Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical modeling of tunneling effects on adjacent structures

The prediction of tunnel induced building deformation becomes a key issue in tunnel construction process. Current design approaches are conservative and lead to unnecessary expenditure in the design and construction of protective measures. This paper introduces a three dimensional elasto-plastic finite difference model to predict building damage due to tunnel construction taking into account the interaction between building stiffness, tunnel and the soil to refine the prediction of tunnel induced building deformation by generating settlement prediction/building damage assessment charts. These charts can be incorporated in the current damage risk assessment and may reduce the number of buildings for which an expensive, detailed evaluation has to be performed. Ground surface deformation and strains induced in building was investigated and compared with field measurement of two case studies; a building due to the construction of Greater Cairo Metro Line3-Phase1, and Neptune House due to the construction of the Jubilee line extension tunnel. Reasonable agreement was found between the observed field measurements and the predicted results using the 3D numerical simulation. and thickness of slab ts. With L being the length of the slab, the second moment of area Islab and the area Aslab are defined as:
The size and shape of snowflake star polymers in dilute solutions: analytical and numerical approaches

We investigate the conformational properties of a multi-branched polymer structure with a dendrimer-like topology, known as a snowflake polymer. This polymer is characterized by two parameters: $f_s$, which represents the functionality of the central star-like core, and $f$, which represents the functionality of the side branching points. To analyze the conformational properties, we have employed various approaches, including analytical methods based on direct polymer renormalization and the Wei's approach as well as numerical molecular dynamics simulations. These methods have allowed us to estimate a size and shape characteristics of the snowflake polymer as functions of $f$ and $f_s$. Our findings consistently demonstrate the effective compactification of the typical polymer conformation as the number of branching points increases. Overall, our study provides valuable insights into the conformational behavior of the snowflake polymer and highlights the impact of branching parameters on its overall compactness.
Transport equations and flows with one-sided Lipschitz velocity fields

We study first- and second-order linear transport equations, as well as ODE and SDE flows, with velocity fields satisfying a one-sided Lipschitz condition. Depending on the time direction, the flows are either compressive or expansive. In the compressive regime, we characterize the stable continuous distributional solutions of both the first and second-order nonconservative transport equations as the unique viscosity solution. Our results in the expansive regime complement the theory of Bouchut, James, and Mancini, and we provide a complete theory for both the conservative and nonconservative equations in Lebesgue spaces, as well as proving the existence, uniqueness, and stability of the regular Lagrangian ODE flow. We also provide analogous results in this context for second order equations and SDEs with degenerate noise coefficients that are constant in the spatial variable.
Virtual Reality Sickness Reduces Attention During Immersive Experiences

In this paper, we show that Virtual Reality (VR) sickness is associated with a reduction in attention, which was detected with the P3b Event-Related Potential (ERP) component from electroencephalography (EEG) measurements collected in a dual-task paradigm. We hypothesized that sickness symptoms such as nausea, eyestrain, and fatigue would reduce the users' capacity to pay attention to tasks completed in a virtual environment, and that this reduction in attention would be dynamically reflected in a decrease of the P3b amplitude while VR sickness was experienced. In a user study, participants were taken on a tour through a museum in VR along paths with varying amounts of rotation, shown previously to cause different levels of VR sickness. While paying attention to the virtual museum (the primary task), participants were asked to silently count tones of a different frequency (the secondary task). Control measurements for comparison against the VR sickness conditions were taken when the users were not wearing the Head-Mounted Display (HMD) and while they were immersed in VR but not moving through the environment. This exploratory study shows, across multiple analyses, that the effect mean amplitude of the P3b collected during the task is associated with both sickness severity measured after the task with a questionnaire (SSQ) and with the number of counting errors on the secondary task. Thus, VR sickness may impair attention and task performance, and these changes in attention can be tracked with ERP measures as they happen, without asking participants to assess their sickness symptoms in the moment.
An overview on the evaluated video retrieval tasks at TRECVID 2022

The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis and retrieval evaluation with the goal of promoting progress in research and development of content-based exploitation and retrieval of information from digital video via open, tasks-based evaluation supported by metrology. Over the last twenty-one years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID has been funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addition, many organizations and individuals worldwide contribute significant time and effort. TRECVID 2022 planned for the following six tasks: Ad-hoc video search, Video to text captioning, Disaster scene description and indexing, Activity in extended videos, deep video understanding, and movie summarization. In total, 35 teams from various research organizations worldwide signed up to join the evaluation campaign this year. This paper introduces the tasks, datasets used, evaluation frameworks and metrics, as well as a high-level results overview.
Optimal Vaccination Policy to Prevent Endemicity: A Stochastic Model

