Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Bayesian uncertainty analysis of climate system properties using Markov chain Monte Carlo methods

A Bayesian uncertainty analysis of 12 parameters of the Bern2.5D climate model is presented. This includes an extensive sensitivity study with respect to the major statistical assumptions. Special attention is given to the parameter representing climate sensitivity. Using the framework of robust Bayesian analysis, the authors first define a nonparametric set of prior distributions for climate sensitivity S and then update the entire set according to Bayes’ theorem. The upper and lower probability that S lies above 4.5°C is calculated over the resulting set of posterior distributions. Furthermore, posterior distributions under different assumptions on the likelihood function are computed. The main characteristics of the marginal posterior distributions of climate sensitivity are quite robust with regard to statistical models of climate variability and observational error. However, the influence of prior assumptions on the tails of distributions is substantial considering the important political implications. Moreover, the authors find that ocean heat change data have a considerable potential to constrain climate sensitivity.
An undergraduate compiler laboratory

A one semester, upper-division undergraduate course in compiler techniques is described. The course is based upon the material contained in Chapter 5 of the text Algorithms + Data Structures &equil; Programs, by Niklaus Wirth. The goals of the course are (1) to introduce students to the fundamental concepts of the design and translation of higher level languages, and (2) to provide an introductory exposure to the related literature. A compiler-writing project of about nine weeks duration is required of all students. Simulators supporting generated code for static and dynamic run-time environments are provided for student use.
The Hidden Object Searching Method for Distributed Autonomous Robotic Systems

In this paper, we present the strategy of object search for distributed autonomous robotic systems (DARS). The DARS are the systems that consist of multiple autonomous robotic agents to whom required functions are distributed. For instance, the agents should recognize their surrounding at where they are located and generate some rules to act upon by themselves. In this paper, we introduce the strategy for multiple DARS robots to search a hidden object at the unknown area. First, we present an area-based action making process to determine the direction change of the robots during their maneuvers. Second, we also present Q learning adaptation to enhance the area-based action making process. Third, we introduce the coordinate system to represent a robot's current location. In the end of this paper, we show experimental results using hexagon-based Q learning to find the hidden object.
The overall performance of the Shannon code

It is well-known that the redundancy of the Shannon code lies in the interval (0, 1). In order to study the overall performance of the Shannon code, we consider its redundancy as a random variable on the set of sources with n symbols, i.e., Rsh(n), and examine its statistical parameters. It is shown that the mean of Rsh(n) gets close to 0.5 for sources with large alphabet size n. Moreover, we observe that its variance tends to zero as n increases. Briefly speaking, for almost all sources with a large alphabet size, the redundancy of the Shannon code is almost 0.5 bits.
The Share Price Puzzle

We document substantial variation in the prices of common stocks in U.S. markets, a phenomenon that apparently results from firms selecting particular price ranges for their stock. We present cross-sectional evidence that variables consistent with Merton’s (1987) model of capital market equilibrium explain roughly 45 percent of the variation in share prices. We also find that measures of trading range and share price appreciation are significant predictors of stock splits, that very widely held firms are less inclined to split their stock than less widely held firms, and that the “investor base” of firms splitting their stock increases compared to other firms.
Properties of Planning with Non-Markovian Rewards

We examine technologies designed to solve decision processes with non-Markovian rewards (NMRDPs). More specifically, target decision processes exhibit Markovian dynamics, calledgrounded dynamics , and desirable behaviours are modelled as state trajectories specified in a temporal logic. Each technology operates by automatically translating NMRDPs into corresponding equivalent MDPs amenable to classical MDP solution methods. They do however differ in their representations of grounded dynamics, the MDP and in the class, structured or non-structured, of MDP solution methods to which they are suited. Therefore two temporal logics and numerous translation procedures have been adopted. This presentation is based on an integrated system for solving NMRDPs which implements these methods and several variants under a common interface; we use it to compare the various approaches and identify some problem features favouring one over the other.
Hybrid traffic engineering: the least path interference algorithm

