Miracle-dz/UniFAR-FTUs huggingface dataset:
Immunomodulatory Effect of Breast Cancer Cell-Derived Exosomes on Macrophage and Dendritic Cells

----------------------------------I LIST OF FIGURES -------------------------------VII LIST OF ABBREVIATIONS --------------------------VIII Chapter 1 -------------------------------------1 Breast Cancer Cell-Derived Exosomes and Macrophage Polarization are Associated with Lymph node Metastasis INTRODUCTION -------------------------------2 MATERIALS AND METHODS -------------------------5 RESULTS ------------------------------------15 DISCUSSION ----------------------------------25 Chapter 2 ------------------------------------31 Noninvasive Photoacoustic Imaging of Dendritic Cell Stimulated with Breast Cancer Cell-Derived Exosomes INTRODUCTION -------------------------------32 MATERIALS AND METHODS ------------------------35 RESULTS -----------------------------------48 DISCUSSION ----------------------------------54 REFERENCES ---------------------------------71 ABSTRACT IN KOREAN ---------------------------84
A Comparative Study of Vectorization-Based Static Test Case Prioritization Methods

To enhance the efficiency of software testing, researchers have studied various test case prioritization (TCP) methods. A topic model-based TCP is one of the promising methods, which expresses test cases by topic vectors and prioritizes them in the order such that the set of already-prioritized test cases have the maximum dispersion in the vector space. However, the topic model is not the only option available for vectorizing test cases. Moreover, the distance metric in the vector space and the scheme to prioritize test cases (the way to find the test case that is the farthest from the set of already-prioritized ones) also have some available options. Because the combinations of the above options have not been well-discussed in the past, this paper conducts a comparative study of 36 TCP methods, which are the combinations of (1) three vectorization methods, (2) three distance metrics, and (3) four prioritization schemes (36=3x3x4). The empirical results show the following findings. The choice of the vectorization method has a significant impact on the testing efficiency: a promising option is Doc2Vec (PVDBoW). The combination with the distance metric may also be impactful: a useful combination is Doc2Vec (PV-DBoW) and Euclidean distance. The third aspect, i.e., the choice of the scheme to find the farthest test case, is not always influential.
An Improved Multi-Class Classification Algorithm based on Association Classification Approach and its Application to Spam Emails

– In our everyday life, we may use email for personal and professional matters. Yet, email benefits have been bedeviled with the remarkable use of annoying, harmful, and fraudulent messages that commonly referred to as spam emails. Several anti-spam campaigns hover around machine learning and data mining techniques were devised in literature. An intelligent data mining approach referred to as Associative Classification (AC) presents itself as a possible method that might efficiently identify spam emails. In this study, an improved Spam Classification based on the Association Classification algorithm (SCAC) is proposed. In addition to the robust rule generation procedure, the improved model creation process, and the enhanced prediction mechanism, the SCAC algorithm is able to derive a new class value that doesn't exist in the original dataset that is the “Uncertain” class value. Hence, the SCAC algorithm doesn't introduce several contributions in the field of AC only but also it has contributed to the spam detection domain. These contributions have mutually reinforced the superb classification abilities of the SCAC algorithm when compared to several other intelligent techniques.
Trajectory adjustment of lagged seasonal forecast ensembles

Seasonal forecasting has became a critical area of development in numerical weather prediction. Reliable forecasts beyond the two week time period are necessary for a number of industrial and societal planning applications and new approaches are being developed to extend the useful range of numerical weather prediction output. We investigate the performance of one such system, the UK Met Office’s GloSea5 system, an ensemble system with the novel feature that ensemble members are initiated in a rolling and staggered manner. Focusing on summer surface temperatures, we show that individual model runs from this system do not exhibit skill beyond the two-week time horizon and indeed substantially under-perform climatological forecasts at longer lead times. However, when combining the ensemble system and applying the Rapid Adjustment of Forecast Trajectories (RAFT) methodology to the individual runs, we show that the combined forecast can achieve performance which is always at least on par with climatology and in many circumstances exhibits modest outperformance.
Investigating neurovascular and metabolic function in healthy and Multiple Sclerosis populations using multi-modal neuroimaging (MEG and fMRI)

