100 research outputs found
Fast Witness Extraction Using a Decision Oracle
The gist of many (NP-)hard combinatorial problems is to decide whether a
universe of elements contains a witness consisting of elements that
match some prescribed pattern. For some of these problems there are known
advanced algebra-based FPT algorithms which solve the decision problem but do
not return the witness. We investigate techniques for turning such a
YES/NO-decision oracle into an algorithm for extracting a single witness, with
an objective to obtain practical scalability for large values of . By
relying on techniques from combinatorial group testing, we demonstrate that a
witness may be extracted with queries to either a deterministic or
a randomized set inclusion oracle with one-sided probability of error.
Furthermore, we demonstrate through implementation and experiments that the
algebra-based FPT algorithms are practical, in particular in the setting of the
-path problem. Also discussed are engineering issues such as optimizing
finite field arithmetic.Comment: Journal version, 16 pages. Extended abstract presented at ESA'1
Hysteretic behavior of angular dependence of exchange bias in FeNi/FeMn bilayers
For FeNi/FeMn bilayers, the angular dependence of exchange bias shows hysteresis between clockwise and counterclockwise rotations, as a new signature. The hysteresis decreases for thick antiferromagnet layers. Calculations have clearly shown that the orientation of antiferromagnet spins also exhibits hysteresis between clockwise and counterclockwise rotations. This furnishes an interpretation of the macroscopic behavior of the ferromagnetic layer in terms of the thermally driven evolution of the magnetic state of the antiferromagnet layer
β-aminobutyric acid induces disease resistance against Botrytis cinerea in grape berries by a cellular priming mechanism
The present study was performed to investigate the effect of β-aminobutyric acid (BABA) treatment on defence activation in grape berries and to analyse its cellular mechanism. The results implied that BABA treatment at an effective concentration of 20 mM significantly inhibited gray mould rot caused by Botrytis cinerea in grape berries by inducing resistance. Accordingly, 20 mM BABA triggered a priming defence in grape suspension cells, since only the BABA-treated cells exhibited an accelerated ability for augmenting defence responses upon the pathogen inoculation. The primed cellular reactions were related to an early H2O2 burst, prompt accumulation of stilbene phytoalexins and activation of PR genes. Thus, we assume that 20 mM BABA can induce resistance to B. cinerea infection in intact grape berries perhaps via intercellular priming defence. Moreover, the BABA-induced priming defence is verified, because no negative effects on cell growth, anthocyanin synthesis, and quality impairment in either grape cells or intact berries were observed under low pathogenic pressure
Rotation of the pinning direction in the exchange bias training effect in polycrystalline NiFe/FeMn bilayers
For polycrystalline NiFe/FeMn bilayers, we have observed and quantified the rotation of the pinning direction in the exchange bias training and recovery effects. During consecutive hysteresis loops, the rotation of the pinning direction strongly depends on the magnetization reversal mechanism of the ferromagnet layer. The interfacial uncompensated magnetic moment of antiferromagnetic grains may be irreversibly switched and rotated when the magnetization reversal process of the ferromagnet layer is accompanied by domain wall motion and domain rotation, respectively
Noise-Resilient Group Testing: Limitations and Constructions
We study combinatorial group testing schemes for learning -sparse Boolean
vectors using highly unreliable disjunctive measurements. We consider an
adversarial noise model that only limits the number of false observations, and
show that any noise-resilient scheme in this model can only approximately
reconstruct the sparse vector. On the positive side, we take this barrier to
our advantage and show that approximate reconstruction (within a satisfactory
degree of approximation) allows us to break the information theoretic lower
bound of that is known for exact reconstruction of
-sparse vectors of length via non-adaptive measurements, by a
multiplicative factor .
Specifically, we give simple randomized constructions of non-adaptive
measurement schemes, with measurements, that allow efficient
reconstruction of -sparse vectors up to false positives even in the
presence of false positives and false negatives within the
measurement outcomes, for any constant . We show that, information
theoretically, none of these parameters can be substantially improved without
dramatically affecting the others. Furthermore, we obtain several explicit
constructions, in particular one matching the randomized trade-off but using measurements. We also obtain explicit constructions
that allow fast reconstruction in time \poly(m), which would be sublinear in
for sufficiently sparse vectors. The main tool used in our construction is
the list-decoding view of randomness condensers and extractors.Comment: Full version. A preliminary summary of this work appears (under the
same title) in proceedings of the 17th International Symposium on
Fundamentals of Computation Theory (FCT 2009
Group testing with Random Pools: Phase Transitions and Optimal Strategy
The problem of Group Testing is to identify defective items out of a set of
objects by means of pool queries of the form "Does the pool contain at least a
defective?". The aim is of course to perform detection with the fewest possible
queries, a problem which has relevant practical applications in different
fields including molecular biology and computer science. Here we study GT in
the probabilistic setting focusing on the regime of small defective probability
and large number of objects, and . We construct and
analyze one-stage algorithms for which we establish the occurrence of a
non-detection/detection phase transition resulting in a sharp threshold, , for the number of tests. By optimizing the pool design we construct
algorithms whose detection threshold follows the optimal scaling . Then we consider two-stages algorithms and analyze their
performance for different choices of the first stage pools. In particular, via
a proper random choice of the pools, we construct algorithms which attain the
optimal value (previously determined in Ref. [16]) for the mean number of tests
required for complete detection. We finally discuss the optimal pool design in
the case of finite
Superselectors: Efficient Constructions and Applications
We introduce a new combinatorial structure: the superselector. We show that
superselectors subsume several important combinatorial structures used in the
past few years to solve problems in group testing, compressed sensing,
multi-channel conflict resolution and data security. We prove close upper and
lower bounds on the size of superselectors and we provide efficient algorithms
for their constructions. Albeit our bounds are very general, when they are
instantiated on the combinatorial structures that are particular cases of
superselectors (e.g., (p,k,n)-selectors, (d,\ell)-list-disjunct matrices,
MUT_k(r)-families, FUT(k, a)-families, etc.) they match the best known bounds
in terms of size of the structures (the relevant parameter in the
applications). For appropriate values of parameters, our results also provide
the first efficient deterministic algorithms for the construction of such
structures
A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic
The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world
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