3,549 research outputs found
Procompetitive Market Access
The view that U.S. businesses are being unfairly hurt by barriers to access in foreign markets has raised demands for market access requirements (MARs) from within U.S. industry and government alike. We show that, contrary to the prevailing wisdom of the recent literature, MARs can be implemented in a procompetitive manner. The basic idea is that the requirement must be implemented in a way that provides the right incentives for increasing aggregate output or lowering prices. We provide two examples to illustrate this point. In the context of a Cournot duopoly, we show that an implementation scheme in which the U.S. firm receives a pre-announced subsidy if the market share target is met leads to increased aggregate output. In a second example, we show that a MAR on an imported intermediate input can lead not only to increased imports of the intermediate good, but also to increased output in the final good market using the input. The intuition is that increasing output of the final good helps to make the MAR less binding and this reduces the marginal cost of production in the final good market. Thus our results buttress the point made in Krishna, Roy and Thursby (1997) that the effects of MARs depend crucially on the details of their implementation.
Channels Reallocation In Cognitive Radio Networks Based On DNA Sequence Alignment
Nowadays, It has been shown that spectrum scarcity increased due to
tremendous growth of new players in wireless base system by the evolution of
the radio communication. Resent survey found that there are many areas of the
radio spectrum that are occupied by authorized user/primary user (PU), which
are not fully utilized. Cognitive radios (CR) prove to next generation wireless
communication system that proposed as a way to reuse this under-utilised
spectrum in an opportunistic and non-interfering basis. A CR is a self-directed
entity in a wireless communications environment that senses its environment,
tracks changes, and reacts upon its findings and frequently exchanges
information with the networks for secondary user (SU). However, CR facing
collision problem with tracks changes i.e. reallocating of other empty channels
for SU while PU arrives. In this paper, channels reallocation technique based
on DNA sequence alignment algorithm for CR networks has been proposed.Comment: 12 page
Can Subsidies for MARs be Procompetitive?
In contrast to recent literature, we show that market access requirements (MARs) can be implemented in a procompetitive manner even in the absence of threats in related markets. By focusing on subsidies that are paid only when the requirement is met, we show that a MAR can increase aggregate output relative to free trade provided that the right set of firms is targeted. In the context of a model with multiple Japanese and US firms, we show that a MAR on US imports is procompetitive as long as the US firms are the ones targeted to receive the subsidy.
Cross Layer Aware Adaptive MAC based on Knowledge Based Reasoning for Cognitive Radio Computer Networks
In this paper we are proposing a new concept in MAC layer protocol design for
Cognitive radio by combining information held by physical layer and MAC layer
with analytical engine based on knowledge based reasoning approach. In the
proposed system a cross layer information regarding signal to interference and
noise ratio (SINR) and received power are analyzed with help of knowledge based
reasoning system to determine minimum power to transmit and size of contention
window, to minimize backoff, collision, save power and drop packets. The
performance analysis of the proposed protocol indicates improvement in power
saving, lowering backoff and significant decrease in number of drop packets.
The simulation environment was implement using OMNET++ discrete simulation tool
with Mobilty framework and MiXiM simulation library.Comment: 8 page
The theories of evolution of Samuel ALexander and Henri Bergson
Thesis (M.A.)--Boston Universit
REPRESENTATION LEARNING FOR ACTION RECOGNITION
The objective of this research work is to develop discriminative representations for human
actions. The motivation stems from the fact that there are many issues encountered while
capturing actions in videos like intra-action variations (due to actors, viewpoints, and duration),
inter-action similarity, background motion, and occlusion of actors. Hence, obtaining
a representation which can address all the variations in the same action while maintaining
discrimination with other actions is a challenging task. In literature, actions have been represented
either using either low-level or high-level features. Low-level features describe
the motion and appearance in small spatio-temporal volumes extracted from a video. Due
to the limited space-time volume used for extracting low-level features, they are not able
to account for viewpoint and actor variations or variable length actions. On the other hand,
high-level features handle variations in actors, viewpoints, and duration but the resulting
representation is often high-dimensional which introduces the curse of dimensionality. In
this thesis, we propose new representations for describing actions by combining the advantages
of both low-level and high-level features. Specifically, we investigate various linear
and non-linear decomposition techniques to extract meaningful attributes in both high-level
and low-level features. In the first approach, the sparsity of high-level feature descriptors is leveraged to build
action-specific dictionaries. Each dictionary retains only the discriminative information
for a particular action and hence reduces inter-action similarity. Then, a sparsity-based
classification method is proposed to classify the low-rank representation of clips obtained
using these dictionaries. We show that this representation based on dictionary learning improves
the classification performance across actions. Also, a few of the actions consist of
rapid body deformations that hinder the extraction of local features from body movements.