We examine here the effects of recurrent vaccination and waning immunity on the establishment of an endemic equilibrium in a population. An individual-based model that incorporates memory effects for transmission rate during infection and subsequent immunity is introduced, considering stochasticity at the individual level. By letting the population size going to infinity, we derive a set of equations describing the large scale behavior of the epidemic. The analysis of the model's equilibria reveals a criterion for the existence of an endemic equilibrium, which depends on the rate of immunity loss and the distribution of time between booster doses. The outcome of a vaccination policy in this context is influenced by the efficiency of the vaccine in blocking transmissions and the distribution pattern of booster doses within the population. Strategies with evenly spaced booster shots at the individual level prove to be more effective in preventing disease spread compared to irregularly spaced boosters, as longer intervals without vaccination increase susceptibility and facilitate more efficient disease transmission. We provide an expression for the critical fraction of the population required to adhere to the vaccination policy in order to eradicate the disease, that resembles a well-known threshold for preventing an outbreak with an imperfect vaccine. We also investigate the consequences of unequal vaccine access in a population and prove that, under reasonable assumptions, fair vaccine allocation is the optimal strategy to prevent endemicity.
CIDGIKc: Distance-Geometric Inverse Kinematics for Continuum Robots

The small size, high dexterity, and intrinsic compliance of continuum robots (CRs) make them well suited for constrained environments. Solving the inverse kinematics (IK), that is finding robot joint configurations that satisfy desired position or pose queries, is a fundamental challenge in motion planning, control, and calibration for any robot structure. For CRs, the need to avoid obstacles in tightly confined workspaces greatly complicates the search for feasible IK solutions. Without an accurate initialization or multiple re-starts, existing algorithms often fail to find a solution. We present CIDGIKc (Convex Iteration for Distance-Geometric Inverse Kinematics for Continuum Robots), an algorithm that solves these nonconvex feasibility problems with a sequence of semidefinite programs whose objectives are designed to encourage low-rank minimizers. CIDGIKc is enabled by a novel distance-geometric parameterization of constant curvature segment geometry for CRs with extensible segments. The resulting IK formulation involves only quadratic expressions and can efficiently incorporate a large number of collision avoidance constraints. Our experimental results demonstrate >98% solve success rates within complex, highly cluttered environments which existing algorithms cannot account for.
Accurate melting point prediction through autonomous physics-informed learning

We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method makes decisions regarding the number of atoms and temperature at which to conduct simulations, and based on the collected data predicts the melting point along with the uncertainty, which can be systematically improved with more data. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making to effectively reduce predictive uncertainty. To validate our approach, we compare the results of 20 melting point calculations from the literature to the results of our calculations, all conducted with same interatomic potentials. Remarkably, we observe significant deviations in about one-third of the cases, underscoring the need for accurate and reliable algorithms for materials property calculations.
Predictions for muon electric and magnetic dipole moments from 
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We calculate chirally enhanced corrections to the muon's electric and magnetic dipole moments in two-Higgs-doublet models extended by vector-like leptons, and we explore a sharp correlation between $h \rightarrow \mu^+ \mu^-$ and the muon's dipole moments in these models. Among many detailed predictions, for a model with new leptons with the same quantum numbers as standard model leptons, we find that $0.38 \lesssim \tan \beta \lesssim 21$ necessarily requires a muon electric dipole moment to be observed at near-future experiments, assuming $h \rightarrow \mu^+ \mu^-$ is measured within $1\%$ of the standard model prediction for the current central value of the measured muon magnetic moment. In all studied models, the predicted values of the electric dipole moment can reach up to current experimental limits. Moreover, we show that in some models there can be two sources of chiral enhancement, parametrizing the correlation between $h \rightarrow \mu^+ \mu^-$ and the dipole moments by a complex number. This leads to sign-preferred predictions for the electric dipole moment.
Emergent Influence Networks in Good-Faith Online Discussions

