Prompt Tuning with Soft Context Sharing for Vision-Language Models

Vision-language models have recently shown great potential on many computer vision tasks. Meanwhile, prior work demonstrates prompt tuning designed for vision-language models could acquire superior performance on few-shot image recognition compared to linear probe, a strong baseline. In real-world applications, many few-shot tasks are correlated, particularly in a specialized area. However, such information is ignored by previous work. Inspired by the fact that modeling task relationships by multi-task learning can usually boost performance, we propose a novel method SoftCPT (Soft Context Sharing for Prompt Tuning) to fine-tune pre-trained vision-language models on multiple target few-shot tasks, simultaneously. Specifically, we design a task-shared meta network to generate prompt vector for each task using pre-defined task name together with a learnable meta prompt as input. As such, the prompt vectors of all tasks will be shared in a soft manner. The parameters of this shared meta network as well as the meta prompt vector are tuned on the joint training set of all target tasks. Extensive experiments on three multi-task few-shot datasets show that SoftCPT outperforms the representative single-task prompt tuning method CoOp [78] by a large margin, implying the effectiveness of multi-task learning in vision-language prompt tuning. The source code and data will be made publicly available

Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, \ie, CLIP, to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCPNet), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly outperforms existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://github.com/jameslahm/SCPNet.Comment: Accepted by IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 202

N₂O Emission from Partial Nitrification and Full Nitrification in Domestic Wastewater Treatment Process

Using actual domestic wastewater as the research object, nitrogen compounds and their combinations were added to different nitrification (partial nitrification, full nitrification) processes to investigate nitrous oxide (N2O) emission and its nitrification mechanisms. The presence of influent NH4+ was the driving force of N2O emission during nitrification. Compared with full nitrification, NO2− in partial nitrification more readily generated N2O by denitrification. Under the proportional gradient of NH4+-N:NO2−-N/NO3−-N, 30:0, 20:10, 10:20, and 0:30, total N2O emissions during partial nitrification were 2.81, 11.30, 65.20, and 11.67 times greater than the total N2O emissions during full nitrification. Full nitrification was more beneficial to N2O emission reduction. This provides a control strategy for N2O emission reduction in wastewater treatment processes under the background of reducing the production of greenhouse gases

The Effect of Salinity on N₂O Emissions during Domestic Wastewater Partial Nitrification Treatment in a Sequencing Batch Reactor

Previous studies have highlighted the salinization caused by the use of seawater to flush toilets and industrial wastewater entering the urban wastewater systems in coastal areas. Thus, in this study, the effect of salinity on N2O emissions during the partial nitrification process, as well as the emission mechanism, was investigated using a partial nitrification system of wastewater as the research object. The results showed that (1) the increase in salinity decreased the oxidation rate of NH4+ and the formation rate of NO2− during partial nitrification; (2) the increase in salinity increased the N2O emissions during NH4+ oxidation and NH2OH oxidation and decreased the formation rate of NO2−-N during hydroxylamine oxidation; (3) the total N2O emissions during hydroxylamine oxidation were less than those during ammonia nitrogen oxidation, and a greater amount of NO2− was reduced to N2 instead of N2O during hydroxylamine oxidation; and (4) a novel finding was that, during partial nitrification with the available organic matter, the N2O emissions via heterotrophic denitrification by heterotrophic bacteria should not be ignored, and the increase in salinity can increase the N2O emissions generated via heterotrophic denitrification. These results would provide a theoretical basis for reducing the N2O emissions in the wastewater treatment process

MOF-Derived CoNi Nanoalloy Particles Encapsulated in Nitrogen-Doped Carbon as Superdurable Bifunctional Oxygen Electrocatalyst

