BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction

Predicting future trajectories of surrounding agents is essential for safety-critical autonomous driving. Most existing work focuses on predicting marginal trajectories for each agent independently. However, it has rarely been explored in predicting joint trajectories for interactive agents. In this work, we propose Bi-level Future Fusion (BiFF) to explicitly capture future interactions between interactive agents. Concretely, BiFF fuses the high-level future intentions followed by low-level future behaviors. Then the polyline-based coordinate is specifically designed for multi-agent prediction to ensure data efficiency, frame robustness, and prediction accuracy. Experiments show that BiFF achieves state-of-the-art performance on the interactive prediction benchmark of Waymo Open Motion Dataset.Comment: 18 pages, 12 figure

SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant and Fast Training

We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only enables field-free magnetization switching with high endurance (> 10^11), but also hosts multiple stable probabilistic states with a low device-to-device variation (< 6.35%). Accordingly, the proposed PBNN outperforms other neural networks by achieving an 18* increase in training speed, while maintaining an accuracy above 97% under the write and read noise perturbations. Furthermore, by applying the binarization process with an additional SOT-MRAM dummy module, we demonstrate an on-chip MNIST inference performance close to the ideal baseline using our SOT-PBNN hardware

Exploiting tertiary lymphoid structures gene signature to evaluate tumor microenvironment infiltration and immunotherapy response in colorectal cancer

BackgroundTertiary lymphoid structures (TLS) is a particular component of tumor microenvironment (TME). However, its biological mechanisms in colorectal cancer (CRC) have not yet been understood. We desired to reveal the TLS gene signature in CRC and evaluate its role in prognosis and immunotherapy response.MethodsThe data was sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Based on TLS-related genes (TRGs), the TLS related subclusters were identified through unsupervised clustering. The TME between subclusters were evaluated by CIBERSORT and xCell. Subsequently, developing a risk model and conducting external validation. Integrating risk score and clinical characteristics to create a comprehensive nomogram. Further analyses were conducted to screen TLS-related hub genes and explore the relationship between hub genes, TME, and biological processes, using random forest analysis, enrichment and variation analysis, and competing endogenous RNA (ceRNA) network analysis. Multiple immunofluorescence (mIF) and immunohistochemistry (IHC) were employed to characterize the existence of TLS and the expression of hub gene.ResultsTwo subclusters that enriched or depleted in TLS were identified. The two subclusters had distinct prognoses, clinical characteristics, and tumor immune infiltration. We established a TLS-related prognostic risk model including 14 genes and validated its predictive power in two external datasets. The model’s AUC values for 1-, 3-, and 5-year overall survival (OS) were 0.704, 0.737, and 0.746. The low-risk group had a superior survival rate, more abundant infiltration of immune cells, lower tumor immune dysfunction and exclusion (TIDE) score, and exhibited better immunotherapy efficacy. In addition, we selected the top important features within the model: VSIG4, SELL and PRRX1. Enrichment analysis showed that the hub genes significantly affected signaling pathways related to TLS and tumor progression. The ceRNA network: PRRX1-miRNA (hsa-miR-20a-5p, hsa-miR-485–5p) -lncRNA has been discovered. Finally, IHC and mIF results confirmed that the expression level of PRRX1 was markedly elevated in the TLS- CRC group.ConclusionWe conducted a study to thoroughly describe TLS gene signature in CRC. The TLS-related risk model was applicable for prognostic prediction and assessment of immunotherapy efficacy. The TLS-hub gene PRRX1, which had the potential to function as an immunomodulatory factor of TLS, could be a therapeutic target for CRC

GAME THEORY AND ITS APPLICATIONS

Bachelor'sBACHELOR OF SCIENCE (HONOURS

Change Detection for High-Resolution Remote Sensing Images Based on a Multi-Scale Attention Siamese Network