Town hall-type debates are increasingly moving online, irrevocably transforming public discourse. Yet, we know relatively little about crucial social dynamics that determine which arguments are more likely to be successful. This study investigates the impact of one's position in the discussion network created via responses to others' arguments on one's persuasiveness in unfacilitated online debates. We propose a novel framework for measuring the impact of network position on persuasiveness, using a combination of social network analysis and machine learning. Complementing existing studies investigating the effect of linguistic aspects on persuasiveness, we show that the user's position in a discussion network influences their persuasiveness online. Moreover, the recognition of successful persuasion further increases this dominant network position. Our findings offer important insights into the complex social dynamics of online discourse and provide practical insights for organizations and individuals seeking to understand the interplay between influential positions in a discussion network and persuasive strategies in digital spaces.
Hyperbolic limit on the early arrival time of bright pulses from PSR J0835−4510 (Vela)

Astronomers have studied the Vela pulsar (PSR J0835−4510) for decades. This study analyses almost 100 h of single-pulse data collected over three consecutive days in 2016 and 2020. The work investigates the fascinating phenomena of the earlier arrival of brighter pulses with their increase in peak intensity. We found a hyperbolic relation between them by constructing integrated pulse profiles using flux density intervals and examining the relationship between pulse arrival time and intensity. We identified a phase limit of −0.85 ± 0.0109 ms for the earliest arrival of the brightest pulses. This study offers exciting prospects for further exploring the emission regions responsible for the Vela pulsar’s regular pulses and giant micro-pulses.
Energy-optimal control of adaptive structures

Adaptive structures are equipped with sensors and actuators to actively counteract external loads such as wind. This can significantly reduce resource consumption and emissions during the life cycle compared to conventional structures. A common approach for active damping is to derive a port-Hamiltonian model and to employ linear-quadratic control. However, the quadratic control penalization lacks physical interpretation and merely serves as a regularization term. Rather, we propose a controller, which achieves the goal of vibration damping while acting energy-optimal. Leveraging the port-Hamiltonian structure, we show that the optimal control is uniquely determined, even on singular arcs. Further, we prove a stable long-time behavior of optimal trajectories by means of a turnpike property. Last, the proposed controller’s efficiency is evaluated in a numerical study.
Quantum speedup for combinatorial optimization with flat energy landscapes

Designing quantum algorithms with a speedup over their classical analogs is a central challenge in quantum information science. Motivated by recent experimental observations of a superlinear quantum speedup in solving the Maximum Independent Set problem on certain unit-disk graph instances [Ebadi et al., Science 376, 6598 (2022)], we develop a theoretical framework to analyze the relative performance of the optimized quantum adiabatic algorithm and a broad class of classical Markov chain Monte Carlo algorithms. We outline conditions for the quantum adiabatic algorithm to achieve a quadratic speedup on hard problem instances featuring flat low-energy landscapes and provide example instances with either a quantum speedup or slowdown. We then introduce an additional local Hamiltonian with no sign problem to the optimized adiabatic algorithm to achieve a quadratic speedup over a wide class of classical simulated annealing, parallel tempering, and quantum Monte Carlo algorithms in solving these hard problem instances. Finally, we use this framework to analyze the experimental observations.
FPGA Implementation of Convolutional Neural Network for Real-Time Handwriting Recognition

Machine Learning (ML) has recently been a skyrocketing field in Computer Science. As computer hardware engineers, we are enthusiastic about hardware implementations of popular software ML architectures to optimize their performance, reliability, and resource usage. In this project, we designed a highly-configurable, real-time device for recognizing handwritten letters and digits using an Altera DE1 FPGA Kit. We followed various engineering standards, including IEEE-754 32-bit Floating-Point Standard, Video Graphics Array (VGA) display protocol, Universal Asynchronous Receiver-Transmitter (UART) protocol, and Inter-Integrated Circuit (I2C) protocols to achieve the project goals. These significantly improved our design in compatibility, reusability, and simplicity in verifications. Following these standards, we designed a 32-bit floating-point (FP) instruction set architecture (ISA). We developed a 5-stage RISC processor in System Verilog to manage image processing, matrix multiplications, ML classifications, and user interfaces. Three different ML architectures were implemented and evaluated on our design: Linear Classification (LC), a 784-64-10 fully connected neural network (NN), and a LeNet-like Convolutional Neural Network (CNN) with ReLU activation layers and 36 classes (10 for the digits and 26 for the case-insensitive letters). The training processes were done in Python scripts, and the resulting kernels and weights were stored in hex files and loaded into the FPGA's SRAM units. Convolution, pooling, data management, and various other ML features were guided by firmware in our custom assembly language. This paper documents the high-level design block diagrams, interfaces between each System Verilog module, implementation details of our software and firmware components, and further discussions on potential impacts.
Correcting discount-factor mismatch in on-policy policy gradient methods

