Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Shapley value of liability games PÉTER CSÓKA – FERENC ILLÉS – TAMÁS SOLYMOSI

In a liability problem, the asset value of an insolvent firm must be distributed among the creditors and the firm itself, when the firm has some freedom in negotiating with the creditors. We model the negotiations using cooperative game theory and analyze the Shapley value to resolve such liability problems. We establish three main monotonicity properties of the Shapley value. First, creditors can only benefit from the increase in their claims or of the asset value. Second, the firm can only benefit from the increase of a claim but can end up with more or with less if the asset value increases, depending on the configuration of small and large liabilities. Third, creditors with larger claims benefit more from the increase of the asset value. Even though liability games are constant-sum games and we show that the Shapley value can be calculated directly from a liability problem, we prove that calculating the Shapley payoff to the firm is NP-hard. JEL codes: C71, C78
Combining textual and non-textual features for e-mail importance estimation

In this work, we present a binary classification problem in which we aim to identify those email messages that the receiver will reply to. The future goal is to develop a tool that informs a knowledge worker which emails are likely to need a reply. The Enron corpus was used to extract training examples. We analysed the word n-grams that characterize the messages that the receiver replies to. Additionally, we compare a Naive Bayes classifier to a decision tree classifier in the task of distinguishing replied from non-replied e-mails. We found that textual features are well-suited for obtaining high accuracy. However, there are interesting differences between recall and precision for the various feature selections.
Constraining the Distribution of Photosynthetic Parameters in the Global Ocean

Global and regional ocean primary production estimates are highly dependent on assumptions concerning the photosynthetic potential of the resident phytoplankton communities. Little is known, however, about global patterns in the distribution of photosynthetic potential and their causes. Here, we review existing literature reporting photosynthetic characteristics of natural populations. From this, we formulate hypotheses regarding abiotic and biotic factors of potential importance in determining photosynthetic performance. These hypotheses are then tested using data we have compiled from nearly all major ocean basins on the maximum rate of photosynthesis, PBmax, and the slope of the photosynthesis vs. light curve, αB (both parameters normalised to chlorophyll) as well as standard environmental variables, size fractioned chlorophyll, taxonomic data (to group), size and biovolume data for pico-, nano-, and micro-phytoplankton. In terms of abiotic variables, depth of sampling, temperature and nutrient availability all can be related to photosynthetic parameters. The most important biotic variable influencing photosynthetic performance was found to be community size distribution and the small component (i.e. the proportion of the phytoplankton community passing through a 10 µm filter) is shown to have both higher PBmax and αB than the larger phytoplankton component. A simple model was used to derive best fit values for PBmax (1.53/2.50 µgC l-1 h-1) and αB (0.025/0.040) for the large/small groups in the subset of the data where taxonomic data were available (both surface and sub-surface samples) using fractioned chlorophyll data and bulk community photosynthetic parameters. Non-metric multidimensional scaling (NMDS) was used to relate the distribution of photosynthetic parameters and dominant (by biovolume) phytoplankton groups. High PBmax was recorded in communities dominated by dinoflagellates, small flagellates and, in warmer waters, picoeukaryotes and Synecococcus. Diatom dominated communities exhibited lower PBmax and were associated with high inorganic nutrients and colder temperatures. That photosynthetic parameters appear closely related to community size distributions and taxonomic group provides some hope for improving the parameterization of photosynthetic performance in global ocean primary production estimates as both of these parameters can be made from remotely sensed optical characteristics of surface waters.
FEVER: Extracting Feature-oriented Changes from Commits

The study of the evolution of highly configurable systems requires a thorough understanding of thee core ingredients of such systems: (1) the underlying variability model; (2) the assets that together implement the configurable features; and (3) the mapping from variable features to actual assets. Unfortunately, to date no systematic way to obtain such information at a sufficiently fine grained level exists.To remedy this problem we propose FEVER and its instantiation for the Linux kernel. FEVER extracts detailed information on changes in variability models (KConfig files), assets (preprocessor based C code), and mappings (Make- files). We describe how FEVER works, and apply it to several releases of the Linux kernel. Our evaluation on 300 ran- domly selected commits, from two different releases, shows our results are accurate in 82.6% of the commits. Furthermore, we illustrate how the populated FEVER graph database thus obtained can be used in typical Linux engineering tasks.
From Words to Sentences & Back: Characterizing Context-dependent Meaning Representations in the Brain

