Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Suffering: An Argument for a Global Moratorium on Synthetic Phenomenology

This paper has a critical and a constructive part. The first part formulates a political demand, based on ethical considerations: Until 2050, there should be a global moratorium on synthetic phenomenology, strictly banning all research that directly aims at or knowingly risks the emergence of artificial consciousness on post-biotic carrier systems. The second part lays the first conceptual foundations for an open-ended process with the aim of gradually refining the original moratorium, tying it to an ever more fine-grained, rational, evidence-based, and hopefully ethically convincing set of constraints. The systematic research program defined by this process could lead to an incremental reformulation of the original moratorium. It might result in a moratorium repeal even before 2050, in the continuation of a strict ban beyond the year 2050, or a gradually evolving, more substantial, and ethically refined view of which — if any — kinds of conscious experience we want to implement in AI systems.
Non-interactive distributed key generation and key resharing

We present a non-interactive publicly verifiable secret sharing scheme where a dealer can construct a Shamir secret sharing of a field element and confidentially yet verifiably distribute shares to multiple receivers. We also develop a non-interactive publicly verifiable resharing scheme where existing share holders of a Shamir secret sharing can create a new Shamir secret sharing of the same secret and distribute it to a set of receivers in a confidential, yet verifiable manner. A public key may be associated with the secret being shared in the form of a group element raised to the secret field element. We use our verifiable secret sharing scheme to construct a non-interactive distributed key generation protocol that creates such a public key together with a secret sharing of the discrete logarithm. We also construct a non-interactive distributed resharing protocol that preserves the public key but creates a fresh secret sharing of the secret key and hands it to a set of receivers, which may or may not overlap with the original set of share holders. Our protocols build on a new pairing-based CCA-secure public-key encryption scheme with forward secrecy. As a consequence our protocols can use static public keys for participants but still provide compromise protection. The scheme uses chunked encryption, which comes at a cost, but the cost is offset by a saving gained by our ciphertexts being comprised only of source group elements and no target group elements. A further efficiency saving is obtained in our protocols by extending our single-receiver encryption scheme to a multi-receiver encryption scheme, where the ciphertext is up to a factor 5 smaller than just having singlereceiver ciphertexts. The non-interactive key management protocols are deployed on the Internet Computer to facilitate the use of threshold BLS signatures. The protocols provide a simple interface to remotely create secret-shared keys to a set of receivers, to refresh the secret sharing whenever there is a change of key holders, and provide proactive security against mobile adversaries.
Investigation of molecular dissolution mechanism of ketoprofen binary and ternary solid dispersions by molecular dynamics simulations

Abstract The research aimed to investigate the molecular dissolution mechanism of both binary and ternary solid dispersions by molecular dynamics simulations. The simulation results indicated that the drug molecules were much easier to be released from surfactant-containing ternary systems than from binary ones. Moreover, sodium dodecyl sulfate as an additive in ternary systems had better effects than Tween 60. The simulation results were in well agreement with the experimental results. This research presented a reasonable explanation of molecular dissolution mechanism for both binary and ternary solid dispersions, which may benefit the future development of solid dispersion formulations.
NUTRIENTES LIMITANTES E AS RELAÇÕES ORGÂNICAS E INORGÂNICAS ENTRE O FÓSFORO, NITROGÊNIO E SILÍCIO NO SISTEMA PELÁGICO DA BES E PNBC

