Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploitation of Antimicrobial Nanoparticles and Their Applications in Biomedical Engineering

Antibiotic resistance is a major threat to public health, which contributes largely to increased mortality rates and costs in hospitals. The severity and widespread nature of antibiotic resistance result in limited treatments to effectively combat antibiotic-resistant pathogens. Nanoparticles have different or enhanced properties in contrast to their bulk material, including antimicrobial efficacy towards a broad range of microorganisms. Their beneficial properties can be utilised in various bioengineering technologies. Thus, antimicrobial nanoparticles may provide an alternative to challenge antibiotic resistance. Currently, nanoparticles have been incorporated into materials, such as fibres, glass and paints. However, more research is required to elucidate the mechanisms of action fully and to advance biomedical applications further. This paper reviews the antimicrobial efficacies and the intrinsic properties of different metallic nanoparticles, their potential mechanisms of action against certain types of harmful pathogens and how these properties may be utilised in biomedical and healthcare products with the aim to reduce cross contaminations, disease transmissions and usage of antibiotics.
SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

We introduce SketchGNN, a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.
Multi-Fidelity Receding Horizon Planning for Multi-Contact Locomotion

When traversing uneven terrain, humans consider their future steps for choosing the best location and timing of their current step. Likewise, when planning multi-contact motions for legged robots (e.g. humanoids), a ‘prediction horizon’ has to be considered. However, planning several steps ahead increases the dimensionality and non-linearity of an already challenging problem, which makes online planning intractable. We propose to reduce the problem complexity by using convex relaxations in the prediction horizon. We realize this idea within a Receding Horizon Planning (RHP) framework to plan dynamically consistent centroidal trajectories of humanoid walking on uneven terrain. This results in a novel formulation that combines an accurate non-convex model with a relaxed convex model, which we call RHP with multiple levels of model fidelity. We evaluate three candidate multi-fidelity RHPs with convex relaxations of the centroidal dynamics in the prediction horizon. The best candidate is 1.4x-3.0x (average 2.4x) faster than the traditional RHP that employs a single dynamics model over the entire look-ahead horizon. We also validate the resultant centroidal trajectories by tracking them with a whole-body inverse dynamics controller in simulation. Lastly, we find that incorporating angular dynamics in the prediction horizon is important to the success of multi-fidelity RHP.
Branched-chain Amino Acid Nitrogen Transfer

A B S T RA C T To investigate the contribution of branched-chain amino acids as a nitrogen source for alanine in vivo, dogs were infused with L-[15N]leucine, L-[U-14C]leucine, L-[2,3,3,3-2H4]alanine, and D-[6,6-2H2]glucose. 14C and 15N isotopic equilibrium in plasma leucine, and deuterium enrichment in arterial and femoral plasma glucose and alanine were achieved within 3 h of initiation of the respective isotope infusion in all animals. The average flux of leucine determined by [15N]leucine was 5.4 Amol kg-' min-', whereas using [14C]leucine it was 3.7 Amol kg-' min-'. Turnover rates for alanine and glucose were 11.0 and 17.2 umol kg-' min-', respectively. [15N]alanine was detected as early as 30 min, but nitrogen isotopic equilibrium in alanine was not achieved until 6 h. The absolute rate of leucine nitrogen transfer to alanine was 1.92 ,umol kg-' min-t, which represented 41-73% (mean 53%) of leucine's nitrogen and 15-20% (mean 18%) of alanine's nitrogen. Fractional extraction of alanine and leucine by the dog hindlimb was 35 and 24%, respectively. Average net alanine balance was -6.7 umol * leg-'1 min-', reflecting a release rate (17.4 ,umol * kg-' min-') that exceeded the rate of uptake (10.8 ,tmol leg-' min-'). Of the leucine taken up by the hindlimb, 34% transferred its nitrogen to alanine and 8%was oxidized to CO2. Since the latter value reflects transamination as well as irreversible catabolism, the nitrogen derived from the oxidation of leucine by the hindlimb could account for only 25% of the observed 15N incorporation into alanine. The significantly faster flux of leucine nitrogen when compared with leucine carbon suggests significant recycling of the leucine a-ketoacid. These studies dem-
International Journal of Swarm Intelligence and Evolutionary Computation

Together Oxytocin and Vasopressin set the neurohypophysial hormones that form a family of structurally and functionally related peptide hormones. However, the biological function of these proteins may vary depending on their taxonomic classification. In our study, using a broad of bioinformatics and machine learning techniques, we described the role of sets of coevolved amino acids in determining the taxonomic classes of neurohypophysial hormone sequences. Withal, it would be possible to correlate that certain taxonomic classes can still be classified from the presence of specific amino acids from these coevolved sets, bringing more light around how molecular evolution can describe the structure and function.
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases

Modern database management systems (DBMS) contain tens to hundreds of critical performance tuning knobs that determine the system runtime behaviors. To reduce the total cost of ownership, cloud database providers put in drastic effort to automatically optimize the resource utilization by tuning these knobs. There are two challenges. First, the tuning system should always abide by the service level agreement (SLA) while optimizing the resource utilization, which imposes strict constrains on the tuning process. Second, the tuning time should be reasonably acceptable since time-consuming tuning is not practical for production and online troubleshooting. In this paper, we design ResTune to automatically optimize the resource utilization without violating SLA constraints on the throughput and latency requirements. ResTune leverages the tuning experience from the history tasks and transfers the accumulated knowledge to accelerate the tuning process of the new tasks. The prior knowledge is represented from historical tuning tasks through an ensemble model. The model learns the similarity between the historical workloads and the target, which significantly reduces the tuning time by a meta-learning based approach. ResTune can efficiently handle different workloads and various hardware environments. We perform evaluations using benchmarks and real world workloads on different types of resources. The results show that, compared with the manually tuned configurations, ResTune reduces 65%, 87%, 39% of CPU utilization, I/O and memory on average, respectively. Compared with the state-of-the-art methods, ResTune finds better configurations with up to ~18x speedups.
Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans

Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able to capture variable-length opinion spans. In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs. Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features. The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our analysis demonstrates that our model can capture aspect-specific opinion spans.
A dataset of open-source safety-critical software A Dataset of Open-Source Safety-Critical Software

We describe the method used to create a dataset of open-source safety-critical software, such as that used for autonomous cars, healthcare, and autonomous aviation, through a systematic and rigorous selection process. The dataset can be used for empirical studies regarding the quality assessment of safety-critical software, its dependencies, and its development process, as well as comparative studies considering software from other domains.
NDT_A_304062 1689..1695

Department of Forensic Genetics and Biology, School of Forensic Medicine, China Medical University, Shenyang, 110122, People’s Republic of China Purpose: Sequencing potentially causal and susceptible genes and genome-wide association studies in samples from Parkinson’s disease (PD) patients has revealed several related loci. The genes for synaptosome-associated protein of 25 kDa (SNAP25), histamineN-methyltransferase (HNMT), FCH and double SH3 domains 1 (FCHSD1) and dopamine βhydroxylase (DBH) are candidate loci and have not been studied in a northern Chinese population. We explored the genetic distribution of four single-nucleotide polymorphisms (rs3746544, rs11558538, rs456998, rs129882) located on SNAP25, HNMT, FCHSD1 and DBH, respectively. Patients and Methods: A total of 330 patients with sporadic PD and 332 healthy controls (HCs) were recruited from a northern Chinese population. Polymerase chain reaction restriction fragment length polymorphism was used to genotype these four SNPs. Results: After statistical analyses and correction of the genotyping results, the mutant-allele T in rs456998 of FCHSD1 was found to be significantly related to reducing the PD risk (P = 0.029, OR = 0.754, 95% CI = 0.586–0.971, power = 0.591). However, rs3746544, rs11558538, and rs129882 did not show an association with PD. Conclusion: FCHSD1 rs456998 may have a protective role in PD in a northern Chinese population, but more studies are needed to support this suggestion.
SCONTO: A Modular Ontology for Supply Chain Representation

Supply Chain Management (SCM) involves coordinating and integrating material, information and money flows, both within and across several companies. The information associated with these flows is perceived differently by distinct companies, raising semantics-related problems. Industry 4.0 and the Digital Supply Chain initiative request a seamless integration. This implies achieving technical, syntactic, semantic and organizational interoperability. For several years, ontologies have been considered the key technology to achieve semantic integration. Therefore, this contribution presents SCOPRO, which is the main module of SCONTO, supply chain ontology. SCOPRO is an ontology that formally describes a supply chain (SC) at various abstraction levels, by specifying its associated business processes based on the SCOR de facto standard and by sharing a precise meaning of the information exchanged among the many stakeholders involved in the SC.
ABC-DIMM: Alleviating the Bottleneck of Communication in DIMM-based Near-Memory Processing with Inter-DIMM Broadcast

