933 research outputs found
Bayesian inference for challenging scientific models
Advances in technology and computation have led to ever more complicated
scientific models of phenomena across a wide variety of fields. Many of these
models present challenges for Bayesian inference, as a result of computationally
intensive likelihoods, high-dimensional parameter spaces or large dataset sizes.
In this thesis we show how we can apply developments in probabilistic machine
learning and statistics to do inference with examples of these types of models.
As a demonstration of an applied inference problem involving a non-trivial
likelihood computation, we show how a combination of optimisation and
MCMC methods along with careful consideration of priors can be used to infer
the parameters of an ODE model of the cardiac action potential.
We then consider the problem of pileup, a phenomenon that occurs in
astronomy when using CCD detectors to observe bright sources. It complicates
the fitting of even simple spectral models by introducing an observation model
with a large number of continuous and discrete latent variables that scales with
the size of the dataset. We develop an MCMC-based method that can work in
the presence of pileup by explicitly marginalising out discrete variables and
using adaptive HMC on the remaining continuous variables. We show with
synthetic experiments that it allows us to fit spectral models in the presence
of pileup without biasing the results. We also compare it to neural Simulation-
Based Inference approaches, and find that they perform comparably to the
MCMC-based approach whilst being able to scale to larger datasets.
As an example of a problem where we wish to do inference with extremely
large datasets, we consider the Extreme Deconvolution method. The method
fits a probability density to a dataset where each observation has Gaussian
noise added with a known sample-specific covariance, originally intended
for use with astronomical datasets. The existing fitting method is batch EM,
which would not normally be applied to large datasets such as the Gaia catalog
containing noisy observations of a billion stars. In this thesis we propose two
minibatch variants of extreme deconvolution, based on an online variation of
the EM algorithm, and direct gradient-based optimisation of the log-likelihood,
both of which can run on GPUs. We demonstrate that these methods provide
faster fitting, whilst being able to scale to much larger models for use with
larger datasets.
We then extend the extreme deconvolution approach to work with non-
Gaussian noise, and to use more flexible density estimators such as normalizing
flows. Since both adjustments lead to an intractable likelihood, we resort to
amortized variational inference in order to fit them. We show that for some
datasets that flows can outperform Gaussian mixtures for extreme deconvolution,
and that fitting with non-Gaussian noise is now possible
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Finite Projected Entangled Pair States for the Hubbard model
We adapt and optimize the projected-pair-entangled-state (PEPS) algorithm on
finite lattices (fPEPS) for two-dimensional Hubbard models and apply the
algorithm to the Hubbard model with nearest-neighbor hopping on a square
lattice. In particular, we formulate the PEPS algorithm using projected
entangled pair operators, incorporate SU(2) symmetry in all tensor indices, and
optimize the PEPS using both iterative-diagonalization-based local bond
optimization and gradient-based optimization of the PEPS. We discuss the
performance and convergence of the algorithm for the Hubbard model on lattice
sizes of up to 8x8 for PEPS states with U(1) symmetric bond dimensions of up to
D = 8 and SU(2) symmetric bond dimensions of up to D = 6. Finally, we comment
on the relative and overall efficiency of schemes for optimizing fPEPS
Quality of experience and access network traffic management of HTTP adaptive video streaming
The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming
A Security RISC: Microarchitectural Attacks on Hardware RISC-V CPUs
Microarchitectural attacks threaten the security of computer systems even in the absence of software vulnerabilities. Such attacks are well explored on x86 and ARM CPUs, with a wide range of proposed but not-yet deployed hardware countermeasures. With the standardization of the RISC-V instruction set architecture and the announcement of support for the architecture by major processor vendors, RISC-V CPUs are on the verge of becoming ubiquitous. However, the microarchitectural attack surface of the first commercially available RISC-V hardware CPUs is not yet explored. This paper analyzes the two commercially-available off-the-shelf 64-bit RISC-V (hardware) CPUs used in most RISC-V systems running a full-fledged commodity Linux system. We evaluate the microarchitectural attack surface, which leads to the introduction