With the advent of every improving information technologies, science and engineering is being being evermore guided by data-driven models and large-scale computations. In this setting, one often is forced to work with models for which the nonlinearities are not derived from first principles and quantitative values for parameters are not known.
With this in mind, I will describe an alternative approach formulated in the language of combinatorics and algebraic topology that is inherently multiscale, amenable to mathematically rigorous results based on discrete descriptions of dynamics, computable, and capable of recovering robust dynamic structures.
To keep the talk grounded, I will discuss the ideas in the context of modeling of gene regulatory networks.
Slides from a project the 2018 Brains, Minds, & Machines summer school, on understanding adversarial examples in deep convolutional neural networks, using attention maps.
A recent direction in Business Process Management studied methodologies to control the execution of Business Processes under several sources of uncertainty in order to always get to the end by satisfying all constraints. Current approaches encode business processes into temporal constraint networks or timed game automata in order to exploit their related strategy synthesis algorithms. However, the proposed encodings can only synthesize single-strategies and fail to handle loops. To overcome these limits I will discuss a recent approach based on supervisory control. The approach considers structured business processes with resources, parallel and mutually exclusive branches, loops, and uncertainty. I will discuss an encoding into finite state automata and prove that their concurrent behavior models exactly all possible executions of the process. After that, I will introduce tentative commitment constraints as a new class of constraints restricting the executions of a process. Finally, I will discuss a tree decomposition of the process that plays a central role in modular supervisory control.
In his ignite talk „The Digital Transformation of Education: A Hyper-Disruptive Era through Blockchain and Generative AI,“ Dr. Alexander Pfeiffer delves into the intricate challenges and potential benefits associated with integrating blockchain technologies and generative AI into the educational landscape. He scrutinizes consensus algorithms and explores sustainable methods of operating blockchain systems, while also examining how smart contracts and transactions can be tailored to meet the specific needs of the educational sector. Alexander underscores the importance of establishing secure digital identities and ensuring robust data protection, while simultaneously casting a critical eye on potential risks and vulnerabilities. The topic of digital identities, facilitated through tokenization, forms a bridge between storing data using blockchain-based databases and the increasingly urgent need for content verification of AI-generated material.
Alexander explores the profound alterations occurring in teaching methodologies, assignment creation, and evaluation processes, shedding light on the hyper-disruptive impact these changes are having on both research and practical applications in education. The production of textual content by educators and students is analyzed with a focus on ensuring clear traceability of content sources and editors, and its proper citation, a critical aspect in the responsible use of AI. In addition to generative text and graphics, AI plays a crucial role in future learning and assignment practices, particularly through adaptive game-based learning and assessment. Alexander will provide a brief glimpse into his game „Gallery-Defender,“ a prototype demonstrating how AI and blockchain can be effectively implemented in serious gaming scenarios.
Furthermore, he emphasizes the imperative for ongoing education and professional development for educational personnel, advocating for a proactive stance in addressing the (legal) challenges associated with AI-generated images and text. This ignite talk aims to provide a balanced and critically reflective perspective on hyper-disruptive technologies, setting the stage for further discourse and exploration in the subsequent discussion.
The simulation of melee combat is central to many contemporary and traditional strategic games and simulations. In order to elevate this element of play from mere exercises of stats-comparison and dice rolling to a meaningful experience of play, strategy games rely on a rich plethora of cultural motives as deciding factors of their mechanic design. On the example of Samurai-themed skirmishing games, my talk elaborates on the impact that (popular) culture and other inspirations have on gaming experiences. It provides concrete examples from Japanese history, its traditional cinema, and postmodern Western reflections of Japanese cultural practices. Based on these insights, it compares four tabletop strategy games, muses on which phenomena they have adapted in their mechanics, and asks why or why not they may succeed in capturing a cultural essence via their rules.
Ultimately, this comparative approach shall serve to decipher the interplay of dice mechanics and aesthetic properties as the longing for a dramatic ideal in tabletop gaming and encourage participants to reflect on the idea in a subsequent, shared gaming experience.
How does a development team expand on an already existing game?
We will look at the two community driven and committee led expansions to the abandoned Tabletop game 'GuildBall' and explore the stages of development that the game went through. The art and lore driven approach employed will show us how rough sketches and concept ideas become a fully fledged ruleset and ultimately miniatures that can be put on the table. We will also explore pitfalls in rules design like over complicating abilities, the lack of streamlining across the game or simply creating expansions who break the game instead of the mold.
Exploring the development and production pipelines for miniatures in the tabletop wargaming industry. Including a look at the career route taken by the speaker, a case study on developing anatomical archetypes for consistent design outcomes, and a brief look at the various production methods available to the industry.
In recent years, we have experienced an exponential growth in the amount of data generated by IoT devices. Data have to be processed strict low latency constraints, that cannot be addressed by conventional computing paradigm and architectures. On top of this, if we consider that we recently hit the limit codified by the Moore’s law, satisfying low-latency requirements of modern applications will become even more challenging in the future. In this talk, we discuss challenges and possibilities of heterogeneous distributed systems in the Post-Moore era.
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.
In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.
Slides from a project the 2018 Brains, Minds, & Machines summer school, on understanding adversarial examples in deep convolutional neural networks, using attention maps.
A recent direction in Business Process Management studied methodologies to control the execution of Business Processes under several sources of uncertainty in order to always get to the end by satisfying all constraints. Current approaches encode business processes into temporal constraint networks or timed game automata in order to exploit their related strategy synthesis algorithms. However, the proposed encodings can only synthesize single-strategies and fail to handle loops. To overcome these limits I will discuss a recent approach based on supervisory control. The approach considers structured business processes with resources, parallel and mutually exclusive branches, loops, and uncertainty. I will discuss an encoding into finite state automata and prove that their concurrent behavior models exactly all possible executions of the process. After that, I will introduce tentative commitment constraints as a new class of constraints restricting the executions of a process. Finally, I will discuss a tree decomposition of the process that plays a central role in modular supervisory control.
