Predicting Changes of Rainfall Erosivity and Hillslope Erosion Risk Across Greater Sydney Region, Australia

Rainfall changes have significant effect on rainfall erosivity and hillslope erosion, but the magnitude of the impact is not well quantified because of the lack of high resolution rainfall data. Recently, the 2-km rainfall projections from regional climate models have become available for the Greater Sydney Region (GSR) at daily time step for the current (1990-2009) and future (2040-2059) periods. These climate projections allow predicting of rainfall erosivity changes and the associated hillslope erosion risk for climate change assessment and mitigation. In this study, we developed a daily rainfall erosivity model for GSR to predict rainfall erosivity from the current and future daily rainfall data. We produced time-series hillslope erosion risk maps using the revised universal soil loss equations on monthly and annual bases for the two contrasting periods. These products were spatially interpolated to a fine resolution (100 m) useful for climate impact assessment and erosion risk mitigation. The spatial variation was assessed based on the state plan regions and the temporal variation on monthly and annual bases. These processes have been implemented in a geographic information system so that they are automated, fast, and repeatable. Our prediction shows relatively good correlation with point-based Pluviograph calculation on rainfall erosivity and the previous study (both R2 and Ec \u3e 0.70). The results indicate that hillslope erosion risk is likely to increase 10-60% in the GSR within the next 50 years, and changes are greater in the coastal and the Blue Mountains, particularly in late summer (January and February). The methodology developed in this study is being extended to south-east Australia

Three results for tau-rigid modules

$\tau$-rigid modules are essential in the $\tau$-tilting theory introduced by Adachi, Iyama and Reiten. In this paper, we give equivalent conditions for Iwanaga-Gorenstein algebras with self-injective dimension at most one in terms of $\tau$-rigid modules. We show that every indecomposable module over iterated tilted algebras of Dynkin type is $\tau$-rigid. Finally, we give a $\tau$-tilting theorem on homological dimension which is an analog to that of classical tilting modules.Comment: 10 pages, to appear in Rocky Mountain Journal of Mathematic

EST analysis of gene expression in the tentacle of Cyanea capillata

AbstractJellyfish, Cyanea capillata, has an important position in head patterning and ion channel evolution, in addition to containing a rich source of toxins. In the present study, 2153 expressed sequence tags (ESTs) from the tentacle cDNA library of C. capillata were analyzed. The initial ESTs consisted of 198 clusters and 818 singletons, which revealed approximately 1016 unique genes in the data set. Among these sequences, we identified several genes related to head and foot patterning, voltage-dependent anion channel gene and genes related to biological activities of venom. Five kinds of proteinase inhibitor genes were found in jellyfish for the first time, and some of them were highly expressed with unknown functions

The Identification of Lymphocyte-Like Cells and Lymphoid-Related Genes in Amphioxus Indicates the Twilight for the Emergency of Adaptive Immune System

To seek evidence of a primitive adaptive immune system (AIS) before vertebrate, we examined whether lymphocytes or lymphocyte-like cells and the related molecules participating in the lymphocyte function existed in amphioxus. Anatomical analysis by electron microscopy revealed the presence of lymphocyte-like cells in gills, and these cells underwent morphological changes in response to microbial pathogens that are reminiscent of those of mammalian lymphocytes executing immune response to microbial challenge. In addition, a systematic comparative analysis of our cDNA database of amphioxus identified a large number of genes whose vertebrate counterparts are involved in lymphocyte function. Among these genes, several genes were found to be expressed in the vicinity of the lymphocyte-like cells by in situ hybridization and up-regulated after exposure to microbial pathogens. Our findings in the amphioxus indicate the twilight for the emergency of AIS before the invertebrate-vertebrate transition during evolution

Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning

We expose the danger of reward poisoning in offline multi-agent reinforcement learning (MARL), whereby an attacker can modify the reward vectors to different learners in an offline data set while incurring a poisoning cost. Based on the poisoned data set, all rational learners using some confidence-bound-based MARL algorithm will infer that a target policy - chosen by the attacker and not necessarily a solution concept originally - is the Markov perfect dominant strategy equilibrium for the underlying Markov Game, hence they will adopt this potentially damaging target policy in the future. We characterize the exact conditions under which the attacker can install a target policy. We further show how the attacker can formulate a linear program to minimize its poisoning cost. Our work shows the need for robust MARL against adversarial attacks

Exact Policy Recovery in Offline RL with Both Heavy-Tailed Rewards and Data Corruption

We study offline reinforcement learning (RL) with heavy-tailed reward distribution and data corruption: (i) Moving beyond subGaussian reward distribution, we allow the rewards to have infinite variances; (ii) We allow corruptions where an attacker can arbitrarily modify a small fraction of the rewards and transitions in the dataset. We first derive a sufficient optimality condition for generalized Pessimistic Value Iteration (PEVI), which allows various estimators with proper confidence bounds and can be applied to multiple learning settings. In order to handle the data corruption and heavy-tailed reward setting, we prove that the trimmed-mean estimation achieves the minimax optimal error rate for robust mean estimation under heavy-tailed distributions. In the PEVI algorithm, we plug in the trimmed mean estimation and the confidence bound to solve the robust offline RL problem. Standard analysis reveals that data corruption induces a bias term in the suboptimality gap, which gives the false impression that any data corruption prevents optimal policy learning. By using the optimality condition for the generalized PEVI, we show that as long as the bias term is less than the ``action gap'', the policy returned by PEVI achieves the optimal value given sufficient data

The Impact of Granting of Forest Certificates on Farmers’ Income—Intermediation Effects Based on Forestland Lease

This study examines the impact of granting forest certificates on farmer income. Linear regression and mediating effect models were used to analyze repeated survey data of 505 households in 50 villages in Jiangxi Province in 2017 and 2018. We examined the impacts of granting forest certificates on forestry income and the total income of rural households, taking into account forestland leases. We draw the following conclusions: first, granting forest certificates has a significant positive effect on total household income but not on forestry income. Second, farmers prefer forestland leasing in their behavior. Granting forest certificates can promote forestland lease out, but the effect on forestland lease in is not obvious. Third, granting forest certificates contributes to the increase in total household income through forestland lease out. Our analysis suggests that the government should increase the proportion of granted forest certificates and improve the policies related to the lease of forestland so as to realize an increase in farmer income

Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region

This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale