Gradient flows and instantons at a Lifshitz point

I provide a broad framework to embed gradient flow equations in non-relativistic field theory models that exhibit anisotropic scaling. The prime example is the heat equation arising from a Lifshitz scalar field theory; other examples include the Allen-Cahn equation that models the evolution of phase boundaries. Then, I review recent results reported in arXiv:1002.0062 describing instantons of Horava-Lifshitz gravity as eternal solutions of certain geometric flow equations on 3-manifolds. These instanton solutions are in general chiral when the anisotropic scaling exponent is z=3. Some general connections with the Onsager-Machlup theory of non-equilibrium processes are also briefly discussed in this context. Thus, theories of Lifshitz type in d+1 dimensions can be used as off-shell toy models for dynamical vacuum selection of relativistic field theories in d dimensions.Comment: 19 pages, 1 figure, contribution to conference proceedings (NEB14); minor typos corrected in v

Labor reallocation: panel evidence from U.S. States

This paper re-examines Lilien’s sectoral shifts hypothesis for U.S. unemployment. We employ a monthly panel that spans from 1990:01 to 2011:12 for 48 U.S. states. Panel unit root tests that allow for cross-sectional dependence reveal the stationarity of unemployment. Within a framework that takes into account dynamics, parameter heterogeneity and cross-sectional dependence in the panel, we show that sectoral reallocation is significant not only at the aggregate level but also at the state level. The magnitude and the statistical significance of the latter as measured by Lilien’s index increases when both heterogeneity and cross-sectional dependence are taken into account

Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption

Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the machine learning model on the raw data to generate activation maps and then sends them to the server to continue the training process. Previous works in the field demonstrated that reconstructing activation maps could result in privacy leakage of client data. In addition to that, existing mitigation techniques that overcome the privacy leakage of SL prove to be significantly worse in terms of accuracy. In this paper, we improve upon previous works by constructing a protocol based on U-shaped SL that can operate on homomorphically encrypted data. More precisely, in our approach, the client applies homomorphic encryption on the activation maps before sending them to the server, thus protecting user privacy. This is an important improvement that reduces privacy leakage in comparison to other SL-based works. Finally, our results show that, with the optimum set of parameters, training with HE data in the U-shaped SL setting only reduces accuracy by 2.65% compared to training on plaintext. In addition, raw training data privacy is preserved

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

Finite W-algebras

Finite versions of W-algebras are introduced by considering (symplectic) reductions of finite dimensional simple Lie algebras. In particular a finite analogue of $W^{(2)}_3$ is introduced and studied in detail. Its unitary and non-unitary, reducible and irreducible highest weight representations are constructed.Comment: 1

139La NMR evidence for phase solitons in the ground state of overdoped manganites

Hole doped transition metal oxides are famous due to their extraordinary charge transport properties, such as high temperature superconductivity (cuprates) and colossal magnetoresistance (manganites). Astonishing, the mother system of these compounds is a Mott insulator, whereas important role in the establishment of the metallic or superconducting state is played by the way that holes are self-organized with doping. Experiments have shown that by adding holes the insulating phase breaks into antiferromagnetic (AFM) regions, which are separated by hole rich clumps (stripes) with a rapid change of the phase of the background spins and orbitals. However, recent experiments in overdoped manganites of the La(1-x)Ca(x)MnO(3) (LCMO) family have shown that instead of charge stripes, charge in these systems is organized in a uniform charge density wave (CDW). Besides, recent theoretical works predicted that the ground state is inhomogeneously modulated by orbital and charge solitons, i.e. narrow regions carrying charge (+/-)e/2, where the orbital arrangement varies very rapidly. So far, this has been only a theoretical prediction. Here, by using 139La Nuclear Magnetic Resonance (NMR) we provide direct evidence that the ground state of overdoped LCMO is indeed solitonic. By lowering temperature the narrow NMR spectra observed in the AFM phase are shown to wipe out, while for T<30K a very broad spectrum reappears, characteristic of an incommensurate (IC) charge and spin modulation. Remarkably, by further decreasing temperature, a relatively narrow feature emerges from the broad IC NMR signal, manifesting the formation of a solitonic modulation as T->0.Comment: 5 pages, 4 figure

TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks

Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze-and-Excitation modules after each convolution and concatenation block. By utilizing cross-validation, an average ensemble technique, and a simple post-processing technique, we obtained dice scores of 88.06, 80.84, and 80.29, and Hausdorff Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online test set.Comment: Accepted at MICCAI BrainLes 201

Irreversibility of World-sheet Renormalization Group Flow

We demonstrate the irreversibility of a wide class of world-sheet renormalization group (RG) flows to first order in $\alpha'$ in string theory. Our techniques draw on the mathematics of Ricci flows, adapted to asymptotically flat target manifolds. In the case of somewhere-negative scalar curvature (of the target space), we give a proof by constructing an entropy that increases monotonically along the flow, based on Perelman's Ricci flow entropy. One consequence is the absence of periodic solutions, and we are able to give a second, direct proof of this. If the scalar curvature is everywhere positive, we instead construct a regularized volume to provide an entropy for the flow. Our results are, in a sense, the analogue of Zamolodchikov's $c$-theorem for world-sheet RG flows on noncompact spacetimes (though our entropy is not the Zamolodchikov $C$-function).Comment: Minor changes, added one citation, version accepted for publicatio

Are there gender, racial or relationship differences in caregiver task difficulty, depressive symptoms and life changes among stroke family caregivers?

OBJECTIVE: To examine differences in caregiver perceptions of task difficulty, depressive symptoms and life changes based on caregiver characteristics of gender, race and type of relationship to the person with stroke. METHODS: A sample of 243 stroke caregivers (females n = 191; males n = 52; non-African Americans n = 184; African Americans n = 59; non-spouses n = 127; spouses n = 116) were interviewed by telephone within 8 weeks of the survivor's discharge to home. Measures included the Oberst Caregiving Burden Scale (OCBS) for task difficulty, Patient Health Questionnaire (PHQ-9) for depressive symptoms and Bakas Caregiving Outcomes Scale (BCOS) for life changes. Three general linear models computed differences in OCBS, PHQ9 and OCBS scores. RESULTS: Significant differences were found on the OCBS for females (p < 0.001) and African American spouses (p < 0.048); on the PHQ9 for females (p < 0.001), non-African Americans (p = 0.047), spouses (p = 0.003) and African-American spouses (p = 0.010); and on the BCOS for females (p = 0.008) and non-African Americans (p = 0.033). CONCLUSIONS: Findings suggest that female and non-African American stroke caregivers are relatively more likely to experience task difficulty, depressive symptoms and negative life changes as a result of providing care. African American spouses were also at risk. Tailoring interventions based on caregivers' characteristics may improve outcomes

Applications of Partial Supersymmetry

I examine quantum mechanical Hamiltonians with partial supersymmetry, and explore two main applications. First, I analyze a theory with a logarithmic spectrum, and show how to use partial supersymmetry to reveal the underlying structure of this theory. This method reveals an intriguing equivalence between two formulations of this theory, one of which is one-dimensional, and the other of which is infinite-dimensional. Second, I demonstrate the use of partial supersymmetry as a tool to obtain the asymptotic energy levels in non-relativistic quantum mechanics in an exceptionally easy way. In the end, I discuss possible extensions of this work, including the possible connections between partial supersymmetry and renormalization group arguments.Comment: 11 pages, harvmac, no figures; typo corrected in identifying info on title pag