Connecting Dualities and Machine Learning

This is a concise graduate level introduction to analytical functional methods in quantum field theory. Functional integral methods provide relatively simple solutions to a wide range of problems in quantum field theory. After introducing the basic mathematical background, this book goes on to study applications and consequences of the formalism to the study of series expansions, measure, phase transitions, physics on spaces with nontrivial topologies, stochastic quantisation, fermions, QED, non-abelian gauge theories, symmetry breaking, the effective potential, finite temperature field theory, instantons and compositeness. Serious attention is paid to the shortcomings of the conventional formalism (e.g. problems of measure) as well as detailed appraisal of the ambiguities of series summation. This book will be of great use to graduate students in theoretical physics wishing to learn the use of functional integrals in quantum field theory. It will also be a useful reference for researchers in theoretical physics, especially those with an interest in experimental and theoretical particle physics and quantum field theory.

Caltech High Energy Physics (HEP) has a broad program in both experimental and theoretical physics. We are known for our creativity and leadership. The future is uncertain and we strive to be involved in all the major areas of experimental and theoretical HEP physics so no matter where the important discoveries occur we are well positioned to play an important role. An outstanding group of postdoctoral scholars, graduate students, staff scientists, and technical and administrative personnel support our efforts in experimental and theoretical physics. The PI’s on this grant are involved in the following program of experimental and theoretical activities: I) EXPERIMENTAL PHYSICS Our CMS group, led by Harvey Newman and Maria Spiropulu, has played a key role in the discovery and interpretation of the Higgs boson and in searches for new physics. They have important hardware responsibilities in both ECAL and HCAL and are also involved in the upgrades needed for the High Luminosity LHC. Newman's group also develops and operates Grid-based computing, networking, and collaborative systems for CMS and the US HEP community. The charged lepton (Mu2e) and quark BaBar flavor physics group is led by David Hitlin and Frank Porter. On Mu2e they have been instrumental in the design of the calorimeter. Construction responsibilities include one third of the crystals and associated readout as well as the calibration system. They also will have responsibility for a major part of the online system software. Although data taking ceased in 2008 the Caltech BaBar group is active on several new forefront analyses. The neutrino group is led by Ryan Patterson. They are central to NOvA's core oscillation physics program, to calibration, and to detector readiness being responsible for the production and installation of 12,000 APD arrays. They have key roles in neutrino appearance and disappearance analysis in MINOS and MINOS+. Sunil Golwala leads the dark matter direct detection effort. Areas of activity include: CDMS II data analysis, contributions to SuperCDMS Soudan operations and analysis, R&D towards SuperCDMS SNOLAB, development of a novel screener for radiocontamination (the BetaCage), and development of new WIMP detector concepts. Ren-Yuan Zhu leads the HEP crystal laboratory for the advanced detector R&D effort. The crystal lab is involved in development of novel scintillating crystals and has proposed several crystal based detector concepts for future HEP experiments at the energy and intensity frontiers. Its current research effort is concentrated on development of fast crystal scintillators with good radiation hardness and low cost. II) THEORETICAL PHYSICS The main theme of Sergei Gukov's current research is the relation between the geometry of quantum group invariants and their categorification, on the one hand, and the physics of supersymmetric gauge theory and string theory, on the other. Anton Kapustin's research spans a variety of topics in non-perturbative Quantum Field Theory (QFT). His main areas of interest are supersymmetric gauge theories, non-perturbative dualities in QFT, disorder operators, Topological Quantum Field Theory, and non-relativistic QFT. He is also interested in the foundations and possible generalizations of Quantum Mechanics. Hirosi Ooguri's current research has two main components. One is to find exact results in Calabi-Yau compactification of string theory. Another is to explore applications of the AdS/CFT correspondence. He also plans to continue his project with Caltech postdoctoral fellows on BPS spectra of supersymmetric gauge theories in diverse dimensions. John Preskill works on quantum information science. This field may lead to important future technologies, and also lead to new understanding of issues in fundamental physics John Schwarz has been exploring a number of topics in superstring theory/M-theory, supersymmetric gauge theory, and their AdS/CFT relationships. Much of the motivation for these studies is the desire to gain a deeper understanding of superstring theory and M-theory. The research interests of Mark Wise span particle physics, cosmology and nuclear physics. His recent work has centered on extensions of the standard model where baryon number and lepton number are gauged and extensions of the standard model that have novel sources of baryon number violation and new sources of charged lepton flavor violation

