Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-driven modal decomposition methods as feature detection techniques for flow problems: a critical assessment

Modal decomposition techniques are showing fast growth in popularity for their good properties as data-driven tools. There are many modal decomposition techniques, yet Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) are the most widespread methods, especially in the field of fluid dynamics. Following their outstanding performance on various applications in several fields, numerous extensions of these techniques have been developed. In this work we present an ambitious review comparing eight different modal decomposition techniques, including most established methods, i.e. POD, DMD and Fast Fourier Transform (FFT); extensions of these classical methods: based either on time embedding systems, Spectral POD (SPOD) and Higher Order DMD (HODMD), or based on scales separation, multi-scale POD (mPOD), multi-resolution DMD (mrDMD); and also a method based on properties of the resolvent operator, the data-driven Resolvent Analysis (RA). The performance of all these techniques will be evaluated on four different testcases: the laminar wake around cylinder, a turbulent jet flow, the three dimensional wake around a cylinder in transient regime and a transient and turbulent wake around a cylinder. All these mentioned datasets are publicly available. First, we show a comparison between the performance of the eight modal decomposition techniques when the datasets are shortened. Next, all the results obtained will be explained in detail, showing both the conveniences and inconveniences of all the methods under investigation depending on the type of application and the final goal (reconstruction or identification of the flow physics).
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?

This work presents a linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading times. First, regression analyses show a strictly monotonic, positive log-linear relationship between perplexity and fit to reading times for the more recently released five GPT-Neo variants and eight OPT variants on two separate datasets, replicating earlier results limited to just GPT-2 (Oh et al., 2022). Subsequently, analysis of residual errors reveals a systematic deviation of the larger variants, such as underpredicting reading times of named entities and making compensatory overpredictions for reading times of function words such as modals and conjunctions. These results suggest that the propensity of larger Transformer-based models to ‘memorize’ sequences during training makes their surprisal estimates diverge from humanlike expectations, which warrants caution in using pre-trained language models to study human language processing.
ML-powered KQI estimation for XR services. A case study on 360-Video

The arise of cutting-edge technologies and services such as XR promise to change the concepts of how day-to-day things are done. At the same time, the appearance of modern and decentralized architectures approaches has given birth to a new generation of mobile networks such as 5G, as well as outlining the roadmap for B5G and posterior. These networks are expected to be the enablers for bringing to life the Metaverse and other futuristic approaches. In this sense, this work presents an ML-based (Machine Learning) framework that allows the estimation of service Key Quality Indicators (KQIs). For this, only information reachable to operators is required, such as statistics and configuration parameters from these networks. This strategy prevents operators from avoiding intrusion into the user data and guaranteeing privacy. To test this proposal, 360-Video has been selected as a use case of Virtual Reality (VR), from which specific KQIs are estimated such as video resolution, frame rate, initial startup time, throughput, and latency, among others. To select the best model for each KQI, a search grid with a cross-validation strategy has been used to determine the best hyperparameter tuning. To boost the creation of each KQI model, feature engineering techniques together with cross-validation strategies have been used. The performance is assessed using MAE (Mean Average Error) and the prediction time. The outcomes point out that KNR (K-Near Neighbors) and RF (Random Forest) are the best algorithms in combination with Feature Selection techniques. Likewise, this work will help as a baseline for E2E-Quality-of-Experience-based network management working in conjunction with network slicing, virtualization, and MEC, among other enabler technologies.
Grant-free Random Access with Self-conjugating Metasurfaces

Recently, grant-free random access schemes have received signiﬁcant attention in the scientiﬁc community as a solution for extremely low-latency massive communications in new industrial Internet-of-things (IIoT) and digital twins applications. Unfortunately, the adoption of such schemes in the mmWave and THz bands is challenging because massive antenna arrays are needed to counteract the high path loss and provide massive access with consequent signiﬁcant signaling overhead for channel estimation and slow beam alignment procedures between the base station (BS) and user equipments (UEs), which are in contrast to the ultra-low-latency requirement, as well as to the need for low hardware complexity and energy consumption. In this paper, we propose the adoption of a self-conjugating metasurface (SCM) at the UE side, where the signal sent by the BS is reﬂected after being conjugated and phase-modulated according to the UE data. Then, a novel grant-free random access protocol is presented capable to detect new UEs and establish parallel multiple-input multiple-output (MIMO) uplink communications with almost zero latency and jitter. This is done in a blind way without the need for RF/ADC chains at the UE side as well as without explicit channel estimation and time-consuming beam alignment schemes.
1642+690: a superluminal quasar

