Miracle-dz/UniFAR-FTUs huggingface dataset:
On Geometry and Symmetries in Classical and Quantum Theories of Gauge Gravity

Spin Foam and Loop approaches to Quantum Gravity reformulate Einstein's theory of relativity in terms of connection variables. The metric properties are encoded in face bivectors/conjugate fluxes that are required to satisfy certain conditions, in order to allow for their geometric interpretation. We show that the (sub-)set of the so-called `volume simplicity constraints' is not implemented properly in the current EPRL-FK spinfoam vertex amplitude, if extended to arbitrary polyhedra. We then propose that a certain knot-invariant of the bivector geometry, induced on the boundary graph, encodes the missing conditions, allowing for reconstruction of a polytope from its two-dimensional faces. Implemented in the quantum amplitude, this leads to corrected semi-classical asymptotics for a hypercuboid, and is conjectured to be non-trivial in more general situations. The analysis of linear version of `volume simplicity' suggests to switch from hypersurface normals to edge lengths, that is from 3-forms directly to tetrads -- in the extended configuration space of the Plebanski constrained formulation. We then give the corresponding dual version of linear simplicity constraints, which prescribe 3d volume for the polyhedral faces in the boundary of a 4d polytope. We also analyse the status of metric/vielbein degrees of freedom and the role of local translations in the classical Einstein-Cartan gravity, viewed as a Poincare gauge theory. The relation with the diffeomorphism symmetry is established through the key concept of development, which generalizes parallel transport of vectors in the geometric theory of Cartan connections. We advocate the latter to be the natural gauge-theoretic framework for the theory of relativity.
Controlling secondary flow in Taylor–Couette turbulence through spanwise-varying roughness

Highly turbulent Taylor–Couette flow with spanwise-varying roughness is investigated experimentally and numerically (direct numerical simulations with an immersed boundary method) to determine the effects of the spacing and spanwise width $s$ of the spanwise-varying roughness on the total drag and on the flow structures. We apply sandgrain roughness, in the form of alternating rough and smooth bands to the inner cylinder. Numerically, the Taylor number is $O(10^{9})$ and the roughness width is varied in the range $0.47\leqslant \tilde{s}=s/d\leqslant 1.23$, where $d$ is the gap width. Experimentally, we explore $Ta=O(10^{12})$ and $0.61\leqslant \tilde{s}\leqslant 3.74$. For both approaches the radius ratio is fixed at $\unicode[STIX]{x1D702}=r_{i}/r_{o}=0.716$, with $r_{i}$ and $r_{o}$ the radius of the inner and outer cylinder respectively. We present how the global transport properties and the local flow structures depend on the boundary conditions set by the roughness spacing $\tilde{s}$. Both numerically and experimentally, we find a maximum in the angular momentum transport as a function of $\tilde{s}$. This can be attributed to the re-arrangement of the large-scale structures triggered by the presence of the rough stripes, leading to correspondingly large-scale turbulent vortices.
OPAL a Versatile Tool for Charged Particle Accelerator Simulations

Many sophisticated computer models have been developed to understand the behaviour of particle accelerators. Even these complex models often do not describe the measured data. Interactions of the beam with external fields, other particles in the same beam and the beam walls all present modelling challenges. These can be challenging to model correctly even with modern supercomputers. This paper describes OPAL (Object Oriented Parallel Accelerator Library), a parallel open source tool for charged-particle optics in linear accelerators and rings, including 3D space charge. OPAL is built from the ground up as a parallel application exemplifying the fact that high performance computing is the third leg of science, complementing theory and experiment. Using the MAD language with extensions, OPAL can run on a laptop as well as on the largest high performance computing systems. The OPAL framework makes it easy to add new features in the form of new C++ classes, enabling the modelling of many physics processes and field types. OPAL comes in two flavours: OPAL-cycl: tracks particles with 3D space charge including neighbouring turns in cyclotrons and FFAs with time as the independent variable. OPAL-t: models beam lines, linacs, rf-photo injectors and complete XFELs excluding the undulator. The code is managed through the git distributed version control system. A suite of unit tests have been developed for various parts of OPAL, validating each part of the code independently. System tests validate the overall integration of different elements.
On the Fairness of Time-Critical Influence Maximization in Social Networks

Influence maximization has found applications in a wide range of real-world problems, for instance, viral marketing of products in an online social network, and propagation of valuable information such as job vacancy advertisements. While existing algorithmic techniques usually aim at maximizing the total number of people influenced, the population often comprises several socially salient groups, e.g., based on gender or race. As a result, these techniques could lead to disparity across different groups in receiving important information. Furthermore, in many applications, the spread of influence is time-critical, i.e., it is only beneficial to be influenced before a deadline. As we show in this paper, such time-criticality of information could further exacerbate the disparity of influence across groups. This disparity could have far-reaching consequences, impacting people’s prosperity and putting minority groups at a big disadvantage. In this work, we propose a notion of group fairness in time-critical influence maximization. We introduce surrogate objective functions to solve the influence maximization problem under fairness considerations. By exploiting the submodularity structure of our objectives, we provide computationally efficient algorithms with guarantees that are effective in enforcing fairness during the propagation process. Extensive experiments on synthetic and real-world datasets demonstrate the efficacy of our proposal.
Shuttle-run sprint training in hypoxia for youth elite soccer players: a pilot study.

