Miracle-dz/UniFAR-FTUs huggingface dataset:
Precision angle measurement in a diffractional spectrometer by means of a ring laser

The measurement of radiation diffraction angles in spectrometers is usually carried out by means of optical encoders or laser interferometers. An angle measurement system based on a ring laser (RL) is described in this paper. The main feature of the RL is that a standing electromagnetic wave in the RL cavity can be considered as a high-precision circular scale. Systems incorporating RLs usually provide an accuracy of angle measurements of several hundredths of an arcsecond in the measurement range 0-. The system described here consists of a RL and an optical interference null-indicator located on a rotary platform optically connected to the crystal of the diffractional spectrometer. An analysis of the measurement principle and measurement errors is presented. It is shown that the main sources of error are the random drift of the phase of the RL output signal and the wideband fluctuation of the null-indicator output signal. Results of measurements of the x-ray line of radiation with an energy of radiation E = 17.47334 keV and corresponding wavelength A are shown. The angle measurement error of the described system turned out to be about 0.05 arcsecond.
COLT: Cyclic Overlapping Lottery Tickets for Faster Pruning of Convolutional Neural Networks

—Pruning refers to the elimination of trivial weights from neural networks. The sub-networks within an overparameterized model produced after pruning are often called Lottery tickets. This research aims to generate winning lottery tickets from a set of lottery tickets that can achieve similar accuracy to the original unpruned network. We introduce a novel winning ticket called Cyclic Overlapping Lottery Ticket (COLT) by data splitting and cyclic retraining of the pruned network from scratch. We apply a cyclic pruning algorithm that keeps only the overlapping weights of different pruned models trained on different data segments. Our results demonstrate that COLT can achieve similar accuracies (obtained by the unpruned model) while maintaining high sparsities. We show that the accuracy of COLT is on par with the winning tickets of Lottery Ticket Hypothesis (LTH) and, at times, is better. Moreover, COLTs can be generated using fewer iterations than tickets generated by the popular Iterative Magnitude Pruning (IMP) method. In addition, we also notice COLTs generated on large datasets can be transferred to small ones without compromising performance, demonstrating its generalizing capability. We conduct all our experiments on Cifar-10, Cifar-100 & TinyImageNet datasets and report superior performance than the state-of-the-art methods. Codes are available at: https: //github.com/ismail31416/COLT
Frenet-Cartesian Model Representations for Automotive Obstacle Avoidance within Nonlinear MPC

In recent years, nonlinear model predictive control (NMPC) has been extensively used for solving automotive motion control and planning tasks. In order to formulate the NMPC problem, different coordinate systems can be used with different advantages. We propose and compare formulations for the NMPC related optimization problem, involving a Cartesian and a Frenet coordinate frame (CCF/ FCF) in a single nonlinear program (NLP). We specify costs and collision avoidance constraints in the more advantageous coordinate frame, derive appropriate formulations and compare different obstacle constraints. With this approach, we exploit the simpler formulation of opponent vehicle constraints in the CCF, as well as road aligned costs and constraints related to the FCF. Comparisons to other approaches in a simulation framework highlight the advantages of the proposed approaches.
Many universality classes in an interface model restricted to non-negative heights.

We present a simple one-dimensional stochastic model with three control parameters and a surprisingly rich zoo of phase transitions. At each (discrete) site x and time t, an integer n(x,t) satisfies a linear interface equation with added random noise. Depending on the control parameters, this noise may or may not satisfy the detailed balance condition, so that the growing interfaces are in the Edwards-Wilkinson or in the Kardar-Parisi-Zhang universality class. In addition, there is also a constraint n(x,t)≥0. Points x where n>0 on one side and n=0 on the other are called "fronts. " These fronts can be "pushed" or "pulled," depending on the control parameters. For pulled fronts, the lateral spreading is in the directed percolation (DP) universality class, while it is in a different universality class for pushed fronts, and another universality class in between. In the DP case, the activity at each active site can in general be arbitrarily large, in contrast to previous realizations of DP. Finally, we find two different types of transitions when the interface detaches from the line n=0 (with 〈n(x,t)〉→const on one side, and →∞ on the other), again with new universality classes. We also discuss a mapping of this model to the avalanche propagation in a directed Oslo rice pile model in specially prepared backgrounds.
Iterative regularization in classification via hinge loss diagonal descent

