1,693 research outputs found
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates
In this paper, we provide a novel construction of the linear-sized spectral
sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous
constructions required running time [BSS14, Zou12], our
sparsification routine can be implemented in almost-quadratic running time
.
The fundamental conceptual novelty of our work is the leveraging of a strong
connection between sparsification and a regret minimization problem over
density matrices. This connection was known to provide an interpretation of the
randomized sparsifiers of Spielman and Srivastava [SS11] via the application of
matrix multiplicative weight updates (MWU) [CHS11, Vis14]. In this paper, we
explain how matrix MWU naturally arises as an instance of the
Follow-the-Regularized-Leader framework and generalize this approach to yield a
larger class of updates. This new class allows us to accelerate the
construction of linear-sized spectral sparsifiers, and give novel insights on
the motivation behind Batson, Spielman and Srivastava [BSS14]
Online Local Learning via Semidefinite Programming
In many online learning problems we are interested in predicting local
information about some universe of items. For example, we may want to know
whether two items are in the same cluster rather than computing an assignment
of items to clusters; we may want to know which of two teams will win a game
rather than computing a ranking of teams. Although finding the optimal
clustering or ranking is typically intractable, it may be possible to predict
the relationships between items as well as if you could solve the global
optimization problem exactly.
Formally, we consider an online learning problem in which a learner
repeatedly guesses a pair of labels (l(x), l(y)) and receives an adversarial
payoff depending on those labels. The learner's goal is to receive a payoff
nearly as good as the best fixed labeling of the items. We show that a simple
algorithm based on semidefinite programming can obtain asymptotically optimal
regret in the case where the number of possible labels is O(1), resolving an
open problem posed by Hazan, Kale, and Shalev-Schwartz. Our main technical
contribution is a novel use and analysis of the log determinant regularizer,
exploiting the observation that log det(A + I) upper bounds the entropy of any
distribution with covariance matrix A.Comment: 10 page
The Computational Power of Optimization in Online Learning
We consider the fundamental problem of prediction with expert advice where
the experts are "optimizable": there is a black-box optimization oracle that
can be used to compute, in constant time, the leading expert in retrospect at
any point in time. In this setting, we give a novel online algorithm that
attains vanishing regret with respect to experts in total
computation time. We also give a lower bound showing
that this running time cannot be improved (up to log factors) in the oracle
model, thereby exhibiting a quadratic speedup as compared to the standard,
oracle-free setting where the required time for vanishing regret is
. These results demonstrate an exponential gap between
the power of optimization in online learning and its power in statistical
learning: in the latter, an optimization oracle---i.e., an efficient empirical
risk minimizer---allows to learn a finite hypothesis class of size in time
. We also study the implications of our results to learning in
repeated zero-sum games, in a setting where the players have access to oracles
that compute, in constant time, their best-response to any mixed strategy of
their opponent. We show that the runtime required for approximating the minimax
value of the game in this setting is , yielding
again a quadratic improvement upon the oracle-free setting, where
is known to be tight
Laboratory evaluation of diatomaceous earth against main stored product insects
The sensitivity of the main external and internal stored product insect pests to the commercial formulation of Detia Degesch Diatomaceous Earth – DDDE - Inerto (DE) was studied in laboratory experiments. The tested insects were adults of internal feeders Sitophilus oryzae Rhyzopertha dominica and external feeders Oryzaephilus surinamensis, Tribolium castaneum, and larvae (third instar) of T.castaneum. The DE was applied to wheat grain of 12% moisture content at concentrations of 0.5, 1.0, 2.0 and 4.0 g/kg of grain. The treated and untreated (control) grain were kept at 28°C and 65 ± 5% r.h. The numbers of dead and survived insects were counted two, three and four weeks after treatment. The number of adult progeny was counted nine weeks after treatment. At a concentration of 0.5 g/kg, mortality of S. oryzae and O. surinamensis after three weeks of exposure to DE were 92 and 86%, respectively. In contrast, mortality of T. castaneum and R. dominica adults was 3 and 37%, respectively. Progeny production of O. surinamensis and T. castaneum at a concentration of 2 g/kg was negligible, since only few individuals were recorded nine weeks after treatment, in comparison with the high progeny production in the control grain. The progeny of S. oryzae was also reduced. In contrast, for R. dominica was reduced only twice, in comparison with the control. In the case of T. castaneum larvae, at a concentration of 2 g/kg, after 4 weeks of exposure, 37% of the larvae emerged to adults, compared with 95% in control. Nine weeks after treatment, the number of F1adults was 100% suppressed. DE efficacy was similar at 4 g/kg. Based on the findings of the present study, the efficacy of the tested DE was influenced by DE concentration, insect species, developmental stage and exposure interval to the treated commodity.Keywords: Diatomaceous earth, Stored product insects, Wheat grai
