Fighting the Mujahideen: Lessons from the Soviet Counter-Insurgency Experience if Afghanistan

The Canadian Forces currently in Afghanistan as part of a NATO coalition are playing a major role in a counter-insurgency campaign directed against a resurgent Taliban threat. Dealing with this menace will not be easy as the Taliban, realizing they cannot defeat NATO’s superior military strength, have resorted to asymmetric actions that strike at the coalition’s will through the cumulative effects of terror and small-scale “hit and run” military operations. Although NATO must contend with these tactics, concentrating solely on the military aspects of the problem will not address the real danger. The true nature of the Taliban’s threat rests in its political strength and not in its military capability. In order to destroy the Taliban’s influence in the region a combination of political, social, economic, and military means are necessary. The complexities of dealing with these issues in a coherent manner are significant, but in the case of Afghanistan there is no precedent. Interestingly, the Soviets faced many of these same challenges while fighting a counter-insurgency campaign against the Mujahideen through much of the 1980s.1 The Soviet experience should be of interest to coalition members as it provides a contemporary example of the challenges of conducting counter-insurgency operations within that country. Contrary to popular belief, the Soviets followed a logical and multifaceted, if somewhat brutal, counter-insurgency strategy in Afghanistan. A critical examination of the Soviet performance reveals that many of their failings can be directly attributed to a lack of resources and in this respect, there are a surprising number of similarities between the operational environment the Soviets faced and the situation that now confronts NATO. This paper will explore aspects of Soviet counterinsurgency operations during their occupation of Afghanistan, and assess strengths and weaknesses relevant to current operations in that country.

Better text compression from fewer lexical n-grams

Word-based context models for text compression have the capacity to outperform more simple character-based models, but are generally unattractive because of inherent problems with exponential model growth and corresponding data sparseness. These ill-effects can be mitigated in an adaptive lossless compression scheme by modelling syntactic and semantic lexical dependencies independently

Reducing the Effects of Detrimental Instances

Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially, the instances in a data set are generally considered equally in machine learning algorithms. Many current approaches for handling noisy and detrimental instances make a binary decision about whether an instance is detrimental or not. In this paper, we 1) extend this paradigm by weighting the instances on a continuous scale and 2) present a methodology for measuring how detrimental an instance may be for inducing a model of the data. We call our method of identifying and weighting detrimental instances reduced detrimental instance learning (RDIL). We examine RIDL on a set of 54 data sets and 5 learning algorithms and compare RIDL with other weighting and filtering approaches. RDIL is especially useful for learning algorithms where every instance can affect the classification boundary and the training instances are considered individually, such as multilayer perceptrons trained with backpropagation (MLPs). Our results also suggest that a more accurate estimate of which instances are detrimental can have a significant positive impact for handling them.Comment: 6 pages, 5 tables, 2 figures. arXiv admin note: substantial text overlap with arXiv:1403.189

A systematic review and meta-synthesis of effective nursing leadership

Introduction. There is a prevailing view that leadership and management development play a vital role in the creating of high performing organisations. Aim of the study. The aim of this research is to review relevant qualitative studies to identify the attributes of effective Nursing Leadership. Material and methods. Healthcare systems have experienced a substantial transformation during recent decades. This has resulted in Nurse Managers dealing with increased levels of systemic complexity and change. It’s been found,that leadership skills may help nurse managers to deal with these difficulties. Nine databases, Nursing Journals, reference lists from relevant publications and grey literature were searched. From over identified 2,000 articles, 394 were reviewed at abstract and 257 reviewed in full. Twelve articles were accepted for the systematic review. Systematic review and meta-synthesis methodologies were employed in the study. Data was collected between November 2006 and January 2007. Results. A variety of effective nursing leadership attributes were identified. Six themes were identified through metasynthesis: personal characteristics, interpersonal relationships, future vision, management of change, managerial competence and clinical experience. Conclusions. Whilst the findings of the research could not be explained by any single leadership theory, all the themes and attributes identified (except clinical experience) could be all identified in the generic leadership theory system, particularly in transformational and charismatic leadership theories. Two recommendations arise from the research. Firstly that further enquiries into Nursing Leadership should encompass the views of other relevant groups, such as patients,and other hospital staff groups. Secondly, these attributes should be empirically tested through quantitative methods

Automatically linking MEDLINE abstracts to the Gene Ontology

Much has been written recently about the need for effective tools and methods for mining the wealth of information present in biomedical literature (Mack and Hehenberger, 2002; Blagosklonny and Pardee, 2001; Rindflesch et al., 2002)—the activity of conceptual biology. Keyword search engines operating over large electronic document stores (such as PubMed and the PNAS) offer some help, but there are fundamental obstacles that limit their effectiveness. In the first instance, there is no general consensus among scientists about the vernacular to be used when describing research about genes, proteins, drugs, diseases, tissues and therapies, making it very difficult to formulate a search query that retrieves the right documents. Secondly, finding relevant articles is just one aspect of the investigative process. A more fundamental goal is to establish links and relationships between facts existing in published literature in order to “validate current hypotheses or to generate new ones” (Barnes and Robertson, 2002)—something keyword search engines do little to support