Miracle-dz/UniFAR-FTUs huggingface dataset:
Cryptoasset Competition and Market Concentration in the Presence of Network Effects

When network products and services become more valuable as their userbase grows (network effects), this tendency can become a major determinant of how they compete with each other in the market and how the market is structured. Network effects are traditionally linked to high market concentration, early-mover advantages, and entry barriers, and in the cryptoasset market they have been used as a valuation tool too. The recent resurgence of Bitcoin has been partly attributed to network effects too. We study the existence of network effects in six cryptoassets from their inception to obtain a high-level overview of the application of network effects in the cryptoasset market. We show that contrary to the usual implications of network effects, they do not serve to concentrate the cryptoasset market, nor do they accord any one cryptoasset a definitive competitive advantage, nor are they consistent enough to be reliable valuation tools. Therefore, while network effects do occur in cryptoasset networks, they are not a defining feature of the cryptoasset market as a whole.
Responsible AI Challenges in End-to-end Machine Learning

Responsible AI is becoming critical as AI is widely used in our everyday lives. Many companies that deploy AI publicly state that when training a model, we not only need to improve its accuracy, but also need to guarantee that the model does not discriminate against users (fairness), is resilient to noisy or poisoned data (robustness), is explainable, and more. In addition, these objectives are not only relevant to model training, but to all steps of end-to-end machine learning, which include data collection, data cleaning and validation, model training, model evaluation, and model management and serving. Finally, responsible AI is conceptually challenging, and supporting all the objectives must be as easy as possible. We thus propose three key research directions towards this vision - depth, breadth, and usability - to measure progress and introduce our ongoing research. First, responsible AI must be deeply supported where multiple objectives like fairness and robust must be handled together. To this end, we propose FR-Train, a holistic framework for fair and robust model training in the presence of data bias and poisoning. Second, responsible AI must be broadly supported, preferably in all steps of machine learning. Currently we focus on the data pre-processing steps and propose Slice Tuner, a selective data acquisition framework for training fair and accurate models, and MLClean, a data cleaning framework that also improves fairness and robustness. Finally, responsible AI must be usable where the techniques must be easy to deploy and actionable. We propose FairBatch, a batch selection approach for fairness that is effective and simple to use, and Slice Finder, a model evaluation tool that automatically finds problematic slices. We believe we scratched the surface of responsible AI for end-to-end machine learning and suggest research challenges moving forward.
Stochastic Learning Approach to Binary Optimization for Optimal Design of Experiments

We present a novel stochastic approach to binary optimization for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations. The OED utility function, namely, the regularized optimality criterion, is cast into a stochastic objective function in the form of an expectation over a multivariate Bernoulli distribution. The probabilistic objective is then solved by using a stochastic optimization routine to find an optimal observational policy. The proposed approach is analyzed from an optimization perspective and also from a machine learning perspective with correspondence to policy gradient reinforcement learning. The approach is demonstrated numerically by using an idealized two-dimensional Bayesian linear inverse problem, and validated by extensive numerical experiments carried out for sensor placement in a parameter identification setup.
Vision-based Vehicle Speed Estimation for ITS: A Survey

: The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits is considered one of the most effective means to increase the road safety. Second, trafﬁc monitoring and forecasting in road networks plays a fundamental role to enhance trafﬁc, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the trafﬁc state. Among the technologies available for the accurate detection of vehicle speed, the use of vision-based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision-based vehicle speed estimation. We describe the terminology, the application domains, and propose a complete taxonomy of a large selection of works that categorizes all stages involved. An overview of performance evaluation metrics and available datasets is provided. Finally, we discuss current limitations and future directions.
Quantitative System-Level Security Verification of the IoV Infrastructure

The Internet of Vehicles (IoV) equips vehicles with connectivity to the Internet and the Internet of Things (IoT) to support modern applications such as autonomous driving. However, the consolidation of complex computing domains of vehicles, the Internet, and the IoT limits the applicability of tailored security solutions. In this paper, we propose a new methodology to quantitatively verify the security of single or system-level assets of the IoV infrastructure. In detail, our methodology decomposes assets of the IoV infrastructure with the help of reference sub-architectures and the 4+1 view model analysis to map identified assets into data, software, networking, and hardware categories. This analysis includes a custom threat modeling concept to perform parameterization of Common Vulnerability Scoring System (CVSS) scores per view model domain. As a result, our methodology is able to allocate assets from attack paths to view model domains. This equips assets of attack paths with our IoV-driven CVSS scores. Our CVSS scores assess the attack likelihood which we use for Markov Chain transition probabilities. This way, we quantitatively verify system-level security among a set of IoV assets. Our results show that our methodology applies to arbitrary IoV attack paths. Based on our parameterization of CVSS scores and our selection of use cases, remote attacks are less likely to compromise location data compared to attacks from close proximity for authorized and unauthorized attackers respectively.
Terahertz detection based on nonlinear Hall effect without magnetic field

