Miracle-dz/UniFAR-FTUs huggingface dataset:
Capacity and Peak-Load Pricing

Several European telecommunications regulatory agencies have recently introduced a fixed capacity charge (flat rate) to regulate access to the incumbent’s network. The purpose of this paper is to show that the optimal capacity charge and the optimal access-minute charge analysed by Armstrong, Doyle, and Vickers (1996) have a similar structure and imply the same payment for the entrant. I extend the analysis to the case where there is a competitor with market power. In this case, the optimal capacity charge should be modified to avoid that the entrant cream-skims the market, fixing a longer or a shorter peak period than the optimal. Finally, I consider a multiproduct setting, where the effect of the product differentiation is exacerbated. JEL Classification Numbers: L13, L51, H54, D43
Rationalization of trusses generated via layout optimization

Numerical layout optimization provides a computationally efficient and generally applicable means of iden tifying the optimal arrangement of bars in a truss. When the plastic layout optimization formulation is used, a wide variety of problem types can be solved using linear programming. However, the solutions obtained are frequently quite complex, particularly when fine numerical discretiza tions are employed. To address this, the efficacy of two rationalization techniques are explored in this paper: (i) in troduction of ‘joint lengths’, and (ii) application of geom etry optimization. In the former case this involves the use of a modified layout optimization formulation, which remains linear, whilst in the latter case a non-linear optimiz ation post-processing step, involving adjusting the locati ons of nodes in the layout optimized solution, is undertaken. Th e two rationalization techniques are applied to example prob lems involving both point and distributed loads, self-weig ht and multiple load cases. It is demonstrated that the introdu ction of joint lengths reduces structural complexity at negl igible computational cost, though generally leads to increase d volumes. Conversely, the use of geometry optimization carries a computational cost but is effective in reducing both structural complexity and the computed volume.
P o S ( I F S 2 0 1 7 ) 0 0 2 Pulsars : implications from Fermi LAT observations and future prospects

Gamma-ray observations with the Fermi Large Area Telescope (LAT) have revealed significant pulsations from more than 200 young and recycled, millisecond pulsars. These observations have demonstrated that pulsars are by far the largest source class in the Galactic plane at GeV energies, and more gamma-ray pulsars are still being revealed by the LAT. In these proceedings I give an overview of the main results from Fermi LAT pulsar observations. I also discuss some of the implications of these results in terms of population statistics and high-energy pulsar emission properties.
Title : Dynamic Power Management using Reinforcement Learning

Energy consumption of electronic devices has become a serious concern in recent years. Energy efficiency is necessary to lengthen the battery lifetime in portable systems, as well as to reduce the operational costs (e.g. cost of electricity) and the environmental impact (e.g. cooling fan noise) of stationary systems. Optimization in design and utilization of both hardware and software is needed in order to achieve more energy efficient systems. Thesis outcomes show that sleeping is indeed feasible in the LAN and in some cases, with very little impact on other protocols. This thesis conducted in-depth research on different types of power management systems and eventually chose the best fit policy. Dynamic Power Management (DPM) system with reinforcement Q-learning methodology is used here to implement efficiency in LAN card. DPM is a design methodology aiming at reducing power consumption of electronic systems by performing selective shutdown of idle system resources. Moreover, reinforcement learning is a machine intelligence approach that has been applied in many different areas. Q-learning is one of the most popular algorithms that perform reinforcement learning. At last, with the desired outcomes of this thesis work, power management issues of LAN card system were solved effectively.
Why People Leave Their Rural Hometown : Evidence from 8 Provinces in China July 22 , 2018

This paper aim to clarify what motivate people to migrate from rural to urban area in China. The focus of most previous studies of migration are restricted to the wage gap between the origin and destination. However, this study uses the RUMiC (2008) data set that has individual characteristics of migrants and stayers, combined with China Statistical Yearbook data, to explore the decision making process on China’s rural to urban migration. This research provides empirical evidence that migration is a joint decision-making process characterized by the choices of migration and destination. The results also show that the living condition in hometowns pushes people to migrate. For example, the probability of moving decreases by 25% if the consumption of the rural area increases by 20% ( 10,000 RMB). † Corresponding author. Osaka School of International Public Policy, Osaka University, Address: 1-31 Machikaneyama, Toyonaka, Osaka 560-0043, JAPAN. Email: zhuhesi@gmail.com
Practical Posterior Error Bounds from Variational Objectives

Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to the widespread use of variational methods is the lack of post-hoc accuracy measures that are both theoretically justified and computationally efficient. In this paper, we provide rigorous bounds on the error of posterior mean and uncertainty estimates that arise from full-distribution approximations, as in variational inference. Our bounds are widely applicable as they require only that the approximating and exact posteriors have polynomial moments. Our bounds are computationally efficient for variational inference in that they require only standard values from variational objectives, straightforward analytic calculations, and simple Monte Carlo estimates. We show that our analysis naturally leads to a new and improved workflow for variational inference. Finally, we demonstrate the utility of our proposed workflow and error bounds on a real-data example with a widely used multilevel hierarchical model.
Percentile-Based Residuals for Model Assessment

Residuals are a key component of diagnosing model fit. The usual practice is to compute standardized residuals using expected values and standard deviations of the observed data, then use these values to detect outliers and assess model fit. Approximate normality of these residuals is key for this process to have good properties, but in many modeling contexts, especially for complex, multi-level models, normality may not hold. In these cases outlier detection and model diagnostics aren't properly calibrated. Alternatively, as we demonstrate, residuals computed from the percentile location of a datum's value in its full predictive distribution lead to well calibrated evaluations of model fit. We generalize an approach described by Dunn and Smyth (1996) and evaluate properties mathematically, via case-studies and by simulation. In addition, we show that the standard residuals can be calibrated to mimic the percentile approach, but that this extra step is avoided by directly using percentile-based residuals. For both the percentile-based residuals and the calibrated standard residuals, the use of full predictive distributions with the appropriate location, spread and shape is necessary for valid assessments.
An Extended Survey on Vehicle Security

The advanced electronic units with wireless capabilities inside modern vehicles have, enhanced the driving experience, but also introduced a myriad of security problems due to the inherent limitations of the internal communication protocol. In the last two decades, a number of security threats have been identified and accordingly, security measures have been proposed. In this paper, we provide a comprehensive review of security threats and countermeasures for the ubiquitous CAN bus communication protocol. Our review of the existing literature leads us to a observation of an overlooked simple, cost-effective, and incrementally deployable solution. Essentially, a reverse firewall, referred to in this paper as an icewall, can be an effective defense against a major class of packet-injection attacks and many denial of service attacks. We cover the fundamentals of the icewall in this paper. Further, by introducing the notion of human-in-the-loop, we discuss the subtle implications to its security when a human driver is accounted for.
Machine learning quantum field theory with local probes

We propose the use of machine learning techniques to address the problem of local measurements in quantum field theory. In particular we discuss how neural networks can efficiently process measurement outcomes from local probes to determine both local and non-local features of the quantum field. As toy examples we show: a) how a particle detector distinguishes boundary conditions imposed on the field without the need of signals propagating from them, and b) how detectors can determine the temperature of the quantum field without thermalizing with it. We discuss how the formalism proposed can be used for any kind of local measurement on a quantum field and, by extension, to local measurements of non-local features in many-body quantum systems.
Learning and Individual Differences

Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections.
Criticality in elastoplastic models of amorphous solids with stress-dependent yielding rates.

We analyze the behavior of different elastoplastic models approaching the yielding transition. We propose two kinds of rules for the local yielding events: yielding occurs above the local threshold either at a constant rate or with a rate that increases as the square root of the stress excess. We establish a family of "static" universal critical exponents which do not depend on this dynamic detail of the model rules: in particular, the exponents for the avalanche size distribution P(S) ∼ S-τSf(S/Ldf) and the exponents describing the density of sites at the verge of yielding, which we find to be of the form P(x) ≃ P(0) + xθ with P(0) ∼ L-a controlling the extremal statistics. On the other hand, we discuss "dynamical" exponents that are sensitive to the local yielding rule. We find that, apart form the dynamical exponent z controlling the duration of avalanches, also the flowcurve's (inverse) Herschel-Bulkley exponent β ([small gamma, Greek, dot above] ∼ (σ - σc)β) enters in this category, and is seen to differ in ½ between the two yielding rate cases. We give analytical support to this numerical observation by calculating the exponent variation in the Hébraud-Lequeux model and finding an identical shift. We further discuss an alternative mean-field approximation to yielding only based on the so-called Hurst exponent of the accumulated mechanical noise signal, which gives good predictions for the exponents extracted from simulations of fully spatial models.
BoolSi: a tool for distributed simulations and analysis of Boolean networks

We present BoolSi, an open-source cross-platform command line tool for distributed simulations of deterministic Boolean networks with synchronous update. It uses MPI standard to support execution on computational clusters, as well as parallel processing on a single computer. BoolSi can be used to model the behavior of complex dynamic networks, such as gene regulatory networks. In particular, it allows for identification and statistical analysis of network attractors. We perform a case study of the activity of a cambium cell to demonstrate the capabilities of the tool.
Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise

