Miracle-dz/UniFAR-FTUs huggingface dataset:
Capture Probability of Colliding Planetesimals: Dynamical Constraints on Accretion of Planets, Satellites, and Ring Particles

We evaluate accretion rate and capture probability of planetesimals in Keplerian motion by three-body trajectory integration and analytic calculation to examine conditions for accretion of planets, satellites, and ring particles. Fragmentation or cratering of colliding bodies is not considered, but the changes of orbits caused by inelastic rebound with given restitution coefficient are calculated without the assumption of inevitable accretion. Numerical results show that with such low relative random velocities that v/ve ≤ 0.1 (ve is the escape velocity), slight inelastic dissipation of kinetic energy is enough to lead most colliding bodies to accretion, but substantial kinetic, energy needs to be dissipated for capture when v ≥ ve. In relatively high-velocity collisions between planetesimals in heliocentric orbits or between satellites around a planet, capture probability, C, which is defined by the ratio of the capture rate to the collision rate, is expressed in terms of the restitution coefficients in normal and tangential direction, en and et, respectively, and a certain critical value, ecr, as follows: (i) C = 1 if en, et < ecr, (ii) C = (e2cr - e2n)/(e2t - e2n.) if en < ecr ≤t et. (iii) C = (e2cr - e2t)/(e2n - e2t) if et < ecr ≤ en. (iv) C = 0 if ecr ≤ en, et. The critical value ecr is a function of v /ve and rp(rp is the ratio of the sum of radii of colliding bodies to the Hill sphere radius), and ecr ≃ 0.7 when v = ve and rp ⪡ 1. These analytic results and the numerical results obtained by trajectory integrations show quite a good agreement for v /ve ≥ 0.2. We find that tidal effects of the central body is not significant as long as v/ve ≥ 0.2 and rp⪡ 1, and we may apply the two-body approximation for capture probability in such cases. Using these results, together with experimental data for the restitution coefficient of ice, we estimate a critical mass of icy satellites above which they accrete by gravitational force. We also investigate the capture probability for collisions between planetary ring particles where rp ≃ 1 and tidal effects are more important. We find that accretion is possible even inside the Roche limit and the capture probability decreases abruptly for rp ≃23, when the target body protrudes out of the Hill sphere. This suggests that in Saturn's B ring gravitational accretion seems quite difficult unless the restitution coefficient is sufficiently small, while accretion of small particles onto large bodies may be possible in A ring unless the material density of particles is substantially smaller than 1 g cm-3.
Hybrid accretion disks in active galactic nuclei. I - Structure and spectra

A unified treatment is presented of the two distinct states of vertically thin AGN accretion disks: a cool (about 10 to the 6th K) optically thick solution, and a hot (about 10 to the 9th K) optically thin solution. A generalized formalism and a new radiative cooling equation valid in both regimes are introduced. A new luminosity limit is found at which the hot and cool alpha solutions merge into a single solution of intermediate optical depth. Analytic solutions for the disk structure are given, and output spectra are computed numerically. This is used to demonstrate the prospect of fitting AGN broadband spectra containing both the UV bump as well as the hard X-ray and gamma-ray tail, using a single accretion disk model. Such models are found to make definite predictions about the observed spectrum, such as the relation between the hard X-ray spectral index, the UV-to-X-ray luminosity ratio, and a feature of about 1 MeV. 39 refs.
Consistent estimation of parameters and quantiles of the three-parameter gamma distribution based on Type-II right-censored data

In this paper, we propose a method of estimation of parameters and quantiles of the three-parameter gamma distribution based on Type-II right-censored data. In the proposed method, under mild conditions, the estimates always exist uniquely, and the estimators have consistency over the entire parameter space. Through Monte Carlo simulations, we further show that the proposed method performs well compared with another prominent method of estimation in terms of bias and root mean-squared error in small-sample situations. Finally, two real data sets are used for illustrating the proposed method.
Feedforward neural networks based on PPS-wavelet activation functions

Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. Neural networks and wavenets have been seen as attractive tools for developing efficient solutions for many real world problems in function approximation. In this paper, it is shown how feedforward neural networks can be built using a different type of activation function referred to as the PPS (polynomial powers of sigmoids)-wavelet. An algorithm is presented to generate a family of PPS-wavelets that can be used to efficiently construct feedforward networks for function approximation.
Finite-Amplitude Wave Activity and Mean Flow Adjustments in the Atmospheric General Circulation. Part I: Quasigeostrophic Theory and Analysis

Abstract A diagnostic relationship between finite-amplitude wave activity and the associated adiabatic adjustments to the zonal-mean zonal wind and temperature is developed in the quasigeostrophic (QG) framework and is applied to a 23-yr segment (1979–2001) of the 40-yr ECMWF Re-Analysis (ERA-40) data. Wave activity is defined in terms of an instantaneous areal displacement of QG potential vorticity (PV) from zonal symmetry. Unlike previous forms, the tendency of wave activity equals exactly the negative of the eddy PV flux (Eliassen–Palm flux divergence) in the conservative limit, even at finite amplitude. This allows one to integrate the transformed Eulerian mean (TEM) theory in time and quantify the departure (adiabatic adjustment) of the zonal-mean state from an eddy-free reference state in terms of the observed wave activity. The structure of wave activity identifies synoptic eddies in the extratropics and planetary waves in the high latitudes of winter-to-spring stratosphere. In addition, a thin lay.
Semantics of logic programs with aggregates

Aggregates are functions that take sets as arguments. Examples are the function that maps a set to the number of its elements or the function which maps a set to its minimal element. Aggregates are frequently used in relational databases and have many applications in combinatorial search problems and knowledge representation. Aggregates are of particular importance for several extensions of logic programming which are used for declarative programming like Answer Set Programming, Abductive Logic Programming, and the logic of inductive definitions (ID-Logic). Aggregate atoms not only allow a broader class of problems to be represented in a natural way but also allow a more compact representation of problems which often leads to faster solving times. Extensions of specific semantics of logic programs with, in many cases, specific aggregate relations have been proposed before. The main contributions of this thesis are: (i) we extend all major semantics of logic programs: the least model semantics of definite logic programs, the standard model semantics of stratified programs, the Clark completion semantics, the well-founded semantics, the stable models semantics, and the three-valued stable semantics; (ii) our framework admits arbitrary aggregate relations in the bodies of rules. We follow a denotational approach in which a semantics is defined as a (set of) fixpoint(s) of an operator associated with a program. The main tool of this work is Approximation Theory. This is an algebraic theory which defines different types of fixpoints of an approximating operator associated with a logic program. All major semantics of a logic program correspond to specific types of fixpoints of an approximating operator introduced by Fitting. We study different approximating operators for aggregate programs and investigate the precision and complexity of the semantics generated by them. We study in detail one specific operator which extends the Fitting operator and whose semantics extends the three-valued stable semantics of logic programs without aggregates. We look at algorithms, complexity, transformations of aggregate atoms and programs, and an implementation in XSB Prolog.
Positive corona streamer as a source of high‐frequency radiation

A model is proposed to evaluate theoretically the radiated power in the LF-VHF band. It is proposed that the positive corona streamer process is responsible for the radiation in the band which is not explained by any other mechanisms, known to explain other frequency regions of the observed spectrum. The calculations show that the power radiated, and its variation with frequency, is similar to that observed by previous workers.
Quench analysis of MgB2 coils with a ferromagnetic matrix

If a superconducting wire includes a ferromagnetic constituent, then magnet design requires a novel approach. We have previously reported on computing the critical current of coils that include ferromagnetic matrix material. Now we introduce a quench analysis in MgB2 solenoid and racetrack coils that utilize conductors with a ferromagnetic matrix. The computer code that was developed uses the commercial software MATLAB and FEMLAB. The code is also capable of simulating quench with coils wound of low-temperature superconductor (LTS) and high-temperature superconductor (HTS). The enhancement to this code compared to the earlier reported quench programs is that the non-linear magnetic behaviour due to the ferromagnetic matrix is taken into account. So far, the code does not take into account the conductor AC losses during current decay and the possible quench back due to the thermal interface.
Theoretical quasar emission line ratios. II. hydrogen La, balmer, and paschen lines, and the balmer continuum

