Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding Fashion Contexts Using Word Embeddings

Personalisation in e-commerce hinges on dynamically uncovering the user’s context via his/her interactions on the portal. The harder the context identification, lesser is the effectiveness of personalisation. Our work attempts to uncover and understand the user’s context to effectively render personalisation for fashion ecommerce. We highlight fashion-domain specific gaps with typical implementations of personalised recommendation systems and present an alternate approach. Our approach hinges on user sessions (clickstream) as a proxy to the context and explores “session vector” as an atomic unit for personalization. The approach to learn context vector incorporates both the fashion product (style) attributes and the users’ browsing signals. We establish various possible user contexts (product clusters) and a style can have a fuzzy membership into multiple contexts. We predict the user’s context using the skip-gram model with negative sampling introduced by Mikolov et al [1]. We are able to decode the context with a high accuracy even for non-coherent sessions.
Cohort analysis: ability to predict future consumption: the cases of fresh fruit in Japan and rice in Korea

Since the second half of the Twentieth Century, there have been substantial changes in food marketing in Japan and in many other countries. Members of younger generations have been exposed to an increasingly wide variety of foods at supermarkets and restaurants. They may also be more accustomed to using convenience foods. It is possible that people’s tastes and preferences are shaped by such aspects of the food environment at the time when they grow up. If so, it is further possible that people born around the same point in history (a “birth cohort”) have shared more similar experiences as young children and teenagers and exhibit more similar patterns of food demand than people born farther apart in time. A growing body of research on food demand allows for such effects attributable to cohort. Recent studies in countries outside of Japan and Asia to allow for cohort effects include Utz (2005), Stewart and Blisard (2008), Aristei, Perali, and Pieroni (2008), Gustavsen and Rickertsen (2009) and Zan and Fan (2010).
Corrélations dans les réseaux de fractures : Caractérisation et conséquences sur les propriétés hydrauliques

L'etude des proprietes hydrauliques des milieux fractures s'inscrit dans le cadre general de la gestion de la ressource en eau et plus particulierement dans le domaine de la gestion des dechets et de leur stockage dans des sites d'enfouissement. Les fractures, qui constituent les chemins d'ecoulements preferentiels dans les milieux fissures, sont distribuees sur une large gamme d'echelles et presentent une geometrie complexe. Le travail de la these est centre sur la caracterisation des correlations spatiales dans les reseaux de fractures et sur l'etude de l'effet induit par ces correlations sur les proprietes de connectivite et hydrauliques de ces milieux. De nombreuses etudes de terrain ont montre que la repartition des fractures n'est pas homogene dans l'espace. Cette evolution de la densite de fracturation suit un modele fractal, qui, couple avec une distribution des longueurs de fractures, constitue un modele pertinent de reseau de fractures. L'organisation spatiale des reseaux se retrouve aussi a un degre superieur dans une correlation positive entre la position des fractures et leur longueur, telle qu'il existe en moyenne une zone d'ecran autour des fractures dont l'aire est correlee a la longueur. Le travail preliminaire de caracterisation de la geometrie des reseaux est complete par une analyse stereologique permettant de relier les proprietes apparentes des reseaux, determinees a partir d'observations 1D ou 2D (transects, puits, affleurements), a leurs proprietes intrinseques (3D). Ainsi, on montre que la dimension fractale d'un reseau 3D peut etre reliee a la dimension fractale apparente du reseau echantillonne sur un affleurement ou un transect. L'etude des proprietes hydrauliques est realisee pour un modele de reseau bidimensionnel presentant les caracteristiques principales associees aux reseaux de fractures, soit une densite de fracturation fractale (exposant D), et une distribution des longueurs de fractures en loi de puissance (exposant a). L'etude prealable de la connectivite montre qu'une correlation fractale forte (D faible) tend a deconnecter les reseaux, alors que la presence de grandes fractures (a faible) induit une augmentation de la connectivite. Le modele de comportement depend donc des valeurs relatives de a et D. Les deux effets se compensent uniquement lorsque a=D+1 (cas self-similaire), et dans ce cas seulement l'etat de connexion des reseaux ne depend pas de l'echelle d'observation. Enfin, l'effet de la correlation spatiale sur les proprietes hydrauliques des reseaux de fractures est soit de premier ordre lorsque a>D+1, puisque alors les reseaux sont deconnectes a grande echelle et par consequent la permeabilite nulle, soit de second ordre lorsque a£D+1: dans ce cas l'effet des longueurs domine et l'evolution de la permeabilite avec l'echelle est peu sensible a la valeur de D, bien que les ecoulements restent chenalises aux fortes densites.
Efficient Circular Flat-Top Pattern Synthesis With Circular Planar Array via Dimensionality Reduction

