Miracle-dz/UniFAR-FTUs huggingface dataset:
Humour theory and videogames: laughter in the slaughter

In this paper we address the value and importance of using humour in videogames. We show how humour can facilitate character interaction, support gameplay, and augment players' intrinsic involvement. We outline humour theories: superiority, incongruity and relief. We then review some videogames, and discuss the roles that humour plays. In the last part of the paper, we discuss more specifically the role of humour in videogames, drawing on the theory of humour to highlight specific functions of humour and how it can benefit the design of videogames. We hope that this paper will contribute to a better understanding of humour and that future designers and educators will be able to use humour to enhance videogames and delight players.
Building energy management and data analytics

Energy efficiency in buildings depends on the way the building is operated. Therefore energy management is the key component for efficient operation. Data analysis of operation data helps to better understand the systems and detect faults and inefficiencies. The facility manager benefits from smart analysis that makes use of machine learning algorithms and innovative visualizations. This analysis is part of a bigger review of the current structure of building automation as it is used in today's buildings. The operation targets in energy efficiency are complex, ambiguous and contradictory: indoor comfort, energy efficiency, high availability and low costs cannot be met at the same time. In order to improve building operation, a novel model of automation is discussed. The foundation of this model is in cognitive automation, since each building is unique in its selection of energy sources, architecture, usage and location, which implies that the building's control system has to be adapted individually. This paper connects the data-driven analysis of operation data with a cognitive concept to be used for operating the energy systems in a building and regarding goals on how to optimally operate while considering constraints about the limits of operation, using the complex, dynamic data from building automation.
Risk prediction models: II. External validation, model updating, and impact assessment

Clinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care. In the first paper of this series of two companion papers, issues relating to prediction model development, their internal validation, and estimating the added value of a new (bio)marker to existing predictors were discussed. In this second paper, an overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals (external validation studies), how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies). Each step is illustrated with empirical examples from the cardiovascular field.
Induction Chemotherapy Improved Long-term Outcomes of Patients with Locoregionally Advanced Nasopharyngeal Carcinoma: A Propensity Matched Analysis of 5-year Survival Outcomes in the Era of Intensity-modulated Radiotherapy

Background: The aim of this study is to evaluate the long-term therapeutic gain of induction chemotherapy (IC) in locoregionally advanced nasopharyngeal carcinoma (NPC) in the era of intensity-modulated radiotherapy (IMRT). Methods: Data on 957 patients with stage T1-2N2-3 or T3-4N1-3 NPC treated with IMRT were retrospectively reviewed. Propensity score matching (PSM) method was adopted to balance influence of various covariates. Patient survival between IC and non-IC groups were compared. Results: For the 318 pairs selected from the original 957 patients by PSM, the median follow-up duration was 57.13 months (range, 1.27-78.1 months). The 5-year overall survival (OS), distant metastasis-free survival (DMFS), disease-free survival (DFS) and locoregional relapse-free survival (LRRFS) rates for IC group vs. non-IC group were 87.2% vs. 80.8% (P = 0.023), 88.1% vs. 83.2% (P = 0.071), 80.7% vs. 71.4% (P = 0.011) and 92.1% vs. 86.7% (P = 0.081), respectively. Multivariate analysis identify IC as an independent prognostic factor for OS (HR, 0.595; 95% CI, 0.397-0.891; P = 0.012) and DFS (HR, 0.627; 95% CI, 0.451-0.872; P = 0.006). After excluding the patients not receiving concurrent chemotherapy, IC was found to be an independent prognostic factor for OS (HR, 0.566; 95% CI, 0.368-0.872; P = 0.01), DMFS (HR, 0.580; 95% CI, 0.367-0.916; P = 0.02) and DFS (HR, 0.633; 95% CI, 0.444-0.903; P = 0.012). Conclusions: IC is an effective treatment modality for patients with stage T1-2N2-3 and T3-4N1-3 NPC, and the incorporation of IC with standard CCRT could achieve the best therapeutic gain.
Reliability and Sharpness in Border Crossing Traffic Interval Prediction

Short-term traffic volume prediction models have been extensively studied in the past few decades. However, most of the previous studies only focus on single-value prediction. Considering the uncertain and chaotic nature of the transportation system, an accurate and reliable prediction interval with upper and lower bounds may be better than a single point value for transportation management. In this paper, we introduce a neural network model called Extreme Learning Machine (ELM) for interval prediction of short-term traffic volume and improve it with the heuristic particle swarm optimization algorithm (PSO). The hybrid PSO-ELM model can generate the prediction intervals under different confidence levels and guarantee the quality by minimizing a multi-objective function which considers two criteria reliability and interval sharpness. The PSO-ELM models are built based on an hourly traffic dataset and compared with ARMA and Kalman Filter models. The results show that ARMA models are the worst for all confidence levels, and the PSO-ELM models are comparable with Kalman Filter from the aspects of reliability and narrowness of the intervals, although the parameters of PSO-ELM are fixed once the training is done while Kalman Filter is updated in an online approach. Additionally, only the PSO-ELMs are able to produce intervals with coverage probabilities higher than or equal to the confidence levels. For the points outside of the prediction levels given by PSO-ELMs, they lie very close to the bounds.
Standard graphs in lens spaces

