Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Automatic Data Labelling of Smart Meter Data for Electricity Theft Detection

The global penetration rate of smart meter installation is ever-growing. The smart meters provide 24/7 continuous recordings of electricity data which is comprised of useful information related to electricity usage and consumers’ behaviors. Anomalies such as electricity theft can be detected using machine learning algorithms. To build a robust and accurate detection model, sufficient labelled data is important to ensure good generalization that adapts properly to unseen data. Nevertheless, manual electricity data labelling is costly and unrealistic in a large-scale population. In this paper, a semi-automatic data labelling algorithm is proposed based on deep convolutional neural network to label the electricity data, with limited amount of labelled data. Results reveal that the algorithm could serve as tradeoff between costly manual labelling and performance of the detection model.
Do General Practitioners in a Visiting Medical Officer Arrangement Improve the Perceived Quality of Care of Rural and Remote Patients? A Qualitative Study in Australia

Background: In rural and remote Australia, general practitioners (GPs) provide care across the continuum from primary to secondary care, often in Visiting Medical Officer (VMO) arrangements with a local hospital. However, little is known about the role of GP-VMOs in improving the perceived quality of care and health outcomes for rural and remote communities. Methods: We collected qualitative data from three GP-VMOs (all aged >55 years) and 10 patients (all aged over 65 years) in three local health districts of New South Wales, Australia. Thirteen in-depth interviews were conducted between October 2020 and February 2021. We employed thematic analysis to identify key roles of GP-VMOs in improving the perceived quality of care and health outcomes of rural and remote patients. Results: Our study advances the current understanding regarding the role of GP-VMOs in improving the perceived quality of services and health outcomes of rural and remote patients. Key roles of GP-VMOs in improving the perceived quality of care include promoting the continuity of care and integrated health services, cultivating trust from local communities, and enhancing the satisfaction of patients. Conclusions: GP-VMOs work across primary and secondary care creating better linkages and promoting the continuity of care for rural and remote communities. Employing GP-VMOs in rural hospitals enables the knowledge and sensitivity gained from their ongoing interactions with patients in primary care to be effectively utilised in the delivery of hospital care.
Supervised Machine Learning for Greedy Agglomeration in Connectomics

This study explores the use of supervised machine learning methods for greedy ag-glomeration in the application of constructing connectomes or neural wiring diagrams that show how neurons are connected to each other. The current approach to this problem is mean aﬃnity greedy agglomeration, which makes locally optimal merge/not-merge decisions. The rationale behind using supervised learning methods is that they may lead to locally optimal merge/not-merge decisions that are more globally optimal than those made by mean aﬃnity agglomeration. The results of this study are inconclusive on whether supervised machine learning methods are better than mean aﬃnity agglomeration because of evaluation issues. Future work should thus explore better methods of evaluating agglomeration performance. In addition, future work should address the speciﬁc weaknesses of the pipeline for producing con-nectomes: misalignment errors and errors where the dendritic spines do not grow out from the dendritic shafts.
Weight-Based Deep Model Compression of DGA Detectors for Small Devices

- The sophisticated model such as deep neural networks (DNNs) based architecture is capable of handling Domain Generating Algorithms (DGAs) malware threats particularly well. Regardless of such remarkable success, DNN models have high computational and storage overhead. It is very difficult to deploy the model into the resource-constrained devices that everyday users have access to, such as mobile phones, home routers, embedded gadgets, or IoT devices. In this thesis, we explore an effective way to compress the DNN model while preserving the effectiveness and enhancing the efficiency. We introduce filter by filter pruning together with additional pre-processing techniques, such as normalization to avoid any biases from dominant values, and a clustering technique to identify redundancy to remove. Adapting the proposed weight-based deep model compression methodology to the pre-trained DGA classifiers, we are able to facilitate the DNN model size reduction by removing 40% of the filters, while preserving the effectiveness of the model. Therefore, reducing energy consumption and memory overhead, our proposed methodology facilitates accessibility of many deep model applications, promotes higher security measures, and enables energy saving for day-to-day users.
Optimization of Shale Gas Sweetening Process Coupling with Claus Process Based on Energy Synthesis

