School of Mathematics and Statistics  Research Publications
http://hdl.handle.net/11343/294
20200930T06:57:00Z

On conformal field theories based on Takiff superalgebras
http://hdl.handle.net/11343/242031
Quella, T
20200414
On conformal field theories based on Takiff superalgebras
We revisit the construction of conformal field theories based on Takiff algebras and superalgebras that was introduced by Babichenko and Ridout. Takiff superalgebras can be thought of as truncated current superalgebras with grading which arise from taking p copies of a Lie superalgebra g and placing them in the degrees s = 0,¼, p  1.Using suitably defined nondegenerate invariant forms we show that Takiff superalgebras give rise to families of conformal field theories with central charge c = psdimg. The resulting conformal field theories are defined in the standard way, i.e. they lend themselves to a Lagrangian description in terms of aWZW model and their chiral energy momentum tensor is the one obtained naturally from the usual Sugawara construction. In view of their intricate representation theory they provide interesting examples of conformal field theories.
Journal Article
20200414T00:00:00Z

Isolating the sources of heterogeneity in nanoengineered particlecell interactions.
http://hdl.handle.net/11343/241848
Johnston, ST; Faria, M; Crampin, EJ
20200527
Isolating the sources of heterogeneity in nanoengineered particlecell interactions.
Nanoengineered particles have the potential to enhance therapeutic success and reduce toxicitybased treatment side effects via the targeted delivery of drugs to cells. This delivery relies on complex interactions between numerous biological, chemical and physical processes. The intertwined nature of these processes has thus far hindered attempts to understand their individual impact. Variation in experimental data, such as the number of particles inside each cell, further inhibits understanding. Here, we present a mathematical framework that is capable of examining the impact of individual processes during particle delivery. We demonstrate that variation in experimental particle uptake data can be explained by three factors: random particle motion; variation in particlecell interactions; and variation in the maximum particle uptake per cell. Without all three factors, the experimental data cannot be explained. This work provides insight into biological mechanisms that cause heterogeneous responses to treatment, and enables precise identification of treatmentresistant cell subpopulations.
Journal Article
20200527T00:00:00Z

DataDriven Approach to MultipleSource Domain Adaptation
http://hdl.handle.net/11343/241495
Stojanov, P; Gong, M; Carbonell, J; Zhang, K
2019
DataDriven Approach to MultipleSource Domain Adaptation
A key problem in domain adaptation is determining what to transfer across different domains. We propose a datadriven method to represent these changes across multiple source domains and perform unsupervised domain adaptation. We assume that the joint distributions follow a specific generating process and have a small number of identifiable changing parameters, and develop a datadriven method to identify the changing parameters by learning lowdimensional representations of the changing classconditional distributions across multiple source domains. The learned lowdimensional representations enable us to reconstruct the targetdomain joint distribution from unlabeled targetdomain data, and further enable predicting the labels in the target domain. We demonstrate the efficacy of this method by conducting experiments on synthetic and real datasets.
Conference Paper
20190101T00:00:00Z

GeometryConsistent Generative Adversarial Networks for OneSided Unsupervised Domain Mapping
http://hdl.handle.net/11343/241494
Fu, H; Gong, M; Wang, C; Batmanghelich, K; Zhang, K; Tao, D
2019
GeometryConsistent Generative Adversarial Networks for OneSided Unsupervised Domain Mapping
Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples. Finding the optimal GXY without paired data is an illposed problem, so appropriate constraints are required to obtain reasonable solutions. One of the most prominent constraints is cycle consistency, which enforces the translated image by GXY to be translated back to the input image by an inverse mapping GYX. While cycle consistency requires the simultaneous training of GXY and GY X, recent studies have shown that onesided domain mapping can be achieved by preserving pairwise distances between images. Although cycle consistency and distance preservation successfully constrain the solution space, they overlook the special properties that simple geometric transformations do not change the semantic structure of images. Based on this special property, we develop a geometryconsistent generative adversarial network (GcGAN), which enables onesided unsupervised domain mapping. GcGAN takes the original image and its counterpart image transformed by a predefined geometric transformation as inputs and generates two images in the new domain coupled with the corresponding geometryconsistency constraint. The geometryconsistency constraint reduces the space of possible solutions while keep the correct solutions in the search space. Quantitative and qualitative comparisons with the baseline (GAN alone) and the stateoftheart methods including CycleGAN and DistanceGAN demonstrate the effectiveness of our method.
Conference Paper
20190101T00:00:00Z