We explore the formation of the hydrogen La, Balmer, and Paschen lines and the Balmer continuum in highly idealized QSO broad emission line clouds (ELCs) of constant temperature and density irradiated by an external source of power-law spectral form. We simultaneously solve the equations governing excitation, ionization, and transfer of both external and diffuse radiation fields. Our calculations show that the typical observed broad emission line radios of La/Ha, Hb/Ha, Pa/Ha, and Balmer continuum/Ha can be understood for ELC conditions in the temperature range 7 x 10/sup 3/< or approx. =T/sub e/< or approx. =2 x 10/sup 4/ K, hydeogen density range 10/sup 8/< or approx. =n/sub H/< or approx. =10/sup 12/ cm/sup -3/, and for external fluxes F< or approx. =/sup -6/ ergs cm/sup -2/ s/sup -1/ at the Lyman continuum limit.
A Cluster Expansion Proof That The Stochastic Exponential Of A Brownian Motion Is A Martingale

Let ${\psi}:\mathbb{R}^{+}\rightarrow\mathbb{R}^{+}$ be a smooth and continuous real function and $\psi\in\mathrm{L}^{2}(\mathbb{R}^{+})$. Let ${B}(t)$ be a standard Brownian motion defined with respect to a probability space $(\Omega,\mathscr{F},{\mathsf{P}})$ and where $d{B}(t)={\xi}(t)dt$ and $t\in\mathbb{R}^{+}$. The process $\xi(t)$ is a Gaussian white noise with expectation $\mathbf{\mathsf{E}}~\xi(t)=0$ and with covariance ${\mathsf{E}}~\xi(t)\xi(s)=\delta(t-s)$. The Dolean-Dades stochastic exponential ${Z}(t)$ is the solution to the linear stochastic differential equation describing a geometric Brownian motion such that $d{Z}(t)=\psi(t){Z}(t)d{B}(t)=\psi(t){Z}(t)\xi(t)dt$. Using a cluster expansion method, and the moment and cumulant generating functions for $\xi(t)$, it is shown that ${Z}(t)$ is a martingale. The original Novikov criteria for ${Z}(t)$ being a true martingale are reproduced and exactly satisfied, namely that \begin{align} {\mathsf{E}}\mathrm{Z}(t)={\mathsf{E}}\exp\left(\int_{o}^{t}{\psi}(u)d{B}(u) -\frac{1}{2}\int_{0}^{t}|{\psi}(u)|^{2}du\right)=1\nonumber \end{align} provided that $\exp\big(\int_{0}^{t}|{\psi}(u)|^{2}du\big)<\infty$ for all $t>0$. However, ${\mathsf{E}}\big[|{Z}(t)|^{p}\big] =\exp(\tfrac{1}{2}p(p-1)\phi(t))$, if $\phi(t)=\int_{0}^{t}|\psi(u)|^{2}du$ is monotone increasing and is a submartingale for all $p>1$.
Full-Spectrum Correlated-k Distribution for Shortwave Atmospheric Radiative Transfer

Abstract The full-spectrum correlated k-distribution (FSCK) method, originally developed for applications in combustion systems, is adapted for use in shortwave atmospheric radiative transfer. By weighting k distributions by the solar source function, the FSCK method eliminates the requirement that the Planck function be constant over a spectral interval. As a consequence, integration may be carried out across the full spectrum as long as the assumption of correlation from one atmospheric level to the next remains valid. Problems with the lack of correlation across the full spectrum are removed by partitioning the spectrum at a wavelength of 0.68 μm into two bands. The resulting two-band approach in the FSCK formalism produces broadband rms clear-sky flux and heating rate errors less than 1% and 6%, respectively, relative to monochromatic calculations and requires only 15 quadrature points per layer, which represents a 60%–90% reduction in computation time relative to other models currently in use. An eva.
Better Automatic Program Repair by Using Bug Reports and Tests Together