The purposes of the present study were to investigate if a) shuttle-run sprint training performed in a normobaric hypoxia chamber of limited size (4.75x2.25m) is feasible, in terms of producing the same absolute training load, when compared to training in normoxia, and b) if such training improves the repeated sprint ability (RSA) and the Yo-Yo intermittent recovery (YYIR) test outcome in young elite soccer players. Players of an elite soccer training Centre (age: 15.3 ± 0.5 years, height: 1.73 ± 0.07 m, body mass: 62.6 ± 6.6 kg) were randomly assigned to a hypoxia or a normoxia training group. Within a 5-week period, players, who were not informed about the hypoxia intervention, performed at least 7 sessions of identical shuttle-run sprint training either in a normal training room (FiO2 = 20.95%) or in a hypoxic chamber (FiO2 = 14.8%; approximately 3300m), both equipped with the same floor. Each training session comprised 3 series of 5x10s back and forth sprints (4.5m) performed at maximal intensity. Recovery time between repetitions was 20s and between series 5min. Before and after the training period the RSA (6 x 40m shuttle sprint with 20 s rest between shuttles) and the YYIR test were performed. The size of the chamber did not restrict the training intensity of the sprint training (both groups performed approximately 8 shuttles during 10s). Training in hypoxia resulted in a lower fatigue slope which indicates better running speed maintenance during the RSA test (p = 0.024). YYIR performance increased over time (p = 0.045) without differences between groups (p > 0.05). This study showed that training intensity of the shuttle-run sprint training was not restricted in a hypoxic chamber of limited size which indicates that such training is feasible. Furthermore, hypoxia compared to normoxia training reduced the fatigue slope during the RSA test in youth soccer players. Key PointsShuttle-run sprint training is feasible in hypoxic chambers of limited size (i.e., 4.75x2.25m). Hypoxia sprint training (RSH), in comparison to normoxia training, might lead to better running speed maintenance during the repeated sprint ability test.
Recent VLBI Results on SN 1986J and the Possibility of FRBs Originating from Inside the Expanding Ejecta of Supernovae

We discuss our VLA and VLBI observations of supernova 1986J, which is characterized by a compact radio-bright component within the expanding shell of ejecta. No other supernova (SN) has such a central component at cm wavelengths. The central component therefore provides a unique probe of the propagation of radio signals at cm wavelengths through the ejecta of a young SN. Such a probe is important in the context of Fast Radio Bursts (FRB), which, in many models, are thought to be produced by young magnetars or neutron stars. The FRB signals would therefore have to propagate through the expanding SN ejecta. Our observations of the Type II SN 1986J show that the ejecta would remain opaque to cm-wave emission like FRBs for at least several decades after the explosion, and by the time the ejecta have become transparent, the contribution of the ejecta to the dispersion measure is likely small.
Harvey: a greybox fuzzer for smart contracts

We present Harvey, an industrial greybox fuzzer for smart contracts, which are programs managing accounts on a blockchain. Greybox fuzzing is a lightweight test-generation approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is guarded by narrow checks. Moreover, most real-world smart contracts transition through many different states during their lifetime, e.g., for every bid in an auction. To explore these states and thereby detect deep vulnerabilities, a greybox fuzzer would need to generate sequences of contract transactions, e.g., by creating bids from multiple users, while keeping the search space and test suite tractable. In this paper, we explain how Harvey alleviates both challenges with two key techniques. First, Harvey extends standard greybox fuzzing with a method for predicting new inputs that are more likely to cover new paths or reveal vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in a targeted and demand-driven way. We have evaluated our approach on 27 real-world contracts. Our experiments show that our techniques significantly increase Harvey's effectiveness in achieving high coverage and detecting vulnerabilities, in most cases orders-of-magnitude faster.
Low-Complexity OFDM Spectral Precoding

