Search results for: implicit neural representations

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

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Authors: Ananya Gangavarapu

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Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.

Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

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Authors: Natalie Riley, Anita Quigley, Robert M. I. Kapsa, George W. Greene

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As the fields of medicine and bionics grow rapidly in technological advancement, the future and success of it depends on the ability to effectively interface between the artificial and the biological worlds. The biggest obstacle when it comes to implantable, electronic medical devices, is maintaining a ‘clean’, low noise electrical connection that allows for efficient sharing of electrical information between the artificial and biological systems. Implant fouling occurs with the adhesion and accumulation of proteins and various cell types as a result of the immune response to protect itself from the foreign object, essentially forming an electrical insulation barrier that often leads to implant failure over time. Lubricin (LUB) functions as a major boundary lubricant in articular joints, a unique glycoprotein with impressive anti-adhesive properties that self-assembles to virtually any substrate to form a highly ordered, ‘telechelic’ polymer brush. LUB does not passivate electroactive surfaces which makes it ideal, along with its innate biocompatibility, as a coating for implantable bionic electrodes. It is the aim of the study to investigate LUB’s anti-fouling properties and its potential as a safe, bioinspired material for coating applications to enhance the performance and longevity of implantable medical devices as well as reducing the frequency of implant replacement surgeries. Native, bovine-derived LUB (N-LUB) and recombinant LUB (R-LUB) were applied to gold-coated mylar surfaces. Fibroblast, chondrocyte and neural cell types were cultured and grown on the coatings under both passive and electrically stimulated conditions to test the stability and anti-adhesive property of the LUB coating in the presence of an electric field. Lactate dehydrogenase (LDH) assays were conducted as a directly proportional cell population count on each surface along with immunofluorescent microscopy to visualize cells. One-way analysis of variance (ANOVA) with post-hoc Tukey’s test was used to test for statistical significance. Under both passive and electrically stimulated conditions, LUB significantly reduced cell attachment compared to bare gold. Comparing the two coating types, R-LUB reduced cell attachment significantly compared to its native counterpart. Immunofluorescent micrographs visually confirmed LUB’s antiadhesive property, R-LUB consistently demonstrating significantly less attached cells for both fibroblasts and chondrocytes. Preliminary results investigating neural cells have so far demonstrated that R-LUB has little effect on reducing neural cell attachment; the study is ongoing. Recombinant LUB coatings demonstrated impressive anti-adhesive properties, reducing cell attachment in fibroblasts and chondrocytes. These findings and the availability of recombinant LUB brings into question the results of previous experiments conducted using native-derived LUB, its potential not adequately represented nor realized due to unknown factors and impurities that warrant further study. R-LUB is stable and maintains its anti-fouling property under electrical stimulation, making it suitable for electroactive surfaces.

Keywords: anti-fouling, bioinspired, cell attachment, lubricin

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Authors: Sarah Kalaouze, Maxine Iannucelli, Kristen Dunfield

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Children’s implicit beliefs regarding intelligence (i.e., intelligence mindsets) influence their motivation, perseverance, and success. Previous research suggests that the way parents perceive failure influences the development of their child’s intelligence mindsets. We invited 151 children-parent dyads (Age= 5–6 years) to complete a series of difficult puzzles over zoom. We assessed parents’ intelligence and failure mindsets using questionnaires and recorded parents’ person/performance-oriented (e.g., “you are smart” or "you were almost able to complete that one) and process-oriented (e.g., “you are trying really hard” or "maybe if you place the bigger pieces first") failure feedback. We were interested in observing the relation between parental mindsets and the type of feedback provided. We found that parents’ intelligence mindsets were not predictive of the feedback they provided children. Failure mindsets, on the other hand, were predictive of failure feedback. Parents who view failure-as-debilitating provided more person-oriented feedback, focusing on performance and personal ability. Whereas parents who view failure-as-enhancing provided process-oriented feedback, focusing on effort and strategies. Taken all together, our results allow us to determine that although parents might already have a growth intelligence mindset, they don’t necessarily have a failure-as-enhancing mindset. Parents adopting a failure-as-enhancing mindset would influence their children to view failure as a learning opportunity, further promoting practice, effort, and perseverance during challenging tasks. The focus placed on a child’s learning, rather than their performance, encourages them to perceive intelligence as malleable (growth mindset) rather than fix (fixed mindset). This implies that parents should not only hold a growth mindset but thoroughly understand their role in the transmission of intelligence beliefs.