E de suma importância ecologica investigar os mecanismos de disponibilidade dos sais inorgânicos dissolvidos e averiguar se no ambiente marinho ha algum nutriente limitante para a produtividade primaria. Esta pesquisa teve por objetivo avaliar as razoes do nitrogenio inorgânico dissolvido (NID) por fosforo inorgânico dissolvido (PID) por silicio inorgânico dissolvido (SID), e estudar as fracoes orgânicas e inorgânicas do nitrogenio (NT) e fosforo (PT) total, atraves de distribuicoes horizontais e verticais e suas relacoes com os parâmetros bioticos e abioticos, e diagramas de mistura. Os resultados mostraram que os percentuais das fracoes orgânicas de PT e NT foram predominantes na camada de superficie e 2a profundidade, enquanto as porcoes inorgânicas foram dominantes nas massas d’agua intermediarias e profunda. A excecao se deu atraves dos valores percentuais de NID maiores em todo o perfil da coluna d’agua, incluindo a camada fotica, durante o verao. Para a massa d’agua mais superficial, este resultado mostrou alteracao no ciclo biogeoquimico referente aos compostos nitrogenados, indicando forte influencia antropica no local, induzido por altas taxas de mineralizacao da materia orgânica. As razoes SID:PID que deveriam assumir valores semelhantes a taxa de Redfield de 16:1, registraram valores muito baixos proximos a 1, na camada fotica, o que indica conspicuamente, que apesar das baixas concentracoes de PID no verao, o nutriente limitante nao e o fosforo, e sim, o silicio reativo, que controla a produtividade primaria e a biomassa fitoplanctonica das diatomaceas na BES e PNBC.
Development of Cathode Materials for Room Temperature Lithium/Sulfur Batteries

University of Wollongong Copyright Warning You may print or download ONE copy of this document for the purpose of your own research or study. The University does not authorise you to copy, communicate or otherwise make available electronically to any other person any copyright material contained on this site. You are reminded of the following: This work is copyright. Apart from any use permitted under the Copyright Act 1968, no part of this work may be reproduced by any process, nor may any other exclusive right be exercised, without the permission of the author. Copyright owners are entitled to take legal action against persons who infringe their copyright. A reproduction of material that is protected by copyright may be a copyright infringement. A court may impose penalties and award damages in relation to offences and infringements relating to copyright material. Higher penalties may apply, and higher damages may be awarded, for offences and infringements involving the conversion of material into digital or electronic form. Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.
Self-assembly and disassembly of stimuli responsive tadpole-like single chain nanoparticles using a switchable hydrophilic/hydrophobic boronic acid cross-linker

Living systems are driven by molecular machines that are composed of folded polypeptide chains, which are assembled together to form multimeric complexes. Although replicating this type of system is a longstanding goal in polymer science, the complexity the structures impose is synthetically very challenging, and generating synthetic polymers to mimic the process of these assemblies appears to be a more appealing approach. To this end, we report a linear polymer programmable for stepwise folding and assembly to higher order structures. To achieve this, a diblock copolymer composed of 4-acryloylmorpholine and glycerol acrylate was synthesised with high precision via reversible addition fragmentation chain transfer polymerisation (Đ < 1.22). Both intramolecular folding and intermolecular assembly were driven by a pH responsive cross-linker, benzene-1,4-diboronic acid. The resulting intramolecular folded single chain nanoparticles were well defined (Đ < 1.16) and successfully assembled into a multimeric structure (Dh = 245 nm) at neutral pH with no chain entanglement. The assembled multimer was observed with a spherical morphology as confirmed by TEM and AFM. These structures were capable of unfolding and disassembling either at low pH or in the presence of sugar. This work offers a new perspective for the generation of adaptive smart materials.
Utilization of eggshell waste as low-cost solid base catalyst for biodiesel production from used cooking oil

A solid CaO-based catalyst of waste eggshell was developed for biodiesel production from used cooking oil. The waste eggshell powder was calcined in air at 90° C for 4 h to convert calcium species in the eggshells into active CaO catalysts. The characterization of CaO catalyst was done by XRD and BET analysis. The CaO catalyst was then introduced for transesterification of used cooking oil (UCO) for testing of its catalytic activity. The experiment was conducted in batch type reactor that consists of three-neck glass equipped by reflux condenser and magnetic stirrer. Before tranesterification process, the UCO was treated by coconut coir powder in order to reduce the free fatty acid content. The result showed that the catalyst was potentially use for transesterification of used cooking oil into biodiesel with relatively high yield of 75.92% was achieved at reaction temperature, reaction time, molar ratio UCO to methanol and catalyst amount of 65° C, 7 h, 1:15 and 6%, respectively.
First Principles Studies of Perovskites for Intermediate Temperature Solid Oxide Fuel Cell Cathodes