Chapter 1 A Trope-Bundle Ontology for Field Theory

The unmanned aerial vehicles (UAVs) have been extensively used in providing intelligence such as target tracking. In our field experiments, a pre-trained deep neural network (DNN) is deployed on the UAV to identify a target from the captured video frames and enable UAV to keep tracking. However, tracking in real time by the DNN requires a lot of computational resources. This motivates us to consider offloading this type of machine learning (ML) tasks to a mobile edge computing (MEC) server. Specifically, we propose a novel hierarchical ML tasks distribution framework for the UAV tracking system, where the UAV is embedded with lower layers of the pre-trained convolutional neural network (CNN) model due to its limited computing capability, while the MEC server with rich computing resources will handle the higher layers of the CNN model. An optimization problem is formulated to minimize the CNN inference delay while taking into account the communications delay, computing time, and ML error. Insights are provided to understand the tradeoff between communications and ML computing in offloading decisions. Numerical results demonstrate the effectiveness of the proposed ML tasks distribution framework with the optimized offloading strategy.

Just as ordinary symmetries relate various forms of matter to each other, and various basic forces to each other, so the novel concept of supersymmetry relates (Fermi) matter to (Bose) force. It is the aim of this book to provide a brief introductory description of the new physical and mathematical ideas involved in formulating supersymmetric theories. The book starts with a physical motivation of supersymmetry, a presentation of the mathematics of Lie superalgebras, supergroups and superspace. Techniques for constructing manifestly globally supersymmetric field theories are given, using the superfield formalism. To allow for a clear flow of ideas, the basic ideas and techniques are worked out in low space dimensionalities where the formulae do not obscure the concepts. Generalizations to four space-time dimensions are then readily come by. Some quantum aspects are discussed. Possible phenomenological applications are not emphasized. Supergravities, locally supersymmetric theories are then considered in 4 and 11 dimensions, in component formalism. An introduction to supersymmetry will be of interest to postgraduate students and researchers in theoretical and particle physics, especially those working in quantum field theory, quantum gravity, general relativity and supergravity. The book will also be of interest to mathematicians with an interest in theoretical physics.

This two-tier conference signals a new phase of philosophers' interest in quantum field theory, which has been growing noticeably in the last few years. However, some prominent physicists have shown their deep suspicions against ignorant philosophers' intrusion into their profession, and have expressed their hostility quite openly. In the philosophy community, some prominent philosophers of physics have also expressed their suspicions against the rationale of moving away from the profound foundational problems raised by Einstein and Bohr, Bohm and Bell, such as those concerning the nature of space-time and measurement, possibility and implications of hidden variables and nonlocality, and stepping into the technical complexity of quantum field theory, which is only an application of quantum mechanics in general without intrinsically distinct philosophical questions to be explored. In order to dispel these suspicions, it is desirable to highlight certain aspects of quantum field theory which require philosophical reflections and deserve further investigations. This discussion intends to suggest that philosophers can learn many important lessons from quantum field theory, and may be of some help in clarifying its conceptual foundations. At this stage of crisis that quantum field theory is experiencing now, the clarification may contribute to the radical transformation of our basic concepts in theoretical physics, which is necessary for a happy resolution of the crisis and the emergence of a new promising fundamental physical theory.

The aim of this paper is to examine one of several candidates for the ontology of today’s most basic theory in physics, quantum field theory (QFT). I will begin by outlining some problems we encounter when we look at different conceptions for the basic entities to which QFT refers. The candidate ontology I wish to consider in this paper is process ontology. I shall characterize process ontology mainly in a negative manner as opposed to substance ontology. The central part will consist in a case study about how the interpretation of Feynman diagrams – the use of which is crucial in QFT – depends on whether we adhere to substance or process ontology. In conclusion I will evaluate my results and point towards some problems faced by process ontology as such.

