104 research outputs found
Monitoring and modeling of nitrogen conversions in membrane-aerated biofilm reactors: Effects of intermittent aeration
Nitrous oxide Production in Membrane-aerated Nitrifying Biofilms: Experimentation and Modelling
Learning Rich Features for Gait Recognition by Integrating Skeletons and Silhouettes
Gait recognition captures gait patterns from the walking sequence of an
individual for identification. Most existing gait recognition methods learn
features from silhouettes or skeletons for the robustness to clothing,
carrying, and other exterior factors. The combination of the two data
modalities, however, is not fully exploited. Previous multimodal gait
recognition methods mainly employ the skeleton to assist the local feature
extraction where the intrinsic discrimination of the skeleton data is ignored.
This paper proposes a simple yet effective Bimodal Fusion (BiFusion) network
which mines discriminative gait patterns in skeletons and integrates with
silhouette representations to learn rich features for identification.
Particularly, the inherent hierarchical semantics of body joints in a skeleton
is leveraged to design a novel Multi-Scale Gait Graph (MSGG) network for the
feature extraction of skeletons. Extensive experiments on CASIA-B and OUMVLP
demonstrate both the superiority of the proposed MSGG network in modeling
skeletons and the effectiveness of the bimodal fusion for gait recognition.
Under the most challenging condition of walking in different clothes on
CASIA-B, our method achieves the rank-1 accuracy of 92.1%.Comment: The paper is under consideration at Multimedia Tools and Application
A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model
Recently, the instruction-tuning of large language models is a crucial area
of research in the field of natural language processing. Due to resource and
cost limitations, several researchers have employed parameter-efficient tuning
techniques, such as LoRA, for instruction tuning, and have obtained encouraging
results In comparison to full-parameter fine-tuning, LoRA-based tuning
demonstrates salient benefits in terms of training costs. In this study, we
undertook experimental comparisons between full-parameter fine-tuning and
LoRA-based tuning methods, utilizing LLaMA as the base model. The experimental
results show that the selection of the foundational model, training dataset
scale, learnable parameter quantity, and model training cost are all important
factors. We hope that the experimental conclusions of this paper can provide
inspiration for training large language models, especially in the field of
Chinese, and help researchers find a better trade-off strategy between training
cost and model performance. To facilitate the reproduction of the paper's
results, the dataset, model and code will be released
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning
Machine Reading Comprehension with Unanswerable Questions is a difficult NLP
task, challenged by the questions which can not be answered from passages. It
is observed that subtle literal changes often make an answerable question
unanswerable, however, most MRC models fail to recognize such changes. To
address this problem, in this paper, we propose a span-based method of
Contrastive Learning (spanCL) which explicitly contrast answerable questions
with their answerable and unanswerable counterparts at the answer span level.
With spanCL, MRC models are forced to perceive crucial semantic changes from
slight literal differences. Experiments on SQuAD 2.0 dataset show that spanCL
can improve baselines significantly, yielding 0.86-2.14 absolute EM
improvements. Additional experiments also show that spanCL is an effective way
to utilize generated questions
Suppression of nitrite-oxidizing bacteria in intermittently aerated biofilm reactors: a model-based explanation
Linearization Point and Frequency Selection for Complex-Valued Electrical Capacitance Tomography
Suppression of nitrite-oxidizing bacteria in intermittently aerated biofilms: a model-based explanation
Feed types driven differentiation of microbial community and functionality in marine integrated multitrophic aquaculture system
Integrated multi trophic aquaculture (IMTA) improves the production of aquatic animals by promoting nutrient utilization through different tropical levels. Microorganisms play an important role in elements cycling, energy flow and farmed-species health. The aim of this study was to evaluate how feed types, fresh frozen fish diet (FFD) or formulated diet (FD), influence the microbial community diversity and functionality in both water and sediment in a marine IMTA system. Preferable water quality, higher animal yields and higher cost efficiency were achieved in the FD pond. Feed types changed the pond bacterial community distribution, especially in the rearing water. The FFD pond was dominated with Cyanobacteria in the water, which played an important role in nitrogen fixation through photosynthesis due to the high nitrogen input of the frozen fish diet. The high carbohydrate composition in the formulated diet triggered higher metabolic pathways related to carbon and lipid metabolism in the water of the FD pond. Sediment had significantly higher microbial diversity than the rearing water. In sediment, the dominating genus, Sulfurovum and Desulfobulbus, were found to be positively correlated by network analysis, which had similar functionality in sulfur transformation. The relatively higher rates of antibiotic biosynthesis in the FFD sediment might be related to the pathogenic bacteria introduced by the trash fish diet. The difference in microbial community composition and metabolic pathways may be associated with the different pathways for nutrient cycling and animal growth performance. The formulated diet was determined to be more ecologically and economically sustainable than the frozen fish diet for marine IMTA pond systems.</p
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