Miracle-dz/UniFAR-FTUs huggingface dataset:
Micro‐Raman study of exfoliated Bi2Sr2CaCu2O8+δ single crystals with different thicknesses

Exfoliated Bi2Sr2CaCu2O8+d (Bi-2212) single crystals were prepared by micromechanical cleavage of bulk Bi-2212 single crystals on SiO2/Si substrates. Room temperature micro-Raman spectra were collected using a 532-nm laser source. The evolutions of the sp
Traffic Flows Analysis in High-Speed Computer Networks Using Time Series

This article explores the required amount of time series points from a high-speed traffic network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series, followed by addressing the minimum amount of points required to obtain accurate estimates of the Hurst exponent in real-time. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behavior, standard deviation, mean square error, and convergence using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed network based on the IEEE 802.3ab standard.
Deep learning stochastic processes with QCD phase transition

It is non-trivial to recognize phase transitions and track dynamics inside a stochastic process because of its intrinsic stochasticity. In this paper, we employ the deep learning method to classify the phase orders and predict the damping coefficient of fluctuating systems under Langevin's description. As a concrete set-up, we demonstrate this paradigm for the scalar condensation in QCD matter near the critical point, in which the order parameter of chiral phase transition can be characterized in a $1+1$-dimensional Langevin equation for $\sigma$ field. In a supervised learning manner, the Convolutional Neural Networks(CNNs) accurately classify the first-order phase transition and crossover based on $\sigma$ field configurations with fluctuations. Noise in the stochastic process does not significantly hinder the performance of the well-trained neural network for phase order recognition. For mixed dynamics with diverse dynamical parameters, we further devise and train the machine to predict the damping coefficients $\eta$ in a broad range. The results show that it is robust to extract the dynamics from the bumpy field configurations.
Bayesian inference on isospin splitting of nucleon effective mass from giant resonances in 208Pb *

From a Bayesian analysis of the electric dipole polarizability, the constrained energy of isovector giant dipole resonance, the peak energy of isocalar giant quadrupole resonance and the constrained energy of isocalar giant monopole resonance in Pb, we extract the isoscalar and isovector effective masses in nuclear matter at saturation density ρ0 as m ∗ s,0/m = 0.87 +0.04 −0.04 and m ∗ v,0/m = 0.78 +0.06 −0.05 at 90% confidence level. The obtained constraints on ms,0 and m ∗ v,0 lead to a positive isospin splitting of nucleon effective mass in asymmetric nuclear matter of isospin asymmetry δ at ρ0 as m ∗ n−p/m = (0.20 −0.14)δ. In addition, the symmetry energy at the subsaturation density ρ ∗ = 0.05 fm−3 is determined to be Esym(ρ ∗) = 16.7 ± 1.3 MeV at 90% confidence level.
CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes

For autonomous vehicles, an accurate calibration for LiDAR and camera is a prerequisite for multi-sensor perception systems. However, existing calibration techniques require either a complicated setting with various calibration targets, or an initial calibration provided beforehand, which greatly impedes their applicability in large-scale autonomous vehicle deployment. To tackle these issues, we propose a novel method to calibrate the extrinsic parameter for LiDAR and camera in road scenes. Our method introduces line features from static straight-line-shaped objects such as road lanes and poles in both image and point cloud and formulates the initial calibration of extrinsic parameters as a perspective-3-lines (P3L) problem. Subsequently, a cost function defined under the semantic constraints of the line features is designed to perform refinement on the solved coarse calibration. The whole procedure is fully automatic and user-friendly without the need to adjust environment settings or provide an initial calibration. We conduct extensive experiments on KITTI and our in-house dataset, quantitative and qualitative results demonstrate the robustness and accuracy of our method.
A Stationary Drake Equation Distribution as a Balance of Birth-death Processes

