Illumination coding meets uncertainty learning: toward reliable AI-augmented phase imaging

We propose a physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging. We design an asymmetric coded illumination scheme to encode high-resolution phase information across a wide field-of-view. We then develop a matching DL algorithm to provide large-SBP phase estimation. We show that this illumination coding scheme is highly scalable in achieving flexible resolution, and robust to experimental variations. We demonstrate this technique on both static and dynamic biological samples, and show that it can reliably achieve 5X resolution enhancement across 4X FOVs using only five multiplexed measurements -- more than 10X data reduction over the state-of-the-art. Typical DL algorithms tend to provide over-confident predictions, whose errors are only discovered in hindsight. We develop an uncertainty learning framework to overcome this limitation and provide predictive assessment to the reliability of the DL prediction. We show that the predicted uncertainty maps can be used as a surrogate to the true error. We validate the robustness of our technique by analyzing the model uncertainty. We quantify the effect of noise, model errors, incomplete training data, and "out-of-distribution" testing data by assessing the data uncertainty. We further demonstrate that the predicted credibility maps allow identifying spatially and temporally rare biological events. Our technique enables scalable AI-augmented large-SBP phase imaging with dependable predictions.Published versio

Supersymmetric Electroweak Phase Transition: Dimensional Reduction versus Effective Potential

We compare two methods of analyzing the finite-temperature electroweak phase transition in the minimal supersymmetric standard model: the traditional effective potential (EP) approach, and the more recently advocated procedure of dimensional reduction (DR). The latter tries to avoid the infrared instabilities of the former by matching the full theory to an effective theory that has been studied on the lattice. We point out a limitation of DR that caused a large apparent disagreement with the effective potential results in our previous work. We also incorporate wave function renormalization into the EP, which is shown to decrease the strength of the phase transition. In the regions of parameter space where both methods are expected to be valid, they give similar results, except that the EP is significantly more restrictive than DR for the range of baryogenesis-allowed values of $\tan\beta$, $m_h$, the critical temperature, and the up-squark mass parameter $m_U$. In contrast, the DR results are consistent with 2\lsim\tan\beta\lsim 4, $m_h<80$ GeV, and $m_U$ sufficiently large to have universality of the squark soft-breaking masses at the GUT scale, in a small region of parameter space. We suggest that the differences between DR and EP are due to higher-order perturbative corrections rather than infrared effects.Comment: 19 pages, Latex, 7 figures, uses epsf.te

On the influence of environment on star forming galaxies

We use our state-of-the-art semi analytic model for GAlaxy Evolution and Assembly (GAEA), and observational measurements of nearby galaxies to study the influence of the environment on the gas content and gaseous/stellar disc sizes of star-forming galaxies. We analyse the origin of differences between physical properties of satellites and those of their central counterparts, identified by matching the Vmax of their host haloes at the accretion time of the satellites. Our model reproduces nicely the differences between centrals and satellites measured for the HI mass, size of the star-forming region, and stellar radii. In contrast, our model predicts larger differences with respect to data for the molecular gas mass and star formation rate. By analysing the progenitors of central and satellite model galaxies, we find that differences in the gas content arise after accretion, and can be entirely ascribed to the instantaneous stripping of the hot gas reservoir. The suppression of cold gas replenishment via cooling and star formation leads to a reduction of the cold gas and of its density. Therefore, more molecular gas is lost than lower density HI gas, and model satellites have less molecular gas and lower star formation rates than observed satellites. We argue that these disagreements could be largely resolved with the inclusion of a proper treatment for ram-pressure stripping of cold gas and a more gradual stripping of the hot gas reservoir. A more sophisticated treatment of angular momentum exchanges, accounting for the multi-phase nature of the gaseous disc is also required.Comment: 15 pages, 9 figures, accepted for publication in MNRA

Scalable and Energy-Efficient Millimeter Massive MIMO Architectures: Reflect-Array and Transmit-Array Antennas

Hybrid analog-digital architectures are considered as promising candidates for implementing millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems since they enable a considerable reduction of the required number of costly radio frequency (RF) chains by moving some of the signal processing operations into the analog domain. However, the analog feed network, comprising RF dividers, combiners, phase shifters, and line connections, of hybrid MIMO architectures is not scalable due to its prohibitively high power consumption for large numbers of transmit antennas. Motivated by this limitation, in this paper, we study novel massive MIMO architectures, namely reflect-array (RA) and transmit-array (TA) antennas. We show that the precoders for RA and TA antennas have to meet different constraints compared to those for conventional MIMO architectures. Taking these constraints into account and exploiting the sparsity of mmWave channels, we design an efficient precoder for RA and TA antennas based on the orthogonal matching pursuit algorithm. Furthermore, in order to fairly compare the performance of RA and TA antennas with conventional fully-digital and hybrid MIMO architectures, we develop a unified power consumption model. Our simulation results show that unlike conventional MIMO architectures, RA and TA antennas are highly energy efficient and fully scalable in terms of the number of transmit antennas.Comment: submitted to IEEE ICC 201

Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty

There is a growing need for methods which can capture uncertainties and answer queries over graph-structured data. Two common types of uncertainty are uncertainty over the attribute values of nodes and uncertainty over the existence of edges. In this paper, we combine those with identity uncertainty. Identity uncertainty represents uncertainty over the mapping from objects mentioned in the data, or references, to the underlying real-world entities. We propose the notion of a probabilistic entity graph (PEG), a probabilistic graph model that defines a distribution over possible graphs at the entity level. The model takes into account node attribute uncertainty, edge existence uncertainty, and identity uncertainty, and thus enables us to systematically reason about all three types of uncertainties in a uniform manner. We introduce a general framework for constructing a PEG given uncertain data at the reference level and develop highly efficient algorithms to answer subgraph pattern matching queries in this setting. Our algorithms are based on two novel ideas: context-aware path indexing and reduction by join-candidates, which drastically reduce the query search space. A comprehensive experimental evaluation shows that our approach outperforms baseline implementations by orders of magnitude