Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Deception Detection from Videos: Using End-to-End Learning Based High-Level Features and Classification Approaches

Deception detection is an interdisciplinary field attracting researchers from psychology, criminology, computer science, and economics. We propose a multimodal approach combining deep learning and discriminative models for automated deception detection. Using video modalities, we employ convolutional end-to-end learning to analyze gaze, head pose, and facial expressions, achieving promising results compared to state-of-the-art methods. Due to limited training data, we also utilize discriminative models for deception detection. Although sequence-to-class approaches are explored, discriminative models outperform them due to data scarcity. Our approach is evaluated on five datasets, including a new Rolling-Dice Experiment motivated by economic factors. Results indicate that facial expressions outperform gaze and head pose, and combining modalities with feature selection enhances detection performance. Differences in expressed features across datasets emphasize the importance of scenario-specific training data and the influence of context on deceptive behavior. Cross-dataset experiments reinforce these findings. Despite the challenges posed by low-stake datasets, including the Rolling-Dice Experiment, deception detection performance exceeds chance levels. Our proposed multimodal approach and comprehensive evaluation shed light on the potential of automating deception detection from video modalities, opening avenues for future research.
Landscape of wave focusing and localisation at low frequencies

High-contrast scattering problems are special among classical wave systems as they allow for strong wave focusing and localisation at low frequencies. We use an asymptotic framework to develop a landscape theory for high-contrast systems that resonate in a subwavelength regime. Our from-first-principles asymptotic analysis yields a characterisation in terms of the generalised capacitance matrix, giving a discrete approximation of the three-dimensional scattering problem. We develop landscape theory for the generalised capacitance matrix and use it to predict the positions of three-dimensional wave focusing and localisation in random and non-periodic systems of subwavelength resonators.
Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

For predictive modeling relying on Bayesian inversion, fully independent, or ``mean-field'', Gaussian distributions are often used as approximate probability density functions in variational inference since the number of variational parameters is twice the number of unknown model parameters. The resulting diagonal covariance structure coupled with unimodal behavior can be too restrictive when dealing with highly non-Gaussian behavior, including multimodality. High-fidelity surrogate posteriors in the form of Gaussian mixtures can capture any distribution to an arbitrary degree of accuracy while maintaining some analytical tractability. Variational inference with Gaussian mixtures with full-covariance structures suffers from a quadratic growth in variational parameters with the number of model parameters. Coupled with the existence of multiple local minima due to nonconvex trends in the loss functions often associated with variational inference, these challenges motivate the need for robust initialization procedures to improve the performance and scalability of variational inference with mixture models. In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference. The procedure begins with an optimization stage in model parameter space in which local gradient-based optimization, globalized through multistart, is used to determine a set of local maxima, which we take to approximate the mixture component centers. Around each mode, a local Gaussian approximation is constructed via the Laplace method. Finally, the mixture weights are determined through constrained least squares regression. Robustness and scalability are demonstrated using synthetic tests. The methodology is applied to an inversion problem in structural dynamics involving unknown viscous damping coefficients.
Helicon waves in a converging-diverging magnetoplasma

Waves propagating along a converging-diverging rf magnetoplasma having the characteristics of a bounded m = 0 helicon mode are reported and characterised. The discharge features a 30 cm separation between the region of radiofrequency energy deposition by a single loop antenna and the region of maximum magnetic field applied by a pair of coils. With 200 W of rf input power, up to a five-fold increase in axial plasma density between the antenna and the magnetic mirror throat is observed together with an Ar II blue-mode. Two dimensional B-dot probe measurements show that the rf magnetic fields are closely guided by the converging-diverging geometry. The wave is characterised as a m = 0 mode satisfying the helicon dispersion relation on-axis with radial boundary conditions approximately matching the radii of the plasma column. Analysis of the wave phase velocity and wave axial damping failed to identify collisionless or collisional wave-plasma coupling mechanisms. Instead, the wave axial amplitude variations can be explained by local wave resonances and possible reflections from localised rapid changes of the refractive index. A Venturi-like effect owing to the funnel-shaped magnetoplasma and conservation of the wave energy may also explain some level of amplitude variations.
Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms

