Miracle-dz/UniFAR-FTUs huggingface dataset:
Near Real-time Map Building with Multi-class Image Set Labeling and Classification of Road Conditions Using Convolutional Neural Networks

ABSTRACT Road Weather Information Systems (RWIS) provide real-time weather information at point locations and are often used to produce road weather forecasts and provide input for pavement forecast models. Compared to the prevalant street cameras, however, RWIS are sometimes limited in availability. Thus, extraction of road conditions data by computer vision can provide a complementary observational data source if it can be done quickly and on large scales. In this paper, we leverage state-of-the-art convolutional neural networks (CNN) in labeling images taken by street and highway cameras located across North America. The final training set included 47,000 images labeled with five classes: dry, wet, snow/ice, poor, and offline. The experiments tested different configurations of six CNNs. The EfficientNet-B4 framework was found to be most suitable to this problem, achieving validation accuracy of 90.6%, although EfficientNet-B0 achieved an accuracy of 90.3% with half the execution time. The classified images were then used to construct a map showing real-time road conditions at various camera locations. The proposed approach is presented in three parts: i) application pipeline, ii) description of the deep learning frameworks, iii) the dataset labeling process and the classification metrics.
Reinforcement Learning-based Autoscaling of Workflows in the Cloud: A Survey

Reinforcement Learning (RL) has demonstrated a great potential for automatically solving decision-making problems in complex uncertain environments. RL proposes a computational approach that allows learning through interaction in an environment with stochastic behavior, where agents take actions to maximize some cumulative short-term and long-term rewards. Some of the most impressive results have been shown in Game Theory where agents exhibited superhuman performance in games like Go or Starcraft 2, which led to its gradual adoption in many other domains including Cloud Computing. Therefore, RL appears as a promising approach for Autoscaling in Cloud since it is possible to learn transparent (with no human intervention), dynamic (no static plans), and adaptable (constantly updated) resource management policies. These are three important distinctive aspects to consider in comparison with other widely used autoscaling policies that are defined as ad-hoc way or statically computed as in solutions based on meta-heuristics. Particularly, workflow autoscaling exploits the Cloud elasticity to optimize the execution of workflows according to given optimization criteria. This is a decision-making problem that demands to decide when and how to scale-up/down computational resources; and how to assign them to the upcoming processing workload. Such actions have to be taken considering some optimization criteria in the Cloud, a dynamic and uncertain environment. Motivated by this, many works apply RL to the autoscaling problem in Cloud. In this work, we survey exhaustively those proposals from major venues, and uniformly compare them based on a set of proposed taxonomies. We also discuss open problems and provide a prospect of future research in the area.
A random-walk model for dark matter halo spins

We extend the random-walk model of Vitvitska et al. for predicting the spins of dark matter halos from their merger histories. Using updated merger rates, orbital parameter distributions, and N-body constraints we show that this model can accurately reproduce the distribution of spin parameters measured in N-body simulations when we include a weak correlation between the spins of halos and the angular momenta of infalling subhalos. We further show that this model is in approximate agreement with the correlation of the spin magnitude over time as determined from N-body simulations, while it slightly underpredicts the correlation in the direction of the spin vector measured from the same simulations. This model is useful for predicting spins from merger histories derived from non-N-body sources, thereby circumventing the need for very high resolution simulations to permit accurate measurements of spins. It may be particularly relevant to modeling systems which accumulate angular momentum from halos over time (such as galactic discs) - we show that this model makes small but significant changes in the distribution of galactic disc sizes computed using the Galacticus semi-analytic galaxy formation model.
Unsupervised Extraction of Market Moving Events with Neural Attention

We present a method to identify relevant events associated with stock price movements without manually labeled data. We train an attention-based neural network, which given a set of news headlines for a given time frame, predicts the price movement of a given stock index (i.e., DOWN, STAY, UP). An attention layer acts as an input selector; it computes a normalized weight for each headline embedding. The weighted average of the embeddings is used to predict the price movement. We present an analysis to understand if, after the network has been trained, the attention layer is capable of generating a global ranking of news events through its unnormalized weights. The ranking should be able to rank relevant financial events higher. In this initial study we use news categories as a proxy for relevance: news belonging to more relevant categories should be ranked higher. Our experiments on four indices suggest that there is an indication that the weights indeed skew the global set of events towards those categories that are more relevant to explain the price change; this effect reflects the performance of the network on stock prediction.
On $R$-triviality of $F_4$-II

Simple algebraic groups of type $F_4$ defined over a field $k$ are the full automorphism groups of Albert algebras over $k$. Let $A$ be an Albert algebra over a field $k$ of arbitrary characteristic. We prove that there is an isotope $A^{(v)}$ of $A$ such that the group $\text{\bf Aut}(A^{(v)})$ is $R$-trivial, in the sense of Manin.
Temporal Information Processing on Noisy Quantum Computers