The brain requires a constant supply of glucose and oxygen to meet metabolic needs at rest and during increased activity. If blood flow is disrupted, or if tissue has difficulty extracting or metabolising nutrients, cell damage or death may occur. Vascular and metabolic impairments in Multiple Sclerosis (MS) are hypothesised to contribute to disease progression. This thesis develops empirical measures of neurovascular coupling using the relationship between magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI). Visual MEG and fMRI responses were reduced in MS. The relationship between them was unchanged suggesting preserved neurovascular coupling. Addressing the same questions with a naturalistic movie stimulus in MS, no coupling differences were found. In a healthy population, results show neurovascular coupling is dependent on brain region and frequency of neural oscillations. Under the hypothesis of reduced cerebral metabolic rate of O2 (CMRO2) contributing to damage in MS, we quantified baseline CMRO2 and cerebral blood flow (CBF), amongst other parameters. This is the first application of dual-calibrated fMRI in MS, involving biophysical modelling of fMRI signals in response to changing inspired CO2 and O2. Reduced baseline CBF and CMRO2 were found in MS, correlating with lesion and grey matter volumes. Relative visual-induced CBF and CMRO2 signals were investigated in MS; a reduction in CBF was found in a small visual region but no visual CMRO2 changes were found, or differences in CBF-CMRO2 coupling. Baseline CBF and CMRO2 signals predicted visual stimulus responses, in both groups. As an alternative to externally supplied gases, we used a breath-hold design to create a CMRO2 movie time-series but report no significant relationships between CMRO2 and MEG. Quantitative functional imaging can detect impairments in resting and stimulus-induced neural oscillations, blood flow and oxygen metabolism in MS, which should be explored further to understand their exact role in disease progression.
SCARLET: SemantiC relAtion discoveRy by harvesting

We present a demo of SCARLET, a technique for discovering relations between two concepts by harvesting the Semantic Web, i.e., automatically finding and exploring multiple and heterogeneous online ontologies. While we have primarily used SCARLET’s relation discovery functionality to support ontology matching and enrichment tasks, it is also available as a stand alone component that can potentially be integrated in a wide range of applications. This demo will focus on presenting SCARLET’s functionality and its different parametric settings that can influence the trade-off between its accuracy and time performance.
An approach towards data change rule based classification of driving maneuver with LSTM network

The proposed work develops a Long Short Term Memory (LSTM) model for multi class classification of driving maneuver from sensor fusion time series dataset. The work also analyzes the significance of sensor fusion data change rule and utilized the idea with deep learning time series multi class classification of driving maneuver. We also proposed some hypotheses which are proven by the experimental results. The proposed model provides Train Accuracy: 99.98, Test Accuracy: 97.2021, Precision: 0.974848, Recall: 0.960154 and F1 score: 0.967028. The Mean Per Class Error (MPCE) is 0.01386. The significant rules can accelerate the feature extraction process of driving data. Moreover, it helps in automatic labeling of unlabeled dataset. Our future approach is to develop a tool for generating categorical label for unlabeled dataset. Besides, we have plan to optimize the proposed classifier using grid search.
The Beneficial Impacts of COVID-19 Lockdowns on Air Pollution: Evidence from Vietnam

Abstract Little evidence currently exists on the effects of COVID-19 lockdowns on air quality in low-income countries, where most air pollution-linked deaths occur. We offer the first study on the lockdown impacts in Vietnam, a poorer country with the worsening air pollution that has had a successful fight against early waves of the pandemic. We compile a new, rich database from various sources including satellite air pollution data for the past year from January 2020 to January 2021 that covers both the pre-pandemic and post-pandemic lockdown periods. Employing the Regression Discontinuity Design method, we find NO2 concentration to decrease by 24 per cent to 32 per cent 2 weeks after the COVID-19 lockdown. While these positive effects on air quality are comparable to those found in previous studies on stricter gasoline regulations or transition to cleaner energy sources, they dissipate after 10 weeks. Our findings are robust to different measures of air quality and model specifications. We also find that mobility restrictions are a potential channel for improved air quality. Finally, our back-of-the-envelope calculations suggest that 2 weeks after the lockdown, the economic gains from better air quality are at least 33.9 million US dollars.
The perceived impact of pedestrianization on local businesses in Al-Muizz Egypt: A case study

