Context-Transformer: Tackling Object Confusion for Few-Shot Detection

Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches.Comment: Accepted by AAAI-202

Vanadium Pentoxide Nanofibers/Carbon Nanotubes Hybrid Film for High-Performance Aqueous Zinc-Ion Batteries

Aqueous zinc-ion batteries (ZIBs) with the characteristics of low production costs and good safety have been regarded as ideal candidates for large-scale energy storage applications. However, the nonconductive and non-redox active polymer used as the binder in the traditional preparation of electrodes hinders the exposure of active sites and limits the diffusion of ions, compromising the energy density of the electrode in ZIBs. Herein, we fabricated vanadium pentoxide nanofibers/carbon nanotubes (V2O5/CNTs) hybrid films as binder-free cathodes for ZIBs. High ionic conductivity and electronic conductivity were enabled in the V2O5/CNTs film due to the porous structure of the film and the introduction of carbon nanotubes with high electronic conductivity. As a result, the batteries based on the V2O5/CNTs film exhibited a higher capacity of 390 mAh g−1 at 1 A g−1, as compared to batteries based on V2O5 (263 mAh g−1). Even at 5 A g−1, the battery based on the V2O5/CNTs film maintained a capacity of 250 mAh g−1 after 2000 cycles with a capacity retention of 94%. In addition, the V2O5/CNTs film electrode also showed a high energy/power density (e.g., 67 kW kg−1/267 Wh kg−1). The capacitance response and rapid diffusion coefficient of Zn2+ (~10−8 cm−2 s−1) can explain the excellent rate capability of V2O5/CNTs. The vanadium pentoxide nanofibers/carbon nanotubes hybrid film as binder-free cathodes showed a high capability and a stable cyclability, demonstrating that it is highly promising for large-scale energy storage applications

Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank

Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existing solutions fall in the paradigm of probabilistic ranking principle (PRP), i.e., first score each item in the candidate set and then perform a sort operation to generate the top ranking list. However, these approaches neglect the contextual dependence among candidate items during individual scoring, and the sort operation is non-differentiable. To bypass the above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework directly generates the permutations of the candidate items without the need for individually scoring and sort operations; and is end-to-end differentiable. As a result, STARank can operate when only the ground-truth permutations are accessible without requiring access to the ground-truth relevance scores for items. For this purpose, STARank first reads the candidate items in the context of the user browsing history, whose representations are fed into a Plackett-Luce module to arrange the given items into a list. To effectively utilize the given ground-truth permutations for supervising STARank, we leverage the internal consistency property of Plackett-Luce models to derive a computationally efficient list-wise loss. Experimental comparisons against 9 the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3 top-N real-world recommendation datasets demonstrate the superiority of STARank in terms of conventional ranking metrics. Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases. STARank can consistently achieve better performance in terms of PBM and UBM simulation-based metrics.Comment: CIKM 202

Lending Interaction Wings to Recommender Systems with Conversational Agents

Recommender systems trained on offline historical user behaviors are embracing conversational techniques to online query user preference. Unlike prior conversational recommendation approaches that systemically combine conversational and recommender parts through a reinforcement learning framework, we propose CORE, a new offline-training and online-checking paradigm that bridges a COnversational agent and REcommender systems via a unified uncertainty minimization framework. It can benefit any recommendation platform in a plug-and-play style. Here, CORE treats a recommender system as an offline relevance score estimator to produce an estimated relevance score for each item; while a conversational agent is regarded as an online relevance score checker to check these estimated scores in each session. We define uncertainty as the summation of unchecked relevance scores. In this regard, the conversational agent acts to minimize uncertainty via querying either attributes or items. Based on the uncertainty minimization framework, we derive the expected certainty gain of querying each attribute and item, and develop a novel online decision tree algorithm to decide what to query at each turn. Experimental results on 8 industrial datasets show that CORE could be seamlessly employed on 9 popular recommendation approaches. We further demonstrate that our conversational agent could communicate as a human if empowered by a pre-trained large language model.Comment: NeurIPS 202

