On the Value Stand of Environmental Law

It is found from the different perspectives of the analysis on anthropocentrism and ecocentrism that strong anthropocentrism is difficult to get out of the excessive self-consciousness that is arrogant to the natural environment, and that there are inextricable theoretical defects and practical problems in ecocentrism. The confrontation between the two has promoted anthropocentrism to evolve to produce a new and more reasonable value-weak anthropocentrism. Weak anthropocentrism can not only overcome the various drawbacks in strong anthropocentrism and ecocentrism, but also avoid damaging the principal status of human and trigger human respect to natural environment; It can not only help to build advanced and mature criminal legislation on environmental pollution, but also actively guide human to make use of environment rationally; It can not only help effectively punish the criminal behavior of serious pollution of environment, and will not hinder human needs of survival and social development. In the sense, the environmental value of weak anthropocentrism is the best choice for the criminal legislation on environmental pollution

Comparative Study of Legislations on Major Domestic and Foreign Environmental Pollution Crimes

Surveying across Germany, Japan, The United Kingdom, and the United States’ environmental pollution crime legislations, there are similarities and differences, and these similar or different models or regulations reflect their different environmental states, legal cultures, legal traditions, political systems, levels of economic development, etc., and have achieved positive results in their own countries. Having been inspired by these countries which are sophisticated in the trend of environmental protection and mature in environmental criminal legislation, our country should also discover a path that is suitable for us according to our own environmental pollution problems and practices. It is suggested that major environmental pollution crimes’ relevant regulations are to be further modified from the perspective of how things ought to be. Possible flaws in the legislative techniques aside, fundamentally speaking, a lot of the other problems or deficiencies stem from just what kind of value system major environmental pollution crimes are systematically constructed and responsibilities allocated. Only by coming from a correct and sound value system can there be an effective guidance to the scientific design of the regulations of these types of crimes and to have it be effective in practice when preventing and remedying major environmental pollutions.

Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy

Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the accuracy of the query results, while satisfying the privacy guarantees. Previous work, notably \cite{LHR+10}, has suggested that with an appropriate strategy, processing a batch of correlated queries as a whole achieves considerably higher accuracy than answering them individually. However, to our knowledge there is currently no practical solution to find such a strategy for an arbitrary query batch; existing methods either return strategies of poor quality (often worse than naive methods) or require prohibitively expensive computations for even moderately large domains. Motivated by this, we propose low-rank mechanism (LRM), the first practical differentially private technique for answering batch linear queries with high accuracy. LRM works for both exact (i.e., $\epsilon$-) and approximate (i.e., ($\epsilon$, $\delta$)-) differential privacy definitions. We derive the utility guarantees of LRM, and provide guidance on how to set the privacy parameters given the user's utility expectation. Extensive experiments using real data demonstrate that our proposed method consistently outperforms state-of-the-art query processing solutions under differential privacy, by large margins.Comment: ACM Transactions on Database Systems (ACM TODS). arXiv admin note: text overlap with arXiv:1212.230

Inquiry Into the Subjectivity of Major Environmental Pollution Crimes: From the Perspective of Weak Anthropocentrism

Now, scholars have done some research on the subjectivity of major environmental pollution crimes, and have raised some different points. It can be said that different people have different points. This paper views from the perspective of weak anthropocentrism, starts with the organization and introduction of the key theories on the subjectivity of contemporary major environmental pollution crimes and the subjective attitude of the major environmental pollution crimes in practice, and then delves into a comparative study of a variety of key theories on the subjectivity of crimes of major environmental pollution, emphasizes the distinction between the intentional and negligent nature of the subjectivity of crimes of major environmental pollution and the problem of strict liability of the subjectivity of crimes of major environmental pollution. Through research, it is argued that it is better to punish and prevent crimes of major environmental pollution if crimes of major environmental pollution incidents are separated, according to subjectivity, into crimes of intentional environmental pollution and crimes of negligent environmental pollution incidents, and the liability principle of crimes of major negligent environmental pollution incidents should be based on the relative strict liability

Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy

Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the accuracy of the query results, while satisfying the privacy guarantees. Previous work, notably the matrix mechanism, has suggested that processing a batch of correlated queries as a whole can potentially achieve considerable accuracy gains, compared to answering them individually. However, as we point out in this paper, the matrix mechanism is mainly of theoretical interest; in particular, several inherent problems in its design limit its accuracy in practice, which almost never exceeds that of naive methods. In fact, we are not aware of any existing solution that can effectively optimize a query batch under differential privacy. Motivated by this, we propose the Low-Rank Mechanism (LRM), the first practical differentially private technique for answering batch queries with high accuracy, based on a low rank approximation of the workload matrix. We prove that the accuracy provided by LRM is close to the theoretical lower bound for any mechanism to answer a batch of queries under differential privacy. Extensive experiments using real data demonstrate that LRM consistently outperforms state-of-the-art query processing solutions under differential privacy, by large margins.Comment: VLDB201

