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Travelled to:
1 × China
1 × Italy
1 × Portugal
1 × South Korea
1 × Sweden
1 × United Kingdom
2 × Germany
3 × France
5 × USA
Collaborated with:
S.Mitra N.Patil H.P.Wong F.Yuan Q.Xu G.Guo A.Lin H.Wei E.P.F.Chan D.Thalmann N.Yorke-Smith L.Wei Junliang Wang S.Bobba G.D.Micheli A.Basu G.Hills M.M.Shulaker R.Ye C.Xu K.Chang C.Long N.Chen X.Deng H.Zhang A.Hazeghi Y.Liu Z.Sun A.Pullini D.Atienza Junjie He Lu Dai Jingsong Bao Chuqiao Xu X.Liu J.Cao W.Qin J.Chen D.Hao Y.Xiong B.Xie L.Zhang H.Mei C.Mackin J.C.Corena S.Kiyomoto S.Marsh J.Vaidya Y.Miyake H.Chen C.Lee L.Liyanage
Talks about:
nanotub (9) carbon (9) circuit (6) trust (5) logic (4) imperfect (3) variat (3) design (3) rate (3) base (3)

Person: Jie Zhang

DBLP DBLP: Zhang:Jie

Contributed to:

DAC 20152015
DATE 20152015
ICSE 20152015
ASE 20142014
SAC 20142014
CIKM 20132013
DAC 20132013
DATE 20132013
RecSys 20132013
ICALP (2) 20122012
DAC 20102010
DATE 20102010
DAC 20092009
DATE 20092009
DATE 20082008
CIKM 20072007
CASE 20192019

Wrote 22 papers:

DAC-2015-LiuZWYX #analysis #difference #encryption #fault #named
DERA: yet another differential fault attack on cryptographic devices based on error rate analysis (YL, JZ, LW, FY, QX), p. 6.
DATE-2015-YeYZX #on the
On the premises and prospects of timing speculation (RY, FY, JZ, QX), pp. 605–608.
ICSE-v2-2015-Zhang #mutation testing #scalability #testing
Scalability Studies on Selective Mutation Testing (JZ), pp. 851–854.
ASE-2014-ZhangCHXXZM #polynomial #search-based
Search-based inference of polynomial metamorphic relations (JZ, JC, DH, YX, BX, LZ, HM), pp. 701–712.
SAC-2014-BasuCKMVG0M #feedback #privacy #social
Privacy preserving trusted social feedback (AB, JCC, SK, SM, JV, GG, JZ, YM), pp. 1706–1711.
SAC-2014-GuoZTBY #empirical #recommendation #trust
From ratings to trust: an empirical study of implicit trust in recommender systems (GG, JZ, DT, AB, NYS), pp. 248–253.
CIKM-2013-XuZCL #overview
Uncovering collusive spammers in Chinese review websites (CX, JZ, KC, CL), pp. 979–988.
DAC-2013-HillsZMSWWM #agile #design #guidelines
Rapid exploration of processing and design guidelines to overcome carbon nanotube variations (GH, JZ, CM, MMS, HW, HSPW, SM), p. 10.
DAC-2013-ZhangYWSX #hardware #named #trust #verification
VeriTrust: verification for hardware trust (JZ, FY, LW, ZS, QX), p. 8.
DATE-2013-WeiSHCLLZWM #challenge
Carbon nanotube circuits: opportunities and challenges (HW, MMS, GH, HYC, CSL, LL, JZ, HSPW, SM), pp. 619–624.
RecSys-2013-GuoZTY #e-commerce #recommendation
Prior ratings: a new information source for recommender systems in e-commerce (GG, JZ, DT, NYS), pp. 383–386.
ICALP-v2-2012-ChenDZZ
Incentive Ratios of Fisher Markets (NC, XD, HZ, JZ), pp. 464–475.
DAC-2010-ZhangBPLWMM #correlation
Carbon nanotube correlation: promising opportunity for CNFET circuit yield enhancement (JZ, SB, NP, AL, HSPW, GDM, SM), pp. 889–892.
DATE-2010-ZhangPLWM
Carbon nanotube circuits: Living with imperfections and variations (JZ, NP, AL, HSPW, SM), pp. 1159–1164.
DAC-2009-PatilLZWM #logic #using
Digital VLSI logic technology using Carbon Nanotube FETs: frequently asked questions (NP, AL, JZ, HSPW, SM), pp. 304–309.
DAC-2009-ZhangPHM
Carbon nanotube circuits in the presence of carbon nanotube density variations (JZ, NP, AH, SM), pp. 71–76.
DATE-2009-BobbaZPAM #design #logic #standard #synthesis
Design of compact imperfection-immune CNFET layouts for standard-cell-based logic synthesis (SB, JZ, AP, DA, GDM), pp. 616–621.
DATE-2009-MitraZPW #logic #using
Imperfection-immune VLSI logic circuits using Carbon Nanotube Field Effect Transistors (SM, JZ, NP, HW), pp. 436–441.
DATE-2008-ZhangPM #design #guidelines #logic
Design Guidelines for Metallic-Carbon-Nanotube-Tolerant Digital Logic Circuits (JZ, NP, SM), pp. 1009–1014.
CIKM-2007-ChanZ #algorithm #evaluation #performance #query
A fast unified optimal route query evaluation algorithm (EPFC, JZ), pp. 371–380.
CASE-2019-HeWDZB #adaptation #detection #fault
An Adaptive Interval Forecast CNN Model for Fault Detection Method (JH, JW, LD, JZ, JB), pp. 602–607.
CASE-2019-XuLWZCQ #approach #learning #performance
An Improved GA-KRR Nested Learning Approach for Refrigeration Compressor Performance Forecasting* (CX, XL, JW, JZ, JC, WQ), pp. 622–627.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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