35,441 research outputs found
Towards sender-based TFRC
Pervasive communications are increasingly sent over mobile devices and personal digital assistants. This trend has
been observed during the last football world cup where cellular phones service providers have measured a significant increase in multimedia traffic. To better carry multimedia traffic, the IETF standardized a new TCP Friendly Rate Control (TFRC) protocol. However, the current receiver-based TFRC design is not well suited to resource limited end systems. We propose a scheme to shift resource allocation and computation to the sender. This sender based approach led us to develop a new algorithm for loss notification and loss rate computation. We demonstrate the gain obtained in terms of memory requirements and CPU processing compared to the current design. Moreover this shifting solves security issues raised by classical TFRC implementations. We have implemented this new sender-based TFRC, named TFRC_light, and conducted measurements under real world conditions
Toward Entity-Aware Search
As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
In this work we present In-Place Activated Batch Normalization (InPlace-ABN)
- a novel approach to drastically reduce the training memory footprint of
modern deep neural networks in a computationally efficient way. Our solution
substitutes the conventionally used succession of BatchNorm + Activation layers
with a single plugin layer, hence avoiding invasive framework surgery while
providing straightforward applicability for existing deep learning frameworks.
We obtain memory savings of up to 50% by dropping intermediate results and by
recovering required information during the backward pass through the inversion
of stored forward results, with only minor increase (0.8-2%) in computation
time. Also, we demonstrate how frequently used checkpointing approaches can be
made computationally as efficient as InPlace-ABN. In our experiments on image
classification, we demonstrate on-par results on ImageNet-1k with
state-of-the-art approaches. On the memory-demanding task of semantic
segmentation, we report results for COCO-Stuff, Cityscapes and Mapillary
Vistas, obtaining new state-of-the-art results on the latter without additional
training data but in a single-scale and -model scenario. Code can be found at
https://github.com/mapillary/inplace_abn
ChoiceRank: Identifying Preferences from Node Traffic in Networks
Understanding how users navigate in a network is of high interest in many
applications. We consider a setting where only aggregate node-level traffic is
observed and tackle the task of learning edge transition probabilities. We cast
it as a preference learning problem, and we study a model where choices follow
Luce's axiom. In this case, the marginal counts of node visits are a
sufficient statistic for the transition probabilities. We show how to
make the inference problem well-posed regardless of the network's structure,
and we present ChoiceRank, an iterative algorithm that scales to networks that
contains billions of nodes and edges. We apply the model to two clickstream
datasets and show that it successfully recovers the transition probabilities
using only the network structure and marginal (node-level) traffic data.
Finally, we also consider an application to mobility networks and apply the
model to one year of rides on New York City's bicycle-sharing system.Comment: Accepted at ICML 201
Improvements on non-equilibrium and transport Green function techniques: the next-generation transiesta
We present novel methods implemented within the non-equilibrium Green
function code (NEGF) transiesta based on density functional theory (DFT). Our
flexible, next-generation DFT-NEGF code handles devices with one or multiple
electrodes () with individual chemical potentials and electronic
temperatures. We describe its novel methods for electrostatic gating, contour
opti- mizations, and assertion of charge conservation, as well as the newly
implemented algorithms for optimized and scalable matrix inversion,
performance-critical pivoting, and hybrid parallellization. Additionally, a
generic NEGF post-processing code (tbtrans/phtrans) for electron and phonon
transport is presented with several novelties such as Hamiltonian
interpolations, electrode capability, bond-currents, generalized
interface for user-defined tight-binding transport, transmission projection
using eigenstates of a projected Hamiltonian, and fast inversion algorithms for
large-scale simulations easily exceeding atoms on workstation computers.
The new features of both codes are demonstrated and bench-marked for relevant
test systems.Comment: 24 pages, 19 figure
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