As a sort of emerging unconventional energy, shale gas has extensive market outlook by virtue of its enormous reserves, and concerns for shale gas exploitation and processing have been raised nowadays. Raw shale gas must be processed to achieve certain specifications before it can be transmitted in pipelines or utilized by consumers. Sweetening is a gas conditioning process to decrease the concentration of acid gases such as hydrogen sulfide and carbon dioxide which are not preferred in sales gas in consideration of heating value specification and corrosion prevention. However, the after-treatment of acid gases is not discussed in many research of sweetening process. In this paper, a flowsheet of shale gas sweetening process is established using Aspen Plus v8.6. Dissolution of light gases and weak electrolyte, absorption of acid gases and reactions in electrolyte solution are considered simultaneously in process modelling. Diethanolamine (DEA) solution is employed as the solvent to separate acid gases from raw shale gas. The optimal feed stage of rich solvent regeneration and reflux ratio of regenerator are analysed to optimize the sweetening flowsheet. A three-stage Claus process is simulated coupling with shale gas sweetening process to convert hydrogen sulfide in acid gas to element sulphur for pollution reduction. A principle is proposed to determine the operating temperature of each Claus reactor which is a decisive parameter on sulfur recovery efficiency and performance of Claus process. Ultimately, the sulfur recovery efficiency of the three-stage Claus process proposed in this paper is 97.35 %. The effectivity of the principle is confirmed by the results reported in literatures. Energy synthesis is then adopted to integrate sweetening process with Claus process in both mass and energy flow. The coupled process provides with more streams than a single sweetening or Claus process, promoting the reasonability of energy utilization. Streams are extracted and matched for heat exchanger network (HEN) synthesis to reduce the energy consumption and total annual cost of the whole process.
Solitary Wave Solutions of Modeled Equations in a Nonlinear Capacitive Electrical Line

Solitary wave is the most used signal for the transmission of information in the different transmission media for the fact that it is robust, localized, and stable. It is particularly that property of non-loss of energy by solitary wave during its movement that has motivated us to find out in which measure one can propagate it in transmission media like electrical line. The choice of electrical line for our study is due to the fact that they are low cost transmission supports and very easy to manufacture. In this article, we define analytically nonlinear properties that must obey the electrical line components so that their media accept to propagate solitary waves. We use the analytical definition of nonlinear charge of capacitors and we apply Kirchhoff laws to the circuit of nonlinear capacitive electrical line to model new higher-order nonlinear partial differential equations which govern the dynamics of solitary waves in the line. The application of Bogning-Djeumen Tchaho-Kofane method that facilitate the construction of solitary wave solutions of nonlinear partial differential equations by the identification of basic hyperbolic function coefficients in a direct and effective manner has permitted to obtain exact solutions of the modeled nonlinear equations. These solution are solitary waves of type Kink and type Pulse that are susceptible to propagate in the line when some conditions we have established are respected. The results obtained in this paper notably the analytical definitions that must undergo nonlinear charges of capacitors in the line, the nonlinear partial differential equations and their exact soliton solutions will permit: The amelioration of the quality of signal susceptible of propagating in the line, to facilitate the choice of the type of the line relative to the choice of the type of signal to transmit, to economize by reducing amplification stations, to increase the mathematical knowledge, to enable the manufacturing of new nonlinear capacitors.
Autoimmunity in MRL/lpr Mice Absence of CD59 Exacerbates Systemic

CD59 is a GPI-anchored membrane regulator of complement expressed on blood cells as well as peripheral tissues. It protects host cells from complement injury by inhibiting formation of the membrane attack complex. Recent studies in mice have suggested also a role of CD59 in T cell immune response that was mechanistically independent of complement. In the present study, we investigated the function of CD59 in the MRL/lpr model of murine lupus. We backcrossed the Cd59a knockout ( Cd59a 2 / 2 ) mouse onto the MRL/lpr background and compared Cd59a +/+ -MRL/lpr and Cd59a 2 / 2 -MRL/lpr littermates for the development of systemic autoimmunity. We found that CD59a deﬁciency signiﬁcantly exacerbated the skin disease and lymphoproliferation characteristic of MRL/lpr mice. It also increased autoantibody titers and caused a higher level of proteinuria in male MRL/ lpr mice. Bone marrow transfer experiments indicated that CD59a expression on both bone marrow–derived cells and peripheral tissues played a role in lymphoproliferation, whereas the skin disease phenotype is determined mainly by local CD59a expression. Importantly, C3 gene deletion or C5 neutralization with a blocking mAb in Cd59a 2 / 2 -MRL/lpr mice did not rescue the proautoimmune phenotype associated with CD59a deﬁciency. These results together suggest that CD59a inhibits systemic autoimmunity in MRL/lpr mice through a complement-independent mechanism. The Journal of Immunology , 2012, 189: 000–000. appeared to have a gender bias, being more striking in male mice than in female mice. found that only male Cd59a 2 / -MRL/lpr mice, but not females, had elevated total serum IgG and IgM levels, higher antichromatin autoantibody titers, and worsened proteinuria when compared with Cd59a -sufﬁcient MRL/lpr mice.
Biochemical and structural characterization of 5 0-methylthioadenosine nucleosidases from Arabidopsis thaliana q

