Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pure Bureaucratic-Entrepreneurial Theory of Deposit Insurance Adoption

www.RofEA. org A Pure Bureaucratic-Entrepreneurial Theory of Deposit Insurance Adoption Empty 15 KAM HON CHU Memorial University of Newfoundland ∗ Empty 15 Empty 15 Based on Becker, Kane, Niskanen, and Peltzman’s ideas, we develop a model to explain why deposit insurance is adopted even though policymakers are aware of its pitfalls in both theory and practice. In our model, the regulator acts as both a bureaucrat and an entrepreneur to maximize his self-interest through administering a deposit insurance scheme. The theory postulates that adoption of deposit insurance is more likely under the following conditions: the scheme is (i) publicly administered and (ii) privately funded, with (iii) non-risk rated insurance premium and (iv) compulsory membership; and there is (v) a larger deposit market with (vi) at least two groups of banks (good vs. bad), (vii) lower government ownership of banks, and (viii) higher economic freedom, such that one group exerts its political influence and gains from deposit insurance. Empirically our theory is supported by the stylized facts, cross-country binary-choice regression results and a case study of Canada. 4 Empty 10
A First-Order Theory of Film Scores for Generation from Lightweight Specifications

This paper proposes a formal theory of the way ﬁlm scores operate for the purpose of enabling semi-automatic generation. Among the contributions are a formalization of the entire generation process as a bit-vector-array satisﬁability problem, an approach to music generation not taken in many previous papers. The paper also formalizes the idea of “thematic” and “stylistic” time-dependent variables and their inherited constraints in speciﬁcation-driven generation. In order to make the result more coherent, the paper formalizes a regular-expression-like grammar of melodic contour. Synthesizing all of these contributions, the result is a program which can take a lightweight speciﬁcation of the relevant information in each scene of a ﬁlm, and produce a coherent and appropriate score to accompany it.
Topological Experience Replay for Fast Q -Learning

State-of-the-art deep Q -learning methods update Q -values using state transition tuples sampled from the experience replay buffer. Often this strategy is to randomly sample or prioritize data sampling based on measures such as the temporal difference (TD) error. Such sampling strategies are agnostic to the structure of the Markov decision process (MDP) and can therefore be data inefﬁcient at propagating reward signals from goal states to the initial state. To accelerate reward propagation, we make use of the MDP structure by organizing the agent’s experience into a graph. Each edge in the graph represents a transition between two connected states. We perform value backups via a breadth-ﬁrst search that expands vertices in the graph starting from the set of terminal states successively moving backward. We empirically show that our method is substantially more data-efﬁcient than several baselines on sparse reward tasks.
FE model updating based on hybrid genetic algorithm and its verification on numerical bridge model

FE model-based dynamic analysis has been widely used to predict the dynamic characteristics of civil structures. In a physical point of view, an FE model is unavoidably different from the actual structure as being formulated based on extremely idealized engineering drawings and design data. The conventional model updating methods such as direct method and sensitivity-based parameter estimation are not flexible for model updating of complex and large structures. Thus, it is needed to develop a model updating method applicable to complex structures without restriction. The main objective of this paper is to present the model updating method based on the hybrid genetic algorithm (HGA) by combining the genetic algorithm as global optimization method and modified Nelder-Mead`s Simplex method as local optimization method. This FE model updating method using HGA does not need the derivation of derivative function related to parameters and without application of complicated inverse analysis methods. In order to allow its application on diversified and complex structures, a commercial FEA tool is adopted to exploit previously developed element library and analysis algorithms. Moreover, an output-level objective function making use of measurement and analytical results is also presented to update simultaneously the stiffness and mass of the analysis model. The numerical examples demonstrated that the proposed method based on HGA is effective for the updating of the FE model of bridge structures.
Continental-scale simulation of burn probabilities, flame lengths, and fire size distribution for the United States

Components of a quantitative risk assessment were produced by simulation of burn probabilities and fire behavior variation for 134 fire planning units (FPUs) across the continental U.S. The system uses fire growth simulation of ignitions modeled from relationships between large fire occurrence and the fire danger index Energy Release Component (ERC). Simulations of 10,000-50,000 years were performed using artificial weather sequences generated by time-series analysis of recorded ERC values at local weather stations. Also needed were monthly distributions of wind speed and direction, as well as spatial data on fuel and topography. Fire suppression was represented by a model of the probability of fire containment by suppression forces, based on fire growth rates, days since ignition, and fuel type. Simulated values of burn probability generally fell within a factor of three of observed values. At the pixel level, burn probabilities vary markedly based on differences in fuels, weather, and topography. The slope of the frequency-magnitude distribution of simulated fire sizes was compared to historic records in each region, demonstrating that the model produced fire size distributions similar to historic patterns. Because model parameters included only a limited suite of weather, fuel, and suppression variables, this result is interpreted to mean that observed fire size distributions are a function of the joint distributions of spatial opportunities for fires to grow to different sizes (dependent on fuels and ignition location) and temporal variability in the length of weather sequences conducive to fire growth. A contribution of this research is the practical aspect of performing fire simulations at national scales for operational planning and ecological research.
A Two-Step Network Intrusion Detection System for Multi-Class Classification (Discussion Paper)

