LambdaOpt: Learn to Regularize Recommender Models in Finer Levels

Recommendation models mainly deal with categorical variables, such as user/item ID and attributes. Besides the high-cardinality issue, the interactions among such categorical variables are usually long-tailed, with the head made up of highly frequent values and a long tail of rare ones. This phenomenon results in the data sparsity issue, making it essential to regularize the models to ensure generalization. The common practice is to employ grid search to manually tune regularization hyperparameters based on the validation data. However, it requires non-trivial efforts and large computation resources to search the whole candidate space; even so, it may not lead to the optimal choice, for which different parameters should have different regularization strengths. In this paper, we propose a hyperparameter optimization method, LambdaOpt, which automatically and adaptively enforces regularization during training. Specifically, it updates the regularization coefficients based on the performance of validation data. With LambdaOpt, the notorious tuning of regularization hyperparameters can be avoided; more importantly, it allows fine-grained regularization (i.e. each parameter can have an individualized regularization coefficient), leading to better generalized models. We show how to employ LambdaOpt on matrix factorization, a classical model that is representative of a large family of recommender models. Extensive experiments on two public benchmarks demonstrate the superiority of our method in boosting the performance of top-K recommendation.Comment: Accepted by KDD 201

Structural basis for regulation of the nucleo-cytoplasmic distribution of Bag6 by TRC35

The metazoan protein BCL2-associated athanogene cochaperone 6 (Bag6) forms a hetero-trimeric complex with ubiquitin-like 4A and transmembrane domain recognition complex 35 (TRC35). This Bag6 complex is involved in tail-anchored protein targeting and various protein quality-control pathways in the cytosol as well as regulating transcription and histone methylation in the nucleus. Here we present a crystal structure of Bag6 and its cytoplasmic retention factor TRC35, revealing that TRC35 is remarkably conserved throughout the opisthokont lineage except at the C-terminal Bag6-binding groove, which evolved to accommodate Bag6, a unique metazoan factor. While TRC35 and its fungal homolog, guided entry of tail-anchored protein 4 (Get4), utilize a conserved hydrophobic patch to bind their respective partners, Bag6 wraps around TRC35 on the opposite face relative to the Get4–5 interface. We further demonstrate that TRC35 binding is critical not only for occluding the Bag6 nuclear localization sequence from karyopherin α to retain Bag6 in the cytosol but also for preventing TRC35 from succumbing to RNF126-mediated ubiquitylation and degradation. The results provide a mechanism for regulation of Bag6 nuclear localization and the functional integrity of the Bag6 complex in the cytosol

Bag6 complex contains a minimal tail-anchor–targeting module and a mock BAG domain

BCL2-associated athanogene cochaperone 6 (Bag6) plays a central role in cellular homeostasis in a diverse array of processes and is part of the heterotrimeric Bag6 complex, which also includes ubiquitin-like 4A (Ubl4A) and transmembrane domain recognition complex 35 (TRC35). This complex recently has been shown to be important in the TRC pathway, the mislocalized protein degradation pathway, and the endoplasmic reticulum-associated degradation pathway. Here we define the architecture of the Bag6 complex, demonstrating that both TRC35 and Ubl4A have distinct C-terminal binding sites on Bag6 defining a minimal Bag6 complex. A crystal structure of the Bag6–Ubl4A dimer demonstrates that Bag6–BAG is not a canonical BAG domain, and this finding is substantiated biochemically. Remarkably, the minimal Bag6 complex defined here facilitates tail-anchored substrate transfer from small glutamine-rich tetratricopeptide repeat-containing protein α to TRC40. These findings provide structural insight into the complex network of proteins coordinated by Bag6

Knowledge Condensation Distillation

Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher network to strengthen a smaller student. Existing methods focus on excavating the knowledge hints and transferring the whole knowledge to the student. However, the knowledge redundancy arises since the knowledge shows different values to the student at different learning stages. In this paper, we propose Knowledge Condensation Distillation (KCD). Specifically, the knowledge value on each sample is dynamically estimated, based on which an Expectation-Maximization (EM) framework is forged to iteratively condense a compact knowledge set from the teacher to guide the student learning. Our approach is easy to build on top of the off-the-shelf KD methods, with no extra training parameters and negligible computation overhead. Thus, it presents one new perspective for KD, in which the student that actively identifies teacher's knowledge in line with its aptitude can learn to learn more effectively and efficiently. Experiments on standard benchmarks manifest that the proposed KCD can well boost the performance of student model with even higher distillation efficiency. Code is available at https://github.com/dzy3/KCD.Comment: ECCV202