A brief analysis on the similarities and differences between the laws and regulations of animal aquatic products in Eurasian Economic Union and similar standards in China

The Eurasian Economic Union (EAEU) was established in 2015. At present, Russia, Kazakhstan, Belarus, Kyrgyzstan and Armenia are member countries. They are all important partners in the construction of the "Belt and Road" initiative. The mandatory technical regulations on animal aquatic products and their products from the EAEU were collected and summarized. The similarities and differences of key contents in relevant EAEU regulations and China’s national food safety standards such as definitions, scopes and categorizations of animal aquatic products and their products, as well as maximum levels of contaminates and pathogenic bacteria, parasitological safety requirements and veterinary drug residues were compared and analyzed. The possible causes of the above similarities and differences of animal aquatic products and their products were briefly discussed. The article could provide references for promoting trade in animal aquatic products and their products between China and EAEU member states, avoiding or resolving potential trade barriers and other issues, as well as for further exchanges and cooperation in technical regulations and standards between the two parties

Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning

Generally, Reinforcement Learning (RL) agent updates its policy by repetitively interacting with the environment, contingent on the received rewards to observed states and undertaken actions. However, the environmental disturbance, commonly leading to noisy observations (e.g., rewards and states), could significantly shape the performance of agent. Furthermore, the learning performance of Multi-Agent Reinforcement Learning (MARL) is more susceptible to noise due to the interference among intelligent agents. Therefore, it becomes imperative to revolutionize the design of MARL, so as to capably ameliorate the annoying impact of noisy rewards. In this paper, we propose a novel decomposition-based multi-agent distributional RL method by approximating the globally shared noisy reward by a Gaussian mixture model (GMM) and decomposing it into the combination of individual distributional local rewards, with which each agent can be updated locally through distributional RL. Moreover, a diffusion model (DM) is leveraged for reward generation in order to mitigate the issue of costly interaction expenditure for learning distributions. Furthermore, the optimality of the distribution decomposition is theoretically validated, while the design of loss function is carefully calibrated to avoid the decomposition ambiguity. We also verify the effectiveness of the proposed method through extensive simulation experiments with noisy rewards. Besides, different risk-sensitive policies are evaluated in order to demonstrate the superiority of distributional RL in different MARL tasks

Thermoelectric Skutterudite Compositions and Methods for Producing the Same

Compositions related to skutterudite-based thermoelectric materials are disclosed. Such compositions can result in materials that have enhanced ZT values relative to one or more bulk materials from which the compositions are derived. Thermoelectric materials such as n-type and p-type skutterudites with high thermoelectric figures-of-merit can include materials with filler atoms and/or materials formed by compacting particles (e.g., nanoparticles) into a material with a plurality of grains each having a portion having a skutterudite-based structure. Methods of forming thermoelectric skutterudites, which can include the use of hot press processes to consolidate particles, are also disclosed. The particles to be consolidated can be derived from (e.g., grinded from), skutterudite-based bulk materials, elemental materials, other non-Skutterudite-based materials, or combinations of such materials

N,N′-Diacetyl-N′-[(4-nitro­phen­oxy)acetyl]acetohydrazide

The asymmetric unit of the title compound, C14H15N3O7, contains two independent mol­ecules which are linked into a pseudocentrosymmetric dimer by a π–π inter­action, as shown by the short distance of 3.722 (5) Å between the centroids of the benzene rings. An extensive network of weak inter­molecular C—H⋯O hydrogen bonds helps to stabilize the crystal packing

Tacotron: Towards End-to-End Speech Synthesis

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires extensive domain expertise and may contain brittle design choices. In this paper, we present Tacotron, an end-to-end generative text-to-speech model that synthesizes speech directly from characters. Given pairs, the model can be trained completely from scratch with random initialization. We present several key techniques to make the sequence-to-sequence framework perform well for this challenging task. Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. In addition, since Tacotron generates speech at the frame level, it's substantially faster than sample-level autoregressive methods.Comment: Submitted to Interspeech 2017. v2 changed paper title to be consistent with our conference submission (no content change other than typo fixes