The traffic engineering mechanisms currently deployed in IP networks are based on routing metrics (cost metrics) which optimize system-wide measures of performance such as average response time, throughput, delay, etc., discounting the diversity of QoS requirements from the mixture of narrow- and broad-band applications carried by the new multi-service Internet. This paper presents a new approach for routing flows in IP networks. The approach referred to as the Least Path Interference (LPI) is based on a route optimization model which (1) moves the traffic away from path interfering links (the path interference quantifying the network reliability) to re-route fewer flows upon link failure and (2) maximizes the link congestion distance (quantifying the network optimality) to reject fewer flows under congestion. LPI implements a hybrid traffic engineering model combining offline estimation of the path interference and online path finding. LPI is based on a simple path selection model where no changes to the traditional routing algorithms are required besides designing a new mixed cost metric to combine reliability and optimality. The Least Path Interfering Algorithm (LPIA); a routing algorithm derived from LPI is applied to compute paths for the traffic offered to a 50-node network. Simulation reveals (1) performance improvements compared to Open Shortest Path (OSPF) and Constraint Shortest Path First (CSPF) routing in terms of routing optimality and network reliability and (2) the same performance as the recently proposed Least Interference Optimization Algorithm (LIOA) algorithm.
The Fanconi Anaemia Components UBE2T and FANCM Are Functionally Linked to Nucleotide Excision Repair

The many proteins that function in the Fanconi anaemia (FA) monoubiquitylation pathway initiate replicative DNA crosslink repair. However, it is not clear whether individual FA genes participate in DNA repair pathways other than homologous recombination and translesion bypass. Here we show that avian DT40 cell knockouts of two integral FA genes – UBE2T and FANCM are unexpectedly sensitive to UV-induced DNA damage. Comprehensive genetic dissection experiments indicate that both of these FA genes collaborate to promote nucleotide excision repair rather than translesion bypass to protect cells form UV genotoxicity. Furthermore, UBE2T deficiency impacts on the efficient removal of the UV-induced photolesion cyclobutane pyrimidine dimer. Therefore, this work reveals that the FA pathway shares two components with nucleotide excision repair, intimating not only crosstalk between the two major repair pathways, but also potentially identifying a UBE2T-mediated ubiquitin-signalling response pathway that contributes to nucleotide excision repair.
Blind Identification of MIMO Channels Using Optimal Periodic Precoding

We propose a method for blind identification of multiple-input mutiple-out (MIMO) finite-impulse response (FIR) channels that exploits cyclostationarity of the received data induced at the transmitters by periodic precoding. It is shown that, by properly choosing the precoding sequence, the MIMO FIR transfer functions, with Mt inputs and Mr outputs, can be identified up to a unitary matrix ambiguity. The transfer functions need not be irreducible or column reduced, and there can be more outputs (MrgesMt) or more inputs (Mr<Mt). The method exploits the linear relation between the covariance matrix of the received data and the "channel product matrices". The method is shown to be robust with respect to channel-order overestimation. The proposed algorithm requires solving linear equations and computing the nonzero eigenvalues and eigenvectors of a Hermitian positive semidefinite matrix. The performance of the algorithm, and indeed the identifiability, depends on the choice of the precoding sequence. We propose a method for optimal selection of the precoding sequence which takes into account the effect of additive channel noise and numerical error in covariance matrix estimation. Simulation results are used to demonstrate the performance of the algorithm
Decentralized Observation Problems

A fundamental observation problem is, given a model of the system to be observed and a specification of the property to be observed, to check whether the property is observable (i.e., the observer can resolve potential ambiguities due to partial observation capabilities) and if so to (automatically) synthesize an observer. We consider a simple case of the above setting, namely, where the system model and specifications are expressed as regular languages over a finite alphabet. In this setting, and in the case of one observer (centralized observation), both checking observability and synthesizing an observer can be done automatically, and the observer is guaranteed to be finite-state. The situation is more complicated in the case of more than one observers (decentralized observation). Here, many definitions are possiblle, depending on where the decisions are taken, as well as on whether the observers are required to be finite-state. We examine some of these possibilities, surveying recent results on the topic and providing some new results as well.
Warping Nodal Levels Via M-Rep Based Diffeomorphism

