1,001 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Evaluating Architectural Safeguards for Uncertain AI Black-Box Components
Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospitalâs new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning
A general framework of unsupervised learning for combinatorial optimization
(CO) is to train a neural network (NN) whose output gives a problem solution by
directly optimizing the CO objective. Albeit with some advantages over
traditional solvers, the current framework optimizes an averaged performance
over the distribution of historical problem instances, which misaligns with the
actual goal of CO that looks for a good solution to every future encountered
instance. With this observation, we propose a new objective of unsupervised
learning for CO where the goal of learning is to search for good initialization
for future problem instances rather than give direct solutions. We propose a
meta-learning-based training pipeline for this new objective. Our method
achieves good empirical performance. We observe that even just the initial
solution given by our model before fine-tuning can significantly outperform the
baselines under various evaluation settings including evaluation across
multiple datasets, and the case with big shifts in the problem scale. The
reason we conjecture is that meta-learning-based training lets the model be
loosely tied to each local optima for a training instance while being more
adaptive to the changes of optimization landscapes across instances.Comment: Our code is available at: https://github.com/Graph-COM/Meta_C
Decoding algorithms for surface codes
Quantum technologies have the potential to solve computationally hard
problems that are intractable via classical means. Unfortunately, the unstable
nature of quantum information makes it prone to errors. For this reason,
quantum error correction is an invaluable tool to make quantum information
reliable and enable the ultimate goal of fault-tolerant quantum computing.
Surface codes currently stand as the most promising candidates to build error
corrected qubits given their two-dimensional architecture, a requirement of
only local operations, and high tolerance to quantum noise. Decoding algorithms
are an integral component of any error correction scheme, as they are tasked
with producing accurate estimates of the errors that affect quantum
information, so that it can subsequently be corrected. A critical aspect of
decoding algorithms is their speed, since the quantum state will suffer
additional errors with the passage of time. This poses a connundrum-like
tradeoff, where decoding performance is improved at the expense of complexity
and viceversa. In this review, a thorough discussion of state-of-the-art
surface code decoding algorithms is provided. The core operation of these
methods is described along with existing variants that show promise for
improved results. In addition, both the decoding performance, in terms of error
correction capability, and decoding complexity, are compared. A review of the
existing software tools regarding surface code decoding is also provided.Comment: 54 pages, 31 figure
Natural type inference
Recently, dynamic language users have started to recognize the value of types in their code. To fulfil this need, many popular dynamic languages have adopted extensions that support type annotations. A prominent example is that of TypeScript which offers a module system, classes, interfaces, and an optional type system on top of JavaScript.
However, providing usable (not too verbose, or complex) types via traditional type inference is more challenging in optional type systems. Motivated by this, we redefine the goal of type inference for optionally typed languages as: infer the maximally natural and sound type, instead of the most general one. By the maximally natural and sound, we refer to a type that (1) is derivable in the type system, and (2) maximally reflects the intention of the programmer with respect to a learnt model.
We formally devise a type inference problem that aids the inference of the maximally natural type. Towards this goal, our problem asks to combine information derived from two sources: (1) from algorithmic type systems using deductive logic-based techniques; and (2) from the source code text using inductive machine learning techniques.
To tackle our formulated problem, we develop two frameworks that combine the two sources of information using mathematical optimization. In the first framework, we formulate the inference problem as a problem in numerical optimization. In the second framework, we map the inference problem into popular problems in discrete optimization: maximum satisfiability (MaxSAT) and Integer Linear Programming (ILP).
Both frameworks are built to be consistent with information derived from the different sources. Moreover, through formal proofs, we validate the soundness and completeness of the developed framework for a core lambda-calculus with named types.
To assess the efficacy of the developed frameworks, we implement them in a tool named Optyper that realizes natural type inference for TypeScript. We evaluate Optyperon TypeSript programs obtained from real world projects. By evaluating our theoretical frameworks we show that, in practice, the combination of logical and natural constraints yields a large improvement in performance over either kind of information individually. Further, we demonstrate that our frameworks out-perform state-of-the-art techniques in type inference to produce natural and sound types
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
2010 GREAT Day Program
SUNY Geneseoâs Fourth Annual GREAT Day.
This file has a supplement of three additional pages, linked in this record.https://knightscholar.geneseo.edu/program-2007/1004/thumbnail.jp
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