Meta-heuristic algorithms for optimized network flow wavelet-based image coding

Optimal multipath selection to maximize the received multiple description coding (MDCs) in a lossy network model is proposed. Multiple description scalar quantization (MDSQ) has been applied to the wavelet coefficients of a color image to generate the MDCs which are combating transmission loss over lossy networks. In the networks, each received description raises the reconstruction quality of an MDC-coded signal (image, audio or video). In terms of maximizing the received descriptions, a greater number of optimal routings between source and destination must be obtained. The rainbow network flow (RNF) collaborated with effective meta-heuristic algorithms is a good approach to resolve it. Two meta-heuristic algorithms which are genetic algorithm (GA) and particle swarm optimization (PSO) have been utilized to solve the multi-objective optimization routing problem for finding optimal routings each of which is assigned as a distinct color by RNF to maximize the coded descriptions in a network model. By employing a local search based priority encoding method, each individual in GA and particle in PSO is represented as a potential solution. The proposed algorithms are compared with the multipath Dijkstra algorithm (MDA) for both finding optimal paths and providing reliable multimedia communication. The simulations run over various random network topologies and the results show that the PSO algorithm finds optimal routings effectively and maximizes the received MDCs with assistance of RNF, leading to reduce packet loss and increase throughput

Genome-wide single-cell-level screen for protein abundance and localization changes in response to DNA damage in S. cerevisiae

An effective response to DNA damaging agents involves modulating numerous facets of cellular homeostasis in addition to DNA repair and cell-cycle checkpoint pathways. Fluorescence microscopy-based imaging offers the opportunity to simultaneously interrogate changes in both protein level and subcellular localization in response to DNA damaging agents at the single-cell level. We report here results from screening the yeast Green Fluorescent Protein (GFP)-fusion library to investigate global cellular protein reorganization on exposure to the alkylating agent methyl methanesulfonate (MMS). Broad groups of induced, repressed, nucleus- and cytoplasm-enriched proteins were identified. Gene Ontology and interactome analyses revealed the underlying cellular processes. Transcription factor (TF) analysis identified principal regulators of the response, and targets of all major stress-responsive TFs were enriched amongst the induced proteins. An unexpected partitioning of biological function according to the number of TFs targeting individual genes was revealed. Finally, differential modulation of ribosomal proteins depending on methyl methanesulfonate dose was shown to correlate with cell growth and with the translocation of the Sfp1 TF. We conclude that cellular responses can navigate different routes according to the extent of damage, relying on both expression and localization changes of specific proteins.National Cancer Institute (U.S.) (R01-CA055042 (now NIEHS R01-ES022872))Massachusetts Institute of Technology. Center for Environmental Health Sciences (Grant NIEHS P30-ES002109)National Cancer Institute (U.S.) (KI Center Grant U54-CA112967)National Cancer Institute (U.S.) (Cancer Center Support Grant P30-CA14051)National Institute of Environmental Health Sciences (R01-ES022872)MIT Faculty Start-up FundMassachusetts Institute of Technology. Computational and Systems Biology Initiative (Merck & Co. Postdoctoral Fellowship

Imagination and the generation of new ideas

A variety of theories have been put forth to explain the function of imagination, most notably that imagination engages and develops children's theory of mind and counterfactual reasoning. Here, we propose that a primary role for imagination is as a cognitive mechanism for efficiently generating new ideas without observing new evidence. Learners must generate hypotheses before they can assess the truth of these hypotheses. Given infinite possibilities, how do learners constrain the process of hypothesis generation? We suggest that learners represent abstract criteria for the solution to a problem and generate solutions that, if true, would solve the problem. As a preliminary test of this idea, we show that, in the absence of any fact of the matter (i.e., when neither prior knowledge nor statistical data distinguishes competing hypotheses), 4–6-year-olds (mean: 63 months) systematically converge on solutions to problems, consistent with an ability to imagine the abstract properties of causal problems and their solutions.National Science Foundation (U.S.) (0744213

Switching Away from Utilitarianism: The Limited Role of Utility Calculations in Moral Judgment.

Our moral motivations might include a drive towards maximizing overall welfare, consistent with an ethical theory called "utilitarianism." However, people show non-utilitarian judgments in domains as diverse as healthcare decisions, income distributions, and penal laws. Rather than these being deviations from a fundamentally utilitarian psychology, we suggest that our moral judgments are generally non-utilitarian, even for cases that are typically seen as prototypically utilitarian. We show two separate deviations from utilitarianism in such cases: people do not think maximizing welfare is required (they think it is merely acceptable, in some circumstances), and people do not think that equal welfare tradeoffs are even acceptable. We end by discussing how utilitarian reasoning might play a restricted role within a non-utilitarian moral psychology