Miracle-dz/UniFAR-FTUs huggingface dataset:
The Kurdish issue: the Kurds between the historical dream

2 Acknowledgement . 3 Chapter 1: Introduction . 6 1.1 Research questions . 12 Chapter 2: Methodology . 13 2.1 Research design . 13 2.2 Reliability, replication and validity of the research . 15 2.3 Data collection . 16 2.4 Choice of theories and relationship to data . 18 Chapter 3: Theory . 20 3.1 Realism theory . 20 3.2 Balance of power . 25 3.3 Security dilemma . 27 3.4 Interventionism . 28 3.5 Basic human needs theory . 30 Chapter 4: Analysis . 32 4.1 Historical background of the Kurds . 32 4.2 Political, civil and cultural rights of the Kurds . 38 4.3 Syrian-Turkish relations and the Kurds . 47 4.4 The role of the Kurds in a balance of power . 50 4.5 States interests in light of realists‘ hypotheses . 57 4.6 The role of the Kurdish diaspora . 67 Chapter 5: Conclusion . 69 List of references . 72 Appendix . 78
Minimum Achievable Peak Age of Information Under Service Preemptions and Request Delay

There is a growing interest in analysing freshness of data in networked systems. Age of Information (AoI) has emerged as a relevant metric to quantify this freshness at a receiver, and minimizing this metric for different system models has received significant research attention. However, a fundamental question remains: what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? We address this question for the average peak AoI (PAoI) statistic by considering generate-at-will source model, service preemptions, and request delays. Our main result is on the characterization of the minimum achievable average PAoI, and we show that it is achieved by a fixed-threshold policy among the set of all causal policies. We use the characterization to provide necessary and sufficient condition for preemptions to be beneficial for a given service-time distribution. Our numerical results, obtained using well-known distributions, demonstrate that the heavier the tail of a distribution the higher the performance gains of using preemptions.
Platelet-Derived Growth Factor-BB is the Dominant Mitogen for Intestinal Smooth Muscle Cells in the Trinitrobenzenesulfonic Acid Model of Rat Colitis

ii Acknowledgements . iv List of Figures.vi Chapter 1 General Introduction . 1 Chapter 2 Mitogenic Factors Promoting Intestinal Smooth Muscle Cell Proliferation. Stanzel, R.D. , Lourenssen, S. , Nair, D.G. , Blennerhassett, M.G. Published in American Journal of Physiology Cell Physiology 2010 299(4):C805-17 . 52 Chapter 3 General Discussion . 94 How Inflammation Affects the Intestine . 107 Understanding the Role of PDGF-BB Signaling in ISMC Hyperplasia: . 110 Induction of PDGF-Rβ Expression . 111 References . 131
Intelligent Fault Detection, Diagnosis and Health Evaluation for Industrial Robots

The focus of this study is development of an intelligent fault detection, diagnosis and health evaluation system for real industrial robots. The system uses principal component analysis based statistical process control with Nelson rules for online fault detection. Several suitable Nelson rules are chosen for sensitive detection. When a variation is detected, the system performs a diagnostic operation to acquire features of the time domain and the frequency domain from the motor encoder, motor current sensor and external accelerometer for fault diagnosis with a multi-class support vector machine. Additionally, a fuzzy logic based robot health index generator is proposed for evaluating the health of the robot, and the generator is an original design to reflect the health status of the robot. Finally, several real aging-related faults are implemented on a six-axis industrial robot, DRV90L7A6213N by Delta Electronics, and the proposed system is validated effectively by the experimental results.
Virulence-resistance plasmids ( pUO-StVR 2-like ) in meat isolates of 1 Salmonella enterica serovar Typhimurium 2 3 4 5 6

