Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale Molecular Dynamics Simulation of Plasma Processing: Application to Plasma Sputtering

Molecular dynamics is an atomistic tool that is able to treat dynamics of atom/molecules/cluster assemblies mainly in the condensed and liquid phases. The goal of the present article is to provide a new methodology for describing all phenomena of plasma processing and beyond such as gas phase chemistry as well. Simulations of condensed matter and liquid processes by molecular dynamics are now readily accessible provided the interaction potentials are available, so quantitative parameters can be deduced as diffusion coefficient, … The situation is less clear for gas phase processes while they operate on larger space and time scales than for condensed phases and at lower specie densities. The present article is proposing a new methodology for describing plasma core interactions in taking into account experimental space and time scales. This is illustrated on a plasma sputtering process and deposition in a single simulation.
RGB-D Tracking of Complex Shapes Using Coarse Object Models

This paper presents a framework for accurately tracking objects of complex shapes with joint minimization of geometric and photometric parameters using a coarse 3D object model with the RGB-D cameras. Tracking with coarse 3D model is remarkably useful for industrial applications. A technique is proposed that uses a combination of point-to-plane distance minimization and photometric error minimization to track objects accurately. The concept of ‘keyframes’ are used in this system of object tracking for minimizing drift. The proposed approach is validated on both simulated and real data. Experimental results show that our approach is more accurate than existing state-of-the-art approaches, especially when dealing with low-textured objects with multiple coplanar faces.
Monocular RGB Hand Pose Inference from Unsupervised Refinable Nets

3D hand pose inference from monocular RGB data is a challenging problem. CNN-based approaches have shown great promise in tackling this problem. However, such approaches are data-hungry, and obtaining real labeled training hand data is very hard. To overcome this, in this work, we propose a new, large, realistically rendered hand dataset and a neural network trained on it, with the ability to refine itself unsupervised on real unlabeled RGB images, given corresponding depth images. We benchmark and validate our method on existing and captured datasets, demonstrating that we strongly compare to or outperform state-of-the-art methods for various tasks ranging from 3D pose estimation to hand gesture recognition.
Study on novel signal processing and simultaneous-fault diagnostic method for wind turbine

High-precision fault diagnosis is important for the widely installed complex industrial product, the wind turbine. However, intelligent monitoring is difficult due to the fuzzy boundaries and individual different variations of the unseen single or simultaneous-fault of such intricate equipment. To solve this problem, this study proposes an ensemble fault diagnostic framework for simultaneous and coupling failure. First, this paper develops novel signal processing methods for effective feature learning and mapping from the non-stationary and nonlinear raw vibrational signals. The adapted variational mode decomposition is introduced based on the particle swarm optimization that applies the minimum mean envelope entropy to optimize the parameters settings. Second, the novel ensemble extreme learning machine-based network is proposed to isolate the faults that applies one extreme learning machine network to count the number of fault scenarios, and the other one to identify the specific single or simultaneous-fault labels. With this scheme, the self-adaptive ensemble extreme learning machine-based fault diagnostic framework is more accurate and faster than the prevailing probabilistic classifier-based methods, as the proposed method does not rely on empirically specified decision-making threshold and generates all the candidate fault labels at the same time. Finally, this study builds the test platform and compares the overall results with the existing feature analysis methods and classifiers. The experimental results verify that the proposed framework detects both single and simultaneous-fault accurately and quickly.
OFFDTAN: A New Approach of Offline Dynamic Taint Analysis for Binaries

