FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

Click-through rate (CTR) prediction is one of the fundamental tasks for online advertising and recommendation. While multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, it has been widely recognized that applying a vanilla MLP network alone is inefficient in learning multiplicative feature interactions. As such, many two-stream interaction models (e.g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction. As the MLP stream learns feature interactions implicitly, existing research focuses mainly on enhancing explicit feature interactions in the complementary stream. In contrast, our empirical study shows that a well-tuned two-stream MLP model that simply combines two MLPs can even achieve surprisingly good performance, which has never been reported before by existing work. Based on this observation, we further propose feature gating and interaction aggregation layers that can be easily plugged to make an enhanced two-stream MLP model, FinalMLP. In this way, it not only enables differentiated feature inputs but also effectively fuses stream-level interactions across two streams. Our evaluation results on four open benchmark datasets as well as an online A/B test in our industrial system show that FinalMLP achieves better performance than many sophisticated two-stream CTR models. Our source code will be available at MindSpore/models.Comment: Accepted by AAAI 2023. Code available at https://xpai.github.io/FinalML

Leak location based on PDS-VMD of leakage-induced vibration signal under low SNR in water-supply pipelines

Leak location in water-supply pipelines is of great significance in order to preserve water resources and reduce economic losses. Cross-correlation (CC) based leak location is a popular and effective method in water-supply pipelines (WSP). However, with a decrease of signal to noise ratio (SNR), the errors of time-delay estimation (TDE) based on CC will become larger making it almost impossible to determine a leakage position. Hence, this work proposes leak location based on a combination of phase difference spectrum and variational mode decomposition (PDS-VMD) of leakage-induced vibration signal under low SNR for WSP. Firstly, the leakage-induced vibration signal is decomposed into several intrinsic mode functions (IMFs) by VMD, where the characteristic frequency band is determined by PDS of cross spectrum of two leakage signals. Then, the energy ratio of leakage signal in characteristic frequency band serves as a guideline to select effective IMF components from the decomposed IMFs. Finally, the selective IMFs are reconstituted into a new signal which can be used to determine a leak position using CC based TDE. In order to verify the effectiveness of the proposed leak location algorithm, the method based on PDS-VMD is compared with that using CC, combination of CC coefficient and VMD (CCC-VMD) using both simulation and experiment. The simulation and experimental results demonstrate that the proposed PDS-VMD method is more suitable for leak location in WSP, which provides immunity to both broadband and narrow band noise under low SNR. © 2020 IEEE

SimpleX: A Simple and Strong Baseline for Collaborative Filtering

Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction encoder, loss function, and negative sampling. While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored. In this work, we show that the choice of loss function as well as negative sampling ratio is equivalently important. More specifically, we propose the cosine contrastive loss (CCL) and further incorporate it to a simple unified CF model, dubbed SimpleX. Extensive experiments have been conducted on 11 benchmark datasets and compared with 29 existing CF models in total. Surprisingly, the results show that, under our CCL loss and a large negative sampling ratio, SimpleX can surpass most sophisticated state-of-the-art models by a large margin (e.g., max 48.5% improvement in NDCG@20 over LightGCN). We believe that SimpleX could not only serve as a simple strong baseline to foster future research on CF, but also shed light on the potential research direction towards improving loss function and negative sampling. Our source code will be available at https://reczoo.github.io/SimpleX.Comment: Accepted by CIKM 2021. Code available at https://reczoo.github.io/Simple

The value of Apolipoprotein B/Apolipoprotein A1 ratio for metabolic syndrome diagnosis in a Chinese population: a cross-sectional study

BACKGROUND: The apoB/apoA1 ratio has been reported to be associated with the metabolic syndrome (MetS), and it may be a more convenient biomarker in MetS predicting. However, whether apoB/apoA1 ratio is a better indicator of metabolic syndrome than other biomarkers and what is the optimal cut-off value of apoB/apoA1 ratio as an indicator of metabolic syndrome in Chinese population remain unknown. Thus, we carried out the current study to assess the predictive value of apoB/apoA1 ratio and determine the optimal cut-off value of apoB/apoA1 ratio for diagnosing MetS in a Chinese population. METHOD: We selected 1,855 subjects with MetS and 6,265 individuals without MetS based on the inclusion and exclusion criteria from the China Health Nutrition Survey (CHNS) in 2009. MetS was identified based on the diagnostic criteria of International Diabetes Federation (2005). Logistic regression was used to estimate the association between the apoB/apoA1 ratio and risk of MetS, and receiver operating characteristics (ROC) curve analysis was performed to test the predictive value of apoB/apoA1 ratio and calculate the appropriate cut-off value. RESULTS: Compared with the lowest quartile of apoB/apoA1 ratio, subjects in the fourth quartile had a higher risk of MetS in both men [odds ratio (OR) = 2.64, 95% confidence interval (CI) =1.82-3.83] and women (OR = 5.18, 95% CI = 3.87-6.92) after adjustment for potential confounders. The optimal cut-off value of apoB/apoA1 ratio for MetS detection was 0.85 in men and 0.80 in women. Comparisons of ROC curves indicated that apoB/apoA1 ratio was better than traditional biomarkers in predicting MetS. CONCLUSION: Our results suggest that, apoB/apoA1 ratio has a promising predictive effectiveness in detection of MetS. An apoB/apoA1 ratio higher than 0.85 in men and 0.80 in women may be a promising and convenient marker of MetS

