Miracle-dz/UniFAR-FTUs huggingface dataset:
u-net CNN based fourier ptychography

Fourier ptychography is a recently explored imaging method for overcoming the diffraction limit of conventional cameras with applications in microscopy and yielding high-resolution images. In order to splice together low-resolution images taken under different illumination angles of coherent light source, an iterative phase retrieval algorithm is adopted. However, the reconstruction procedure is slow and needs a good many of overlap in the Fourier domain for the continuous recorded low-resolution images and is also worse under system aberrations such as noise or random update sequence. In this paper, we propose a new retrieval algorithm that is based on convolutional neural networks. Once well trained, our model can perform high-quality reconstruction rapidly by using the graphics processing unit. The experiments demonstrate that our model achieves better reconstruction results and is more robust under system aberrations.
Transformations of the transfinite plane

Abstract We study the existence of transformations of the transfinite plane that allow one to reduce Ramsey-theoretic statements concerning uncountable Abelian groups into classical partition relations for uncountable cardinals. To exemplify: we prove that for every inaccessible cardinal $\kappa $, if $\kappa $ admits a stationary set that does not reflect at inaccessibles, then the classical negative partition relation $\kappa \nrightarrow [\kappa ]^2_\kappa $ implies that for every Abelian group $(G,+)$ of size $\kappa $, there exists a map $f:G\rightarrow G$ such that for every $X\subseteq G$ of size $\kappa $ and every $g\in G$, there exist $x\neq y$ in X such that $f(x+y)=g$.
Mitigating flicker noise in high-precision photometry

Context. In photometry, the short-timescale stellar variability (“flicker”), such as that caused by granulation and solar-like oscillations, can reach amplitudes comparable to the transit depth of Earth-sized planets and is correlated over the typical transit timescales. It can introduce systematic errors on the inferred planetary parameters when a small number of transits are observed. Aims. The objective of this paper is to characterize the statistical properties of the flicker noise and quantify its impact on the inferred transit parameters. Methods. We used the extensive solar observations obtained with SoHO/VIRGO to characterize flicker noise. We simulated realistic transits across the solar disk using SDO/HMI data and used these to obtain transit light curves, which we used to estimate the errors made on the transit parameters due to the presence of real solar noise. We make these light curves publicly available. To extend the study to a wider parameter range, we derived the properties of flicker noise using Kepler observations and studied their dependence on stellar parameters. Finally, we predicted the limiting stellar apparent magnitude for which the properties of the flicker noise can be extracted using high-precision CHEOPS and PLATO observations. Results. Stellar granulation is a stochastic colored noise, and is stationary with respect to the stellar magnetic cycle. Both the flicker correlation timescales and amplitudes increase with the stellar mass and radius. If these correlations are not taken into account when fitting for the parameters of transiting exoplanets, this can bias the inferred parameters. In particular, we find errors of up to 10% on the ratio between the planetary and stellar radius (Rp∕Rs) for an Earth-sized planet orbiting a Sun-like star. Conclusions. Flicker will significantly affect the inferred parameters of transits observed at high precision with CHEOPS and PLATO for F and G stars. Dedicated modeling strategies need to be developed to accurately characterize both the star and the transiting exoplanets.
Inverse Design of Potential Singlet Fission Molecules using a Transfer Learning Based Approach

Singlet fission has emerged as one of the most exciting phenomena known to improve the efficiencies of different types of solar cells and has found uses in diverse optoelectronic applications. The range of available singlet fission molecules is, however, limited as to undergo singlet fission, molecules have to satisfy certain energy conditions. Recent advances in material search using inverse design has enabled the prediction of materials for a wide range of applications and has emerged as one of the most efficient methods in the discovery of suitable materials. It is particularly helpful in manipulating large datasets, uncovering hidden information from the molecular dataset and generating new structures. However, we seldom encounter large datasets in structure prediction problems in material science. In our work, we put forward inverse design of possible singlet fission molecules using a transfer learning based approach where we make use of a much larger ChEMBL dataset of structurally similar molecules to transfer the learned characteristics to the singlet fission dataset.
Adaptive learning rates for support vector machines working on data with low intrinsic dimension

We derive improved regression and classification rates for support vector machines using Gaussian kernels under the assumption that the data has some low-dimensional intrinsic structure that is described by the box-counting dimension. Under some standard regularity assumptions for regression and classification we prove learning rates, in which the dimension of the ambient space is replaced by the box-counting dimension of the support of the data generating distribution. In the regression case our rates are minimax optimal, whereas in the classification case our rates are of the form of the best known. Furthermore, we show that a training validation approach for choosing the hyperparameters of an SVM in a data dependent way achieves the same rates adaptively, that is without any knowledge on the data generating distribution.
A Scaling Algorithm for Weighted f-Factors in General Graphs