Keywords: mindset(s), failure, intelligence, parental feedback, parents

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Authors: Ahmad Fahim Nasiry, Shigeo Honma

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We examine two-dimensional oil displacement by water in a petroleum reservoir. The pore fluid is immiscible, and the porous media is homogenous and isotropic in the horizontal direction. Buckley-Leverett theory and a combination of Laplacian and Darcy’s law are used to study the fluid flow through porous media, and the Laplacian that defines the dispersion and diffusion of fluid in the sand using heavy oil is discussed. The reservoir is homogenous in the horizontal direction, as expressed by the partial differential equation. Two main factors which are observed are the water saturation and pressure distribution in the reservoir, and they are evaluated for predicting oil recovery in two dimensions by a physical and mathematical simulation model. We review the numerical simulation that solves difficult partial differential reservoir equations. Based on the numerical simulations, the saturation and pressure equations are calculated by the iterative alternating direction implicit method and the iterative alternating direction explicit method, respectively, according to the finite difference assumption. However, to understand the displacement of oil by water and the amount of water dispersion in the reservoir better, an interpolated contour line of the water distribution of the five-spot pattern, that provides an approximate solution which agrees well with the experimental results, is also presented. Finally, a computer program is developed to calculate the equation for pressure and water saturation and to draw the pressure contour line and water distribution contour line for the reservoir.

Keywords: numerical simulation, immiscible, finite difference, IADI, IDE, waterflooding

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

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The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

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Authors: Nihan Akdemir

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This research aims to investigate the role of fashion in the context of the deconstruction of gender stereotypes. Expectation of society and culture related to the biological structure of the individual corresponds to the gender. At this point there are some unseen rules which are given to person even from his/her childhoods according to the sex and gender, are called stereotypes. With basic example, girls should wear pink, and the boys should wear blue. Or boys do not wear skirt and the woman must behave like a woman. There are also many many stereotypes like them. But the clothing style the individual uses to express his or her gender identity may not match the expectations of the community and society. In the context of big role of the clothing, these stereotypes could be deconstructed because clothes are the visible expression of gender identity of the person. And fashion is a big part of this structure because fashion is a pioneer of what people wear in other words fashion tells to people what should they wear this season. Nowadays fashion has also meant about expressing identity independent of whether you were born male or female. Many fashion brands prepare their collections in the concept of ‘gender fluid’ by deconstructions. It means that fashion is opening the roads for being more free about the gender identity. The representations of gender fluidity through fashion help bring a sense of normality to people who are trying to find the self-confidence to express who they want to be. Maybe the voice of the streets carries this point to the catwalks firstly, and then it becomes a trend. All these items have been explained with visual images and supported by the literature investigations. And the results are showed that the numbers of collections about it are increasing and fashion sector takes this issue into consideration. And this new approach reached to the streets.

Keywords: fashion, gender identity, gender stereotypes, trend

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Authors: Mathias Ebot, Päivi Harinen

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This article focuses on challenges of parenthood for Sub-Saharan African fathers in Finland. In this analysis Finland represents a Nordic welfare society where family relationships are strongly guided by national family policies and discourses. These policies are based on both traditional ideas of a proper Finnish family, as well as on the contemporary waves of female liberation and emphasis on children’s rights. We analyze how especially Cameroonian fathers perceive their parenthood positions and how they characterize and frame their fathering experiences in relation to the mainstream sociopolitical, cultural and national representations of fatherhood in Finland. The analysis is based on interviews and narrative reports collected among Cameroonian fathers living in Finland with their African spouses. The scrutiny shows that in the context where the mainstream cultural and national family representation is created by equality between parents and also between parents and their children, and where “good fatherhood” is created by embodied presence and warm relationships with children these fathers have difficulties: They have to fulfill another fatherhood duty – bread-winning – and thus ensure their labor possibilities all the time, from very marginalized positions of the labor market. When comparing their fatherhood position with the one in Cameroon they also feel embarrassed as the Finnish educational system teaches and encourages their children to challenge their authority as up-raising adults, which in Cameroon could not be possible.

Keywords: Cameroonian fathers, perception, fathering experiences, Finland

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Authors: Arjun Chatterjee

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The India-China border clash in Galwan valley in June 2020, the first deadly skirmish between the two Asian giants in the Himalayan border area in over four decades, highlighted the need to examine the notion of ‘authenticity’ in journalistic practices. Information emanating from such remotely located, sparsely populated, and not well-demarcated international land borders have limited sources, restricted to official sources, which have their own narrative. Geopolitical goals and ambitions embolden narratives of nationalism in the media, and these often challenge the notion and understanding of authenticity in journalism. The Indian press, contrary to the Chinese press, which is state-owned, is diverse and also confrontational, where narratives of nationalism are differentially interpreted, embedded, and realised. This paper examines how authenticity has become a variable, rather than a constant, in conflict reporting of the Sino-Indian border clash and how authenticity is interpreted similarly or differently in conflict journalism. The paper reports qualitative textual analysis of two leading English language newspapers – The Times of India and The Hindu, and two mainstream regional language newspapers, Amar Ujala (Hindi) and Ananda Bazar Patrika (Bengali), to evaluate the ways in which representations of information function in conflict reporting and to recontextualize (and thus change or modify the meaning of) that which they represent, and with what political and cultural implications.

Keywords: India-China, framing, conflict, media narratives, border dispute

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Authors: Lian Yang

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Object-oriented programming (OOP) is the dominant programming paradigm in today’s software industry and it has literally enabled average software developers to develop millions of commercial strength software applications in the era of INTERNET revolution over the past three decades. On the other hand, the lack of strict mathematical model and domain constraint features at the language level has long perplexed the computer science academia and OOP engineering community. This situation resulted in inconsistent system qualities and hard-to-understand designs in some OOP projects. The difficulties with regards to fix the current situation are also well known. Although the power of OOP lies in its unbridled flexibility and enormously rich data modeling capability, we argue that the ambiguity and the implicit facade surrounding the conceptual model of a class and an object should be eliminated as much as possible. We listed the five major usage of class and propose to separate them by proposing new language constructs. By using well-established theories of set and FSM, we propose to apply certain simple, generic, and yet effective constraints at OOP language level in an attempt to find a possible solution to the above-mentioned issues regarding OOP. The goal is to make OOP more theoretically sound as well as to aid programmers uncover warning signs of irregularities and domain-specific issues in applications early on the development stage and catch semantic mistakes at runtime, improving correctness verifiability of software programs. On the other hand, the aim of this paper is more practical than theoretical.

Keywords: new language constructs, set theory, FSM theory, user defined value type, function groups, membership qualification attribute (MQA), check-constraint (CC)

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Authors: Toktam Khatibi

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Self-supervised learning (SSL) includes machine learning models that are trained on one aspect and/or one part of the input to learn other aspects and/or part of it. SSL models are divided into two different categories, including pre-text task-based models and contrastive learning ones. Pre-text tasks are some auxiliary tasks learning pseudo-labels, and the trained models are further fine-tuned for downstream tasks. However, one important disadvantage of SSL using pre-text task solving is defining an appropriate pre-text task for each image dataset with a variety of image modalities. Therefore, it is required to design an appropriate pretext task automatically for each dataset and each downstream task. To the best of our knowledge, the automatic designing of pretext tasks for image analysis has not been considered yet. In this paper, we present a framework based on In-context learning that describes each task based on its input and output data using a pre-trained image transformer. Our proposed method combines the input image and its learned description for optimizing the pre-text task design and its hyper-parameters using Meta-learning models. The representations learned from the pre-text tasks are fine-tuned for solving the downstream tasks. We demonstrate that our proposed framework outperforms the compared ones on unseen tasks and image modalities in addition to its superior performance for previously known tasks and datasets.

Keywords: in-context learning (ICL), meta learning, self-supervised learning (SSL), vision-language domain, transformers

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Authors: Tugba Metinyurt

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Microaggressions are small, everyday verbal and behavioral slights that communicate derogatory messages to individuals on the basis of their group membership. They are often unintentional and not intended to do harm, and yet research has shown that their cumulative effect can be quite detrimental. The current pilot study focuses on the role of bystanders disrupting gender microaggressions and potential barriers of challenging them in the academic workplace at University of Massachusetts Lowell (UML). The participants in this study included 9 male and 20 female from faculty of different disciplines at UML. A Barriers to Intervening Questionnaire asks respondents 1) to rate barriers to intervening in situations described in three short vignettes and 2) to identify more general factors that make it more or less likely that UML faculty will intervene in microaggressions as bystanders through response to an open-ended question. Responses to the questionnaire scales that ask about respondents’ own reactions to the vignettes indicated that faculty may hesitate to interrupt gender microaggressions to avoid being perceived as offensive, losing their relationship with their coworkers, and engaging possible arguments. Responses to the open-ended question, which asked more generally about perceived barriers, revealed a few additional barriers; lack of interpersonal and institutional support, repercussion to self, personal orientation/personality, and privilege. Interestingly, participants tended to describe the obstacles presented in the questionnaire as unlikely to prevent them from intervening, yet the same barriers were suggested to be issues for others on the open-ended questions. Limitations and future directions are discussed. The barriers identified in this research can inform efforts to create bystander trainings to interrupt microaggressions in the academic workplaces.

