Miracle-dz/UniFAR-FTUs huggingface dataset:
Control of Center of Mass Motion during Walking Predicts Gait and Balance in People with Incomplete Spinal Cord Injury

Background: There is evidence that ambulatory people with incomplete spinal cord injury (iSCI) have an impaired ability to control lateral motion of their whole-body center of mass (COM) during walking. This impairment is believed to contribute to functional deficits in gait and balance, however that relationship is unclear. Thus, this project examines the relationship between the ability to control lateral COM motion during walking and functional measures of gait and balance in people with iSCI. Methods: We assessed the ability to control lateral COM motion during walking and conducted clinical gait and balance outcome measures on twenty ambulatory adults with chronic iSCI (C1-T10 injury, American Spinal Injury Association Impairment Scale C or D). To assess their ability to control lateral COM motion, participants performed three treadmill walking trials. During each trial, real-time lateral COM position and a target lane were projected on the treadmill. Participants were instructed to keep their lateral COM position within the lane. If successful, an automated control algorithm progressively reduced the lane width, making the task more challenging. If unsuccessful, the lane width increased. The adaptive lane width was designed to challenge each participants maximum capacity to control lateral COM motion during walking. To quantify control of lateral COM motion, we calculated lateral COM excursion during each gait cycle and then identified the minimum lateral COM excursion occurring during five consecutive gait cycles. Our clinical outcome measures were Berg Balance Scale (BBS), Timed Up and Go test (TUG), 10-Meter Walk Test (10MWT) and Functional Gait Assessment (FGA). We used a Spearman correlation analysis (rho) to examine the relationship between minimum lateral COM excursion and clinical measures. Results: Minimum lateral COM excursion had significant moderate correlations with BBS (rho=-0.54, p=0.014), TUG (rho=0.59, p=0.007), 10MWT-preferred (rho=-0.59, p=0.006), and FGA (rho=-0.59, p=0.007) and a significant strong correlation with 10MWT-fast (rho=-0.68, p=0.001). Conclusion: Control of lateral COM motion during walking predicts a wide range of clinical gait and balance measures in people with iSCI. This finding suggests the ability to control lateral COM motion during walking could be a contributing factor to gait and balance in people with iSCI.
Negatively buoyant starting jets

The initial development of negatively buoyant jets has been investigated experimentally and numerically, focusing on the role played by gravity in the evolution of the leading vortex ring. Under the experimental conditions considered in this work, the densimetric Froude number, Fr=ρjUj2/[(ρ0−ρj)gD], which represents the ratio between the jet momentum and the buoyancy forces, emerges as the most relevant parameter characterizing the dynamics of the flow. Two different flow regimes have been observed depending on the Froude number: for sufficiently small Fr, the vortex ring generated initially is pushed radially away by gravity forces before it has time to detach from the shear layer originating at the orifice. On the other hand, when the Froude number is larger than a critical value, Fr>Frc∼1, the vortex ring detaches from the injection orifice and propagates downstream into the stagnant ambient followed by a trailing jet until it eventually reaches a maximum penetration depth. In order to clarify the mech.
FE-TCM: Filter-Enhanced Transformer Click Model for Web Search

Constructing click models and extracting implicit relevance feedback information from interaction between users and search engines are very important for improving the ranking of search results. Neural networks are effective for modeling users’ click behavior, and we propose a novel Filter-Enhanced Transformer Click Model (FE-TCM) for web search. The model uses the powerful Transformer model as the backbone network for feature extraction and innovatively add a filter layer. Firstly, in order to reduce the influence of noise on user behavior data, we use the learnable filters to filter the log noise. Secondly, following the examination hypothesis, we model the attraction estimator and examination predictor respectively to output attractiveness scores and examination probabilities. A novel transformer model is used to learn the deeper representation among different features. Finally, we apply the different combination functions to integrate attractiveness scores and examination probabilities into the click prediction. From our experiments on two real-world session datasets, it is proved that FE-TCM outperforms the existing click models for the click prediction.
Job recommendations: benchmarking of collaborative filtering methods for classifieds

