Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning

Artificial intelligence-empowred Real-Time Bidding (AIRTB) is regarded as one of the most enabling technolo-gies for online advertising. It has attracted significant research attention from diverse fields such as pattern recognition, game theory and mechanism design. Despite of its remarkable development and deployment, the AIRTB system can sometimes harm the interest of its participants (e.g., depleting the advertisers’ budget with various kinds of fraud). As such, building trustworthy AIRTB auctioning systems has emerged as an important direction of research in this field in recent years. Due to the highly interdisciplinary nature of this field and a lack of a comprehensive survey, it is a challenge for researchers to enter this field and contribute towards building trustworthy AIRTB technologies. This paper bridges this important gap in trustworthy AIRTB literature. We start by analysing the key concerns of various AIRTB stakeholders and identify three main dimensions of trust building in AIRTB, namely security, robustness and fairness. For each of these dimensions, we propose a unique taxonomy of the state of the art, trace the root causes of possible breakdown of trust, and discuss the necessity of the given dimension. This is followed by a comprehensive review of existing strategies for fulfilling the requirements of each trust dimension. In addition, we discuss the promising future directions of research essential towards building trustworthy AIRTB systems to benefit the field of online advertising.
Phase Diagram of Mixed-Dimensional Anisotropic t-J-Models

We study the phase diagram of two different mixed-dimensional $t-J_z-J_{\perp}$-models on the square lattice, in which the hopping amplitude $t$ is only nonzero along the $x$-direction. In the first, bosonic, model, the spin exchange amplitude $J_{\perp}$ is negative and isotropic along the $x$ and $y$ directions of the lattice, and $J_z$ is isotropic and positive. The low-energy physics is characterized by spin-charge separation: the holes hop as free fermions in an easy-plane ferromagnetic background. In the second model, $J_{\perp}$ is restricted to the $x$-axis while $J_z$ remains isotropic and positive. The model is agnostic to particle statistics, and shows stripe patterns with anti-ferromagnetic N{\'e}el order at low temperature and high hole concentrations, in resemblance of the mixed-dimensional $t-J_z$ and $t-J$ models. At lower hole concentration, a very strong first order transition and hysteresis loop is seen extending to a remarkably high 14(1)% hole doping.
Simulating scalar field theories on quantum computers with limited resources

We present a quantum algorithm for implementing $\phi^4$ lattice scalar field theory on qubit computers. The field is represented in the discretized field amplitude basis. The number of qubits and elementary gates required by the implementation of the evolution operator is proportional to the lattice size. The algorithm allows efficient $\phi^4$ state preparation for a large range of input parameters in both the normal and broken-symmetry phases. The states are prepared using a combination of variational and adiabatic evolution methods. First, the ground state of a local Hamiltonian, which includes the $\phi^4$ self-interaction, is prepared using short variational circuits. Next, this state is evolved by switching on the coupling between the lattice sites adiabatically. The parameters defining the local Hamiltonian are adjustable and constitute the input of our algorithm. We present a method to optimize these parameters in order to reduce the adiabatic time required for state preparation. For preparing broken-symmetry states, the adiabatic evolution problems caused by crossing the phase transition critical line and by the degeneracy of the broken-symmetry ground state can be addressed using an auxiliary external field which gradually turns off during the adiabatic process. We show that the time dependence of the external field during the adiabatic evolution is important for addressing the broken-symmetry ground state degeneracy. The adiabatic time dependence on the inverse error tolerance can be reduced from quadratic to linear by using a field strength that decreases exponentially in time relative to one that decreases linearly.
Uncovering extreme nonlinear dynamics in solids through time-domain field analysis

