Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient identification, localization and quantification of grapevine inflorescences and flowers in unprepared field images using Fully Convolutional Networks

Yield and its prediction is one of the most important tasks in grapevine breeding purposes and vineyard management. Commonly, this trait is estimated manually right before harvest by extrapolation, which mostly is labor-intensive, destructive and inaccurate. In the present study an automated image-based workflow was developed for quantifying inflorescences and single flowers in unprepared field images of grapevines, i.e. no artificial background or light was applied. It is a novel approach for non-invasive, inexpensive and objective phenotyping with high-throughput. First, image regions depicting inflorescences were identified and localized. This was done by segmenting the images into the classes "inflorescence" and "non-inflorescence" using a Fully Convolutional Network (FCN). Efficient image segmentation hereby is the most challenging step regarding the small geometry and dense distribution of single flowers (several hundred single flowers per inflorescence), similar color of all plant organs in the fore- and background as well as the circumstance that only approximately 5 % of an image show inflorescences. The trained FCN achieved a mean Intersection Over Union (IOU) of 87.6 % on the test data set. Finally, single flowers were extracted from the "inflorescence"-areas using Circular Hough Transform. The flower extraction achieved a recall of 80.3 % and a precision of 70.7 % using the segmentation derived by the trained FCN model. Summarized, the presented approach is a promising strategy in order to predict yield potential automatically in the earliest stage of grapevine development which is applicable for objective monitoring and evaluations of breeding material, genetic repositories or commercial vineyards.
SRCB Neural Machine Translation Systems in WAT 2018

This is the first time SRCB participates in WAT. This paper describes the Neural Machine Translation systems for the shared translation tasks of WAT 2018. We participated in ASPEC tasks, and submitted results on English-Japanese, Japanese-English and Japanese-Chinese three language pairs. We employed Transformer as baseline model, and experimented subword segmentation, relative position representation and model ensembling. Experiments show that all these methods can yield substantial improvements.
Efficient Large-Scale Stereo Reconstruction using Variational Methods

This thesis investigates the use of convex variational methods for depth reconstruction from optical imagery and fusion of multiple depth maps into combined depth maps with higher accuracy. Dense depth reconstruction from two or more camera views are an important subject of research in computer vision, since measurement density is much higher than other depth sensing techniques, namely active depth sensing via infrared pattern projection or Lidar and Radar based techniques even though the latter ones are more accurate and robust in depth. Other advantages of cameras are their low costs and low power consumption due to their passive sensing principle. Approaches are ranging from autonomous driving cars, obstacle avoidance or surveying UAVs up to detailed reconstruction of remote terrains using space borne imagery. In particular, we propose a fast algorithm for high-accuracy large-scale outdoor dense stereo reconstruction. To this end, we present a structure-adaptive second-order Total Generalized Variation (TGV) regularization which facilitates the emergence of planar structures by enhancing the discontinuities along building facades. Instead of solving the arising optimization problem by a coarse-to-fine approach, we propose a quadratic relaxation approach which is solved by an augmented Lagrangian method. This technique allows for capturing large displacements and fine structures simultaneously. For the application in autonomous driving, we further present an algorithm for dense and direct large-scale visual SLAM that runs in real-time on a commodity notebook. We developed a fast variational dense 3D reconstruction algorithm which robustly integrates data terms from multiple images thus enhancing quality of the image matching. An additional property of this variational reconstruction framework is the ability to integrate sparse depth priors (e.g. from RGB-D sensors or LiDAR data) into the early stages of the visual depth reconstruction, leading to an implicit sensor fusion scheme for a variable number of heterogenous depth sensors. Embedded into a keyframe-based SLAM framework, this results in a memory efficient representation of the scene and therefore (in combination with loop-closure detection and pose tracking via direct image alignment) enables us to densely reconstruct large scenes in real-time. Finally, applied to space-borne remote sensing, we present an algorithm for robustly fusing digital surface models (DSM) with different ground sampling distances and confidences, using explicit surface priors to obtain locally smooth surface models. The optimization using L1 based differences between the separate DSMs and in-
PR EP RI NT Improving Visual Feature Extraction in Glacial Environments