Significance Despite the enormous potential of terahertz technology, fast, sensitive, and broadband terahertz detection at room temperature has been a challenge. We propose a method for long-wavelength photodetection using topological quantum materials without invoking any diode or junction region. The inherently quadratic relation between transverse current and input voltage at zero magnetic field is used to rectify the incident terahertz or infrared electric field into a direct current. We identify the Weyl semimetal NbP and ferroelectric semiconductor GeTe for terahertz/infrared photodetection with large current responsivity without external bias. We propose a method for broadband long-wavelength photodetection using the nonlinear Hall effect in noncentrosymmetric quantum materials. The inherently quadratic relation between transverse current and input voltage at zero magnetic field is used to rectify the incident terahertz or infrared electric field into a direct current, without invoking any diode. Our photodetector operates at zero external bias with fast response speed and has zero threshold voltage. Remarkably, the intrinsic current responsivity due to the Berry curvature mechanism is a material property independent of the incident frequency or the scattering rate, which can be evaluated from first-principles electronic structure calculations. We identify the Weyl semimetal NbP and ferroelectric semiconductor GeTe for terahertz/infrared photodetection with large current responsivity without external bias.
Multi-point dimensionality reduction to improve projection layout reliability

I n ordinary Dimensionality Reduction (DR), each data instance in a high dimensional space (original space), or on a distance matrix denoting original space distances, is mapped to (projected onto) one point in a low dimensional space (visual space), building a layout of projected points trying to preserve as much as possible some property of data such as distances, neighbourhood relationships, and/or topology structures, with the ultimate goal of approximating semantic properties of data with preserved geometric properties or topology structures in visual space. In this paper, the concept of Multi-point Dimensionality Reduction is elaborated on where each data instance can be mapped to (projected onto) possibly more than one point in visual space by providing the ﬁrst general solution (algorithm) for it as a move in the direction of improving reliablity, usability and interpretability of dimensionality reduction. Furthermore by allowing the points in visual space to be split into two layers while maintaining the possibility of having more than one projection (mapping) per data instance , the beneﬁt of separating more reliable points from less reliable points is dicussed notwithstanding the eﬀort to improve less reliable points. The proposed solution (algorithm) in this paper, named Layered Vertex Splitting Data Embedding (LVSDE), is built upon and extends a combination of ordinary DR and graph drawing techniques. Based on the experiments of this paper on some data sets, the particular proposed algorithm (LVSDE) practically outperforms popular ordinary DR methods visually (semantics, group separation, subgroup detection or combinational group detection) in a way that is easily explainable.
Hierarchical Multitask Learning with Dependency Parsing for Japanese Semantic Role Labeling Improves Performance of Argument Identification

With the advent of FrameNet and PropBank, many semantic role labeling (SRL) systems have been proposed in English. Although research on Japanese predicate argument structure analysis (PASA) has been conducted, most studies focused on surface cases. There are only few previous works on Japanese SRL for deep cases, and their models' accuracies are low. Therefore, we propose a hierarchical multitask learning method with dependency parsing (DP) and show that our model achieves state-of-the-art results in Japanese SRL. Also, we conduct experiments with a joint model that performs both argument identification and argument classification simultaneously. The result suggests that multitasking with DP is mainly effective for argument identification.
2-aminooxazole in Astrophysical Environments: IR Spectra and Destruction Cross Sections for Energetic Processing

2-aminooxazole (2AO), a N-heterocyclic molecule, has been proposed as an intermediate in prebiotic syntheses. It has been demonstrated that it can be synthesized from small molecules such as cyanamide and glycoaldehyde, which are present in interstellar space. The aim of this work is to provide infrared (IR) spectra, in the solid phase for conditions typical of astrophysical environments and to estimate its stability toward UV photons and cosmic rays. IR (4000–600 cm−1) absorption spectra at 20 K, 180 K, and 300 K, IR band strengths, and room-temperature UV (120–250 nm) absorption spectra are given for the first time for this species. Destruction cross sections of ≈9.5 10−18 cm2 and ≈2 10−16 cm2 were found in the irradiation at 20 K of pure 2AO and 2AO:H2O ices with UV (6.3–10.9 eV) photons or 5 keV electrons, respectively. These data were used to estimate half-life times for the molecule in different environments. It is estimated that 2AO could survive UV radiation and cosmic rays in the ice mantles of dense clouds beyond cloud collapse. In contrast, it would be very unstable on the surface of cold solar system bodies like Kuiper Belt objects, but the molecule could still survive within dust grain agglomerates or cometesimals.
Apertif view of the OH megamaser IRAS 10597+5926: OH 18 cm satellite lines in wide-area H I surveys

