Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Caption Generator using CNN-LSTM Model

- Caption for an image is generated by combining the keywords and punctuation, while there is no proper sentence to the caption which describes the image. Objects in the images are to be classified into human, animal, and plants by the model. To process these things, image processing and natural language processing are used to define the model. This can be done using CNN as an encoding vector layer by layer format, which will give a good idea about the image then they can be processed by the LSTM model, which will be used to predict or generate caption word by word. There are many datasets, which we can use to train our model. We are using the Flickr 8k dataset which is derived from the ImageNet dataset, which is very efficient for image processing. This model then generates a caption and we compare these results with the test dataset to generate the BLEU score. This BLEU score is used in determining whether the model is good or not.
Differentiating comorbidities and predicting prognosis in idiopathic normal pressure hydrocephalus using cerebrospinal fluid biomarkers

Idiopathic normal pressure hydrocephalus (iNPH) is a condition resulting from impaired cerebrospinal fluid (CSF) absorption and excretion characterized by a triad of symptoms comprising dementia, gait disturbance (impaired trunk balance), and urinary incontinence. CSF biomarkers not only assist in diagnosis but are also important for analyzing the pathology and understanding appropriate treatment indications. As the neuropathological findings characteristic of iNPH have yet to be defined, there remains no method to diagnose iNPH with 100% sensitivity and specificity. Neurotoxic proteins are assumed to be involved in the neurological symptoms of iNPH, particularly the appearance of cognitive impairment. The symptoms of iNPH can be reversed by improving CSF turnover through shunting. However, early diagnosis is essential as once neurodegeneration has progressed, pathological changes become irreversible and symptom improvement is minimal, even after shunting. Combining a variety of diagnostic methods may lead to a more definitive diagnosis and accurate prediction of the prognosis following shunt treatment. Identifying comorbidities in iNPH using CSF biomarkers does not contraindicate shunting-based intervention, but does limit the improvement in symptoms it yields, and provides vital information for predicting post-treatment prognosis.
Multi-Object Tracking in Drone Videos

In this report, three popular methods for multi-pedestrian tracking are extended to a multi-category setting and tested on a large drone-based dataset. A thorough comparison of the algorithms is presented and a common shortcoming is identiﬁed. Building on this, a new tracking-by-detection based approach is developed that outperforms the other methods by a large margin. In addition, a state-of-the-art object detection model is adapted for the drone imagery, since no public detections are available for the dataset.
An Evaluation of Kahoot Application and Its Environment as a Learning Tool

 Abstract— Over the past 20 years, internet has seen continual advancement and with the advent of online technology, various types of web-based games have been developed. Games are frequently being used among different age groups from baby boomers to generation Z. Games are not only used for entertainment but also utilized as a learning approach transmitting education to a level that is more interesting and effective for students. One of the popular web-based education games is Kahoot with growing popularity and usage, which is being used in different fields of studies. However, little knowledge is available on university students’ perception of Kahoot environment and application for learning subjects. Hence, the objective of the current study is to investigate students’ perceptions of Kahoot application and environment as a learning tool. The study employed a survey approach by distributing Google Forms –created questionnaire, with high level of reliability index, to 62 students (11 males and 51 females). The findings show that students have positive attitudes towards Kahoot application and its environment for learning. Regarding Kahoot application, it was indicated that activities created using Kahoot are more interesting for students, Kahoot is useful for collaborative learning, and Kahoot enhances interest in learning lesson. In terms of Kahoot environment, it was found that using this application through mobile is easy for students, its design is simple and useful, Kahoot-created activities can easily be shared, and the application can easily be used on any platform. The findings of the study have implications for instructors, policymakers and curriculum developers.
Acute or Chronic? the Long-Term Impact of the COVID Crisis on Economic Output

There is a substantial literature on the lasting damage to economic output from financial crises. The COVID crisis is not, in the first instance, a financial crisis and nor was the eve of the pandemic characterised by large scale macroeconomic imbalances. But it is not financial crises qua financial crises that cause long term damage, but a number of channels which operate during financial crises and other recessions, some of which are at work now as well. We identify balance sheet repair, belief scarring, labour market hysteresis, slowing structural reform momentum, and policy error as relevant transmissions channels of long term damage at work during this crisis. Our judgement is that the COVID crisis will impart a permanent levels shock to post-pandemic global economic output of 3% of GDP. This is a third the levels damage after the GFC, consistent with the nature of the shock and the more encouraging monetary, fiscal and structural policy responses. But this is still a substantial amount of long-term damage relative to 'typical' downturns, reflecting the depth of the pandemic shock and the channels of damage identified.
Five Dimensional Grain Boundary Distribution for Yttria Stabilized Zirconia Based on Experimentally Determined Macroscopic Boundary Parameters

