1,784 research outputs found
A study of dynamical syn-eco symbiotic system with bio-economic aspect at interior equilibrium
This paper proposes a multi species ecosystem with optimal harvesting of the prey (S1), and Super predator (S3).The predator (S2) is surviving on the prey and the super predator is surviving on predator. The mathematical model is defined by three non-linear ordinary differential equations .The existence of bionomic equilibrium of the system has been discussed. The Local stability is discussed using Routh-Hurwitz‘s criteria and global stability by Lyapunov’s function. The analytical stability criteria are supported by numerical simulations using Mat lab
Two-Hop Routing with Traffic-Differentiation for QoS Guarantee in Wireless Sensor Networks
This paper proposes a Traffic-Differentiated Two-Hop Routing protocol for
Quality of Service (QoS) in Wireless Sensor Networks (WSNs). It targets WSN
applications having different types of data traffic with several priorities.
The protocol achieves to increase Packet Reception Ratio (PRR) and reduce
end-to-end delay while considering multi-queue priority policy, two-hop
neighborhood information, link reliability and power efficiency. The protocol
is modular and utilizes effective methods for estimating the link metrics.
Numerical results show that the proposed protocol is a feasible solution to
addresses QoS service differenti- ation for traffic with different priorities.Comment: 13 page
What Can We Learn Privately?
Learning problems form an important category of computational tasks that
generalizes many of the computations researchers apply to large real-life data
sets. We ask: what concept classes can be learned privately, namely, by an
algorithm whose output does not depend too heavily on any one input or specific
training example? More precisely, we investigate learning algorithms that
satisfy differential privacy, a notion that provides strong confidentiality
guarantees in contexts where aggregate information is released about a database
containing sensitive information about individuals. We demonstrate that,
ignoring computational constraints, it is possible to privately agnostically
learn any concept class using a sample size approximately logarithmic in the
cardinality of the concept class. Therefore, almost anything learnable is
learnable privately: specifically, if a concept class is learnable by a
(non-private) algorithm with polynomial sample complexity and output size, then
it can be learned privately using a polynomial number of samples. We also
present a computationally efficient private PAC learner for the class of parity
functions. Local (or randomized response) algorithms are a practical class of
private algorithms that have received extensive investigation. We provide a
precise characterization of local private learning algorithms. We show that a
concept class is learnable by a local algorithm if and only if it is learnable
in the statistical query (SQ) model. Finally, we present a separation between
the power of interactive and noninteractive local learning algorithms.Comment: 35 pages, 2 figure
Novel Organotin(IV)-Schiff Base Complexes: Synthesis, Characterization, Antimicrobial Activity, and DNA Interaction Studies
Four organotin(IV) complexes with 2-(2-hydroxybenzylideneamino)isoindoline-1,3-dione (L1), and 4-(4-hydroxy-3-methoxybenzylideneamino-N-(pyrimidin-2-yl)benzenesulfonamide (L2) were synthesized and well characterized by analytical and spectral studies. The synthesized compounds were tested for antimicrobial activity by disc diffusion method. The DNA binding of the complexes 1 and 3 with CT-DNA has been performed with absorption spectroscopy, which showed that both the complexes are avid binders of CT-DNA. Also the nuclease activity of complexes 1 and 3 with plasmid DNA (pUC19) was studied using agarose gel electrophoresis. The complex 1 can act as effective DNA cleaving agent when compared to complex 3 resulting in the nicked form of DNA under physiological conditions. The gel was run both in the absence and presence of the oxidizing agent
The FastMap Algorithm for Shortest Path Computations
We present a new preprocessing algorithm for embedding the nodes of a given
edge-weighted undirected graph into a Euclidean space. The Euclidean distance
between any two nodes in this space approximates the length of the shortest
path between them in the given graph. Later, at runtime, a shortest path
between any two nodes can be computed with A* search using the Euclidean
distances as heuristic. Our preprocessing algorithm, called FastMap, is
inspired by the data mining algorithm of the same name and runs in near-linear
time. Hence, FastMap is orders of magnitude faster than competing approaches
that produce a Euclidean embedding using Semidefinite Programming. FastMap also
produces admissible and consistent heuristics and therefore guarantees the
generation of shortest paths. Moreover, FastMap applies to general undirected
graphs for which many traditional heuristics, such as the Manhattan Distance
heuristic, are not well defined. Empirically, we demonstrate that A* search
using the FastMap heuristic is competitive with A* search using other
state-of-the-art heuristics, such as the Differential heuristic
Imaging Studies of photodamage and self-healing in disperse orange 11 dye-doped PMMA
We report on optical imaging studies of self-healing after laser-induced
photodamage in disperse orange 11 dye doped into poly(methyl methacrylate)
(PMMA) polymer. In particular, the high spatial-contrast image of the damage
track made by a line focus pump laser allows the recovery rates to be measured
as a function of burn dose using the relationship between transverse distance
and pump intensity profile. The time evolution of the damaged population
results in an intensity-independent time constant of {\tau} = 490\pm23 min, in
agreement with independent measurements of the time evolution of amplified
spontaneous emission. Also observed is a damage threshold above which the
material does not fully recover.Comment: 5 pages, 8 figure
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