Itinerant Delivery of Popular Data via WIDE Hot Spots Abstract -Wireless Information Delivery Environment (WIDE) is a distributed data dissemination system, which uses IEEE 802.11b technology. WIDE aims to deliver popular information services to registered mobile clients in WLAN hot spots. Data delivery is based on broadcasting and multicasting to provide scalability and efficient use of the wireless channel.Reliability is assured with a combination of Forward Error Correction (FEC), data carousel, and ARQ techniques. This paper presents the proposed system architecture with the details of reliable and secure data dissemination mechanisms. Functional evaluation of the proposed system and mechanisms on the implemented prototype are also included in this paper. The dominant use of WLANs is to provide Internet access for mobile users in distributed hot spots. Users can search for and access the information over the Internet with the help of their wireless capable PDAs or laptop computers even while they are itinerant. However, there are some scenarios where there is highly correlated demand for specific information, which we refer as popular data. Course notes, web pages and announcements for students in a campus environment; detailed information about the objects for visitors in an exhibition; or product and price information and promotions for customers in a shopping-mall can be regarded as popular data. The content of these data may change in time depending on the subject of the data, as in the cases of the content of a web page or a news service. The aim of WIDE system is to bring such popular information ready to the fingertips of WLAN users rather than make them search for it.. The WIDE system can be defined as a collection of hot spots providing high bandwidth radio link for data services in moderate-sized and low-mobility (walk-through) environments.. The system takes on the concept of discontinuous service provision because of the nonubiquitous nature of hot spots. For many types of information, discontinuous coverage is sufficient. Email, music, or e-book services can be classified within this category.. WIDE hot spots can be found in locations where there is the appropriate user density, but users have to walk through the service area in order to actually access the service, and then take their service away for later consumption. As users pass through the coverage area of the system, the most recent version of the information services in the user profile will be automatically downloaded to their mobile terminals without any user intervention. A typical WIDE client and its user interface are illustrated in Figure 1 . Interesting deployment scenarios are possible for large-scale networks. WIDE hot spots located at traffic posts and/or bus stops may provide municipal services to both pedestrians and to the cars waiting for the red light or for the bus. Users of the system may subscribe to the information services related to that region, including local maps, weather reports, shop, and restaurant locations, as well as road conditions and traffic congestion reports.. The motivation of the system proposed in this paper is the Infostation concept [1, 2] . Hot spots of the WIDE system are analogous to Infostation cells in that both offer discontinuous service provision. There are ongoing projects on the Infostation concept. One of these projects focuses primarily on the data link layer and below to enable drive-through data reception [3] and another one focuses on developing Infostation applications and transport layer mechanisms to support those applications for walk-through scenarios [4, 5] . Rover Technology [6] and Mobius [7] are other ongoing projects specialized on employing location tracking to decide the services to deliver to the users, and scheduling algorithms for large-scale data dissemination systems respectively. Wideray [8] is a commercial product providing some of the functionality of WIDE.. However, Wideray is based on GSM networks, not on distributed WLAN hotspots and uses a hardware called Jack utilizing IrDA to provide service requiring user interaction.. In Section 2, we give a brief overview of the design principles to implement the WIDE system. Section 3 presents the system architecture and communication design of WIDE. The details of the mechanisms included in WIDE are presented in Section 4. Section 5 presents the functional evaluation of the proposed system design on the implemented WIDE prototype.. Section 6 concludes the paper, suggesting some future works. The design of an information delivery system, namely WIDE, which targets itinerant clients, is presented in this paper. The system uses the WLAN hot spot concept to disseminate high-bandwidth popular data in a distributed manner. IEEE 802.11b technology is used as the underlying infrastructure for the proposed system. The overall system design is shaped according to some design principles presented in Section 2. The system architecture is constructed for a moderate-size network with the assumption of walk-through scenarios. The communication protocols are presented in view of the communication cycle concept. The mechanisms, which are required for reliable and secure data dissemination, are presented briefly. Proposed data delivery mechanisms are tested on a "proof of concept" prototype and some results on the functional evaluation are reported. Readers interested in the details of the design, implementation and other tests performed on the prototype can refer to the WIDE technical report [21] .. Although most of the ideas were theoretically proposed before, it should be noted that the strength of the paper is the presentation of the original overall system design and the actual implementation of the prototype using IEEE 802.11b technology. The idea in this paper is to analyze the existing solutions for related problems in such systems and combine the solutions in a systematic way in order to come up with a working, real-life, easily deployable prototype and present the functionality of it.. In future studies, our aim is to conduct large-scale performance experiments on the implemented prototype with realistic number of clients and servers and find out how the system behaves under different load conditions. Additionally, we want to modify the design of the system and the implementation of the prototype to improve the power-awareness of the system by dozing the Wi-Fi card at every unnecessary period in the communication cycle. Moreover, we plan to improve the system architecture in terms of scalability and robustness. For this purpose, it is being planned to add backup authentication and profiling services to the current design. With these additions, the system will be able to operate under heavy load conditions without affecting the performance dramatically. We also want to modify the WIDE system to deliver location based information services to its clients. For this purpose we need to find the physical location of the client. WIDE may be integrated with WLAN Tracker [22] , but will require modifications to the current design. Currently WIDE offers file delivery services. In the future framework, the system can be improved to give streaming and upload services such as music and video streaming, and e-mail transfer requiring special coding and security issues.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A Knowledge base model for complex forging die machining Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined Design of forged part and forging die is obtained by the study of metal flow from the die cavities to the gutter and flash through the parting surface. This type of study was performed in different sections of the functional part using a wire-frame model (Bagard, 1997; Mawussi, 1995; Sun, Sequin, 2001 ). Then models of forged part and forging die are designed by machining assistant who is generally in charge of the definition of the final shapes and the generation of the machining process. In this approach, the final shapes of forged parts and forging dies was obtained according to the know-how of the machining assistant and the available machining resources.. Today, die designers define forged part according to the whole shape of the functional part. Therefore design of the final shapes of dies and forged parts is transferred to engineering and design department. This evolution is closely linked with the development of powerful software tools which allow simulating metal flow in the 3D model of die cavities (TRANSVALOR, Material forming simulation, http://www.transvalor.com; Kulon, Mynors, Broomhead, 2006) . The die model obtained in the engineering and design department is processed by the machining assistant.. Today, die models have more complex shape. Indeed, the introduction of close die forging process ( Fig. 1) increases the complexity and the finishing requirements of die shapes in order to limit machining operation (Doege, Bohnsack, 2000) . Application of a unique standard machining strategy and a choice of one cutting tool for the machining of a whole surface of a given forging die are generally unsatisfactory because of the variations of cutting speed and feed rate. Approach presented in this paper is aimed to decompose the die geometry in several machining area in order to limit the variations of cutting speed and feed rate. The decomposition process represents the CAD/CAM integration in the field of the machining of forging dies and it can be extended to complex shape parts.. The generation of the machining process of dies by the machining assistant is based on several expert knowledge which are linked to technological and topological data, machining strategies, cutting tools and machining sequence components. The detailed analysis of these expert knowledge made it possible to highlight their relations and associations to the different information being able to be processed during the decomposition of the forging die shape. The data handled at this stage as their relations were gathered in the machining process model which was used as a basis for machining knowledge formalization and integration.. Knowledge formalization in the machining process generation has been carried out at two stages starting from the representation of the CAD model of die by STL facets. In the first stage the difficulty linked to the variation of cutting speed contact areas between cutting tools and the shape of the die have been analysed. Knowledge formalization at this stage leads to the generation of contact area map which is based on the orientation of the normal vector of each facet of the STL model compared to the tool axis direction. The decomposition of the normal vector by projection respectively to the tool axis direction and the plane H perpendicular to this tool axis direction defines the orientation indicator and the contact area indicator. These two indicators are useful for the integration of knowledge associated to cutting tools. The second stage of knowledge formalization is based on the difficulty to keep the orientation of the normal vectors of several facets in the same plane. When a plane parallel to the tool axis direction exists, it defines a machining continuity and corresponds to the basic plane for the parallel planes strategy. Maps generated at this stage for the search of continuity planes are based on a continuity indicator defined according to a given machining direction. The continuity indicator provided information for the integration of knowledge associated to machining strategies. Extraction of geometrical features from the combination of the two types of maps is not automatic. This is one of the points which must be developed in next work. The knowledge integration being based on several expert rules, the geometric feature extraction process must be based on artificial intelligence approaches like neural network.. The industrial case study presented in the final part of the paper shown a complete application of the knowledge formalization for the decomposition of the die shape into geometrical features. The machining features created by associating cuttings tools and machining strategies to the geometrical features can be used by a machining assistant to generate the machining sequences.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Intelligent data entry assistant for XML using ensemble learning XML has emerged as the primary standard of data representation and data exchange [13] . Although many software tools exist to assist the XML implementation process, data must be manually entered into the XML documents.Current form filling technologies are mostly for simple data entry and do not provide support for the complexity and nested structures of XML grammars. This paper presents SmartXAutofill, an intelligent data entry assistant for predicting and automating inputs for XML documents based on the contents of historical document collections in the same XML domain. SmartXAutofill incorporates an ensemble classifier, which integrates multiple internal classification algorithms into a single architecture. Each internal classifier uses approximate techniques to propose a value for an empty XML field, and, through voting, the ensemble classifier determines which value to accept. As the system operates it learns which internal classification algorithms work better for a specific XML document domain and modifies its weights (confidence) in their predictive ability. As a result, the ensemble classifier adapts itself to the specific XML domain, without the need to develop special learners for the infinite number of domains that XML users have created. We evaluated our system performance using data from eleven different XML domains. The results show that the ensemble classifier adapted itself to different XML document domains, and most of the time (for 9 out of 11 domains) produced predictive accuracies as good as or better than the best individual classifier for a domain. In this paper we presented an ensemble classifier that assists the data entry into XML documents by predicting the values of XML nodes. The ensemble integrates individual classification algorithms and learns which ones predict the values of XML nodes more accurately. Our approach is different from current autofill technologies, since our internal classifiers use approximate predictive techniques (e.g naïve conditional probability or partial syntactic match).. Current autofill technologies, on the other hand, require a perfect match between the data in the document being completed and some document in memory. Our ensemble also differs from traditional ensemble classifiers, since it integrates disparate classification and prediction algorithms, instead of algorithms induced by the same methodology through boosting.. Our experiments showed that the ensemble adapted itself to a variety of XML domains, learned the best predictive algorithm for a node in an XML domain, and in 9 out of 11 test domains performed as well as or better than the best individual predictive algorithm. The accuracy achieved ranged greatly from domain to domain, with lowest predictive accuracy of 15.95% and best accuracy of 100%. The average accuracy of the ensemble was 44.3% and the median accuracy 37.9%. The system performed in real-time, with worst performance of approximately 60 milliseconds, satisfying the requirement that the autofill be faster than the user. Now that we have verified that the ensemble approach works, we intend to study which classifiers to include in the ensemble to improve its lower end performance. Other possible extensions include tweaking the rewards formula for the ensemble, and exploring more complex classifiers such as Neural Networks or Support Vector Machines that may require off-line retraining. We will also study the use of ensemble-based prediction in different applications including web forms and databases. Finally, it will be interesting to study why simple prediction techniques seem to perform much better than complex ones, and whether this is a feature constrained to specific XML domains, or a more general feature of XML documents. In our experiments we expected that the ensemble classifier would work at least as well as the best performing internal classification algorithm for a domain, and also that for different domains it would match the performance of different internal classifiers. This was the case in 9 out of the 11 domains where we tested our algorithms. We currently have no explanation as to why the ensemble did worse than the best algorithm in the UWM and NASA domains (although in both cases it did significantly better than the second-best classifier); it is possible that more training of the ensemble would have improved its performance in these two cases.. An interesting observation is that the ensemble learns to prefer different algorithms for different nodes in the same domain, leading to adaptation and learning at the lowest possible level. This would explain why it performed better than the best individual predictor in 9 domains: the ensemble learns the best algorithm per node instead of per domain, while individual, internal algorithms make predictions based on the whole document in a domain.. An unexpected result was that the best individual predictive algorithms were most often the simplest ones: recency and frequency, while more complex algorithms were seldom the better ones, and often had much lower accuracy.

