Miracle-dz/UniFAR-FTUs huggingface dataset:
Photogrammetry-based structural damage detection by tracking a visible laser line

Research works on photogrammetry have received tremendous attention in the past few decades. One advantage of photogrammetry is that it can measure displacement and deformation of a structure in a fully non-contact, full-field manner. As a non-destructive evaluation method, photogrammetry can be used to detect structural damage by identifying local anomalies in measured deformation of a structure. Numerous methods have been proposed to measure deformations by tracking exterior features of structures, assuming that the features can be consistently identified and tracked on sequences of digital images captured by cameras. Such feature-tracking methods can fail if the features do not exist on captured images. One feasible solution to the potential failure is to artificially add exterior features to structures. However, painting and mounting such features can introduce unwanted permanent surficial modifications, mass loads, and stiffness changes to structures. In this article, a photogrammetry-based structural damage detection method is developed, where a visible laser line is projected to a surface of a structure, serving as an exterior feature to be tracked; the projected laser line is massless and its existence is temporary. A laser-line-tracking technique is proposed to track the projected laser line on captured digital images. Modal parameters of a target line corresponding to the projected laser line can be estimated by conducting experimental modal analysis. By identifying anomalies in curvature mode shapes of the target line and mapping the anomalies to the projected laser line, structural damage can be detected with identified positions and sizes. An experimental investigation of the damage detection method was conducted on a damaged beam. Modal parameters of a target line corresponding to a projected laser line were estimated, which compared well with those from a finite element model of the damaged beam. Experimental damage detection results were validated by numerical ones from the finite element model.
Helping effort in a dominance hierarchy

In many cooperatively breeding species, group members form a dominance hierarchy or queue to inherit the position of breeder. Models aimed at understanding individual variation in helping behavior, however, rarely take into account the effect of dominance rank on expected future reproductive success and thus the potential direct fitness costs of helping. Here we develop a kin-selection model of helping behavior in multimember groups in which only the highest ranking individual breeds. Each group member can invest in the dominant's offspring at a cost to its own survivorship. The model predicts that lower ranked subordinates, who have a smaller probability of inheriting the group, should work harder than higher ranked subordinates. This prediction holds regardless of whether the intrinsic mortality rate of subordinates increases or decreases with rank. The prediction does not necessarily hold, however, where the costs of helping are higher for lower ranked individuals: a situation that may be common in vertebrates. The model makes two further testable predictions: that the helping effort of an individual of given rank should be lower in larger groups, and the reproductive success of dominants should be greater where group members are more closely related. Empirical evidence for these predictions is discussed. We argue that the effects of rank on stable helping effort may explain why attempts to correlate individual helping effort with relatedness in cooperatively breeding species have met with limited success. Copyright 2005.
PIK Report No. 102

Most models used in climate economics so far are full-employment models. In Germany, as in other countries suffering from persistent unemployment, climate policy can only be successful if it is embedded in an economic policy that generates additional jobs at a large scale. To investigate the possibility for such embedding, a long-term model of the German economy is needed. lagom sim is such a model. It is a dynamic stochastic general equilibrium model; its dynamics is based on a sequence of temporary equilibria with incomplete markets. The model architecture works without needing the problematic fictions of a macro-economic representative agent or a Walrasian auctioneer. The mechanisms of demand and supply on different markets display the frictions of real markets, where price adjustments take time. First simulation results suggest that it is possible to reduce German unemployment by half in a decade by means of suitable incentives for financial markets. This possibility resembles past experiences of Sweden and Austria. If poorly managed, it would result in a massive increase of greenhouse gas emissions. At the same time, it offers opportunities for a Pareto improving climate policy that combines higher growth with lower emissions. To investigate these opportunities in more detail, further model developments are recommended. The lagom family of models is emerging out of the work of the research group on globalization and financial markets at PIK currently involving A. Biewald, H. Förster, A. Haas, C.Jaeger, J. Krause, F. Meissner, and M. Welp. The help of Rupert Klein in producing lagom sim has been invaluable. Moreover, two sets of ideas on economic dynamics have been particularly inspiring developed by K. Hasselmann at MPI in Hamburg, and by S. Hallegatte and J.-C. Hourcade at CIRED in Paris. Finally, I am grateful to the Potsdam University students attending the course on the German economy in summer 2005 for their curiosity, questions, and comments. Responsibility for errors stays firmly with the author. This document describes lagom sim version 0.1. The model is implemented using the Vensim software for modelling dynamical systems. The code is available from the author upon request. Comments are welcome to carlo.jaeger@pikpotsdam.de. lagom is a swedish word denoting a sense of balance and harmony, with a flavor of equilibrium but richer, perhaps akin to the chinese dao.
How liberals and conservatives respond to feasibility and desirability appeals in anti-tobacco campaigns

