Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations

Multi-task learning (MTL) has been successfully used in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks. However, challenge still remains in balancing the trade-off of various tasks since model performance is sensitive to the relationships between them. Less correlated or even conflict tasks will deteriorate the performance by introducing unhelpful or negative information. Therefore, it is important to efficiently exploit and learn fine-grained feature representation corresponding to each task. In this paper, we propose an Adaptive Pattern Extraction Multi-task (APEM) framework, which is adaptive and flexible for large-scale industrial application. APEM is able to fully utilize the feature information by learning the interactions between the input feature fields and extracted corresponding tasks-specific information. We first introduce a DeepAuto Group Transformer module to automatically and efficiently enhance the feature expressivity with a modified set attention mechanism and a Squeeze-and-Excitation operation. Second, explicit Pattern Selector is introduced to further enable selectively feature representation learning by adaptive task-indicator vectors. Empirical evaluations show that APEM outperforms the state-of-the-art MTL methods on public and real-world financial services datasets. More importantly, we explore the online performance of APEM in a real industrial-level recommendation scenario.Comment: 18 pages, 9 figure

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

State of the Art of Adaptive Dynamic Programming and Reinforcement Learning

This article introduces the state-of-the-art development of adaptive dynamic programming and reinforcement learning (ADPRL). First, algorithms in reinforcement learning (RL) are introduced and their roots in dynamic programming are illustrated. Adaptive dynamic programming (ADP) is then introduced following a brief discussion of dynamic programming. Researchers in ADP and RL have enjoyed the fast developments of the past decade from algorithms, to convergence and optimality analyses, and to stability results. Several key steps in the recent theoretical developments of ADPRL are mentioned with some future perspectives. In particular, convergence and optimality results of value iteration and policy iteration are reviewed, followed by an introduction to the most recent results on stability analysis of value iteration algorithms

Influence of aromatic heterocycle of conjugated side chains on photovoltaic performance of benzodithiophene-based wide-bandgap polymers

Extensive efforts have been focused on the study of wide-band gap (WBG) polymers due to their important applications in multiple junction and ternary blend organic solar cells. Herein, three WBG copolymers named PBDT(X)-T1 (X = O, S, Se) were synthesized based on the benzodithiophene (BDT) donor unit and 1,3-bis(5-bromothiophen-2-yl)-5,7-bis(2-ethylhexyl)-4H,8H-benzo[1,2-c:4,5-c???]dithiophene-4,8-dione (T1) acceptor unit. Different aromatic heterocycle groups (furan, thiophene and selenophene) were introduced to modify the BDT unit to investigate the influence of conjugated side chains on the photovoltaic properties of conjugated polymers. Photophysical properties, electrochemistry, charge transport and crystalline properties of the polymers were studied to discuss the role of chalcogen atoms on the performance of conjugated polymers. Solar cells based on these three WBG copolymers were fabricated. Among them, the PBDT(Se)-T1-based solar cell shows the best photovoltaic performance with the highest power conversion efficiency (PCE) of 8.52%, an open-circuit voltage (Voc) of 0.91 V, and a high fill factor (FF) of 72%. The high crystallinity and preferential face-on orientation in the blend film partially explain the superior photovoltaic performance achieved in PBDT(Se)-T1-based solar cells. The results indicate the important role of chalcogen atoms in conjugated side chains and that high photovoltaic performance can be realized through side chain engineering of BDT-based WBG conjugated polymers.clos