Miracle-dz/UniFAR-FTUs huggingface dataset:
Tight Bounds for Monotone Minimal Perfect Hashing

The monotone minimal perfect hash function (MMPHF) problem is the following indexing problem. Given a set $S= \{s_1,\ldots,s_n\}$ of $n$ distinct keys from a universe $U$ of size $u$, create a data structure $DS$ that answers the following query: \[ RankOp(q) = \text{rank of } q \text{ in } S \text{ for all } q\in S ~\text{ and arbitrary answer otherwise. } \] Solutions to the MMPHF problem are in widespread use in both theory and practice. The best upper bound known for the problem encodes $DS$ in $O(n\log\log\log u)$ bits and performs queries in $O(\log u)$ time. It has been an open problem to either improve the space upper bound or to show that this somewhat odd looking bound is tight. In this paper, we show the latter: specifically that any data structure (deterministic or randomized) for monotone minimal perfect hashing of any collection of $n$ elements from a universe of size $u$ requires $\Omega(n \cdot \log\log\log{u})$ expected bits to answer every query correctly. We achieve our lower bound by defining a graph $\mathbf{G}$ where the nodes are the possible ${u \choose n}$ inputs and where two nodes are adjacent if they cannot share the same $DS$. The size of $DS$ is then lower bounded by the log of the chromatic number of $\mathbf{G}$. Finally, we show that the fractional chromatic number (and hence the chromatic number) of $\mathbf{G}$ is lower bounded by $2^{\Omega(n \log\log\log u)}$.
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning

Clustering is a ubiquitous tool in unsupervised learning. Most of the existing self-supervised representation learning methods typically cluster samples based on visually dominant features. While this works well for image-based self-supervision, it often fails for videos, which require understanding motion rather than focusing on background. Using optical flow as complementary information to RGB can alleviate this problem. However, we observe that a naive combination of the two views does not provide meaningful gains. In this paper, we propose a principled way to combine two views. Specifically, we propose a novel clustering strategy where we use the initial cluster assignment of each view as prior to guide the final cluster assignment of the other view. This idea will enforce similar cluster structures for both views, and the formed clusters will be semantically abstract and robust to noisy inputs coming from each individual view. Additionally, we propose a novel regularization strategy to address the feature collapse problem, which is common in cluster-based self-supervised learning methods. Our extensive evaluation shows the effectiveness of our learned representations on downstream tasks, e.g., video retrieval and action recognition. Specifically, we outperform the state of the art by 7% on UCF and 4% on HMDB for video retrieval, and 5% on UCF and 6% on HMDB for video classification
Entropic Density Functional Theory

A formulation of density functional theory (DFT) is constructed as an application of the method of maximum entropy for an inhomogeneous fluid in thermal equilibrium. The use of entropy as a systematic method to generate optimal approximations is extended from the classical to the quantum domain. This process introduces a family of trial density operators that are parameterized by the particle density. The optimal density operator is that which maximizes the quantum entropy relative to the exact canonical density operator. This approach reproduces the variational principle of DFT and allows a simple proof of the Hohenberg–Kohn theorem at finite temperature. Finally, as an illustration, we discuss the Kohn–Sham approximation scheme at finite temperature.
Turbulent Prandtl number from isotropically forced turbulence

Abstract Turbulent motions enhance the diffusion of large-scale flows and temperature gradients. Such diffusion is often parameterized by coefficients of turbulent viscosity ($\nu _{t}$) and turbulent thermal diffusivity ($\chi _{t}$) that are analogous to their microscopic counterparts. We compute the turbulent diffusion coefficients by imposing sinusoidal large-scale velocity and temperature gradients on a turbulent flow and measuring the response of the system. We also confirm our results using experiments where the imposed gradients are allowed to decay. To achieve this, we use weakly compressible three-dimensional hydrodynamic simulations of isotropically forced homogeneous turbulence. We find that the turbulent viscosity and thermal diffusion, as well as their ratio the turbulent Prandtl number, $\textit {Pr}_{t} = \nu _{t}/\chi _{t}$, approach asymptotic values at sufficiently high Reynolds and Péclet numbers. We also do not find a significant dependence of $\textit {Pr}_{t}$ on the microscopic Prandtl number $\textit {Pr} = \nu /\chi$. These findings are in stark contrast to results from the $k{-}\epsilon$ model, which suggests that $\textit {Pr}_{t}$ increases monotonically with decreasing $\textit {Pr}$. The current results are relevant for the ongoing debate on, for example, the nature of the turbulent flows in the very-low-$\textit {Pr}$ regimes of stellar convection zones.
Numerical simulation of coherent backscattering and temporal intensity correlations in random media

