Event-based hyperspace analogue to language for query expansion

Bag-of-words approaches to information retrieval (IR) are effective but assume independence between words. The Hyperspace Analogue to Language (HAL) is a cognitively motivated and validated semantic space model that captures statistical dependencies between words by considering their co-occurrences in a surrounding window of text. HAL has been successfully applied to query expansion in IR, but has several limitations, including high processing cost and use of distributional statistics that do not exploit syntax. In this paper, we pursue two methods for incorporating syntactic-semantic information from textual ‘events’ into HAL. We build the HAL space directly from events to investigate whether processing costs can be reduced through more careful definition of word co-occurrence, and improve the quality of the pseudo-relevance feedback by applying event information as a constraint during HAL construction. Both methods significantly improve performance results in comparison with original HAL, and interpolation of HAL and relevance model expansion outperforms either method alone

A fractal-based model for soil water characteristic curve over entire range of water content

Soil water characteristic curve (SWCC) has been an important role in hydraulic engineering, civil engineer and petroleum engineering, etc. Most of SWCC models neglected the film flow in the dry state, so that they cannot accurately describe the SWCC over entire range of water content. In this work, an alternative fractal model is proposed to predict the SWCC over entire range of water content by combining Campbell and Shiozawa model and Tao model. The proposed model can well predict twelve sets of experimental data, and its parameters, including the fractal dimension, the saturated volumetric water content, the matric suction at oven-dry condition, and the air-entry value, accord with theoretical value. The results show that there is a strong linear relationship between volumetric water content and matrix suction in log-log scale for different fractal pore-size distribution of soils. In addition, good agreement is obtained between the experimental data and the model predictions in all of the cases.Cited as: Jin, T., Cai, X., Chen, Y., Jiang, S., Wei, W. A fractal-based model for soil water characteristic curve over entire range of water content. Capillarity, 2019, 2(4): 66-75, doi: 10.26804/capi.2019.04.0

Schisandra chinensis (Turcz.) Baill. essential oil exhibits antidepressant-like effects and against brain oxidative stress through Nrf2/HO-1 pathway activation.

The present study aimed to evaluate the antidepressant-like effect of essential oils from Schisandra chinensis (Turcz.) Baill. (SEO) and its possible mechanisms of SEO. The behavioral despair mouse model in vivo and H2O2-induced PC12 cells model in vitro were employed. And the potential effective components were identified by the spectrum-effect relationships analysis. SEO significantly decreased the immobility time in the forced swimming test and tail suspension test, which indicated a promising antidepressant-like effect of SEO in depressed mice. The decreased levels of SOD, GSH, and CAT, and increased levels of MDA were significantly reversed by SEO treatment, which showed good antioxidant activities both in vitro and in vivo. Besides, SEO significantly promoted the nuclear translocation of Nrf2 and the expression of HO-1 in depressed mice and H2O2-induced PC12 cells. The histopathological examination results showed a potential neuronal protective effect of SEO in the hippocampus and cortex. Furthermore, the upregulation of PI3K/AKT/GSK3β signaling was observed after SEO treatment in the H2O2-induced PC12 cells. Additionally, based on the spectrum-effect relationship analysis, 9 peaks were identified as positively correlated with the antioxidant activity of SEO. These results suggested that SEO promoted Nrf2/HO-1 pathway to improve the oxidative stress status and exerted the antidepressant-like effects

Towards Imperceptible Backdoor Attack in Self-supervised Learning

Self-supervised learning models are vulnerable to backdoor attacks. Existing backdoor attacks that are effective in self-supervised learning often involve noticeable triggers, like colored patches, which are vulnerable to human inspection. In this paper, we propose an imperceptible and effective backdoor attack against self-supervised models. We first find that existing imperceptible triggers designed for supervised learning are not as effective in compromising self-supervised models. We then identify this ineffectiveness is attributed to the overlap in distributions between the backdoor and augmented samples used in self-supervised learning. Building on this insight, we design an attack using optimized triggers that are disentangled to the augmented transformation in the self-supervised learning, while also remaining imperceptible to human vision. Experiments on five datasets and seven SSL algorithms demonstrate our attack is highly effective and stealthy. It also has strong resistance to existing backdoor defenses. Our code can be found at https://github.com/Zhang-Henry/IMPERATIVE

Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders

Self-supervised learning (SSL) is increasingly attractive for pre-training encoders without requiring labeled data. Downstream tasks built on top of those pre-trained encoders can achieve nearly state-of-the-art performance. The pre-trained encoders by SSL, however, are vulnerable to backdoor attacks as demonstrated by existing studies. Numerous backdoor mitigation techniques are designed for downstream task models. However, their effectiveness is impaired and limited when adapted to pre-trained encoders, due to the lack of label information when pre-training. To address backdoor attacks against pre-trained encoders, in this paper, we innovatively propose a mutual information guided backdoor mitigation technique, named MIMIC. MIMIC treats the potentially backdoored encoder as the teacher net and employs knowledge distillation to distill a clean student encoder from the teacher net. Different from existing knowledge distillation approaches, MIMIC initializes the student with random weights, inheriting no backdoors from teacher nets. Then MIMIC leverages mutual information between each layer and extracted features to locate where benign knowledge lies in the teacher net, with which distillation is deployed to clone clean features from teacher to student. We craft the distillation loss with two aspects, including clone loss and attention loss, aiming to mitigate backdoors and maintain encoder performance at the same time. Our evaluation conducted on two backdoor attacks in SSL demonstrates that MIMIC can significantly reduce the attack success rate by only utilizing <5% of clean data, surpassing seven state-of-the-art backdoor mitigation techniques

The Sixth Transmembrane Segment Is a Major Gating Component of the TMEM16A Calcium-Activated Chloride Channel.

Calcium-activated chloride channels (CaCCs) formed by TMEM16A or TMEM16B are broadly expressed in&nbsp;the nervous system, smooth muscles, exocrine glands, and other tissues. With two calcium-binding sites and a pore within each monomer, the dimeric CaCC exhibits voltage-dependent calcium sensitivity. Channel activity also depends on the identity of permeant anions. To understand how CaCC regulates neuronal signaling and how CaCC is, in turn, modulated by neuronal activity, we examined the molecular basis of CaCC gating. Here, we report&nbsp;that voltage modulation of TMEM16A-CaCC involves voltage-dependent occupancy of calcium- and anion-binding site(s) within the membrane electric field as well as a voltage-dependent conformational change intrinsic to the channel protein. These gating modalities all critically depend on the sixth transmembrane segment

Effects of simulated litter inputs on plant-microbe carbon pool trade-offs in degraded alpine meadows

IntroductionLitter, as a major carbon source in alpine meadow ecosystems, seriously affect the variation of plant-microbe carbon pools in alpine meadows. In order to study the response of plant-microbial biomass carbon pool trade-offs in degraded alpine meadow to litter inputs.MethodsWe investigated the effects of different levels of litter inputs on the carbon pools of alpine meadows plant aboveground communities, the carbon pools of the root, and the total carbon pools of the plant communities and the soil microbial biomass carbon pools, and clarified the variable factors that affect the balance of the plant-microbial biomass carbon pools and the process of influencing the trade-offs.Result(1) Litter inputs had a positive effect on plant carbon pools, and the aboveground community carbon pools, root carbon pools, total plant community carbon pools and soil microbial biomass carbon pools of alpine meadows were all maximized in the T3 treatment. (2) The trade-off analyses showed that the trade-off relationships of ungrazed alpine meadows TPCPMBCP in the following order under different levels of litter treatments: T1(0.0414) &gt; T2 (0.0269) &gt; T0 (0.0086) &gt; T3 (0.0012), the trade-off relationship of TPCP-MBCP in lightly grazed alpine meadows was in the order of T2 (0.0494) &gt; T3 (0.0140) &gt; T0 (0.0097) &gt; T1 (0.002), and the tradeoff relationship of TPCP-MBCP in moderately grazed alpine meadows was in the order of T3 (0.0383) &gt; T1 (0.0307) &gt; T2 (0.0196) &gt; T0 (0.0005). (3) Propensity analysis showed that the TPCP-MBCP trade-offs tended to favor MBCP under ungrazed, lightly grazed and moderately grazed meadows under the T1 treatment. (4) Structural equation modeling showed that RB and APC were positively correlation, RCP was significantly negatively correlated with the TPCPMBCP trade-off in lightly grazed grassland (P&lt;0.05), and MBC was significantly positively correlated with the TPCP-MBCP trade-off in moderately grazed grassland (P&lt;0.05).DiscussionThere was no uniform pattern in TPCP-MBCP trade-off and propensities in ungrazed, lightly grazed, and moderately grazed alpine meadows under different levels of litter inputs. This study can help to optimize the grazing management measures, predict the changes of carbon pools in alpine meadows and clarify the transfer and storage of carbon pools between plants and microorganisms, so as to provide a theoretical basis for the study of carbon pools in degraded alpine meadows