Bidirectional motion of filaments: Role of motor proteins and passive cross linkers

In eukaryotic cells, motor proteins (MP) bind to cytoskeletal filaments and move along them in a directed manner generating active stresses. During cell division a spindle structure of overlapping antiparallel microtubules (MT) form whose stability and dynamics under the influence of MPs has been studied extensively. Although passive cross linkers (PCL) were known to provide structural stability to filamentous network, consequences of the interplay between ATP dependent active forces of MPs and passive entropic forces of PCLs on MT overlap remains largely unexplored. Here, we formulate and characterize a model to study this, using linear stability analysis and numerical integration. In presence of PCLs, we find dynamic phase transitions with changing activity exhibiting regimes of stable partial overlap with or without oscillations, instability towards complete overlap, and stable limit cycle oscillations that emerge via a supercritical Hopf bifurcation characterized by an oscillation frequency determined by the MP and PCL parameters. We show that the overlap dynamics and stability depend crucially on whether both the MTs of overlapping pair are movable or one is immobilized, having potential implications for in vivo and in vitro studies.Comment: 13 pages, 9 figure

Financial Time Series Forecasting using Agent Based Models in Equity and FX Markets

We investigate the application of machine learning Agent Based Modelling (ABM) techniques to model and forecast various financial markets including Foreign Exchange and Equities, especially models that could reproduce the time-series properties of the financial variables. We model the economy by considering non-equilibrium economics. We adopt the features that are required for modelling non-equilibrium economics using ABMs and replicate the non-equilibrium nature of the financial markets by considering a set of bounded rational heterogeneous agents, with different strategies that are ranked according to their performance in the market. We consider markets where there are different agents interacting among themselves and forming some sort of patterns. For example, the patterns are equity prices or exchange rates. While the agents have been interacting in the artificial market, the generated patterns (price dynamics) they co-produce would match with the real financial time-series. In order to get the best fit to the real market, we need to search for the best set of artificial heterogeneous agents that represents the underlying market. Evolutionary computing techniques are used in order to search for a suitable set of agent configuration in the market. We verify the forecasting performance of the artificial markets by comparing that with the real financial market by conducting out-of-sample tests

Dispatch guided allocation optimization for effective emergency response

Plant-pollinator interaction networks are bipartite networks representing the mutualistic interactions between a set of plant species and a set of pollinator species. Data on these networks are collected by field biologists, who count visits from pollinators to flowers. Ecologists study the structure and function of these networks for scientific, conservation, and agricultural purposes. However, little research has been done to understand the underlying mechanisms that determine pairwise interactions or to predict new links from networks describing the species community. This paper explores the use of latent factor models to predict interactions that will occur in new contexts (e.g. a different distribution of the set of plant species) based on an observed network. The analysis draws on algorithms and evaluation strategies developed for recommendation systems and introduces them to this new domain. The matrix factorization methods compare favorably against several baselines on a pollination dataset collected in montane meadows over several years. Incorporating both positive and negative implicit feedback into the matrix factorization methods is particularly promising

Strategic Planning for Setting up Base Stations In Emergency Medical Systems

Emergency Medical Systems (EMSs) are an important component of public health-care services. Improving infrastructure for EMS and specifically the construction of base stations at the ”right” locations to reduce response times is the main focus of this paper. This is a computationally challenging task because of the: (a) exponentially large action space arising from having to consider combinations of potential base locations, which themselves can be significant; and (b) direct impact on the performance of the ambulance allocation problem, where we decide allocation of ambulances to bases. We present an incremental greedy approach to discover the placement of bases that maximises the service level of EMS. Using the properties of submodular optimisation we show that our greedy algorithm provides quality guaranteed solutions for one of the objectives employed in real EMSs. Furthermore, we validate our derived policy by employing a real-life event driven simulator that incorporates the real dynamics of EMS. Finally, we show the utility of our approaches on a real-world dataset from a large asian city and demonstrate significant improvement over the best known approaches from literature

Incentivizing the use of bike trailers for dynamic repositioning in bike sharing systems

Bike Sharing System (BSS) is a green mode of transportation that is employed extensively for short distance travels in major cities of the world. Unfortunately, the users behaviour driven by their personal needs can often result in empty or full base stations, thereby resulting in loss of customer demand. To counter this loss in customer demand, BSS operators typically utilize a fleet of carrier vehicles for repositioning the bikes between stations. However, this fuel burning mode of repositioning incurs a significant amount of routing, labor cost and further increases carbon emissions. Therefore, we propose a potentially self-sustaining and environment friendly system of dynamic repositioning, that moves idle bikes during the day with the help of bike trailers. A bike trailer is an add-on to a bike that can help with carrying 3-5 bikes at once. Specifically, we make the following key contributions: (i) We provide an optimization formulation that generates “repositioning” tasks so as to minimize the expected lost demand over past demand scenarios; (ii) Within the budget constraints of the operator, we then design a mechanism to crowdsource the tasks among potential users who intend to execute repositioning tasks; (iii) Finally, we provide extensive results on a wide range of demand scenarios from a real-world data set to demonstrate that our approach is highly competitive to the existing fuel burning mode of repositioning while being green

Probabilistic Inference Based Message-Passing for Resource Constrained DCOPs

Distributed constraint optimization (DCOP) is an important framework for coordinated multiagent decision making. We address a practically use-ful variant of DCOP, called resource-constrained DCOP (RC-DCOP), which takes into account agents ’ consumption of shared limited resources. We present a promising new class of algorithm for RC-DCOPs by translating the underlying co-ordination problem to probabilistic inference. Us-ing inference techniques such as expectation-maximization and convex optimization machinery, we develop a novel convergent message-passing al-gorithm for RC-DCOPs. Experiments on standard benchmarks show that our approach provides bet-ter quality than previous best DCOP algorithms and has much lower failure rate. Comparisons against an efficient centralized solver show that our ap-proach provides near-optimal solutions, and is sig-nificantly faster on larger instances.

Robust Repositioning to Counter Unpredictable Demand in Bike Sharing Systems

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

A New Outlook in Lymphatic Filariasis Elimination in India

In India, human lymphatic filariasis (LF) is the most common vector-borne disease after malaria. It is a roundworm nematode parasitic helminthiases group of diseases under Filarioidea type of infection. The parasites are found in the lymphatic system, damage the system leading to deformities of body organs. Of the eight human filarial parasites, Wuchereria bancrofti, Brugia malayi and B. timori are involved with the lymphatic system. Globally W. bancrofti is the most predominant species sharing 90% of the burden. In India, W. bancrofti and B. malayi are present. The revised control strategy was aimed at a single-dose mass drug administration (MDA) and home-based morbidity management. The Elimination of LF (ELF) was initiated in 2004 in 202 districts which were expanded later in 256 districts after a pilot study in LF endemic districts initiated in 1997. The initial start of ELF campaign was with a single drug, i.e. diethylcarbamazine (DEC), but later in 2007, a combination of two drugs DEC and albendazole (ALB) were given through MDA. Now a third drug ivermectin (IVM) has been added to accelerate the elimination process by 2020 which is the global goal of elimination under Global Programme to Eliminate Lymphatic Filariasis (GPELF)