Improvement of phosphine fumigation by the use of Speedbox
Today, phosphine is turning to be a major fumigant for controlling insects in stored products. However, few limitations, such as low temperatures and relatively long exposure time, limit the phosphine use. In order to improve phosphine application, a special devise, containing a heater and a ventilator, called "Speedbox" has been developed by Detia Degesch GmbH Germany. For studying the effectiveness of phosphine fumigation using Speedbox, we have conducted two kinds of experiments: one in a fumigation room (Pilot) and other in commercial warehouse. For pilot fumigation, adults, pupae and late larvae of Sitophilus oryzae, Rhyzopertha dominica, Oryzaephilus surinamensis, Trogoderma granarium and Callosobruchus maculatus, and all stages of Tribolium castaneum Herbst, Plodia interpunctella and Ephestia cautella were used as test insects. One to three Degesch Plates (about 2-6 g of phosphine gas per m3) were used. Exposure time was 1 to 3 days. The phosphine concentrtion was monitored by Bedfont device model 415. At 4 g/m3 for 48 ha maximum of phosphine concentration of 1460 ppm was reached. The total mortality of all tested insects and stages was recorded, except the eggs of E. cautella (98%). The commercial stack fumigation was done at the dosages of 2-4 g/m3, exposure time of 2-4 days and commodity temperatures of 6-17ºC. At a target concentration of 4 g/m3, 2 hours after beginning of the treatment, the concentration of the gas has reached 414 ppm, with a maximum of 1480 ppm. The total mortality of tested insects at adult, late larvae and pupae stages was recorded. The use of Speedbox allows one-day decrease in the plates degassing time, recirculation of the gas and its event distribution in the treated space and controlling major stored product insects for shorter exposure time at low temperatures. Keywords: Fumigation; Posphine; Speedbox; Stored-product insect
On k-Column Sparse Packing Programs
We consider the class of packing integer programs (PIPs) that are column
sparse, i.e. there is a specified upper bound k on the number of constraints
that each variable appears in. We give an (ek+o(k))-approximation algorithm for
k-column sparse PIPs, improving on recent results of and
. We also show that the integrality gap of our linear programming
relaxation is at least 2k-1; it is known that k-column sparse PIPs are
-hard to approximate. We also extend our result (at the loss
of a small constant factor) to the more general case of maximizing a submodular
objective over k-column sparse packing constraints.Comment: 19 pages, v3: additional detail
Direct Exoplanet Detection Using L1 Norm Low-Rank Approximation
We propose to use low-rank matrix approximation using the component-wise
L1-norm for direct imaging of exoplanets. Exoplanet detection by direct imaging
is a challenging task for three main reasons: (1) the host star is several
orders of magnitude brighter than exoplanets, (2) the angular distance between
exoplanets and star is usually very small, and (3) the images are affected by
the noises called speckles that are very similar to the exoplanet signal both
in shape and intensity. We first empirically examine the statistical noise
assumptions of the L1 and L2 models, and then we evaluate the performance of
the proposed L1 low-rank approximation (L1-LRA) algorithm based on visual
comparisons and receiver operating characteristic (ROC) curves. We compare the
results of the L1-LRA with the widely used truncated singular value
decomposition (SVD) based on the L2 norm in two different annuli, one close to
the star and one far away.Comment: 13 pages, 4 figures, BNAIC/BeNeLearn 202
Dynamics of Transformation from Segregation to Mixed Wealth Cities
We model the dynamics of the Schelling model for agents described simply by a
continuously distributed variable - wealth. Agents move to neighborhoods where
their wealth is not lesser than that of some proportion of their neighbors, the
threshold level. As in the case of the classic Schelling model where
segregation obtains between two races, we find here that wealth-based
segregation occurs and persists. However, introducing uncertainty into the
decision to move - that is, with some probability, if agents are allowed to
move even though the threshold level condition is contravened - we find that
even for small proportions of such disallowed moves, the dynamics no longer
yield segregation but instead sharply transition into a persistent mixed wealth
distribution. We investigate the nature of this sharp transformation between
segregated and mixed states, and find that it is because of a non-linear
relationship between allowed moves and disallowed moves. For small increases in
disallowed moves, there is a rapid corresponding increase in allowed moves, but
this tapers off as the fraction of disallowed moves increase further and
finally settles at a stable value, remaining invariant to any further increase
in disallowed moves. It is the overall effect of the dynamics in the initial
region (with small numbers of disallowed moves) that shifts the system away
from a state of segregation rapidly to a mixed wealth state.
The contravention of the tolerance condition could be interpreted as public
policy interventions like minimal levels of social housing or housing benefit
transfers to poorer households. Our finding therefore suggests that it might
require only very limited levels of such public intervention - just sufficient
to enable a small fraction of disallowed moves, because the dynamics generated
by such moves could spur the transformation from a segregated to mixed
equilibrium.Comment: 12 pages, 7 figure
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