of 3 new microarchitectural attack techniques: Cache+Time, a novel cache-line-granular cache attack without shared memory, Flush+Fault exploiting the Harvard cache architecture for Flush+Reload, and CycleDrift exploiting unprivileged access to instruction-retirement information. Additionally, we show that many known attacks are applicable to these RISC-V CPUs, mainly due to non-existing hardware countermeasures and instruction-set subtleties that do not consider the microarchitectural attack surface. We demonstrate our attacks in 6 case studies, including the first RISC-V-specific microarchitectural KASLR break and a CycleDrift-based method for detecting kernel activity. Based on our analysis, we stress the need to consider the microarchitectural attack surface during every step of a CPU design, including custom instruction-set extensions
Decision-making with gaussian processes: sampling strategies and monte carlo methods
We study Gaussian processes and their application to decision-making in the real world. We begin by reviewing the foundations of Bayesian decision theory and show how these ideas give rise to methods such as Bayesian optimization. We investigate practical techniques for carrying out these strategies, with an emphasis on estimating and maximizing acquisition functions. Finally, we introduce pathwise approaches to conditioning Gaussian processes and demonstrate key benefits for representing random variables in this manner.Open Acces
Developmental Bootstrapping of AIs
Although some current AIs surpass human abilities in closed artificial worlds
such as board games, their abilities in the real world are limited. They make
strange mistakes and do not notice them. They cannot be instructed easily, fail
to use common sense, and lack curiosity. They do not make good collaborators.
Mainstream approaches for creating AIs are the traditional manually-constructed
symbolic AI approach and generative and deep learning AI approaches including
large language models (LLMs). These systems are not well suited for creating
robust and trustworthy AIs. Although it is outside of the mainstream, the
developmental bootstrapping approach has more potential. In developmental
bootstrapping, AIs develop competences like human children do. They start with
innate competences. They interact with the environment and learn from their
interactions. They incrementally extend their innate competences with
self-developed competences. They interact and learn from people and establish
perceptual, cognitive, and common grounding. They acquire the competences they
need through bootstrapping. However, developmental robotics has not yet
produced AIs with robust adult-level competences. Projects have typically
stopped at the Toddler Barrier corresponding to human infant development at
about two years of age, before their speech is fluent. They also do not bridge
the Reading Barrier, to skillfully and skeptically draw on the socially
developed information resources that power current LLMs. The next competences
in human cognitive development involve intrinsic motivation, imitation
learning, imagination, coordination, and communication. This position paper
lays out the logic, prospects, gaps, and challenges for extending the practice
of developmental bootstrapping to acquire further competences and create
robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure
Increasing the Reliability of Power and Communication Networks via Robust Optimization
Uncertainty plays an increasingly significant role in the planning and operation of complex networked infrastructure. The inclusion of variable renewable energy in power systems makes ensuring basic grid requirements such as transmission line constraints and the power balance between supply and demand more involved. Likewise, data traffic in communication networks varies greatly with user preferences and service availability, and with communication networks carrying more traffic than ever due to the surge in network-enabled devices, coping with the highly variable data flows between server and end-users becomes more crucial for the network's overall stability.
Within this context, we propose in this thesis new adaptable methods for optimizing flows in power and communication systems that explicitly consider the growing variability in these systems to guarantee optimal operation with a flexible degree of reliability. The proposed methods use a robust optimization framework, making constraints dependent on uncertain factors tractable by replacing originally stochastic conditions with deterministic counterparts. The primary benefit of robust methods is that they ensure the system is feasible for any values of the uncertain variables within a given continuous set of possible realizations. This, however, can lead to excessively conservative solutions. Therefore, we also investigate how to reduce the conservativeness of the proposed algorithms.