A review of studies on the numerical simulation of coherent effects in random media performed by using exact analytic results is presented. The simulation procedure is based on a comparison of the Monte-Carlo method and the iteration solution of the Bethe—Salpeter equation. The results of calculations of the time correlation function and the interference component of coherent backscattering for scalar and electromagnetic fields are described. The simulation results are compared for the first time with known generalisations of the Milne solution and are in good agreement with experimental data. The interference component of backscattered low-coherent radiation is calculated for the first time. The localisation of backscattered low-coherent laser radiation along the penetration depth is described. The theory and numerical simulation predict, in accordance with the experiment, a considerable broadening of the backscattering peak with decreasing the coherence length, which opens up essentially new possibilities for the use of this effect, especially for medical diagnostics.
Compact power divider for wideband in-phase and out-of-phase performances using parallel coupled lines

A novel structure that can operate as an in-phase and out-of-phase power divider across 100% fractional bandwidth is presented. The proposed structure adopts two pairs of parallel-coupled lines to enable changing the operation from in-phase to out-of-phase while improving the return loss and isolation of a traditional Wilkinson power divider. Moreover, the device includes a stepped impedance T-junction for input port impedance matching. Changing the status of two terminals of one pair of coupled lines from open-ended to short-ended allows achieving changing the operation from in-phase to out-of-phase performance. Using a chip capacitor between the coupled lines enables realising the needed tight coupling without the need to have narrow gaps. A prototype is fabricated and measured. Simulated and measured results show more than 10 dB of return loss and more than 15 dB of isolation across the investigated band 1-3 GHz. In addition, the device provides <1° phase imbalance and 0.2 dB amplitude imbalance in the in-phase and out-of-phase states across the whole frequency band.
Improved Generative Model for Weakly Supervised Chest Anomaly Localization via Pseudo-paired Registration with Bilaterally Symmetrical Data Augmentation

Image translation based on a generative adversarial network (GAN-IT) is a promising method for precise localization of abnormal regions in chest X-ray images (AL-CXR). However, heterogeneous unpaired datasets undermine existing methods to extract key features and distinguish normal from abnormal cases, resulting in inaccurate and unstable AL-CXR. To address this problem, we propose an improved two-stage GAN-IT involving registration and data augmentation. For the ﬁrst stage, we introduce an invertible deep-learning-based registration technique that virtually and reasonably converts unpaired data into paired data for learning registration maps. This novel approach achieves high registration performance. For the second stage, we apply data augmentation to diversify anomaly locations by swapping the left and right lung regions on the uniform registered frames, further improving the performance by alleviating imbalance in data distribution showing left and right lung lesions. Our method is intended for application to existing GAN-IT models, allowing existing architecture to beneﬁt from key features for translation. By showing that the AL-CXR performance is uniformly improved when applying the proposed method, we believe that GAN-IT for AL-CXR can be deployed in clinical environments, even if learning data are scarce.
Perencanaan Strategis Sistem Informasi Rumah Sakit Kusuma Ungaran dengan Metode Anita Cassidy

Sistem Informasi (SI) dan Teknologi Informasi (TI) pada saat ini merupakan suatu kebutuhan mendasar menunjang jalannya proses bisnis pada suatu organisasi, institusi atau suatu perusahaan baik pemberi layanan jasa atau jual beli. RS Kusuma Ungaran merupakan suatu institusi kesehatan yang sedang bertumbuh dan sedang mengembangkan proses bisnisnya. Dalam pengembangan proses bisnis tersebut, RS Kusuma Ungaran membutuhan sistem informasi yang dapat mengakomodir semua proses bisnis yang ada pada rumah sakit ini kedepannya. Maka dibutuhkan suatu perencanaan strategis sistem informasi untuk dapat membangun sistem informasi yang sesuai dengan kebutuhan dan proses bisnis dari RS Kusuma Ungaran, dan juga untuk dapat bersaing dengan rumah sakit lain yang sudah menerapkan sistem informasi untuk meningkatkan pelayanannya. Pada RS Kusuma Ungaran ini sudah menggunakan suatu sistem informasi yaitu SIMRS (Sistem Informasi Manajemen Rumah Sakit), yang dirasa belum cukup untuk dapat mengakomodir seluruh proses bisnis yang ada pada RS Kusuma Ungaran dan yang akan dikembangkan selanjutnya. Perencanaan Strategis Sistem Informasi yang akan dibahas adalah perencanaan strategis sitem informasi dengan metode yang dibuat oleh Anita Cassidy dalam bukunya yaitu A Practical Guide to Information Systems Strategic Planning (Second Edition, 2006) dengan menerapkan empat fase dalam buku ini yaitu visioning, analysis, direction dan recommendation. Penelitian ini menghasilkan sebuah dokumen perencanaan strategis yang disebut dokumen Perencanaan Strategis Sistem Informasi atau dokumen PSSI. Perencanaan strategis sistem informasi menjadi salah satu acuan pengembangan sistem informasi pada RS Kusuma Ungaran. Kata Kunci : perencanaan strategis sistem informasi, Anita Cassidy, visioning, analysis, direction, recommendation
Composite elastic magnet films with hard magnetic feature

