Learning with Augmented Features for Heterogeneous Domain Adaptation

We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods.Comment: ICML201

DIFFUSE OPTICAL MEASUREMENTS OF HEAD AND NECK TUMOR HEMODYNAMICS FOR EARLY PREDICTION OF CHEMO-RADIATION THERAPY OUTCOMES

Chemo-radiation therapy is a principal modality for the treatment of head and neck cancers, and its efficacy depends on the interaction of tumor oxygen with free radicals. In this study, we adopted a novel hybrid diffuse optical instrument combining a commercial frequency-domain tissue oximeter (Imagent) and a custom-made diffuse correlation spectroscopy (DCS) flowmeter, which allowed for simultaneous measurements of tumor blood flow and blood oxygenation. Using this hybrid instrument we continually measured tumor hemodynamic responses to chemo-radiation therapy over the treatment period of 7 weeks. We also explored monitoring dynamic tumor hemodynamic changes during radiation delivery. Blood flow data analysis was improved by simultaneously extracting multiple parameters from one single autocorrelation function curve measured by DCS. Patients were classified into two groups based on clinical outcomes: a complete response (CR) group and an incomplete response (IR) group with remote metastasis and/or local recurrence within one year. Interestingly, we found human papilloma virus (HPV-16) status largely affected tumor homodynamic responses to therapy. Significant differences in tumor blood flow index (BFI) and reduced scattering coefficient (μs’) between the IR and CR groups were observed in HPV-16 negative patients at Week 3. Significant differences in oxygenated hemoglobin concentration ([HbO2]) and blood oxygen saturation (StO2) between the two groups were found in HPV-16 positive patients at Week 1 and Week 3, respectively. Receiver operating characteristic curves were constructed and results indicated high sensitivities and specificities of these hemodynamic parameters for early (within the first three weeks of the treatment) prediction of one-year treatment outcomes. Measurement of tumor hemodynamics may serve as a predictive tool allowing treatment selection based on biologic tumor characteristics. Ultimately, reduction of side effects in patients not benefiting from radiation treatment may be feasible

Robustness of Network of Networks with Interdependent and Interconnected links

Robustness of network of networks (NON) has been studied only for dependency coupling (J.X. Gao et. al., Nature Physics, 2012) and only for connectivity coupling (E.A. Leicht and R.M. D Souza, arxiv:0907.0894). The case of network of n networks with both interdependent and interconnected links is more complicated, and also more closely to real-life coupled network systems. Here we develop a framework to study analytically and numerically the robustness of this system. For the case of starlike network of n ER networks, we find that the system undergoes from second order to first order phase transition as coupling strength q increases. We find that increasing intra-connectivity links or inter-connectivity links can increase the robustness of the system, while the interdependency links decrease its robustness. Especially when q=1, we find exact analytical solutions of the giant component and the first order transition point. Understanding the robustness of network of networks with interdependent and interconnected links is helpful to design resilient infrastructures

Percolation on interacting networks with feedback-dependency links

When real networks are considered, coupled networks with connectivity and feedback-dependency links are not rare but more general. Here we develop a mathematical framework and study numerically and analytically percolation of interacting networks with feedback-dependency links. We find that when nodes of between networks are lowly connected, the system undergoes from second order transition through hybrid order transition to first order transition as coupling strength increases. And, as average degree of each inter-network increases, first order region becomes smaller and second-order region becomes larger but hybrid order region almost keep constant. Especially, the results implies that average degree \bar{k} between intra-networks has a little influence on robustness of system for weak coupling strength, but for strong coupling strength corresponding to first order transition system become robust as \bar{k} increases. However, when average degree k of inter-network is increased, the system become robust for all coupling strength. Additionally, when nodes of between networks are highly connected, the hybrid order region disappears and the system first order region becomes larger and secondorder region becomes smaller. Moreover, we find that the existence of feedback dependency links between interconnecting networks makes the system extremely vulnerable by comparing non-feedback condition for the same parameters.First author draf

On a generalized Mazur–Ulam question: Extension of isometries between unit spheres of Banach spaces

AbstractWe call a Banach space X admitting the Mazur–Ulam property (MUP) provided that for any Banach space Y, if f is an onto isometry between the two unit spheres of X and Y, then it is the restriction of a linear isometry between the two spaces. A generalized Mazur–Ulam question is whether every Banach space admits the MUP. In this paper, we show first that the question has an affirmative answer for a general class of Banach spaces, namely, somewhere-flat spaces. As their immediate consequences, we obtain on the one hand that the question has an approximately positive answer: Given ε>0, every Banach space X admits a (1+ε)-equivalent norm such that X has the MUP; on the other hand, polyhedral spaces, CL-spaces admitting a smooth point (in particular, separable CL-spaces) have the MUP

Unusual Compression Behavior of Columbite TiO2 via First-Principles Calculations

The physical mechanisms behind the reduction of the bulk modulus of a high-pressure cubic TiO2 phase are confirmed by first-principles calculations. An unusual and abrupt change occurs in the dependence of energy on pressure at 43 GPa, indicating a pressure-induced phase transition from columbite TiO2 to a newly-identified modified fluorite TiO2 with a Pca21 symmetry. Oxygen atom displacement in Pca21 TiO2 unexpectedly reduces the bulk modulus by 34% relative to fluorite TiO2. This discovering provides a direct evidence for understanding the compressive properties of such groups of homologous materialsComment: [email protected] or [email protected]