Academic literature on the topic 'Partial virtual machine'

Smartphones help us with almost every activity and task nowadays. The features and hardware of the phone can be leveraged to make apps for online payment, content consumption, and creation, accessibility, etc. These devices can also be used to help and assist visually challenged and guide them in their daily activities. As the visually challenged sometimes face difficulty in sensing the objects or humans in the surroundings, they require guidance or help in recognizing objects, human faces, reading text, and other activities. Hence, this Android application has been proposed to help and assist people with partial vision impairment. The application will make use of technologies like face detection, object and text recognition, barcode scanner, and a basic voice-based chatbot which can be used to execute basic commands implemented through Deep Learning, Artificial Intelligence, and Machine Learning. The application will be able to detect the number of faces, recognize the object in the camera frame of the application, read out the text from newspapers, documents, etc, and open the link detected from the barcode, all given as output to the user in the form of voice.

Double-column vehicle lift is a lifting equipment commonly used in auto repair and maintenance units, which is widely applied to the repair and maintenance of small vehicles such as cars. The design is a double-column vehicle lift driven by hydraulic. It mainly consists of lifting device, synchronous drive, column and bracket arm. The hydraulic circuit of hydraulic lifting device is designed. Currently, the articles concerning solidworks in the design of hydraulic drive lift only focus on unilateral appliance. When it comes to the design of the system, most of them concentrate on part modeling, virtual assembly, finite element analysis of parts ,etc, or a partial function of certain lifting machine. Seldom of them make a systematic research concerning motion stimulation. This paper, however, makes a three-dimensional virtual modeling for the lifting machine by the use of solidworks. In addition, it makes a structure verification of lift with assembly and motion simulation.

With more and more attention being paid to anti-shock of the warship by many country’s navy, it is of practical significance to study the anti-shock characteristic of the diesel engine which belongs to the A class facility on warship. Owing to the high price of shock test, simulation combined with partial shock test methods are adopted by many countries to ensure the facility’s anti-shock performance. A numeric virtual platform of TBD234V6 diesel engine generator set is built by means of software MSC.ADAMS and four machine feet of the diesel engine are modeled flexibly. The virtual shock test of the generator set is conducted on this platform according to the German shock standard BV043/85 and anti-shock performance limits of the machine feet is obtained. Hammer experiment is also carried out on the real generator set. After the comparison of the simulation results and the experiment results, the deficiency aspects need to be improved are pointed out at last.

Abstract Motivation Partial atomic charges are usually used to calculate the electrostatic component of energy in many molecular modeling applications, such as molecular docking, molecular dynamics simulations, free energy calculations and so forth. High-level quantum mechanics calculations may provide the most accurate way to estimate the partial charges for small molecules, but they are too time-consuming to be used to process a large number of molecules for high throughput virtual screening. Results We proposed a new molecule descriptor named Atom-Path-Descriptor (APD) and developed a set of APD-based machine learning (ML) models to predict the partial charges for small molecules with high accuracy. In the APD algorithm, the 3D structures of molecules were assigned with atom centers and atom-pair path-based atom layers to characterize the local chemical environments of atoms. Then, based on the APDs, two representative ensemble ML algorithms, i.e. random forest (RF) and extreme gradient boosting (XGBoost), were employed to develop the regression models for partial charge assignment. The results illustrate that the RF models based on APDs give better predictions for all the atom types than those based on traditional molecular fingerprints reported in the previous study. More encouragingly, the models trained by XGBoost can improve the predictions of partial charges further, and they can achieve the average root-mean-square error 0.0116 e on the external test set, which is much lower than that (0.0195 e) reported in the previous study, suggesting that the proposed algorithm is quite promising to be used in partial charge assignment with high accuracy. Availability and implementation The software framework described in this paper is freely available at https://github.com/jkwang93/Atom-Path-Descriptor-based-machine-learning Supplementary information Supplementary data are available at Bioinformatics online.

