1,458 research outputs found
A foundation for synthesising programming language semantics
Programming or scripting languages used in real-world systems are seldom designed
with a formal semantics in mind from the outset. Therefore, the first step for developing well-founded analysis tools for these systems is to reverse-engineer a formal
semantics. This can take months or years of effort.
Could we automate this process, at least partially? Though desirable, automatically reverse-engineering semantics rules from an implementation is very challenging,
as found by Krishnamurthi, Lerner and Elberty. They propose automatically learning
desugaring translation rules, mapping the language whose semantics we seek to a simplified, core version, whose semantics are much easier to write. The present thesis
contains an analysis of their challenge, as well as the first steps towards a solution.
Scaling methods with the size of the language is very difficult due to state space
explosion, so this thesis proposes an incremental approach to learning the translation
rules. I present a formalisation that both clarifies the informal description of the challenge by Krishnamurthi et al, and re-formulates the problem, shifting the focus to the
conditions for incremental learning. The central definition of the new formalisation is
the desugaring extension problem, i.e. extending a set of established translation rules
by synthesising new ones.
In a synthesis algorithm, the choice of search space is important and non-trivial,
as it needs to strike a good balance between expressiveness and efficiency. The rest
of the thesis focuses on defining search spaces for translation rules via typing rules.
Two prerequisites are required for comparing search spaces. The first is a series of
benchmarks, a set of source and target languages equipped with intended translation
rules between them. The second is an enumerative synthesis algorithm for efficiently
enumerating typed programs. I show how algebraic enumeration techniques can be applied to enumerating well-typed translation rules, and discuss the properties expected
from a type system for ensuring that typed programs be efficiently enumerable.
The thesis presents and empirically evaluates two search spaces. A baseline search
space yields the first practical solution to the challenge. The second search space is
based on a natural heuristic for translation rules, limiting the usage of variables so that
they are used exactly once. I present a linear type system designed to efficiently enumerate translation rules, where this heuristic is enforced. Through informal analysis
and empirical comparison to the baseline, I then show that using linear types can speed
up the synthesis of translation rules by an order of magnitude
How to Turn Your Knowledge Graph Embeddings into Generative Models
Some of the most successful knowledge graph embedding (KGE) models for link
prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based
models. Under this perspective they are not amenable for exact
maximum-likelihood estimation (MLE), sampling and struggle to integrate logical
constraints. This work re-interprets the score functions of these KGEs as
circuits -- constrained computational graphs allowing efficient
marginalisation. Then, we design two recipes to obtain efficient generative
circuit models by either restricting their activations to be non-negative or
squaring their outputs. Our interpretation comes with little or no loss of
performance for link prediction, while the circuits framework unlocks exact
learning by MLE, efficient sampling of new triples, and guarantee that logical
constraints are satisfied by design. Furthermore, our models scale more
gracefully than the original KGEs on graphs with millions of entities
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
The Appropriation of Value from Knowledge: Three Essays on Technological Discontinuities, Market Entry, and Patent Strategy
Knowledge accumulation and protection are critical considerations of the firm. How does the capability to appropriate value from knowledge affect firm strategies in the industries? To answer this question, I develop a new theory and evidence to argue that appropriate value from knowledge is a central consideration in firms’ capabilities and decisions to deal with technological changes and intellectual property issues. In particular, I examine the relatedness of products and markets, the strategic uses of patents, and how firms can successfully adapt to concerns regarding technological changes and intellectual property leakage. Throughout my three dissertation chapters, I find evidence that the capability to appropriate value from knowledge can affect how firms behave in consistent and essential ways. These findings provide important implications for knowledge-based views of the firm and strategy-based recommendations in terms of the management of knowledge assets
Comparing the production of a formula with the development of L2 competence
This pilot study investigates the production of a formula with the development of L2 competence over proficiency levels of a spoken learner corpus. The results show that the formula
in beginner production data is likely being recalled holistically from learners’ phonological
memory rather than generated online, identifiable by virtue of its fluent production in absence
of any other surface structure evidence of the formula’s syntactic properties. As learners’ L2
competence increases, the formula becomes sensitive to modifications which show structural
conformity at each proficiency level. The transparency between the formula’s modification
and learners’ corresponding L2 surface structure realisations suggest that it is the independent
development of L2 competence which integrates the formula into compositional language,
and ultimately drives the SLA process forward
Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Response
Efficiently obtaining the up-to-date information in the disaster-stricken
area is the key to successful disaster response. Unmanned aerial vehicles
(UAVs), workers and cars can collaborate to accomplish sensing tasks, such as
data collection, in disaster-stricken areas. In this paper, we explicitly
address the route planning for a group of agents, including UAVs, workers, and
cars, with the goal of maximizing the task completion rate. We propose
MANF-RL-RP, a heterogeneous multi-agent route planning algorithm that
incorporates several efficient designs, including global-local dual information
processing and a tailored model structure for heterogeneous multi-agent
systems. Global-local dual information processing encompasses the extraction
and dissemination of spatial features from global information, as well as the
partitioning and filtering of local information from individual agents.