In his ignite talk „The Digital Transformation of Education: A Hyper-Disruptive Era through Blockchain and Generative AI,“ Dr. Alexander Pfeiffer delves into the intricate challenges and potential benefits associated with integrating blockchain technologies and generative AI into the educational landscape. He scrutinizes consensus algorithms and explores sustainable methods of operating blockchain systems, while also examining how smart contracts and transactions can be tailored to meet the specific needs of the educational sector. Alexander underscores the importance of establishing secure digital identities and ensuring robust data protection, while simultaneously casting a critical eye on potential risks and vulnerabilities. The topic of digital identities, facilitated through tokenization, forms a bridge between storing data using blockchain-based databases and the increasingly urgent need for content verification of AI-generated material.
Alexander explores the profound alterations occurring in teaching methodologies, assignment creation, and evaluation processes, shedding light on the hyper-disruptive impact these changes are having on both research and practical applications in education. The production of textual content by educators and students is analyzed with a focus on ensuring clear traceability of content sources and editors, and its proper citation, a critical aspect in the responsible use of AI. In addition to generative text and graphics, AI plays a crucial role in future learning and assignment practices, particularly through adaptive game-based learning and assessment. Alexander will provide a brief glimpse into his game „Gallery-Defender,“ a prototype demonstrating how AI and blockchain can be effectively implemented in serious gaming scenarios.
Furthermore, he emphasizes the imperative for ongoing education and professional development for educational personnel, advocating for a proactive stance in addressing the (legal) challenges associated with AI-generated images and text. This ignite talk aims to provide a balanced and critically reflective perspective on hyper-disruptive technologies, setting the stage for further discourse and exploration in the subsequent discussion.
The simulation of melee combat is central to many contemporary and traditional strategic games and simulations. In order to elevate this element of play from mere exercises of stats-comparison and dice rolling to a meaningful experience of play, strategy games rely on a rich plethora of cultural motives as deciding factors of their mechanic design. On the example of Samurai-themed skirmishing games, my talk elaborates on the impact that (popular) culture and other inspirations have on gaming experiences. It provides concrete examples from Japanese history, its traditional cinema, and postmodern Western reflections of Japanese cultural practices. Based on these insights, it compares four tabletop strategy games, muses on which phenomena they have adapted in their mechanics, and asks why or why not they may succeed in capturing a cultural essence via their rules.
Ultimately, this comparative approach shall serve to decipher the interplay of dice mechanics and aesthetic properties as the longing for a dramatic ideal in tabletop gaming and encourage participants to reflect on the idea in a subsequent, shared gaming experience.
How does a development team expand on an already existing game?
We will look at the two community driven and committee led expansions to the abandoned Tabletop game 'GuildBall' and explore the stages of development that the game went through. The art and lore driven approach employed will show us how rough sketches and concept ideas become a fully fledged ruleset and ultimately miniatures that can be put on the table. We will also explore pitfalls in rules design like over complicating abilities, the lack of streamlining across the game or simply creating expansions who break the game instead of the mold.
Exploring the development and production pipelines for miniatures in the tabletop wargaming industry. Including a look at the career route taken by the speaker, a case study on developing anatomical archetypes for consistent design outcomes, and a brief look at the various production methods available to the industry.
In recent years, we have experienced an exponential growth in the amount of data generated by IoT devices. Data have to be processed strict low latency constraints, that cannot be addressed by conventional computing paradigm and architectures. On top of this, if we consider that we recently hit the limit codified by the Moore’s law, satisfying low-latency requirements of modern applications will become even more challenging in the future. In this talk, we discuss challenges and possibilities of heterogeneous distributed systems in the Post-Moore era.
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.
In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.
Function-as-a-Service (FaaS) is the latest paradigm of cloud computing in which developers deploy their codes as serverless functions, while the entire underlying platform and infrastructure is completely managed by cloud providers. Each cloud provider offers a huge set of cloud services and many libraries to simplify development and deployment, but only inside their clouds, often in a single cloud region. With such „help“ of cloud providers, users are locked to use resources and services of the selected cloud provider, which are often limited. Moreover, such heterogeneous and distributed environment of multiple cloud regions and providers challenge scientists to engineer cloud applications, often in a form of serverless workflows. In this talk, I will present our design principle „code once, run everywhere, with everything“. In particular, I will present challenges and our approaches and techniques how to program, model, orchestrate, and run distributed serverless workflow applications in federated FaaS.
As the network softwarization trend started by SDN and NFV keeps evolving, the hardware/software continuum becomes more relevant than ever, offering new offloading/acceleration opportunities at node and network-wide scales. This talk will review evolving transformations behind network softwarization with a special focus on network refactoring and offloading trends leading to “fluid networks planes”, characterized by multiple candidate options for the specific HW/SW embodiment and the location of chained network functions, from the edge to core, from one administrative provider to another, from programmable silicon to portable lightweight virtualized containers. The talk will overview concrete examples from the literature with a special focus on the role of Machine Learning to assist key (automated) decision-making steps. Lastly, the talk will conclude with a glimpse on ongoing ML work applied to Youtube video QoE prediction in live 5G networks.
