The exact sample complexity of PAC-learning problems with unit VC dimension

The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine learning problems, which may be used to obtain upper and lower bounds on the number of training examples needed to learn to prescribed levels of accuracy. Most of the known bounds apply to the Probably Approximately Correct (PAC) framework, which is the framework within which we work in this paper. For a learning problem with some known VC dimension, much is known about the order of growth of the sample-size requirement of the problem, as a function of the PAC parameters. The exact value of sample-size requirement is however less well-known, and depends heavily on the particular learning algorithm being used. This is a major obstacle to the practical application of the VC dimension. Hence it is important to know exactly how the sample-size requirement depends on VC dimension, and with that in mind, we describe a general algorithm for learning problems having VC dimension 1. Its sample-size requirement is minimal (as a function of the PAC parameters), and turns out to be the same for all non-trivial learning problems having VC dimension 1. While the method used cannot be naively generalised to higher VC dimension, it suggests that optimal algorithm-dependent bounds may improve substantially on current upper bounds.

On the annealed VC entropy for margin classifiers: A statistical mechanics study

Using techniques from Statistical Physics, the annealed VC entropy for hyperplanes in high dimensional spaces is calculated as a function of the margin for a spherical Gaussian distribution of inputs.

Reduction of Secondary Degeneration after Spinal Cord Injury by Acute Delivery of Proinflammatory Growth Factors

INTRODUCTION Inflammation is a protective attempt to facilitate the removal of damaged tissue and to initiate the healing response in other tissues. However, after spinal cord injury (SCI), this response is prolonged leading to secondary degeneration and glial scarring. Here, we investigate the potential of sustained delivery of pro-inflammatory factors vascular endothelial growth factor (VEGF) and platelet derived growth factor (PDGF) to increase early inflammatory events and promote inflammatory resolution. Method Animal ethics approval was obtained from the Queensland University of Technology. Adult Wistar-Kyoto rats (12-16 weeks old) were subjected to laminectomies and T10 hemisections. Animals were then randomised to treatment (implantation of osmotic pump (Alzet) loaded with 5ug VEGF & 5 ug PDGF) or control groups (lesion control or lesion plus pump delivering PBS). Rats were sacrificed at one month and the spinal cords were harvested and examined by immunohistology, using anti-neurofilament-200(NF200) and anti- ionized calcium binding adapter molecule 1 (Iba1). One way ANOVA was used for statistic analysis. Results At 1 month, active pump-treated cords showed a high level of axonal filament throughout the defects as compared to the control groups. The mean lesion size, as measured by NF200, was 0.47mm2 for the lesion control, 0.39mm2 for the vehicle control and 0.078mm2 for the active pump group. Significant differences were detected between the active pump group and the two control groups (AP vs LC p= 0.017 AG vs VC p= 0.004). Iba-1 staining also showed significant differences in the post-injury inflammatory response. Discussion We have shown that axons and activated microglia are co-located in the lesion of the treated cord. We hypothesise the delivery of VEGF/PDGF increases the local vessel permeability to inflammatory cells and activates these along with the resident microglia to threshold population, which ultimately resolved the prolonged inflammation. Here, we have shown that maintaining the inflammatory signals for at least 7 days improved the morphology of the injured cord. Conclusion This study has shown that boosting inflammation, by delivery VEGF/PDGF, in the early phase of SCI helps to reduce secondary degeneration and may promote inflammation resolution. This treatment may provide a platform for other neuro-regenrative therapies.

