Descent c-Wilf Equivalence

Let $S_n$ denote the symmetric group. For any $\sigma \in S_n$, we let $\mathrm{des}(\sigma)$ denote the number of descents of $\sigma$, $\mathrm{inv}(\sigma)$ denote the number of inversions of $\sigma$, and $\mathrm{LRmin}(\sigma)$ denote the number of left-to-right minima of $\sigma$. For any sequence of statistics $\mathrm{stat}_1, \ldots \mathrm{stat}_k$ on permutations, we say two permutations $\alpha$ and $\beta$ in $S_j$ are $(\mathrm{stat}_1, \ldots \mathrm{stat}_k)$-c-Wilf equivalent if the generating function of $\prod_{i=1}^k x_i^{\mathrm{stat}_i}$ over all permutations which have no consecutive occurrences of $\alpha$ equals the generating function of $\prod_{i=1}^k x_i^{\mathrm{stat}_i}$ over all permutations which have no consecutive occurrences of $\beta$. We give many examples of pairs of permutations $\alpha$ and $\beta$ in $S_j$ which are $\mathrm{des}$-c-Wilf equivalent, $(\mathrm{des},\mathrm{inv})$-c-Wilf equivalent, and $(\mathrm{des},\mathrm{inv},\mathrm{LRmin})$-c-Wilf equivalent. For example, we will show that if $\alpha$ and $\beta$ are minimally overlapping permutations in $S_j$ which start with 1 and end with the same element and $\mathrm{des}(\alpha) = \mathrm{des}(\beta)$ and $\mathrm{inv}(\alpha) = \mathrm{inv}(\beta)$, then $\alpha$ and $\beta$ are $(\mathrm{des},\mathrm{inv})$-c-Wilf equivalent.Comment: arXiv admin note: text overlap with arXiv:1510.0431

Wet Torrefaction of forest Residues – Combustion Kinetics

AbstractFresh branches of Norway spruce and birch were torrefied in hot compressed water at varied temperatures(175, 200, or 225°C) and for 30minutes. The combustion of untreated and torrefied branchesin synthetic air (21% O2 and 79% N2) wasexperimentally studied by means ofa thermogravimetric analyzer, followed by a kinetic analysis adopting the distributed activation energy model. It appears that, wet torrefaction has significant effects on the combustion reactivity of forest residues. Compared with the raw materials, wet-torrefied branches are less reactive during devolatilization, but more reactive in the char combustion stage

Process modeling and optimization for torrefaction of forest residues

This work aims to build a comprehensive biomass torrefaction model, which can provide a wide range of information essential for industrialization and commercialization of the process. Norwegian forest residue (birch branches) was chosen as feedstock. The model is capable of presenting detailed distributions of main and by-products from the torrefaction process. In addition, important fuel properties (ultimate analysis and heating value) of the main solid product after torrefaction can be predicted. The model is validated and simulation results show good agreement with available experimental data in the literature. Reduction in mass and energy yields as well as improvement in heating value of torrefied biomass with increasing torrefaction temperature are observed. Trends for carbon, oxygen and hydrogen contents are also consistent with other experimental works. Moreover, overall energy consumption and process energy efficiency can be estimated from the model. It reveals that drying accounts for 76-80% of the total heat demand. Furthermore, the process energy efficiency reduces with increasing temperature, thus torrefaction at high temperatures is not advisable. More importantly, process optimization shows that optimal conditions for torrefaction of birch branches are 30 min holding time and a temperature between 275 and 278 °C.acceptedVersio

Sustainability assessment of Vietnam's electricity planning: Using section 1 of the 2009 hydropower sustainability assessment protocol

The Draft Hydropower Sustainability Assessment Protocol (HSAP) was first introduced in Vietnam at the National Consultant Workshop organized by Vietnam Water Partnership (VNWP) in November 2009. Although the structure of HSAP is relatively complex and new to Vietnam, the participants (from Government agencies, experts, investors, and Vietnamese and international civil society organizations) had the impression that the HSAP has the potential to be a useful tool for participatory assessment of the sustainability of a hydropower project and broader planning. With the assistance of the M-POWER (Mekong Program on Water, Environment and Resilience), a national group of experts in multiple disciplines from government agencies, national organisations and NGOs was mobilized to conduct a rapid sustainability assessment of the energy and hydropower development policy and plan in Vietnam. Section I of the draft HSAP 2009 was used as an assessment tool. The assessment focused on the quality of the process of developing and implementing the strategic development of the electricity sector in general and hydropower development of Vietnam in particular. Even though the rapid assessment framework of HSAP was quite new to the Assessment Team and the assessment subjects are broad, the Team and participants in this trial learned positive and negative lessons that can serve as a basis for future assessment exercises to enable deeper and more comprehensive assessment. The assessment report includes four major parts: 1) Introduction and background, 2) Water and hydropower development in Vietnam; 3) Rapid assessment - process and discussion of results; and 4) Lessons learned from the assessment and recommendations for draft HSAP 2009

HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System

Measuring the confidence of AI models is critical for safely deploying AI in real-world industrial systems. One important application of confidence measurement is information extraction from scanned documents. However, there exists no solution to provide reliable confidence score for current state-of-the-art deep-learning-based information extractors. In this paper, we propose a complete and novel architecture to measure confidence of current deep learning models in document information extraction task. Our architecture consists of a Multi-modal Conformal Predictor and a Variational Cluster-oriented Anomaly Detector, trained to faithfully estimate its confidence on its outputs without the need of host models modification. We evaluate our architecture on real-wold datasets, not only outperforming competing confidence estimators by a huge margin but also demonstrating generalization ability to out-of-distribution data.Comment: Document Intelligence @ KDD 2021 Worksho