The dynamics of networks enables the function of a variety of systems we rely on every day, from gene regulation and metabolism in the cell to the distribution of electric power and communication of information. Understanding, steering and predicting the function of interacting nonlinear dynamical systems, in particular if they are externally driven out of equilibrium, relies on obtaining and evaluating suitable models, posing at least two major challenges. First, how can we extract key structural system features of networks if only time series data provide information about the dynamics of (some) units? Second, how can we characterize nonlinear responses of nonlinear multi-dimensional systems externally driven by fluctuations, and consequently, predict tipping points at which normal operational states may be lost? Here we report recent progress on nonlinear response theory extended to predict tipping points and on model-free inference of network structural features from observed dynamics.
When it comes to integrating digital technologies into the classroom in higher education, many teachers face similar challenges. Nevertheless, it is difficult for teachers to share experiences because it is usually not possible to transfer successful teaching scenarios directly from one area to another, as subject-specific characteristics make it difficult to reuse them. To address this problem, instructional scenarios can be described as patterns that have been used previously in educational contexts. Patterns can capture proven teaching strategies and describe instructional scenarios in a consistent structure that can be reused. Because priorities for content, methods, and tools are different in each domain, a consensus-tested taxonomy was first developed with the goal of modeling a domain-independent database to collect digital instructional practices. In addition, this presentation will present preliminary insights into a data-driven approach to identifying effective instructional practices from interdisciplinary data as patterns. A web-based application will be developed for this that can both collect teaching/learning scenarios and individually extract scenarios from patterns for a learning platform.
The advent of fog and edge computing has prompted predictions that they will take over the traditional cloud for information processing and knowledge extraction in Internet of Things (IoT) systems. Notwithstanding the fact that fog and edge computing have undoubtedly large potential, these predictions are probably oversimplified and wrongly portray the relations between cloud, fog and edge computing.
Concretely, fog and edge computing have been introduced as an extension of the cloud services towards the data sources, thus forming the computing continuum. The computing continuum enables the creation of a new type of services, spanning across distributed infrastructures, supporting various IoT applications. These applications have a large spectrum of requirements, burdensome to meet with "distant'' cloud data centers. However, the introduction of the computing continuum raises multiple challenges for management, deployment and orchestration of complex distributed applications, such as: increased network heterogeneity, limited resource capacity of edge devices, fragmented storage management, high mobility of edge devices and limited support of native monolithic applications. These challenges primarily concern the complexity and the large diversity of the devices, managed by different entities (cloud providers, universities, private institutions), which range from single-board computers such as Raspberry Pis to powerful multi-processor servers.
Therefore, in this talk, we will discuss novel algorithms for low latency, scalable, and sustainable computing over heterogeneous resources for information processing and reasoning, thus enabling transparent integration of IoT applications. We will tackle the heterogeneity challenge of dynamically changing topologies of the computing infrastructure and present a novel concept for sustainable processing at scale.
East-west oriented photovoltaic power system is a new trend in orienting photovoltaic system. This lecture presents an evaluation of east–west oriented photovoltaic power system. A comparison between east–west oriented photovoltaic system and south oriented photovoltaic system in terms of cost of energy and technical requirement is conducted is presented in this lecture. In addition to that, the benefits of using east–west oriented photovoltaic system are discussed in this paper.
Randomized Signature or random feature selection are two instances of machine learning, where randomly chosen structures appear to be highly expressive. We analyze several aspects of the theory behind it, show that these structures have several theoretically attractive properties and introduce two classes of examples from finance (joint works with Christa Cuchiero, Lukas Gonon, Lyudmila Grigoryeva, Martin Larsson, and Juan-Pablo Ortega).
We live in a “digital” world, the separation between physical and virtual makes (almost) no sense anymore. Here, the Corona pandemic has also acted as an accelerator/magnifier demonstrating that the future of our digital society is here with all its possibilities, but also shortcomings.
In his talk, Hannes Werthner will briefly reflect on the history of computer science, and then discuss the need for an interdisciplinary response to these shortcomings. Such an answer is the Digital Humanism, which looks at this interplay of technology and humankind, it analyzes, and, most importantly, tries to influence the complex interplay of technology and humankind, for a better society and life. In the second part he will discuss this approach, and show what was achieved since its first workshop in 2019, and what lies ahead.
In the latest years, we have witnessed a growing number of media transmitted and stored on computers and mobile devices. For this reason, there is an actual need to employ smart compression algorithms to reduce the size of our media files. However, such techniques are often responsible for severe reduction of user perceived quality. In this talk we present several approaches we have developed to restore degraded images and videos to match their original quality, making use of Generative Adversarial Networks. The aim of the talk is to highlight the main features of our research work, including the advantages of our solution, the current challenges and the possible directions for future improvements.
Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.
Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to decide which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose an algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN's cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai's CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region.
Artificial neural networks have been adopted for a broad range of tasks in multimedia analysis and processing, such as visual and acoustic classification, extraction of multimedia descriptors or image and video coding. The trained neural networks for these applications contain a large number of parameters (weights), resulting in a considerable size. Thus, transferring them to a number of clients using them in applications (e.g., mobile phones, smart cameras) benefits from a compressed representation of neural networks.
MPEG Neural Network Coding and Representation is the first international standard for efficient compression of neural networks (NNs). The standard is designed as a toolbox of compression methods, which can be used to create coding pipelines. It can be either used as an independent coding framework (with its own bitstream format) or together with external neural network formats and frameworks. For providing the highest degree of flexibility, the network compression methods operate per parameter tensor in order to always ensure proper decoding, even if no structure information is provided. The standard contains compression-efficient quantization and an arithmetic coding scheme (DeepCABAC) as core encoding and decoding technologies, as well as neural network parameter pre-processing methods like sparsification, pruning, low-rank decomposition, unification, local scaling, and batch norm folding. NNR achieves a compression efficiency of more than 97% for transparent coding cases, i.e. without degrading classification quality, such as top-1 or top-5 accuracies.