25 During a screening of Salmonella enterica in foods of animal origin four isolates of serovar 26 Typhimurium carrying hybrid virulence-resistance plasmids were detected. Three of them, 27 one from pork and two from chicken meat, contained pUO-StVR2, a derivative of the 28 pSLT virulence plasmid with the blaOXA-1, catA1, aadA1, sulI and tet(B) genes, which 29 confer resistance to ampicillin, chloramphenicol, streptomycin-spectinomycin, 30 sulfonamides and tetracycline, respectively. The fourth isolate harboured a pUO-StVR2 31 variant (termed ν8) with an additional dfrA10 gene responsible for resistance to 32 trimethoprim. This gene is part of the orf513-dfrA10-qacEΔ1-sul1 element 33 characteristically found in complex class 1 integrons. Pulsed-field gel electrophoresis 34 identified two XbaI-BlnI combined profiles: X2-B2 generated from the three pUO-StVR2 35 isolates, and X12-B17 shown by the pUO-StVR2-ν8 isolate. The same profiles have also 36 been found in clinical and ill pig isolates, supporting chicken and pork meat as vehicles for 37 transmission of Typhimurium carrying pUO-StVR2-like plasmids. 38 39 40 41 42 43 44 45 46 47
Evaluation of Cr (VI) remediation potential of Eichornia sp in conjunction with chromium-resistant bacterial strains

Purpose: Evaluation of Cr (VI) removal by indigenous chromium resistant bacterial strains alone and in combination with Eichornia sp. Methods: Three chromium resistant bacterial strains S-4 O chrobactrum grignonense , SF-5 Bacillus sp. and S-6 Ochrobactrum pseudogrignonenses were isolated from industrial effluent. The isolated chromium-resistant bacterial strains were subjected to heavy-metal resistance profiling. Cr (VI) reduction was evaluated in mobilized as well as immobilized forms. The phytoremediation potential of Eichornia sp. in conjunction with chromium resistant bacterial isolates was also determined. Fourier transform infra-red (FTIR) spectroscopy was performed to rule out the involvement of various functional groups in the binding activity of Cr (VI). Results: Three bacterial strains resisted up to 1000 μg/mL of potassium dichromate (K 2 CrO 4 ). Bacterial strains S-4, SF-5 and S-6 showed significant Cr (VI) removal in mobilized state (84.93, 85.85 and 83.97% respectively) compared with immobilized state (41.27, 37.99, 37.96 %) at an initial concentration of 500 μg/mL of chromate. Bacterial strains caused reduction in chromate uptake in inoculated plants relative to control plants. FTIR spectra revealed significant changes in the absorption peaks, reflecting the binding of Cr (VI) ions with bacterial cell surface under stress conditions. Conclusion: The selected isolates tested possess the ability to remove Cr (VI) synergistically with Eichornia sp. Keywords: Phytoremediation, Heavy-metal, Pollution, Chromium-resistant bacteria
DOE-NEER Project ) Report for Phase III and final project report Phase Stability under Irradiation of Precipitates and Solid Solutions in Model Alloys and in ODS Alloys Relevant

The overall objective of this program is to investigate the irradiation-altered phase stability of oxide precipitates in ODS steels and of model alloy solid solutions of associated systems. This information can be used to determine whether the favorable mechanical properties of these steels are maintained under irradiation, thus addressing one of the main materials research issues for this class of steels as identified by the GenIV working groups. The research program will also create fundamental understanding of the irradiation precipitation/dissolution problem by studying a “model” system in which the variables can be controlled and their effects understood individually.
Déséquilibre thermique dans un plasma d'air ensemencé d'aluminium

Plusieurs technologies utilisent l’aluminium en raison de ses proprietes variees allant de sa maniabilite, de sa legerete, de sa resistance a la corrosion, de son apport energetique augmentant la puissance explosive du nitrate d'ammonium… On le retrouve dans plusieurs secteurs industriels. Dans ces systemes les electrodes et/ou les fils sont faits d'aluminium. Durant l'interaction du plasma d’air et du metal en aluminium, la composition chimique joue un role cle sur les phases condensees. En outre le champ electrique peut atteindre une valeur tres elevee, entrainant le plasma a etre hors de l'equilibre thermique. Les electrons obtiennent une temperature T_e plus haute que T_l celle des especes chimiques lourdes. Aux temperatures basses, les phases condensees peuvent etre conductrices ou isolantes ce qui peut perturber le fonctionnement electrique du systeme qui est par ailleurs de plus en plus miniaturise. Nous presenterons d’abord l’influence du desequilibre thermique sur les phases condensees, puis nous tenterons d’evaluer le desequilibre thermique en fonction du champ electrique.
Nutribullets Hybrid: Multi-document Health Summarization