Classifieds provide many challenges for recommendation methods, due to the limited information regarding users and items. In this paper, we explore recommendation methods for classifieds using the example of OLX Jobs. The goal of the paper is to benchmark different recommendation methods for jobs classifieds in order to improve advertisements' conversion rate and user satisfaction. In our research, we implemented methods that are scalable and represent different approaches to recommendation, namely ALS, LightFM, Prod2Vec, RP3beta, and SLIM. We performed a laboratory comparison of methods with regard to accuracy, diversity, and scalability (memory and time consumption during training and in prediction). Online A/B tests were also carried out by sending millions of messages with recommendations to evaluate models in a real-world setting. In addition, we have published the dataset that we created for the needs of our research. To the best of our knowledge, this is the first dataset of this kind. The dataset contains 65,502,201 events performed on OLX Jobs by 3,295,942 users, who interacted with (displayed, replied to, or bookmarked) 185,395 job ads in two weeks of 2020. We demonstrate that RP3beta, SLIM, and ALS perform significantly better than Prod2Vec and LightFM when tested in a laboratory setting. Online A/B tests also demonstrated that sending messages with recommendations generated by the ALS and RP3beta models increases the number of users contacting advertisers. Additionally, RP3beta had a 20% greater impact on this metric than ALS.
Supercharging Distributed Computing Environments For High Performance Data Engineering

The data engineering and data science community has embraced the idea of using Python&R dataframes for regular applications. Driven by the big data revolution and artificial intelligence, these applications are now essential in order to process terabytes of data. They can easily exceed the capabilities of a single machine, but also demand significant developer time&effort. Therefore it is essential to design scalable dataframe solutions. There have been multiple attempts to tackle this problem, the most notable being the dataframe systems developed using distributed computing environments such as Dask and Ray. Even though Dask/Ray distributed computing features look very promising, we perceive that the Dask Dataframes/Ray Datasets still have room for optimization. In this paper, we present CylonFlow, an alternative distributed dataframe execution methodology that enables state-of-the-art performance and scalability on the same Dask/Ray infrastructure (thereby supercharging them! ). To achieve this, we integrate a high performance dataframe system Cylon, which was originally based on an entirely different execution paradigm, into Dask and Ray. Our experiments show that on a pipeline of dataframe operators, CylonFlow achieves 30x more distributed performance than Dask Dataframes. Interestingly, it also enables superior sequential performance due to the native C++ execution of Cylon. We believe the success of Cylon&CylonFlow extends beyond the data engineering domain, and can be used to consolidate high performance computing and distributed computing ecosystems.
Suboptimality analysis of receding horizon quadratic control with unknown linear systems and its applications in learning-based control

—For a receding-horizon controller with a known system and with an approximate terminal value function, it is well-known that increasing the prediction horizon can improve its control performance. However, when the prediction model is inexact, a larger prediction horizon also causes propagation and accumulation of the prediction error. In this work, we aim to analyze the effect of the above trade-off between the modeling error, the terminal value function error, and the prediction horizon on the performance of a nominal receding-horizon linear quadratic (LQ) controller. By developing a novel perturbation result of the Riccati difference equation, a performance upper bound is obtained and suggests that for many cases, the prediction horizon should be either 1 or + ∞ to improve the control performance, depending on the relative difference between the modeling error and the terminal value function error. The obtained suboptimality performance bound is also applied to provide end-to-end performance guarantees, e.g., regret bounds, for nominal receding-horizon LQ controllers in a learning-based setting.
Nonperturbative strong coupling at timelike momenta