Social robots adopt an emotional touch to interact with users inducing and transmitting humanlike emotions. Natural interaction with humans needs to be in real time and well grounded on the full availability of information on the environment. These robots base their way of communicating on direct interaction (touch, listening, view), supported by a range of sensors on the surrounding environment that provide a radially central and partial knowledge on it. Over the past few years, social robots have been demonstrated to implement different features, going from biometric applications to the fusion of machine learning environmental information collected on the edge. This article aims at describing the experiences performed and still ongoing and characterizes a simulation environment developed for the social robot Pepper that aims to foresee the new scenarios and benefits that tactile connectivity will enable.

Virtual manufacturing reflects the nature of manufacturing in the computer implementation. It is the comprehensive development and application of computer simulation technology and virtual reality technology in manufacturing. It is also a new concept in manufacturing industry, for which enterprises use information integration as bases. This article therefore describes the practical applications of the tobacco harvesting in virtual manufacturing system. Based on the whole to part-based design idea, we adopt a brand new design method which uses the interaction of three-dimensional design, graphic design and partial tests. Meanwhile, it uses soliderworks software to create three-dimensional modeling and motion simulation for the tobacco harvesting system. Specifically, for the design part, we first conduct the constraint and connecting assembly of the established three-dimensional model of the tobacco harvest system to form an entirety. Then we carry on the motion simulation tests, which show the kinetic characteristics of the system in the virtual environment. The final step is data processing and error modification. The result indicates that this method not only improves the accuracy and efficiency of design manufacturing and reduces relevant costs, but also improves the collection level of the leaf tobacco harvesting machine, reduces harvesting costs with a relative high value.

Abstract All-cellulose composites (ACCs) were prepared from filter paper via partial dissolution in the ionic liquid 1-ethyl-3-methylimidazolium acetate, and material tensile properties were investigated using various approaches. One is based on data directly taken from a tensile testing machine, and the other uses two-cameras stereovision with digital image correlation (DIC) technique. In the latter case, virtual extensometer with different locations on the sample and averaging over sample surface were tested. Nominal and true stress–strain dependences were built and Young's modulus, tensile strength, elongation at maximal stress and toughness were evaluated as a function of ACC density. A minor difference was observed for the stress–strain dependences derived from different approaches which use the DIC technique, most probably because of low ACC deformation. However, the results reveal that the nominal stress–strain curve from DIC is significantly different from that which is directly derived from the data provided by machine sensors thus strongly impacting Young’s modulus and elongation at break values. This study provides an insight into the evaluation of the mechanical properties of ACCs. Graphic abstract

UMView is a partial virtual machine and userspace hypervisor capable of intercepting system calls and modifying their behavior according to the calling process' view. In order to provide flexibility and modularity UMView supports modules loadable at runtime using a plugin architecture. UMView in particular is the implementation of the View-OS concept which negates the global view assumption which is so radically established in the world of OSes and virtualization.

The technology of partial virtualization is a revolutionary approach to the world of virtualization. It lies directly in-between full system virtual machines (like QEMU or XEN) and application-related virtual machines (like the JVM or the CLR). The ViewOS project is the flagship of such technique, developed by the Virtual Square laboratory, created to provide an abstract view of the underlying system resources on a per-process basis and work against the principle of the Global View Assumption. Virtual Square provides several different methods to achieve partial virtualization within the ViewOS system, both at user and kernel levels. Each of these approaches have their own advantages and shortcomings. This paper provides an analysis of the different virtualization methods and problems related to both the generic and partial virtualization worlds. This paper is the result of an in-depth study and research for a new technology to be employed to provide partial virtualization based on ELF dynamic binaries. It starts with a mild analysis of currently available virtualization alternatives and then goes on describing the ViewOS system, highlighting its current shortcomings. The vloader project is then proposed as a possible solution to some of these inconveniences with a working proof of concept and examples to outline the potential of such new virtualization technique. By injecting specific code and libraries in the middle of the binary loading mechanism provided by the ELF standard, the vloader project can promote a streamlined and simplified approach to trace system calls. With the advantages outlined in the following paper, this method presents better performance and portability compared to the currently available ViewOS implementations. Furthermore, some of itsdisadvantages are also discussed, along with their possible solutions.

