DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective

Several prior studies have suggested that word frequency biases can cause the Bert model to learn indistinguishable sentence embeddings. Contrastive learning schemes such as SimCSE and ConSERT have already been adopted successfully in unsupervised sentence embedding to improve the quality of embeddings by reducing this bias. However, these methods still introduce new biases such as sentence length bias and false negative sample bias, that hinders model's ability to learn more fine-grained semantics. In this paper, we reexamine the challenges of contrastive sentence embedding learning from a debiasing perspective and argue that effectively eliminating the influence of various biases is crucial for learning high-quality sentence embeddings. We think all those biases are introduced by simple rules for constructing training data in contrastive learning and the key for contrastive learning sentence embedding is to mimic the distribution of training data in supervised machine learning in unsupervised way. We propose a novel contrastive framework for sentence embedding, termed DebCSE, which can eliminate the impact of these biases by an inverse propensity weighted sampling method to select high-quality positive and negative pairs according to both the surface and semantic similarity between sentences. Extensive experiments on semantic textual similarity (STS) benchmarks reveal that DebCSE significantly outperforms the latest state-of-the-art models with an average Spearman's correlation coefficient of 80.33% on BERTbase

AutoAMG(θ\theta): An Auto-tuned AMG Method Based on Deep Learning for Strong Threshold

Algebraic Multigrid (AMG) is one of the most used iterative algorithms for solving large sparse linear equations $Ax=b$. In AMG, the coarse grid is a key component that affects the efficiency of the algorithm, the construction of which relies on the strong threshold parameter $\theta$. This parameter is generally chosen empirically, with a default value in many current AMG solvers of 0.25 for 2D problems and 0.5 for 3D problems. However, for many practical problems, the quality of the coarse grid and the efficiency of the AMG algorithm are sensitive to $\theta$; the default value is rarely optimal, and sometimes is far from it. Therefore, how to choose a better $\theta$ is an important question. In this paper, we propose a deep learning based auto-tuning method, AutoAMG($\theta$) for multiscale sparse linear equations, which are widely used in practical problems. The method uses Graph Neural Networks (GNNs) to extract matrix features, and a Multilayer Perceptron (MLP) to build the mapping between matrix features and the optimal $\theta$, which can adaptively output $\theta$ values for different matrices. Numerical experiments show that AutoAMG($\theta$) can achieve significant speedup compared to the default $\theta$ value

Neuropathic Injury-Induced Plasticity of GABAergic System in Peripheral Sensory Ganglia

GABA is a major inhibitory neurotransmitter in the mammalian central nervous system (CNS). Inhibitory GABAA channel circuits in the dorsal spinal cord are the gatekeepers of the nociceptive input from the periphery to the CNS. Weakening of these spinal inhibitory mechanisms is a hallmark of chronic pain. Yet, recent studies have suggested the existence of an earlier GABAergic “gate” within the peripheral sensory ganglia. In this study, we performed systematic investigation of plastic changes of the GABA-related proteins in the dorsal root ganglion (DRG) in the process of neuropathic pain development. We found that chronic constriction injury (CCI) induced general downregulation of most GABAA channel subunits and the GABA-producing enzyme, glutamate decarboxylase, consistent with the weakening of the GABAergic inhibition at the periphery. Strikingly, the α5 GABAA subunit was consistently upregulated. Knock-down of the α5 subunit in vivo moderately alleviated neuropathic hyperalgesia. Our findings suggest that while the development of neuropathic pain is generally accompanied by weakening of the peripheral GABAergic system, the α5 GABAA subunit may have a unique pro-algesic role and, hence, might represent a new therapeutic target

