Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer simulation of confined prolate hard spherocylinder liquids between hard walls

ABSTRACT In present work, using canonical Monte Carlo simulation method, the packing fraction profiles and order parameters of prolate hard spherocylinder (HSC) fluid between hard walls are calculated. The hard particles and hard particle–wall interactions are considered. The confinement effects on transition packing fraction of HSCs with aspect ratios and 5 are calculated. In slit pore, while the bulk packing fraction increases, an orientationally ordered phase can form. We conclude that the required packing fraction for forming liquid crystal phases will decrease in confinement. Also while the molecular anisotropy increases, the isotropic–nematic transition packing fraction will decrease in confinement. We could not find the exact or simulation results for comparison with packing fraction profiles of confined HSCs. The results in the case of confined are compared with the simulation results of confined hard Gaussian overlap fluid of elongation . The results are corresponded, qualitatively. Also the confinement isotropic–nematic transition packing fractions are compared with the simulation and fundamental mixed measure theory (FMMT) results of bulk prolate HSCs. Graphical Abstract
Planetary nebula abundances in dwarf galaxies

Abstract The framework of the present review is as follows. The Local Group (LG) is populated by dwarf galaxies belonging to different morphological types. Apparently these galaxies are very different. The questions naturally raised from this framework are many. Is there an evolutionary sequence among them? Do they share common progenitors? Is the environment at the origin of their differences? The method we propose to answer to these questions is by investigating the chemical evolution of dwarf galaxies and their mass-metallicity relation (MZR). To this aim we use metallicities derived from planetary nebulae, since this stellar population is present in the star-forming (dwarf irregular, dIrr) as well as in the quiescent (dwarf spheroidal, dSph) galaxies. The results, actually, show that both dIrr and dSph galaxies of the LG follow the same MZR, at variance with the differences claimed in the past. These results are in good agreement with the recently derived MZR, based on stellar instead of the nebular metallicities of the LG dwarf galaxies. Moreover, our MZR is also consistent with the global MZR of SLOAN star-forming galaxies, which spans a wider stellar mass range (~ 106 - 1011 M⊙) than the LG dwarfs.
Towards a holistic approach to the socio-historical analysis of vernacular photos

Although one of the most popular practices in photography since the end of the 19th century, an increase in scholarly interest in family photo albums dates back to the early 1980s. Such collections of photos may reveal sociological and historical insights regarding speciic cultures and times. They are, however, in most cases scattered among private homes and only available on paper or photographic ilm, thus making their collection and analysis by historians, socio-cultural anthropologists, and cultural theorists very cumbersome. Computer-based methodologies could aid such a process in various ways, speeding the cataloging step, for example, with the use of modern computer vision techniques. We here investigate such an approach, introducing the design and development of a multimedia application that may automatically catalog vernacular pictures drawn from family photo albums. To this aim, we introduce the IMAGO dataset, which is composed of photos belonging to family albums assembled at the University of Bologna’s Rimini campus since 2004. Exploiting the proposed application, IMAGO has ofered the opportunity of experimenting with photos taken between the years 1845 and 2009. In particular, it has been possible to estimate their socio-historical content, i.e., the dates and contexts of the images, without resorting to any other sources of information. Exceeding our initial expectations, such an approach has revealed its merit not only in terms of performance but also in terms of the foreseeable implications for the beneit of socio-historical research. To the best of our knowledge, this contribution is among the few that move along this path at the intersection of socio-historical studies, multimedia computing, and artiicial intelligence.
The survey of planetary nebulae in Andromeda (M 31)

