The Benefit of Information Sharing in a Logistics Outsourcing Context

The goal of this article is to examine the value of information sharing in outsourcing of logistics activities. Our examination is in the context of a fairly complex network in which location and capacity of carriers are considered. The current research also examines the moderating effect of network settings on the benefit of information sharing. A core component of our methodology is use of computational experiments to provide a variety of logistics network conditions under which we investigate information sharing value. The investigation involves comparing two strategies, namely full and no information sharing. Underlying the experiments are procedures to optimise the network under each strategy. The procedures are based on exact methods that combine integer linear programming with exhaustive enumeration. To gauge the robustness of the insights, we applied formal analysis of variance techniques to the data from the numerical experiments. The obtained insights are helpful to managers for selecting appropriate logistics service providers and level of information exchange

Epitope prediction improved by multitask support vector machines

Motivation: In silico methods for the prediction of antigenic peptides binding to MHC class I molecules play an increasingly important role in the identification of T-cell epitopes. Statistical and machine learning methods, in particular, are widely used to score candidate epitopes based on their similarity with known epitopes and non epitopes. The genes coding for the MHC molecules, however, are highly polymorphic, and statistical methods have difficulties to build models for alleles with few known epitopes. In this case, recent works have demonstrated the utility of leveraging information across alleles to improve the performance of the prediction. Results: We design a support vector machine algorithm that is able to learn epitope models for all alleles simultaneously, by sharing information across similar alleles. The sharing of information across alleles is controlled by a user-defined measure of similarity between alleles. We show that this similarity can be defined in terms of supertypes, or more directly by comparing key residues known to play a role in the peptide-MHC binding. We illustrate the potential of this approach on various benchmark experiments where it outperforms other state-of-the-art methods

When orthography is not enough: the effect of lexical stress in lexical decision.

Three lexical decision experiments were carried out in Italian, in order to verify if stress dominance (the most frequent stress type) and consistency (the proportion and number of existent words sharing orthographic ending and stress pattern) had an effect on polysyllabic word recognition. Two factors were manipulated: whether the target word carried stress on the penultimate (dominant; graNIta, seNIle 'slush, senile') or on the antepenultimate (non-dominant) syllable (MISsile, BIbita 'missile, drink'), and whether the stress neighborhood was consistent (graNIta, MISsile) or inconsistent (seNIle, BIbita) with the word\u2019s stress pattern. In Experiment 1 words were mixed with nonwords sharing the word endings, which made words and nonwords more similar to each other. In Experiment 2 words and nonwords were presented in lists blocked for stress pattern. In Experiment 3 we used a new set of nonwords, which included endings with (stress) ambiguous neighborhoods and/or with low number of neighbors, and which were overall less similar to words. In all three experiments there was an advantage for words with penultimate (dominant) stress, and no main effect of stress neighborhood. However, the dominant stress advantage decreased in Experiments 2 and 3. Finally, in Experiment 4 the same materials used in Experiment 1 were also used in a reading aloud task, showing a significant consistency effect, but no dominant stress advantage. The influence of stress information in Italian word recognition is discussed

A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing systems to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas