Business Survey Data: Do They Help in Forecasting the Macro Economy?

In this paper we examine whether data from business tendency surveys are useful for forecasting the macro economy in the short run. Our analyses primarily concern the growth rates of real GDP but we also evaluate forecasts of other variables such as unemployment, price and wage inflation, interest rates, and exchange-rate changes. The starting point is a so-called dynamic factor model (DFM), which is used both as a framework for dimension reduction in forecasting and as a procedure for filtering out unimportant idiosyncratic noise in the underlying survey data. In this way, it is possible to model a rather large number of noise-reduced survey variables in a parsimoniously parameterised vector autoregression (VAR). To assess the forecasting performance of the procedure, comparisons are made with VARs that either use the survey variables directly, are based on macro variables only, or use other popular summary indices of economic activity. As concerns forecasts of GDP growth, the procedure turns out to outperform the competing alternatives in most cases. For the other macro variables, the evidence is more mixed, suggesting in particular that there often is little difference between the DFM-based indicators and the popular summary indices of economic activity.Business survey data; Dynamic factor models; Macroeconomic forecasting

Minimal Phylogenetic Supertrees and Local Consensus Trees

The problem of constructing a minimally resolved phylogenetic supertree (i.e., having the smallest possible number of internal nodes) that contains all of the rooted triplets from a consistent set R is known to be NP-hard. In this paper, we prove that constructing a phylogenetic tree consistent with R that contains the minimum number of additional rooted triplets is also NP-hard, and develop exact, exponential-time algorithms for both problems. The new algorithms are applied to construct two variants of the local consensus tree; for any set S of phylogenetic trees over some leaf label set L, this gives a minimal phylogenetic tree over L that contains every rooted triplet present in all trees in S, where ``minimal\u27\u27 means either having the smallest possible number of internal nodes or the smallest possible number of rooted triplets. The second variant generalizes the RV-II tree, introduced by Kannan, Warnow, and Yooseph in 1998

Initiating a Smart Tourism Ecosystem: A Public Actor Perspective

Smart tourism ecosystems are an emerging phenomenon; however, how these ecosystems are initiated by city actors is under-explored in the existing literature. In this paper, we conduct a qualitative case study to investigate the initiation of a de novo smart tourism ecosystem in the City of Gothenburg—the European capital of smart tourism 2020. Göteborg & Co, as a public organization, is initiating a digital Destination Data Platform (DDP) as the core of its tourism ecosystem and is working on involving non-focal actors to shape the surrounding ecosystem. Our findings extend the existing research on innovation ecosystems by highlighting a hybrid public-private focal actor in the smart tourism ecosystem. We also underline how a public focal actor leverages its unique public position and legal obligations to involve non-focal actors and orchestrating the ecosystem. Finally, we suggest a conceptual model for a smart tourism ecosystem focusing on the place and purpose of control points

On Finding the Adams Consensus Tree

This paper presents a fast algorithm for finding the Adams consensus tree of a set of conflicting phylogenetic trees with identical leaf labels, for the first time improving the time complexity of a widely used algorithm invented by Adams in 1972 [1]. Our algorithm applies the centroid path decomposition technique [9] in a new way to traverse the input trees\u27 centroid paths in unison, and runs in O(k n log n) time, where k is the number of input trees and n is the size of the leaf label set. (In comparison, the old algorithm from 1972 has a worst-case running time of O(k n^2).) For the special case of k = 2, an even faster algorithm running in O(n cdot frac{log n}{loglog n}) time is provided, which relies on an extension of the wavelet tree-based technique by Bose et al. [6] for orthogonal range counting on a grid. Our extended wavelet tree data structure also supports truncated range maximum queries efficiently and may be of independent interest to algorithm designers

Flexible taxonomic assignment of ambiguous sequencing reads

<p>Abstract</p> <p>Background</p> <p>To characterize the diversity of bacterial populations in metagenomic studies, sequencing reads need to be accurately assigned to taxonomic units in a given reference taxonomy. Reads that cannot be reliably assigned to a unique leaf in the taxonomy (<it>ambiguous reads</it>) are typically assigned to the lowest common ancestor of the set of species that match it. This introduces a potentially severe error in the estimation of bacteria present in the sample due to false positives, since all species in the subtree rooted at the ancestor are implicitly assigned to the read even though many of them may not match it.</p> <p>Results</p> <p>We present a method that maps each read to a node in the taxonomy that minimizes a penalty score while balancing the relevance of precision and recall in the assignment through a parameter <it>q</it>. This mapping can be obtained in time linear in the number of matching sequences, because LCA queries to the reference taxonomy take constant time. When applied to six different metagenomic datasets, our algorithm produces different taxonomic distributions depending on whether coverage or precision is maximized. Including information on the quality of the reads reduces the number of unassigned reads but increases the number of ambiguous reads, stressing the relevance of our method. Finally, two measures of performance are described and results with a set of artificially generated datasets are discussed.</p> <p>Conclusions</p> <p>The assignment strategy of sequencing reads introduced in this paper is a versatile and a quick method to study bacterial communities. The bacterial composition of the analyzed samples can vary significantly depending on how ambiguous reads are assigned depending on the value of the <it>q </it>parameter. Validation of our results in an artificial dataset confirm that a combination of values of <it>q </it>produces the most accurate results.</p

