Efficient Downlink Channel Reconstruction for FDD Multi-Antenna Systems

In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. We first estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high estimation accuracy. Simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting that the promising application of the proposed scheme in large-scale antenna array systems

Team Totemics

Team Totemics integrates the Surrealist cadavre exquis as a design strategy to advance remote learning and as a pedagogical tool for cultivating fellowship amongst students in the wake of the COVID-19 pandemic. Despite the immense significance of the integration of computational methods in design education, there remains a need for theorization and critical exposition of the interaction between building technology and digital making in online design pedagogy. Team Totemics creates materials for discussing, exhibiting, and demonstrating pedagogies based on the principle of multiple origins suggested by the exquisite corpse. The outcomes inform student learning and faculty research at the nexus of digital composition, social collectivity and structural empathy

Hierarchical Multi-Bottleneck Classification Method And Its Application to DNA Microarray Expression Data

The recent development of DNA microarray technology is creating a wealth of gene expression data. Typically these datasets have high dimensionality and a lot of varieties. Analysis of DNA microarray expression data is a fast growing research area that interfaces various disciplines such as biology, biochemistry, computer science and statistics. It is concluded that clustering and classification techniques can be successfully employed to group genes based on the similarity of their expression patterns. In this paper, a hierarchical multi-bottleneck classification method is proposed, and it is applied to classify a publicly available gene microarray expression data of budding yeast Saccharomyces cerevisiae.Singapore-MIT Alliance (SMA

Triple-loop networks with arbitrarily many minimum distance diagrams

Minimum distance diagrams are a way to encode the diameter and routing information of multi-loop networks. For the widely studied case of double-loop networks, it is known that each network has at most two such diagrams and that they have a very definite form "L-shape''. In contrast, in this paper we show that there are triple-loop networks with an arbitrarily big number of associated minimum distance diagrams. For doing this, we build-up on the relations between minimum distance diagrams and monomial ideals.Comment: 17 pages, 8 figure

Immediate Risk for Cardiovascular Events in Hip Fracture Patients: A Population-Based Cohort Study

Background: Emerging evidence showed that bone metabolism and cardiovascular diseases (CVD) are closely related. We previously observed a potential immediate risk of cardiovascular mortality after hip fracture. However, whether there is an immediate risk of cardiovascular events after hip fracture is unclear. The aim of this study was to evaluate the risk for major adverse cardiovascular events (MACEs) between patients having experienced falls with and without hip fracture. / Methods: This retrospective population-based cohort study used data from a centralized electronic health record database managed by Hong Kong Hospital Authority. Patients having experienced falls with and without hip fracture were matched by propensity score (PS) at a 1:1 ratio. Adjusted associations between hip fracture and risk of MACEs were evaluated using competing risk regression after accounting for competing risk of death. / Results: Competing risk regression showed that hip fracture was associated with increased one-year risk of MACEs (hazard ratio [HR], 1.27; 95% CI, 1.21 to 1.33; p<0.001), with a 1-year cumulative incidence difference of 2.40% (1.94% to 2.87%). The HR was the highest in the first 90-day after hip fracture (HR of 1.32), and such an estimate was continuously reduced in 180-day, 270-day, and 1-year after hip fracture. / Conclusions: Hip fracture was associated with increased immediate risk of MACEs. This study suggested that a prompt evaluation of MACE among older adults aged 65 years and older who are diagnosed with hip fracture irrespectively of cardiovascular risk factors may be important, as early management may reduce subsequent risk of MACE

Ganoderma lucidum polysaccharides enhance CD14 endocytosis of LPS and promote TLR4 signal transduction of cytokine expression

We have previously reported that a well-characterized glycoprotein fraction containing fucose residues in an extract of Ganoderma lucidum polysaccharides (EORP) exerts certain immuno-modulation activity by stimulating the expression of inflammatory cytokines via TLR4. Continuing our studies, we have demonstrated that EORP increases the surface expression of CD14 and TLR4 within murine macrophages J774A.1 cells in vitro, and further promotes LPS binding and uptake by J774A.1 cells in a CD14-dependent fashion. Moreover, we observed the co-localization of internalized LPS with lysosome- and Golgi-apparatus markers within 5 min after J774A.1 cells stimulated with LPS. In addition, EORP pretreatment of J774A.1 cells and human blood-derived primary macrophages, followed by LPS stimulation, results in the super-induction of interleukin-1beta (IL-1) expression. Endocytosis inhibitors: such as cytochalasin D and colchicine effectively block EORP-enhanced LPS internalization by J774A.1 cells; yet they fail to decrease the LPS-induced phosphorylation of certain mitogen-activated protein kinases, and IL-1 mRNA and proIL-1 protein expression, indicating that LPS internalization by J774A.1 cells is not associated with LPS-dependent activation. Our current results could provide a potential EORP-associated protection mechanism for bacteria infection by enhancing IL-1 expression and the clearance of contaminated LPS by macrophages. J. Cell. Physiol. 212: 537–550, 2007. © 2007 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56052/1/21050_ftp.pd

Fake News Detection with Heterogeneous Transformer

The dissemination of fake news on social networks has drawn public need for effective and efficient fake news detection methods. Generally, fake news on social networks is multi-modal and has various connections with other entities such as users and posts. The heterogeneity in both news content and the relationship with other entities in social networks brings challenges to designing a model that comprehensively captures the local multi-modal semantics of entities in social networks and the global structural representation of the propagation patterns, so as to classify fake news effectively and accurately. In this paper, we propose a novel Transformer-based model: HetTransformer to solve the fake news detection problem on social networks, which utilises the encoder-decoder structure of Transformer to capture the structural information of news propagation patterns. We first capture the local heterogeneous semantics of news, post, and user entities in social networks. Then, we apply Transformer to capture the global structural representation of the propagation patterns in social networks for fake news detection. Experiments on three real-world datasets demonstrate that our model is able to outperform the state-of-the-art baselines in fake news detection