The document provides an overview of digital electronics basics and combinational logic. It discusses digital versus analog signals, how digital signals assume discrete voltage values, and noise margins in digital circuits. It then explains what combinational logic is, giving an example of a simple AND gate circuit to make an instant decision based on inputs. The document discusses representing logic using truth tables and Boolean algebra, and provides examples of logic expressions and circuits for AND, OR, NAND, NOR and other gates. It also discusses how to simplify logic expressions and convert between representations.
Stephen Friend IBC Next Generation Sequencing & Genomic Medicine 2011-08-03Sage Base
This document discusses a drug development approach called Arch2POCM that moves from disease targets to clinical validation. It focuses on using data intensive science to build better disease maps by generating massive amounts of data from comprehensive population monitoring and integrating genotypic, gene expression, and trait data into probabilistic models. This allows the direct identification of causal genes for disease.
Artificial neural networks (ANNs) are mathematical models inspired by biological neural networks. ANNs consist of interconnected groups of artificial neurons similar to biological neurons in the brain. ANNs learn by adjusting the connection weights between units or nodes through training data, allowing them to learn patterns and make predictions. They can learn complex patterns through parallel processing and remain functional even if damaged, making them useful for applications like pattern recognition, data processing, and robotics.
Stephen Friend Institute of Development, Aging and Cancer 2011-11-28Sage Base
Stephen Friend presented on using data intensive science and bionetworks to build better models of disease. He discussed how current disease models often oversimplify pathways and indications, failing to capture disease complexity. Integrating large datasets using computational models hosted in "compute spaces" could generate more accurate disease maps. However, barriers include a lack of data sharing, standardized tools and incentives. Sage Bionetworks aims to establish a commons for collaborative model building to accelerate disease understanding and new therapies.
Linear discriminant analysis (LDA) is a method used to classify observations into categories. LDA finds a linear combination of features that best separates two or more classes of objects. It assumes normal distributions of data and equal class prior probabilities. LDA seeks projections of high-dimensional data onto a line or plane that best separates the classes.
The document provides an overview of digital electronics basics and combinational logic. It discusses digital versus analog signals, how digital signals assume discrete voltage values, and noise margins in digital circuits. It then explains what combinational logic is, giving an example of a simple AND gate circuit to make an instant decision based on inputs. The document discusses representing logic using truth tables and Boolean algebra, and provides examples of logic expressions and circuits for AND, OR, NAND, NOR and other gates. It also discusses how to simplify logic expressions and convert between representations.
Stephen Friend IBC Next Generation Sequencing & Genomic Medicine 2011-08-03Sage Base
This document discusses a drug development approach called Arch2POCM that moves from disease targets to clinical validation. It focuses on using data intensive science to build better disease maps by generating massive amounts of data from comprehensive population monitoring and integrating genotypic, gene expression, and trait data into probabilistic models. This allows the direct identification of causal genes for disease.
Artificial neural networks (ANNs) are mathematical models inspired by biological neural networks. ANNs consist of interconnected groups of artificial neurons similar to biological neurons in the brain. ANNs learn by adjusting the connection weights between units or nodes through training data, allowing them to learn patterns and make predictions. They can learn complex patterns through parallel processing and remain functional even if damaged, making them useful for applications like pattern recognition, data processing, and robotics.
Stephen Friend Institute of Development, Aging and Cancer 2011-11-28Sage Base
Stephen Friend presented on using data intensive science and bionetworks to build better models of disease. He discussed how current disease models often oversimplify pathways and indications, failing to capture disease complexity. Integrating large datasets using computational models hosted in "compute spaces" could generate more accurate disease maps. However, barriers include a lack of data sharing, standardized tools and incentives. Sage Bionetworks aims to establish a commons for collaborative model building to accelerate disease understanding and new therapies.
Linear discriminant analysis (LDA) is a method used to classify observations into categories. LDA finds a linear combination of features that best separates two or more classes of objects. It assumes normal distributions of data and equal class prior probabilities. LDA seeks projections of high-dimensional data onto a line or plane that best separates the classes.
This document provides an overview of statistical tests commonly used in neuroimaging such as t-tests, ANOVAs, and regression. It discusses the purposes of these tests and how they are applied. T-tests are used to compare means, for example to determine if the difference between two conditions is statistically significant. ANOVAs examine variances and can be used when comparing more than two groups. Regression allows describing and predicting the relationship between variables and is useful in the general linear model approach used in SPM. Key assumptions and calculations for each method are outlined.
