Miracle-dz/UniFAR-FTUs huggingface dataset:
Sizing of Defect Using Magnetic Memory Signal Based on the Reconstruction Algorithm

A metal magnetic memory (MMM) testing technique is an extensively used qualitative method for locating the position of possible damages in ferromagnetic metal structures. The mechanism of the MMM technique is described by the magnetomechanical effect under the constant magnetizing field of the Earth. Discontinuous defect remarkably affects MMM signals. For the exploration of the influences of defect on MMM signals and the determination of a method to size the defect, experimentation and reconstruction calculation were performed in this paper. Stress was imported into a specimen fabricated with a circular defect to generate the stress-induced MMM signal. The defect size and the residual magnetic field of the specimen were measured after loading. The stress-induced magnetization inside the specimen was reconstructed with measured magnetic field signals using a reconstruction algorithm. The influences of the discontinuous defect on the stress-induced magnetization were quantitatively analyzed. A method for defect imaging and sizing using the MMM technique was proposed. Results corroborate that the reconstructed defect width is congruent with the actual defect width, thereby confirming the effectiveness and applicability of the proposed method.
Discrete Laplace mixture model with applications in forensic genetics

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Radial shape discrimination testing for new-onset neovascular age-related macular degeneration in at-risk eyes

We investigated the performance of the handheld radial shape discrimination (hRSD) test in detecting the development of neovascular AMD (nAMD) in a prospective, longitudinal, observational study. Patients diagnosed with unilateral nAMD, with no nAMD in the other eye (the study eye, SE), completed the hRSD test on consecutive, routine clinic visits up to a maximum of 12, or until they were diagnosed with nAMD in the SE based on slit-lamp biomicroscopy and spectral-domain OCT assessment, with fluorescein angiography confirmation. Masked grading was carried out to confirm the diagnosis of nAMD, and to ensure no cases of nAMD were missed. Receiver operating characteristics (ROC) analysis was used to explore the diagnostic performance of the hRSD test relative to clinical diagnosis. Data were available from 179 patients of whom 19 (10.6%; "converters") developed nAMD in the SE. The mean hRSD threshold at conversion was -0.47 (95% CI -0.38 to -0.55) logMAR compared to -0.53 (-0.50 to -0.57) logMAR in 160 non-converters. hRSD threshold in the converters began to decline 190 days before diagnosis of nAMD. The ROC curve demonstrated that at an hRSD cut-off of -0.60 logMAR, sensitivity was 0.79 (0.54-0.94) with a specificity of 0.54 (0.46-0.62); positive and negative predictive values were 0.16 and 0.96 respectively. We conclude that the hRSD test has moderate sensitivity for detecting the earliest stages of nAMD in the at-risk fellow eyes of patients with unilateral nAMD, compared to clinical diagnosis. Given its relative inexpensiveness, ease of use and the inherent connectivity of the platforms it can be presented on, it may have a role in early detection of nAMD in the population at large.
Distant speech recognition of natural spontaneous multi-party conversations

Distant speech recognition (DSR) has gained wide interest recently. While deep networks keep improving ASR overall, the performance gap remains between using close-talking recordings and distant recordings. Therefore the work in this thesis aims at providing some insights for further improvement of DSR performance. The investigation starts with collecting the first multi-microphone and multi-media corpus of natural spontaneous multi-party conversations in native English with the speaker location tracked, i.e. the Sheffield Wargame Corpus (SWC). The state-of-the-art recognition systems with the acoustic models trained standalone and adapted both show word error rates (WERs) above 40% on headset recordings and above 70% on distant recordings. A comparison between SWC and AMI corpus suggests a few unique properties in the real natural spontaneous conversations, e.g. the very short utterances and the emotional speech. Further experimental analysis based on simulated data and real data quantifies the impact of such influence factors on DSR performance, and illustrates the complex interaction among multiple factors which makes the treatment of each influence factor much more difficult. The reverberation factor is studied further. It is shown that the reverberation effect on speech features could be accurately modelled with a temporal convolution in the complex spectrogram domain. Based on that a polynomial reverberation score is proposed to measure the distortion level of short utterances. Compared to existing reverberation metrics like C50, it avoids a rigid early-late-reverberation partition without compromising the performance on ranking the reverberation level of recording environments and channels. Furthermore, the existing reverberation measurement is signal independent thus unable to accurately estimate the reverberation distortion level in short recordings. Inspired by the phonetic analysis on the reverberation distortion via self-masking and overlap-masking, a novel partition of reverberation distortion into the intra-phone smearing and the inter-phone smearing is proposed, so that the reverberation distortion level is first estimated on each part and then combined.
Beyond Spatial Proximity - Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data

Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3–4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors’ spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations.
A neural network cost function for highly class-imbalanced data sets

We introduce a new cost function for the training of a neural network classifier in conditions of high class imbalance. This function, based on an approximate confusion matrix, represents a balance of sensitivity and specificity and is thus well suited to problems where cost functions such as the mean squared error and cross entropy are prone to overpredicting the majority class. The benefit of the new measure is shown on a set of common class-imbalanced datasets using the Matthews Correlation Coefficient as an independent scoring measure.
Growing Tiny Publics

Drawing from fieldwork of 14 small food farms in the Midwest, we describe the on-the-ground, practical challenges of doing and communicating sustainability when local food production is not well-supported. We illustrate how farmers enact learned and honed tactics of sustainability at key sites such as farmers' markets and the Internet with consumers. These tactics reveal tensions with dominant discourse from government, Big Ag, and popular culture. The success of these tactics depends on farmers having fortitude--control, resilience, and the wherewithal to be exemplars of sustainability. In our discussion, we highlight how the local farmers' social movement work constitutes loosely organized small groups connecting others to an amorphous idea of a sustainable society--one that sustains an environmental, economic, local, cultural, and physical way of life. Using Fine's concept of tiny publics, we identify design opportunities for supporting this less directed kind of social movement.
Efficient Hardware Acceleration of CNNs using Logarithmic Data Representation with Arbitrary log-base

Efficient acceleration of Deep Neural Networks is a manifold task. In order to save memory requirements and reduce energy consumption we propose the use of dedicated accelerators with novel arithmetic processing elements which use bit shifts instead of multipliers. While a regular power-of-2 quantization scheme allows for multiplierless computation of multiply-accumulate-operations, it suffers from high accuracy losses in neural networks. Therefore, we evaluate the use of powers-of-arbitrary-log-bases and confirmed their suitability for quantization of pre-trained neural networks. The presented method works without retraining of the neural network and therefore is suitable for applications in which no labeled training data is available. In order to verify our proposed method, we implement the log-based processing elements into a neural network accelerator on an FPGA. The hardware efficiency is evaluated in terms of FPGA utilization and energy requirements in comparison to regular 8-bit-fixed-point multiplier based acceleration. Using this approach hardware resources are minimized and power consumption is reduced by 22.3%.
New postoperative conduction abnormalities after the implantation of a rapid-deployment aortic valve prosthesis.

OBJECTIVES Sutureless and rapid-deployment aortic valves have enlarged the selection of prostheses available for surgical aortic valve replacement. However, the rate of postoperative pacemaker implantation seems to have increased. METHODS In addition to pacemaker implantation, we were interested in the incidence of new postoperative conduction abnormalities, namely left bundle branch block, right bundle branch block and atrioventricular block III after the implantation of the EDWARDS INTUITY Elite™ sutureless prosthesis. All patients undergoing isolated or concomitant surgical aortic valve replacement between May 2014 and May 2017 were included in the study. RESULTS Two hundred patients were operated on. Of them, 14 patients dropped out because of concomitant endocardial or epicardial ablation or because of septal myectomy. Three more patients dropped out because the treatment was converted to conventional aortic valve replacement, and 183 patients remained. At the time of discharge from hospital, 38.1% of the 183 patients had new left bundle branch block, 2.5% of patients had new right bundle branch block and 9.6% had atrioventricular block III requiring either a pacemaker or defibrillator. The postoperative mean transvalvular gradient over the prosthesis was 8.4 ± 3.4 mmHg, the occurrence of paravalvular leakage more than trivial was 1% and hospital mortality was 3.3%. CONCLUSIONS The incidence of new postoperative conduction abnormalities is considerable with the EDWARDS INTUITY Elite rapid-deployment aortic valve prosthesis. We will focus our future research on the follow-up of patients with postoperative left bundle branch block, which was the most frequently observed form of new conduction abnormality.
compboost: Modular Framework for Component-Wise Boosting

In high-dimensional prediction problems, especially in the p ≫ n situation, feature selection is an essential tool. A fundamental method for problems of this type is componentwise gradient boosting, which automatically selects from a pool of base learners – e.g. simple linear effects or component-wise smoothing splines (Schmid & Hothorn, 2008) – and produces a sparse additive statistical model. Boosting these kinds of models maintains interpretability and enables unbiased model selection in high-dimensional feature spaces (Hofner, Hothorn, Kneib, & Schmid, 2012).
Exosomes Released from Tumor-Associated Macrophages Transfer miRNAs That Induce a Treg/Th17 Cell Imbalance in Epithelial Ovarian Cancer