Demographic strategies of a dominant tree species in response to logging in a degraded subtropical forest in Southeast China

International audienceAbstractKey messageThe demography of pioneer tree species (Pinus massonianaLamb.) is significantly affected by logging in Southeast China. Logging negatively affects the population growth rate ofP. massoniana, which facilitates the growth of individual trees but has no effect on reproduction probability. The survival and growth of seedlings contribute the most to population growth.ContextSubtropical forest degradation caused by unreasonable disturbances is closely related to anthropogenic activities in Southeast China, and the frequent small-scale logging activity by local people was the dominated disturbance regime in forests in this region over the past several decades.AimsThe objective of this study is to evaluate the demographic consequences of logging on Pinus massoniana, a pioneer tree species, at individual level (survival, growth, and fecundity) and population level (the population growth rate and size distribution) over short-term period.MethodsThe size of tree individuals was combined with vital rates using various modeling approaches based on demographic data from three annual censuses. The integral projection model (IPM) was constructed and used to conduct comparative demographic analyses.ResultsLogging negatively affected the population growth rate: from a slight expansion before logging to a moderate decline after logging. This study found a significant reduction in seedling recruitment after logging, and plant growth and mortality were slightly enhanced. The survival of seedlings greatly contributes to population growth rate compared to other life stages for both periods (before and after logging) while its relative importance decreases after logging. Seedling growth is also important to population growth, and its relative importance increased after logging. Shrinkage and fecundity have a minimal contribution effect on the population growth rate.ConclusionGrowing plants in a nursery with a similar demography to P. massoniana could be beneficial for pioneer species regeneration in that this will improve the survival rate and growth of small individuals after logging

Case Report: Primary hepatic neuroendocrine tumor: two cases report with literature review

Background & AimsPrimary hepatic neuroendocrine tumors (PHNETs) are rare malignant liver tumors that present diagnostic challenges owing to their rarity and absence of specific clinical features. This study aimed to investigate the characteristics of this rare liver tumor to enhance our understanding of the disease, improve diagnostic accuracy, and explore standardized diagnostic and treatment approaches.Case descriptionDuring physical examination, two elderly women, aged 64 and 74 years, were found to have liver masses. 18F-FDG Positron Emission Tomography-Computed Tomography (18F-FDG PET-CT) and Ga68-DOTATATE PET-CT scans of both individuals revealed multiple liver masses that were initially suspected to be hepatic neuroendocrine tumors. Subsequent puncture pathology confirmed the diagnosis of neuroendocrine tumors. Furthermore, in Case 1, the tumor was also detected by 18F-FDG PET-CT in the lung, suggesting a metastatic tumor, in conjunction with liver immunohistochemistry and imaging findings. Laboratory tests revealed no significant abnormalities in liver function or autoimmune liver disease indicators, and there was no evidence of viral hepatitis infection. However, partial hepatectomy was not indicated for cases with distant metastasis or multiple space-occupying lesions. Individualized treatment approaches have been developed for such situations. A large portion of the tumor underwent Transarterial Embolization (TAE), and targeted combination chemotherapy or endocrine therapy was administered based on the pathological results. During regular follow-ups a 13 and 12 months, the tumor remained stable. The patients’ quality of life was good, and their psychological well-being was healthy. They led active lifestyles, demonstrated a thorough understanding of their disease and its progression, and actively cooperated during the follow-up process.ConclusionOur findings suggest that a combination of serological, radiological, and immunohistochemical examinations can aid in the diagnosis of PHNET. In addition, we determined that TAE combined with drug therapy could be an effective method for controlling PHNET progression. Regular postoperative follow-ups are important for monitoring the prognosis and tumor progression status of patients with PHNET

Revealing historical observations and future projections of precipitation over Northwest China based on dynamic downscaled CMIP6 simulations