Introducción El acelerado ritmo al que se genera y crece la información en la sociedad actual y la posible llegada de la tecnología de transistor a sus límites de tamaño exige la puesta en marcha de soluciones para el procesado eficiente de datos en campos específicos de aplicación. Contenido Esta tesis doctoral de carácter transdisciplinar a medio camino entre la ingeniería electrónica y la química computacional presenta soluciones optimizadas en hardware y en software para la construcción y el procesado eficiente de bases de datos moleculares. En primer lugar se propone y se estudia el funcionamiento de bloques digitales que implementan funciones en lógica pulsante estocástica orientadas a tareas de reconocimiento de objetos. Especialmente se proponen y analizan diseños digitales para la construcción de generadores de números aleatorios (RNG) como base de estos sistemas que han sido implementados en dispositivos Field Programable Gate Array (FPGA). En segundo lugar se propone y se evalúa un conjunto reducido de descriptores moleculares para la caracterización de compuestos orgánicos y la generación de bases de datos moleculares. Estos descriptores recogen información sobre la distribución de la carga molecular en el espacio y la energía electrostática. Las bases de datos generadas con estos descriptores se han procesado utilizando sistemas de computación convencionales en software y mediante sistemas de computación estocástica implementados en hardware mediante el uso de circuitería digital programable. Finalmente se proponen optimizaciones para la estimación del potencial electrostático molecular (MEP) y para el cálculo de los puntos de interacción molecular derivados (SSIP). Conclusiones Por una parte, los resultados obtenidos ponen de manifiesto la importancia de la uniformidad de los RNG en el período de evaluación para poder implementar sistemas de computación estocástica de alta fiabilidad. Además, los RNG propuestos tienen una naturaleza aperiódica que minimiza las posibles correlaciones entre señales, haciendo que sean adecuados para la implementación de sistemas de computación estocástica. Por otra parte, el conjunto de descriptores moleculares propuestos PED han demostrado obtener muy buenos resultados en comparación con otros métodos presentes en la literatura. Este hecho se ha discutido mediante los parámetros Area Under The Curve (AUC) y Enrichment Factor (EF) obtenidos de las curvas promedio Receiving Operating Characteristic (ROC). Además, se ha mostrado como la eficacia de los descriptores aumenta cuando se implementan en sistemas de clasificación con aprendizaje supervisado, haciéndolos adecuados para la construcción de un sistema de predicción de dianas terapéuticas eficiente. En esta tesis, además, se ha determinado que los MEP calculados utilizando la teoría DFT y el conjunto de bases B3LYP/6-31*G en la superficie con densidad electrónica 0,01 au correlacionan bien con datos experimentales debido presumiblemente a la mayor contribución de las propiedades electrostáticas locales reflejadas en el MEP. Las parametrizaciones propuestas en función del tipo de hibridación atómica pueden haber contribuido también a esta mejora. Los cálculos realizados en dichas superficies suponen mejoras en un factor cinco en la velocidad de procesamiento del MEP. Dado el aceptable ajuste a datos experimentales del método propuesto para el cálculo del MEP aproximado y de los SSIP, éste se puede utilizar con el fin de obtener los SSIP para bases de datos moleculares extensas o en macromoléculas como proteínas de manera muy rápida (ya que la velocidad de procesamiento obtenida puede alcanzar del orden de cinco mil átomos procesados por segundo utilizando un solo procesador). Estas técnicas resultan de especial interés dadas las numerosas aplicaciones de los SSIP como por ejemplo el cribado virtual de cocristales o la predicción de energías libres en disolución.
Introducció El creixement accelerat de les dades en la societat actual i l'arribada de la tecnologia del transistor als límits físics exigeix la proposta de metodologies per al processament eficient de dades. Contingut Aquesta tesi doctoral, de caràcter transdisciplinària i a mig camí entre els camps de l'enginyeria electrònica i la química computacional presenta solucions optimitzades en maquinari i en programari per tal d’accelerar el processament de bases de dades moleculars. En primer lloc es proposa i s'estudia el funcionament de blocs digitals que implementen funcions de lògica polsant estocàstica aplicades a tasques de reconeixement d'objectes. En concret es proposen i analitzen dissenys específics per a la construcció de generadors de nombres aleatoris (RNG) com a sistemes bàsics per al funcionament dels sistemes de computació estocàstics implementats en dispositius programables com les Field Programable Gate Array (FPGA). En segon lloc es proposen i avaluen un conjunt reduït de descriptors moleculars especialment orientats a la caracterització de compostos orgànics. Aquests descriptors reuneixen la informació sobre la distribució de càrrega molecular i les energies electroestàtiques. Les bases de dades generades amb aquests descriptors s’han processat emprant sistemes de computació convencionals en programari i mitjançant sistemes basats en computació estocàstica implementats en maquinari programable. Finalment es proposen optimitzacions per al càlcul del potencial electroestàtic molecular (MEP) calculat mitjançant la teoria del funcional de la densitat (DFT) i dels punts d’interacció que se’n deriven (SSIP). Conclusions Per una banda, els resultats obtinguts posen de manifest la importància de la uniformitat del RNG en el període d’avaluació per a poder implementar sistemes de computació estocàstics d’alta fiabilitat. A més, els RNG proposats presenten una font d’aleatorietat aperiòdica que minimitza les correlacions entre senyals, fent-los adequats per a la implementació de sistemes de computació estocàstica. Per una altra banda, el conjunt de descriptors moleculars proposats PED, han demostrat obtenir molts bons resultats en comparació amb els mètodes presents a la literatura. Aquest fet ha estat discutit mitjançant l’anàlisi dels paràmetres Area Under The Curve (AUC) i Enrichment Factor (EF) de les curves Receiving Operating Characteristic (ROC) analitzades. A més, s’ha mostrat com l’eficàcia dels descriptors augmenta de manera significativa quan s’implementen en sistemes de classificació amb aprenentatge supervisat com les finestres de Parzen, fent-los adequats per a la construcció d’un sistema de predicció de dianes terapèutiques eficient. En aquesta tesi doctoral, a més, s’ha trobat que els MEP calculats mitjançant la teoria DFT i el conjunt de bases B3LYP/6-31*G en la superfície amb densitat electrònica 0,01 au correlacionen bé amb dades experimentals possiblement a causa de la contribució més gran de les propietats electroestàtiques locals reflectides en el MEP. Les parametritzacions proposades en funció del tipus d’hibridació atòmica han contribuït també a la millora dels resultats. Els càlculs realitzats en aquestes superfícies suposen un guany en un factor cinc en la velocitat de processament del MEP. Donat l’acceptable ajust a les dades experimentals del mètode proposat per al càlcul del MEP aproximat i dels SSIP que se’n deriven, aquest procediment es pot emprar per obtenir els SSIP en bases de dades moleculars extenses i en macromolècules (com ara proteïnes) d’una manera molt ràpida (ja que la velocitat de processament obtinguda arriba fins als cinc mil àtoms per segon amb un sol processador). Les tècniques proposades en aquesta tesi doctoral resulten d’interès donades les nombroses aplicacions que tenen els SSIP com per exemple, en el cribratge virtual de cocristalls o en la predicció d’energies lliures en dissolució.
Introduction Because of the generalized data growth in the nowadays digital era and due to the fact that we are possibly living on the last days of the Moore’s law, there exists a good reason for being focused on the development of technical solutions for efficient data processing. Contents In this transdisciplinary thesis between electronic engineering and computational chemistry, it's shown optimal solutions in hardware and software for molecular database processing. On the first hand, there's proposed and studied a set of stochastic computing systems in order to implement ultrafast pattern recognition applications. Specially, it’s proposed and analyzed specific digital designs in order to create digital Random Number Generators (RNG) as a base for stochastic functions. The digital platform used to generate the results is a Field Programmable Gate Array (FPGA). On the second hand, there's proposed and evaluated a set of molecular descriptors in order to create a compact molecular database. The proposed descriptors gather charge and molecular geometry information and they have been used as a database both in software conventional computing and in hardware stochastic computing. Finally, there's a proposed a set of optimizations for Molecular Electrostatic Potential (MEP) and Surface Site Interaction Points (SSIP). Conclusions Firstly, the results show the relevance of the uniformity of the RNG within the evaluation period in order to implement high precision stochastic computing systems. In addition, the proposed RNG have an aperiodic behavior which avoid some potential correlations between stochastic signals. This property makes the proposed RNG suitable for implementation of stochastic computing systems. Secondly, the proposed molecular descriptors PED have demonstrated to provide good results in comparison with other methods that are present in the literature. This has been discussed by the use of Area Under the Curve (AUC) and Enrichment Factor (EF) of averaged Receiving Operating Characteristic (ROC) curves. Furthermore, the performance of the proposed descriptors gets increased when they are implemented in supervised machine learning algorithms making them appropriate for therapeutic target predictions. Thirdly, the efficient molecular database characterization and the usage of stochastic computing circuitry can be used together in order to implement ultrafast information processing systems. On the other hand, in this thesis, it has been found that the MEP calculated by using DFT and B3LYP/6-31*G basis at 0.01 au density surface level has good correlation with experimental data. This fact may be due to the important contribution of local electrostatics and the refinement performed by the parameterization of the MEP as a function of the orbital atom type. Additionally, the proposed calculation over 0.01 au is five times faster than the calculation over 0.002 au. Finally, due to acceptable agreement between experimental data and theoretical results obtained by using the proposed calculation for MEP and SSIP, the proposed method is suitable for being applied in order to quickly process big molecular databases and macromolecules (the processing speed can achieve five thousand molecules per second using a single processor). The proposed techniques have special interest with the purpose of finding the SSIP because the big number of applications they have as for instance in virtual cocrystal screening and calculation of free energies in solution.