In this paper, we propose a numerical methodology for finding the closed-loop Nash equilibrium of stochastic delay differential games through deep learning. These games are prevalent in finance and economics where multi-agent interaction and delayed effects are often desired features in a model, but are introduced at the expense of increased dimensionality of the problem. This increased dimensionality is especially significant as that arising from the number of players is coupled with the potential infinite dimensionality caused by the delay. Our approach involves parameterizing the controls of each player using distinct recurrent neural networks. These recurrent neural network-based controls are then trained using a modified version of Brown's fictitious play, incorporating deep learning techniques. To evaluate the effectiveness of our methodology, we test it on finance-related problems with known solutions. Furthermore, we also develop new problems and derive their analytical Nash equilibrium solutions, which serve as additional benchmarks for assessing the performance of our proposed deep learning approach.
Reliability of Noisy Quantum Computing Devices

Noisy intermediate-scale quantum (NISQ) devices are valuable platforms for testing the tenets of quantum computing, but these devices are susceptible to errors arising from de-coherence, leakage, cross-talk and other sources of noise. This raises concerns for ensuring the stability of program results when using NISQ devices as strategies for mitigating errors generally require well-characterized and reliable error models. Here, we quantify the reliability of NISQ devices by assessing the necessary conditions for generating stable results within a given tolerance. We use similarity metrics derived from device characterization data to analyze the stability of performance across several key features: gate fidelities, de-coherence time, SPAM error, and cross-talk error. We bound the behavior of these metrics derived from their joint probability distribution, and we validate these bounds using numerical simulations of the Bernstein-Vazirani circuit tested on a superconducting transmon device. Our results enable the rigorous testing of reliability in NISQ devices and support the long-term goals of stable quantum computing.
Askey-Wilson signed measures and open ASEP in the shock region

We introduce a family of multi-dimensional Askey-Wilson signed measures. We offer an explicit description of the stationary measure of the open asymmetric simple exclusion process (ASEP) in the full phase diagram, in terms of integrations with respect to these Askey-Wilson signed measures. Using our description, we provide a rigorous derivation of the density profile and limit fluctuations of open ASEP in the entire shock region, including the high and low density phases as well as the coexistence line. This in particular confirms the existing physics postulations of the density profile.
Attention-based Multi-Source-Free Domain Adaptation for EEG Emotion Recognition

Electroencephalography (EEG) based emotion recognition in affective brain-computer interfaces has advanced significantly in recent years. Unsupervised domain adaptation (UDA) methods have been successfully used to mitigate the need for large amounts of training data, which is required due to the inter-subject variability of EEG signals. Typical UDA solutions require access to raw source data to leverage the knowledge learned from the labelled source domains (previous subjects) across the target domain (a new subject), raising privacy concerns. To tackle this issue, we propose Attention-based Multi-Source-Free Domain Adaptation (AMFDA) for EEG emotion recognition. AMFDA attempts to transfer knowledge of source models to the target domain by aggregating adapted source models based on a set of learnable weights without accessing the source data. While the classifiers of source models are frozen, the set of learnable weights and the feature extractors are learned based on information maximization and a novel self-supervised pseudo-labelling method. A channel-wise attention layer is also used in the proposed framework to enhance the performance of source models, which in turn improves the performance of target models. We conducted extensive experiments on SEED and SEED-IV. The experimental results demonstrate that the proposed AMFDA method performs comparably to UDA state-of-the-art methods.
Augmented Lagrangian Pattern Search Based Multi‐Agent Model Predictive Control of Rhine‐Meuse Delta