Automated program repair is already deployed in industry, but concerns remain about repair quality. Recent research has shown that one of the main reasons repair tools produce incorrect (but seemingly correct) patches is imperfect fault localization (FL). This paper demonstrates that combining information from natural-language bug reports and test executions when localizing faults can have a significant positive impact on repair quality. For example, existing repair tools with such FL are able to correctly repair 7 defects in the Defects4J benchmark that no prior tools have repaired correctly. We develop, Blues, the first information-retrieval-based, statement-level FL technique that requires no training data. We further develop RAFL, the first unsupervised method for combining multiple FL techniques, which outperforms a supervised method. Using RAFL, we create SBIR by combining Blues with a spectrum-based (SBFL) technique. Evaluated on 815 real-world defects, SBIR consistently ranks buggy statements higher than its underlying techniques. We then modify three state-of-the-art repair tools, Arja, SequenceR, and SimFix, to use SBIR, SBFL, and Blues as their internal FL. We evaluate the quality of the produced patches on 689 real-world defects. Arja and SequenceR significantly benefit from SBIR: Arja using SBIR correctly repairs 28 defects, but only 21 using SBFL, and only 15 using Blues; SequenceR using SBIR correctly repairs 12 defects, but only 10 using SBFL, and only 4 using Blues. SimFix, (which has internal mechanisms to overcome poor FL), correctly repairs 30 defects using SBIR and SBFL, but only 13 using Blues. Our work is the first investigation of simultaneously using multiple software artifacts for automated program repair, and our promising findings suggest future research in this directions is likely to be fruitful.
Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning

Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians in diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form diagnostic B-mode images. However, the components of breast tissue, such as glandular tissue, fat, and lesions, differ in sound speed. Given a constant sound speed assumption, these differences can degrade the quality of reconstructed images via phase aberration. Sound speed images can be a powerful tool for improving image quality and identifying diseases if properly estimated. To this end, we propose a supervised deep-learning approach for sound speed estimation from analytic ultrasound signals. We develop a large-scale simulated ultrasound dataset that generates representative breast tissue samples by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We adopt a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map. The simulated tissue is interrogated with a plane wave transmit sequence, and the complex-value reconstructed images are used as input for the convolutional network. The network is trained on the sound speed distribution map of the simulated data, and the trained model can estimate sound speed given reconstructed pulse-echo signals. We further incorporate thermal noise augmentation during training to enhance model robustness to artifacts found in real ultrasound data. To highlight the ability of our model to provide accurate sound speed estimations, we evaluate it on simulated, phantom, and in-vivo breast ultrasound data.
Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity

Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in learning fields. The validity of existing works heavily rely on either a restrictive Lower-Level Strong Convexity (LLSC) condition or on solving a series of approximation subproblems with high accuracy or both. In this work, by averaging the upper and lower level objectives, we propose a single loop Bi-level Averaged Method of Multipliers (sl-BAMM) for BLO that is simple yet efficient for large-scale BLO and gets rid of the limited LLSC restriction. We further provide non-asymptotic convergence analysis of sl-BAMM towards KKT stationary points, and the comparative advantage of our analysis lies in the absence of strong gradient boundedness assumption, which is always required by others. Thus our theory safely captures a wider variety of applications in deep learning, especially where the upper-level objective is quadratic w.r. t. the lower-level variable. Experimental results demonstrate the superiority of our method.
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup

On-device Deep Neural Network (DNN) inference consumes significant computing resources and development efforts. To alleviate that, we propose LUT-NN, the first system to empower inference by table lookup, to reduce inference cost. LUT-NN learns the typical features for each operator, named centroid, and precompute the results for these centroids to save in lookup tables. During inference, the results of the closest centroids with the inputs can be read directly from the table, as the approximated outputs without computations. LUT-NN integrates two major novel techniques: (1) differentiable centroid learning through backpropagation, which adapts three levels of approximation to minimize the accuracy impact by centroids; (2) table lookup inference execution, which comprehensively considers different levels of parallelism, memory access reduction, and dedicated hardware units for optimal performance. LUT-NN is evaluated on multiple real tasks, covering image and speech recognition, and nature language processing. Compared to related work, LUT-NN improves accuracy by 66% to 92%, achieving similar level with the original models. LUT-NN reduces the cost at all dimensions, including FLOPs (≤ 16×), model size (≤ 7×), latency (≤ 6.8×), memory (≤ 6.5×), and power (≤ 41.7%).
Compressed sensing for inverse problems and the sample complexity of the sparse Radon transform