Causal Discovery with Linear NonGaussian Models under Measurement Error: Structural Identifiability Results.
http://hdl.handle.net/11343/241493
Zhang, K; Gong, M; Ramsey, J; Batmanghelich, K; Spirtes, P; Glymour, C
2018
Causal Discovery with Linear NonGaussian Models under Measurement Error: Structural Identifiability Results.
Causal discovery methods aim to recover the causal process that generated purely observational data. Despite its successes on a number of real problems, the presence of measurement error in the observed data can produce serious mistakes in the output of various causal discovery methods. Given the ubiquity of measurement error caused by instruments or proxies used in the measuring process, this problem is one of the main obstacles to reliable causal discovery. It is still unknown to what extent the causal structure of relevant variables can be identified in principle. This study aims to take a step towards filling that void. We assume that the underlining process or the measurementerror free variables follows a linear, nonGuassian causal model, and show that the socalled ordered group decomposition of the causal model, which contains major causal information, is identifiable. The causal structure identifiability is further improved with different types of sparsity constraints on the causal structure. Finally, we give rather mild conditions under which the whole causal structure is fully identifiable.
Conference Paper
20180101T00:00:00Z

Deep Ordinal Regression Network for Monocular Depth Estimation
http://hdl.handle.net/11343/241492
Fu, H; Gong, M; Wang, C; Batmanghelich, K; Tao, D
2018
Deep Ordinal Regression Network for Monocular Depth Estimation
Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an illposed problem. Recent methods have gained significant improvement by exploring imagelevel information and hierarchical features from deep convolutional neural networks (DCNNs). These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions. Besides, existing depth estimation networks employ repeated spatial pooling operations, resulting in undesirable lowresolution feature maps. To obtain highresolution depth maps, skipconnections or multilayer deconvolution networks are required, which complicates network training and consumes much more computations. To eliminate or at least largely reduce these problems, we introduce a spacingincreasing discretization (SID) strategy to discretize depth and recast depth network learning as an ordinal regression problem. By training the network using an ordinary regression loss, our method achieves much higher accuracy and faster convergence in synch. Furthermore, we adopt a multiscale network structure which avoids unnecessary spatial pooling and captures multiscale information in parallel. The proposed deep ordinal regression network (DORN) achieves stateoftheart results on three challenging benchmarks, i.e., KITTI [16], Make3D [49], and NYU Depth v2 [41], and outperforms existing methods by a large margin.
Conference Paper
20180101T00:00:00Z

Causal discovery and forecasting in nonstationary environments with statespace models
http://hdl.handle.net/11343/241491
Huang, B; Zhang, K; Gong, M; Glymour, C
2019
Causal discovery and forecasting in nonstationary environments with statespace models
In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments. In this paper, we study causal discovery and forecasting for nonstationary time series. By exploiting a particular type of statespace model to represent the processes, we show that nonstationarity helps to identify the causal structure, and that forecasting naturally benefits from learned causal knowledge. Specifically, we allow changes in both causal strengths and noise variances in the nonlinear statespace models, which, interestingly, renders both the causal structure and model parameters identifiable. Given the causal model, we treat forecasting as a problem in Bayesian inference in the causal model, which exploits the timevarying property of the data and adapts to new observations in a principled manner. Experimental results on synthetic and realworld data sets demonstrate the efficacy of the proposed methods.
Conference Paper
20190101T00:00:00Z

LikelihoodFree Overcomplete ICA and Applications In Causal Discovery
http://hdl.handle.net/11343/241490
Chenwei, DING; Gong, M; Zhang, K; Tao, D
2020
LikelihoodFree Overcomplete ICA and Applications In Causal Discovery
Causal discovery witnessed significant progress over the past decades. In particular, many recent causal discovery methods make use of independent, nonGaussian noise to achieve identifiability of the causal models. Existence of hidden direct common causes, or confounders, generally makes causal discovery more difficult; whenever they are present, the corresponding causal discovery algorithms can be seen as extensions of overcomplete independent component analysis (OICA). However, existing OICA algorithms usually make strong parametric assumptions on the distribution of independent components, which may be violated on real data, leading to suboptimal or even wrong solutions. In addition, existing OICA algorithms rely on the Expectation Maximization (EM) procedure that requires computationally expensive inference of the posterior distribution of independent components. To tackle these problems, we present a LikelihoodFree Overcomplete ICA algorithm (LFOICA) that estimates the mixing matrix directly by backpropagation without any explicit assumptions on the density function of independent components. Thanks to its computational efficiency, the proposed method makes a number of causal discovery procedures much more practically feasible. For illustrative purposes, we demonstrate the computational efficiency and efficacy of our method in two causal discovery tasks on both synthetic and real data.
Conference Paper
20200101T00:00:00Z