Hence, we propose to use a dictionary which is trained on convolutional neural network
(CNN) features of the human body in various poses to reliably identify actors from the
background. Particularly, we demonstrate the efficacy of sparse representation in the identification
of the human body under rapid and substantial deformation.
In the first two approaches, sparsity-based representation is developed to improve discriminability
using class-specific dictionaries that utilize action labels. However, developing
an unsupervised representation of actions is more beneficial as it can be used to both
recognize similar actions and localize actions. We propose to exploit inter-action similarity
to train a universal attribute model (UAM) in order to learn action attributes (common and
distinct) implicitly across all the actions. Using maximum aposteriori (MAP) adaptation,
a high-dimensional super action-vector (SAV) for each clip is extracted. As this SAV contains
redundant attributes of all other actions, we use factor analysis to extract a novel lowvi
dimensional action-vector representation for each clip. Action-vectors are shown to suppress
background motion and highlight actions of interest in both trimmed and untrimmed
clips that contributes to action recognition without the help of any classifiers.
It is observed during our experiments that action-vector cannot effectively discriminate
between actions which are visually similar to each other. Hence, we subject action-vectors
to supervised linear embedding using linear discriminant analysis (LDA) and probabilistic
LDA (PLDA) to enforce discrimination. Particularly, we show that leveraging complimentary
information across action-vectors using different local features followed by discriminative
embedding provides the best classification performance. Further, we explore
non-linear embedding of action-vectors using Siamese networks especially for fine-grained
action recognition. A visualization of the hidden layer output in Siamese networks shows
its ability to effectively separate visually similar actions. This leads to better classification
performance than linear embedding on fine-grained action recognition.
All of the above approaches are presented on large unconstrained datasets with hundreds
of examples per action. However, actions in surveillance videos like snatch thefts are
difficult to model because of the diverse variety of scenarios in which they occur and very
few labeled examples. Hence, we propose to utilize the universal attribute model (UAM)
trained on large action datasets to represent such actions. Specifically, we show that there
are similarities between certain actions in the large datasets with snatch thefts which help
in extracting a representation for snatch thefts using the attributes from the UAM. This
representation is shown to be effective in distinguishing snatch thefts from regular actions
with high accuracy.In summary, this thesis proposes both supervised and unsupervised approaches for representing
actions which provide better discrimination than existing representations. The
first approach presents a dictionary learning based sparse representation for effective discrimination
of actions. Also, we propose a sparse representation for the human body based
on dictionaries in order to recognize actions with rapid body deformations. In the next
approach, a low-dimensional representation called action-vector for unsupervised action
recognition is presented. Further, linear and non-linear embedding of action-vectors is
proposed for addressing inter-action similarity and fine-grained action recognition, respectively.
Finally, we propose a representation for locating snatch thefts among thousands of
regular interactions in surveillance videos
A stochastic chemical dynamic approach to correlate autoimmunity and optimal vitamin-D range
Motivated by several recent experimental observations that vitamin-D could
interact with antigen presenting cells (APCs) and T-lymphocyte cells (T-cells)
to promote and to regulate different stages of immune response, we developed a
coarse grained kinetic model in an attempt to quantify the role of vitamin-D in
immunomodulatory responses. Our kinetic model, developed using the ideas of
chemical network theory, leads to a system of nine coupled equations that we
solve both by direct and by stochastic (Gillespie) methods. Both the analyses
consistently provide detail information on the dependence of immune response to
the variation of critical rate parameters. We find that although vitamin-D
plays a negligible role in the initial immune response, it exerts a profound
influence in the long term, especially in helping the system to achieve a new,
stable steady state. The study explores the role of vitamin-D in preserving an
observed bistability in the phase diagram (spanned by system parameters) of
immune regulation, thus allowing the response to tolerate a wide range of
pathogenic stimulation which could help in resisting autoimmune diseases. We
also study how vitamin-D affects the time dependent population of dendritic
cells that connect between innate and adaptive immune responses. Variations in
dose dependent response in anti-inflammatory and pro-inflammatory T-cell
populations to vitamin-D correlate well with recent experimental results. Our
kinetic model allows for an estimation of the range of optimum level of
vitamin-D required for smooth functioning of the immune system and for control
of both hyper-regulation and inflammation. Most importantly, the present study
reveals that an overdose or toxic level of vitamin-D or any steroid analogue
could give rise to too large a tolerant response, leading to an inefficacy in
adaptive immune function.Comment: arXiv admin note: substantial text overlap with arXiv:1304.719
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