Emergence of biased errors in imperfect photonic circuits

We study the impact of experimental imperfections in integrated photonic circuits. We discuss the emergence of a moderate biased error in path encoding, and investigate its correlation with properties of the optical paths. Our analysis connects and deepens previous studies in this direction, revealing potential issues for high-precision tests and optical implementations of machine learning.Comment: 14 pages, 10 figures, code available at https://zenodo.org/record/492448

Biologically Inspired Sensing and MIMO Radar Array Processing

The contributions of this dissertation are in the fields of biologically inspired sensing and multi-input multi-output: MIMO) radar array processing. In our research on biologically inspired sensing, we focus on the mechanically coupled ears of the female Ormia ochracea. Despite the small distance between its ears, the Ormia has a remarkable localization ability. We statistically analyze the localization accuracy of the Ormia\u27s coupled ears, and illustrate the improvement in the localization performance due to the mechanical coupling. Inspired by the Ormia\u27s ears, we analytically design coupled small-sized antenna arrays with high localization accuracy and radiation performance. Such arrays are essential for sensing systems in military and civil applications, which are confined to small spaces. We quantitatively demonstrate the improvement in the antenna array\u27s radiation and localization performance due to the biologically inspired coupling. On MIMO radar, we first propose a statistical target detection method in the presence of realistic clutter. We use a compound-Gaussian distribution to model the heavy tailed characteristics of sea and foliage clutter. We show that MIMO radars are useful to discriminate a target from clutter using the spatial diversity of the illuminated area, and hence MIMO radar outperforms conventional phased-array radar in terms of target-detection capability. Next, we develop a robust target detector for MIMO radar in the presence of a phase synchronization mismatch between transmitter and receiver pairs. Such mismatch often occurs due to imperfect knowledge of the locations as well as local oscillator characteristics of the antennas, but this fact has been ignored by most researchers. Considering such errors, we demonstrate the degradation in detection performance. Finally, we analyze the sensitivity of MIMO radar target detection to changes in the cross-correlation levels: CCLs) of the received signals. Prior research about MIMO radar assumes orthogonality among the received signals for all delay and Doppler pairs. However, due to the use of antennas which are widely separated in space, it is impossible to maintain this orthogonality in practice. We develop a target-detection method considering the non-orthogonality of the received data. In contrast to the common assumption, we observe that the effect of non-orthogonality is significant on detection performance

The Shape of Strangeness: Transverse Spherocity and Underlying Event studies of φ and its relation to Ξ in √s = 13 TeV pp collisions

Through ultrarelativistic particle collisions at the LHC, it is possible to deconfine quarks and gluons. This deconfinement gives rise to a strongly interacting medium, referred to as the Quark-Gluon Plasma (QGP). One of the earliest proposed and observed signatures of the QGP was the enhanced production of strange hadrons since the medium can thermally produce strange quarks. However, recent studies in small systems, such as proton–proton (pp) and proton-lead (pPb) collisions, have exhibited similar features. These findings arequite puzzling, as the formation of a QGP in these small collision systems challenges current theoretical frameworks.In this Thesis, I present two different studies on the production of φ mesons, in relation to Ξ baryons, in pp collisions at √s = 13 TeV, measured with the ALICE apparatus. Both of these studies aim to investigate the origin of strange hadron enhancement in high-multiplicity pp collisions. First, I report measurements of φ production as a function of the event-shape observable Unweighted Transverse Spherocity, SOpT=1. With SOpT=1 , it is possible to categorize events by their azimuthal topology. I utilize SOpT=1 to contrast particle production in collisionsdominated by many soft initial interactions, with collisions dominated by a single hard scattering. I find that strangeness enhancement is prominent in soft, isotropic topologies, whereas events with di-jet topologies showcase a clear suppression of strange particles.The second study presents the production of φ mesons and Ξ hadrons as a function of the Relative Transverse Activity RT . With RT , one can control the size of the Underlying Event (UE). By varying RT, it is therefore possible to study the interplay between particle production from hard fragmentation in jets, and soft particles produced by the UE. The reported results suggest that strange particle production is mainly a feature of the UE. When put together, the two studies suggest that high-multiplicity pp collisions are in general dominated by soft physics, which is also responsible for the strangeness enhancement, whilehigh-multiplicity events dominated by hard physics are rare outliers

Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models

This paper considers the specification of covariance structures with tail estimates. We focus on two aspects: (i) the estimation of the VaR-CoVaR risk matrix in the case of larger number of time series observations than assets in a portfolio using quantile predictive regression models without assuming the presence of nonstationary regressors and; (ii) the construction of a novel variable selection algorithm, so-called, Feature Ordering by Centrality Exclusion (FOCE), which is based on an assumption-lean regression framework, has no tuning parameters and is proved to be consistent under general sparsity assumptions. We illustrate the usefulness of our proposed methodology with numerical studies of real and simulated datasets when modelling systemic risk in a network