CTVIS: Consistent Training for Online Video Instance Segmentation

The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS). Instance embedding learning is directly supervised by the contrastive loss computed upon the contrastive items (CIs), which are sets of anchor/positive/negative embeddings. Recent online VIS methods leverage CIs sourced from one reference frame only, which we argue is insufficient for learning highly discriminative embeddings. Intuitively, a possible strategy to enhance CIs is replicating the inference phase during training. To this end, we propose a simple yet effective training strategy, called Consistent Training for Online VIS (CTVIS), which devotes to aligning the training and inference pipelines in terms of building CIs. Specifically, CTVIS constructs CIs by referring inference the momentum-averaged embedding and the memory bank storage mechanisms, and adding noise to the relevant embeddings. Such an extension allows a reliable comparison between embeddings of current instances and the stable representations of historical instances, thereby conferring an advantage in modeling VIS challenges such as occlusion, re-identification, and deformation. Empirically, CTVIS outstrips the SOTA VIS models by up to +5.0 points on three VIS benchmarks, including YTVIS19 (55.1% AP), YTVIS21 (50.1% AP) and OVIS (35.5% AP). Furthermore, we find that pseudo-videos transformed from images can train robust models surpassing fully-supervised ones.Comment: Accepted by ICCV 2023. The code is available at https://github.com/KainingYing/CTVI

Structure and distinct supramolecular organization of a PSII-ACPII dimer from a cryptophyte alga Chroomonas placoidea

Cryptophyte algae are an evolutionarily distinct and ecologically important group of photosynthetic unicellular eukaryotes. Photosystem II (PSII) of cryptophyte algae associates with alloxanthin chlorophyll a/c-binding proteins (ACPs) to act as the peripheral light-harvesting system, whose supramolecular organization is unknown. Here, we purify the PSII-ACPII supercomplex from a cryptophyte alga Chroomonas placoidea (C. placoidea), and analyze its structure at a resolution of 2.47 & Aring; using cryo-electron microscopy. This structure reveals a dimeric organization of PSII-ACPII containing two PSII core monomers flanked by six symmetrically arranged ACPII subunits. The PSII core is conserved whereas the organization of ACPII subunits exhibits a distinct pattern, different from those observed so far in PSII of other algae and higher plants. Furthermore, we find a Chl a-binding antenna subunit, CCPII-S, which mediates interaction of ACPII with the PSII core. These results provide a structural basis for the assembly of antennas within the supercomplex and possible excitation energy transfer pathways in cryptophyte algal PSII, shedding light on the diversity of supramolecular organization of photosynthetic machinery

PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation

Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions are restricted to pre-defined, static sparsity patterns due to significant overheads associated with preprocessing. Efficient execution of dynamic sparse computation often faces the misalignment between the GPU-friendly tile configuration for efficient execution and the sparsity-aware tile shape that minimizes coverage wastes (non-zero values in tensor). In this paper, we propose PIT, a deep-learning compiler for dynamic sparsity. PIT proposes a novel tiling mechanism that leverages Permutation Invariant Transformation (PIT), a mathematically proven property, to transform multiple sparsely located micro-tiles into a GPU-efficient dense tile without changing the computation results, thus achieving both high GPU utilization and low coverage waste. Given a model, PIT first finds feasible PIT rules for all its operators and generates efficient GPU kernels accordingly. At runtime, with the novel SRead and SWrite primitives, PIT rules can be executed extremely fast to support dynamic sparsity in an online manner. Extensive evaluation on diverse models shows that PIT can accelerate dynamic sparsity computation by up to 5.9x (average 2.43x) over state-of-the-art compilers

HOXA9 Reprograms the Enhancer Landscape to Promote Leukemogenesis

Aberrant expression of HOXA9 is a prominent feature of acute leukemia driven by diverse oncogenes. Here we show that HOXA9 overexpression in myeloid and B progenitor cells leads to significant enhancer reorganizations with prominent emergence of leukemia-specific de novo enhancers. Alterations in the enhancer landscape lead to activation of an ectopic embryonic gene program. We show that HOXA9 functions as a pioneer factor at de novo enhancers and recruits CEBPα and the MLL3/MLL4 complex. Genetic deletion of MLL3/MLL4 blocks histone H3K4 methylation at de novo enhancers and inhibits HOXA9/MEIS1-mediated leukemogenesis in vivo. These results suggest that therapeutic targeting of HOXA9-dependent enhancer reorganization can be an effective therapeutic strategy in acute leukemia with HOXA9 overexpressio

A Mutation in Intracellular Loop 4 Affects the Drug-Efflux Activity of the Yeast Multidrug Resistance ABC Transporter Pdr5p

Multidrug resistance protein Pdr5p is a yeast ATP-binding cassette (ABC) transporter in the plasma membrane. It confers multidrug resistance by active efflux of intracellular drugs. However, the highly polymorphic Pdr5p from clinical strain YJM789 loses its ability to expel azole and cyclohexmide. To investigate the role of amino acid changes in this functional change, PDR5 chimeras were constructed by segmental replacement of homologous BY4741 PDR5 fragments. Functions of PDR5 chimeras were evaluated by fluconazole and cycloheximide resistance assays. Their expression, ATPase activity, and efflux efficiency for other substrates were also analyzed. Using multiple lines of evidence, we show that an alanine-to-methionine mutation at position 1352 located in the predicted short intracellular loop 4 significantly contributes to the observed transport deficiency. The degree of impairment is likely correlated to the size of the mutant residue