O O O O O B O B I B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Ribosome

kinetics

and

aa-tRNA

competition

determine

rate

and

fidelity

of

peptide

synthesis

It

is

generally

accepted

that

the

translation

rate

depends

on

the

availability

of

cognate

aa-tRNAs.

In

this

study

it

is

shown

that

the

key

factor

that

determines

translation

rate

is

the

competition

between

near-cognate

and

cognate

aa-tRNAs.

The

transport

mechanism

in

the

cytoplasm

is

diffusion,

thus

the

competition

between

cognate,

near-cognate

and

non-cognate

aa-tRNAs

to

bind

to

the

ribosome

is

a

stochastic

process.

Two

competition

measures

are

introduced;

C(i)

and

R(i)

(i

are

quotients

of

the

arrival

frequencies

of

near-cognates

vs.

cognates

and

noncognates

vs.

cognates,

respectively.

Furthermore,

the

reaction

rates

of

bound

cognates

differ

from

those

of

bound

near-cognates.

If

a

near-cognate

aa-tRNA

binds

to

the

A

site

of

the

ribosome,

it

may

be

rejected

at

the

anti-codon

recognition

step

or

proofreading

step

or

it

may

be

accepted.

Regardless

of

its

fate,

the

near-cognates

and

non-cognates

have

caused

delays

of

varying

duration

to

the

observed

rate

of

translation.

Rate

constants

have

been

measured

at

a

temperature

of

°C

by

(Gromadski,

K.B.,

Rodnina,

M.V.,

Kinetic

determinants

of

high-fidelity

tRNA

discrimination

on

the

ribosome.

Mol.

Cell

13,[191][192][193][194][195][196][197][198][199][200].

These

rate

constants

have

been

re-evaluated

at

°C,

using

experimental

data

at

°C

and

°C

(Varenne,

S.,

et

al.,

Translation

in

a

non-uniform

process:

effect

of

tRNA

availability

on

the

rate

of

elongation

of

nascent

polypeptide

chains.

J.

Mol.

Biol.