Recent Machine Learning systems in vision and language processing have drawn attention to single-word vector spaces, where concepts are represented by a set of basic features or attributes based on textual and perceptual input. However, such representations are still shallow and fall short from symbol grounding. In contrast, Grounded Cognition theories such as CAR (Concept Attribute Representation; Binder et al., 2009) provide an intrinsic analysis of word meaning in terms of sensory, motor, spatial, temporal, affective and social features, as well as a mapping to corresponding brain networks. Building on this theory, this research aims to understand an intriguing effect of grounding, i.e. how word meaning changes depending on context. CAR representations of words are mapped to fMRI images of subjects reading different sentences, and the contributions of each word determined through Multiple Linear Regression and the FGREP nonlinear neural network. As a result, the FGREP model in particular identifies significant changes on the CARs for the same word used in different sentences, thus supporting the hypothesis that context adapts the meaning of words in the brain. In future work, such context-modified word vectors could be used as representations for a natural language processing system, making it more effective and robust.
Seedling Stage Strategies as a Means of Habitat Specialization in Herbaceous Plants

The regeneration niche has been little investigated in studies of community assembly and plant distribution. We examined adaptive associations between seedling traits and habitat specialization. Two habitat contrasts were investigated across several evolutionary lineages of angiosperms: species specialized to forest vs. open habitats and to dry vs. wet habitats. We also tested whether effects of shade and drought vary independently or, alternatively, if shade may amplify effects on drought-stressed plants. Seedling response in terms of growth rate, height, slenderness, specific leaf area (SLA) and degree of elongation (longest internode; petiole or leaf-sheath depending on species' morphology) to light and watering treatments was assessed. We used a factorial design involving three light regimes and two watering frequencies. The open-shaded habitat contrast and the dry-wet habitat contrast were investigated using six and five pairs of congeneric species, respectively. The congeneric species pair design controlled for confounding effects of evolutionary history prior to divergence in habitat specialization. Seedling growth rate generally decreased with shade and reduced watering frequency. Plant height was generally largest at intermediate light. Specialization to shaded habitats was associated with a more conservative growth strategy, i.e. showing a more modest growth response to increasing light. Species from all habitats showed the highest relative elongation at intermediate light, except for the moist-habitat species, for which elongation increased with shade. Contrary to our expectations, species from dry habitats grew bigger than species from moist habitats in all treatments. SLA responded to the light treatment, but not to watering regime. The contrasting light and moisture conditions across habitats appear to not have selected for differences in SLA. We conclude that seedling phase strategies of resource allocation in temperate herbs contribute to their habitat specialization. Habitat-specific seedling strategies and trade-offs in response to resource availability and environmental conditions may be important to adaptive specialization.
Search-Based Crash Reproduction and Its Impact on Debugging

Software systems fail. These failures are often reported to issue tracking systems, where they are prioritized and assigned to responsible developers to be investigated. When developers debug software, they need to reproduce the reported failure in order to verify whether their fix actually prevents the failure from happening again. Since manually reproducing each failure could be a complex task, several automated techniques have been proposed to tackle this problem. Despite showing advancements in this area, the proposed techniques showed various types of limitations. In this paper, we present EvoCrash, a new approach to automated crash reproduction based on a novel evolutionary algorithm, called Guided Genetic Algorithm (GGA). We report on our empirical study on using EvoCrash to reproduce 54 real-world crashes, as well as the results of a controlled experiment, involving human participants, to assess the impact of EvoCrash tests in debugging. Based on our results, EvoCrash outperforms state-of-the-art techniques in crash reproduction and uncovers failures that are undetected by classical coverage-based unit test generation tools. In addition, we observed that using EvoCrash helps developers provide fixes more often and take less time when debugging, compared to developers debugging and fixing code without using EvoCrash tests.
Gut Microbiota in Human Adults with Type 2 Diabetes Differs from Non-Diabetic Adults

Background Recent evidence suggests that there is a link between metabolic diseases and bacterial populations in the gut. The aim of this study was to assess the differences between the composition of the intestinal microbiota in humans with type 2 diabetes and non-diabetic persons as control. Methods and Findings The study included 36 male adults with a broad range of age and body-mass indices (BMIs), among which 18 subjects were diagnosed with diabetes type 2. The fecal bacterial composition was investigated by real-time quantitative PCR (qPCR) and in a subgroup of subjects (N = 20) by tag-encoded amplicon pyrosequencing of the V4 region of the 16S rRNA gene. The proportions of phylum Firmicutes and class Clostridia were significantly reduced in the diabetic group compared to the control group (P = 0.03). Furthermore, the ratios of Bacteroidetes to Firmicutes as well as the ratios of Bacteroides-Prevotella group to C. coccoides-E. rectale group correlated positively and significantly with plasma glucose concentration (P = 0.04) but not with BMIs. Similarly, class Betaproteobacteria was highly enriched in diabetic compared to non-diabetic persons (P = 0.02) and positively correlated with plasma glucose (P = 0.04). Conclusions The results of this study indicate that type 2 diabetes in humans is associated with compositional changes in intestinal microbiota. The level of glucose tolerance should be considered when linking microbiota with metabolic diseases such as obesity and developing strategies to control metabolic diseases by modifying the gut microbiota.
Learning Behavior Characterizations for Novelty Search