Distributions showing the frequencies of occurrence of grain boundaries as functions of their 5 macroscopic boundary parameters have been computed for a set of yttria-stabilized zirconia samples sintered at different conditions and thus having different grain sizes. For this purpose, datasets containing both grain misorientation and boundary plane parameters have been collected by the means of the 3D EBSD technique. Then, the 5-parameter distributions have been computed using the approach based on kernel density estimation. A few top peaks are visible in all of the obtained distributions. Their heights exceed the level of the random distribution by at least about 3 multiples of the corresponding statistical errors. The list of favored grain boundaries includes the Σ3/(111) and Σ11/(1‾13) boundaries, as well as the boundaries having both the boundary plane (100) and misorientation by 10° to 37° about the [100] axis.
Labor Market Choices of Migrants and Redistributive Policies

We experimentally study economic migrants’ willingness to take up work and integrate into society, and, in turn, destination country citizens’ willingness to allow economic migrants to pursue formal work and integrate into society and its social security and welfare system. We find clear evidence for a reciprocal relationship between the individuals in these roles. The labor market participation of economic migrants co-moves with destination countries’ openness to welcoming them. We conclude that supporting economic migrants in early labor market attachment is crucial to support a mutually beneficial co-existence in a society.
The Nirenberg Problem on Half Spheres: A Bubbling-off Analysis

In this paper, we perform a refined blow-up analysis of finite energy approximated solutions to a Nirenberg-type problem on half spheres. The latter consists of prescribing, under minimal boundary conditions, the scalar curvature to be a given function. In particular, we give a precise location of blow-up points and blow-up rates. Such an analysis shows that the blow-up picture of the Nirenberg problem on half spheres is far more complicated that in the case of closed spheres. Indeed, besides the combination of interior and boundary blow ups, there are nonsimple blow-up points for subcritical solutions having zero or nonzero weak limit. The formation of such nonsimple blowups is governed by a vortex problem, unveiling an unexpected connection with Euler equations in fluid dynamic and mean fields type equations in mathematical physics.
Online Range Image-based Pole Extractor for Long-term LiDAR Localization in Urban Environments

Reliable and accurate localization is crucial for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, lamps, etc., are ideal landmarks for localization in urban environments due to their local distinctiveness and long-term stability. In this paper, we present a novel, accurate, and fast pole extraction approach that runs online and has little computational demands such that this information can be used for a localization system. Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point cloud explicitly and enables fast pole extraction for each scan. We test the proposed pole extraction and localization approach on different datasets with different LiDAR scanners, weather conditions, routes, and seasonal changes. The experimental results show that our approach outperforms other state-of-the-art approaches, while running online without a GPU. Besides, we release our pole dataset to the public for evaluating the performance of pole extractor, as well as the implementation of our approach.
Globular Clusters and Streaming Velocities: Testing the New Formation Channel in High-resolution Cosmological Simulations

The formation of globular clusters and their relation to the distribution of dark matter have long puzzled astronomers. One of the most recently proposed globular cluster formation channels ties ancient star clusters to the large-scale streaming velocity of baryons relative to dark matter in the early universe. These streaming velocities affect the global infall of baryons into dark matter halos, the high-redshift halo mass function, and the earliest generations of stars. In some cases, streaming velocities may result in dense regions of dark matter-free gas that becomes Jeans unstable, potentially leading to the formation of compact star clusters. We investigate this hypothesis using cosmological hydrodynamical simulations that include a full chemical network and the formation and destruction of H2, a process crucial for the formation of the first stars. We find that high-density gas in regions with significant streaming velocities is indeed somewhat offset from the centers of dark matter halos, but this offset is typically significantly smaller than the virial radius. Gas outside of dark matter halos never reaches Jeans-unstable densities in our simulations. We postulate that low-level (Z ≈ 10−3 Z ⊙) metal enrichment by Population III supernovae may enable cooling in the extra-virial regions, allowing gas outside of dark matter halos to cool to the cosmic microwave background temperature and become Jeans unstable. Follow-up simulations that include both streaming velocities and metal enrichment by Population III supernovae are needed to understand if streaming velocities provide one path for the formation of globular clusters in the early universe.
MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Images