Previous critiques of the Drake Equation have highlighted its deterministic nature, implying that the number of civilizations is the same at all times. Here, I build upon earlier work and present a stochastic formulation. The birth of civilizations within the galaxy is modeled as following a uniform rate (Poisson) stochastic process, with a mean rate of λ C . Each then experiences a constant hazard rate of collapse, which defines an exponential distribution with rate parameter λ L . Thus, the galaxy is viewed as a frothing landscape of civilization birth and collapse. Under these assumptions, I show that N in the Drake Equation must follow another Poisson distribution, with a mean rate (λ C /λ L ). This is used to demonstrate why the Copernican Principle does not allow one to infer N, as well evaluating the algebraic probability of being alone in the galaxy.
On the Experiences of Adopting Automated Data Validation in an Industrial Machine Learning Project

Data errors are a common challenge in machine learning (ML) projects and generally cause significant performance degradation in ML-enabled software systems. To ensure early detection of erroneous data and avoid training MLmodels using bad data, research and industrial practice suggest incorporating a data validation process and tool in the ML system development process. Aim: The study investigates the adoption of a data validation process and tool in industrial ML projects. The data validation process demands significant engineering resources for tool development and maintenance. Thus, it is important to identify the best practices for their adoption especially by development teams that are in the early phases of deploying ML-enabled software systems. Method: Action research was conducted at a large-software intensive organization in telecommunications, specifically within the analytics R&D organization for an ML use case of classifying faults from returned hardware telecommunication devices. Results: Based on the evaluation results and learning from our action research, we identified three best practices, three benefits, and two barriers to adopting the data validation process and tool in ML projects. We also propose a data validation framework(DVF) for systematizing the adoption of a data validation process. Conclusions: The results show that adopting a data validation process and tool in ML projects is an effective approach to testing ML-enabled software systems. It requires having an overview of the level of data (feature, dataset, cross-dataset, data stream) at which certain data quality tests can be applied.
Linear stability of a solid–vapour interface

We investigate a system consisting of a condensed phase in contact with its vapour. We derive similarity solutions for vapour and temperature profiles and calculate the condition for the presence of vapour supersaturation adjacent to the condensed phase. We analyse the linear stability of a solid–vapour interface with varying atmospheric conditions. The instability is qualitatively similar to the Mullins–Sekerka instability in binary alloys but the results highlight the important parameters for the solid/vapour problem. We derive the neutral stability condition and results are applied to frost flowers, which are small hoar-frost-like crystals that grow on sea ice, and to physical vapour deposition. The results are applicable to many problems in the wide field of condensed-phase/vapour systems.
TypeShift: A User Interface for Visualizing the Typing Production Process

TypeShift is a tool for visualizing linguistic patterns in the timing of typing production. Language production is a complex process which draws on linguistic, cognitive and motor skills. By visualizing holistic trends in the typing process, TypeShift aims to elucidate the often noisy information signals that are used to represent typing patterns, both at the word-level and character-level. It accomplishes this by enabling a researcher to compare and contrast specific linguistic phenomena, and compare an individual typing session to multiple group averages. Finally, although TypeShift was originally designed for typing data, it can easy be adapted to accommodate speech data, as well. A web demo is available at https://angoodkind.shinyapps. io/TypeShift/. The source code can be accessed at https://github.com/angoodkind/TypeShift.
Deep reinforcement learning models the emergent dynamics of human cooperation

Collective action demands that individuals eﬃciently coordinate how much, where, and when to cooperate. Laboratory experiments have extensively explored the ﬁrst part of this process, demonstrating that a variety of social-cognitive mechanisms inﬂuence how much individuals choose to invest in group eﬀorts. However, experimental research has been unable to shed light on how social cognitive mechanisms contribute to the where and when of collective action. We leverage multi-agent deep reinforcement learning to model how a social-cognitive mechanism—speciﬁcally, the intrinsic motivation to achieve a good reputation—steers group behavior toward speciﬁc spatial and temporal strategies for collective action in a social dilemma. We also collect behavioral data from groups of human participants challenged with the same dilemma. The model accurately predicts spatial and temporal patterns of group behavior: in this public goods dilemma, the intrinsic motivation for reputation catalyzes the development of a non-territorial, turn-taking strategy to coordinate collective action.
Numerical studies of a detonation analogue

