The proceedings contain 55 papers. The topics discussed include: low-power listening goes multi-channel;PhyTraces: simulating new RF environments with physical layer traces;a novel wake-up receiver with addressing cap...
ISBN:
(纸本)9781479946198
The proceedings contain 55 papers. The topics discussed include: low-power listening goes multi-channel;PhyTraces: simulating new RF environments with physical layer traces;a novel wake-up receiver with addressing capability for wireless sensor nodes;symmetric coherent link degree, adaptive throughput-transmission power for wireless sensor networks;indoor occupant positioning system using active RFID deployment and particle filters;harnessing non-uniform transmit power levels for improved sequence based localization;FlashTrack: a fast, in-network tracking system for sensor networks;crowd-sensing with polarized sources;a dual-sensor enabled indoor localization system with Crowdsensing spot survey;on decentralized in-network aggregation in real-world scenarios with crowd mobility;and the information funnel: exploiting named data for information-maximizing data collection.
Under sampling is a popular technique for unbalanced datasets to reduce the skew in class distributions. However, it is well-known that under sampling one class modifies the priors of the training set and consequently...
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Under sampling is a popular technique for unbalanced datasets to reduce the skew in class distributions. However, it is well-known that under sampling one class modifies the priors of the training set and consequently biases the posterior probabilities of a classifier. In this paper, we study analytically and experimentally how under sampling affects the posterior probability of a machine learning model. We formalize the problem of under sampling and explore the relationship between conditional probability in the presence and absence of under sampling. Although the bias due to under sampling does not affect the ranking order returned by the posterior probability, it significantly impacts the classification accuracy and probability calibration. We use Bayes Minimum Risk theory to find the correct classification threshold and show how to adjust it after under sampling. Experiments on several real-world unbalanced datasets validate our results.
Spiking neural networks (SNNs) have generated considerable excitement because of their computational properties, believed to be superior to conventional von Neumann machines, and sharing properties with living brains....
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Spiking neural networks (SNNs) have generated considerable excitement because of their computational properties, believed to be superior to conventional von Neumann machines, and sharing properties with living brains. Yet progress building these systems has been limited because we lack a design methodology. We present a gene-driven network growth algorithm that enables a genetic algorithm (evolutionary computation) to generate and test SNNs. The genome length for this algorithm grows O(n) where n is the number of neurons; n is also evolved. The genome not only specifies the network topology, but all its parameters as well. In experiments, the algorithm discovered SNNs that effectively produce a robust spike bursting behavior given tonic inputs, an application suitable for central pattern generators. Even though evolution did not include perturbations of the input spike trains, the evolved networks showed remarkable robustness to such perturbations. On a second task, a sequence detector, several related discriminating designs were found, all made “errors” in that they fired when input spikes were simultaneous (i.e. not strictly in sequence), but not when they were out of sequence. They also fired when the sequence was too close for the teacher to have declared they were in sequence. That is, evolution produced these behaviors even though it was not explicitly rewarded for doing so. We are optimistic that this technology might be scaled up to produce robust SNN designs that humans would be hard pressed to produce.
Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times s...
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Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.
Protein alignment is a basic step for many molecular biology researches. The BLOSUM matrices, especially BLOSUM62, are the de facto standard matrices for protein alignments. However, after widely utilization of the ma...
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Protein alignment is a basic step for many molecular biology researches. The BLOSUM matrices, especially BLOSUM62, are the de facto standard matrices for protein alignments. However, after widely utilization of the matrices for 15 years, programming errors were surprisingly found in the initial version of source codes for their generation. And amazingly, after bug correction, the "intended" BLOSUM62 matrix performs consistently worse than the "miscalculated" one. In this paper, we find linear relationships among the eigenvalues of the matrices and propose an algorithm to find optimal unified eigenvectors. With them, we can parameterize matrix BLOSUMx for any given variable x that could change continuously. We compare the effectiveness of our parameterized isentropic matrix with BLOSUM62. Furthermore, an iterative alignment and matrix selection process is proposed to adaptively find the best parameter and globally align two sequences. Experiments are conducted on aligning 13,667 families of Pfam database and on clustering MHC II protein sequences, whose improved accuracy demonstrates the effectiveness of our proposed method.
Parallel implementations of molecular dynamics (MD) simulation require significant inter-node communication, but off-chip communication bandwidth is not scaling as quickly as on-chip logic density. We present three ne...
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ISBN:
(纸本)9781479986507
Parallel implementations of molecular dynamics (MD) simulation require significant inter-node communication, but off-chip communication bandwidth is not scaling as quickly as on-chip logic density. We present three network features targeting this problem that have been implemented in Anton 2, a massively parallel special-purpose supercomputer for MD simulations. The first is a mechanism to dynamically identify packets that do not need to be delivered to all endpoints within a multicast tree, these packets are filtered to conserve network bandwidth. The second is hardware for in-network reductions that supports over a thousand concurrent neighbourhood reductions per node and fast all-to-all global reductions. The third is a log-weight synchronization mechanism for multicast-reduce communication patterns that can be used to efficiently detect the completion of reduction operations when the number of summands is difficult to predict. We use the combination of packet filtering, in-network reductions and log-weight synchronization to decrease the communication requirements of MD simulations by as much as 51% on Anton 2, yielding application-level performance improvements of up to 14%.
The 2008 ieee symposium on computational intelligence in bioinformatics and computational biology was held at the Sun Valley Resort in Sun Valley, Idaho on September 15-17.
The 2008 ieee symposium on computational intelligence in bioinformatics and computational biology was held at the Sun Valley Resort in Sun Valley, Idaho on September 15-17.
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