The world is going through a challenging phase due to the disastrous effect caused by the COVID-19 pandemic on the healthcare system and the economy. The rate of spreading, post-COVID-19 symptoms, and the occurrence of new strands of COVID-19 have put the healthcare systems in disruption across the globe. Due to this, the task of accurately screening COVID-19 cases has become of utmost priority. Since the virus infects the respiratory system, Chest X-Ray is an imaging modality that is adopted extensively for the initial screening. We have performed a comprehensive study that uses CXR images to identify COVID-19 cases and realized the necessity of having a more generalizable model. We utilize MobileNetV2 architecture as the feature extractor and integrate it into Capsule Networks to construct a fully automated and lightweight model termed as MobileCaps. MobileCaps is trained and evaluated on the publicly available dataset with the model ensembling and Bayesian optimization strategies to efficiently classify CXR images of patients with COVID-19 from non-COVID-19 pneumonia and healthy cases. The proposed model is further evaluated on two additional RT-PCR confirmed datasets to demonstrate the generalizability. We also introduce MobileCaps-S and leverage it for performing severity assessment of CXR images of COVID-19 based on the Radiographic Assessment of Lung Edema (RALE) scoring technique. Our classification model achieved an overall recall of 91.60, 94.60, 92.20, and a precision of 98.50, 88.21, 92.62 for COVID-19, non-COVID-19 pneumonia, and healthy cases, respectively. Further, the severity assessment model attained an R$
Optimal squeezing for quantum target detection

It is believed that the optimal performance of a quantum lidar or radar in the absence of an idler and only using Gaussian resources cannot exceed the performance of a semiclassical setup based on coherent states and homodyne detection. Here we disprove this conjecture by showing that an idlerfree squeezed-based setup can beat this benchmark. More generally, we show that probes whose displacement and squeezing are jointly optimized can strictly outperform coherent states with the same mean number of input photons for both the problems of quantum illumination and reading.
Social Fabric: Tubelet Compositions for Video Relation Detection

This paper strives to classify and detect the relationship between object tubelets appearing within a video as a 〈subject-predicate-object〉 triplet. Where existing works treat object proposals or tubelets as single entities and model their relations a posteriori, we propose to classify and detect predicates for pairs of object tubelets a priori. We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives. These primitives are learned over all relations, resulting in a compact representation able to localize and classify relations from the pool of co-occurring object tubelets across all timespans in a video. The encoding enables our two-stage network. In the first stage, we train Social Fabric to suggest proposals that are likely interacting. We use the Social Fabric in the second stage to simultaneously finetune and predict predicate labels for the tubelets. Experiments demonstrate the benefit of early video relation modeling, our encoding and the two-stage architecture, leading to a new state-of-the-art on two benchmarks. We also show how the encoding enables query-by-primitive-example to search for spatio-temporal video relations. Code: https://github.com/shanshuo/Social-Fabric.
A cut-and-fold self-sustained compliant oscillator for autonomous actuation of origami-inspired robots

Origami-inspired robots are of particular interest due to their potential for rapid and accessible design and fabrication of elegant designs and complex functionalities through cutting and folding of flexible two-dimensional sheets or even strings, that is, printable manufacturing. Yet, origami robots still require bulky rigid components or electronics for actuation and control to accomplish tasks with reliability, programmability, ability to output substantial force, and durability, restricting their full potential. In this study, we present a printable self-sustained compliant oscillator that generates periodic actuation using only constant electrical power, without discrete components or electronic control hardware. This oscillator is robust (9 out of 10 prototypes worked successfully on the first try), configurable (with tunable periods from 3 to 12 s), powerful (can overcome hydrodynamic resistance to consistently propel a swimmer at ∼1.6 body lengths/min or 3.66 mm/s), and long lasting (∼103 cycles); it enables driving macroscale devices with prescribed autonomous behaviors, for example, locomotion and sequencing. This oscillator is also fully functional underwater and in high magnetic fields. Our analytical model characterizes essential parameters of the oscillation period, enabling programmable design of the oscillator. The printable oscillator can be integrated into origami-inspired systems seamlessly and monolithically, allowing rapid design and prototyping; the resulting integrated devices are lightweight, low cost, compliant, electronic free, and nonmagnetic, enabling practical applications in extreme areas. We demonstrate the functionalities of the oscillator with: (1) autonomous gliding of a printable swimmer, (2) LED flashing, and (3) fluid stirring. This work paves the way for realizing fully printable autonomous robots with high integration of actuation and control.
Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021