Dynamic taint analysis is a powerful technique for tracking the flow of sensitive information. Different approaches have been proposed to accelerate this process in an online or offline manner. Unfortunately, most of these approaches still have performance bottlenecks and thus reduce analytical efficiency. To address this limitation, we present OFFDTAN, a new approach of offline dynamic taint analysis for binaries. OFFDTAN can be described in terms of four stages: dynamic information acquisition, vulnerability modeling, offline analysis, and backtrace analysis. It first records program runtime information and models the stack buffer overflow vulnerabilities and controlled jump vulnerabilities. Then it performs offline analysis and backtrace analysis to locate vulnerabilities. We implement OFFDTAN on the basis of QEMU virtual machine and apply it to off-the-shelf applications. In order to illustrate how our approach works, we first employ a case study. Furthermore, six applications have been verified so as to evaluate our approach. Experimental results demonstrate that our approach is correct and effective. Compared with other offline analysis tools, OFFDTAN has much lower application runtime overhead.
DISRUPTION AND RECOVERY OF BACTERIAL COMMUNITY STRUCTURE OF AN ATLANTIC FOREST SOIL AFTER EXPOSURE TO GASOHOL

This article reports the effects of gasohol on the genetic of a bacterial community of a tropical Atlantic Forest soil. Hydrocarbon and ethanol biodegradation was accompanied by CO2 emission. Gasohol had an immediate impact on genetic structure of bacteria and on respiratory metabolism of soil microbial community. Cluster analysis of DGGE band pattern indicated a shift in the community structure between the fifth and fortieth days after contamination. At 60 days after contamination, the DGGE profile of the bacterial community in the contaminated soil was similar to that found in the non-contaminated control. Gasohol addition increased the respiratory rate of the soil, peaking at 3 days and returning to basal level at 15 days after contamination. We concluded that gasohol contamination causes a strong transient impact on soil microbial community structure that is completely reversed after a few days following contaminant removal. Secondary succession after contamination resulted in a bacterial community of identical genetic structure to that found before contamination. Our results point out to a high resilience of microbial community established in Atlantic Forest soil.
A Novel Trust Taxonomy for Shared Cyber Threat Intelligence

Cyber threat intelligence sharing has become a focal point for many organizations to improve resilience against cyberattacks. The objective lies in sharing relevant information achieved through automating as many processes as possible without losing control or compromising security. The intelligence may be crowdsourced from decentralized stakeholders to collect and enrich existing information. Trust is an attribute of actionable cyber threat intelligence that has to be established between stakeholders. Sharing information about vulnerabilities requires a high level of trust because of the sensitive information. Some threat intelligence platforms/providers support trust establishment through internal vetting processes; others rely on stakeholders to manually build up trust. The latter may reduce the amount of intelligence sources. This work presents a novel trust taxonomy to establish a trusted threat sharing environment. 30 popular threat intelligence platforms/providers were analyzed and compared regarding trust functionalities. Trust taxonomies were analyzed and compared. Illustrative case studies were developed and analyzed applying our trust taxonomy.
Named Entity Recognition with Extremely Limited Data

Traditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search. Named entity taggers themselves are typically trained on thousands or tens of thousands of examples labeled by humans. However, there is a long tail of named entities classes, and for these cases, labeled data may be impossible to find or justify financially. We propose exploring named entity recognition as a search task, where the named entity class of interest is a query, and entities of that class are the relevant "documents". What should that query look like? Can we even perform NER-style labeling with tens of labels? This study presents an exploration of CRF-based NER models with handcrafted features and of how we might transform them into search queries.
Extracting Non-Functional Requirements from Unstructured Text