Time-domain analysis of harmonic fields with sub-cycle resolution is now experimentally viable due to the emergence of sensitive, on-chip techniques for petahertz-scale optical-field sampling. We demonstrate how such a time-domain, field-resolved analysis uncovers the extreme nonlinear electron dynamics responsible for high-harmonic generation within solids. Time-dependent density functional theory was used to simulate harmonic generation from a solid-state band-gap system driven by near- to mid-infrared waveforms. Particular attention was paid to regimes where both intraband and interband emission mechanisms play a critical role in shaping the nonlinear response. We show that a time-domain analysis of the harmonic radiation fields identifies the interplay between intra- and interband dynamical processes underlying the nonlinear light generation. With further analysis, we show that changes to the dominant emission regime can occur after only slight changes to the peak driving intensity and central driving wavelength. Time-domain analysis of harmonic fields also reveals, for the first time, the possibility of rapid changes in the dominant emission mechanism within the temporal window of the driving pulse envelope. Finally, we examine the experimental viability of performing time-domain analysis of harmonic fields with sub-cycle resolution using realistic parameters.
New Five Roots to Solve Quantic Equation in General Forms by Using Radical Expressions Along With New Theorems

- This paper presents new formulary solutions for quantic polynomial equations in general forms, where we present five solutions for any fifth degree polynomial equation with real coefficients, and thereby having the possibility to calculate the five roots of any quantic equation nearly simultaneously. The proposed roots for fifth degree polynomials in this paper are structured basing on new proposed solutions for fourth degree polynomial equations, which we developed in order to reduce the expression of any quantic polynomial to an expression of quartic polynomial.
Model-Based Imitation Learning for Urban Driving

An accurate model of the environment and the dynamic agents acting in it offers great potential for improving motion planning. We present MILE: a Model-based Imitation LEarning approach to jointly learn a model of the world and a policy for autonomous driving. Our method leverages 3D geometry as an inductive bias and learns a highly compact latent space directly from high-resolution videos of expert demonstrations. Our model is trained on an offline corpus of urban driving data, without any online interaction with the environment. MILE improves upon prior state-of-the-art by 31% in driving score on the CARLA simulator when deployed in a completely new town and new weather conditions. Our model can predict diverse and plausible states and actions, that can be interpretably decoded to bird's-eye view semantic segmentation. Further, we demonstrate that it can execute complex driving manoeuvres from plans entirely predicted in imagination. Our approach is the first camera-only method that models static scene, dynamic scene, and ego-behaviour in an urban driving environment. The code and model weights are available at https://github.com/wayveai/mile.
Pell’s equation, sum-of-squares and equilibrium measures on a compact set

We first interpret Pell's equation satisfied by Chebyshev polynomials for each degree t, as a certain Positivstellensatz, which then yields for each integer t, what we call a generalized Pell's equation, satisfied by reciprocals of Christoffel functions of ''degree'' 2t, associated with the equilibrium measure $\mu$ of the interval [--1, 1] and the measure (1 -- x 2)d$\mu$. We next extend this point of view to arbitrary compact basic semi-algebraic set S $\subset$ R n and obtain a generalized Pell's equation (by analogy with the interval [--1, 1]). Under some conditions, for each t the equation is satisfied by reciprocals of Christoffel functions of ''degree'' 2t associated with (i) the equilibrium measure $\mu$ of S and (ii), measures gd$\mu$ for an appropriate set of generators g of S. These equations depend on the particular choice of generators that define the set S. In addition to the interval [--1, 1], we show that for t = 1, 2, 3, the equations are indeed also satisfied for the equilibrium measures of the 2D-simplex, the 2D-Euclidean unit ball and unit box. Interestingly, this view point connects orthogonal polynomials, Christoffel functions and equilibrium measures on one side, with sum-of-squares, convex optimization and certificates of positivity in real algebraic geometry on another side.
Deriving closures for bubbly flows using direct numerical simulations