This document outlines key concepts in linear models and estimation that will be covered in the STA721 Linear Models course, including:
1) Linear regression models decompose observed data into fixed and random components.
2) Maximum likelihood estimation finds parameter values that maximize the likelihood function.
3) Linear restrictions on the mean vector μ define a subspace and equivalent parameterizations represent the same subspace.
4) Inference should be independent of the parameterization or coordinate system used to represent μ.
The document discusses discrete and continuous random variables. It defines discrete random variables as variables that can take on countable values, like the number of heads from coin flips. Continuous random variables can take any value within a range, like height. The document explains how to calculate and interpret the mean, standard deviation, and probabilities of events for both types of random variables using examples like Apgar scores for babies and heights of young women.
- An artificial neural network consists of simple processing units called neurons connected in a network similar to a biological neural system. Each neuron receives inputs from other neurons and processes the inputs using an activation function to determine its output.
- A common type of artificial neural network has three layers of neurons: an input layer, a hidden layer, and an output layer. The input layer receives information from the environment, the hidden layer transforms the inputs, and the output layer provides the network's predictions or decisions.
- Each neuron calculates a weighted sum of its inputs and passes the result through an activation function to determine its output. During training, the weights are adjusted to minimize error between the network's predictions and correct outputs.
This document discusses probability distributions for random variables. It introduces discrete distributions like the binomial and Poisson distributions which are used for counting experiments. It also introduces continuous distributions like the normal distribution which are defined over continuous ranges of values. Key concepts covered include probability density functions, cumulative distribution functions, and how to relate random variables with specific parameters to standard distributions. Examples are provided to illustrate concepts like modeling the number of plant stems in a sampling area with a Poisson distribution.
Stephen Friend Norwegian Academy of Science and Letters 2011-11-02Sage Base
The document discusses using data intensive science to build better models of disease. It argues that the current pharmaceutical model is broken because it lacks sufficient understanding of disease biology. Most drug candidates fail because existing disease models oversimplify complex conditions. The author proposes using large datasets and computational modeling to map molecular pathways and construct causal models of diseases. This could provide a more mechanistic understanding of diseases and their heterogeneity to identify true drivers and better predict treatment responses. Pilots are needed to determine if this approach can modify diseases rather than just treat symptoms by moving beyond lists of altered genes and proteins to causal networks.
The document discusses measures of dispersion such as variance, standard deviation, and the coefficient of variation. It defines variance as the average squared deviation from the mean and standard deviation as the positive square root of the variance. The coefficient of variation measures relative dispersion by dividing the standard deviation by the mean. It is unit-free and allows for comparison across distributions. The document also covers Chebyshev's inequality and how it relates to the proportion of data within a given number of standard deviations from the mean.
The document provides an overview of digital electronics basics and combinational logic. It discusses digital versus analog signals, how digital signals assume discrete voltage values, and noise margins in digital circuits. It then explains what combinational logic is, giving an example of a simple AND gate circuit to make an instant decision based on inputs. The document discusses representing logic using truth tables and Boolean algebra, and provides examples of logic expressions and circuits for AND, OR, NAND, NOR and other gates. It also discusses how to simplify logic expressions and convert between representations.
This document summarizes a presentation on causally regularized machine learning. It discusses how machine learning is increasingly impacting daily life through applications like personalized recommendations. However, current ML techniques rely heavily on correlation without consideration for causation, leading to problems like lack of explainability, instability, and sensitivity to sample biases. The presentation proposes addressing these issues by bringing concepts from causal inference into machine learning, which could lead to models that are more explainable, stable, and robust. It outlines several causal inference methods like matching, propensity score methods, and direct confounder balancing that could help bridge the gap between causality and machine learning.
Faster, More Effective Flowgraph-based Malware ClassificationSilvio Cesare
Silvio Cesare is a PhD candidate at Deakin University researching malware detection and automated vulnerability discovery. His current work extends his Masters research on fast automated unpacking and classification of malware. He presented this work last year at Ruxcon 2010. His system uses control flow graphs and q-grams of decompiled code as "birthmarks" to detect unknown malware samples that are suspiciously similar to known malware, reducing the need for signatures. He evaluated the system on 10,000 malware samples with only 10 false positives. The system provides improved effectiveness and efficiency over his previous work in 2010.