Regarding the construction of the model structure for heterogeneous
multi-agent, we perform the following work. We design the same data structure
to represent the states of different agents, prove the Markovian property of
the decision-making process of agents to simplify the model structure, and also
design a reasonable reward function to train the model. Finally, we conducted
detailed experiments based on the rich simulation data. In comparison to the
baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has
exhibited a significant improvement in terms of task completion rate
Detecting tropical peatland degradation: combining remote sensing and organic geochemistry
Tropical peatlands are important carbon stores that are vulnerable to drainage and conversion to agriculture. Protection and restoration of peatlands are increasingly recognised as key nature based solutions that can be implemented as part of climate change mitigation. Identification of peatland areas that are important for protection and restauration with regards to the state of their carbon stocks, are therefore vital for policy makers. In this paper we combined organic geochemical analysis by Rock-Eval (6) pyrolysis of peat collected from sites with different land management history and optical remote sensing products to assess if remotely sensed data could be used to predict peat conditions and carbon storage. The study used the North Selangor Peat Swamp forest, Malaysia, as the model system. Across the sampling sites the carbon stocks in the below ground peat was ca 12 times higher than the forest (median carbon stock held in ground vegetation 114.70 Mg ha-1 and peat soil 1401.51 Mg ha-1). Peat core sub-samples and litter collected from Fire Affected, Disturbed Forest, and Managed Recovery locations (i.e. disturbed sites) had different decomposition profiles than Central Forest sites. The Rock-Eval pyrolysis of the upper peat profiles showed that surface peat layers at Fire Affected, Disturbed Forest, and Managed Recovery locations had lower immature organic matter index (I-index) values (average I-index range in upper section 0.15 to -0.06) and higher refractory organic matter index (R -index) (average R-index range in upper section 0.51 to 0.65) compared to Central Forest sites indicating enhanced decomposition of the surface peat. In the top 50 cm section of the peat profile, carbon stocks were negatively related to the normalised burns ratio (NBR) (a satellite derived parameter) (Spearman’s rho = -0.664, S = 366, p-value = <0.05) while there was a positive relationship between the hydrogen index and the normalised burns ratio profile (Spearman’s rho = 0.7, S = 66, p-value = <0.05) suggesting that this remotely sensed product is able to detect degradation of peat in the upper peat profile. We conclude that the NBR can be used to identify degraded peatland areas and to support identification of areas for conversation and restoration
Markov field models of molecular kinetics
Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions
Dynamical Modularity in Automata Models of Biochemical Networks
Given the large size and complexity of most biochemical regulation and
signaling networks, there is a non-trivial relationship between the micro-level
logic of component interactions and the observed macro-dynamics. Here we
address this issue by formalizing the existing concept of pathway modules,
which are sequences of state updates that are guaranteed to occur (barring
outside interference) in the dynamics of automata networks after the
perturbation of a subset of driver nodes. We present a novel algorithm to
automatically extract pathway modules from networks and we characterize the
interactions that may take place between modules. This methodology uses only
the causal logic of individual node variables (micro-dynamics) without the need
to compute the dynamical landscape of the networks (macro-dynamics).
Specifically, we identify complex modules, which maximize pathway length and
require synergy between their components. This allows us to propose a new take
on dynamical modularity that partitions complex networks into causal pathways
of variables that are guaranteed to transition to specific states given a
perturbation to a set of driver nodes. Thus, the same node variable can take
part in distinct modules depending on the state it takes. Our measure of
dynamical modularity of a network is then inversely proportional to the overlap
among complex modules and maximal when complex modules are completely
decouplable from one another in the network dynamics. We estimate dynamical
modularity for several genetic regulatory networks, including the Drosophila
melanogaster segment-polarity network. We discuss how identifying complex
modules and the dynamical modularity portrait of networks explains the
macro-dynamics of biological networks, such as uncovering the (more or less)
decouplable building blocks of emergent computation (or collective behavior) in
biochemical regulation and signaling.Comment: 42 pages, 7 figure
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!
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