To address the problems in remote sensing image change detection such as missed detection of features at different scales and incomplete region detection, this paper proposes a high-resolution remote sensing image change detection model (Multi-scale Attention Siamese Network, MASNet) based on a Siamese network and multi-scale attention mechanism. The MASNet model took the Siamese structure of the ResNet-50 network to extract features of different simultaneous images and then applied the attention module to feature maps of different scales to generate multi-scale feature representations. Meanwhile, an improved contrastive loss function was adopted to enhance the learning of change features and improving the imbalance problem between unchanged and changed samples. Furthermore, to address the current time-consuming and laborious phenomenon of manually annotating datasets, we provided a change detection dataset from Yunnan Province in China (YNCD) that contains 1540 pairs of 256 × 256 bi-temporal images with a spatial resolution of 1 m. Then, model training and change detection applications were studied by expanding a small number of experimental area samples into the existing public datasets. The results showed that the overall accuracy of the MASNet model for change detection in the experimental area is 95.34%, precision rate is 79.78%, recall rate is 81.52%, and F1 score is 80.64%, which are better than those of six comparative models (FC-EF, FC-Siam-Diff, FC-Siam-Conc, PAN, MANet, and STANet). This verifies the effectiveness of the MASNet model as well as the feasibility of change detection by expanding existing public datasets

Hydrogen Absorption Performance and O₂ Poisoning Resistance of Pd/ZrCo Composite Film

In order to enhance the hydrogen absorption performance and poisoning resistance of ZrCo to O2, Pd/ZrCo composite films were prepared by direct current magnetron sputtering. The results show that the initial hydrogen absorption rate of the Pd/ZrCo composite film increased significantly due to the catalytic effect of Pd compared with the ZrCo film. In addition, the hydrogen absorption properties of Pd/ZrCo and ZrCo were tested in poisoned hydrogen mixed with 1000 ppm O2 at 10–300 °C, where the Pd/ZrCo films maintained a better resistance to O2 poisoning below 100 °C. The mechanism of poisoning was investigated jointly by first-principles calculation combined with SEM-EDS elemental mapping tests. It is shown that the poisoned Pd layer maintained the ability to promote the decomposition of H2 into hydrogen atoms and their rapid transfer to ZrCo

Effects of Perceptual Learning on Deprivation Amblyopia in Children with Limbal Dermoid: A Randomized Controlled Trial

Limbal dermoid (LD) is a congenital ocular tumor that causes amblyopia and damages visual acuity (VA) and visual function. This study evaluated the therapeutic efficacy of perceptual learning (PL) toward improving contrast sensitivity function (CSF) and VA. A total of 25 children with LD and 25 normal children were compared in terms of CSF and VA. The LD group was further randomly allocated into two arms: nine underwent PL combined with patching and eight underwent patching only; eight patients quit the amblyopia treatment. The primary outcome was the area under log CSF (AULCSF), and the secondary outcome was the best corrected VA (BCVA). The CSF was obviously reduced in the LD group compared with that in the normal group. Moreover, the difference in the changes in the AULCSF between the PL and patching groups after 6 months of training was 0.59 (95% CI: 0.32, 0.86, p p < 0.001). Children suffering from LD with amblyopia exhibited CSF deficits and VA loss simultaneously. PL could improve CSF and VA in the amblyopic eye better than patching

Synergistic Anticancer Activity of Photo- and Chemoresponsive Nanoformulation Based on Polylysine-Functionalized Graphene

Multimodal therapeutic agents based on nanomaterials for cancer combination therapy have attracted increasing attention. In this report, a novel photo- and chemoactive nanohybrid was fabricated by assembling photosensitizer Zn­(II)–phthalocyanine (ZnPc) and anticancer drug doxorubicin (DOX) on the biocompatible poly-l-lysine (PLL)-grafted graphene (G-PLL). This nanocomplex of G-PLL/DOX/ZnPc showed excellent physiochemical properties, including high solubility and stability in biological solutions, high drug loading efficiency, pH-triggered drug release, and ability to generalize ¹O₂ under light excitation. Compared to free drug molecules, cells treated with G-PLL/DOX/ZnPc showed a higher cellular uptake. In particular, G-PLL/DOX/ZnPc elicited a remarkable synergistic anticancer activity owing to combined photodynamic and chemotherapeutic effects. The combination dose reduction indexes revealed that combining DOX with ZnPc provided strong synergistic effects (combination index < 0.1) against three cancer cell lines tested (HeLa, MCF-7, and B16). Thus, this study demonstrates programmable dual-modality therapy exemplified by G-PLL/DOX/ZnPc to synergistically treat cancers