Abstract As an aid to deciding a computational strategy for problems involving strong detonation waves, we have applied a variety of numerical techniques to a mathematical problem devised by Fickett, which exhibits many of the essential computational difficulties and possesses analytical solution corresponding to overdriven and underdriven reacting flows. Classical shock capturing methods such as those of MacCormack and Godunov, do not produce acceptable solutions. Better solutions can be provided by Flux Difference Methods (Roe's method) and by Random Choice Methods (still in one dimension), especially by using a new variant that yields second order accuracy. Adaptive gridding techniques for these methods are currently being investigated with encouraging preliminary results.
Taming English modals

In the present paper, I pursue a Construction Grammar (CxG) characterization of English modal auxiliaries (e.g., can-could, must, have (got) to, should, ought to, need to, will-would) that seeks to add to established lexical approaches. It is argued that Construction Grammar (e.g., Goldberg, 1995, 2006; Tomasello, 2003) can successfully account for underlying modality patterns, the understanding of which can lead to distinct gains for both linguistics and second language acquisition research. To that end, some of the tenets of CxG are invoked.
Fast On-Device Adaptation for Spiking Neural Networks Via Online-Within-Online Meta-Learning

Spiking Neural Networks (SNNs) have recently gained popularity as machine learning models for on-device edge intelligence for applications such as mobile healthcare management and natural language processing due to their low power profile. In such highly personalized use cases, it is important for the model to be able to adapt to the unique features of an individual with only a minimal amount of training data. Meta-learning has been proposed as a way to train models that are geared towards quick adaptation to new tasks. The few existing meta-learning solutions for SNNs operate offline and require some form of backpropagation that is incompatible with the current neuromorphic edge-devices. In this paper, we propose an online-within-online meta-learning rule for SNNs termed OWOML-SNN, that enables lifelong learning on a stream of tasks, and relies on local, backprop-free, nested updates.
Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder–decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. These numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.
Neural Network-based Quantization for Network Automation

Deep Learning methods have been adopted in mobile networks, especially for network management automation where they provide means for advanced machine cognition. Deep learning methods utilize cutting-edge hardware and software tools, allowing complex cognitive algorithms to be developed. In a recent paper, we introduced the Bounding Sphere Quantization (BSQ) algorithm, a modification of the k-Means algorithm, that was shown to create better quantizations for certain network management use-cases, such as anomaly detection. However, BSQ required a significantly longer time to train than k-Means, a challenge which can be overcome with a neural network-based implementation. In this paper, we present such an implementation of BSQ that utilizes state-of-the-art deep learning tools to achieve a competitive training speed.
Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor

A single synaptic device with inherent learning and memory functions is demonstrated based on an amorphous InGaZnO (α‐IGZO) memristor; several essential synaptic functions are simultaneously achieved in such a single device, including nonlinear transmission characteristics, spike‐rate‐dependent and spike‐timing‐dependent plasticity, long‐term/short‐term plasticity (LSP and STP) and “learning‐experience” behavior. These characteristics bear striking resemblances to certain learning and memory functions of biological systems. Especially, a “learning‐experience” function is obtained for the first time, which is thought to be related to the metastable local structures in α‐IGZO. These functions are interrelated: frequent stimulation can cause an enhancement of LTP, both spike‐rate‐dependent and spike‐timing‐dependent plasticity is the same on this point; and, the STP‐to‐LTP transition can occur through repeated “stimulation” training. The physical mechanism of device operation, which does not strictly follow the memristor model, is attributed to oxygen ion migration/diffusion. A correlation between short‐term memory and ion diffusion is established by studying the temperature dependence of the relaxation processes of STP and ion diffusion. The realization of important synaptic functions and the establishment of a dynamic model would promote more accurate modeling of the synapse for artificial neural network.
Forecasting the Remaining Duration of an Ongoing Solar Flare

