Miracle-dz/UniFAR-FTUs huggingface dataset:
Variability of apatite fission-track annealing kinetics: III. Extrapolation to geological time scales

Abstract A new model for examining fission-track data from natural specimens has been developed on the basis of new laboratory data describing fission-track annealing in a wide variety of apatites and the empirical correction for fission-track length anisotropy presented in earlier papers. Using revised and simplified statistical methods, we examine how well various empirical equations are able to fit the laboratory data and reproduce expected behavior on geological time scales. Based on the latter criterion, we find that so-called “fanning Arrhenius” models of mean track length are not the bestsuited for our data. Instead, we find that fitting c-axis projected lengths with a model that incorporates some curvature on an Arrhenius plot produces results that are in better agreement with the available geological benchmarks. In examining the relative annealing behavior of apatites with different resistance to annealing, we find that the laboratory-time-scale behavior of any two apatites can be reproduced well by a simple one- or two-parameter equation. This function is used to convert the reduced fission-track length of one apatite that has undergone a certain time-temperature history into the length that would be measured in a second, less-resistant apatite that has undergone the same history. Using this conversion, we create a single model that encompasses the annealing behavior of all of the apatites we studied. The predictions made by this model match closely those made by fits to data for individual apatites. We therefore infer that, although the conversion equation is imperfect, it presents an excellent practical solution to characterizing the range of kinetic variability for annealing of fission tracks in apatite.
X-TREPAN: A MULTI CLASS REGRESSION

In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
Customer collaboration in new product development : the moderating impact of internal R&D efforts

In this study, we examine the moderating impact of internal R&D efforts on the relationship between customer collaboration and both, new-to-the-firm innovation performance and newto- the-market innovation performance. Conducting OLS regression analyses, supplemented with the Johnson-Neyman technique, on a sample of 305 firms, we observe that firms can benefit from customer collaboration in terms of new-to-the-firm innovation performance. In addition, we observe that firms can benefit from customer collaboration in terms of new-tothe- market innovation performance only when at least 22% of the employees are employed in order to support internal R&D activities. These findings have important theoretical and managerial implications in the field of open innovation.
A note on the matrix arithmetic-geometric mean inequality

This note proves the following inequality: If $n=3k$ for some positive integer $k$, then for any $n$ positive definite matrices $\bA_1,\bA_2,\dots,\bA_n$, the following inequality holds: \begin{equation*}\label{eq:main} \frac{1}{n^3} \, \Big\|\sum_{j_1,j_2,j_3=1}^{n}\bA_{j_1}\bA_{j_2}\bA_{j_3}\Big\| \,\geq\, \frac{(n-3)! }{n! } \, \Big\|\sum_{\substack{j_1,j_2,j_3=1,\\\text{$j_1$, $j_2$, $j_3$ all distinct}}}^{n}\bA_{j_1}\bA_{j_2}\bA_{j_3}\Big\|, \end{equation*} where $\|\cdot\|$ represents the operator norm. This inequality is a special case of a recent conjecture proposed by Recht and R\'{e} (2012).
Bounds on Energy, Magnetic Helicity, and Cross Helicity Dissipation Rates of Approximate Deconvolution Models of Turbulence for MHD Flows

We consider a family of high accuracy, approximate deconvolution models of turbulent magnetohydrodynamic flows. For body force driven turbulence, we prove directly from the model's equations of motion the following bounds on the model's time averaged energy dissipation rate ⟨ϵADM⟩, time averaged cross helicity dissipation rate ⟨γ×, ADM⟩ and magnetic helicity dissipation rate ⟨γmag,ADM⟩, where 𝒰, ℬ, L are the global velocity scale, global magnetic field scale and length scale, R is a dimensionless constant related to fluid and magnetic Reynolds numbers, given precisely in Section 2.2, S is the reciprocal of the product of fluid density times free-space permeability and δ is the LES filter radius.
GLUTAMINE SYNTHETASE PROTEIN

The effects of chronic liver insufficiency resulting from end-to-side portacaval anastomosis (PCA) on glutamine synthetase (GS) activities, protein and gene expression were studied in brain, liver and skeletal muscle of male adult rats. Four weeks following PCA, activities of GS in cerebral cortex and cerebellum were reduced by 32% and 37% (p<0.05) respectively whereas GS activities in muscle were increased by 52% (p<0.05). GS activities in liver were decreased by up to 90% (p<0.01), a finding which undoubtedly reflects the loss of GS-rich perivenous hepatocytes following portal-systemic shunting. Immunoblotting techniques revealed no change in GS protein content of brain regions or muscle but a significant loss in liver of PCA rats. GS mRNA determined by semi-quantitative RT-PCR was also significantly decreased in the livers of PCA rats compared to sham-operated controls. These findings demonstrate that PCA results in a loss of GS gene expression in the liver and that brain does not show a compensatory induction of enzyme activity, rendering it particularly sensitive to increases in ammonia in chronic liver failure. The finding of a post-translational increase of GS in muscle following portacaval shunting suggests that, in chronic liver failure, muscle becomes the major organ responsible for the removal of excess blood-borne ammonia.
Finite Element Analysis of Cable-Stayed Bridges with Appropriate Initial Shapes Under Seismic Excitations Focusing on Deck-Stay Interaction