The combination of machine learning and quantum computing has emerged as a promising approach for addressing previously untenable problems. Reservoir computing is an efficient learning paradigm that utilizes nonlinear dynamical systems for temporal information processing, i.e., processing of input sequences to produce output sequences. Here we propose quantum reservoir computing that harnesses complex dissipative quantum dynamics. Our class of quantum reservoirs is universal, in that any nonlinear fading memory map can be approximated arbitrarily closely and uniformly over all inputs by a quantum reservoir from this class. We describe a subclass of the universal class that is readily implementable using quantum gates native to current noisy gate-model quantum computers. Proof-of-principle experiments on remotely accessed cloud-based superconducting quantum computers demonstrate that small and noisy quantum reservoirs can tackle high-order nonlinear temporal tasks. Our theoretical and experimental results pave the path for attractive temporal processing applications of near-term gate-model quantum computers of increasing fidelity but without quantum error correction, signifying the potential of these devices for wider applications including neural modeling, speech recognition and natural language processing, going beyond static classification and regression tasks.
Control of liquid crystal skyrmions towards designing re-configurable materials

Recently there has been significant research efforts towards materials by design, self assembly at various length scales, and functional soft materials. Here, we propose that skyrmions in chiral nematic liquid crystals are suitable building blocks for the realization the above goals due to the possibility of individually controlling them. Skyrmions have attracted much and rapidly growing interest due to their topological properties and unique aspects for potential novel applications such as data storage and soft robotics. While not real particles, these skyrmions behave like particles-they interact with each other and can be actuated by means of electric field, surface anchoring, and light. On the other hand, they are field configurations which have properties not possessed by real particles. In this letter, we outline some of the basic mechanisms and knobs by which LC skyrmions can be confined and moved for realization of custom material properties. Our work may trigger experimental efforts in this direction.
Causal inference for data-driven debugging and decision making in cloud computing

Cloud computing involves complex technical and economical systems and interactions. This brings about various challenges, two of which are: (1) debugging and control to optimize the performance of computing systems, with the help of sandbox experiments, and (2) privacy-preserving prediction of the cost of ``spot'' resources for decision making of cloud clients. In this paper, we formalize debugging by counterfactual probabilities and control by post-(soft-)interventional probabilities. We prove that counterfactuals can approximately be calculated from a ``stochastic'' graphical causal model (while they are originally defined only for ``deterministic'' functional causal models), and based on this sketch a data-driven approach to address problem (1). To address problem (2), we formalize bidding by post-(soft-)interventional probabilities and present a simple mathematical result on approximate integration of ``incomplete'' conditional probability distributions. We show how this can be used by cloud clients to trade off privacy against predictability of the outcome of their bidding actions in a toy scenario. We report experiments on simulated and real data.
Regularity of fully non-linear elliptic equations on Hermitian manifolds. II.

In this paper we investigate the regularity and solvability of solutions to Dirichlet problem for fully non-linear elliptic equations with gradient terms on Hermitian manifolds, which include among others the Monge-Ampere equation for $(n-1)$-plurisubharmonic functions. Some significantly new features of regularity assumptions on the boundary and boundary data are obtained, which reveal how the shape of the boundary influences such regularity assumptions. Such new features follow from quantitative boundary estimates which specifically enable us to apply a blow-up argument to derive the gradient estimate. Interestingly, the subsolutions are constructed when the background space is moreover a product of a closed Hermitian manifold with a compact Riemann surface with boundary.
Some Insights into Lifelong Reinforcement Learning Systems

A lifelong reinforcement learning system is a learning system that has the ability to learn through trail-and-error interaction with the environment over its lifetime. In this paper, I give some arguments to show that the traditional reinforcement learning paradigm fails to model this type of learning system. Some insights into lifelong reinforcement learning are provided, along with a simplistic prototype lifelong reinforcement learning system.
Unconstrained Biometric Recognition: Summary of Recent SOCIA Lab. Research

The development of biometric recognition solutions able to work in visual surveillance conditions, i.e., in unconstrained data acquisition conditions and under covert protocols has been motivating growing efforts from the research community. Among the various laboratories, schools and research institutes concerned about this problem, the SOCIA: Soft Computing and Image Analysis Lab. , of the University of Beira Interior, Portugal, has been among the most active in pursuing disruptive solutions for obtaining such extremely ambitious kind of automata. This report summarises the research works published by elements of the SOCIA Lab. in the last decade in the scope of biometric recognition in unconstrained conditions. The idea is that it can be used as basis for someone wishing to entering in this research topic.
Impact and Outcomes of Pretreatment Total Serum Testosterone on Localized Prostate Cancer Patients

