End-to-End ML-Driven Feedback Loops in DevOps Pipelines

When employed with DevOps processes, Quantum computing may transform CI / CD pipelines by providing better methods for computation. In this paper, the author investigates the potential of quantum computing to optimize CI/CD solutions regarding function performance, security measures, and resource utilization in software development settings. Simulation reports are then given to explain the outcome of different cases using real-time information on the increased speed and resource management performance. The research shows that quantum algorithms improve the efficiency of pipeline automation and testing methods, decreasing the time it takes to deploy and limiting mistakes. Drawbacks of adopting quantum computing in DevOps, including resource management and optimizing algorithms, are presented with ways to overcome some problems. This study offers insights into the future of the CI/CD pipeline, making it possible for organizations to develop effective software delivery systems. Published Date: 28 April 2021

It is well known that noise is inevitable in gene regulatory networks due to the low-copy numbers of molecules and local environmental fluctuations. The prediction of noise effects is a key issue in ensuring reliable transmission of information. Interlinked positive and negative feedback loops are essential signal transduction motifs in biological networks. Positive feedback loops are generally believed to induce a switch-like behavior, whereas negative feedback loops are thought to suppress noise effects. Here, by using the signal sensitivity (susceptibility) and noise amplification to quantify noise propagation, we analyze an abstract model of the Myc/E2F/MiR-17-92 network that is composed of a coupling between the E2F/Myc positive feedback loop and the E2F/Myc/miR-17-92 negative feedback loop. The role of the feedback loop on noise effects is found to depend on the dynamic properties of the system. When the system is in monostability or bistability with high protein concentrations, noise is consistently suppressed. However, the negative feedback loop reduces this suppression ability (or improves the noise propagation) and enhances signal sensitivity. In the case of excitability, bistability, or monostability, noise is enhanced at low protein concentrations. The negative feedback loop reduces this noise enhancement as well as the signal sensitivity. In all cases, the positive feedback loop acts contrary to the negative feedback loop. We also found that increasing the time scale of the protein module or decreasing the noise autocorrelation time can enhance noise suppression; however, the systems sensitivity remains unchanged. Taken together, our results suggest that the negative/positive feedback mechanisms in coupled feedback loop dynamically buffer noise effects rather than only suppressing or amplifying the noise.

DevOps focuses on collaboration between different teams in an organization to achieve rapid deployment of software and services to end-users by automating the software delivery infrastructure. According to Dyck et al. [1] DevOps is a software process that emphasizes collaboration within and between different teams involved in software development. According to a study from CA Technologies [5], 88% of 1425 organization executives stated that they have adopted DevOps, or are planning to adopt DevOps in the next five years. According to Puppet Labs' 2015 State of DevOps Report [2], organizations that have adopted DevOps experienced 60 times fewer failures and deploy 30 times more frequently than organizations that have not adopted DevOps. Despite the popularity, security aspects of DevOps remain a concern for organizations that want to adopt DevOps [5]. In organizations that use DevOps practices, developers can commit and deploy their software changes at a rapid rate using an automated pipeline. At such a rapid rate, if the security team operates in isolation without close collaboration with the development and operations teams, then the rapidly deployed software changes might not undergo the adequate security reviews, potentially leading to vulnerable software. Bringing security principles within the DevOps process can help the organization in achieving better quality of software by integrating security checks into the phases of development, testing, and deployment.

This paper studies the performance of classical and quantum machine learning models for sentiment analysis task. Here, popular machine learning algorithms viz support vector machine (SVM), gradient boosting (GB) and random forest (RF) are compared with variational quantum classifier (VQC) using two sets of parameterized circuits viz EfficientSU2 and RealAmplitudes. For experimenting with VQC, IBM Quantum Experience and IBM Qiskit were used while for classical machine learning models, scikit-learn was used. It was found that the performance of the VQC was slightly better than popular machine learning algorithms. For our experiments, we have used popular restaurant sentiment analysis dataset. The extracted features from this dataset and then after applying PCA reduced the feature set into 5 features. Quantum ML models were trained using 100 epochs and 150 epochs. Overall, four Quantum ML models were trained and three Classical ML models were trained. The performance of the trained models was evaluated using standard evaluation measures viz, Accuracy, Precision, Recall, F-Score etc. In all the cases EfficientSU2 based model with 100 Epochs performed better than all other models. Efficient SU2 model with 100 epochs produced an accuracy of 74.5% and an F-Score of 0.7605 which were highest across all the trained models.

The emergence of powerful LLMs has led to a paradigm shift in Natural Language Understanding and Natural Language Generation. But the properties that make LLMs so valuable for these tasks -- creativity, ability to produce fluent speech, and ability to quickly and effectively abstract information from large corpora -- also present new challenges to evaluating their outputs. The rush to market has led teams to fall back on quick, cost-effective automatic evaluations which offer value, but do not obviate the need for human judgments in model training and evaluation. We argue that when end users need to agree with the decisions made by ML models -- e.g. in toxicity detection or in extraction of main points for summarization -- models should be trained and tested on data that represent the preferences of those users. This paper primarily discusses the role of human feedback in labeling and judgment tasks for model training and evaluation. We first propose methods for disentangling noise from signal in labeling tasks. We show that noise in labeling disagreement can be minimized by adhering to proven methodological best practices, while signal in labeling disagreement can be maximized to play an integral role in model training and evaluation tasks. We illustrate best practices by providing a case study in which two guardrail classifiers are evaluated, using human judgments to align final model behavior to user preferences. We aim for this paper to provide researchers and professionals with guidelines to integrating human judgments into their ML and generative AI evaluation toolkit when working toward achieving accurate and unbiased features that align with users’ needs and expectations.