A network intrusion detection system aims to discover any unauthorised access to computer networks by analysing the network traffic for signs of malicious activity. In this paper, we present a two-step system for network intrusion detection. The first step comprises a Triplet network that processes the flow-based characteristics of the historical network traffic data to learn an embedding space, where distances between samples labelled with opposite classes are greater than distances between samples labelledwith thesameclass. We takeadavantageofthis embeddingspacetoseparate thenormalsamples from the malicious ones. The second step uses a multi-class eXtreme Gradient Boosting classifier to recognize the attack family of the detected malicious flows. The experiments prove the effectiveness of the proposed system as it leads to higher accuracy when compared to several, recent competitors.
CADET: Debugging and Fixing Misconfigurations using Counterfactual Reasoning

Modern computing platforms are highly-configurable with thousands of interacting configuration options. However, configuring these systems is challenging and misconfigurations can cause unexpected non-functional faults. This paper proposes CADET (short for Causal Debugging Toolkit) that enables users to identify, explain , and fix the root cause of non-functional faults early and in a principled fashion. CADET builds a causal model by observing the performance of the system under different configurations. Then, it uses casual path extraction followed by counterfactual reasoning over the causal model to (a) identify the root causes of non-functional faults, (b) estimate the effects of various configuration options on the performance objective(s), and (c) prescribe candidate repairs to the relevant configuration options to fix the non-functional fault. We evaluated CADET on 5 highly-configurable systems by comparing with state-of-the-art configuration optimization and ML-based debugging approaches. The experimental results indicate that CADET can find effective repairs for faults in multiple non-functional properties with (at most) 13% more accuracy, 32% higher gain, and 13 × speed-up than other ML-based performance debugging methods. Compared to multi-objective optimization approaches, CADET can find fixes (at most) 8 × faster with comparable or better performance gain. Our study of non-functional faults re-ported in NVIDIA’s forum shows that CADET can find 14% better repairs than the experts’ advice in less than 30 minutes.
Estimation of Annual Average Daily Traffic for Nonstate Roads in a Florida County

A study was undertaken to develop a methodology to estimate annual average daily traffic (AADT) for nonstate roads in urbanized areas in Florida. The current practice related to the estimation of AADT for nonstate roads has been of great concern to the Florida Department of Transportation because of the potential lack of accuracy in the estimated data. For this study, a multiple regression model was developed for estimating AADT on nonstate roads. The model utilized a large sample size (data from 450 count stations in Broward County) and involved the investigation of up to 12 initial variables. Various methods, including geographic information systems (GIS), were explored to convert the current digital data and aggregate them into suitable forms for statistical analysis. Statistical tests were performed and the results showed that the most important contributing predictors are roadway characteristics, such as the number of lanes, functional classification, and area type. Socioeconomic variables, including population, dwelling units, automobile ownership, employment numbers, and school enrollment in the surrounding area, have an insignificant impact on AADT. Further analyses revealed the deficiency of traditional roadway functional classification, the need to improve the method of road classification, and the need for alternative methods to account for the impact of economic activities on AADT.
FLOWER: A comprehensive dataflow compiler for high-level synthesis

FPGAs have found their way into data centers as accelerator cards, making reconfigurable computing more accessible for high-performance applications. At the same time, new high-level synthesis compilers like Xilinx Vitis and runtime libraries such as XRT attract software programmers into the reconfigurable domain. While software programmers are familiar with task-level and data-parallel programming, FPGAs often require different types of parallelism. For example, data-driven parallelism is mandatory to obtain satisfactory hardware designs for pipelined dataflow architectures. However, software programmers are often not acquainted with dataflow architectures— resulting in poor hardware designs. In this work we present FLOWER, a comprehensive compiler infrastructure that provides automatic canonical transformations for high-level synthesis from a domain-specific library. This allows programmers to focus on algorithm implementations rather than low-level optimizations for dataflow architectures. We show that FLOWER allows to synthesize efficient implementations for high-performance streaming applications targeting System-on-Chip and FPGA accelerator cards, in the context of image processing and computer vision.
Naturalistic Physical Adversarial Patch for Object Detectors

