Stiffness-Driven Design and Interface Debonding Study of FRP Sandwich Structures for Bridges

Bridge decks entirely made of fiber reinforced polymer (FRP) materials are a potential solution to fast construction in bridge engineering. This study mainly focuses on the stiffness-driven design of FRP decks for short-span slab bridges and the interface debonding of an FRP sandwich structure with honeycomb cores. As is evidenced by the analytical and experimental results in this study, these two topics are closely related to the application of FRP materials in bridge deck construction. The design verification of an FRP slab bridge showed that its design should be controlled by stiffness rather than strength. The tests of the FRP sandwich panels at cold temperatures indicated that interface debonding might occur even at the service load level. In order to facilitate the stiffness-driven design of typical FRP slab bridges in practice, this study proposed equivalent strip width expressions which allow them to be designed by Timoshenko beam theory. The key factors for the expressions were identified and a design procedure was recommended in this study as well. Finally, this study investigated the application of tilted sandwich debond (TSD) tests to the interface debonding study of the sandwich structure. This study showed that TSD tests with proper modifications could be used to measure interfacial fracture toughness at different mixed-mode ratios. Recommendations concerning experimental setups and the data reduction method associated with TSD tests were also suggested in this study

ProCC: Progressive Cross-primitive Consistency for Open-World Compositional Zero-Shot Learning

Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space, which induces a tremendously large output space containing all possible state-object compositions. Existing works either learn the joint compositional state-object embedding or predict simple primitives with separate classifiers. However, the former heavily relies on external word embedding methods, and the latter ignores the interactions of interdependent primitives, respectively. In this paper, we revisit the primitive prediction approach and propose a novel method, termed Progressive Cross-primitive Consistency (ProCC), to mimic the human learning process for OW-CZSL tasks. Specifically, the cross-primitive consistency module explicitly learns to model the interactions of state and object features with the trainable memory units, which efficiently acquires cross-primitive visual attention and avoids cross-primitive feasibility scores. Moreover, considering the partial-supervision setting (pCZSL) as well as the imbalance issue of multiple tasks prediction, we design a progressive training paradigm to enable the primitive classifiers to interact to obtain discriminative information in an easy-to-hard manner. Extensive experiments on three widely used benchmark datasets demonstrate that our method outperforms other representative methods on both OW-CZSL and pCZSL settings by

MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition

Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier considering the predicted probabilities of each known family with a threshold-based detection to achieve the MOSR. However, our observation reveals that the feature distributions of malware samples are extremely similar to each other even between known and unknown families. Thus the obtained classifier may produce overly high probabilities of testing unknown samples toward known families and degrade the model performance. In this paper, we propose the Multi-modal Dual-Embedding Networks, dubbed MDENet, to take advantage of comprehensive malware features (i.e., malware images and malware sentences) from different modalities to enhance the diversity of malware feature space, which is more representative and discriminative for down-stream recognition. Last, to further guarantee the open-set recognition, we dually embed the fused multi-modal representation into one primary space and an associated sub-space, i.e., discriminative and exclusive spaces, with contrastive sampling and rho-bounded enclosing sphere regularizations, which resort to classification and detection, respectively. Moreover, we also enrich our previously proposed large-scaled malware dataset MAL-100 with multi-modal characteristics and contribute an improved version dubbed MAL-100+. Experimental results on the widely used malware dataset Mailing and the proposed MAL-100+ demonstrate the effectiveness of our method.Comment: 14 pages, 7 figure

DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning

Online Class-Incremental (OCI) learning has sparked new approaches to expand the previously trained model knowledge from sequentially arriving data streams with new classes. Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones. Existing literature have applied the data augmentation (DA) to alleviate the model forgetting, while the role of DA in OCI has not been well understood so far. In this paper, we theoretically show that augmented samples with lower correlation to the original data are more effective in preventing forgetting. However, aggressive augmentation may also reduce the consistency between data and corresponding labels, which motivates us to exploit proper DA to boost the OCI performance and prevent the CF problem. We propose the Enhanced Mixup (EnMix) method that mixes the augmented samples and their labels simultaneously, which is shown to enhance the sample diversity while maintaining strong consistency with corresponding labels. Further, to solve the class imbalance problem, we design an Adaptive Mixup (AdpMix) method to calibrate the decision boundaries by mixing samples from both old and new classes and dynamically adjusting the label mixing ratio. Our approach is demonstrated to be effective on several benchmark datasets through extensive experiments, and it is shown to be compatible with other replay-based techniques.Comment: 10 pages, 7 figures and 3 table