The

key

results

of

the

study

are:

(i)

the

average

time

(at

°C)

to

add

an

amino

acid,

as

defined

by

the

ith

codon,

to

the

nascent

peptide

chain

is:

τ(i)

(in

ms);

(ii)

the

misreading

frequency

is

directly

proportional

to

the

near-cognate

competition,

E(i)

(iii)

the

competition

from

near-cognates,

and

not

the

availability

of

cognate

aa-tRNAs,

is

the

most

important

factor

that

determines

the

translation

rate

-the

four

codons

with

highest

near-cognate

competition

(in

the

case

of

E.

coli)

are

[GCC]

[CGG]

[AGG]

[GGA],

which

overlap

only

partially

with

the

rarest

codons:

[AGG]

[CCA]

[GCC]

[CA

C];

(iv)

based

on

the

kinetic

rates

at

°C,

the

average

time

to

insert

a

cognate

amino

acid

is

ms

and

the

average

delay

to

process

a

nearcognate

aa-tRNA

is

ms

and

(vii)

the

model

also

provides

estimates

of

the

vacancy

times

of

the

A

site

of

the

ribosome

-an

important

factor

in

frameshifting.

investigated

the

role

of

intracellular

tRNA

concentrations

in

the

dynamics

and

regulation

of

protein

synthesis.

Their

three

major

conclusions

are:

the

ribosome's

idling

in

the

open

A

site

in

its

stochastic

search

for

a

cognate

or

near-cognate

aa-tRNA

is

the

rate-limiting

step

in

the

peptidyl

elongation

cycle,

since

translation

and

translocation

steps

occur

much

faster;

imbalances

in

tRNA

concentrations

in

the

cytoplasm

lead

to

variations

in

the

translation

rate

and

the

possible

role

of

mRNA

secondary

structure

in

non-uniform

translation

rates

is

minor,

at

least

for

proteins

they

investigated.

Another

important

point

they

raised

is

our

incomplete

knowledge

of

the

decoding

spectrum

(cf.

point

on

p.

of

Varenne

et

al.

Table

(Varenne

et

al.,

p.

summarizes

average

synthesis

rates

for

various

proteins

in

E.

coli.

The

rates

which

have

been

measured

at

°C

(Rate

B

in

their

Table

compare

very

closely

with

our

theoretical

results,

which

use

the

kinetic

rates

of

Gromadski

and

Rodnina

measured

at

°C.

Heyd

and

Drew

who

also

based

their

model

on

the

mechanism

proposed

by

Rodnina

and

co-workers,

obtained

an

average

translation

rate

of

amino

acids/s.

It

is

interesting

to

note

their

explanation

of

their

low

translation

rates;

they

ascribe

it

to

possible

errors

in

the

measured

kinetic

rates

(cf.

p.

but

they

did

not

consider

the

difference

between

the

kinetic

rates

at

°C

and

°C.

Gilchrist

and

Wagner

developed

a

model

for

single

mRNA

translation.

They

did

include

ribosome

recycling

in

their

model,

but

ribosome-ribosome

interaction

was

not

considered.

Variation

in

elongation

rates

was

assumed

to

be

directly

proportional

to

tRNA

availability

and

scaled

to

give

an

average

rate

of

amino

acids/s.

They

defined

'nonsense

errors'

as

one

of

the

following

events:

(i)

ribosome

release

from

the

mRNA

prior

to

completion

of

the

translation

process,

(ii)

frameshifting

and

(iii)

false

termination.

However,

their

model

did

not

consider

the

important

issue

of

misreading

errors.

Furthermore,

the

same

probabilities

for

these

events

were

assigned

to

all

codons.

Farabaugh

provided

many

examples

of

codon-dependent

frameshifting.

Sipley

and

Goldman

found

that

extended

pauses

in

translation,

during

which

the

A-site

is

vacant,

increase

the

frequency

of

frameshifting.

In

addition,

alleles

that

translate

slowly

are

prone

to

frameshift

more

frequently.

Conversely,

if

they

increased

the

concentrations

of

cognate

tRNA,

then

the

A-site

vacancy

time

was

reduced

and

the

frameshift

frequency

dropped.

The

aforementioned

results

are

consistent

with

those

of

Rosenberg

et

al.

who

found

that

mRNA

containing

high

numbers

of

the

rare

codon

AGG

caused

its

corresponding

tRNA

to

be

sequestered

and

the

ribosome

stalled

at

the

first

of

two

consecutive

AGG

codons.

The

stalled

ribosome

frameshifted,

hopped,

or

terminated

translation

as

an

upstream

translating

ribosome

approached..

Ribosome-ribosome

interaction

becomes

a

factor

to

consider

if

there

are

sections

on

the

mRNA

that

translate

sufficiently

slowly

to

cause

trailing

ribosomes

to

stall

behind

the

one

in

a

slow

mRNA

section.

Zouridis

and

Hatzimanikatis

found

that

translation

is

initiation-or

elongation-limited

for

low

or

intermediate

polysome

sizes,

respectively,

and

terminationlimited

for

high

polysome

sizes.

After

calculating

an

effective

elongation

rate

constant

which

varies

with

the

degree

of

ribosome

crowding,

they

concluded

that

protein

synthesis

rates

are

maximized

when

polysome

size

corresponds

to

a

set

of

effective

elongation

rate

constants

while

avoiding

crowding

along

the

mRNA.

Koontz

found

that

varying

the

concentration

of

Trp-tRNA

Trp

in

cells

increased

the

polysome

size

distribution,

supporting

a

model

in

which

a

translating

ribosome

stalls

(and

subsequent

ribosomes

are

backed

up

behind

it)

at

Trp

codons

when

the

concentration

of

the

corresponding

tRNA

is

low.

Kremling

derived

a

phenomenological

model

of

translation

of

prokaryotes

that

also

includes

RNA

transcription.

The

model

provides

expressions

for

the

steady-state

population

of

ribosomes

on

both

nascent

and

complete

mRNAs..

An

interesting

study

was

reported

by

Wahab

et

al.

They

observed

that

hypomodified

tRNA

led

to

a

decrease

in

protein

synthesis

and

growth

rates

in

E.

coli.

But

they

also

found

that

cells

can

tolerate

a

large

increase

in

concentration

of

a

single

tRNA

with

little

effect

on

growth.

The

single

modified

tRNA

Leu

also

did

not

markedly

affect

overall

misreading

errors.

Sorensen

et

al.

studied

translation

rates

of

some

"frequent"

and

"infrequent"

codons

and

found

that

the

infrequent

codons

were

translated

slower

than

the

frequent

codons

by

a

factor

of

six.

They

proposed

that

there

might

be

individual

codons

which

are

translated

slower

than

frequent

codons.

They

also

determined

that

"an

extraordinary

drain

of

the

corresponding

tRNAs"

was

not

the

cause

of

the

infrequent

codons'

slow

translation.

This

result

points

to

the

competition

between

cognates

and

near-cognates

as

a

plausible

explanation

of

reduction

in

translation

rate,

instead

of

tRNA

depletion.

The

following

major

conclusions

follow

from

the

analysis

and

the

results

of

the

mathematical

modeling:

a.

The

arrival

frequencies

of

tRNA

species

at

a

ribosome

A

site

have

been

calculated

on

the

basis

of

transport

by

diffusion.

b.

The

competition

measure

C(i)

of

the

ith

codon

is

the

quotient

of

the

arrival

frequencies

of

its

near-cognates

and

the

arrival

frequencies

of

its

cognates.

c.

An

overall

kinetic

expression

for

translation

has

been

derived

and

it

includes

the

steps

of

peptidyl

transfer

and

ribosome

translocation.

d.

There

is

a

linear

relationship

between

the

average

translation

time

of

the

ith

codon

and

its

competition

measure:

τ(i)

e.

Codons

with

high

competition

measures

have

high

frequencies

of

misreading

errors.

The

relation

is

given

by:

E(i)

f.

The

correlation

between

tRNA

availability

and

translation

time

is

not

perfect.

