1,177 research outputs found
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs
As communication protocols evolve, datacenter network utilization increases.
As a result, congestion is more frequent, causing higher latency and packet
loss. Combined with the increasing complexity of workloads, manual design of
congestion control (CC) algorithms becomes extremely difficult. This calls for
the development of AI approaches to replace the human effort. Unfortunately, it
is currently not possible to deploy AI models on network devices due to their
limited computational capabilities. Here, we offer a solution to this problem
by building a computationally-light solution based on a recent reinforcement
learning CC algorithm [arXiv:2207.02295]. We reduce the inference time of RL-CC
by x500 by distilling its complex neural network into decision trees. This
transformation enables real-time inference within the -sec decision-time
requirement, with a negligible effect on quality. We deploy the transformed
policy on NVIDIA NICs in a live cluster. Compared to popular CC algorithms used
in production, RL-CC is the only method that performs well on all benchmarks
tested over a large range of number of flows. It balances multiple metrics
simultaneously: bandwidth, latency, and packet drops. These results suggest
that data-driven methods for CC are feasible, challenging the prior belief that
handcrafted heuristics are necessary to achieve optimal performance
Segment Routing based Traffic Engineering
In modern networks, the increasing volume of network traffic and the diverse range of services with varying requirements necessitate the implementation of more advanced routing decisions and traffic engineering. This academic study proposes a QoS adaptive mechanism called ”Sepitto”, which utilizes Segment routing protocols, specifically SRv6, to address network-traffic control and congestion avoidance. Sepitto leverages data-plane traffic to convey Linux Qdisc statistics, such as queue size, packet drops, and buffer occupancy, in each Linux-based virtual router. By incorporating this information, edge routers become aware of the current network status, enabling them to make informed decisions regarding traffic paths based on QoS classes. SRv6 is employed to direct traffic along desired paths, avoiding congested links and minimizing queuing delays and overall latency. Moreover, Sepitto offers network administrators an interface to customize decision-making processes based on their policies, assigning costs to network graph edges by associating the provided statistics to a certain cost. To incorporate these costs, the implementation employs the Dijkstra algorithm to determine the path with the lowest cost. Performance analysis of Sepitto reveals minimal overhead compared to traditional routing methods, while effectively mitigating network congestion. The results demonstrate that Sepitto reduces traffic round-trip time during congestion while maintaining differentiated treatment for various QoS classes
From crystal to adsorption : new insights into layered double hydroxides derived sorbent materials for carbon capture
With the goal of designing mixed metal oxides (MMOs) that have better CO2 sorption
performance, such as sorption capacities greater than 1.35 mmol/g at the intermediate
temperature range (200 – 400 ˚C), detailed investigation was performance on the MMOs
and their precursor, layered double hydroxides (LDHs). The novelty of work lies in the
crystal chemistry approach, which uses the crystal lattice parameter “a” of precursors
LDHs to obtain the “true” chemical composition of sorbent material. Two systematic
studies based on this approach were conducted to study the effects of different variables on
the CO2 adsorption performance of resultant MMOs, e.g., xcrystal of LDH phase, synthesis
methods and choice of precursors. Synthesis methods was found to be the most important
variable affecting the properties of LDHs and MMOs phase; and co-precipitation method
produces LDH-derived MMOs sorbent with the more desirable CO2 capture performance,
compared to the urea hydrolysis method. The results obtained from these systematic studies
allow the establishment of a new crystal-chemical model for Mg-Al based LDHs that is
fundamentally sound and more accurate when obtaining x from a. Finally, unambiguous
characterization of the equilibrium isotherms and diffusion coefficient of pristine Mg-Al
MMOs was conducted for the intermediate temperature range, using gravimetric and zero
length column method. In the low-pressure region (< 8 bar) and 200 ˚C, the equilibrium
capacities and diffusional coefficient values of pristine MMOs are found comparable to
those promoted with alkali metal salts (AMS). However, in the high-pressure region (8 - 30
bar) and temperature range between 300 – 400 ˚C, the AMS-promoted MMOs shows
almost a two-fold increase in the equilibrium capacities. Overall, the present work set a
reference case for the CO2 adsorption performance of LDH-derived MMOs
Learning, future cost and role of offshore renewable energy technologies in the North Sea energy system