This paper proposes a new large-scale mask-compliant spectral precoder (LS-MSP) for orthogonal frequency division multiplexing systems. In this paper, we first consider a previously proposed mask-compliant spectral precoding scheme that utilizes a generic convex optimization solver which suffers from high computational complexity, notably in large-scale systems. To mitigate the complexity of computing the LS-MSP, we propose a divide-and-conquer approach that breaks the original problem into smaller rank 1 quadratic-constraint problems and each small problem yields closed-form solution. Based on these solutions, we develop three specialized first-order low-complexity algorithms, based on 1) projection on convex sets and 2) the alternating direction method of multipliers. We also develop an algorithm that capitalizes on the closed-form solutions for the rank 1 quadratic constraints, which is referred to as 3) semianalytical spectral precoding. Numerical results show that the proposed LS-MSP techniques outperform previously proposed techniques in terms of the computational burden while complying with the spectrum mask. The results also indicate that 3) typically needs 3 iterations to achieve similar results as 1) and 2) at the expense of a slightly increased computational complexity.
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

Click through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems. Recent years have witnessed the success of both the deep learning based model and attention mechanism in various tasks in computer vision (CV) and natural language processing (NLP). How to combine the attention mechanism with deep CTR model is a promising direction because it may ensemble the advantages of both sides. Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features. In this paper, we propose a new neural CTR model named Field Attentive Deep Field-aware Factorization Machine (FAT-DeepFFM) by combining the Deep Field-aware Factorization Machine (DeepFFM) with Compose-Excitation network (CENet) field attention mechanism which is proposed by us as an enhanced version of Squeeze-Excitation network (SENet) to highlight the feature importance. We conduct extensive experiments on two real-world datasets and the experiment results show that FAT-DeepFFM achieves the best performance and obtains different improvements over the state-of-the-art methods. We also compare two kinds of attention mechanisms (attention before explicit feature interaction vs. attention after explicit feature interaction) and demonstrate that the former one outperforms the latter one significantly.
Artificial intelligence technology in oncology: a new technological paradigm

Artificial Intelligence (AI) technology is based on theory and development of computer systems able to perform tasks that normally require human intelligence. In this context, deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior. Application of these methods to medical imaging can assist pathologists in the detection of cancer subtype, gene mutations and/or metastases for applying appropriate therapies. The purpose of this study is to show the emerging application of AI in medical imaging to detect lung and breast cancer. Moreover, this study shows the comparative evolutionary pathways of this emerging technology for three critical cancers: lung, breast and thyroid. A main finding of this study is the recognition that, since the late 1990, the sharp increase of technological trajectories of AI technology applied in cancer imaging seems to be driven by high rates of mortality of some types of cancer (e.g., lung and breast) in order to find new techniques for a more accurate detection, characterization and monitoring as well as to apply efficiently anticancer therapies that increase the progression-free survival of patients: the so-called mortality-driven AI technological trajectories. Results also suggest that this new technology can generate a technological paradigm shift for diagnostic assessment of any cancer type. However, application of these methods to medical imaging requires further assessment and validation to assist pathologists to increase the efficiency of their workflow in both routine tasks and critical cases of diagnostics.
Deep Session Interest Network for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction plays an important role in many industrial applications, such as online advertising and recommender systems. How to capture users' dynamic and evolving interests from their behavior sequences remains a continuous research topic in the CTR prediction. However, most existing studies overlook the intrinsic structure of the sequences: the sequences are composed of sessions, where sessions are user behaviors separated by their occurring time. We observe that user behaviors are highly homogeneous in each session, and heterogeneous cross sessions. Based on this observation, we propose a novel CTR model named Deep Session Interest Network (DSIN) that leverages users' multiple historical sessions in their behavior sequences. We first use self-attention mechanism with bias encoding to extract users' interests in each session. Then we apply Bi-LSTM to model how users' interests evolve and interact among sessions. Finally, we employ the local activation unit to adaptively learn the influences of various session interests on the target item. Experiments are conducted on both advertising and production recommender datasets and DSIN outperforms other state-of-the-art models on both datasets.
Catching butterflies in the sky: Extended catalog of winged or X-shaped radio sources from the latest FIRST data release

We present a catalog of 290 "winged" or X-shaped radio galaxies (XRGs) extracted from the latest (2014 December 17) data release of the "Very Large Array Faint Images of the Radio Sky at Twenty centimeter. " We have combined these radio images with their counterparts in the TIFR GMRT sky survey at 150 MHz, in an attempt to identify any low surface brightness radio emission present in these sources. This has enabled us to assemble a sample of 106 "strong" XRG candidates and 184 "probable" XRG candidates whose XRG designation needs to be verified by further observations. The present sample of 290 XRG candidates is almost twice as large as the number of XRGs currently known. Twenty-five of our 290 XRG candidates (9 "strong" and 16 "probable") are identified as quasars. Double-peaked narrow emission lines are seen in the optical spectra of three of the XRG candidates (two "strong" and one "probable"). Nearly 90% of the sample is located in the FR II domain of the Owen-Ledlow diagram. A few of the strong XRG candidates have a rather flat radio spectrum (spectral index alpha flatter than -0.3) between 150 MHz and 1.4 GHz, or between 1.4 and 5 GHz. Since this is not expected for lobe-dominated extragalactic radio sources (like nearly all known XRGs), these sources are particularly suited for follow-up radio imaging and near-simultaneous measurement of the radio spectrum.
Activity of four allelic forms of glutathione S-transferase hGSTP1-1 for diol epoxides of polycyclic aromatic hydrocarbons.