© 2012 Liu and Wang, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Finite Element Analysis of Cable-Stayed Bridges with Appropriate Initial Shapes Under Seismic Excitations Focusing on Deck-Stay Interaction
Modeling dust as component minerals in the Community Atmosphere Model: Development of framework and impact on radiative forcing

Abstract. The mineralogy of desert dust is important due to its effect on radiation, clouds and biogeochemical cycling of trace nutrients. This study presents the simulation of dust radiative forcing as a function of both mineral composition and size at the global scale, using mineral soil maps for estimating emissions. Externally mixed mineral aerosols in the bulk aerosol module in the Community Atmosphere Model version 4 (CAM4) and internally mixed mineral aerosols in the modal aerosol module in the Community Atmosphere Model version 5.1 (CAM5) embedded in the Community Earth System Model version 1.0. 5 (CESM) are speciated into common mineral components in place of total dust. The simulations with mineralogy are compared to available observations of mineral atmospheric distribution and deposition along with observations of clear-sky radiative forcing efficiency. Based on these simulations, we estimate the all-sky direct radiative forcing at the top of the atmosphere as + 0.05 Wm−2 for both CAM4 and CAM5 simulations with mineralogy. We compare this to the radiative forcing from simulations of dust in release versions of CAM4 and CAM5 (+0.08 and +0.17 Wm−2) and of dust with optimized optical properties, wet scavenging and particle size distribution in CAM4 and CAM5, −0.05 and −0.17 Wm−2, respectively. The ability to correctly include the mineralogy of dust in climate models is hindered by its spatial and temporal variability as well as insufficient global in situ observations, incomplete and uncertain source mineralogies and the uncertainties associated with data retrieved from remote sensing methods.
Year Paper tmle : An R Package for Targeted Maximum Likelihood Estimation

Targeted maximum likelihood estimation (TMLE) presents an approach for construction of an efficient double-robust semi-parametric substitution estimator of a target feature of the data generating distribution, such as a statistical association measure or a causal effect parameter. tmle is a recently developed R package that implements TMLE for estimation of the effect of a binary treatment at a single point in time on an outcome of interest, controlling for user supplied covariates: the additive treatment effect, the relative risk, the odds ratio. The package allows outcome data with missingness, and experimental units that contribute repeated records of the point-treatment data structure, thereby allowing this package to analyze longitudinal data structures. The TMLE of the direct effect of the binary treatment, controlling for a binary intermediate variable on the pathway from treatment to the outcome, is also implemented. Estimation of the parameters of a marginal structural model for binary treatments is also provided. Relevant factors of the likelihood may be modeled or fit by user-specified commands, or fit dataadaptively internally. Effect estimates, variances, p-values, and 95% confidence intervals are provided by the software.
A Fast Search Algorithm for Large Video Database Using HOG Based Features

In this paper, we propose a novel fast video search algorithm for large video database. Histogram of Oriented Gradients (HOG) has been reported which can be reliably applied to object detection, especially pedestrian detection. We use HOG based features as a feature vector of a frame image in this study. Combined with active search, a temporal pruning algorithm, fast and robust video search can be achieved. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip more accurately and robust against Gaussian noise than conventional fast video search algorithm.
A side sensitive modified group runs control chart to detect shifts in the process mean

Gadre and Rattihalli [5] have introduced the Modified Group Runs (MGR) control chart to identify the increases in fraction non-conforming and to detect shifts in the process mean. The MGR chart reduces the out-of-control average time-to-signal (ATS), as compared with most of the well-known control charts. In this article, we develop the Side Sensitive Modified Group Runs (SSMGR) chart to detect shifts in the process mean. With the help of numerical examples, it is illustrated that the SSMGR chart performs better than the Shewhart's X¯ chart, the synthetic chart [12], the Group Runs chart [4], the Side Sensitive Group Runs chart [6], as well as the MGR chart [5]. In some situations it is also superior to the Cumulative Sum chart p9] and the exponentially weighed moving average chart [10]. In the steady state also, its performance is better than the above charts.
Performance Evaluation of Priority-Based CDMA Systems in the Presence of Multirate Poisson Traffic