Context. The Andromeda (M 31) galaxy displays several substructures in its inner halo. Different simulations associate their origin with either a single relatively massive merger, or with a larger number of distinct, less massive accretions. Aims. The origin of these substructures as remnants of accreted satellites or perturbations of the pre-existing disc would be encoded in the properties of their stellar populations (SPs). The metallicity and star formation history of these distinct populations leave traces on their deep [O III] 5007 Å planetary nebulae luminosity function (PNLF). By characterizing the morphology of the PNLFs, we constrain their origin. Methods. From our 54 sq. deg. deep narrow-band [O III] survey of M 31, we identify planetary nebulae in six major inner-halo substructures: the Giant Stream, North East Shelf, G1 Clump, Northern Clump, Western Shelf, and Stream D. We obtain their PNLFs and those in two disc annuli, with galactocentric radii of RGC = 10–20 kpc and RGC = 20–30 kpc. We measure PNLF parameters from cumulative fits and statistically compare the PNLFs in each substructure and disc annulus. We link these deep PNLF parameters and those for the Large Magellanic Cloud (LMC) to published metallicities and resolved stellar population-age measurements for their parent SPs. Results. The absolute magnitudes (M*) of the PNLF bright cut-off for these sub-populations span a significant magnitude range, despite being located at the same distance and having a similar line-of-sight extinction. The M* values of the Giant Stream, W Shelf, and Stream D PNLFs are fainter than those predicted by PN evolution models by 0.6, 0.8, and 1.5 mag, respectively, assuming the measured metallicity of the parent stellar populations. The faint-end slope of the PNLF increases linearly with decreasing fraction of stellar mass younger than 5 Gyr across the M 31 regions and the LMC. From their PNLFs, the Giant Stream and NE Shelf are consistent with being stellar debris from an infalling satellite, while the G1 Clump appears to be linked with the pre-merger disc with an additional contribution from younger stars. Conclusions. The SPs of the substructures are consistent with those predicted by simulations of a single fairly massive merger event that took place 2–3 Gyr ago in M31. Stream D has an unrelated, distinct origin. Furthermore, this study provides independent evidence that the faint-end of the PNLF is preferentially populated by planetary nebulae evolved from older stars.
Long Range Arena: A Benchmark for Efficient Transformers

Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models. To this date, there is no well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide spectrum of tasks and datasets makes it difficult to assess relative model quality amongst many models. This paper proposes a systematic and unified benchmark, LRA, specifically focused on evaluating model quality under long-context scenarios. Our benchmark is a suite of tasks consisting of sequences ranging from $1K$ to $16K$ tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. We systematically evaluate ten well-established long-range Transformer models (Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers, Synthesizers, Sparse Transformers, and Longformers) on our newly proposed benchmark suite. LRA paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle. Our benchmark code will be released at this https URL.
Probing Accretion Turbulence in the Galactic Centre with EHT Polarimetry

We explore the origin and the influence of the interstellar scattering on the observation of Sgr A*, and the method to mitigate this scattering via Event Horizon Telescope (EHT) polarimetry. Interstellar scattering is due to the existence of inhomogeneous plasma screens between the Earth and Sgr A*. At radio wavelengths, this scattering adds and removes small structures in the images. In the EHT observation, the scattering contaminates the image by moving power from long baselines to short baselines in a fashion that may be described by a linear transformation characterized by the Scattering Shift Kernel. However, for credible interstellar magnetic field strengths, this transformation is insensitive to polarization. Therefore, it is possible to distinguish intrinsic and scattered structures via the image power spectra constructed in di↵erent polarization components. Via numerical experiments, we demonstrate a method for reconstructing intrinsic structure information. We do this for two cases: a toy model in which we show that this method accurately reproduces the characteristics of controlled image fluctuations, and general relativistic magnetohydrodynamic simulation images. Specifically, we show that the ratio of the power spectra obtained independently for di↵erent polarization components is independent of the scattering screen given the current observational limitations of the EHT. Therefore, these power spectra ratios provide a window directly into the magnetohydrodynamic turbulence believed to drive accretion onto black holes.
An Empirical Investigation of the Determinants of Firm Financial Performance

In this paper, we examine potential determinants of firm financial performance using data from 177 USA listed companies for three distinct periods; prior to GFC, during the GFC, and post GFC. Based on the literature we have selected a number of possible determinants and have categorized them into four different groups to facilitate the analysis. They are; (i) executive director and CEO remuneration and incentivisation factors, (ii) institutional ownership factors, (iii) board practice and diversity factors, (iv) remuneration committees and remuneration consultants’ factors. The market capitalisation (MCAP) is used as the dependent variable because actual profits and profit forecasts through continuous market disclosure have an immediate influence on share price, which in turn alters the MCAP of the respective company.Based on the results, the study concludes that for all three periods covered executive director and CEO remuneration variables are the most important determinants of financial performance of listed companies.
A High Order Well-Balanced Finite Volume WENO Scheme for a Blood Flow Model in Arteries