Carbon-encapsulated transition metal catalysts have caught the interest of researchers in the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) due to their distinctive architectures and highly tunable electronic structures. In this work, we synthesized N-doped carbon encapsulated with CoNi nanoalloy particles (CoNi@NC) as the electrocatalysts. The metal-organic skeleton ZIF-67 nanocubes were first synthesized, and then Ni2+ ions were inserted to generate CoNi-ZIF precursors by a simple ion-exchange route, which was followed by pyrolysis and with urea for the introduction of nitrogen (N) at a low temperature to synthesize CoNi@NC composites. The results reveal that ZIF-67 pyrolysis can dope more N atoms in the carbon skeleton and that the pyrolysis temperature influences the ORR and OER performances. The sample prepared by CoNi@NC pyrolysis at 650 °C has a high N content (9.70%) and a large specific surface area (167 m2 g−1), with a positive ORR onset potential (Eonset) of 0.89 V vs. RHE and half-wave potential (E1/2) of 0.81 V vs. RHE in 0.1 M KOH, and the overpotential of the OER measured in 1 M KOH was only 286 mV at 10 mA cm−2. The highly efficient bifunctional ORR/OER electrocatalysts synthesized by this method can offer some insights into the design and synthesis of complex metal-organic frameworks (MOFs) hybrid structures and their derivatives as functional materials in energy storage

Constructing 3D Skeleton on Commercial Copper Foil via Electrophoretic Deposition of Lithiophilic Building Blocks for Stable Lithium Metal Anodes

Lithium (Li) metal has been regarded as the "Holy Grail" of Li battery anodes thanks to its high theoretic specific capacity and low reduction potential, but uneven formation of Li dendrites and uncontrollable Li volume changes hinder the practical applications of Li metal anodes. A three-dimensional (3D) current collector is one of the promising strategies to address the above issues if it can be compatible with current industrialized process. Here, Au-decorated carbon nanotubes (Au@CNTs) are electrophoretically deposited on commercial Cu foil as a 3D lithiophilic skeleton to regulate Li deposition. The thickness of the as-prepared 3D skeleton can be accurately controlled by adjusting the deposition time. Benefitting from the reduced localized current density and improved Li affinity, the Au@CNTs-deposited Cu foil (Au@CNTs@Cu foil) achieves uniform Li nucleation and dendrite-free Li deposition. Compared with bare Cu foil and CNTs deposited Cu foil (CNTs@Cu foil), the Au@CNTs@Cu foil exhibits enhanced Coulombic efficiency and better cycling stability. In the full-cell configuration, the Au@CNTs@Cu foil with predeposited Li shows superior stability and rate performance. This work provides a facial strategy to directly construct a 3D skeleton on commercial Cu foils with lithiophilic building blocks for stable and practical Li metal anodes

Facile Synthesis of Co Nanoparticles Embedded in N-Doped Carbon Nanotubes/Graphitic Nanosheets as Bifunctional Electrocatalysts for Electrocatalytic Water Splitting

Developing robust and cost-effective electrocatalysts to boost hydrogen evolution reactions (HERs) and oxygen evolution reactions (OERs) is crucially important to electrocatalytic water splitting. Herein, bifunctional electrocatalysts, by coupling Co nanoparticles and N-doped carbon nanotubes/graphitic nanosheets (Co@NCNTs/NG), were successfully synthesized via facile high-temperature pyrolysis and evaluated for water splitting. The morphology and particle size of products were influenced by the precursor type of the cobalt source (cobalt oxide or cobalt nitrate). The pyrolysis product prepared using cobalt oxide as a cobalt source (Co@NCNTs/NG-1) exhibited the smaller particle size and higher specific surface area than that of the pyrolysis products prepared using cobalt nitrate as a cobalt source (Co@NCNTs/NG-2). Notably, Co@NCNTs/NG-1 displayed much lower potential −0.222 V vs. RHE for HER and 1.547 V vs. RHE for OER at the benchmark current density of 10 mA cm−2 than that of Co@NCNTs/NG-2, which indicates the higher bifunctional catalytic activities of Co@NCNTs/NG-1. The water-splitting device using Co@NCNTs/NG-1 as both an anode and cathode demonstrated a potential of 1.92 V to attain 10 mA cm−2 with outstanding stability for 100 h. This work provides a facile pyrolysis strategy to explore highly efficient and stable bifunctional electrocatalysts for water splitting