Finding undetected protein associations in cell signaling by belief propagation

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.Comment: 6 pages, 3 figures, 1 table, Supporting Informatio

Underwater Data Collection Using Robotic Sensor Networks

We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.United States. Office of Naval Research (ONR N00014-09-1-0700)United States. Office of Naval Research (ONR N00014-07-1-00738)National Science Foundation (U.S.) (NSF 0831728)National Science Foundation (U.S.) (NSF CCR-0120778)National Science Foundation (U.S.) (NSF CNS-1035866

Energy Scaling Laws for Distributed Inference in Random Fusion Networks

The energy scaling laws of multihop data fusion networks for distributed inference are considered. The fusion network consists of randomly located sensors distributed i.i.d. according to a general spatial distribution in an expanding region. Among the class of data fusion schemes that enable optimal inference at the fusion center for Markov random field (MRF) hypotheses, the scheme with minimum average energy consumption is bounded below by average energy of fusion along the minimum spanning tree, and above by a suboptimal scheme, referred to as Data Fusion for Markov Random Fields (DFMRF). Scaling laws are derived for the optimal and suboptimal fusion policies. It is shown that the average asymptotic energy of the DFMRF scheme is finite for a class of MRF models.Comment: IEEE JSAC on Stochastic Geometry and Random Graphs for Wireless Network

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks

The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included

The Case Against Copyright: A Comparative Institutional Analysis of Intellectual Property Regimes

Contemporary debates over intellectual property ( IP ) generally evidence positions that appear to line up at opposite ends of the same axis, with one side arguing for more rights for IP owners under each major regime - patent, trademark, and copyright - and the other side arguing for fewer. Approaching from what some may see as a more IP view, this paper offers the counterintuitive suggestion to consider abolishing one of these IP regimes - copyright, at least with respect to the entertainment industry, which represents one of that regime\u27s most commercially significant users. This realization is in fact consistent with the underlying view because the view is not accurately seen as even being directed to the more or less debate; and instead is focused on means as much as ends. In keeping with this means-directed approach, the paper provides the first comprehensive analysis of IP regimes using the set of tools from the field of new institutional economics. In so doing the paper offers the first normative case for IP that connects the path breaking literature on the theory of property rights generally with the seminal theories of the firm, transaction costs, and agency costs. Underlying this paper\u27s stark departure from both the more and less bodies of the IP literature is the realization that the institutional structure of the present copyright regime may make the social costs of the present copyright regime too high, for at least the entertainment industry, while at the same time preventing it from providing the coordination benefits an IP regime normatively should provide. Building on this, the paper begins to explore for the first time whether the recent patent and trademark regimes have institutional structures that may allow them to provide these coordination benefits better, and with lower social costs. The paper thereby suggests how the patent and trademark regimes of yesterday may obsolete the copyright system of today

Improving the accuracy of weed species detection for robotic weed control in complex real-time environments

Alex Olsen applied deep learning and machine vision to improve the accuracy of weed species detection in real time complex environments. His robotic weed control prototype, AutoWeed, presents a new efficient tool for weed management in crop and pasture and has launched a startup agricultural technology company