Keywords: academic workplace, bystander behavior, implicit bias, microaggressions

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Authors: Hemant Kumar, Nilendu Das

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A study based on ANN structure gives us the information to predict the size of the future in realizing a past event. ANN, IMD (Indian meteorological department) data and remote sensing were used to enable a number of parameters for calculating the size that may occur in the future. A threshold selected specifically above the high-frequency harvest reached the area during the selected seismic activity. In the field of human and local biodiversity it remains to obtain the right parameter compared to the frequency of impact. But during the study the assumption is that predicting seismic activity is a difficult process, not because of the parameters involved here, which can be analyzed and funded in research activity.

Keywords: ANN, Bayesion class, earthquakes, IMD

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Authors: Mark R. Skopec, Hamdi M. Issa, Henock B. Taddese, Kate Ippolito, Matthew J. Harris

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In peer review, there is a skew toward research from high-income countries, otherwise known as geographic bias. Research from well-known and prestigious institutions is often favored in the peer review process and is more frequently cited in biomedical research. English clinicians have been found to rate research from low-income countries worse compared to the same research presented as if from high-income countries. This entrenched bias, which is rooted in the perceived superiority of academic institutions in high-income countries is damaging in many regards. Crucially, it reinforces colonial roots by strengthening the dominance of knowledge bases in high-income contexts and perpetuates the perceived inferiority of research from low-income settings. We report on the interventions that Imperial College London is conducting to “decolonize” the higher education curriculum – a root and branch review of reading material in the Masters of Public Health course; identification of unconscious bias against low-income country research in faculty and staff; in-depth interviews with faculty members on their experiences and practices with respect to inclusion of low-income country research in their own teaching and learning practice; and exploring issues surrounding entrenched biases and structural impediments for enabling desirable changes. We intend to use these findings to develop frameworks and approaches, including workshops and online resources, to effect sustainable changes to diversify the curriculum at Imperial College London.

Keywords: curriculum design, diversity, geographic bias, higher education, implicit associations, inclusivity

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Authors: Onaza Ajmal

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The purpose of the study is to explore the power relations linguistically and visually with reference to the representation of gender, race, violence, and empowerment through male characters and female superheroes from the two selected Marvel comics, Ms. Marvel (2014) and Captain Marvel (2019-). The study also aims to elaborate on the different cultural backgrounds of female superheroes and their choices and behaviors concerning the male characters. Moreover, it also seeks to explore whether the female superheroes reassert or resists the established gender roles. Using the tenets of critical discourse analysis (CDA) and feminist critical discourse analysis (FCDA) by Lazar (2005), the study analyzed the power relations from a feminist viewpoint. The linguistic analysis of textual features such as ‘adjectives’, ‘lexical items’, ‘metaphors’, and ‘use of pronouns’, etc., found in the selected comics is carried out under the framework of CDA given by Fairclough (1989). Kress and van Leeuwen's model of reading images (2006) are used to analyze the visual images in this study. The findings of the study show that despite the empowering nature of female superheroes, the unequal power relations between male and female characters are established linguistically and visually, which further sustains and reinforces the racial and patriarchal gender ideologies in the selected comics. Moreover, it is recommended that the female representations in the feminist themes of empowerment with respect to the Pakistani female superheroes should also be explored for further research.

Keywords: feminist critical discourse analysis, patriarchal gender ideology, power relations, superhero comics

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Authors: Pakpoom Hannapha

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This paper investigates the implications of social and political contexts in Thai Post’s news articles written by a columnist, Khon Plai Soy. Samples included twenty eight news articles published between 28th May 2015 and 28th June 2015 selected and analyzed according to Semiotics including implications, connotation, cultural politics, and Thai usage in newspaper articles. The data analysis can be divided into two parts; first, an analysis of signs/signifiers appearing in the articles and second, an analysis of the columnist’s purposes. This study demonstrated representations of signs in the selected articles that were categorized into four groups: events, actions, persons, and organizations. In this study, implications of the news articles were analyzed in two aspects according to Semiotics. It was found that the columnist mostly points out purposes for education, facts, and personal opinions in his works. Also, he offers some solutions to problems discussed in the articles. The writer often explicated knowledge and facts in accordance with either his personal opinions or problem-solutions. According to the research result, studying the implications of news articles in the Thai Post based on the Semiotic approach can help clarify and understand connotative meanings in terms of contents and the writer’s purposes. This paper can enhance readers’ understanding of Semiotic implications through signs and meanings in the texts and thus be used as a model to explore other political news articles