The identification of lymph node drainage levels in the neck from CT scans is necessary for the proper treatment of cancer of the head and neck with radiation therapy. However, to comply with the guidelines one must segment at least 20 fiducial anatomic objects on each side, and then apply complex rules to determine six boundaries for each of nine different levels. To reduce physician time and standardize results it is necessary to automate the entire process. In this paper, we propose solving this problem by deforming the nodal levels from an atlas patient to target patient in a way combining two novel ideas: a) The large deformation diffeomorphic transformations of curved landmark paths; b) a scheme provided by m-reps and their geodesic paths for producing curved paths between corresponding points associated with the fiducial objects. This method is applicable to a wide variety of problems of mapping between an atlas and a target image with automatically segmentable structures.
Exploiting speaker segmentations for automatic role detection. An application to broadcast news documents

In the field of automatic audiovisual content-based indexing and structuring, finding events like interviews, debates, reports, or live commentaries requires to bridge the gap between low-level feature extraction and such high-level event detection. In our work, we consider that detecting speaker role to enrich interaction sequences between speakers is a first step to reach this goal. The generic method we propose follows a data mining approach. We assume that speaker roles can emerge from parameters extracted from speaker segmentations without taking any prior information into account. Each speaker is then represented by a feature vector carrying temporal, signal and prosodic information. In this paper, we study how methods for dimensionality reduction and classification can help to recognize speaker roles. This method is applied to the corpus of the ESTER2 evaluation campaign and our best result reaches about 72% of well recognized roles that corresponds to nearly 79% of speech time.
Green and Blue Spaces and Behavioral Development in Barcelona Schoolchildren: The BREATHE Project

Background: Green spaces have been associated with improved mental health in children; however, available epidemiological evidence on their impact on child behavioral development is scarce. Objectives: We investigated the impact of contact with green spaces and blue spaces (beaches) on indicators of behavioral development and symptoms of attention deficit/hyperactivity disorder (ADHD) in schoolchildren. Methods: This study was based on a sample of 2,111 schoolchildren (7–10 years of age) from 36 schools in Barcelona in 2012. We obtained data on time spent in green spaces and beaches and Strengths and Difficulties Questionnaires (SDQ) from parents, and ADHD/DSM-IV questionnaires from teachers. Surrounding greenness was abstracted as the average Normalized Difference Vegetation Index (NDVI) in buffers of 100 m, 250 m, and 500 m around each home address. Proximity to green spaces was defined as living within 300 m of a major green space (≥ 0.05 km2). We applied quasi-Poisson mixed-effects models (with school random effect) to separately estimate associations between indicators of contact with green spaces and SDQ and ADHD total and subscale scores. Results: We generally estimated beneficial associations between behavioral indicators and longer time spent in green spaces and beaches, and with residential surrounding greenness. Specifically, we found statistically significant inverse associations between green space playing time and SDQ total difficulties, emotional symptoms, and peer relationship problems; between residential surrounding greenness and SDQ total difficulties and hyperactivity/inattention and ADHD/DSM-IV total and inattention scores; and between annual beach attendance and SDQ total difficulties, peer relationship problems, and prosocial behavior. For proximity to major green spaces, the results were not conclusive. Conclusion: Our findings support beneficial impacts of contact with green and blue spaces on behavioral development in schoolchildren. Citation: Amoly E, Dadvand P, Forns J, López-Vicente M, Basagaña X, Julvez J, Alvarez-Pedrerol M, Nieuwenhuijsen MJ, Sunyer J. 2014. Green and blue spaces and behavioral development in Barcelona schoolchildren: the BREATHE Project. Environ Health Perspect 122:1351–1358; http://dx.doi. org/10.1289/ehp.1408215
DWI-ASPECTS as a Predictor of Dramatic Recovery After Intravenous Recombinant Tissue Plasminogen Activator Administration in Patients With Middle Cerebral Artery Occlusion

