On Deterministic Sketching and Streaming for Sparse Recovery and Norm Estimation

We study classic streaming and sparse recovery problems using deterministic linear sketches, including l1/l1 and linf/l1 sparse recovery problems (the latter also being known as l1-heavy hitters), norm estimation, and approximate inner product. We focus on devising a fixed matrix A in R^{m x n} and a deterministic recovery/estimation procedure which work for all possible input vectors simultaneously. Our results improve upon existing work, the following being our main contributions: * A proof that linf/l1 sparse recovery and inner product estimation are equivalent, and that incoherent matrices can be used to solve both problems. Our upper bound for the number of measurements is m=O(eps^{-2}*min{log n, (log n / log(1/eps))^2}). We can also obtain fast sketching and recovery algorithms by making use of the Fast Johnson-Lindenstrauss transform. Both our running times and number of measurements improve upon previous work. We can also obtain better error guarantees than previous work in terms of a smaller tail of the input vector. * A new lower bound for the number of linear measurements required to solve l1/l1 sparse recovery. We show Omega(k/eps^2 + klog(n/k)/eps) measurements are required to recover an x' with |x - x'|_1 <= (1+eps)|x_{tail(k)}|_1, where x_{tail(k)} is x projected onto all but its largest k coordinates in magnitude. * A tight bound of m = Theta(eps^{-2}log(eps^2 n)) on the number of measurements required to solve deterministic norm estimation, i.e., to recover |x|_2 +/- eps|x|_1. For all the problems we study, tight bounds are already known for the randomized complexity from previous work, except in the case of l1/l1 sparse recovery, where a nearly tight bound is known. Our work thus aims to study the deterministic complexities of these problems

Cluster Editing: Kernelization based on Edge Cuts

Kernelization algorithms for the {\sc cluster editing} problem have been a popular topic in the recent research in parameterized computation. Thus far most kernelization algorithms for this problem are based on the concept of {\it critical cliques}. In this paper, we present new observations and new techniques for the study of kernelization algorithms for the {\sc cluster editing} problem. Our techniques are based on the study of the relationship between {\sc cluster editing} and graph edge-cuts. As an application, we present an ${\cal O}(n^2)$-time algorithm that constructs a $2k$ kernel for the {\it weighted} version of the {\sc cluster editing} problem. Our result meets the best kernel size for the unweighted version for the {\sc cluster editing} problem, and significantly improves the previous best kernel of quadratic size for the weighted version of the problem

Use of the Spine AdVerse Events Severity (SAVES) System to Categorize and Report Adverse Events in Spine Surgery.

Introduction: Analysis of adverse events (AEs) in spine surgery has historically been retrospective, utilizing hospital administrative data. Our objective was to determine the incidence, severity and effect on hospital length of stay (LOS) for AEs in spine surgery using the Spine AdVerse Events Severity (SAVES V2) system. Methods: AEs for all surgical spine patients at our institution were prospectively collected for 18 months and correlated with retrospective data from operative reports and H&Ps. Statistical analyses compared patient demographics, diagnoses, and surgical characteristics to hospital length of stay and likelihood of adverse events. Results: This system captured 75% (765/977) of surgical cases for all indications over the study period. 73% (541/743) of patients experienced at least one AE, with an average of 1.2 AEs per patient (range 0-5). The most common AEs were pain control (31%), urinary retention (9.7%), wound infection (6.3%), and incidental durotomy (5.8%). For patients experiencing at least one AE, 30% had no effect on LOS, 48% increased LOS by 1-2 days, 15% increased LOS by 3-7 days, and 7% had prolonged LOS greater than 8 days. Our system captured 25.4% more adverse events (60.0% vs. 34.6%) than hospital administrative data. Univariate analysis revealed patient age, emergent surgery, diagnostic and surgical categories, and spine region to be predictors of both AEs and LOS. Instrumentation was predictive of increased LOS but not AEs. The type of AE was strongly associated with LOS. Multivariable analysis of AE likelihood demonstrated emergent surgery to be the strongest independent predictor with an adjusted odds ratio of 8.5 versus elective surgery. Discussion: Spine surgery is associated with a high incidence of adverse events, which often prolong hospital length of stay. Better characterization of adverse events and their predictors could lead to improved management strategies that reduce patient morbidity and mortality