Adaptive smoothing of multi-shell diffusion-weighted magnetic resonance data by msPOAS

In this article we present a noise reduction method (msPOAS) for multi-shell diffusion-weighted magnetic resonance data. To our knowledge, this is the first smoothing method which allows simultaneous smoothing of all q-shells. It is applied directly to the diffusion weighted data and consequently allows subsequent analysis by any model. Due to its adaptivity, the procedure avoids blurring of the inherent structures and preserves discontinuities. MsPOAS extends the recently developed position-orientation adaptive smoothing (POAS) procedure to multi-shell experiments. At the same time it considerably simplifies and accelerates the calculations. The behavior of the algorithm msPOAS is evaluated on diffusion-weighted data measured on a single shell and on multiple shells

Adaptive smoothing of multi-shell diffusion-weighted magnetic resonance data by msPOAS

In this article we present a noise reduction method (msPOAS) for multi-shell diffusionweighted magnetic resonance data. To our knowledge, this is the first smoothing method which allows simultaneous smoothing of all q-shells. It is applied directly to the diffusion weighted data and consequently allows subsequent analysis by any model. Due to its adaptivity, the procedure avoids blurring of the inherent structures and preserves discontinuities. MsPOAS extends the recently developed positionorientation adaptive smoothing (POAS) procedure to multi-shell experiments. At the same time it considerably simplifies and accelerates the calculations. The behavior of the algorithm msPOAS is evaluated on diffusion-weighted data measured on a single shell and on multiple shells

Adaptive smoothing of multi-shell diffusion-weighted magnetic resonance data by msPOAS

In this article we present a noise reduction method (msPOAS) for multi-shell diffusion-weighted magnetic resonance data. To our knowledge, this is the first smoothing method which allows simultaneous smoothing of all q-shells. It is applied directly to the diffusion weighted data and consequently allows subsequent analysis by any model. Due to its adaptivity, the procedure avoids blurring of the inherent structures and preserves discontinuities. MsPOAS extends the recently developed position-orientation adaptive smoothing (POAS) procedure to multi-shell experiments. At the same time it considerably simplifies and accelerates the calculations. The behavior of the algorithm msPOAS is evaluated on diffusion-weighted data measured on a single shell and on multiple shells

Explore the Node Representation Learning on Heterogeneous Information Networks

Node representation learning (NRL) has shown incredible success in recent years. It compresses the nodes as low-dimensional vectors, which can accurately represent the characteristics of the nodes. While many researchers have applied NRL to heterogeneous information networks (HIN), most of them only focus on the quality of the node embedding itself or some basic downstream tasks, such as node classification and link prediction. In this thesis, we study the following three problems to explore the power of graph representation learning on different heterogeneous information network mining tasks. Firstly, we investigate the problem of the meta-path prediction problem. Given an HIN H, a head node h, a meta-path P, and a tail node t, the meta-path prediction aims to predict whether h can be linked to t by an instance of P. Most existing solutions either require predefined meta-paths, which limits their scalability to schema-rich HINs and long meta-paths, or do not aim to predict the existence of an instance of P. To address these issues, we propose a novel prediction model, called ABLE, by exploiting the Attention mechanism and BiLSTM for Embedding. We conduct extensive experiments on four real datasets. The empirical results show that ABLE outperforms the state-of-the-art methods by up to 20\% and 22\% of improvement of AUC and AP scores, respectively. Secondly, we focus on the node importance value estimation problem. Node importance estimation is a fundamental task in graph data analysis. Extensive studies have focused on this task, and various downstream applications have benefited from it, such as recommendation, resource allocation optimization, and missing value completion. However, existing works either focus on the homogeneous network or only study importance-based ranking. We are the first to consider the node importance values as heterogeneous values in HINs. A typical HIN is built of several distinguished node types where each type has its own measure of importance value. This characteristic makes the above problem more challenging than computing the node importance in conventional homogeneous networks. In this thesis, we formally introduce the problem of node importance value estimation in HINs; that is, given the importance values of a subset of nodes in an HIN, we aim to estimate the importance values of the remaining nodes. To solve this problem, we propose an effective graph neural network (GNN) model, called HIN Importance Value Estimation Network (HIVEN). Extensive experiments on real-world HIN datasets demonstrate that HIVEN superiorly outperforms the baseline methods. Thirdly, we study the node importance estimation problem in dynamic HIN. The node importance in HIN is highly co-related to the HIN topology, while the node importance can also in turn influence the change of the HIN structure. All existing works assume that the HIN is static, and ignore their co-evolutionary natures. In addition, the historical node importance information is always available, which can further help to get accurate node importance estimation. Thus, we propose a novel temporal GNN model, CoGNN. We experimented with real-world dynamic HIN datasets and show that the proposed model outperforms the state of the arts