Background and Purpose— In patients with middle cerebral artery trunk occlusion we investigated whether the diffusion-weighted imaging- the Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) predicts short-term neurological recovery after intravenous recombinant tissue plasminogen activator administration, and investigated how DWI-ASPECTS relates to clinical outcome. Methods— Dramatic recovery was defined as a ≥10-point reduction or a total National Institutes of Health Stroke Scale score of 0 to 1 at 24 hours and 7 days. Early recanalization was defined as recanalization within 1 hours after intravenous recombinant tissue plasminogen activator. Favorable outcome at 3 months was defined as a modified Rankin Scale score of 0 to 2. Results— Sixty-six patients (median age [interquartile], 79 [70–85] years, male; 34 [52%]) were enrolled. DWI-ASPECTS was 6 (5–9). Dramatic recovery was seen in 16 (24%) and 26 (39%) patients at 24 hours and on day 7, respectively. Early recanalization occurred in 22 (33%) patients. DWI-ASPECTS ≥7 was an independent predictor of dramatic recovery at 24 hours (odds ratio, 100.85; 95% confidence interval, 4.29–2371.40; P=0.004) and 7 days (odds ratio, 14.15; 95% confidence interval, 2.21–90.48; P=0.005). Although the favorable outcome rate was not significantly different between patients with DWI-ASPECTS ≥7 with and without early recanalization (60% versus 31%; P=0.228), it was statistically more frequent in patients with DWI-ASPECTS <7 with early recanalization than those without early recanalization (38% versus 0%; P=0.017). Conclusions— DWI-ASPECTS predicted short-term recovery in patients with middle cerebral artery trunk occlusion receiving intravenous recombinant tissue plasminogen activator. In patients with lower DWI-ASPECTS, there may still be benefit from early recanalization.
Flow in a wavy-walled channel lined with a poroelastic layer

Motivated by physiological flows in capillaries, venules and the pleural space, the pressure-driven flow of a Newtonian fluid in a two-dimensional wavy-walled channel is investigated theoretically. The sinusoidal wavy shape is due to the configuration of underlying cells, their nuclei and intercellular junctions or clefts. The walls are lined with a thin poroelastic layer that models the glycocalyx coating of the cell surface. The upper and lower wavy walls are offset axially by the phase angle $\Phi $, where $\Phi\,{ =}\, 0$ ($\upi$) yields an antisymmetric (symmetric) channel. Biphasic theory is employed for the poroelastic layer and the flow is solved by a lubrication approximation using a small parameter, $\delta\,{\ll}\,1$, where $\delta$ is the channel width/wavelength ratio. The velocity fields in the core and layer are determined as perturbation expansions in $\delta^2$ and finite-Reynolds-number effects occur at $O(\delta^2)$ assuming $\delta^2\hbox{\it Re}\,{\ll}\,1$. When the hydraulic resistivity, $\alpha$, the ratio of the channel width to the Darcy permeability, is sufficiently large and $\Phi$ is near enough to $\upi$, the flow develops a trapped recirculation eddy within the glycocalyx layer near the widest part of the channel. This can be of significance to transport through the cellular boundary, since that location corresponds to intercellular clefts through which important fluid and solute exchange occurs. Increasing $|\Phi\,{-}\,\upi |$ diminishes the recirculation region. Increasing the Reynolds number moves the recirculation slightly upstream. Both layer velocity and wall shear stresses decrease as $\alpha$ increases and support the appearance of flow recirculation. Further, the wavy geometry allows a portion of the flow to enter and exit the layer, which provides a mechanism for convective transport between these two regions that otherwise have only diffusive interactions. The relevant Péclet number is $\hbox{\it Pe}\,{=}\,V^*_nb/D$ where $D$ is molecular diffusivity and $V^*_n$ is the normal velocity to the glycocalyx layer. For large molecules, $\hbox{\it Pe}\,{=}\,O(10^2)$ or higher, so the convective transport is important. The solid displacement, dictated by the layer flow field, increases as $\alpha$ increases.
A Reduced-State-Space Markov Chain Monte Carlo Method for Iterative Spatial Multiplexing MIMO