Iterative regularization is a classic idea in regularization theory, that has recently become popular in machine learning. On the one hand, it allows to design efficient algorithms controlling at the same time numerical and statistical accuracy. On the other hand it allows to shed light on the learning curves observed while training neural networks. In this paper, we focus on iterative regularization in the context of classification. After contrasting this setting with that of regression and inverse problems, we develop an iterative regularization approach based on the use of the hinge loss function. More precisely we consider a diagonal approach for a family of algorithms for which we prove convergence as well as rates of convergence. Our approach compares favorably with other alternatives, as confirmed also in numerical simulations.
On the importance of 3D stress state in 2D earthquake rupture simulations with off-fault deformation

During the last decades, many numerical models have been developed to investigate the conditions for seismic and aseismic slip. Those models explore the behavior of frictional faults, embedded in either elastic or inelastic media, and submitted to a far field loading (seismic cycle models), or initial stresses (single dynamic rupture models). Those initial conditions impact both on-fault and off-fault dynamics. Because of the sparsity of direct measurements of fault stresses, modelers have to make assumptions about these initial conditions. To this day, Anderson's theory is the only framework that can be used to link fault generation and reactivation to the three-dimensional stress field. In this work we look at the role of the three dimensional stress field in modelling a 2D strike-slip fault under plane-strain conditions. We show that setting up an incorrect initial stress field, based on Anderson's theory, can lead to underestimation of the damage zone width by up to a factor of six, for the studied cases. Moreover, because of the interactions between fault slip and off-fault deformation, initial stress field influences the rupture propagation. Our study emphasizes the need to set up the correct initial 3D stress field, even in 2D numerical simulations.
Mass Relations, Unification, and Proton Decay in the Mirror GUT Model

We propose an SU(5)×U(1)X×U(1)PQ model without SUSY because the SUSY particles have not been observed yet. The U(1)X gauge symmetry is the generalization of the U(1)B−L gauge symmetry and U(1)PQ symmetry is the global Peccei-Quinn symmetry. We introduce three mirror families in order to unify the SM gauge couplings and avoid the restriction of proton lifetime. In order to obtain the difference of the masses for the down-type quarks and charged leptons, we also introduce the 45 representation Higgs in addition to the 5 representation one. In this paper, we discuss the mass relations between the SM and mirror particles and identify the mass scales of the mirror particles. Since the new particles exist in the intermediate energy scale and contribute to the renormalization group equation, the SM gauge couplings unify successfully at high energy. Our model can be tested by the future proton decay search, e.g., the Hyper-Kamiokande experiment expected as τp(p → π0e+) < 1.0× 1035 years. Also, by using the mass relations between the active and mirror neutrinos, we estimate the lower bound of the heavy neutrino masses.
Does the Universe have its own mass?

Within the framework of the previously proposed formulation of the quantum theory of gravity in terms of world histories, it was suggested that the universe has its own mass. This quantity is analogous to the mass of a particle in relativistic mechanics. The mass of the universe is a distribution of non-zero values of gravitational constraints, which arises and changes in time as a consequence of the initial conditions for fundamental dynamic variables. A formulation of the Euclidean quantum theory of gravity is also proposed to determine the initial state, which can be the source of the universe’s own mass. Being unrelated to ordinary matter, the distribution of its own mass aﬀects the geometry of space and forms a dedicated frame of reference. The existence of selected reference systems is taken into account by the corresponding modiﬁcation of the system of quantum gravitational links. A variant of such a modiﬁcation of the Wheeler-De Witt equation is the operator representation of gravitational constraints, which, together with the state of the universe, determines the parameters of the reference system in the form of a distribution of the spinor ﬁeld on a spatial section.
Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning

Zero-Shot Learning has been a highlighted research topic in both vision and language areas. Recently, most existing methods adopt structured knowledge information to model explicit correlations among categories and use deep graph convolutional network to propagate information between different categories. However, it is difficult to add new categories to existing structured knowledge graph, and deep graph convolutional network suffers from over-smoothing problem. In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation. Our semantic enhanced knowledge graph can further enhance the correlations among categories and make it easy to absorb new categories. To propagate information on the knowledge graph, we propose a novel Residual Graph Convolutional Network (ResGCN), which can effectively alleviate the problem of over-smoothing. Experiments conducted on the widely used large-scale ImageNet-21K dataset and AWA2 dataset show the effectiveness of our method, and establish a new state-of-the-art on zero-shot learning. Moreover, our results on the large-scale ImageNet-21K with various feature extraction networks show that our method has better generalization and robustness.
ROS-Based Multi-Agent Systems COntrol Simulation Testbed (MASCOT)