ABSTRACT Using two experiments, this study aims to investigate how politically liberal or conservative message recipients respond to anti-tobacco appeals. The results show that in Study 1, respondents were exposed to a message about price policy. In Study 2, they were exposed to a message about a warning policy. In both studies, liberal participants more favorably evaluated anti-tobacco messages emphasizing feasibility rather than desirability, whereas conservative participants more positively evaluated messages emphasizing desirability rather than feasibility. Implications for policymakers and marketers are discussed.
Notions of Weak Pseudorandomness and GF (2 n )-Polynomials

We study the role of polynomials over the finite field GF (2) in constructing and attacking various weak notions of pseudorandomness. First, we consider the construction of weak pseudorandom generators using GF (2)-polynomials. We study the generator of [Nisan, STOC’90], that fools space-bounded nonuniform distinguishers, whose standard instantiation can be seen as a GF (2)-polynomial map. Among other results, we show that its resilience to (nonuniform) space-bounded machines is sensitive to the order of its output bits, and rule out some natural generalizations of the generator. We also consider GF (2)-polynomial-based generators that have small bias, meaning that they fool all (bit-)linear tests. We present a simple construction of a small-bias generator (called the geometric generator), which is similar (but not identical) to the powering construction presented by [Alon et al., RS&A’92]. A variant of this generator has shorter seed than the classical constructions of Alon et al., if the reciprocal of the bias required is polylogarithmic in the output length. We also use a variant of the geometric generator to construct a separation between small-bias and fooling small space that has good parameters, giving an exponential-stretch generator with exponentially small bias that is O(1)-space distinguishable (by a nonuniform machine). Second, we study how GF (2)-polynomials can be used to distinguish various distributions from random ones. We provide a full picture of how various notions of GF (2)-linear distinguishers relate to standard linear (that is, GF (2)-linear) tests, and derive a reduction from having small-bias to fooling GF (2)-linear tests. In fact, this reduction is used to prove the small bias of the geometric generator mentioned above. We also conduct a study of GF (2)bilinear and GF (2)-quadratic forms, and in specific characterize the sets of GF (2)-bilinear forms that can be computed exactly, or approximated to various rates, by GF (2)-bilinear forms. Higher-degree polynomials are also studied, and specifically we consider a recent result of [Viola, CCC’08] that showed that the sum of d independent instances of a small-bias generator fools polynomials of degree at most d. We show the tightness of this result with respect to the number of instances required, showing an explicit degree-d+ 1 polynomial that distinguishes this construction from random with constant gap.
mGluR‐dependent persistent firing in entorhinal cortex layer III neurons

Persistent firing is believed to be a crucial mechanism for memory function including working memory. Recent in vivo and in vitro findings suggest an involvement of metabotropic glutamate receptors (mGluRs) in persistent firing. Using whole‐cell patch‐recording techniques in a rat entorhinal cortex (EC) slice preparation, we tested whether EC layer III neurons display persistent firing due to mGluR activation, independently of cholinergic activation. Stimulation of the angular bundle drove persistent firing in 90% of the cells in the absence of a cholinergic agonist. The persistent firing was typically stable for > 4.5 min at which point persistent firing was terminated by the experimenter. The average frequency of the persistent firing was 2.1 Hz, ranging from 0.4 to 5.5 Hz. This persistent firing was observed even in the presence of atropine (2 μm), suggesting that the persistent firing can occur independent of cholinergic activation. Furthermore, ionotropic glutamate and GABAergic synaptic blockers (2 mm kynurenic acid, 100 μm picrotoxin and 1 μm CGP55845) did not block the persistent firing. On the other hand, blockers of group I mGluRs (100 μm LY367385 and 20 μm MPEP) completely blocked or suppressed the persistent firing. An agonist of group I mGluRs (20 μm DHPG) greatly enhanced the persistent firing induced by current injection. These results indicate that persistent firing can be driven through group I mGluRs in entorhinal layer III neurons, suggesting that glutamatergic synaptic input alone could enable postsynaptic neurons to hold input signals in the form of persistent firing.
A high-speed systolic array for computing third-order cumulants