Next-generation cellular networks are expected to support a wide range of communication technologies and user devices. Under such conditions, proper modelling of user traffic and system performance evaluation is of major importance. In this paper, we employ mathematical modelling techniques to analyse the performance of a single CDMA cell under the presence of user-generated multirate Poisson traffic. We also explicitly consider different traffic priority classes in the system. The proposed model enables us to determine the resource occupancy of the cellular system at different system states and to accurately calculate call blocking probabilities for different services. In particular, we describe the CDMA system as a continuous-time Markov chain and derive formulas for system state probabilities. We also perform thorough simulation experiments to validate the accuracy of the derived equations.
THE EFFECTS OF CHEATING ON DECEPTION DETECTION DURING A SOCIAL DILEMMA

Research by social psychologists and others consistently finds that people are poor at detecting attempted deception by others. However, Tooby and Cosmides (cognitive psychologists who favor evolutionary analyses of behavior) have argued and shown that humans have evolved a special “cognitive module” for detecting cheaters. Their research suggests that people are good at detecting cheating by group members. These two literatures seem to be at odds with one another. The hypothesis of this research was that when participants are told a lie by a fellow group member whose attempted deception involves cheating on a task that affects their outcomes, they will be good at detecting deception. In this experiment, participants played blackjack in groups using a social dilemma paradigm. Participants’ outcomes were either interdependent or independent with a confederate’s outcomes. It was predicted that participants whose outcomes were interdependent with the confederate would be better at detecting deception by the confederate than those participants whose outcomes were independent from the confederate’s outcomes. Results indicate that when judging other participants’ lies interdependent players were more successful at deception detection than independent players but were not more sensitive to the lies. This effect may be driven by the truth bias, people assume that their interaction partners are truthful which would explain why sensitivity measures (which remove response biases) did not show the hypothesized effect. Independent players were not more successful or sensitive when judging the confederate’s lies. The failure to find the hypothesized effect may be due to methodological factors. Both participants heard may have had their cheating detection modules activated when hearing the instructions for the experiment which implied that cheating could occur. Overall success rates support this idea because they were significantly higher than success rates reached by most deception detection research (50%) which may be indicative that both participants cheating detection modules were active. Results also indicate that as the number of lies told increases overall success decreases but success at detecting lies and sensitivity increase. Thus the more lies that are told the better people are at catching them.
Second-order topological corner states with ultracold atoms carrying orbital angular momentum in optical lattices

We study the single-particle properties of a system formed by ultracold atoms loaded into the manifold of $l=1$ Orbital Angular Momentum (OAM) states of an optical lattice with a diamond chain geometry. Through a series of successive basis rotations, we show that the OAM degree of freedom induces phases in some tunneling amplitudes of the tight-binding model that are equivalent to a net $\pi$ flux through the plaquettes and give rise to a topologically non-trivial band structure and protected edge states. In addition, we demonstrate that quantum interferences between the different tunneling processes involved in the dynamics may lead to Aharanov-Bohm caging in the system. All these analytical results are confirmed by exact diagonalization numerical calculations.
EUI Working Papers

This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. We argue that noncausal autoregressive models are especially well suited for modeling expectations. Unlike conventional causal autoregressive models, they explicitly show how the considered economic variable is a¤ected by expectations and how expectations are formed. Noncausal autoregressive models can also be used to examine the related issue of backward-looking or forward-looking dynamics of an economic variable. We show in the paper how the parameters of a noncausal autoregressive model can be estimated by the method of maximum likelihood and how related test procedures can be obtained. Because noncausal autoregressive models cannot be distinguished from conventional causal autoregressive models by second order properties or Gaussian likelihood, a detailed discussion on their speci cation is provided. Motivated by economic applications we explicitly use a forward-looking autoregressive polynomial in the formulation of the model. This is di¤erent from the practice used in previous statistics literature on noncausal autoregressions and, in addition to its economic motivation, it is also convenient from a statistical point of view. In particular, it facilitates obtaining likelihood based diagnostic tests for the speci ed orders of the backward-looking and forward-looking autoregressive polynomials. Such test procedures are not only useful in the speci cation of the model but also in testing economically interesting hypotheses such as whether the considered variable only exhibits forward-looking behavior. As an empirical application, we consider modeling the U.S. ination dynamics which, according to our results, is purely forward-looking. y We thank Antti Ripatti for useful comments. Financial support from the Academy of Finland and the Okobank Group Research Foundation is gratefully acknowledged. This research was completed while the second author was a Fernand Braudel Fellow in the Economics Department of the European Univeristy Institute. * Department of Economics, University of Helsinki, P.O. Box 17 (Arkadiankatu 7), FIN00014 University of Helsinki, Finland, e-mail: markku.lanne@helsinki. ** Department of Mathematics and Statistics, University of Helsinki, P.O. Box 68 (Gustaf Hällströmin katu 2b), FIN00014 University of Helsinki, Finland, e-mail: pentti.saikkonen@helsinki.
DOMAIN-INVARIANT REPRESENTATION LEARNING