Glacial science could benefit tremendously from autonomous robots, but previous glacial robots have had perception issues in these colorless and featureless environments, specifically with visual feature extraction. Glaciologists use near-infrared imagery to reveal the underlying heterogeneous spatial structure of snow and ice, and we theorize that this hidden near-infrared structure could produce more and higher quality features than available in visible light. We took a custom camera rig to Igloo Cave at Mt. St. Helens to test our theory. The camera rig contains two identical machine vision cameras, one which was outfitted with multiple filters to see only near-infrared light. We extracted features from short video clips taken inside Igloo Cave at Mt. St. Helens, using three popular feature extractors (FAST, SIFT, and SURF). We quantified the number of features and their quality for visual navigation using feature correspondence and the epipolar constraint. Our results indicate that near-infrared imagery produces more features that tend to be of higher quality than that of visible light imagery.
The thiazolidinediones (TZDs)—of which the two most frequently prescribed are rosiglitazone and pioglitazone—are insulin-sensitizing oral antidiabetic agents that are an effective treatment for type 2 diabetes. The primary pharmacological actions of these drugs are to increase adipose and muscle ins

prescribed are rosiglitazone and pioglitazone—are insulin-sensitizing oral antidiabetic agents that are an effective treatment for type 2 diabetes. The primary pharmacological actions of these drugs are to increase adipose and muscle insulin sensitivity and to inhibit gluconeogenesis in the liver. In type 2 diabetes patients, rosiglitazone reduces plasma levels of fasting glucose, glycated haemoglobin (HBA1c), and insulin. 1 It may also improve β-cell function. However, emerging evidence regarding the relationship between the use of TZDs and bone loss in type 2 diabetes patients gives cause for concern.
Response-Based and Counterfactual Learning for Sequence-to-Sequence Tasks in NLP

Many applications nowadays rely on statistical machine-learnt models, such as a rising number of virtual personal assistants. To train statistical models, typically large amounts of labelled data are required which are expensive and difficult to obtain. In this thesis, we investigate two approaches that alleviate the need for labelled data by leveraging feedback to model outputs instead. Both scenarios are applied to two sequence-to-sequence tasks for Natural Language Processing (NLP): machine translation and semantic parsing for question-answering. Additionally, we define a new question-answering task based on the geographical database OpenStreetMap (OSM) and collect a corpus, NLmaps v2, with 28,609 question-parse pairs. With the corpus, we build semantic parsers for subsequent experiments. Furthermore, we are the first to design a natural language interface to OSM, for which we specifically tailor a parser. The first approach to learn from feedback given to model outputs, considers a scenario where weak supervision is available by grounding the model in a downstream task for which labelled data has been collected. Feedback obtained from the downstream task is used to improve the model in a response-based on-policy learning setup. We apply this approach to improve a machine translation system, which is grounded in a multilingual semantic parsing task, by employing ramp loss objectives. Next, we improve a neural semantic parser where only gold answers, but not gold parses, are available, by lifting ramp loss objectives to non-linear neural networks. In the second approach to learn from feedback, instead of collecting expensive labelled data, a model is deployed and user-model interactions are recorded in a log. This log is used to improve a model in a counterfactual off-policy learning setup. We first exemplify this approach on a domain adaptation task for machine translation. Here, we show that counterfactual learning can be applied to tasks with large output spaces and, in contrast to prevalent theory, deterministic logs can successfully be used on sequence-to-sequence tasks for NLP. Next, we demonstrate on a semantic parsing task that counterfactual learning can also be applied when the underlying model is a neural network and feedback is collected from human users. Applying both approaches to the same semantic parsing task, allows us to draw a direct comparison between them. Response-based on-policy learning outperforms counterfactual off-policy learning, but requires expensive labelled data for the downstream task, whereas interaction logs for counterfactual learning can be easier to obtain in various scenarios.
Presence-only Habitat Suitability Modelling Using Unclassified Landsat ETM + Imagery : Fine-resolution Maps for Common Small Mammal Species in Bulgaria

In recent years, habitat suitability modelling, HSM (or species distribution modelling, SDM) is playing an increasingly important role in studying distribution patterns of organisms (Guisan and Thuiller 2005). These models are based on the analysis of the interrelationship between the known occurrences of a particular species and the state of environmental variables that are important for its existence in this place. They allow to extrapolate these relationships in unexplored regions, or into hypothetical scenarios of future or past climatic conditions (Guisan and Thuiller 2005). Over the last years a large amount of environmental data has become available for analyzing species distributions in the form of interpolated digital maps with environmental measurements. These environmental layers include abiotic factors, such as temperature, precipitation, or their proxies such as elevation, slope, aspect, topographic wetness index, hill shading, etc. Climatic variables typically have a great range of continuous values, and are often used for predicting species distributions at large scales. In contrast, topographic data are often used on the rePresence-only Habitat Suitability Modelling Using Unclassified Landsat ETM+ Imagery: Fine-resolution Maps for Common Small Mammal Species in Bulgaria
Mathematisches Forschungsinstitut Oberwolfach Report No . 46 / 2004 Theory of the Riemann Zeta and Allied Functions Organised