Hard magnetic materials with high remnant magnetic moment, Mr, have unique advantages that can achieve bi-directional (push–pull) movement in an external magnetic field. This paper presents the results on the fabrication and testing of novel composite elastic permanent magnet films. The microsize hard barium ferrite powder, NdFeB powder, and different silicone elastomers have been used to fabricate various large elongation hard magnetic films. Three different fabrication methods, screen-coating processing, moulding processing and squeegee-coating processing, have been investigated, and the squeegee-coating process was proven to be the most successful method. The uniform composite elastic permanent magnet films range from 40 µm to 216 µm in thickness have been successfully fabricated. These films were then magnetized in the thickness direction after fabrication. They exhibited permanent magnet behaviour; for instance, the film (0.640 mm3 in volume) made of polydimethyl siloxane (PDMS) and hard barium ferrite powders is measured to give a coercive force, Hc, of 3.24 × 105 A m−1 and Mr of 1.023 × 10−5 A m2, and the film (0.504 mm3 in volume) made of PDMS and NdFeB powders gives 1.55 × 105 A m−1 Hc and 8.081 × 10−5 A m2 Mr. These composite elastic permanent magnet films' mechanical properties, like Young's modulus and deflection force, have been evaluated. To validate the films' Young's modulus, a finite-element computer simulation (ANSYS®) is used and one film is chosen whose Young's modulus (16.60 MPa) is confirmed by the simulation results with ANSYS®. The large elongation composite elastic permanent magnet film provides an excellent diaphragm material, which plays an important role in the micropump or valve. The movement of the 126 µm thick film with 4.5 mm diameter made of PDMS and NdFeB powders has been tested in a 0.21 Tesla external magnetic field. It was proven to have large deflection of 125 µm.
Three-dimensional phase averaging of time-resolved PIV measurement data

A three-dimensional phase-averaging technique is proposed and applied to the investigation of large-scale coherent structures in the cylinder wake. The flow fields are measured with a time-resolved PIV system with high sampling rate and large internal memory block. As a vector processing algorithm, a cross-correlation method is used in conjunction with the FFT (fast Fourier transform) analysis and a window shifting technique. Phase-locked three-dimensional vortical structures are reconstructed from the phase-averaged data obtained in several two-dimensional planes. In the process of phase averaging, the reference phases are compensated with some phase-lags and a `vortex offset technique' is proposed, which freezes the secondary vortices with respect to the centreline of respective planes.
Classification methods for noise transients in advanced gravitational-wave detectors

Noise of non-astrophysical origin will contaminate science data taken by the advanced laser interferometer gravitational-wave observatory and advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors in the upcoming science runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time–frequency scan of each event. Here, we present three new algorithms designed for the automatic classification of noise transients in advanced detectors. Two of these algorithms are based on principal component analysis. They are principal component analysis for transients and an adaptation of LALInference burst. The third algorithm is a combination of an event generator called wavelet detection filter and machine learning techniques for classification. We test these algorithms on simulated data sets, and we show their ability to automatically classify transients by frequency, signal to noise ratio and waveform morphology.
L'un et le multiple. L'imparfait de l'indicatif en français : valeur en langue et usages en discours

Cette these porte sur l'imparfait de l'indicatif en francais. Elle a pour objet l'etude du signifie de l'imparfait et du fonctionnement de ce temps dans ses differents usages. Il s'agit ici d'articuler la valeur de l'imparfait a la multiplicite des effets de sens auxquels il est lie en discours. Ce travail s'interroge ainsi, dans une premiere partie, sur les outils dont dispose l'analyse linguistique pour decrire les temps verbaux et sur les principales approches actuelles de l'imparfait. A partir de cette reflexion, nous proposons un signifie de ce temps et un dispositif theorique traitant de la production de sens en discours. Nous mettons en œuvre dans une seconde partie notre approche en l'appliquant a un corpus de donnees recueillies dans des textes litteraires, journalistiques et interactionnels. Nous nous focalisons alors sur l'etude des emplois temporels et modaux de l'imparfait et sur les relations temporelles auxquelles il peut etre associe.
Boosting 3D Object Detection via Object-Focused Image Fusion

