity between English–Urdu word pairs. The results showed that the path length similarity measure performs better for the Google and Bing translated words. The newly created corpus and evaluation results are freely available online for further research and development.

Estimating time-dependent entropy production from non-equilibrium trajectories

The rate of entropy production provides a useful quantitative measure of a non-equilibrium system and estimating it directly from time-series data from experiments is highly desirable. Several approaches have been considered for stationary dynamics, some of which are based on a variational characterization of the entropy production rate. However, the issue of obtaining it in the case of non-stationary dynamics remains largely unexplored. Here, we solve this open problem by demonstrating that the variational approaches can be generalized to give the exact value of the entropy production rate even for non-stationary dynamics. On the basis of this result, we develop an efficient algorithm that estimates the entropy production rate continuously in time by using machine learning techniques and validate our numerical estimates using analytically tractable Langevin models in experimentally relevant parameter regimes. Our method only requires time-series data for the system of interest without any prior knowledge of the system’s parameters.

A novel time-based surface EMG measure for quantifying hypertonia in paretic arm muscles during daily activities after hemiparetic stroke

After stroke, paretic arm muscles are constantly exposed to abnormal neural drive from the injured brain. As such, hypertonia, broadly defined as an increase in muscle tone, is prevalent especially in distal muscles, which impairs daily function or in long-term leads to a flexed resting posture in the wrist and fingers. However, there currently is no quantitative measure that can reliably track how hypertonia is expressed on daily basis. In this study, we propose a novel time-based surface electromyography (sEMG) measure that can overcome the limitations of the coarse clinical scales often measured in functionally irrelevant context and the magnitude-based sEMG measures that suffer from signal non-stationarity. We postulated that the key to robust quantification of hypertonia is to capture the true baseline in sEMG for each measurement session, by which we can define the relative duration of activity over a short time segment continuously tracked in a sliding window fashion. We validate that the proposed measure of sEMG active duration is robust across parameter choices (e.g., sampling rate, window length, threshold criteria), robust against typical noise sources present in paretic muscles (e.g., low signal-to-noise ratio, sporadic motor unit action potentials), and reliable across measurements (e.g., sensors, trials, and days), while providing a continuum of scale over the full magnitude range for each session. Furthermore, sEMG active duration could well characterize the clinically observed differences in hypertonia expressed across different muscles and impairment levels. The proposed measure can be used for continuous and quantitative monitoring of hypertonia during activities of daily living while at home, which will allow for the study of the practical effect of pharmacological and/or physical interventions that try to combat its presence.

Single Image-Based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy

High-resolution functional 2-photon microscopy of neural activity is a cornerstone technique in current neuroscience, enabling, for instance, the image-based analysis of relations of the organization of local neuron populations and their temporal neural activity patterns. Interpreting local image intensity as a direct quantitative measure of neural activity presumes, however, a consistent within- and across-image relationship between the image intensity and neural activity, which may be subject to interference by illumination artifacts. In particular, the so-called vignetting artifact—the decrease of image intensity toward the edges of an image—is, at the moment, widely neglected in the context of functional microscopy analyses of neural activity, but potentially introduces a substantial center-periphery bias of derived functional measures. In the present report, we propose a straightforward protocol for single image-based vignetting correction. Using immediate-early gene-based 2-photon microscopic neural image data of the mouse brain, we show the necessity of correcting both image brightness and contrast to improve within- and across-image intensity consistency and demonstrate the plausibility of the resulting functional data.

Pregnancy outcomes with differences in grain consumption: a randomized controlled trial

Objectives Contemporary obstetrics has begun to appreciate the importance of diet in pregnancy, but guidelines are not based on robust data. The hypothesis that a whole grains diet improves pregnancy outcomes is tested in this study. We compared maternal and neonatal outcomes for a pregnancy diet containing 75% of total carbohydrates as refined grains with outcomes for a diet with 75% of total carbohydrates as whole grains. Methods This was a randomized interventional study in a clinic population over the last 4–7 months of normal pregnancy with extensive compliance measures. Besides obstetrical and neonatal outcomes, anthropometric measurements were done. In addition to food frequency questionnaires (FFQs), total plasma alkyl resorcinols, a unique quantitative measure of whole grains, were used as a measure of whole grain consumption. Results The data show effective compliance and no difference in outcomes between the diets with regard to maternal weight gain, birth weights, subcutaneous fat and glucose tolerance. Conclusions Ensuring compliance to a proper pregnancy diet resulted in satisfactory weight gain and normal outcomes even when the proportion of whole grains consumed is only 25% of total carbohydrates. www.ClinicalTrials.gov NCT03232762, Effects of Diet on Pregnancy Outcome and Child Obesity.