Correlation of dts by er. sanyam s. saini me (reg) 2012-14Sanyam Singh
This document discusses correlation of discrete-time signals. It defines correlation as a measure of similarity between two data sequences. Correlation techniques are widely used in signal processing applications like radar target detection. Cross correlation compares two separate signals, while auto correlation compares a signal to itself. Properties of correlation include detecting signals in noise and recognizing patterns. Examples of cross correlation, auto correlation and correlation of periodic sequences are provided. The main application discussed is using correlation for radar target detection.
Gene Extrapolation Models for Toxicogenomic DataNacho Caballero
1) The document describes using gene expression data from landmark genes to build predictive models for extrapolating the expression of regular genes not in the landmark set.
2) Three different model types are evaluated: linear regression, elastic net, and neural networks. Elastic net is shown to outperform linear regression in terms of signal-to-noise ratio for the extrapolated expressions.
3) The performance of the different extrapolation models is assessed on their ability to predict carcinogenicity classifiers, and the correlation between expressions from different microarray platforms is examined.
Multi Level Modelling&Weights Workshop Kiel09egebhardt72
This document provides an overview of multiple regression analysis and multilevel modelling, with examples using PISA data. It discusses key concepts like plausible values, student weights, and replicate weights in PISA. It also compares single-level regression to multilevel modelling, explaining how multilevel modelling accounts for clustering in data by decomposing variance within and between clusters. Weighting approaches for multilevel modelling using PISA data are also considered.
Soft computing is an emerging approach to computing that aims to mimic human reasoning and learning in uncertain and imprecise environments. It includes neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that are difficult to model mathematically, while exploiting tolerance for uncertainty like humans. Some applications of soft computing include consumer appliances, robotics, food preparation devices, and game playing. Soft computing is well-suited for problems not solvable by traditional computing due to its characteristics of tractability, low cost, and high machine intelligence.
EXPERT SYSTEMS AND SOLUTIONS
Project Center For Research in Power Electronics and Power Systems
IEEE 2010 , IEEE 2011 BASED PROJECTS FOR FINAL YEAR STUDENTS OF B.E
Email: expertsyssol@gmail.com,
Cell: +919952749533, +918608603634
www.researchprojects.info
OMR, CHENNAI
IEEE based Projects For
Final year students of B.E in
EEE, ECE, EIE,CSE
M.E (Power Systems)
M.E (Applied Electronics)
M.E (Power Electronics)
Ph.D Electrical and Electronics.
Training
Students can assemble their hardware in our Research labs. Experts will be guiding the projects.
EXPERT GUIDANCE IN POWER SYSTEMS POWER ELECTRONICS
We provide guidance and codes for the for the following power systems areas.
1. Deregulated Systems,
2. Wind power Generation and Grid connection
3. Unit commitment
4. Economic Dispatch using AI methods
5. Voltage stability
6. FLC Control
7. Transformer Fault Identifications
8. SCADA - Power system Automation
we provide guidance and codes for the for the following power Electronics areas.
1. Three phase inverter and converters
2. Buck Boost Converter
3. Matrix Converter
4. Inverter and converter topologies
5. Fuzzy based control of Electric Drives.
6. Optimal design of Electrical Machines
7. BLDC and SR motor Drives
This document discusses matrix factorization techniques for recommendation systems. It explains that user-item interaction data can be represented as a matrix and decomposed into two lower-rank matrices that capture latent features. One matrix represents users and the other represents items. The document outlines an alternating least squares algorithm to compute the decomposed matrices and discusses how the technique can be implemented in Apache Mahout and Myrrix for scalable recommendations.
The document appears to be a list of numbers paired with "30° CRS4" repeated many times. It includes the phrases "Enjoy the reading!" and names Christian Solinas as the President of the Autonomous Region of Sardinia.
Tutti a Iscol@ 2017, presentazione della Linea B2: Laboratori Extracurriculari Didattici Tecnologici.
L'iniziativa è promossa da: Regione Autonoma della Sardegna (Assessorato della Pubblica Istruzione);
Agenzia Regionale Sardegna Ricerche;
CRS4.