The mathematical universe of the quantum topos, which is formulated on the basis of classical Boolean snapshots, delivers a neo-realist description of quantum mechanics that preserves realism. The main contribution of this article is developing formal objectivity in physical theories beyond quantum mechanics in the topos-theory approach. It will be shown that neo-realist responses to non-perturbative structures of quantum field theory do not preserve realism. In this regard, the method of Feynman graphons is applied to reframe the task of describing objectivity in quantum field theory in terms of replacing the standard Hilbert-space/operator-algebra ontology with a new context category built from a certain family of topological Hopf subalgebras of the topological Hopf algebra of renormalization as algebraic/combinatorial data tied to non-perturbative structures. This topological-Hopf-algebra ontology, which is independent of instrumentalist probabilities, enables us to reconstruct gauge field theories on the basis of the mathematical universe of the non-perturbative topos. The non-Boolean logic of the non-perturbative topos cannot be recovered by classical Boolean snapshots, which is in contrast to the quantum-topos reformulation of quantum mechanics. The article formulates a universal version of the non-perturbative topos to show that quantum field theory is a globally and locally neo-realist theory which can be reconstructed independent of the standard Hilbert-space/operator-algebra ontology. Formal objectivity of the universal non-perturbative topos offers a new route to build objective semantics for non-perturbative structures.

Recently there has been a rapidly growing demand for faster machine learning (ML) processing in data centers and migration of ML inference applications to edge devices. These developments have prompted both industry and academia to explore custom accelerators to optimize ML executions for performance and power. However, identifying which accelerator is best equipped for performing a particular ML task is challenging, especially given the growing range of ML tasks, the number of target environments, and the limited number of integrated modeling tools. To tackle this issue, it is of paramount importance to provide the computer architecture research community with a common framework capable of performing a comprehensive, uniform, and fair comparison across different accelerator designs targeting a particular ML task. To this aim, we propose a new framework named TFLITE-SOC (System On Chip) that integrates a lightweight system modeling library (SystemC) for fast design space exploration of custom ML accelerators into the build/execution environment of Tensorflow Lite (TFLite), a highly popular ML framework for ML inference. Using this approach, we are able to model and evaluate new accelerators developed in SystemC by leveraging the language's hierarchical design capabilities, resulting in faster design prototyping. Furthermore, any accelerator designed using TFLITE-SOC can be benchmarked for inference with any DNN model compatible with TFLite, which enables end-to-end DNN processing and detailed (i.e., per DNN layer) performance analysis. In addition to providing rapid prototyping, integrated benchmarking, and a range of platform configurations, TFLITE-SOC offers comprehensive performance analysis of accelerator occupancy and execution time breakdown as well as a rich set of modules that can be used by new accelerators to implement scaling up studies and optimized memory transfer protocols. We present our framework and demonstrate its utility by considering the design space of a TPU-like systolic array and describing possible directions for optimization. Using a compression technique, we implement an optimization targeting reducing the memory traffic between DRAM and on-device buffers. Compared to the baseline accelerator, our optimized design shows up to 1.26x speedup on accelerated operations and up to 1.19x speedup on end-to-end DNN execution.

Datasets of labeled network traces are essential for a multitude of machine learning (ML) tasks in networking, yet their availability is hindered by privacy and maintenance concerns, such as data staleness. To overcome this limitation, synthetic network traces can often augment existing datasets. Unfortunately, current synthetic trace generation methods, which typically produce only aggregated flow statistics or a few selected packet attributes, do not always suffice, especially when model training relies on having features that are only available from packet traces. This shortfall manifests in both insufficient statistical resemblance to real traces and suboptimal performance on ML tasks when employed for data augmentation. In this paper, we apply diffusion models to generate high-resolution synthetic network traffic traces. We present NetDiffusion, a tool that uses a finely-tuned, controlled variant of a Stable Diffusion model to generate synthetic network traffic that is high fidelity and conforms to protocol specifications. Our evaluation demonstrates that packet captures generated from NetDiffusion can achieve higher statistical similarity to real data and improved ML model performance than current state-of-the-art approaches (e.g., GAN-based approaches). Furthermore, our synthetic traces are compatible with common network analysis tools and support a myriad of network tasks, suggesting that NetDiffusion can serve a broader spectrum of network analysis and testing tasks, extending beyond ML-centric applications.