Compressed sensing allows for the recovery of sparse signals from few measurements, whose number is proportional to the sparsity of the unknown signal, up to logarithmic factors. The classical theory typically considers either random linear measurements or subsampled isometries and has found many applications, including accelerated magnetic resonance imaging, which is modeled by the subsampled Fourier transform. In this work, we develop a general theory of infinite-dimensional compressed sensing for abstract inverse problems, possibly ill-posed, involving an arbitrary forward operator. This is achieved by considering a generalized restricted isometry property, and a quasi-diagonalization property of the forward map. As a notable application, for the first time, we obtain rigorous recovery estimates for the sparse Radon transform (i.e., with a finite number of angles $\theta_1,\dots,\theta_m$), which models computed tomography. In the case when the unknown signal is $s$-sparse with respect to an orthonormal basis of compactly supported wavelets, we prove exact recovery under the condition \[ m\gtrsim s, \] up to logarithmic factors.
AMP: A unified framework boosting low resource automatic speech recognition

We propose a novel text-to-speech (TTS) data augmentation framework for low resource automatic speech recognition (ASR) tasks, named phoneme audio mix up (PAMP). The PAMP method is highly interpretable and can incorporate prior knowledge of pronunciation rules. Furthermore, PAMP can be easily deployed in almost any language, extremely for low resource ASR tasks. Extensive experiments have demonstrated the great effectiveness of PAMP on low resource ASR tasks: we achieve a 10.84% character error rate (CER) on the common voice Cantonese ASR task, bringing a great relative improvement of about 30% compared to the previous state-of-the-art which was achieved by ﬁne-tuning the wav2vec2 pretrained model.
CoMap: Proactive Provision for Crowdsourcing Map in Automotive Edge Computing

Crowdsourcing data from connected and automated vehicles (CAVs) is a cost-efficient way to achieve high-definition maps with up-to-date transient road information. Achieving the map with deterministic latency performance is, however, challenging due to the unpredictable resource competition and distributional resource demands. In this paper, we propose CoMap, a new crowdsourcing high definition (HD) map to minimize the monetary cost of network resource usage while satisfying the percentile requirement of end-to-end latency. We design a novel CROP algorithm to learn the resource demands of CAV offloading, optimize offloading decisions, and proactively allocate temporal network resources in a fully distributed manner. In particular, we create a prediction model to estimate the uncertainty of resource demands based on Bayesian neural networks and develop a utilization balancing scheme to resolve the imbalanced resource utilization in individual infrastructures. We evaluate the performance of CoMap with extensive simulations in an automotive edge computing network simulator. The results show that CoMap reduces up to 80.4% average resource usage as compared to existing solutions.
DivBO: Diversity-aware CASH for Ensemble Learning

The Combined Algorithm Selection and Hyperparameters optimization (CASH) problem is one of the fundamental problems in Automated Machine Learning (AutoML). Motivated by the success of ensemble learning, recent AutoML systems build post-hoc ensembles to output the final predictions instead of using the best single learner. However, while most CASH methods focus on searching for a single learner with the best performance, they neglect the diversity among base learners (i.e., they may suggest similar configurations to previously evaluated ones), which is also a crucial consideration when building an ensemble. To tackle this issue and further enhance the ensemble performance, we propose DivBO, a diversity-aware framework to inject explicit search of diversity into the CASH problems. In the framework, we propose to use a diversity surrogate to predict the pair-wise diversity of two unseen configurations. Furthermore, we introduce a temporary pool and a weighted acquisition function to guide the search of both performance and diversity based on Bayesian optimization. Empirical results on 15 public datasets show that DivBO achieves the best average ranks (1.82 and 1.73) on both validation and test errors among 10 compared methods, including post-hoc designs in recent AutoML systems and state-of-the-art baselines for ensemble learning on CASH problems.