Omega results for the Riemann zeta function and the error terms in the summatory functions of arithmetic functions R. Balasubramanian Let f(s) = ∑∞ n=1 ann −s be a Dirichlet series, convergent in σ > 1 and analytically continuable in σ > −a with a finite number of poles. Here a is a positive real number or ∞ . We assume that the poles are situated in |t| 6 A . We further suppose that f(s) is of finite order, that is there exists a constant B > 0 , such that |f(σ+ it)| = O ( |t|+1)B ) for |t| > A . In this talk, we shall discuss the lower bound for ∫ T+H T |f(σ+ it) dt and some applications to arithmetic function. We start with some preliminary observations. First note that | exp(sin z)| 6 exp ( − exp(y/2) ) where z = x+ iy . The exponential decay of exp(sin z) immediately yields Lemma 1. If |x| < π/6 , x 6= 0 , then ∫ ∞ −∞ ∣∣ exp(sin z) z ∣∣ dy = O ( log 1 |x| ) . We shall employ an averaging technique which we explain. Fix T sufficiently large, H 6 (log logT ) 6 T, U = H, r = 100. Instead of considering a function g(t), it becomes advantageous to consider g̃(t) = ∫ u 0 du1 ∫ u 0 du2 · · · ∫ u 0 g(t−S)dur , where S = u1 + u2 + . + ur . For Dirichlet series, which are functions of s = σ + it the averaging is done with respect to the variable t. Lemma 2. Let a(n), b(n) be complex numbers which are o(n) . Let
Fostering secure attachment: experiences of animal companions in the foster home

This study sought to use attachment theory as a lens through which to explore children’s relationships with animal companions in the context of long-term foster care. Inductive and deductive thematic analyses of longitudinal case study data from eight children and their foster families suggested (a) that children’s relationships with animal companions satisfied attachment-related functions in their own right and (b) that animal companions also helped to soften perceptions of foster caregivers, facilitating opportunities for the development of closeness. Animals in the foster home may therefore play an important part in helping children to find and develop secure, warm, and loving relationships. ARTICLE HISTORY Received 24 October 2016 Accepted 6 January 2017
Bridging the Gap between Online Discussions and Formal Models of Argumentation

Formal models of argumentation in the field of artificial intelligence (AI) research were originally developed as a tool for logical inference on the basis of incomplete or inconsistent knowledge. Recently, AI argumentation formalisms are also being used to represent and evaluate real-world argumentations. Software tools that support argumentation can benefit from the semantic evaluation mechanisms that are provided by formal argumentation models, while the application to real-world scenarios improves the scope and relevance of formal models of argumentation in AI. This research is part of the Ph.D. program “Online Participation,” supported by the North Rhine-Westphalian funding scheme “Fortschrittskollegs. ” One of the program’s goals is to improve the quality of software-supported argumentation. This thesis contributes to this improvement by strengthening the connection between formal argumentation models from AI and software-supported discussions among humans on both sides. The first part of the contribution of this thesis is an extension of the formal model of abstract argumentation frameworks. In its basic form, an argumentation framework is given by a set of arguments and a binary attack relation on these arguments. In the proposed extended model of incomplete argumentation frameworks, individual arguments and individual attacks may be uncertain—such elements may or may not be part of the discussion. The extended model allows the representation of a wider range of scenarios that can arise in real argumentation settings, such as intermediate states in elicitation processes or the restricted knowledge of a single participant about the state of a discussion. The technical contribution of this part of the thesis is a full analysis of how the added notion of uncertainty affects the computational complexity of core reasoning tasks in abstract argumentation—namely, the verification and acceptance problems—for various evaluation semantics. Compared to the computational complexity of the respective problems for standard argumentation frameworks, for incomplete argumentation frameworks we observe a jump in complexity for most variants of the acceptance problems, but not for most variants of the verification problem. The second part of the contribution of this thesis is the development and implementation of translations from discussion data generated by the d-bas web tool for dialog-based argumentation to three different AI argumentation formalisms, namely, abstract argumentation frameworks, abstract dialectical frameworks, and the ASPIC+ framework. The translations are proved to satisfy established quality criteria. We developed the tool dabasco to implement these translations, which enables d-bas to utilize the full range of existing software tools for reasoning problems in the three argumentation models. The argument pipeline consisting of d-bas, dabasco, and these tools can automatically determine whether, e.g., a statement is acceptable or whether a participant’s opinion is consistent, thus improving feedback for operators and participants of discussions.
EZPlanes : Ensemble of 0 / 1 loss local minima hyperplanes for classification

We make several new contributions towards optimizing the 0/1 loss for binary classification. We first present a new objective function and local search algorithm and show that it optimizes 0/1 loss better than previously published programs on datasets from the UCI repository. We then present a new method EZplanes that builds an ensemble of 0/1 loss local minima hyperplanes with our local search algorithm and then uses non-linearized outputs of the ensemble as new features for final linear classification. We show that EZplanes achieves a lower mean error than state of the art methods such as gradient boosting and multi-layer perceptron in cross-validation studies on 37 randomly selected datasets from the UCI repository. We provide a freely available C implementation of our method at http:/ezplanes.sourceforge. net.
On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data