We compute the strong coupling constant of Landau gauge QCD in the full complex momentum plane, both directly and via spectral reconstruction. In particular, we consider the Taylor coupling given by the product of ghost and gluon dressing functions. Assuming spectral representations for the latter, we first show that also the coupling obeys such a representation. The subsequent spectral reconstruction of the coupling data, obtained from 2+1 flavour lattice QCD results for the ghost and gluon, is based on a probabilistic inversion of this representation using Gaussian process regression with analytically enforced asymptotics. In contradistinction, our direct calculation relies on earlier reconstruction results for the ghost and gluon spectral functions themselves, as well as data obtained in functional QCD. Apart from its relevance for studies of resonances or scattering processes, the calculation also serves as a non-trivial benchmark of our reconstruction approach. The results show remarkable agreement, testifying to the reliability of the method.
FORMATION OF FORMALDEHYDE AND CARBON DIOXIDE ON AN ICY GRAIN ANALOG USING FAST HYDROGEN ATOMS

Formaldehyde (H2CO) and carbon dioxide (CO2) were produced in collisions of a superthermal, 3 eV beam of H(2S) atoms with CO molecules adsorbed on a gold surface at 4.8 K. The reaction-generated products were detected and analyzed using the techniques of temperature programmed desorption (TPD), quadrupole mass spectrometry, and a novel application of the Metropolis algorithm, random-walk procedure to identify the unique fractionation patterns of H2CO and CO2 from the patterns of other species such as N2, CO, and H2O embedded in the CO blanket and devolved in the TPD/mass spectrometry process. Reaction sequences are given to account for the formation of H2CO and CO2.
Single Higgs boson production at electron-positron colliders in gauge-Higgs unification

We examine contributions to single Higgs boson production processes via $Z'$ and $W'$ bosons in the $SU(3)_C\times SO(5)_W\times U(1)_X$ gauge-Higgs unification (GHU) model. In particular, we analyze the cross sections of three single Higgs boson production processes $e^-e^+\to Zh$, $e^-e^+\to \nu\bar{\nu}h$, and $e^-e^+\to e^-e^+h$ in the SM and the GHU model. For the Higgs strahlung process $e^-e^+\to Zh$, we show that for a parameter region satisfying the current experimental constraints, a maximum deviation from the SM is about up to 6% for the center-of-mass energy of the initial electron and positron $\sqrt{s}=250$ GeV and that from the SM is about up to 20% for $\sqrt{s}=500$ GeV, depending on the initial polarization of electron and positron. The deviation from the SM is monotonically increasing with respect to $\sqrt{s}$ for $\sqrt{s}\lesssim 1$ TeV. By using the Higgs strahlung process, it is possible to explore up to the region of tens of TeV in terms of the Kaluza-Klein (KK) mass. We also show that the sign of the bulk mass of a lepton multiplet in the GHU model can be determined by examining the deviation of the left-right symmetry of the $e^-e^+\to Zh$ process from the SM. The contributions to the cross sections of the $e^-e^+\to \nu\bar{\nu}h$ and $e^-e^+\to e^-e^+h$ processes via the $Z'$ and $W'$ bosons are relatively small compared to that for $e^-e^+\to Zh$ at least for $\sqrt{s}\leq 1$ TeV. As is the same as in the SM, the $e^-e^+\to\nu\bar{\nu}h$ process gives a main contribution to the single Higgs boson production processes for $\sqrt{s}\gtrsim 500$ GeV, but large deviations from the SM are only observed on energy scales close to the masses of the first KK gauge bosons or higher.
Sequential learning and control: Targeted exploration for robust performance

We present a novel dual control strategy for uncertain linear systems based on targeted harmonic exploration and gain-scheduling with performance and excitation guarantees. In the proposed sequential approach, robust control is implemented after exploration with the main feature that the exploration is optimized with respect to the robust control performance. Specifically, we leverage recent results on finite excitation using spectral lines to determine a high probability lower bound on the resultant finite excitation of the exploration data. This provides an a priori upper bound on the remaining model uncertainty after exploration, which can further be leveraged in a gain-scheduling controller design that guarantees robust performance. This leads to a semidefinite program-based design which computes an exploration strategy with finite excitation bounds and minimal energy, and a gain-scheduled controller with probabilistic performance bounds that can be implemented after exploration. The effectiveness of our approach and its benefits over common random exploration strategies are demonstrated with an example of a system which is 'hard to learn'.
Multi-Agent Interplay in a Competitive Survival Environment