In Large Scale Systems the concept of centrality fails due to the lack of centralized computing capability. The control of such systems has to be performed using multiple control agents. In this case, the matter of interactions among neighboring subsystems needs to be considered. In this paper, a water control system in the Netherlands is studied as a real large scale system. A multi‐agent scheme is applied to control the flow through the system which is decomposed into two interconnected subsystems. Each agent employs a model‐based predictive control (MPC) technique. The model of this large scale system is nonlinear and nonconvex. Therefore, an augmented Lagrangian pattern search optimization algorithm is used to implement multi‐agent MPC for this system. This proposed algorithm is applied by each control agent to solve its own interconnected optimization problem, at each subsystem of whole the water system. Simulation results show the effectiveness of the proposed approach.
Pulsed Laser Ablation of the Solid Targets in a Liquid Environment

Abstract In this paper, the viability of laser ablation of gold target immersed in medical distillated water used to produce nanoparticles having morphological, structural and compositional properties for medical applications is explored. The morphological properties were investigated by means of scanning electron microscopy (SEM). Energy-dispersive X-ray spectroscopy (EDX) analysis was used to reveal the composition of resulting particles. The absorbance in the wavelengths range of 300-800 nm can be assigned to the small gold nanoparticles.
Validity of the Fitbit Charge HR2 for Measuring Step Count and Heart Rate During Exercise

Wearable activity trackers have gained popularity over the past decade, developing into a multi-billion dollar industry. The potential of these devices to support behavior change interventions are promising. Yet, before such interventions can be considered, the examination of the validity of fitness trackers is imperative. The present study examines one of the most popular devices in the market, the Fitbit Charge HR2, which was released in 2016. Currently, there is a lack of evidence of its validity to track activity indicators (e.g., heart rate, steps). PURPOSE: To test the validity of the Fitbit Charge HR2 monitor in step count and heart rate (HR) during exercise. METHODS: Thirty-two healthy volunteers (age 22.7±6.1, 20 female) participated in the study. Participants completed four 6-minute trials (i.e., lying in a supine position and moving at 3.5, 5, and 6.5 miles per hour (mph) on a treadmill). Data were collected on the Fitbit Charge HR2, a Polar H7 chest strap, and by a count of videotaped steps. Statistical analyses included mean absolute percentage errors, one-sample t-tests, and intraclass correlation coefficients (ICC). RESULTS: The Fitbit Charge HR2 significantly underestimated HR for all four trials (mean differences=-1.7-12.7, SDs=9.318.4; ps<.01). For HR, ICCs ranged from . 44-. 66, indicating only modest agreement between the Fitbit and the Polar H7 chest strap. The Fitbit Charge HR2 also significantly underestimated step count for walking at 3.5 mph (mean difference=-13.0, SD=19.0; p<.01) with an acceptable ICC (.77). However, for running at 5 and 6.5 mph, no significant underestimation was found. Accordingly, the ICCs revealed an excellent agreement of . 99 between the Fitbit and counted steps for both trials. CONCLUSION: The Fitbit Charge HR2 acceptably measured step count for moving at jogging or running speeds. For the measurement of HR and step count at lower speeds the accuracy was questionable and the use of other devices may be advisable.
Parameter and State Estimation of Shear Buildings Using Spline Interpolation and Linear Integral Filters

A parameter identification method and a high gain observer are proposed in order to identify the model and to recover the state of a seismically excited shear building using acceleration responses of the ground and instrumented floors levels, as well as the responses at noninstrumented floors, which are reconstructed by means of cubic spline shape functions. The identification method can be implemented online or offline and uses Linear Integral Filters, whose bandwidth must enclose the spectrum of a seismically excited building. On the other hand, the proposed state observer estimates the displacements and velocities of all the structure floors using the model estimated by the identification method. The observer allows obtaining a fast response and reducing the state estimation error, while depending on a single gain. The performance of the parameter and state estimators is verified through experiments carried out on a five-story small scale building.
A Web-centric entity-salience based system for determining Notability of entities for Wikipedia