A novel method is presented to efficiently synthesize circular flat-top patterns with circular planar arrays, employing dimensionality reduction techniques. Considering the symmetry of the circular planar array geometry, we define the distance from each element to the array geometric center as the independent variable, then expand the excitation distribution using the even-order polynomials of a single Chebyshev polynomials. With the help of such polynomial expansion, we can synthesize pattern by solving polynomial coefficients instead of directly solving array excitation distribution, significantly reducing the number of optimization variables by over 98% compared with the number of array elements, which effectively converts a high-dimensional problem into a more manageable low-dimensional one. Two synthesis examples involving circular planar arrays with equispaced and nonequispaced elements are conducted to validate the effectiveness and advantages of our proposed method.
Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

Smart home monitoring systems via internet of things (IoT) are required for taking care of elders at home. They provide the flexibility of monitoring elders remotely for their families and caregivers. Activities of daily living are an efficient way to effectively monitor elderly people at home and patients at caregiving facilities. The monitoring of such actions depends largely on IoT-based devices, either wireless or installed at different places. This paper proposes an effective and robust layered architecture using multisensory devices to recognize the activities of daily living from anywhere. Multimodality refers to the sensory devices of multiple types working together to achieve the objective of remote monitoring. Therefore, the proposed multimodal-based approach includes IoT devices, such as wearable inertial sensors and videos recorded during daily routines, fused together. The data from these multi-sensors have to be processed through a pre-processing layer through different stages, such as data filtration, segmentation, landmark detection, and 2D stick model. In next layer called the features processing, we have extracted, fused, and optimized different features from multimodal sensors. The final layer, called classification, has been utilized to recognize the activities of daily living via a deep learning technique known as convolutional neural network. It is observed from the proposed IoT-based multimodal layered system’s results that an acceptable mean accuracy rate of 84.14% has been achieved.
Solution of Definite Integrals using Functional Link Artificial Neural Networks

This paper discusses a new method to solve definite integrals using artificial neural networks. The objective is to build a neural network that would be a novel alternative to pre-established numerical methods and with the help of a learning algorithm, be able to solve definite integrals, by minimising a well constructed error function. The proposed algorithm, with respect to existing numerical methods, is effective and precise and well-suited for purposes which require integration of higher order polynomials. The observations have been recorded and illustrated in tabular and graphical form.
The mapping of marsh vegetation using aircraft multispectral scanner data

A test was conducted to determine if salinity regimes in coastal marshland could be mapped and monitored by the identification and classification of marsh vegetative species from aircraft multispectral scanner data. The data was acquired at 6.1 km (20,000 ft.) on October 2, 1974, over a test area in the coastal marshland of southern Louisiana including fresh, intermediate, brackish, and saline zones. The data was classified by vegetational species using a supervised, spectral pattern recognition procedure. Accuracies of training sites ranged from 67% to 96%. Marsh zones based on free soil water salinity were determined from the species classification to demonstrate a practical use for mapping marsh vegetation.
Particle radiation produced by accelerated systems and their analogy with damped oscillators

