Miracle-dz/UniFAR-FTUs huggingface dataset:
Marginal maximum likelihood estimation methods for the tuning parameters of ridge, power ridge, and generalized ridge regression

ABSTRACT This study introduces fast marginal maximum likelihood (MML) algorithms for estimating the tuning (shrinkage) parameter(s) of the ridge and power ridge regression models, and an automatic plug-in MML estimator for the generalized ridge regression model, in a Bayesian framework. These methods are applicable to multicollinear or singular covariate design matrices, including matrices where the number of covariates exceeds the sample size. According to analyses of many real and simulated datasets, these MML-based ridge methods tend to compare favorably to other tuning parameter selection methods, in terms of computation speed, prediction accuracy, and ability to detect relevant covariates.
Reducing the impact of state space explosion in Stochastic Automata Networks

A solucao de modelos markovianos com grande espaco de estados e um dos maiores desafios da area de avaliacao de desempenho de sistemas. Os formalismos estruturados, como as Redes de Automatos Estocasticos (SAN), foram propostos para descrever multiplos componentes atraves de automatos, cujas transicoes sao regidas por eventos locais ou sincronizantes, com taxas de ocorrencia constantes ou funcionais. Devido a capacidade de representacao modular de SAN, atraves do uso de algebra tensorial (ou de Kronecker), armazena-se o gerador infinitesimal do modelo de forma compacta e eficiente em memoria. Os metodos numericos de solcao que calculam a distribuicao estacionaria das probabilidades sao adaptados a estas representacoes tensoriais. A operacao basica e a multiplicacao vetor-descritor, que e produto de um vetor de probabilidades por termos tensoriais compostos por matrizes normalmente esparsas. O principal algoritmo chama-se Shuffle e e caracterizado pelo acesso e embaralhamento de posicoes do vetor quando multiplicado pelas matrizes de cada termo. Este metodo e considerado extremamente eficaz no armazenamento em memoria, entretanto apresenta um tempo de processamento alto para a solucao de modelos reais. Propoe-se um algoritmo hibrido e mais flexivel para a multiplicacao vetor-descritor, chamado Split, que coloca o algoritmo Shuffle em perspectiva, apresentando ganhos significativos no tempo de execucao para diversas classes de modelos, sem onerar os recursos computacionais. Entretanto, quando os modelos aumentam em escala, este algoritmo numerico torna-se inadequado devido ao problema da explosao do espaco de estados. Para mitigar o impacto deste problema propoe-se o uso de solucoes alternativas de simulacao, as quais buscam estimar a distribuicao estacionaria de probabilidades tao proximas quanto possivel das solucoes analiticas, baseando-se na execucao de longas trajetorias. Utiliza-se a tecnica de simulacao baseada em amostragem perfeita (tambem chamada de simulacao exata), para fornecer amostras confiaveis da distribuicao estacionaria atraves do casamento de trajetorias, em tempo de simulacao reverso. Esta difere-se da simulacao tradicional por evitar o periodo transiente e a escolha aleatoria de um estado inicial. Mostra-se a viabilidade destes algoritmos aplicados a SAN, principalmente quando se detectam propriedades de monotonicidade nos modelos. Espacos de estados parcialmente ordenados e propriedades de monotonicidade permitem a execucao de um numero reduzido de trajetorias em paralelo para obtencao de uma amostra. A analise numerica iterativa e a simulacao de modelos estocasticos sao abordagens que apresentam vantagens e limitacoes quando aplicadas a solucao de modelos estruturados como SAN. A principal contribuicao desta tese foca na reducao do impacto da explosao do espaco de estados de modelos markovianos descritos em SAN, propondo solucoes quando o tempo de computacao das tecnicas analiticas e muito longo ou quando os requisitos de armazenamento do vetor de probabilidade excedem a capacidade de memoria das tecnologias correntes.
A Systematic Study of Feature Selection Methods for Learning to Rank Algorithms

This article describes how feature selection for learning to rank algorithms has become an interesting issue. While noisy and irrelevant features influence performance, and result in an overfitting problem in ranking systems, reducing the number of features by illuminating irrelevant and noisy features is a solution. Several studies have applied feature selection for learning to rank, which promote efficiency and effectiveness of ranking models. As the number of features and consequently the number of irrelevant and noisy features is increasing, systematic a review of Feature selection for learning to rank methods is required. In this article, a framework to examine research on feature selection for learning to rank (FSLR) is proposed. Under this framework, the authors review the most state-of-the-art methods and suggest several criteria to analyze them. FSLR offers a structured classification of current algorithms for future research to: a) properly select strategies from existing algorithms using certain criteria or b) to find ways to develop existing methodologies.
In-Depth Buffers