First Principles Studies of Perovskites for Intermediate Temperature Solid Oxide Fuel Cell Cathodes Omotayo Akande Salawu Fundamental advances in cathode materials are key to lowering the operating temperature of solid oxide fuel cells (SOFCs). Detailed understanding of the structural, electronic and defect formation characteristics are essential for rational design of cathode materials. In this thesis we employ first principles methods to study La(Mn/Co)O3 and LnBaCo2O5+δ (Ln = Pr, Gd; δ = 0.5, 1) as cathode for SOFCs. Specifically, factors affecting the O vacancy formation and migration are investigated. We demonstrate that for LaMnO3 the anisotropy effects often neglected at high operating temperatures become relevant when the temperature is lowered. We show that this fact has consequences for the material properties and can be further enhanced by strain and Sr doping. Tensile strain promotes both the O vacancy formation and migration in pristine and Sr doped LaMnO3, while Sr doping enhances the O vacancy formation but not the migration. The effect of A-site hole doping (Mg, Ca or Ba) on the electronic and magnetic properties as well as the O vacancy formation and migration in LaCoO3 are studied. All three dopants are found to facilitate O vacancy formation. Substitution of La with Ba/Mg yields the lowest O vacancy formation energy for low/intermediate spin Co, implying that not only the structure, but also the spin state of Co is a key parameter. Only for low spin Co the ionic radius is correlated with the O migration barrier. Enhanced migration for intermediate spin Co is ascribed to the availability of additional space at the transition state. 5 For LnBaCo2O5+δ we compare the O vacancy formation in GdBaCo2O5.5 (Pmmm symmetry) and GdBaCo2O6 (P4/mmm symmetry), and the influence of Sr doping. The O vacancy formation energy is demonstrated to be smaller in the already O deficient compound. This relation is maintained under Sr doping. It turns out that Sr doping can be utilized to significantly enhance the O vacancy formation in both compounds. The observed trends are explained on a microscopic level. Furthermore, we consider antisite defects as they may modify the electronic and O migration properties but are rarely studied in double perovskite oxides. It turns out that O vacancy formation is significantly easier in PrBaCo2O5.5 than in GdBaCo2O5.5, the difference in formation energy being hardly modified by antisite defects. Finally, having established that the O vacancy formation energy is significantly lower in PrBaCo2O5.5 than in GdBaCo2O5.5, we study the O Frenkel energy and migration of O ions in PrBa(Co/Fe)2O5.5. The electronic structure and charge redistribution during defect formation are analyzed. We demonstrate that Co↔Fe substitution strongly affects the formation of defects and, consequently, the O migration. The low O Frenkel energy points to a high concentration of O vacancies. The migration of the O ions shows a distinct anisotropy.
The characterisation, photocatalytic performance, and theoretical investigation of small gold clusters supported on titanium dioxide nanoparticles