This talk presents an overview of the context, technical features, and characteristics of the NN coding standard, and discusses ongoing topics such as incremental neural network representation.
The computing continuum extends the high-performance cloud data centers with energy-efficient and low-latency devices close to the data sources located at the edge of the network. However, the heterogeneity of the computing continuum raises multiple challenges related to application and data management. These include (i) how to efficiently provision compute and storage resources across multiple control domains across the computing continuum, (ii) how to decompose and schedule an application, and (iii) where to store an application source and the related data. To support these decisions, we explore in this thesis, novel approaches for (i) resource characterization and provisioning with detailed performance, mobility, and carbon footprint analysis, (ii) application and data decomposition with increased reliability, and (iii) optimization of application storage repositories. We validate our approaches based on a selection of use case applications with complementary resource requirements across the computing continuum over a real-life evaluation testbed.
Locating universities in regions where they should spur regional development has been recognized as an important regional political “instrument“ since the middle of the twentieth century. The expected impacts on regional development range from economic, political, demographic, infrastructural, cultural, educational, to social. Thereby various studies confirm that a) the university-region setting is unique and context-specific and b) universities will not spur regional development autonomously or inevitably.
The presentation will show the potentials but also challenges of universities being located in rural regions. This refers to balancing acts between internationality and regional focus, economic performance and societal engagement, curriculum development and demands of the regional/national labor market etc. Particular attention is paid to income students (origin, attraction) as well as the migration of graduates. Hereby the presentations builds on insights from former and ongoing projects with national and international case studies (Germany, The Netherlands). The presentation will conclude with insights on strategies how universities in rural siting regions dealt with this multiple challenges as well as how the regional environment influenced the role the universities resumed.
A major challenge for the next decade is to design virtual and augmented reality systems (VR at large) for real-world use cases such as healthcare, entertainment, e-education, and high-risk missions. This requires VR systems to operate at scale, in a personalized manner, remaining bandwidth-tolerant whilst meeting quality and latency criteria. One key challenge to reach this goal is to fully understand and anticipate user behaviours in these mixed reality settings.
This can be accomplished only by a fundamental revolution of the network and VR systems that have to put the interactive user at the heart of the system rather than at the end of the chain. With this goal in mind, in this talk, we describe our current researches on user-centric systems. First, we describe our view-port based streaming strategies for 360-degree video. Then, we present more in details our research on of users‘ behaviour analysis, when users interact with the 360-degree content. Specifically, we describe a set of metrics that allows us to identify key behaviours among users and quantify the level of similarity of these behaviours. Specifically, we present our clique-based clustering methodology, information theory and trajectory base in-depth analysis. Finally, we conclude with an overview of the extension of this work to navigation within volumetric video sequences.
In this talk, I will present the recent advancements on 5G for what concerns support for “the media vertical sector”, i.e., use cases involving the transmission of audiovisual content. I will begin by introducing the research that TNO has conducted on this topic in the past few years, starting with the H2020 TRIANGLE project, were we first adapted network orchestration to “communicate” with media orchestration components, such as a DASH Aware Network Element (DANE). Then, I will explain how we created media-specific 5G slices in the context of the H2020 5GINFIRE project, and what benefits media service providers can expect. I will further discuss about the advantages that edge computing offers to video production, based on our results from the H2020 FLAME project. Finally, I will give an overview of the standardization activities around this topic. I will conclude my talk with an outlook on future developments and offer some reflections on what researchers, telecom operators and service providers can expect.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Function-as-a-Service (FaaS) is the latest paradigm of cloud computing in which developers deploy their codes as serverless functions, while the entire underlying platform and infrastructure is completely managed by cloud providers. Each cloud provider offers a huge set of cloud services and many libraries to simplify development and deployment, but only inside their clouds, often in a single cloud region. With such „help“ of cloud providers, users are locked to use resources and services of the selected cloud provider, which are often limited. Moreover, such heterogeneous and distributed environment of multiple cloud regions and providers challenge scientists to engineer cloud applications, often in a form of serverless workflows. In this talk, I will present our design principle „code once, run everywhere, with everything“. In particular, I will present challenges and our approaches and techniques how to program, model, orchestrate, and run distributed serverless workflow applications in federated FaaS.
As the network softwarization trend started by SDN and NFV keeps evolving, the hardware/software continuum becomes more relevant than ever, offering new offloading/acceleration opportunities at node and network-wide scales. This talk will review evolving transformations behind network softwarization with a special focus on network refactoring and offloading trends leading to “fluid networks planes”, characterized by multiple candidate options for the specific HW/SW embodiment and the location of chained network functions, from the edge to core, from one administrative provider to another, from programmable silicon to portable lightweight virtualized containers. The talk will overview concrete examples from the literature with a special focus on the role of Machine Learning to assist key (automated) decision-making steps. Lastly, the talk will conclude with a glimpse on ongoing ML work applied to Youtube video QoE prediction in live 5G networks.
The dynamics of networks enables the function of a variety of systems we rely on every day, from gene regulation and metabolism in the cell to the distribution of electric power and communication of information. Understanding, steering and predicting the function of interacting nonlinear dynamical systems, in particular if they are externally driven out of equilibrium, relies on obtaining and evaluating suitable models, posing at least two major challenges. First, how can we extract key structural system features of networks if only time series data provide information about the dynamics of (some) units? Second, how can we characterize nonlinear responses of nonlinear multi-dimensional systems externally driven by fluctuations, and consequently, predict tipping points at which normal operational states may be lost? Here we report recent progress on nonlinear response theory extended to predict tipping points and on model-free inference of network structural features from observed dynamics.