We present a new approach for rendering all triangles in a model in front-to-back order without the need for sorting at runtime. The method can be used for rendering order-dependent transparency effects, or to minimize overdraw, for example. The key distinguishing component in the approach is its negligible runtime cost and therefore the ease with which it can be incorporated into rendering engines. More specifically, given a viewpoint, the runtime simply selects a presorted triangle list, which we call in-depth buffers, to be rendered at full speed. These in-depth buffers are even optimized for post-transform vertex cache efficiency. The result is unmatched in front-to-back rendering performance. The difficulty is computing the smallest set of in-depth buffers required. This reduces to a graph problem that we prove to be NP-hard. Nevertheless, we have found an optimization heuristic that produces good results, particularly when visually imperceptible fragment ordering mistakes are allowed.
Comparison of PD, PID and Sliding-Mode Position Controllers for V–Tail Quadcopter Stability

In this paper, a comparison of PD, PID, and SMC position controllers for a V-tail quadcopter is presented. First, a customized design of the V-tail quadcopter is shown to know the parameters of this structure and compare them with the commonly $\times $ structure quadcopter used in most papers. Then, the dynamic analysis of the V-tail quadcopter using the Newton–Euler formulation is presented. The main contribution of this paper remains in the design and Lyapunov stability analysis of the PD, PID, and SMC position controllers for the V-tail quadcopter because the robot manipulator methodology was used, treating the V-tail quadcopter as a robot manipulator. The simulation results validate the proposed controllers and algorithms for the V-tail quadcopter when the three controllers reach the desired position. Also, a non-conventional variable is introduced to study the stability analysis of unmanned aerial vehicles when controlled by PID position controller. Finally, a comparison between the three designed controllers for the V-tail quadcopter is presented, where the differences between each can be appreciated. So that, for the first time, three controllers for the V-tail quadcopter designed using the robot manipulator theory is presented.
Robust Student Network Learning

Deep neural networks bring in impressive accuracy in various applications, but the success often relies on heavy network architectures. Taking well-trained heavy networks as teachers, classical teacher–student learning paradigm aims to learn a student network that is lightweight yet accurate. In this way, a portable student network with significantly fewer parameters can achieve considerable accuracy, which is comparable to that of a teacher network. However, beyond accuracy, the robustness of the learned student network against perturbation is also essential for practical uses. Existing teacher-student learning frameworks mainly focus on accuracy and compression ratios, but ignore the robustness. In this paper, we make the student network produce more confident predictions with the help of the teacher network, and analyze the lower bound of the perturbation that will destroy the confidence of the student network. Two important objectives regarding prediction scores and gradients of examples are developed to maximize this lower bound, to enhance the robustness of the student network without sacrificing the performance. Experiments on benchmark data sets demonstrate the efficiency of the proposed approach to learning robust student networks that have satisfying accuracy and compact sizes.
Spondyloarthritis in Chile

advanced cirrhosis (F4). Despite following the recommended guidelines for laboratory monitoring (with the exception of a 20-month time period where the patient's laboratory tests were measured less frequently than the recommended interval), we were unable to predict the development of severe liver fibrosis. Current rheumatologic guidelines focus on performing further diagnostic testing for liver disease in the subset of patients with persistent laboratory abnormalities. Rheumatology guidelines have served both patents and clinicians using MTX well for over 20 years. Of note, in small studies with RA patients on MTX, isolated liver enzyme abnormalities were not reflective of degree of fibrosis. The current rheumatology guidelines defer to the American Association for the Study of Liver Disease for patients on MTX with additional risk factors for liver disease (such as obesity and the potential for NAFLD, as in our patient). For several liver conditions, hepatologists measure LFTs every 3 months until stability is established and then every 3 to 6 months thereafter. Transient elastography has proven to be a useful tool for screening for hepatic fibrosis in a number of common liver diseases, and as a noninvasive imaging modality, it can be done for serial monitoring of patients without the risks associated with liver biopsies. Transient elastography may be useful in the assessment of liver fibrosis in RA patients on long-term MTX, especially in those with additional risk factors including elevated BMI, concurrent hepatotoxic medication, and impaired kidney function. We are interested in following patients on MTX with multiple risk factors for liver disease with TE in conjunction with the routine laboratory testing recommended by the ACR to assess the degree of concordance between these screening modalities in a larger patient population. We would also be interested in assessing if TE could be used intermittently in patients with no additional risk factors for liver disease, as a means of reducing the frequency of recommended laboratory testing. We believe that a North American study is needed in order to clarify if TE is an effective and cost-efficient modality for following the ever-increasing number of patients with rising cumulative MTX exposure.
Optimal Constraint Collection for Core-Selecting Path Mechanism