CFD modeling of dispersed multiphase flows can be quite challenging because of the wide range of time- and length-scales involved. A modern methodology to bridge the different scales is multi-scale modeling, which involves applying different (types of) models to describe phenomena prevailing at different time and length scales. This approach requires however, closure equations for the unresolved sub-grid phenomena in the higher level models. These closures can in principle be obtained from analytical theory, experiments and direct numerical simulations (DNS), each with their own strong and weak points. Analytical theory is limited to idealized situations, for instance spherical bubbles in the limit of high Reynolds numbers, while experiments are timeconsuming, costly and easily influenced by disturbances and contaminations and often not all relevant quantities can be measured simultaneously and with the desired accuracy. A third and relatively new path is to use DNS, which is not restricted to any idealized situation nor suffers from experimental difficulties. One of the strongest points is the freedom to change any of the physical properties or other parameters (geometry, operating parameters, etc.) at will and study their influence independently at great detail, having all the information on all variables (such as flow field, pressure field, etc.) available. The objective of this thesis was to improve a 3D Front Tracking model and to use it to obtain closures for the drag, lift and virtual mass forces acting on single bubbles rising in an initially quiescent infinite liquid. Using periodic boundary conditions, also the influence of neighboring bubbles (referred to as ‘swarm effects’) on the drag force was studied. In addition, dedicated experiments have been performed to validate the numerical results for the drag and lift forces acting on single bubbles.
Disks of Stars in the Galactic Center Triggered by Tidal Disruption Events

In addition to a supermassive black hole (SMBH), the central parsec of the Milky Way hosts over 100 massive, high-velocity young stars whose existence, and organization of a subset of them in one, or possibly two, misaligned disks, is puzzling. Due to a combination of low medium density and strong tidal forces in the vicinity of Sgr A*, stars are not expected to form. Here we propose a novel scenario for their in situ formation: a jetted tidal disruption event (TDE) from an older wandering star triggers an episode of positive feedback of star formation in the plane perpendicular to the jet, as demonstrated via numerical simulations in the context of jet-induced feedback in galactic outflows. An overpressured cocoon surrounding the jet shock-compresses clumps to densities high enough to resist the SMBH tidal field. The TDE rate of 10−5–10−4 yr−1 per galaxy, out of which a few percent of events are jetted, implies a jetted TDE event per galaxy to occur every few million years. This timescale is interestingly of the same order of the age of the disk stars. The mass function predicted by our mechanism is top heavy. Additionally, since TDEs are isotropic, our model predicts a random orientation for the disk of stars with respect to the plane of the galaxy and, due to the relatively high TDE rate, can account for multiple disks of stars with uncorrelated orientations.
Bayesian estimation of the autocovariance of a model error in time series

Autocovariance of the error term in a time series model plays a key role in the estimation and inference for the model that it belongs to. Typically, some arbitrary parametric structure is assumed upon the error to simplify the estimation, which inevitably introduces potential model-misspecification. We thus conduct nonparametric estimation of it. To avoid the difficult bandwidth selection issue under the traditional nonparametric truncation approach, this paper conducts the Bayesian estimation of its spectral density in a frequency domain. To this end, we consider two cases: fixed error variance and time-varying one. Each approach is taken to estimate the spectral density of the autocovariance and the model parameters. The methodology is applied to exchange rate forecasting and proves to compete favorably against some benchmark models, including the random walk without drift.
Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons

Automatically generated emotion arcs—that capture how an individual or a population feels over time—are widely used in industry and research. However, there is little work on evaluating the generated arcs. This is in part due to the difﬁculty of establishing the true (gold) emotion arc. Our work, for the ﬁrst time, systematically and quantitatively evaluates automatically generated emotion arcs. We also compare two common ways of generating emotion arcs: Machine-Learning (ML) models and Lexicon-Only (LexO) methods. Using a number of diverse datasets, we systematically study the relationship between the quality of an emotion lexicon and the quality of the emotion arc that can be generated with it. We also study the relationship between the quality of an instance-level emotion detection system (say from an ML model) and the quality of emotion arcs that can be generated with it. We show that despite being markedly poor at instance level, LexO methods are highly accurate at generating emotion arcs by aggregating information from hundreds of instances. This has wide-spread implications for commercial development, as well as research in psychology, public health, digital humanities, etc. that values simple interpretable methods and dis-prefers the need for domain-speciﬁc training data, programming expertise, and high-carbon-footprint models.
A Second Wave of UD Hebrew Treebanking and Cross-Domain Parsing