In this paper, we develop convergence analysis of a modified line search method for objective functions whose value is computed with noise and whose gradient estimates are inexact and possibly random. The noise is assumed to be bounded in absolute value without any additional assumptions. We extend the framework based on stochastic methods from [Cartis and Scheinberg, 2018] which was developed to provide analysis of a standard line search method with exact function values and random gradients to the case of noisy function. We introduce a condition on the gradient which when satisfied with some sufficiently large probability at each iteration, guarantees convergence properties of the line search method. We derive expected complexity bounds for convex, strongly convex and nonconvex functions.
Algebras of coloured Petri nets and their applications to modelling and verification

This document surveys my research work about a framework of composable coloured Petri nets. This framework is organised around a core model of coloured Petri nets that can be extended with various features: control flow, synchronous communication, exceptions , threads, functions and time. Extensions are made in such a way that the underlying model of coloured Petri net is preserved and the usual analysis tools and techniques can still be used. However, specific verifications issues arise from the proposed approaches; we discuss these issues and propose solutions to enable for efficient verification. An implementation of the framework is also presented, as well as applications to the modelling and verification of security protocols, and to the modelling and analysis of biological regulatory networks in the context of developmental processes.
“Lose 30?lbs in 30 days”: Assigning responsibility for deceptive advertising of weight-loss products

Purpose – The aim of this paper is to outline key social marketing issues apparent in deceptive weight-loss advertising, from the perspective of government policy-makers, manufacturers, the media, and consumers. The purpose is to examine the complexity of one aspect of the obesity battle and provide a framework for coordinated and integrated social marketing initiatives from a multiple stakeholder perspective. Design/methodology/approach – The results of deceptive weight-loss advertising are framed using the harm chain model, and the paper offers recommended solutions based on a framework of marketing, education and policy changes across the network of stakeholders. Findings – This paper concludes that a resolution to the harm created by deceptive weight-loss advertising can be achieved by the creation of a more holistic, system-wide solution to this important health and policy issue. This networked approach must involve all aspects of harm in a multi-stakeholder solution, including both upstream and downstream integration. Specific recommendations are made for policy-makers, manufacturers, the media, and consumers to achieve this goal. Social implications – From a marketing perspective, analyzing the issue of deceptive weight-loss advertising using the harm chain allows for the creation of a more holistic, system-wide solution involving stakeholders in all aspects of harm for this important health and policy issue. Originality/value – This research examines the problem of obesity and weight-loss advertising from the unique perspective of the harm chain framework. The authors make unified recommendations for various stakeholders including industry, media, government and consumers, in order to direct integrated social marketing and consumer-oriented strategies within this industry. Keyword(s): Deceptive advertising; Social marketing; Harm chain; Public policy; Advertising.
Detecting HandBall Events in Video

We analyze videos in which a hand interacts with a basketball. In this work, we present a computational system which detects and classifies hand-ball events, given the trajectories of a hand and ball. Our approach is to determine non-gravitational parts of the ball’s motion using only the motion of the hand as a reliable cue for hand-ball events. This thesis makes three contributions. First, we show that hand motion can be segmented using piecewise fifth-order polynomials inspired by work in motor control. We make the remarkable experimental observation that hand-ball events have a phenomenal correspondence to the segmentation breakpoints. Second, by fitting a context-dependent gravitational model to the ball over an adaptive window, we can isolate places where the hand is causing non-gravitational motion of the ball. Finally, given a precise segmentation, we use the measured velocity steps (force impulses) on the ball to detect and classify various event types.
Audit for web application MSc Research Project Cloud Computing

Though security has become a major concern of a user using a cloud application, it is the responsibility of the cloud provider to secure the collected users data. The most targeted layer of a cloud service is the application layer containing too many loop holes through which an attacker can steal the users data. The service provided by each SaaS provider may differ from each one, but the threat level of all those cloud services are same. So the pressure of tightening the security of the application is with the cloud service provider. The provider has to analyse the possible ways his cloud service can be attacked, this analysis will help them to frame the preventive measures. The problem in this is not framing those preventive measures or implementing them, but identifying all the methods or syntax present in the source code which has the possibility to expose taint content and verifying whether the needed measures are implemented before updating them in live. The main aim of this research is to identify those codes and verify whether necessary measures are implemented to prevent the application from any attacks.
Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