PiP: Planning-informed Trajectory Prediction for Autonomous Driving

It is critical to predict the motion of surrounding vehicles for self-driving planning, especially in a socially compliant and flexible way. However, future prediction is challenging due to the interaction and uncertainty in driving behaviors. We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. Our approach is differentiated from the traditional manner of prediction, which is only based on historical information and decoupled with planning. By informing the prediction process with the planning of ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets. Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.Comment: European Conference on Computer Vision (ECCV) 2020; Project page at http://haoran-song.github.io/planning-informed-predictio

Robust Respiration Sensing Based on Wi-Fi Beamforming

Currently, the robustness of most Wi-Fi sensing systems is very limited due to that the target’s reflection signal is quite weak and can be easily submerged by the ambient noise. To address this issue, we take advantage of the fact that Wi-Fi devices are commonly equipped with multiple antennas and introduce the beamforming technology to enhance the reflected signal as well as reduce the time-varying noise. We adopt the dynamic signal energy ratio for sub-carrier selection to solve the location dependency problem, based on which a robust respiration sensing system is designed and implemented. Experimental results show that when the distance between the target and the transceiver is 7m,the mean absolute error of the respiration sensing system is less than0.729bpm and the corresponding accuracy reaches 94.79%, which out performs the baseline methods

Flatband Line States in Photonic Super-Honeycomb Lattices

We establish experimentally a photonic super-honeycomb lattice (sHCL) by use of a cw-laser writing technique, and thereby demonstrate two distinct flatband line states that manifest as noncontractible-loop-states in an infinite flatband lattice. These localized states (straight and zigzag lines) observed in the sHCL with tailored boundaries cannot be obtained by superposition of conventional compact localized states because they represent a new topological entity in flatband systems. In fact, the zigzag-line states, unique to the sHCL, are in contradistinction with those previously observed in the Kagome and Lieb lattices. Their momentum-space spectrum emerges in the high-order Brillouin zone where the flat band touches the dispersive bands, revealing the characteristic of topologically protected bandcrossing. Our experimental results are corroborated by numerical simulations based on the coupled mode theory. This work may provide insight to Dirac like 2D materials beyond graphene

Association of interleukin-10 gene polymorphisms with breast cancer in a Chinese population

BACKGROUD: Interleukin-10(IL-10) is a multifunctional cytokine with both immunosuppressive and antiangiogenic functions. Polymorphisms in the IL-10 gene promoter genetically determine interindividual differences in IL-10 production. This study was performed to determined whether polymorphisms in the IL-10 gene promoter were associated with breast cancer in a Chinese Han population. METHODS: We genotyped 315 patients with breast cancer and 322 healthy control subjects for -1082A/G, -819T/C and -592A/C single nucleotide polymorphisms in the promoter region of the IL-10 gene by polymerase chain reactionerestriction fragment length polymorphism (PCR-RFLP). RESULTS: There were no significant differences in genotype, allele, or haplotype frequencies in all three loci between patients and healthy controls. Analysis of breast cancer prognostic and predictive factors revealed that the -1082AA genotype was associated with a significantly increased risk of lymph node (LN) involvement (P = 0.041) and larger tumor size (P = 0.039) at the time of diagnosis. Furthermore, in the haplotype analysis of IL-10 gene, we found that patients carrying ATA haplotype were in higher LN involvement (p = 0.022) and higher tumor stage(p = 0.028) of breast cancer at the time of diagnosis compared with others. CONCLUSIONS: Our findings suggest that IL-10 promoter polymorphisms participate in the progression of breast cancer rather than in its initial development in Chinese Han women