The pace of cost decline of offshore renewable energy technologies significantly impacts their role in the North Sea energy transition. However, a good understanding of their remains a critical knowledge gap in the literature. Therefore, this thesis aims to quantify the future role of offshore renewables in the North Sea energy transition and assess the impact of cost development on their optimal deployments. The following findings were observed in this thesis, 1) Fixed-bottom offshore wind is well established in the North Sea region and is already competitive with onshore renewables 2) Floating wind is emerging and their current costs are high, but it can reach about 40 EUR/MWh by early 2040 and would require 44 billion EUR of learning investment.3) Grid connection costs will become a major factor as wind farm moves further away. Policy actions and innovation is needed in this space to avoid increasing integration costs. 4) Offshore wind (fixed-bottom and floating) can play a significant role in the North Sea energy system, comprising 498 GW of deployments in 2050 (222 GW of fixed-bottom and 276 GW of floating wind) and contributing up to a maximum of 51% of total power generation in the North Sea power system. 5) The role of the investigated low-TRL offshore renewables, including the tidal stream, wave technology, and bioethanol, was limited in all scenarios considered, as they remain expensive compared to other mature technologies in the system
Cognitive Decay And Memory Recall During Long Duration Spaceflight
This dissertation aims to advance the efficacy of Long-Duration Space Flight (LDSF) pre-flight and in-flight training programs, acknowledging existing knowledge gaps in NASA\u27s methodologies. The research\u27s objective is to optimize the cognitive workload of LDSF crew members, enhance their neurocognitive functionality, and provide more meaningful work experiences, particularly for Mars missions.The study addresses identified shortcomings in current training and learning strategies and simulation-based training systems, focusing on areas requiring quantitative measures for astronaut proficiency and training effectiveness assessment. The project centers on understanding cognitive decay and memory loss under LDSF-related stressors, seeking to establish when such cognitive decline exceeds acceptable performance levels throughout mission phases. The research acknowledges the limitations of creating a near-orbit environment due to resource constraints and the need to develop engaging tasks for test subjects. Nevertheless, it underscores the potential impact on future space mission training and other high-risk professions. The study further explores astronaut training complexities, the challenges encountered in LDSF missions, and the cognitive processes involved in such demanding environments. The research employs various cognitive and memory testing events, integrating neuroimaging techniques to understand cognition\u27s neural mechanisms and memory. It also explores Rasmussen\u27s S-R-K behaviors and Brain Network Theory’s (BNT) potential for measuring forgetting, cognition, and predicting training needs. The multidisciplinary approach of the study reinforces the importance of integrating insights from cognitive psychology, behavior analysis, and brain connectivity research. Research experiments were conducted at the University of North Dakota\u27s Integrated Lunar Mars Analog Habitat (ILMAH), gathering data from selected subjects via cognitive neuroscience tools and Electroencephalography (EEG) recordings to evaluate neurocognitive performance. The data analysis aimed to assess brain network activations during mentally demanding activities and compare EEG power spectra across various frequencies, latencies, and scalp locations. Despite facing certain challenges, including inadequacies of the current adapter boards leading to analysis failure, the study provides crucial lessons for future research endeavors. It highlights the need for swift adaptation, continual process refinement, and innovative solutions, like the redesign of adapter boards for high radio frequency noise environments, for the collection of high-quality EEG data. In conclusion, while the research did not reveal statistically significant differences between the experimental and control groups, it furnished valuable insights and underscored the need to optimize astronaut performance, well-being, and mission success. The study contributes to the ongoing evolution of training methodologies, with implications for future space exploration endeavors
An investigation of energy saving behaviour for residential buildings in Nigeria