Quantitative assessment of information quality in textual sources for landslide inventories

Landslide research chiefly relies on digital inventories for a multitude of spatial, temporal, and/or process analyses. In respect thereof, many landslide inventories are populated with information from textual documents (e.g., news articles, technical reports) due to effectiveness. However, information detail can vary greatly in these documents and the question arises whether such textual information is suitable for landslide inventories. The present work proposes to define the usefulness of textual source types as a probability to find landslide information, weighted with adaptable parameter requirements. To illustrate the method with practical results, a German landslide dataset has been examined. It was found that three combined source types (administrative documents, expert opinions, and news articles) give an 89 % chance to detect useful information on three defined parameters (location, date, and process type). In conclusion, the definition of usefulness as a probability makes it an intuitive, quantitative measure that is suitable for a wide range of applicants. Furthermore, a priori knowledge of usefulness allows for focusing on a few source types with the most promising outcome and thus increases the effectiveness of textual data acquisition and digitalisation for landslide inventories.

Establishing and Testing a Quantitative Measure for Evolving Third Place Characteristics

Third places offer and promote social experiences beneficial for building interpersonal relationships. This study has two goals: 1) establish a scale that tests if an environment is characteristic of third place characteristics and 2) use this scale with four virtual environments (Facebook, Snapchat, Instagram, and Twitter) to test the proficiency of third place characteristics as representative of virtual third places. A research-informed scale was created and tested with a sample of 354 participants. Confirmatory factor analysis verified a nine-factor solution, with each subscale reporting acceptable reliability (range: .89 to .96). This scale was tested with 140 participants to verify if certain social media qualified as third places. MANOVAs revealed that Facebook adheres most closely to the majority of third place characteristics, followed by Snapchat, Instagram, and Twitter respectively. The proposed scale can be used with other virtual environments to measure if they qualify as third places.

Introduction to logical entropy and its relationship to Shannon entropy

We live in the information age. Claude Shannon, as the father of the information age, gave us a theory of communications that quantified an “amount of information,” but, as he pointed out, “no concept of information itself was defined.” Logical entropy provides that definition. Logical entropy is the natural measure of the notion of information based on distinctions, differences, distinguishability, and diversity. It is the (normalized) quantitative measure of the distinctions of a partition on a set-just as the Boole–Laplace logical probability is the normalized quantitative measure of the elements of a subset of a set. And partitions and subsets are mathematically dual concepts – so the logic of partitions is dual in that sense to the usual Boolean logic of subsets, and hence the name “logical entropy.” The logical entropy of a partition has a simple interpretation as the probability that a distinction or dit (elements in different blocks) is obtained in two independent draws from the underlying set. The Shannon entropy is shown to also be based on this notion of information-as-distinctions; it is the average minimum number of binary partitions (bits) that need to be joined to make all the same distinctions of the given partition. Hence all the concepts of simple, joint, conditional, and mutual logical entropy can be transformed into the corresponding concepts of Shannon entropy by a uniform non-linear dit-bit transform. And finally logical entropy linearizes naturally to the corresponding quantum concept. The quantum logical entropy of an observable applied to a state is the probability that two different eigenvalues are obtained in two independent projective measurements of that observable on that state.

Co-Speech Movement in Conversational Turn-Taking

This study investigates co-speech movements as a function of the conversational turn exchange type, the type of speech material at a turn exchange, and the interlocutor’s role as speaker or listener. A novel interactive protocol that mixes conversation and (non-read) nursery rhymes works to elicit many speech turns and co-speech movements within dyadic speech interaction. To evaluate a large amount of data, we use the density of co-speech movement as a quantitative measure. Results indicate that both turn exchange type and participant role are associated with variation in movement density for head and brow co-speech movement. Brow and head movement becomes denser as speakers approach overlapping speech exchanges, indicating that speakers increase their movement density as an interruptive exchange is approached. Similarly, head movement generally increases after such overlapping exchanges. Lastly, listeners display a higher rate of co-speech movement than speakers, both at speech turns and remote from them. Brow and head movements generally behave similarly across speech material types, conversational roles, and turn exchange types. On the whole, the study demonstrates that the quantitative co-speech movement density measure advanced here is useful in the study of co-speech movement and turn-taking.