Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum localization and electronic transport in covalently functionalized carbon nanotubes

Carbon nanotubes are of central importance for applications in nano-electronics thanks to their exceptional transport properties. They can be used as sensors, for example in biological applications, provided that they are functionalized to detect specific molecules. Due to their one-dimensional geometry the carbon nanotubes are very sensitive to the phenomenon of Anderson localization and it is therefore essential to know how the functionalization modifies their conduction properties and if they remain good conductors. Here we present a study of the quantum localization induced by functionalization in metallic single walled carbon nanotubes (SWCNT) with circumferences up to 15 nm. We consider resonant and non-resonant adsorbates that represent two types of covalently functionalized groups with strong and moderate scattering properties. The present study provides a detailed analysis of the localization behaviour and shows that the localization length can decrease down to 20–50 nm at concentrations of about 1 percent of adsorbates. On this basis we discuss the possible electronic transport mechanisms which can be either metallic like or insulating like with variable range hopping.
Machine learning as ecology

Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms—including support vector machines (SVMs)—have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark performance using the MNIST dataset. Our work provides a new ecological lens through which we can view statistical learning and opens the possibility of designing ecosystems for machine learning.
Unlabelled Far-field Deeply Subwavelength Superoscillatory Imaging (DSSI)

Recently it was reported that deeply subwavelength features of free space superoscillatory electromagnetic fields can be observed experimentally and used in optical metrology with nanoscale resolution [Science 364, 771 (2019)]. Here we introduce a new type of imaging, termed Deeply Subwavelength Superoscillatory Imaging (DSSI), that reveals the fine structure of a physical object through its far-field scattering pattern under superoscillatory illumination. The object is reconstructed from intensity profiles of scattered light recorded for different positions of the object in the superoscillatory field. The reconstruction is performed with a convolutional neural network trained on a large number of scattering events. We show that DSSI offers resolution far beyond the conventional 'diffraction limit'. In modelling experiments, a dimer comprising two subwavelength opaque particles is imaged with a resolution exceeding ${\lambda}/200$.
On the Merge of k-NN Graph

<italic>k</italic>-nearest neighbor graph is a fundamental data structure in many disciplines such as information retrieval, data-mining, pattern recognition, and machine learning, etc. In the literature, considerable research has been focusing on how to efficiently build an approximate <italic>k</italic>-nearest neighbor graph (<italic>k</italic>-NN graph) for a fixed dataset. Unfortunately, a closely related issue of how to merge two existing <italic>k</italic>-NN graphs has been overlooked. In this paper, we address the issue of <italic>k</italic>-NN graph merging in two different scenarios. In the first scenario, a symmetric merge algorithm is proposed to combine two approximate <italic>k</italic>-NN graphs. The algorithm facilitates large-scale processing by the efficient merging of <italic>k</italic>-NN graphs that are produced in parallel. In the second scenario, a joint merge algorithm is proposed to expand an existing <italic>k</italic>-NN graph with a raw dataset. The algorithm enables the incremental construction of a hierarchical approximate <italic>k</italic>-NN graph. Superior performance is attained when leveraging the hierarchy for NN search of various data types, dimensionality, and distance measures.
Characterization of the Particle-induced Background of XMM-Newton EPIC-pn: Short- and Long-term Variability

The particle-induced background of X-ray observatories is produced by galactic cosmic ray (GCR) primary protons, electrons, and He ions. Events due to direct interaction with the detector are usually removed by onboard processing. The interactions of these primary particles with the detector environment produce secondary particles that mimic X-ray events from celestial sources, and are much more difficult to identify. The filter-wheel closed data from the XMM-Newton EPIC-pn camera in small window mode (SWM) contains both the X-ray-like background events, and the events due to direct interactions with the primary particles. From this data, we demonstrate that X-ray-like background events are spatially correlated with the primary particle interaction. This result can be used to further characterize and reduce the non-X-ray background in silicon-based X-ray detectors in current and future missions. We also show that spectrum and pattern fractions of secondary particle events are different from those produced by cosmic X-rays.
Adaptive Cooperative Multi-hop Transmission in Ad Hoc Networks