Codons

which

are

served

by

minor

tRNA

species

such

as

[UGU]

can

be

translated

as

fast

as

codons

which

are

served

by

abundant

codons

such

as

[CUG].

g.

The

average

time

to

insert

an

amino

acid

from

a

cognate

aa-tRNA

is

ms.

The

result

is

based

on

the

kinetic

parameters

of

Gromadski

and

Rodnina

which

have

been

measured

at

°C

and

adjusted

to

°C.

h.

The

average

delay

that

is

caused

by

the

binding

of

a

near-cognate

aa-tRNA

is

ms.

i.

There

is

great

variability

in

the

rates

of

translation.

Rates

vary

between

and

amino

acids/s

within

the

same

set

of

synonymous

codons.

j.

Codon

optimization

for

recombinant

DNA

expression

is

not

only

the

process

to

use

the

most

abundant

synonymous

codons.

A

linear

application

of

our

theory

suggests

that

synonymous

codons

be

selected

with

minimum

competition

measure.

If

the

objective

is

not

to

minimize

the

translation

time,

but

to

produce

as

many

functional

proteins

as

possible,

one

begins

to

question

the

role

of

codons

with

slow

translation

rates

(i.e.

high

competition

measures).

If

their

role

is

primarily

to

slow

down

the

translation

process

to

allow

time

for

a

nascent

polypeptide

chain

to

fold,

before

the

next

domain

is

synthesized,

then

any

substitution

by

codons

with

faster

translation

rates

compromises

the

folding

process.

If

this

argument

is

correct,

then

it

is

unfortunately

not

possible

to

decouple

the

codon

optimization

from

the

protein

folding

process.

However,

it

is

conjectured

(cf.

Thanaraj

and

Argos

that

one

role

of

slow-translating

codons

is

to

allow

time

for

an

orderly,

sequential

folding

of

multi-domain

proteins.

B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


3D object search through semantic component In this paper, we present a novel concept named semantic component for 3D object search which describes a key component that semantically defines a 3D object. In most cases, the semantic component is intra-category stable and therefore can be used to construct an efficient 3D object retrieval scheme. By segmenting an object into segments and learning the similar segments shared by all the objects in the same category, we can summarize what human uses for object recognition, from the analysis of which we develop a method to find the semantic component of an object. In our experiments, the proposed method is justified and the effectiveness of our algorithm is also demonstrated. Effective representation and visualization of an object in 3D are of great importance in current multimedia applications. Though we can obtain 3D raw data 1 much more easier due to the advance of technology, machine understanding and characterization of the data are still challenging tasks. Some basic concepts, such as how similar one object is to the other, are still hard to define mathematically such that they can match our visual perception. These difficulties result in deficiency in content based searching, indexing, and classification of 3D objects.. By tackling 3D objects from different aspects, many description features and similarity measures have been proposed in the literature. Some applications can be tried online (such as Princeton 3D model search engine 2 ). Regarding the characteristics used, most methods that handle 3D objects can be categorized into five groups: (i) tackling an object as a whole [9, 10] , (ii) local features [1, 11] , (iii) spatial maps and distributions [12, 8, 13] , (iv) structures [7, 2, 16] , and (v) 2D view based [3, 14] . To deal with more shape variations and to achieve better performance, a mixture of methods from different groups is a reasonable approach often used by researchers.. In a common scenario, a feature is extracted from an object and then is used in a specific application, such as object retrieval, to justify its effectiveness. In this application, usually the features from two 3D objects are compared by a similarity measure. This procedure has been followed many years with some fundamental flaws ignored. In these flaws, the most inconspicuous is that an extracted feature depends on its corresponding object in some specific shape/pose. The feature extraction in this way is far from the approach involving in human recognition of 3D objects.. Take the recognition of the objects in Figure 1 as an example. By giving a feature f and feature data fa, f b , fc, and f d extracted from the four objects. Machine takes two feature data as input and then gives a similarity estimation. Consider the same horse with different poses shown in (a) and (b). Ideally, fa and f b should be exactly the same. However, they usually should exhibit some difference if we want the feature f has more discriminative power, such that in the case of comparing (c) and (d), fc and f d can be separated from each other. Therefore, a proper tradeoff between invariance and discriminativity has to be carefully considered when we design a feature. Unfortunately, determining such a tradeoff is a very tricky task. However, human can easily overcome the difficulty by learning from given objects, perceiving different and similar components between objects and then pay distinct attentions to various aspects of objects. Can machine learn in a similar manner?. Notice that the objects from (a) to (d) in Figure 1 share some very similar components -their limbs. They are not distinctive among the objects and play a minor role in the recognition of these objects, i.e., we may not notice the difference of limbs but still can easily conclude that both (a) and (b) are horses, and at the same time distinguish (c) from (d). Ignoring components like the limbs leads to the following benefits for machine recognition of 3D objects:. i) The intra-category invariance is increased. For example, for the two objects (a) and (b) in Figure 1 , considering the main bodies of them without the limbs, they can be easily matched.. ii) The inter-category variance is increased too. Take (c) and (d) in Figure 1 as an example. Similar limbs between them could mess up the obtained feature data and raise the similarity between them.. Through extensive observation, we know that most objects contain a key component, by which they are semantically defined. The fact that we can recognize a horse and distinguish it from a cow is not because of their limbs but their main bodies. For a general object, determining its visual key component is not trivial. In what follows, we discuss how to define the key component, called semantic component, for a given object and develop an algorithm to locate it. We also explore its applications and discuss its pros and cons. In this paper, we have presented a novel concept named semantic component to describe the key component that semantically defines an object. In most cases, the semantic component is intra-category stable and therefore can be used to construct an efficient object retrieval scheme. We have also proposed a method to segment an object and then to find its semantic component. In the experiments, our theory is justified and the effectiveness of the proposed scheme is demonstrated.. From our experiments, we also notice that our scheme gives less improvement when dealing with objects without articulated parts. In many cases like this, the semantic component of an object is the object itself. Sometimes, we cannot find semantic component for an object. Take snake as an example. Given a set of learning samples in various configurations, We can hardly learn a component shared alike among the samples since the shape of a snake body is equally tenuous at any part. With our theory we can expect a structure involved measure, like those using skeleton, can achieve a better performance on these objects.. In our future work, we plan to build an object component warehouse including a comprehensive collection of everyday objects. With it we can learn which part or structure is semantically important to each object in the warehouse, from which we can obtain a better understanding of human perception of 3D objects.