La radiosource 1642+690 contient un cœur compact avec un jet s'etendant sur 5'' vers le sud. On presente deux cartes VLBI a 5 GHz, obtenues en 1980 et 1983 qui montrent que la structure de la source est semblable a celles des sources superluminiques 3C273 et 3C345. L'objet optique associe est un quasar avec z=0,75
$1/N$ expansion of the D3-D5 defect CFT

We consider four dimensional $U(N)$ $\mathcal N=4$ SYM theory interacting with a 3d $\mathcal N=4$ theory living on a codimension-one interface and holographically dual to the D3-D5 system without flux. Localization captures several observables in this dCFT, including its free energy, related to the defect expectation value, and single trace $\frac{1}{2}$-BPS composite scalars. These quantities may be computed in a hermitian one-matrix model with non-polynomial single-trace potential. We exploit the integrable Volterra hierarchy underlying the matrix model and systematically study its $1/N$ expansion at any value of the 't Hooft coupling. In particular, the strong coupling regime is determined -- up to non-perturbative exponentially suppressed corrections -- by differential relations that constrain higher order terms in the $1/N$ expansion. The analysis is extended to the model with $SU(N)$ gauge symmetry by resorting to the more general Toda lattice equations.
Drop encapsulation and bubble bursting in surfactant-laden flows in capillary channels

We present a parametric study of the unsteady phenomena associated with the flow of elongated gas bubbles travelling through liquid-filled square capillaries under high Weber number conditions. These conditions consistently induce the formation of a re-entrant jet at the back of the bubble that commonly gives way to a deep liquid cavity. Subsequent steps include pinch-off events in the cavity to generate one or multiple encapsulated drops which may coalesce, in conjunction with the bursting of the bubble-liquid interface by either the cavity or the drops. Some of these interfacial instabilities have previously been reported experimentally (Olbricht 1996) and numerically (Izbassarov&Muradoglu 2016) for liquid-liquid flow in microchannels. We carry out three-dimensional direct numerical simulations based on a hybrid interface-tracking/level-set method capable of accounting for the presence and dynamic exchange of surfactants between the liquid bulk phase and the liquid-gas interface. Our results indicate that the delicate interplay amongst inertia, capillarity, viscosity, surfactant adsorption/desorption kinetics, and Marangoni stresses has a dramatic influence over the non-axisymmetric morphological structures of the encapsulated drops-elongated bubble. This strong coupling also influences the pinch-off time, penetration depth of the cavity, and number, size, and velocity of the encapsulated drops across the bubble. The observed phenomena are summarised in three main morphological regimes based on surfactant-related parameters and dimensionless groups. A discussion of the flow regime maps is also provided.
Patterning of morphogenetic anisotropy fields

Significance Life has brought about a remarkable diversity of organism shapes. During development, an organism is sculpted by mechanical forces that are generated within its tissues and directed by orientational patterns, as found in the orientation of muscle fibers. How orientational order is coupled to the morphogen concentration fields that organize the body plan is not well understood. Using a minimal model for how orientational patterns couple to gradients of morphogen expression, we examine how they jointly organize the shape of developing organisms. The model recapitulates the reorganization of muscle fiber orientation during key developmental stages of the freshwater polyp Hydra. Our results lay a foundation for disentangling the interplay between tissue mechanics, orientational order, and morphogen gradients.
Permissionless Refereed Tournaments

Scalability problems in programmable blockchains have created a strong demand for secure methods that move the bulk of computation outside the blockchain. One of the preferred solutions to this problem involves off-chain computers that compete interactively to prove to the limited blockchain that theirs is the correct result of a given intensive computation. Each off-chain computer spends effort linear on the cost of the computation, while the blockchain adjudicates disputes spending only logarithmic effort. However, this effort is multiplied by the number of competitors, rendering disputes that involve a significant number of parties impractical and susceptible to Sybil attacks. In this paper, we propose a practical dispute resolution algorithm by which a single honest competitor can win disputes while spending effort linear on the cost of the computation, but only logarithmic on the number of dishonest competitors. This algorithm is a novel, stronger primitive for building permissionless fraud-proof protocols, which doesn't rely on complex economic incentives to be enforced.
A Novel Method for Lane-change Maneuver in Urban Driving Using Predictive Markov Decision Process