Ulteriori informazioni: http://iscola-lineab2.crs4.it/
Sardegna Ricerche
CRS4
Presentazione del progetto "Iscol@ Linea B": laboratori didattici innovativi finalizzati all’apertura al territorio delle Istituzioni scolastiche. Regione Autonoma della Sardegna, Agenzia Sardegna Ricerche, CRS4
This document provides an overview of statistical tests commonly used in neuroimaging such as t-tests, ANOVAs, and regression. It discusses the purposes of these tests and how they are applied. T-tests are used to compare means, for example to determine if the difference between two conditions is statistically significant. ANOVAs examine variances and can be used when comparing more than two groups. Regression allows describing and predicting the relationship between variables and is useful in the general linear model approach used in SPM. Key assumptions and calculations for each method are outlined.
This document outlines key concepts in linear models and estimation that will be covered in the STA721 Linear Models course, including:
1) Linear regression models decompose observed data into fixed and random components.
2) Maximum likelihood estimation finds parameter values that maximize the likelihood function.
3) Linear restrictions on the mean vector μ define a subspace and equivalent parameterizations represent the same subspace.
4) Inference should be independent of the parameterization or coordinate system used to represent μ.
The document discusses discrete and continuous random variables. It defines discrete random variables as variables that can take on countable values, like the number of heads from coin flips. Continuous random variables can take any value within a range, like height. The document explains how to calculate and interpret the mean, standard deviation, and probabilities of events for both types of random variables using examples like Apgar scores for babies and heights of young women.
- An artificial neural network consists of simple processing units called neurons connected in a network similar to a biological neural system. Each neuron receives inputs from other neurons and processes the inputs using an activation function to determine its output.
- A common type of artificial neural network has three layers of neurons: an input layer, a hidden layer, and an output layer. The input layer receives information from the environment, the hidden layer transforms the inputs, and the output layer provides the network's predictions or decisions.
- Each neuron calculates a weighted sum of its inputs and passes the result through an activation function to determine its output. During training, the weights are adjusted to minimize error between the network's predictions and correct outputs.
This document discusses probability distributions for random variables. It introduces discrete distributions like the binomial and Poisson distributions which are used for counting experiments. It also introduces continuous distributions like the normal distribution which are defined over continuous ranges of values. Key concepts covered include probability density functions, cumulative distribution functions, and how to relate random variables with specific parameters to standard distributions. Examples are provided to illustrate concepts like modeling the number of plant stems in a sampling area with a Poisson distribution.
Stephen Friend Norwegian Academy of Science and Letters 2011-11-02Sage Base
The document discusses using data intensive science to build better models of disease. It argues that the current pharmaceutical model is broken because it lacks sufficient understanding of disease biology. Most drug candidates fail because existing disease models oversimplify complex conditions. The author proposes using large datasets and computational modeling to map molecular pathways and construct causal models of diseases. This could provide a more mechanistic understanding of diseases and their heterogeneity to identify true drivers and better predict treatment responses. Pilots are needed to determine if this approach can modify diseases rather than just treat symptoms by moving beyond lists of altered genes and proteins to causal networks.
The document discusses measures of dispersion such as variance, standard deviation, and the coefficient of variation. It defines variance as the average squared deviation from the mean and standard deviation as the positive square root of the variance. The coefficient of variation measures relative dispersion by dividing the standard deviation by the mean. It is unit-free and allows for comparison across distributions. The document also covers Chebyshev's inequality and how it relates to the proportion of data within a given number of standard deviations from the mean.
The document provides an overview of digital electronics basics and combinational logic. It discusses digital versus analog signals, how digital signals assume discrete voltage values, and noise margins in digital circuits. It then explains what combinational logic is, giving an example of a simple AND gate circuit to make an instant decision based on inputs. The document discusses representing logic using truth tables and Boolean algebra, and provides examples of logic expressions and circuits for AND, OR, NAND, NOR and other gates. It also discusses how to simplify logic expressions and convert between representations.
This document summarizes a presentation on causally regularized machine learning. It discusses how machine learning is increasingly impacting daily life through applications like personalized recommendations. However, current ML techniques rely heavily on correlation without consideration for causation, leading to problems like lack of explainability, instability, and sensitivity to sample biases. The presentation proposes addressing these issues by bringing concepts from causal inference into machine learning, which could lead to models that are more explainable, stable, and robust. It outlines several causal inference methods like matching, propensity score methods, and direct confounder balancing that could help bridge the gap between causality and machine learning.