Threats of climate change, global warming and uncertainty about future energy prices have sparked a global discussion about energy efficiency, particularly energy saving behaviour in residential buildings. Numerous challenges have been faced in achieving energy savings, with specific concern on energy consumption behaviour of building occupants. Accordingly, governments have set targets through policies for the reduction of energy emission, these have been adopted by the building industry through policies on energy efficiency in buildings including public private partnership in energy management and the development of near zero energy buildings.Previous studies have shown that occupant behaviour can result in a significant amount of variance in building energy use. To address these challenges in line with objectives of sustainable development goal (clean and sustainable energy and climate action), as well as energy efficiency in residential buildings, this research investigated key factors as well as practices that determine and limit energy saving behaviour in residential buildings from a different cultural perspective. Nigeria has been constantly confronted with an electricity demand that exceeded supply capacity. The increased demand for electricity can be attributed to growing populations, increased commercial activity and industrialisation. Households are a significant contributor to the rapidly increasing electricity demand as identified. Energy providers resort to ‘load shedding’ of electricity supply between communities and industries and even long-term electrical outage due to limited supply. It is also important to understand how the actions of occupants affect energy consumption behaviour in residential buildings. To reduce electricity demand and save energy, this research exploited literature on energy saving behaviour and behaviour change. The research study was conducted based on a sequential exploratory mixed method and consists of two key phases. Firstly, qualitative data was collected using semi-structure interview with eighteen experts in the energy and construction industry in Nigeria. The purpose of which was to provide an insight into residential energy consumption behaviour and the barriers faced in the adoption of sustainable energy sources. Analysis from the result shows that cost of energy is a major driver to the adoption of energy saving practices as there are no compulsory regulatory agencies to enforce and facilitate the migration to a more sustainable and innovative society. Furthermore, results also show that there is a need for continuous awareness on energy saving behavioural change, a need for government subsidies on renewable energy, government checks and standardization of energy efficient appliances imported into the country could improve the trust towards sustainable choices and promote efficient energy use. The second phase involved a household survey with 317 households from the case study area. The survey instrument was developed based on the constructs of energy culture framework, sociodemographic factors and physical environment. The hypothesised relationship from the conceptual model were tested using structural equation modelling (SEM). The results indicated that energy practices, material culture, attitude perception cognitive and social norms with behaviour changes were statistically significant, with attitude perception cognitive and social norms having the least impact on behaviour change. Additionally, the correlations from the constructs shows a direct relationship with behaviour change in achieving energy efficiency and energy saving approach while a deliberate policy to achieve energy efficiency and energy saving practices is vital to achieve sustainable development goals. The outcome from this work provided a better understanding of drivers and barriers to energy use behaviour and will inform future energy policy and interventions related to household energy saving. It also will contribute to the existing body of knowledge as well as give policy direction of governments towards climate action and some specific objectives of sustainable development goals
Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage
We investigate the role of projection heads, also known as projectors, within
the encoder-projector framework (e.g., SimCLR) used in contrastive learning. We
aim to demystify the observed phenomenon where representations learned before
projectors outperform those learned after -- measured using the downstream
linear classification accuracy, even when the projectors themselves are linear.
In this paper, we make two significant contributions towards this aim.
Firstly, through empirical and theoretical analysis, we identify two crucial
effects -- expansion and shrinkage -- induced by the contrastive loss on the
projectors. In essence, contrastive loss either expands or shrinks the signal
direction in the representations learned by an encoder, depending on factors
such as the augmentation strength, the temperature used in contrastive loss,
etc. Secondly, drawing inspiration from the expansion and shrinkage phenomenon,
we propose a family of linear transformations to accurately model the
projector's behavior. This enables us to precisely characterize the downstream
linear classification accuracy in the high-dimensional asymptotic limit. Our
findings reveal that linear projectors operating in the shrinkage (or
expansion) regime hinder (or improve) the downstream classification accuracy.