Lane-change maneuver has always been a challenging task for both manual and autonomous driving, especially in an urban setting. In particular, the uncertainty in predicting the behavior of other vehicles on the road leads to indecisive actions while changing lanes, which, might result in traffic congestion and cause safety concerns. This paper analyzes the factors related to uncertainty such as speed range change and lane change so as to design a predictive Markov decision process for lane-change maneuver in the urban setting. A hidden Markov model is developed for modeling uncertainties of surrounding vehicles. The reward model uses the crash probabilities and the feasibility/distance to the goal as primary parameters. Numerical simulation and analysis of two traffic scenarios are completed to demonstrate the effectiveness of the proposed approach.
Structure-guided Image Outpainting

—Deep learning techniques have made considerable progress in image inpainting, restoration, and reconstruction in the last few years. Image outpainting, also known as image extrapolation, lacks attention and practical approaches to be fulﬁlled, owing to difﬁculties caused by large-scale area loss and less legitimate neighboring information. These difﬁculties have made outpainted images handled by most of the existing models unrealistic to human eyes and spatially inconsistent. When upsampling through deconvolution to generate fake content, the naive generation methods may lead to results lacking high-frequency details and structural authenticity. Therefore, as our novelties to handle image outpainting problems, we introduce structural prior as a condition to optimize the generation quality and a new semantic embedding term to enhance perceptual sanity. we propose a deep learning method based on Generative Adversarial Network (GAN) and condition edges as structural prior in order to assist the generation. We use a multi-phase adversarial training scheme that comprises edge inference training, contents inpainting training, and joint training. The newly added semantic embedding loss is proved effective in practice.
Shaft inflation in Randall-Sundrum model

Shaft inflation is a model in which the inflaton potential approaches a plateau far from the origin, while it mimics chaotic inflation near the origin. Meanwhile, the Randall-Sundrum type II model (RSII) is an interesting extra-dimensional model to study cosmological phenomenology. In this paper, we study shaft inflation in the RSII model. We find that the predictions are in excellent agreement with observation. The fundamental five-dimensional Planck scale is found to be M 5 ≃ 1016 GeV, which is consistent with the lower bound M 5 ≳ 109 GeV obtained from experimental Newtonian gravitational bound. This is an important result that can be used to explore further the implications of extra dimension in other contexts.
Radiation from a spherical acoustic source near a scattering sphere

The radiation from a spherical source vibrating with an arbitrary, axisymmetric, time‐harmonic velocity distribution while positioned at an arbitrary point wholly outside a fluid sphere while both are embedded in another infinite fluid medium, is computed. The translational addition theorem for spherical wave functions is used to express series of wave modes centered at one sphere in terms of modes centered at the other, thereby facilitating the task of satisfying the boundary conditions. By letting the fluid sphere become either infinitely dense or infinitely compliant, the special cases of a spherical source radiating in the presence of a perfectly reflecting spherical scatterer are obtained. The source output and farfield directional response pattern are computed as a function of source and fluid sphere separation for some representative values of relative fluid density and sound speed. Numerous results are presented showing the modification of the radiation load on monopole and dipole sources of vario.
Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models

Question: Can an encoder-decoder architecture pretrained on a large dataset of longitudinal electronic health records improves patient outcome predictions? Findings: In this prognostic study of 6.8 million patients, our denoising sequence-to-sequence prediction model of multiple outcomes outperformed state-of-the-art models scuh pretrained BERT on a broad range of patient outcomes, including intentional self-harm and pancreatic cancer. Meaning: Deep bidirectional and autoregressive representation improves patient outcome prediction.
Combining mutation and recombination statistics to infer clonal families in antibody repertoires

B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution and dynamics. We present HILARy (High-precision Inference of Lineages in Antibody Repertoires), an efficient, fast and precise method to identify clonal families from high-throughput sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and dN/dS ratio do not depend on the junction length. We also identify a broad range of selection pressures scanning two orders of magnitude.
Mapping and Manipulation of Topological Singularities: From Photonic Graphene to T-Graphene

Topological singularities (TSs) in momentum space give rise to intriguing fundamental phenomena as well as unusual material properties, attracting a great deal of interest in the past decade. Recently, we have demonstrated universal momentum-to-real-space mapping of TSs and pseudospin angular momentum conversion using photonic honeycomb (graphene-like) and Lieb lattices. Such mapping arises from the Berry phase encircling the Dirac or Dirac-like cones, and is thus of topological origin. In this paper, we briefly present previous observations of topological charge conversion, and then we present our first theoretical analysis and experimental demonstration of TS mapping in a new T-graphene lattice. Unlike other lattices, there are two coexisting but distinct TSs located at different high-symmetry points in the first Brillouin zone of T-graphene, which enables controlled topological charge conversion in the same lattice. We show active manipulation of the TS mapping, turning the two TSs into vortices of different helicities, or one into a high-order vortex but the other into a quadrupole. Such TS manipulation and pseudospin-to-orbital conversion may find applications in optical communications and quantum information, and may bring insight into the study of other Dirac-like structures with multiple TSs beyond the 2D photonic platform.
Adaptive RiskAware Bidding with Budget Constraint in Display Advertising

