14,559 research outputs found
How and Why has American Media Shifted the On-Screen Image of Asian Americans from Stereotypical Roles to Lead Roles?
Growing up as an Asian American, I never really had a role model of sort that I could relate to entirely. There were the general characters in movies that I looked up to but there was never that one character that I could possibly see myself as because there was never any lead characters that looked like me. Every time an Asian character was showed on-screen they were never a normal relatable person; they were always rather a living stereotype or unrealistic. Asians are always portrayed as intellectuals that are very awkward and geeky or one who has a very thick ethnic accent. Apart from Aladdin and Jackie Chan there never was a core Asian character who I looked up to as a role model. So, growing up there was always a stigma towards me that I had to be very Asian or very smart.
Ever since Asians immigrated to the United States, they were being portrayed by the media in one form or another. The portrayal was often stereotyped extensively which affected the public’s viewpoint towards Asian Americans. But in the recent decade the portrayal of Asian Americans has shifted to a more positive and everyday image. So, I wondered about how this happened and asked my research question, “How and Why has American Media Shifted the On-Screen Image of Asian Americans from Stereotypical Roles to Lead Roles?”https://scholarscompass.vcu.edu/uresposters/1259/thumbnail.jp
The Ergodic Capacity of Phase-Fading Interference Networks
We identify the role of equal strength interference links as bottlenecks on
the ergodic sum capacity of a user phase-fading interference network, i.e.,
an interference network where the fading process is restricted primarily to
independent and uniform phase variations while the channel magnitudes are held
fixed across time. It is shown that even though there are cross-links,
only about disjoint and equal strength interference links suffice to
determine the capacity of the network regardless of the strengths of the rest
of the cross channels. This scenario is called a \emph{minimal bottleneck
state}. It is shown that ergodic interference alignment is capacity optimal for
a network in a minimal bottleneck state. The results are applied to large
networks. It is shown that large networks are close to bottleneck states with a
high probability, so that ergodic interference alignment is close to optimal
for large networks. Limitations of the notion of bottleneck states are also
highlighted for channels where both the phase and the magnitudes vary with
time. It is shown through an example that for these channels, joint coding
across different bottleneck states makes it possible to circumvent the capacity
bottlenecks.Comment: 19 page
Elements of Cellular Blind Interference Alignment --- Aligned Frequency Reuse, Wireless Index Coding and Interference Diversity
We explore degrees of freedom (DoF) characterizations of partially connected
wireless networks, especially cellular networks, with no channel state
information at the transmitters. Specifically, we introduce three fundamental
elements --- aligned frequency reuse, wireless index coding and interference
diversity --- through a series of examples, focusing first on infinite regular
arrays, then on finite clusters with arbitrary connectivity and message sets,
and finally on heterogeneous settings with asymmetric multiple antenna
configurations. Aligned frequency reuse refers to the optimality of orthogonal
resource allocations in many cases, but according to unconventional reuse
patterns that are guided by interference alignment principles. Wireless index
coding highlights both the intimate connection between the index coding problem
and cellular blind interference alignment, as well as the added complexity
inherent to wireless settings. Interference diversity refers to the observation
that in a wireless network each receiver experiences a different set of
interferers, and depending on the actions of its own set of interferers, the
interference-free signal space at each receiver fluctuates differently from
other receivers, creating opportunities for robust applications of blind
interference alignment principles
On the Asymptotic Capacity of -Secure -Private Information Retrieval with Graph Based Replicated Storage
The problem of private information retrieval with graph-based replicated
storage was recently introduced by Raviv, Tamo and Yaakobi. Its capacity
remains open in almost all cases. In this work the asymptotic (large number of
messages) capacity of this problem is studied along with its generalizations to
include arbitrary -privacy and -security constraints, where the privacy
of the user must be protected against any set of up to colluding servers
and the security of the stored data must be protected against any set of up to
colluding servers. A general achievable scheme for arbitrary storage
patterns is presented that achieves the rate , where
is the total number of servers, and each message is replicated at least
times. Notably, the scheme makes use of a special structure
inspired by dual Generalized Reed Solomon (GRS) codes. A general converse is
also presented. The two bounds are shown to match for many settings, including
symmetric storage patterns. Finally, the asymptotic capacity is fully
characterized for the case without security constraints for arbitrary
storage patterns provided that each message is replicated no more than
times. As an example of this result, consider PIR with arbitrary graph based
storage () where every message is replicated at exactly servers.
For this -replicated storage setting, the asymptotic capacity is equal to
where is the maximum size of a -matching in a
storage graph . In this undirected graph, the vertices correspond
to the set of servers, and there is an edge between vertices
only if a subset of messages is replicated at both servers and
The Capacity of Private Computation
We introduce the problem of private computation, comprised of distributed
and non-colluding servers, independent datasets, and a user who wants to
compute a function of the datasets privately, i.e., without revealing which
function he wants to compute, to any individual server. This private
computation problem is a strict generalization of the private information
retrieval (PIR) problem, obtained by expanding the PIR message set (which
consists of only independent messages) to also include functions of those
messages. The capacity of private computation, , is defined as the maximum
number of bits of the desired function that can be retrieved per bit of total
download from all servers. We characterize the capacity of private computation,
for servers and independent datasets that are replicated at each
server, when the functions to be computed are arbitrary linear combinations of
the datasets. Surprisingly, the capacity,
, matches the capacity of PIR with
servers and messages. Thus, allowing arbitrary linear computations does
not reduce the communication rate compared to pure dataset retrieval. The same
insight is shown to hold even for arbitrary non-linear computations when the
number of datasets
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