In path auctions, strategic bidders make bids for commodities. Each edge of the graph stands for a commodity and the weight on the edge represents the prime cost. Auctioneer needs to purchase a sequence of edges in order to get a path from one vertex to another at a low cost. Path auctions can be considered as a kind of combinatorial reverse-auctions. Computing prices in core-selecting combinatorial auctions is a computationally hard problem, the same is true in core-selecting path auctions. This problem can be solved by core constraint generation(CCG) algorithm. However, we find that there are many redundant constraints and the constraint collection can be conciser in core-selecting path mechanism. In this paper, 1) we put forward a new approach to get the constraint collection, and reduce the constraint number from exponential O(2^n) to polynomial O(n^2), where n is the network diameter; 2) we prove that the new constraint collection is not only equivalent to the original collection, but also has no redundant constraint in the worst case; 3) we validate our approach on real-world datasets and obtain excellent results. Furthermore, we provide new insights to think over the core-selecting mechanism in combinatorial auctions.
BIBIM: A Prototype Multi-Partition Aware Heterogeneous New Memory

We describe a prototype multi-partition aware new memory controller and subsystem, BIBIM, which precisely integrates DRAM with 3x nm phase change RAM (PRAM) modules. In this work, we reveal the main challenges of a new type of PRAMs in getting closer to a main processor by evaluating our real 3x nm PRAM with diverse persistent memory benchmarks. BIBIM implements hybrid cache logic into a 2x nm FPGA device, which can hide the long latency imposed by the underlying PRAM modules as well as can support persistent operations. The cache logic of our controller is also able to serve multiple read requests while writing data into a target PRAM bank by considering the multi-partition architecture of such new memory. The evaluation results demonstrate that the read and write latencies of our BIBIM are 115 ns and 125 ns, which are 38% and 99% shorter than those of a pure PRAM-based memory subsystem. In addition, BIBIM can remove blocking reads by 53%, on average, thereby shortening the average latency of write-after-read memory operations by 48%.
Benchmark Framework for Virtual Students' Behaviours

This paper demonstrates the integration and evaluation of different atmosphere models into Virtual Reality (VR) training for teacher education. We developed three behaviour models to simulate different levels of class discipline. We evaluated their performances using a combination of objective and subjective measurements. Our initial results suggest that the more believable and distinguishable classroom atmospheres are produced by creating more consistent behaviours across virtual students. Our results confirm the importance of similar behaviours to elicit a particular atmosphere.
Data-driven recovery of hand depth using CRRF on stereo images

Hand pose is emerging as an important interface for human-computer interaction. This paper presents a data-driven method to estimate a high-quality depth map of a hand from a stereoscopic camera input by introducing a novel superpixel based regression framework that takes advantage of the smoothness of the depth surface of the hand. To this end, we introduce Conditional Regressive Random Forest (CRRF), a method that combines a Conditional Random Field (CRF) and a Regressive Random Forest (RRF) to model the mapping from a stereo RGB image pair to a depth image. The RRF provides a unary term that adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. While the RRF makes depth prediction for each super-pixel independently, the CRF unifies the prediction of depth by modeling pair-wise interactions between adjacent superpixels. Experimental results show that CRRF can generate a depth image more accurately than the leading contemporary techniques using an inexpensive stereo camera.
Modified Token Based Congestion Control Scheme for Opportunistic Networks

To address congestion issues in Opportunistic Networks (OppNets), a modified token-based congestion control with adaptive forwarding mechanism is proposed. The mechanism allows the network nodes holding a valid token to inject message into the network or other neighboring node. At the point of congestion, the algorithm has the potential to redirect the traffic from a more congested node to congestion free node for the purpose of effective resource utilization and fairness in the network. Tokens are evenly distributed throughout the network. Using the opportunistic network environment (ONE) simulator, we illustrate the performance of a modified token-based congestion control algorithm, which results in reduction of dropped messages and network transit time due to congestion across all the scenarios considered. At different queue sizes of (QS-10, QS-20, QS-30 and QS-40), modified token based congestion control algorithm has 13.91%, 10.71%, 5.46%, and 4.22%, respectively, reduction in dropped messages. In addition, at the greatest connected component values of 50%, 60%, 70% and 80%, the modified token-based congestion control has 8.34%, 2.19%, 4.61%, and 7.63%, respectively, decrease in network transit time. These results are substantial, because they indicate a reduction in both network storage and time.
Semantic Mapping for Autonomous Robots in Urban Environments

Increasing autonomy and interactivity of mobile robotic technologies require the inclusion of semantic information in environment representations. This thesis focuses on representations and methods for semantic mapping in particular for urban environments. It covers the estimation of spatial relations between objects using Probabilistic Logic, including a novel inference method for Markov Logic Networks. Moreover, the benefits of combining different sources of semantic information with sensor data are shown in a scene interpretation and a semantic localization task.
An Imbalanced Malicious Domains Detection Method Based on Passive DNS Traffic Analysis