When it comes to integrating digital technologies into the classroom in higher education, many teachers face similar challenges. Nevertheless, it is difficult for teachers to share experiences because it is usually not possible to transfer successful teaching scenarios directly from one area to another, as subject-specific characteristics make it difficult to reuse them. To address this problem, instructional scenarios can be described as patterns that have been used previously in educational contexts. Patterns can capture proven teaching strategies and describe instructional scenarios in a consistent structure that can be reused. Because priorities for content, methods, and tools are different in each domain, a consensus-tested taxonomy was first developed with the goal of modeling a domain-independent database to collect digital instructional practices. In addition, this presentation will present preliminary insights into a data-driven approach to identifying effective instructional practices from interdisciplinary data as patterns. A web-based application will be developed for this that can both collect teaching/learning scenarios and individually extract scenarios from patterns for a learning platform.
The advent of fog and edge computing has prompted predictions that they will take over the traditional cloud for information processing and knowledge extraction in Internet of Things (IoT) systems. Notwithstanding the fact that fog and edge computing have undoubtedly large potential, these predictions are probably oversimplified and wrongly portray the relations between cloud, fog and edge computing.
Concretely, fog and edge computing have been introduced as an extension of the cloud services towards the data sources, thus forming the computing continuum. The computing continuum enables the creation of a new type of services, spanning across distributed infrastructures, supporting various IoT applications. These applications have a large spectrum of requirements, burdensome to meet with "distant'' cloud data centers. However, the introduction of the computing continuum raises multiple challenges for management, deployment and orchestration of complex distributed applications, such as: increased network heterogeneity, limited resource capacity of edge devices, fragmented storage management, high mobility of edge devices and limited support of native monolithic applications. These challenges primarily concern the complexity and the large diversity of the devices, managed by different entities (cloud providers, universities, private institutions), which range from single-board computers such as Raspberry Pis to powerful multi-processor servers.
Therefore, in this talk, we will discuss novel algorithms for low latency, scalable, and sustainable computing over heterogeneous resources for information processing and reasoning, thus enabling transparent integration of IoT applications. We will tackle the heterogeneity challenge of dynamically changing topologies of the computing infrastructure and present a novel concept for sustainable processing at scale.
East-west oriented photovoltaic power system is a new trend in orienting photovoltaic system. This lecture presents an evaluation of east–west oriented photovoltaic power system. A comparison between east–west oriented photovoltaic system and south oriented photovoltaic system in terms of cost of energy and technical requirement is conducted is presented in this lecture. In addition to that, the benefits of using east–west oriented photovoltaic system are discussed in this paper.
Randomized Signature or random feature selection are two instances of machine learning, where randomly chosen structures appear to be highly expressive. We analyze several aspects of the theory behind it, show that these structures have several theoretically attractive properties and introduce two classes of examples from finance (joint works with Christa Cuchiero, Lukas Gonon, Lyudmila Grigoryeva, Martin Larsson, and Juan-Pablo Ortega).
We live in a “digital” world, the separation between physical and virtual makes (almost) no sense anymore. Here, the Corona pandemic has also acted as an accelerator/magnifier demonstrating that the future of our digital society is here with all its possibilities, but also shortcomings.
In his talk, Hannes Werthner will briefly reflect on the history of computer science, and then discuss the need for an interdisciplinary response to these shortcomings. Such an answer is the Digital Humanism, which looks at this interplay of technology and humankind, it analyzes, and, most importantly, tries to influence the complex interplay of technology and humankind, for a better society and life. In the second part he will discuss this approach, and show what was achieved since its first workshop in 2019, and what lies ahead.
In the latest years, we have witnessed a growing number of media transmitted and stored on computers and mobile devices. For this reason, there is an actual need to employ smart compression algorithms to reduce the size of our media files. However, such techniques are often responsible for severe reduction of user perceived quality. In this talk we present several approaches we have developed to restore degraded images and videos to match their original quality, making use of Generative Adversarial Networks. The aim of the talk is to highlight the main features of our research work, including the advantages of our solution, the current challenges and the possible directions for future improvements.
Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.
Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to decide which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose an algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN's cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai's CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region.
Artificial neural networks have been adopted for a broad range of tasks in multimedia analysis and processing, such as visual and acoustic classification, extraction of multimedia descriptors or image and video coding. The trained neural networks for these applications contain a large number of parameters (weights), resulting in a considerable size. Thus, transferring them to a number of clients using them in applications (e.g., mobile phones, smart cameras) benefits from a compressed representation of neural networks.
MPEG Neural Network Coding and Representation is the first international standard for efficient compression of neural networks (NNs). The standard is designed as a toolbox of compression methods, which can be used to create coding pipelines. It can be either used as an independent coding framework (with its own bitstream format) or together with external neural network formats and frameworks. For providing the highest degree of flexibility, the network compression methods operate per parameter tensor in order to always ensure proper decoding, even if no structure information is provided. The standard contains compression-efficient quantization and an arithmetic coding scheme (DeepCABAC) as core encoding and decoding technologies, as well as neural network parameter pre-processing methods like sparsification, pruning, low-rank decomposition, unification, local scaling, and batch norm folding. NNR achieves a compression efficiency of more than 97% for transparent coding cases, i.e. without degrading classification quality, such as top-1 or top-5 accuracies.
This talk presents an overview of the context, technical features, and characteristics of the NN coding standard, and discusses ongoing topics such as incremental neural network representation.