In this paper, we propose a novel deep convolutional neural network to solve the general multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different from other methods based on deep learning, our network architecture is designed by drawing inspirations from a new proposed multi-modal convolutional sparse coding (MCSC) model. The key feature of the proposed network is that it can automatically split the common information shared among different modalities, from the unique information that belongs to each single modality, and is therefore denoted with CU-Net, i.e., common and unique information splitting network. Specifically, the CU-Net is composed of three modules, i.e., the unique feature extraction module (UFEM), common feature preservation module (CFPM), and image reconstruction module (IRM). The architecture of each module is derived from the corresponding part in the MCSC model, which consists of several learned convolutional sparse coding (LCSC) blocks. Extensive numerical results verify the effectiveness of our method on a variety of MIR and MIF tasks, including RGB guided depth image super-resolution, flash guided non-flash image denoising, multi-focus and multi-exposure image fusion.
The SPTpol Extended Cluster Survey

We describe the observations and resultant galaxy cluster catalog from the 2770 deg2 SPTpol Extended Cluster Survey (SPT-ECS). Clusters are identified via the Sunyaev–Zel’dovich (SZ) effect and confirmed with a combination of archival and targeted follow-up data, making particular use of data from the Dark Energy Survey (DES). With incomplete follow-up we have confirmed as clusters 244 of 266 candidates at a detection significance ξ ≥ 5 and an additional 204 systems at 4 < ξ < 5. The confirmed sample has a median mass of and a median redshift of z = 0.49, and we have identified 44 strong gravitational lenses in the sample thus far. Radio data are used to characterize contamination to the SZ signal; the median contamination for confirmed clusters is predicted to be ∼1% of the SZ signal at the ξ > 4 threshold, and <4% of clusters have a predicted contamination >10% of their measured SZ flux. We associate SZ-selected clusters, from both SPT-ECS and the SPT-SZ survey, with clusters from the DES redMaPPer sample, and we find an offset distribution between the SZ center and central galaxy in general agreement with previous work, though with a larger fraction of clusters with significant offsets. Adopting a fixed Planck-like cosmology, we measure the optical richness–SZ mass ( ) relation and find it to be 28% shallower than that from a weak-lensing analysis of the DES data—a difference significant at the 4σ level—with the relations intersecting at λ = 60. The SPT-ECS cluster sample will be particularly useful for studying the evolution of massive clusters and, in combination with DES lensing observations and the SPT-SZ cluster sample, will be an important component of future cosmological analyses.
Cryopreservation protocol for equine platelet-rich plasma Protocolo de criopreservação para plasma rico em plaquetas de equinos Liomara

In this preliminary study, a new equine platelet-rich plasma (PRP) cryopreservation protocol was evaluated. PRP was obtained by a double centrifugation technique of whole blood collected from 8 adult healthy ponies. A fresh sample of PRP was analyzed for total platelet count, mean platelet volume (MPV), and platelet morphology. Upon morphological evaluation, 200 platelets were counted using a differential interference contrast microscope with a 40x phase objective and classified as activated (with pseudopodia), inactivated (normal discoid shape), or uncertain state (spherical shape, without pseudopodia). Two other PRP samples, one containing DMSO as a cryoprotectant and the other without DMSO, were stored in a mechanical freezer at –80oC. After 14 days, the frozen samples were thawed and submitted to the same analysis as described above. The fresh PRP showed a platelet count of 830 (±95.3) x103 μL-1, an MPV of 5.2 (±0.07) fL, and composed of 4% activated platelets. There was no significant difference in platelet count, MPV, and activated platelets between fresh and 6% DMSO frozen PRP samples (617.9±65.5x103μL-1; 5.3±0.06fL; 9.5%) (p>0.05). On the other hand, samples frozen without DMSO showed a significantly lower platelet count (519.6±66.1x103μL-1), higher MPV (5.7±0.08fL), and more activated platelets (13.9%) than the other groups (p<0.05). The 6% DMSO was able to preserve platelet morphology in PRP stored at –80oC for 14 days, but studies on platelet function of thawed PRP are still needed.
N A ] 8 S ep 2 01 6 Tighter bound of Sketched Generalized Matrix Approximation

Generalized matrix approximation plays a fundamental role in many machine learning problems, such as CUR decomposition, kernel approximation, and matrix low rank approximation. Especially with Today’s applications involved in larger and larger dataset, more and more efficient generalized matrix approximation algorithems become a crucially important research issue. In this paper, we find new sketching techniques to reduce the size of the original data matrix to develop new matrix approximation algorithms. Our results derive a much tighter bound for the approximation than previous works: we obtain a (1 + ǫ) approximation ratio with small sketched dimensions which implies a more efficient generalized matrix approximation.