This paper reports the Machine Translation (MT) systems submitted by the IIITT team for the English->Marathi and English->Irish language pairs LoResMT 2021 shared task. The task focuses on getting exceptional translations for rather low-resourced languages like Irish and Marathi. We fine-tune IndicTrans, a pretrained multilingual NMT model for English->Marathi, using external parallel corpus as input for additional training. We have used a pretrained Helsinki-NLP Opus MT English->Irish model for the latter language pair. Our approaches yield relatively promising results on the BLEU metrics. Under the team name IIITT, our systems ranked 1, 1, and 2 in English->Marathi, Irish->English, and English->Irish, respectively.
A panoramic view of the circumgalactic medium in the photoionized precipitation model

We consider a model of the circumgalactic medium (CGM) in which feedback maintains a constant ratio of cooling time to free-fall time throughout the halo, so that the entire CGM is marginally unstable to multiphase condensation. This ‘precipitation model’ is motivated by observations of multiphase gas in the cores of galaxy clusters and the haloes of massive ellipticals. From the model, we derive the density and temperature profiles for the CGM around galaxies with masses similar to the Milky Way. After taking into consideration the geometrical position of our Solar system in the Milky Way, we show that the CGM model is consistent with observed O vi, O vii, O viii column densities and the ratio of O vii and O viii column densities only if temperature fluctuations with a lognormal dispersion σln T ∼ 0.6–1.0 are included. We show that O vi column densities observed around star-forming galaxies require systematically greater values of σln T than around passive galaxies, implying a connection between star formation in the disc and the state of the CGM. Photoionization by an extragalactic ultraviolet background radiation does not significantly change these CGM features for galaxies like the Milky Way but has much greater and significant effects on the CGM of lower mass galaxies.
Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic

Spatial-temporal prediction is a critical problem for intelligent transportation, which is helpful for tasks such as traffic control and accident prevention. Previous studies rely on large-scale traffic data collected from sensors. However, it is unlikely to deploy sensors in all regions due to the device and maintenance costs. This paper addresses the problem via outdoor cellular traffic distilled from over two billion records per day in a telecom company, because outdoor cellular traffic induced by user mobility is highly related to transportation traffic. We study road intersections in urban and aim to predict future outdoor cellular traffic of all intersections given historic outdoor cellular traffic. Furthermore, we propose a new model for multivariate spatial-temporal prediction, mainly consisting of two extending graph attention networks (GAT). First GAT is used to explore correlations among multivariate cellular traffic. Another GAT leverages the attention mechanism into graph propagation to increase the efficiency of capturing spatial dependency. Experiments show that the proposed model significantly outperforms the state-of-the-art methods on our dataset.
Thin-shell approach for modeling superconducting tapes in the H-φ finite-element formulation

This paper presents a novel finite-element (FE) approach for the electromagnetic modeling of superconducting coated conductors with transport currents. We combine a thin-shell (TS) method to the H-φ-formulation to avoid the meshing difficulties related to the high aspect ratio of these conductors and reduce the computational burden in simulations. The interface conditions in the TS method are defined using an auxiliary 1-D FE discretization of N elements along the thinnest dimension of the conductor. This procedure permits the approximation of the superconductor’s nonlinearities inside the TS in a time-transient analysis. Four application examples of increasing complexity are discussed: (1) single coated conductor, (2) two closely packed conductors carrying anti-parallel currents, (3) a stack of 20 superconducting tapes and (4) a full representation of a high-temperature superconducting tape comprising a stack of thin films. In all these examples, the profiles of both the tangential and normal components of the magnetic field show good agreement with a reference solution obtained with the standard 2-D H-φ-formulation. Results are also compared with the widely used T-A-formulation. This formulation is shown to be dual to the TS model with a single FE (N = 1) in the auxiliary 1-D systems. The increase of N in the TS model is shown to be advantageous at small inter-tape separation and low transport current since it allows the tangential components of the magnetic field to penetrate the thin region. The reduction in computational cost without compromising accuracy makes the proposed model promising for the simulation of large-scale superconducting applications.
Towards a Multispectral RGB-IR-UV-D Vision System — Seeing the Invisible in 3D

In this paper, we present the development of a sensing system with the capability to compute multispectral point clouds in real-time. The proposed multi-eye sensor system effectively registers information from the visible, (long-wave) infrared, and ultraviolet spectrum to its depth sensing frame, thus enabling to measure a wider range of surface features that are otherwise hidden to the naked eye. For that, we designed a new cross-calibration apparatus that produces consistent features which can be sensed by each of the cameras, therefore, acting as a multispectral "chessboard". The performance of the sensor is evaluated with two different cases of studies, where we show that the proposed system can detect "hidden" features of a 3D environment.