The computing continuum extends the high-performance cloud data centers with energy-efficient and low-latency devices close to the data sources located at the edge of the network. However, the heterogeneity of the computing continuum raises multiple challenges related to application and data management. These include (i) how to efficiently provision compute and storage resources across multiple control domains across the computing continuum, (ii) how to decompose and schedule an application, and (iii) where to store an application source and the related data. To support these decisions, we explore in this thesis, novel approaches for (i) resource characterization and provisioning with detailed performance, mobility, and carbon footprint analysis, (ii) application and data decomposition with increased reliability, and (iii) optimization of application storage repositories. We validate our approaches based on a selection of use case applications with complementary resource requirements across the computing continuum over a real-life evaluation testbed.
Locating universities in regions where they should spur regional development has been recognized as an important regional political “instrument“ since the middle of the twentieth century. The expected impacts on regional development range from economic, political, demographic, infrastructural, cultural, educational, to social. Thereby various studies confirm that a) the university-region setting is unique and context-specific and b) universities will not spur regional development autonomously or inevitably.
The presentation will show the potentials but also challenges of universities being located in rural regions. This refers to balancing acts between internationality and regional focus, economic performance and societal engagement, curriculum development and demands of the regional/national labor market etc. Particular attention is paid to income students (origin, attraction) as well as the migration of graduates. Hereby the presentations builds on insights from former and ongoing projects with national and international case studies (Germany, The Netherlands). The presentation will conclude with insights on strategies how universities in rural siting regions dealt with this multiple challenges as well as how the regional environment influenced the role the universities resumed.
A major challenge for the next decade is to design virtual and augmented reality systems (VR at large) for real-world use cases such as healthcare, entertainment, e-education, and high-risk missions. This requires VR systems to operate at scale, in a personalized manner, remaining bandwidth-tolerant whilst meeting quality and latency criteria. One key challenge to reach this goal is to fully understand and anticipate user behaviours in these mixed reality settings.
This can be accomplished only by a fundamental revolution of the network and VR systems that have to put the interactive user at the heart of the system rather than at the end of the chain. With this goal in mind, in this talk, we describe our current researches on user-centric systems. First, we describe our view-port based streaming strategies for 360-degree video. Then, we present more in details our research on of users‘ behaviour analysis, when users interact with the 360-degree content. Specifically, we describe a set of metrics that allows us to identify key behaviours among users and quantify the level of similarity of these behaviours. Specifically, we present our clique-based clustering methodology, information theory and trajectory base in-depth analysis. Finally, we conclude with an overview of the extension of this work to navigation within volumetric video sequences.
In this talk, I will present the recent advancements on 5G for what concerns support for “the media vertical sector”, i.e., use cases involving the transmission of audiovisual content. I will begin by introducing the research that TNO has conducted on this topic in the past few years, starting with the H2020 TRIANGLE project, were we first adapted network orchestration to “communicate” with media orchestration components, such as a DASH Aware Network Element (DANE). Then, I will explain how we created media-specific 5G slices in the context of the H2020 5GINFIRE project, and what benefits media service providers can expect. I will further discuss about the advantages that edge computing offers to video production, based on our results from the H2020 FLAME project. Finally, I will give an overview of the standardization activities around this topic. I will conclude my talk with an outlook on future developments and offer some reflections on what researchers, telecom operators and service providers can expect.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
4. Still Useful Today
Restricted 3 Body Problem
W.S. Koon, M. W. Lo, J. E. Marsden,
S. D. Ross, Heteroclinic connections
between periodic orbits and
resonance transitions in celestial
mechanics, Chaos, 2000
Motivation: “the design of trajectories
for space missions such as the
Genesis Discovery Mission.”
d2
x
dt2
2
dy
dt
= ⌦x
d2
y
dt2
+ 2
dx
dt
= ⌦y
⌦(x, y) =
x2
+ y2
2
+
1 µ
r1
+
µ2
r2
+
µ(1 µ)
2
Genesis Discovery Mission
Equations involve explicit
analytic expressions.
5. Worth Noting: We can
compute orbits that exhibit
fascinating complexity.
How does this help our
understanding?
u(x, y, z, t) is velocity field
p(x, y, z, t) is pressure field
T(x, y, z, t) is temperature field
Pr 1
✓
@u
@t
+ u · ru
◆
= rp + r2
u + RaTˆz
@T
@t
+ u · rT = r2
T
r · u = 0
Boussinesq Equations
Mark Paul, VA Tech
6. The 3-body Problem ≈1890
Jules Henri Poincare
1854-1912
Chaotic dynamics exists.
Understanding the solution of a
single initial value problem is not
sufficient.
S ⇢ Rn
is an invariant set if '(t, S) = S for all times t.
': R ⇥ Rn
! Rn
(t, x) 7! '(t, x)
Flow:
initial
condition
time
value of solution
at time t
Consider all solutions:
Map: f : Rn
! Rn
x 7! f(x) := '(⌧, x)
⌧ > 0 is a fixed time.
Examples: equilibria, periodic orbits, connecting orbits, strange
attractors
7. The equivalence relation:
Two maps f : X ! X and g: Y ! Y are topologically conjugate if
there exists a homeomorphism h: X ! Y such that h f = g h.
0 2 ⇤ is a bifurcation point if for any neighborhood U of 0 there
exists 1 2 U such that f 0 is not conjugate to f 1
The places of change:
f(x, r) = fr(x) = rx(1 x)<latexit 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Worth Noting:
Bifurcations are
occurring on all scales.
How does this help
our understanding?
8. Estimated number of malaria cases in 2010: between 219 and 550 million
Estimated number of deaths due to malaria in 2010: 600,000 to 1,240,000
Malaria may have killed half of all the people that ever lived. And more
people are now infected than at any point in history. There are up to
half a billion cases every year, and about 2 million deaths - half of those
are children in sub-Saharan Africa. J. Whitfield, Nature, 2002
Resistance is now common against all classes of antimalarial drugs
apart from artemisinins. … Malaria strains found in five countries in
the Greater Mekong Subregion are resistant to combination therapies
that include artemisinins, and may therefore be untreatable.