Specific and Shared Causal Relation Modeling and MechanismBased Clustering
http://hdl.handle.net/11343/241489
Huang, B; Zhang, K; Xie, P; Gong, M; Xing, EP; Glymour, C
2020
Specific and Shared Causal Relation Modeling and MechanismBased Clustering
Stateoftheart approaches to causal discovery usually assume a fixed underlying causal model. However, it is often the case that causal models vary across domains or subjects, due to possibly omitted factors that affect the quantitative causal effects. As a typical example, causal connectivity in the brain network has been reported to vary across individuals, with significant differences across groups of people, such as autistics and typical controls. In this paper, we develop a unified framework for causal discovery and mechanismbased group identification. In particular, we propose a specific and shared causal model (SSCM), which takes into account the variabilities of causal relations across individuals/groups and leverages their commonalities to achieve statistically reliable estimation. The learned SSCM gives the specific causal knowledge for each individual as well as the general trend over the population. In addition, the estimated model directly provides the group information of each individual. Experimental results on synthetic and realworld data demonstrate the efficacy of the proposed method.
Conference Paper
20200101T00:00:00Z

Twin Auxilary Classifiers GAN
http://hdl.handle.net/11343/241488
Gong, M; Xu, Y; Li, C; Zhang, K; Batmanghelich, K
2020
Twin Auxilary Classifiers GAN
Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (ACGAN), which generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier. However, the diversity of the generated samples by ACGAN tends to decrease as the number of classes increases, hence limiting its power on largescale data. In this paper, we identify the source of the low diversity issue theoretically and propose a practical solution to solve the problem. We show that the auxiliary classifier in ACGAN imposes perfect separability, which is disadvantageous when the supports of the class distributions have significant overlap. To address the issue, we propose Twin Auxiliary Classifiers Generative Adversarial Net (TACGAN) that further benefits from a new player that interacts with other players (the generator and the discriminator) in GAN. Theoretically, we demonstrate that TACGAN can effectively minimize the divergence between the generated and realdata distributions. Extensive experimental results show that our TACGAN can successfully replicate the true data distributions on simulated data, and significantly improves the diversity of classconditional image generation on real datasets.
Conference Paper
20200101T00:00:00Z

Causal Discovery from NonIdentical Variable Sets
http://hdl.handle.net/11343/241487
Huang, B; Zhang, K; Gong, M; Glymour, C
2020
Causal Discovery from NonIdentical Variable Sets
A number of approaches to causal discovery assume that there are no hidden confounders and are designed to learn a fixed causal model from a single data set. Over the last decade, with closer cooperation across laboratories, we are able to accumulate more variables and data for analysis, while each lab may only measure a subset of them, due to technical constraints or to save time and cost. This raises a question of how to handle causal discovery from multiple data sets with nonidentical variable sets, and at the same time, it would be interesting to see how more recorded variables can help to mitigate the confounding problem. In this paper, we propose a principled method to uniquely identify causal relationships over the integrated set of variables from multiple data sets, in linear, nonGaussian cases. The proposed method also allows distribution shifts across data sets. Theoretically, we show that the causal structure over the integrated set of variables is identifiable under testable conditions. Furthermore, we present two types of approaches to parameter estimation: one is based on maximum likelihood, and the other is likelihood free and leverages generative adversarial nets to improve scalability of the estimation procedure. Experimental results on various synthetic and realworld data sets are presented to demonstrate the efficacy of our methods.
Conference Paper
20200101T00:00:00Z