Faster, More Effective Flowgraph-based Malware ClassificationSilvio Cesare
Silvio Cesare is a PhD candidate at Deakin University researching malware detection and automated vulnerability discovery. His current work extends his Masters research on fast automated unpacking and classification of malware. He presented this work last year at Ruxcon 2010. His system uses control flow graphs and q-grams of decompiled code as "birthmarks" to detect unknown malware samples that are suspiciously similar to known malware, reducing the need for signatures. He evaluated the system on 10,000 malware samples with only 10 false positives. The system provides improved effectiveness and efficiency over his previous work in 2010.
Correlation of dts by er. sanyam s. saini me (reg) 2012-14Sanyam Singh
This document discusses correlation of discrete-time signals. It defines correlation as a measure of similarity between two data sequences. Correlation techniques are widely used in signal processing applications like radar target detection. Cross correlation compares two separate signals, while auto correlation compares a signal to itself. Properties of correlation include detecting signals in noise and recognizing patterns. Examples of cross correlation, auto correlation and correlation of periodic sequences are provided. The main application discussed is using correlation for radar target detection.
Gene Extrapolation Models for Toxicogenomic DataNacho Caballero
1) The document describes using gene expression data from landmark genes to build predictive models for extrapolating the expression of regular genes not in the landmark set.
2) Three different model types are evaluated: linear regression, elastic net, and neural networks. Elastic net is shown to outperform linear regression in terms of signal-to-noise ratio for the extrapolated expressions.
3) The performance of the different extrapolation models is assessed on their ability to predict carcinogenicity classifiers, and the correlation between expressions from different microarray platforms is examined.
Multi Level Modelling&Weights Workshop Kiel09egebhardt72
This document provides an overview of multiple regression analysis and multilevel modelling, with examples using PISA data. It discusses key concepts like plausible values, student weights, and replicate weights in PISA. It also compares single-level regression to multilevel modelling, explaining how multilevel modelling accounts for clustering in data by decomposing variance within and between clusters. Weighting approaches for multilevel modelling using PISA data are also considered.
Soft computing is an emerging approach to computing that aims to mimic human reasoning and learning in uncertain and imprecise environments. It includes neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that are difficult to model mathematically, while exploiting tolerance for uncertainty like humans. Some applications of soft computing include consumer appliances, robotics, food preparation devices, and game playing. Soft computing is well-suited for problems not solvable by traditional computing due to its characteristics of tractability, low cost, and high machine intelligence.
EXPERT SYSTEMS AND SOLUTIONS
Project Center For Research in Power Electronics and Power Systems
IEEE 2010 , IEEE 2011 BASED PROJECTS FOR FINAL YEAR STUDENTS OF B.E
Email: expertsyssol@gmail.com,
Cell: +919952749533, +918608603634
www.researchprojects.info
OMR, CHENNAI
IEEE based Projects For
Final year students of B.E in
EEE, ECE, EIE,CSE
M.E (Power Systems)
M.E (Applied Electronics)
M.E (Power Electronics)
Ph.D Electrical and Electronics.
Training
Students can assemble their hardware in our Research labs. Experts will be guiding the projects.
EXPERT GUIDANCE IN POWER SYSTEMS POWER ELECTRONICS
We provide guidance and codes for the for the following power systems areas.
1. Deregulated Systems,
2. Wind power Generation and Grid connection
3. Unit commitment
4. Economic Dispatch using AI methods
5. Voltage stability
6. FLC Control
7. Transformer Fault Identifications
8. SCADA - Power system Automation
we provide guidance and codes for the for the following power Electronics areas.
1. Three phase inverter and converters
2. Buck Boost Converter
3. Matrix Converter
4. Inverter and converter topologies
5. Fuzzy based control of Electric Drives.
6. Optimal design of Electrical Machines
7. BLDC and SR motor Drives
This document discusses matrix factorization techniques for recommendation systems. It explains that user-item interaction data can be represented as a matrix and decomposed into two lower-rank matrices that capture latent features. One matrix represents users and the other represents items. The document outlines an alternating least squares algorithm to compute the decomposed matrices and discusses how the technique can be implemented in Apache Mahout and Myrrix for scalable recommendations.