The computational complexity involved in the estimation of higher-order statistics far exceeds that of conventional second-order statistics. This paper presents a parallel-processing system that speeds up the computational process of third-order cumulants. An algorithm for estimating third-order cumulants based on matrix multiplications is presented. Then, a special structure of systolic array system for matrix multiplications is developed. The structure and complexity of the array as well as the internal structure and the delay of the processing elements are discussed. The system is suitable for VLSI implementation.
Research on Direct 3D ECT Imaging: Sensor Modeling and Image Reconstruction

The main work of this paper is to research direct 3D ECT imaging technology. Based on the finite element modeling of a 3D ECT imaging sensor, the sensor sensitivity matrix and simulation data of capacitance measurements were obtained. And then 3D images were reconstructed directly by using linear back projection (LBP), traditional Land Weber iteration and improved Land Weber iteration methods. The reconstruction results show that the images obtained by the improved Land Weber iteration method proposed in this paper are much better than those by LBP and traditional Land Weber iteration method.
The Skeleton of the Internet

Research works concerning AS (Autonomous Systems) level Internet topology measurements typically aim at obtaining near-complete maps of the AS structure. In this paper, we take a fundamentally different approach by inspecting several concurrently visible local views of the AS graph stored at individual BGP route servers. We find that each of these views exhibits the characteristic properties of complex graphs having power-law degree distribution, large clustering coefficient and the small world property. As a main contribution, the intersection of these views is investigated to identify the skeleton of the Internet consisting of edges seen by most of the ASes. Our measurements support the surprising claim that this skeleton is a scale-free complex network, having a giant connected component with a dense part in its heart forming the critical AS level core. We identify the edges in the skeleton as critical infrastructure, any changes of which induces an Internet-wide effect with BGP updates propagating to all ASes. Finally, we reinterpret the path inflation metric using the local view approach and show that local path inflation can be very diverse in different ASes.
Efficacy and efficiency of algorithm-based fault-tolerance on GPUs

Computer simulations drive innovations in science and industry, and they are gaining more and more importance. However, their high computational demand generates extraordinary challenges for computing systems. Typical high-performance computing systems, which provide sufficient performance and high reliability, are extremely expensive. Modern GPUs offer high performance at very low costs, and they enable simulation applications on the desktop. However, they are increasingly prone to transient effects and other reliability threats. To fulfill the strict reliability requirements in scientific computing and simulation technology, appropriate fault tolerance measures have to be integrated into simulation applications for GPUs. Algorithm-Based Fault Tolerance on GPUs has the potential to meet these requirements. In this work we investigate the efficiency and the efficacy of ABFT for matrix operations on GPUs. We compare ABFT against fault tolerance schemes that are based on redundant computations and we evaluate its error detection capabilities.
Splatting Errors and Antialiasing

The paper describes three new results for volume rendering algorithms utilizing splatting. First, an antialiasing extension to the basic splatting algorithm is introduced that mitigates the spatial aliasing for high resolution volumes. Aliasing can be severe for high resolution volumes or volumes where a high depth of field leads to converging samples along the perspective axis. Next, an analysis of the common approximation errors in the splatting process for perspective viewing is presented. In this context, we give different implementations, distinguished by efficiency and accuracy, for adding the splat contributions to the image plane. We then present new results in controlling the splatting errors and also show their behavior in the framework of our new antialiasing technique. Finally, current work in progress on extensions to splatting for temporal antialiasing is demonstrated. We present a simple but highly effective scheme for adding motion blur to fast moving volumes.
Image classification based on support vector machine and the fusion of complementary features

Image Classification based on BOW (Bag-of-words) has broad application prospect in pattern recognition field but the shortcomings are existed because of single feature and low classification accuracy. To this end we combine three ingredients: (i) Three features with functions of mutual complementation are adopted to describe the images, including PHOW (Pyramid Histogram of Words), PHOC (Pyramid Histogram of Color) and PHOG (Pyramid Histogram of Orientated Gradients). (ii) The improvement of traditional BOW model is presented by using dense sample and an improved K-means clustering method for constructing the visual dictionary. (iii) An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the fusion of multiple features is adopted. Experiments carried out on Caltech 101 database confirm the validity of the proposed approach. From the experimental results can be seen that the classification accuracy rate of the proposed method is improved by 7%-17% higher than that of the traditional BOW methods. This algorithm makes full use of global, local and spatial information and has significant improvements to the classification accuracy.
What factors influence ward nurses’ recognition of and response to patient deterioration? An integrative review of the literature