Cooperative communication techniques have been proposed in order to improve the quality of the received signals at the receivers by using the diversity added by du plication of signals sent by relay terminals situating between e ach transmission pair. This paper proposes an adaptive cooperation technique for frame transmissions in Ad Hoc networks that is compatible to both of the basic ac cess mode and the optional access mode of IEEE 802.11 Medium Access Control (MAC) protocol. The transmission mode for each data frame is adaptively switched between a cooperative mode and a non-cooperative mode based on the absence of ackn owledge (ACK) frame. Simulations show that transmission performance is improved by decreasing the number of retransmissions due to frame errors; thus, chances of multi-hop mode transitions that are costly in time and bandwi dth are alleviated. The analysis of the proposition performance indicates the interest of the adaptation paradigm. It puts forward that, in addition to the channel quality parameter, the channel availability parameter must be concerned in the adaptation process. Keywords-adaptive cooperation; cooperative transmissions; IEEE 802.11
Scaffolding Teachers' Efforts to Implement Problem-Based Learning

Despite prevalent recommendations for the adoption of problem-based learning (PBL) approaches, the transition to PBL teaching is not easy. Given the general lack of experience most teachers have with open-ended teaching strategies, novice PBL instructors are likely to encounter difficulties in all aspects of instruction: planning, implementing, and assessing. More specifically, researchers have reported that instructors experience frustration with the amount of time it takes to implement problem-based experiences, report difficulty transitioning students into more active roles, and note struggles with effectively assessing student learning.
Study of the Fly algorithm for 2-D and 3-D image reconstruction

The aim of this study is to investigate the behaviour and application of an evolutionary algorithm (EA) based on a particular approach of cooperative co-evolution algorithm (CCEA), the Parisian Approach. It evolves and keeps an entire population as an optimal solution to the problem instead of keeping only the best individual in classical EAs. The CCEA we selected is called the “Fly algorithm”. It is named after flies, because the individuals are extremely primitive and correspond to three-dimensional (3-D) points. This algorithm has been relatively overlooked despite showing promising results in real-time robotic and image reconstruction in tomography. Our focus in this study is on two types of applications: medical imaging and digital art. i) In the medical application, we aim to improve quantitative results in 3-D reconstructed volumes in positron emission tomography (PET). We investigate the use of density fields, based on Metaballs and on Gaussian functions respectively, to obtain a realistic output. We also investigate how to exploit individuals’ fitness to modulate their individual footprint in the final reconstructed volume. An individual’s fitness can be seen as a level of confidence in its 3-D position. The resulting volumes are compared with previous work in terms of normalised-cross correlation. In our test cases, data fidelity increases by more than 10% when density fields are used instead of using a naive approach. Our method also provides reconstructions comparable to those obtained using well-established techniques used in medicine (e.g., filtered back-projection (FBP) and ordered subset expectation-maximization (OSEM)). Our algorithm relies heavily on the mutation operator. We propose 4 different fully adaptive mutation operators: basic mutation, adaptive mutation variance, dual mutation and directed mutation. Their impact on the algorithm efficiency is analysed and validated on PET reconstruction. ii) In the digital art application, we present the first application of the Fly algorithm in digital art. This branch of digital art is called “evolutionary art”. The motivation is to evaluate the algorithm with a much more complex structure of flies. They are still defined as simplistic primitives (3-D points) but with colours, sizes and rotations. Different visual effects were investigated, such as mosaic-like images and spray paint rendering. An online survey (including 41 participates) was conducted to validate our approach. Participants compared our results with similar ones generated with open-source software (GIMP). Again, our method shows promising results. In conclusion, our investigations confirm that the Fly algorithm works well with a complex search space. We demonstrate a fast and accurate solution to optimise a set of parameters in both applications. The Fly algorithm can improve reconstructed image quality compared to FBP and OSEM in medical application and to GIMP in digital art application.
Coprecipitation of Aluminum Goethite and Amorphous Al-Hydroxy-Sulfate Using Urea and Characterization of the Thermal Decomposition Products

