My publications


Lixia Xiao, Da Chen, Ibrahim Hemadeh, Pei Xiao, Tao Jiang (2020)Graph Theory assisted Bit-to-Index-Combination Gray Coding for Generalized Index Modulation, In: IEEE Transactions on Wireless Communications Institute of Electrical and Electronics Engineers (IEEE)

Generalized index modulation (GIM) which implicitly conveys information by the activated indices is a promising technique for next-generation wireless networks. Due to the prohibitive challenge of bit-to-index combination (IC) mapping optimization, conventional GIM system obtains the bit-to-IC mapping table randomly, which may suffer from some performance loss. To circumvent this issue, we propose a low-complexity graph theory assisted bit-to-IC gray coding for GIM systems by minimizing the average hamming distance (HD) between any two ICs having one different value. Specifically, we decompose and transform the optimization problem into two subproblems using the graph theory, i.e., 1) Select an IC set whose corresponding graph has the minimum degree; 2) Design a bit-to-IC mapping principle to minimize the weight of the selected graph. Low complexity algorithms are developed to solve the subproblems with a significant reduced complexity. Both simulation and theoretical results are shown that the GIM systems with our proposed mapping table are capable of providing significant performance gains over the conventional counterparts without the need for any additional feedback-link and without extra computational complexity. It is also shown that the proposed bit-to-IC mapping table is straightforward for any GIM systems over generalized fading channels.

Chen Li, Svetlana Stoma, Luca A. Lotta, Sophie Warner, Eva Albrecht, Alessandra Allione, Pascal P. Arp, Linda Broer, Jessica L. Bruxton, Alexessander Da Silva Couto Alves, Joris Deelen, Iryna O. Fedko, Scott D. Gordon, Tao Jiang, Robert Karlsson, Nicola Kerrison, Taylor K. Loe, Massimo Mangino, Yuri Milaneschi, Benjamin Miraglio, Natalia Pervjakova, Alessia Russo, Ida Surakka, Ashley van der Spek, Josine E. Verhoeven, Najaf Amin, Marian Beekman, Alexandra I. Blakemore, Frederico Canzian, Stephen E. Hamby, Jouke-Jan Hottenga, Peter D. Jones, Pekka Jousilahti, Reedik Magi, Sarah E. Medland, Grant W. Montgomery, Dale R. Nyholt, Markus Perola, Kirsi H. Pietilainen, Veikko Salomaa, Elina Sillanpaa, H. Eka Suchiman, Diana van Heemst, Gonneke Willemsen, Antonio Agudo, Heiner Boeing, Dorret I. Boomsma, Maria-Dolores Chirlaque, Guy Fagherazzi, Pietro Ferrari, Paul Franks, Christian Gieger, Johan Gunnar Eriksson, Marc Gunter, Sara Hagg, Iiris Hovatta, Liher Imaz, Jaakko Kaprio, Rudolf Kaaks, Timothy Key (2020)Genome-wide Association Analysis in Humans Links Nucleotide Metabolism to Leukocyte Telomere Length, In: American Journal of Human Genetics106(3)pp. 389-404 Elsevier

Leukocyte telomere length (LTL) is a heritable biomarker of genomic aging. In this study, we perform a genome-wide meta-analysis of LTL by pooling densely genotyped and imputed association results across large-scale European-descent studies including up to 78,592 individuals. We identify 49 genomic regions at a false dicovery rate (FDR) < 0.05 threshold and prioritize genes at 31, with five highlighting nucleotide metabolism as an important regulator of LTL. We report six genome-wide significant loci in or near SENP7, MOB1B, CARMIL1, PRRC2A, TERF2, and RFWD3, and our results support recently identified PARP1, POT1, ATM, and MPHOSPH6 loci. Phenome-wide analyses in >350,000 UK Biobank participants suggest that genetically shorter telomere length increases the risk of hypothyroidism and decreases the risk of thyroid cancer, lymphoma, and a range of proliferative conditions. Our results replicate previously reported associations with increased risk of coronary artery disease and lower risk for multiple cancer types. Our findings substantially expand current knowledge on genes that regulate LTL and their impact on human health and disease.

TAO JIANG, NECATI CIHAN CAMGOZ, RICHARD BOWDEN (2021)Skeletor: Skeletal Transformers for Robust Body-Pose Estimation

Predicting 3D human pose from a single monoscopic video can be highly challenging due to factors such as low resolution, motion blur and occlusion, in addition to the fundamental ambiguity in estimating 3D from 2D. Approaches that directly regress the 3D pose from independent images can be particularly susceptible to these factors and result in jitter, noise and/or inconsistencies in skeletal estimation. Much of which can be overcome if the temporal evolution of the scene and skeleton are taken into account. However, rather than tracking body parts and trying to temporally smooth them, we propose a novel transformer based network that can learn a distribution over both pose and motion in an unsupervised fashion. We call our approach Skeletor. Skeletor overcomes inaccuracies in detection and corrects partial or entire skeleton corruption. Skeletor uses strong priors learn from on 25 million frames to correct skeleton sequences smoothly and consistently. Skeletor can achieve this as it implicitly learns the spatio-temporal context of human motion via a transformer based neural network. Extensive experiments show that Skeletor achieves improved performance on 3D human pose estimation and further provides benefits for downstream tasks such as sign language translation.

Lixia Xiao, Da Chen, Ibrahim Hemadeh, Pei Xiao, Tao Jiang (2020)Generalized Space Time Block Coded Spatial Modulation for Open-Loop Massive MIMO Downlink Communication Systems, In: IEEE Transactions on Communications Institute of Electrical and Electronics Engineers

In this paper, we propose a generalized space-time block coded spatial modulation (GSTBC-SM) scheme for openloop massive multiple-input and multiple-output (MIMO) downlink communication systems. Specifically, we firstly partition the information bits into multiple groups with each group modulated by the spatial modulation (SM), where the SM symbols are invoked for orthogonal STBC (OSTBC) and quasi-orthogonal STBC (Q-OSTBC) structures. Then, message passing (MP) and block minimum mean square equalization (B-MMSE) detectors are designed for our GSTBC-SM systems, to achieve near-optimal performance with significantly reduced complexity in massive MIMO configurations. Finally, we derive the theoretical average bit error probability (ABEP) of the proposed scheme. The main contribution is that the propose scheme achieves high transmission rate and diversity gain even with small number of radio frequency (RF) chains at the transmitter. Simulation results verify the theoretical derivations and show that the proposed GSTBCSM scheme provides near 20 dB gain over the conventional GSTBC scheme under massive MIMO configurations. Index Terms—Spatial Modulation (SM), Space Time Block Coding (STBC), High throughput, Diversity gain.