Solving hard-exploration environments in an important challenge in Reinforcement Learning. Several approaches have been proposed and studied, such as Intrinsic Motivation, co-evolution of agents and tasks, and multi-agent competition. In particular, the interplay between multiple agents has proven to be capable of generating human-relevant emergent behaviour that would be difficult or impossible to learn in single-agent settings. In this work, an extensible competitive environment for multi-agent interplay was developed, which features realistic physics and human-relevant semantics. Moreover, several experiments on different variants of this environment were performed, resulting in some simple emergent strategies and concrete directions for future improvement. The content presented here is part of the author's thesis"Multi-Agent Interplay in a Competitive Survival Environment"for the Master's Degree in Artificial Intelligence and Robotics at Sapienza University of Rome, 2022.
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisions

Current interpretability methods focus on explaining a particular model's decision through present input features. Such methods do not inform the user of the sufficient conditions that alter these decisions when they are not desirable. Contrastive explanations circumvent this problem by providing explanations of the form"If the feature $X>x$, the output $Y$ would be different''. While different approaches are developed to find contrasts; these methods do not all deal with mutability and attainability constraints. In this work, we present a novel approach to locally contrast the prediction of any classifier. Our Contrastive Entropy-based explanation method, CEnt, approximates a model locally by a decision tree to compute entropy information of different feature splits. A graph, G, is then built where contrast nodes are found through a one-to-many shortest path search. Contrastive examples are generated from the shortest path to reflect feature splits that alter model decisions while maintaining lower entropy. We perform local sampling on manifold-like distances computed by variational auto-encoders to reflect data density. CEnt is the first non-gradient-based contrastive method generating diverse counterfactuals that do not necessarily exist in the training data while satisfying immutability (ex. race) and semi-immutability (ex. age can only change in an increasing direction). Empirical evaluation on four real-world numerical datasets demonstrates the ability of CEnt in generating counterfactuals that achieve better proximity rates than existing methods without compromising latency, feasibility, and attainability. We further extend CEnt to imagery data to derive visually appealing and useful contrasts between class labels on MNIST and Fashion MNIST datasets. Finally, we show how CEnt can serve as a tool to detect vulnerabilities of textual classifiers.
Stability for Time-Domain Elastic Wave Equations

This paper is concerned with the inverse scattering problem involving the time-domain elastic wave equations in a bounded $d$-dimensional domain. First, an explicit reconstruction formula for the density is established by means of the Dirichlet-to-Neumann operator. The reconstruction is mainly based on the modified boundary control method and complex geometric optics solutions for the elastic wave. Next, the stable observability is obtained by a Carleman estimate. Finally, the stability for the density is presented by the connect operator.
Electromagnetic field analysis in remote field eddy current testing systems

A model and a general nondestructive testing (NDT) and nondestructive evaluation (NDE) package are presented for developing and optimizing electromagnetic sensors. The package has been applied to sensors for remote field control and provides much information for optimizing, for example, the distance between excitation and measurement coils. Some precautions must be taken to obtain high-quality results. The mesh must be the same in order to compare materials with and without a crack. Also, in the NDT technique the measurement coil is far from the excitation coil, so that the study domain, including air, is very large. The quality of the results can be increased by coupling the finite-element method on this domain to the boundary integral equation for the exterior domain. >
Redshift evolution of cosmic birefringence in CMB anisotropies

