Fill 'er up? Fill what up?
نویسندگان
چکیده
منابع مشابه
TCP - BFA : Bu er Fill
The main goal of a congestion avoidance algorithm is to maximize throughput and minimize delay (Jain & Ramakrishnan 1988). While TCP Reno achieves high throughput, it tends to consume all of the buuer space at the bottleneck router, causing large delays. In this paper we propose a simple scheme that modiies TCP Reno's congestion avoidance algorithm by throttling back the opening of the congesti...
متن کاملTCP-BFA: Bu er Fill Avoidance
The main goal of a congestion avoidance algorithm is to maximize throughput and minimize delay (Jain & Ramakrishnan 1988). While TCP Reno achieves high throughput, it tends to consume all of the bu er space at the bottleneck router, causing large delays. In this paper we propose a simple scheme that modi es TCP Reno's congestion avoidance algorithm by throttling back the opening of the congesti...
متن کاملHow to Fill Up Merkle-Damgård Hash Functions
Many of the popular Merkle-Damg̊ard hash functions have turned out to be not collision-resistant (CR). The problem is that we no longer know if these hash functions are even second-preimage-resistant (SPR) or one-way (OW), without the underlying compression functions being CR. We remedy this situation by introducing the “split padding” into a current Merkle-Damg̊ard hash function H. The patched h...
متن کاملFill-up versus interpolation methods for phrase-based SMT adaptation
This paper compares techniques to combine diverse parallel corpora for domain-specific phrase-based SMT system training. We address a common scenario where little in-domain data is available for the task, but where large background models exist for the same language pair. In particular, we focus on phrase table fill-up: a method that effectively exploits background knowledge to improve model co...
متن کاملA Probabilistic Feature-Based Fill-up for SMT
In this paper, we describe an effective translation model combination approach based on the estimation of a probabilistic Support Vector Machine (SVM). We collect domain knowledge from both in-domain and general-domain corpora inspired by a commonly used data selection algorithm, which we then use as features for the SVM training. Drawing on previous work on binary-featured phrase table fill-up...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Blood
سال: 2016
ISSN: 0006-4971,1528-0020
DOI: 10.1182/blood-2016-05-713040