Understanding, assessing, comparing, and managing risks related to the energy transition from fossil fuel to renewables – for Norway and India

There is growing worry about the future supply of fossil-fuel-based energy and its environmental consequences. There is call for the globe to switch from fossil fuels to renewable energy sources. (Teles et al., 2015). On the other hand, the speed and scale of this shift remain uncertain and arguable. (Gribkova & Milshina, 2022). The energy transition is inherently risky. (Poudineh et al., 2019). The main objectives of the thesis are to gain improved knowledge of the risks related to the energy transition from fossil fuel to renewables for Norway and India, and contribute to improve the assessment and management of these risks. The energy industry is responsible for nearly three-quarters of the emissions that have already increased world average temperatures by 1.1 degrees Celsius since pre-industrial times, with evident effects on weather and climate extremes. The energy industry must be at the center of the climate change solution. (IEA, 2021b). The thesis performs risk analysis for both nations using Bayesian network, compares and demonstrates the variations in the study's outcomes, as well as the different risk management approaches that they may use. The Bayesian network events and consequences are interlinked, and the sequence of action may or may not be followed as demonstrated as it depends on the various factors and the probability of occurrences of scenarios involving these factors. Factors such as government policies encouraging renewable energy and energy efficiency, technology and innovation, people expectations, Covid-19 will all play a role in the sequence. The thesis further shows that socioeconomic factors influence the risks and the energy transition for both the countries. Risk comparison demonstrates that a same risk problem in two distinct situations (here, two separate nations) is not identical. The risk comparison underlines the importance of conducting a context assessment first in order to have a better understanding of risk. Risk management strategies are suggested in this thesis for the management of risks for Norway and India which contributes to improved risk management of the energy transition risks for Norway and India. Risk informed strategy is used in the thesis wherein risk treatment methods are suggested for the identified risk sources and initiating events. When one wants to choose between several solution alternatives for the energy transition problem then the author suggests that a multi-attribute analysis is a better approach for decision making because there are several factors influencing the decision-making process, including energy sources, energy demands, population, economy, geography, political goals and strategies, ethical factors, social factors, personal factors, infrastructure needs, citizen psychology, societal preference, speed of transition, and in general its magnitude. The author believes that the governments must try to strike a balance between the various attributes. These questions have no definitive solutions. The author of this thesis emphasizes that whether precautionary principle be given more or less weight is the choice of the decision maker. Companies should propose alternative uses of oil and gas utilities to successfully tackle the energy transition barrier and enhance the degree of risk acceptability and tolerance in the energy market. For productive operations, organizations should take effective precautions and adopt contemporary risk acceptability models such as ALARP. By performing risk analysis and comparing the risks these countries face in achieving the Paris Climate Agreement and the Sustainable Development Goals using various risk management strategies, this thesis contributes to a better understanding of the energy transition risks and improved risk assessment & risk management for Norway and India

Data-Efficient Contrastive Self-supervised Learning: Easy Examples Contribute the Most

Self-supervised learning (SSL) learns high-quality representations from large pools of unlabeled training data. As datasets grow larger, it becomes crucial to identify the examples that contribute the most to learning such representations. This enables efficient SSL by reducing the volume of data required for learning high-quality representations. Nevertheless, quantifying the value of examples for SSL has remained an open question. In this work, we address this for the first time, by proving that examples that contribute the most to contrastive SSL are those that have the most similar augmentations to other examples, in expectation. We provide rigorous guarantees for the generalization performance of SSL on such subsets. Empirically, we discover, perhaps surprisingly, the subsets that contribute the most to SSL are those that contribute the least to supervised learning. Through extensive experiments, we show that our subsets outperform random subsets by more than 3% on CIFAR100, CIFAR10, and STL10. Interestingly, we also find that we can safely exclude 20% of examples from CIFAR100 and 40% from STL10, without affecting downstream task performance.Comment: Accepted to ICML 202

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines, a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. Synaptic sampling machines perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate & fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based synaptic sampling machines outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware

Fundamental studies of structure and crystallinity of low and high molecular weight poly(3-hexylthiophene) P3HT by means of synchrotron X-ray diffraction