3D object detection has achieved remarkable progress by taking point clouds as the only input. However, point clouds often suffer from incomplete geometric structures and the lack of semantic information, which makes detectors hard to accurately classify detected objects. In this work, we focus on how to effectively utilize object-level information from images to boost the performance of point-based 3D detector. We present DeMF, a simple yet effective method to fuse image information into point features. Given a set of point features and image feature maps, DeMF adaptively aggregates image features by taking the projected 2D location of the 3D point as reference. We evaluate our method on the challenging SUN RGB-D dataset, improving state-of-the-art results by a large margin (+2.1 mAP@0.25 and +2.3mAP@0.5). Code is available at https://github.com/haoy945/DeMF.
Spectroscopic analysis of new-born massive stars in SMC N 81

We present the first results of a spectroscopic study of young massive stars in the SMC High Excitation Blob N81. These stars have Mv values which are ~ 2 mag smaller than those of nor mal dwarf stars. Their UV STIS spectra reveal features typical of O stars, but surprisingly weak wind components. The preliminary modelling of these spectr a with the code CMFGEN (Hillier & Miller 1998) indicates a mass loss rate of the order 10e-9 Mdot/yr. If confirmed, such a weak wind may indicate either a breakdown of the wind-momentum luminosity relation at low luminosity, or a steeper slope of this relation at low metallicity.
Reweighting Monte Carlo Mixtures

Markov chain Monte Carlo (e. g., the Metropolis algorithm, Hastings algorithm, and Gibbs sampler) is a general multivariate simulation method applicable to a wide range of problems. It permits sampling from any stochastic process whose density is known up to a constant of proportionality. The Gibbs sampler has recently received much attention as a method of simulating from posterior distributions in Bayesian inference, but Markov chain Monte Carlo is no less important in frequentist inference with applications in maximum likelihood, hypothesis testing, and the parametric bootstrap. It is most useful when combined with importance reweighting so that a Monte Carlo sample from one distribution can be used for inference about many distributions. In Bayesian inference, reweighting permits the calculation of posteriors corresponding to a range of priors using a Monte Carlo sample from just one posterior. In likelihood inference, reweighting permits the calculation of the whole likelihood function using a Monte Carlo sample from just one distribution in the model. Given this estimate of the likelihood, a parametric bootstrap calculation of the sampling distribution of the maximum likelihood estimate can be done using just one more Monte Carlo sample. Although reweighting can save much calculation, it does not work well unless the distribution being reweighted places appreciable mass in all regions of interest. Hence it is often not advisable to sample from a distribution in the model. Reweighting a mixture of distributions in the model may perform much better. But using such a mixture gives rise to another problem when the densities are known only up to constants of proportionality. These normalizing constants must be calculated to obtain the mixture density. Direct Monte Carlo estimation, though possible, is very ine cient. A new method, reverse logistic regression, accurately estimates these constants, permitting the use of these mixture estimates in Markov chain Monte Carlo.
Frontiers of Quantum Gravity: shared challenges, converging directions

Understanding the quantum nature of spacetime and gravity remains one of the most ambitious goals of theoretical physics. It promises to provide key new insights into fundamental particle theory, astrophysics, cosmology and the foundations of physics. Despite this common goal, the community of quantum gravity researchers is sometimes seen as divided into sub-communities working on different, mutually exclusive approaches. In practice however, recent years have shown the emergence of common techniques, results and physical ideas arising from different sub-communities, suggesting exciting new prospects for collaboration and interaction between traditionally distinct approaches. In this White Paper we discuss some of the common themes which have seen a growing interest from various directions, and argue that focusing on them will help the quantum gravity community as a whole towards shared objectives.
What Made This Test Flake? Pinpointing Classes Responsible for Test Flakiness

Flaky tests are defined as tests that manifest non-deterministic behaviour by passing and failing intermittently for the same version of the code. These tests cripple continuous integration with false alerts that waste developers’ time and break their trust in regression testing. To mitigate the effects of flakiness, both researchers and industrial experts proposed strategies and tools to detect and isolate flaky tests. However, flaky tests are rarely fixed as developers struggle to localise and understand their causes. Additionally, developers working with large codebases often need to know the sources of non-determinism to preserve code quality, i.e., avoid introducing technical debt linked with non-deterministic behaviour, and to avoid introducing new flaky tests. To aid with these tasks, we propose re-targeting Fault Localisation techniques to the flaky component localisation problem, i.e., pinpointing program classes that cause the non-deterministic behaviour of flaky tests. In particular, we employ Spectrum-Based Fault Localisation (SBFL), a coverage-based fault localisation technique commonly adopted for its simplicity and effectiveness. We also utilise other data sources, such as change history and static code metrics, to further improve the localisation. Our results show that augmenting SBFL with change and code metrics ranks flaky classes in the top-1 and top-5 suggestions, in 26% and 47% of the cases. Overall, we successfully reduced the average number of classes inspected to locate the first flaky class to 19% of the total number of classes covered by flaky tests. Our results also show that localisation methods are effective in major flakiness categories, such as concurrency and asynchronous waits, indicating their general ability to identify flaky components.