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

Six-month adherence to Statin use and subsequent risk of major adverse cardiovascular events (MACE) in patients discharged with acute coronary syndromes

Acknowledgements: The authors thank all participants who contributed to the study. Funding: CPACS-1 was funded by unrestricted educational grants from Guidant and Sanofi-Aventis, and grants from The Royal Australasian College of Physicians. AP is supported by an Australian National Heart Foundation Career Development Award. CPACS-2 was funded by an unrestricted grant from Sanofi-Aventis China. The George Institute for Global Health at Peking University Health Science Center sponsored the study and owns the data. Data analyses and reports were supported by Beijing Science and Technology Key Research Plan (D151100002215001). However, the authors are solely responsible for the design, analyses, the drafting and editing of the manuscript, and its final contents.Peer reviewedPublisher PD

Influence of Tasks Duration Variability on Task-Based Runtime Schedulers

In the context of HPC platforms, individual nodes nowadays consist in heterogenous processing resources such as GPU units and multicores. Those resources share communication and storage resources , what induces complex co-scheduling effects, and makes it hard to predict the exact duration of a task or of a communication. To cope with these issues, runtime dynamic schedulers such as StarPU have been developed. These systems base their decisions at runtime on the state of the platform and possibly on static priorities of tasks computed offline. In this paper, our goal is to quantify performance variability in the context of HPC heterogeneous nodes, by focusing on very regular dense linear algebra kernels. Then, we analyze the impact of this variability on a dynamic runtime scheduler such as StarPU, in order to analyze whether the strategies that have been designed in the context of MapReduce applications to cope with stragglers could be transferred to HPC systems, or if the dynamic nature of runtime schedulers is enough to cope with actual performance variations

Highly Efficient and Selective Photocatalytic Nonoxidative Coupling of Methane to Ethylene over Pd-Zn Synergistic Catalytic Sites

Photocatalytic nonoxidative coupling of CH4 to multicarbon (C2+) hydrocarbons (e.g., C2H4) and H2 under ambient conditions provides a promising energy-conserving approach for utilization of carbon resource. However, as the methyl intermediates prefer to undergo self-coupling to produce ethane, it is a challenging task to control the selective conversion of CH4 to higher value-added C2H4. Herein, we adopt a synergistic catalysis strategy by integrating Pd-Zn active sites on visible light-responsive defective WO3 nanosheets for synergizing the adsorption, activation, and dehydrogenation processes in CH4 to C2H4 conversion. Benefiting from the synergy, our model catalyst achieves a remarkable C2+ compounds yield of 31.85 mu mol center dot g-1 center dot h-1 with an exceptionally high C2H4 selectivity of 75.3% and a stoichiometric H2 evolution. In situ spectroscopic studies reveal that the Zn sites promote the adsorption and activation of CH4 molecules to generate methyl and methoxy intermediates with the assistance of lattice oxygen, while the Pd sites facilitate the dehydrogenation of methoxy to methylene radicals for producing C2H4 and suppress overoxidation. This work demonstrates a strategy for designing efficient photocatalysts toward selective coupling of CH4 to higher value-added chemicals and highlights the importance of synergistic active sites to the synergy of key steps in catalytic reactions.Peer reviewe

Intelligent reflecting surface networks with multiorder-reflection effect: system modeling and critical bounds

In this paper, we model, analyze and optimize the multi-user and multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks. We first derive a complete MUMOR IRS network model applicable for the arbitrary times of reflections, size and number of IRSs/reflectors. The optimal condition for achieving sum rate upper bound with one IRS in a closed-form function and the analytical condition to achieve interference-free transmission are derived, respectively. Leveraging this optimal condition, we obtain the MUMOR sum rate upper bound of the IRS network with different network topologies, where the linear graph (LG), complete graph (CG) and null graph (NG) topologies are considered. Simulation results verify our theories and derivations and demonstrate that the sum rate upper bounds of different network topologies are under a K-fold improvement given K-piece IRS