Renewable photocatalytic systems that can use solar radiation to produce chemical fuels are appealing technologies that have garnered much interest over the last few decades. The development of novel gold-based heterogeneous photocatalysts has the potential to drive this new form of green chemistry. In this thesis, ligand-protected, atomically precise gold clusters supported on titanium dioxide nanoparticles are characterised by various surface science techniques and their photocatalytic potential for water photolysis is investigated. Given the difficultly associated with acquiring experimental data in the incredibly small size regime of clusters, complementary density functional theory calculations are also performed to match with experimental data and to help elucidate the interactions occurring between gold atoms and the titanium dioxide surface. The characterisation of these gold clusters begins with the far infra-red absorption spectra of isolated Au6(Ph2P(CH2)3PPh2)4(NO3)2, Au8(PPh3)8(NO3), Au9(PPh3)8(NO3)3, Au11(PPh3)8Cl3, Pd(PPh3)Au6(PPh3)6(NO3)2, and Pt(H)(PPh3)(AuPPh3)7(NO3)2 recorded using synchrotron light. These experiments reveal a series of unique peaks between 50 and 475 cm that are assigned to specific vibrational modes by comparison with density functional theory calculations. The distinct peaks for each cluster can be assigned to the calculated cluster core vibrations: 80.4 and 84.1 cm for Au6; 165.1 and 166.4 cm for Au8; 170.1 and 185.2 cm for Au9; 173.7 and 182.2 cm for Au11; 158.9, 195.2, and 206.7 cm for Au6Pd; and 156.3, 171.8, and 173.5 cm for Au7Pt. There is also a strong absorption for all clusters near 420 cm that are assigned to P-Ph3 vibrations. Characterisation is continued with the use of Synchrotron X-ray and TEM techniques to investigate the full effect of treatments that are undertaken to remove the organic protective ligands after the gold clusters are supported upon titanium dioxide. These experiments show an increase in particle size and loss of ligands with successively harsher post-treatments. Acidic pre-treatment and the form of the titanium dioxide support are shown to have a strong impact on the severity of agglomeration and ligand loss. These results are also supported by X-ray absorption near edge structure and extended X-ray absorption near edge structure analysis of complementary X-ray absorption experiments.
Structure Prior Guided Deep Network for Compressive Sensing Image Reconstruction From Big Data

Recent research on image reconstruction from Compressive Sensing theory has achieved great success due to the devolopment of deep convolutional neural networks(DCNN). However, commonly used loss function suffers from a limitation in that the reconstruction data should obey an unimodal Gaussian distribution which is disobedience to the real distribution of natural image data [1]. In this work, we propose an structure prior guided compressive sensing network called SGCSNet. The key portion of SGCSNet is the adaptive structure guided block(dubbed as ASGB) in reconstruction network. By fully using the image structure prior as guidance constraint to decompose the multi modal distribution into multiple unimodal distributions based on ASGB, SGCSNet could improve the visual effect of the details in restored image for commonly used loss function like $l_{2}$ loss. In addition, we introduce gram matrix instead of $l_{2}$ norm to depict the distance between feature layers. The gram matrix can express the difference of features from a global views and improve the texture details of image restoration. Experiments show that SGCSNet has a better performance and could obtain more realistic results than the existing state of the art method.
Developed of Real Time Simulator for Wind Energy Systems

This paper proposes a developed real time simulator for variable speed wind energy system using two devices of dSPACE real time interface (RTI), one of them contains the wind turbine and the other contains the rest of power circuit with the control circuits, due to lake of real model of wind turbine and a permanent magnet synchronous generator. The results of the real time simulation provide that, maximum power generation control technique at the converter of generator side has been achieved, the voltage of DC-link has been kept constant, finally the grid unity power factor has been attained using a simple PI controller cascaded with a hysteresis current controller on the voltage source inverter (VSI) at the grid side. The suggested developed real time simulator design gives more benefits for both research and application of wind energy system whereas the testing in the field is expensive and consuming time.
Codec Network for Speech Bandwidth Extension

In order to further improve the performance of speech bandwidth extension, this paper proposes an end-to-end codec network model for speech bandwidth extension. It is modeled on the time-domain waveform and consists of encoder network, long short-term memory (LSTM) and decoder network. The encoder network is responsible for feature extraction and data dimensionality reduction of the input data, the LSTM is responsible for extracting the context-dependent information of the speech signal, and the decoder network is responsible for wideband speech reconstruction of the potential features output by the LSTM. In addition, this paper also proposes a time-frequency perception loss function to guide model training to generate more accurate time-domain waveforms and more realistic frequency-domain spectrum. Experimental results show that the reconstructed wideband speech generated by the model achieves better results in both subjective and objective evaluation.
Player Behavior Modeling for Enhancing Role-Playing Game Engagement

