8,301 research outputs found
The body in the library: adventures in realism
This essay looks at two aspects of the virtual ‘material world’ of realist fiction: objects encountered by the protagonist and the latter’s body. Taking from Sartre two angles on the realist pact by which readers agree to lend
their bodies, feelings, and experiences to the otherwise ‘languishing signs’ of the text, it goes on to examine two sets of first-person fictions published between 1902 and 1956 — first, four modernist texts in which banal objects defy and then gratify the protagonist, who ends up ready and almost able to write; and, second, three novels in which the body of the protagonist is indeterminate in its sex, gender, or sexuality. In each of these cases, how do we as readers make texts work for us as ‘an adventure of the body’
Public Health Laboratories: Unprepared and Overwhelmed
Addresses the role of public health labs within the public health system and their ability to respond to specific chemical weapon events. Provides recommendations for improving response to terrorism as well as more conventional threats
Thermal conduction in molecular chains: Non-Markovian effects
We study the effect of non-Markovian reservoirs on the heat conduction
properties of short to intermediate size molecular chains. Using classical
molecular dynamics simulations, we show that the distance dependence of the
heat current is determined not only by the molecular properties, rather it is
also critically influenced by the spectral properties of the heat baths for
both harmonic and anharmonic molecular chains. For highly correlated reservoirs
the current of an anharmonic chain may exceed the flux of the corresponding
harmonic system. Our numerical results are accompanied by a simple single-mode
heat conduction model that can capture the intricate distance dependence
obtained numerically
Auctions with Severely Bounded Communication
We study auctions with severe bounds on the communication allowed: each
bidder may only transmit t bits of information to the auctioneer. We consider
both welfare- and profit-maximizing auctions under this communication
restriction. For both measures, we determine the optimal auction and show that
the loss incurred relative to unconstrained auctions is mild. We prove
non-surprising properties of these kinds of auctions, e.g., that in optimal
mechanisms bidders simply report the interval in which their valuation lies in,
as well as some surprising properties, e.g., that asymmetric auctions are
better than symmetric ones and that multi-round auctions reduce the
communication complexity only by a linear factor
F as in Fat: How Obesity Policies Are Failing in America, 2005
Examines national and state obesity rates and government policies. Challenges the research community to focus on major research questions to inform policy decisions, and policymakers to pursue actions to combat the obesity crisis
Large-scale structure and the redshift-distance relation
In efforts to demonstrate the linear Hubble law v = Hr from galaxy
observations, the underlying simplicity is often obscured by complexities
arising from magnitude-limited data. In this paper we point out a simple but
previously unremarked fact: that the shapes and orientations of structures in
redshift space contain in themselves independent information about the
cosmological redshift-distance relation.
The orientations of voids in the CfA slice support the Hubble law, giving a
redshift-distance power index p = 0.83 +/- 0.36 (void data from Slezak, de
Lapparent, & Bijoui 1993) or p = 0.99 +/- 0.38 (void data from Malik &
Subramanian 1997).Comment: 11 pages (AASTeX), 4 figures, to appear in the Astrophysical Journal
Letter
Information based clustering
In an age of increasingly large data sets, investigators in many different
disciplines have turned to clustering as a tool for data analysis and
exploration. Existing clustering methods, however, typically depend on several
nontrivial assumptions about the structure of data. Here we reformulate the
clustering problem from an information theoretic perspective which avoids many
of these assumptions. In particular, our formulation obviates the need for
defining a cluster "prototype", does not require an a priori similarity metric,
is invariant to changes in the representation of the data, and naturally
captures non-linear relations. We apply this approach to different domains and
find that it consistently produces clusters that are more coherent than those
extracted by existing algorithms. Finally, our approach provides a way of
clustering based on collective notions of similarity rather than the
traditional pairwise measures.Comment: To appear in Proceedings of the National Academy of Sciences USA, 11
pages, 9 figure
Motif Discovery through Predictive Modeling of Gene Regulation
We present MEDUSA, an integrative method for learning motif models of
transcription factor binding sites by incorporating promoter sequence and gene
expression data. We use a modern large-margin machine learning approach, based
on boosting, to enable feature selection from the high-dimensional search space
of candidate binding sequences while avoiding overfitting. At each iteration of
the algorithm, MEDUSA builds a motif model whose presence in the promoter
region of a gene, coupled with activity of a regulator in an experiment, is
predictive of differential expression. In this way, we learn motifs that are
functional and predictive of regulatory response rather than motifs that are
simply overrepresented in promoter sequences. Moreover, MEDUSA produces a model
of the transcriptional control logic that can predict the expression of any
gene in the organism, given the sequence of the promoter region of the target
gene and the expression state of a set of known or putative transcription
factors and signaling molecules. Each motif model is either a -length
sequence, a dimer, or a PSSM that is built by agglomerative probabilistic
clustering of sequences with similar boosting loss. By applying MEDUSA to a set
of environmental stress response expression data in yeast, we learn motifs
whose ability to predict differential expression of target genes outperforms
motifs from the TRANSFAC dataset and from a previously published candidate set
of PSSMs. We also show that MEDUSA retrieves many experimentally confirmed
binding sites associated with environmental stress response from the
literature.Comment: RECOMB 200
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