B I I O B I O O O O O O O O O B I O B I I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Ensemble methods for uplift modeling Abstract Uplift modeling is a branch of machine learning which aims at predicting the causal effect of an action such as a marketing campaign or a medical treatment on a given individual by taking into account responses in a treatment group, containing individuals subject to the action, and a control group serving as a background. The resulting model can then be used to select individuals for whom the action will be most profitable. This paper analyzes the use of ensemble methods: bagging and random forests in uplift modeling. We perform an extensive experimental evaluation to demonstrate that the application of those methods often results in spectacular gains in model performance, turning almost useless single models into highly capable uplift ensembles. The gains are much larger than those achieved in case of standard classification. We show that those gains are a result of high ensemble diversity, which in turn is a result of the differences between class probabilities in the treatment and control groups being harder to model than the class probabilities themselves. The feature of uplift modeling which makes it difficult thus also makes it amenable to the application of ensemble methods. As a result, bagging and random forests emerge from our evaluation as key tools in the uplift modeling toolbox. Machine learning is primarily concerned with the problem of classification, where the task is to predict, based on a number of attributes, the class to which an instance belongs, or the conditional probability of it belonging to each of the classes. Unfortunately, classification is not well suited to many problems in marketing or medicine to which it is applied. Consider a direct marketing campaign where potential customers receive a mailing offer. A typical application of machine learning techniques in this context involves selecting a small pilot sample of customers who receive the campaign. Next, a classifier is built based on the pilot campaign outcomes and used to select customers to whom the offer should be mailed. As a result, the customers most likely to buy after the campaign will be selected as targets.. Unfortunately this is not what a marketer wants! Some of the customers would have bought regardless of the campaign; targeting them resulted in unnecessary costs. Other customers were actually going to make a purchase but were annoyed by the campaign. The result is a loss of a sale or even a complete loss of the customer (churn). While the second case may seem unlikely, it is a well known phenomenon in the marketing community (Hansotia and Rukstales 2002; Radcliffe and Surry 2011) .. In order to run a truly successful campaign, we need, instead, to be able to select customers who will buy because of the campaign, i.e., those who are likely to buy if targeted, but unlikely to buy otherwise. Similar problems arise in medicine where some patients may recover without actually being treated and some may be hurt by the therapy's side effects more than by the disease itself.. Uplift modeling provides a solution to this problem. The approach employs two separate training sets: treatment and control. The objects in the treatment dataset have been subject to some action, such as a medical treatment or a marketing campaign. The control dataset contains objects which have not been subject to the action and serve as a background against which its effect can be assessed. Instead of modeling class probabilities, uplift modeling attempts to model the difference between conditional class probabilities in the treatment and control groups. This way, the causal influence of the action can be modeled, and the method is able to predict the true gain (with respect to taking no action) from targeting a given individual. To date, uplift modeling has been successfully applied in real life business settings. An American bank used uplift modeling to turn an unsuccessful mailing campaign into a profitable one (Grundhoefer 2009 ). Applications have also been reported in minimizing churn at mobile telecoms (Radcliffe and Simpson 2008) .. Ensemble methods are a class of highly successful machine learning algorithms which combine several different models to obtain an ensemble which is, hopefully, more accurate than its individual members. The goal of this paper is to evaluate selected ensemble methods in the context of uplift modeling. Our comparison will be focused on bagging and Random Forests (which is a form of bagging using additional randomization), two very popular ensemble techniques, which, as we demonstrate, offer exceptionally good performance. Boosting, another important technique, is beyond the scope of this paper as adapting it to uplift modeling requires an extensive theoretical treatment and merits a separate investigation. Further, we provide an explanation for good performance of those methods which, in our opinion, is that the nature of uplift modeling naturally leads to highly diverse ensembles. The 'uplift signal' is weak compared to changes in conditional class probabilities which makes the prediction problems difficult; the members of the ensemble are thus very sensitive to noise introduced by random sampling and/or randomized decision tree splits which makes them very different from each other.. In practice, uplift modeling is frequently applied in the marketing domain which in itself is likely (we do not have access to a large enough collection of real marketing datasets to demonstrate this experimentally) to promote ensemble diversity due to the so called correlation problem (Abe et al. 2004 ), i.e., the fact that predictor variables are usually very weakly correlated with customer behavior.. The contribution of this paper is to provide a thorough analysis of ensemble methods in the uplift modeling domain. First we discuss how various types of uplift decision trees can be combined into ensembles. Then we provide an extensive experimental evaluation on real and artificial datasets showing excellent performance of such methods. We also discuss theoretical properties of uplift ensembles and provide an explanation for their good performance based on the concept of ensemble diversity. Although the use of ensemble methods in uplift modeling has already been mentioned in the literature Radcliffe and Surry (2011) and Guelman et al. (2012) , to the best of our knowledge this is the first detailed treatment of the subject including both theoretical analysis and thorough experimental verification.. The remaining part of the paper is organized as follows: Sect. 2.1 gives a literature overview, Sect. 3 describes ensemble methods in the context of uplift modeling, and the experimental Sect. 4 demonstrates excellent performance of those methods. Section 5 then offers an explanation for this good performance through an analysis of model diversity. Finally, Sect. 6 concludes the paper. The paper presented a theoretical and experimental investigation of the effectiveness of ensemble methods in uplift modeling. The analysis includes two practically important types of uplift models: the double classifier approach and trees which model the net gain directly. Although uplift ensembles have been mentioned before in the literature, this paper is the first to provide a thorough analysis and evaluation, and the first to point out that uplift modeling is especially well suited to the application of such methods. Our experiments on real and artificial data demonstrate that ensemble methods often bring dramatic improvements in performance, turning useless single trees into highly capable ensembles. In some cases the Area Under the Uplift Curve of an ensemble was triple that of the base learner. We demonstrate that features specific to uplift modeling naturally promote high of diversity of ensemble members. Interestingly, this is especially true in cases where uplift modeling itself is difficult.. Further, we compare bagging and Random Forests in the uplift modeling context. We show that Random Forests provide more diverse ensembles at the expense of their members being slightly weaker. In practice both methods perform very well; which one is better is very much case dependent. Random Forests outperform bagging only if increased diversity is able to offset the decrease in individual members' strength.. The most important conclusion of the paper is that ensemble methods come out from the analysis as key uplift modeling tools capable of achieving excellent results. The improvements are typically much bigger than in the case of classification where ensembles are most commonly applied. Despite its practical appeal, uplift modeling has received surprisingly little attention in the literature. In this section we will present the related work. We begin with the motivation for uplift modeling and related techniques and a brief overview of ensemble methods, then we discuss the available uplift modeling algorithms, and finally present current references on using ensemble methods with uplift models.. The first publication explicitly discussing uplift modeling was Radcliffe and Surry (1999) . It presents a thorough motivation including several use cases. General discussions of uplift modeling and its applications can also be found in Hansotia and Rukstales (2002) and Radcliffe and Surry (2011) .. Experiments involving control groups are becoming common in website optimization, where they are used with so called A/B tests or multivariate tests (Kohavi et al. 2009 ). The focus of those methods is, however, different from uplift modeling as their main goal is to verify the overall effectiveness of a change in website design, not selecting the right design for each customer (looking into specific subgroups is usually mentioned only in the diagnostic context). Another related technique is action rule discovery (Adomavicius and Tuzhilin 1997; Raś et al. 2009 ) which is concerned with finding actions which should be taken to achieve a specific goal. This is different from uplift modeling which aims at identifying groups on which a predetermined action will have the most positive effect. Contrast sets introduced by Bay and Pazzani (2001) allow for finding subgroups in two datasets on which a specified quantity differs significantly. This is different from uplift modeling which aims at predicting this difference at the level of single records.. The most popular ensemble methods are bagging (Breiman 1996) , boosting (Freund and Schapire 1997) and Random Forests (Breiman 2001) . Other ensemble methods exist, such as Extremely Randomized Trees (Geurts et al. 2006) or Random Decision Trees (Fan et al. 2003) . Essentially, those methods differ by the way randomness is injected into the tree learning algorithm to ensure that models in the ensemble are diverse. In Liu et al. (2008) a unifying framework is proposed which encompasses many approaches to randomization. As we mentioned in Sect. 1, this paper will only look into bagging and Random Forests.