Wearable Structured Mental-Sensing-Graph Measurement

The emotional assessment under internet of things (IoT) architecture can support researchers to establish the relationships between human social and physiological signals and emotions. In this paper, a wearable emotion sensing system is developed under narrow band internet of things (NB-IoT) wireless communication technology. The wearable sensing device integrates social linked sensors including voice, activity, and heart rate. Using this system, a dating experiment is set up to investigate multimodal factors of male’s attractiveness perception. In particular, the multimodal data are fused in a graph structure, and this further leads to a graph convolutional neural networks model for emotion evaluation and a mental-sensing-graph intelligent interpreter. Different types of mental-sensing-graphs are fused during the training stage, and the model achieves a verification accuracy of 0.93. The intrinsic relationships among the multimodal data have been captured by the subgraphs which have star-shaped structures, and the center of the subgraphs are mostly audio node. The obtained results show that the attractiveness perception of the male participants in dating is more aligned to language communication. The results also reveal that when the male participants date highly attractive women during the experiments, a significant correlation is observed between the multimodal features and the attractiveness perception levels

Altered Regional and Circuit Resting-State Activity Associated with Unilateral Hearing Loss

The deprivation of sensory input after hearing damage results in functional reorganization of the brain including cross-modal plasticity in the sensory cortex and changes in cognitive processing. However, it remains unclear whether partial deprivation from unilateral auditory loss (UHL) would similarly affect the neural circuitry of cognitive processes in addition to the functional organization of sensory cortex. Here, we used resting-state functional magnetic resonance imaging to investigate intrinsic activity in 34 participants with UHL from acoustic neuroma in comparison with 22 matched normal controls. In sensory regions, we found decreased regional homogeneity (ReHo) in the bilateral calcarine cortices in UHL. However, there was an increase of ReHo in the right anterior insular cortex (rAI), the key node of cognitive control network (CCN) and multimodal sensory integration, as well as in the left parahippocampal cortex (lPHC), a key node in the default mode network (DMN). Moreover, seed-based resting–state functional connectivity analysis showed an enhanced relationship between rAI and several key regions of the DMN. Meanwhile, lPHC showed more negative relationship with components in the CCN and greater positive relationship in the DMN. Such reorganizations of functional connectivity within the DMN and between the DMN and CCN were confirmed by a graph theory analysis. These results suggest that unilateral sensory input damage not only alters the activity of the sensory areas but also reshapes the regional and circuit functional organization of the cognitive control network

Loss of Conformational Stability in Calmodulin upon Methionine Oxidation

We have used electrospray ionization mass spectrometry (ESI-MS), circular dichroism (CD), and fluorescence spectroscopy to investigate the secondary and tertiary structural consequences that result from oxidative modification of methionine residues in wheat germ calmodulin (CaM), and prevent activation of the plasma membrane Ca-ATPase. Using ESI-MS, we have measured rates of modification and molecular mass distributions of oxidatively modified CaM species (CaMox) resulting from exposure to H2O2. From these rates, we find that oxidative modification of methionine to the corresponding methionine sulfoxide does not predispose CaM to further oxidative modification. These results indicate that methionine oxidation results in no large-scale alterations in the tertiary structure of CaMox, because the rates of oxidative modification of individual methionines are directly related to their solvent exposure. Likewise, CD measurements indicate that methionine oxidation results in little change in the apparent α-helical content at 28°C, and only a small (0.3 ± 0.1 kcal mol−1) decrease in thermal stability, suggesting the disruption of a limited number of specific noncovalent interactions. Fluorescence lifetime, anisotropy, and quenching measurements of N-(1-pyrenyl)-maleimide (PMal) covalently bound to Cys26 indicate local structural changes around PMal in the amino-terminal domain in response to oxidative modification of methionine residues in the carboxyl-terminal domain. Because the opposing globular domains remain spatially distant in both native and oxidatively modified CaM, the oxidative modification of methionines in the carboxyl-terminal domain are suggested to modify the conformation of the amino-terminal domain through alterations in the structural features involving the interdomain central helix. The structural basis for the linkage between oxidative modification and these global conformational changes is discussed in terms of possible alterations in specific noncovalent interactions that have previously been suggested to stabilize the central helix in CaM