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

High-Performance Organic Electrochemical Transistors and Neuromorphic Devices Comprising Naphthalenediimide-Dialkoxybithiazole Copolymers Bearing Glycol Ether Pendant Groups

Organic electrochemical transistors (OECTs) have emerged as building blocks for low power circuits, biosensors, and neuromorphic computing. While p-type polymer materials for OECTs are well developed, the choice of high-performance n-type polymers is limited, despite being essential for cation and metabolite biosensors, and crucial for constructing complementary circuits. N-type conjugated polymers that have efficient ion-to-electron transduction are highly desired for electrochemical applications. In this contribution, three non-fused, planar naphthalenediimide (NDI)-dialkoxybithiazole (2Tz) copolymers, which systematically increase the amount of polar tri(ethylene glycol) (TEG) side chains: PNDI2OD-2Tz (0 TEG), PNDIODTEG-2Tz (1 TEG), PNDI2TEG-2Tz (2 TEG), are reported. It is demonstrated that the OECT performance increases with the number of TEG side chains resulting from the progressively higher hydrophilicity and larger electron affinities. Benefiting from the high electron mobility, excellent ion conduction capability, efficient ion-to-electron transduction, and low-lying lowest unoccupied molecular orbital energy level, the 2 TEG polymer achieves close to 105 on-off ratio, fast switching, 1000 stable operation cycles in aqueous electrolyte, and has a long shelf life. Moreover, the higher number TEG chain substituted polymer exhibits good conductance state retention over two orders of magnitudes in electrochemical resistive random-access memory devices, highlighting its potential for neuromorphic computing

Uber die Welch-Fraenkelschen Bazillen und ihre Verwandten im Darmkanal

Der Welch-Fraenkelsche Bazillus, der sich als ein Bodenbakterium weit im Boden verbreitet, ist obligater Anaerobier, und bekannt als Erreger des Gasoedems, welches eine chirurgisch und insbesonders kriegschirurgisch wichtige Wundinfektionskrankheit ist. Die Tatsache, dass dieses Stabchen nicht nur aus dem Boden, sondern auch aus menschlichem Darm gezuchtet wird, hat viele Autoren schon bisweilen beschaftigt. Dieser Bazillus ist Gram-positiv, gross und kraftig, abgerundet, unbeweglich und sporuliert nicht im gewohnlichen Nahrboden. Die Milch wurde sturmisch mit Gasbildung vergoren. Schon nach 20 Stunden schwamm das Kasein deutlich geschieden in truber, immer klarer werdender Molke. Die Kaseingerinnsel wurden ferner nicht wieder durch Peptonisierung gelost, noch kam es nach dem leicht sauerlich bleibenden Geruch zu Eiweissfaulniss. Die oben beschriebene Eigenschaft, die sog. Sturmische Gerinnung , ist charakteristisches Merkmal zur Diagnose-stellung des Welch-Fraenkelschen Bazillus. Der Verfasser hat aus 172 Faeces der Menschen 244 Stamme der Welch-Fraenkelschen Bazillen und 39 ahnliche Stamme, die von erstem Bazillus deutlich voneinander in Eigenstumlichkeit abweicht gezuchtet; d. h. dem Letzteren fehlt die sturmische Gerinnung. Diesen Bazillus nennt der Verfasser einen Verwandten der Welch-Fraenkelschen Bazillen , und vergleicht dieses Stabchen morphologisch und biologisch mit den Welch-Fraenkelschen Bazillen, welche er aus menschlichen Faeces und klassischem Gasoedem isoliert hat. Morphologie: Die Verwandten sind ebenso mit abgerundetem Ende 0.8-1.0μ breit, 4.0-1.0μ, nicht haufig 15μ in der Lange, zuweilen leicht gekrummt, und werden zarter als Welch-Fraenkelche Bazillen beobachtet. Sporen und Kapseln: Nicht nur in gewohnlichen Nahrboden, sondern auch in den alkalischen-, natriumphosphathaltigen Nahrsubstraten und im Hirnbrei nahm der Verfasser keine Sporen oder Kapseln wahr. Aber nur bei aus Gasoedem gezuchtetem Stamm beobachtete er beide im spezifischen antiserumhaltigen Nahrboden. Kolonientypen: Auf d

Hydrothermal Fabrication of Spindle-Shaped ZnO/Palygorskite Nanocomposites Using Nonionic Surfactant for Enhancement of Antibacterial Activity