50-Methylthioadenosine (MTA) and S-adenosylhomocysteine (SAH) are important metabolites in all living organisms. Two similar nucleosidases for hydrolyzing MTA in Arabidopsis thaliana (AtMTAN1 and AtMTAN2) exist, but only AtMTAN2 shows markedly broad substrate specificity for hydrolysis of SAH. To examine the biochemical characteristics of AtMTAN2, it was over-expressed in Escherichia coli and purified to homogeneity. Spectroscopic assays confirm AtMTAN2 catalyzes MTA as well as SAH hydrolysis, compared to AtMTAN1 which only hydrolyzes MTA. In addition, crystal structure of the AtMTAN2 enzyme in complex with, adenine was determined at 2.9 Å resolution. Finally, a structural comparison of AtMTAN2 performed with previously determined structures of AtMTAN1 and an E. coli homolog provides clues for the substrate specificity of MTA nucleosidases in A. thaliana. 2009 Elsevier Inc. All rights reserved.
Leveraging Natural Language Processing to Analyse the Temporal Behavior of Extremists on Social Media

—Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the analysis of social media content and the detection of extremists and radical views expressed online. However, existing approaches simplify the concept of radicalization to a binary problem in which individuals are classified as extremists or non-extremists. Such binary approaches do not capture the radicalization process’s complexity that is influenced by many aspects such as social interactions, the impact of opinion leaders, and peer pressure. Moreover, the longitudinal analysis of users’ interactions and profile evolution over time is lacking in the literature. Aiming at addressing those limitations, this work proposes a sophisticated framework for the analysis of the temporal behavior of extremists on social media platforms. Far-right extremism during the Trump presidency was used as a case study, and a large dataset of over 259,000 tweets was collected to train and test our models. The results obtained are very promising and encourage the use of advanced social media analytics in the support of effective and timely decision-making.
Leveraging Distributed Storage Redundancy in Datacenters

All distributed storage systems replicate data objects, providing built-in redundancy that is designed to help the system withstand failures. Such redundancy is unavoidable because withstanding failures is a critical goal of distributed systems, and redundancy is the only way to tolerate failures that cause loss of data access. However, with the proliferation of data, it is becoming ever more paramount to reduce the costs of distributed storage systems. To balance the need to reduce storage costs and the need to withstand failures, this thesis explores two ways we can leverage the unavoidable redundancy in distributed storage systems to eliminate additional storage overheads in other parts of the storage stack. The first system we present is Replex. The key end-to-end observation in this work is that distributed secondary indices duplicate the work done by replication. Secondary indices often store full copies of data objects, in addition to the replicas of data objects that are created by default to handle failures. In Replex, we eliminate the additional storage overhead of secondary indices by treating them as data replicas during replication time. The second system we present is DIRECT. The key end-to-end observation here is that the redundancy created by replication can and should be used to correct bit errors at the hardware level. Traditionally, disks are expected to abstract bit errors from software, and in fact flash devices are shipped with aggressive internal error correction mechanisms to prevent errors from percolating to the user for the calculated lifetime of the device. In DIRECT, we argue that the underlying premise that disks should not expose bit errors is incorrect. In doing so, DIRECT enables the use of flash devices well beyond their advertised lifetime, which is a huge cost savings for datacenter operators. Therefore, by applying existing storage redundancy to enable two key properties in datacenter storage systems – secondary indexing and flash reliability – this thesis shows that distributed storage systems can be designed without burdensome storage overheads.
Orca: Server-assisted Multicast for Datacenter Networks

Group communications appear in various large-scale datacenter applications. These applications, however, do not cur-rently beneﬁt from multicast, despite its potential substantial savings in network and processing resources. This is because current multicast systems do not scale and they impose con-siderable state and communication overheads. We propose a new architecture, called Orca, that addresses the challenges of multicast in datacenter networks. Orca divides the state and tasks of the data plane among switches and servers, and it partially ofﬂoads the management of multicast sessions to servers. Orca signiﬁcantly reduces the state at switches, minimizes the bandwidth overhead, incurs small and constant processing overhead, and does not limit the size of multicast sessions. We implemented Orca in a testbed to demonstrate its performance in terms of throughput, consumption of server resources, packet latency, and the impact of server failures. We also implemented a sample multicast application in our testbed, and showed that Orca can substantially reduce its communication time, through optimizing the data transfer between nodes using multicast instead of unicast. In addition, we simulated a datacenter consisting of 27,648 hosts and handling 1M multicast sessions, and we compared Orca versus the state-of-art system in the literature. Our results show that Orca reduces the switch state by up to two orders of magnitude, the communication overhead by up to 19X, and the control overhead by up to 14X, compared to the state-of-art.
High rate of BA.1, BA.1.1 and BA.2 infection in triple vaccinated