Modern offices are crowded with personal computers. While studies have shown these to be idle most of the time, they remain powered, consuming up to 60% of their peak power. Hardware based solutions engendered by PC vendors (e.g., low power states, Wake-on-LAN) have proven unsuccessful because, in spite of user inactivity, these machines often need to remain network active in support of background applications that maintain network presence. Recent solutions have been proposed that perform consolidation of idle desktop virtual machines. However, desktop VMs are often large requiring gigabytes of memory. Consolidating such VMs, creates large network transfers lasting in the order of minutes, and utilizes server memory inefficiently. When multiple VMs migrate simultaneously, each VM’s experienced migration latency grows, and this limits the use of VM consolidation to environments in which only a few daily migrations are expected per VM. This thesis introduces partial VM migration, an approach that transparently migrates only the working set of an idle VM, by migrating memory pages on-demand. It creates a partial replica of the desktop VM on the consolidation server by copying only VM metadata, and transferring pages to the server, as the VM accesses them. This approach places desktop PCs in low power state when inactive and resumes them to running state when pages are needed by the VM running on the consolidation server. Jettison, our software prototype of partial VM migration for off-the-shelf PCs, can deliver 78% to 91% energy savings during idle periods lasting more than an hour, while providing low migration latencies of about 4 seconds, and migrating minimal state that is under an order of magnitude of the VM’s memory footprint. In shorter idle periods of up to thirty minutes, Jettison delivers savings of 7% to 31%. We present two approaches that increase energy savings attained with partial VM migration, especially in short idle periods. The first, Context-Aware Selective Resume, expedites PC resume and suspend cycle times by supplying a context identifier at desktop resume, and initializing only devices and code that are relevant to the context. CAESAR, the Context-Aware Selective Resume framework, enables applications to register context vectors that are invoked when the desktop is resumed with matching context. CAESAR increases energy savings in short periods of five minutes to an hour by up to 66%. The second approach, the low power page cache, embeds network accessible low power hardware in the PC, to enable serving of pages to the consolidation server, while the PC is in low power state. We show that Oasis, our prototype page cache, addresses the shortcomings of energy-oriented on-demand page migration by increasing energy savings, especially during short idle periods. In periods of up to an hour, Oasis increases savings by up to twenty times.

