Deep Policy Dynamic Programming for Vehicle Routing Problems

Routing problems are a class of combinatorial problems with many practical applications. Recently, end-to-end deep learning methods have been proposed to learn approximate solution heuristics for such problems. In contrast, classical dynamic programming (DP) algorithms guarantee optimal solutions, but scale badly with the problem size. We propose Deep Policy Dynamic Programming (DPDP), which aims to combine the strengths of learned neural heuristics with those of DP algorithms. DPDP prioritizes and restricts the DP state space using a policy derived from a deep neural network, which is trained to predict edges from example solutions. We evaluate our framework on the travelling salesman problem (TSP), the vehicle routing problem (VRP) and TSP with time windows (TSPTW) and show that the neural policy improves the performance of (restricted) DP algorithms, making them competitive to strong alternatives such as LKH, while also outperforming most other 'neural approaches' for solving TSPs, VRPs and TSPTWs with 100 nodes.Comment: 21 page

Self-organization of actin filament orientation in the dendritic-nucleation/array-treadmilling model

Author Posting. © The Author(s), 2006. This is the author's version of the work. It is posted here by permission of National Academy of Sciences of the USA for personal use, not for redistribution. The definitive version was published in Proceedings of the National Academy of Sciences 104 (2007): 7086-7091, doi:10.1073/pnas.0701943104.The dendritic-nucleation/array-treadmilling model provides a conceptual framework for the generation of the actin network driving motile cells. We have incorporated it into a 2-D, stochastic computer model to study lamellipodia via the self-organization of filament orientation patterns. Essential dendritic-nucleation sub-models were incorporated, including discretized actin monomer diffusion, Monte-Carlo filament kinetics, and flexible filament and plasma membrane mechanics. Model parameters were estimated from the literature and simulation, providing values for the extent of the leading edge branching/capping-protective zone (5.4 nm) and the auto-catalytic branch rate (0.43 /s). For a given set of parameters the system evolved to a steady state filament count and velocity, at which total branching and capping rates were equal only for specific orientations; net capping eliminated others. The standard parameter set evoked a sharp preference for the ±35 deg. filaments seen in lamellipodial electron micrographs, requiring ~ 12 generations of successive branching to adapt to a 15 deg. change in protrusion direction. This pattern was robust with respect to membrane surface and bending energies and to actin concentrations, but required protection from capping at the leading edge and branching angles greater than 60 deg. A +70/0/-70 deg. pattern was formed with flexible filaments ~ 100 nm or longer and with velocities less than ~ 20% of free polymerization rates

Constraint Programming for Multi-criteria Conceptual Clustering

International audienceA conceptual clustering is a set of formal concepts (i.e., closed itemsets) that defines a partition of a set of transactions. Finding a conceptual clustering is an N P-complete problem for which Constraint Programming (CP) and Integer Linear Programming (ILP) approaches have been recently proposed. We introduce new CP models to solve this problem: a pure CP model that uses set constraints, and an hybrid model that uses a data mining tool to extract formal concepts in a preprocessing step and then uses CP to select a subset of formal concepts that defines a partition. We compare our new models with recent CP and ILP approaches on classical machine learning instances. We also introduce a new set of instances coming from a real application case, which aims at extracting setting concepts from an Enterprise Resource Planning (ERP) software. We consider two classic criteria to optimize, i.e., the frequency and the size. We show that these criteria lead to extreme solutions with either very few small formal concepts or many large formal concepts, and that compromise clusterings may be obtained by computing the Pareto front of non dominated clusterings

Intrinsic dynamic behavior of fascin in filopodia

Author Posting. © American Society for Cell Biology, 2007. This article is posted here by permission of American Society for Cell Biology for personal use, not for redistribution. The definitive version was published in Molecular Biology of the Cell 18 (2007): 3928-3940, doi:10.1091/mbc.E07-04-0346.Recent studies showed that the actin cross-linking protein, fascin, undergoes rapid cycling between filopodial filaments. Here, we used an experimental and computational approach to dissect features of fascin exchange and incorporation in filopodia. Using expression of phosphomimetic fascin mutants, we determined that fascin in the phosphorylated state is primarily freely diffusing, whereas actin bundling in filopodia is accomplished by fascin dephosphorylated at serine 39. Fluorescence recovery after photobleaching analysis revealed that fascin rapidly dissociates from filopodial filaments with a kinetic off-rate of 0.12 s–1 and that it undergoes diffusion at moderate rates with a coefficient of 6 µm2s–1. This kinetic off-rate was recapitulated in vitro, indicating that dynamic behavior is intrinsic to the fascin cross-linker. A computational reaction–diffusion model showed that reversible cross-linking is required for the delivery of fascin to growing filopodial tips at sufficient rates. Analysis of fascin bundling indicated that filopodia are semiordered bundles with one bound fascin per 25–60 actin monomers.This work was supported by a National Institutes of Health F31National Research Service Award NS055565-01 (to Y.S.A.), Northwestern University Pulmonary and Critical Care Division T32 (to T.E.S.), and National Institutes of Health grant GM-70898 (to G.G.B.)

AFM-Detected Apoptotic Changes in Morphology and Biophysical Property Caused by Paclitaxel in Ishikawa and HeLa Cells

The apoptosis of cancer cells is associated with changes in the important cell properties including morphology, surface roughness and stiffness. Therefore, the changes in morphology and biophysical properties can be a good way of evaluating the anticancer activity of a drug. This study examined the effect of paclitaxel on the properties of Ishikawa and HeLa cells using atomic force microscopy (AFM), and the relationship between the changes in morphology and the biophysical properties and apoptosis was discussed. The viability and proliferation of the cells were analyzed using the methylthiazol tetrazolium (MTT) method and a TUNEL assay to confirm cellular apoptosis due to a paclitaxel treatment. AFM observations clearly showed the apoptotic morphological and biophysical changes in Ishikawa and HeLa cells. After the paclitaxel treatment, the cell membrane was torn and holed, the surface roughness was increased, and the stiffness was decreased. These changes were observed more apparently after a 24 h treatment and in Ishikawa cells compared to HeLa cells. The MTT and TUNEL assays results revealed the Ishikawa cells to be more sensitive to paclitaxel than HeLa cells and definite apoptosis occurred after a 24 h treatment. These results showed good agreement with the AFM results. Therefore, research on the morphological and biophysical changes by AFM in cancer cells will help to evaluate the anticancer activities of the drugs