Cancer cell intrinsic TIM-3 induces glioblastoma progression

Porous Network Concrete: a bio-inspired building component to make concrete structures self-healing

This paper will explore the concept of resilience from its roots in ecology to the application of this ecological concept of resilience to social and community resilience in the context of climate change. In this context, resilience is seen as a property of complex adaptive communities rather than of individuals. This paper will explore how this ecological concept of resilience has been taken up both by climate adaptation research and by the Transition Town movement. This ecological concept of resilience is at odds with the individualism of both psychological and economic approaches to resilience in relation to climate change.

This study aims to explore the benefits and challenges of DevOps adoption in the rapidly evolving landscape of DevOps and Information Technology (IT) firms. DevOps is a software development approach that emphasises communication and collaboration between software developers and IT operations teams, aiming to streamline processes and enhance software delivery. Despite the growing popularity of DevOps, there are several challenges to its adoption, including stakeholder confusion, a lack of clear processes and guidelines, and a lack of empirical studies that discuss the challenges of DevOps. To establish a foundation of understanding and provide insights into the benefits, challenges, and guidelines for DevOps adoption, this study uses a two-fold approach, including a systematic literature review and conducts semi-structured interviews involving six organisations of various sizes. The interviews aimed to obtain evidence of DevOps adoption in practice and to detail real scenarios and explain the role of each category during DevOps adoption. The study provides insights into the challenges faced by software organisations in adopting the DevOps culture and the benefits of DevOps adoption. The study also proposes DevOps adoption guidelines based on the findings. The findings contribute to the existing literature on DevOps adoption and provide valuable recommendations for software organisations.

Bistable switches have important roles in cellular decision-making processes. Bistability can be the consequence of positive or double-negative feedback loops. Although necessary, such feedback is not sufficient for bistability, which also requires nonlinearity. Nonlinearity can be provided by synergy of multiple feedback loops or by an ultrasensitive response within a single feedback loop. However, these two possibilities are not mutually exclusive; a combination of them is also possible. Here we analyze a biochemical regulatory network that controls a crucial cell cycle transition in all eukaryotic cells and contains multiple redundant feedback loops and nonlinearity. We show in this realistic biological example that two redundant feedback loops have different effects on the position of one of the saddle-node bifurcations of the system, which determines where the system switches. This illustrates that even though the roles of positive and double-negative feedbacks have been regarded as equivalent, the difference in their architectures can lead to differences in their effects on the system. We speculate that this conclusion could be general for other bistable systems with redundant feedback loops.

In this chapter, we introduce several applications of machine learning and deep learning in different domains, including sensor and time-series, image and vision, text and natural language processing, relational data, energy, manufacturing, social media, health, security, and Internet-of-Things (IoT) applications. Until 2010, traditional ML models such as SVMs and decision trees have enjoyed successes in various tasks, including handwritten digit classification, face detection, and pattern recognition. Though traditional ML models are easy to interpret, the model’s inputs need to be well-designed, handcrafted features. On the other hand, deep learning models circumvent this problem and directly take the raw data as input and provide end-to-end learning capability. There is an unprecedented increase in machine learning and deep learning applications, especially with the emergence of fast mobile devices with access to cloud computing. While cloud computing provides the necessary computational power to train deep learning models, trained models can be easily deployed in the cloud or on embedded devices at the edge of the cloud to carry out the inference.

Anomaly detection is increasingly important to handle the amount of sensor data in Edge and Fog environments, Smart Cities, as well as in Industry 4.0. To ensure good results, the utilized ML models need to be updated periodically to adapt to seasonal changes and concept drifts in the sensor data. Although the increasing resource availability at the edge can allow for in-situ execution of model training directly on the devices, it is still often offloaded to fog devices or the cloud. In this paper, we propose Local-Optimistic Scheduling (LOS), a method for executing periodic ML model training jobs in close proximity to the data sources, without overloading lightweight edge devices. Training jobs are offloaded to nearby neighbor nodes as necessary and the resource consumption is optimized to meet the training period while still ensuring enough resources for further training executions. This scheduling is accomplished in a decentralized, collaborative and opportunistic manner, without full knowledge of the infrastructure and workload. We evaluated our method in an edge computing testbed on real-world datasets. The experimental results show that LOS places the training executions close to the input sensor streams, decreases the deviation between training time and training period by up to 40% and increases the amount of successfully scheduled training jobs compared to an in-situ execution.

Mathematical Modeling and Validation of Glucose Compensation of the Neurospora Circadian Clock

Feedback loops and types of adaptation in the modelling of land-use decisions in an agent-based simulation

This paper offers a case study of a large UK local authority adult care department as it introduces a new practice model. The change programme is examined through a lens of complexity theory. Complexity offers a way of understanding constantly changing systems and uses concepts such as ‘feedback loops’ and recognition of ‘emergent’ behaviours to offer a means to anticipate patterns of change in otherwise unpredictable systems. In this study, complexity is found to offer a helpful understanding of behaviour across the complex adaptive system of a large organisation but it falls short at recognising the cause of behaviours that can be obstructive. The author suggests the use of psychoanalytic theory to acknowledge the emotion that might be the driver for resistance to change and notices how effectively the two theories applied together can bring rich understanding as well as strategic oversight to a complex social care system. This study provides a useful, theoretically informed insight into change management and leadership within a social work agency that is moving away from transactional practice and promoting creative, autonomous, relationship-based practice. The study advances understanding of how complexity and psychoanalytic theory can be used in tangent to support organisational change within social care.