Aluminum substitution is a common phenomenon in environmental iron oxides and oxyhydroxides, affecting the color, magnetic character, surface features, etc. Several methods for preparing Al-substituted iron oxyhydroxides can be found in the literature, resulting in samples with particular properties. In the present study, the synthesis of aluminum-substituted goethites, AlxFe1-xOOH with 0 ⩽ x ⩽ 0.15, by homogeneous precipitation and the transformation to aluminum-substituted hematites, (AlxFe1-x)O3, are presented. The goethite samples were produced at 90°C from solutions of urea and iron and aluminum nitrates in the presence of ammonium sulfate (GU series). Although attempts were made to incorporate up to 33 mole% of Al into the goethite, only ~15 mole% was found to be within the structure, due to the final pH, ~7, of the synthesis. Another feature of these goethites was a lateral alignment of the tabular particles. By heating batches of the GU samples at 400°C and 800°C, two series of Al-hematites were obtained, denoted here as the HX400 and HX800 samples, respectively. X-ray diffraction, thermal analysis, Karl-Fischer titration, transmission electron microscopy, and Mössbauer spectroscopy were used to characterize the samples. The X-ray patterns showed the samples to be pure iron phases, with particle sizes of ~10 nm for the GU and HX400 samples, and of ~70 nm for the HX800 samples. An inversion in the intensities of the (104) and (110) diffraction peaks of hematite was observed to be dependent on the aluminum substitution and was explained by small particle sizes, shape anisotropy, and the presence of nanopores. The cell parameters of both GU and HX samples showed a small decrease with increasing aluminum substitution up to x ≈ 0.15. The amount of adsorbed sulfate, presumably as an aluminum hydroxy sulfate gel, increased with aluminum substitution in all GU and HX samples, reaching a maximum of ~6.5 wt. % for the highest substitution. Heating at 100°C did not remove all of the adsorbed water, and significantly higher temperatures were required to achieve complete removal. Mossbauer spectra at 295 K and 80 K are typical for small-particle goethite and hematite, and revealed that Al-for-Fe substitution in all samples seems to be limited to ~15 mol. %.
Assessment of MPPT Techniques During the Faulty Conditions of PV System

The contribution of Distributed Generation (DG) systems like wind energy systems and solar Photovoltaic (PV) systems on the generation of electricity has increased. Out of these DG systems, the PV systems have gained wide popularity, because of the availability of solar energy throughout the day. Depending on the size of PV installations, a large number of PV modules can be interconnected in the form of series and parallel connection. Since a large number of modules are interconnected, it is possible for the faults in a PV array to occur due to the failure of protection system, which can cause damage to the PV module and also the decrease in the output power. This paper presents the tracking of a maximum power point under the faulty conditions of 12x5 PV array. The fault conditions that have been considered in the PV array are open circuit fault, line to ground, line to line and failure of bypass diodes. Perturb and observe, incremental conductance and fuzzy logic controller are the maximum power point tracking techniques that have been implemented. For each of the fault conditions, the results have been presented in terms of the maximum power tracked, tracking time and tracking efficiency.
Stochastic Geometry Based Analysis of Candidate Technologies for 5G Cellular Systems

Future fifth generation (5G) cellular standards incorporate new emerging technologies such as cognitive radio, device to device (D2D), interweave, energy harvesting, massive MIMO, and millimeter wave networks. However, the achievable gains of these technologies are limited by the spatial randomness of nodes and network interference. Spatial locations of nodes are increasingly random. Characterizing the impacts of this randomness on system parameters is a main motivation of this thesis. Moreover, when two networks coexist in the same geographical area, interference occurs between the networks in addition to interference within each network. The interplay between different technologies is also of high interest as they will be deployed in tandem. Therefore, the main communication problem addressed in this thesis is the characterization of interference among spatially random nodes for co-existing networks under emerging technologies. The main contributions of this thesis are categorized as follows: 1) Development of power control and receiver association schemes for an annular underlay CR network and investigation of their performance, 2) Development of co-operative beacon detection schemes for interweave CR networks and investigation of their performance, 3) Investigation of the feasibility of wireless energy harvesting for an underlay CR network using stochastic geometry and markov chain based models, 4) Quantifying the effect of performance degradation for an underlaid receiver when base stations use massive MIMO, and 5) Development of an analytical framework to analize performance degradation to an underlaid D2D network employing millimeter wave frequencies. It is shown that stochastic geometry models provide vital insights into key network design considerations while incorporating most system and channel parameters. The research outcomes
Image sharing privacy policy on social networks using A3P

User Image sharing social site maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users inadvertently shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. The solution relies on an image classification framework for image categories which may be associated with similar policies and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to user’s social features. Image Sharing takes place both among previously established groups of known people or social circles and also increasingly with people outside the users social circles, for purposes of social discovery-to help them identify new peers and learn about peers interests and social surroundings, Sharing images within online content sharing sites, therefore, may quickly lead to unwanted disclosure. The aggregated information can result in unexpected exposure of one’s social environment and lead to abuse of one’s personal information.
How Players Speak to an Intelligent Game Character Using Natural Language Messages