Weighted fairness with MCR guarantee and a general-purpose explicit rate allocation algorithm for ATM ABR service

In this paper, weighted fair rate allocation for ATM available bit rate (ABR) service is discussed with the concern of the minimum cell rate (MCR). Weighted fairness with MCR guarantee has been discussed recently in the literature. In those studies, each ABR virtual connection (VC) is first allocated its MCR, then the remaining available bandwidth is further shared among ABR VCs according to their weights. For the weighted fairness defined in this paper, the bandwidth is first allocated according to each VC's weight; if a VC's weighted share is less than its MCR, it should be allocated its MCR instead of the weighted share. This weighted fairness with MCR guarantee is referred to as extended weighted (EXW) fairness. Certain theoretical issues related to EXW, such as its global solution and bottleneck structure, are first discussed in the paper. A distributed explicit rate allocation algorithm is then proposed to achieve EXW fairness in ATM networks. The algorithm is a general-purpose explicit rate algorithm in the sense that it can realise almost all the fairness principles proposed for ABR so far whilst only minor modifications may be needed.

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Shifting : one-inclusion mistake bounds and sample compression

We present new expected risk bounds for binary and multiclass prediction, and resolve several recent conjectures on sample compressibility due to Kuzmin and Warmuth. By exploiting the combinatorial structure of concept class F, Haussler et al. achieved a VC(F)/n bound for the natural one-inclusion prediction strategy. The key step in their proof is a d = VC(F) bound on the graph density of a subgraph of the hypercube—oneinclusion graph. The first main result of this paper is a density bound of n [n−1 <=d-1]/[n <=d] < d, which positively resolves a conjecture of Kuzmin and Warmuth relating to their unlabeled Peeling compression scheme and also leads to an improved one-inclusion mistake bound. The proof uses a new form of VC-invariant shifting and a group-theoretic symmetrization. Our second main result is an algebraic topological property of maximum classes of VC-dimension d as being d contractible simplicial complexes, extending the well-known characterization that d = 1 maximum classes are trees. We negatively resolve a minimum degree conjecture of Kuzmin and Warmuth—the second part to a conjectured proof of correctness for Peeling—that every class has one-inclusion minimum degree at most its VCdimension. Our final main result is a k-class analogue of the d/n mistake bound, replacing the VC-dimension by the Pollard pseudo-dimension and the one-inclusion strategy by its natural hypergraph generalization. This result improves on known PAC-based expected risk bounds by a factor of O(logn) and is shown to be optimal up to an O(logk) factor. The combinatorial technique of shifting takes a central role in understanding the one-inclusion (hyper)graph and is a running theme throughout.

Shifting : one-inclusion mistake bounds and sample compression

We present new expected risk bounds for binary and multiclass prediction, and resolve several recent conjectures on sample compressibility due to Kuzmin and Warmuth. By exploiting the combinatorial structure of concept class F, Haussler et al. achieved a VC(F)/n bound for the natural one-inclusion prediction strategy. The key step in their proof is a d=VC(F) bound on the graph density of a subgraph of the hypercube—one-inclusion graph. The first main result of this report is a density bound of n∙choose(n-1,≤d-1)/choose(n,≤d) < d, which positively resolves a conjecture of Kuzmin and Warmuth relating to their unlabeled Peeling compression scheme and also leads to an improved one-inclusion mistake bound. The proof uses a new form of VC-invariant shifting and a group-theoretic symmetrization. Our second main result is an algebraic topological property of maximum classes of VC-dimension d as being d-contractible simplicial complexes, extending the well-known characterization that d=1 maximum classes are trees. We negatively resolve a minimum degree conjecture of Kuzmin and Warmuth—the second part to a conjectured proof of correctness for Peeling—that every class has one-inclusion minimum degree at most its VC-dimension. Our final main result is a k-class analogue of the d/n mistake bound, replacing the VC-dimension by the Pollard pseudo-dimension and the one-inclusion strategy by its natural hypergraph generalization. This result improves on known PAC-based expected risk bounds by a factor of O(log n) and is shown to be optimal up to a O(log k) factor. The combinatorial technique of shifting takes a central role in understanding the one-inclusion (hyper)graph and is a running theme throughout

The diagnostic accuracy of telegeriatrics for the diagnosis of dementia via video conferencing

