Faculty Research Publications (Electrical Engineering and Computer Science)
http://hdl.handle.net/1957/7303
This collection includes scholarly articles produced by EECS faculty and students.
Wed, 30 Jul 2014 19:34:16 GMT
20140730T19:34:16Z

Effect of band offset on carrier transport and infrared detection in InP quantum dots/Si nanoheterojunction grown by metalorganic chemical vapor deposition technique
http://hdl.handle.net/1957/50454
Effect of band offset on carrier transport and infrared detection in InP quantum dots/Si nanoheterojunction grown by metalorganic chemical vapor deposition technique
Halder, Nripendra N.; Biswas, Pranab; Nagabhushan, B.; Kundu, Souvik; Biswas, D.; Banerji, P.
Epitaxy of IIIV semiconductors on Si gets recent interest for next generation system on
heterogeneous chip on wafer. The understanding of band offset is thus necessary for describing
the charge transport phenomenon in these heterojunctions. In this work, xray photoemission
spectroscopy has been used to determine the band offsets in a heterojunction made of InP
quantum dots on Si. The valence and conduction band offset was found to be 0.12 eV and
0.35 eV, respectively, with a typeII band lineup. Deviation from theoretical prediction and
previously published reports on quasi similar systems have been found and analyzed on the basis
of the effect of strain, surface energy, shift in the electrostatic dipole and charge transfer at the
interface. The carrier transport mechanisms along with different device parameters in the
heterojunction have been studied for a temperature range of 180–300 K. This heterojunction is
found to behave as an efficient infrared photodetector with an ON/OFF ratio of 21 at a reverse
bias of 2V. The corresponding rise and decay time was found to be 132 ms and 147 ms,
respectively.
This is the publisher’s final pdf. The published article is copyrighted by the American Institute of Physics Publishing and can be found at: http://scitation.aip.org/content/aip/journal/jap.
Wed, 28 May 2014 00:00:00 GMT
http://hdl.handle.net/1957/50454
20140528T00:00:00Z

Anomalous diffusion of Ga and As from semiinsulating GaAs substrate into MOCVD grown ZnO films as a function of annealing temperature and its effect on charge compensation
http://hdl.handle.net/1957/50453
Anomalous diffusion of Ga and As from semiinsulating GaAs substrate into MOCVD grown ZnO films as a function of annealing temperature and its effect on charge compensation
Biswas, Pranab; Halder, Nripendra N.; Kundu, Souvik; Banerji, P.; Shripathi, T.; Gupta, M.
See article for Abstract.
This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the American Institute of Physics Publishing. The published article can be found at: http://scitation.aip.org/content/aip/journal/adva;jsessionid=72iqa9pms2si7.xaiplive02.
Thu, 01 May 2014 00:00:00 GMT
http://hdl.handle.net/1957/50453
20140501T00:00:00Z

Extending Type Inference to Variational Programs
http://hdl.handle.net/1957/50333
Extending Type Inference to Variational Programs
Chen, Sheng; Erwig, Martin; Walkingshaw, Eric
Through the use of conditional compilation and related tools, many software projects can be used to generate a huge
number of related programs. The problem of typing such variational software is difficult. The bruteforce strategy
of generating all variants and typing each one individually is (1) usually infeasible for efficiency reasons and (2)
produces results that do not map well to the underlying variational program. Recent research has focused mainly
on efficiency and addressed only the problem of type checking. In this work we tackle the more general problem of
variational type inference and introduce variational types to represent the result of typing a variational program. We
introduce the variational lambda calculus (VLC) as a formal foundation for research on typing variational programs.
We define a type system for VLC in which VLC expressions are mapped to correspondingly variational types. We
show that the type system is correct by proving that the typing of expressions is preserved over the process of
variation elimination, which eventually results in a plain lambda calculus expression and its corresponding type.
We identify a set of equivalence rules for variational types and prove that the type unification problem modulo these
equivalence rules is unitary and decidable; we also present a sound and complete unification algorithm. Based on
the unification algorithm, the variational type inference algorithm is an extension of algorithm W. We show that
it is sound and complete and computes principal types. We also consider the extension of VLC with sum types, a
necessary feature for supporting variational data types, and demonstrate that the previous theoretical results also
hold under this extension. Finally, we characterize the complexity of variational type inference and demonstrate the
efficiency gains over the bruteforce strategy.
This is an author's peerreviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the Association for Computing Machinery and can be found at: http://toplas.acm.org/.
Sat, 01 Mar 2014 00:00:00 GMT
http://hdl.handle.net/1957/50333
20140301T00:00:00Z

You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems
http://hdl.handle.net/1957/50207
You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems
Groce, Alex; Kulesza, Todd; Zhang, Chaoqiang; Shamasunder, Shalini; Burnett, Margaret; Wong, WengKeen; Stumpf, Simone; Das, Shubhomoy; Shinsel, Amber; Bice, Forrest; McIntosh, Kevin
How do you test a program when only a single user, with no expertise in software testing, is able to determine if the
program is performing correctly? Such programs are common today in the form of machinelearned classifiers. We consider the
problem of testing this common kind of machinegenerated program when the only oracle is an end user: e.g., only you can
determine if your email is properly filed. We present test selection methods that provide very good failure rates even for small test
suites, and show that these methods work in both largescale random experiments using a “gold standard” and in studies with
real users. Our methods are inexpensive and largely algorithmindependent. Key to our methods is an exploitation of properties
of classifiers that is not possible in traditional software testing. Our results suggest that it is plausible for timepressured end
users to interactively detect failures—even very hardtofind failures—without wading through a large number of successful (and
thus less useful) tests. We additionally show that some methods are able to find the arguably most difficulttodetect faults of
classifiers: cases where machine learning algorithms have high confidence in an incorrect result.
©2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. This is the author's peerreviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the Institute of Electrical and Electronics Engineers and can be found at: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32.
Sat, 01 Mar 2014 00:00:00 GMT
http://hdl.handle.net/1957/50207
20140301T00:00:00Z