The Impact of Socialist Imprinting and Search for Knowledge on Resource Change: An Empirical Study of Firms in Lithuania

In this paper we examine how firms change their resources in response to exogenous shocks in their business environment. Building on core ideas from the literatures on organizational imprinting and firm resources, we suggest that founding conditions differentially imprint firm resources. These initial imprinting differentials in turn influence the search for knowledge required to adapt or change firm resources in the face of external change in their business environment. We also suggest that the level of imprinting and the location of search independently and jointly influence the success with which firms are able to change their resources. We use survey-based data from a set of firms in Central Europe that experienced an exogenous shock in 1989-1991 to test our arguments. We develop a measure of pre-shock imprinting (called socialist imprinting) on resources and use it to predict where firms will search for knowledge to undertake change in the post-shock period and how successful that change will be. We find that the level of socialist imprinting influences the search location for knowledge to change key resources and activities following the shock. In terms of the success of change undertaken, we see that distant search for knowledge is positively linked to it. We also observe that the level of imprinting and search location jointly impact the success of change; for resources with higher socialist imprinting, distant search was more effective than local search. This research makes three important contributions in the context of existing research on organizational imprinting and firm level change. One, it focuses on firm-level resources to examine the impact of imprinting. Two, we examine how differences in resource level imprinting influence the search for new knowledge required to transform these resources. Three, we demonstrate that the interaction between the level of imprinting and the nature of search has important influences on firm performance. Our findings also provide insights to practitioners and policy makers who deal with firms in transitional economies. Practitioners can better understand how to undertake firm level change more effectively in the context of sudden exogenous shock. For policy makers, both of domestic and international institutions, understanding the change process can help formulate assistance programs more effectively.http://deepblue.lib.umich.edu/bitstream/2027.42/39830/3/wp446.pd

Fast search of sequences with complex symbol correlations using profile context-sensitive HMMS and pre-screening filters

Recently, profile context-sensitive HMMs (profile-csHMMs) have been proposed which are very effective in modeling the common patterns and motifs in related symbol sequences. Profile-csHMMs are capable of representing long-range correlations between distant symbols, even when these correlations are entangled in a complicated manner. This makes profile-csHMMs an useful tool in computational biology, especially in modeling noncoding RNAs (ncRNAs) and finding new ncRNA genes. However, a profile-csHMM based search is quite slow, hence not practical for searching a large database. In this paper, we propose a practical scheme for making the search speed significantly faster without any degradation in the prediction accuracy. The proposed method utilizes a pre-screening filter based on a profile-HMM, which filters out most sequences that will not be predicted as a match by the original profile-csHMM. Experimental results show that the proposed approach can make the search speed eighty times faster

Large neighborhood search for the most strings with few bad columns problem

In this work, we consider the following NP-hard combinatorial optimization problem from computational biology. Given a set of input strings of equal length, the goal is to identify a maximum cardinality subset of strings that differ maximally in a pre-defined number of positions. First of all, we introduce an integer linear programming model for this problem. Second, two variants of a rather simple greedy strategy are proposed. Finally, a large neighborhood search algorithm is presented. A comprehensive experimental comparison among the proposed techniques shows, first, that larger neighborhood search generally outperforms both greedy strategies. Second, while large neighborhood search shows to be competitive with the stand-alone application of CPLEX for small- and medium-sized problem instances, it outperforms CPLEX in the context of larger instances.Peer ReviewedPostprint (author's final draft

Comparing the content of instruments assessing environmental factors using the International Classification of Functioning, Disability and Health

Purpose: To describe and compare the content of instruments that assess environmental factors using the International Classification of Functioning, Disability and Health (ICF). Methods: A systematic search of PubMed, CINAHL and PEDro databases was conducted using a pre-determined search strategy. The identified instruments were screened independently by two investigators, and meaningful concepts were linked to the most precise ICF category according to published linking rules. Results: Six instruments were included, containing 526 meaningful concepts. Instruments had between 20% and 98% of items linked to categories in Chapter 1. The highest percentage of items from one instrument linked to categories in Chapters 2–5 varied between 9% and 50%. The presence or absence of environmental factors in a specific context is assessed in 3 instruments, while the other 3 assess the intensity of the impact of environmental factors. Discussion: Instruments differ in their content, type of assessment, and have several items linked to the same ICF category. Most instruments primarily assess products and technology (Chapter 1), highlighting the need to deepen the discussion on the theory that supports the measurement of environmental factors. This discussion should be thorough and lead to the development of methodologies and new tools that capture the underlying concepts of the ICF

A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors

Facilitating academic words learning: a data-driven approach using a collocation consultation system built from open access research papers

It is essential and beneficial for ESP students to master collocations of a set of core academic words. Corpus analysis tools (e.g. concordancers) have been widely used in facilitating collocation learning, and promising results have been demonstrated in the literature. This paper presents a learner friendly collocation consultation system built from 50,000 open access research papers made available by CORE (https://core.ac.uk/). The research papers are grouped into four disciplines: Arts and Humanities, Physical Sciences, Life Sciences and Social Sciences. From these articles, useful syntactic-based word combinations (e.g., verb+noun, noun+noun, adjective+noun) are extracted, organized by syntactic patterns, sorted by frequency, and linked to their context sentences. Learners can search collocations and look up the usage of an academic word in any of these four disciplines by simply entering the word or selecting it from one of pre-compiled academic word lists. The paper will also show how the system was used in an initial study carried out with 15 international students studying computer science at University of Waikato, New Zealand