Spatial-spectral Terahertz Networks

This paper focuses on the spatial-spectral terahertz (THz) networks, where transmitters equipped with leaky-wave antennas send information to their receivers at the THz frequency bands. As a directional and nearly planar antenna, the leaky-wave antenna allows for information transmissions with narrow beams and high antenna gains. The conventional large antenna arrays are confronted with challenging issues such as scaling limits and path discovery in the THz frequencies. Therefore, this work exploits the potential of leaky-wave antennas in the dense THz networks, to establish low-complexity THz links. By addressing the propagation angle-frequency coupling effects, the transmission rate is analyzed. The results show that the leaky-wave antenna is efficient for achieving the high-speed transmission rate. The co-channel interference management is unnecessary when the THz transmitters with large subchannel bandwidths are not extremely dense. A simple subchannel allocation solution is proposed, which enhances the transmission rate compared with the same number of subchannels with the equal allocation of the frequency band. After subchannel allocation, a low-complexity power allocation method is proposed to improve the energy efficiency.Comment: accepted by the IEEE Transactions on Wireless Communication

Unsupervised Cross-Task Generalization via Retrieval Augmentation

Humans can perform unseen tasks by recalling relevant skills that are acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this paper, we aim to improve such cross-task generalization ability of massive multi-task language models such as T0 (Sanh et al., 2021) in an unsupervised setting. We propose a retrieval-augmentation method named ReCross that takes a few unlabelled examples as queries to retrieve a small subset of upstream data and uses them to update the multi-task model for better generalization. Our empirical results show that the proposed ReCross consistently outperforms non-retrieval baselines by a significant margin.Comment: Project website: https://inklab.usc.edu/ReCross

Differential and Joint Effects of Metformin and Statins on Overall Survival of Elderly Patients with Pancreatic Adenocarcinoma: A Large Population-Based Study.

Background: Published evidence indicates that individual use of metformin and statin is associated with reduced cancer mortality. However, their differential and joint effects on pancreatic cancer survival are inconclusive.Methods: We identified a large population-based cohort of 12,572 patients ages 65 years or older with primary pancreatic ductal adenocarcinoma (PDAC) diagnosed between 2008 and 2011 from the Surveillance, Epidemiology, and End Results (SEER)-Medicare-linked database. Exposure to metformin and statins was ascertained from Medicare Prescription Drug Event files. Cox proportional hazards models with time-varying covariates adjusted for propensity scores were used to assess the association while controlling for potential confounders.Results: Of 12,572 PDAC patients, 950 (7.56%) had used metformin alone, 4,506 (35.84%) had used statin alone, and 2,445 (19.45%) were dual users. Statin use was significantly associated with improved overall survival [HR, 0.94; 95% confidence interval (CI), 0.90-0.98], and survival was more pronounced in postdiagnosis statin users (HR, 0.69; 95% CI, 0.56-0.86). Metformin use was not significantly associated with overall survival (HR, 1.01; 95% CI, 0.94-1.09). No beneficial effect was observed for dual users (HR, 1.00; 95% CI, 0.95-1.05).Conclusions: Our findings suggest potential benefits of statins on improving survival among elderly PDAC patients; further prospective studies are warranted to corroborate the putative benefit of statin therapy in pancreatic cancer.Impact: Although more studies are needed to confirm our findings, our data add to the body of evidence on potential anticancer effects of statins. Cancer Epidemiol Biomarkers Prev; 26(8); 1225-32. ©2017 AACR

Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding

We propose an effective two-stage approach to tackle the problem of language-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task. In the first stage, we propose an Augmented 2D Temporal Adjacent Network (Augmented 2D-TAN) to temporally ground the target moment corresponding to the given description. Primarily, we improve the original 2D-TAN from two aspects: First, a temporal context-aware Bi-LSTM Aggregation Module is developed to aggregate clip-level representations, replacing the original max-pooling. Second, we propose to employ Random Concatenation Augmentation (RCA) mechanism during the training phase. In the second stage, we use pretrained MDETR model to generate per-frame bounding boxes via language query, and design a set of hand-crafted rules to select the best matching bounding box outputted by MDETR for each frame within the grounded moment.Comment: Best Paper Award at the 3rd Person in Context (PIC) Challenge CVPR Workshop 202