We present a method for generating comparative summaries that highlights similarities and contradictions in input documents. The key challenge in creating such summaries is the lack of large parallel training data required for training typical summarization systems. To this end, we introduce a hybrid generation approach inspired by traditional concept-to-text systems. To enable accurate comparison between different sources, the model first learns to extract pertinent relations from input documents. The content planning component uses deterministic operators to aggregate these relations after identifying a subset for inclusion into a summary. The surface realization component lexicalizes this information using a text-infilling language model. By separately modeling content selection and realization, we can effectively train them with limited annotations. We implemented and tested the model in the domain of nutrition and health -- rife with inconsistencies. Compared to conventional methods, our framework leads to more faithful, relevant and aggregation-sensitive summarization -- while being equally fluent.
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods

The increasing availability of data presents an opportunity to calibrate unknown parameters which appear in complex models of phenomena in the biomedical, physical and social sciences. However, model complexity often leads to parameter-to-data maps which are expensive to evaluate and are only available through noisy approximations. This paper is concerned with the use of interacting particle systems for the solution of the resulting inverse problems for parameters. Of particular interest is the case where the available forward model evaluations are subject to rapid fluctuations, in parameter space, superimposed on the smoothly varying large scale parametric structure of interest. {A motivating example from climate science is presented, and ensemble Kalman methods (which do not use the derivative of the parameter-to-data map) are shown, empirically, to perform well. Multiscale analysis is then used to analyze the behaviour of interacting particle system algorithms when rapid fluctuations, which we refer to as noise, pollute the large scale parametric dependence of the parameter-to-data map. Ensemble Kalman methods and Langevin-based methods} (the latter use the derivative of the parameter-to-data map) are compared in this light. The ensemble Kalman methods are shown to behave favourably in the presence of noise in the parameter-to-data map, whereas Langevin methods are adversely affected. On the other hand, Langevin methods have the correct equilibrium distribution in the setting of noise-free forward models, whilst ensemble Kalman methods only provide an uncontrolled approximation, except in the linear case. Therefore a new class of algorithms, ensemble Gaussian process samplers, which combine the benefits of both ensemble Kalman and Langevin methods, are introduced and shown to perform favourably.
User-Generated Text Corpus for Evaluating Japanese Morphological Analysis and Lexical Normalization

Morphological analysis (MA) and lexical normalization (LN) are both important tasks for Japanese user-generated text (UGT). To evaluate and compare different MA/LN systems, we have constructed a publicly available Japanese UGT corpus. Our corpus comprises 929 sentences annotated with morphological and normalization information, along with category information we classified for frequent UGT-specific phenomena. Experiments on the corpus demonstrated the low performance of existing MA/LN methods for non-general words and non-standard forms, indicating that the corpus would be a challenging benchmark for further research on UGT.
M-Net with Bidirectional ConvLSTM for Cup and Disc Segmentation in Fundus Images

Glaucoma is a severe eye disease that is known to deteriorate optic never fibers, causing cup size to increase, which could result in permanent loss of vision. Glaucoma is the second leading cause of blindness after cataract, but glaucoma being more dangerous as it is not curable. Early diagnoses and treatment of glaucoma can help to slow the progression of glaucoma and its damages. For the detection of glaucoma, the Cup to Disc ratio (CDR) provides significant information. The CDR depends heavily on the accurate segmentation of cup and disc regions. In this paper, we have proposed a modified M-Net with bidirectional convolution long short-term memory (LSTM), based on joint cup and disc segmentation. The proposed network combines features of encoder and decoder, with bidirectional LSTM. Our proposed model segments cup and disc regions based on which the abnormalities in cup to disc ratio can be observed. The proposed model is tested on REFUGE2 data, where our model achieves a dice score of 0.92 for optic disc and an accuracy of 98.99% in segmenting cup and disc regions.
A Tip of the Red Giant Branch Distance of 22.1 ± 1.2 Mpc to the Dark Matter Deficient Galaxy NGC 1052–DF2 from 40 Orbits of Hubble Space Telescope Imaging