World Health Organization
Malaria is of great public health concern, and seems likely to be the
vector-borne disease most sensitive to long-term climate change.
World Health Organization
A Current Problem: Malaria
9. Malaria: P. falciparum
48 hour cycle
1-2 minutes
All genes (5409)
1.5$
0.0$
&1.5$
Standard$devia0ons$from$
mean$expression$(z&score)$
High
Low
0$ 10$ 20$ 30$ 40$
0me$in#vitro#(hours)$
50$ 60$
periodic$genes$(43)$
10 20 30 40 50 600
Task: Characterize the dynamics
with the goal of affecting the
dynamics with drugs.
A proposed network
A differential equation dx
dt = f(x, ) is proba-
bly a reasonable model for the dynamics, but
I do not have an analytic description of f or
estimates of the parameters .
Malaria is
• Sequenced
• Poorly annotated
10. RB-E2F pathwayCancer
Poorly quantified: biochemistry,
e.g. reaction rates, binding
energies, etc., not known
What is (are) appropriate model(s) for dynamics?
This is a dynamic process: timing
and sequencing of events is
essential
Yao, et. al., MSB, 2011
Deregulation of the RB–E2F pathway is implicated in
most, if not all, human cancers.
11. Biological
Model
Biological
Data/Phenotype
Hill functions: 1+4
parameters˙x = f(x, )
x 2 RN
, 2 RM
Physics/Math
Model
Yao et.al. follow
a traditional
approach
Yao, et. al., MSB, 2011
Remark: Typical model
considered by Yao et.al.
has ≈ 30 parameters.
Strategy: Choose 20,000 random parameter values and evaluate.
Quality of model = QM = # parameters with bistability
20,000
A worry: 230
= 1, 073, 741, 824
13. The Lac Operon Ozbudak et al. Nature 2004
Network Model
1
⌧y
˙y = ↵
RT
RT + R(x)
y
1
⌧x
˙x = y x
R(x) =
RT
1 +
⇣
x
x0
⌘n
ODE Model
Data
ODES are great modeling tools,
but should be handled with care.
parameter values
↵ =
84.4
1 + (G/8.1)1.2
+ 16.1
= . . .
16. Order Theory
and
Dynamics
;
{a} {b}
{ac} {ab} {bf}
{abc} {abd} {abe} {abf}
{abcd} {abde} {abcf} {abef}
{abcde} {abcdf} {abcef} {abdef}{abdeg}
{abcdeg} {abcdef} {abdefg} {abdefh}
{abcdefg} {abcdefh}
{abcdefh}
17. -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
A Definition from Continuous Dynamics
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Let ': R ⇥ X ! X be a flow. A set N ⇢ X is an attracting block if
'(t, cl(N)) ⇢ int(N) for all t > 0.<latexit 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Attracting blocks are what we can hope to see from time series data.
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18. Let (L, ^, _, 0, 1) denote a finite bounded distributive lattice.
Birkhoff’s Theorem
Birkhoff’s Theorem:
O(J_
(L)) ⇠= L
J_
(O(P)) ⇠= P
Let (P, <) denote a partially ordered set (poset).
c
b’b
a
P
The lattice of down sets of (P, <) is
O(P) := {U ⇢ P | if x 2 U and y < x then y 2 U} .
{a, b, b0
, c}
{a, b, b0
}
{a, b0
}{a, b}
{a}
;
O(P)
The poset of join irreducible elements of L is
J_
(L) := {x 2 L | if x = a _ b, then a = x or b = x}
J_
(O(P))
19. Let X be a compact metric space. phase space
TopologyDynamics
Use Birkhoff to define poset (P := J_
(A), <)
G(L) denoted atoms of L “smallest” elements of L
For each p 2 P define a Morse tile M(p) := cl(A pred(A))
Declare a bounded sublattice A ⇢ L to be a lattice of attracting blocks
Space of all
approximations
Reg(X) denotes the lattice of
regular closed subsets of X.
L is a finite bounded atomic
sublattice of Reg(X)
The chosen approximation
scheme
20. Example
Morse tiles M(p)
Let F0
(x) = f(x).
-4 40
Atoms of lattice: G(L) = {[n, n + 1] | n = 4, . . . , 3}
Phase space: X = [ 4, 4] ⇢ R
P
1 2
3
Birkhoff
How does this relate to a differential
equation dx
dt = f(x)?
-4 40
F
F
(bistability)
A
Lattice of attracting blocks: A = {[ 3, 1], [1, 3], [ 3, 1] [ [1, 3], [ 4, 4]}
Attracting blocks are regions of phase
space that are forward invariant with
time.
F
21. Biological
Model
Biological
Data/Phenotype
˙x = f(x, )
x 2 RN
, 2 RM
Physics/Math
Model
Traditional
approach
Finite
Computational Model
Part I
Part II Part III
Order theory
Algebraic topology
Dynamic Structures
Generated
from Regulatory
Networks
22. p1 p0
p2
p3
Vertices: States
Edges: Dynamics
Simple decomposition
of Dynamics:
Recurrent
Nonrecurrent
(gradient-like)
Linear time Algorithm!
Morse Graph
of state transition graph
State Transition Graph F : X !! X
An essential computational tool
23. p1 p0
p2
p3
P
O
S
E
T
Morse Graph
of F : X !! X
Join Irreducible
J_
(A)
Birkhoff’s Theorem implies that
the Morse graph and the lattice
of Attractors are equivalent.
