Miracle-dz/UniFAR-FTUs huggingface dataset:
On an inverse Robin spectral problem

We consider the problem of the recovery of a Robin coefficient on a part γ ⊂ ∂Ω of the boundary of a bounded domain Ω from the principal eigenvalue and the boundary values of the normal derivative of the principal eigenfunction of the Laplace operator with Dirichlet boundary condition on ∂Ω\γ. We prove the uniqueness, as well as local Lipschitz stability of the inverse problem. Moreover, we present an iterative reconstruction algorithm with numerical computations in two dimensions showing the accuracy of the method.
Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet

Significance Single-molecule localization microscopy has developed from a specialized technique into a widely used method across biological and chemical science. However, to see one molecule, unwanted light from the sample (background) must be minimized. More importantly, nonuniform background can seriously degrade the localization process. So far, addressing this problem has been challenging. We provide a robust, general, and easy-to-implement framework rooted in deep learning to accurately and rapidly estimate arbitrarily structured background so that this interfering structure can be removed. The method works both for conventional microscopes and also for complex 3-dimensional designs based on engineering the point-spread function. Accurate background estimation is a critically useful tool for extracting maximum information from single-molecule images. Background fluorescence, especially when it exhibits undesired spatial features, is a primary factor for reduced image quality in optical microscopy. Structured background is particularly detrimental when analyzing single-molecule images for 3-dimensional localization microscopy or single-molecule tracking. Here, we introduce BGnet, a deep neural network with a U-net-type architecture, as a general method to rapidly estimate the background underlying the image of a point source with excellent accuracy, even when point-spread function (PSF) engineering is in use to create complex PSF shapes. We trained BGnet to extract the background from images of various PSFs and show that the identification is accurate for a wide range of different interfering background structures constructed from many spatial frequencies. Furthermore, we demonstrate that the obtained background-corrected PSF images, for both simulated and experimental data, lead to a substantial improvement in localization precision. Finally, we verify that structured background estimation with BGnet results in higher quality of superresolution reconstructions of biological structures.
Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity

Music source separation performance has greatly improved in recent years with the advent of approaches based on deep learning. Such methods typically require large amounts of labelled training data, which in the case of music consist of mixtures and corresponding instrument stems. However, stems are unavailable for most commercial music, and only limited datasets have so far been released to the public. It can thus be difficult to draw conclusions when comparing various source separation methods, as the difference in performance may stem as much from better data augmentation techniques or training tricks to alleviate the limited availability of training data, as from intrinsically better model architectures and objective functions. In this paper, we present the synthesized Lakh dataset (Slakh) as a new tool for music source separation research. Slakh consists of high-quality renderings of instrumental mixtures and corresponding stems generated from the Lakh MIDI dataset (LMD) using professional-grade sample-based virtual instruments. A first version, Slakh2100, focuses on 2100 songs, resulting in 145 hours of mixtures. While not fully comparable because it is purely instrumental, this dataset contains an order of magnitude more data than MUSDB18, the de facto standard dataset in the field. We show that Slakh can be used to effectively augment existing datasets for musical instrument separation, while opening the door to a wide array of data-intensive music signal analysis tasks.
Towards Ethical Machines Via Logic Programming

Autonomous intelligent agents are playing increasingly important roles in our lives. They contain information about us and start to perform tasks on our behalves. Chatbots are an example of such agents that need to engage in a complex conversations with humans. Thus, we need to ensure that they behave ethically. In this work we propose a hybrid logic-based approach for ethical chatbots.
Recent highlights with baryons from lattice QCD

Highlights from recent computations in lattice QCD involving baryons are presented. Calcula tions of the proton mass and spin decompositions are discussed, a percent level determination of the nucleon axial coupling is described, and determinations of the proton and neutron electromagnetic form factors and light-cone parton distribution functions are outlined. Recent results applying the so-called Luscher method to meson-baryon systems are presented. Key points emphasized are that much better precision with disconnected diagrams is being achieved, incorporating multi-hadron operators is now feasible, and more and more studies are being done with physical quark masses.
CrackGAN: A Labor-Light Crack Detection Approach Using Industrial Pavement Images Based on Generative Adversarial Learning

Fully convolutional network is a powerful tool for per-pixel semantic segmentation/detection. However, it is problematic when coping with crack detection using industrial pavement images: the network may easily "converge" to the status that treats all the pixels as background (BG) and still achieves a very good loss, named "All Black" phenomenon, due to the data imbalance and the unavailability of accurate ground truths (GTs). To tackle this problem, we introduce crack-patch-only (CPO) supervision and generative adversarial learning for end-to-end training, which forces the network to always produce crack-GT images while reserves both crack and BG-image translation abilities by feeding a larger-size crack image into an asymmetric U-shape generator to overcome the "All Black" issue. The proposed approach is validated using four crack datasets; and achieves state-of-the-art performance comparing with that of the recently published works in efficiency and accuracy.
Coded Aperture Imaging in High-energy Astrophysics