This thesis focuses on two classes of problems in power and communication systems, flow control and the placement of flow-controlling devices. In power systems, flow control refers to actions that induce changes in the power carried by transmission lines to minimize or maximize a specific objective value while considering the electrical grid's physical constraints. Some examples of power flow control actions are the change of switching equipment's state, regulation of generators' set points, and the management of the so-called Flexible AC Transmission Systems (FACTS) devices. For the last two action types, we propose a robust approach to optimize the corresponding control policies. As for communication networks, (data) flow control is implemented at each router in the network. These routers define the path and the rate data is forwarded using routing tables. We show that it is possible to robustly design policies to adapt these routing tables that optimize the data flows in the network depending on the instantaneous rate of the system's exogenous inputs. For both flow problems, we employ a robust optimization framework where affine-linear functions parametrize the flow control policies. The parametrized policies can be efficiently computed via linear or quadratic programming, depending on the system's constraints.
Furthermore, we consider the placement problems in the form of FACTS placement and the embedding of virtual networks in an existing communication network to improve the reliability of the network systems. Both problems are formulated as robust Mixed-Integer Linear Programs (MILP). However, because finding provable optimal solutions in large networks is computationally challenging, we also develop approximate algorithms that can yield near-optimal results while being several times faster to solve than the original MILP. In the proposed robust framework, the flow control and the placement of controlling-devices problems are solved together to take into account the coupling effects of the two optimization measures.
We demonstrate the proposed methodology in a series of use cases in power and communication systems. We also consider applications in Smart Grids, where communication and electric networks are closely interlinked. E.g., communication infrastructure enables real-time monitoring of the status of power grids and sending timely control signals to devices controlling the electric flow. Due to the increasing number of renewable energy resources, Smart Grids must adapt to fast changes in operating conditions while meeting application-dependent reliability requirements. The robust optimization methods introduced in this thesis can thus use the synergy between flexible power and communication systems to provide secure and efficient Smart Grid operation
Online Algorithms with Randomly Infused Advice
We introduce a novel method for the rigorous quantitative evaluation of online algorithms that relaxes the "radical worst-case" perspective of classic competitive analysis. In contrast to prior work, our method, referred to as randomly infused advice (RIA), does not make any assumptions about the input sequence and does not rely on the development of designated online algorithms. Rather, it can be applied to existing online randomized algorithms, introducing a means to evaluate their performance in scenarios that lie outside the radical worst-case regime.
More concretely, an online algorithm ALG with RIA benefits from pieces of advice generated by an omniscient but not entirely reliable oracle. The crux of the new method is that the advice is provided to ALG by writing it into the buffer ? from which ALG normally reads its random bits, hence allowing us to augment it through a very simple and non-intrusive interface. The (un)reliability of the oracle is captured via a parameter 0 ? ? ? 1 that determines the probability (per round) that the advice is successfully infused by the oracle; if the advice is not infused, which occurs with probability 1 - ?, then the buffer ? contains fresh random bits (as in the classic online setting).
The applicability of the new RIA method is demonstrated by applying it to three extensively studied online problems: paging, uniform metrical task systems, and online set cover. For these problems, we establish new upper bounds on the competitive ratio of classic online algorithms that improve as the infusion parameter ? increases. These are complemented with (often tight) lower bounds on the competitive ratio of online algorithms with RIA for the three problems
On Invariance, Equivariance, Correlation and Convolution of Spherical Harmonic Representations for Scalar and Vectorial Data
The mathematical representations of data in the Spherical Harmonic (SH)
domain has recently regained increasing interest in the machine learning
community. This technical report gives an in-depth introduction to the
theoretical foundation and practical implementation of SH representations,
summarizing works on rotation invariant and equivariant features, as well as
convolutions and exact correlations of signals on spheres. In extension, these
methods are then generalized from scalar SH representations to Vectorial
Harmonics (VH), providing the same capabilities for 3d vector fields on spheresComment: 106 pages, tech repor
- …