Although existing malicious domains detection techniques have shown great success in many real-world applications, the problem of learning from imbalanced data is rarely concerned with this day. But the actual DNS traffic is inherently imbalanced; thus how to build malicious domains detection model oriented to imbalanced data is a very important issue worthy of study. This paper proposes a novel imbalanced malicious domains detection method based on passive DNS traffic analysis, which can effectively deal with not only the between-class imbalance problem but also the within-class imbalance problem. The experiments show that this proposed method has favorable performance compared to the existing algorithms.
Performance Improvement of File Operations on OverlayFS for Containers

Server consolidation with virtualization is a popular method to address the issue of a large amount of power consumption of inter-connected computers in data centers. The more computers are consolidated, the more energy is saved. However, highly consolidating, wherein many servers are consolidated into one physical computer, results in large performance decline. Especially, I/O performance is severely decreased as reported. In this work, we focus on Docker, a popular container-based virtualizing system, and OverlayFS. OverlayFS is widely recognized method for improving I/O performance in Docker. First, we evaluate the performance of file operations on OverlayFS. In particular, we focus on the performance of file writing involving copy_up and show that the performance is severely low. Second, we investigate the performance and behavior the filesystem during copy_up and demonstrate that synchronization is the most important issue. Third, we discuss a method for improving the performance by disabling this synchronization. Fourth, we evaluate the improving method and show that the method can significantly improve the writing performance with copy_up by 680 times at most.
Um Modelo de Falhas em Cascata para Sistemas Globais de Gerenciamento de Identidades

The increasing number of portable devices requires a global identity management system. However, these systems are cascading failures prone, that is, failures arosed by the coordinated exploitation of common vulnerabilities among identities providers to manipulate the authentication operations. Although there are theoretical models to analyze cascading failures resulted from nodes overload, cascading failures effects from the coordinated exploitation of vulnerabilities in authentication operations remains no investigated. This paper presents an analytical cascading failure model for identity management systems, enabling the investigation these failures. Results from a case study indicate that failure dissemination in biggest sets of identities providers can compromise a identity management system in about 100%. Resumo. A explosão do número de dispositivos portáteis conectados tem demandado sistemas de gerenciamento de identidades globais. Entretanto, esses sistemas são propensos a falhas em cascata, as quais são oriundas da exploração coordenada de vulnerabilidades em comum entre provedores de identidades para manipular as operações de autenticação. Apesar de existirem modelos teóricos para analisar falhas em cascata resultantes da sobrecarga da capacidade de nós, o efeito de falhas em cascata decorrente da exploração de vulnerabilidades nas operações de autenticações em sistemas de gerenciamento de identidades permanece em sua maioria desconhecidos. Este trabalho apresenta um modelo analítico de falhas em cascata para sistemas de gerenciamento de identidades, possibilitando a investigação dessas falhas. Resultados de um estudo de caso indicam que a disseminação de falhas nos conjuntos maiores de provedores de identidades pode comprometer até 100% do sistema.
A Mobility Link Service for NDN Consumer Mobility

Named Data Networking (NDN) supports the consumer mobility service by letting a consumer reissue an interest. This method is straightforward, but it may cause several drawbacks, including unnecessary handover overhead and long handover delay. We concentrate on the NDN communication model in which the pair of an interest and a data packet is considered a single communication working set (i.e., transaction unit). In this respect, reissuing an interest means creating a new transaction due to the connection damaged by the movement of a consumer. It makes all states of the current transaction useless, and this is where the drawbacks arise. In order to enhance the consumer mobility service, we propose Mobility Link Service (MLS) operated in NDN face which is responsible for management of a connection for a transaction. MLS reuses the existing states of a transaction by establishing a connection for the transaction instead of creating a new one. In addition, MLS in NDN face makes consumer mobility service transparent to the NDN forwarding plane. Therefore, the consumer mobility service and the NDN architecture can evolve independently. The performance evaluation shows that MLS reduces the amount of retransmitted data and handover latency compared with the existing NDN mobility solution.
Using Sentence-Level Neural Network Models for Multiple-Choice Reading Comprehension Tasks

Comprehending unstructured text is a challenging task for machines because it involves understanding texts and answering questions. In this paper, we study the multiple-choice task for reading comprehension based on MC Test datasets and Chinese reading comprehension datasets, among which Chinese reading comprehension datasets which are built by ourselves. Observing the above-mentioned training sets, we find that “sentence comprehension” is more important than “word comprehension” in multiple-choice task, and therefore we propose sentence-level neural network models. Our model firstly uses LSTM network and a composition model to learn compositional vector representation for sentences and then trains a sentence-level attention model for obtaining the sentence-level attention between the sentence embedding in documents and the optional sentences embedding by dot product. Finally, a consensus attention is gained by merging individual attention with the merging function. Experimental results show that our model outperforms various state-of-the-art baselines significantly for both the multiple-choice reading comprehension datasets.