B I O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O B O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O B O B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O B O B I O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O B I O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O B O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I


User-generated descriptions of individual images versus labels of groups of images: A comparison using basic level theory Although images are visual information sources with little or no text associated with them, users still tend to use text to describe images and formulate queries. This is because digital libraries and search engines provide mostly text query options and rely on text annotations for representation and retrieval of the semantic content of images. While the main focus of image research is on indexing and retrieval of individual images, the general topic of image browsing and indexing, and retrieval of groups of images has not been adequately investigated. Comparisons of descriptions of individual images as well as labels of groups of images supplied by users using cognitive models are scarce. This work fills this gap. Using the basic level theory as a framework, a comparison of the descriptions of individual images and labels assigned to groups of images by 180 participants in three studies found a marked difference in their level of abstraction. Results confirm assertions by previous researchers in LIS and other fields that groups of images are labeled using more superordinate level terms while individual image descriptions are mainly at the basic level. Implications for design of image browsing interfaces, taxonomies, thesauri, and similar tools are discussed.

O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O


Robust image-based visual servoing using invariant visual information This paper deals with the use of invariant visual features for visual servoing. New features are proposed to control the 6 degrees of freedom of a robotic system with better linearizing properties and robustness to noise than the state of the art in image-based visual servoing. We show in this paper that by using these features the behavior of image-based visual servoing in task space can be significantly improved. Several experimental results are provided and validate our proposal. Image-based visual servoing (IBVS) consists of using feedback information defined directly in the images provided by a vision sensor to control the motion of a dynamic system. Theoretically, IBVS is suitable only for small displacements. However in practice, it is robust and efficient for large displacements, but less than optimal in terms of 3-D motion [3] . A large spectrum of visual features can be extracted and used in the control loop. Besides, the choice of features directly influences the closed-loop dynamics in task-space. The points are the most simple and common features that can be extracted from an image. However, such features suffer from control coupling and non-linearities between the image space and 3D space. In practice, this can lead to unpredictable behavior in the task space but also to divergence or convergence to local minima in some situations, especially when large displacements are to be performed. Other features can be derived from the points in image to improve the system behavior. Features including the distance between two points in the image plane and the orientation of the line connecting those two points were proposed in [8] . The relative area of two projected surfaces was proposed in [20] as a feature. A vanishing point and the horizon line were selected in [15] , which ensures good decoupling between translational and rotational degrees of freedom (DOFs). In [11] , the coordinates of points are expressed in a cylindrical coordinate system instead of the classical Cartesian one to improve the robot trajectory.. This paper belongs to a series of works aiming at the selection of optimal visual features for IBVS [17] , [18] , [19] . These previous works were concerned with approaches that consider performance measures to choose visual features with good decoupling and linearizing properties. The main idea is to apply non-linear transformations to classical image visual features (point coordinates, contours or image region) in order to obtain new visual information with better linearizing properties. The results obtained using such approaches have shown a superior behavior in task space and convergence rate with respect to point coordinates (for instance) [18] . However, applying non-linear transformations on data obtained from the sensor space changes the noise distribution. For instance, if the image noise is Gaussian white, the noise on invariant features proposed in [18] and [19] is no more Gaussian white since the applied transformation is nonlinear. In practice, if the noise level is low, the use of invariants allows obtaining adequate robustness. If the noise level increases, the use of invariants can lead to less robustness at convergence compared to the classical visual features. In this paper, further to proposing new invariants allowing better linearizing properties and decoupling than those proposed in [18] and [19] , we address the problem of robustness to noise.. The features used in this paper are computed from the projection of points onto the unit sphere. This means that the method proposed can work not only with classical perspective cameras but can also be applied to wide-angle cameras obeying the unified model [9, 2] . Wide-angle cameras include catadioptric systems that combine mirrors and conventional cameras to create omnidirectional cameras providing 360 o panoramic views of a scene, or dioptric fish-eye lenses [1, 5] . It is highly desirable that such imaging systems have a single viewpoint [1] , [16] . i.e there exists a single center of projection, so that every pixel in the sensed image measures the irradiance of the light passing through the same viewpoint in one particular direction.. In the next section, the unified camera model and some basic definitions related to visual servoing are recalled as well as the notations used in the following of the paper. In Section 3, a new visual feature vector is proposed. More precisely, the first part of the feature vector we propose is defined by a new rotation vector computed from a set of projected points onto the unit sphere. We will show that this rotation vector has a direct link with the rotational motion (i.e the camera rotational velocities and the vector entries are linked with the identity matrix). Similarly to rotational velocities, a new feature derived from an invariant to rotation computed from projected points onto the unit sphere is proposed in this section to approach a linear system. The interaction matrix related to the proposed feature is derived and shown to behave as a constant matrix with respect to point depth under some assumptions. The invariant to rotation will be used to control the translational motion independently of the rotational ones. Furthermore, the sensitivity to image noise is considered by taking into account the noise propagation from image to the space of new features. Finally, in Section 4, numerous experimental results are presented confirming the validity of the proposed approach. To summarize, the new feature vector s n we propose is composed of two parts. The first part s t is devoted to control the translational motions. It is composed of the set of s wij computed from all combinations of two projected points on the sphere. The second part is devoted to control the rotational motions. It is defined by the rotation vector θu described in paragraph 3.1.3. The interaction matrix related to s n can be expressed as follows:. where L nv is the interaction matrix that links the variation of s t and the translational motions (see (52)) and where L ω (θ, u) and ζ nv are given respectively by (26) and (33). This leads to the following visual servoing control law: In this paper, we have proposed new visual features for image-based visual servoing from a set of matched points. We have firstly proposed a rotation vector computed from all the projected points and providing direct link with the rotational velocities. Then, a set of invariants to rotation has been proposed and used to control the translational velocities. The propagation of noise from the image points coordinates to the features space has been taken into account to improve the robustness of the control law with respect to measurement noise. The theoretical results have been validated with several simulation and experimental results.. By combining these equations with (13), we obtain:. from which, we obtain:. Identically, it is possible to prove that:. Combining (60), (61) and (15) it yields:. Therefore, by combining (60), (61) and (62), one obtains: . By taking the derivative of (14), we obtain:. Furthermore, the time variation of v n3 can be computed using: . Furthermore, after plugging (67), (68) and (72) into (30), we obtain:. Remind that the interaction matrix related to points on the unit sphere [10] is given by:. The interaction matrices related to P v 1 and P v 2 can be obtained by taking the derivative of (12) and combining the result with (74):. Let us consider that ζ n = ζ nv v + ζ nω ω and show that the direct link between ζ and the rotational velocities obtained in [19] is still valid for the new features (i.e. ζ nω = −I 3 ). Combining (75), (76) and (73), it can be obtained:. We have. ] × , which leads to:. Identically, it can be noticed that . Since the triplet (v n1 , v n2 , v n3 ) is a direct and ortho-normal basis, we have:. By plugging the last three equations in (79), we obtain:. Furthermore, we have:. where