This paper presents a simulation testbed developed for testing and demonstration of decentralized control algorithms designed for multi-agent systems. Aimed at bridging a gap between theory and practical deployment of such algorithms, this testbed provides a simulator with multi-agent systems having quadcopter agents. It is used to test the control algorithms designed assuming simple agent dynamics like a single or double integrator. This is based on the fact that under certain assumptions quadcopter dynamics can be modeled as a double integrator system. A gazebo simulator with a physics engine as ODE (Open Dynamics Engine) is used for simulating the dynamics of the quadcopter. Robot Operating System is used to develop communication networks and motion control algorithms for the different agents in the simulation testbed. The performance of the test bed is analyzed by implementing linear control laws such as position control, leaderless consensus, leader-follower consensus, and non-linear control law for min-max time consensus. This work is published as an open-source ROS package under MIT license at https: //github. com/Avi241/mascot. A docker image is also developed for easy setup of the system.
Reduced order unsteady aerodynamic and aeroelastic models using Karhunen-Loeve eigenmodes

For tlie first time, Karhunen M v e (KL) decomposition is used to form a nearly optimal sa of basis vectors for constructing highly efficient and accurate reduced order models of the discretized Euler equations for time accurate unsteady aerodynamic and aeroelastic applicatfons to two dimensional isolated airfoils in compressible flow. These models are shown to duplicate the response of the large. primitive variable (density, velocity, etc.) computational fluid dynamics based models, but with O( the degrees of freedom and computational time. The technique for constructing the reduced order models is hoth straight forward and inexpensive since, unlike previous eigenvector based reduced order aerodynamic and aeroelastic models, there is no need to solve the eigenvalue prob! em for the very large system of equations given by the discretized Euler equations. Also, unlike the previous methods. the use of a quasi-static correction is not required.
TypeFormer: Transformers for Mobile Keystroke Biometrics

The broad usage of mobile devices nowadays, the sensitiveness of the information contained in them, and the shortcomings of current mobile user authentication methods are calling for novel, secure, and unobtrusive solutions to verify the users' identity. In this article, we propose TypeFormer, a novel Transformer architecture to model free-text keystroke dynamics performed on mobile devices for the purpose of user authentication. The proposed model consists in Temporal and Channel Modules enclosing two Long Short-Term Memory (LSTM) recurrent layers, Gaussian Range Encoding (GRE), a multi-head Self-Attention mechanism, and a Block-Recurrent structure. Experimenting on one of the largest public databases to date, the Aalto mobile keystroke database, TypeFormer outperforms current state-of-the-art systems achieving Equal Error Rate (EER) values of 3.25% using only 5 enrolment sessions of 50 keystrokes each. In such way, we contribute to reducing the traditional performance gap of the challenging mobile free-text scenario with respect to its desktop and fixed-text counterparts. Additionally, we analyse the behaviour of the model with different experimental configurations such as the length of the keystroke sequences and the amount of enrolment sessions, showing margin for improvement with more enrolment data. Finally, a cross-database evaluation is carried out, demonstrating the robustness of the features extracted by TypeFormer in comparison with existing approaches.
Understanding the Complexity Gains of Single-Task RL with a Curriculum

Reinforcement learning (RL) problems can be challenging without well-shaped rewards. Prior work on provably efficient RL methods generally proposes to address this issue with dedicated exploration strategies. However, another way to tackle this challenge is to reformulate it as a multi-task RL problem, where the task space contains not only the challenging task of interest but also easier tasks that implicitly function as a curriculum. Such a reformulation opens up the possibility of running existing multi-task RL methods as a more efficient alternative to solving a single challenging task from scratch. In this work, we provide a theoretical framework that reformulates a single-task RL problem as a multi-task RL problem defined by a curriculum. Under mild regularity conditions on the curriculum, we show that sequentially solving each task in the multi-task RL problem is more computationally efficient than solving the original single-task problem, without any explicit exploration bonuses or other exploration strategies. We also show that our theoretical insights can be translated into an effective practical learning algorithm that can accelerate curriculum learning on simulated robotic tasks.
Doubly Robust Diﬀerence-in-Diﬀerences with General Treatment Patterns

We develop a diﬀerence-in-diﬀerences method in a general setting in which the treatment variable of interest may be non-binary and its value may change in each time period. It is generally diﬃcult to estimate treatment parameters deﬁned with the potential outcome given the entire path of treatment adoption, as each treatment path may be experienced by only a small number of observations. We propose an empirically tractable alternative using the concept of eﬀective treatment, which summarizes the treatment path into a low-dimensional variable. Under a parallel trends assumption conditional on observed covariates, we show that doubly robust diﬀerence-in-diﬀerences estimands can identify certain average treatment eﬀects for movers, even when the chosen eﬀective treatment is misspeciﬁed. We consider doubly robust estimation and multiplier bootstrap inference, which are asymptotically justiﬁable if either an outcome regression function for stayers or a generalized propensity score is correctly parametrically speciﬁed. We illustrate the usefulness of our method by estimating the instantaneous and dynamic eﬀects of union membership on wages.
The Nice Feature of a Sort of Orthogonal Finitely Supported Five-Variant Wavelet Packages