We prove the conjecture of Menasco and Zhang that a completely tubing compressible tangle consists of at most two families of parallel strands. This conjecture is related to problems concerning graphs in 3-manifolds, and follows from a theorem that states that a 1-vertex graph in M is standard, in a certain sense, if and only if the exteriors of all its nontrivial subgraphs are handlebodies.
Hypervisor-based fault tolerance

Protocols to implement a fault-tolerant computing system are described. These protocols augment the hypervisor of a virtual-machine manager and coordinate a primary virtual machine with its backup. The result is a fault-tolerant computing system. No modification to hardware, operating system, or application programs is required. A prototype system was constructed for HP's PA-RISC instruction-set architecture. The prototype was able to run programs about a factor of 2 slower than a bare machine would.
VLSI Implementation of digital frequency sensors as hardware countermeasure

Non-invasive attacks are considered among the more serious threats for the hardware security due to there not exist evidences of them. Among these attacks, the clock glitch attack is one of the easier implementations. The typical countermeasure in front of this attack is a frequency sensor. In this paper, a VLSI design of a frequency digital sensor is presented. The simulation results, in a 0.35um standard CMOS technology, show a minimum timing resolution of 1.91ns and a minimum allowed period of 6.8ns. Finally, this implementation has been compared with an analog solution. The digital solution improves to the analog solution in timing resolution, power consumption, area consumption and propagation delay.
Black- and White-Box Self-testing COTS Components

Development of a software system from existing components can surely have various benefits, but can also entail a series of problems. One type of problems is caused by a limited exchange of information between the developer and user of a component, i.e. the developer of a componentbased system. A limited exchange of information cannot only require the testing by the user but it can also complicate this tasks, since vital artifacts, source code in particular, might not be available. Self-testing components can be one response in such situation. This paper describes an enhancement of the Self-Testing COTS Components (STECC) Method so that an appropriately enabled component is not only capable of white-box testing its methods but also capable of black-box testing.
Nitric Oxide-Mediated Maintenance of Redox Homeostasis Contributes to NPR1-Dependent Plant Innate Immunity Triggered by Lipopolysaccharides1[C][W]

The perception of lipopolysaccharides (LPS) by plant cells can lead to nitric oxide (NO) production and defense gene induction. However, the signaling cascades underlying these cellular responses have not yet been resolved. This work investigated the biosynthetic origin of NO and the role of NONEXPRESSOR OF PATHOGENESIS-RELATED GENES1 (NPR1) to gain insight into the mechanism involved in LPS-induced resistance of Arabidopsis (Arabidopsis thaliana). Analysis of inhibitors and mutants showed that LPS-induced NO synthesis was mainly mediated by an arginine-utilizing source of NO generation. Furthermore, LPS-induced NO caused transcript accumulation of alternative oxidase genes and increased antioxidant enzyme activity, which enhanced antioxidant capacity and modulated redox state. We also analyzed the subcellular localization of NPR1 to identify the mechanism for protein-modulated plant innate immunity triggered by LPS. LPS-activated defense responses, including callose deposition and defense-related gene expression, were found to be regulated through an NPR1-dependent pathway. In summary, a significant NO synthesis induced by LPS contributes to the LPS-induced defense responses by up-regulation of defense genes and modulation of cellular redox state. Moreover, NPR1 plays an important role in LPS-triggered plant innate immunity.
Inferring gas consumption and pollution emission of vehicles throughout a city

