6,897 research outputs found
Route Swarm: Wireless Network Optimization through Mobility
In this paper, we demonstrate a novel hybrid architecture for coordinating
networked robots in sensing and information routing applications. The proposed
INformation and Sensing driven PhysIcally REconfigurable robotic network
(INSPIRE), consists of a Physical Control Plane (PCP) which commands agent
position, and an Information Control Plane (ICP) which regulates information
flow towards communication/sensing objectives. We describe an instantiation
where a mobile robotic network is dynamically reconfigured to ensure high
quality routes between static wireless nodes, which act as source/destination
pairs for information flow. The ICP commands the robots towards evenly
distributed inter-flow allocations, with intra-flow configurations that
maximize route quality. The PCP then guides the robots via potential-based
control to reconfigure according to ICP commands. This formulation, deemed
Route Swarm, decouples information flow and physical control, generating a
feedback between routing and sensing needs and robotic configuration. We
demonstrate our propositions through simulation under a realistic wireless
network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
Interpolators -- estimators that achieve zero training error -- have
attracted growing attention in machine learning, mainly because state-of-the
art neural networks appear to be models of this type. In this paper, we study
minimum norm (``ridgeless'') interpolation in high-dimensional least
squares regression. We consider two different models for the feature
distribution: a linear model, where the feature vectors
are obtained by applying a linear transform to a vector of i.i.d.\ entries,
(with ); and a nonlinear model,
where the feature vectors are obtained by passing the input through a random
one-layer neural network, (with ,
a matrix of i.i.d.\ entries, and an
activation function acting componentwise on ). We recover -- in a
precise quantitative way -- several phenomena that have been observed in
large-scale neural networks and kernel machines, including the "double descent"
behavior of the prediction risk, and the potential benefits of
overparametrization.Comment: 68 pages; 16 figures. This revision contains non-asymptotic version
of earlier results, and results for general coefficient
Genomic Analysis of Drosophila Neuronal Remodeling: A Role for the RNA-Binding Protein Boule as a Negative Regulator of Axon Pruning
Drosophila mushroom body (MB) {gamma} neurons undergo axon pruning during metamorphosis through a process of localized degeneration of specific axon branches. Developmental axon degeneration is initiated by the steroid hormone ecdysone, acting through a nuclear receptor complex composed of USP (ultraspiracle) and EcRB1 (ecdysone receptor B1) to regulate gene expression in MB {gamma} neurons. To identify ecdysone-dependent gene expression changes in MB {gamma} neurons at the onset of axon pruning, we use laser capture microdissection to isolate wild-type and mutant MB neurons in which EcR (ecdysone receptor) activity is genetically blocked, and analyze expression changes by microarray. We identify several molecular pathways that are regulated in MB neurons by ecdysone. The most striking observation is the upregulation of genes involved in the UPS (ubiquitin–proteasome system), which is cell autonomously required for {gamma} neuron pruning. In addition, we characterize the function of Boule, an evolutionarily conserved RNA-binding protein previously implicated in spermatogenesis in flies and vertebrates. boule expression is downregulated by ecdysone in MB neurons at the onset of pruning, and forced expression of Boule in MB {gamma} neurons is sufficient to inhibit axon pruning. This activity is dependent on the RNA-binding domain of Boule and a conserved DAZ (deleted in azoospermia) domain implicated in interactions with other RNA-binding proteins. However, loss of Boule does not result in obvious defects in axon pruning or morphogenesis of MB neurons, suggesting that it acts redundantly with other ecdyonse-regulated genes. We propose a novel function for Boule in the CNS as a negative regulator of developmental axon pruning
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Every Kid in a Park Climate Change Academies: Notes from the field
Students learn about climate change at on-site academies, with examples from Indiana Dunes National Park and Cape Cod National Seashore
Deterministic Time-Space Tradeoffs for k-SUM
Given a set of numbers, the -SUM problem asks for a subset of numbers
that sums to zero. When the numbers are integers, the time and space complexity
of -SUM is generally studied in the word-RAM model; when the numbers are
reals, the complexity is studied in the real-RAM model, and space is measured
by the number of reals held in memory at any point.
We present a time and space efficient deterministic self-reduction for the
-SUM problem which holds for both models, and has many interesting
consequences. To illustrate:
* -SUM is in deterministic time and space
. In general, any
polylogarithmic-time improvement over quadratic time for -SUM can be
converted into an algorithm with an identical time improvement but low space
complexity as well. * -SUM is in deterministic time and space
, derandomizing an algorithm of Wang.
* A popular conjecture states that 3-SUM requires time on the
word-RAM. We show that the 3-SUM Conjecture is in fact equivalent to the
(seemingly weaker) conjecture that every -space algorithm for
-SUM requires at least time on the word-RAM.
* For , -SUM is in deterministic time and
space
Digital zero noise extrapolation for quantum error mitigation
Zero-noise extrapolation (ZNE) is an increasingly popular technique for
mitigating errors in noisy quantum computations without using additional
quantum resources. We review the fundamentals of ZNE and propose several
improvements to noise scaling and extrapolation, the two key components in the
technique. We introduce unitary folding and parameterized noise scaling. These
are digital noise scaling frameworks, i.e. one can apply them using only
gate-level access common to most quantum instruction sets. We also study
different extrapolation methods, including a new adaptive protocol that uses a
statistical inference framework. Benchmarks of our techniques show error
reductions of 18X to 24X over non-mitigated circuits and demonstrate ZNE
effectiveness at larger qubit numbers than have been tested previously. In
addition to presenting new results, this work is a self-contained introduction
to the practical use of ZNE by quantum programmers.Comment: 11 pages, 7 figure
COVID-19 is increasing the power of Brazil’s criminal groups
Data from various states suggest that COVID-19 lockdowns have done little to reduce the use of violence by criminal groups in Brazil. What has changed is governance, with criminal actors adapting to coronavirus by imposing curfews, restricting movement, promoting public-health messages, and discouraging price gouging – alongside their usual practices of extortion and drug trafficking. Such changes in violence and governance indicate that Brazil’s non-state armed groups continue to augment their power, and these gains may well persist once the pandemic has receded, write Ryan Berg (American Enterprise Institute) and Andrea Varsori (Urban Violence Research Network)
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