Building a Small and Informative Phylogenetic Supertree

We combine two fundamental, previously studied optimization problems related to the construction of phylogenetic trees called maximum rooted triplets consistency (MAXRTC) and minimally resolved supertree (MINRS) into a new problem, which we call q-maximum rooted triplets consistency (q-MAXRTC). The input to our new problem is a set R of resolved triplets (rooted, binary phylogenetic trees with three leaves each) and the objective is to find a phylogenetic tree with exactly q internal nodes that contains the largest possible number of triplets from R. We first prove that q-MAXRTC is NP-hard even to approximate within a constant ratio for every fixed q >= 2, and then develop various polynomial-time approximation algorithms for different values of q. Next, we show experimentally that representing a phylogenetic tree by one having much fewer nodes typically does not destroy too much triplet branching information. As an extreme example, we show that allowing only nine internal nodes is still sufficient to capture on average 80% of the rooted triplets from some recently published trees, each having between 760 and 3081 internal nodes. Finally, to demonstrate the algorithmic advantage of using trees with few internal nodes, we propose a new algorithm for computing the rooted triplet distance between two phylogenetic trees over a leaf label set of size n that runs in O(q n) time, where q is the number of internal nodes in the smaller tree, and is therefore faster than the currently best algorithms for the problem (with O(n log n) time complexity [SODA 2013, ESA 2017]) whenever q = o(log n)

New and Improved Algorithms for Unordered Tree Inclusion

The tree inclusion problem is, given two node-labeled trees P and T (the "pattern tree" and the "text tree"), to locate every minimal subtree in T (if any) that can be obtained by applying a sequence of node insertion operations to P. Although the ordered tree inclusion problem is solvable in polynomial time, the unordered tree inclusion problem is NP-hard. The currently fastest algorithm for the latter is from 1995 and runs in O(poly(m,n) * 2^{2d}) = O^*(2^{2d}) time, where m and n are the sizes of the pattern and text trees, respectively, and d is the maximum outdegree of the pattern tree. Here, we develop a new algorithm that improves the exponent 2d to d by considering a particular type of ancestor-descendant relationships and applying dynamic programming, thus reducing the time complexity to O^*(2^d). We then study restricted variants of the unordered tree inclusion problem where the number of occurrences of different node labels and/or the input trees\u27 heights are bounded. We show that although the problem remains NP-hard in many such cases, it can be solved in polynomial time for c = 2 and in O^*(1.8^d) time for c = 3 if the leaves of P are distinctly labeled and each label occurs at most c times in T. We also present a randomized O^*(1.883^d)-time algorithm for the case that the heights of P and T are one and two, respectively

Förändring av en kunskapsintensiv försäljningsenhet

Vår studie syftar till att undersöka hur kunskap överförs inom en försäljningsenhet, med avsikt att etablera en gemensam förståelse för hur kundvärde kan skapas. Vidare vill vi öka förståelsen för vilka nyckelfaktorer som påverkar potentialen till förändring för att uppnå önskad arbetsprocedur. Metod: Vi har valt att utföra en fördjupad fallstudie med ett abduktivt förhållningsätt gentemot teoretiska och empririska data. Insamlingen av vårt empiriska material har skett genom semistrukturerade intervjuer. Teoretiska perspektiv: Studiens teoretiska ansats utgörs av Sales Management, Knowledge Management samt Change Management. Utifrån dessa teoretiska perspektiv har ett teoretiskt ramverk skapats med fokus på de faktorer som vi anser vara av stor betydelse för att uppnå en önskad arbetsprocedur. Empirisk forskning: Vår studie grundar sig i studier kring SCA- Packaging och deras försäljningsenheter belägna i Malmö samt Värnamo. Slutsats: För att en säljenhet ska förändras finner vi att inställningen till ny kunskap och att individerna lär sig av varandra genom integration av olika kunskapsspecifika avdelningar är viktiga faktorer. Vidare är det av betydelse att företaget kommunicerar behovet till förändringen för att säljarna genom vilja att förändras blir motiverade till att vara delaktiga i förändringsprocessen. Om företaget består utav olika försäljningsavdelningar som besitter generell kunskap om produkten och försäljning är organisationens struktur avgörande för att motverka att intern konkurrens skapas