Foundational Hebrew NLP tasks such as segmentation, tagging and parsing, have relied to date on various versions of the Hebrew Treebank (HTB, Sima’an et al. 2001). However, the data in HTB, a single-source newswire corpus, is now over 30 years old, and does not cover many aspects of contemporary Hebrew on the web. This paper presents a new, freely available UD treebank of Hebrew stratified from a range of topics selected from Hebrew Wikipedia. In addition to introducing the corpus and evaluating the quality of its annotations, we deploy automatic validation tools based on grew (Guillaume, 2021), and conduct the first cross domain parsing experiments in Hebrew. We obtain new state-of-the-art (SOTA) results on UD NLP tasks, using a combination of the latest language modelling and some incremental improvements to existing transformer based approaches. We also release a new version of the UD HTB matching annotation scheme updates from our new corpus.
Superpixel Perception Graph Neural Network for Intelligent Defect Detection

Aero-engine is the core component of aircraft and other spacecraft. The high-speed rotating blades provide power by sucking in air and fully combusting, and various defects will inevitably occur, threatening the operation safety of aero-engine. Therefore, regular inspections are essential for such a complex system. However, existing traditional technology which is borescope inspection is labor-intensive, time-consuming, and experience-dependent. To endow this technology with intelligence, a novel superpixel perception graph neural network (SPGNN) is proposed by utilizing a multi-stage graph convolutional network (MSGCN) for feature extraction and superpixel perception region proposal network (SPRPN) for region proposal. First, to capture complex and irregular textures, the images are transformed into a series of patches, to obtain their graph representations. Then, MSGCN composed of several GCN blocks extracts graph structure features and performs graph information processing at graph level. Last but not least, the SPRPN is proposed to generate perceptual bounding boxes by fusing graph representation features and superpixel perception features. Therefore, the proposed SPGNN always implements feature extraction and information transmission at the graph level in the whole SPGNN pipeline, and SPRPN and MSGNN mutually benefit from each other. To verify the effectiveness of SPGNN, we meticulously construct a simulated blade dataset with 3000 images. A public aluminum dataset is also used to validate the performances of different methods. The experimental results demonstrate that the proposed SPGNN has superior performance compared with the state-of-the-art methods. The source code will be available at https://github.com/githbshang/SPGNN.
Hubble captures multiply-imaged ionizing radiation from strongly lensed galaxy at z=2.4

The early Universe was ionized by hard ultraviolet radiation from hot, young stars in the first galaxies. These galaxies contain absorbing neutral Hydrogen which must be configured favorably to allow ionizing escape; only a few dozen cases have been detected at any redshift. Here we present Hubble observations of the brightest known lensed galaxy, the Sunburst Arc, revealing bright, multiply-imaged escape from a compact stellar region through a narrow channel in an optically thick gas. Gravitational lensing magnification has allowed the clearest view yet of how ionizing photons escape galaxies, providing a unique window to the last major phase transition of the Universe, the epoch of re-ionization. The multiple sight lines to the source probe absorption by intergalactic neutral Hydrogen on unprecedentedly small scales.
Tumbling of polymers in semidilute solution under shear flow

The tumbling dynamics of individual polymers in semidilute solution is studied by large-scale non-equilibrium mesoscale hydrodynamic simulations. We find that the tumbling time is equal to the non-equilibrium relaxation time of the polymer end-to-end distance along the flow direction and strongly depends on concentration. In addition, the normalized tumbling frequency as well as the widths of the alignment distribution functions for a given concentration-dependent Weissenberg number exhibit a weak concentration dependence in the cross-over regime from a dilute to a semidilute solution. For semidilute solutions a universal behavior is obtained. This is a consequence of screening of hydrodynamic interactions at polymer concentrations exceeding the overlap concentration.
MYH1F promotes the proliferation and differentiation of chicken skeletal muscle satellite cells into myotubes

