Improving Multi-Task Generalization via Regularizing Spurious Correlation

Multi-Task Learning (MTL) is a powerful learning paradigm to improve generalization performance via knowledge sharing. However, existing studies find that MTL could sometimes hurt generalization, especially when two tasks are less correlated. One possible reason that hurts generalization is spurious correlation, i.e., some knowledge is spurious and not causally related to task labels, but the model could mistakenly utilize them and thus fail when such correlation changes. In MTL setup, there exist several unique challenges of spurious correlation. First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other. Second, the confounder between task labels brings in a different type of spurious correlation to MTL. We theoretically prove that MTL is more prone to taking non-causal knowledge from other tasks than single-task learning, and thus generalize worse. To solve this problem, we propose Multi-Task Causal Representation Learning framework, aiming to represent multi-task knowledge via disentangled neural modules, and learn which module is causally related to each task via MTL-specific invariant regularization. Experiments show that it could enhance MTL model's performance by 5.5% on average over Multi-MNIST, MovieLens, Taskonomy, CityScape, and NYUv2, via alleviating spurious correlation problem.Comment: Published on NeurIPS 202

Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)

Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. This skew hurts recommender quality especially for the item slices without much user feedback. While there have been many research advances made in academia, deploying these methods in production is very difficult and very few improvements have been made in industry. One challenge is that these methods often hurt overall performance; additionally, they could be complex and expensive to train and serve. In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost. We first find that the predictions of user preferences are biased under long-tail distributions. The bias comes from the differences between training and serving data in two perspectives: 1) the item distributions, and 2) user's preference given an item. Most existing methods mainly attempt to reduce the bias from the item distribution perspective, ignoring the discrepancy from user preference given an item. This leads to a severe forgetting issue and results in sub-optimal performance. To address the problem, we design a novel Cross Decoupling Network (CDN) (i) decouples the learning process of memorization and generalization on the item side through a mixture-of-expert architecture; (ii) decouples the user samples from different distributions through a regularized bilateral branch network. Finally, a new adapter is introduced to aggregate the decoupled vectors, and softly shift the training attention to tail items. Extensive experimental results show that CDN significantly outperforms state-of-the-art approaches on benchmark datasets. We also demonstrate its effectiveness by a case study of CDN in a large-scale recommendation system at Google.Comment: Accepted by KDD 2023 Applied Data Science (ADS) trac

In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine

Serum and plasma contain abundant biological information that reflect the body’s physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases

A novel double bridging-slipping (DBS) concept to overcome deformation incompatibility of textile reinforced-engineering cementitious composite (TR-ECC)

Due to the deformation incompatibility between the engineering cementitious composite (ECC) matrix and embedded fiber reinforced polymer (FRP), conventional textile reinforced (TR)-ECC usually fails in premature FRP rupture when subjected to a tensile load. To address this issue, a novel double bridging-slipping (DBS)-based concept is proposed in this study. Apart from bridging-slipping capacity of incorporated short fibers in the ECC matrix, the presence of FRP strip can contribute to the other bridging-slipping mechanism for enhancing the tensile performance of the whole specimen. The embedment zone of FRP strips can bridge the crack tips of ECC matrix through the FRP-ECC interfacial bond stress, and the slippage of FRP strips in the uncracked zone can compensate for the excessive deformation of ECC matrix in the multi-cracking zone. Experimental results show that compared to the conventional TR-ECC, the proposed DBS-based TR-ECC with the same FRP volumetric ratio of 0.113 % can significantly improve the ultimate tensile strain and toughness by 148.3 % and 38.0 %, respectively. As DBS-based TR-ECC facilitates the combination of superior strength of FRP textiles and excellent toughness of ECC materials, it has a promising application in infrastructure projects

Influence of cold rolling deformation on mechanical properties and corrosion behavior of Ti-6Al-3Nb-2Zr-1Mo alloy

The effects of cold deformation on tensile properties and corrosion behavior of the Ti-6Al-3Nb-2Zr-1Mo alloy are investigated in the present work. The microstructure of the sample was characterized by means of x-ray diffractometry, scanning electron microscope and electron backscattered diffraction. The corrosion behavior of the alloy in HCl solution was characterized by potentiodynamic polarization test. The results show that the α laths became kinked after cold rolling and the interlamellar spacing decreased to about 0.45 μ m at the rolling reduction of 50%. The kinking process closely linked with the development of shear bands within the colonies. EBSD investigations indicated that the α lath exhibited a (0001) texture in the 50% cold-rolled alloy. With the increase of cold deformation strain, the Yield strength of the alloy increases from 811 MPa to 943 MPa. The corrosion resistance of processed samples was higher than as-received sample. Experimental results showed that deformation substructure and texture had an influence on the corrosion rate of this alloy

Effect of the Addition of Steel Fibers on the Bonding Interface and Tensile Properties of Explosion-Welded 2A12 Aluminum Alloy and SS-304 Steel

First of all, the explosion-welding method was adopted to prepare steel fiber-reinforced steel-aluminum composite plates. Secondly, the smooth particle hydrodynamic (SPH) method was used to investigate the effect of introducing steel fibers to a vortex region created at the bonding interface of the steel-aluminum composite plate. Thirdly, the following conclusions were drawn through an analysis of the vortex region with the assistance of scanning electron microscopy and energy-dispersive X-ray spectroscopy. A brittle intermetallic compound FeAl was produced in the vortex region in an environment characterized by high temperature, high pressure, and high strain rate, resulting in cracks, holes and pores. In addition, the hardness of the vortex area was less than the estimated value, which is mainly because the main element in the vortex area was 2A12 aluminum with low hardness, and there were cracks, holes, pores and other defects that caused hardness reduction. Although the addition of steel fibers caused defects at the bond interface, the addition of steel fibers was effective in improving the tensile resistance performance of steel-aluminum composite panels to a certain extent. In addition, the larger the fiber diameter, the more significant the increase in tensile resistance

Novel Reassortant Influenza A(H5N8) Viruses in Domestic Ducks, Eastern China

Domestic ducks are natural reservoirs of avian influenza viruses and serve as reassortant hosts for new virus subtypes. We isolated 2 novel influenza A(H5N8) viruses from domestic ducks in eastern China, sequenced their genomes, and tested their pathogenicity in chickens and mice. Circulation of these viruses may pose health risks for humans