Conjugated polymers are the one of the most promising candidates for the active layer of low-cost Organic-field-effect transistors (OFETs). The charge carrier mobility of these conjugated polymers is the key material property, limiting the performance of the devices. The structural ordering along the whole thickness of film, especially near to interface region play an important role of controlling such electrical properties which ultimately gives high device performance. In the present work using complementary techniques I have studied structural properties of P3HT films as a function of molecular weight and film thickness. Detailed structural studies have been done to understand this polythiophene families internal structural ordering and consequently its correlation with charge carrier mobility. This work focussed on the dependence of the charge transport on morphology of the best known polythiophene group member i.e. regioregular poly(3-hexylthiophene). P3HT films (thin as well as bulk) with different molecular weights provided an ideal system for correlating morphological changes in conjugated polymers to resulting changes in charge transport seeing that the charge carrier mobility in OFETs was found to increase up to four orders-of-magnitude as the molecular weight (Mw) of P3HT is increased from 2500 g/mol to 30,000 g/mol. Grazing incidence X-ray scattering (GIXS) with combination of its complementary techniques like Atomic Force Microscopy (AFM) and Transmission Electron Microscopy (TEM) were used to measure changes in the crystallinity and crystal orientation associated with varying the annealing conditions, substrate surface treatment at a constant Mw. In particular, X-ray grazing-incidence diffraction (GID) used for depth-resolved structural analysis. We have noticed the several diffraction peaks which are associated with crystalline ordering within the films for low as well as HMW fractions (LMW, HMW) of P3HT besides the amorphous scattering from disordered part of the polymer sample. Both molecular weight fractions display a well pronounced periodicity normal to the surface due to stacking of main chains. The inter-planar distances for such stacked sheets were found to be 1.5 and 1.6 nm for low and high molecular weight fractions, respectively. These films are formally amorphous having the crystalline domains dispersed in amorphous matrix. Here, a textured and strong orientation effects are observed. On the other hand, for the low molecular weight fraction, the X-ray diffraction data are different at the surface and for the bulk samples, where the CCD images show a randomly orientated powder without any preferential orientation of nanocrystallites. Our results for LMW P3HT indicate the overall higher crystallinity, better in-plane stacking and the concentration of highly oriented crystals, but the mobility is more than a factor of 100 lower than HMW P3HT. These counterintuitive results indicate the charge carrier mobility of conjugated polymers is coupled to several different aspects of the morphology. In the case of the LMW films, the ordered regions are embedded in amorphous matrix which isolates the crystallites from their next neighbours. Whereas in HMW films, the long chains connect the small ordered regions and provide a smooth pathway for charges to move through the film. The molecular structure and morphology of an organic semiconductor are important key factors which control the properties of the interface between the organic film and the insulator, thus a part of research has also focused on interface engineering. We have modified the interface layer interaction by varying the dielectric layers (HMDS, OTS). Our GIXS results on samples with a chemically modified surface showed highly oriented crystals that were nucleated from the substrate and correlate with variations in charge transport for the first 5-10 layers. Correlation between the structural, thermal properties with the OFET performance gives strong evidence that the transport properties of layers prepared from both fractions of poly(3-hexylthiophene) is largely determined by the crystallinity of the samples and in part, responsible for the strong dependence of the OFET mobilities for polymer OFETs on the preparation conditions. Regarding the comparison of electronic and structural properties for HMW fraction, we have found that the mobility remains constant over a wide range of film thicknesses. A clear interface region found for thick HMW P3HT films and it is responsible for charge transport in the OFET measurements. The existence of amorphous regions in between highly-crystalline lamellae (or nanofibrils) for both short and long chain P3HT samples, controls the charge carrier mobility of such semi-crystalline system. Therefore control of this amorphous region could hold the answer for high charge carrier mobility of any semicrystalline polymers.Konjugierte Polymere gehören zu den vielversprechendsten Kandidaten für die aktive Schicht von preiswerten organischen Field-Effekt Transistoren (OFETs). Für die Leistungsfähigkeit dieser OFETs ist die Ladungsmobilität der konjugierten Polymere die wichtigste Eigenschaft . In dieser Arbeit befindet sich eine rigorose fundamentale Strukturanalyse von regioregularen poly(3-hexylthiophene) (P3HT) und seine Korrelation mit dem Ladungstransport, sowie die Kontrolle dieser Strukturen basierend auf Schichtdicken mit erhöhtem molekularen Gewicht. Sowohl die Grenzschichten zwischen dielektrischem Material und organischen Halbleiter als auch zwischen Luft und organischen Halbleiter wurden untersucht. Die Verbesserung der Aktivschichtmorphologie und -komposition werden diskutiert basierend auf Röntgenstreumethoden und AFM- sowie TEM-Techniken. Aus den Ergebnissen wird ein Grundverständnis für solche Polymere abgeleitet, die die Strukturdisparität als Funktion von molekularem Gewicht und seiner Korrelation mit den Transporteigenschaften erstellt. Dies kann eine Anleitung für die Entwicklung von neuen chemischen Strukturen sein und das Verständnis der Variation dieser Struktureigenschaften fördern. Gleichzeitig ist dies ein Modell, in dem die Verbesserung der Mobilität in diesen konjugierten Polymeren durch eine bessere Strukturordnung erreicht werden kann

Entanglement on linked boundaries in Chern-Simons theory with generic gauge groups

We study the entanglement for a state on linked torus boundaries in $3d$ Chern-Simons theory with a generic gauge group and present the asymptotic bounds of R\'enyi entropy at two different limits: (i) large Chern-Simons coupling $k$, and (ii) large rank $r$ of the gauge group. These results show that the R\'enyi entropies cannot diverge faster than $\ln k$ and $\ln r$, respectively. We focus on torus links $T(2,2n)$ with topological linking number $n$. The R\'enyi entropy for these links shows a periodic structure in $n$ and vanishes whenever $n = 0 \text{ (mod } \textsf{p})$, where the integer $\textsf{p}$ is a function of coupling $k$ and rank $r$. We highlight that the refined Chern-Simons link invariants can remove such a periodic structure in $n$.Comment: 31 pages, 5 figure

Hydrogeophysical Investigation of Contaminant Distribution at a Closed Landfill in Southwestern Ontario, Canada

The study looked at the application of geophysical and groundwater modeling methods to investigate the underground leachate distribution at a closed municipal landfill. Firstly, the apparent conductivity of the landfill was mapped using two coil separations. The resulting maps displayed a high conductive zone in the western portion of the site with measurements averaging between 35mS/m to 3000mS/m. The resistivity of the same high conductive zone was measured with resistivity profiles showing the waste material occupying the upper sand aquifer as a low resistivity anomaly ranging between 1.2 - 6 ohm*m. Results from the geophysical surveys were used to prepare two conceptual models (S-N and W-E) of the landfill. The groundwater modeling results show the contaminants occupying mainly the upper sand aquifer and most of the silt/sand aquitard after 1000 years. In most cases the lower sand aquifer remained free from contamination

Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks

The large computing and memory cost of deep neural networks (DNNs) often precludes their use in resource-constrained devices. Quantizing the parameters and operations to lower bit-precision offers substantial memory and energy savings for neural network inference, facilitating the use of DNNs on edge computing platforms. Recent efforts at quantizing DNNs have employed a range of techniques encompassing progressive quantization, step-size adaptation, and gradient scaling. This paper proposes a new quantization approach for mixed precision convolutional neural networks (CNNs) targeting edge-computing. Our method establishes a new pareto frontier in model accuracy and memory footprint demonstrating a range of quantized models, delivering best-in-class accuracy below 4.3 MB of weights (wgts.) and activations (acts.). Our main contributions are: (i) hardware-aware heterogeneous differentiable quantization with tensor-sliced learned precision, (ii) targeted gradient modification for wgts. and acts. to mitigate quantization errors, and (iii) a multi-phase learning schedule to address instability in learning arising from updates to the learned quantizer and model parameters. We demonstrate the effectiveness of our techniques on the ImageNet dataset across a range of models including EfficientNet-Lite0 (e.g., 4.14MB of wgts. and acts. at 67.66% accuracy) and MobileNetV2 (e.g., 3.51MB wgts. and acts. at 65.39% accuracy)

Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity

Spike-timing-dependent plasticity (STDP) incurs both causal and acausal synaptic weight updates, for negative and positive time differences between pre-synaptic and post-synaptic spike events. For realizing such updates in neuromorphic hardware, current implementations either require forward and reverse lookup access to the synaptic connectivity table, or rely on memory-intensive architectures such as crossbar arrays. We present a novel method for realizing both causal and acausal weight updates using only forward lookup access of the synaptic connectivity table, permitting memory-efficient implementation. A simplified implementation in FPGA, using a single timer variable for each neuron, closely approximates exact STDP cumulative weight updates for neuron refractory periods greater than 10 ms, and reduces to exact STDP for refractory periods greater than the STDP time window. Compared to conventional crossbar implementation, the forward table-based implementation leads to substantial memory savings for sparsely connected networks supporting scalable neuromorphic systems with fully reconfigurable synaptic connectivity and plasticity.Comment: Submitted to BioCAS 201

Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators

The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters. Various methods of memory organisation targeting energy-efficient digital accelerators have been investigated in the past, however, they do not completely encapsulate the energy costs at a system level. To address this shortcoming and to account for various overheads, we synthesize the controller and memory for different encoding schemes and extract the energy costs from these synthesized blocks. Additionally, we introduce functional encoding for structured connectivity such as the connectivity in convolutional layers. Functional encoding offers a 58% reduction in the energy to implement a backward pass and weight update in such layers compared to existing index-based solutions. We show that for a 2 layer spiking neural network trained to retain a spatio-temporal pattern, bitmap (PB-BMP) based organization can encode the sparser networks more efficiently. This form of encoding delivers a 1.37x improvement in energy efficiency coming at the cost of a 4% degradation in network retention accuracy as measured by the van Rossum distance.Comment: submitted to ISCAS202