Most prior works on physical adversarial attacks mainly focus on the attack performance but seldom enforce any restrictions over the appearance of the generated adversarial patches. This leads to conspicuous and attention-grabbing patterns for the generated patches which can be easily identified by humans. To address this issue, we pro-pose a method to craft physical adversarial patches for object detectors by leveraging the learned image manifold of a pretrained generative adversarial network (GAN) (e.g., BigGAN and StyleGAN) upon real-world images. Through sampling the optimal image from the GAN, our method can generate natural looking adversarial patches while maintaining high attack performance. With extensive experiments on both digital and physical domains and several independent subjective surveys, the results show that our proposed method produces significantly more realistic and natural looking patches than several state-of-the-art base-lines while achieving competitive attack performance. 1
Learning with Noisy Labels via Sparse Regularization: Supplementary Materials

since 1− ηk k−1 > 0 and ∑k i=1 L(v, i) is a constant, then f ∗ minimizes R(f) if and only if f∗ minimizes R(f). Theorem 2. In a multi-class classification problem, we let f : X → Pv, where v is a fixed vector. If RL(f) = 0, ∀f : X → Pv, ∀L ∈ L and 0 ≤ L ≤ C k−1 , L is noise-tolerant under asymmetric or class-conditional noise when ηy,i < 1− ηy with ∑ k ̸=y ηy,i = ηy , ∀x. Proof. For asymmetric or class-conditional noise, we have R L(f) = Ex,y(1− ηy)L(f(x), y) + Ex,y ∑
Large signal dielectric losses in electrostrictive materials

The dielectric loss factor tan (delta) is a critical parameter in transducer design and performance prediction, as it is directly related to the electrical energy lost to Joule heating. A method for calculating the equivalent loss factor of 'quasi linear' materials from a measured major polarization vs. field loop by extending the standard definition of tan (delta) for a linear lossy capacitor to nonlinear materials is presented. To extract effective loss tangents for minor loops from the major loops, an area correction algorithm was implemented. This algorithm proves to be nearly exact for all bias and drive levels in the case of an ideal linear capacitor. In most cases, the effective tan (delta) calculated from a major loop agrees fairly well with that calculated from a directly measured minor loop. Finally, the behavior of the loss tangent as a function of the dc bias field, ac drive field, prestress level and temperature will be examined. It shall be shown that, in general, the effective loss factor of lead magnesium niobate-lead titanate decreases with increasing temperature, consistent with its transition from a piezoelectric to an electrostrictive material, but increases with prestress. At a fixed temperature and prestress level, the loss factor increases as the bias or drive levels decrease. However, at certain bias levels, the loss tangent is practically the same, regardless of the drive level.
Green Road Intersections by Efficient Traffic Light Scheduling Algorithm

Reducing the fuel consumption and gas emissions of traveling vehicles over the road networks have been intensively investigated over the road networks. Vehicles produce a high percentage of pollution to the atmosphere that encourages governments and organizations to invest and work toward reducing their emissions. The behavior of the same vehicle varies over downtown and highway scenarios in terms of efficiency and pollution parameters. This is due to the effects of the architectural design of the traversed road and the driving behavior. In this work, we propose an efficient traffic light scheduling algorithm. It considers the real-time traffic characteristics of the competing traffic flows at a signalized road intersection. This is to produce an efficient schedule that minimizes the total required fuel of vehicles and produces less emissions while passing safely through that intersection. An extensive set of experiments have been used to verify the performance of the proposed scheduling algorithm compared to other efficient traffic light scheduling algorithms.
Non-Linear Arrhenius Behavior of Self-Diffusion in β-Ti and Mo

While anomalous diffusion coefficients with non-Arrhenius like temperature dependence are observed in a number of metals, a conclusive comprehensive framework of explanation has not been brought forward to date. Here, we use first-principles calculations based on density functional theory to calculate self-diffusion coefficients in the bcc metals Mo and β-Ti by coupling quasiharmonic transition state theory and large displacement phonon calculations and show that anharmonicity from thermal expansion is the major reason for the anomalous temperature dependence. We use a modified Debye approach to quantify the thermal expansion over the entire temperature range and introduce a method to relax the vacancy structure in a mechanically unstable crystal such as β-Ti. Thermal expansion is found to weakly affect the activation enthalpy but has a strong effect on the prefactor of the diffusion coefficient, reproducing the non-linear, non-Arrhenius “anomalous” self-diffusion in both bcc systems with good agreement between calculation and experiment. The proposed methodology is general and simple enough to be applicable to other mechanically unstable crystals.
The relationships among relatedness frustration, affiliation motivation, and WeChat engagement, moderated by relatedness satisfaction

