Diffusion of Context and Credit Information in Markovian Models

This paper studies the problem of ergodicity of transition probability matrices in Markovian models, such as hidden Markov models (HMMs), and how it makes very difficult the task of learning to represent long-term context for sequential data. This phenomenon hurts the forward propagation of long-term context information, as well as learning a hidden state representation to represent long-term context, which depends on propagating credit information backwards in time. Using results from Markov chain theory, we show that this problem of diffusion of context and credit is reduced when the transition probabilities approach 0 or 1, i.e., the transition probability matrices are sparse and the model essentially deterministic. The results found in this paper apply to learning approaches based on continuous optimization, such as gradient descent and the Baum-Welch algorithm.Comment: See http://www.jair.org/ for any accompanying file

J. Scott Ockey v. Christena White : Brief of Appellant

We develop kernels for measuring the similarity between relational instances using background knowledge expressed in first-order logic. The method allows us to bridge the gap between traditional inductive logic programming (ILP) representations and statistical approaches to supervised learning. Logic programs are first used to generate proofs of given visitor programs that use predicates declared in the available background knowledge. A kernel is then defined over pairs of proof trees. The method can be used for supervised learning tasks and is suitable for classification as well as regression. We report positive empirical results on Bongard-like and M-of-N problems that are difficult or impossible to solve with traditional ILP techniques, as well as on real bioinformatics and chemoinformatics data sets.status: publishe

Deep Tree Transductions - A Short Survey

The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.Comment: To appear in the Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019). arXiv admin note: text overlap with arXiv:1809.0909

Thermal weed control in Photinia x Fraseri “Red Robin” container nurseries

A near-zero tolerance policy on weeds by markets for nursery crops calls for weed-free container-grown plants, and forces growers to frequently remove weeds. Thermal weed control could represent a novel method to control weeds in shrubs from container nurseries, thus avoiding the use of herbicides and mulches. The aims of this study were to develop custom-built machinery for thermal weed control in container nurseries and to test the weed control efficiency of flame weeding and steaming in Photinia x fraseri "Red Robin" containers. A liquefied petroleum gas (LPG) fed flamer and a steamer with a dedicated diffuser were built. Four treatments were applied for a total period of 24 months: steaming once every four months, steaming once every two months, flame weeding once every two months or once a month. Temperature values measured at different depths in the substrate after thermal applications were recorded and analyzed. Photinia x fraseri features (height, diameter, and dry biomass) and aesthetic parameters as affected by thermal treatments were also evaluated. The trend in temperature values of the substrate over time followed a two-phase exponential decay. All the thermal treatments lead to a continuous near-100% weed control level, which is the level required by growers for aesthetic reasons. No damages caused by heat on Photinia x fraseri were observed. Container nursery producers could thus adopt thermal methods as a substitute for chemical solutions for weed control management

Innovative crop and weed management strategies for organic spinach: crop yield and weed suppression.

In organic agriculture, it is important to tackle crop and weed management from a system perspective to make it effective, especially in poorly competitive crops such as vegetables. For that reason, we developed two innovative integrated crop and weed management systems for a field vegetable crop sequence in a commercial organic farm that we have been comparing to a standard farm system from 2006 to 2008. The three systems are applied to a spinach-potato-cabbage-tomato two-year crop sequence and include different levels of technical innovation: Standard Crop Management System (SCMS); Intermediate Crop Management System (ICMS); and Advanced Crop Management System (ACMS). ICMS is based on a sequence of physical weed management treatments, whereas ACMS also includes a subterranean clover (Trifolium subterraneum) living mulch. In this paper we analyse the results obtained on spinach (Spinacia oleracea) in terms of crop yield and weed suppression. Both innovative systems increased total spinach fresh weight yield compared to SCMS, despite higher weed biomass. In ACMS, total weed biomass decreased linearly with increasing biomass of the subterranean clover living mulch