This provides the first theoretical explanation as to why (linear) projectors
impact the downstream performance of learned representations. Our theoretical
findings are further corroborated by extensive experiments on both synthetic
data and real image data
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Operational modal analysis and prediction of remaining useful life for rotating machinery
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe significance of rotating machinery spans areas from household items to vital industry sectors, such as aerospace, automotive, railway, sea transport, resource extraction, and manufacturing. Hence, our technologised society depends on efficient and reliable operation of rotating machinery. To contribute to this aim, this thesis leverages measurable quantities during its operation for structural-mechanical evaluation employing Operational Modal Analysis (OMA) and the prediction of Remaining Useful Life (RUL). Modal parameters determined by OMA are central for the design, test, and validation of rotating machinery. This thesis introduces the first open parametric simulation dataset of rotating machinery during an acceleration run. As there is a lack of similar open datasets suitable for OMA, it lays a foundation for improved reproducibility and comparability of future research. Based on this, the Averaged Order-Based Modal Analysis (AOBMA) method is developed. The novel addition of scaling and weighted averaging of individual machine orders in AOBMA alleviates the analysis effort of the existing Order-Based Modal Analysis (OBMA) method by providing a unified set of modal parameters with higher accuracy. As such, AOBMA showed a lower mean absolute relative error of 0.03 for damping ratio estimations across compared modes while OBMA provided an error value of 0.32 depending on the processed order. At excitation with high harmonic contributions, AOBMA also resulted in the highest number of accurately identified modes among the compared methods. At a harmonic ratio of 0.8, for example, AOBMA identified an average of 11.9 modes per estimation, while OBMA and baseline OMA followed with 9.5 and 9 modes, respectively. Moreover, it is the first study, which systematically evaluates the impact of excitation conditions on the compared methods and finds an advantage of OBMA and AOBMA over traditional OMA regarding mode shape estimation accuracy. While OMA can be used to evaluate significant structural changes, Machine Learning (ML) methods have seen substantially greater success in condition monitoring, including RUL prediction. However, as these methods often require large amounts of time and cost-
intensive training data, a novel data-efficient RUL prediction methodology is introduced, taking advantage of distinct healthy and faulty condition data. When the number of training sequences from an open dataset is reduced to 5%, an average prediction Root Mean Square Error (RMSE) of 24.9 operation cycles is achieved, outperforming the baseline method with an RMSE of 28.1. Motivated by environmental considerations, the impact of data reduction on the training duration of several method variants is quantified. When the full training set is
utilised, the most resource-saving variant of the proposed approach achieves an average training duration of 8.9% compared to the baseline method
Mesoscopic Physics of Quantum Systems and Neural Networks
We study three different kinds of mesoscopic systems – in the intermediate region between macroscopic and microscopic scales consisting of many interacting constituents:
We consider particle entanglement in one-dimensional chains of interacting fermions. By employing a field theoretical bosonization calculation, we obtain the one-particle entanglement entropy in the ground state and its time evolution after an interaction quantum quench which causes relaxation towards non-equilibrium steady states. By pushing the boundaries of the numerical exact diagonalization and density matrix renormalization group computations, we are able to accurately scale to the thermodynamic limit where we make contact to the analytic field theory model. This allows to fix an interaction cutoff required in the continuum bosonization calculation to account for the short range interaction of the lattice model, such that the bosonization result provides accurate predictions for the one-body reduced density matrix in the Luttinger liquid phase.
Establishing a better understanding of how to control entanglement in mesoscopic systems is also crucial for building qubits for a quantum computer. We further study a popular scalable qubit architecture that is based on Majorana zero modes in topological superconductors. The two major challenges with realizing Majorana qubits currently lie in trivial pseudo-Majorana states that mimic signatures of the topological bound states and in strong disorder in the proposed topological hybrid systems that destroys the topological phase. We study coherent transport through interferometers with a Majorana wire embedded into one arm.
By combining analytical and numerical considerations, we explain the occurrence of an amplitude maximum as a function of the Zeeman field at the onset of the topological phase – a signature unique to MZMs – which has recently been measured experimentally [Whiticar et al., Nature Communications, 11(1):3212, 2020]. By placing an array of gates in proximity to the nanowire, we made a fruitful connection to the field of Machine Learning by using the CMA-ES algorithm to tune the gate voltages in order to maximize the amplitude of coherent transmission. We find that the algorithm is capable of learning disorder profiles and even to restore Majorana modes that were fully destroyed by strong disorder by optimizing a feasible number of gates.
Deep neural networks are another popular machine learning approach which not only has many direct applications to physical systems but which also behaves similarly to physical mesoscopic systems. In order to comprehend the effects of the complex dynamics from the training, we employ Random Matrix Theory (RMT) as a zero-information hypothesis: before training, the weights are randomly initialized and therefore are perfectly described by RMT. After training, we attribute deviations from these predictions to learned information in the weight matrices.
Conducting a careful numerical analysis, we verify that the spectra of weight matrices consists of a random bulk and a few important large singular values and corresponding vectors that carry almost all learned information. By further adding label noise to the training data, we find that more singular values in intermediate parts of the spectrum contribute by fitting the randomly labeled images. Based on these observations, we propose a noise filtering algorithm that both removes the singular values storing the noise and reverts the level repulsion of the large singular values due to the random bulk
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