We study the imprints of a cosmological redshift-dependent pseudoscalar field $\phi$ on the rotation of cosmic microwave background (CMB) linear polarization generated by a coupling $ \phi F^{\mu\nu} \tilde F_{\mu \nu}$. We show how either phenomenological or theoretically motivated redshift dependence of the pseudoscalar field, such as those in models of Early Dark Energy, Quintessence or axion-like dark matter, lead to CMB polarization and temperature-polarization power spectra which exhibit a multipole dependence which goes beyond the widely adopted approximation in which the redshift dependence of the linear polarization angle is neglected. Because of this multipole dependence, the isotropic birefringence effect due to a general coupling $\phi F^{\mu\nu} \tilde F_{\mu \nu}$ is not degenerate with a systematic calibration angle uncertainty. By taking this multipole dependence into account, we calculate the parameters of these phenomenological and theoretical redshift dependence of the pseudoscalar field which can be detected by future CMB polarization experiments on the basis of a $\chi^2$ analysis for a Wishart likelihood. As a final example of our approach, we compute by Markov Chain MonteCarlo (MCMC) the minimal coupling $g_\phi$ in Early Dark Energy which could be detected by future experiments, with or without marginalizing on a systematic rotation angle uncertainty.
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient

A foundational problem in reinforcement learning and interactive decision making is to understand what modeling assumptions lead to sample-efficient learning guarantees, and what algorithm design principles achieve optimal sample complexity. Recently, Foster et al. (2021) introduced the Decision-Estimation Coefficient (DEC), a measure of statistical complexity which leads to upper and lower bounds on the optimal sample complexity for a general class of problems encompassing bandits and reinforcement learning with function approximation. In this paper, we introduce a new variant of the DEC, the Constrained Decision-Estimation Coefficient, and use it to derive new lower bounds that improve upon prior work on three fronts: - They hold in expectation, with no restrictions on the class of algorithms under consideration. - They hold globally, and do not rely on the notion of localization used by Foster et al. (2021). - Most interestingly, they allow the reference model with respect to which the DEC is defined to be improper, establishing that improper reference models play a fundamental role. We provide upper bounds on regret that scale with the same quantity, thereby closing all but one of the gaps between upper and lower bounds in Foster et al. (2021). Our results apply to both the regret framework and PAC framework, and make use of several new analysis and algorithm design techniques that we anticipate will find broader use.
Dark photon dark matter from an oscillating dilaton

We present a mechanism for generating ultralight dark photon dark matter in the early Universe via a dilatonlike scalar field coupled to the dark photon's kinetic term. Energy is initially stored in the condensate of the dilaton, which resonantly produces dark photons when it begins oscillating in the early Universe. While similar scenarios with axion--dark-photon couplings require large coupling coefficients to fully populate the dark photon, the dilatonic coupling features a unique regime: When the dark photon's mass is half that of the dilaton, dark photons are copiously produced even when the dilaton undergoes small-amplitude oscillations. Scenarios consistent with the cosmic microwave background allow for ultralight vector dark matter with mass as light as $10^{-20}$ eV.
Robust Gaussian Process Regression with Huber Likelihood

Gaussian process regression in its most simplified form assumes normal homoscedastic noise and utilizes analytically tractable mean and covariance functions of predictive posterior distribution using Gaussian conditioning. Its hyperparameters are estimated by maximizing the evidence, commonly known as type II maximum likelihood estimation. Unfortunately, Bayesian inference based on Gaussian likelihood is not robust to outliers, which are often present in the observational training data sets. To overcome this problem, we propose a robust process model in the Gaussian process framework with the likelihood of observed data expressed as the Huber probability distribution. The proposed model employs weights based on projection statistics to scale residuals and bound the influence of vertical outliers and bad leverage points on the latent functions estimates while exhibiting a high statistical efficiency at the Gaussian and thick tailed noise distributions. The proposed method is demonstrated by two real world problems and two numerical examples using datasets with additive errors following thick tailed distributions such as Students t, Laplace, and Cauchy distribution.