The Treg/Th17 ratio is altered in epithelial ovarian cancer. Exosomal miRNAs from tumor-associated macrophages contribute to this T-cell imbalance, which promotes an immune suppressive tumor microenvironment and favors progression and metastasis of epithelial ovarian cancer cells. The immune microenvironment is crucial for epithelial ovarian cancer (EOC) progression and consists of tumor-associated macrophages (TAM) and T lymphocytes, such as regulatory T cells (Treg) and T helper 17 (Th17) cells. In this study, the Treg/Th17 ratio was significantly higher in EOC in situ and in metastatic peritoneal tissues than in benign ovarian tumors and benign peritoneum. The Treg/Th17 ratio was associated with histologic grade and was an independent prognostic factor for overall survival of EOC patients. On the basis of microarray analysis of exosomes derived from TAMs, we identified miRNAs enriched in the exosomes, including miR-29a-3p and miR-21-5p. When the two miRNA mimics were transfected into CD4+ T cells, they directly suppressed STAT3 and regulated Treg/Th17 cells, inducing an imbalance, and they had a synergistic effect on STAT3 inhibition. Taken together, these results indicate that exosomes mediate the interaction between TAMs and T cells, generating an immune-suppressive microenvironment that facilitates EOC progression and metastasis. These findings suggest that targeting these exosomes or their associated miRNAs might pave the way for the development of novel treatments for EOC.
The MCAPL Framework including the Agent Infrastructure Layer an Agent Java Pathfinder

The MCAPL (Model-checking Agent Programming Languages) framework is a suite of tools for building interpreters for agent programming languages and verifying the correctness of programs running in these interpreters using the model checking technique. It consists of the Agent Infrastructure Layer (AIL) toolkit for building interpreters for rational agent programming languages (BDI languages) as introduced by (Rao and Georgeff 1992) and the Agent JavaPathFinder (AJPF) model checker (L. A. Dennis et al. 2012). AJPF extends the JavaPathfinder (JPF) model checker (Visser et al. 2003) to prove Linear Temporal Logic properties of BDI programs. The distribution also contains a number of programming languages implemented in the AIL. Chief among these are Gwendolen (Louise A. Dennis 2017), the EASS variant of Gwendolen that can be used to program hybrid autonomous systems and GOAL (Hindriks et al. 2001).
TGR5 Activation Promotes Stimulus-Secretion Coupling of Pancreatic β-Cells via a PKA-Dependent Pathway

The Takeda-G-protein-receptor-5 (TGR5) mediates physiological actions of bile acids. Since it was shown that TGR5 is expressed in pancreatic tissue, a direct TGR5 activation in β-cells is currently postulated and discussed. The current study reveals that oleanolic acid (OLA) affects murine β-cell function by TGR5 activation. Both a Gαs inhibitor and an inhibitor of adenylyl cyclase (AC) prevented stimulating effects of OLA. Accordingly, OLA augmented the intracellular cAMP concentration. OLA and two well-established TGR5 agonists, RG239 and tauroursodeoxycholic acid (TUDCA), acutely promoted stimulus-secretion coupling (SSC). OLA reduced KATP current and elevated current through Ca2+ channels. Accordingly, in mouse and human β-cells, TGR5 ligands increased the cytosolic Ca2+ concentration by stimulating Ca2+ influx. Higher OLA concentrations evoked a dual reaction, probably due to activation of a counterregulating pathway. Protein kinase A (PKA) was identified as a downstream target of TGR5 activation. In contrast, inhibition of phospholipase C and phosphoinositide 3-kinase did not prevent stimulating effects of OLA. Involvement of exchange protein directly activated by cAMP 2 (Epac2) or farnesoid X receptor (FXR2) was ruled out by experiments with knockout mice. The proposed pathway was not influenced by local glucagon-like peptide 1 (GLP-1) secretion from α-cells, shown by experiments with MIN6 cells, and a GLP-1 receptor antagonist. In summary, these data clearly demonstrate that activation of TGR5 in β-cells stimulates insulin secretion via an AC/cAMP/PKA-dependent pathway, which is supposed to interfere with SSC by affecting KATP and Ca2+ currents and thus membrane potential.
pyPhenology: A python framework for plant phenology modelling