In this integrative review, we aimed to: first, identify and summarize published studies relating to ward nurses' recognition of and response to patient deterioration; second, to critically evaluate studies that described or appraised the practice of ward nurses in recognizing and responding to patient deterioration; and third, identify gaps in the literature for further research.
Epsilon PKC Increases Brain Mitochondrial SIRT1 Protein Levels via Heat Shock Protein 90 following Ischemic Preconditioning in Rats

Ischemic preconditioning is a neuroprotective mechanism whereby a sublethal ischemic exposure is protective against a subsequent lethal ischemic attack. We previously demonstrated that SIRT1, a nuclear localized stress-activated deacetylase, is vital for ischemic preconditioning neuroprotection. However, a recent study demonstrated that SIRT1 can also localize to the mitochondria. Mitochondrial localized SIRT1 may allow for a direct protection of mitochondria following ischemic preconditioning. The objective of this study was to determine whether ischemic preconditioning increases brain mitochondrial SIRT1 protein levels and to determine the role of PKCɛ and HSP90 in targeting SIRT1 to the mitochondria. Here we report that preconditioning rats, with 2 min of global cerebral ischemia, induces a delayed increase in non-synaptic mitochondrial SIRT1 protein levels which was not observed in synaptic mitochondria. This increase in mitochondrial SIRT1 protein was found to occur only in neuronal cells and was mediated by PKCε activation. Inhibition of HSP90, a protein chaperone involved in mitochondrial protein import, prevented preconditioning induced increases in mitochondrial SIRT1 and PKCε protein. Our work provides new insights into a possible direct role of SIRT1 in modulating mitochondrial function under both normal and stress conditions, and to a possible role of mitochondrial SIRT1 in activating preconditioning induced ischemic tolerance.
Risk factors associated with first-year students’ intention to drop out from a university in South Africa

ABSTRACT Researchers have been investigating student dropout, and possible reasons leading to it, for quite a long time. Cumulatively, the results of these research attempts show that drop out of students depends on several factors, of which only a relatively small number can directly be dealt with by universities. Therefore, the overall research problem investigated in this study was to timeously identify risk factors impacting on first-year students’ intention to drop out from a university in South Africa in order to manage the potential effect of these factors. By using a cross-sectional quantitative research design, data for the study were collected from first-year students who completed an online survey questionnaire. One of the survey questions requested the respondents to indicate whether they have already (eight weeks after enrolling for the first time) considered dropping out of university; 11.5% of the respondents answered ‘Yes’ to the question. The data was analysed using partial least squares structural equation modelling (PLS-SEM) to get an indication of the effects (direct and indirect) of constructed latent variables on students’ intention to drop out. The outcomes show that only addressing the direct effects leading to drop out (as universities usually do) is not enough and that the indirect effects should also be identified and addressed if one wants to make progress in solving the dropout problem.
Celebrating diversity: a mixture of experts approach for runtime mapping in dynamic environments

Matching program parallelism to platform parallelism using thread selection is difficult when the environment and available resources dynamically change. Existing compiler or runtime approaches are typically based on a one-size fits all policy. There is little ability to either evaluate or adapt the policy when encountering new external workloads or hardware resources. This paper focuses on selecting the best number of threads for a parallel application in dynamic environments. It develops a new scheme based on a mixture of experts approach. It learns online which, of a number of existing policies, or experts, is best suited for a particular environment without having to try out each policy. It does this by using a novel environment predictor as a proxy for the quality of an expert thread selection policy. Additional expert policies can easily be added and are selected only when appropriate. We evaluate our scheme in environments with varying external workloads and hardware resources.We then consider the case when workloads use affinity scheduling or are themselves adaptive and show that our approach, in all cases, outperforms existing schemes and surprisingly improves workload performance. On average, we improve 1.66x over OpenMP default, 1.34x over an online scheme, 1.25x over an offline policy and 1.2x over a state-of-art analytic model. Determining the right number and type of experts is an open problem and our initial analysis shows that adding more experts improves accuracy and performance.
Blind Image Quality Assessment via Convolutional Neural Network

This paper proposed a novel blind image quality assessment method that is created by training a convolutional neural network to learn discriminant features of image quality and fitting the features with a support vector regression to get an evaluation score. The pooling procedure is help to reduce the feature dimension and improve computation efficiency. The proposed method does not need any hand-crafted features contrast with most previous BIQA methods. It achieves better performance than previous BIQA methods on LIVE database. The experimental results show that the proposed method has good consistency, robustness and efficiency.
DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY

Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background.