Non-functional requirements (NFRs) of a software system describe desired quality attributes rather than specific user-visible features; NFRs model stakeholder expectations about pervasive system properties such as performance, security, reliability, and usability. Failing to meet these expectations can result in systems that, while functionally complete, may lead to user dissatisfaction and ultimately to the failure of the product. While NFRs may be documented, tracked, and evaluated in a variety of ways during development, there is no single common approach to doing so. In this work, we investigate extracting information about NFRs that may be contained in source code and source code comments, since they are often considered to be the ultimate source of ground truth about a software system. Specifically, we examine how often NFRs are mentioned explicitly or implicitly in source code comments by using natural language processing (NLP) techniques, and we evaluate how effectively they can be identified using machine learning (ML). We modeled the problem as a text classification problem in which the goal is to identify comments about NFRs, and we evaluated the classifiers using example systems from the electronic health records (EHR) domain. The best performance was achieved using SVM classifier, with an F1 measure of 0.86. Our results indicate that using supervised method for our problem outperforms unsupervised methods which try to find common NFR patterns in comments. Comparing our results to previous studies shows that NFRs can be extracted more accurately from source code comments compared to other software artifacts (e.g., SRS or RFP documents). Moreover, we found that bag-of-words features are more effective compared to more complicated features (i.e., doc2vec) for the problem of extracting NFRs from source code comments.
Time-dependent inhibition (TDI) of CYP1A2 by a CYP3A4-mediated reactive metabolite: proposal for a novel mechanism of irreversible TDI by a non-suicide substrate

Abstract 1. The purpose of this study was to clarify the mechanism of DSP-1053 time-dependent inhibition (TDI) for CYP1A2. 2. DSP-1053 inhibited time- and concentration-dependently CYP1A2 activity in human liver microsomes even in a dilution assay. However, DSP-1053 was not metabolized by recombinant human CYP1A2. These findings indicate that the inhibitory effect of DSP-1053 on CYP1A2 does not follow a general mechanism-based inhibition (MBI) because it did not seem to be a suicide substrate. 3. In fact, CYP1A2 was not inhibited with DSP-1053 pre-incubation in recombinant human CYP1A2. On the other hand, CYP1A2 was potently inhibited after pre-incubation with DSP-1053 in a mixture of human recombinant CYP1A2 and CYP3A4. In addition, DSP-1053 TDI of CYP1A2 in human liver microsomes was drastically reduced not only by addition of a CYP3A4 inhibitor, but also by addition of potassium cyanide (KCN), which is a trapping agent for iminium ions. We also confirmed in this study that CYP1A2 suicide inhibition by DSP-1053 metabolites generated by CYP3A4 had only minimal role in DSP-1053 TDI of CYP1A2. 4. In conclusion, a possible mechanism for DSP-1053 TDI of CYP1A2 is that DSP-1053 iminium ion, which is generated by CYP3A4, departs from CYP3A4 without inhibiting it and covalently binds to CYP1A2.
SDF-1/CXCR7 Chemokine Signaling is Induced in the Peri-Infarct Regions in Patients with Ischemic Stroke

Stromal-derived factor-1 (SDF-1, also known as CXCL12) and its receptors CXCR4 and CXCR7 play important roles in brain repair after ischemic stroke, as SDF-1/ CXCR4/CXCR7 chemokine signaling is critical for recruiting stem cells to sites of ischemic injury. Upregulation of SDF-1/CXCR4/CXCR7 chemokine signaling in the ischemic regions has been well-documented in the animal models of ischemic stroke, but not in human ischemic brain. Here, we found that protein expression of SDF-1 and CXCR7, but not CXCR4, were significantly increased in the cortical peri-infarct regions (penumbra) after ischemic stroke in human, compared with adjacent normal tissues and control subjects. Double-label fluorescence immunohistochemistry shows that SDF-1 and CXCR4 proteins were expressed in neuronal cells and astrocytes in the normal brain tissue and peri-infarct regions. CXCR7 protein was also observed in neuronal cells and astrocytes in the normal cortical regions, but predominantly in astrocytes in the penumbra of ischemic brain. Our data suggest that ischemic stroke in human leads to an increase in the expression of SDF-1 and CXCR7, but not CXCR4, in the peri-infarct cerebral cortex. Our findings suggest that chemokine SFD-1 is expressed not only in animal models of stroke, but also in the human brain after an ischemic injury. In addition, unlike animals, CXCR7 may be the primary receptor of SDF-1 in human stroke brain.
Distributed economic MPC: a framework for cooperative control problems