This paper instantly infers the gas consumption and pollution emission of vehicles traveling on a city's road network in a current time slot, using GPS trajectories from a sample of vehicles (e.g., taxicabs). The knowledge can be used to suggest cost-efficient driving routes as well as identifying road segments where gas has been wasted significantly. The instant estimation of the emissions from vehicles can enable pollution alerts and help diagnose the root cause of air pollution in the long run. In our method, we first compute the travel speed of each road segment using the GPS trajectories received recently. As many road segments are not traversed by trajectories (i.e., data sparsity), we propose a Travel Speed Estimation (TSE) model based on a context-aware matrix factorization approach. TSE leverages features learned from other data sources, e.g., map data and historical trajectories, to deal with the data sparsity problem. We then propose a Traffic Volume Inference (TVI) model to infer the number of vehicles passing each road segment per minute. TVI is an unsupervised Bayesian Network that incorporates multiple factors, such as travel speed, weather conditions and geographical features of a road. Given the travel speed and traffic volume of a road segment, gas consumption and emissions can be calculated based on existing environmental theories. We evaluate our method based on extensive experiments using GPS trajectories generated by over 32,000 taxis in Beijing over a period of two months. The results demonstrate the advantages of our method over baselines, validating the contribution of its components and finding interesting discoveries for the benefit of society.
JPMiner: Mining Frequent Jump Patterns from Graph Databases

A major challenge in frequent subgraph mining is the sheer size of its mining results. In many cases, allow minimum support may generate an explosive number of frequent subgraphs, which severely restricts the usage of frequent sub graph mining. In this paper, we study anew problem of mining frequent jump patterns from graph databases. Mining frequent jump patterns can dramatically reduce the number of output graph patterns, and still capture interesting graph patterns. By integrating the operation of checking jump patterns into the well-known DFS code tree enumeration framework, we present an efficient algorithm JPMiner for this new problem. We experimentally evaluate various aspects of Jupiter using both real and synthetic datasets. Experimental results demonstrate that the number of frequent jump patterns is much smaller than that of closed frequent graph patterns, and JPMiner is efficient and scalable in mining frequent jump patterns.
Congestion prediction in floorplanning

Routability optimization has become the major concern in floorplanning. In traditional floor planners, area minimization is an important issue. Due to the recent advances in VLSI technology, interconnect has become a dominant factor to the overall performance of a circuit. Routability prediction is thus very important in the floorplanning stage. In this paper, we propose a new congestion model to predict the congestion after detailed routing which is not confined to the assumption of shortest Manhattan distance routes. We have compared our new models and some existing models with the actual congestion measures obtained by global routing some placement results (using the Capo placer as stated in A. E. Caldwell et al. (2000)) with a publicly available maze router. Results show that our models can make significant improvement in estimation accuracy over the other models.
A Theory of Program Comprehension: Joining Vision Science and Program Comprehension

There exists an extensive literature on vision science, on the one hand, and on program comprehension, on the other hand. However, these two domains of research have been so far rather disjoint. Indeed, several cognitive theories have been proposed to explain program comprehension. These theories explain the processes taking place in the software engineers’ minds when they understand programs. They explain how software engineers process available information to perform their tasks but not how software engineers acquire this information. Vision science provides explanations on the processes used by people to acquire visual information from their environment. Joining vision science and program comprehension provides a more comprehensive theoretical framework to explain facts on program comprehension, to predict new facts, and to frame experiments. We join theories in vision science and in program comprehension; the resulting theory is consistent with facts on program comprehension and helps in predicting new facts, in devising experiments, and in putting certain program comprehension concepts in perspective.
A wavelet-based continuous classification scheme for multifunction myoelectric control

This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity.
Interplay Between HGF/SF–Met-Ras Signaling, Tumor Metabolism and Blood Flow as a Potential Target for Breast Cancer Therapy

High glucose uptake and increase blood flow is a characteristic of most metastatic tumors. Activation of Ras signaling increases glycolytic flux into lactate, de novo nucleic acid synthesis and uncoupling of ATP synthase from the proton gradient. Met tyrosine kinase receptor signaling upon activation by its ligand, hepatocyte growth factor/scatter factor (HGF/SF), increases glycolysis, oxidative phosporylation, oxygen consumption, and tumor blood volume. Ras is a key factor in Met signaling. Using the Ras inhibitor S-trans,trans-farnesylthiosalicylic acid (FTS), we investigated interplay between HGF/SF-Met–Ras signaling, metabolism, and tumor blood-flow regulation. In vitro, HGF/SF-activated Met increased Ras activity, Erk phosphorylation, cell motility and glucose uptake, but did not affect ATP. FTS inhibited basal and HGF/SF-induced signaling and cell motility, while further increasing glucose uptake and inhibiting ATP production. In vivo, HGF/SF rapidly increased tumor blood volume. FTS did not affect basal blood-flow but abolished the HGF/SF effect. Our results further demonstrate the complex interplay between growth-factor-receptor signaling and cellular and tumor metabolism, as reflected in blood flow. Inhibition of Ras signaling does not affect glucose consumption or basal tumor blood flow but dramatically decreases ATP synthesis and the HGF/SF induced increase in tumor blood volume. These findings demonstrate that the HGF/SF-Met–Ras pathway critically influences tumor-cell metabolism and tumor blood-flow regulation. This pathway could potentially be used to individualize tumor therapy based on functional molecular imaging, and for combined signaling/anti-metabolic targeted therapy.
Measuring Odor Threshold Using a Simplified Olfactory Measurement Method