Real-time bidding (RTB) has become a major paradigm of display advertising. Each ad impression generated from a user visit is auctioned in real time, where demand-side plat- form (DSP) automatically provides bid price usually relying on the ad impression value estimation and the optimal bid price determination. However, the current bid strategy over- looks the randomness of the user behaviors (e.g., click) and the cost uncertainty caused by the auction competition. In this work, we propose a novel adaptive risk-aware bidding algorithm with budget constraint via reinforcement learn- ing, which is the rst to simultaneously consider estimation uncertainty and the dynamic risk tendency of a DSP. Specif- ically, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a speci c state and market environment via a sequen- tial decision process. Additionally, we theoretically unveil the intrinsic relation between the uncertainty and the risk tendency based on value at risk (VaR). Consequently, we propose two instantiations to model risk tendency, includ- ing an expert knowledge-based formulation embracing three essential properties and an adaptive learning method based on self-supervised reinforcement learning. We conduct ex- periments on public datasets and show that the proposed framework achieves better performance in terms of the num- ber of clicks under di erent budget constraints 1.
Spectrum of light scattered coherently or incoherently by a collection of atoms.

We discuss the spectrum of light scattered by N atomic dipoles. We present examples illustrating the nature of contributions proportional to N and N 2 in «coherent» and «incoherent» forward and side scattering. We use a detector-based formalism that differs in some respects from the formalism of Sundaram and Milonni [Phys. Rev. A 41, 6571 (1990)]
Matrix Based Adaptive Short Block Cipher

Every day, millions of credit cards are swiped and transactions are carried out across the world. Due to numerous forms of unethical digital activities, users are vulnerable to credit card fraud, phishing, identity theft, etc. This paper outlines a novel block encryption algorithm involving multiple private keys and a resilient trapdoor function that ensures data security while maintaining an optimal run time and space complexity. The proposed scheme consists of an irrepressible trapdoor based on a depressed cubic function and a unique key generation algorithm that uses Fibonacci sequences and invertible square matrices for improved security. The paper involves data obtained from compre-hensive crypt analysis exploiting the strengths and weaknesses of the system and comments on its potential large-scale industry applications.
Formulating the fluctuations of space-time and a justification for applying fractional gravity

It is well known that in quantum gravity, the very geometry of space and time is subject to continual fluctuation. The mathematical formulation for this old theory is still lacking. This article formulates this more than forty-year-old theory of quantum gravity. On the other hand, recent attention has been paid to fractional gravity. Although this type of gravity leads to brilliant results, we have no deep reason for applying it other than that it works. In this paper, it is demonstrated that quantum gravity equations become fractional when space-time fluctuations are taken into account. Therefore, here a reasonable root and argument for applying fractional gravity are found: ``Fractional quantum gravity is generated by stochastic fluctuations of space-time''. For clarification, Einstein-Hilbert theory along with a scalar field is investigated in deformed and non-deformed minisuperspaces. The results illustrate a transition from decelerated to accelerated expansion (late-time-accelerated expansion).
Design of Chemical Propellant Thruster to Deorbit Nano satellite: StudSat II

An increase in the application of the satellite has skyrocketed the number of satellites, especially in the low earth orbit (LEO). The major concern today is after end-of-life, these satellites become debris which negatively affects the space environment. As per the International Guidelines of the European Space Agency, it is mandatory to deorbit the satellite within 25years of end-of-life. This paper is aimed to design the solid chemical propellant thruster to deorbit the StudSat-2 from its original orbit to the lower orbit. StudSat-2 carries the heritage of StudSat-1 which was successfully launched on 12th July 2010 AD and is the first Pico-Satellite in India by the undergraduate students of seven engineering colleges. This paper explains how a solid monopropellant thruster could be used to deorbit the satellite after the end-of-life with the least difficulty compared to other active and passive methods of deorbiting. The deorbiting mechanism consists of a solid propellant, Convergent-Divergent nozzle, ignition system, and electronic actuators. The components of the thruster were designed in the CATIA V5, and the combustion studies and flow analysis were done in ANSYS. The concept of Hohmann transfer was used to deorbit the satellite and STK was used to simulate it.