Introduction The suitability of video conferencing (VC) technology for clinical purposes relevant to geriatric medicine is still being established. This project aimed to determine the validity of the diagnosis of dementia via VC. Methods This was a multisite, noninferiority, prospective cohort study. Patients, aged 50 years and older, referred by their primary care physician for cognitive assessment, were assessed at 4 memory disorder clinics. All patients were assessed independently by 2 specialist physicians. They were allocated one face-to-face (FTF) assessment (Reference standard – usual clinical practice) and an additional assessment (either usual FTF assessment or a VC assessment) on the same day. Each specialist physician had access to the patient chart and the results of a battery of standardized cognitive assessments administered FTF by the clinic nurse. Percentage agreement (P0) and the weighted kappa statistic with linear weight (Kw) were used to assess inter-rater reliability across the 2 study groups on the diagnosis of dementia (cognition normal, impaired, or demented). Results The 205 patients were allocated to group: Videoconference (n = 100) or Standard practice (n = 105); 106 were men. The average age was 76 (SD 9, 51–95) and the average Standardized Mini-Mental State Examination Score was 23.9 (SD 4.7, 9–30). Agreement for the Videoconference group (P0= 0.71; Kw = 0.52; P < .0001) and agreement for the Standard Practice group (P0= 0.70; Kw = 0.50; P < .0001) were both statistically significant (P < .05). The summary kappa statistic of 0.51 (P = .84) indicated that VC was not inferior to FTF assessment. Conclusions Previous studies have shown that preliminary standardized assessment tools can be reliably administered and scored via VC. This study focused on the geriatric assessment component of the interview (interpretation of standardized assessments, taking a history and formulating a diagnosis by medical specialist) and identified high levels of agreement for diagnosing dementia. A model of service incorporating either local or remote administered standardized assessments, and remote specialist assessment, is a reliable process for enabling the diagnosis of dementia for isolated older adults.

Effect of body position on measurements of diffusion capacity after exercise

Background--Pulmonary diffusing capacity for carbon monoxide (Dlco), alveolar capillary membrane diffusing capacity (Dm), and pulmonary capillary blood volume (Vc) are all significantly reduced after exercise. Objective--To investigate whether measurement position affects this impaired gas transfer. Methods--Before and one, two, and four hours after incremental cycle ergometer exercise to fatigue, single breath Dlco, Dm, and Vc measurements were obtained in 10 healthy men in a randomly assigned supine and upright seated position. Results--After exercise, Dlco, Dm, and Vc were significantly depressed compared with baseline in both positions. The supine position produced significantly higher values over time for Dlco (5.22 (0.13) v 4.66 (0.15) ml/min/mm Hg/l, p = 0.022) and Dm (6.78 (0.19) v 6.03 (0.19) ml/min/mm Hg/l, p = 0.016), but there was no significant position effect for Vc. There was a similar pattern of change over time for Dlco, Dm, and Vc in the two positions. Conclusions--The change in Dlco after exercise appears to be primarily due to a decrease in Vc. Although the mechanism for the reduction in Vc cannot be determined from these data, passive relocation of blood to the periphery as the result of gravity can be discounted, suggesting that active vasoconstriction of the pulmonary vasculature and/or peripheral vasodilatation is occurring after exercise.

ACE research vignette 030 : Is venture capital investment the key to success?

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. This vignette, written by Professor Per Davidsson, reports on a paper which synthesizes available research on the effects of VC funding on the performance of the funded firm.

