64 research outputs found
Surrogate Assisted Optimisation for Travelling Thief Problems
The travelling thief problem (TTP) is a multi-component optimisation problem
involving two interdependent NP-hard components: the travelling salesman
problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP
solvers modify the underlying TSP and KP solutions in an iterative and
interleaved fashion. The TSP solution (cyclic tour) is typically changed in a
deterministic way, while changes to the KP solution typically involve a random
search, effectively resulting in a quasi-meandering exploration of the TTP
solution space. Once a plateau is reached, the iterative search of the TTP
solution space is restarted by using a new initial TSP tour. We propose to make
the search more efficient through an adaptive surrogate model (based on a
customised form of Support Vector Regression) that learns the characteristics
of initial TSP tours that lead to good TTP solutions. The model is used to
filter out non-promising initial TSP tours, in effect reducing the amount of
time spent to find a good TTP solution. Experiments on a broad range of
benchmark TTP instances indicate that the proposed approach filters out a
considerable number of non-promising initial tours, at the cost of omitting
only a small number of the best TTP solutions
Solving Travelling Thief Problems using Coordination Based Methods
A travelling thief problem (TTP) is a proxy to real-life problems such as
postal collection. TTP comprises an entanglement of a travelling salesman
problem (TSP) and a knapsack problem (KP) since items of KP are scattered over
cities of TSP, and a thief has to visit cities to collect items. In TTP, city
selection and item selection decisions need close coordination since the
thief's travelling speed depends on the knapsack's weight and the order of
visiting cities affects the order of item collection. Existing TTP solvers deal
with city selection and item selection separately, keeping decisions for one
type unchanged while dealing with the other type. This separation essentially
means very poor coordination between two types of decision. In this paper, we
first show that a simple local search based coordination approach does not work
in TTP. Then, to address the aforementioned problems, we propose a human
designed coordination heuristic that makes changes to collection plans during
exploration of cyclic tours. We further propose another human designed
coordination heuristic that explicitly exploits the cyclic tours in item
selections during collection plan exploration. Lastly, we propose a machine
learning based coordination heuristic that captures characteristics of the two
human designed coordination heuristics. Our proposed coordination based
approaches help our TTP solver significantly outperform existing
state-of-the-art TTP solvers on a set of benchmark problems. Our solver is
named Cooperation Coordination (CoCo) and its source code is available from
https://github.com/majid75/CoCoComment: expanded and revised version of arXiv:1911.0312
Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees
Toxicity prediction of chemical compounds is a grand challenge. Lately, it
achieved significant progress in accuracy but using a huge set of features,
implementing a complex blackbox technique such as a deep neural network, and
exploiting enormous computational resources. In this paper, we strongly argue
for the models and methods that are simple in machine learning characteristics,
efficient in computing resource usage, and powerful to achieve very high
accuracy levels. To demonstrate this, we develop a single task-based chemical
toxicity prediction framework using only 2D features that are less compute
intensive. We effectively use a decision tree to obtain an optimum number of
features from a collection of thousands of them. We use a shallow neural
network and jointly optimize it with decision tree taking both network
parameters and input features into account. Our model needs only a minute on a
single CPU for its training while existing methods using deep neural networks
need about 10 min on NVidia Tesla K40 GPU. However, we obtain similar or better
performance on several toxicity benchmark tasks. We also develop a cumulative
feature ranking method which enables us to identify features that can help
chemists perform prescreening of toxic compounds effectively
The Schroedinger Problem, Levy Processes Noise in Relativistic Quantum Mechanics
The main purpose of the paper is an essentially probabilistic analysis of
relativistic quantum mechanics. It is based on the assumption that whenever
probability distributions arise, there exists a stochastic process that is
either responsible for temporal evolution of a given measure or preserves the
measure in the stationary case. Our departure point is the so-called
Schr\"{o}dinger problem of probabilistic evolution, which provides for a unique
Markov stochastic interpolation between any given pair of boundary probability
densities for a process covering a fixed, finite duration of time, provided we
have decided a priori what kind of primordial dynamical semigroup transition
mechanism is involved. In the nonrelativistic theory, including quantum
mechanics, Feyman-Kac-like kernels are the building blocks for suitable
transition probability densities of the process. In the standard "free" case
(Feynman-Kac potential equal to zero) the familiar Wiener noise is recovered.
In the framework of the Schr\"{o}dinger problem, the "free noise" can also be
extended to any infinitely divisible probability law, as covered by the
L\'{e}vy-Khintchine formula. Since the relativistic Hamiltonians
and are known to generate such laws, we focus on
them for the analysis of probabilistic phenomena, which are shown to be
associated with the relativistic wave (D'Alembert) and matter-wave
(Klein-Gordon) equations, respectively. We show that such stochastic processes
exist and are spatial jump processes. In general, in the presence of external
potentials, they do not share the Markov property, except for stationary
situations. A concrete example of the pseudodifferential Cauchy-Schr\"{o}dinger
evolution is analyzed in detail. The relativistic covariance of related waveComment: Latex fil
Laparoscopy in management of appendicitis in high-, middle-, and low-income countries: a multicenter, prospective, cohort study.
BACKGROUND: Appendicitis is the most common abdominal surgical emergency worldwide. Differences between high- and low-income settings in the availability of laparoscopic appendectomy, alternative management choices, and outcomes are poorly described. The aim was to identify variation in surgical management and outcomes of appendicitis within low-, middle-, and high-Human Development Index (HDI) countries worldwide. METHODS: This is a multicenter, international prospective cohort study. Consecutive sampling of patients undergoing emergency appendectomy over 6 months was conducted. Follow-up lasted 30 days. RESULTS: 4546 patients from 52 countries underwent appendectomy (2499 high-, 1540 middle-, and 507 low-HDI groups). Surgical site infection (SSI) rates were higher in low-HDI (OR 2.57, 95% CI 1.33-4.99, p = 0.005) but not middle-HDI countries (OR 1.38, 95% CI 0.76-2.52, p = 0.291), compared with high-HDI countries after adjustment. A laparoscopic approach was common in high-HDI countries (1693/2499, 67.7%), but infrequent in low-HDI (41/507, 8.1%) and middle-HDI (132/1540, 8.6%) groups. After accounting for case-mix, laparoscopy was still associated with fewer overall complications (OR 0.55, 95% CI 0.42-0.71, p < 0.001) and SSIs (OR 0.22, 95% CI 0.14-0.33, p < 0.001). In propensity-score matched groups within low-/middle-HDI countries, laparoscopy was still associated with fewer overall complications (OR 0.23 95% CI 0.11-0.44) and SSI (OR 0.21 95% CI 0.09-0.45). CONCLUSION: A laparoscopic approach is associated with better outcomes and availability appears to differ by country HDI. Despite the profound clinical, operational, and financial barriers to its widespread introduction, laparoscopy could significantly improve outcomes for patients in low-resource environments. TRIAL REGISTRATION: NCT02179112
Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial
Background
Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy
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