Datasets of labeled network traces are essential for a multitude of machine learning (ML) tasks in networking, yet their availability is hindered by privacy and maintenance concerns, such as data staleness. To overcome this limitation, synthetic network traces can often augment existing datasets. Unfortunately, current synthetic trace generation methods, which typically produce only aggregated flow statistics or a few selected packet attributes, do not always suffice, especially when model training relies on having features that are only available from packet traces. This shortfall manifests in both insufficient statistical resemblance to real traces and suboptimal performance on ML tasks when employed for data augmentation. In this paper, we apply diffusion models to generate high-resolution synthetic network traffic traces. We present NetDiffusion, a tool that uses a finely-tuned, controlled variant of a Stable Diffusion model to generate synthetic network traffic that is high fidelity and conforms to protocol specifications. Our evaluation demonstrates that packet captures generated from NetDiffusion can achieve higher statistical similarity to real data and improved ML model performance than current state-of-the-art approaches (e.g., GAN-based approaches). Furthermore, our synthetic traces are compatible with common network analysis tools and support a myriad of network tasks, suggesting that NetDiffusion can serve a broader spectrum of network analysis and testing tasks, extending beyond ML-centric applications.

The emerging paradigm of edge computing has revolutionized network applications, delivering computational power closer to the end-user. Consequently, Machine Learning (ML) tasks, typically performed in a data centre (Centralized Learning - CL), can now be offloaded to the edge (Edge Learning - EL) or mobile devices (Federated Learning - FL). While the inherent flexibility of such distributed schemes has drawn considerable attention, a thorough investigation on their resource consumption footprint is still missing.In our work, we consider a FL scheme and two EL variants, representing varying proximity to the end users (data sources) and corresponding levels of workload distribution across the network; namely Access Edge Learning (AEL), where edge nodes are essentially co-located with the base stations and Regional Edge Learning (REL), where they lie towards the network core. Based on real systems' measurements and user mobility traces, we devise a realistic simulation model to evaluate and compare the performance of the considered ML schemes under an image classification task. Our results indicate that FL and EL can act as viable alternatives to CL. Edge learning effectiveness is shaped by the configuration of edge nodes in the network with REL achieving the prominent combination of accuracy and bandwidth needs. Energy-wise, edge learning is shown to offer an attractive choice (for involved stakeholders) to offload centralised ML tasks.

Holography, Locality and Symmetries of The Universe Xiao Xiao It is an interesting question that, with a well tested duality between the quantum gravity in anti de Sitter space and a quantum field theory in one lower dimension, whether quantum gravity in a cosmological background has a well defined dual description. In large N limit, this duality could be a correspondence between an approximately local gravity theory describing cosmology and a quantum field theory. In dS/CFT, the quantum field theory is a Euclidean CFT living at the conformal boundary of de Sitter space, in large N limit, we should expect the local observables in de Sitter cosmology be recovered from the CFT. We explicitly develop this construction for scalar fields and derive the operator map at lowest order of 1 N expansion. Having addressed the fundamental question of how local fields in de Sitter cosmology arise via holography, we focus on the theory of cosmological perturbations that is described in terms of local field theory. The curvature perturbations during inflation, which originated from quantum fluctuations of inflaton and which induced the CMB inhomogeneity we see today, as well as the large scale structure, can be described as Goldstone boson fields which nonlinearly realize a subset of general coordinate transformations as residual symmetries. This fact puts strong constraints on the behavior of the cosmological correlation functions, and a series of consistency relations constraining the soft limits of these correlation functions can be derived as Ward identities.

A central task of theoretical physics is to analyse spectral properties of quantum mechanical observables. In this endeavour, Mourre’s conjugate operator method emerged as an effective tool in the spectral theory of Schrödinger operators. This paper introduces a novel class of examples from relativistic quantum field theory that are amenable to Mourre’s method. By assuming Lorentz covariance and the spectrum condition, we derive a limiting absorption principle for the energy-momentum operators and provide new proofs of the absolute continuity of the energy-momentum spectra. Moreover, under the assumption of dilation covariance, we show that the spectrum of the relativistic mass operator is purely absolutely continuous in (0,∞)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(0,\\infty )$$\\end{document}.

“In recent years the ideas of decay of a metastable state by quantum tunnelling of a field, and also of tunnelling between degenerate vacua, have played a major role in particle physics. Analogous problems occur also in condensed-matter physics, where they tend to be more tractable quantitatively because the role of the field is mainly to provide dissipation for the “primary” tunnelling variable. While the explicitly field-theoretic aspects of the problem are in this sense rather trivial, many of the techniques popular in quantum field theory have been applied successfully to such condensed-state problems, and I review here some of these applications.”