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Science for the Poor: How One Woman Challenged Researchers, Ranchers, and Loggers in Amazonia

Patricia Shanley
2006 Ecology and Society  
not lose their forests for meager sums.  ...  Having defended her family homestead near the city of Cameta against loggers in the late 1980s, Glória Gaia became interested in strengthening the information base of other villagers so that they would  ...  Through Glória's interest, we learned that the research we had conducted for years along the Capim River resonated in a new setting.  ... 
doi:10.5751/es-01928-110228 fatcat:xfxun7bpwjgfzbmxmjur72hzkq

Tianshou: a Highly Modularized Deep Reinforcement Learning Library [article]

Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Hang Su, Jun Zhu
2021 arXiv   pre-print
We present Tianshou, a highly modularized python library for deep reinforcement learning (DRL) that uses PyTorch as its backend.  ...  Acknowledgments We thank Haosheng Zou for his pioneering work of TensorFlow-based Tianshou before version 0.1.1.  ...  We thank Peng Zhong, Qiang He, Yi Su, Qing Xiao, Qifan Li, Yan Li, and others for their extraordinary contributions to Tianshou.  ... 
arXiv:2107.14171v2 fatcat:wutwzwu4dnfptaf2dgqsg2fto4

CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning [article]

Yi Su and Lequn Wang and Michele Santacatterina and Thorsten Joachims
2019 arXiv   pre-print
Both offline A/B-testing and off-policy learning require a counterfactual estimator that evaluates how some new policy would have performed, if it had been used instead of the logging policy.  ...  In addition, it is sub-differentiable such that it can be used for learning, unlike the SWITCH estimator.  ...  Off-policy evaluation refers to the problem of using S for estimating the expected reward (or loss) R of a new policy π R(π) = E x∼P (x) Eȳ ∼π(ȳ|x) E r∼D(r|x,ȳ) [r]. (2) Off-policy learning uses S for  ... 
arXiv:1811.02672v4 fatcat:3yczwbrmfzgoxlje43nxaqvuny

Master Graduation Thesis: A Lightweight and Distributed Container-based Framework [article]

Qifan Deng, Rajkumar Buyya
2021 arXiv   pre-print
It provides a mechanism for scheduling heterogeneous IoT applications and implements several scheduling policies.  ...  Experimental results show FogBus2's scheduling policy improves the response time of IoT applications by 53\% compared to other policies.  ...  The framework needs to obtain the ability to integrate multiple scheduling policies.  ... 
arXiv:2108.03562v1 fatcat:tuwsu7ycrrbn7ei4hgtcv4aauq

Vizarel: A System to Help Better Understand RL Agents [article]

Shuby Deshpande, Jeff Schneider
2020 arXiv   pre-print
Visualization tools for supervised learning have allowed users to interpret, introspect, and gain intuition for the successes and failures of their models.  ...  While reinforcement learning practitioners ask many of the same questions, existing tools are not applicable to the RL setting.  ...  Acknowledgements We thank Benjamin Eysenbach for valuable discussions and feedback over the initial drafts of this work. This work is supported by the CMU Argo AI Center.  ... 
arXiv:2007.05577v1 fatcat:ucegzcuoh5aflofdatdaerngbi

Continuous Tamper-Proof Logging Using TPM 2.0 [chapter]

Arunesh Sinha, Limin Jia, Paul England, Jacob R. Lorch
2014 Lecture Notes in Computer Science  
A fundamental requirement for reliable auditing is the integrity of the log entries.  ...  This paper presents an infrastructure for secure logging that is capable of detecting the tampering of logs by powerful adversaries residing on the device where logs are generated.  ...  One application is enforcing multiple policies on the same system modularly.  ... 
doi:10.1007/978-3-319-08593-7_2 fatcat:7mhg6tgjnrbkrbmo6wrsdgqjcq

Revisiting offline evaluation for implicit-feedback recommender systems

Olivier Jeunen
2019 Proceedings of the 13th ACM Conference on Recommender Systems - RecSys '19  
More specifically, we believe there is much room for improvement in temporal evaluation, off-policy evaluation, and moving beyond using just clicks to evaluate performance.  ...  Alternatively, in an online evaluation setting, multiple versions of the system are deployed and various metrics for those systems are recorded.  ...  Impression-data for Presentation Bias Impression-data further has its use in the off-policy evaluation setting described in Section 3.  ... 
doi:10.1145/3298689.3347069 dblp:conf/recsys/Jeunen19 fatcat:tlm64i2mbza6hequt4xyrhl4zu

Promoting Distributed Accountability in the Cloud

Smitha Sundareswaran, Anna Squicciarini, Dan Lin, Shuo Huang
2011 2011 IEEE 4th International Conference on Cloud Computing  
In particular, we leverage the programmable capability of Java JAR files to enclose our logging mechanism together with users' data and policies.  ...  In case there exist multiple loggers for the same set of data items, the log harmonizer will merge log records from them before sending back to the data owner.  ...  It has two major components: logger and log harmonizer. The logger is strongly coupled with user's data (either single or multiple data items).  ... 
doi:10.1109/cloud.2011.57 dblp:conf/IEEEcloud/SundareswaranSLH11 fatcat:sbrvo5jfrzamzdf26blyx5xlt4