Local businesses in historic centers are not only a simple service, for tourists and local communities, but also a significant element of local identity and a part of the cultural heritage of the place. Whereas pedestrianization has been widely adopted as a means to revitalizing historic centers and as a key factor in promoting tourism, evidence has revealed that the buy-in of local businesses (as one of the main beneficiaries) is vital to ensure the success of pedestrianization schemes and to get support for future ones. This study aims to explore the perceived impact of pedestrianization from the local businesses perspective within the Egyptian context. Adopting a case-study approach, Al-Muizz street: the main commercial lifeline for the Historic Fatimid Cairo, which has been lately pedestrianized, is evaluated. Using field observation and questionnaire methods, data is collected and analyzed. Results show that despite the positive impact perceived due to pedestrianization, different types of businesses have not been similarly benefited from such a scheme.
Physiological and potentially pathogenic microbial flora in stone curlew (Burhinus oedicnemus), southeastern Sicily

European stone curlew (Burhinus oedicnemus) is a Palearctic species with high conservation interest. This species nests on the ground, in open canopies with sparse herbaceous vegetation, and is typically found next to areas of intense agro-pastoral activity, where it feeds on invertebrates present in ruminant droppings. This study aimed to investigate the enteric, ocular, and oral bacterial flora of stone curlew and determine the possible occurrence of pathogenic bacteria. Furthermore, the study aimed to determine how epidemiological factors shape the bacterial flora. Fecal samples, cloacal, conjunctival and oral swabs from 61individuals of B. oedicnemus were taken in three different agro-pastoral areas of the southeastern Sicily. The presence of commensal and potentially pathogenic bacteria in the samples was evaluated by standard methods. The bacteriological analysis revealed the presence of 215 Gram – and 92 Gram + strains belonging to 23 different genera (12 families). Potentially pathogenic species including Salmonella enterica, Shigella dysenteriae, Staphylococcus aureus, and Enterococcus spp. have been identified. To our knowledge, this is the first study to determine the presence of potentially pathogenic bacteria in stone curlew living in a semi-natural habitat. Some of the detected bacterial species are potentially pathogenic not only for wild species but also for domestic animals and humans. Altogether, our results suggest that stone curlew from agro-pastoral areas are being colonized with commensal or potentially pathogenic bacteria from agricultural or human sources; the prevalence of bacteria is probably influenced by environmental and alimentary factors. B. oedicnemus can, therefore, be considered a good indicator of environmental contamination by bacteria deriving from human activities, which are potentially threatening stone curlew and other wild birds species‎.
Facial Appearance Modifications using SKPCA-Derived Features Extracted from Convolutional Autoencoder’s Latent Space

The presented paper proposes a method that enables image object appearance editing by modifying its appearance-related high-level attributes. First, attribute-related features get extracted from a latent representation of an image generator and then, their contents gets modified, which results in producing the assumed appearance alterations. Convolutional Autoencoder (CAE) has been adopted as an image manipulation framework and face appearance, characterized by four attributes: age, smile intensity, facial hair intensity and gender, was chosen for modifications. To extract attribute-related features from CAE’s latent representation, Supervised Kernel Principal Component Analysis (SKPCA) was used, as this transformation is able to disentangle complex, nonlinear image-to-attribute relationships. The method has been evaluated using large-scale face dataset CelebA. Qualitative results show that realistically-looking appearance modifications can be obtained. To quantify plausibility of introduced modifications, face recognition experiments on altered face images were performed, delivering on average 95% classification accuracy, for twenty-six category dataset.
Wearable Antenna for an Ankle Bracelet Device