Manufacturing equipments in semiconductor factories (fabs) provide abundant data and opportunities for data-driven process monitoring and modeling. In particular, virtual metrology (VM) is an active area of research. Traditional monitoring techniques using univariate statistical process control charts do not provide immediate feedback to quality excursions, hindering the implementation of fab-wide advanced process control initiatives. VM models or inferential sensors aim to bridge this gap by predicting of quality measurements instantaneously using tool fault detection and classification (FDC) sensor measurements. The existing research in the field of inferential sensor and VM has focused on comparing regressions algorithms to demonstrate their feasibility in various applications. However, two important areas, data pretreatment and post-deployment model maintenance, are usually neglected in these discussions. Since it is well known that the industrial data collected is of poor quality, and that the semiconductor processes undergo drifts and periodic disturbances, these two issues are the roadblocks in furthering the adoption of inferential sensors and VM models. In data pretreatment, batch data collected from FDC systems usually contain inconsistent trajectories of various durations. Most analysis techniques requires the data from all batches to be of same duration with similar trajectory patterns. These inconsistencies, if unresolved, will propagate into the developed model and cause challenges in interpreting the modeling results and degrade model performance. To address this issue, a Constrained selective Derivative Dynamic Time Warping (CsDTW) method was developed to perform automatic alignment of trajectories. CsDTW is designed to preserve the key features that characterizes each batch and can be solved efficiently in polynomial time. Variable selection after trajectory alignment is another topic that requires improvement. To this end, the proposed Moving Window Variable Importance in Projection (MW-VIP) method yields a more robust set of variables with demonstrably more long-term correlation with the predicted output. In model maintenance, model adaptation has been the standard solution for dealing with drifting processes. However, most case studies have already preprocessed the model update data offline. This is an implicit assumption that the adaptation data is free of faults and outliers, which is often not true for practical implementations. To this end, a moving window scheme using Total Projection to Latent Structure (T-PLS) decomposition screens incoming updates to separate the harmless process noise from the outliers that negatively affects the model. The integrated approach was demonstrated to be more robust. In addition, model adaptation is very inefficient when there are multiplicities in the process, multiplicities could occur due to process nonlinearity, switches in product grade, or different operating conditions. A growing structure multiple model system using local PLS and PCA models have been proposed to improve model performance around process conditions with multiplicity. The use of local PLS and PCA models allows the method to handle a much larger set of inputs and overcome several challenges in mixture model systems. In addition, fault detection sensitivities are also improved by using the multivariate monitoring statistics of these local PLS/PCA models. These proposed methods are tested on two plasma etch data sets provided by Texas Instruments. In addition, a proof of concept using virtual metrology in a controller performance assessment application was also tested.