The document appears to be a list of numbers paired with "30° CRS4" repeated many times. It includes the phrases "Enjoy the reading!" and names Christian Solinas as the President of the Autonomous Region of Sardinia.
Tutti a Iscol@ 2017, presentazione della Linea B2: Laboratori Extracurriculari Didattici Tecnologici.
L'iniziativa è promossa da: Regione Autonoma della Sardegna (Assessorato della Pubblica Istruzione);
Agenzia Regionale Sardegna Ricerche;
CRS4.
Ulteriori informazioni: http://iscola-lineab2.crs4.it/
Sardegna Ricerche
CRS4
Presentazione del progetto "Iscol@ Linea B": laboratori didattici innovativi finalizzati all’apertura al territorio delle Istituzioni scolastiche. Regione Autonoma della Sardegna, Agenzia Sardegna Ricerche, CRS4
I progressi tecnologici raggiunti nel campo delle strategie di sequenziamento degli acidi nucleici ("Next Generation Sequencing", NGS) permettono oramai di ottenere con facilità le informazioni contenute all’interno dell’intero genoma umano. Ma solo una piccola percentuale (stimata a 1,6%) del genoma umano viene tradotto nelle proteine che fanno funzionare il corpo umano. Il sequenziamento esomico ("Whole exome sequencing") si concentra proprio sulle parti del genoma che codificano le proteine ("i geni") perché la ricerca di varianti in tali regioni permette di trovare le modificazioni funzionali delle proteine che sono associate a malattie. Dovendo sequenziare solo circa 1/60 dell’intero genoma si ha la possibilità di avere una migliore accuratezza e di ridurre tempi e costi del sequenziamento. Per questo motivo il sequenziamento esomico è diventato uno dei metodi di diagnosi genetica più utilizzato dai medici (sopratutto nel caso in cui non ci siano ipotesi sui geni coinvolti nella malattia).
1) The document presents a method for spatial velocity analysis of near-surface seismic data using a global simultaneous multi-parameter optimization approach.
2) It compares different implementations of spatial velocity analysis, including using common reflection surface (CRS) operators with 1x3, 3x1, and 1x2 parameter searches, and finds that using a 1x2 parameter diffraction operator for the global search followed by local 1x3 reflection optimization provides accurate results with less computational cost than other methods.
3) The method is demonstrated on an example of ultra-shallow seismic data from a field survey, and velocity models derived from the analysis are used for stacking, tomography, and migration, improving the quality and interpretability of
Valentina Spanu: esempi di applicazioni di GIS Partecipativo; gestione delle riserve idriche, energia solare, ristrutturazione di un edificio scolastico in Marocco; riduzione del rischio di disastro naturale in Georgia.
Alfonso Damiano (Università di Cagliari) Tecnologie ICT per le reti intelligenti di energia - evoluzione dei sistemi di distribuzione elettrica, anche con riferimento alla situazione della Regione; smart grid, micro grid e virtual power plant; stato della ricerca nel settore; potenzialità offerte dall'integrazione tra sistema elettrico e sistema della mobilità; reti intelligenti in una visione di smart city.
Workshop organizzato dal CRS4 nell'ambito della Collana di seminari per la valorizzazione e trasferimento dei risultati della Ricerca.
Viene illustrato il problema della raccolta efficiente e scalabile dei dati da potenziali sorgenti di Big Data. Inoltre verrà fatta una carrellata su alcuni tra i più popolari software utilizzabili in una pipeline di data streaming in realtime e/o batch analysis.
La caratterizzazione chimico-analitica del profilo metabolico di una serie di pazienti di sindrome fiobromialgica e di controlli, è stata integrata con un approccio modellistico per validare l'ipotesi che i lipidi sovra-rappresentati nei pazienti fossero in grado di interagire, attivandolo, con il recettore deputato alla modulazione dei meccanismi biologici del dolore, il PAFR, in maniera simile a quanto fatto dal ligando endogeno PAF. Al momento attuale non esistono test di laboratorio o marcatori biologici che possano confermare lo stato di malattia, per cui questo approccio rappresenta un primo passo verso la definizione di biomarcatori per la diagnosi e per il monitoraggio.