Purpose To investigate how pretreatment testosterone levels correlate with progression-free survival, metastasis-free survival, and overall survival in a propensity-adjusted localized prostate cancer population. Methods Men diagnosed with clinical NCCN-risk stratified very-low, low, intermediate, high, and/or very-high risk prostate cancer who had a baseline total serum testosterone level≥100 ng/dl measured within the 100 days preceding the first definitive therapy were identified from our prospectively gathered institutional database. Cohorts below (100–239 ng/dl), within (240–593 ng/dl), or above (594 + ng/dl) one standard deviation from the mean testosterone level (416 ng/dl) were used for comparison. Progression-free, metastasis-free, and overall survival were evaluated. A separate cohort of men not receiving ADT was used to evaluate testosterone recovery after various treatment modalities (surgery, external beam radiation, brachytherapy, or combined EBRT + Brachy). Results There was no statistically significant difference between the low, average, and high testosterone cohorts for PFS, MFS, or OS. In men not using ADT, there were no statistically significant changes in testosterone levels 1 year after therapy, regardless of therapy type. Conclusion In men with serum testosterone levels >=100 ng/dl at diagnosis, baseline testosterone does not impact PFS, MFS, or OS. Recovery of testosterone back to baseline is expected for men undergoing either surgery, external beam or brachytherapy, or combined modality radiation when not using ADT.
Parametric analysis of flux creep-flow model by using genetic algorithm

The pinning parameters for numerical calculation based on the ﬂux creep-ﬂow model are determined by using genetic algorithm (GA), which has been applied to many practical determination for parameters. Several estimation functions which describe the distance between the experimental and calculated results by GA were proposed, and the di ﬀ erence between the results were calculated. It is found that the pinning parameters of the ﬂux creep-ﬂow model are successfully deduced by GA. The di ﬀ erence between the calculated and experimental results and the calculation time are found to be largely depended on the estimation functions.
Progress in diagnosis and treatment of cesarean scar pregnancy

Cesarean scar pregnancy (CSP) is a special ectopic pregnancy, and the incidence of the disease increases with the increase in cesarean section rate in recent years. Because CSP usually has massive bleeding and other risks, early diagnosis and early treatment is particularly important. Based on the status quo of diagnosis and treatment of CSP, this review summarizes the progress of current commonly used diagnostic technologies, treatment and their corresponding advantages and disadvantages. UAE is a classical treatment at present, but its main risk destroys ovarian function, causes intrauterine adhesions, which may affect re-fertility, so a more optimized treatment plan is worth exploring. The new treatments mentioned in this review (high-intensity focused ultrasound, lauromacrogol) have certain feasibility, which can be further extended and verified in future research. Keyword: Cesarean scar pregnancy diagnosis treatment Cesarean scar pregnancy (CSP) is a rare ectopic pregnancy in which the embryo implantation is located in the scar of the uterine incision of the original cesarean section, where it grows and develops [1]. Since Larsen first reported CSP in 1978, the disease has been gradually recognized [2]. Previous scholars reported that the incidence of this disease was 1/18001/2216, accounting for 6.1% of all ectopic pregnancies and 0.15% of pregnant women with cesarean section history [1, 3]. In China, the cesarean section rate is the highest in the world [4], in 2011 alone, the cesarean section rate reached 54.9% [5]. In recent years, with the opening of the two-child policy, the rise of cesarean section rate and the further improvement of diagnostic technology, the incidence of CSP shows a significant increasing trend. If CSP patients cannot be effectively and timely treated, they may suffer from massive bleeding, placenta previa, placenta implantation, uterine rupture, even hysterectomy and lifethreatening risks [6, 7]. Therefore, it is recommended to terminate the pregnancy immediately once the diagnosis is made. So far, there are no unified standards and guidelines for the diagnosis and treatment of CSP [8]. This article reviews and discusses the main diagnostic and therapeutic methods and their recent progress.
Strength Assessment of Imperfect Stiffened Panels Using Modified Stress Strain Curves

The objective of the present thesis is to analyze the ultimate strength of steel plates and stiffened panels accounting for residual stresses, welding sequences and corrosion degradation and to develop a fast finite element approach based on modified stress strain curves. The analysis is performed by finite element method employing commercial software. The heat source of welding is defined both as pre-stresses and a moving heat one, in order to simulate welding processes and consequently residual stresses. The modified stress strain curves are developed for three finite element models of structural components and accounting for different levels of residual stresses and boundary conditions. The modified material stress strain curves also account for the effect of the welding sequence, shakedown and corrosion degradation. The modified stress strain curves can be directly used for nonlinear finite element analyses of ultimate strength of steel plates and stiffened panels. This work also analyses the effect of shakedown on ultimate strength. A mathematical model is developed to account for a time dependent residual stress reduction which is used to evaluate ultimate strength.
Diatom frustules enhancing the efficiency of gel polymer electrolyte based dye-sensitized solar cells with multilayer photoelectrodes