Abstract In this paper, we propose a distributed economic MPC algorithm for cooperative control of several self-interested interacting dynamical systems. Each system considers its own, local, performance criterion, and coordination between the systems is enforced via coupling constraints. The proposed control strategy consists of a distributed optimization algorithm, used to determine an overall optimal steady-state, whose current iterates at each time are then used by each system to compute a control input in an economic MPC framework. We analyze the properties of the proposed algorithm and prove convergence results for the resulting overall closed-loop system. Furthermore, we apply our results to the problem of synchonizing several agents with conflicting objective.
Needed in Psychology: Theoretical Precision

Europe's Journal of Psychology, 2018, Vol. 14(1), 1–6, doi:10.5964/ejop.v14i1. 1602 Published (VoR): 2018-03-12. *Corresponding author at: Department of Communication and Psychology, Aalborg University, Kroghstræde 3, 9220 Aalborg Ø, Denmark. E-mail: jvalsiner@gmail.com This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Innovative Natural Ingredients-Based Multiple Emulsions: The Effect on Human Skin Moisture, Sebum Content, Pore Size and Pigmentation

The increased interest in natural cosmetics has resulted in a higher market demand for preservative-free products based on herbal ingredients. An innovative W/O/W type emulsions containing herbal extracts were prepared directly; its cation form was induced by an ethanolic rosemary extract and stabilized using weak herbal gels. Due to the wide phytochemical composition of herbal extracts and the presence of alcohol in the emulsion system, which can cause skin irritation, sensitization or dryness when applied topically, the safety of the investigated drug delivery system is necessary. The aim of our study was to estimate the potential of W/O/W emulsions based on natural ingredients for skin irritation and phototoxicity using reconstructed 3D epidermis models in vitro and to evaluate in vivo its effect on human skin moisture, sebum content and pigmentation by biomedical examination using a dermatoscopic camera and corneometer. According to the results obtained after in vitro cell viability test the investigated emulsion was neither irritant nor phototoxic to human skin keratinocytes. W/O/W emulsion did not cause skin dryness in vivo, despite the fact that it contained ethanol. We can conclude that the emulsion is safe for use as a leave-on product due to the positive effect on human skin characteristics or as a semisolid pharmaceutical base where active compounds could be encapsulated.
Microscale optoelectronic infrared-to-visible upconversion devices and their use as injectable light sources

Significance Materials and devices that upconvert photons from low to high frequencies are of tremendous interest for broad applications in bio- and chemical sensing, infrared imaging, display, and photovoltaics. Here we develop self-powered, micrometer-scale optoelectronic upconversion devices for infrared-to-visible photon upconversion. The realized microscale optoelectronic device exhibits a practical efficiency of 1.5% under incoherent low-power illumination, with emission intensity linearly dependent on excitation. Other remarkable advances over conventional approaches include broadband absorption, wide-emission spectral tunability (red, yellow, and blue), and fast dynamics. Encapsulated ultraminiaturized devices can be implanted into subdermal tissues and provide stable operation in behaving animals. Demonstrations of optogenetic stimulation in living animals present their potential for biomedical uses. Optical upconversion that converts infrared light into visible light is of significant interest for broad applications in biomedicine, imaging, and displays. Conventional upconversion materials rely on nonlinear light-matter interactions, exhibit incidence-dependent efficiencies, and require high-power excitation. We report an infrared-to-visible upconversion strategy based on fully integrated microscale optoelectronic devices. These thin-film, ultraminiaturized devices realize near-infrared (∼810 nm) to visible [630 nm (red) or 590 nm (yellow)] upconversion that is linearly dependent on incoherent, low-power excitation, with a quantum yield of ∼1.5%. Additional features of this upconversion design include broadband absorption, wide-emission spectral tunability, and fast dynamics. Encapsulated, freestanding devices are transferred onto heterogeneous substrates and show desirable biocompatibilities within biological fluids and tissues. These microscale devices are implanted in behaving animals, with in vitro and in vivo experiments demonstrating their utility for optogenetic neuromodulation. This approach provides a versatile route to achieve upconversion throughout the entire visible spectral range at lower power and higher efficiency than has previously been possible.
Maternal antibody and the maintenance of a lyssavirus in populations of seasonally breeding African bats