Background: Booster vaccine doses offer protection against severe COVID-19 caused by omicron but are less effective against infection. Characteristics such as serological correlates of protection, viral abundance and clearance of omicron infections in triple vaccinated individuals are scarce. Methods: We conducted a 4-week twice-weekly SARS-CoV-2 qPCR screening shortly after an mRNA vaccine booster in 375 healthcare workers. Anti-Spike IgG levels and neutralization titers were determined at study start. qPCR-positive participants were sampled repeatedly for two weeks and monitored for symptoms. Result: In total 82 (cumulative incidence 22%) omicron infections were detected, divided between BA.1, BA.1. 1 and BA.2. Only 10% of infected participants remained asymptomatic. Viral load peaked at day 3 and live virus could be detected for up to 9 days after first PCR-positive sample. Presence of symptoms correlated to elevated viral load (p<0.0001), but despite resolution of symptoms most participants showed Ct levels <30 at day 9. While post-booster antibody titers were similar in those with and without subsequent breakthrough infection (p>0.05), high antibody titers were linked to reduced viral load (p<0.01) and time to viral clearance (p<0.01). No significant differences were observed for viral load and time to viral clearance between BA.1, BA.1. 1 and BA.2 infected individuals. Conclusion: We report high incidence of omicron infections despite recent booster vaccination in triple vaccinated individuals. Vaccine-induced antibody titres seem to play a limited role in risk of omicron infection. High viral load and secretion of live virus for up to nine days may increase transmission in a triple vaccinated population. remained participants that tested positive during the
A Morphological Feature-Oriented Algorithm for Extracting Impervious Surface Areas Obscured by Vegetation in Collaboration with OSM Road Networks in Urban Areas

Remote sensing is the primary way to extract the impervious surface areas (ISAs). However, the obstruction of vegetation is a long-standing challenge that prevents the accurate extraction of urban ISAs. Currently, there are no general and systematic methods to solve the problem. In this paper, we present a morphological feature-oriented algorithm, which can make use of the OSM road network information to remove the obscuring effects when the ISAs are extracted. Very high resolution (VHR) images of Wuhan, China, were used in experiments to verify the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm can improve the accuracy and completeness of ISA extraction by our previous deep learning-based algorithm. In the proposed algorithm, the overall accuracy (OA) is 86.64%. The results show that the proposed algorithm is feasible and can extract the vegetation-obscured ISAs effectively and precisely.
Investigations On Breakdown Voltages In Chemically Treated Natural Esters When Used As Liquid Dielectric

Most commonly used liquid insulators like mineral oil and silicone fluid serve dual purpose of insulation and cooling in electrical apparatus, particularly transformers. Mineral oil is neither biocompatible nor biodegradable and this may lead to health hazard. While silicone fluid is biocompatible, it is only partly bio-degradable. Each of these has certain drawbacks particularly with respect to environmental impact and biodegradability. Thus, search for a suitable dielectric fluid to replace the mineral oil and expensive silicone fluid is of importance in the present scenario. This paper presents assessment of dielectric properties of natural esters as compared to mineral oil and silicone fluid. The natural esters, due to their superior environmental safety and bio-degradability can be considered as liquid dielectrics. This paper presents the performance study on Indigenous natural esters as liquid dielectric codenamed as IO18 and IO-19. The effect of temperature is investigated on the samples before and after chemical treatment. Three food grade anti-oxidants are added to the samples in different concentrations. The study shows that there is significant improvement in the dielectric properties
L EARNING A RTICULATED R IGID

Being able to reproduce physical phenomena, ranging from light interaction to contact mechanics, simulators are becoming increasingly useful to more and more application domains where real-world interaction or labeled data is difficult to obtain. Despite the gain in attention, it requires significant human effort to configure simulators to accurately reproduce real-world behaviors. We introduce a pipeline that combines inverse rendering with differentiable simulation to create digital twins of real-world articulated mechanisms from depth or RGB videos. Our approach automatically discovers joint types and estimates their kinematic parameters, while the dynamic properties of the overall mechanism are tuned to attain physically accurate simulations. On a real-world coupled pendulum system observed through RGB video, we correctly determine its articulation and simulation parameters, such that its motion can be reproduced accurately in a physics engine. Having learned a simulator from depth video, we demonstrate on a simulated cartpole that a model-predictive controller can leverage such dynamics model to control nonlinear systems. We provide further results and details on our project website at https://eric-heiden.github. io/video2sim. (a) Simulation derived from RGB video (b) Inferred articulation Figure 1: Our proposed framework infers articulated rigid body dynamics simulations from video. In this example, Rott’s pendulum is identified from real RGB camera footage of the system in motion. In (a), the original video is compared to the simulated motion in the learned physics engine. In (b), the inferred joints are visualized on the left, where blue cylinders correspond to the axes of the revolute joints. The estimated kinematic tree is shown on the right.