The Unruh effect predicts how uniformly accelerated observers will perceive a change in the vacuum state. This shows that the concept of particle number depends on the acceleration of the reference frame. Although this is a result of quantum field theory, its experimental verification is still questioned, mainly due to the high accelerations required. In this work we study a quantum oscillator with only one complex coordinate and a damping term acting as perturbation, which has all the characteristics of the Unruh effect in second quantization for an accelerated observer. The Bogoliubov transformation connecting the two different vacuum states is obtained. This leads to an explicit formula for the particle occupation number as a function of energy and acceleration. Furthermore, it is shown that our analogue system contains an effective temperature that depends on the observer's sudden acceleration, seen as a friction force. The purpose of this work is to demonstrate that quanta production (particles or energy packets) is inevitable under the premises of quantum field theory.
Constraining the output of discrete‐time systems using sliding mode control with a novel sliding hyperplane

Sliding mode control strategies are widely applicable to systems subject to disturbance and modeling uncertainties. However, since sliding mode controllers are typically only concerned with the evolution of a virtual sliding variable, potential state constraints placed on the system are often neglected when applying such strategies. Motivated by this problem, in this article, we propose a novel sliding mode control strategy for discrete‐time systems with the aim of satisfying constraints placed on their output. The proposed approach uses a new sliding hyperplane designed to ensure a linear relationship between the sliding variable and the system output. The main contribution of the article involves demonstrating that when using this approach, one can indirectly impose bounds on the output rate of change by limiting the convergence rate of the sliding variable to zero. The obtained result has been documented for two cases. In the first part of the article, the proposed approach has been applied to systems with full state information always known. Then, the strategy has been extended to plants with only output signal available, where the full state needs to be estimated.
Extreme Events and Land Use Decisions under Climate Change in Tart Cherry Industry in Michigan

This paper studies a land use switching model as a measure of adaptation to climate change in tart cherry industry in Michigan. In order to capture the effects of extreme events, we employ a real options land conversion model where an underlying stochastic process allows a Poisson-type jump component. We compare land use decisions under the increased frequency and severity of extreme weather events as well as well-known gradual climate change. We find that when decision makers are allowed to optimize dynamically and to learn, gradual changes and extreme events can lead to different likelihoods of adaptation occurring as well as different adaptation incentives even when traditional net present value (NPV) calculations are equal. We suggest that although gradual change imposes higher incentive to switch than the extreme events, the realized action may be dominated by the extreme events, especially extreme magnitude change.
IMRT in the treatment of locally advanced or inoperable NSCLC in the pre-durvalumab era: clinical outcomes and pattern of relapses, experience from the Oscar Lambret Center

Background Intensity-modulated conformal radiotherapy (IMRT) has become the technique of choice for the treatment of locally advanced or inoperable non-small cell lung cancer (NSCLC). Nevertheless, this technique presents dosimetric uncertainties, particularly in treating moving targets such as pulmonary neoplasms. Moreover, it theoretically increases the risk of isolated nodal failure (INF) due to reduced incidental irradiation. Objective The objective of this study was to evaluate the efficacy and safety of IMRT in patients with inoperable NSCLC and to describe the pattern of relapses. Methods Patients with locally advanced NSCLC treated with radiotherapy and chemotherapy between 2015 and 2018 at the Oscar Lambret Center were retrospectively included in the study. Overall and progression-free survival were estimated using the Kaplan–Meier method. The cumulative incidence of the different components of relapse was estimated using the Kalbfleisch and Prentice method. Prognostic factors for relapse/death were investigated using the Cox model. A comparison with literature data was performed using a one-sample log-rank test. Results Seventy patients were included, and 65 patients (93%) had stage III disease. All the patients received chemotherapy, most frequently with cisplatin and navelbine. The dose received was 66 Gy administered in 33 fractions. The median follow-up and survival were 49.1 and 39.1 months, respectively. A total of 35 deaths and 43 relapses, including 29 with metastatic components, were reported. The overall survival rates at 1 and 2 years were 80.2% (95% confidence interval 68.3%-88.0%) and 67.2% (95% confidence interval 54.2%-77.3%), respectively. Locoregional relapse was observed in 14 patients, including two INF, one of which was located in the lymph node area adjacent to the clinical target volume. Median relapse-free survival was 15.2 months. No variable was statistically associated with the risk of relapse/death in multivariate analysis. Seven patients (10%) experienced grade 3 or higher toxicity. Conclusion The use of IMRT for locally advanced or inoperable NSCLC led to favorable long-term clinical outcomes. The rate of locoregional relapse, particularly isolated lymph node failure, was low and comparable with that of the three-dimensional radiotherapy series, as was the rate of early and late toxicities.
Looking Back: Tracing the Trajectory of Four Dominican Women Who Learned 'To Do' School