BIM to GIS-based building model conversion in support of urban energy simulation

In the context of growing population and increasing greenhouse gas emissions, urban energy planning is becoming a topic of high concern. With the rapid pace of urban construction, it is claimed that 40% of America’s total energy consumption is accounted by the building sector. Energy modelling and simulation are believed to be effective in supporting urban energy planning. Today, the growing availability of 3D city models has facilitated energy simulations at various scales. While Building Information Model (BIM) provides users with the possibility to explore the energy consumption alternatives of a single building, GIS-based city-wide building models offer the chance to simulate urban energy demand at the city scale. Considering that most of the existing GIS-based building energy simulations are using models with a lower Level of Detail (LoD), the aim of this study is to extract geometrically detailed and semantically correct information from BIM models to construct GIS models with a higher LoD in order to support more accurate Building energy simulations. Semantics of BIM models from different sources are matched with that of GIS-based building models, and geometry of BIM models are converted to conform to GIS-based building model standards. Specific concern is given to the extraction of openings that are absent from lower LoD CityGML. After conversion, the result is used to perform a simple energy assessment to see how much influence a higher LoD CityGML model has on the energy simulation of a building.Urban energy planning is becoming a hot topic in the context of growing population and increasing greenhouse gas emissions. With the rapid pace of urban construction, it is claimed that 40% of America’s total energy consumption is accounted by the building sector. Energy modelling and simulation are believed to be effective in supporting urban energy planning. Today, the growing availability of 3D city models has facilitated energy simulations at various scales. While Building Information Model (BIM) provides users with detailed information of a single building and allows the evaluation energy consumption in different scenarios, GIS-based city-wide building models offer the chance to simulate urban energy demand at the city scale. For the lack of detail in most GIS based 3D models, the energy simulation from these models are generally biased. A general workflow has been developed in the thesis to convert BIM to a detailed GIS-based city model. After conversion, the result is used to perform a simple energy assessment to see how much influence a more detailed GIS-based model has on the energy simulation of a building. The result shows that taking extra information from BIM to improve the result of energy simulation of a GIS-based model is feasible. And the magnitude of the improvement is worth considerable concern

Application of Alkyl Amidopropyl Betaine in Fire Fighting Foam Extinguishing Agent

In order to improve the fire extinguishing performance of foam fire extinguishing agents in nonpolar liquid fires, the application of alkyl amidopropyl betaine with different chain lengths in aqueous film-forming foam fire extinguishing agents was studied. The relationship between the structure of alkylamidopropyl betaine and surface tension, foaming property and foam stability was analyzed. On this basis, different foam fire extinguishing agent formulations were formed, and then the surface tension, foam performance and fire extinguishing performance of each formulation were tested. The results show that the alkyl chain of alkylamidopropyl betaine is directly proportional to the foaming property. The shorter the alkyl chain, the less oily the foam is and the better the foam’s anti-burning performance. The combination of alkylamidopropyl betaine with different chain lengths is conducive to comprehensive product foam performance and oleophobic performance to achieve the best fire extinguishing effect

The Role of Spatial Frequency Information in Face Classification by Race

It was found that face classification by race is more quickly for other-race than own-race faces (other-race classification advantage, ORCA). Controlling the spatial frequencies of face images, the current study investigated the perceptual processing differences based on spatial frequencies between own-race and other-race faces that might account for the ORCA. Regardless of the races of the observers, the own-race faces were classified faster and more accurately for broad-band faces than for both lower and higher spatial frequency (SF) faces, whereas, although other-race faces were classified less accurately for higher SF than for either broad-band or lower SF faces, there was no difference between broad-band and lower SF conditions of other-race faces. Although it was not evident for higher SF condition, the ORCA was more evident for lower SF than that for broad-band faces. The present data indicate that global/configural information is needed for subordinate race categorization of faces and that an important source of ORCA is application of global/configural computations by default while categorizing an own-race face but not while categorizing an other-race face

Analysis of the Mixed Control System of Damper and TMD

Conference Name:International Conference on Advanced Engineering Materials and Technology (AEMT2011). Conference Address: Sanya, PEOPLES R CHINA. Time:JUL 29-31, 2011.Many new control techniques and energy dissipation systems which can decrease the response of wind vibration and earthquake. But there is less research on the mixed control system of Damper and TMD. In order to improve the shortcomings of Damper system and TMD system, combining their respective advantages reasonably, the mixed control system of Damper and TMD were analyzed in this paper. A 20-storey Benchmark model was used to compare the effects of various control systems under 2-directional earthquake. Taking the displacement reducing coefficient (DRF) as the objective function, Damper system, TMD system, and the mixed control system of Damper and TMD are optimally designed based on Genetic Algorithm (GA). Numerical results show that the mixed control system of dampers and TMD proposed in the paper can work in coordination and complement each other to achieve better control effect