Hard X-ray and low-energy gamma-ray coded-aperture imaging (CAI) instruments have been highly successful as high-energy surveyors and transient-source discoverers and trackers over the past decades. Albeit having relatively low sensitivity as compared with focusing instruments, coded-aperture telescopes still represent a very good choice for simultaneous, high cadence spectral measurements of individual point sources in large source fields. Here, I present a review of the fundamentals of CAI instruments in high-energy astrophysics, with an emphasis on the fundamental aspects of the technique, coded-mask instrument characteristics, and properties of the reconstructed images.
Optimization of the Belief-Propagation Algorithm for Distributed Detection by Linear Data-Fusion Techniques

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a priori describing the statistical behavior of the wireless environment concerned. In addition, we propose a blind threshold adaptation method to guarantee a certain performance level in a BP-based distributed detection system. To clarify the points discussed, we design a novel linear-BP-based distributed spectrum sensing scheme for cognitive radio networks and illustrate the performance improvement obtained, over an existing BP-based detection method, via computer simulations.
Computer assisted detection and modelling of paediatric airway pathology from medical images

This thesis proposes a novel computer assisted detection framework to analyse airway shape change and to detect signs of paediatric pulmonary tuberculosis (TB). The method can accurately distinguish TB patients with airway involvement, from non-TB patients using CT scans. This model is also applied to X-ray radiographs to segment the airways. As a first step, a CT airway segmentation algorithm is proposed, and then evaluated as part of the EXACT’09 airway segmentation challenge. Algorithms are then implemented to extract the airway centreline, detect branch points and label each airway branch. A number of cases had the appearance of complete obstruction in some bronchi, and a method is presented to identify and segment beyond these obstructions. A statistical shape model is developed using airway shape variation, which requires correspondence between airways. Thus, a method to register regions of the airway tree is proposed. This method generates landmarks on the airway surface. Using these landmarks, a template mesh is then aligned to each airway by thin-plate-spline warp and local vertex alignment. This develops a corresponding surface mesh representation for each airway that can be used for statistical analysis. A statistical model of the variation of local regions of the airway is constructed and features derived from this are used to train a classifier to detect abnormal airway variation. The method is able to accurately detect TB in unseen cases. It is also compared to second method based on automated bronchi measurements. Finally, a method is developed that uses the previous 3D model to segment the airways in 2D radiographs. This uses an optimisation algorithm to fit the 3D model to image features in the radiograph. These methods create novel tools for airway analysis and have wider applications in medical imaging.
Hubble Space Telescope Spectroscopy of a Planetary Nebula in an M31 Open Cluster: Hot-bottom Burning at 3.4 M⊙

We use imaging and spectroscopy from the Hubble Space Telescope (HST) to examine the properties of a bright planetary nebula (PN) projected within M31's young open cluster B477-D075. We show that the probability of a chance superposition of the PN on the cluster is small, ≲2%. Moreover, the radial velocity of the PN is the same as that of the cluster within the measurement error of ∼10 km s−1. Given the expected ∼70 km s−1 velocity dispersion in this region, ∼8 kpc from M31's nucleus, the velocity data again make it extremely likely that the PN belongs to the cluster. Applying isochrone fitting to archival color–magnitude photometric data from the HST Advanced Camera for Surveys, we determine the cluster age and metallicity to be 290 Myr and Z = 0.0071, respectively, implying an initial mass of for any PN produced by the cluster. From HST’s Space Telescope Imaging Spectrograph observations and Cloudy photoionization modeling, we find that the PN is likely a Type I planetary, with a nitrogen abundance that is enhanced by ∼5–6 times over the solar value scaled to the cluster metallicity. If the PN is indeed a cluster member, these data present strong empirical evidence that hot-bottom burning occurs in asymptotic giant branch stars with initial masses as low as 3.4 M⊙.
The Star Formation History and Dynamics of the Ultra-diffuse Galaxy Dragonfly 44 in MOND and MOG