Allelic forms of hGSTP1-1 which differ from each other by their catalytic properties and, structurally, by the amino acid(s) in position(s) 104 or (and) 113 are known to exist in human populations. The four possible isoforms of hGSTP1-1 with isoleucine or valine in position 104 and with alanine or valine in position 113 were produced by site-directed mutagenesis of the cDNA followed by bacterial expression and purification of the proteins. Glutathione-conjugating activity was measured with the diol epoxides of benzo(a)pyrene and chrysene, as well as with the model substrate 1-chloro-2,4-dinitrobenzene. Isoenzymes with valine in position 104 were more effective with the diol epoxides of polycyclic aromatic hydrocarbons but less effective with 1-chloro-2,4-dinitrobenzene than the isoforms with isoleucine 104. In addition, the transition A113V in the presence of V104 caused a pronounced increase in catalytic efficiency for the benzo(a)pyrene but not the chrysene diol epoxide. It is proposed that amino acid 113 functions as part of a clamp that lines the mouth of the water channel leading to the active sites of the hGSTP1-1 dimer and controls the access to substrates. Therefore, the hydrophobicity and the size of residue 113 are important in co-determining the substrate specificity of the isoenzymes. The widely different activities of the allelic isoforms toward carcinogenic diol epoxides of polycyclic aromatic hydrocarbons may help to explain the correlation between cancer susceptibility and genotype at the hGSTP1 locus that has been found by others.
Early Low-mass Galaxies and Star-cluster Candidates at z ∼ 6–9 Identified by the Gravitational-lensing Technique and Deep Optical/Near-infrared Imaging

We present very faint dropout galaxies at z ∼ 6−9 with a stellar mass M⋆ down to that are found in deep optical/near-infrared (NIR) images of the full data sets of the Hubble Frontier Fields (HFF) program in conjunction with deep ground-based and Spitzer images and gravitational-lensing magnification effects. We investigate stellar populations of the HFF dropout galaxies with the optical/NIR photometry and BEAGLE models made of self-consistent stellar population synthesis and photoionization models, carefully including strong nebular emission impacting on the photometry. We identify 453 galaxies with . Our best-estimate function is comparable to a model of star formation duration time of 100 Myr that is assumed in Bouwens et al. We derive the galaxy stellar mass functions (GSMFs) at z ∼ 6–9 that agree with those obtained by previous studies at , and that extend to . Estimating the stellar mass densities with the GSMFs, we find a very slow evolution from z ∼ 9 to z ∼ 6–7, which is consistent with the one estimated from star formation rate density measurements. In conjunction with the estimates of the galaxy effective radii Re on the source plane, we have pinpointed four objects with low stellar masses ( ) and very compact morphologies ( pc) that are comparable with those of globular clusters (GCs) in the Milky Way today. These objects are candidates of star clusters, some of which may be related to GCs today.
Shock-induced arrhythmogenesis in the human heart: A computational modelling study

Electrical defibrillation by application of a strong shock to the heart is the only effective treatment against lethal cardiac arrhythmias such as ventricular fibrillation. A large body of experimental and computational research has been devoted to understanding shock-induced effects on the heart in an attempt to improve defibrillation efficacy. However, most of the research has been performed in small animal hearts, and in particular rabbits. The difference in size between rabbits and humans might limit the extrapolation of the results to the clinical setting. In this paper, we present, for the first time, computer simulations of shock-induced effects on a human ventricular model with realistic ion channel dynamics and fibre architecture. Bidomain simulations using the human ventricular model were performed using the Chaste open source simulation package. The parallel performance of the software package was highly improved in order to meet the computational requirements of these kind of studies.
The behavior of gravitational force at small scales

We point out the idea that, at small scales, gravity can be described by the standard degrees of freedom of general relativity, plus a scalar particle and a degree of freedom of a new type: the fakeon. This possibility leads to fundamental implications in understanding gravitational force at quantum level as well as phenomenological consequences in the corresponding classical theory.
Latent Universal Task-Specific BERT

This paper describes a language representation model which combines the Bidirectional Encoder Representations from Transformers (BERT) learning mechanism described in Devlin et al. (2018) with a generalization of the Universal Transformer model described in Dehghani et al. (2018). We further improve this model by adding a latent variable that represents the persona and topics of interests of the writer for each training example. We also describe a simple method to improve the usefulness of our language representation for solving problems in a specific domain at the expense of its ability to generalize to other fields. Finally, we release a pre-trained language representation model for social texts that was trained on 100 million tweets.