The content on Wikipedia is growing rapidly, and it is essential to moderate this uploaded content, for the efficient functioning of Wikipedia. We utilize the ”Notability” guidelines, decided by the editors of Wikipedia, and design a system to automatically identify if an entity warrants its own article on Wikipedia or not. This system is based on web-based entity features and their text-based salience encodings, and functions irrespective of an entity’s category.
FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING

This paper presents the evaluation of a daily inflow forecasting model using a tool that facilitates the analysis of mathematical models for hydroelectric plants. The model is based on a Fuzzy Inference System. An offline version of the Expectation Maximization algorithm is employed to adjust the model parameters. The tool integrates different inflow forecasting models into a single physical structure. It makes uniform and streamlines the management of data, prediction studies, and presentation of results. A case study is carried out using data from three Brazilian hydroelectric plants of the Parana basin, Tiete River, in southern Brazil. Their activities are coordinated by Operator of the National Electric System (ONS) and inspected by the National Agency for Electricity (ANEEL). The model is evaluated considering a multi-step ahead forecasting task. The graphs allow a comparison between observed and forecasted inflows. For statistical analysis, it is used the mean absolute percentage error, the root mean square error, the mean absolute error, and the mass curve coefficient. The results show an adequate performance of the model, leading to a promising alternative for daily inflow forecasting.
Modeling SARS-CoV-2 Infection Risk in Various Office Building HVAC Systems

The SARS-CoV-2 pandemic aroused great interest in the HVAC community regarding both the design of new systems and management and operation strategies for existing ones. Risk management plays a key role in all-air systems, which are based on air recirculation among all spaces, and also in primary air systems. Following an explanation of the analytical approach, this article assesses the infection risk in several HVAC system layouts. It takes into account the role of air renewal (ventilation) and recirculation and the lowering of infection risk due to virus removal or inactivation (through mechanical or electrostatic filtration or inactivation technologies such as UV-C irradiation and ionization), with special attention to the management of airflows depending on plant layout.
Supplementary for GEMS: Generating Efficient Meta-Subnets

The model architecture is similar to the original MAML [2] paper, i.e., 4 modules with 3 x 3 convolutions and 64 filters with a stride of 2, followed by batch normalization, a ReLU nonlinearity and 2 × 2 max-pooling. The MLP is a fully connected network, where each layer consists of 2N hidden units, where N is the number of layers of the base learner network. The ReLU activation function is placed between the MLP layers.
Information Aggregation with Asymmetric Asset Payoffs∗

We study noisy aggregation of dispersed information in financial markets beyond the usual parametric restrictions imposed on preferences, information, and return distributions. This allows a general characterization of asset returns by means of a risk-neutral probability measure that features excess weight on tail risks. Using this characterization, we show that noisy aggregation of dispersed information provides a unified explanation for several prominent crosssectional return anomalies such as returns to skewness, returns to disagreement and interaction effects between the two. Moreover, this characterization can be linked to observable moments such as forecast dispersion and accuracy, and simple calibrations suggest the model can account for a significant fraction of empirical return anomalies. We thank Bruno Biais, John Geanakoplos, Narayana Kocherlakota, Felix Kubler, Stephen Morris, Guillaume Plantin, Jeremy Stein, Jean Tirole, Xavier Vives, Olivier Wang, Eric Young, and audiences at numerous conferences and seminars for helpful comments. This paper builds on and supersedes our earlier working paper “A theory of asset prices based on heterogeneous information” (Albagli, Hellwig, Tsyvinski 2011, 2015). Hellwig gratefully acknowledges financial support from the European Research Council (starting grant agreement 263790), the Institut Universitaire de France as well as funding from the French National Research Agency (ANR) under grant ANR-17-EURE-0010 (Investissements d’Avenir). Tsyvinski is grateful to NSF (grant 0551175) for support and EIEF for hospitality.
Empirical multi-wavelength prediction method for very high energy gamma-ray emitting BL Lacertae objects (cid:63)