Innovazione e infrastrutture cloud per lo sviluppo di applicativi web e mobile orientato alla geomatica in contesto Smart City. Roberto Demontis (CRS4)
Questo corso riguarda gli aspetti teorici della propagazione delle onde sismiche e i principali legami tra caratteristiche di propagazione (velocità, attenuazione) e caratteristiche geometriche e fisico-meccaniche dei materiali del sottosuolo. Successivamente, saranno illustrati gli aspetti pratici dell'utilizzo dei metodi sismici a riflessione per la caratterizzazione dei suoli e delle rocce, delineando gli aspetti essenziali delle fasi di acquisizione, elaborazione ed interpretazione dei dati e le loro applicazioni in campo ingegneristico. Infine verranno dati alcuni cenni sul principio di funzionamento del GPR e sulle sue applicazioni pratiche.
Viene presentato e discusso (in inglese) in dettaglio l'utilizzo della piattaforma EIAGRID/SmartGEO in due casi studio significativi per le applicazioni geotecniche e ambientali. Al termine, l'utente interessato dovrebbe essere in grado di utilizzare in modo autonomo la piattaforma attraverso il portale SmartGEO.
Viene descritta la piattaforma EiAGRID/SmartGeo, un portale di calcolo e analisi dati per sismica a riflessione e acquisizioni GPR multioffset, che mette a disposizione dell'utente una serie di servizi di calcolo e di processing accessibili attraverso un'interfaccia Web basata su un'infrastruttura Grid. La piattaforma consente all'utente in campo, tramite un dispositivo client (laptop, PC, tablet, etc.), di usufruire di una serie di servizi computazionali che risiedono e girano su server remoti, secondo il paradigma SaaS (Software as a Service). Verranno illustrate le soluzioni modellistiche e tecnologiche adottate e alcuni risultati ottenuti su dati reali.
This document summarizes trends in mobile graphics presented by Marco Agus and Marcos Balsa at the Visual Computing conference at UniCa in June 2015. It discusses how mobile devices are using techniques like remote rendering, mixed mobile/remote rendering, image-based and model-based methods to render 3D graphics. It also explores hardware acceleration methods for mobile like parallel pipelines, real-time ray tracing, and multi-rate approaches to improve frame rates and rendering quality on mobile. The document focuses on visualization techniques for large meshes, complex lighting, and volume rendering on mobile devices.
This document provides an overview of mobile graphics and development. It discusses the evolution of mobile devices and graphics through movies and games. It outlines the increasing capabilities of mobile devices including processing power, memory, and connectivity. It covers operating systems, programming languages, CPU architectures including ARM and graphics processing unit architectures used in mobile devices. It provides details on graphics development for mobile systems.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
8. What are Gene networks?
ACTIVATOR 1 ACTIVATOR 2 REPRESSOR 1
A1 A2 R1
T1
TARGET 1
9. What are Gene networks?
Metabolic space
Metabolite 1 Metabolite 2
Protein space
Protein 2
Complex 3:4
Protein 4
Protein 1 Protein 3
Gene 2
Gene 3
Gene 1
Gene space Gene 4
15. Experimental strategies
‘Observational data’
Repeated measurements of a given tissue/cell type
without experimental intervention
ALLOWS ONLY FOR INFERRING UNDIRECTED NETWORKS
‘Perturbation data’
Creating targeted perturbations and measuring
systems dynamic responses (steady states or
time-series)
ALLOWS FOR INFERRING DIRECTED NETWORKS
16. Observational data
Gene C activity level
Gene B activity level
Gene A activity level Gene A activity level
20. Perturbation analysis
Measure gene-expression in unperturbed (WT) state
Perturb each gene and measure gene-expression responses
Perturb X2
Perturb X1
(over-express,
knock-down)
All perturbed
21. Perturbation analysis
Distinguish direct from indirect edges:
Algebraic relation between the deviation matrix X (perturbed levels –
wild type levels) and the network matrix (encoding the network A of
direct interactions)
−1
a11 0 0 0 a15 ∆x11 0 0 0 ∆x15
a21 a22 0 0 0 ∆x21 ∆x22 0 0 ∆x25
0 a a33 0 0 = ∆x31 ∆x32 ∆x33 0 ∆x35
32
0 0 a43 a44 a54 ∆x41 ∆x42 ∆x43 ∆x44 ∆x45
0 0 0 0 a55 0 0 0 0 ∆x55
22. Linear modeling approach
d∆xi n
= ∑ aij ∆x j + ∆u i
dt j
n n
0 = ∑ aij ∆x j + ∆ui ∑ a ∆xij j = − ∆ui
j j
JX = −U
J = {aij } Effect of gene j on rate of change of gene i
U = {u kk } Diagonal perturbation matrix
X = {xik } Change in gene i expression after perturbation k
J = − UX −1
R = U −1 J = − X −1
23. Further reading
Trends Genet. 2002 Aug;18(8):395-8
Scheinine, A., Mentzen, W., Pieroni E., Fotia, G., Maggio, F., Mancosu, G. and de la
Fuente, A. (2009) Inferring Gene Networks: Dream or nightmare? Part 2: Challenges 4
and 5. Annals of the New York Academy of Sciences 1158: 287301
25. Perturbation analysis
Weight estimation for edge i→j: change in the
mRNA level xi,j of gene j after knockout of gene i
Z-score:
xi , j = x⋅, j
Wi , j =
s⋅, j
26. Transitive reduction
The edge weight measures the total causal effect of
a gene on another gene: direct or mediated?