The observed line-of-sight velocity dispersion σlos of the ultra-diffuse galaxy Dragonfly 44 (DF44) requires a Newtonian dynamical mass-to-light ratio of solar units. This is well outside the acceptable limits of our stellar population synthesis (SPS) models, which we construct using the integrated galactic initial mass function (IGIMF) theory. Assuming DF44 is in isolation and using Jeans analysis, we calculate σlos profiles of DF44 in Milgromian dynamics (MOND) and modified gravity (MOG) theories without invoking dark matter. Comparing with the observed kinematics, the best-fitting MOND model has and a constant orbital anisotropy of . In MOG, we first fix its two theoretical parameters α and μ based on previous fits to the observed rotation curve data of The HI Nearby Galaxy Survey (THINGS). The DF44 σlos profile is best fit with , larger than plausible SPS values. MOG produces a σlos profile for DF44 with acceptable Mdyn/LI and isotropic orbits if α and μ are allowed to vary. MOND with the canonical a0 can explain DF44 at the 2. 40σ confidence level (1.66%) if considering both its observed kinematics and typical star formation histories in an IGIMF context. However, MOG is ruled out at 5. 49σ (P-value of 4.07 × 10−8) if its free parameters are fixed at the highest values consistent with THINGS data.
Influence of Toroidal Flow on Stationary Density of Collisionless Plasmas

Starting from the given passive particle equilibrium particle cylindrical profiles, we built self-consistent stationary conditions of the Maxwell-Vlasov equation at thermodynamic equilibrium with non-flat density profiles. The solutions to the obtained equations are then discussed. It appears that the presence of an azimuthal (poloidal) flow in the plasma can ensure radial confinement, while the presence of a longitudinal (toroidal) flow can enhance greatly the confinement. Moreover in the global physically reasonable situation, we find that no unstable point can emerge in the effective integrable Hamiltonian of the individual particles, hinting at some stability of the confinement when considering a toroidal geometry in the large aspect ratio limit.
Multi-epoch X-ray burst modelling: MCMC with large grids of 1D simulations

Type-I X-ray bursts are recurring thermonuclear explosions on the surface of accreting neutron stars. Matching observed bursts to computational models can help to constrain system properties, such as the neutron star mass and radius, crustal heating rates, and the accreted fuel composition, but systematic parameter studies to date have been limited. We apply Markov chain Monte Carlo methods to 1D burst models for the first time, and obtain system parameter estimations for the ‘Clocked Burster’, GS 1826−238, by fitting multiple observed epochs simultaneously. We explore multiple parameters which are often held constant, including the neutron star mass, crustal heating rate, and hydrogen composition. To improve the computational efficiency, we precompute a grid of 3840 kepler models – the largest set of 1D burst simulations to date – and by interpolating over the model grid, we can rapidly sample burst predictions. We obtain estimates for a CNO metallicity of $Z_\mathrm{CNO} = 0.010^{+0.005}_{-0.004}$, a hydrogen fraction of $X_0 = 0.74^{+0.02}_{-0.03}$, a distance of $d \sqrt{\xi _\mathrm{b}} = 6.5^{+0.4}_{-0.6}\, \mathrm{kpc}$ , and a system inclination of $i = {69^{+2}_{-3}}^{\circ }$.
An Unpaired Sketch-to-Photo Translation Model

Sketch-based image synthesis aims to generate a photo image given a sketch. It is a challenging task; because sketches are drawn by non-professionals and only consist of strokes, they usually exhibit shape deformation and lack visual cues, i.e., colors and textures. Thus translation from sketch to photo involves two aspects: shape and color (texture). Existing meth-ods cannot handle this task well, as they mostly focus on solving one translation. In this work, we show that the key to this task lies in decomposing the translation into two sub-tasks, shape translation and colorization. Correspondingly, we propose a model consisting of two sub-networks, with each one tackling one sub-task. We also ﬁnd that, when translating shapes, speciﬁc drawing styles aﬀect the generated results signiﬁcantly and may even lead to failure. To make our model more robust to drawing style variations, we design a data augmentation strategy and re-purpose an attention module, aiming to make our model pay less attention to distracted regions of a sketch. Besides, a conditional module is adapted for color translation to improve diversity and increase users’ control over the generated results. Both quantitative and qualitative comparisons are presented to show the superiority of our approach. In addition, as a side beneﬁt, our model can synthesize high-quality sketches from photos inversely. We also demonstrate how these generated photos and sketches can beneﬁt other applications, such as sketch-based image retrieval.
Measure Contribution of Participants in Federated Learning

Federated Machine Learnig (FML) creates an ecosystem for multiple parties to collaborate on building models while protecting data privacy for the participants. A measure of the contribution for each party in FML enables fair credits allocation. In this paper we develop simple but powerful techniques to fairly calculate the contributions of multiple parties in FML, in the context of both horizontal FML and vertical FML. For Horizontal FML we use deletion method to calculate the grouped instance influence. For Vertical FML we use Shapley Values to calculate the grouped feature importance. Our methods open the door for research in model contribution and credit allocation in the context of federated machine learning.
Population transfer via a dissipative structural continuum

