Approximated Gene Expression Trajectories (AGETs) for Gene Regulatory Network Inference on Cell Tracks

The study of pattern formation has benefited from reverse-engineering gene regulatory network (GRN) structure from spatio-temporal quantitative gene expression data. Traditional approaches omit tissue morphogenesis, hence focusing on systems where the timescales of pattern formation and morphogenesis can be separated. In such systems, pattern forms as an emergent property of the underlying GRN. This is not the case in many animal patterning systems, where patterning and morphogenesis are simultaneous. To address pattern formation in these systems we need to adapt our methodologies to explicitly accommodate cell movements and tissue shape changes. In this work we present a novel framework to reverse-engineer GRNs underlying pattern formation in tissues experiencing morphogenetic changes and cell rearrangements. By combination of quantitative data from live and fixed embryos we approximate gene expression trajectories (AGETs) in single cells and use a subset to reverse-engineer candidate GRNs using a Markov Chain Monte Carlo approach. GRN fit is assessed by simulating on cell tracks (live-modelling) and comparing the output to quantitative data-sets. This framework outputs candidate GRNs that recapitulate pattern formation at the level of the tissue and the single cell. To our knowledge, this inference methodology is the first to integrate cell movements and gene expression data, making it possible to reverse-engineer GRNs patterning tissues undergoing morphogenetic changes.

Converting Motion Capture Into Editable Keyframe Animation: Fast, Optimal, and Generic Keyframe Selection

<p>Motion capture is attractive to visual effects studios because it offers a fast and automatic way to create animation directly from actors' movements. Despite extensive research efforts toward motion capture processing and motion editing, animations created using motion capture are notoriously difficult to edit. We investigate this problem and develop a technique to reverse engineer editable keyframe animation from motion capture. Our technique for converting motion capture into editable animation is to select keyframes from the motion capture that correspond to those an animator might have used to create the motion from scratch. As the first contribution presented by this thesis, we survey both traditional and contemporary animation practice to define the types of keyframes created by animators following conventional animation practices. As the second contribution, we develop a new keyframe selection algorithm that uses a generic objective function; using different implementations, we can define different criteria to which keyframes are selected. After presenting the algorithm, we return to the problem of converting motion capture into editable animation and design three implementations of the objective function that can be used together to select animator-like keyframes. Finally, as a minor contribution to conclude the thesis, we present a simple interpolation algorithm that can be used to construct a new animation from only the selected keyframes. In contrast to previous research in the topic of keyframe selection, our technique is novel in that we have designed it to provide selections of keyframes that are similar in structure to those used by animators following conventional practices. Consequently, both animators and motion editors can adjust the resulting animation in much the same way as their own, manually created, content. Furthermore, our technique offers an optimal guarantee paired with fast performance for practical editing situations, which has not yet been achieved in previous research. In conclusion, the contributions of this thesis advance the state of the art in the topic by introducing the first fast, optimal, and generic keyframe selection algorithm. Ultimately, our technique is not only well suited to the problem of recovering editable animation from motion capture, but can also be used to select keyframes for other purposes - such as compression or pattern identification - provided that an appropriate implementation of the objective function can be imagined and employed.</p>

Converting Motion Capture Into Editable Keyframe Animation: Fast, Optimal, and Generic Keyframe Selection

<p>Motion capture is attractive to visual effects studios because it offers a fast and automatic way to create animation directly from actors' movements. Despite extensive research efforts toward motion capture processing and motion editing, animations created using motion capture are notoriously difficult to edit. We investigate this problem and develop a technique to reverse engineer editable keyframe animation from motion capture. Our technique for converting motion capture into editable animation is to select keyframes from the motion capture that correspond to those an animator might have used to create the motion from scratch. As the first contribution presented by this thesis, we survey both traditional and contemporary animation practice to define the types of keyframes created by animators following conventional animation practices. As the second contribution, we develop a new keyframe selection algorithm that uses a generic objective function; using different implementations, we can define different criteria to which keyframes are selected. After presenting the algorithm, we return to the problem of converting motion capture into editable animation and design three implementations of the objective function that can be used together to select animator-like keyframes. Finally, as a minor contribution to conclude the thesis, we present a simple interpolation algorithm that can be used to construct a new animation from only the selected keyframes. In contrast to previous research in the topic of keyframe selection, our technique is novel in that we have designed it to provide selections of keyframes that are similar in structure to those used by animators following conventional practices. Consequently, both animators and motion editors can adjust the resulting animation in much the same way as their own, manually created, content. Furthermore, our technique offers an optimal guarantee paired with fast performance for practical editing situations, which has not yet been achieved in previous research. In conclusion, the contributions of this thesis advance the state of the art in the topic by introducing the first fast, optimal, and generic keyframe selection algorithm. Ultimately, our technique is not only well suited to the problem of recovering editable animation from motion capture, but can also be used to select keyframes for other purposes - such as compression or pattern identification - provided that an appropriate implementation of the objective function can be imagined and employed.</p>

Repurposing Oil and Gas Reservoirs for Blue Hydrogen Production

Hydrogen is critical to achieving the NetZero Target set by the UK government in 2050. There have been concerted efforts to produce more hydrogen from renewable sources (green hydrogen) to reduce the impact on the environment. The arguments have been that hydrogen produced from hydrocarbon sources contribute largely to CO 2 emission in the atmosphere causing global warming. While this is true, the reality however is that the increasing demand projections for hydrogen have not been met by green hydrogen. At present, nearly all industrial hydrogen are produced from hydrocarbon sources (Muradov 2017). CO 2emission is a major by-product of blue hydrogen production. However, there is a need to reverse engineer the hydrogen process from hydrocarbons, explore hydrogen production directly from the reservoir and retain the accompanying CO 2from being released into the surface. Using a depleted reservoir as feedstock, one method of doing this is by in-situ hydrogen production through thermal combustion of the hydrocarbon reservoirs.