The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the domain-specific latent feature representations directly in the hidden activation space. Although some standard distribution matching approaches exist that can be interpreted as the matching of weighted sums of moments, e.g. Maximum Mean Discrepancy, an explicit order-wise matching of higher order moments has not been considered before. We propose to match the higher order central moments of probability distributions by means of order-wise moment differences. Our model does not require computationally expensive distance and kernel matrix computations. We utilize the equivalent representation of probability distributions by moment sequences to define a new distance function, called Central Moment Discrepancy (CMD). We prove that CMD is a metric on the set of probability distributions on a compact interval. We further prove that convergence of probability distributions on compact intervals w. r. t. the new metric implies convergence in distribution of the respective random variables. We test our approach on two different benchmark data sets for object recognition (Office) and sentiment analysis of product reviews (Amazon reviews). CMD achieves a new state-of-the-art performance on most domain adaptation tasks of Office and outperforms networks trained with Maximum Mean Discrepancy, Variational Fair Autoencoders and Domain Adversarial Neural Networks on Amazon reviews. In addition, a post-hoc parameter sensitivity analysis shows that the new approach is stable w. r. t. parameter changes in a certain interval. The source code of the experiments is publicly available1.
Preliminary results on the genetic diversity of Carpinus betulus in Carpathian populations.

The aim of this study was to obtain data upon the genetic structure of Carpinus betulus in populations from Romania. Four recently characterized nuclear microsatellite markers have been tested in three Carphatian populations. Between 14 and 23 alleles per marker were observed. On average, the number of alleles per locus (Na) and the effective number of alleles (Ne) per locus was 1.745 and 1.525, respectively. The expected heterozygosity (He) for the populations ranged from 0.289 to 0.325. A total of five private alleles were observed in two populations.
STOCHASTIC DEEP LEARNING ALGORITHMS

The question why deep learning algorithms generalize so well has attracted increasing research interest. However, most of the well-established approaches, such as hypothesis capacity, stability or sparseness, have not provided complete explanations (Zhang et al., 2017; Kawaguchi et al., 2017). In this work, we focus on the robustness approach (Xu & Mannor, 2012), i.e., if the error of a hypothesis will not change much due to perturbations of its training examples, then it will also generalize well. As most deep learning algorithms are stochastic (e.g., Stochastic Gradient Descent, Dropout, and Bayes-by-backprop), we revisit the robustness arguments of Xu & Mannor, and introduce a new approach – ensemble robustness – that concerns the robustness of a population of hypotheses. Through the lens of ensemble robustness, we reveal that a stochastic learning algorithm can generalize well as long as its sensitiveness to adversarial perturbations is bounded in average over training examples. Moreover, an algorithm may be sensitive to some adversarial examples (Goodfellow et al., 2015) but still generalize well. To support our claims, we provide extensive simulations for different deep learning algorithms and different network architectures exhibiting a strong correlation between ensemble robustness and the ability to generalize.
0068 TITLE : Defining Translational Reprogramming in Tuberous Sclerosis Complex

It is common wisdom that codon usage bias has evolved in the selection for efficient translation, in which highly expressed genes are encoded predominantly by optimal codons. However, a growing body of evidence suggests regulatory roles for non-optimal codons in translation dynamics. Here we report that in mammalian cells, non-optimal codons play a critical role in promoting selective mRNA translation during amino acid starvation. During starvation, in contrast to genes encoding ribosomal proteins whose translation is highly sensitive to amino acid deprivation, translation of genes involved in the cellular protein degradation pathways remains unaffected. We found that these two gene groups bear different codon composition, with non-optimal codons being highly enriched in genes encoding the ubiquitin–proteasome system. Supporting the selective tRNA charging model originally proposed in Escherichia coli, we demonstrated that tRNA isoacceptors decoding rare codons are maintained in translating ribosomes under amino acid starvation. Finally, using luciferase reporters fused with endogenous gene-derived sequences, we show that codon optimality contributes to differential mRNA translation in response to amino acid starvation. These results highlight the physiological significance of codon usage bias in cellular adaptation to stress.
The effects of oxide traps on the large-signal transient response of analog MOS circuits

Hysteresis in the threshold voltage of MOSFETs due to oxide traps is discussed, which can impose serious limitations on the accuracy and speed of analog circuits. The measured magnitude of the input-referred hysteresis ranges from 100 mu V to more than 1 mV in NMOS devices stressed with positive gate-source voltages ranging from 1 to 5 V on microsecond-to-millisecond time scales. This hysteresis is explained by a model in which electrons tunnel to oxide traps close to the interface. >