We present the serendipitous detection of the two main OH maser lines at 1667 and 1665 MHz associated with IRAS 10597+5926 at z⊙ = 0.19612 in the untargeted Apertif Wide-area Extragalactic imaging Survey (AWES), and the subsequent measurement of the OH 1612 MHz satellite line in the same source. With a total OH luminosity of log(L/L⊙) = 3.90 ± 0.03, IRAS 10597+5926 is the fourth brightest OH megamaser (OHM) known. We measure a lower limit for the 1667/1612 ratio of R1612 > 45.9, which is the highest limiting ratio measured for the 1612 MHz OH satellite line to date. OH satellite line measurements provide a potentially valuable constraint by which to compare detailed models of OH maser pumping mechanisms. Optical imaging shows that the galaxy is likely a late-stage merger. Based on published infrared and far ultraviolet fluxes, we find that the galaxy is an ultra-luminous infrared galaxy (ULIRG) with log(LTIR/L⊙) = 12.24 that is undergoing a starburst with an estimated star formation rate of 179 ± 40 M⊙ yr−1. These host galaxy properties are consistent with the physical conditions responsible for very bright OHM emission. Finally, we provide an update on the predicted number of OH masers that may be found in AWES and estimate the total number of OH masers that will be detected in each of the individual main and satellite OH 18 cm lines.
On the Verification and Validation of AI Navigation Algorithms

This paper explores the state of the art on to methods to verify and validate navigation algorithms for autonomous surface ships. We perform a systematic mapping study to find research works published in the last 10 years proposing new algorithms for autonomous navigation and collision avoidance and we have extracted what verification and validation approaches have been applied on these algorithms. We observe that most research works use simulations to validate their algorithms. However, these simulations often involve just a few scenarios designed manually. This raises the question if the algorithms have been validated properly. To remedy this, we propose the use of a systematic scenario-based testing approach to validate navigation algorithms extensively.
Data-Driven Learning for the Mori-Zwanzig Formalism: A Generalization of the Koopman Learning Framework

A theoretical framework which unifies the conventional Mori-Zwanzig formalism and the approximate Koopman learning is presented. In this framework, the Mori-Zwanzig formalism, developed in statistical mechanics to tackle the hard problem of construction of reduced-order dynamics for high-dimensional dynamical systems, can be considered as a natural generalization of the Koopman description of the dynamical system. We next show that similar to the approximate Koopman learning methods, data-driven methods can be developed for the Mori-Zwanzig formalism with Mori's linear projection operator. We developed two algorithms to extract the key operators, the Markov and the memory kernel, using time series of a reduced set of observables in a dynamical system. We adopted the Lorenz `96 system as a test problem and solved for the operators, which exhibit complex behaviors which are unlikely to be captured by traditional modeling approaches, in Mori-Zwanzig analysis. The nontrivial Generalized Fluctuation Dissipation relationship, which relates the memory kernel with the two-time correlation statistics of the orthogonal dynamics, was numerically verified as a validation of the solved operators. We present numerical evidence that the Generalized Langevin Equation, a key construct in the Mori-Zwanzig formalism, is more advantageous in predicting the evolution of the reduced set of observables than the conventional approximate Koopman operators.
A reversed Kerr traveling wave parametric amplifier

Traveling wave parametric amplification in a nonlinear medium provides broadband quantumnoise limited gain and is a remarkable resource for the detection of electromagnetic radiation. This nonlinearity is at the same time the key to the amplification phenomenon but also the cause of a fundamental limitation: poor phase matching between the signal and the pump. Here we solve this issue with a new phase matching mechanism based on the sign reversal of the Kerr nonlinearity. We present a novel traveling wave parametric amplifier composed of a chain of superconducting nonlinear asymmetric inductive elements (SNAILs) which allows this sign reversal when biased with the proper magnetic flux. Compared to previous state of the art phase matching approaches, this reversed Kerr phase matching mechanism avoids the presence of gaps in transmission, reduces gain ripples, and allows in situ tunability of the amplification band over an unprecedented wide range. Besides such notable advancements in the amplification performance, with direct applications to superconducting quantum computing, the in-situ tunability of the nonlinearity in traveling wave structures, with no counterpart in optics to the best of our knowledge, opens exciting experimental possibilities in the general framework of microwave quantum optics and single-photon detection.
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team