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Highly configurable platforms for embedded computing systems Abstract Integrated circuit (IC) chip capacities are increasing at a tremendous rate, leading to system-on-a-chip (SOC) designs. Such capacities allow embedded computing system designers to create single-chip systems with massive functionality. Future IC technologies promise further advances in both transistor capacity and processor speeds. But at some point one begins to ask: How much is enough?. Consider the following analogy. Meeting your family's basic needs on a $20,000 annual salary would be a challenge. Increasing that salary to $40,000 would make a big difference, and $80,000 would be even better. However, at some point, working for further increases would reach a point of diminishing returns. An increase from $100 million to $200 million would be nice, but it probably wouldn't change your life much, and few would even notice the difference between $1 billion and $2 billion.. Similarly, meeting your basic embedded computing needs with a 20,000-transitor silicon budget would also be a challenge. 20,000 transistors (roughly the silicon budget two decades ago) are barely enough to implement an 8-bit microprocessor. Increasing the budget to 40,000 transistors would make a big difference, and 80,000 would be even better. Again, at some point, working for further increases would reach a point of diminishing returns. An increase from 100 million transistors to 200 million transistors (modern chip sizes) would be nice, but would not change most designers' systems all that much, and few designers would even notice the difference between 1 billion and 2 billion transistors.. Moore's law states that chip capacity doubles every 18 months. However, ASIC vendor data shows that most designs greatly underuse that capacity. Mainstream embedded systems designers are simply no longer screaming for higher-capacity chips the way they once were. One reason is that a few hundred million transistors are enough to provide plenty of computing ability. Another reason, known as the productivity gap, is that designer productivity increases have not kept pace with chip capacity increases, meaning designers often cannot create designs that utilize all those available transistors.. IC design costs are also increasing at a rapid rate. Table 1 shows sample non-recurring engineering (NRE) costs for different CMOS IC technologies [6] . At 0.8 µm Furthermore, designing high-end ICs that utilize the available transistor capacity requires significantly more time during design. While the productivity gap is partially responsible for longer design times, the time it takes for a design to be manufactured at a fabrication facility and returned to the designers in the form of an initial IC is also increasing. Table 1 provides the turnaround times for various technology nodes. The turnaround times for manufacturing an IC have almost doubled between 0.8 µm and 0.13 µm technologies. Longer turnaround times lead to larger design costs and even possible loss of revenue if the design is late to the market. Furthermore, long turnaround times become a larger burden on designers when designs flaws lead to multiple respins, delaying a products entrance into the market.. The problems of increasing design costs and long turnaround times are made even more noticeable due to increasing market pressures. Market windows, the time during which a company seeks to introduce a product into the market, are shrinking. The design of new ICs are increasingly being driven by time to market concerns. Due to these concerns, design features or requirements of new systems are often modified into order to get the system to market faster.. Increases in design costs and design time as well as time to market concerns limit the number of situations that can justify producing designs using the latest IC technology. Less than 1,000 out of every 10,000 ASIC designs have volumes high enough to justify fabrication at 0.13 µm [6] . Therefore, if design costs and design times for producing a high-end IC are becoming increasingly large, will high-end ICs be produced at all? Yes, but only a few designs will be able to justify doing so. There will always be systems that make use of the transistor capacity of the latest IC technology. However, for most mainstream embedded systems designers, producing a high-end IC is not feasible.. ICs that are sold in small volumes typically have a high per IC cost. On the other hand, ICs with high volume sales have lower per IC costs because the NRE design costs and other initial design costs can be amortized over the high volume. The three plots in Figure 1 illustrate the idea that cost per IC decreases with higher volumes due to amortization of design costs.. The plots correspond to leading edge IC technology in the 1990s, 2000s, and 2010s. For higher volumes of ICs, initial design costs can be amortized over larger numbers, resulting in lower cost per IC -hence, the plots slope down to the right. Figure 1 also includes a shaded region illustrating the volumes and acceptable cost per IC for mainstream embedded system products. The top of the region represents the highest acceptable cost per IC, and the right side of the region represents the highest volumes we might expect to see, in mainstream systems. The figure illustrates that 1990s technologies were affordable enough to be considered by mainstream designers, while the 2010s technologies are out of range. Few systems could tolerate the high cost per IC, or have the very high volumes, to justify creating a new IC in those technologies.. High-end chips will still be produced in 2010 technologies, but will either require extremely large volume or have high costs. Thus, to achieve the high volumes, high-end ICs will more likely be produced in the form of prefabricated programmable platform ICs, which could be used across a wide variety of embedded systems. Cost and market pressure trends point strongly towards the use of pre-fabricated platforms in mainstream embedded system design. Such platforms must be highly configurable in order to achieve acceptable performance and energy across a wide range of applications. We have investigated two aspects of such configurability. We have designed a highly configurable cache and shown that tuning the cache configuration to a particular application can yield significant energy savings. We have partitioned software among a microprocessor and same-chip FPGA and shown significant speedups and energy savings. Many other researchers are working in the direction of highly-configurable architectures too. Extensive future work remains, not only in designing configurable architectures, but also in developing automated tools to assist in finding the best configuration. Towards this end, we are investigating not only the development of traditional desktop tools, but also desktop tools that interact with an executing platform, and even platforms that transparently tune themselves to an executing application.