HONEYPOT TECHNOLOGIES FOR MALWARE DETECTION AND ANALYSIS

In this paper, we offer a brief summary of latest developments in honeypot technologies, used for malware detection and analysis. This includes not only honeypot software, but also methodologies to analyze captured honeypot data. As such, our focus in this work is to keep track of current developments related to traffic analysis, especially honeypot technologies, as a means of data capture and interpretation of malicious traffic. Zero-day attacks are still very hard to predict, then handle, by any security platform. Means to successfully predict an attack is of paramount importance to the world of cybersecurity. Effective network security administration depends, to a great extent, on the understanding of existing and emerging threats propagated over the web. In order to protect information systems and its users, it is of crucial importance to collect accurate, concise, high-quality information about malicious activities, for security researchers to be able to reverse-engineer, then understand and stop a malicious actor.

Adaptive Manufacturing for Healthcare During the COVID-19 Emergency and Beyond

During the COVID-19 pandemic, global health services have faced unprecedented demands. Many key workers in health and social care have experienced crippling shortages of personal protective equipment, and clinical engineers in hospitals have been severely stretched due to insufficient supplies of medical devices and equipment. Many engineers who normally work in other sectors have been redeployed to address the crisis, and they have rapidly improvised solutions to some of the challenges that emerged, using a combination of low-tech and cutting-edge methods. Much publicity has been given to efforts to design new ventilator systems and the production of 3D-printed face shields, but many other devices and systems have been developed or explored. This paper presents a description of efforts to reverse engineer or redesign critical parts, specifically a manifold for an anaesthesia station, a leak port, plasticware for COVID-19 testing, and a syringe pump lock box. The insights obtained from these projects were used to develop a product lifecycle management system based on Aras Innovator, which could with further work be deployed to facilitate future rapid response manufacturing of bespoke hardware for healthcare. The lessons learned could inform plans to exploit distributed manufacturing to secure back-up supply chains for future emergency situations. If applied generally, the concept of distributed manufacturing could give rise to “21st century cottage industries” or “nanofactories,” where high-tech goods are produced locally in small batches.

Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)

Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony. Notably, the employment of OS leads to a rapid increase in the number of Android users. However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. Although practitioners conduct intrusion detection analyses, such as static analysis, there is an inadequate number of review articles discussing the research efforts on this type of analysis. Therefore, this study discusses the articles published from 2009 until 2019 and analyses the steps in the static analysis (reverse engineer, features, and classification) with taxonomy. Following that, the research issue in static analysis is also highlighted. Overall, this study serves as the guidance for novice security practitioners and expert researchers in the proposal of novel research to detect malware through static analysis.

Testing and Acceptance of Semi-Machined Turbine Rotor Forging Elements

Turbine wheels are critical elements and the integrity of their forgings is extremely important. The procurement of wheel forgings utilized oversized outside diameters in order to provide ample amount of material from which test material was cut out. Test specimens were produced in accordance with relevant ASTM standards. Test specimens were divided into three groups; tension, impact and hardness / metallography. Tension and impact tests at sub-zero, room and elevated temperatures were conducted in presence of owner’s inspectors at an independent certified laboratory. Once all specimens passed the tests, the semi-machined forgings were released to the machining facility. Machined forgings were then sent for over-speed tests at sub-zero and elevated temperatures. Over-speed testing under sub-zero temperature was the ultimate test of the forgings. Over-speed testing of the forgings under elevated temperatures served to minimize residual tensile stresses at the bore of the wheel and convert them to beneficial compressive stresses. The above described tests of turbine Elements forging are critical when the owner selects third party producers to reverse engineer and manufacture these elements. This paper details the selection of the tests, the execution of the owner’s acceptance testing program, especially the over-speed tests, and how this helps to ensure the high integrity of critical rotating elements for a mid-size heavy industrial frame gas turbine.

Disentangling a deep learned volume formula

We present a simple phenomenological formula which approximates the hyperbolic volume of a knot using only a single evaluation of its Jones polynomial at a root of unity. The average error is just 2.86% on the first 1.7 million knots, which represents a large improvement over previous formulas of this kind. To find the approximation formula, we use layer-wise relevance propagation to reverse engineer a black box neural network which achieves a similar average error for the same approximation task when trained on 10% of the total dataset. The particular roots of unity which appear in our analysis cannot be written as e2πi/(k+2) with integer k; therefore, the relevant Jones polynomial evaluations are not given by unknot-normalized expectation values of Wilson loop operators in conventional SU(2) Chern-Simons theory with level k. Instead, they correspond to an analytic continuation of such expectation values to fractional level. We briefly review the continuation procedure and comment on the presence of certain Lefschetz thimbles, to which our approximation formula is sensitive, in the analytically continued Chern-Simons integration cycle.

A Neural Model of RNA Splicing: Learning Motif Distances with Self-Attention and Toeplitz Max Pooling

Alternative RNA splicing is an important regulator of tissue development and specificity, and a relevant mechanism in cancer progression. There exists a strong motivation to disentangle the rules that govern RNA splicing, in part because this knowledge may one day yield new clinically relevant diagnostic tools and therapeutics. It is no easy to task to reverse engineer how the splicesome machinery choreographs the removal and addition of RNA elements following transcription. Here, we propose an interpretable neural network called the Toeplitz ATtention Architecture (TATA), which learns distance-dependent motif interactions through a novel Toeplitz max pool layer that captures the relative distance between interacting CNN filters. TATA is a completely transparent ``clear-box'' solution: every model parameter is human-interpretable. We validate TATA on simulated data, then apply it to real data to identify putative cis-regulatory elements that interact with primary RNA splice sites.