The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team. During these collaborations, there is a knowledge mismatch between AI developers, who are skilled in data science, and external stakeholders who are typically not. This difference leads to communication gaps, and the onus falls on AI developers to explain data science concepts to their collaborators. In this paper, we report on a study including analyses of both interviews with AI developers and artifacts they produced for communication. Using the analytic lens of shared mental models, we report on the types of communication gaps that AI developers face, how AI developers communicate across disciplinary and organizational boundaries, and how they simultaneously manage issues regarding trust and expectations.
Cosmology from Non‐Minimal Geometry‐Matter Coupling

We construct a cosmological model from the inception of the Friedmann‐Lemâitre‐Robertson‐Walker metric into the field equations of the f(R,Lm)$f(R,L_m)$ gravity theory, with R being the Ricci scalar and Lm$L_m$ being the matter lagrangian density. The formalism is developed for a particular f(R,Lm)$f(R,L_m)$ function, namely R/16π+(1+σR)Lm$R/16\pi +(1+\sigma R)L_{m}$ , with σ being a constant that carries the geometry‐matter coupling. Our solutions are remarkably capable of evading the Big‐Bang singularity as well as predict the cosmic acceleration with no need for the cosmological constant, but simply as a consequence of the geometry‐matter coupling terms in the Friedmann‐like equations.
A spin Hamiltonian for BaCdVO(PO$_4$)$_2$

Single crystal inelastic neutron scattering is used to study spin wave excitations in the fully polarized state of the frustrated quantum ferro-antiferromagnet BaCdVO(PO4)2. The data analysis is based on a Heisenberg spin Hamiltonian that includes as many distinct nearest-neighbor and next-nearest neighbor interactions as allowed by crystal symmetry. All 8 such exchange constants are obtained in a simultaneous fit to over 150 scans across the dispersion manifold. This establishes a definitive quantitative model of this material. It turns out to be substantially different from the one assumed in numerous previous studies based on powder experiments.
Towards interpreting ML-based automated malware detection models: a survey

Malware is being increasingly threatening and malware detectors based on traditional signature-based analysis are no longer suitable for current malware detection. Recently, the models based on machine learning (ML) are developed for predicting unknown malware variants and saving human strength. However, most of the existing ML models are black-box, which made their pre-diction results undependable, and therefore need further interpretation in order to be effectively deployed in the wild. This paper aims to examine and categorize the existing researches on ML-based malware detector interpretability. We first give a detailed comparison over the previous work on common ML model inter-pretability in groups after introducing the principles, attributes, evaluation indi-cators and taxonomy of common ML interpretability. Then we investigate the interpretation methods towards malware detection, by addressing the importance of interpreting malware detectors, challenges faced by this field, solutions for migitating these challenges, and a new taxonomy for classifying all the state-of-the-art malware detection interpretability work in recent years. The highlight of our survey is providing a new taxonomy towards malware detection interpreta-tion methods based on the common taxonomy summarized by previous re-searches in the common field. In addition, we are the first to evaluate the state-of-the-art approaches by interpretation method attributes to generate the final score so as to give insight to quantifying the interpretability. By concluding the results of the recent researches, we hope our work can provide suggestions for researchers who are interested in the interpretability on ML-based malware de-tection models.
Continual Learning of Knowledge Graph Embeddings

In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications. However, while robots often observe previously unknown concepts, these representations typically assume that all concepts are known a priori, and incorporating new information requires all concepts to be learned afresh. Our work relaxes this limiting assumption of existing representations and tackles the incremental knowledge graph embedding problem by leveraging the principles of a range of continual learning methods. Through an experimental evaluation with several knowledge graphs and embedding representations, we provide insights about trade-offs for practitioners to match a semantics-driven robotics applications to a suitable continual knowledge graph embedding method.
Spin dynamics from a constrained magnetic tight-binding model

A dynamics of the precession of coupled atomic moments in the tight-binding (TB) approximation is presented. By implementing an angular penalty functional in the energy that captures the magnetic effective fields self-consistently, the motion of the orientation of the local magnetic moments is observed faster than the variation of their magnitudes. This allows the computation of the effective atomic magnetic fields that are found consistent with the Heisenberg’s exchange interaction, by comparison with classical atomistic spin dynamics on Fe, Co and Ni magnetic clusters.