This study used the Simplified Olfactory Measurement Method to clarify the fundamental characteristics of substances with offensive odors present in human living environments and to measure the odor detection threshold of these substances, which have relatively high boiling points and high molecular weights. Results showed that propanoic acid was the highest in those substances, with an odor threshold of 49 ppb, and phenylacetic acid was the lowest in those substances, with an odor threshold of 2.4×10 -6 ppb. Although there was a gap between the Simplified Olfactory Measurement Method and the Triangle Odor Bag Method, the former still fell in the range of thresholds that can occur between panels. Furthermore, the threshold for the relationship between thresholds of straight-chain fatty acids and molecular carbon numbers gradually decreased for carbon numbers from three to five and increased again in six. That trend resembles the result of the Triangle Odor Bag Method.
A novel model for testing the efficiency of removal of calcium hydroxide from complex root canal anatomies

The purpose of this study was to evaluate the efficacy of several irrigation protocols in the removal of calcium hydroxide [Ca(OH)2 ] from simulated internal root resorption cavities in a complex root canal anatomy model. The 20° to 35° curved mesiobuccal roots of 94 maxillary molars were sectioned longitudinally; internal resorption cavities were prepared in the apical third of the canal walls. Calcium hydroxide was placed into the cavities and the root halves reassembled. Four teeth were used as controls, and 90 teeth were randomly divided into six experimental groups (n = 15), according to the irrigation protocols used: syringe irrigation; H2 O2 (HP); Navitip FX; Vibringe-syringe; Vibringe-NaviTip FX; ultrasonically activated irrigation (UAI) using an ultrasonic K-file. In the HP group, 2.5% NaOCl and 3% H2 O2 were used, while 2.5% NaOCl and 17% EDTA were used in the remaining groups. Stereomicroscope images and radiographs were used to measure the remaining Ca(OH)2 . The model proved to be suitable for simulating complex anatomy. Positive correlation was found between stereomicroscope and radiographic analyses (P < 0.05). UAI removed significantly more Ca(OH)2 than the other experimental groups (P < 0.05). The HP group was the least efficient protocol (P < 0.05). It would appear that a reliable model has been developed that simulates complex root canal anatomy. Irrigant activation protocols enhanced Ca(OH)2 removal.
Boolean CP Decomposition of Binary Tensors: Uniqueness and Algorithm

We propose an algorithm to perform the low-rank Boolean Canonical Polyadic Decomposition (BCPD) of a binary tensor. The proposed approach is based on the AO-ADMM strategy introduced in [1] and uses a post-nonlinear mixture model for binary sources. We show that this new method is better suited for low-rank approximation of binary tensors compared to other similar methods. We also provide an easy-to-check uniqueness condition for the BCPD. This is the first time that such a condition is derived for Boolean decompositions.
Intractability Results for Integration and Discrepancy

We mainly study multivariate (uniform or Gaussian) integration defined for integrand spaces Fd such as weighted Sobolev spaces of functions of d variables with smooth mixed derivatives. The weight ? j moderates the behavior of functions with respect to the jth variable. For ? j? 1, we obtain the classical Sobolev spaces whereas for decreasing ? j's the weighted Sobolev spaces consist of functions with diminishing dependence on the jth variables. We study the minimal errors of quadratures that use n function values for the unit ball of the space Fd. It is known that if the smoothness parameter of the Sobolev space is one, then the minimal error is the same as the discrepancy. Let n(? , Fd) be the smallest n for which we reduce the initial error, i.e., the error with n=0, by a factor ? The main problem studied in this paper is to determine whether the problem is intractable, i.e., whether n(? , Fd) grows faster than polynomially in ? 1 or d. In particular, we prove intractability of integration (and discrepancy) if limsupd? dj=1? j/lnd=∞. Previously, such results were known only for restricted classes of quadratures. For ? j? 1, the, following bounds hold for discrepancy ? with boundary conditions1.0628d(1+o(1))? n(? , Fd)? 1.5d? 2,asd? ∞,? without boundary conditions1.0463d(1+o(1))? n(? , Fd)? 1.125d? 2,asd? ∞. These results are obtained by analyzing arbitrary linear tensor product functionals Id defined over weighted tensor product reproducing kernel Hilbert spaces Fd of functions of d variables. We introduce the notion of a decomposable kernel. For reproducing kernels that have a decomposable part we prove intractability of all Id with non-zero subproblems with respect to the decomposable part of the kernel, as long as the weights satisfy the condition mentioned above.