The current study was designed based on the two-process model of the relatedness need to investigate the cyber-psychological mechanisms in the relationship between relatedness frustration and social networking sites (SNSs) behaviors. Chinese college students (N = 494; 190 males; Mage = 18.81 ± .92) were recruited to complete online questionnaires to measure relatedness frustration, relatedness satisfaction, affiliation motivation, and WeChat engagement. Path analyses indicated that relatedness frustration was directly related to defensive WeChat engagement and indirectly related to WeChat involvement and active engagement via affiliation motivation. Affiliation motivation played a significant mediating role, with the relationship between affiliation motivation and WeChat involvement being moderated by relatedness satisfaction. Specifically, this relationship existed only when the level of relatedness satisfaction was high. This study helps to understand motivational coping mechanisms among people with different levels of relatedness satisfaction in relation to SNSs after experiencing relatedness frustration. Potential limitations and future directions of this paper to the cyber-psychology literature are discussed.
END-TO-END INDUSTRIAL PRINT EQUIPMENT RECOMMENDATION AS A SERVICE

Rather than just evaluating price/performance of the discrete pieces of equipment, industrial print service providers (PSPs) are working (without automated tools) to put together well matched, flexible solutions since any mismatch in capabilities or capacities will greatly reduce ROI. This design challenge is made more difficult by the variety of equipment suppliers offering multiple devices with similar functionality yet no standard vocabulary for comparison. An automated way to reason out the best combination of equipment is needed. It needs to provide a good comparative study and also address capability and capacity matching questions so that solution architects can recommend the right solutions to the PSPs. We profile here our approach and a prototype tool named Production Designer, which is based on an open source electronic design automation toolkit Ptolemy II that selects the right candidate configurations based on static and dynamic behavior of the system and the desired business objective. 1 PROBLEM STATEMENT
Influences of Built Environment on Walking and Cycling by Latent Segments of Aging Population

Walking and bicycling are relatively easy ways for the growing group of older adults to be physically active. The built environment may facilitate or restrict participation in walking and bicycling trips. A study was done to describe aspects of participation in walking and bicycling activities and to explore their role for various segments of the aging population. Specifically, the effect of environmental characteristics, various types of land use, and degree of urbanization on participation in walking and bicycling, for both leisure and transportation, was examined. Detailed individual travel data were correlated with objectively defined environmental attributes. The results show that people make more walking trips in more highly urbanized areas, whereas they use their bikes more often in less-urbanized areas. A significant effect was found for the impact of total surface of recreation areas on both types of physical activity. Specifically, highly urbanized neighborhoods with a low percentage of recreation and green areas are related to physical inactivity. The study suggests that compact, high-density neighborhoods may not be a good urban design concept if health is considered.
Research and Analysis of Brain Glioma Imaging Based on Deep Learning

The incidence of glioma is increasing year by year, seriously endangering people's health. Magnetic resonance imaging (MRI) can effectively provide intracranial images of brain tumors and provide strong support for the diagnosis and treatment of the disease. Accurate segmentation of brain glioma has positive significance in medicine. However, due to the strong variability of the size, shape, and location of glioma and the large differences between different cases, the recognition and segmentation of glioma images are very difficult. Traditional methods are time-consuming, labor-intensive, and inefficient, and single-modal MRI images cannot provide comprehensive information about gliomas. Therefore, it is necessary to synthesize multimodal MRI images to identify and segment glioma MRI images. This work is based on multimodal MRI images and based on deep learning technology to achieve automatic and efficient segmentation of gliomas. The main tasks are as follows. A deep learning model based on dense blocks of holes, 3D U-Net, is proposed. It can automatically segment multimodal MRI glioma images. U-Net network is often used in image segmentation and has good performance. However, due to the strong specificity of glioma, the U-Net model cannot effectively obtain more details. Therefore, the 3D U-Net model proposed in this paper can integrate hollow convolution and densely connected blocks. In addition, this paper also combines classification loss and cross-entropy loss as the loss function of the network to improve the problem of category imbalance in glioma image segmentation tasks. The algorithm proposed in this paper has been used to perform a lot of experiments on the BraTS2018 dataset, and the results prove that this model has good segmentation performance.
Low-frequency noise behavior in GaN HEMTs on silicon substrate

In this paper, we report low frequency noise (LFN) data obtained on passivated AlGaN/GaN HEMT’s grown by MBE on a silicon substrate. In order to localize the LFN sources, we have measured all the extrinsic gate and drain current noise generators and their coherence versus bias in the linear regime. We have found that the gate noise sources result from leakage phenomena at gate-source and gate-drain regions. Drain noise sources are mostly located in the active channel below the gate and they feature an equivalent Hooge coefficient of about 10-3. Secondly, in order to build a LFN model that fits the requirements of a CAD simulator, we have measured the LFN sources for numerous bias points in the saturation region and therefore we have studied the bias dependence of the different noise sources under normal operating conditions. Results show that the gate terminal noise current impacts heavily the overall LFN of the transistor contrary to others III-V HEMTs, and that specific bias conditions are needed in order to reduce the LFN.