The warming climate driven by global change has great potential in altering regional and global hydrologic cycles, thus leading to considerable changes in spatial variability and temporal pattern of precipitation. Northwest China (NW) has witnessed a significant wetting trend over the past decades, while the persistence of this wetting trend and potential changes in precipitation under future climate impacts remains elusive. In this study, long-term meteorological observations were used to probe historical variations of precipitation from 1951 to 2020, and the WRF model was employed as a regional climate model to examine future precipitation patterns over NW. Two 9-year downscaled WRF simulations were conducted comprising of historical (WRF-HIST; 2012–2020) and future climate change scenarios (WRF-SSP585; 2047–2055) using bias-corrected global climate model outputs from Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with ground observations, the WRF model exhibited strong capability in capturing the spatial pattern and temporal variations of precipitation across the NW. Intense precipitation was mainly found in stations located at northern NW and southeastern NW. Summertime precipitation substantially contributed to annual precipitation over the study region. Future precipitation projections suggest significant decreases of precipitation across the southern and eastern NW, with a stronger reduction magnitude in summer. Further, extreme precipitation events were projected to decrease in spring and summer, suggesting that the NW may become drier and the wetting trend may shift to another pattern in the 2050s under the SSP585 climate scenario. Overall, this study reveals historical and future potential changes in precipitation over NW through a high-resolution, dynamically downscaled dataset from WRF modeling, which in turn will help inform regional mitigation and adaption on potential impacts of future climate change on NW

Estimation of biogenic VOC emissions and their corresponding impact on ozone and secondary organic aerosol formation in China

Biogenic volatile organic compounds (BVOC) play an important role in global environmental chemistry and climate. In the present work, biogenic emissions from China in 2017 were estimated based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN). The effects of BVOC emissions on ozone and secondary organic aerosol (SOA) formation were investigated using the WRF-CMAQ modeling system. Three parallel scenarios were developed to assess the impact of BVOC emissions on China's ozone and SOA formation in July 2017. Biogenic emissions were estimated at 23.54 Tg/yr, with a peak in the summer and decreasing from southern to northern China. The high BVOC emissions across eastern and southwestern China increased the surface ozone levels, particularly in the BTH (Beijing-Tianjin-Hebei), SCB (Sichuan Basin), YRD (Yangtze River Delta) and central PRD (Pearl River Delta) regions, with increases of up to 47 μg m−3 due to the sensitivity of VOC-limited urban areas. In summer, most SOA concentrations formed over China are from biogenic sources (national average of 70%). And SOA concentrations in YRD and SCB regions are generally higher than other regions. Excluding anthropogenic emissions while keeping biogenic emissions unchanged results that SOA concentrations reduce by 60% over China, which indicates that anthropogenic emissions can interact with biogenic emissions then facilitate biogenic SOA formation. It is suggested that controlling anthropogenic emissions would result in reduction of both anthropogenic and biogenic SOA.Peer reviewe

Decreasing Coalbed Methane Formation Damage Using Microfoamed Drilling Fluid Stabilized by Silica Nanoparticles

Coalbed methane (CBM) reservoirs in China are featured in remarkable nanosized pores below 200 nm, acknowledged natural cleats, and tectonic fractures. This paper discussed the possibility that a clay free microfoamed drilling fluid could be stabilized by silica nanoparticles (CFMDF-NP) so as to avoid formation damage of CBM drilling. In accordance with the experimental results of foaming capacity and foam stability test, basic drilling fluid performance appraisal, micromorphology observation, swelling test, and gas permeability test, the mechanism of the CFMDF-NP was discussed in this paper. The results indicated that, with 10–20 nm nano-SiO2, the foaming volume of traditional foamed drilling fluid could be improved by up to 50% and an increased half-life period by up to 200%. Chemically treated nano-SiO2 dispersions functioned as a foam stabilizer and a foaming agent as well. The CFMDF-NP had controllable density (0.7~1 g/cm3) and excellent rheological and sealing properties, which could satisfy the drilling requirements of the low pressure coal seams. With 5–8 mm slicing on the contaminated side of coal cores, the contaminated zone could be removed and the recovery rate of gas permeability could reach up to 70%. The CFMDF-NP laid good technical foundation to decrease formation damage of CBM reservoir