The policy gradient theorem gives a convenient form of the policy gradient in terms of three factors: an action value, a gradient of the action likelihood, and a state distribution involving discounting called the \emph{discounted stationary distribution}. But commonly used on-policy methods based on the policy gradient theorem ignores the discount factor in the state distribution, which is technically incorrect and may even cause degenerate learning behavior in some environments. An existing solution corrects this discrepancy by using $\gamma^t$ as a factor in the gradient estimate. However, this solution is not widely adopted and does not work well in tasks where the later states are similar to earlier states. We introduce a novel distribution correction to account for the discounted stationary distribution that can be plugged into many existing gradient estimators. Our correction circumvents the performance degradation associated with the $\gamma^t$ correction with a lower variance. Importantly, compared to the uncorrected estimators, our algorithm provides improved state emphasis to evade suboptimal policies in certain environments and consistently matches or exceeds the original performance on several OpenAI gym and DeepMind suite benchmarks.
Speech signal endpoint detection and separation based on improved fuzzy ART

This paper analyzes and discusses the saturation problem of the connection weight vector of the fuzzy ART when the processing sample's number is bigger and the inaccurate problem of classification with some special case, and proposes the improved IFART algorithm on this foundation, it is applied to the speech signal endpoint detection and separation, the experimental results show that this algorithm can have better classification effect under the noise environments of difference.
Inferring Hierarchical Structure in Multi-Room Maze Environments

Cognitive maps play a crucial role in facilitating flexible behaviour by representing spatial and conceptual relationships within an environment. The ability to learn and infer the underlying structure of the environment is crucial for effective exploration and navigation. This paper introduces a hierarchical active inference model addressing the challenge of inferring structure in the world from pixel-based observations. We propose a three-layer hierarchical model consisting of a cognitive map, an allocentric, and an egocentric world model, combining curiosity-driven exploration with goal-oriented behaviour at the different levels of reasoning from context to place to motion. This allows for efficient exploration and goal-directed search in room-structured mini-grid environments.
Safe Risk-Averse Bayesian Optimization for Controller Tuning

Controller tuning and parameter optimization are crucial in system design to improve both the controller and underlying system performance. Bayesian optimization (BO) has been established as an efficient model-free method for controller tuning and adaptation. Standard methods, however, are not enough for high-precision systems to be robust with respect to unknown input-dependent noise and stable under safety constraints. In this work, we present a novel data-driven approach, RAGoOSe, for safe controller tuning in the presence of heteroscedastic noise, combining safe learning with risk-averse Bayesian optimization. We demonstrate the method for synthetic benchmark and compare its performance to established BO-based tuning methods. We further evaluate RaGoose performance on a real precision-motion system utilized in semiconductor industry applications and compare it to the built-in auto-tuning routine.
Solving overparametrized systems of random equations: I. Model and algorithms for approximate solutions

Consider the problem of solving a system of equations ${\boldsymbol F}({\boldsymbol x})= {\boldsymbol 0}$, subject to $\|{\boldsymbol x}\|_2=1$, whereby ${\boldsymbol F}:{\mathbb R}^d\to{\mathbb R}^n$ is a random nonlinear map. More precisely, $ {\boldsymbol F}({\boldsymbol x}) = (F_1({\boldsymbol x}),\dots,F_n({\boldsymbol x})) $ where the $ F_i(\,\cdot\, ) $'s are i.i. d. rotationally invariant Gaussian processes. We study this problem under the proportional asymptotics $n,d\to\infty$, $n/d\to\alpha\in [0,1)$ and establish results about the existence of solutions and polynomial-time algorithms to find them. First, we establish upper and lower bounds $\alpha_{UB}$, $\alpha_{LB}$ on the threshold for existence of solutions. Namely, if the number of equations per variable satisfies $\alpha<\alpha_{LB}$, then the system admits exact solutions with high probability, while for $\alpha>\alpha_{UB}$, no solutions exist, even in an approximate sense. We then analyze several algorithms to find solutions: gradient descent, Hessian descent, and a two-phase algorithm. In particular, for Hessian descent and the two-phase algorithm, we characterize their thresholds $\alpha_{HD}$, $\alpha_{TP}$. Namely, for $\alpha<\alpha_{HD}$ (or $\alpha<\alpha_{TP}$) the algorithm finds an approximate solution with high probability, while for $\alpha>\alpha_{HD}$ (respectively $\alpha>\alpha_{TP}$), it does not. Finally, we compare the theoretical predictions within this model to empirical results obtained with structured systems of nonlinear equations.