In this article, the notion of orthogonal nonseparable five-variant wavelet packages, whi- ch is the generalization of orthogonal univariate wavelet packages, is introduced. A new approach for constructing the wavelet packages is presented by iteration method as well wavelet frames. The orthogonality properties for five-dimensional wavelet packages are discussed. Three orthogonality formulas concerning these wavelet packages are estabished. A necessary and sufficient condition for the existence of the pyramid decomposition scheme of space is presented.
Statistical Mechanics of Generalization In Graph Convolution Networks

Graph neural networks (GNN) have become the default machine learning model for relational datasets, including protein interaction networks, biological neural networks, and scientiﬁc collaboration graphs. We use tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model. The derived curves are phenomenologically rich: they explain the distinction between learning on homophilic and heterophilic graphs and they predict double descent whose existence in GNNs has been questioned by recent work. Our results are the ﬁrst to accurately explain the behavior not only of a stylized graph learning model but also of complex GNNs on messy real-world datasets. To wit, we use our analytic insights about homophily and heterophily to improve performance of state-of-the-art graph neural networks on several heterophilic benchmarks by a simple addition of negative self-loop ﬁlters.
Dynamic Circular Formation Of Multi-Agent Systems With Obstacle Avoidance And Size Scaling: A Flocking Approach

Formation control with the flocking approach is an efficient method that can reach the formation without determining the agent's position. This paper focuses on reaching the circular formation around the leader or target with a specific geometric pattern for the second-order multi-agent system. This means that the polygon formation is formed with arbitrary initial conditions. To create the circular formation, two potential function terms have been used for agent-agent and leader-agent interaction. In our approach, if some faults occur during the circular formation and some agents fail, the regular polygon formation will still form with fewer agents. Obstacle avoidance for a single-circle formation and collision-free motion is guaranteed. A circular formation with size scaling is proposed to better maneuver and pass through obstacles. Also, several circles with the desired radius can be reached with changes in the agent-leader potential function. In this work, optimization algorithms with different scenarios are compared to calculate the parameters of our algorithm.
Cache-Aided Multi-User Private Information Retrieval using PDAs

We consider the problem of cache-aided multi-user private information retrieval (MuPIR). In this problem, each of K cache-equipped users wants to privately retrieve a file out of N files replicated across B non-colluding servers. The user caches are filled with some arbitrary function of the files before the users decide their demands, known as the placement phase. Then in the delivery phase, users decide their demands and send queries to the servers to retrieve their desired files. This paper proposes MuPIR schemes that utilize placement delivery arrays (PDAs) to characterize placement and delivery. Proposed MuPIR schemes significantly reduce subpacketization levels while slightly increasing the download cost for the users. The proposed scheme also substantially reduces the upload cost for the users. For PDAs based on Ali-Niesen scheme for centralized coded caching, we show that our scheme is order optimal in terms of download cost. We recover the optimal single-user PIR scheme presented by Tian et al. in "Capacity-Achieving Private Information Retrieval Codes With Optimal Message Size and Upload Cost" as a special case. Our scheme also achieves optimal rate for single-user cache-aided PIR setup as described by R. Tondon in "The capacity of cache aided private information retrieval".
Dispenser printed composite thermoelectric thick films for thermoelectric generator applications

This paper describes novel processes for preparing thermoelectric composite materials compatible with thick film dispenser printing fabrication processes. Optimization of process parameters to improve thermoelectric properties is introduced. We explore the use of n-type Bi2Te3 and p-type Sb2Te3 materials to achieve properties suitable for use in low cost high aspect ratio microscale thermoelectric generators. Printable thermoelectric inks consisted of dispersed semiconductor powders in an epoxy resin system. Thick films were printed on glass substrates and cured at temperatures ranging from 150 to 350 °C. The best achievable power factors for n-type Bi2Te3-epoxy and p-type Sb2Te3-epoxy composite films were 1.5×10−4 W/m K2 and 8.4×10−4 W/m K2, respectively. Figure of merit (ZT) values for n-type Bi2Te3-epoxy and p-type Sb2Te3-epoxy composites were 0.16 and 0.41, respectively, which are much higher than previously reported ZT values for composite thermoelectric materials.