Boosting Semi-Supervised Learning with Contrastive Complementary Labeling

Semi-supervised learning (SSL) has achieved great success in leveraging a large amount of unlabeled data to learn a promising classifier. A popular approach is pseudo-labeling that generates pseudo labels only for those unlabeled data with high-confidence predictions. As for the low-confidence ones, existing methods often simply discard them because these unreliable pseudo labels may mislead the model. Nevertheless, we highlight that these data with low-confidence pseudo labels can be still beneficial to the training process. Specifically, although the class with the highest probability in the prediction is unreliable, we can assume that this sample is very unlikely to belong to the classes with the lowest probabilities. In this way, these data can be also very informative if we can effectively exploit these complementary labels, i.e., the classes that a sample does not belong to. Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs based on the complementary labels and adopts contrastive learning to make use of all the unlabeled data. Extensive experiments demonstrate that CCL significantly improves the performance on top of existing methods. More critically, our CCL is particularly effective under the label-scarce settings. For example, we yield an improvement of 2.43% over FixMatch on CIFAR-10 only with 40 labeled data.Comment: typos corrected, 5 figures, 3 tables

Temporal Interest Network for Click-Through Rate Prediction

The history of user behaviors constitutes one of the most significant characteristics in predicting the click-through rate (CTR), owing to their strong semantic and temporal correlation with the target item. While the literature has individually examined each of these correlations, research has yet to analyze them in combination, that is, the quadruple correlation of (behavior semantics, target semantics, behavior temporal, and target temporal). The effect of this correlation on performance and the extent to which existing methods learn it remain unknown. To address this gap, we empirically measure the quadruple correlation and observe intuitive yet robust quadruple patterns. We measure the learned correlation of several representative user behavior methods, but to our surprise, none of them learn such a pattern, especially the temporal one. In this paper, we propose the Temporal Interest Network (TIN) to capture the quadruple semantic and temporal correlation between behaviors and the target. We achieve this by incorporating target-aware temporal encoding, in addition to semantic embedding, to represent behaviors and the target. Furthermore, we deploy target-aware attention, along with target-aware representation, to explicitly conduct the 4-way interaction. We performed comprehensive evaluations on the Amazon and Alibaba datasets. Our proposed TIN outperforms the best-performing baselines by 0.43\% and 0.29\% on two datasets, respectively. Comprehensive analysis and visualization show that TIN is indeed capable of learning the quadruple correlation effectively, while all existing methods fail to do so. We provide our implementation of TIN in Tensorflow

Infrared Imaging of Magnetic Octupole Domains in Non-collinear Antiferromagnets

Magnetic structure plays a pivotal role in the functionality of antiferromagnets (AFMs), which not only can be employed to encode digital data but also yields novel phenomena. Despite its growing significance, visualizing the antiferromagnetic domain structure remains a challenge, particularly for non-collinear AFMs. Currently, the observation of magnetic domains in non-collinear antiferromagnetic materials is feasible only in Mn$_{3}$Sn, underscoring the limitations of existing techniques that necessitate distinct methods for in-plane and out-of-plane magnetic domain imaging. In this study, we present a versatile method for imaging the antiferromagnetic domain structure in a series of non-collinear antiferromagnetic materials by utilizing the anomalous Ettingshausen effect (AEE), which resolves both the magnetic octupole moments parallel and perpendicular to the sample surface. Temperature modulation due to the AEE originating from different magnetic domains is measured by the lock-in thermography, revealing distinct behaviors of octupole domains in different antiferromagnets. This work delivers an efficient technique for the visualization of magnetic domains in non-collinear AFMs, which enables comprehensive study of the magnetization process at the microscopic level and paves the way for potential advancements in applications.Comment: National Science Review in pres

Topic clustering within chatbots

Nous commençons le projet en explorant un grand nombre de méthodes NLP qui s'apparentent au Topic Clustering. Ensuite, nous choisissons parmi ces techniques une qui nous semble la plus prometteuse, à savoir les Contextualised Word Embeddings. Dans notre projet, nous nous intéressons plus particulièrement à ELMo, que l'on compare à d'autres modèles similaires en terme de performance. En optimisant les hyper-paramètres des modèles construits, nous obtenons un f1-score supérieur de 0.9. Nous poussons l'étude un peu plus loin, en examinant non seulement la détection de sujet, mais aussi la génération d'une réponse adéquate, avec un mécanisme d'attention particulièrement efficace dans le cas d'un utilisateur non-coopératif. Enfin, nous étudions comment les 3 modèles décrits peuvent être combinés pour répondre à la problématique du projet

One-Step Self-Assembly Synthesis α-Fe2O3 with Carbon-Coated Nanoparticles for Stabilized and Enhanced Supercapacitors Electrode

A cocoon-like α-Fe2O3 nanocomposite with a novel carbon-coated structure was synthesized via a simple one-step hydrothermal self-assembly method and employed as supercapacitor electrode material. It was observed from electrochemical measurements that the obtained α-Fe2O3@C electrode showed a good specific capacitance (406.9 Fg−1 at 0.5 Ag−1) and excellent cycling stability, with 90.7% specific capacitance retained after 2000 cycles at high current density of 10 Ag−1. These impressive results, presented here, demonstrated that α-Fe2O3@C could be a promising alternative material for application in high energy density storage

Incorporation of Poly(Ionic Liquid) with PVDF-HFP-Based Polymer Electrolyte for All-Solid-State Lithium-Ion Batteries

A solid-state polymer electrolyte membrane is formed by blending poly(vinylidene fluoride-co-hexafluoropropylene) with the synthesized copolymer of poly(methyl methacrylate-co-1-vinyl-3-butyl-imidazolium bis(trifluoromethanesulfonyl)imide, in which lithium bis(trifluoromethane)sulfonimide molecules are applied as the source of lithium ions. The accordingly formed membrane that contains 14 wt.% of P(MMA-co-VBIm-TFSI), 56 wt.% of PVDF-HFP, and 30 wt.% of LiTFSI manifests the best electrochemical properties, achieving an ionic conductivity of 1.11 × 10−4 S·cm−1 at 30 °C and 4.26 × 10−4 S·cm−1 at 80 °C, a Li-ion transference number of 0.36, and a wide electrochemical stability window of 4.7 V (vs. Li/Li+). The thus-assembled all-solid-state lithium-ion battery of LiFePO4/SPE/Li delivers a discharge specific capacity of 148 mAh·g−1 in the initial charge–discharge cycle at 0.1 C under 60 °C. The capacity retention of the cell is 95.2% after 50 cycles at 0.1 C and the Coulombic efficiency remains close to 100% during the cycling process

Protic ionic liquid modified electrocatalyst enables robust anode under cell reversal condition

Pt/C has been commercially used as anode electrocatalyst for fuel cells but generally exhibits limited durability under conditions of fuel starvation and subsequent cell reversal. Herein we report an improved scaffold concept to simultaneously stabilize the catalyst against particle growth and reduce the adverse effects of cell reversal by modifying Pt/C with suitable protic ionic liquids (PILs). The modified Pt/C catalysts show enhanced cell reversal tolerance because of their high activity towards oxygen evolution reaction (OER), up to 300 mV lower overpotential compared to the unmodified Pt/C. Moreover, the PIL modified catalysts show better resistance to the loss of electrochemical surface area (ECSA) under simulated cell reversal conditions. The results indicate that modification of Pt/C catalysts with PILs is a promising strategy to enhance the stability and durability of electrocatalysts in fuel cell applications with the risk of frequent fuel starvation events, such as automotive fuel cells.status: publishe