In order to improve the antibacterial performance of natural palygorskite, spindle-like ZnO/palygorskite (ZnO/PAL) nanocomposites with controllable growth of ZnO on the surface of PAL were prepared in the presence of non-ionic surfactants using an easy-to-operate hydrothermal method. The obtained ZnO/PAL nanocomposites have a novel and special spindle-shaped structure and good antibacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), and are also low cost. The minimum inhibitory concentrations of ZnO/PAL nanocomposites toward E. coli and S. aureus reached 1.5 and 5 mg/mL, respectively

Impulse Noise Removal by L1 Weighted Nuclear Norm Minimization

In recent years, the nuclear norm minimization (NNM) as a convex relaxation of the rank minimization has attracted great research interest. By assigning different weights to singular values, the weighted nuclear norm minimization (WNNM) has been utilized in many applications. However, most of the work on WNNM is combined with the l2data-fidelity term, which is under additive Gaussian noise assumption. In this paper, we introduce the L1-WNNM model, which incorporates the l1-data-fidelity term and the regularization from WNNM. We apply the alternating direction method of multipliers (ADMM) to solve the non-convex minimization problem in this model. We exploit the low rank prior on the patch matrices extracted based on the image non-local self-similarity and apply the L1-WNNM model on patch matrices to restore the image corrupted by impulse noise. Numerical results show that our method can effectively remove impulse noise

Densified MoS2/Ti3C2 films with balanced porosity for ultrahigh volumetric capacity sodium-ion battery

Developing high volumetric energy density sodium-ion batteries (SIBs) is indispensable for catering to the miniaturization and flexibility of various consumer electronics. Herein, we have reported the flexible and compact MoS2/Ti3C2 hybrid films with balanced porosity, where the few-layered MoS2 nanosheets are parallelly intercalated into the Ti3C2 interlayer space in virtue of strong electrostatic effect and difference in their sizes. The hybrid films have been stabilized by the two-dimensional (2D) confinement effect and the Ti-S-Mo bonds with a high density of ~2.9 g cm−3. Furthermore, the dual 2D compounds intrinsically possess satisfied ions conductivity, and meanwhile give rapid electrons transfer after assembling such superstructure. When directly used as SIB anode, the MoS2/Ti3C2 hybrid films deliver an exceptional volumetric specific capacity of 1510 mAh cm−3 at 0.28 mA cm−2 and 650 mAh cm−3 at 14 mA cm−2. The specific capacity remains unchanged after 300 cycles at 1.4 mA cm−2. More significantly, the areal specific capacity shows a linear relationship with the increase of film thickness from 9.6 to 43.1 μm without sacrificing the volumetric capacity. © 2020 Elsevier B.V.National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [21975074, 21838003]; Basic Research Program of Shanghai [17JC1402300]; Shanghai Scientific and Technological Innovation Project [18JC1410500]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [222201718002

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

Click-through rate (CTR) prediction is one of the fundamental tasks in online advertising and recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, but it has been widely shown that applying a vanilla MLP network alone is ineffective in learning complex feature interactions. As such, many two-stream models (e.g., Wide&Deep, DeepFM, and DCN) have recently been proposed, aiming to integrate two parallel sub-networks to learn feature interactions from two different views for enhanced CTR prediction. In addition to one MLP stream that learns feature interactions implicitly, most of the existing research focuses on designing another stream to complement the MLP stream with explicitly enhanced feature interactions. Instead, this paper presents a simple two-stream feature interaction model, namely FinalMLP, which employs only MLPs in both streams yet achieves surprisingly strong performance. In contrast to sophisticated network design in each stream, our work enhances CTR modeling through a feature selection module, which produces differentiated feature inputs to two streams, and a group-wise bilinear fusion module, which effectively captures stream-level interactions across two streams. We show that FinalMLP achieves competitive or even better performance against many existing two-stream CTR models on four open benchmark datasets and also brings significant CTR improvements during an online A/B test in our industrial news recommender system. We envision that the simple yet effective FinalMLP model could serve as a new strong baseline for future development of two-stream CTR models. Our source code will be available at MindSpore/models and FuxiCTR/model_zoo

Multiple Robust Learning for Recommendation

In recommender systems, a common problem is the presence of various biases in the collected data, which deteriorates the generalization ability of the recommendation models and leads to inaccurate predictions. Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate. In this paper, we propose a multiple robust (MR) estimator that can take the advantage of multiple candidate imputation and propensity models to achieve unbiasedness. Specifically, the MR estimator is unbiased when any of the imputation or propensity models, or a linear combination of these models is accurate. Theoretical analysis shows that the proposed MR is an enhanced version of DR when only having a single imputation and propensity model, and has a smaller bias. Inspired by the generalization error bound of MR, we further propose a novel multiple robust learning approach with stabilization. We conduct extensive experiments on real-world and semi-synthetic datasets, which demonstrates the superiority of the proposed approach over state-of-the-art methods