LOOKING BACK: TRACING THE TRAJECTORY OF FOUR DOMINICAN WOMEN WHO LEARNED ‘TO DO’ SCHOOL by Maria Montalvo-Balbed This study examines the educational trajectories, critical life events, variations and commonalities of four Dominican educators in the U.S. South who, despite being nonEnglish speakers and immigrants, were successful in attending college and obtaining degrees in education beyond a Master’s degree. This study explores their life journeys in order to understand and account for various factors which allowed and propelled them to be successful in school and rise to leadership positions in education. The themes which emerge are directly related to the various types of capital and social assets that are embodied within the Community Cultural Wealth framework, e.g. aspirational, linguistic, familial, social, navigational, and resistant. Patterns found among the participants’ paths include: success stories, academic resilience, exposure, mentors, and the historical context of their immigration. The findings suggest that the women’s own strong academic expectations, coupled with high levels of academic and emotional support were key factors in their educational and career success. This study extends the existing body of research literature on the persistent underachievement of immigrant and English Learners through an expanded understanding and application of the concepts of cultural capital and cultural responsiveness in education.
Multibeam Transmit-Reflect-Array Antenna Using Alternating Transmission and Reflection Elements for Space–Air–Ground–Sea Integrated Network

A bidirectional multibeam transmit-reflect-array (TRA) antenna has been proposed for the space–air–ground–sea integrated network (SAGSIN). The TRA consists of alternating transmission element (TE) and reflection element (REs), and its aperture phase distribution comprises focusing and periodic phases. The combination of different transmission and reflection apertures with varying periodic phase differences can achieve TRA with different numbers of transmitted and reflected beams. Total TE and RE with linearly varying phases with thickness are used to analyze the relationship between the gains of transmitted and reflected beams. It has been proved that the gain ratio of the transmitted and reflected beams can be adjusted by changing the area ratio of the transmission and reflection aperture. To verify universality, a single-layer TRA has been designed for verification, and the simulation and measurement results are in good agreement. The measurement results have demonstrated that the proposed TRA antenna can realize the tri-beam transmission and dual-beam reflection.
UMA-MF: A Unified Multi-CPU/GPU Asynchronous Computing Framework for SGD-Based Matrix Factorization

Recent research has shown that collaborative computing of CPUs and GPUs in the same system can effectively accelerate large-scale SGD-based matrix factorization (MF), but it faces the problem of limited scalability due to parameter synchronization in the server. Theoretically, asynchronous methods can overcome this shortcoming. However, through a series of tests, observations, and analyses, we realize that developing an effective asynchronous multi-CPU/GPU MF framework faces several major design challenges: the underutilized CPUs, high communication overhead, and the asynchronous data safety issue. This article presents a unified multi-CPU/GPU asynchronous computing framework for SGD-based matrix factorization, named UMA-MF. UMA-MF treats CPUs and GPUs in the system as distributed workers that train matrix datasets in parallel and update feature parameters asynchronously. It provides a cache-friendly CPU external working mode, which can improve the CPU's cache hit rate, thereby promoting the efficient use of CPUs. It offers an algorithm to find the shortest communication ring topology of heterogeneous CPU/GPU workers and builds computing-communication pipelines to minimize the communication overhead. It implements a wait-free structure and load-balanced data distribution to achieve asynchronous data safety. UMA-MF can effectively accelerate SGD-based MF on multi-CPU/GPU systems in an asynchronous way. On a physical platform with configurations ranging from single processor system to 2CPUs--4CPUs system, for five common datasets Netfix, R1, R2, Goodreads, and de-dense, UMA-MF achieves up to 3.56x speedup compared with HCC-MF, which is the state-of-the-art multi-CPU/GPU synchronous computing framework for SGD-based MF. UMA-MF also shows good scalability. When the system is scaled to 2CPUs-4GPUs, the training time speedup of UMA-MF can reach 70%--97% of the ideal speedup.
A High-Order Time-Domain Discontinuous Galerkin Integral Equation Method for Analysis of Multiscale PEC Objects

