19,641 research outputs found
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
Towards Fast and Scalable Private Inference
Privacy and security have rapidly emerged as first order design constraints.
Users now demand more protection over who can see their data (confidentiality)
as well as how it is used (control). Here, existing cryptographic techniques
for security fall short: they secure data when stored or communicated but must
decrypt it for computation. Fortunately, a new paradigm of computing exists,
which we refer to as privacy-preserving computation (PPC). Emerging PPC
technologies can be leveraged for secure outsourced computation or to enable
two parties to compute without revealing either users' secret data. Despite
their phenomenal potential to revolutionize user protection in the digital age,
the realization has been limited due to exorbitant computational,
communication, and storage overheads.
This paper reviews recent efforts on addressing various PPC overheads using
private inference (PI) in neural network as a motivating application. First,
the problem and various technologies, including homomorphic encryption (HE),
secret sharing (SS), garbled circuits (GCs), and oblivious transfer (OT), are
introduced. Next, a characterization of their overheads when used to implement
PI is covered. The characterization motivates the need for both GCs and HE
accelerators. Then two solutions are presented: HAAC for accelerating GCs and
RPU for accelerating HE. To conclude, results and effects are shown with a
discussion on what future work is needed to overcome the remaining overheads of
PI.Comment: Appear in the 20th ACM International Conference on Computing
Frontier
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Pipeline quantum processor architecture for silicon spin qubits
Noisy intermediate-scale quantum (NISQ) devices seek to achieve quantum
advantage over classical systems without the use of full quantum error
correction. We propose a NISQ processor architecture using a qubit `pipeline'
in which all run-time control is applied globally, reducing the required number
and complexity of control and interconnect resources. This is achieved by
progressing qubit states through a layered physical array of structures which
realise single and two-qubit gates. Such an approach lends itself to NISQ
applications such as variational quantum eigensolvers which require numerous
repetitions of the same calculation, or small variations thereof. In exchange
for simplifying run-time control, a larger number of physical structures is
required for shuttling the qubits as the circuit depth now corresponds to an
array of physical structures. However, qubit states can be `pipelined' densely
through the arrays for repeated runs to make more efficient use of physical
resources. We describe how the qubit pipeline can be implemented in a silicon
spin-qubit platform, to which it is well suited to due to the high qubit
density and scalability. In this implementation, we describe the physical
realisation of single and two qubit gates which represent a universal gate set
that can achieve fidelities of , even under typical
qubit frequency variations.Comment: 21 pages (13 for main + 8 for supplement), 9 figures (4 for main + 5
for supplement
Reinforcement learning in large state action spaces
Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios.
This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory).
In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications
Swarm Reinforcement Learning For Adaptive Mesh Refinement
The Finite Element Method, an important technique in engineering, is aided by
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow
for a favorable trade-off between computational speed and simulation accuracy.
Classical methods for AMR depend on task-specific heuristics or expensive error
estimators, hindering their use for complex simulations. Recent learned AMR
methods tackle these problems, but so far scale only to simple toy examples. We
formulate AMR as a novel Adaptive Swarm Markov Decision Process in which a mesh
is modeled as a system of simple collaborating agents that may split into
multiple new agents. This framework allows for a spatial reward formulation
that simplifies the credit assignment problem, which we combine with Message
Passing Networks to propagate information between neighboring mesh elements. We
experimentally validate the effectiveness of our approach, Adaptive Swarm Mesh
Refinement (ASMR), showing that it learns reliable, scalable, and efficient
refinement strategies on a set of challenging problems. Our approach
significantly speeds up computation, achieving up to 30-fold improvement
compared to uniform refinements in complex simulations. Additionally, we
outperform learned baselines and achieve a refinement quality that is on par
with a traditional error-based AMR strategy without expensive oracle
information about the error signal.Comment: Version 1 of this paper is a preliminary workshop version that was
accepted as a workshop paper in the ICLR 2023 Workshop on Physics for Machine
Learnin
Accelerated Benders Decomposition for Variable-Height Transport Packaging Optimisation
This paper tackles the problem of finding optimal variable-height transport
packaging. The goal is to reduce the empty space left in a box when shipping
goods to customers, thereby saving on filler and reducing waste. We cast this
problem as a large-scale mixed integer problem (with over seven billion
variables) and demonstrate various acceleration techniques to solve it
efficiently in about three hours on a laptop. We present a KD-Tree algorithm to
avoid exhaustive grid evaluation of the 3D-bin-packing, provide analytical
transformations to accelerate the Benders decomposition, and an efficient
implementation of the Benders sub problem for significant memory savings and a
three order of magnitude runtime speedup
Using machine learning to predict pathogenicity of genomic variants throughout the human genome
Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität.
Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores.
Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt.
Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity.
Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants.
The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency.
In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org
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A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics. Sentiment analysis is a typical example of such domains. In the last few years, by leveraging the modeling power of quantum probability (a non-classical probability stemming from quantum mechanics methodology) and deep neural networks, a range of novel quantum-cognitively inspired models for sentiment analysis have emerged and performed well. This survey presents a timely overview of the latest developments in this fascinating cross-disciplinary area. We first provide a background of quantum probability and quantum cognition at a theoretical level, analyzing their advantages over classical theories in modeling the cognitive aspects of sentiment analysis. Then, recent quantum-cognitively inspired models are introduced and discussed in detail, focusing on how they approach the key challenges of the sentiment analysis task. Finally, we discuss the limitations of the current research and highlight future research directions
A High-Performance Implementation of Atomistic Spin Dynamics Simulations on x86 CPUs
Atomistic spin dynamics simulations provide valuable information about the
energy spectrum of magnetic materials in different phases, allowing one to
identify instabilities and the nature of their excitations. However, the time
cost of evaluating the dynamical correlation function
increases quadratically as the number of spins , leading to significant
computational effort, making the simulation of large spin systems very
challenging. In this work, we propose to use a highly optimized general matrix
multiply (GEMM) subroutine to calculate the dynamical spin-spin correlation
function that can achieve near-optimal hardware utilization. Furthermore, we
fuse the element-wise operations in the calculation of into
the in-house GEMM kernel, which results in further performance improvements of
44\% - 71\% on several relatively large lattice sizes when compared to the
implementation that uses the GEMM subroutine in OpenBLAS, which is the
state-of-the-art open source library for Basic Linear Algebra Subroutine
(BLAS).Comment: 18 (short) pages, 6 figure
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