US2RO: Union of Superpoints to Recognize Objects

The creation of interactive virtual reality (VR) applications from 3D scanned content usually includes a lot of manual and repetitive work. Our research aim is to develop agents that recognize objects to enhance the creation of interactive VR applications. We trained partition agents in our superpoint growing environment that we extended with an expert function. This expert function solves the sparse reward signal problem of the previous approaches and enables to use a variant of imitation learning and deep reinforcement learning with dense feedback. Additionally, the function allows to calculate a performance metric for the degree of imitation for different partitions. Furthermore, we introduce an environment to optimize the superpoint generation. We trained our agents with 1182 scenes of the ScanNet data set. More specifically, we trained different neural network architectures with 1170 scenes and tested their performance with 12 scenes. Our intermediate results are promising such that our partition system might be able to assist the VR application development from 3D scanned content in near future.

Implementation of K-Means Clustering Algorithm in Determining Classification of the Spread of the COVID-19 Virus in Bali

The COVID-19 virus or also known as SARS-Cov-2 is an infectious disease caused by the Coronavirus which attacks the human respiratory system. The COVID-19 case has affected all provinces in Indonesia, including Bali. There is a total of 7481 cases in Bali and this is due to the lack of understanding of the community towards the COVID-19 prone areas in Bali. Therefore, it is necessary to group the areas prone to COVID-19 in Bali. One of the clustering algorithms is K-Means, this algorithm uses several groups for the placement of some data with a partition system. The grouping will be carried out using data from the Bali COVID-19 Task Force website on September 18, 2020, using RapidMiner application. The results obtained divided Bali into 3 clusters with Denpasar as the center of the highest spread of COVID-19 in Bali as the red zone, then Badung, Buleleng, Bangli, Gianyar, and Karangasem in the yellow zone, and other districts in the green zone.

Three ParA dimers cooperatively assemble on type Ia partition promoters

Accurate DNA segregation is essential for faithful inheritance of genetic material. In bacteria, this process is mainly ensured by a partition system (Par) composed of two proteins, ParA and ParB, and a centromere site. The auto-regulation of Par operon expression is important for efficient partitioning, and is primarily mediated by ParA for type Ia plasmid partition systems. For the plasmid F, four ParAF monomers were proposed to bind to four repeated sequences in the promoter region. By contrast, using quantitative surface plasmon resonance, we showed that three ParAF dimers bind to this region. We uncovered that one perfect inverted repeat (IR) motif, consisting of two hexamer sequences spaced by 28-bp, constitutes the primary ParAF DNA binding site. A similar but degenerated motif overlaps the former. ParAF binding to these motifs is well supported by biochemical and modeling analyses. In addition, molecular dynamics simulations predict that the winged-HTH domain displays high flexibility, which may favor the cooperative ParA binding to the promoter region. We propose that three ParAF dimers bind cooperatively to overlapping motifs thus covering the promoter region. A similar organization is found on both closely related and distant plasmid partition systems, suggesting that such promoter organization for auto-regulated Par operons is widespread and may have evolved from a common ancestor.

Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study

<div>Bayesian optimization is a popular sequential decision strategy that can be used for environmental monitoring. In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km²) using the Bayesian optimization approach with a Voronoi Partition system. The system manages to quickly approximate the real unknown distribution map of a water quality parameter using Gaussian Processes as surrogate models. Furthermore, to select new water quality measurement locations, an acquisition function adapted to vehicle energy constraints is used. Moreover, a Voronoi Partition system helps to distributing the workload with all the available vehicles, so that robustness and scalability is assured. For evaluation purposes, we use both the mean squared error and computational efficiency. The results showed that our method manages to efficiently monitor the Ypacarai Lake, and also provides confident approximate models of water quality parameters. It has been observed that, for every vehicle, the resulting surrogate model improves by 38%.</div>

Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study

<div>Bayesian optimization is a popular sequential decision strategy that can be used for environmental monitoring. In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km²) using the Bayesian optimization approach with a Voronoi Partition system. The system manages to quickly approximate the real unknown distribution map of a water quality parameter using Gaussian Processes as surrogate models. Furthermore, to select new water quality measurement locations, an acquisition function adapted to vehicle energy constraints is used. Moreover, a Voronoi Partition system helps to distributing the workload with all the available vehicles, so that robustness and scalability is assured. For evaluation purposes, we use both the mean squared error and computational efficiency. The results showed that our method manages to efficiently monitor the Ypacarai Lake, and also provides confident approximate models of water quality parameters. It has been observed that, for every vehicle, the resulting surrogate model improves by 38%.</div>

IMPLEMENTASI ALGORITMA K-MEANS DALAM MENENTUKAN TINGKAT PENYEBARAN PANDEMI COVID-19 DI SUMATERA BARAT

Corona viruses (CoV) are part of a family of viruses that cause illnesses ranging from the flu to more severe illnesses such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). This virus can be transmitted from person to person through small droplets from the nose or mouth when coughing, sneezing or speaking. Because the spread of this virus is very fast, it requires fast handling so that this virus does not spread, one of which is by implementing health protocols, namely maintaining distance, washing hands and using masks. All provinces in Indonesia have not been spared from this virus, including the province of West Sumatra. Classification of the spread of this virus is necessary in order to break the chain of its spread. One of the techniques used in this grouping is k-means, which uses several groups to assign multiple data to a partition system. The results of this study indicate that the regions in the first cluster have the highest rates of positive cases and patients who die, while the areas in the second and third clusters have the potential for the spread of Covid-19 which is also a concern of the government.

Experimental study of the seismic performance of plasterboard partition walls with seismic gaps

It is now widely recognized that the performance of non-structural elements is crucial to the performance of building systems during earthquakes. Field surveys and experimental studies have shown that light steel or timber framed plasterboard partition walls are particularly vulnerable. The objective of this study is to investigate the seismic performance of a novel seismic gap partition system with angled return walls under quasi-static cyclic loading applied obliquely and to investigate the benefits of using acrylic gap-filler in the seismic gaps. Two specimens were tested: a steel stud specimen and a timber stud specimen. Observed drift capacities were significantly greater than traditional plasterboard partition systems. Equations were used to predict the drift at which damage state 1 (DS1) and damage state 2 (DS2) would initiate. The equation used to estimate the drift at the onset of DS1 accurately predicted the onset of plaster cracking but overestimated the drift at which the gap filling material was damaged. The equation used to predict the onset of DS2 provided a lower bound for both specimens and also when used to predict results of previous experimental tests on seismic gap systems. The gap-filling material reduced the drift at the onset of DS1, however, it had a beneficial effect on the re-centring behaviour of the linings. Out-of-plane displacements and return wall configuration did not appear to significantly impact the onset of plaster cracking in the specimens.

Robust and conserved stochastic self-assembly mechanism for dynamic ParB-parS partition complexes on bacterial chromosomes and plasmids

SummaryChromosome and plasmid segregation in bacteria are mostly driven by ParABS systems. These DNA partitioning machineries rely on large nucleoprotein complexes assembled on centromere sites (parS). However, the mechanism of how a few parS-bound ParB proteins nucleate the formation of highly concentrated ParB clusters remains unclear despite several proposed physico-mathematical models. We discriminated between these different models by varying some key parameters in vivo using the plasmid F partition system. We found that ‘Nucleation & caging’ is the only coherent model recapitulating in vivo data. We also showed that the stochastic self-assembly of partition complexes (i) does not directly involve ParA, (ii) results in a dynamic structure of discrete size independent of ParB concentration, and (iii) is not perturbed by active transcription but is by protein complexes. We refined the ‘Nucleation & Caging’ model and successfully applied it to the chromosomally-encoded Par system of Vibrio cholerae, indicating that this stochastic self-assembly mechanism is widely conserved from plasmids to chromosomes.