The numerical simulations for the blood flow in arteries by high order accurate schemes have a wide range of applications in medical engineering. The blood flow model admits the steady state solutions, in which the flux gradient is non-zero and is exactly balanced by the source term. In this paper, we present a high order finite volume weighted essentially non-oscillatory (WENO) scheme, which preserves the steady state solutions and maintains genuine high order accuracy for general solutions. The well-balanced property is obtained by a novel source term reformulation and discretisation, combined with well-balanced numerical fluxes. Extensive numerical experiments are carried out to verify well-balanced property, high order accuracy, as well as good resolution for smooth and discontinuous solutions. AMS subject classifications: 74S10
Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications

Convolutional neural networks (CNNs) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, this paper shows how a tradeoff can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data.
RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion

In recent years, many efforts have been made to complete knowledge graphs (KGs) by various graph embedding methods, most of which only focus on static KGs (SKGs) without considering the time dependency of facts. However, KGs in reality are dynamic and there exists correlations between facts with different timestamps. Due to the sparsity of temporal KGs (TKGs), SKG embedding methods cannot be directly applied to TKGs. And existing methods of TKG embedding suffer from two issues: (1) they follow the pattern of SKG embedding where all facts need to be retrained when a new timestamp appears; (2) they don't provide a general way to transplant SKG embedding methods to TKGs and therefore lack extensibility. In this paper, we propose a novel Recursive Temporal Fact Embedding Framework (RTFE) to transplant translation-based or graph neural network-based SKG embedding methods to TKGs. In the recursive way, timestamp parameters provide a good starting point for the next future timestamp. And existing SKG embedding models can be used as components. Experiments on TKGs show that our proposed framework (1) outperforms the state-of-the-art baseline model in the entity prediction task on fact datasets; (2) achieves similar performance compared with the state-of-the-art baseline model in relation prediction task on fact datasets; and (3) shows performance in the entity prediction task on event datasets.
Syntactic parameters and spin glass models of language change

We develop a model of language change, based on syntactic parameters treated like spin variables in a spin glass model, where the vertices of the underlying graph represent a set of languages, and edges connecting them represent language interaction. As a measure of the strength of language interaction we use data from the MIT MediaLab, while for syntactic parameters we use the SSWL database and Table A of [22]. We develop a new type of spin glass model, based on Ising and Potts models on a graph coupled at the vertices, where the vertex interactions encode entailment relations between syntactic parameters. We study the dynamics of the system via Monte Carlo simulation and a version of the Metropolis–Hastings algorithm. We find that, for independent parameters with no entailment relations, in the low temperature range the parameters tend to align over time according to the most prevalent value in the initial configuration. While in the case with entailment more interesting equilibrium configurations appear depending on the strength of the entailment relation: we focus in particular on the cases of the Strong Deixis and Strong Anaphoricity and the Partial Definiteness and Definiteness Checking parameters. We interpret the temperature variable of the spin glass model as a measure of whether syntactic parameters are best considered as deterministic or probabilistic variables.
Monge–Ampère measures for toric metrics on abelian varieties

Toric metrics on a line bundle of an abelian variety $A$ are the invariant metrics under the natural torus action coming from Raynaud's uniformization theory. We compute here the associated Monge-Amp\`ere measures for the restriction to any closed subvariety of $A$. This generalizes the computation of canonical measures done by the first author from canonical metrics to toric metrics and from discrete valuations to arbitrary non-archimedean fields.
L ] 1 3 N ov 2 01 8 Theoretical Analysis of Adversarial Learning : A Minimax Approach

We propose a general theoretical method for analyzing the risk bound in the presence of adversaries. In particular, we try to fit the adversarial learning problem into theminimax framework. We first show that the original adversarial learning problem could be reduced to aminimax statistical learning problem by introducing a transport map between distributions. Then we prove a risk bound for this minimax problem in terms of covering numbers. In contrast to previous minimax bounds in [22, 15], our bound is informative when the radius of the ambiguity set is small. Our method could be applied to multi-class classification problems and commonly-used loss functions such as hinge loss and ramp loss. As two illustrative examples, we derive the adversarial risk bounds for kernel-SVM and deep neural networks. Our results indicate that a stronger adversary might have a negative impact on the complexity of the hypothesis class and the existence of margin could serve as a defense mechanism to counter adversarial attacks. ∗UBTECH Sydney AI Centre and the School of Computer Science in the Faculty of Engineering and Information Technologies at The University of Sydney, NSW, 2006, Australia, zhtu3055@uni.sydney.edu.au, zjin8228@uni.sydney.edu.au, dacheng.tao@sydney.edu.au.
Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training