Pathogens causing acute disease and death or lasting immunity require specific spatial or temporal processes to persist in populations. Host traits, such as maternally-derived antibody (MDA) and seasonal birthing affect infection maintenance within populations. Our study objective is to understand how viral and host traits lead to population level infection persistence when the infection can be fatal. We collected data on African fruit bats and a rabies-related virus, Lagos bat virus (LBV), including through captive studies. We incorporate these data into a mechanistic model of LBV transmission to determine how host traits, including MDA and seasonal birthing, and viral traits, such as incubation periods, interact to allow fatal viruses to persist within bat populations. Captive bat studies supported MDA presence estimated from field data. Captive bat infection-derived antibody decayed more slowly than MDA, and while faster than estimates from the field, supports field data that suggest antibody persistence may be lifelong. Unobserved parameters were estimated by particle filtering and suggest only a small proportion of bats die of disease. Pathogen persistence in the population is sensitive to this proportion, along with MDA duration and incubation period. Our analyses suggest MDA produced bats and prolonged virus incubation periods allow viral maintenance in adverse conditions, such as a lethal pathogen or strongly seasonal resource availability for the pathogen in the form of seasonally pulsed birthing.
A Directed Acyclic Graph Approach to Online Log Parsing

Logs are widely used in modern software system management because they are often the only data accessible that record system events at runtime. In recent years, because of the ever-increasing log size, data mining techniques are often utilized to help developers and operators conduct system reliability management. A typical log-based system reliability management procedure is to first parse log messages because of their unstructured format; and apply data mining techniques on the parsed logs to obtain critical system behavior information. Most of existing research studies focus on offline log parsing, which need to parse logs in batch mode. However, software systems, especially distributed systems, require online monitoring and maintenance. Thus, a log parser that can parse log messages in a streaming manner is highly in demand. To address this problem, we propose an online log parsing method, namely Drain, based on directed acyclic graph, which encodes specially designed rules for parsing. Drain can automatically generate a directed acyclic graph for a new system and update the graph according to the incoming log messages. Besides, Drain frees developers from the burden of parameter tuning by allowing them use Drain with no pre-defined parameters. To evaluate the performance of Drain, we collect 11 log datasets generated by real-world systems, ranging from distributed systems, Web applications, supercomputers, operating systems, to standalone software. The experimental results show that Drain has the highest accuracy on all 11 datasets. Moreover, Drain obtains 37.15\%$\sim$ 97.14\% improvement in the running time over the state-of-the-art online parsers. We also conduct a case study on a log-based anomaly detection task using Drain in the parsing step, which determines its effectiveness in system reliability management.
Online Discussion and the Moral Pathway to Identity Politicization and Collective Action

Research on the mobilizing potential of the Internet has produced some controversy between optimistic vs. skeptical perspectives. Although some attention has been paid to the effects of online discussions on collective participation, very little is known about how people’s experience of online interactions affects the key psychosocial predictors of collective action. The present research investigated whether use of the Internet as a channel for deliberation influenced the moral pathway to collective mobilization by shaping users’ politicized identity, thereby indirectly influencing collective action. Results showed that when people perceived online discussions as a constructive communication context, their politicized identity was imbued with the meaning of responding to a moral obligation, and willingness to participate in collective action was sustained. However, when participants perceived that online discussions were not constructive, their identification with the movement did not refer to moral obligation, and intention to participate in collective action was not sustained. Our discussion focuses on the need to deepen investigation of how people experience the particularities of interacting online, and on how this can affect psychosocial processes leading to collective action.