Keywords: semiotic analysis, Thai social contexts, Thai Post’s news, articles

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Authors: Saurav S. Rath, Birendra K. David

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Generic pharmaceutical industry has to operate in strict timelines of product development and scale-up from lab to plant. Hence, detailed product & process understanding and implementation of appropriate mechanistic modelling and Quality-by-design (QbD) approaches are imperative in the product life cycle. This work provides example cases of such efforts in topical dosage products. Topical products are typically in the form of emulsions, gels, thick suspensions or even simple solutions. The efficacy of such products is determined by characteristics like rheology and morphology. Defining, and scaling up the right manufacturing process with a given set of ingredients, to achieve the right product characteristics presents as a challenge to the process engineer. For example, the non-Newtonian rheology varies not only with CPPs and CMAs but also is an implicit function of globule size (CQA). Hence, this calls for various mechanistic models, to help predict the product behaviour. This paper focusses on such models obtained from computational fluid dynamics (CFD) coupled with population balance modelling (PBM) and constitutive models (like shear, energy density). In a special case of the use of high shear homogenisers (HSHs) for the manufacture of thick emulsions/gels, this work presents some findings on (i) scale-up algorithm for HSH using shear strain, a novel scale-up parameter for estimating mixing parameters, (ii) non-linear relationship between viscosity and shear imparted into the system, (iii) effect of hold time on rheology of product. Specific examples of how this approach enabled scale-up across 1L, 10L, 200L, 500L and 1000L scales will be discussed.

Keywords: computational fluid dynamics, morphology, quality-by-design, rheology

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Authors: Karthika Chandran, Pulkit Prakash, Amitabh Das, Santhosh P. N.

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Rare earth orthoferrites (RFeO₃) exhibit interesting and useful magnetic properties like multiferroicity, magnetodielectric coupling, spin reorientation (SR) and exchange bias. B site doped RFeO₃ are attracting attention due to the complex and tuneable magnetic transitions. In this work, 45% Mn-doped HoFeO₃ polycrystalline sample (HoFe₀.₅₅Mn₀.₄₅O₃) was synthesized by a solid-state reaction method. The magnetic structure and transitions were studied by magnetization measurements and neutron powder diffraction methods. The neutron diffraction patterns were taken at 13 different temperatures from 7°K to 300°K (7°K and 25°K to 300°K in 25°K intervals). The Rietveld refinement was carried out by using a FULLPROF suite. The magnetic space groups and the irreducible representations were obtained by SARAh module. The room temperature neutron diffraction refinement results indicate that the sample crystallizes in an orthorhombic perovskite structure with Pnma space group with lattice parameters a = 5.6626(3) Ǻ, b = 7.5241(3) Ǻ and c = 5.2704(2) Ǻ. The temperature dependent magnetization (M-T) studies indicate the presence of two magnetic transitions in the system ( TN Fe/Mn~330°K and TSR Fe/Mn ~290°K). The inverse susceptibility vs. temperature curve shows a linear behavior above 330°K. The Curie-Weiss fit in this region gives negative Curie constant (-34.9°K) indicating the antiferromagnetic nature of the transition. The neutron diffraction refinement results indicate the presence of mixed magnetic phases Γ₄(AₓFᵧG

Keywords: neutron powder diffraction, rare earth orthoferrites, Rietveld analysis, spin reorientation

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: Jocelyn Lachance, Francis Jauréguiberry

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In a few years, the exceptionality of the situation of an individual who could be reached at any time and at any time was replaced by the normality of instantly hearing the voice or immediately seeing the face of the person. This participates in the transformation of our representations of time and space, which gives rise to new expectations. Expectations that parents formulate more or less clearly to their children. The obligation to remain reachable seems to be asserting itself as a general norm which, having imposed itself on adults, now extends to the youngest. In the case of parents and their children, the rationale for this ongoing connection is not always based on actual and imminent dangers. It is the potential for dangerous events that underpins the indisputable argument for the importance of remaining reachable. It is the contingent nature of the risks that imposes itself on these young people as an argument of authority. By entering this connected world, the younger generations also end up adhering in many cases to this reassuring standard of connection. Many teenagers in ours researches nonetheless firmly believe that their freedom of movement is subject to the obligation to carry their smartphone with them. In this way, a connection "pact" is generally established, concluded under pressure, which implies first and foremost that contact be possible at any time, hence the importance of keeping it within reach, and often of '' be attentive to calls and texts sent by parents, at the risk of losing a recently acquired freedom. In this context, if adolescents are growing up in a connected world today, it is also because of the connection the parents are expecting from them. In our conference, by evoking situations reported by teenagers and parents of teenagers during our surveys, we propose to think about the role of the parents in making their child connected and about the desire of the disconnection of the teens.