We propose a model to study quantum population transfer via a structural continuum. The model is composed of two spins which are coupled to two bosonic modes separately by two control pulses, and the two bosonic modes are coupled to a common structural continuum. We show that efficient population transfer can be achieved between the two spins by using a multi-level stimulated Raman adiabatic passage (STIRAP) across the continuum, which we refer to as straddle STIRAP via continuum. We also consider the stability of this model against different control parameters and show that efficient population transfer can be achieved even in presence a moderate dissipation.
Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition

In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching. Previous works addressing this challenge mainly focused on word-level aspects such as word embeddings. However, in many cases, languages share common subwords, especially for closely related languages, but also for languages that are seemingly irrelevant. Therefore, we propose Hierarchical Meta-Embeddings (HME) that learn to combine multiple monolingual word-level and subword-level embeddings to create language-agnostic lexical representations. On the task of Named Entity Recognition for English-Spanish code-switching data, our model achieves the state-of-the-art performance in the multilingual settings. We also show that, in cross-lingual settings, our model not only leverages closely related languages, but also learns from languages with different roots. Finally, we show that combining different subunits are crucial for capturing code-switching entities.
Leyenda: An Adaptive, Hybrid Sorting Algorithm for Large Scale Data with Limited Memory

Sorting is the one of the fundamental tasks of modern data management systems. With Disk I/O being the most-accused performance bottleneck and more computation-intensive workloads, it has come to our attention that in heterogeneous environment, performance bottleneck may vary among different infrastructure. As a result, sort kernels need to be adaptive to changing hardware conditions. In this paper, we propose Leyenda, a hybrid, parallel and efficient Radix Most-Significant-Bit (MSB) MergeSort algorithm, with utilization of local thread-level CPU cache and efficient disk/memory I/O. Leyenda is capable of performing either internal or external sort efficiently, based on different I/O and processing conditions. We benchmarked Leyenda with three different workloads from Sort Benchmark, targeting three unique use cases, including internal, partially in-memory and external sort, and we found Leyenda to outperform GNU's parallel in-memory quick/merge sort implementations by up to three times. Leyenda is also ranked the second best external sort algorithm on ACM 2019 SIGMOD programming contest and forth overall.
Where Are the Electromagnetic-wave Counterparts of Stellar-mass Binary Black Hole Mergers?

Multimessenger astronomy, combining gravitational-wave (GW) and electromagnetic-wave (EM) observations, has a huge impact on physics, astrophysics, and cosmology. However, the majority of sources detected with currently running ground-based GW observatories are binary black hole (BBH) mergers, which disappointingly were expected to have no EM counterparts. In this Letter, we propose that if a BBH merger happens in a gaseous disk around a supermassive black hole, the merger can be accompanied by a transient radio flare like a fast radio burst (FRB). We argue that the total mass and the effective spin derived from GW detection can be used to distinguish such a source from other channels of BBH mergers. If this prediction is confirmed with future observations, multimessenger astronomy can be greatly improved. The mystery of the origin of FRBs could also be revealed partially.
A Proof of the Twin Prime Conjecture

The twin prime conjecture 'There are infinitely many twin primes' is a very old unsolved mathematical problem. This paper develops a sieve to extract all twin primes on the level of their generators up to any limit. The sieve uses only elementary methodes. With this sieve the twin prime conjecture finally can be proved indirectly.
Specification and Optimal Reactive Synthesis of Run-time Enforcement Shields

A system with sporadic errors (SSE) is a controller which produces high quality output but it may occasionally violate a critical requirement REQ(I,O). A run-time enforcement shield is a controller which takes (I,O) (coming from SSE) as its input, and it produces a corrected output O' which guarantees the invariance of requirement REQ(I,O'). Moreover, the output sequence O' must deviate from O "as little as possible" to maintain the quality. In this paper, we give a method for logical specification of shields using formulas of logic Quantified Discrete Duration Calculus (QDDC). The specification consists of a correctness requirement REQ as well as a hard deviation constraint HDC which must both be mandatorily and invariantly satisfied by the shield. Moreover, we also use quantitative optimization to give a shield which minimizes the expected value of cumulative deviation in an H-optimal fashion. We show how tool DCSynth implementing soft requirement guided synthesis can be used for automatic synthesis of shields from a given specification. Next, we give logical formulas specifying several notions of shields including the k-Stabilizing shield of Bloem "et al." as well as the Burst-error shield of Wu "et al.", and a new e,d-shield. Shields can be automatically synthesized for all these specifications using the tool DCSynth. We give experimental results showing the performance of our shield synthesis tool in relation to previous work. We also compare the performance of the shields synthesized under diverse hard deviation constraints in terms of their expected deviation and the worst case burst-deviation latency.