Role-playing games (RPGs) are one of the most exciting and most rapidly expanding genres of online games. Virtual characters that are not controlled by players, have become an integral part, which helps to advance narratives of RPGs. Believable characters can enhance game engagement and further improve player retention. However, game players easily find that most characters’ behaviors are limited and improbable, resulting in a less meaningful game experience. In this work, we propose a framework to model game behaviors to learn behavior patterns of human players. Based on the learned behavior patterns, it generates human-like action sequences that can be used for the design of believable virtual characters in RPGs, so as to enhance game engagement. Specifically, considering the influence of game context in behavior patterns, we integrate game context (players’ levels and game classes) with actions together to model behaviors. We propose a long-term memory cell on actions and game context to learn the hidden representations. We also introduce an attention mechanism to measure the contribution of the actions previously performed to the next action. Given only one action, our model can generate action sequences by predicting the succeeding action based on the previously generated actions. The model was evaluated on a real-world data set of over 22 000 players and more than 51 million action logs of an RPG game in 21 days. The results demonstrate the state-of-the-art performance.
Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing. Previous methods usually assume this issue as a job-assignment optimization problem and only consider the current VMHs’ loads; however, without considering loads of VMHs after balancing, these methods can only gain limited effectiveness in real environments. In this study, two genetic-based methods are integrated and presented. First, performance models of virtual machines (VMs) are extracted from their creating parameters and corresponding performance measured in a cloud computing environment. The gene expression programming (GEP) is applied for generating symbolic regression models that describe the performance of VMs and are used for predicting loads of VMHs after load-balance. Secondly, with the VMH loads estimated by GEP, the genetic algorithm considers the current and the future loads of VMHs and decides an optimal VM-VMH assignment for migrating VMs and performing load-balance. The performance of the proposed methods is evaluated in a real cloud-computing environment, Jnet, wherein these methods are implemented as a centralized load balancing mechanism. The experimental results show that our method outperforms previous methods, such as heuristics and statistics regression.
A Multi-Layer Capsule-based Forensics Model for Fake Detection of Digital Visual Media

the dangers generated from synthesized multimedia are increasing every day. The creation of the so-called Deepfakes multimedia is vastly evolving, making the detection task harder every day. Researchers and corporations are interested in exploring the technology limits and are coming up with new tools every year to create more robust fake media. In this paper, a new enhanced fake video detection model is introduced addressing many of the face-swapping threats and the low generalization problem. A preprocessing stage is proposed to minimize the noise in the data to enhance their quality. The proposed architecture uses a modified application of capsule neural networks (CapsNet) with an enhanced routing technique. It does not require a lot of training data and generates a small number of training parameters making it fast to build. The model was trained and tested using the DFDC-P dataset and the results have proven that it outperformed other detectors in terms of detection recall, weighted precision, and F1 score.
DBSCAN Is Semi-Spectral Clustering*

Spectral clustering and DBSCAN are two famous clustering methods, the former reduces data dimensionality by spectrum of similarity matrix, and then utilizes kmeans to cluster data in low dimensional space. While DBSCAN performs clustering by finding different density regions that depart from each other, which makes it seems quite different from spectral clustering, and there is little literal discusses the relationship between them. In this paper, we revisit DBSCAN from Similarity Graph and Graph Cut point of views, uncover the underlying relationship between DBSCAN and spectral clustering, which proves that DBSCAN can be explained and rewritten under the framework of spectral clustering. Furthermore, eigenvectors are often approximately resolved, and k-means is usually used in the final stage, often converges in local optimization. Hence, we rewrite spectral clustering by using nearest neighbor query instead of k-means to obtain exact result. Experimental results address that the revised spectral clustering method can obtain the same result as DBSCAN on core point set. Therefore, we come to the conclusion that DBSCAN is semi-spectral clustering. The work of this paper theoretically illustrates that spectral clustering is as good as DBSCAN, and both algorithms can be replaced with each other to avoid their own disadvantages.