Aim: We have collected the most complete multi-wavelength (6 . 0 − 6 . 0 × 10 − 18 cm) dataset of very high energy (VHE) γ -ray emitting (TeV) BL Lacs, which are the most numerous extragalactic VHE sources. Using signiﬁcant correlations between di ﬀ erent bands, we aim to identify the best TeV BL Lac candidates that can be discovered by the current and next generation of imaging air Cherenkov telescopes. Methods: We formed ﬁve datasets from lower energy data, i.e. radio, mid-infrared, optical, X-rays, and GeV γ -ray, and ﬁve VHE γ -ray datasets to perform a correlation study between di ﬀ erent bands and to construct the prediction method. The low energy datasets were averaged for individual sources, while the VHE γ -ray data were divided into subsets according to the ﬂux state of the source. We then looked for signiﬁcant correlations and determined their best-ﬁt parameters. Using the best-ﬁt parameters we predicted the level of VHE γ -ray ﬂux for a sample of 182 BL Lacs, which have not been detected at TeV energies. We identiﬁed the most promising TeV BL Lac candidates based on the predicted VHE γ -ray ﬂux for each source. Results: We found 14 signiﬁcant correlations between radio, mid-infrared, optical, γ -ray, and VHE γ -ray bands. The correlation between optical and VHE γ -ray luminosity is established for the ﬁrst time. We attribute this to the more complete sample and more accurate handling of host galaxy ﬂux in our work. We found nine BL Lac candidates whose predicted VHE γ -ray ﬂux is high enough for detection in less than 25 h with current imaging air Cherenkov telescopes.
The Realm of Patient Specific Implants (PSI) In Maxillofacial Prosthodontics: A Review

: Reconstruction and augmentation of congenital and acquired maxillofacial defectsare deemed quite a challenge to experienced doctors. Meticulous rehabilitation of the elaborate “three-dimensional” (3D) facial forms necessitates detailed shaping and sculpting of autologous grafts and manufactured implants, and conforming them to the primary skeletal structure to reestablish anatomical facial structures. Inaccurate shaping of off-the-shelf implants could end in suboptimal formed restorations. Nevertheless, recent developments in computer-aided design/computer-aided manufacturing (CAD/ CAM) have devised contemporary alternatives for fabricating patient-specific implants (PSIs), with enhanced accuracy, resulting in restoration with improved contour and outcomes. This article collates available evidence relating to the benefits and limitations of Patient-specific implants, thereby revealing the potentiality of Patient-specific implants (PSI) as a viable option for the rehabilitation of oral and maxillofacial structures in near future.
Coexistence of two dune scales in a lowland river

Abstract. A secondary scale of bedforms, superimposed on larger, primary dunes, has been observed in fluvial systems worldwide. This notwithstanding, very little is known about the morphological behavior and characteristics of this secondary scale. This study aims to better characterize and understand how two dune scales coexist in fluvial systems and how both scales adapt over time and space, considering their interdependence. The study is based on analysis of a large biweekly multibeam echo sounding dataset from the river Waal, a lowland sand-bedded river. Results reveal that the secondary dune scale is ubiquitous across space and time and not limited to specific flow or transport conditions. Whereas primary dunes lengthen during low flows, secondary dune height, lee slope angle, and length correlate with discharge. Secondary dune size and migration strongly depend on the primary dune lee slope angle and height. Secondary dunes can migrate over the lee slope of low-angled primary dunes, and their height is inversely correlated to the upstream primary dune height and lee slope angle. In the Waal river, a lateral variation in bed grain size, attributed to shipping, largely affects dune morphology. Primary dunes are lower and less often present in the southern lane, where grain sizes are smaller. Here, secondary bedforms are more developed. At peak discharge, secondary bedforms even become the dominant scale, whereas primary dunes entirely disappear but are re-established during lower flows.