Keywords: connection, disconnection, smartphone, parents, ritual

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Authors: Sakae Suzuki

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Although Japanese students study English for six years in secondary schools, they demonstrate little success with it when they enter higher education. Learners’ beliefs can predict the future behavior of students, so it may be effective to investigate how learners’ beliefs limit their success and how beliefs might be nudged in a positive direction. While many researchers still depend on a questionnaire called BALLI to reveal explicit beliefs, alternative approaches, especially those designed to reveal implicit beliefs, might be helpful for promoting learning. The present study seeks to identify beliefs with a discursive approach using visual metaphors and narratives. Employing a sociocultural framework, this study investigates how students’ beliefs are revealed by drawings of themselves and their surrounding environments and artifacts while they are engaged in language learning. Research questions are: (1) Can we identify beliefs through an analysis of students’ visual narratives? (2) What environments and artifacts can be found in students’ drawings, and what do they mean? (3) To what extent do students see language learning as a solitary, rather than a social, activity? Participants are university students majoring in science and technology in Japan. The questionnaire was administered to 70 entering students in April, 2014. Data included students drawings of themselves as learners of English as well as written descriptions of students’ backgrounds, English-learning experiences, and analogies and metaphors that they used in written descriptions of themselves as learners. Data will be analyzed qualitatively and quantitatively. Anticipated results include students’ perceptions of themselves as language learners, including their sense of agency, awareness of artifacts, and social contexts of language learning. Comments will be made on implications for teaching, as well as the use of visual narratives as research tools, and recommended further research.

Keywords: drawings, learners' beliefs, metaphors, BALLI

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Authors: K. Dhanalakshmi, N. Deepak, E. Manikandan, S. Kanagaraj, M. Sulthan Ariff Rahman, P. Chilambarasan C. Abhimanyu, C. A. Akaash Emmanuel Raj, V. R. Sanal Kumar

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In this paper using a validated double precision, density-based implicit standard k-ε model, the detailed 2D and 3D numerical studies have been carried out to examine the external flow choking at wing-in-ground (WIG) effect craft. The CFD code is calibrated using the exact solution based on the Sanal flow choking condition for adiabatic flows. We observed that at the identical WIG effect conditions the numerically predicted 2D boundary layer blockage is significantly higher than the 3D case and as a result, the airfoil exhibited an early external flow choking than the corresponding wing, which is corroborated with the exact solution. We concluded that, in lieu of the conventional 2D numerical simulation, it is invariably beneficial to go for a realistic 3D simulation of the wing in ground effect, which is analogous and would have the aspects of a real-time parametric flow. We inferred that under the identical flying conditions the chances of external flow choking at WIG effect is higher for conventional aircraft than an aircraft facilitating a divergent channel effect at the bottom surface of the fuselage as proposed herein. We concluded that the fuselage and wings integrated geometry optimization can improve the overall aerodynamic performance of WIG craft. This study is a pointer to the designers and/or pilots for perceiving the zone of danger a priori due to the anticipated external flow choking at WIG effect craft for safe flying at the close proximity of the terrain and the dynamic surface of the marine.

Keywords: boundary layer blockage, chord dominated ground effect, external flow choking, WIG effect

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Authors: Snehal G. Teli, R. J. Shelke

Abstract:

CNN and MultiUNet models are the framework for the proposed method for enhancing and reconstructing underwater images. Multiscale merging of features and regeneration are both performed by the MultiUNet. CNN collects relevant features. Extensive tests on benchmark datasets show that the proposed strategy performs better than the latest methods. As a result of this work, underwater images can be represented and interpreted in a number of underwater applications with greater clarity. This strategy will advance underwater exploration and marine research by enhancing real-time underwater image processing systems, underwater robotic vision, and underwater surveillance.