Novelty search and related diversity-driven algorithms provide a promising approach to overcoming deception in complex domains. The behavior characterization (BC) is a critical choice in the application of such algorithms. The BC maps each evaluated individual to a behavior, i.e., some vector representation of what the individual is or does during evaluation. Search is then driven towards diversity in a metric space of these behaviors. BCs are built from hand-designed features that are limited by human expertise, or upon generic descriptors that cannot exploit domain nuance. The main contribution of this paper is an approach that addresses these shortcomings. Generic behaviors are recorded from evolution on several training tasks, and a new BC is learned from them that funnels evolution towards successful behaviors on any further tasks drawn from the domain. This approach is tested in increasingly complex simulated maze-solving domains, where it outperforms both hand-coded and generic BCs, in addition to outperforming objective-based search. The conclusion is that adaptive BCs can improve search in many-task domains with little human expertise.
A Validated Set of Smells in Model-View-Controller Architectures

Code smells are symptoms of poor design and implementation choices that may hinder code comprehension, and possibly increase change-and defect-proneness. A vast catalogue of smells has been defined in the literature, and it includes smells that can be found in any kind of system (e.g., God Classes), regardless of their architecture. On the other hand, software systems adopting specific architectures (e.g., the Model-View-Controller pattern) can be also affected by other types of poor practices. We surveyed and interviewed 53 MVC developers to collect bad practices to avoid while working on Web MVC applications. Then, we followed an open coding procedure on the collected answers to define a catalogue of six Web MVC smells, namely Brain Repository, Fat Repository, Promiscuous Controller, Brain Controller, Laborious Repository Method, and Meddling Service. Then, we ran a study on 100 MVC projects to assess the impact of these smells on code change-and defect-proneness. In addition, we surveyed 21 developers to verify their perception of the defined smells. The achieved results show that the Web MVC smells (i) more often than not, increase change-and defect-proneness of classes, and (ii) are perceived by developers as severe problems.
SATT: Tailoring Code Metric Thresholds for Different Software Architectures

Code metric analysis is a well-known approach for assessing the quality of a software system. However, current tools and techniques do not take the system architecture (e.g., MVC, Android) into account. This means that all classes are assessed similarly, regardless of their specific responsibilities. In this paper, we propose SATT (Software Architecture Tailored Thresholds), an approach that detects whether an architectural role is considerably different from others in the system in terms of code metrics, and provides a specific threshold for that role. We evaluated our approach on 2 different architectures (MVC and Android) in more than 400 projects. We also interviewed 6 experts in order to explain why some architectural roles are different from others. Our results shows that SATT can overcome issues that traditional approaches have, especially when some architectural role presents very different metric values than others.
Global metabolic effect of manipulating pyruvate dehydrogenase complex activity in mammalian cells

III DECLARATION BY AUTHOR V PUBLICATIONS DURING CANDIDATURE VI ACKNOWLEDGEMENTS IX TABLE OF CONTENTS XI LIST OF FIGURES XV LIST OF TABLES XVI SELECTED ABBREVIATIONS AND SYMBOLS XVII CHAPTER 1 1 GENERAL INTRODUCTION 1 1.1. Mammalian cell metabolism and the Warburg effect 2 1.2. Systems biology 3 1.3. Aims of this project 5 CHAPTER 2 6 LITERATURE REVIEW 6 2.1. Mammalian cell central metabolism and ATP generation 7 2.1.1. Glycolysis 8 2.1.2. TCA cycle and oxidative phosphorylation 9 2.1.3. Pyruvate dehydrogenase complex: connecting glycolysis and TCA cycle 10 2.2. Aerobic glycolysis 13 2.2.1. Aerobic glycolysis: a metabolic phenotype mostly observed in rapid proliferating cells 13 2.2.2. The Warburg effect in industrial relevant cell lines 15 2.3. Dichloroacetate 17 2.3.1. Dichloroacetate indirectly increases PDC activity 17 2.3.2. Dichloroacetate in the focus of cancer research 18 CHAPTER 3 21 DYNAMIC METABOLIC FLUX ANALYSIS USING B-SPLINES TO STUDY THE EFFECTS OF TEMPERATURE SHIFT ON CHO CELL METABOLISM 21 3.1. Abstract 22 3.2. Introduction 22 3.3. Materials and Methods 24 3.3.1. Dynamic metabolic flux analysis (DMFA) using B-Spline fitting 24 3.3.2. Heuristic algorithm to determine the placement and number of knots 27 3.3.3. Metabolic model 29 3.3.4. Cell culture 29 3.3. 5. Extracellular metabolites and antibody concentration analysis 30 3.4. Results and discussion 30 3.4. 1. Comparing B-DMFA against L-DMFA 30 3.4. 2. Effects of temperature shift on cell size and productivity of CHO-XL99 cells 34
Designing a Self-Management App for Young People With Type 1 Diabetes: Methodological Challenges, Experiences, and Recommendations