The incorporation of nanostructures that improve light scattering and dye adsorption has been suggested for dye-sensitized solar cells (DSSCs), but the manufacture of photonic and nanostructured materials with the desired properties is not an easy task. In nature, however, the process of light-harvesting for photosynthesis has, in some cases, evolved structures with remarkable wavelength-sensitive light-trapping properties. The present work is focused on enhancing the efficiency of quasi solid-state DSSCs by capitalizing on the light trapping properties of diatom frustules since they provide complex 3-dimensional structures for scattering and trapping light. This study reports a promising approach to prepare TiO2 nanocrystal (14 nm) based photo-electrodes by utilizing the waveguiding and photon localization effects of nanostructured diatom frustules for enhancing light harvesting without deteriorating the electron conduction. Single and double-layered photo-electrodes were prepared with different frustule/nanocrystal combinations and conformations on transparent conductive oxide substrates. This study clearly reports impressive efficiency and short circuit current density enhancements of about 35% and 39%, respectively, due to the incorporation of diatom frustules extracted from a ubiquitous species. The SEM images obtained in this work reveal that the produced thin films had a remarkable surface coverage of evenly distributed frustules within the TiO2 nanoparticle layer. To the best of our knowledge, this study reports the first quasi solid-state DSSC based on a photo-electrode with incorporated bio-formed nanostructures.
Effective bounds on multiplicatively dependent orbits of integer polynomials modulo S-integers

We obtain effective bounds on the heights of algebraic integers whose orbits contain multiplicatively dependent values modulo S-integers. Our method is based on a new upper bound on the so-called S-height of polynomial values over the ring of integers of $\mathbb{K}$. Our results provide an effective variant of a recent result of A.B\'erczes, A.Ostafe, I.E.Shparlinski and J.H. Silverman (arXiv:1811.04971) on multiplicative dependence modulo a finitely generated subgroup by eliminating the use of non-effective results by K.F. Roth and G.Faltings.
Verifying Software Vulnerabilities in IoT Cryptographic Protocols

Internet of Things (IoT) is a system that consists of a large number of smart devices connected through a network. The number of these devices is increasing rapidly, which creates a massive and complex network with a vast amount of data communicated over that network. One way to protect this data in transit, i.e., to achieve data confidentiality, is to use lightweight encryption algorithms for IoT protocols. However, the design and implementation of such protocols is an error-prone task; flaws in the implementation can lead to devastating security vulnerabilities. These vulnerabilities can be exploited by an attacker and affect users' privacy. There exist various techniques to verify software and detect vulnerabilities. Bounded Model Checking (BMC) and Fuzzing are useful techniques to check the correctness of a software system concerning its specifications. Here we describe a framework called Encryption-BMC and Fuzzing (EBF) using combined BMC and fuzzing techniques. We evaluate the application of EBF verification framework on a case study, i.e., the S-MQTT protocol, to check security vulnerabilities in cryptographic protocols for IoT.
General Optimal Stopping with Linear Costs

This article treats both discrete time and continuous time stopping problems for general Markov processes on the real line with general linear costs. Using an auxiliary function of maximum representation type, conditions are given to guarantee the optimal stopping time to be of threshold type. The optimal threshold is then characterized as the root of that function. For random walks our results condense in the fact that all combinations of concave increasing pay-off functions and convex cost functions lead to a one-sided solution. For Levy processes an explicit way to obtain the auxiliary function and the threshold is given by use of the ladder height processes. Lastly, the connection from discrete and continuous problem and possible approximation of the latter one via the former one is discussed.
Integrated microtransit services with chance-constrained dynamic pricing and demand learning

The design of integrated mobility-on-demand services requires jointly considering the interactions between traveler choice behavior and operators' operation policies to design a financially sustainable pricing scheme. However, most existing studies focus on the supply side perspective, disregarding the impact of customer choice behavior in the presence of co-existing transport networks. We propose a modeling framework for dynamic integrated mobility-on-demand service operation policy evaluation with two service options: door-to-door rideshare and rideshare with transit transfer. A new constrained dynamic pricing model is proposed to maximize operator profit, taking into account the correlated structure of different modes of transport. User willingness to pay is considered as a stochastic constraint, resulting in a more realistic ticket price setting while maximizing operator profit. Unlike most studies, which assume that travel demand is known, we propose a demand learning process to calibrate customer demand over time based on customers' historical purchase data. We evaluate the proposed methodology through simulations under different scenarios on a test network by considering the interactions of supply and demand in a multimodal market. Different scenarios in terms of customer arrival intensity, vehicle capacity, and the variance of user willingness to pay are tested. Results suggest that the proposed chance-constrained assortment price optimization model allows increasing operator profit while keeping the proposed ticket prices acceptable.