The large and diffuse galaxies NGC 1052–DF2 and NGC 1052–DF4 have been found to have very low dark matter content and a population of luminous globular clusters (GCs). Accurate distance measurements are key to interpreting these observations. Recently, the distance to NGC 1052–DF4 was found to be 20.0 ± 1.6 Mpc by identifying the tip of the red giant branch (TRGB) in 12 orbits of Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) imaging. Here we present 40 orbits of HST ACS data for NGC 1052–DF2 and use these data to measure its TRGB. The TRGB is readily apparent in the color–magnitude diagram. Using a forward model that incorporates photometric uncertainties, we find a TRGB magnitude of m F814W,TRGB = 27.67 ± 0.10 mag. The inferred distance is D TRGB = 22.1 ± 1.2 Mpc, consistent with the previous surface brightness fluctuation distances to the bright elliptical galaxy NGC 1052. The new HST distance rules out the idea that some of NGC 1052–DF2's unusual properties can be explained if it were at ∼13 Mpc; instead, it implies that the galaxy’s GCs are even more luminous than had been derived using the previous distance of 20 Mpc. The distance from NGC 1052–DF2 to NGC 1052–DF4 is well-determined at 2.1 ± 0.5 Mpc, significantly larger than the virial diameter of NGC 1052. We discuss the implications for formation scenarios of the galaxies and for the external field effect, which has been invoked to explain the intrinsic dynamics of these objects in the context of modified Newtonian dynamics.
Multimodal Fusion Refiner Networks

Tasks that rely on multi-modal information typically include a fusion module that combines information from different modalities. In this work, we develop a Refiner Fusion Network (ReFNet) that enables fusion modules to combine strong unimodal representation with strong multimodal representations. ReFNet combines the fusion network with a decoding/defusing module, which imposes a modality-centric responsibility condition. This approach addresses a big gap in existing multimodal fusion frameworks by ensuring that both unimodal and fused representations are strongly encoded in the latent fusion space. We demonstrate that the Refiner Fusion Network can improve upon performance of powerful baseline fusion modules such as multimodal transformers. The refiner network enables inducing graphical representations of the fused embeddings in the latent space, which we prove under certain conditions and is supported by strong empirical results in the numerical experiments. These graph structures are further strengthened by combining the ReFNet with a Multi-Similarity contrastive loss function. The modular nature of Refiner Fusion Network lends itself to be combined with different fusion architectures easily, and in addition, the refiner step can be applied for pre-training on unlabeled datasets, thus leveraging unsupervised data towards improving performance. We demonstrate the power of Refiner Fusion Networks on three datasets, and further show that they can maintain performance with only a small fraction of labeled data.
DJXPerf: Identifying Memory Inefficiencies via Object-Centric Profiling for Java

Java is the “go-to” programming language choice for developing scalable enterprise cloud applications. In such systems, even a few percent CPU time savings can offer a significant competitive advantage and cost savings. Although performance tools abound for Java, those that focus on the data locality in the memory hierarchy are rare. In this paper, we first categorize data locality issues in Java programs. We then present DJXPerf, a lightweight, object-centric memory profiler for Java, which associates memory-hierarchy performance metrics (e.g., cache/TLB misses) with Java objects. DJXPerf uses statistical sampling of hardware performance monitoring counters to attribute metrics to not only source code locations but also Java objects. DJXPerf presents Java object allocation contexts combined with their usage contexts and presents them ordered by the poor locality behaviors. DJXPerf’s performance measurement, object attribution, and presentation techniques guide optimizing object allocation, layout, and access patterns. DJXPerf incurs only ~8.5% runtime overhead and ∼6% memory overhead on average, requiring no modifications to hardware, OS, Java virtual machine, or application source code, which makes it attractive to use in production. Guided by DJXPerf, we study and optimize a number of Java and Scala programs, including well-known benchmarks and real-world applications, and demonstrate significant speedups.
How Transferable are Reasoning Patterns in VQA?