What is observable? A X is an attractor if F(A) = A
p1 p0
p1, p0
p2, p1, p0
p3, p2, p1, p0
Lower Sets O(M)
;
Lattice of Attractors
of F : X !! X
_ = [
^ = maximal attractor in Com
putable
Observable
24. Biological Model
Assume xi decays. dxi
dt = ixi
dxi
dt = ixi + ⇤i(x)dxi
dt = ixi + ⇤i(xj)
How do I want to interpret this information?
What differential equation do I want to use?
Proposed model:
dx2
dt
x1
✓2,1
u2,1
l2,1
x1 represses the
production of x2.
1 2
x1 activates the
production of x2.
1 2
Parameters
1/node
3/edge
For x1 < ✓2,1 we ask about sign ( 2x2 + u2,1).
For x1 > ✓2,1 we ask about sign ( 2x2 + l2,1).
xi denotes amount of species i.
j,i(xi) =
(
uj,i if xi < ✓j,i
`j,i if xi > ✓j,i
Focus on sign of ixi + i,j(xj) ixi + +
i,j(xj)
25. 12
✓2,1
✓1,2
x1
x2
Phase space: X = (0, 1)2
If 1✓2,1 + 1,2(x2) > 0
If 1✓2,1 + 1,2(x2) < 0
Example (The Toggle Switch)
Parameter space is a subset of (0, 1)8
Fix z a regular parameter value.
z is a regular parameter value if
0 < i
0 < `i,j < ui,j,
0 < ✓i,k 6= ✓j,k, and
0 6= i✓j,i + ⇤i(x)
26. ✓2,1
✓1,2
x1
x2
Need to Construct State Transition Graph Fz : X !! X
Example (The Toggle Switch) 12
Fix z a regular parameter value.
Vertices
X corresponds to all rectangular
domains and co-dimension 1 faces
defined by thresholds ✓.
Faces pointing in map to their domain.
Domains map to their faces pointing
out.
Edges
If no outpointing faces domain maps
to itself.
27. 12The Toggle Switch
✓2,1
✓1,2
x1
x2
Assume: l1,2 < 1✓2,1 < u1,2
2✓1,2 < l2,1
Morse
Graph
FP{0,1}
Fix z a regular parameter value.
Constructing state transition
graph Fz : X !! X
Check signs of i✓j,i + i,j(xj)
30. Signal control of the Toggle Switch
1 2S
The rate of change of x1 is given by
1x1 + s · 1,2(x2)
signal
strength
choice of logic
We care about sign of
1✓2,1 + s · 1,2(x2)
(7)
FP(1,1)
1✓2,1 < sl1,2 < su1,2
2✓1,2 < l2,1 < u2,1
(8)
FP(1,0)
1✓2,1 < sl1,2 < su1,2
l2,1 < 2✓1,2 < u2,1
(9)
FP(1,0)
1✓2,1 < sl1,2 < su1,2
u2,1 < u2,1 < 2✓1,2
(4)
FP(0,1)
sl1,2 < 1✓2,1 < su1,2
2✓1,2 < l2,1 < u2,1
(5)
FP(0,1) FP(1,0)
sl1,2 < 1✓2,1 < su1,2
l2,1 < 2✓1,2 < u2,1
(6)
FP(1,0)
sl1,2 < 1✓2,1 < su1,2
l2,1 < u2,1 < 2✓1,2
(1)
FP(0,1)
sl1,2 < su1,2 < 1✓2,1
2✓1,2 < l2,1 < u2,1
(2)
FP(0,1)
sl1,2 < su1,2 < 1✓2,1
l2,1 < 2✓1,2 < u2,1
(3)
FP(0,0)
sl1,2 < su1,2 < 1✓2,1
u2,1 < u2,1 < 2✓1,2
DSGRN database
Increasingsignals
Use the product structure to count paths
1
4
7
2
5
8
3
6
9
Each graph gives rise to 6
possible monotone signal paths
1 ! 2 ! 3 1 ! 2 2 ! 3 21 3
Only one path 2 ! 5 ! 8 gives rise to hysteresis.
1
18
score:
31. Biological
Model
Biological
Data/Phenotype
˙x = f(x, )
x 2 RN
, 2 RM
Physics/Math
Model
Traditional
approach
Finite
Computational Model
Part I
Part II Part III
Order theory
Algebraic topology
Choosing Models
based on
Robustness of
Phenotype
32. What is the Phenotype?
Significance: Deregulation of the RB–
E2F pathway is implicated in most, if
not all, human cancers.
Phenomena: Rb-E2F is a
resettable bistable switch
Bistability: Two equilibria:
(A) E2F low = quiescence
(B) E2F high = proliferation
Resettable bistability:
Bistable state: B
When growth signals → 0
B → A
A
B
S
Hysteresis:
A
B
S
33. Revisiting Yao et. al.
DSGRN strategy
Construct all subnetworks with 3 nodes
satisfying the following properties:
Every node has an out edge.
There is at most one edge from one
node to another node.
Query product graphs over MD for resettable bistability and hysteresis.
FP(MD,RP,EE) Quiescence:= FP(*,*,*,0) Proliferation:= FP(*,*,*,m)
34. Top choices of Yao, et. al.
based on resettable
bistability
MD
RP
EE
21%
19%
Hysteresis
Resettable
Bistability
MD
RP
EE
17%
17%
MD
RP
EE
MD
RP
EE
8%
18%
MD
RP
EE
8%
16%
6%
13%
MD
RP
EE
4%
12%
DSGRN Results
37. Thank-you for your Attention
Homology + Database Software
chomp.rutgers.edu
Rutgers
S. Harker
MSU
T. Gedeon
B. Cummings
FAU
W. Kalies
VU Amsterdam
R. Vandervorst