Phenology, the timing of biological events, is an important aspect of environmental systems and phenology models have been in use since the mid 20th century (Chuine & Régnière, 2017). Phenology is a well established field of research and there are numerous model comparison studies which attempt to find the model which best explains a specific phenological event (Basler, 2016; Tang et al., 2016). Many phenology models are well established (Chuine, de Cortazar-Atauri, Kramer, & Hänninen, 2013), yet studies frequently implement a custom codebase to evaluate them. The pyPhenology package attempts to create a common modelling framework which can be used and referenced in phenology research. This will allow other researchers to check the implementation of mathematical models and add new models without rewriting core functionality.
Simultaneous sparse coding with Laplacian scale mixture prior for image restoration

Abstract. Group-based sparse representation modeling of natural images codes each patch in a group as a sparse linear combination from an over-complete dictionary and assumes that the coding coefficients of each patch have a common set of nonzero support. This model has shown great success in image restoration (IR) applications; however, current group-based sparse models are simple extensions of traditional L0 or L1 sparse models and lack spatial adaption and principled fashion. We propose a group-based sparse model named as simultaneously sparse coding with Laplacian scale mixture (LSM) prior through probabilistic approach. In this model, the representation coefficients of each patch in group are characterized by the same LSM prior, which is dominated by the shared scale variables. By imposing a hyperprior distribution over the scale variables, the sparse coefficients and shared scale variables can be jointly estimated via alternating optimization. Furthermore, we generalize this model to process more general IR problems under the plug-and-play alternating direction method of multipliers framework. Extensive experiments on image denoising, deblurring, and single-image super-resolution demonstrate that the proposed method achieves notable objective and subjective improvements over many state-of-the-art restored methods.
Live fingerprint detection using magnitude of perceived spatial stimuli and local phase information

Abstract. Fingerprint recognition systems are widely used for authentication purposes in security systems. However, fingerprint recognition systems can easily be spoofed by imitations of fingerprints using various spoof materials. A compact and discriminative set of features is needed to discriminate between live and spoof fingerprints. We explore combined Shepard magnitude and orientation for live fingerprint detection using independent quantization of global and local features extracted in spatial and frequency domain. The spatial domain features that are extracted comprise of the magnitude of perceived spatial stimuli that is computed from the net variation of perceived edge information. Rotation invariance is achieved by extracting local features based on phase information of significant frequency components in the frequency domain. The concatenated feature vector associated with a fingerprint image is represented as a two-dimensional histogram. The support vector machine classifier is used to classify the fingerprint as either live or spoof. Experiments are performed on three databases, i.e., the fingerprint liveness detection (LivDet) competition databases of 2011, 2013, and 2015. Results showed a reduction in average error rate to 5.8, 2.2, and 5.3 on LivDet 2011, 2013, and 2015, respectively.
Quantitative bounds on the security-critical resource consumption of JavaScript apps

Current resource policies for mobile phone apps are based on permissions that unconditionally grant or deny access to a resource like private data, sensors and services. In reality, the legitimacy of an access may be context-dependent for example, depending on how often a resource is accessed and in which situation. This thesis presents research into providing bounds on the access of JavaScript apps to security and privacyrelevant resources on mobile devices. The investigated bounds are quantitative and interaction-dependent: for example, permitting one access each time the user presses a specified button. Two novel systems are presented with different approaches to providing these bounds. The system PhoneWrap injects a quantitative policy into an app and enforces the bound dynamically during runtime by monitoring the resource consumption and the user interaction. If the injected bound is exceeded, the resource request is replaced by a deny action. This way, PhoneWrap restricts the unwanted behaviour while the expected functionality can be performed. Policies for this system describe the UI elements which trigger the expected resource consumption and the number of resource units consumed for each interaction. The enforcement of the policies is achieved via wrapping the critical APIs using JavaScript internal features. The injection of a policy can be performed automatically. PhoneWrap is the first system using the lightweight wrapping method to inject policies directly into mobile apps and the first to combine quantitative policies with interaction-dependencies. The second system AmorJiSe statically analyses the resource consumption of a given JavaScript program. This system automatically infers amortised annotations on top of given JavaScript data types. The amortised annotations symbolise reserved resource units stored in the data structures. This way the amount of resource units available to the app is expressed dependent on the size of the data structures. The resulting function types of the UI handlers can be used to extract interaction-dependent bounds. The correctness of these bounds is proven in relation to a resource-aware operational semantics. AmorJiSe extends the known amortised type paradigm to JavaScript with its dynamic object structures and applies this paradigm to the novel domain of mobile resources. Although, the two systems are based on similar resource models and produce similar resource bounds, they use different methods with different properties which are presented in this dissertation.