Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product Manifold

In graph representation learning, it is important that the complex geometric structure of the input graph, e.g. hidden relations among nodes, is well captured in embedding space. However, standard Euclidean embedding spaces have a limited capacity in representing graphs of varying structures. A promising candidate for the faithful embedding of data with varying structure is product manifolds of component spaces of different geometries (spherical, hyperbolic, or euclidean). In this paper, we take a closer look at the structure of product manifold embedding spaces and argue that each component space in a product contributes differently to expressing structures in the input graph, hence should be weighted accordingly. This is different from previous works which consider the roles of different components equally. We then propose WEIGHTED-PM, a data-driven method for learning embedding of heterogeneous graphs in weighted product manifolds. Our method utilizes the topological information of the input graph to automatically determine the weight of each component in product spaces. Extensive experiments on synthetic and real-world graph datasets demonstrate that WEIGHTED-PM is capable of learning better graph representations with lower geometric distortion from input data, and performs better on multiple downstream tasks, such as word similarity learning, top-$k$ recommendation, and knowledge graph embedding

Fermion masses in the economical 3-3-1 model

We show that, in frameworks of the economical 3-3-1 model, all fermions get masses. At the tree level, one up-quark and two down-quarks are massless, but the one-loop corrections give all quarks the consistent masses. This conclusion is in contradiction to the previous analysis in which, the third scalar triplet has been introduced. This result is based on the key properties of the model: First, there are three quite different scales of vacuum expectation values: \om \sim {\cal O}(1) \mathrm{TeV}, v \approx 246 \mathrm{GeV} and $u \sim {\cal O}(1) \mathrm{GeV}$. Second, there exist two types of Yukawa couplings with different strengths: the lepton-number conserving couplings $h$'s and the lepton-number violating ones $s$'s satisfying the condition in which the second are much smaller than the first ones: $s \ll h$. With the acceptable set of parameters, numerical evaluation shows that in this model, masses of the exotic quarks also have different scales, namely, the $U$ exotic quark ($q_U = 2/3$) gains mass $m_U \approx 700$ GeV, while the D_\al exotic quarks (q_{D_\al} = -1/3) have masses in the TeV scale: m_{D_\al} \in 10 \div 80 TeV.Comment: 20 pages, 8 figure

Enterprise integration lessons learned, Journal of Telecommunications and Information Technology, 2004, nr 4

This document describes the lessons learned from a United States Navy enterprise integration initiative called Web Enabled Navy (WEN). WEN was initiated in April 2001 with the foci of integrating navy resources and providing a single-point-of-access, Web environment to all business and operational applications. The navy's applications operate within a complex network environment that spans commands afloat, ashore, and overseas. The challenges addressed are similar to those faced by large, multi-national corporations and include some unusual characteristics including islands of intermittently, bandwidth-limited, connected information consumers. Both technical and management lessons learned will be described and denoted as either prerequisite or success factors

A Cosine Similarity-based Method for Out-of-Distribution Detection

The ability to detect OOD data is a crucial aspect of practical machine learning applications. In this work, we show that cosine similarity between the test feature and the typical ID feature is a good indicator of OOD data. We propose Class Typical Matching (CTM), a post hoc OOD detection algorithm that uses a cosine similarity scoring function. Extensive experiments on multiple benchmarks show that CTM outperforms existing post hoc OOD detection methods.Comment: Accepted paper at ICML 2023 Workshop on Spurious Correlations, Invariance, and Stability. 10 pages (4 main + appendix

Microfluidic impedance biosensors for monitoring a single and multiple cancer cells in anticancer drug treatments

In this work, we present a novel microfluidic impedance biosensor chip for trapping both a single and multiple cancer cells and monitoring their response to the anti-cancer drug treatment. By designing different sizes of working microelectrodes together with the V-shaped cell capture structures, a single or multiple cells are trapped on the microelectrodes surfaces. In addition, by utilizing the passive pumping method, cells can be trapped and positioned inside the microchannels without the need of using the outer micro pump or syringe. The impedance change induced by the response of cells to the anticancer drug Cisplatin treatment was successfully recorded. The proposed biosensor chip has a great potential for applications in cancer cell research, drug screening, and quantification of cancer cells from various tumor stages. The results of this study open potential research collaborations about development of cost-effective devices and lab-on-chips for early disease detection, studies of cancerous cells and their response to anti-cancer drugs to optimize cancer treatments, characterisation of mechanical properties of cells, new drug delivery mechanisms, and micro and nano manufacturing

Policy volatility and growth

The paper aims to examine how fiscal and monetary volatility might affect the balanced economic growth rate using a standard monetary growth model characterized by nominal wage rigidity and productive public spending. The model shows that any type of shock — monetary or fiscal — can generate either a negative or positive relationship between short-run volatility and long-run growth, critically de- pending on the size of government and the elasticity of output with respect to labor/ capital. In particular, given the labor income share, it shows that excessive government spending may cause the impact of fiscal volatility on long-run growth to turn from positive to negative. In addition, a rise in the volatility of the monetary shock is capable of generating either an increase or decrease in the mean of growth. With the range of the labor share values in reality, the model produces results consistent with the fact that the relationship between volatility and growth is generally found empirically to be more negative in developing than in developed countries. The model can be seen as a further explanation for the ambiguous empirical evidence in the existing literature.info:eu-repo/semantics/publishedVersio