Domain Generalization via Balancing Training Difficulty and Model Capability

Domain generalization (DG) aims to learn domain-generalizable models from one or multiple source domains that can perform well in unseen target domains. Despite its recent progress, most existing work suffers from the misalignment between the difficulty level of training samples and the capability of contemporarily trained models, leading to over-fitting or under-fitting in the trained generalization model. We design MoDify, a Momentum Difficulty framework that tackles the misalignment by balancing the seesaw between the model's capability and the samples' difficulties along the training process. MoDify consists of two novel designs that collaborate to fight against the misalignment while learning domain-generalizable models. The first is MoDify-based Data Augmentation which exploits an RGB Shuffle technique to generate difficulty-aware training samples on the fly. The second is MoDify-based Network Optimization which dynamically schedules the training samples for balanced and smooth learning with appropriate difficulty. Without bells and whistles, a simple implementation of MoDify achieves superior performance across multiple benchmarks. In addition, MoDify can complement existing methods as a plug-in, and it is generic and can work for different visual recognition tasks.Comment: 11 pages, 6 figures, Accepted by ICCV 202

Design of Mobile Communication Indoor Distribution System

With the development of economy, the improvement of people's living standard has been increasing and theconstruction of buildings is becoming more demanding. These buildings are large in scale and of good quality andhave a strong shielding effect on mobile phone signals. In the middle of the large-scale buildings, undergroundshopping malls, underground parking and other environments, the mobile communication signal is weak hence thephone cannot be used normally and forming a mobile communication blind area and shadow area. In the middlefl oor, due to the surrounding diff erent base station signal overlap, the ping-pong eff ect, frequent switching of mobilephones and even dropped calls are seriously aff ecting the normal use of mobile phones. In the building's higher fl oors,due to the height of the base station antenna, it has abnormal coverage and there is also mobile communication blindspot. In addition, in some buildings, although the phone can answer normal call but the user density, base stationchannel congestion and mobile phone line is diffi cult. In particular, the network coverage, capacity and quality ofmobile communication are the key factors for operators to gain competitive advantage. Network coverage, networkcapacity and network quality fundamentally refl ects the mobile network service level and is the theme of all mobilenetwork optimization work. The indoor coverage system is produced under this background. According to the relevantstatistics in some areas of indoor traffi c in the total traffi c accounted for a higher proportion. Therefore, strengtheningthe indoor coverage is of great signifi cance to improve the quality of mobile communication

Long Short-Term Sample Distillation

In the past decade, there has been substantial progress at training increasingly deep neural networks. Recent advances within the teacher--student training paradigm have established that information about past training updates show promise as a source of guidance during subsequent training steps. Based on this notion, in this paper, we propose Long Short-Term Sample Distillation, a novel training policy that simultaneously leverages multiple phases of the previous training process to guide the later training updates to a neural network, while efficiently proceeding in just one single generation pass. With Long Short-Term Sample Distillation, the supervision signal for each sample is decomposed into two parts: a long-term signal and a short-term one. The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates. Moreover, the teachers for each sample are unique, such that, overall, the model learns from a very diverse set of teachers. Comprehensive experimental results across a range of vision and NLP tasks demonstrate the effectiveness of this new training method.Comment: published as a conference paper at AAAI 202

Knowledge Graph Link Prediction Fusing Description and Structural Features

Knowledge graph generally has the problem of incomplete knowledge, which makes link prediction an important research content of knowledge graph. Existing models only focus on the embedding representation of triples. On the one hand, in terms of model input, only the embedding representation of entities and relations is randomly initialized, and the description information of entities and relations is not incorporated, which will lack semantic information; on the other hand, in decoding, the influence of the structural features of the triplet itself on the link prediction results is ignored. Aiming at the above problems, this paper proposes a knowledge graph link prediction model BFGAT (graph attention network link prediction based on fusion of description information and structural features) that integrates description information and structural features. The BFGAT model uses the BERT pretraining model to encode the description information of entities and relations, and integrates the description information into the embedding representation of entities and relations to solve the problem of missing semantic information. In the coding process, graph attention mechanism is used to aggregate the information of adjacent nodes to solve the problem that the target node can obtain more information. The embedding representation of triples is spliced into a matrix in the decoding process, using a method based on CNN convolution pooling to solve the problem of triple structural features. The model is subjected to detailed experiments on the public datasets FB15k-237 and WN18RR, and the experiments show that the BFGAT model can effectively improve the effect of knowledge graph link prediction

Regulation of NDVI and ET negative responses to increased atmospheric vapor pressure deficit by water availability in global drylands

Atmospheric vapor pressure deficit (VPD, indicative of atmospheric water conditions) has been identified as a major driver of global vegetation dynamics. Drylands, including deserts, temperate grasslands, savannas, and dry forests, are more sensitive to water conditions and affect carbon, nitrogen, and water cycles. However, our knowledge is limited on the way increasing VPD affects vegetation growth and evapotranspiration (ET) in global drylands. In this study, we used long-term satellite datasets combined with multiple statistical analyses to examine the relationship between the satellite-derived normalized difference vegetation index (NDVI), a proxy for vegetation growth, and ET to VPD across global drylands. We found that significant decreases in NDVI and ET predominantly influenced the NDVI (RVPD − NDVI) and ET (RVPD − ET) responses to VPD in both the savannas and dry forests of South American, African, and Australian savannas and dry forests, as well as in temperate grasslands (e.g., Eurasian steppes and American prairies). Notably, more than 60% of global drylands exhibited significantly negative RVPD − NDVI and RVPD − ET values. In contrast, the percentage of significantly negative RVPD − NDVI and RVPD − ET decreased to <10% in cold drylands (>60° N). In predominantly warm drylands (60° N~60° S), negative VPD effects were significantly and positively regulated by soil water availability, as determined by multiple linear regression models. However, these significant regulatory effects were not observed in cold drylands. Moving-window analyses further revealed that temporal changes in RVPD − NDVI and RVPD − ET were positively correlated with changes in the Standardized Precipitation Evapotranspiration Index (SPEI). In warm drylands, areas with increasing RVPD − NDVI and RVPD − ET over time showed an increasing trend in the SPEI, whereas areas with a decreasing SPEI showed a negative trend in RVPD − NDVI and RVPD − ET values over time. Given the increasing atmospheric dryness due to climate change, this study highlighted the importance of re-evaluating the representation of the role of water availability in driving the response of the carbon-water cycle to increased VPD across global drylands

Measurement and correlation of the solubility of telmisartan (form A) in nine different solvents from 277.85 to 338.35 K

The solubility of telmisartan (form A) in nine organic solvents (chloroform, dichloromethane, ethanol, toluene, benzene, 2-propanol, ethyl acetate, methanol and acetone) was determined by a laser monitoring technique at temperatures from 277.85 to 338.35 K. The solubility of telmisartan (form A) in all of the nine solvents increased with temperature as did the rates at which the solubility increased except in chloroform and dichloromethane. The mole fraction solubility in chloroform is higher than that in dichloromethane, which are both one order of magnitude higher than those in the other seven solvents at the experimental temperatures. The solubility data were correlated with the modified Apelblat equation and λh equations. The results show that the λh equation is in better agreement with the experimental data than the Apelblat equation. The relative root mean square deviations (σ) of the λh equation are in the range from 0.004 to 0.45 %. The dissolution enthalpies, entropies and Gibbs energies of telmisartan in these solvents were estimated by the Van’t Hoff equation and the Gibbs equation. The melting point and the fusion enthalpy of telmisartan were determined by differential scanning calorimetry