Rho GTPases mediate the mechanosensitive lineage commitment of neural stem cells

Adult neural stem cells (NSCs) play important roles in learning and memory and are negatively impacted by neurological disease. It is known that biochemical and genetic factors regulate self-renewal and differentiation, and it has recently been suggested that mechanical and solid-state cues, such as extracellular matrix (ECM) stiffness, can also regulate the functions of NSCs and other stem cell types. However, relatively little is known of the molecular mechanisms through which stem cells transduce mechanical inputs into fate decisions, the extent to which mechanical inputs instruct fate decisions versus select for or against lineage-committed blast populations, or the in vivo relevance of mechanotransductive signaling molecules in native stem cell niches. Here we demonstrate that ECM-derived mechanical signals act through Rho GTPases to activate the cellular contractility machinery in a key early window during differentiation to regulate NSC lineage commitment. Furthermore, culturing NSCs on increasingly stiff ECMs enhances RhoA and Cdc42 activation, increases NSC stiffness, and suppresses neurogenesis. Likewise, inhibiting RhoA and Cdc42 or downstream regulators of cellular contractility rescues NSCs from stiff matrix- and Rho GTPase-induced neurosuppression. Importantly, Rho GTPase expression and ECM stiffness do not alter proliferation or apoptosis rates indicating that an instructive rather than selective mechanism modulates lineage distributions. Finally, in the adult brain, RhoA activation in hippocampal progenitors suppresses neurogenesis, analogous to its effect in vitro. These results establish Rho GTPase-based mechanotransduction and cellular stiffness as biophysical regulators of NSC fate in vitro and RhoA as an important regulatory protein in the hippocampal stem cell niche.

Bisphenol A degradation enhanced by air bubbles via advanced oxidation using in situ generated ferrous ions from nano zero-valent iron/palygorskite composite materials

Novel nano zero-valent iron/palygorskite composite materials prepared by evaporative and centrifuge methods are tested for the degradation of bisphenol A in an aqueous medium. A systematic study is presented which showed that nano zero-valent iron material has little effect on bisphenol A degradation. When hydrogen peroxide was added to initiate the reaction, some percentage of bisphenol A removal (∼20%) was achieved; however, with the aid of air bubbles, the percentage removal can be significantly increased to ∼99%. Compared with pristine nano zero-valent iron and commercial iron powder, nano zero-valent iron/palygorskite composite materials have much higher reactivity towards bisphenol A and these materials are superior as they have little impact on the solution pH. However, for pristine nano zero-valent iron, it is difficult to maintain the reaction system at a favourable low pH which is a key factor in maintaining high bisphenol A removal. All materials were characterized by X-ray diffraction, scanning electron microscopy, elemental analysis, transmission electron microscopy and X-ray photoelectron spectroscopy. The optimum conditions were obtained based on a series of batch experiments. This study has extended the application of nano zero-valent iron/palygorskite composites as effective materials for the removal of phenolic compounds from the environment.

Inheritance of qualitative and quantitative trypsin inhibitor variants in Pisum

A trypsin inhibitor locus (Tri) has been mapped close to Vc-2 on Pisum (pea) linkage group 5 using recombinant inbred lines derived from crosses of genotypes showing qualitative variation in seed trypsin inhibitors. F2 seed populations derived from crosses between lines showing qualitative variation in trypsin inhibitors as well as quantitative variation in inhibitor activity showed an association between the segregation of the structural variation and relative activity levels. Clones complementary to Pisum trypsin inhibitor mRNA were used in hybridization analyses which showed that the segregation of protein polymorphisms reflected directly the segregation of polymorphisms associated with the structural genes.

Family Mobility : Reconciling Career Opportunities and Educational Strategy

Family mobility decisions reveal much about how the public and private realms of social life interact and change. This sociological study explores how contemporary families reconcile individual members’ career and education projects within the family unit over time and space, and unpacks the intersubjective constraints on workforce mobility. This Australian mixed methods study sampled Defence Force families and middle class professional families to illustrate how families’ educational projects are necessarily and deeply implicated in issues of workforce mobility and immobility, in complex ways. Defence families move frequently, often absorbing the stresses of moving through ‘viscous’ institutions as private troubles. In contrast, the selective mobility of middle class professional families and their ‘no go zones’ contribute to the public issue of poorly serviced rural communities. Families with different social, material and vocational resources at their disposal are shown to reflexively weigh the benefits and risks associated with moving differently. The book also explore how priorities shift as children move through educational phases. The families’ narratives offer empirical windows on larger social processes, such as the mobility imperative, the gender imbalance in the family’s intersubjective bargains, labour market credentialism, the social construction of place, and the family’s role in the reproduction of class structure.