Results 1  10
of
1,688,676
Critical Power for Asymptotic Connectivity in Wireless Networks
, 1998
"... : In wireless data networks each transmitter's power needs to be high enough to reach the intended receivers, while generating minimum interference on other receivers sharing the same channel. In particular, if the nodes in the network are assumed to cooperate in routing each others ' pack ..."
Abstract

Cited by 548 (19 self)
 Add to MetaCart
; packets, as is the case in ad hoc wireless networks, each node should transmit with just enough power to guarantee connectivity in the network. Towards this end, we derive the critical power a node in the network needs to transmit in order to ensure that the network is connected with probability one
Design and Evaluation of a WideArea Event Notification Service
 ACM Transactions on Computer Systems
"... This paper presents SIENA, an event notification service that we have designed and implemented to exhibit both expressiveness and scalability. We describe the service's interface to applications, the algorithms used by networks of servers to select and deliver event notifications, and the strat ..."
Abstract

Cited by 789 (32 self)
 Add to MetaCart
This paper presents SIENA, an event notification service that we have designed and implemented to exhibit both expressiveness and scalability. We describe the service's interface to applications, the algorithms used by networks of servers to select and deliver event notifications
A Critical Point For Random Graphs With A Given Degree Sequence
, 2000
"... Given a sequence of nonnegative real numbers 0 ; 1 ; : : : which sum to 1, we consider random graphs having approximately i n vertices of degree i. Essentially, we show that if P i(i \Gamma 2) i ? 0 then such graphs almost surely have a giant component, while if P i(i \Gamma 2) i ! 0 the ..."
Abstract

Cited by 511 (8 self)
 Add to MetaCart
Given a sequence of nonnegative real numbers 0 ; 1 ; : : : which sum to 1, we consider random graphs having approximately i n vertices of degree i. Essentially, we show that if P i(i \Gamma 2) i ? 0 then such graphs almost surely have a giant component, while if P i(i \Gamma 2) i ! 0 then almost surely all components in such graphs are small. We can apply these results to G n;p ; G n;M , and other wellknown models of random graphs. There are also applications related to the chromatic number of sparse random graphs.
Using Daily Stock Returns: The Case of Event Studies
 Journal of Financial Economics
, 1985
"... This paper examines properties of daily stock returns and how the particular characteristics of these data affect event study methodologies. Daily data generally present few difficulties for event studies. Standard procedures are typically wellspecified even when special daily data characteristics ..."
Abstract

Cited by 763 (2 self)
 Add to MetaCart
This paper examines properties of daily stock returns and how the particular characteristics of these data affect event study methodologies. Daily data generally present few difficulties for event studies. Standard procedures are typically wellspecified even when special daily data characteris
Earthquake Shakes Twitter Users: Realtime Event Detection by Social Sensors
 In Proceedings of the Nineteenth International WWW Conference (WWW2010). ACM
, 2010
"... Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its realtime nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence ..."
Abstract

Cited by 484 (3 self)
 Add to MetaCart
promptly, simply by observing the tweets. As described in this paper, we investigate the realtime interaction of events such as earthquakes, in Twitter, and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features
Time Discounting and Time Preference: A Critical Review
 Journal of Economic Literature
, 2002
"... www.people.cornell.edu/pages/edo1/. ..."
Unrealistic optimism about future life events
 Journal of Personality and Social Psychology
, 1980
"... Two studies investigated the tendency of people to be unrealistically optimistic about future life events. In Study 1, 258 college students estimated how much their own chances of experiencing 42 events differed from the chances of their classmates. Overall, they rated their own chances to be above ..."
Abstract

Cited by 493 (0 self)
 Add to MetaCart
Two studies investigated the tendency of people to be unrealistically optimistic about future life events. In Study 1, 258 college students estimated how much their own chances of experiencing 42 events differed from the chances of their classmates. Overall, they rated their own chances to be above
Memory Consistency and Event Ordering in Scalable SharedMemory Multiprocessors
 In Proceedings of the 17th Annual International Symposium on Computer Architecture
, 1990
"... Scalable sharedmemory multiprocessors distribute memory among the processors and use scalable interconnection networks to provide high bandwidth and low latency communication. In addition, memory accesses are cached, buffered, and pipelined to bridge the gap between the slow shared memory and the f ..."
Abstract

Cited by 735 (18 self)
 Add to MetaCart
and the fast processors. Unless carefully controlled, such architectural optimizations can cause memory accesses to be executed in an order different from what the programmer expects. The set of allowable memory access orderings forms the memory consistency model or event ordering model for an architecture.
A comparison of event models for Naive Bayes text classification
, 1998
"... Recent work in text classification has used two different firstorder probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multivariate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey ..."
Abstract

Cited by 1002 (27 self)
 Add to MetaCart
Recent work in text classification has used two different firstorder probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multivariate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a unigram language model with integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997). This paper aims to clarify the confusion by describing the differences and details of these two models, and by empirically comparing their classification performance on five text corpora. We find that the multivariate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizesproviding on average a 27% reduction in error over the multivariate Bernoulli model at any vocabulary size.
A MaximumEntropyInspired Parser
, 1999
"... We present a new parser for parsing down to Penn treebank style parse trees that achieves 90.1% average precision/recall for sentences of length 40 and less, and 89.5% for sentences of length 100 and less when trained and tested on the previously established [5,9,10,15,17] "stan dard" se ..."
Abstract

Cited by 963 (19 self)
 Add to MetaCart
We present a new parser for parsing down to Penn treebank style parse trees that achieves 90.1% average precision/recall for sentences of length 40 and less, and 89.5% for sentences of length 100 and less when trained and tested on the previously established [5,9,10,15,17] "stan dard
Results 1  10
of
1,688,676