Markov Chain Monte Carlo (MCMC) method ap- plied as Multiple-Input-Multiple-Output (MIMO) detector has shown near capacity performance. However, the conventional MCMC method suffers from an error floor in the high signal-to- noise (SNR) region. This paper proposes a novel robust reduced- state-space MCMC (RSS-MCMC) method, which utilizes the ap rioriinformation for the first time to qualify the reliable decoded bits from the entire signal space. The new robust MCMC method is developed to deal with the unreliable bits by using the reliably decoded bit information to cancel the interference that they generate. The performance comparison shows that the new technique has improved performance compared to the conventional approach, and further complexity reduction can be obtained with the assistance of the ap rioriinformation. Furthermore, the complexity and performance tradeoff of the new method can be optimized for practical realizations.
Performance Improvement of Audio-Visual Speech Recognition with Optimal Reliability Fusion

In state-of-the-art ASR technology, audio and video (AV) information based speech recognition is one of key challenges to cope with noise problem. AV fusion is one of the robust approaches for ASR. The main issues of AV fusion is where and how to integrate the two modalities' information. To enhance the AV fusion performance the paper [1] has proposed the optimum reliability fusion (ORF) and applied the ORF to AV speaker identification. In this paper we adopt the ORF based fusion in AV based speech recognition and evaluate the performance improvement in that domain. The ORF's main idea is to introduce weighting factors in score-base reliability measure (SCRM) for solving the over- or under-estimation problem in SCRM calculation. Our AV speech recognition system is implemented for Korean digit recognition using SAMSUMG AV database. Experimental results show that ORF effectively reduce the relative error rate of 42.8% in comparison with the baseline system adopt the previous AV fusion scheme [2].
Calcium-Activated Potassium Channels in Ischemia–Reperfusion: Learning for the Clinical Application

The article by Tano and Gollasch published in Frontiers in Physiology reviewed the involvement of Ca++-sensitive K+ channels in ischemia and reperfusion, with cardio-vascular and brain models mostly discussed (1), where the increased Na+ inflow activates Na+–Ca++ exchanger, and leads to cell membrane depolarization (2). Activated Na+–Ca++ exchanger works to pump Na+ out and Ca++ in (2). The increase in intracellular Ca++ results in activation of various Ca++-sensitive K+ channels to establish K+ influx and hyperpolarization (1). In the scenario of lung transplantation, the graft is subjected to ischemia followed by reperfusion (following standard transplantation or during ex vivo perfusion). Graft ischemia results in inhibition of Na+–K+ ATPase, inhibition of K+ ATP channels, drop of intracellular K+, and the absence of flow favors cell membrane depolarization (2). Cell membrane depolarization and inactive K+ ATP channels would be associated with increased NADPH oxidase (NOX2) activity and increased production of reactive oxygen species (ROS) (2). ROS results in inflammasomes priming (3). Decreased intracellular K+ results in inflammasomes activation. Inflammasomes activation results in caspase 1 activation, which activates pro IL1β and pro IL18 (3). Both IL1β and IL18 are able to induce IL6. Accordingly, the enhancement of Ca++-sensitive K+ channels during lung graft ischemia would be expected to provide protection through antagonizing membrane depolarization (i.e., favoring hyperpolarization), which would attenuate ROS production, leading to abortion of inflammasomes priming and activation, and accordingly the release of pro-inflammatory cytokines. The Toronto team of lung transplantation has achieved significant inhibition of cytokines production within the lung graft through gene therapy during ex vivo lung perfusion (adenoviral IL10 delivery), which correlated with decreased incidence of primary graft dysfunction and chronic lung allograft dysfunction after transplantation (4). However, another study reported similar level of inhibited cytokines production through inhalation of 2% hydrogen. This was achieved through the up-regulation of hemeoxygenase-1 (HO-1) (5). HO-1 catalyzes the production of carbon monoxide, which activates big conductance Ca++-activated K+ channels (6). These findings highlight the possible protective role of the enhancement of Ca++-activated K+ channels during lung graft ischemia. Accordingly, further studies should be conducted to investigate the actual status of these channels during lung graft ischemia prior to transplantation. In addition, pharmacological activation of these channels could be a good target to protect the lung graft during transplantation, with corresponding improvement of the clinical outcome.