Compressed SelfAttention for Deep Metric Learning
http://hdl.handle.net/11343/241486
Ziye, C; Gong, M; Xu, Y; Wang, C; Zhang, K; Du, B
2020
Compressed SelfAttention for Deep Metric Learning
In this paper, we aim to enhance selfattention (SA) mechanism for deep metric learning in visual perception, by capturing richer contextual dependencies in visual data. To this end, we propose a novel module, named compressed selfattention (CSA), which significantly reduces the computation and memory cost with a neglectable decrease in accuracy with respect to the original SA mechanism, thanks to the following two characteristics: i) it only needs to compute a small number of base attention maps for a small number of base feature vectors; and ii) the output at each spatial location can be simply obtained by an adaptive weighted average of the outputs calculated from the base attention maps. The high computational efficiency of CSA enables the application to highresolution shallow layers in convolutional neural networks with little additional cost. In addition, CSA makes it practical to further partition the feature maps into groups along the channel dimension and compute attention maps for features in each group separately, thus increasing the diversity of longrange dependencies and accordingly boosting the accuracy. We evaluate the performance of CSA via extensive experiments on two metric learning tasks: person reidentification and local descriptor learning. Qualitative and quantitative comparisons with latest methods demonstrate the significance of CSA in this topic.
Conference Paper
20200101T00:00:00Z

GenerativeDiscriminative Complementary Learning
http://hdl.handle.net/11343/241485
Xu, Y; Gong, M; Chen, J; Liu, T; Zhang, K; Batmanghelich, K
2020
GenerativeDiscriminative Complementary Learning
The majority of stateoftheart deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on highquality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases. In this paper, we study the complementary learning problem. Unlike ordinary labels, complementary labels are easy to obtain because an annotator only needs to provide a yes/no answer to a randomly chosen candidate class for each instance. We propose a generativediscriminative complementary learning method that estimates the ordinary labels by modeling both the conditional (discriminative) and instance (generative) distributions. Our method, we call Complementary Conditional GAN (CCGAN), improves the accuracy of predicting ordinary labels and is able to generate highquality instances in spite of weak supervision. In addition to the extensive empirical studies, we also theoretically show that our model can retrieve the true conditional distribution from the complementarilylabeled data.
Conference Paper
20200101T00:00:00Z

Youth Depression Alleviation with Antiinflammatory Agents (YoDAA): a randomised clinical trial of rosuvastatin and aspirin
http://hdl.handle.net/11343/241374
Berk, M; Mohebbi, M; Dean, OM; Cotton, SM; Chanen, AM; Dodd, S; Ratheesh, A; Amminger, GP; Phelan, M; Weller, A; Mackinnon, A; Giorlando, F; Baird, S; Incerti, L; Brodie, RE; Ferguson, NO; Rice, S; Schafer, MR; Mullen, E; Hetrick, S; Kerr, M; Harrigan, SM; Quinn, AL; Mazza, C; McGorry, P; Davey, CG
20200117
Youth Depression Alleviation with Antiinflammatory Agents (YoDAA): a randomised clinical trial of rosuvastatin and aspirin
BACKGROUND: Inflammation contributes to the pathophysiology of major depressive disorder (MDD), and antiinflammatory strategies might therefore have therapeutic potential. This trial aimed to determine whether adjunctive aspirin or rosuvastatin, compared with placebo, reduced depressive symptoms in young people (1525 years). METHODS: YoDAA, Youth Depression Alleviation with Antiinflammatory Agents, was a 12week tripleblind, randomised, controlled trial. Participants were young people (aged 1525 years) with moderate to severe MDD (MADRS mean at baseline 32.5 ± 6.0; N = 130; age 20.2 ± 2.6; 60% female), recruited between June 2013 and June 2017 across six sites in Victoria, Australia. In addition to treatment as usual, participants were randomised to receive aspirin (n = 40), rosuvastatin (n = 48), or placebo (n = 42), with assessments at baseline and weeks 4, 8, 12, and 26. The primary outcome was change in the MontgomeryÅsberg Depression Rating Scale (MADRS) from baseline to week 12. RESULTS: At the a priori primary endpoint of MADRS differential change from baseline at week 12, there was no significant difference between aspirin and placebo (1.9, 95% CI ( 2.8, 6.6), p = 0.433), or rosuvastatin and placebo ( 4.2, 95% CI ( 9.1, 0.6), p = 0.089). For rosuvastatin, secondary outcomes on selfrated depression and global impression, quality of life, functioning, and mania were not significantly different from placebo. Aspirin was inferior to placebo on the Quality of Life Enjoyment and Satisfaction Questionnaire (QLESQSF) at week 12. Statins were superior to aspirin on the MADRS, the Clinical Global Impressions Severity Scale (CGIS), and the Negative Problem Orientation Questionnaire scale (NPOQ) at week 12. CONCLUSIONS: The addition of either aspirin or rosuvastatin did not to confer any beneficial effect over and above routine treatment for depression in young people. Exploratory comparisons of secondary outcomes provide limited support for a potential therapeutic role for adjunctive rosuvastatin, but not for aspirin, in youth depression. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12613000112763. Registered on 30/01/2013.
Journal Article
20200117T00:00:00Z