The solar X‐ray irradiance is significantly heightened during the course of a solar flare, which can cause radio blackouts due to ionization of the atoms in the ionosphere. As the duration of a solar flare is not related to the size of that flare, it is not directly clear how long those blackouts can persist. Using a random forest regression model trained on data taken from X‐ray light curves, we have developed a direct forecasting method that predicts how long the event will remain above background levels. We test this on a large collection of flares observed with GOES‐15, and show that it generally outperforms simple linear regression, giving a median error of less than 2 min for the approximate end time of a flare. This random forest model is computationally light enough to be performed in real time, allowing for the prediction to be made during the course of a flare.
Detecting quantum entanglement with unsupervised learning

Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average. Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.
A facile approach for fabrication of mechanically strong graphene/polypyrrole films with large areal capacitance for supercapacitor applications

Substantial progress has been made in free-standing flexible graphene-based films for flexible supercapacitors. However, there are limited reports on the areal capacitance of these electrodes, which is an important parameter for practical applications, especially in miniaturized electronic devices. Herein we report the facile fabrication of robust flexible graphene/polypyrrole nanoparticle films. PPy NPs act as the “spacer” between graphene layers creating hierarchical structures. This free-standing film shows excellent mechanical properties with a fracture strength of 16.89 MPa and Young's modulus of 11.77 MPa. The resulting film electrode delivers a large areal specific capacitance of 216 mF cm−2, which is higher or comparable to other graphene/conducting polymer composite films. Moreover, this composite film exhibits a high capacitance retention rate of 87% after 5000 charge/discharge cycles and a fast relaxation time constant of 2.51 s. These excellent properties all suggest their prospective use in flexible energy storage devices.
InFillmore: Neural Frame Lexicalization for Narrative Text Infilling

We propose a structured extension to bidirectional-context conditional language generation, or “inﬁlling,” inspired by Frame Semantic theory (Fillmore, 1976). Guidance is provided through two approaches: (1) model ﬁne-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive lexically constrained decoding that leverages frame semantic lexical units. Automatic and human evaluations conﬁrm that frame-guided generation allows for explicit manipulation of intended inﬁll semantics, with minimal loss in distinguishability from human-generated text. Our methods ﬂexibly apply to a variety of use scenarios, and we provide an interactive web demo. 1
Risk aware optimization of water sensor placement

This paper addresses the identification of optimal "sensing spots", within a network for monitoring the spread of "effects" triggered by "events". Many real-world problems fit into this general framework: we focused on the early detection of contamination events in Water Distribution Networks (WDN). We model the sensor placement as a bi-objective optimization problem, aiming at minimizing the mean and standard deviation of detection time over a set of different simulated contamination events and solved using NSGA-II. A problem-specific data structure is proposed enabling a deeper analysis of empirical convergence of the population.
Causal Analysis of Agent Behavior for AI Safety

As machine learning systems become more powerful they also become increasingly unpredictable and opaque. Yet, finding human-understandable explanations of how they work is essential for their safe deployment. This technical report illustrates a methodology for investigating the causal mechanisms that drive the behaviour of artificial agents. Six use cases are covered, each addressing a typical question an analyst might ask about an agent. In particular, we show that each question cannot be addressed by pure observation alone, but instead requires conducting experiments with systematically chosen manipulations so as to generate the correct causal evidence.
A Study on Fairness and Trust Perceptions in Automated Decision Making

Automated decision systems are increasingly used for consequential decision making -- for a variety of reasons. These systems often rely on sophisticated yet opaque models, which do not (or hardly) allow for understanding how or why a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield undesirable (e.g., unfair) outcomes because their sanity is difficult to assess and calibrate in the first place. In this work, we conduct a study to evaluate different attempts of explaining such systems with respect to their effect on people's perceptions of fairness and trustworthiness towards the underlying mechanisms. A pilot study revealed surprising qualitative insights as well as preliminary significant effects, which will have to be verified, extended and thoroughly discussed in the larger main study.