Exact Weight Subgraphs and the k-Sum Conjecture

We consider the Exact-Weight-H problem of finding a (not necessarily induced) subgraph H of weight 0 in an edge-weighted graph G. We show that for every H, the complexity of this problem is strongly related to that of the infamous k-Sum problem. In particular, we show that under the k-Sum Conjecture, we can achieve tight upper and lower bounds for the Exact-Weight-H problem for various subgraphs H such as matching, star, path, and cycle. One interesting consequence is that improving on the O(n^3) upper bound for Exact-Weight-4-Path or Exact-Weight-5-Path will imply improved algorithms for 3-Sum, 5-Sum, All-Pairs Shortest Paths and other fundamental problems. This is in sharp contrast to the minimum-weight and (unweighted) detection versions, which can be solved easily in time O(n^2). We also show that a faster algorithm for any of the following three problems would yield faster algorithms for the others: 3-Sum, Exact-Weight-3-Matching, and Exact-Weight-3-Star

Boundary work: An interpretive ethnographic perspective on negotiating and leveraging cross-cultural identity

The complexity of global organizations highlights the importance of members’ ability to span diverse boundaries that may be defined by organization structures, national borders, and/or a variety of cultures associated with organization, nation-based societal and work cultures, industries, and/or professions. Based on ethnographic research in a Japan–US binational firm, the paper describes and analyzes the boundary role performance of the firm\u27s Japanese members. It contributes toward theory on boundary spanning by introducing a “cultural identity negotiation” conceptual framework. We show boundary spanning as a process shaped through the interplay of the contextual issues that make a boundary problematic; an individual\u27s multiple repertoires of cultural knowledge; and the individual boundary spanner\u27s “negotiation”, through interaction with others, of his/her cultural identities – the sense of “who I am” as a cultural being that is fundamental to an individual\u27s self-concept. At the same time, we make transparent the epistemological and methodological foundations of an interpretive ethnographic approach, demonstrating its value for understanding complex organizational processes. Research findings have practical implications for the selection and training of an organization\u27s employees, particularly of persons who may be considered “bicultural”

A new research agenda for managing socio-cultural integration

Post-acquisition socio-cultural integration has received increasing attention from both scholars and practitioners since the early 1990s. During the past decade, research has increasingly focused on emotions and identity in mergers and acquisitions. This chapter introduces the reader to the vibrant research field and its relevance. This section sets the scene for the book, which provides a deeper understanding of how emotions—both positive and negative—as well as values and identity enable a deeper socio-cultural integration after a merger or acquisition, and how leadership plays a crucial role in making it all happen. This chapter also highlights how the Nordic approach to post-acquisition socio-cultural integration refers to a large community of Nordic academics focusing on the softer social and human side of acquisition, often relying on a huge variety of qualitative methods, and to Nordic companies that are not afraid of adopting a more collaborative approach to post-acquisition integration

Cyclic and Sleep-Like Spontaneous Alternations of Brain State Under Urethane Anaesthesia

Background: Although the induction of behavioural unconsciousness during sleep and general anaesthesia has been shown to involve overlapping brain mechanisms, sleep involves cyclic fluctuations between different brain states known as active (paradoxical or rapid eye movement: REM) and quiet (slow-wave or non-REM: nREM) stages whereas commonly used general anaesthetics induce a unitary slow-wave brain state. Methodology/Principal Findings: Long-duration, multi-site forebrain field recordings were performed in urethaneanaesthetized rats. A spontaneous and rhythmic alternation of brain state between activated and deactivated electroencephalographic (EEG) patterns was observed. Individual states and their transitions resembled the REM/nREM cycle of natural sleep in their EEG components, evolution, and time frame (,11 minute period). Other physiological variables such as muscular tone, respiration rate, and cardiac frequency also covaried with forebrain state in a manner identical to sleep. The brain mechanisms of state alternations under urethane also closely overlapped those of natural sleep in their sensitivity to cholinergic pharmacological agents and dependence upon activity in the basal forebrain nuclei that are the major source of forebrain acetylcholine. Lastly, stimulation of brainstem regions thought to pace state alternations in sleep transiently disrupted state alternations under urethane. Conclusions/Significance: Our results suggest that urethane promotes a condition of behavioural unconsciousness tha