We study the maximum weight perfect $f$-factor problem on any general simple graph $G=(V,E,w)$ with positive integral edge weights $w$, and $n=|V|$, $m=|E|$. When we have a function $f:V\rightarrow \mathbb{N}_+$ on vertices, a perfect $f$-factor is a generalized matching so that every vertex $u$ is matched to $f(u)$ different edges. The previous best algorithms on this problem have running time $O(m f(V))$ [Gabow 2018] or $\tilde{O}(W(f(V))^{2.373}))$ [Gabow and Sankowski 2013], where $W$ is the maximum edge weight, and $f(V)=\sum_{u\in V}f(u)$. In this paper, we present a scaling algorithm for this problem with running time $\tilde{O}(mn^{2/3}\log W)$. Previously this bound is only known for bipartite graphs [Gabow and Tarjan 1989]. The running time of our algorithm is independent of $f(V)$, and consequently it first breaks the $\Omega(mn)$ barrier for large $f(V)$ even for the unweighted $f$-factor problem in general graphs.
Hihooi: A Database Replication Middleware for Scaling Transactional Databases Consistently

With the advent of the Internet and Internet-connected devices, modern business applications can experience rapid increases as well as variability in transactional workloads. Database replication has been employed to scale performance and improve availability of relational databases but past approaches have suffered from various issues including limited scalability, performance versus consistency tradeoffs, and requirements for database or application modifications. This paper presents Hihooi, a replication-based middleware system that is able to achieve workload scalability, strong consistency guarantees, and elasticity for existing transactional databases at a low cost. A novel replication algorithm enables Hihooi to propagate database modifications asynchronously to all replicas at high speeds, while ensuring that all replicas are consistent. At the same time, a fine-grained routing algorithm is used to load balance incoming transactions to available replicas in a consistent way. Our thorough experimental evaluation with several well-established benchmarks shows how Hihooi is able to achieve almost linear workload scalability for transactional databases.
Neural Mesh Refiner for 6-DoF Pose Estimation

How can we effectively utilise the 2D monocular image information for recovering the 6D pose (6-DoF) of the visual objects? Deep learning has shown to be effective for robust and real-time monocular pose estimation. Oftentimes, the network learns to regress the 6-DoF pose using a naive loss function. However, due to a lack of geometrical scene understanding from the directly regressed pose estimation, there are misalignments between the rendered mesh from the 3D object and the 2D instance segmentation result, e.g., bounding boxes and masks prediction. This paper bridges the gap between 2D mask generation and 3D location prediction via a differentiable neural mesh renderer. We utilise the overlay between the accurate mask prediction and less accurate mesh prediction to iteratively optimise the direct regressed 6D pose information with a focus on translation estimation. By leveraging geometry, we demonstrate that our technique significantly improves direct regression performance on the difficult task of translation estimation and achieve the state of the art results on Peking University/Baidu - Autonomous Driving dataset and the ApolloScape 3D Car Instance dataset. The code can be found at \url{this https URL}.
Clustering of local extrema in Planck CMB maps

The clustering of local extrema including peaks and troughs will be exploited to assess Gaussianity, asymmetry and the footprint of cosmic strings network on the CMB random field observed by {\it Planck} satellite. The number density of local extrema reveals some non-resolved shot noise in {\it Planck} maps. The \texttt{SEVEM} data has maximum number density of peaks, $n_{pk}$, and troughs, $n_{tr}$, compared to other observed maps. The cumulative of $n_{pk}$ and $n_{tr}$ above and below a threshold, $\vartheta$, for all {\it Planck} maps except for the 100GHz band are compatible with the Gaussian random field. The unweighted Two-Point Correlation Function (TPCF), $\Psi(\theta;\vartheta)$, of the local extrema illustrates significant non-Gaussianity for angular separation $\theta\le 15'$ for all available thresholds. Our results show that to put the feasible constraint on the amplitude of the mass function based on the value of $\Psi$ around the {\it Doppler peak} ($\theta\approx 70'-75'$), we should consider $\vartheta\gtrsim+1.0$. The scale independent bias factors for peak above a threshold for large separation angle and high threshold level are in agreement with that of expected for a pure Gaussian CMB. Unweighted TPCF of local extrema demonstrates a level of rejecting Gaussian hypothesis in \texttt{SMICA}. Genus topology also confirms the Gaussian hypothesis for different component separation maps. Tessellating CMB map with disk of size $6^{\circ}$ based on $n_{pk}$ and $\Psi_{pk-pk}$ demonstrate statistical symmetry in {\it Planck} maps. Combining all maps and applying the $\Psi_{pk-pk}$ puts the upper bound on the cosmic string's tension: $G\mu^{(up)} \lesssim 5.00\times 10^{-7}$.
Some new rational Gushel fourfolds