Since its inception, Visual Question Answering (VQA) is notoriously known as a task, where models are prone to exploit biases in datasets to find shortcuts instead of performing high-level reasoning. Classical methods address this by removing biases from training data, or adding branches to models to detect and remove biases. In this paper, we argue that uncertainty in vision is a dominating factor preventing the successful learning of reasoning in vision and language problems. We train a visual oracle and in a large scale study provide experimental evidence that it is much less prone to exploiting spurious dataset biases compared to standard models. We propose to study the attention mechanisms at work in the visual oracle and compare them with a SOTA Transformer-based model. We provide an in-depth analysis and visualizations of reasoning patterns obtained with an online visualization tool which we make publicly available1. We exploit these insights by transferring reasoning patterns from the oracle to a SOTA Transformer-based VQA model taking standard noisy visual inputs via fine-tuning. In experiments we report higher overall accuracy, as well as accuracy on infrequent answers for each question type, which provides evidence for improved generalization and a decrease of the dependency on dataset biases.
Universally-optimal distributed algorithms for known topologies

Many distributed optimization algorithms achieve existentially-optimal running times, meaning that there exists some pathological worst-case topology on which no algorithm can do better. Still, most networks of interest allow for exponentially faster algorithms. This motivates two questions: (i) What network topology parameters determine the complexity of distributed optimization? (ii) Are there universally-optimal algorithms that are as fast as possible on every topology? We resolve these 25-year-old open problems in the known-topology setting (i.e., supported CONGEST) for a wide class of global network optimization problems including MST, (1+є)-min cut, various approximate shortest paths problems, sub-graph connectivity, etc. In particular, we provide several (equivalent) graph parameters and show they are tight universal lower bounds for the above problems, fully characterizing their inherent complexity. Our results also imply that algorithms based on the low-congestion shortcut framework match the above lower bound, making them universally optimal if shortcuts are efficiently approximable.
Seeing Thru Walls: Visualizing Mobile Robots in Augmented Reality

We present an approach for visualizing mobile robots through an Augmented Reality headset when there is no line-of-sight visibility between the robot and the human. Three elements are visualized in Augmented Reality: 1) Robot’s 3D model to indicate its position, 2) An arrow emanating from the robot to indicate its planned movement direction, and 3) A 2D grid to represent the ground plane. We conduct a user study with 18 participants, in which each participant are asked to retrieve objects, one at a time, from stations at the two sides of a T-junction at the end of a hallway where a mobile robot is roaming. The results show that visualizations improved the perceived safety and efficiency of the task and led to participants being more comfortable with the robot within their personal spaces. Furthermore, visualizing the motion intent in addition to the robot model was found to be more effective than visualizing the robot model alone. The proposed system can improve the safety of automated warehouses by increasing the visibility and predictability of robots.
Extending Hierarchical Delta Debugging with Hoisting

Minimizing failing test cases is an important pre-processing step on the path of debugging. If much of a test case that triggered a bug does not contribute to the actual failure, then the time required to fix the bug can increase considerably. However, test case reduction itself can be a time consuming task, especially if done manually. Therefore, automated minimization techniques have been proposed, the minimizing Delta Debugging (DDMIN) and the Hierarchical Delta Debugging (HDD) algorithms being the most well known. DDMIN does not need any information about the structure of the test case, thus it works for any kind of input. If the structure is known, however, it can be utilized to create smaller test cases faster. This is exemplified by HDD, which works on tree-structured inputs, pruning subtrees at each level of the tree with the help of DDMIN.In this paper, we propose to extend HDD with a reduction method that does not prune subtrees, but replaces them with compatible subtrees further down the hierarchy, called hoisting. We have evaluated various combinations of pruning and hoisting on multiple test suites and found that hoisting can help to further reduce the size of test cases by as much as 80% compared to the baseline HDD. We have also compared our results to other state-of-the-art test case reduction algorithms and found that HDD extended with hoisting can produce smaller output in most of the cases.