USP13 antagonizes gp78 to maintain functionality of a chaperone in ER-associated degradation

Physiological adaptation to proteotoxic stress in the endoplasmic reticulum (ER) requires retrotranslocation of misfolded proteins into the cytoplasm for ubiquitination and elimination by ER-associated degradation (ERAD). A surprising paradox emerging from recent studies is that ubiquitin ligases (E3s) and deubiquitinases (DUBs), enzymes with opposing activities, can both promote ERAD. Here we demonstrate that the ERAD E3 gp78 can ubiquitinate not only ERAD substrates, but also the machinery protein Ubl4A, a key component of the Bag6 chaperone complex. Remarkably, instead of targeting Ubl4A for degradation, polyubiquitination is associated with irreversible proteolytic processing and inactivation of Bag6. Importantly, we identify USP13 as a gp78-associated DUB that eliminates ubiquitin conjugates from Ubl4A to maintain the functionality of Bag6. Our study reveals an unexpected paradigm in which a DUB prevents undesired ubiquitination to sharpen substrate specificity for an associated ubiquitin ligase partner and to promote ER quality control

Structural basis for regulation of the nucleo-cytoplasmic distribution of Bag6 by TRC35

The metazoan protein BCL2-associated athanogene cochaperone 6 (Bag6) forms a hetero-trimeric complex with ubiquitin-like 4A and transmembrane domain recognition complex 35 (TRC35). This Bag6 complex is involved in tail-anchored protein targeting and various protein quality-control pathways in the cytosol as well as regulating transcription and histone methylation in the nucleus. Here we present a crystal structure of Bag6 and its cytoplasmic retention factor TRC35, revealing that TRC35 is remarkably conserved throughout the opisthokont lineage except at the C-terminal Bag6-binding groove, which evolved to accommodate Bag6, a unique metazoan factor. While TRC35 and its fungal homolog, guided entry of tail-anchored protein 4 (Get4), utilize a conserved hydrophobic patch to bind their respective partners, Bag6 wraps around TRC35 on the opposite face relative to the Get4–5 interface. We further demonstrate that TRC35 binding is critical not only for occluding the Bag6 nuclear localization sequence from karyopherin α to retain Bag6 in the cytosol but also for preventing TRC35 from succumbing to RNF126-mediated ubiquitylation and degradation. The results provide a mechanism for regulation of Bag6 nuclear localization and the functional integrity of the Bag6 complex in the cytosol

Relationship between gray matter structure and age in children and adolescents with high-functioning autism spectrum disorder

ObjectiveThe present study used magnetic resonance imaging to investigate the difference in the relationship between gray matter structure and age in children and adolescents with autism spectrum disorder (ASD) and typically developing (TD) subjects.MethodsAfter screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 111 children and adolescents (7–18 years old) with high-functioning ASD and 151 TD subjects matched for age, sex and full IQ were included in the current study. By using the voxel-based morphological analysis method, gray matter volume/density (GMV/GMD) maps were obtained for each participant. Then, a multiple regression analysis was performed for ASD and TD groups, respectively to estimate the relationship between GMV/GMD and age with gender, education, site, and IQ scores as covariates. Furthermore, a z-test was used to compare such relationship difference between the groups.ResultsResults showed that compared with TD, the GMD of ASD showed stronger positive correlations with age in the prefrontal cortex, and a stronger negative correlation in the left inferior parietal lobule, and a weaker positive correlation in the right inferior parietal lobule. The GMV of ASD displayed stronger positive correlations with age in the prefrontal cortex and cerebellum.ConclusionThese findings may provide evidence to support that the brain structure abnormalities underlying ASD during childhood and adolescence may differ from each other