G1 G2 G3
X
The initial network can have many feed-forward
loops
Not essential for reachability
We want to remain with only “essential” edges
27. Further reading
Pinna, A., Soranzo, N. and de la Fuente, A. (2010) From Knockouts to Networks:
Establishing Direct Cause-Effect Relationships through Graph Analysis, PLoS ONE 5(10),
e12912 (DREAM4 Special Collection)
28. Figure 7 from: GeneNetWeaver: In silico benchmark generation and performance profiling of
network inference methods. Schaffter T, Marbach D, Floreano D. Bioinformatics (2011) 27 (16):
2263-2270.
29. Overview of the
presentation
• Introduction to Gene networks
• Gene network inference
• Differential networking in disease
30. Disease studies
?
Group 1 (healthy tissue, Group 2 (tumor tissue,
treated with medicine, not treated with medicine,
tumor stage X, etc.) tumor stage Y, etc.)
37. Lung cancer miRNAs?
Bhattacharjee,A. et al. (2001) Classification of human lung carcinomas by
mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc.
Natl Acad. Sci., 98, 13790-13795.
Family name Seed N. of target genestarget P-value for Notes on LungCancer3, 10000 permutations
N. of in TargetScan with at least one conserved site
genes also GSCA
in LungCancer3 dataset
miR-1293 GGGUGGU 73 23 0.0022
miR-28/28-3p ACUAGAU 77 19 0.0024 upregulated in serum copy number of lung cancer patients w.r.t. healthy [1]
miR-1244 AGUAGUU 147 53 0.0027
miR-1269 UGGACUG 77 21 0.0048
miR-1224/1224-5p UGAGGAC 88 34 0.0050
miR-578 UUCUUGU 229 65 0.0052
miR-1305 UUUCAAC 414 106 0.0060
miR-433 UCAUGAU 207 63 0.0061
highly specific marker for squamous cell lung carcinoma [2] and non-small cell
lung cancer [3]; located in a region amplified in lung cancer; upregulated in
miR-205 CCUUCAU 288 92 0.0063 lung cancer tissues w.r.t. noncancerous lung tissues [4]
miR-1237 CCUUCUG 177 42 0.0082
miR-520a-5p/525-5p UCCAGAG 296 79 0.0085
miR-582-3p AACUGGU 97 46 0.0086
miR-568 UGUAUAA 308 85 0.0087
miR-432 CUUGGAG 133 37 0.0090 member of miR-127 cluster, which is downregulated in tumors [5]
miR-524-3p/525-3p AAGGCGC 38 10 0.0091
miR-513c UCUCAAG 223 64 0.0094
miR-370 CCUGCUG 239 52 0.0096 downregulated after lung development [6]
[1] Chen, X., et al. - Cell Res. 18(10) pp. 997–1006 – 2008
[2] Lebanony, D., et al. - J. Clinical Oncology 27(12) – pp. 2030-2037 – 2009
[3] Markou, A., et al. – Clin. Chem. 54(10) – pp. 1696-1704 – 2008
[4] Yanaihara, N., et al. - Cancer Cell 9(3) – pp. 189-198 – 2006
[5] Saito, Y., et al. - Cancer Cell 9(6) – pp. 435-443 – 2006
[6] Williams, A. E., et al. - Dev. Dyn. 236(2) – pp. 572-580 – 2007