9,802 research outputs found
Generalized LIL for geometrically weighted random series in Banach spaces
AbstractLet {X,Xn;n⩾0} be a sequence of independent and identically distributed random variables, taking values in a separable Banach space B with topological dual B⁎. Considering the geometrically weighted series ξ(β)=∑n=0∞βnXn for 0<β<1, motivated by Einmahl and Li (2005, 2008), a general law of the iterated logarithm for ξ(β) is established
SSthreshless Start: A Sender-Side TCP Intelligence for Long Fat Network
Measurement shows that 85% of TCP flows in the internet are short-lived flows
that stay most of their operation in the TCP startup phase. However, many
previous studies indicate that the traditional TCP Slow Start algorithm does
not perform well, especially in long fat networks. Two obvious problems are
known to impact the Slow Start performance, which are the blind initial setting
of the Slow Start threshold and the aggressive increase of the probing rate
during the startup phase regardless of the buffer sizes along the path. Current
efforts focusing on tuning the Slow Start threshold and/or probing rate during
the startup phase have not been considered very effective, which has prompted
an investigation with a different approach. In this paper, we present a novel
TCP startup method, called threshold-less slow start or SSthreshless Start,
which does not need the Slow Start threshold to operate. Instead, SSthreshless
Start uses the backlog status at bottleneck buffer to adaptively adjust probing
rate which allows better seizing of the available bandwidth. Comparing to the
traditional and other major modified startup methods, our simulation results
show that SSthreshless Start achieves significant performance improvement
during the startup phase. Moreover, SSthreshless Start scales well with a wide
range of buffer size, propagation delay and network bandwidth. Besides, it
shows excellent friendliness when operating simultaneously with the currently
popular TCP NewReno connections.Comment: 25 pages, 10 figures, 7 table
Information Filtering on Coupled Social Networks
In this paper, based on the coupled social networks (CSN), we propose a
hybrid algorithm to nonlinearly integrate both social and behavior information
of online users. Filtering algorithm based on the coupled social networks,
which considers the effects of both social influence and personalized
preference. Experimental results on two real datasets, \emph{Epinions} and
\emph{Friendfeed}, show that hybrid pattern can not only provide more accurate
recommendations, but also can enlarge the recommendation coverage while
adopting global metric. Further empirical analyses demonstrate that the mutual
reinforcement and rich-club phenomenon can also be found in coupled social
networks where the identical individuals occupy the core position of the online
system. This work may shed some light on the in-depth understanding structure
and function of coupled social networks
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