Human-like computing will require machines to communicate interactively with people, to take in human knowledge and preferences, to explain the machine’s actions, and to generally coordinate human and machine behaviour. But even the simplest communicative interactions raise deep theoretical challenges. Human and machine spontaneously have to “agree” on the mapping between possible signals and messages. Such mappings are extremely flexible and influenced by large numbers of contextual factors in real-world interactions. Typically, there are many candidate signal-message mappings. Successful communication requires that both parties agree on the same mapping. We outline experimental and theoretical work exploring an approach to these problems based on “virtual bargaining,” according to which each communicative party attempts to infer which mapping would be agreed were prior communication possible. This chapter explores how such problems are solved by humans, and suggests directions for building computational models of these processes.

This paper discloses in synthesis a super-computation computer architecture (CA) model, presently a provisional Patent Application at INPI (nº 116408). The outline is focused on a method to perform computation at or near the speed of light, resorting to an inversion of the Princeton CA. It expands from isomorphic binary/RGB (typical) digital “images”, in a network of (UTM)s over Turing-machines (M)s. From the binary/RGB code, an arithmetic theory of (typical) digital images permits fully synchronous/orthogonal calculus in parallelism, wherefrom an exponential surplus is achieved. One such architecture depends on any “cell”- like exponential-prone basis such as the “pixel”, or rather the RGB “octet-byte”, limited as it may be, once it is congruent with any wave-particle duality principle in observable objects under the electromagnetic spectrum and reprogrammable designed. Well-ordered instructions in binary/RGB modules are, further, programming composed to alter the structure of the Internet, in virtual/virtuous eternal recursion/recurrence, under man-machine/machine-machine communication ontology.