Background Young people with type 1 diabetes often struggle to self-manage their disease. Mobile health (mHealth) apps show promise in supporting self-management of chronic conditions such as type 1 diabetes. Many health care providers become involved in app development. Unfortunately, limited information is available to guide their selection of appropriate methods, techniques, and tools for a participatory design (PD) project in health care. Objective The aim of our study was to develop an mHealth app to support young people in self-managing type 1 diabetes. This paper presents our methodological recommendations based on experiences and reflections from a 2-year research study. Methods A mixed methods design was used to identify user needs before designing the app and testing it in a randomized controlled trial. App design was based on qualitative, explorative, interventional, and experimental activities within an overall iterative PD approach. Several techniques and tools were used, including workshops, a mail panel, think-aloud tests, and a feasibility study. Results The final mHealth solution was “Young with Diabetes” (YWD). The iterative PD approach supported researchers and designers in understanding the needs of end users (ie, young people, parents, and health care providers) and their assessment of YWD, as well as how to improve app usability and feasibility. It is critical to include all end user groups during all phases of a PD project and to establish a multidisciplinary team to provide the wide range of expertise required to build a usable and useful mHealth app. Conclusions Future research is needed to develop and evaluate more efficient PD techniques. Health care providers need guidance on what tools and techniques to choose for which subgroups of users and guidance on how to introduce an app to colleagues to successfully implement an mHealth app in health care organizations. These steps are important for anyone who wants to design an mHealth app for any illness.
avoiding conflicts between the physical and digital model in tabletop interfaces with robotic tangibles

In TUIs, physical/digital conﬂicts can occur when the digital model does not match the model implied by the spatial lay-out of tangibles. We show how tangible tabletop interfaces (TTI) can be modiﬁed to allow robot movement of tangi-bles, thereby avoiding conﬂicts. We present RoMo, an open source Java library that allow existing TTI applications to perform robot movement, and demonstrate its functionality with three applications.
Besting the Quiz Master: Crowdsourcing Incremental Classification Games

Cost-sensitive classification, where the features used in machine learning tasks have a cost, has been explored as a means of balancing knowledge against the expense of incrementally obtaining new features. We introduce a setting where humans engage in classification with incrementally revealed features: the collegiate trivia circuit. By providing the community with a web-based system to practice, we collected tens of thousands of implicit word-by-word ratings of how useful features are for eliciting correct answers. Observing humans' classification process, we improve the performance of a state-of-the art classifier. We also use the dataset to evaluate a system to compete in the incremental classification task through a reduction of reinforcement learning to classification. Our system learns when to answer a question, performing better than baselines and most human players.
Virtual trackballs revisited

Rotation of three-dimensional objects by a two-dimensional mouse is a typical task in computer-aided design, operation simulations, and desktop virtual reality. The most commonly used rotation technique is a virtual trackball surrounding the object and operated by the mouse pointer. We review and provide a mathematical foundation for virtual trackballs. The first, but still popular, virtual trackball was described by Chen et al. (1998). We show that the virtual trackball by Chen et al. does not rotate the object along the intended great circular arc on the virtual trackball and we give a correction. Another popular virtual trackball is Shoemake's quaternion implementation (1992), which we show to be a special case of the virtual trackball by Chen et al. Shoemake extends the scope of the virtual trackball to the full screen. Unfortunately, Shoemake's virtual trackball is inhomogeneous and discontinuous with consequences for usability. Finally, we review Bell's virtual trackball (1998) and discuss studies of the usability of virtual trackballs.
Scaling law for effective heat diffusivity in ELMy H-mode plasmas

Transport simulations of high density scenarios of ITER and other reactor-grade devices require a scaling law for the effective heat diffusivity, χ, in the ELMy H-mode regime. A comprehensive empirical scaling, χH98, compatible with the ITER reference scaling, ITERH-98P(y, 2), for the thermal energy confinement time has been set up. It follows from a power law ansatz for χ and integration of the single-fluid energy equation and recovers all the exponents of the global confinement law. The dependences on temperature and temperature gradient are consistent with the power degradation of confinement and the experimental χ profiles. The χH98 scaling is validated by JET, DIII-D, ASDEX Upgrade and ASDEX discharges covering a wide parameter range. Simulations of the inductive scenario of ITER with χH98 yield an energy confinement time which agrees with the global scaling prediction.