O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O


Approaching Throughput-optimality in Distributed CSMA Scheduling Algorithms with Collisions Multiple Access)-like distributed algorithms can achieve the maximal throughput in wireless networks (and task processing networks) under certain assumptions. One important, but idealized assumption is that the sensing time is negligible, so that there is no collision. In this paper, we study more practical CSMAbased scheduling algorithms with collisions. First, we provide a Markov chain model and give an explicit throughput formula which takes into account the cost of collisions and overhead. The formula has a simple form since the Markov chain is "almost" time-reversible. Second, we propose transmission-length control algorithms to approach throughput optimality in this case. Sufficient conditions are given to ensure the convergence and stability of the proposed algorithms. Finally, we characterize the relationship between the CSMA parameters (such as the maximum packet lengths) and the achievable capacity region. Efficient resource allocation is essential to achieve high utilization of a class of networks with resource-sharing constraints, such as wireless networks and stochastic processing networks (SPN [5] ). In wireless networks, certain links cannot transmit at the same time due to the interference constraints among them. In a SPN, two tasks cannot be processed simultaneously if they both require monopolizing a common resource. A scheduling algorithm determines which link to activate (or which task to process) at a given time without violating these constraints. Designing efficient distributed scheduling algorithms to achieve high throughput is especially a challenging task [4] .. Maximal-weight scheduling (MWS) [3] is a classical throughput-optimal algorithm. That is, MWS can stabilize all queues in the network as long as the arrival rates are within the capacity region. MWS operates in slotted time. In each slot, a set of non-conflicting links (called an "independent set", or "IS") that have the maximal weight (i.e., summation of queue lengths) are scheduled. However, implementing MWS in general networks is quite difficult for two reasons. (i) MWS is inherently a centralized algorithm and is not amenable to distributed implementation; (ii) finding a maximal-weighted IS This work is supported by MURI grant BAA 07-036. 18 . Some main results of this paper appeared in [29] . This is the longer version of a same-titled paper to appear in IEEE/ACM Transactions on Networking.. (in each slot) is NP-complete in general and is hard even for centralized algorithms.. On the other hand, there has been active research on low-complexity but suboptimal scheduling algorithms. For example, reference [6] shows that Maximal Scheduling can only guarantee a fraction of the network capacity. A related algorithm has been studied in [7] in the context of IEEE 802.11 networks. Longest-Queue-First (LQF) algorithm (see, for example, [9] , [10] , [11] , [12] ), which greedily schedules queues in the descending order of the queue lengths, tends to achieve higher throughput than Maximal Scheduling, although it is not throughput-optimal in general [10] . Reference [13] proposed random-access-based algorithms that can achieve performance comparable to that of maximal-size scheduling.. Recently, we proposed a distributed adaptive CSMA (Carrier Sense Multiple Access) algorithm [16] that is throughputoptimal for a general interference model, under certain assumptions (further explained later). The algorithm has a few desirable features. It is distributed (i.e., each node only uses its own backlog information), asynchronous (i.e., nodes do not need to synchronize their transmission) and requires no control message. (In [17] , Rajagopalan and Shah independently proposed a similar randomized algorithm in the context of optical networks. Reference [18] showed that under a "node-exclusive" interference model, CSMA can be made throughput-optimal in an asymptotic regime.) We have also developed a joint algorithm in [16] that combined the adaptive CSMA scheduling with congestion control to approach the maximal total utility of competing data flows.. However, the algorithms in [16] assume an idealized CSMA protocol ( [14] , [25] , [26] , [27] ), meaning that the sensing is instantaneous, so that conflicting links do not transmit at the same time (i.e., collisions do not occur). In many situations, however, this is an unnatural assumption. For example, in CSMA/CA wireless networks, due to the propagation delay and processing time, sensing is not instantaneous. Instead, time can be viewed as divided into discrete minislots, and collisions happen if multiple conflicting links try to transmit in the same minislot. When a collision occurs, all links that are involved lose their packets, and will try again later.. In this paper, we study this important practical issue when designing CSMA-based scheduling algorithms. We follow four main steps: (1) we first present a Markov chain model for a simple CSMA protocol with collisions, and give an explicit throughput formula (in section II) that has a simple form since the Markov chain is "almost" time-reversible; (2) we show that the algorithms in [16] can be extended to approach throughput optimality even with collisions (section III); (3) we give sufficient conditions to ensure the convergence/stability of the proposed algorithms (section III); (4) finally, we discuss the tradeoff between the achievable capacity region and shortterm fairness, and we characterize the relationship between the CSMA parameters (such as the maximum packet lengths) and the achievable capacity region.. Although step (2) can be viewed as a generalization of [16] , we believe that this generalization is important and non-trivial. The importance, as mentioned above, is because collisions are unavoidable in CSMA/CA wireless networks, and the collision-free model used in [16] does not provide enough accuracy. The generalization is non-trivial for the following reasons.. Step (2) requires the result of step (1) . In step (1) , the Markov chain used to model the CSMA protocol is no longer time-reversible as in [16] . We need to re-define the state space in order to compute the service rates it can provide. Interestingly, as a result of our design the chain is "almost" time-reversible which can be exploited. In step (2) , in view of the expression of service rates derived in step (1) , it is important to realize that adjusting the backoff times as in [16] does not lead to the desirable throughput-optimal property. Instead, one should adjust the transmission lengths. Different from [16] , we further show that the "optimal" CSMA parameters (in this case the mean transmission lengths) are unique. This fact is needed to establish the convergence of our algorithm in step (3) .. We note that in a recent work [8] , Ni and Srikant also proposed a CSMA-like algorithm to achieve near-optimal throughput with collisions taken into account. The algorithm in [8] uses synchronized and alternate control phase and data phase. It is designed to realize a discrete-time CSMA (in the data phase) which has the same stationary distribution as its continuous counterpart in [16] . The control phase does not contribute to the throughput and can be viewed as the protocol overhead. Different from [8] , our algorithm here is asynchronous, and has more resemblance to the RTS/CTS mode in IEEE 802.11. Although the algorithm in [8] is quite elegant and could potentially be applied to other timeslotted systems, we believe that it is an interesting problem to understand how to use the asynchronous algorithm to achieve throughput-optimality. Also, due to its similarity to the RTS/CTS mode of IEEE 802.11, the throughput analysis in this paper could also deepen the understanding of 802.11 in general topologies. In this paper, we have studied CSMA-based scheduling algorithms with collisions. We first provided a model and gave a throughput formula which takes into account the cost of collisions and overhead. The formula has a simple product form. Next, we designed distributed algorithms where each link adaptively updates its mean transmission length to approach the throughput-optimality, and provided sufficient conditions to ensure the convergence and stability of the algorithms. We also characterized the relationship between the algorithm parameters and the achievable capacity region.. Finally, simulations results were presented to illustrate and verify the main results.. In the algorithm, the transmission probabilities of the links are chosen to be fixed at a reasonable level, since we have shown that adjusting the transmission lengths alone is sufficient to approach throughput-optimality (the main goal of this paper). However, the choices of the transmission probabilities p k 's has an effect on the probability of collisions among the probe packets. In the future, we would like to further study whether the adjustment of transmission probabilities can be combined with the algorithm. Also, we are interested to further explore the short-term fairness and the case with hidden nodes.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Leakage current reduction in VLSI systems There is a growing need to analyze and optimize the stand-by component of power in digital circuits designed for portable and battery-powered applications. Since these circuits remain in stand-by (or sleep) mode significantly longer than in active mode, their stand-by current, and not their active switching current, determines their battery life. Hence, stringent specifications are being placed on the stand-by (or leakage) current drawn by such devices. As the power supply voltage is reduced, the threshold voltage of transistors is scaled down to maintain a constant switching speed. Since reducing the threshold voltage increases the leakage of a device exponentially, leakage current has become a dominant factor in the design of VLSI circuits. In this paper, we describe a method that uses simultaneous dynamic voltage scaling (DVS) and adaptive body biasing (ABB) to reduce the total power consumption of a processor under dynamic computational workloads. Analytical models of the leakage current, dynamic power, and frequency as a function of supply voltage and body bias are derived and verified with SPICE simulation. Given these models, we show how to derive an analytical expression for the optimal trade-off between supply voltage and body bias, given a required clock frequency and duration of operation. The proposed method is then applied to a processor and is compared with DVS alone for workloads obtained using real-time monitoring of processor utilization for four typical applications. Power consumption has become an overriding constraint for microprocessor designs, not only in mobile environments, but in desktop and server applications as well. Traditionally, the supply voltage of microprocessors has been set at the maximum allowable voltage based on device breakdown potentials, and the processor is run at maximum clock frequency. During typical use, however, the dynamic load of applications running on a processor results in substantial periods of operation where the maximum performance of the processor is not required. A number of methods have been proposed that take advantage of these periods of low utilization by scaling the supply voltage and clock frequency, resulting in a reduction in dynamic power consumption [1] [2] [3] .. While these dynamic voltage scaling (DVS) methods are effective in addressing the dynamic power consumption, they are not as effective in reducing the leakage or static power. As minimum feature size continues to shrink, the scaling of the maximum supply voltage requires a reduction in the threshold voltage which results in an exponential increase of leakage current with each new technology generation. Even in today's technologies, it is not uncommon for leakage power to comprise as much as 20% of the total power consumption [4] . As technologies continue to scale, it is expected that leakage power consumption will become comparable to dynamic power consumption [5] . Furthermore, during periods of low utilization, the lower clock frequency and lower corresponding voltage level result in reduced dynamic power consumption, thereby causing leakage power to dominant. We examined an energy reduction technique through simultaneous implementation of DVS and ABB and presented an analytical expression for power consumption and processor performance as functions of three control parameters (frequency, supply voltage, and body bias voltage). A closed-form method for finding the proper V dd and V bs for optimal power consumption was also presented. Furthermore, this optimal solution was easily obtained using process parameters from SPICE simulation and design specifications. The optimal parameters were applied to both actual and simulated workloads for a 600MHz, 0.18µm mobile processor. The results show that the simultaneous implementation of DVS and ABB power scaling techniques produce an average energy reduction of 23% in a 0.18µm process and 39% in a predicted 0.07µm process over DVS alone when scaling performance between 50 -100%. Energy reductions of nearly 50% were achieved through more aggressive performance scaling (10 -100%) in the 0.07µm process. The results also suggest that as technology scales and leakage power increases, simultaneous DVS and ABB scaling will become more effective.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O