Assessment of serum symmetric dimethylarginine and creatinine concentrations in hyperthyroid cats before and after a fixed dose of orally administered radioiodine
http://hdl.handle.net/11343/240445
Yu, L; Lacorcia, L; Finch, S; Johnstone, T
2020
Assessment of serum symmetric dimethylarginine and creatinine concentrations in hyperthyroid cats before and after a fixed dose of orally administered radioiodine
Background: Serum symmetric dimethylarginine (SDMA) is a sensitive renal biomarker for detecting early chronic kidney disease (CKD) in nonhyperthyroid cats, but knowledge regarding its performance in hyperthyroid cats remains limited.
Objectives: To determine the relationship between serum SDMA, creatinine and total thyroxine (TT4) concentrations in hyperthyroid cats before (T0) and 3 months after (T1) receiving a PO fixed dose of radioiodine.
Animals: Eighty client‐owned hyperthyroid cats.
Methods: Prospective cohort study. Serum TT4, and SDMA, creatinine concentrations, and urine specific gravity were measured at T0 and T1. Nonparametric tests were used to determine the relationship among SDMA, and creatinine and TT4 concentrations. Agreement between SDMA and creatinine regarding CKD staging at both time points was assessed using Goodman and Kruskal's gamma statistic.
Results: Mean serum SDMA concentration increased after treatment of hyperthyroidism. However, 21 of 75 cats experienced a decrease in SDMA between T0 and T1, whereas creatinine decreased in only 2 cats. A moderate correlation between SDMA and creatinine was seen at T1 (r = 0.53; P < .001) but not at T0 (r = 0.13; P = .25). Where assessable at T1, poor agreement was observed between SDMA and creatinine and CKD stage (Goodman and Kruskal's gamma 0.20; P = .29).
Conclusions and clinical importance: Discordant outcomes between SDMA and creatinine after radioiodine treatment in cats with hyperthyroidism suggest extrarenal factors may interfere with the reliability of SDMA to adequately reflect renal function. As a result, SDMA should not be interpreted in isolation in hyperthyroid cats treated with radioiodine.
Journal Article
20200101T00:00:00Z

A few clarifications on MIRIBEL
http://hdl.handle.net/11343/235829
Faria, M; Bjornmalm, M; Crampin, EJ; Caruso, F
20200101
A few clarifications on MIRIBEL
We are inspired by the responses1,2,3,4 to our recently proposed ‘reporting standards’ for bio–nano research (Minimum Information Reporting in Bio–Nano Experimental Literature — MIRIBEL)5. However, we wish to clarify several points made in MIRIBEL and discuss a few concerns raised by the community, with a view toward encouraging uptake of the MIRIBEL standard and improving future research.
Journal Article
20200101T00:00:00Z

The meansquare dichotomy spectrum and a bifurcation to a meansquare attractor
http://hdl.handle.net/11343/51722
Son Doan, T; Rasmussen, M; E. Kloeden, P
2015
The meansquare dichotomy spectrum and a bifurcation to a meansquare attractor
The dichotomy spectrum is introduced for linear meansquare random dynamical systems, and it is shown that for finitedimensional meanfield stochastic differential equations, the dichotomy spectrum consists of finitely many compact intervals. It is then demonstrated that a change in the sign of the dichotomy spectrum is associated with a bifurcation from a trivial to a nontrivial meansquare random attractor.
Journal Article
20150101T00:00:00Z

Learning arithmetic blocks: a concrete model for teaching decimals
http://hdl.handle.net/11343/35042
Archer, Shona; Condon, Caroline; STACEY, KAYE; STEINLE, VICKI; McCarthy, Heather; Helme, Sue; Sullivan, Gerard; Tromp, Calvin
2006
Learning arithmetic blocks: a concrete model for teaching decimals
This booklet is an introduction to using the LAB model with your students. It outlines a number of activities using LAB to assist students in gaining an understanding of the decimal number system.
Copyright confirmation in progress. Any queries to UMERenquiries@unimelb.edu.au
Book
20060101T00:00:00Z