Using a high-order time-domain discontinuous Galerkin approach, the transient electromagnetic (EM) scattering from perfectly electric conducting complicated multiscale objects is examined in this work. High-order hierarchical vector basis functions and curved triangular elements are used to reduce the unknowns of the surface integral equation for multiscale EM simulation. A discontinuous Galerkin method is proposed to join nearby subdomains’ conformal/nonconformal surface grids together. Numerical examples are provided to further support the precision and effectiveness of the suggested strategy.
Homologous Anatomical-Based Facial-Metrics Application to Down Syndrome Face Recognition

Down syndrome (DS) is one of the prominent neuro-developmental diseases which are distinguished by atypical fractionation behaviors, physical traits, and other mental disabilities. Current techniques of recognizing the syndrome need genetic testing through clinical studies, which is usually expensive and challenging to get. In order to simplify the classification approach, computer-aided facial analysis methods incorporating machine learning and morphometrics are crucial. Thus, this study proposes Homologous Anatomical-based Histogram of Oriented Gradients plus Support Vector Machine (HAB-HOG/SVM) to automatically detects and extracts 74 homologous facial landmarks from the subjects (DS patient and healthy control) face image and Chord-Transformed Principal Components (CT-PC) as features extraction method for classification. The novelty of this method relies on the automatic acquisition of landmark data which is conceptually simple, robust, computationally efficient, and annotation error-free and the feature extraction technique applies which is simplified enough to follow. The experiment reports recognition accuracy of 56.82% and 98.86% for Classical Principal Components (CPC) and Chord-Transformed PC, respectively. The results demonstrate that the suggested method outperformed not only the CPC but also the previously presented state-of-the-art methods in the domain of DS face recognition.
Effectiveness of the Sellick maneuver for painless gastroscopy in patients with esophageal hiatal hernia: A randomized, self‑control trial

The Sellick maneuver is used for endotracheal intubation to prevent the occurrence of gastroesophageal reflux. The aim of the present study was to observe the effect of the Sellick maneuver on safety, esophageal closure status, gastric mucosal fold extension status, and positive detection rate of lesions in patients with esophageal hiatal hernia under painless gastroscopy. A total of 40 patients with esophageal hiatal hernia who underwent painless gastroscopy were screened for the use of the Sellick maneuver, in which the operator applied pressure to the cervical cricoid cartilage during the examination. The status of esophageal closure at the are pressed, examination time, gastric mucosal fold extension score, positive rate of lesion detection, and reflux of gastric juice or gastric contents, amongst other parameters were assessed. After using the Sellick maneuver, the state of esophageal closure during gastroscopy was significantly better than the no-Sellick maneuver group (P<0.05), and the extension scores of the greater curvature folds of the gastric body, the lateral folds of the lesser curvature of the gastric body, and the mucosal folds of the fundus were significantly higher than that of the no-Sellick maneuver (all P<0.05). The number of gastric polyps and gastric lesions (gastric ulcers and mucosal hyperplasia, amongst others) examined with the Sellick maneuver was significantly higher than the no-Sellick maneuver group (P<0.01). The Sellick maneuver effectively improved the extension of gastric mucosal folds during gastroscopy in patients with esophageal hiatal hernia, increased the positive detection rate of gastric lesions, and shortened the endoscopy time.