Keywords: convolutional neural network, image enhancement, machine learning, multiunet, underwater images

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Authors: Ali Hamza

Abstract:

Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Lei Zhang, Jean-Michel Ghidaglia, Anela Kumbaro

Abstract:

This work focuses on numerical simulation of two-phase flows based on the bi-fluid six-equation model widely used in many industrial areas, such as nuclear power plant safety analysis. A pressure-based numerical method is adopted in our studies due to the fact that in two-phase flows, it is common to have a large range of Mach numbers because of the mixture of liquid and gas, and density-based solvers experience stiffness problems as well as a loss of accuracy when approaching the low Mach number limit. This work extends the semi-implicit pressure solver in the nuclear component CUPID code, where the governing equations are solved on unstructured grids with co-located variables to accommodate complicated geometries. A conservative version of the solver is developed in order to capture exactly the shock in one-phase flows, and is extended to two-phase situations. An inter-facial pressure term is added to the bi-fluid model to make the system hyperbolic and to establish a well-posed mathematical problem that will allow us to obtain convergent solutions with refined meshes. The ability of the numerical method to treat phase appearance and disappearance as well as the behavior of the scheme at low Mach numbers will be demonstrated through several numerical results. Finally, inter-facial mass and heat transfer models are included to deal with situations when mass and energy transfer between phases is important, and associated industrial numerical benchmarks with tabulated EOS (equations of state) for fluids are performed.

Keywords: two-phase flows, numerical simulation, bi-fluid model, unstructured grids, phase appearance and disappearance

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Authors: Abdul Halim Abdullah, Nur Liyana Zainal Abidin, Mahani Mokhtar

Abstract:

Problem-solving is an activity which can encourage students to use Higher Order Thinking Skills (HOTS). Learning fractions can be challenging for students since empirical evidence shows that students experience difficulties in solving the fraction problems. However, visual methods can help students to overcome the difficulties since the methods help students to make meaningful visual representations and link abstract concepts in Mathematics. Therefore, the purpose of this study was to investigate whether there were any changes in students’ HOTS at the four highest levels when learning the fractions by using Thinking Blocks. 54 students participated in a quasi-experiment using pre-tests and post-tests. Students were divided into two groups. The experimental group (n=32) received a treatment to improve the students’ HOTS and the other group acted as the control group (n=22) which used a traditional method. Data were analysed by using Mann-Whitney test. The results indicated that during post-test, students who used Thinking Blocks showed significant improvement in their HOTS level (p=0.000). In addition, the results of post-test also showed that the students’ performance improved significantly at the four highest levels of HOTS; namely, application (p=0.001), analyse (p=0.000), evaluate (p=0.000), and create (p=0.000). Therefore, it can be concluded that Thinking Blocks can effectively encourage students to use the four highest levels of HOTS which consequently enable them to solve fractions problems successfully.

Keywords: Thinking Blocks, Higher Order Thinking Skills (HOTS), fractions, problem solving

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Authors: V.Barbakadze, L.Gogilashvili, L.Amiranashvili, M.Merlani, K.Mulkijanyan

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The high-molecular fractions from the several species of two genera (Symphytum and Anchusa) of Boraginaceae family Symphytum asperum, S. caucasicum, S. officinale, and Anchusa italica were isolated. According to IR, 13C and 1H NMR, 2D heteronuclear 1H/13C HSQC spectral data and 1D NOE experiment, the main structural element of these preparations was found to be a regularly substituted polyoxyethylene, namely poly[3-(3,4-dihydroxyenyl)glyceric acid] (PDPGA) or poly[oxy-1-carboxy-2-(3,4-dihydroxyphenyl)ethylene]. Such caffeic acid-derived biopolymer to our knowledge has not been known and has been identified for the first time. This compound represents a new class of natural polyethers with a residue of 3-(3,4-dihydroxyphenyl)glyceric acid as the repeating unit. Most of the carboxylic groups of PDPGA from A. italica unlike the polymer of S. asperum, S. caucasicum, and S. officinale are methylated. The 2D DOSY experiment gave the similar diffusion coefficient for the methylated and non-methylated signals of A. italica PDPGA. Both sets of signals fell in the same horizontal. This would imply a similar molecular weight for methylated and non-methylated polymers. This was further evidenced by graphic representations of the intensity decay of the 1H signals of aromatic H-2″ and H-1 at δ 7.16 and 5.24 and that of the methoxy group at δ 3.85. These three signals essentially showed the same curve shape. According to results of in vitro and in vivo experiments PDPGA of S.asperum and S.caucasicum could be considered as potential anti-inflammatory, wound healing and anti-cancer therapeutic agent.