Wearable devices, as well as their related applications and services, are more and more important in the steady increase of the global IP traffic. They are now an important part of the Internet of Things (IoT)/Internet of Everything (IoE). The proposed structure is a dual-band antenna with two metal layers for use in security, health/medical care, and personnel tracking applications. This dissertation proposes an antenna of 82.6 x 20 x 3.5 mm with three parallel rectangular slots of 46.5 x 1.5 mm, equispaced of 1 mm, placed in ground plane and near the lowest band port. Its prototype was built and tested. The antenna operates in the Wireless-Fidelity (Wi-Fi) and Bluetooth (BT) bands, in the 2.45 GHz Industrial, Scientific and Medical (ISM) band, and in Global Positioning System (GPS)/GLONASS/Galileo L1 bands. In two distinct stages, Scattering-parameters (S-parameters), bandwidths, feeding, radiating patterns and radiating efficiency were analysed. First in free-space, due to isolation issues, it became necessary to approach decoupling techniques. In a second stage, the antenna was studied when folded and integrated in an ankle bracelet, inspired by a commercial application, and then when placed close to an ankle and foot model. The Specific Absorption Rate (SAR) was also evaluated. Regarding the results of the first stage, there was a satisfactory agreement between the simulation and the experimental Sparameters results in the bands of interest, which validates the procedure used. The second stage led to promising simulation results for a proof of concept.
Articial States

Arti cial states are those in which political borders do not coincide with a division of nationalities desired by the people on the ground. We propose and compute for all countries in the world two new measures how arti cial states are. One is based on measuring how borders split ethnic groups into two separate adjacent countries. The other one measures how straight land borders are, under the assumption the straight land borders are more likely to be arti cial. We then show that these two measures seem to be highly correlated with several measures of political and economic
Effect of customized fertilizers on the growth and yield of wheat (Triticum aestivum L.) under eastern Uttar Pradesh

A field experiment was conducted at Agronomy Research Farm of Narendra Deva University of Agriculture and Technology, Kumarganj, Faizabad (U.P.) during Rabi 2015-16 in randomized block design with four replications. The treatment consisted of T1 (control), T2 (RDF), T3 (Soil test-based recommendation), T4 (Indo-Gulf Customized Fertilizer), T5 (TCL Customized Fertilizer) and T6 (Farmer’s Practice). All the growth parameters viz. plant height (cm), number of tillers (m), and yield attributes like number of grains spike, test weight, grain and straw yield (q ha), were maximum under T3 (Soil test based recommendation), which was at par with T4 (Indo-Gulf Customized Fertilizer) and T5 (TCL Customized Fertilizer) and significantly superior over T1 (control), T2 (RDF) and T6 (Farmer’s Practice). At harvest stage the tallest plants of 111.11 cm were recorded with T3 (Soil test based recommendation) followed by Indo-Gulf Customized Fertilizer (108.34cm) and TCL Customized Fertilizer (106.64cm) and the number of tillers meter at harvest (379.25) were found under T3 (Soil test based recommendation) which were 1.28% and 1.71% higher than the T4 (Indo-Gulf Customized Fertilizer) and T5 (TCL Customized Fertilizer) treatments respectively. The maximum grain yield of wheat (46.64 q ha) was recorded with T3 (Soil test-based recommendation) which was significantly higher over than control (20.08 qha), farmer’s practice (30.78 qha) and RDF (38.10 qha) while at par with TCL Customized Fertilizer (43.92 qha) and Indo-Gulf Customized Fertilizer (44.97 qha). The grain yield of wheat was increased with the treatment T3 (Soil test-based recommendation) 22.41% and 51.53% over RDF and farmer’s practise respectively. Similarly, 3.71% and 6.19% over Indo-Gulf Customized Fertilizer and TCL Customized Fertilizer respectively. The yield difference between both the customized fertilizers was not upto the level of significance.
Learning Arithmetic Operations With A Multistep Deep Learning

Deep neural networks are difficult to train when applied to tasks that can be expressed as algorithmic procedures. In this article, we propose to study how the explicit guidance of a network through all steps of the algorithm, using external memory and active choice of inputs, can improve its learning capability. The idea is to take inspiration from a child’s learning and running through a procedure via interaction with an external support such as a paper. We show that this mechanism applied to a simple multilayer perceptron can significantly improve its performance when learning either a multi-digit addition or multiplication, which are simple but yet challenging operations to learn.
Wave Propagation and Deep Propagation A description of two new algorithms for Inclusion Based Points-to Analysis.

This paper describes two new algorithms for solving inclusion based points-to analysis. The ﬁrst algorithm, the Wave Propagation Method, is a modiﬁed version of an early technique presented by Pearce et al. ; however, it greatly improves on the execution of its predecessor. The second al-gorithm, the Deep Propagation Method, is a more light-weighted analysis, that requires less memory. We have compared these algorithms with three state-of-the-art techniques by Hardekopf-Lin, Heintze-Tardieu and Pearce-Kelly-Hankin. Our experiments show that Deep Propagation has the best average execution time across a suite of 17 large benchmarks, the lowest memory requirements in absolute numbers, and the fastest absolute times for benchmarks under 100,000 lines of code. The memory-hungry Wave Propagation has the fastest absolute running times in a memory rich execution environment, matching the speed of the best known points-to algorithms in large benchmarks.
Convexity of the orbit-closed C-numerical range and majorization

We introduce and investigate the orbit-closed C-numerical range, a natural modification of the C-numerical range of an operator introduced for C trace-class by Dirr and vom Ende. Our orbit-closed C-numerical range is a conservative modification of theirs because these two sets have the same closure and even coincide when C is finite rank. Since Dirr and vom Ende's results concerning the C-numerical range depend only on its closure, our orbit-closed C-numerical range inherits these properties, but we also establish more. For C self-adjoint, Dirr and vom Ende were only able to prove that the closure of their C-numerical range is convex and asked whether it is convex without taking the closure. We establish the convexity of the orbit-closed C-numerical range for self-adjoint C without taking the closure by providing a characterization in terms of majorization, unlocking the door to a plethora of results which generalize properties of the C-numerical range known in finite dimensions or when C has finite rank. Under rather special hypotheses on the operators, we also show the C-numerical range is convex, thereby providing a partial answer to the question posed by Dirr and vom Ende.
Adversarial Robustness of Model Sets

Machine learning models are vulnerable to very small adversarial input perturbations. Here, we study the question of whether the list of predictions made by a list of models can also be changed arbitrarily by a single small perturbation. Clearly, this is a harder problem since one has to simultaneously mislead several models using the same perturbation, where the target classes assigned to the models might differ. This attack has several applications over models designed by different manufacturers for a similar purpose. One might want a single perturbation that acts differently on each model; like only mis-leading a subset, or making each model predict a different label. Also, one might want a perturbation that misleads each model the same way and thereby create a transferable perturbation. Current approaches are not applicable for this general problem directly. Here, we propose an algorithm that is able to find a perturbation that satisfies several kinds of attack patterns. For example, all the models could have the same target class, or different random target classes, or target classes designed to be maximally contradicting. We evaluated our algorithm using three model sets consisting of publicly available pre-trained ImageNet models of varying capacity and architecture. We demonstrate that, in all the scenarios, our method is able to find visually insignificant perturbations that achieve our target adversarial patterns.1
BIRATIONAL AUTOMORPHISMS OF NODAL

It is well-known that a nonsingular minimal cubic surface is birationally rigid, a group of its birational selfmaps is generated by biregular selfmaps and birational involutions such that all relations between the latter are implied by standard relations between reflections on an elliptic curve. It is also known that a factorial nodal quartic threefold is birationally rigid and its group of birational selfmaps is generated by biregular ones and certain birational involutions. We prove that all relations between these involutions are implied by standard relations on elliptic curves, complete a proof of birational rigidity over a non-closed field and describe the situations when some of the birational involutions in question become regular (and, in particular, complete the proof of the initial theorem on birational rigidity, since some details were not established in the original paper of M. Mella).