Lesson ideas and activities for teaching decimals
http://hdl.handle.net/11343/35041
Condon, Caroline; Archer, Shona; STACEY, KAYE; STEINLE, VICKI; Scott, Nick; Helme, Sue; Sullivan, Gerard; Tromp, Calvin
2006
Lesson ideas and activities for teaching decimals
The Department of Science and Mathematics Education has produced this booklet to assist teachers with students learning to work confidently with decimal numbers. It contains many classroom activities that will motivate and engage students making the teaching and learning of decimals both enjoyable and effective.
Copyright confirmation in progress. Any queries to UMERenquires@unimelb.edu.au; Further information regarding the book is available at http://staff.edfac.unimelb.edu.au/~kayecs/projects/decprojlink.htm
Book
20060101T00:00:00Z

Frobenius circulant graphs of valency four
http://hdl.handle.net/11343/33003
Thomson, Alison; ZHOU, SANMING
2008
Frobenius circulant graphs of valency four
A first kind Frobenius graph is a Cayley graph Cay.K; S/ on the Frobenius kernel of a Frobenius group K o H such that S D aH for some a 2 K with haH i D K, where H is of even order or a is an involution. It is known that such graphs admit ‘perfect’ routing and gossiping schemes. A circulant graph is a Cayley graph on a cyclic group of order at least three. Since circulant graphs are widely used as models for interconnection networks, it is thus highly desirable to characterize those which are Frobenius of the first kind. In this paper we first give such a characterization for connected 4valent circulant graphs, and then describe optimal routing and gossiping schemes for those which are first kind Frobenius graphs. Examples of such graphs include the 4valent circulant graph with a given diameter and maximum possible order.
© 2008 Australian Mathematical Society. Online edition of the journal is available at http://journals.cambridge.org/JAZ
Journal Article
20080101T00:00:00Z

Queueing analysis of network traffic: methodology and visualization tools
http://hdl.handle.net/11343/30695
ROLLS, D.; MICHAILIDIS, G.; HERNANDEZCAMPOS, F.
2005
Queueing analysis of network traffic: methodology and visualization tools
Journal Article
20050101T00:00:00Z

The density of the time to ruin for a Sparre Andersen process with Erlang arrivals and exponential claims
http://hdl.handle.net/11343/29538
DICKSON, D.; HUGHES, B.; ZHANG, L.
2005
The density of the time to ruin for a Sparre Andersen process with Erlang arrivals and exponential claims
Journal Article
20050101T00:00:00Z

Positive and realpositive solutions to the equation axa* = c in C*algebras
http://hdl.handle.net/11343/29238
CVETKOVIILIC, D.; DAJIC, A.; KOLIHA, J.
2007
Positive and realpositive solutions to the equation axa* = c in C*algebras
Journal Article
20070101T00:00:00Z

Finite symmetric graphs with 2arc transitive quotients II
http://hdl.handle.net/11343/29231
LU, Z.; ZHOU, S.
2007
Finite symmetric graphs with 2arc transitive quotients II
Journal Article
20070101T00:00:00Z

Specht modules and semisimplicity criteria for Brauer and BirmanMurakamiWenzl algebras
http://hdl.handle.net/11343/29226
ENYANG, J.
2007
Specht modules and semisimplicity criteria for Brauer and BirmanMurakamiWenzl algebras
Journal Article
20070101T00:00:00Z

Some infinite soluble groups, their modules, and the arithmeticity of associated automorphism groups
http://hdl.handle.net/11343/29223
BROOKES, C.; GROVES, J.
2005
Some infinite soluble groups, their modules, and the arithmeticity of associated automorphism groups
Journal Article
20050101T00:00:00Z

An application of classical invariant theory to identifiability in nonparametric mixtures
http://hdl.handle.net/11343/29222
ELMORE, R.; HALL, P.; NEEMAN, A.
2005
An application of classical invariant theory to identifiability in nonparametric mixtures
Journal Article
20050101T00:00:00Z

Spectral curves and the mass of hyperbolic monopoles
http://hdl.handle.net/11343/29213
NORBURY, P.; ROMAO, N.
2007
Spectral curves and the mass of hyperbolic monopoles
Journal Article
20070101T00:00:00Z

Angle structures and normal surfaces
http://hdl.handle.net/11343/27840
LUO, F.; TILLMANN, S.
2008
Angle structures and normal surfaces
C1  Refereed Journal Article
Journal Article
20080101T00:00:00Z

Alcove walks, Hecke algebras, spherical functions, crystals and column strict tableaux
http://hdl.handle.net/11343/27835
RAM, A.
2006
Alcove walks, Hecke algebras, spherical functions, crystals and column strict tableaux
C1  Refereed Journal Article
Journal Article
20060101T00:00:00Z