Abstract In diploid organisms, interactions between alleles determine phenotypic variation. In previous experiments, only MYH1F was found to show both ASE (spatiotemporal allele-specific expression) and TRD (allelic transmission ratio distortion) characteristics in the pectoral muscle by comparing the genome-wide allele lists of hybrid populations (F1) of meat- and egg- type chickens. In addition, MYH1F is a member of the MYH gene family, which plays an important role in skeletal muscle and non-muscle cells of animals, but the specific expression and function of this gene in chickens are still unknown. Therefore, qRT-PCR was used to detect the expression of MYH1F in different tissues of chicken. Proliferation and differentiation of chicken skeletal muscle satellite cells (SMSCs) have been detected by transfection of MYH1F-specific small interfering RNA (siRNA). The results showed that the expression of MYH1F in chicken skeletal muscle was higher than that in other tissues. Combined with CCK-8 assay, EdU assay, immunofluorescence, and Western blot Assay, it was found that MYH1F knockdown could significantly suppress the proliferation of chicken SMSCs and depress the differentiation and fusion of the cells. These results suggest that MYH1F plays a critical role in myogenesis in poultry, which is of great significance for exploring the regulatory mechanisms of muscle development and improving animal productivity.
K-shell spectroscopy in hot plasmas: Stark effect, Breit interaction and QED corrections

The broadening of lines by Stark effect is widely used for inferring electron density and temperature in plasmas. Stark-effect calculations often rely on atomic data (transition rates, energy levels,.) not always exhaustive and/or valid only for isolated atoms. In this work, we first present a recent development in the detailed opacity code SCO-RCG for K-shell spectroscopy. The approach is adapted from the work of Gilles and Peyrusse. Neglecting non-diagonal terms in dipolar and collision operators, the line profile is expressed as a sum of Voigt functions associated to the Stark components. The formalism relies on the use of parabolic coordinates and the relativistic fine-structure of Lyman lines is included by diagonalizing the hamiltonian matrix associated to quantum states having the same principal quantum number n. The SCO-RCG code enables one to investigate plasma environment effects, the impact of the microfield distribution, the decoupling between electron and ion temperatures and the role of satellite lines (such as Li-like 1s nl n'l' - 1s2 nl, Be-like, etc.). Atomic-structure calculations have reached levels of accuracy which require evaluation of Breit interaction and many-electron quantum electro-dynamics (QED) contributions. Although much work was done for QED effects (self-energy and vacuum polarization) in hydrogenic atoms, the case of an arbitrary number of electrons is more complicated. Since exact analytic solutions do not exist, a number of heuristic methods have been used to approximate the screening of additional \'electrons in the self-energy part. We compare different ways of including such effects in atomic-structure codes (Slater-Condon, Multi-Configuration Dirac-Fock, etc.).
Extended Kerr–Schild spacetimes: general properties and some explicit examples

We study extended Kerr–Schild (xKS) spacetimes, i.e. extensions of the Kerr–Schild (KS) ansatz where, in addition to the null KS vector, a spacelike vector field appears in the metric. In contrast to the KS case, we obtain only a necessary condition under which the KS vector is geodetic. However, it is shown that this condition becomes sufficient if we appropriately restrict the geometry of the null KS and spacelike vector. It turns out that xKS spacetimes with a geodetic KS vector are of Weyl type I and the KS vector has the same optical properties in the full and background spacetimes. In the case of Kundt xKS spacetimes, the compatible Weyl types are further restricted and a few examples of such metrics are provided. The relation of pp-waves to the class of Kundt xKS spacetimes is briefly discussed. We also show that the CCLP black hole, as an example of an expanding xKS spacetime, is of Weyl type I i , which specializes to type D in the uncharged or non-rotating limit, and that its optical matrix satisfies the optical constraint.