AI-driven characters that learn directly from human input are rare in digital games, but recent advances in several fields of machine learning suggests that they may soon be much more feasible to create. This study explores the design space for interacting with such a character through natural language text dialogue. We conducted an observational study with 18 high school students, who played Minecraft alongside a Wizard of Oz prototype of a companion AI character that learned from their actions and inputs. In this paper, we report on an analysis of the 186 natural language messages that players sent to the character, and review key variations in syntax, function and writing style. We find that players’ behaviour and language was differentiated by the extent to which they expressed an anthropomorphic view of the AI character and the level of interest that they showed in interacting with it.
Recognition of arm-movement electroencephalography (EEG) using motor-related intrinsic mode functions filtering and cross mutual information: World Congress on Medical Physics and Biomedical Engineering: Neuroengineering, Neural Systems, Rehabilitation and Prosthetics

In this paper, we propose trial-specific and sub- ject-specific filters to extract the motor-related compartment for the recognition of EEG signals induced by the left- or right- arm movement. Such motor-related filters are the intrinsic mode functions (IMF), which were produced by the decompo- sition of signals in C3 or C4 motor channels using the empiri- cal mode decomposition (EMD), with the peak frequency per- taining to the mu rhythm within alpha band (8-12Hz) or beta band (16-25Hz). After these trial-specific and subject-specific filters were applied on all channels, the cross mutual informa- tion (CMI) of filtered signals between any two channels was computed. The average classification rates for five healthy subjects obtained from the proposed filters related to the alpha and beta bandpass filtering with whole-brain CMI maps were 77.4% and 88.3%, respectively, which were superior to the 72.2%, and 77.7% obtained from the same filtering but with only CMI vectors related to motor signals in C3 and C4, re- spectively.
OPTIMIZATION OF ELECTROLYTE COMPOSITION FOR THE CATHODIC TEMPLATE DEPOSITION OF Ni ( OH ) 2-BASED ELECTROCHROMIC FILMS ON FTO GLASS

A series of experiments have been conducted in order to determine the optimal concentration of polyvinyl alcohol (PVA) for deposition of electrochromic nickel hydroxide films onto fluorine-doped tin oxide (FTO), using cathodic template deposition method. Fundamental physicochemical properties of electrolytes for cathodic template deposition method with different concentration of PVA have been studied. It was found that higher concentrations of PVA resulted in higher adhesion of films to FTO substrate and higher performance; however, excessive concentrations resulted in degradation of film performance. It was also found, the electrolyte viscosity is directly dependant on PVA concentration, however, no correlation between PVA concentration and conductivity have been found. A hypothesis was proposed to explain such behavior.
Phytochemical screening and quantification of phytoconstituents in Gmelina arborea fruits extracts

The aims of present investigation are to carried out phytochemical screening and quantify phytoconstituents of Gmelina arborea (Verbenaceae) fruits extracts. The ethyl acetate, methanolic and water extract of G. arborea were screened and phytochemical analysis showed the presence of carbohydrates, saponin, alkaloid, flavonoids, phenolic compound, steroid and triterpenes. The quantification of total phenolic and flavonoids were carried out using Folin–Ciocalteu method and aluminium chloride method respectively while saponin content determined using vanillin sulphuric acid in G. arborea fruits extracts. Crude alkaloid content was determined in G. arborea fruit powder. TLC (Thin layer chromatography) is also performed on methanol extract of fruits of G. arborea. The Results revealed that ethyl acetate extract showed highest amount of phenolic and flavonoids content whereas the highest saponin content was found in methanolic extract of fruits.
CoCo Design, Risk Shifting Incentives and Capital Regulation

Contingent convertible capital (CoCo) is a debt instrument that converts to equity or is written off if the issuing bank fails to meet a distress threshold. The conversion increases the issuer's loss-absorption capacity, but results in wealth transfers between CoCo holders and shareholders, which may change risk-shifting incentives to shareholders. Higher risk increases the probability of CoCo conversion, while lowering the wealth transfer. We show that for Principal-Write-Down (PWD) CoCos, the net effect is to always increase risk-shifting incentives, while for equity-converting CoCos, it depends on the extent of dilution after conversion. We integrate the analysis in a game-theoretic optimal capital regulation framework and show that use of PWD or insuffciently dilutive CE CoCos requires higher capital requirements for given asset structure to offset the rising risk-shifting incentives these instruments give rise to.