213 research outputs found
Seeding Rate and Row-Spacing Effects on Seed Yield and Yield Components of \u3cem\u3eLeymus chinensis\u3c/em\u3e (Trin.) Tzvel.
Chinese sheepgrass (Leymus chinensis (Trin.) Tzvel.) is widely distributed in the eastern portion of the Inner Mongolian Plateau and the Songnen Grassland of China. This grass is highly salt, cold and drought tolerant and has been the major source of forage for cows and other ruminants in China (Gao et al. 2012). Seed yield of this grass is very low under native conditions because of the low heading percentage and percentage of seed set (Wang et al. 2010). The Hexi Corridor, located in China’s northwestern Gansu Province, is the seed production center of China because of its dry, sunny climate and favorable irrigation conditions. Our field study was conducted to determine the optimum seeding rate and row-spacing for seed production of Chinese sheepgrass in the Hexi Corridor, where this grass has not been previously grown
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Multivariate time series forecasting with hierarchical structure is widely
used in real-world applications, e.g., sales predictions for the geographical
hierarchy formed by cities, states, and countries. The hierarchical time series
(HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation.
In the previous works, hierarchical information is only integrated in the
reconciliation step to maintain coherency, but not in forecasting step for
accuracy improvement. In this paper, we propose two novel tree-based feature
integration mechanisms, i.e., top-down convolution and bottom-up attention to
leverage the information of the hierarchical structure to improve the
forecasting performance. Moreover, unlike most previous reconciliation methods
which either rely on strong assumptions or focus on coherent constraints
only,we utilize deep neural optimization networks, which not only achieve
coherency without any assumptions, but also allow more flexible and realistic
constraints to achieve task-based targets, e.g., lower under-estimation penalty
and meaningful decision-making loss to facilitate the subsequent downstream
tasks. Experiments on real-world datasets demonstrate that our tree-based
feature integration mechanism achieves superior performances on hierarchical
forecasting tasks compared to the state-of-the-art methods, and our neural
optimization networks can be applied to real-world tasks effectively without
any additional effort under coherence and task-based constraint
Assessing contributions of agricultural and nonagricultural emissions to atmospheric ammonia in a Chinese megacity
Ammonia (NH3) is the predominant alkaline gas in the atmosphere contributing to formation of fine particles—a leading environmental cause of increased morbidity and mortality worldwide. Prior findings suggest that NH3 in the urban atmosphere derives from a complex mixture of agricultural (mainly livestock production and fertilizer application) and nonagricultural (e.g., urban waste, fossil fuel-related emissions) sources; however, a citywide holistic assessment is hitherto lacking. Here we show that NH3 from nonagricultural sources rivals agricultural NH3 source contributions in the Shanghai urban atmosphere. We base our conclusion on four independent approaches: (i) a full-year operation of a passive NH3 monitoring network at 14 locations covering urban, suburban, and rural landscapes; (ii) model-measurement comparison of hourly NH3 concentrations at a pair of urban and rural supersites; (iii) source-specific NH3 measurements from emission sources; and (iv) localized isotopic signatures of NH3 sources integrated in a Bayesian isotope mixing model to make isotope-based source apportionment estimates of ambient NH3. Results indicate that nonagricultural sources and agricultural sources are both important contributors to NH3 in the urban atmosphere. These findings highlight opportunities to limit NH3 emissions from nonagricultural sources to help curb PM2.5 pollution in urban China
Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction
Automatic bill payment is an important part of business operations in fintech
companies. The practice of deduction was mainly based on the total amount or
heuristic search by dividing the bill into smaller parts to deduct as much as
possible. This article proposes an end-to-end approach of automatically
learning the optimal deduction paths (deduction amount in order), which reduces
the cost of manual path design and maximizes the amount of successful
deduction. Specifically, in view of the large search space of the paths and the
extreme sparsity of historical successful deduction records, we propose a deep
hierarchical reinforcement learning approach which abstracts the action into a
two-level hierarchical space: an upper agent that determines the number of
steps of deductions each day and a lower agent that decides the amount of
deduction at each step. In such a way, the action space is structured via prior
knowledge and the exploration space is reduced. Moreover, the inherited
information incompleteness of the business makes the environment just partially
observable. To be precise, the deducted amounts indicate merely the lower
bounds of the available account balance. To this end, we formulate the problem
as a partially observable Markov decision problem (POMDP) and employ an
environment correction algorithm based on the characteristics of the business.
In the world's largest electronic payment business, we have verified the
effectiveness of this scheme offline and deployed it online to serve millions
of users
Combining machine learning and human judgment in author disambiguation
ABSTRACT Author disambiguation in digital libraries becomes increasingly difficult as the number of publications and consequently the number of ambiguous author names keep growing. The fully automatic author disambiguation approach could not give satisfactory results due to the lack of signals in many cases. Furthermore, human judgment on the basis of automatic algorithms is also not suitable because the automatically disambiguated results are often mixed and not understandable for humans. In this paper, we propose a Labeling Oriented Author Disambiguation approach, called LOAD, to combine machine learning and human judgment together in author disambiguation. LOAD exploits a framework which consists of high precision clustering, high recall clustering, and top dissimilar clusters selection and ranking. In the framework, supervised learning algorithms are used to train the similarity functions between publications and a clustering algorithm is further applied to generate clusters. To validate the effectiveness and efficiency of the proposed LOAD approach, comprehensive experiments are conducted. Comparing to conventional author disambiguation algorithms, the LOAD yields much more accurate results to assist human labeling. Further experiments show that the LOAD approach can save labeling time dramatically
A New Approach to Query Segmentation for Relevance Ranking in Web Search
Abstract In this paper, we try to determine how best to improve state-ofthe-art methods for relevance ranking in web searching by query segmentation. Query segmentation is meant to separate the input query into segments, typically natural language phrases. We propose employing the re-ranking approach in query segmentation, which first employs a generative model to create the top k candidates and then employs a discriminative model to re-rank the candidates to obtain the final segmentation result. The method has been widely utilized for structure prediction in natural language processing, but has not been applied to query segmentation, as far as we know. Furthermore, we propose a new method for using the results of query segmentation in relevance ranking, which takes both the original query words and the segmented query phrases as units of query representation. We investigate whether our method can improve three relevance models, namely n-gram BM25, key n-gram model and term dependency model, within the framework of learning to rank. Our experimental results on large scale web search datasets show that our method can indeed significantly improve relevance ranking in all three cases
- …