As an application of results proved in [arXiv:1707.00999, arXiv:1909.01263], and continuing with the constructions initiated in [arXiv:1910.12838, arXiv:2002.07026] of examples of Gushel-Mukai fourfolds through computer-aided methods, here we exhibit some other rational Gushel-Mukai fourfolds of special type.
Impedanzunabhängige Defibrillation mit physiologischer Impulsform

1. Patientengerechte Dosierung Eine individuelle Dosierung der Defibrillationsintensitat wird erreicht, wenn anstelle der Energie die Vorgabe der (mittleren) Stromstarke I und der Ladung Q herangezogen wird. 2. Optimierung der Defibrillation Fur die Zielfindung einer optimierten Defibrillation ist eine Kommunikation von Medizinern, Ingenieuren und Betriebswirten erforderlich. Es wird ein Werkzeug vorgestellt, das die Zusammenhange der einzelnen Parameter visualisiert. 3. Realisierung
A proof of Hall's conjecture on length of ray images under starlike mappings of order α

Assume that \(f\) lies in the class of starlike functions of order \(\alpha\in[0,1)\), that is, which are regular and univalent for \(|z|<1\) and such that Re\(\left(\frac{zf'(z)}{f(z)}\right)>\alpha\) for \(|z|<1.\) In this paper we show that for each \(\alpha\in[0,1)\), the following sharp inequality holds: \(|f(re^{i\theta})|^{-1}\int_{0}^{r}|f'(ue^{i\theta})|\,du\leq\frac{\Gamma(\frac{1}{2})\Gamma(2-\alpha)}{\Gamma(\frac{3}{2}-\alpha)}\) for every \(r<1\) and \(\theta\). This settles the conjecture of Hall (1980) positively.
Hypoglycaemia in elderly patients with type 2 diabetes mellitus: a review of risk factors, consequences and prevention

The prevalence of diabetes in the elderly is high. Hypoglycaemia is a treatment‐related complication of diabetes. Owing to age‐related changes, decreased perception of hypoglycaemic symptoms, comorbidities and polypharmacy, elderly diabetic persons are at a greater risk of developing hypoglycaemia. Other risk factors include longer duration of diabetes, history of previous hypoglycaemic episodes, intensive glycaemic control, poor nutrition or missed meals and recent hospitalisation. Insulin and sulphonylureas are the medication classes most frequently associated with hypoglycaemia. Other antidiabetic medications typically cause hypoglycaemia only when used in combination with sulphonylureas or insulin. Hypoglycaemia in older people is associated with loss of independence, increased morbidity and mortality. Prevention of hypoglycaemia requires recognition of the risk, identification of precipitating factors, patient and caregiver education, appropriate prescribing and ongoing health professional support.
Tensorflow Audio Models in Essentia

Essentia is a reference open-source C++/Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions with pre-trained deep learning models, and are designed to offer flexibility of use, easy extensibility, and real-time inference. To show the potential of this new interface with TensorFlow, we provide a number of pre-trained state-of-the-art music tagging and classification CNN models. We run an extensive evaluation of the developed models. In particular, we assess the generalization capabilities in a cross-collection evaluation utilizing both external tag datasets as well as manual annotations tailored to the taxonomies of our models.
Data-Driven Metadata Tagging for Building Automation Systems: A Unified Architecture

This article presents a Unified Architecture for automated point tagging of Building Automation System data, based on a combination of data-driven approaches. Advanced energy analytics applications-including fault detection and diagnostics and supervisory control-have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical Building Automation System implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: 1. commercial retail buildings and 2. office buildings from the National Renewable Energy Laboratory campus. The proposed methodology correctly applied 85-90 percent and 70-75 percent of the tags in each of these test scenarios, respectively.
Testing broad-line region models with reverberation mapping