Keywords: caffeic acid-derived polyether, poly[3-(3, 4-dihydroxyphenyl)glyceric acid], poly[oxy-1-carboxy-2-(3, 4-dihydroxyphenyl)ethylene], symphytum, anchusa

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Authors: Alexander Vandesompele, Joni Dambre

Abstract:

The diversity in dendritic arborization, as first illustrated by Santiago Ramon y Cajal, has always suggested a role for dendrites in the functionality of neurons. In the past decades, thanks to new recording techniques and optical stimulation methods, it has become clear that dendrites are not merely passive electrical components. They are observed to integrate inputs in a non-linear fashion and actively participate in computations. Regardless, in simulations of neural networks dendritic structure and functionality are often overlooked. Especially in a machine learning context, when designing artificial neural networks, point neuron models such as the leaky-integrate-and-fire (LIF) model are dominant. These models mimic the integration of inputs at the neuron soma, and ignore the existence of dendrites. In this work, the LIF point neuron model is extended with a simple form of dendritic computation. This gives the LIF neuron increased capacity to discriminate spatiotemporal input sequences, a dendritic functionality as observed in another study. Simulations of the spiking neurons are performed using the Bindsnet framework. In the common LIF model, incoming synapses are independent. Here, we introduce a dependency between incoming synapses such that the post-synaptic impact of a spike is not only determined by the weight of the synapse, but also by the activity of other synapses. This is a form of short term plasticity where synapses are potentiated or depressed by the preceding activity of neighbouring synapses. This is a straightforward way to prevent inputs from simply summing linearly at the soma. To implement this, each pair of synapses on a neuron is assigned a variable,representing the synaptic relation. This variable determines the magnitude ofthe short term plasticity. These variables can be chosen randomly or, more interestingly, can be learned using a form of Hebbian learning. We use Spike-Time-Dependent-Plasticity (STDP), commonly used to learn synaptic strength magnitudes. If all neurons in a layer receive the same input, they tend to learn the same through STDP. Adding inhibitory connections between the neurons creates a winner-take-all (WTA) network. This causes the different neurons to learn different input sequences. To illustrate the impact of the proposed dendritic mechanism, even without learning, we attach five input neurons to two output neurons. One output neuron isa regular LIF neuron, the other output neuron is a LIF neuron with dendritic relationships. Then, the five input neurons are allowed to fire in a particular order. The membrane potentials are reset and subsequently the five input neurons are fired in the reversed order. As the regular LIF neuron linearly integrates its inputs at the soma, the membrane potential response to both sequences is similar in magnitude. In the other output neuron, due to the dendritic mechanism, the membrane potential response is different for both sequences. Hence, the dendritic mechanism improves the neuron’s capacity for discriminating spa-tiotemporal sequences. Dendritic computations improve LIF neurons even if the relationships between synapses are established randomly. Ideally however, a learning rule is used to improve the dendritic relationships based on input data. It is possible to learn synaptic strength with STDP, to make a neuron more sensitive to its input. Similarly, it is possible to learn dendritic relationships with STDP, to make the neuron more sensitive to spatiotemporal input sequences. Feeding structured data to a WTA network with dendritic computation leads to a significantly higher number of discriminated input patterns. Without the dendritic computation, output neurons are less specific and may, for instance, be activated by a sequence in reverse order.

Keywords: dendritic computation, spiking neural networks, point neuron model

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Authors: Mai Awad

Abstract:

This paper aims to focus on the phenomenon of martyrdom—particularly its performative aspect—and how social and popular cultural representations address the multiple meanings of the loaded image of a Palestinian martyr. This focus will help us to explore the possible reasons that might push Palestinians to consider pursuing “martyrdom” or suicide operations. Tracing what happened in the past and what is currently happening (that is, haunting) will aid in theorizing how the act/practice of “martyrdom” is produced. It is believed that there are social and political forces, particularly in a colonial society like Palestine, that influence the subject and its experience. But what is unique about this paper is its attempt to disclose the invisible, hidden narratives and complexities of Palestinian life that we do not see. By giving “martyrs” a chance to speak and express their own narratives—since “martyrs” usually leave written letters for their families, which are published after their death—this study must broaden the whole picture and discuss what is missing. The analytic method to be used: For the methodology, the paper recruits discourse analysis as a method for tracing the emergence, circulation, and productivity of the martyrdom discourse across a range of social practices in Palestinians’ everyday life after the Nakba. The paper analyzes the letters that “martyrs” left to their families, relatives, and the Palestinian community after their death. By letting “martyrs” speak for themselves and hearing their unique discourses, the research would suggest that more explanation is needed to describe the “martyr” identity. Hence, it is not possible to study the “martyr” identity in Palestine without understanding the colonial context that governs it and shapes their subjective experience.

Keywords: martyrdom, palestine, haunting, nakba 1948

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