Near-Memory Processing (NMP) systems that integrate accelerators within DIMM (Dual-Inline Memory Module) buffer chips potentially provide high performance with relatively low design and manufacturing costs. However, an inevitable communication bottleneck arises when considering the main memory bus among peer DIMMs and the host CPU. This communication bottleneck roots in the bus-based nature and the limited point-to-point communication pattern of the main memory system. The aggregated memory bandwidth of DIMM- based NMP scales with the number of DIMMs. When the number of DIMMs in a channel scales up, the per-DIMM point-to-point communication bandwidth scales down, whereas the computation resources and local memory bandwidth per DIMM stay the same. For many important sparse data-intensive workloads like graph applications and sparse tensor algebra, we identify that communication among DIMMs and the host CPU easily dominates their processing procedure in previous DIMM-based NMP systems, which severely bottlenecks their performance.To tackle this challenge, we propose that inter-DIMM broadcast should be implemented and utilized in the main memory system of DIMM-based NMP. On the hardware side, the main memory bus naturally scales out with broadcast, where per- DIMM effective bandwidth of broadcast remains the same as the number of DIMMs grows. On the software side, many sparse applications can be implemented in a form such that broadcasts dominate their communication. Based on these ideas, we design ABC-DIMM, which Alleviates the Bottleneck of Communication in DIMM-based NMP, consisting of integral broadcast mechanisms and Broadcast-Process programming framework, with minimized modifications to commodity software-hardware stack. Our evaluation shows that ABC-DIMM offers an 8.33 × geo-mean speedup over a 16-core CPU baseline, and outperforms two NMP baselines by 2.59 × and 2.93 × on average.
Performance Analysis and Tuning in Multicore Environments

Performance analysis is the task of monitor the behavior of a program execution. The main goal is to find out the possible adjustments that might be done in order improve the performance. To be able to get that improvement it is necessary to find the different causes of overhead. Nowadays we are already in the multicore era, but there is a gap between the level of development of the two main divisions of multicore technology (hardware and software). When we talk about multicore we are also speaking of shared memory systems, on this master thesis we talk about the issues involved on the performance analysis and tuning of applications running specifically in a shared Memory system. We move one step ahead to take the performance analysis to another level by analyzing the applications structure and patterns. We also present some tools specifically addressed to the performance analysis of OpenMP multithread application. At the end we present the results of some experiments performed with a set of OpenMP scientific application.
Fake News Detection Website

: Fake news, one of the biggest new-age problems has the potential to mould opinions and influence decisions. The proliferation of fake news on social media and Internet is deceiving people to an extent which needs to be stopped. The existing systems are inefficient in giving a precise statistical rating for any given news claim. Also, the restrictions on input and category of news make it less varied. This paper proposes a system that classifies unreliable news into different categories after computing an F-score. This system aims to use various classification techniques to help achieve maximum accuracy.
Management Control Systems and Performance: The Mediating Role of Organisational Capabilities in Malaysia

This study focuses on the interactive use of Management Control System (MCS) and the identification of threats and opportunities that enables management to respond quickly to changes in the environment. The study aims to examine, from a resource-based view (RBV) perspective, the relationship between interactive use of MCS and organisational capabilities (market responsiveness, strategic networking, process reliability and efficiency, innovativeness and learning) as well as their subsequent impact on organisational performance. An empirical study was conducted involving 70 public-listed companies in Malaysia. The analysis included the use of descriptive and multiple regression tests. The findings provide empirical evidence that interactive MCS facilitates the development of organisational capabilities and that organisational capabilities play a mediating role in the relationship between MCS interactive usage and organisational performance. This study indicates that companies in Malaysia still focus on financial measures rather than on non-financial ones in most of their decision making. Managers who are shortterm focused or financially oriented in their decision making may jeopardise the long-term sustainability of their organisations’ performance.
LogStore: A Cloud-Native and Multi-Tenant Log Database

With the prevalence of cloud computing, more and more enterprises are migrating applications to cloud infrastructures. Logs are the key to helping customers understand the status of their applications running on the cloud. They are vital for various scenarios, such as service stability assessment, root cause analysis and user activity profiling. Therefore, it is essential to manage the massive amount of logs collected on the cloud and tap their value. Although various log storages have been widely used in the past few decades, it is still a non-trivial problem to design a cost-effective log storage for cloud applications. It faces challenges of heavy write throughput of tens of millions of log records per second, retrieval on PB-level logs and massive hundreds of thousands of tenants. Traditional log processing systems cannot satisfy all these requirements. To address these challenges, we propose the cloud-native log database LogStore. It combines shared-nothing and shared-data architecture, and utilizes highly scalable and low-cost cloud object storage, while overcoming the bandwidth limitations and high latency of using remote storage when writing a large number of logs. We also propose a multi-tenant management method that physically isolates tenant data to ensure compliance and flexible data expiration policies, and uses a novel traffic scheduling algorithm to mitigate the impact of traffic skew and hotspots among tenants. In addition, we design an efficient column index structure LogBlock to support queries with full-text search, and combined several query optimization techniques to reduce query latency on cloud object storage. LogStore has been deployed in Alibaba Cloud on a large scale (more than 500 machines), processing logs of more than 100 GB per second, and has been running stably for more than two years.
SED FITTING WITH MARKOV CHAIN MONTE CARLO: METHODOLOGY AND APPLICATION TO Z=3.1 LYα-EMITTING GALAXIES