New reverberation mapping (RM) measurements, combined with accurate luminosities and line ratios, provide strong constraints on the location of the line emitting gas in the broad line region (BLR) of active galactic nuclei (AGN). In this paper I present new calculations of radiation pressure and magnetic pressure confined clouds and apply them to a ``generic AGN'' and to NGC 5548. The new calculations are in good agreement with the observed lags of all broad emission lines, and with the luminosities of La, Civ 1549, Ovi 1035 and Heii 1640. They are also in reasonable agreement with the luminosities of Mgii 2798 and the 1990A blend of Ciii] and Siiii] lines for high metallicity gas. They explain the changes in time-lag following an increase in continuum luminosity and their dependencies on the inner and outer boundaries of the BLR. They also predict very strong Balmer and Paschen continua with important implications to continuum RM experiments. However, the calculated Balmer and Paschen line luminosities are too weak, by factors of 2-5. This ``Balmer line crisis'' was noted in several earlier works and is now confirmed and constrained by RM measurements that were not available in the past. It seems that present photoionization codes that use the escape probability formalism, fail to correctly compute the Balmer line luminosities in high density, large optical depth gas.
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS

Theoretical analysis of the divide-and-conquer based distributed learning with least square loss in the reproducing kernel Hilbert space (RKHS) have recently been explored within the framework of learning theory. However, the studies on learning theory for general loss functions and hypothesis spaces remain limited. To fill the gap, we study the risk performance of distributed empirical risk minimization (ERM) for general loss functions and hypothesis spaces. The main contributions are two-fold. First, we derive two tight risk bounds under certain basic assumptions on the hypothesis space, as well as the smoothness, Lipschitz continuity, strong convexity of the loss function. Second, we further develop a more general risk bound for distributed ERM without the restriction of strong convexity.
Fabric surface characterization: assessment of deep learning-based texture representations using a challenging dataset

Abstract Tactile sensing or fabric hand plays a critical role in an individual’s decision to buy a certain fabric from the range of available fabrics for a desired application. Therefore, textile and clothing manufacturers have long been in search of an objective method for assessing fabric hand, which can then be used to engineer fabrics with a desired hand. In this paper, we explore how to characterize surface properties (e.g. smoothness) of materials. We formulate the problem as a fine-grained texture classification problem, and study how deep learning-based texture representation techniques can help tackle the task. We introduce a new, challenging microscopic material surface dataset (CoMMonS), geared towards an automated fabric quality assessment mechanism in an intelligent manufacturing system. Additionally, we propose a multi-level texture encoding and representation network (MuLTER), which extracts texture details and structural information. Our dataset and source code are available at https://ghassanalregib.info/software-and-datasets.
A bird's-eye view of phylosymbiosis: weak signatures of phylosymbiosis among all 15 species of cranes

In numerous animal clades, the evolutionary history of host species drives patterns of gut microbial community structure, resulting in more divergent microbiota with increasing phylogenetic distance between hosts. This phenomenon, termed phylosymbiosis, has been observed in diverse evolutionary lineages, but has been difficult to detect in birds. Previous tests of phylosymbiosis among birds have been conducted using wild individuals, and thus interspecific differences in diet and environment may have masked a phylogenetic signal. Therefore, we tested for phylosymbiosis among all 15 species of cranes (family Gruidae) housed in the same captive environment and maintained on identical diets. 16S rRNA sequencing revealed that crane species harbour distinct gut microbiota. Overall, we detected marginally significant patterns of phylosymbiosis, the strength of which was increased when including the estimates of absolute microbial abundance (rather than relative abundance) derived from microbial densities determined by flow cytometry. Using this approach, we detected the statistically significant signatures of phylosymbiosis only after removing male cranes from our analysis, suggesting that using mixed-sex animal cohorts may prevent the detection of phylosymbiosis. Though weak compared with mammals (and especially insects), these results provide evidence of phylosymbiosis in birds. We discuss the potential differences between birds and mammals, such as transmission routes and host filtering, that may underlie the differences in the strength of phylosymbiosis.
Within-island diversification in a passerine bird

The presence of congeneric taxa on the same island suggests the possibility of in situ divergence, but can also result from multiple colonizations of previously diverged lineages. Here, using genome-wide data from a large population sample, we test the hypothesis that intra-island divergence explains the occurrence of four geographical forms meeting at hybrid zones in the Reunion grey white-eye (Zosterops borbonicus), a species complex endemic to the small volcanic island of Reunion. Using population genomic and phylogenetic analyses, we reconstructed the population history of the different forms. We confirmed the monophyly of the complex and found that one of the lowland forms is paraphyletic and basal relative to others, a pattern highly consistent with in situ divergence. Our results suggest initial colonization of the island through the lowlands, followed by expansion into the highlands, which led to the evolution of a distinct geographical form, genetically and ecologically different from the lowland ones. Lowland forms seem to have experienced periods of geographical isolation, but they diverged from one another by sexual selection rather than niche change. Overall, low dispersal capabilities in this island bird combined with both geographical and ecological opportunities seem to explain how divergence occurred at such a small spatial scale.