In this work, we propose a novel single-shot and keypoints-based framework for monocular 3D objects detection using only RGB images, called KM3D-Net. We design a fully convolutional model to predict object keypoints, dimension, and orientation, and then combine these estimations with perspective geometry constraints to compute position attribute. Further, we reformulate the geometric constraints as a differentiable version and embed it into the network to reduce running time while maintaining the consistency of model outputs in an end-to-end fashion. Benefiting from this simple structure, we then propose an effective semi-supervised training strategy for the setting where labeled training data is scarce. In this strategy, we enforce a consensus prediction of two shared-weights KM3D-Net for the same unlabeled image under different input augmentation conditions and network regularization. In particular, we unify the coordinate-dependent augmentations as the affine transformation for the differential recovering position of objects and propose a keypoints-dropout module for the network regularization. Our model only requires RGB images without synthetic data, instance segmentation, CAD model, or depth generator. Nevertheless, extensive experiments on the popular KITTI 3D detection dataset indicate that the KM3D-Net surpasses all previous state-of-the-art methods in both efficiency and accuracy by a large margin. And also, to the best of our knowledge, this is the first time that semi-supervised learning is applied in monocular 3D objects detection. We even surpass most of the previous fully supervised methods with only 13\% labeled data on KITTI.
Software Algorithm MappingSensors Benchmarking Scheduling Static Dynamic Hybrid Simulation Functional Specialisation Architecture Detailed Pro fi ling Holistic Optimisation Design Space Exploration Multi-domain Design Space Exploration Motion-aware Design Space Exploration Comparative Crowdsourcing

Visual understanding of 3D environments in realtime, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. The major contributions we present are (1) tools and methodology for systematic quantitative evaluation of SLAM algorithms, (2) automated, machine-learning-guided exploration of the algorithmic and implementation design space with respect to multiple objectives, (3) end-to-end simulation tools to enable optimisation of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various SLAM algorithmic approaches, and (4) tools for delivering, where appropriate, accelerated, adaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.
Operational results with fast automatic beam-based LHC collimator alignment

Abstract. The CERN Large Hadron Collider (LHC) is the largest and highest-energy particle accelerator ever built. It is designed to collide particles at a centre-of-mass energy of 14 TeV to explore the fundamental forces and constituents of matter. Due to the potentially destructive high-energy particle beams, with a total design energy of 362 MJ, the collider is equipped with a series of machine protection systems. The beam cleaning or collimation system is designed to passively intercept and absorb particles at large amplitudes. The cleaning efficiency depends heavily on the accurate positioning of the jaws with respect to the beam trajectory. Beam-based collimator alignment is currently the only feasible technique that can be used to determine the beam centre and beam size at the collimator locations. If the alignment is performed without any automation, it can require up to 30 hours to complete for all collimators. This reduces the beam time available for physics experiments. This article provides a brief recap of the algorithms and software developed to automate and speed up the alignment procedure, and presents the operational results achieved with fast automatic beam-based alignment in the 20112013 LHC runs.
Modified teleparallel theories of gravity

Teleparallel gravity is an alternative formulation of gravity which has the same field equations as General Relativity (GR), therefore, it is also known as the Teleparallel equivalent of General Relativity (TEGR). This theory is a gauge theory of the translations with the torsion tensor being non-zero but with a vanishing curvature tensor, hence, the manifold is globally flat. An interesting approach for understanding the late-time accelerating behaviour of the Universe is called modified gravity where GR is extended or modified. In the same spirit, since TEGR is equivalent to GR, one can consider its modifications and study if they can describe the current cosmological observations. This thesis is devoted to studying several modified Teleparallel theories of gravity with emphasis on late-time cosmology. Those Teleparallel theories are in general different to the modified theories based on GR, but one can relate and classify them accordingly. Various Teleparallel theories are presented and studied such as Teleparallel scalar-tensor theories, quintom models, Teleparallel non-local gravity, and f(T,B) gravity and its extensions (coupled with matter, extensions of new GR and Gauss-Bonnet) where T is the scalar torsion and B is the boundary term which is related with the Ricci scalar via R=-T+B.