Ensuring Distributed Accountability for Data Sharing in the Cloud

Smitha Sundareswaran, Anna Squicciarini, Dan Lin
2012 IEEE Transactions on Dependable and Secure Computing  
In particular, we propose an object-centered approach that enables enclosing our logging mechanism together with users' data and policies.  ...  The logger is strongly coupled with user's data (either single or multiple data items).  ...  In case there exist multiple loggers for the same set of data items, the log harmonizer will merge log records from them before sending back to the data owner.  ... 
doi:10.1109/tdsc.2012.26 fatcat:njrwvzm575fxff6x7ybcbhtzpq

Beyond Perception: The Experience of Risk and Stigma in Community Contexts

Robin S Gregory, Theresa A Satterfield
2002 Risk Analysis  
from trade-off analysis and narrative approaches.  ...  Concerns about stigmatization are an important influence on the development of risk management and communication policies for a wide range of technologies and products such as those associated with hazardous  ...  Loggers have come to be viewed in many quarters as agents of destruction, interested in their own well-being and profit and willing to trade off long-term losses to the ecosystem in return for short-term  ... 
doi:10.1111/0272-4332.00017 pmid:12022681 fatcat:kluklcmg6vgbhlr4sxnq73yfxy

Context-for-wireless

Ahmad Rahmati, Lin Zhong
2007 Proceedings of the 5th international conference on Mobile systems, applications and services - MobiSys '07  
We consequently devise algorithms that can effectively learn from context information and estimate the probability distribution of Wi-Fi network conditions.  ...  We show that an ideal selection policy can more than double the battery lifetime of a commercial mobile phone, and the improvement varies with data transfer patterns and Wi-Fi availability.  ...  is powered off.  ... 
doi:10.1145/1247660.1247681 dblp:conf/mobisys/RahmatiZ07 fatcat:56udffyicjddtmsbdgu6cq3dni

Resource Management in Edge and Fog Computing using FogBus2 Framework [article]

Mohammad Goudarzi, Qifan Deng, Rajkumar Buyya
2021 arXiv   pre-print
Besides, we provide a step-by-step guideline to set up an integrated computing environment, containing multiple cloud service providers (Hybrid-cloud) and edge devices, which is a prerequisite for any  ...  Finally, we demonstrate how to implement and integrate several new IoT applications and custom scheduling and scalability policies with the FogBus2 framework.  ...  Accordingly, the FogBus2 framework offers a configurable cooling-off period for the Task Executor components, during which containers keep waiting for the next incoming request of the same type before  ... 
arXiv:2108.00591v1 fatcat:oyabeeh3hzgt7n3wxlsohx7g2y

Triply Robust Off-Policy Evaluation [article]

Anqi Liu, Hao Liu, Anima Anandkumar, Yisong Yue
2019 arXiv   pre-print
We propose a robust regression approach to off-policy evaluation (OPE) for contextual bandits. We frame OPE as a covariate-shift problem and leverage modern robust regression tools.  ...  Our robust regression method is compatible with deep learning, and is thus applicable to complex OPE settings that require powerful function approximators.  ...  In International Conference on Machine Learning (ICML). [He et al., 2019] He, L., Xia, L., Zeng, W., Ma, Z.-M., Zhao, Y., and Yin, D. (2019). Off-policy learning for multiple loggers.  ... 
arXiv:1911.05811v2 fatcat:n53dpuf6o5bblpyv26dthba5ge

Panama's illegal rosewood logging boom from Dalbergia retusa

Ella Vardeman, Julie Velásquez Runk
2020 Global Ecology and Conservation  
For the last seventy-five years, Panama's main use of cocobolo rosewood (Dalbergia retusa) was in small pieces for artisanal carvings, its state of conservation favoring merchantable timber for recent  ...  press to relay to the public; 3) to show how logging changed geographically as the boom progressed; 4) to demonstrate how Panama and the international community responded to the global boom with new policies  ...  learned to cut and move illicit logs.  ... 
doi:10.1016/j.gecco.2020.e01098 fatcat:cmpcuasd2nea3koag4vjkxtcgy

Using environmental monitoring to complement in-depth qualitative interviews in cold homes research

Anna Cronin de Chavez, Jan Gilbertson, Angela Mary Tod, Peter Nelson, Vanessa Powell-Hoyland, Catherine Homer, Adelaide Lusambili, Ben Thomas
2017 Indoor and Built Environment  
Using fuel poverty as an identifier for those at risk does not always capture everyday exposure to cold homes due to variations in financial trade-offs and behavioural factors.  ...  The paper concludes with recommendations for future development and implementation of the research method.  ...  Multiple reasons were identified to explain why people had turned heating systems off even when it was very cold.  ... 
doi:10.1177/1420326x17719491 fatcat:rnctjtyopvgwnghxf576c5rvza
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