The Multiple-Network Poroelastic Theory (MPET) is a numerical model to characterize the transport of multiple fluid networks in the brain, which overcomes the problem of conducting separate analyses on individual fluid compartments and losing the interactions between tissue and fluids, in addition to the interaction between the different fluids themselves. In this paper, the blood perfusion results from MPET modeling are partially validated using cerebral blood flow (CBF) data obtained from arterial spin labeling (ASL) magnetic resonance imaging (MRI), which uses arterial blood water as an endogenous tracer to measure CBF. Two subjects—one healthy control and one patient with unilateral middle cerebral artery (MCA) stenosis are included in the validation test. The comparison shows several similarities between CBF data from ASL and blood perfusion results from MPET modeling, such as higher blood perfusion in the gray matter than in the white matter, higher perfusion in the periventricular region for both the healthy control and the patient, and asymmetric distribution of blood perfusion for the patient. Although the partial validation is mainly conducted in a qualitative way, it is one important step toward the full validation of the MPET model, which has the potential to be used as a testing bed for hypotheses and new theories in neuroscience research.
Toward a Differential and Situated View of Assessment Literacy: Studying Teachers' Responses to Classroom Assessment Scenarios

Research has consistently demonstrated that teachers’ assessment actions have a significant influence on students’ learning experience and achievement. While much of the assessment research to date has investigated teachers’ understandings of assessment purposes, their developing assessment literacy, or specific classroom assessment practices, few studies have explored teachers’ differential responses to specific and common classroom assessment scenarios. Drawing on a contemporary view of assessment literacy, and providing empirical evidence for assessment literacy as a differential and situated professional competency, the purpose of this study is to explore teachers’ approaches to assessment more closely by examining their differential responses to common classroom assessment scenarios. By drawing on data from 453 beginning teachers who were asked to consider their teaching context and identify their likely actions in response to common assessment scenarios, this paper makes a case for a situated and contextualized view of assessment work, providing an empirically-informed basis for reconceptualizing assessment literacy as negotiated, situated, and differential across teachers, scenarios, and contexts. Data from survey that presents teachers with assessment scenarios are analyzed through descriptive statistics and significance testing to observe similarities and differences by scenario and by participants' teaching division (i.e., elementary and secondary). The paper concludes by considering implications for assessment literacy theory and future related research.
Psychological Flexibility as a Resilience Factor in Individuals With Chronic Pain

Resilience factors have been suggested as key mechanisms in the relation between symptoms and disability among individuals with chronic pain. However, there is a need to better operationalize resilience and to empirically evaluate its role and function. The present study examined psychological flexibility as a resilience factor in relation to symptoms and functioning among 252 adults with chronic pain applying for participation in a digital ACT-based self-help treatment. Participants completed measures of symptoms (pain intensity, and anxiety), functioning (pain interference and depression), as well as the hypothesized resilience factor psychological flexibility (measured as avoidance, value obstruction, and value progress). As expected, symptoms, functioning and resilience factors were significantly associated. Hierarchical linear regression analyses showed that psychological flexibility significantly contributed to the prediction of pain interference and depression when adjusting for age, pain and anxiety. Also, participants with low levels of psychological flexibility were more likely to be on sick leave. Furthermore, a series of multiple mediation analyses showed that psychological flexibility had a significant indirect effect on the relationship between symptoms and functioning. Avoidance was consistently shown to contribute to the indirect effect. Results support previous findings and suggest the importance of psychological flexibility as a resilience factor among individuals with chronic pain and anxiety.
Finger orientation as an additional input dimension for touchscreens

Since the first digital computer in 1941 and the first personal computer back in 1975, the way we interact with computers has radically changed. The keyboard is still one of the two main input devices for desktop computers which is accompanied most of the time by a mouse or trackpad. However, the interaction with desktop and laptop computers today only make up a small percentage of current interaction with computing devices. Today, we mostly interact with ubiquitous computing devices, and while the first ubiquitous devices were controlled via buttons, this changed with the invention of touchscreens. Moreover, the phone as the most prominent ubiquitous computing device is heavily relying on touch interaction as the dominant input mode. Through direct touch, users can directly interact with graphical user interfaces (GUIs). GUI controls can directly be manipulated by simply touching them. However, current touch devices reduce the richness of touch input to two-dimensional positions on the screen. In this thesis, we investigate the potential of enriching a simple touch with additional information about the finger touching the screen. We propose to use the user’s finger orientation as two additional input dimensions. We investigate four key areas which make up the foundation to fully understand finger orientation as an additional input technique. With these insights, we provide designers with the foundation to design new gestures sets and use cases which take the finger orientation into account. We first investigate approaches to recognize finger orientation