We present GalMC, a MCMC algorithm designed to fit the spectral energy distributions (SED) of galaxies to infer physical properties such as age, stellar mass, dust reddening, metallicity, redshift, and star formation rate. We describe the features of the code and the extensive tests conducted to ensure that our procedure leads to unbiased parameter estimation and accurate evaluation of uncertainties. We compare its performance to grid-based algorithms, showing that the efficiency in CPU time is ∼ 100 times better for MCMC for a three dimensional parameter space and increasing with the number of dimensions. We use GalMC to fit the stacked SEDs of two samples of Lyman Alpha Emitters (LAEs) at redshift z=3.1. Our fit reveals that the typical LAE detected in the IRAC 3.6 μm band has age = 0.67 [0.37 -1.81] Gyr and stellar mass = 3.2 [2.5 4.2] ×10 M⊙, while the typical LAE not detected at 3.6 μm has age = 0.06 [0.01-0.2] Gyr and stellar mass = 2 [1.1 3.4] ×10 M⊙. The SEDs of both stacks are consistent with the absence of dust. The data do not significantly prefer exponential with respect to constant star formation history. The stellar populations of these two samples are consistent with the previous study by Lai et al, with some differences due to the improved modeling of the stellar populations. A constraint on the metallicity of z=3.1 LAEs from broad-band photometry, requiring Z < Z⊙ at 95% confidence, is found here for the first time.
with Position Based

In this paper, I will describe my implementation of simulating ﬂuid dynamics using Smoothed Particle Hydrodynamics (SPH) from the previous work of [M¨uller et al. 2003] and Particle Based Dynamics (PBD) from [Macklin and M¨uller 2013]. The goal is to have a quick and stable algorithm for the use of real-time applications.
The Eternity Bike-Bringing Active Safety to the Bicycle

To meet humans’ mobility needs in the future, cycling could become a relevant alternative to established transport modes. However, bicycles are increasingly involved in accidents, and they did not benefit from active safety technology as implemented in traditional cars. In this position paper, we present our vision of the “Eternity Bike”, a (partly) automated bike with active safety functions that will fully integrate into the automated and cooperative transport systems of the future. We discuss relevant challenges from a technical and a human factors perspective and propose a set of research questions which will guide us in its development.
FUZZY BASED IMAGE SEGMENTATION USING SPATIAL/FREQUENCY INFORMATION

: This article presents a multiphase fuzzy region competition model that takes into account spatial and frequency information for image segmentation. In the proposed energy functional, each region is represented by a fuzzy membership function and a data fidelity term. It measures the conformity of spatial and frequency data within each region. Gaussian densities parameters are determined jointly with the segmentation process. To efficiently solve the minimization of the energy functional, an alternate minimization procedure is adopted and Chambolle’s fast duality projection algorithm is used. The proposed method is applied to synthetic and natural textures as well as real-world natural images.
Effect of MT2A on apoptosis and proliferation in HL60 cells

Although accumulating evidence has revealed that metallothioneins (MTs) and its family member MT2A are strongly linked to the risk of various solid tumors, researches on the occurrence and development of acute myeloid leukemia (AML) have rarely been investigated. Here, we constructed a lentiviral vector with MT2A over-expression and the interfering plasmids with MT2A expression inhibition to study the influence of MT2A on the bioactivities of HL60 cells. After cells were infected with a lentiviral vector containing the MT2A gene, both transcription and translation levels of MT2A were significantly increased in the over-expressed group in comparison with control groups. In vitro experiments, all results demonstrated that cell reproductive capacity was inhibited, but cell apoptosis rate was significantly increased. Together, the expression of apoptosis-related protein Bcl2 was remarkably reduced, while a high expression level of Bax protein was detected. Further experiments revealed that up-regulation of MT2A induced cell apoptosis and promoted G2/M phase arrest. The mechanism may be associated with down-regulated p-IκB-α and cyclinD1 expression and up-regulated IκB-α expression in the nuclear factor-kappaB (NF-κB) pathway. On the contrary, MT2A expression was down-regulated by interfering plasmids. We found that cell proliferative potential was notably increased in the interfering group compared with the negative and untreated group. What's more, MT2A may be closely related to AML cell proliferation and function via the NF-κB signal pathway.